From e3a038001af418a93ca20b78e989342dd5d52598 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 10 Dec 2024 10:56:19 +0100
Subject: [PATCH 001/277] Initial commit

---
 LICENSE   | 21 +++++++++++++++++++++
 README.md |  1 +
 2 files changed, 22 insertions(+)
 create mode 100644 LICENSE
 create mode 100644 README.md

diff --git a/LICENSE b/LICENSE
new file mode 100644
index 00000000..2057cb8f
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 ack00gar
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
new file mode 100644
index 00000000..b334a680
--- /dev/null
+++ b/README.md
@@ -0,0 +1 @@
+# VR-Funscript-AI-Generator
\ No newline at end of file

From e1d5b83059de790b259f32aa54b576774351c88d Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 10 Dec 2024 11:09:36 +0100
Subject: [PATCH 002/277] Initial commit

Initial commit
---
 FSGenerator.py                                | 324 ++++++++
 .../Data/com.apple.CoreML/model.mlmodel       |   0
 .../Data/com.apple.CoreML/weights/weight.bin  |   0
 .../Manifest.json                             |  18 +
 utils/FunscriptHandler.py                     | 227 ++++++
 utils/KalmanFilter.py                         |  36 +
 utils/ObjectDetector.py                       |  44 +
 utils/ObjectTracker.py                        | 770 ++++++++++++++++++
 utils/SceneCutsDetect.py                      |  88 ++
 utils/VideoReaderFFmpeg.py                    | 252 ++++++
 utils/Visualizer.py                           |  96 +++
 utils/config.py                               |  40 +
 12 files changed, 1895 insertions(+)
 create mode 100644 FSGenerator.py
 create mode 100644 models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/model.mlmodel
 create mode 100644 models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/weights/weight.bin
 create mode 100644 models/k00gar-11n-200ep-best.mlpackage/Manifest.json
 create mode 100644 utils/FunscriptHandler.py
 create mode 100644 utils/KalmanFilter.py
 create mode 100644 utils/ObjectDetector.py
 create mode 100644 utils/ObjectTracker.py
 create mode 100644 utils/SceneCutsDetect.py
 create mode 100644 utils/VideoReaderFFmpeg.py
 create mode 100644 utils/Visualizer.py
 create mode 100644 utils/config.py

diff --git a/FSGenerator.py b/FSGenerator.py
new file mode 100644
index 00000000..9b1856ce
--- /dev/null
+++ b/FSGenerator.py
@@ -0,0 +1,324 @@
+import time
+import os
+import cv2
+import json
+from utils.config import class_names, class_priority_order, class_reverse_match, class_colors
+from ultralytics import YOLO
+from utils import ObjectTracker as OT
+from utils import FunscriptHandler as FH
+from tqdm import tqdm
+from utils import Visualizer as VS
+from utils.SceneCutsDetect import detect_scene_changes
+
+
+# Define the BoxRecord class
+class BoxRecord:
+    def __init__(self, box, conf, cls, class_name):
+        self.box = box
+        self.conf = conf
+        self.cls = cls
+        self.class_name = class_name
+
+    def __iter__(self):
+        return iter((self.box, self.conf, self.cls, self.class_name))
+
+
+# Define the Result class
+class Result:
+    def __init__(self, image_width):
+        self.frame_data = {}
+        self.image_width = image_width
+
+    def add_record(self, frame_id, box_record):
+        if frame_id in self.frame_data:
+            self.frame_data[frame_id].append(box_record)
+        else:
+            self.frame_data[frame_id] = [box_record]
+
+    def map_class_type_to_name(self, class_type, x1, x2, image_width):
+        if class_type in ['foot', 'hand']:
+            # Call it left if it is mainly on the left of the frame, right otherwise
+            if (x1 + x2) / 2 < image_width / 2:
+                class_name = 'right ' + class_type
+            else:
+                class_name = 'left ' + class_type
+        else:
+            class_name = class_type
+        return class_name
+
+    def get_boxes(self, frame_id):
+        itemized_boxes = []
+        if frame_id not in self.frame_data:
+            raise KeyError(f"No records found for frame ID {frame_id}")
+        boxes = self.frame_data[frame_id]
+        for box, conf, cls, class_name in boxes:
+            target_class_name = self.map_class_type_to_name(class_name, box[0], box[2], self.image_width)
+            itemized_boxes.append((box, conf, cls, target_class_name))
+        sorted_boxes = sorted(
+            itemized_boxes,
+            key=lambda x: class_priority_order.get(x[3], 7)  # Sort by class name priority
+        )
+        return sorted_boxes  # sorted_boxes
+
+    def get_all_frame_ids(self):
+        return list(self.frame_data.keys())
+
+def update_sex_position(position, new_position, frame_id, reason=None):
+    if position is not None and new_position is not None and position != new_position:
+        due_to = " given " + reason if reason else ""
+        print(f"Sex position changed from {position} to {new_position}{due_to}")
+        position_changes.append((frame_id, position, new_position, reason))
+        return new_position
+    return position
+
+def write_dataset(file_path, data):
+    print(f"Exporting data...")
+    export_start = time.time()
+    # if file exists, rename it
+    if os.path.exists(file_path):
+        os.rename(file_path, file_path + ".bak")
+    with open(file_path, 'w') as f:
+        json.dump(data, f)
+    export_end = time.time()
+    print(f"Done in {export_end - export_start}.")
+def extract_yolo_data(model_file, video_path, frame_start, frame_end=None, TestMode = False, isVR = False):
+    records = []
+    test_result = Result(320)
+
+    cap = cv2.VideoCapture(video_path)
+    cap.set(cv2.CAP_PROP_POS_FRAMES, frame_start)
+
+    if frame_end:
+        last_frame = frame_end
+    else:
+        last_frame = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    # Load the YOLO11 model
+    model = YOLO(model_file, task="detect")
+
+    # Loop through the video frames
+    for frame_pos in tqdm(range(frame_start, last_frame), ncols=None, desc="Performing YOLO detection on frames"):
+        # Read a frame from the video
+        success, frame = cap.read()
+
+        if success:
+            if isVR:
+                # only keep the center third of the left half of the frame
+                frame = frame[:, frame.shape[1] // 6:frame.shape[1] // 3, :]
+
+            # Run YOLO11 tracking on the frame, persisting tracks between frames
+            # yolo_results = model.predict(frame, verbose=False, conf=0.5)  #track(frame, persist=True, verbose=False)
+            yolo_results = model.track(frame, persist=True, conf=0.5, verbose=False)
+
+            #if results[0].boxes.id is None:
+            if yolo_results[0].boxes.cls is None:
+                continue
+
+            # Get the boxes and track IDs
+            #track_ids = results[0].boxes.id.cpu().tolist()
+            boxes = yolo_results[0].boxes.xywh.cpu()
+            classes = yolo_results[0].boxes.cls.cpu().tolist()
+            confs = yolo_results[0].boxes.conf.cpu().tolist()
+
+            # for track_id, cls, conf, box in zip(track_ids, classes, confs, boxes):
+            for cls, conf, box in zip(classes, confs, boxes):
+                x, y, w, h = box.int().tolist()
+                x1 = x - w // 2
+                y1 = y - h // 2
+                x2 = x + w // 2
+                y2 = y + h // 2
+                #record = [frame_pos, int(track_id), int(cls), round(conf, 1), x1, y1, x2, y2]
+                record = [frame_pos, int(cls), round(conf, 1), x1, y1, x2, y2]
+                records.append(record)
+                if TestMode:
+                    print(f"Record : {record}")
+                    print(f"For class id: {int(cls)}, getting: {class_reverse_match.get(int(cls), 'unknown')}")
+                    test_box = [[x1, y1, x2, y2], round(conf, 1), int(cls), class_reverse_match.get(int(cls), 'unknown')]
+                    print(f"Test box: {test_box}")
+                    test_result.add_record(frame_pos, test_box)
+            if TestMode:
+                # display the results from YOLO
+                yolo_results[0].plot()
+                cv2.imshow("YOLO11", yolo_results[0].plot())
+                cv2.waitKey(1)
+                # Making sure we did not mess the classes
+                sorted_boxes = test_result.get_boxes(frame_pos)
+                print(f"Sorted boxes : {sorted_boxes}")
+
+                for box in sorted_boxes:
+                    cv2.rectangle(frame, (box[0][0], box[0][1]), (box[0][2], box[0][3]), (0, 255, 0), 2)
+                    cv2.putText(frame, f"{box[3]}", (box[0][0], box[0][1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+                    #cv2.putText(frame, f"{int(cls)}: {int(conf*100)}%", (record[4], record[5] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+                cv2.imshow("YOLO11 test boxes Tracking", frame)
+                cv2.waitKey(1)
+            if frame_pos == last_frame-1:
+                print("Triggered here")
+                write_dataset(video_path[:-4] + f"_rawyolo.json", records)
+                break
+        else:
+            # Break the loop if the end of the video is reached
+            print("Triggered there")
+            write_dataset(video_path[:-4] + f"_rawyolo.json", records)
+            break
+    # Release the video capture object and close the display window
+    cap.release()
+    cv2.destroyAllWindows()
+
+def load_yolo_data_from_file(file_path):
+    with open(file_path, 'r') as f:
+        data = json.load(f)
+    return data
+
+def make_data_boxes(records, image_x_size):
+    # Create Result instance
+    result = Result(image_x_size)
+
+    # Populate the Result instance with BoxRecord instances
+    for record in records:
+        frame_idx, cls, conf, x1, y1, x2, y2 = record
+        box = [x1, y1, x2, y2]
+        class_name = class_reverse_match.get(cls, 'unknown')
+        box_record = BoxRecord(box, conf, cls, class_name)
+        result.add_record(frame_idx, box_record)
+    return result
+
+def analyze_tracking_results(results, image_y_size, video_path, frame_start = None, frame_end = None, TestMode = False):
+    list_of_frames = results.get_all_frame_ids()
+    visualizer = VS.Visualizer()
+
+    cap = cv2.VideoCapture(video_path)
+    nb_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    if not frame_start:
+        frame_start = 0
+
+    if not frame_end:
+        frame_end = nb_frames
+
+    if TestMode:
+        cap.set(cv2.CAP_PROP_POS_FRAMES, frame_start)
+
+    # if a file already exists, directly load the cuts
+    if os.path.exists(video_path[:-4] + f"_cuts.json"):
+        print(f"Loading cuts from {video_path[:-4] + f'_cuts.json'}")
+        with open(video_path[:-4] + f"_cuts.json", 'r') as f:
+            cuts = json.load(f)
+    else:
+        scene_list = detect_scene_changes(video_path, "Left", 0.9, frame_start, frame_end)
+        print(f"Analyzing frames {frame_start} to {frame_end}")
+        cuts = [scene[1] for scene in scene_list]
+        # remove last entry in cuts
+        cuts = cuts[:-1]
+        # save to a file
+        with open(video_path[:-4] + f"_cuts.json", 'w') as f:
+            json.dump(cuts, f)
+
+    tracker = OT.ObjectTracker()
+
+    for frame_pos in tqdm(range(frame_start, frame_end), ncols=None, desc="Analyzing tracking results"):
+
+        if frame_pos in cuts:
+            print(f"Reaching cut at frame {frame_pos}")
+            previous_distances = tracker.previous_distances
+            tqdm.write(f"Reinitializing tracker with previous distances: {previous_distances}")
+            tracker = OT.ObjectTracker()
+            tracker.previous_distances = previous_distances
+
+        if frame_pos in list_of_frames:
+            sorted_boxes = results.get_boxes(frame_pos)
+
+            tracker.tracking_logic(sorted_boxes, frame_pos, image_y_size)
+
+            if tracker.distance:
+                funscript_frames.append(frame_pos)
+                funscript_distances.append(tracker.distance)
+
+        if TestMode:
+            ret, frame = cap.read()
+            frame = frame[:, :frame.shape[1] // 2, :]
+            if tracker.tracked_body_part in class_names and tracker.boxes[tracker.tracked_body_part] is not None:
+                frame = visualizer.draw_bounding_box(frame,
+                                                          tracker.boxes[tracker.tracked_body_part],
+                                                          tracker.tracked_body_part,
+                                                          class_colors[tracker.tracked_body_part],
+                                                          int(image_x_size / 2))
+            if tracker.locked_penis_box is not None:
+                frame = visualizer.draw_bounding_box(frame, tracker.locked_penis_box,
+                                                              "Locked_Penis",
+                                                              class_colors['penis'],
+                                                              int(image_x_size / 2))
+            if funscript_distances:
+                frame = visualizer.draw_gauge(frame, funscript_distances[-1])
+                #frame = visualizer.draw_limited_graph(frame, funscript_distances,
+                #                                               funscript_frames, 200)
+
+            cv2.imshow("Combined Results", frame)
+            cv2.waitKey(1)
+    return funscript_frames, funscript_distances
+
+
+def parse_yolo_data_looking_for_penis(data, start_frame):
+    for line in data:
+        if line[0] >= start_frame and line[1] == 0:  # class_types.get("penis"):
+            print(f"First instance of Penis found in frame {line[0]}")
+            return line[0]
+
+
+
+
+# MAIN logic
+
+# YOLO model file
+yolo_model = "models/k00gar-11n-200ep-best.mlpackage"
+
+video_list = []
+# video_list.append("/Users/k00gar/Downloads/wankzvr-shocum-180_180x180_3dh_LR.mp4")
+# video_list.append("/Users/k00gar/Downloads/ARPorn_Angel Youngs_Angel Of Nurse_4000p_2K_original_FISHEYE190_alpha.mp4")
+# video_list.append("/Users/k00gar/Downloads/Milfvr - Honeymoon In Vega - Vanessa Vega_1080p.mp4")
+# video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_Sugar-baby_ Drooling Kitty_1920p_48559_FISHEYE190.mp4")
+# video_list.append("/Volumes/Crucial/pn/EBVR-018-B_AI_upscaled.mp4")
+# video_list.append("/Volumes/Crucial/pn/NaugthyAmericaVR.My.Wifes.Hot.Friend.23.09.15.Blake.Blossom.Oculus.8k.4096.mp4")
+# video_list.append("/Volumes/WD Elements/VRPn/CzechVR.723.I.m.Here.to.Serve.Agatha.Vega.Oculus.8K..mp4")
+# video_list.append("/Volumes/WD Elements/VRPn/721.czechvr.3d.7680x3840.60fps.oculusrift_uhq_h265.mp4")
+# video_list.append("/Users/k00gar/Downloads/JVR_100199.mp4")
+# video_list.append("/Users/k00gar/Downloads/JVR_100200.mp4")
+# video_list.append("/Users/k00gar/Downloads/JVR_100201.mp4")
+video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_Poolside Romance_1920p_39107_FISHEYE190.mp4")
+
+TestMode = False
+
+for video_path in video_list:
+    print(f"Processing video: {video_path}")
+    cap = cv2.VideoCapture(video_path)
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    image_y_size = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
+    image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH) // 3
+    print(f"Image size: {image_x_size}x{image_y_size}")
+    cap.release()
+
+    frame_start = 0   # 61000  # 0
+    frame_end = None  #64000  # None
+
+    position_changes = []
+    sex_position = ""
+    funscript_frames = []
+    funscript_distances = []
+
+    # Process the video
+    extract_yolo_data(yolo_model, video_path, frame_start, frame_end, TestMode)
+
+    yolo_data = load_yolo_data_from_file(video_path[:-4] + f"_rawyolo.json")
+
+    results = make_data_boxes(yolo_data, image_x_size)
+
+    first_penis_frame = parse_yolo_data_looking_for_penis(yolo_data, 0)
+
+    frame_start = max(max(first_penis_frame - int(fps), frame_start - int(fps)), 0)
+
+    analyze_tracking_results(results, image_y_size, video_path, frame_start, frame_end, TestMode)
+
+    funscript_handler = FH.FunscriptGenerator()
+    funscript_handler.generate(funscript_frames, funscript_distances, video_path[:-4] + "_kAI.funscript", fps, TestMode)
+
+    print(f"Finished processing video: {video_path}")
+
diff --git a/models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/model.mlmodel b/models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/model.mlmodel
new file mode 100644
index 00000000..e69de29b
diff --git a/models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/weights/weight.bin
new file mode 100644
index 00000000..e69de29b
diff --git a/models/k00gar-11n-200ep-best.mlpackage/Manifest.json b/models/k00gar-11n-200ep-best.mlpackage/Manifest.json
new file mode 100644
index 00000000..cac3aa39
--- /dev/null
+++ b/models/k00gar-11n-200ep-best.mlpackage/Manifest.json
@@ -0,0 +1,18 @@
+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "3EE8E38E-A97B-4918-8FA3-64EE837C9D1B": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        },
+        "6FDCFB65-8E94-427D-8CB9-606F5C6DE5F2": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        }
+    },
+    "rootModelIdentifier": "3EE8E38E-A97B-4918-8FA3-64EE837C9D1B"
+}
diff --git a/utils/FunscriptHandler.py b/utils/FunscriptHandler.py
new file mode 100644
index 00000000..e5062874
--- /dev/null
+++ b/utils/FunscriptHandler.py
@@ -0,0 +1,227 @@
+import os
+import json
+from simplification.cutil import simplify_coords
+import matplotlib.pyplot as plt
+
+class FunscriptGenerator:
+    def generate(self, frames, distances, output_path, fps, TestMode = False):
+        print(f"Generating funscript based on {len(distances)} distances...")
+
+        self.points = list(zip(frames, distances))
+
+        self.filtered_positions = simplify_coords(self.points, 10.0)  # Use VW algorithm
+
+        self.write_funscript(self.filtered_positions, output_path, fps)
+
+        # for alternative version, make every point 0 if distance is under 20
+        points_v2 = []
+        multiplier = 1.2
+        for i in range(len(self.filtered_positions)):
+            if self.filtered_positions[i][1] < 10:
+                distance = 0
+            else:
+                if self.filtered_positions[i][1] < 50:
+                    distance = int(self.filtered_positions[i][1] / multiplier)
+                else:
+                    distance = min(int(self.filtered_positions[i][1] * multiplier), 100)
+            # print(f"Point {i}: was {self.filtered_positions[i][1]}, now is {distance}")
+            points_v2.append((self.filtered_positions[i][0], distance))
+
+        write_path = output_path[:-10] + '_remapped.funscript'
+        self.write_funscript(points_v2, write_path, fps)
+
+        if TestMode:
+            # plot a comparative graph
+            self.plot_comparison(self.points, self.filtered_positions, points_v2)
+
+
+    def write_funscript(self, distances, output_path, fps):
+        with open(output_path, 'w') as f:
+            f.write('{"version":"1.0","inverted":false,"range":95,"author":"kAI","actions":[{"at": 0, "pos": 100},')
+            i = 0
+            for frame, position in distances:
+                time_ms = int(frame * 1000 / fps)
+                if i > 0:
+                    f.write(",")
+                f.write(f' {{"at": {time_ms}, "pos": {int(position)}}}')
+                i += 1
+            f.write("]}\n")
+
+    def dump_section(self, section_id, frames, distances, output_path, fps):
+        print(f"Generating json file for section {section_id} content based on {len(distances)} distances...")
+        points = []
+        # build points based on the list of frames, distances
+        for i in range(len(frames)):
+            points.append((int(frames[i] * 1000 / fps), int(distances[i])))
+        filtered_positions = simplify_coords(points, 5.0)
+        print(f"Filtered positions: {len(filtered_positions)}")
+
+        # Convert filtered positions to a list of dictionaries
+        filtered_data = [{"at": timing_ms, "pos": distance} for timing_ms, distance in filtered_positions]
+
+        # Create the output file path
+        output_file = f"{output_path}_section_{section_id}.json"
+
+        # Dump filtered positions to json file
+        with open(output_file, 'w') as f:
+            json.dump(filtered_data, f, indent=4)
+
+        print(f"Section {section_id} dumped to {output_file}")
+
+    def assemble_sections(self, number_of_sections, output_path):
+        # assemble json section files to a final funscript file
+        final_data = []
+
+        # Load each section's JSON file and append its data to the final data
+        for section_id in range(1, number_of_sections + 1):
+            section_file = f"{output_path}_section_{section_id}.json"
+            with open(section_file, 'r') as f:
+                section_data = json.load(f)
+                final_data.extend(section_data)
+
+        # Sort the final data by frame number
+        final_data.sort(key=lambda x: x['at'])
+
+        # Create the final funscript file
+        final_file = f"{output_path}"
+        #with open(final_file, 'w') as f:
+        #    json.dump(final_data, f, indent=4)
+        with open(output_path, 'w') as f:
+            #f.write("[\n")
+            f.write('{"version":"1.0","inverted":false,"range":95,"author":"kAI","actions":[')
+            i = 0
+            for timing_ms, position in self.filtered_positions:
+                if i > 0:
+                    f.write(",")
+                f.write(f' {{"at": {timing_ms}, "pos": {int(position)}}}')
+                i += 1
+            f.write("]}\n")
+
+        print(f"Final funscript file assembled and saved to {final_file}")
+
+    def plot_comparison(self, original_points, rdp_filtered_points, vw_filtered_points):
+        plt.figure(figsize=(12, 6))
+
+        # Plot original points
+        plt.plot([p[0] for p in original_points], [p[1] for p in original_points], 'b-', label='Original Points')
+
+        # Plot RDP filtered points
+        plt.plot([p[0] for p in rdp_filtered_points], [p[1] for p in rdp_filtered_points], 'r-', label='RDP Filtered Points')
+
+        # Plot VW filtered points
+        plt.plot([p[0] for p in vw_filtered_points], [p[1] for p in vw_filtered_points], 'g-', label='VW Filtered Points')
+
+        plt.xlabel('Frames')
+        plt.ylabel('Distances')
+        plt.title('Comparison of RDP and VW Filtered Positions')
+        plt.legend()
+        plt.savefig('comparison_plot.png')
+        plt.show()
+        #plt.savefig('comparison_plot.png')
+
+    def smooth_distance_ema(self, distances, alpha=0.3):
+        smoothed = [distances[0]]
+        for i in range(1, len(distances)):
+            smoothed.append(alpha * distances[i] + (1 - alpha) * smoothed[-1])
+        return [[i, smoothed[i]] for i in range(len(smoothed))]
+
+    def keep_significant_points(self, positions):
+        significant = [positions[0]]
+        for i in range(1, len(positions) - 1):
+            if positions[i][1] != positions[i - 1][1] and positions[i][1] != positions[i + 1][1]:
+                significant.append(positions[i])
+        significant.append(positions[-1])
+        return significant
+
+    def filter_positions(self, positions, fps):
+        if not positions:
+            return []
+
+        filtered_positions = [positions[0]]  # Start with the first position
+
+        min_interval_ms = 50  # Minimum interval between points in milliseconds
+        slope_threshold = 0.2  # Adjusted slope threshold for gradual changes
+
+        def calculate_slope(pos1, time1, pos2, time2):
+            return (pos2 - pos1) / (time2 - time1) if (time2 - time1) != 0 else 0
+
+        for i in range(1, len(positions) - 1):
+            current_pos = positions[i]
+
+            # Skip None values
+            if current_pos is None:
+                continue
+
+            prev_pos = positions[i - 1]
+            next_pos = positions[i + 1]
+
+            # Skip consecutive duplicate positions
+            if current_pos[1] == filtered_positions[-1][1] and current_pos[1] == next_pos[1]:
+                continue
+
+            # Calculate slopes
+            slope_prev = calculate_slope(prev_pos[1], prev_pos[0], current_pos[1], current_pos[0])
+            slope_next = calculate_slope(current_pos[1], current_pos[0], next_pos[1], next_pos[0])
+            slope_diff = abs(slope_next - slope_prev)
+
+            is_local_extreme = ((current_pos[1] >= prev_pos[1] and current_pos[1] > next_pos[1])
+                                or (current_pos[1] > prev_pos[1] and current_pos[1] >= next_pos[1])
+                                or (current_pos[1] <= prev_pos[1] and current_pos[1] < next_pos[1])
+                                or (current_pos[1] < prev_pos[1] and current_pos[1] <= next_pos[1]))
+
+            # Add to filtered lists based on conditions
+            if (is_local_extreme or slope_diff > slope_threshold) and (abs(
+                    current_pos[0] - filtered_positions[-1][0]) * 1000 / fps) > min_interval_ms:
+                filtered_positions.append(current_pos)
+
+        # Ensure the last point meets the interval requirement
+        if len(filtered_positions) > 1 and positions[-1][0] - filtered_positions[-1][0] >= min_interval_ms:
+            filtered_positions.append(positions[-1])
+
+        return filtered_positions
+
+    def load_funscript(self, funscript_path):
+        # if the funscript path exists
+        if not os.path.exists(funscript_path):
+            print(f"Funscript not found at {funscript_path}")
+            return None, None, None, None
+
+        with open(funscript_path, 'r') as f:
+            try:
+                print(f"Loading funscript from {funscript_path}")
+                data = json.load(f)
+            except json.JSONDecodeError as e:
+                print(f"JSONDecodeError: {e}")
+                print(f"Error occurred at line {e.lineno}, column {e.colno}")
+                print("Dumping the problematic JSON data:")
+                f.seek(0)  # Reset file pointer to the beginning
+                print(f.read())
+                return None, None, None, None
+
+        times = []
+        positions = []
+
+        for action in data['actions']:
+            times.append(action['at'])
+            positions.append(action['pos'])
+        print(f"Loaded funscript with {len(times)} actions")
+
+        # Access the chapters
+        chapters = data.get("metadata", {}).get("chapters", [])
+
+        relevant_chapters_export = []
+        irrelevant_chapters_export = []
+        # Print the chapters
+        for chapter in chapters:
+            print(f"Chapter: {chapter['name']}, Start: {chapter['startTime']}, End: {chapter['endTime']}")
+            # convert 00:00:00 to milliseconds
+            startTime_ms = int(chapter['startTime'].split(':')[0]) * 60 * 60 * 1000 + int(
+                chapter['startTime'].split(':')[1]) * 60 * 1000 + int(chapter['startTime'].split(':')[2]) * 1000
+            endTime_ms = int(chapter['endTime'].split(':')[0]) * 60 * 60 * 1000 + int(
+                chapter['endTime'].split(':')[1]) * 60 * 1000 + int(chapter['endTime'].split(':')[2]) * 1000
+            if chapter['name'] in ['POV Kissing', 'Close Up', 'Asslicking', 'Creampie']:
+                irrelevant_chapters_export.append([chapter['name'], startTime_ms, endTime_ms])
+            else:  # if chapter['name'] == 'Blow Job':
+                relevant_chapters_export.append([chapter['name'], startTime_ms, endTime_ms])
+
+        return times, positions, relevant_chapters_export, irrelevant_chapters_export
\ No newline at end of file
diff --git a/utils/KalmanFilter.py b/utils/KalmanFilter.py
new file mode 100644
index 00000000..eec9b87c
--- /dev/null
+++ b/utils/KalmanFilter.py
@@ -0,0 +1,36 @@
+import numpy as np
+import cv2
+
+class KalmanFilter:
+    def __init__(self):
+        self.kf = cv2.KalmanFilter(4, 2)
+        self.kf.measurementMatrix = np.array([[1, 0, 0, 0],
+                                              [0, 1, 0, 0]], np.float32)
+        self.kf.transitionMatrix = np.array([[1, 0, 1, 0],
+                                             [0, 1, 0, 1],
+                                             [0, 0, 1, 0],
+                                             [0, 0, 0, 1]], np.float32)
+        self.kf.processNoiseCov = np.eye(4, dtype=np.float32) * 0.03
+        self.position = None
+        self.detected = False
+        self.prediction_count = 0
+        self.half_height = 20
+        self.half_width = 20
+        self.touching = False
+
+    def update(self, measurement):
+        measured = np.array([[np.float32(measurement[0])],
+                             [np.float32(measurement[1])]])
+        self.kf.correct(measured)
+        self.position = measurement
+        self.detected = True
+        self.prediction_count = 0
+
+    def predict(self):
+        prediction = self.kf.predict()
+        self.position = (prediction[0] - self.half_width, prediction[1] - self.half_height,
+                         prediction[0] + self.half_width, prediction[1] + self.half_height)
+        self.detected = False
+        self.prediction_count += 1
+        return prediction[0]
+
diff --git a/utils/ObjectDetector.py b/utils/ObjectDetector.py
new file mode 100644
index 00000000..d8fb7610
--- /dev/null
+++ b/utils/ObjectDetector.py
@@ -0,0 +1,44 @@
+from ultralytics import YOLO
+import gc
+
+class ObjectDetector:
+    def __init__(self, model_name):
+        self.model = YOLO(model_name, task='detect')
+        self.class_priority_order = {
+            "glans": 0, "penis": 1, "breast": 2, "navel": 3, "pussy": 4, "butt": 5, "face": 6
+        }
+        self.class_default_priority = 7
+        class_types = {'face': 0, 'hand': 1, 'penis': 2, 'glans': 3, 'pussy': 4, 'butt': 5,
+                       'anus': 6, 'breast': 7, 'navel': 8, 'foot': 9}
+
+        # Define priority order
+        class_priority_order = {"glans": 0, "penis": 1, "breast": 2, "navel": 3, "pussy": 4, "butt": 5, "face": 6}
+        class_default_priority = 7  # For classes not explicitly listed
+
+    def detect(self, frame):
+        results = self.model(frame, verbose=False)
+        boxes = []
+        for result in results:
+            for *box, conf, cls in result.boxes.data.cpu().numpy():
+                #print(f"Detected {result.names[int(cls)]} with confidence {conf} and box {box}")
+                class_name = self.map_class_type_to_name(result.names[int(cls)], box[0], box[2], frame.shape[1])
+                boxes.append((box, conf, cls, class_name))
+        # Sort the collected boxes by priority
+        sorted_boxes = sorted(
+            boxes,
+            key=lambda x: self.class_priority_order.get(x[3], self.class_default_priority)  # Sort by class name priority
+        )
+        del results
+        gc.collect()
+        return sorted_boxes
+
+    def map_class_type_to_name(self, class_type, x1, x2, image_width):
+        if class_type in ['foot', 'hand']:
+            # Call it left if it is mainly on the left of the frame, right otherwise
+            if (x1 + x2) / 2 < image_width / 2:
+                class_name = 'left ' + class_type
+            else:
+                class_name = 'right ' + class_type
+        else:
+            class_name = class_type
+        return class_name
diff --git a/utils/ObjectTracker.py b/utils/ObjectTracker.py
new file mode 100644
index 00000000..710d3398
--- /dev/null
+++ b/utils/ObjectTracker.py
@@ -0,0 +1,770 @@
+from utils import KalmanFilter as KF
+from collections import deque
+import numpy as np
+import cv2
+from utils.config import class_names
+
+class ObjectTracker:
+    def __init__(self, state=None):
+        self.class_names = class_names
+        self.tracked_objects = {
+            class_name: {'kf': KF.KalmanFilter(),
+                         'position': None, 'detected': False, 'touching': False, 'prediction_count': 0}
+            for class_name in class_names
+        }
+        self.detection_interval = 10
+        self.frame_count = 0
+        self.trackers = {}
+
+        self.distance_kf = KF.KalmanFilter()
+
+        self.frame = None
+        self.image_y_size = 0
+
+        self.occlusion = False
+
+        self.minimum_penis_consecutive_detections = 2
+
+        self.penis_box, self.locked_penis_box = None, None
+        self.glans_detected = False
+        self.locked_penis_height = 0
+        self.breast_tracking = False
+        self.distance = 100
+        self.raw_distance = 100
+        self.previous_distances = [100, 100, 100]
+        self.tracked_body_part = "Nothing"
+
+        self.face_distance = 100
+        self.hand_distance = 100
+        self.previous_hand_distance = 100
+        self.previous_face_distance = 100
+        self.smoothed_distance = 100
+        self.smoothed_foot_distance = 100
+
+        # Handjob / Blowjob tweaking and stabilization parameters
+        self.switch_threshold_multiplier = 1.5  # Movement must be 1.5x higher to switch
+        self.required_frames_to_switch = 5  # 3 Number of consecutive frames needed to switch
+        self.cooldown_frames = 10  # 5 Frames to wait before allowing another switch
+        self.last_tracked_body_part = None
+        self.switch_cooldown = 0  # Frames left in cooldown
+        # Movement tracking
+        self.face_hands_movements = {}
+        self.previous_hand_distance = 0
+        self.face_distances = []  # List to store recent face distances
+        self.right_hand_distances = []  # List to store recent right hand distances
+        self.left_hand_distances = []  # List to store recent left hand distances
+
+        # Footjob tweaking and stabilization parameters
+        # Stabilization parameters for feet
+        self.foot_switch_threshold_multiplier = 1.5  # Movement must be 1.5x higher to switch
+        self.required_foot_frames_to_switch = 3  # Number of consecutive frames needed to switch
+        self.foot_cooldown_frames = 5  # Cooldown period for switching foot tracking
+        self.last_tracked_foot = None
+        self.foot_switch_cooldown = 0  # Frames left in cooldown
+        # Movement tracking for feet
+        self.foot_movements = {}
+        self.right_foot_distances = []  # List to store recent right foot distances
+        self.left_foot_distances = []  # List to store recent left foot distances
+
+        self.sex_position = "Not relevant"
+        self.prev_sex_position = "Not relevant"
+
+        #self.positions = {class_name: [] for class_name in class_names}
+        self.positions = {class_name: deque(maxlen=60) for class_name in class_names}
+
+        # Initialize normalized_positions as a dictionary of dictionaries
+        self.normalized_positions = {class_name: deque(maxlen=10) for class_name in class_names}
+        self.normalized_positions['navel'].append(100)
+        self.normalized_positions['breast'].append(100)
+        #    'breast': deque(maxlen=10),
+        #    'navel': deque(maxlen=10),
+            # add other classes as needed
+        #}
+
+        #self.normalized_positions = []
+
+        self.moving_average_window = 5  # Number of frames to consider for moving average
+
+        # Counters for consecutive detections and non-detections
+        self.consecutive_penis_detections = 0
+        self.consecutive_no_penis_detections = 0
+
+        self.penetration = False
+        self.penetration_mode = 'No penetration'
+        self.tracked_body_part = None
+
+        self.max_predictions = 600
+
+        if state:
+            tracked_objects_state = state.get('tracked_objects_state', {})
+            for class_name, obj_state in tracked_objects_state.items():
+                if class_name in self.tracked_objects:
+                    self.tracked_objects[class_name]['kf'].mean = obj_state['mean']
+                    self.tracked_objects[class_name]['kf'].covariance = obj_state['covariance']
+                    self.tracked_objects[class_name]['position'] = obj_state['position']
+                    self.tracked_objects[class_name]['detected'] = obj_state['detected']
+                    self.tracked_objects[class_name]['touching'] = obj_state['touching']
+                    self.tracked_objects[class_name]['prediction_count'] = obj_state['prediction_count']
+
+            distance_kf_state = state.get('distance_kf_state', {})
+            if distance_kf_state:
+                self.distance_kf.mean = distance_kf_state['mean']
+                self.distance_kf.covariance = distance_kf_state['covariance']
+
+            self.tracked_body_part = state.get('tracked_body_part', "Nothing")
+            self.sex_position = state.get('sex_position', "Not relevant")
+            self.sex_position_reason = state.get('sex_position_reason', "")
+
+            positions = state.get('positions', {})
+            for class_name, pos_list in positions.items():
+                if class_name in self.positions:
+                    self.positions[class_name].extend(pos_list)
+
+            normalized_positions = state.get('normalized_positions', {})
+            for class_name, norm_pos_list in normalized_positions.items():
+                if class_name in self.normalized_positions:
+                    self.normalized_positions[class_name].extend(norm_pos_list)
+
+            self.face_hands_movements = state.get('face_hands_movements', {})
+            self.foot_movements = state.get('foot_movements', {})
+            self.right_hand_distances = state.get('right_hand_distances', [])
+            self.left_hand_distances = state.get('left_hand_distances', [])
+            self.right_foot_distances = state.get('right_foot_distances', [])
+            self.left_foot_distances = state.get('left_foot_distances', [])
+            self.consecutive_penis_detections = state.get('consecutive_penis_detections', 0)
+            self.consecutive_no_penis_detections = state.get('consecutive_no_penis_detections', 0)
+            self.penetration = state.get('penetration', False)
+            self.penetration_mode = state.get('penetration_mode', 'No penetration')
+            self.switch_cooldown = state.get('switch_cooldown', 0)
+            self.foot_switch_cooldown = state.get('foot_switch_cooldown', 0)
+
+    def get_state(self):
+        state = {
+            'tracked_objects_state': {
+                class_name: {
+                    'mean': obj['kf'].mean,
+                    'covariance': obj['kf'].covariance,
+                    'position': obj['position'],
+                    'detected': obj['detected'],
+                    'touching': obj['touching'],
+                    'prediction_count': obj['prediction_count']
+                }
+                for class_name, obj in self.tracked_objects.items()
+            },
+            'distance_kf_state': {
+                'mean': self.distance_kf.mean,
+                'covariance': self.distance_kf.covariance
+            },
+            'tracked_body_part': self.tracked_body_part,
+            'sex_position': self.sex_position,
+            'sex_position_reason': self.sex_position_reason,
+            'positions': {
+                class_name: list(pos)
+                for class_name, pos in self.positions.items()
+            },
+            'normalized_positions': {
+                class_name: list(norm_pos)
+                for class_name, norm_pos in self.normalized_positions.items()
+            },
+            'face_hands_movements': self.face_hands_movements,
+            'foot_movements': self.foot_movements,
+            'right_hand_distances': self.right_hand_distances,
+            'left_hand_distances': self.left_hand_distances,
+            'right_foot_distances': self.right_foot_distances,
+            'left_foot_distances': self.left_foot_distances,
+            'consecutive_penis_detections': self.consecutive_penis_detections,
+            'consecutive_no_penis_detections': self.consecutive_no_penis_detections,
+            'penetration': self.penetration,
+            'penetration_mode': self.penetration_mode,
+            'switch_cooldown': self.switch_cooldown,
+            'foot_switch_cooldown': self.foot_switch_cooldown
+        }
+        return state
+
+    def reinit_with_distance(self, distance):
+        self.__init__()
+        self.distance = distance
+
+    def initialize_trackers(self, sorted_boxes):
+        params = cv2.TrackerVit_Params()
+        params.net = "models/vit/object_tracking_vittrack_2023sep.onnx"
+
+        for box, conf, cls, class_name in sorted_boxes:
+            if class_name not in self.trackers:
+                tracker = cv2.TrackerVit_create(params)
+                tracker.init(self.frame, tuple(box))
+                self.trackers[class_name] = tracker
+
+    def update_trackers(self):
+        for class_name, tracker in self.trackers.items():
+            success, box = tracker.update(self.frame)
+            if success:
+                self.tracked_objects[class_name]['position'] = box
+                self.tracked_objects[class_name]['detected'] = True
+            else:
+                self.tracked_objects[class_name]['detected'] = False
+
+    def update_distance(self, raw_distance):
+        rounded_distance = round(raw_distance/5)*5
+        #self.previous_distances.pop(0)
+        #self.previous_distances.append(raw_distance)
+        ema_distance = int(0.7 * rounded_distance + 0.1 * self.previous_distances[1] + 0.2 * self.previous_distances[2])
+        # cap the change in distance to a maximum of +5 or -5 vs previous distance
+        if abs(ema_distance - self.previous_distances[-1]) > 15:
+            ema_distance = self.previous_distances[-1] + np.sign(ema_distance - self.previous_distances[-1]) * 15
+            #print(f"Capping distance change to {ema_distance} vs {raw_distance}")
+        elif abs(ema_distance - self.previous_distances[-1]) < 2:
+            # if the change is less than 2, use the previous distance to eliminate jitter
+            #print(f"Skipping distance change to {ema_distance} vs {self.previous_distances[-1]} because change is less than 4")
+            ema_distance = self.previous_distances[-1]
+        self.previous_distances.pop(0)
+        self.previous_distances.append(ema_distance)
+        #self.raw_distance = raw_distance
+        if raw_distance is not None:
+            # keep the distance between 0 and 100
+            #raw_distance = max(0, min(100, int(raw_distance)))
+            # Update the Kalman filter with the measurement
+            #raw_distance_2d = (raw_distance, 0)
+            ema_distance_2d = (ema_distance, 0)
+            #filtered_distance = self.distance_kf.update(raw_distance_2d)
+            #filtered_distance = self.distance_kf.update(raw_distance_2d)
+            self.distance_kf.update(ema_distance_2d)
+            #filtered_distance = float(self.distance_kf.predict()[0])
+            filtered_distance = ema_distance
+        else:
+            # Predict without updating
+            #filtered_distance = self.distance_kf.predict()
+            filtered_distance = int(float(self.distance_kf.predict()[0]) / 5) * 5
+            # keep the distance between 0 and 100
+            filtered_distance = max(0, min(100, filtered_distance))
+        # print(f"Raw distance vs filtered distance: {raw_distance} vs {filtered_distance}")
+        return filtered_distance
+
+    def update_tracking(self, class_name, box, fallback_class, tracked_item):
+        tracked = self.tracked_objects[class_name]
+        if box is not None:
+            # Object detected: Update Kalman filter
+            tracked['kf'].update([(box[0] + box[2]) / 2, (box[1] + box[3]) / 2])
+            tracked['position'] = box
+            tracked['detected'] = True
+            tracked['half_height'] = (box[3] - box[1]) // 2
+            tracked['half_width'] = (box[2] - box[0]) // 2
+            tracked['touching'] = self.boxes_overlap(self.locked_penis_box, box)
+            tracked['prediction_count'] = 0  # Reset prediction count when object is detected
+        # Only perform prediction for the tracked item
+        elif class_name == tracked_item:
+            # Object not detected: Predict position based on the Kalman filter
+            # print(f"Kalman prediction for {class_name}: {tracked['prediction_count'] + 1} / {int(self.max_predictions)}")
+            tracked['kf'].predict()
+            # print(f"Kalman prediction for {class_name}: {tracked['prediction_count'] + 1} / {int(self.max_predictions)}")
+            tracked['detected'] = False
+            tracked['position'] = tracked['kf'].position
+            tracked['touching'] = self.boxes_overlap(self.locked_penis_box, tracked['position'])
+
+            # Use fallback if primary tracked object is lost and prediction count exceeds 60
+            if tracked['prediction_count'] >= self.max_predictions:
+                if fallback_class and self.tracked_objects[fallback_class]['detected']:
+                    print(f"Handling occlusion of {class_name}, fallback to {fallback_class}")
+                    tracked['position'] = self.tracked_objects[fallback_class]['position']
+                else:
+                    print(f"No fallback available for {class_name}, deactivating tracking")
+                    tracked['position'] = None
+                    tracked['detected'] = False
+                    tracked['touching'] = False
+                    tracked['prediction_count'] = 0
+                    self.tracked_body_part = None
+
+                    # print(f"Fallback to {fallback_class}")
+                    #draw_bounding_box(image, tracked['position'], fallback_class)
+                #else:
+                    # print(f"No fallback available for {class_name}")
+
+        return tracked['position']
+
+    def boxes_overlap(self, box1, box2):
+        if box1 is None or box2 is None:
+            return False
+        x1, y1, x2, y2 = box1
+        x3, y3, x4, y4 = box2
+        if x2 < x3 or x4 < x1 or y2 < y3 or y4 < y1:
+            return False
+        return True
+
+    def calculate_distance(self, penis_box, other_box, class_name):
+        """Calculate distance between penis box and other body part box."""
+        px1, py1, px2, py2 = penis_box
+        ox1, oy1, ox2, oy2 = other_box
+
+        min_reevaluation = py1
+        max_reevaluation = py2
+
+        # Retrieve normalized distance for class_name
+        normalized_distance = self.normalized_positions[class_name][-1]
+
+        if class_name in ['left foot', 'right foot', 'breast', 'left hand', 'right hand']:
+            # Calculate distance from middle of hand/foot box to bottom of penis box
+            pos = (oy1 + oy2) // 2
+            min_reevaluation = py1 + ((oy2 - oy1) * 0.2)
+            max_reevaluation = py2 - ((oy2 - oy1) / 3)
+
+        else:
+            # Calculate distance from bottom of other box to bottom of penis box
+            pos = oy2
+            if class_name == 'butt':
+                min_reevaluation = py1 + ((oy2 - oy1) * 0.2)
+            elif class_name == 'face':
+                min_reevaluation = py1 + ((py2 - py1) * 0.1)
+                max_reevaluation = py1 + ((py2 - py1) * 0.6)
+            elif class_name == 'pussy':
+                max_reevaluation = py2 - ((oy2 - oy1) // 2)
+
+        secured_distance = max(0, max_reevaluation - pos)
+        # Normalize distance to 0-100 scale
+        max_distance = max_reevaluation - min_reevaluation
+
+        if normalized_distance:
+            if max_distance == 0:
+                return normalized_distance
+            else:
+                normalized_distance = (int(max(0, min(100, (secured_distance / max_distance) * 100))) + normalized_distance) //2
+        else:
+            if max_distance == 0:
+                return 100
+            else:
+                normalized_distance = int(max(0, min(100, (secured_distance / max_distance) * 100)))
+        # print(f"{class_name} distance: secured : {secured_distance}, max distance: {max_distance},  normalized: {normalized_distance}")
+        return normalized_distance
+
+    def detect_sex_position_change(self, sex_position, reason):
+        if sex_position != self.sex_position and sex_position == self.prev_sex_position:
+            print(f"Sex position changed from {self.sex_position} to {sex_position} given {reason}")
+            self.sex_position = sex_position
+            self.sex_position_reason = reason
+        else:
+            self.prev_sex_position = sex_position
+
+    def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
+        #self.frame = frame
+        self.image_y_size = image_y_size
+        self.frame_count += 1
+
+        #if self.frame_count % self.detection_interval == 0:
+        #    self.initialize_trackers(sorted_boxes)
+        #else:
+        #    self.update_trackers()
+
+        # initialize values
+        self.foot_detected = False
+        self.glans_detected = False
+        self.tracking_states = {class_name: False for class_name in class_names}
+        self.boxes = {class_name: None for class_name in class_names}
+
+        #for class_name in class_names:
+        #    # check if class_name is in sorted_boxes
+        #    if any(box[3] == class_name for box in sorted_boxes):
+        #        self.tracking_states[class_name] = True
+
+        for box, conf, cls, class_name in sorted_boxes:
+            if class_name == "glans" and conf > 0.5:
+                self.glans_detected = True
+                if conf > 0.8 and self.penetration:
+                    self.penetration = False
+                    # print(f"Penetration ended at frame {current_frame_id}")
+
+            elif class_name == "penis" and conf > 0.5:
+                self.boxes['penis'] = box
+                self.penis_handling(box, conf, current_frame_id)
+
+            elif class_name == "navel" and conf > 0.5:
+                self.navel_handling(box)
+
+            if class_name in ["breast", "navel"] and conf > 0.5:
+                self.log_and_normalize_pos(box, class_name)
+
+            elif self.locked_penis_box and class_name not in ["penis", "glans"]:
+                if conf > 0.4:
+                    self.update_tracking(class_name, box, None, self.tracked_body_part)
+                else:
+                    fallback_class = {'pussy': 'breast', 'breast': 'navel'}.get(class_name)
+                    self.update_tracking(class_name, None, fallback_class, self.tracked_body_part)
+
+                #if class_name not in ["breast", "navel"] and not self.boxes_overlap(locked_penis_box, box):
+                #    continue
+
+                if class_name not in ["breast", "navel"] and not self.tracked_objects[class_name]['touching']:
+                    continue
+
+                if class_name in ["left foot", "right foot", "left hand", "right hand", "butt", "face", "pussy"] and self.tracked_objects[class_name]['touching']:
+                    self.log_and_normalize_pos(box, class_name)
+
+                self.boxes[class_name] = box
+                self.tracking_states[class_name] = True
+        if self.tracked_body_part == "Nothing":
+            self.update_distance(100)
+
+        if self.tracked_body_part and self.tracked_body_part != "Nothing":
+            if not self.tracking_states[self.tracked_body_part]:
+                if not self.occlusion:
+                    #print(f"Lost tracking of {self.tracked_body_part} at frame {current_frame_id}, suspecting occlusion")
+                    self.occlusion = True
+                self.update_tracking(self.tracked_body_part, None, None, self.tracked_body_part)
+            else:
+                if self.occlusion:
+                    #print(f"Found back {self.tracked_body_part} at frame {current_frame_id}")
+                    self.occlusion = False
+
+        if self.breast_tracking:
+            if self.boxes['breast'] is not None and not self.boxes_overlap(self.locked_penis_box, self.boxes['breast']):
+            #if self.boxes['breast'] is not None and not self.tracked_objects['breast']['touching']:
+                # experimenting to handle delay in breast tracking when boobs reach peak or low position
+                #projected_frame_id = current_frame_id  # + int(fps//5)
+                #distances[projected_frame_id] = normalized_positions['breast'].get(current_frame_id, 100)
+                distance = self.normalized_positions['breast'][-1]
+                self.distance = self.update_distance(distance)
+                self.tracked_body_part = "breast"
+                self.detect_sex_position_change("Closeup missionary", "breast tracking activated")
+                if not self.penetration:
+                    self.penetration = True
+                    print(f"Penetration started at frame {current_frame_id}")
+        else:
+            if self.penis_box is None and self.penetration and self.tracked_body_part == "pussy":
+                # trying to handle pussy occlusion
+                if self.tracking_states['navel']:
+                    print(f"Handling pussy occlusion at frame {current_frame_id}")
+                    #self.update_distance(self.normalized_positions['navel'].get(current_frame_id, 100))
+                    self.update_distance(self.normalized_positions['navel'][-1])
+
+            #elif self.penis_box is not None and self.locked_penis_box is not None:
+            elif self.locked_penis_box:
+                # Dealing with irrelevant positions
+                if self.tracking_states['pussy'] and self.tracking_states['butt']:
+                    # position likely to be Closeup
+                    self.handle_closeup_position("presence of pussy and butt")
+
+                elif (self.tracking_states['pussy'] and (self.boxes['pussy'][3] - self.boxes['pussy'][1]) >
+                      image_y_size * 0.1):
+                    # position likely to be Closeup
+                    self.handle_closeup_position("size of pussy in frame")
+
+                # Now dealing with relevant positions
+                elif self.boxes['pussy'] is not None and self.tracking_states['pussy'] and self.boxes['breast'] is not None:
+                    # position likely to be CowGirl / Missionary
+                    self.handle_CowGirl_Missionary_position("presence of pussy and breast", current_frame_id, 'pussy')
+                    if self.tracked_objects['pussy']['touching'] and not self.penetration:
+                        self.penetration = True
+                        print(f"Penetration started at frame {current_frame_id}")
+
+                elif self.boxes['butt'] is not None and self.tracking_states['butt']:
+                    # position likely to be Reverse Cowgirl or Doggy / Pronebone
+                    if self.tracking_states['left foot'] or self.tracking_states['right foot']:
+                        # position likely to be Reverse Cowgirl
+                        self.handle_RevCowGirl_Doggy_Pronebone_position(
+                            "Reverse Cowgirl","presence of foot", current_frame_id)
+                    elif self.tracking_states['anus']:
+                        self.handle_RevCowGirl_Doggy_Pronebone_position(
+                            "Doggy / Pronebone", "presence of anus", current_frame_id)
+                    else:
+                        self.handle_RevCowGirl_Doggy_Pronebone_position(
+                            "Doggy / Pronebone", "absence of foot",current_frame_id)
+                    if self.tracked_objects['butt']['touching'] and not self.penetration:
+                        self.penetration = True
+                        print(f"Penetration started at frame {current_frame_id}")
+
+                elif ((self.boxes['face'] is not None and self.tracking_states['face']) or
+                      ((self.boxes['left hand'] is not None and self.tracking_states['left hand']) or
+                       (self.boxes['right hand'] is not None and self.tracking_states['right hand']))) and not self.penetration:
+
+                    self.handle_Blowjob_Handjob_position("presence of face or hand", current_frame_id)
+
+                elif self.boxes['navel'] is not None and not self.boxes_overlap(self.locked_penis_box, self.boxes['navel']) and self.penetration:
+                    # position likely to be CowGirl / Missionary, with occluded pussy
+                    self.handle_CowGirl_Missionary_position("presence of navel during penetration", current_frame_id, 'navel')
+
+                elif self.boxes['breast'] is not None and self.tracking_states['breast'] and not self.penetration and self.tracked_objects["breast"]['touching']:
+                    # position likely to be Boobjob
+                    self.handle_Boobjob_position("presence of breast", current_frame_id)
+
+                elif ((self.boxes['left foot'] is not None and self.tracking_states['left foot']) or (
+                        self.boxes['right foot'] is not None and self.tracking_states['right foot'])) and not self.penetration:
+                    # position likely to be Footjob
+                    self.handle_Footjob_position("presence of foot", current_frame_id)
+                #else:
+                #    # Kalman ?
+                #    self.distance = self.previous_distance
+
+        # Check for non-detection of penis
+        if self.boxes['penis'] is None:
+            self.consecutive_no_penis_detections += 1
+            self.consecutive_penis_detections = 0  # Reset the detection counter
+
+            # Deactivate locked_penis_box after x consecutive non-detections
+            if self.consecutive_no_penis_detections >= self.max_predictions:
+                if self.locked_penis_box is not None:
+                    self.locked_penis_box = None
+                    print(
+                        f"Penis tracking deactivated at frame {current_frame_id} after {self.consecutive_no_penis_detections} consecutive non-detections")
+                    #print("Reinitializing tracker")
+                    #self.__init__(self.class_names)
+                    self.penis_box = None
+
+    def penis_handling(self, box, conf, current_frame_id):
+        if self.penis_box is None:
+            print(f"Penis detected at frame {current_frame_id} with confidence {conf}")
+
+        self.penis_box = box
+
+        px1, py1, px2, py2 = self.penis_box
+        current_height = py2 - py1
+
+        if self.glans_detected:
+            self.locked_penis_height = current_height
+        elif current_height > self.locked_penis_height:
+            self.locked_penis_height = current_height
+
+        if self.locked_penis_box:
+            self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
+
+        self.consecutive_penis_detections += 1
+        self.consecutive_no_penis_detections = 0
+
+        if self.consecutive_penis_detections >= self.minimum_penis_consecutive_detections and self.locked_penis_box is None:
+            self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
+            # print(f"Locked penis box activated at frame {current_frame_id}")
+
+        if (self.image_y_size - py1) / self.image_y_size < 0.1 and not self.breast_tracking and self.tracked_objects['breast']['detected']:
+            # print("Breast tracking mode activated given penis position in lower 10th of frame")
+            self.breast_tracking = True
+
+        return self.penis_box, self.locked_penis_box, self.breast_tracking
+
+    def navel_handling(self, box):
+        if (self.image_y_size - box[1]) / self.image_y_size < 0.15 and not self.breast_tracking:
+            print("Breast tracking mode activated given navel position in lower 15th of frame")
+            self.breast_tracking = True
+
+    def log_and_normalize_pos(self, box, class_name):
+        self.boxes[class_name] = box
+        _, y1, _, y2 = box
+        mid_y = (y1 + y2) / 2
+        self.positions[class_name].append(mid_y)
+
+        min_y, max_y = min(self.positions[class_name]), max(self.positions[class_name])
+        normalized_y = (100 - int(100 * ((mid_y - min_y) / (max_y - min_y)))) if min_y != max_y else 100
+        #self.normalized_positions[class_name][current_frame_id] = normalized_y
+        self.normalized_positions[class_name].append(normalized_y)
+
+    def handle_closeup_position(self, reason):
+        self.distance = 100
+        self.update_distance(self.distance)
+        self.tracked_body_part = "Nothing"
+        self.detect_sex_position_change("Closeup", reason)
+
+    def handle_CowGirl_Missionary_position(self, reason, current_frame_id, body_part):
+        if body_part == "pussy":
+            distance = self.calculate_distance(self.locked_penis_box, self.boxes[body_part], body_part)
+            # trying to add up the navel distance and average
+            #if self.normalized_positions['navel'].get(current_frame_id, 100) is not None:
+            #    distance = 0.3 * distance + 0.7 * self.normalized_positions['navel'].get(current_frame_id, 100)
+            if self.normalized_positions['navel'][-1] is not None:
+                distance = 0.3 * distance + 0.7 * self.normalized_positions['navel'][-1]
+        elif body_part == "navel":
+            #distance = self.normalized_positions['navel'].get(current_frame_id, 100)
+            distance = self.normalized_positions['navel'][-1]
+        self.distance = self.update_distance(distance)
+        self.tracked_body_part = body_part
+        self.detect_sex_position_change("Cowgirl / Missionary", reason)
+
+    def handle_RevCowGirl_Doggy_Pronebone_position(self, position, reason, current_frame_id):
+        if self.boxes['butt'][3] > self.locked_penis_box[3]:
+            # print(f"Actuating locked penis box to fit butt lower position at frame {current_frame_id}")
+            self.locked_penis_box = (
+                self.locked_penis_box[0], self.locked_penis_box[1],
+                self.locked_penis_box[2], self.boxes['butt'][3])
+        distance = self.calculate_distance(self.locked_penis_box, self.boxes['butt'], 'butt')
+        self.distance = self.update_distance(distance)
+        self.tracked_body_part = "butt"
+        self.detect_sex_position_change(position, reason)
+
+    def handle_Blowjob_Handjob_position(self, reason, current_frame_id):
+        distance = None
+
+        if self.tracking_states['face'] and self.tracked_objects['face']['touching']:
+            if len(self.face_distances) > 3:
+                self.face_distances.pop(0)
+            distance = self.calculate_distance(self.locked_penis_box, self.boxes['face'], 'face')
+            #print(f"Face distance: {distance}")
+            self.face_distances.append(distance)
+            self.face_hands_movements['face'] = abs(max(self.face_distances) - min(self.face_distances))
+            # if no left hand or no right hand, return
+            if not self.tracked_objects['left hand']['touching'] and not self.tracked_objects['right hand']['touching']:
+                self.distance = self.update_distance(distance)
+                self.tracked_body_part = "face"
+                self.detect_sex_position_change("Blowjob / Handjob", "presence of face only")
+                return
+        else:
+            self.face_hands_movements['face'] = 0
+
+        if self.tracking_states['left hand'] and self.tracked_objects['left hand']['touching']:
+            if len(self.left_hand_distances) > 3:
+                self.left_hand_distances.pop(0)
+            distance = self.calculate_distance(self.locked_penis_box, self.boxes['left hand'], 'left hand')
+            #print(f"Left hand distance: {distance}")
+            self.left_hand_distances.append(distance)
+            self.face_hands_movements['left hand'] = abs(max(self.left_hand_distances) - min(self.left_hand_distances))
+            if not self.tracked_objects['right hand']['touching'] and not self.tracked_objects['face']['touching']:
+                self.distance = self.update_distance(distance)
+                self.tracked_body_part = "left hand"
+                self.detect_sex_position_change("Blowjob / Handjob", "presence of left hand only")
+                return
+        else:
+            self.face_hands_movements['left hand'] = 0
+
+        if self.tracking_states['right hand'] and self.tracked_objects['right hand']['touching']:
+            if len(self.right_hand_distances) > 3:
+                self.right_hand_distances.pop(0)
+            distance = self.calculate_distance(self.locked_penis_box, self.boxes['right hand'], 'right hand')
+            #print(f"Right hand distance: {distance}")
+            self.right_hand_distances.append(distance)
+            self.face_hands_movements['right hand'] = abs(
+                max(self.right_hand_distances) - min(self.right_hand_distances))
+            if not self.tracked_objects['left hand']['touching'] and not self.tracked_objects['face']['touching']:
+                self.distance = self.update_distance(distance)
+                self.tracked_body_part = "right hand"
+                self.detect_sex_position_change("Blowjob / Handjob", "presence of right hand only")
+                return
+        else:
+            self.face_hands_movements['right hand'] = 0
+
+        # Determine the most moving body part
+        most_moving_part = max(self.face_hands_movements, key=self.face_hands_movements.get, default=None)
+        #print(f"Most moving part: {most_moving_part}")
+        max_movement = self.face_hands_movements.get(most_moving_part, 0)
+        current_tracked_movement = self.face_hands_movements.get(self.tracked_body_part, 0)
+
+        if most_moving_part and most_moving_part != self.tracked_body_part:
+            if (
+                    max_movement > current_tracked_movement * self.switch_threshold_multiplier and
+                    self.switch_cooldown == 0
+            ):
+                if most_moving_part == self.last_tracked_body_part:
+                    self.consecutive_switch_frames += 1
+                else:
+                    self.consecutive_switch_frames = 1  # Reset counter if different part
+                self.last_tracked_body_part = most_moving_part
+
+                if self.consecutive_switch_frames >= self.required_frames_to_switch:
+                    self.tracked_body_part = most_moving_part
+                    self.consecutive_switch_frames = 0
+                    self.switch_cooldown = self.cooldown_frames  # Set cooldown
+                    #print(f"Switching to: {self.tracked_body_part} due to sustained movement")
+            else:
+                self.consecutive_switch_frames = 0  # Reset counter if condition not met
+        else:
+            self.consecutive_switch_frames = 0  # Reset if no significant movement detected
+
+        # Cooldown logic
+        if self.switch_cooldown > 0:
+            self.switch_cooldown -= 1
+
+        # Update position and distance for the tracked body part
+        if self.tracked_body_part in ['face', 'left hand', 'right hand']:
+            self.detect_sex_position_change("Blowjob / Handjob", reason)
+            if self.tracked_body_part == "face":
+                distance = self.face_distances[-1]
+            elif self.tracked_body_part == "left hand":
+                distance = self.left_hand_distances[-1]
+            elif self.tracked_body_part == "right hand":
+                distance = self.right_hand_distances[-1]
+
+            # Smooth transitions with a moving average
+            #if not hasattr(self, 'smoothed_distance'):
+            #    self.smoothed_distance = distance
+            #    print(f"Smoothed distance initialized to {self.smoothed_distance}")
+            #else:
+            #    smoothing_factor = 0.2  # Adjust for smoother transitions (lower = smoother)
+            #    self.smoothed_distance = self.smoothed_distance * (1 - smoothing_factor) + self.distance * smoothing_factor
+            #    print(f"Smoothed distance updated to {self.smoothed_distance}")
+            #self.distance = self.update_distance(self.smoothed_distance)
+
+            self.distance = self.update_distance(distance)
+
+            # Log the tracked information
+            # print(f"Tracked: {self.tracked_body_part}, Position: {self.sex_position}, Distance: {int(self.smoothed_distance)}")
+
+    def handle_Footjob_position(self, reason, current_frame_id):
+        distance = 100
+
+        if self.tracking_states['left foot'] and self.tracked_objects['left foot']['touching']:
+            if len(self.left_foot_distances) > 3:
+                self.left_foot_distances.pop(0)
+            self.left_foot_distances.append(
+                self.calculate_distance(self.locked_penis_box, self.boxes['left foot'], 'left foot'))
+            self.foot_movements['left foot'] = abs(max(self.left_foot_distances) - min(self.left_foot_distances))
+
+        if self.tracking_states['right foot'] and self.tracked_objects['right foot']['touching']:
+            if len(self.right_foot_distances) > 3:
+                self.right_foot_distances.pop(0)
+            self.right_foot_distances.append(
+                self.calculate_distance(self.locked_penis_box, self.boxes['right foot'], 'right foot'))
+            self.foot_movements['right foot'] = abs(max(self.right_foot_distances) - min(self.right_foot_distances))
+
+        # Determine the most moving foot
+        most_moving_foot = max(self.foot_movements, key=self.foot_movements.get, default=None)
+        max_foot_movement = self.foot_movements.get(most_moving_foot, 0)
+        current_tracked_foot_movement = self.foot_movements.get(self.tracked_body_part, 0)
+
+        if most_moving_foot and most_moving_foot != self.tracked_body_part:
+            # Check if movement is significantly higher than the currently tracked foot
+            if (
+                    max_foot_movement > current_tracked_foot_movement * self.foot_switch_threshold_multiplier and
+                    self.foot_switch_cooldown == 0
+            ):
+                # Increment confidence counter
+                if most_moving_foot == self.last_tracked_foot:
+                    self.consecutive_foot_switch_frames += 1
+                else:
+                    self.consecutive_foot_switch_frames = 1  # Reset counter if different foot
+
+                self.last_tracked_foot = most_moving_foot
+
+                # Switch only if sustained for required frames
+                if self.consecutive_foot_switch_frames >= self.required_foot_frames_to_switch:
+                    self.tracked_body_part = most_moving_foot
+                    self.consecutive_foot_switch_frames = 0
+                    self.foot_switch_cooldown = self.foot_cooldown_frames  # Set cooldown
+                    # print(f"Switching to: {self.tracked_body_part} due to sustained foot movement")
+            else:
+                self.consecutive_foot_switch_frames = 0  # Reset counter if condition not met
+        else:
+            self.consecutive_foot_switch_frames = 0  # Reset if no significant movement detected
+
+        # Cooldown logic
+        if self.foot_switch_cooldown > 0:
+            self.foot_switch_cooldown -= 1
+
+        # Update position and distance for the tracked foot
+        if self.tracked_body_part in ['left foot', 'right foot']:
+            self.detect_sex_position_change("Footjob", reason)
+            if self.tracked_body_part == "left foot":
+                distance = self.left_foot_distances[-1]
+            elif self.tracked_body_part == "right foot":
+                distance = self.right_foot_distances[-1]
+
+        # Smooth transitions with a moving average
+        foot_smoothing_factor = 0.2  # Adjust for smoother transitions (lower = smoother)
+        if hasattr(self, 'smoothed_foot_distance'):
+            self.smoothed_foot_distance = self.smoothed_foot_distance * (
+                        1 - foot_smoothing_factor) + distance * foot_smoothing_factor
+        else:
+            self.smoothed_foot_distance = distance
+
+        # Log the tracked foot information
+        # print(f"Tracked: {self.tracked_body_part}, Position: {self.sex_position}, Distance: {self.smoothed_foot_distance}")
+        self.distance = self.update_distance(self.smoothed_foot_distance)
+
+    def handle_Boobjob_position(self, reason, current_frame_id):
+        distance = self.calculate_distance(self.locked_penis_box, self.boxes['breast'], 'breast')
+        self.distance = self.update_distance(distance)
+        self.tracked_body_part = "breast"
+        self.detect_sex_position_change("Boobjob", reason)
+
diff --git a/utils/SceneCutsDetect.py b/utils/SceneCutsDetect.py
new file mode 100644
index 00000000..7b25b400
--- /dev/null
+++ b/utils/SceneCutsDetect.py
@@ -0,0 +1,88 @@
+import cv2
+import numpy as np
+import tqdm
+
+def compute_histogram(frame):
+    # Convert the frame to the HSV color space
+    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+    # Compute the histogram for the H channel
+    hist = cv2.calcHist([hsv], [0], None, [256], [0, 256])
+    # Normalize the histogram
+    cv2.normalize(hist, hist)
+    return hist
+
+def compare_histograms(hist1, hist2):
+    # Use the correlation method to compare histograms
+    return cv2.compareHist(hist1, hist2, cv2.HISTCMP_CORREL)
+
+def detect_scene_changes(video_path, crop = None, threshold=0.97, frame_start = 0, frame_end = None):
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        print("Error: Could not open video.")
+        return
+
+    frame_count = 0
+    prev_cut = 0
+    fps_step = 2 * int(cap.get(cv2.CAP_PROP_FPS))
+    total_frames_base = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    if frame_start != 0:
+        if frame_end is None:
+            total_frames = total_frames_base - frame_start
+        else:
+            total_frames = frame_end - frame_start
+    else:
+        total_frames = total_frames_base
+
+    total_frames_to_parse = int(total_frames / fps_step)
+    scene_changes = []
+    prev_hist = None
+
+    for frame_pos in tqdm.tqdm(range(total_frames_to_parse)):
+        cap.set(cv2.CAP_PROP_POS_FRAMES, frame_pos * fps_step)
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        if crop == "Left":
+            # only left side of the frame
+            frame = frame[:, :frame.shape[1] // 2]
+        elif crop == "Right":
+            # only right side of the frame
+            frame = frame[:, frame.shape[1] // 2:]
+
+        current_hist = compute_histogram(frame)
+
+        if prev_hist is not None:
+            similarity = compare_histograms(prev_hist, current_hist)
+            if similarity < threshold:
+                # we have a scene cut in between (frame_pos - 1)*fps and frame_pos * fps
+                tqdm.tqdm.write(
+                    f"Raw Scene change detected at frame {frame_pos * fps_step}, time: {int(cap.get(cv2.CAP_PROP_POS_MSEC) / 1000 // 60)} min {int(cap.get(cv2.CAP_PROP_POS_MSEC) / 1000 % 60)} sec")
+                scene_changes.append([prev_cut, frame_pos * fps_step])
+                prev_cut = frame_pos * fps_step
+
+        prev_hist = current_hist
+
+    scenes = []
+    for scene in scene_changes:
+        if len(scenes) == 0:
+            scenes.append(scene)
+        else:
+            if scene[1] - scenes[-1][1] < 5000:
+                scenes[-1][1] = scene[1]
+            else:
+                scenes.append(scene)
+
+    # add the last scene
+    if len(scenes) == 0:
+        scenes.append([0, total_frames_base])
+    else:
+        if scenes[-1][1] != total_frames_base:
+            scenes.append([scenes[-1][1], total_frames_base])
+
+    print(f"Found {len(scenes)} relevant scenes: {scenes}.")
+
+    cap.release()
+    return scenes
+
+
diff --git a/utils/VideoReaderFFmpeg.py b/utils/VideoReaderFFmpeg.py
new file mode 100644
index 00000000..90b9491d
--- /dev/null
+++ b/utils/VideoReaderFFmpeg.py
@@ -0,0 +1,252 @@
+import subprocess
+import cv2
+import numpy as np
+import ffmpeg
+import time
+
+
+class VideoReaderFFmpeg:
+    def __init__(self, video_path):
+        self.video_path = video_path
+        self._initialize_video_info()
+        self.playback_speed = 1.0  # Default is 1.0 (real-time)
+        self.paused = False
+        self.start_frame = 0
+        self.current_frame_number = 0
+        self.current_time = 0
+        self.process = None
+        self.frame_size = None
+
+    def _initialize_video_info(self):
+        """ Retrieves video metadata like fps, total frames, resolution, etc. using ffprobe. """
+        try:
+            probe = ffmpeg.probe(self.video_path)
+            video_streams = [stream for stream in probe['streams'] if stream['codec_type'] == 'video']
+            video_stream = video_streams[0]  # First video stream
+
+            self.fps = eval(video_stream['r_frame_rate'])
+            self.width = int(video_stream['width'])
+            self.height = int(video_stream['height'])
+            self.codec = video_stream['codec_name']
+            self.total_frames = int(video_stream['nb_frames']) if 'nb_frames' in video_stream else None
+            self.duration = float(video_stream['duration']) * 1000  # Duration in milliseconds
+
+            print(f"FPS: {self.fps}, Resolution: {self.width}x{self.height}, "
+                  f"Codec: {self.codec}, Total Frames: {self.total_frames}, Duration: {self.duration:.2f} ms")
+        except Exception as e:
+            print(f"Error initializing video info: {e}")
+
+    def _start_process(self, start_frame=0):
+        """ Start the FFmpeg process to read frames. """
+        start_time = (start_frame / self.fps) * 1000  # Convert to milliseconds
+        self.current_frame_number = start_frame
+        self.process = (
+            ffmpeg.input(self.video_path, ss=start_time / 1000)  # Convert back to seconds for FFmpeg
+            .output('pipe:', format='rawvideo', pix_fmt='bgr24')
+            .run_async(pipe_stdout=True, pipe_stderr=subprocess.PIPE)
+        )
+        self.frame_size = self.width * self.height * 3  # For 'bgr24', 3 bytes per pixel
+
+    def prev_initialize_video_info(self):
+        """ Retrieves video metadata like fps, total frames, resolution, etc. using ffprobe. """
+        # Retrieve FPS, frame count, codec, width, and height
+        try:
+            probe = ffmpeg.probe(self.video_path)
+            video_streams = [stream for stream in probe['streams'] if stream['codec_type'] == 'video']
+            if not video_streams:
+                raise ValueError("No video streams found in the input file.")
+            video_stream = video_streams[0]  # First video stream
+
+            self.fps = eval(video_stream['r_frame_rate'])
+            self.width = int(video_stream['width'])
+            self.height = int(video_stream['height'])
+            self.codec = video_stream['codec_name']
+            self.total_frames = int(video_stream['nb_frames']) if 'nb_frames' in video_stream else None
+            self.duration = float(video_stream['duration']) * 1000  # Duration in milliseconds
+
+            print(f"FPS: {self.fps}, Resolution: {self.width}x{self.height}, "
+                  f"Codec: {self.codec}, Total Frames: {self.total_frames}, Duration: {self.duration:.2f} ms")
+
+        except ffmpeg.Error as e:
+            print(f"FFmpeg error: {e.stderr.decode('utf8')}")
+            raise
+
+    def get_frame(self, frame_number: int, crop = None, vr_roi= None) -> np.ndarray:
+        """ Retrieve a specific frame by number, optionally crop or resize. """
+        return self._get_frame_by_time_or_number(frame_number=frame_number, crop=crop, vr_roi=vr_roi)
+
+    def get_frame_at_time(self, time_milliseconds, crop=None, vr_roi=None):
+        """ Retrieve a frame at a specific time in the video (in milliseconds), with crop and resize options. """
+        return self._get_frame_by_time_or_number(time_milliseconds=time_milliseconds, crop=crop, vr_roi=vr_roi)
+
+    def _get_frame_by_time_or_number(self, frame_number=None, time_milliseconds=None, crop=None, vr_roi=None):
+        """ Internal function to retrieve a frame either by frame number or time, with optional crop/resize. """
+        try:
+            if frame_number is not None:
+                time_milliseconds = (frame_number / self.fps) * 1000  # Convert frame number to milliseconds
+            elif time_milliseconds is None:
+                raise ValueError("Either frame_number or time_milliseconds must be provided.")
+
+            # FFmpeg cropping command if needed
+            ffmpeg_input = ffmpeg.input(self.video_path, ss=time_milliseconds / 1000)  # Convert back to seconds for FFmpeg
+
+            cmd = (
+                ffmpeg_input
+                .output('pipe:', vframes=1, format='image2', vcodec='rawvideo', pix_fmt='bgr24')
+                .run_async(pipe_stdout=True, pipe_stderr=True)
+            )
+
+            frame_size = self.width * self.height * 3  # For 'bgr24', 3 bytes per pixel
+            raw_frame = cmd.stdout.read(frame_size)
+            frame = np.frombuffer(raw_frame, np.uint8).reshape((self.height, self.width, 3))
+
+            if crop:
+                if crop == "Left":
+                    frame = frame[:, :frame.shape[1] // 2, :]
+                elif crop == "Right":
+                    frame = frame[:, frame.shape[1] // 2:, :]
+                elif crop == "Top":
+                    frame = frame[:frame.shape[0] // 2, :, :]
+                elif crop == "Bottom":
+                    frame = frame[frame.shape[0] // 2:, :, :]
+            if vr_roi:
+                panel_height, panel_width = frame.shape[:2]
+                frame = frame[2 * (panel_height // 5):, (panel_width // 4):(3 * panel_width // 4)]
+
+            # Close FFmpeg process
+            cmd.stdout.close()
+            cmd.wait()
+
+            return frame
+
+        except ffmpeg.Error as e:
+            print(f"FFmpeg error: {e.stderr.decode()}")
+            return None
+
+    def read(self, crop=None, vr_roi=None, resize=None):
+        """ Read the next frame from the video. Returns (ret, frame). """
+        if self.process is None:
+            self._start_process(start_frame=self.start_frame)
+
+        try:
+            in_bytes = self.process.stdout.read(self.frame_size)
+            if not in_bytes:
+                return False, None  # End of video
+            frame = np.frombuffer(in_bytes, np.uint8).reshape([self.height, self.width, 3])
+            if crop:
+                if crop == "Left":
+                    frame = frame[:, :frame.shape[1] // 2, :]
+                elif crop == "Right":
+                    frame = frame[:, frame.shape[1] // 2:, :]
+                elif crop == "Top":
+                    frame = frame[:frame.shape[0] // 2, :, :]
+                elif crop == "Bottom":
+                    frame = frame[frame.shape[0] // 2:, :, :]
+            if vr_roi:
+                panel_height, panel_width = frame.shape[:2]
+                frame = frame[2 * (panel_height // 5):, (panel_width // 4):(3 * panel_width // 4)]
+
+            self.current_frame_number += 1
+            self.current_time = (self.current_frame_number / self.fps) * 1000
+            return True, frame
+        except Exception as e:
+            print(f"Error reading frame: {e}")
+            return False, None
+
+    def read_frames(self, start_frame=0, crop=None, resize=None):
+        """
+        Generator function that yields frames starting from a specific frame.
+        You can crop and resize frames on the fly.
+        """
+        # Seek to the starting frame by calculating the time in milliseconds
+        start_time = (start_frame / self.fps) * 1000  # Convert to milliseconds
+        process = (
+            ffmpeg.input(self.video_path, ss=start_time / 1000)  # Convert back to seconds for FFmpeg
+            .output('pipe:', format='rawvideo', pix_fmt='bgr24')
+            .run_async(pipe_stdout=True, pipe_stderr=subprocess.PIPE)
+        )
+
+        frame_size = self.width * self.height * 3  # For 'bgr24', 3 bytes per pixel
+        frame_number = start_frame
+
+        try:
+            while True:
+                in_bytes = process.stdout.read(frame_size)
+                if not in_bytes:
+                    break  # End of video
+                frame = np.frombuffer(in_bytes, np.uint8).reshape([self.height, self.width, 3])
+
+                # Apply crop
+                if crop:
+                    x, y, w, h = crop
+                    frame = frame[y:y + h, x:x + w]
+
+                # Apply resize
+                if resize:
+                    new_w, new_h = resize
+                    frame = cv2.resize(frame, (new_w, new_h))
+
+                self.current_frame_number = frame_number
+                yield frame, frame_number, frame_number * 1000 / self.fps  # Return frame, frame number, and timestamp in ms
+
+                # Adjust for playback speed
+                if not self.paused:
+                    time.sleep(1 / (self.fps * self.playback_speed))
+                    frame_number += 1
+        finally:
+            process.stdout.close()
+            process.wait()
+
+
+
+    def pause(self):
+        self.paused = True
+
+    def resume(self):
+        self.paused = False
+
+    def set_playback_speed(self, speed):
+        """ Set playback speed (1.0 = normal speed, 2.0 = 2x, 0.5 = 0.5x, etc.). """
+        self.playback_speed = speed
+
+    def seek_and_read(self, seek_frame, crop=None, resize=None):
+        """ Seeks to a specific time and returns the next frame with crop and resize options. """
+        frame = self.get_frame(seek_frame) #, crop=crop, resize=resize)
+        return frame
+
+    def get_metadata(self):
+        """ Return the video's metadata as a dictionary. """
+        return {
+            'fps': self.fps,
+            'width': self.width,
+            'height': self.height,
+            'codec': self.codec,
+            'total_frames': self.total_frames,
+            'duration': self.duration,
+        }
+
+    def release(self):
+        """ Release the resources and close the FFmpeg process. """
+        if self.process:
+            self.process.stdout.close()
+            self.process.wait()
+            self.process = None
+
+"""
+# Example Usage
+if __name__ == '__main__':
+    video_path = 'your_video.mp4'
+    reader = VideoReaderFFmpeg(video_path)
+
+    # Set playback speed to 2x
+    reader.set_playback_speed(2.0)
+
+    # Read and display frames at 2x speed
+    for frame, frame_num, timestamp in reader.read_frames(start_frame=0, crop=(0, 0, reader.width // 2, reader.height)):
+        print(f"Frame: {frame_num}, Time: {timestamp:.2f} ms")
+        cv2.imshow('Frame', frame)
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    cv2.destroyAllWindows()
+"""
\ No newline at end of file
diff --git a/utils/Visualizer.py b/utils/Visualizer.py
new file mode 100644
index 00000000..b36a7eb6
--- /dev/null
+++ b/utils/Visualizer.py
@@ -0,0 +1,96 @@
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+class Visualizer:
+    def draw_bounding_box(self, image, box, label, color, offset_x=0):
+        x1, y1, x2, y2 = map(int, box)
+        x1 += offset_x
+        x2 += offset_x
+        cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)
+        (label_width, label_height), baseline = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
+        cv2.rectangle(image, (x1, y1 - label_height - baseline), (x1 + label_width, y1), color, -1)
+        cv2.putText(image, label, (x1, y1 - baseline), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
+        return image
+
+    def draw_gauge(self, image, distance):
+        gauge_width = 20
+        gauge_height = 200
+        gauge_x = int(3 * image.shape[1] / 4)
+        gauge_y = int(3 * image.shape[0] / 5)
+        cv2.rectangle(image, (gauge_x, gauge_y), (gauge_x + gauge_width, gauge_y + gauge_height), (0, 0, 0), -1)
+        fill_height = int((distance / 100) * gauge_height)
+        cv2.rectangle(image, (gauge_x, gauge_y + gauge_height - fill_height), (gauge_x + gauge_width, gauge_y + gauge_height), (0, 255, 0), -1)
+        return image
+
+    def draw_limited_graph(self, image, distances, frames, nb_frames):
+        """Plot the frame indices vs positions for the last nb frames."""
+        # Determine the number of frames to plot
+        num_frames_to_plot = min(nb_frames, len(distances))
+
+        # Get the last 'num_frames_to_plot' frames
+        last_frame_indices = frames[-num_frames_to_plot:]
+        last_positions = distances[-num_frames_to_plot:]
+
+        graph_height = image.shape[0] // 4
+        graph_width = image.shape[1] - 20 - 10
+        graph_x = 10
+        graph_y = image.shape[0] // 10
+
+        fig = plt.figure(figsize=(graph_width / 100, graph_height / 100), dpi=100)
+        ax = fig.add_subplot(111)
+        ax.plot(range(len(last_frame_indices)), last_positions, color='green', linewidth=1)
+        ax.set_ylim(0, 100)
+        ax.set_xlim(0, nb_frames)  # Limit the x-axis to nb_frames
+        fig.canvas.draw()
+
+        # Get the width and height of the figure
+        width, height = fig.canvas.get_width_height()
+
+        # Convert the figure to an image
+        graph_image = np.frombuffer(fig.canvas.buffer_rgba(), dtype='uint8').reshape((height, width, 3))
+        graph_image = cv2.cvtColor(graph_image, cv2.COLOR_RGBA2RGB)  # Convert from RGBA to RGB
+        graph_image = cv2.resize(graph_image, (graph_width, graph_height))
+
+        #graph_image = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8').reshape(
+        #    fig.canvas.get_width_height()[::-1] + (3,))
+        #graph_image = np.frombuffer(fig.canvas.buffer_rgba(), dtype='uint8').reshape(
+        #    fig.canvas.get_width_height()[::-1] + (4,))
+        #graph_image = cv2.resize(graph_image, (graph_width, graph_height))
+
+        image[graph_y:graph_y + graph_height, graph_x:graph_x + graph_width] = cv2.addWeighted(
+            image[graph_y:graph_y + graph_height, graph_x:graph_x + graph_width],
+            0.5,
+            graph_image,
+            0.5,
+            0
+        )
+        plt.close(fig)
+        return image
+
+    def draw_graph(self, image, distances, frames, reference_positions, reference_frames):
+        graph_height = image.shape[0] // 4
+        graph_width = image.shape[1] - 20 - 10
+        graph_x = 10
+        graph_y = image.shape[0] // 10
+        fig = plt.figure(figsize=(graph_width / 100, graph_height / 100), dpi=100)
+        ax = fig.add_subplot(111)
+        if reference_positions:
+            ax.plot(reference_frames, reference_positions, color='red', linewidth=1)
+        ax.plot(range(len(distances)), distances, color='green', linewidth=1)
+        # Draw the last 600 frames
+
+        ax.set_ylim(0, 100)
+        ax.set_xlim(0, len(distances))
+        fig.canvas.draw()
+        graph_image = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8').reshape(fig.canvas.get_width_height()[::-1] + (3,))
+        graph_image = cv2.resize(graph_image, (graph_width, graph_height))
+        image[graph_y:graph_y + graph_height, graph_x:graph_x + graph_width] = cv2.addWeighted(
+            image[graph_y:graph_y + graph_height, graph_x:graph_x + graph_width],
+            0.5,
+            graph_image,
+            0.5,
+            0
+        )
+        plt.close(fig)
+        return image
\ No newline at end of file
diff --git a/utils/config.py b/utils/config.py
new file mode 100644
index 00000000..0d7ddd3a
--- /dev/null
+++ b/utils/config.py
@@ -0,0 +1,40 @@
+# Class types
+class_types = {'face': 0, 'hand': 1, 'penis': 2, 'glans': 3, 'pussy': 4, 'butt': 5,
+                       'anus': 6, 'breast': 7, 'navel': 8, 'foot': 9}
+
+class_reverse_match = {0: 'penis', 1: 'glans', 2: 'pussy', 3: 'butt', 4: 'anus', 5: 'breast',
+                       6: 'navel', 7: 'hand', 8: 'face', 9: 'foot'}
+
+class_priority_order = {
+            "glans": 0, "penis": 1, "breast": 2, "navel": 3, "pussy": 4, "butt": 5, "face": 6
+        }
+
+# Define class names with their corresponding indices
+class_names = {
+    'face': 0,
+    'left hand': 1, 'right hand': 1,
+    'penis': 2,
+    'glans': 3,
+    'pussy': 4,
+    'butt': 5,
+    'anus': 6,
+    'breast': 7,
+    'navel': 8,
+    'left foot': 9, 'right foot': 9
+}
+
+# Define colors for each class
+class_colors = {
+    "penis": (255, 0, 0),               # red
+    "glans": (0, 128, 0),               # green
+    "pussy": (0, 0, 255),               # blue
+    "butt": (255, 255, 0),              # yellow
+    "anus": (128, 0, 128),              # purple
+    "breast": (255, 165, 0),            # orange
+    "navel": (0, 255, 255),             # cyan
+    "left hand": (255, 0, 255),         # magenta
+    "right hand": (255, 0, 255),        # magenta
+    "face": (0, 255, 0),                # lime
+    "left foot": (165, 42, 42),         # brown
+    "right foot": (165, 42, 42)         # brown
+}
\ No newline at end of file

From 642d13c46ab386a929cd06fb8244c156597eaa10 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 10 Dec 2024 11:18:36 +0100
Subject: [PATCH 003/277] Delete models/k00gar-11n-200ep-best.mlpackage
 directory

---
 .../Data/com.apple.CoreML/model.mlmodel        |  0
 .../Data/com.apple.CoreML/weights/weight.bin   |  0
 .../Manifest.json                              | 18 ------------------
 3 files changed, 18 deletions(-)
 delete mode 100644 models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/model.mlmodel
 delete mode 100644 models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/weights/weight.bin
 delete mode 100644 models/k00gar-11n-200ep-best.mlpackage/Manifest.json

diff --git a/models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/model.mlmodel b/models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/model.mlmodel
deleted file mode 100644
index e69de29b..00000000
diff --git a/models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/weights/weight.bin
deleted file mode 100644
index e69de29b..00000000
diff --git a/models/k00gar-11n-200ep-best.mlpackage/Manifest.json b/models/k00gar-11n-200ep-best.mlpackage/Manifest.json
deleted file mode 100644
index cac3aa39..00000000
--- a/models/k00gar-11n-200ep-best.mlpackage/Manifest.json
+++ /dev/null
@@ -1,18 +0,0 @@
-{
-    "fileFormatVersion": "1.0.0",
-    "itemInfoEntries": {
-        "3EE8E38E-A97B-4918-8FA3-64EE837C9D1B": {
-            "author": "com.apple.CoreML",
-            "description": "CoreML Model Specification",
-            "name": "model.mlmodel",
-            "path": "com.apple.CoreML/model.mlmodel"
-        },
-        "6FDCFB65-8E94-427D-8CB9-606F5C6DE5F2": {
-            "author": "com.apple.CoreML",
-            "description": "CoreML Model Weights",
-            "name": "weights",
-            "path": "com.apple.CoreML/weights"
-        }
-    },
-    "rootModelIdentifier": "3EE8E38E-A97B-4918-8FA3-64EE837C9D1B"
-}

From ac9a43699f4be3a6b74416130aa7170dae1504e4 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 10 Dec 2024 11:20:52 +0100
Subject: [PATCH 004/277] Add files via upload

---
 .../Data/com.apple.CoreML/model.mlmodel       | Bin 0 -> 158971 bytes
 .../Data/com.apple.CoreML/weights/weight.bin  | Bin 0 -> 5230240 bytes
 .../Manifest.json                             |  18 ++++++++++++++++++
 3 files changed, 18 insertions(+)
 create mode 100644 models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/model.mlmodel
 create mode 100644 models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/weights/weight.bin
 create mode 100644 models/k00gar-11n-200ep-best.mlpackage/Manifest.json

diff --git a/models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/model.mlmodel b/models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/model.mlmodel
new file mode 100644
index 0000000000000000000000000000000000000000..b3679754b41cc275dc0c4077195089d84d05de81
GIT binary patch
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HcmV?d00001

diff --git a/models/k00gar-11n-200ep-best.mlpackage/Manifest.json b/models/k00gar-11n-200ep-best.mlpackage/Manifest.json
new file mode 100644
index 00000000..cac3aa39
--- /dev/null
+++ b/models/k00gar-11n-200ep-best.mlpackage/Manifest.json
@@ -0,0 +1,18 @@
+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "3EE8E38E-A97B-4918-8FA3-64EE837C9D1B": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        },
+        "6FDCFB65-8E94-427D-8CB9-606F5C6DE5F2": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        }
+    },
+    "rootModelIdentifier": "3EE8E38E-A97B-4918-8FA3-64EE837C9D1B"
+}

From 68b6373a178b4bd80550b05487c75bcb806a64da Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 10 Dec 2024 11:21:53 +0100
Subject: [PATCH 005/277] init

---
 .../Data/com.apple.CoreML/model.mlmodel       | Bin 0 -> 158971 bytes
 .../Data/com.apple.CoreML/weights/weight.bin  | Bin 0 -> 5230240 bytes
 .../Manifest.json                             |  18 ++++++++++++++++++
 3 files changed, 18 insertions(+)
 create mode 100644 models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/model.mlmodel
 create mode 100644 models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/weights/weight.bin
 create mode 100644 models/k00gar-11n-200ep-best.mlpackage/Manifest.json

diff --git a/models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/model.mlmodel b/models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/model.mlmodel
new file mode 100644
index 0000000000000000000000000000000000000000..b3679754b41cc275dc0c4077195089d84d05de81
GIT binary patch
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literal 0
HcmV?d00001

diff --git a/models/k00gar-11n-200ep-best.mlpackage/Manifest.json b/models/k00gar-11n-200ep-best.mlpackage/Manifest.json
new file mode 100644
index 00000000..cac3aa39
--- /dev/null
+++ b/models/k00gar-11n-200ep-best.mlpackage/Manifest.json
@@ -0,0 +1,18 @@
+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "3EE8E38E-A97B-4918-8FA3-64EE837C9D1B": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        },
+        "6FDCFB65-8E94-427D-8CB9-606F5C6DE5F2": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        }
+    },
+    "rootModelIdentifier": "3EE8E38E-A97B-4918-8FA3-64EE837C9D1B"
+}

From 7a17a16917065dca0d2c931fed7f04a15672b73d Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sat, 21 Dec 2024 16:11:08 +0100
Subject: [PATCH 006/277] init

---
 Compare_models_and_resolutions.py |   33 +
 FSGenerator.py                    |  217 +++--
 utils/Debugger.py                 |  299 +++++++
 utils/Display_debug_results.py    |   42 +
 utils/FFmpegVideoCapture.py       |  134 +++
 utils/FunscriptHandler.py         |   41 +-
 utils/ObjectTracker.py            | 1297 +++++++++++++++++++++++------
 utils/ObjectTracker_v2.py         |  417 ++++++++++
 utils/PoseEstimation.py           |  136 +++
 utils/VideoReaderFFmpeg.py        |  330 +++-----
 10 files changed, 2464 insertions(+), 482 deletions(-)
 create mode 100644 Compare_models_and_resolutions.py
 create mode 100644 utils/Debugger.py
 create mode 100644 utils/Display_debug_results.py
 create mode 100644 utils/FFmpegVideoCapture.py
 create mode 100644 utils/ObjectTracker_v2.py
 create mode 100644 utils/PoseEstimation.py

diff --git a/Compare_models_and_resolutions.py b/Compare_models_and_resolutions.py
new file mode 100644
index 00000000..e5415a67
--- /dev/null
+++ b/Compare_models_and_resolutions.py
@@ -0,0 +1,33 @@
+import cv2
+import numpy as np
+import json
+from ultralytics import YOLO
+
+# Example usage
+if __name__ == "__main__":
+
+    frame_id=32900
+    # Replace with your video paths and YOLO models
+    video = []
+    video.append("/Users/k00gar/Downloads/ARPorn_Sasha Tatcha_Fit and Fired Up_4000p_8K_original_FISHEYE190_alpha_orig.mp4")
+    #video.append("/Users/k00gar/Downloads/ARPorn_Sasha Tatcha_Fit and Fired Up_4000p_8K_original_FISHEYE190_alpha.mp4")
+
+    models = []
+    models.append("models/k00gar-11n-200ep-best.mlpackage")
+    models.append("models/k00gar-11s-198ep-best.mlpackage")
+    models.append("models/k00gar-11m-134ep-best.mlpackage")
+
+    for model in models:
+        for video_path in video:
+            cap = cv2.VideoCapture(video_path)
+            # position at frame 32000
+            cap.set(cv2.CAP_PROP_POS_FRAMES, frame_id)
+            ret, frame = cap.read()
+            cap.release()
+            frame = frame[:, frame.shape[1] // 6:frame.shape[1] // 3, :]
+            # Load the YOLO model
+            yolo_instance = YOLO(model)
+            results = yolo_instance(frame)
+            for result in results:
+                print(f"Results for model {model} and video {video_path}")
+                result.show()
diff --git a/FSGenerator.py b/FSGenerator.py
index 9b1856ce..74a3e113 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -2,14 +2,16 @@
 import os
 import cv2
 import json
-from utils.config import class_names, class_priority_order, class_reverse_match, class_colors
-from ultralytics import YOLO
-from utils import ObjectTracker as OT
-from utils import FunscriptHandler as FH
 from tqdm import tqdm
-from utils import Visualizer as VS
-from utils.SceneCutsDetect import detect_scene_changes
+from ultralytics import YOLO
 
+from utils.config import class_names, class_priority_order, class_reverse_match, class_colors
+from utils.ObjectTracker_v2 import ObjectTracker
+from utils.FunscriptHandler import FunscriptGenerator
+from utils.Visualizer import Visualizer
+from utils.Debugger import Debugger
+from utils.SceneCutsDetect import detect_scene_changes
+from utils.VideoReaderFFmpeg import VideoReaderFFmpeg
 
 # Define the BoxRecord class
 class BoxRecord:
@@ -49,7 +51,8 @@ def map_class_type_to_name(self, class_type, x1, x2, image_width):
     def get_boxes(self, frame_id):
         itemized_boxes = []
         if frame_id not in self.frame_data:
-            raise KeyError(f"No records found for frame ID {frame_id}")
+            return itemized_boxes
+            # raise KeyError(f"No records found for frame ID {frame_id}")
         boxes = self.frame_data[frame_id]
         for box, conf, cls, class_name in boxes:
             target_class_name = self.map_class_type_to_name(class_name, box[0], box[2], self.image_width)
@@ -63,13 +66,6 @@ def get_boxes(self, frame_id):
     def get_all_frame_ids(self):
         return list(self.frame_data.keys())
 
-def update_sex_position(position, new_position, frame_id, reason=None):
-    if position is not None and new_position is not None and position != new_position:
-        due_to = " given " + reason if reason else ""
-        print(f"Sex position changed from {position} to {new_position}{due_to}")
-        position_changes.append((frame_id, position, new_position, reason))
-        return new_position
-    return position
 
 def write_dataset(file_path, data):
     print(f"Exporting data...")
@@ -85,7 +81,9 @@ def extract_yolo_data(model_file, video_path, frame_start, frame_end=None, TestM
     records = []
     test_result = Result(320)
 
-    cap = cv2.VideoCapture(video_path)
+    #cap = cv2.VideoCapture(video_path)
+    cap = VideoReaderFFmpeg(video_path, is_VR=isVR)
+    # cap = FFmpegVideoCapture(video_path)
     cap.set(cv2.CAP_PROP_POS_FRAMES, frame_start)
 
     if frame_end:
@@ -104,16 +102,20 @@ def extract_yolo_data(model_file, video_path, frame_start, frame_end=None, TestM
         if success:
             if isVR:
                 # only keep the center third of the left half of the frame
-                frame = frame[:, frame.shape[1] // 6:frame.shape[1] // 3, :]
+                #frame = frame[:, frame.shape[1] // 6:frame.shape[1] // 3, :]
+                frame = frame[:, frame.shape[1] // 3:2*frame.shape[1] // 3, :]
 
             # Run YOLO11 tracking on the frame, persisting tracks between frames
             # yolo_results = model.predict(frame, verbose=False, conf=0.5)  #track(frame, persist=True, verbose=False)
-            yolo_results = model.track(frame, persist=True, conf=0.5, verbose=False)
+            yolo_results = model.track(frame, persist=True, conf=0.3, verbose=False)
 
             #if results[0].boxes.id is None:
             if yolo_results[0].boxes.cls is None:
                 continue
 
+            if len(yolo_results[0].boxes) == 0 and not TestMode:
+                continue
+
             # Get the boxes and track IDs
             #track_ids = results[0].boxes.id.cpu().tolist()
             boxes = yolo_results[0].boxes.xywh.cpu()
@@ -136,6 +138,13 @@ def extract_yolo_data(model_file, video_path, frame_start, frame_end=None, TestM
                     test_box = [[x1, y1, x2, y2], round(conf, 1), int(cls), class_reverse_match.get(int(cls), 'unknown')]
                     print(f"Test box: {test_box}")
                     test_result.add_record(frame_pos, test_box)
+                #if DebugMode:
+                #    debugger.log_frame(frame_pos,
+                #                       bounding_boxes={'box': [x1, y1, x2, y2],
+                #                                       'conf': conf,
+                #                                       'class_id': int(cls),
+                #                                       'class_name': class_reverse_match.get(int(cls), 'unknown')})
+
             if TestMode:
                 # display the results from YOLO
                 yolo_results[0].plot()
@@ -151,15 +160,7 @@ def extract_yolo_data(model_file, video_path, frame_start, frame_end=None, TestM
                     #cv2.putText(frame, f"{int(cls)}: {int(conf*100)}%", (record[4], record[5] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
                 cv2.imshow("YOLO11 test boxes Tracking", frame)
                 cv2.waitKey(1)
-            if frame_pos == last_frame-1:
-                print("Triggered here")
-                write_dataset(video_path[:-4] + f"_rawyolo.json", records)
-                break
-        else:
-            # Break the loop if the end of the video is reached
-            print("Triggered there")
-            write_dataset(video_path[:-4] + f"_rawyolo.json", records)
-            break
+    write_dataset(video_path[:-4] + f"_rawyolo.json", records)
     # Release the video capture object and close the display window
     cap.release()
     cv2.destroyAllWindows()
@@ -184,11 +185,16 @@ def make_data_boxes(records, image_x_size):
 
 def analyze_tracking_results(results, image_y_size, video_path, frame_start = None, frame_end = None, TestMode = False):
     list_of_frames = results.get_all_frame_ids()
-    visualizer = VS.Visualizer()
+    visualizer = Visualizer()
 
-    cap = cv2.VideoCapture(video_path)
+    #cap = cv2.VideoCapture(video_path)
+    cap = VideoReaderFFmpeg(video_path, is_VR=isVR)
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    # cap = FFmpegVideoCapture(video_path)
     nb_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
 
+    offset_x = int(image_x_size / 3)
+
     if not frame_start:
         frame_start = 0
 
@@ -197,12 +203,16 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start = No
 
     if TestMode:
         cap.set(cv2.CAP_PROP_POS_FRAMES, frame_start)
+    else:
+        cap.release()
 
     # if a file already exists, directly load the cuts
     if os.path.exists(video_path[:-4] + f"_cuts.json"):
         print(f"Loading cuts from {video_path[:-4] + f'_cuts.json'}")
         with open(video_path[:-4] + f"_cuts.json", 'r') as f:
             cuts = json.load(f)
+        print(f"Loaded {len(cuts)} cuts : {cuts}")
+
     else:
         scene_list = detect_scene_changes(video_path, "Left", 0.9, frame_start, frame_end)
         print(f"Analyzing frames {frame_start} to {frame_end}")
@@ -213,15 +223,17 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start = No
         with open(video_path[:-4] + f"_cuts.json", 'w') as f:
             json.dump(cuts, f)
 
-    tracker = OT.ObjectTracker()
+    funscript_frames = []
+    tracker = ObjectTracker(fps, frame_start)
 
-    for frame_pos in tqdm(range(frame_start, frame_end), ncols=None, desc="Analyzing tracking results"):
+    for frame_pos in tqdm(range(frame_start, frame_end), ncols=80, desc="Analyzing tracking results"):
 
         if frame_pos in cuts:
             print(f"Reaching cut at frame {frame_pos}")
             previous_distances = tracker.previous_distances
-            tqdm.write(f"Reinitializing tracker with previous distances: {previous_distances}")
-            tracker = OT.ObjectTracker()
+            # tqdm.write
+            print(f"Reinitializing tracker with previous distances: {previous_distances}")
+            tracker = ObjectTracker(fps, frame_pos)
             tracker.previous_distances = previous_distances
 
         if frame_pos in list_of_frames:
@@ -231,7 +243,36 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start = No
 
             if tracker.distance:
                 funscript_frames.append(frame_pos)
-                funscript_distances.append(tracker.distance)
+                funscript_distances.append(int(tracker.distance))
+
+            if DebugMode:
+                bounding_boxes = []  # Initialize an empty list for bounding boxes
+                for box in sorted_boxes:
+                    bounding_boxes.append({
+                        'box': box[0],
+                        'conf': box[1],
+                        'class_name': box[3]
+                    })
+                debugger.log_frame(frame_pos,
+                                   bounding_boxes=bounding_boxes,  # Pass the list of bounding boxes
+                                   variables={
+                                       'distance': tracker.distance,
+                                       'Penetration': tracker.penetration,
+                                       'Grinding': tracker.grinding,
+                                       'sex_position': tracker.sex_position,
+                                       #'sex_position': tracker.state_manager.state,
+                                       'sex_position_reason': tracker.sex_position_reason,
+                                       'tracked_body_part': tracker.tracked_body_part,
+                                       'locked_penis_box': tracker.locked_penis_box,
+                                       'glans_detected': tracker.glans_detected,
+                                       'cons._glans_detections': tracker.consecutive_detections['glans'],
+                                       'cons._glans_non_detections': tracker.consecutive_non_detections['glans'],
+                                       'cons._penis_detections': tracker.consecutive_detections['penis'],
+                                       'cons._penis_non_detections': tracker.consecutive_non_detections['penis'],
+                                       'breast_tracking': tracker.breast_tracking,
+                                       'left hand normalized': tracker.normalized_positions['left hand'][-1],
+                                       'right hand normalized': tracker.normalized_positions['right hand'][-1],
+                                   })
 
         if TestMode:
             ret, frame = cap.read()
@@ -241,12 +282,17 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start = No
                                                           tracker.boxes[tracker.tracked_body_part],
                                                           tracker.tracked_body_part,
                                                           class_colors[tracker.tracked_body_part],
-                                                          int(image_x_size / 2))
+                                                          offset_x)
             if tracker.locked_penis_box is not None:
                 frame = visualizer.draw_bounding_box(frame, tracker.locked_penis_box,
                                                               "Locked_Penis",
                                                               class_colors['penis'],
-                                                              int(image_x_size / 2))
+                                                              offset_x)
+            if tracker.glans_detected:
+                frame = visualizer.draw_bounding_box(frame, tracker.boxes['glans'],
+                                                              "Glans",
+                                                              class_colors['glans'],
+                                                              offset_x)
             if funscript_distances:
                 frame = visualizer.draw_gauge(frame, funscript_distances[-1])
                 #frame = visualizer.draw_limited_graph(frame, funscript_distances,
@@ -254,22 +300,55 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start = No
 
             cv2.imshow("Combined Results", frame)
             cv2.waitKey(1)
-    return funscript_frames, funscript_distances
+
+    funscript_data = list(zip(funscript_frames, funscript_distances))
+
+    points = "["
+    for i in range(len(funscript_frames)):
+        if i != 0:
+            points += ","
+        points += f"[{funscript_frames[i]}, {funscript_distances[i]}]"
+        # points += f'{{funscript_frames[i]}, {funscript_distances[i]}}'
+    points += "]"
+    # write the raw data to a json file
+    with open(video_path[:-4] + f"_rawfunscript.json", 'w') as f:
+        #json.dump(points, f)
+        json.dump(funscript_data, f)
+    return funscript_data #funscript_frames, funscript_distances
 
 
 def parse_yolo_data_looking_for_penis(data, start_frame):
+    consecutive_frames = 0
+    frame_detected = 0
+    penis_frame = 0
     for line in data:
-        if line[0] >= start_frame and line[1] == 0:  # class_types.get("penis"):
-            print(f"First instance of Penis found in frame {line[0]}")
-            return line[0]
-
+        if line[0] >= start_frame and line[1] == 0 and line[2] >= 0.5:
+            penis_frame = line[0]
+        #if line[0] >= start_frame and line[1] == 1 and line[2] >= 0.5:  # actually, Glans --class_types.get("penis"):
+        if line[0] == penis_frame and line[1] == 1 and line[2] >= 0.5:
+            if frame_detected == 0:
+                frame_detected = line[0]
+                consecutive_frames += 1
+            elif line[0] == frame_detected + 1:
+                consecutive_frames += 1
+                frame_detected = line[0]
+            else:
+                consecutive_frames = 0
+                frame_detected = 0
 
+            if consecutive_frames >= 10:
+                print(f"First instance of Glans/Penis found in frame {line[0] - 4}")
+                return line[0] - 4
 
 
 # MAIN logic
 
 # YOLO model file
-yolo_model = "models/k00gar-11n-200ep-best.mlpackage"
+#yolo_model = "models/k00gar-11n-200ep-best.mlpackage"
+yolo_model = "models/k00gar-11s-198ep-best.mlpackage"
+#yolo_model = "models/k00gar-11m-134ep-best.mlpackage"
+
+funscript_data = []
 
 video_list = []
 # video_list.append("/Users/k00gar/Downloads/wankzvr-shocum-180_180x180_3dh_LR.mp4")
@@ -283,16 +362,48 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
 # video_list.append("/Users/k00gar/Downloads/JVR_100199.mp4")
 # video_list.append("/Users/k00gar/Downloads/JVR_100200.mp4")
 # video_list.append("/Users/k00gar/Downloads/JVR_100201.mp4")
-video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_Poolside Romance_1920p_39107_FISHEYE190.mp4")
+# video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_Poolside Romance_1920p_39107_FISHEYE190.mp4")
+# video_list.append("/Users/k00gar/Downloads/SLR_UpCloseVR_UP Close VR with Connie Perignon_1920p_49954_LR_180.mp4")
+# video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_CJ Miles Sexperience_1920p_33250_MKX200.mp4")
+# video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_She Gives The Best Gifts_1920p_35951_MKX200.mp4")
+
+#video_list.append("/Users/k00gar/Downloads/VRLatina_Innocence_Unveiled_2000p_180_180x180_3dh_LR.mp4")
+#video_list.append("/Users/k00gar/Downloads/Katrina Jade [ADD] Oct 16, 2024R_6kvr265_reenc.mp4")
+#video_list.append("/Users/k00gar/Downloads/milfvr-daisy fuentes-jizzlejuice-jizzlejuice-3600p-180_180x180_3dh_LR_reenc.mp4")
+#video_list.append("/Users/k00gar/Downloads/BaDoinkVR_Peaches_n_Cream_5k_180_180x180_3dh_LR.mp4")
+# video_list.append("/Users/k00gar/Downloads/730-czechvr-3d-7680x3840-60fps-oculusrift_uhq_h265.mp4")
+#video_list.append("/Users/k00gar/Downloads/ARPorn_Sasha Tatcha_Fit and Fired Up_4000p_8K_original_FISHEYE190_alpha.mp4")
+
+#video_list.append("/Users/k00gar/Downloads/WAVR 278 A - Ichika Matsumoto, Aoi Kururugi, Mitsuki Nagisa - 3 Little Sisters.mp4")
+#video_list.append("/Users/k00gar/Downloads/HNVR 097 B.mp4")
+#video_list.append("/Users/k00gar/Downloads/BadoinkVR_Lights_Camera_Satisfaction_4K_HEVC_180_180x180_3dh.mp4")
+#video_list.append("/Users/k00gar/Downloads/2022-09-09 - TonightsGirlfriend - Kenna James.mp4")
+
+video_list.append("/Users/k00gar/Downloads/VideoFile.mp4")
 
 TestMode = False
+DebugMode = True
+isVR = True
+
+# wait 60 minutes before continuing
+# time.sleep(60 * 40)
+
 
 for video_path in video_list:
     print(f"Processing video: {video_path}")
-    cap = cv2.VideoCapture(video_path)
+    #funscript_data = []
+
+    debugger = Debugger(video_path, output_dir=video_path[:-4])
+
+    #cap = cv2.VideoCapture(video_path)
+    cap = VideoReaderFFmpeg(video_path, is_VR=isVR)
     fps = cap.get(cv2.CAP_PROP_FPS)
     image_y_size = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
-    image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH) // 3
+    image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
+
+    if isVR:
+        image_x_size = image_x_size // 3
+
     print(f"Image size: {image_x_size}x{image_y_size}")
     cap.release()
 
@@ -305,20 +416,30 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
     funscript_distances = []
 
     # Process the video
-    extract_yolo_data(yolo_model, video_path, frame_start, frame_end, TestMode)
 
+    # Run the YOLO detection and saves result to _rawyolo.json file
+    #extract_yolo_data(yolo_model, video_path, frame_start, frame_end, TestMode, isVR)
+
+    # Load YOLO detection results from file
     yolo_data = load_yolo_data_from_file(video_path[:-4] + f"_rawyolo.json")
 
     results = make_data_boxes(yolo_data, image_x_size)
 
+    # Looking for the first instance of penis within the YOLO results
     first_penis_frame = parse_yolo_data_looking_for_penis(yolo_data, 0)
 
+    # Deciding whether we start from there or from a user specified later frame
     frame_start = max(max(first_penis_frame - int(fps), frame_start - int(fps)), 0)
 
-    analyze_tracking_results(results, image_y_size, video_path, frame_start, frame_end, TestMode)
+    # Performing the tracking part and generation of the raw funscript data
+    funscript_data = analyze_tracking_results(results, image_y_size, video_path, frame_start, frame_end, TestMode)
+
+    debugger.save_logs()
+
+    funscript_handler = FunscriptGenerator()
 
-    funscript_handler = FH.FunscriptGenerator()
-    funscript_handler.generate(funscript_frames, funscript_distances, video_path[:-4] + "_kAI.funscript", fps, TestMode)
+    # Simplifying the funscript data and generating the file
+    funscript_handler.generate(video_path[:-4] + f"_rawfunscript.json", funscript_data, fps, TestMode)
 
     print(f"Finished processing video: {video_path}")
 
diff --git a/utils/Debugger.py b/utils/Debugger.py
new file mode 100644
index 00000000..390ce2ba
--- /dev/null
+++ b/utils/Debugger.py
@@ -0,0 +1,299 @@
+import json
+import time
+
+import cv2
+import numpy as np
+from utils.config import class_colors
+from scipy.interpolate import interp1d
+from utils.Visualizer import Visualizer
+from utils.VideoReaderFFmpeg import VideoReaderFFmpeg
+
+class Debugger:
+    def __init__(self, video_path, output_dir):
+        self.video_path = video_path
+        self.output_dir = output_dir
+        self.logs = {}
+
+    def log_frame(self, frame_id, variables=None, bounding_boxes=None):
+        """
+        Log or update the state of variables and bounding boxes for a specific frame.
+        """
+        if frame_id not in self.logs:
+            self.logs[frame_id] = {
+                'variables': {},
+                'bounding_boxes': []
+            }
+
+        if variables:
+            self.logs[frame_id]['variables'].update(variables)
+
+        if bounding_boxes:
+            self.logs[frame_id]['bounding_boxes'].extend(bounding_boxes)
+
+    def save_logs(self):
+        """
+        Save the logs to a JSON file.
+        """
+        log_file = f"{self.output_dir}_debug_logs.json"
+
+        # Custom function to convert NumPy types to native Python types
+        def default(obj):
+            if isinstance(obj, np.integer):
+                return int(obj)
+            elif isinstance(obj, np.floating):
+                return float(obj)
+            elif isinstance(obj, np.ndarray):
+                return obj.tolist()
+            else:
+                raise TypeError(f"Object of type {obj.__class__.__name__} is not JSON serializable")
+
+        # Save logs with the custom default function
+        with open(log_file, 'w') as f:
+            json.dump(self.logs, f, indent=4, default=default)
+        print(f"Logs saved to {log_file}")
+
+    def load_logs(self):
+        """
+        Load existing logs from a JSON file.
+        """
+        log_file = f"{self.output_dir}_debug_logs.json"
+        try:
+            with open(log_file, 'r') as f:
+                self.logs = json.load(f)
+            print(f"Logs loaded from {log_file}")
+        except FileNotFoundError:
+            print(f"Log file {log_file} not found. Starting with empty logs.")
+        except json.JSONDecodeError:
+            print(f"Error decoding JSON from {log_file}. Starting with empty logs.")
+
+    def display_frame(self, frame_id):
+        """
+        Display the logged frame with bounding boxes and variable states.
+        """
+        str_frame_id = str(frame_id)
+        if str_frame_id not in self.logs:
+            print(f"No logs found for frame {frame_id}")
+            return
+
+        # Load the frame from the video
+        cap = cv2.VideoCapture(self.video_path)
+        cap.set(cv2.CAP_PROP_POS_FRAMES, frame_id)
+        ret, frame = cap.read()
+        cap.release()
+
+        if not ret:
+            print(f"Failed to read frame {frame_id}")
+            return
+
+        frame = frame[:, frame.shape[1] // 6:frame.shape[1] // 3, :]
+
+        # Draw bounding boxes with color coding
+        for box in self.logs[str_frame_id]['bounding_boxes']:
+            x1, y1, x2, y2 = box['box']
+            class_name = box['class_name']
+            conf = box['conf']
+
+            print(f"{class_name}: conf {conf}, box: [{x1}, {y1}, {x2}, {y2}]")
+
+            # Get the color for the class from the class_colors dictionary
+            color = class_colors.get(class_name, (0, 255, 0))  # Default to green if class not found
+
+            # Draw the bounding box
+            cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
+
+            # Add the class name and confidence score as text
+            cv2.putText(frame, f"{class_name} {conf:.2f}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+
+        # Display variables line by line
+        variables = self.logs[str_frame_id]['variables']
+        y_offset = 30  # Initial vertical offset for the first variable
+        for key, value in variables.items():
+            variable_text = f"{key}: {value}"
+            print(variable_text)
+            cv2.putText(frame, variable_text, (10, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
+            y_offset += 20  # Move down by 20 pixels for the next variable
+
+        # Draw the locked_penis_box if it exists
+        locked_penis_box = variables.get('locked_penis_box')
+        if locked_penis_box:
+            x1, y1, x2, y2 = locked_penis_box
+            color = class_colors.get('penis', (0, 255, 0))  # Use the color for "penis" or default to green
+            cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
+            cv2.putText(frame, "Locked Penis", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+
+        # Show the frame
+        cv2.imshow(f"Debug Frame {frame_id}", frame)
+        cv2.waitKey(0)
+        cv2.destroyAllWindows()
+
+    def debug_frame(self, frame_id, variables, bounding_boxes):
+        """
+        Log and display the frame for debugging.
+        """
+        self.log_frame(frame_id, variables, bounding_boxes)
+        self.display_frame(frame_id)
+
+    def play_video(self, start_frame=0, duration=15, rolling_window_size=100, record=False, downsize_ratio = 1):
+        """
+        Play the video from a specified frame or timestamp, displaying variables, bounding boxes,
+        and rolling window curves for distance and funscript data.
+        """
+
+        visualizer = Visualizer()
+
+        # Load the video
+        #cap = cv2.VideoCapture(self.video_path)
+        cap = VideoReaderFFmpeg(self.video_path, is_VR=True)
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        print(f"Total frames: {total_frames}, FPS: {fps}")
+
+        if record:
+            ret, frame = cap.read()
+            if cap.is_VR:
+                frame_copy = frame.copy()
+                frame_copy = frame_copy[:, frame.shape[1] // 3:2 * frame.shape[1] // 3, :]
+            # Define the output video writer
+            output_path = self.video_path.replace(".mp4", "_debug.mp4")
+            fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # Codec for MP4
+            out = cv2.VideoWriter(output_path, fourcc, fps, (frame_copy.shape[1] // downsize_ratio, frame_copy.shape[0] // downsize_ratio))
+
+            if not out.isOpened():
+                print(f"Error: Could not open video writer for {output_path}")
+                cap.release()
+                return
+
+        # Set the starting frame
+        cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame)
+
+        # Load the funscript file
+        funscript_path = self.video_path.replace(".mp4", ".funscript")
+        try:
+            with open(funscript_path, 'r') as f:
+                funscript_data = json.load(f)
+        except FileNotFoundError:
+            print(f"Funscript file not found at {funscript_path}")
+            funscript_data = None
+
+        # Initialize rolling window buffers
+        distance_buffer = np.zeros(rolling_window_size)
+        funscript_buffer = np.zeros(rolling_window_size)
+
+        # Prepare funscript interpolation
+        if funscript_data:
+            actions = funscript_data.get('actions', [])
+            funscript_times = [action['at'] for action in actions]
+            funscript_positions = [action['pos'] for action in actions]
+            print(f"len of funscript_times: {len(funscript_times)}")
+            funscript_interpolator = interp1d(funscript_times, funscript_positions, kind='linear',
+                                              fill_value="extrapolate")
+
+        frame_id = start_frame
+        end_frame = start_frame + int(duration * fps)  # Calculate the end frame based on duration
+
+        while frame_id < end_frame:
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            # Crop the frame (as done in display_frame)
+            #frame = frame[:, frame.shape[1] // 6:frame.shape[1] // 3, :]
+            if cap.is_VR:
+                frame_copy = frame.copy()
+                frame_copy = frame_copy[:, frame.shape[1] // 3:2 * frame.shape[1] // 3, :]
+
+            # Display variables and bounding boxes
+            str_frame_id = str(frame_id)
+            if str_frame_id in self.logs:
+                variables = self.logs[str_frame_id]['variables']
+                bounding_boxes = self.logs[str_frame_id]['bounding_boxes']
+
+                # Draw bounding boxes
+                for box in bounding_boxes:
+                    x1, y1, x2, y2 = box['box']
+                    class_name = box['class_name']
+                    conf = box['conf']
+                    color = class_colors.get(class_name, (0, 255, 0))
+                    cv2.rectangle(frame_copy, (x1, y1), (x2, y2), color, 2)
+                    cv2.putText(frame_copy, f"{class_name} {conf:.2f}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color,
+                                2)
+
+                # Display variables
+                y_offset = 30
+                for key, value in variables.items():
+                    variable_text = f"{key}: {value}"
+                    cv2.putText(frame_copy, variable_text, (10, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
+                    y_offset += 20
+
+                # Draw the locked_penis_box if it exists
+                locked_penis_box = variables.get('locked_penis_box')
+                if locked_penis_box:
+                    x1, y1, x2, y2 = locked_penis_box
+                    color = class_colors.get('penis', (0, 255, 0))
+                    cv2.rectangle(frame_copy, (x1, y1), (x2, y2), color, 2)
+                    cv2.putText(frame_copy, "Locked Penis", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+
+            # Update rolling window buffers
+            distance = variables.get('distance', 0)
+            funscript_value = self._get_funscript_value(funscript_interpolator, frame_id, fps) if funscript_data else 0
+            print(f"Funscript value: {funscript_value}")
+            visualizer.draw_gauge(frame_copy, funscript_value)
+            distance_buffer = np.roll(distance_buffer, -1)
+            distance_buffer[-1] = distance
+            funscript_buffer = np.roll(funscript_buffer, -1)
+            funscript_buffer[-1] = funscript_value
+
+            # Draw rolling window curves
+            graph_height = int(frame_copy.shape[0] * 0.2)  # 20% of the frame height
+            graph_y_start = y_offset + 10  # Start the graph right under the variables
+            self._draw_rolling_window_curve(frame_copy, distance_buffer, (0, 255, 0), 0.5, graph_height, graph_y_start)
+            self._draw_rolling_window_curve(frame_copy, funscript_buffer, (255, 0, 0), 0.5, graph_height, graph_y_start)
+
+            # Show the frame
+            cv2.imshow(f"Debug Video", frame_copy)
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                break
+            # if spacebar is pressed, pause
+            if cv2.waitKey(1) & 0xFF == 32:
+                time.sleep(10)
+
+
+
+            if record:
+                # resize the frame, divide dimensions by 2
+                frame = cv2.resize(frame_copy, (frame_copy.shape[1] // downsize_ratio, frame_copy.shape[0] // downsize_ratio))
+                out.write(frame_copy)
+
+            frame_id += 1
+
+        cap.release()
+        if record:
+            out.release()
+        cv2.destroyAllWindows()
+
+    def _get_funscript_value(self, interpolator, frame_id, fps):
+        """
+        Get the interpolated funscript value for a given frame.
+        """
+        time_in_milliseconds = int((frame_id / fps) * 1000)
+        return interpolator(time_in_milliseconds)
+
+    def _draw_rolling_window_curve(self, frame, buffer, color, alpha, graph_height, graph_y_start):
+        """
+        Draw a rolling window curve on the frame as a transparent overlay with a fixed Y-axis range of 0 to 100.
+        """
+        overlay = frame.copy()
+        height, width, _ = frame.shape
+
+        # Ensure the buffer values are within the range 0 to 100
+        buffer_clipped = np.clip(buffer, 0, 100)
+
+        for i in range(len(buffer_clipped) - 1):
+            x1 = int(width * (i / len(buffer_clipped)))
+            x2 = int(width * ((i + 1) / len(buffer_clipped)))
+            y1 = graph_y_start + graph_height - int((buffer_clipped[i] / 100) * graph_height)
+            y2 = graph_y_start + graph_height - int((buffer_clipped[i + 1] / 100) * graph_height)
+            cv2.line(overlay, (x1, y1), (x2, y2), color, 2)
+
+        cv2.addWeighted(overlay, alpha, frame, 1 - alpha, 0, frame)
+
diff --git a/utils/Display_debug_results.py b/utils/Display_debug_results.py
new file mode 100644
index 00000000..8b57c455
--- /dev/null
+++ b/utils/Display_debug_results.py
@@ -0,0 +1,42 @@
+from Debugger import Debugger
+
+#video="/Users/k00gar/Downloads/BadoinkVR_Lights_Camera_Satisfaction_4K_HEVC_180_180x180_3dh.mp4"
+
+#video = "/Users/k00gar/Downloads/Katrina Jade [ADD] Oct 16, 2024R_6kvr265_reenc.mp4"
+video = "/Users/k00gar/Downloads/VideoFile.mp4"
+#video = "/Users/k00gar/Downloads/ARPorn_Sasha Tatcha_Fit and Fired Up_4000p_8K_original_FISHEYE190_alpha.mp4"
+
+#video = "/Users/k00gar/Downloads/2022-09-09 - TonightsGirlfriend - Kenna James.mp4"
+
+
+# Katrina Jade ADD
+frame = 77000
+#frame = 126000
+
+#BadoinkVR Lights Camera
+
+#frame = 53100  # messed up bj
+
+#frame = 90000
+#frame = 77400
+
+#Kenna James TNGF debug section
+#frame = 80000
+#frame = 36900
+#frame = 153000 # says CG should be handjob
+#frame = 75600
+#frame = 100000 # should be grinding but BJ....
+#frame = 72500  #BJ
+#frame = 50400 #Grinding
+
+#frame = 42920
+
+#frame = 35312
+
+debugger = Debugger(video, video[:-4])
+
+debugger.load_logs()
+
+#debugger.display_frame(frame)
+
+debugger.play_video(frame, record=False, downsize_ratio=3)
\ No newline at end of file
diff --git a/utils/FFmpegVideoCapture.py b/utils/FFmpegVideoCapture.py
new file mode 100644
index 00000000..b72cd4ac
--- /dev/null
+++ b/utils/FFmpegVideoCapture.py
@@ -0,0 +1,134 @@
+import ffmpeg
+import numpy as np
+import cv2  # For OpenCV constants
+
+
+class FFmpegVideoCapture:
+    def __init__(self, video_path):
+        """
+        Initialize the FFmpegVideoCapture with a video file.
+
+        Args:
+            video_path (str): Path to the video file.
+        """
+        self.video_path = video_path
+        self.probe = ffmpeg.probe(video_path)  # Use the correct probe function
+        video_stream = next((stream for stream in self.probe['streams'] if stream['codec_type'] == 'video'), None)
+        if video_stream is None:
+            raise ValueError("No video stream found in the input file.")
+
+        self.width = int(video_stream['width'])
+        self.height = int(video_stream['height'])
+        self.fps = int(video_stream['r_frame_rate'].split('/')[0]) / int(video_stream['r_frame_rate'].split('/')[1])
+        self.total_frames = int(video_stream.get('nb_frames', -1))
+        self.frame_count = 0
+
+        # Initialize FFmpeg process
+        self.process = None
+        self.start_process()
+
+    def start_process(self):
+        """
+        Start the FFmpeg process to read frames.
+        """
+        try:
+            self.process = (
+                ffmpeg
+                .input(self.video_path)
+                .output('pipe:', format='rawvideo', pix_fmt='rgb24')
+                .run_async(pipe_stdout=True)
+            )
+        except ffmpeg.Error as e:
+            raise RuntimeError(f"Failed to start FFmpeg process: {e}")
+
+    def read(self):
+        """
+        Read the next frame from the video.
+
+        Returns:
+            bool: True if the frame was read successfully, False otherwise.
+            np.ndarray: The frame as a NumPy array (or None if no frame was read).
+        """
+        if not self.process:
+            return False, None  # Process not initialized or already released
+
+        in_bytes = self.process.stdout.read(self.width * self.height * 3)
+        if not in_bytes:
+            return False, None
+
+        frame = np.frombuffer(in_bytes, dtype=np.uint8).reshape((self.height, self.width, 3))
+        self.frame_count += 1
+        return True, frame
+
+    def get(self, prop_id):
+        """
+        Get a property of the video.
+
+        Args:
+            prop_id (int): Property ID (e.g., cv2.CAP_PROP_FPS, cv2.CAP_PROP_FRAME_HEIGHT, etc.).
+
+        Returns:
+            float: The value of the property.
+        """
+        if prop_id == cv2.CAP_PROP_FRAME_WIDTH:
+            return self.width
+        elif prop_id == cv2.CAP_PROP_FRAME_HEIGHT:
+            return self.height
+        elif prop_id == cv2.CAP_PROP_FPS:
+            return self.fps
+        elif prop_id == cv2.CAP_PROP_FRAME_COUNT:
+            return self.total_frames
+        elif prop_id == cv2.CAP_PROP_POS_FRAMES:
+            return self.frame_count
+        else:
+            return 0.0  # Unsupported property
+
+    def set(self, prop_id, value):
+        """
+        Set a property of the video.
+
+        Args:
+            prop_id (int): Property ID (e.g., cv2.CAP_PROP_POS_FRAMES).
+            value (float): The value to set.
+
+        Returns:
+            bool: True if the property was set successfully, False otherwise.
+        """
+        if prop_id == cv2.CAP_PROP_POS_FRAMES:
+            # Seek to the specified frame
+            if value < 0 or (self.total_frames > 0 and value >= self.total_frames):
+                return False  # Invalid frame number
+
+            # Calculate the time in seconds to seek to
+            seek_time = value / self.fps
+
+            # Restart the FFmpeg process with the seek time
+            self.release()
+            try:
+                self.process = (
+                    ffmpeg
+                    .input(self.video_path, ss=seek_time)  # Seek to the specified time
+                    .output('pipe:', format='rawvideo', pix_fmt='rgb24')
+                    .run_async(pipe_stdout=True)
+                )
+                self.frame_count = value
+                return True
+            except ffmpeg.Error as e:
+                raise RuntimeError(f"Failed to start FFmpeg process after seek: {e}")
+        else:
+            return False  # Unsupported property
+
+    def release(self):
+        """
+        Release the FFmpeg process and clean up resources.
+        """
+        if self.process:
+            self.process.stdout.close()
+            self.process.wait()
+            self.process = None
+
+    def __del__(self):
+        """
+        Ensure the FFmpeg process is released when the object is deleted.
+        """
+        self.release()
\ No newline at end of file
diff --git a/utils/FunscriptHandler.py b/utils/FunscriptHandler.py
index e5062874..9d39e332 100644
--- a/utils/FunscriptHandler.py
+++ b/utils/FunscriptHandler.py
@@ -4,15 +4,39 @@
 import matplotlib.pyplot as plt
 
 class FunscriptGenerator:
-    def generate(self, frames, distances, output_path, fps, TestMode = False):
-        print(f"Generating funscript based on {len(distances)} distances...")
+    def generate(self, raw_funscript_path, funscript_data, fps, TestMode = False):
+        output_path = raw_funscript_path[:-18] + '.funscript'
+        if len(funscript_data) == 0:
+            # Read the funscript data from the JSON file
+            with open(raw_funscript_path, 'r') as f:
+                print(f"Loading funscript from {raw_funscript_path}")
+                try:
+                    data = f.read() #json.load(f)
+                    data = eval(data)
+                except:
+                    print(f"Error loading funscript from {raw_funscript_path}")
+        else:
+            data = funscript_data
 
-        self.points = list(zip(frames, distances))
+        try:
+            print(f"Generating funscript based on {len(data)} points...")
 
-        self.filtered_positions = simplify_coords(self.points, 10.0)  # Use VW algorithm
+            filter_coeff = 12.0
 
-        self.write_funscript(self.filtered_positions, output_path, fps)
+            self.filtered_positions = simplify_coords(data, filter_coeff)  # Use VW algorithm
 
+            print(f"Lenghth of filtered positions: {len(self.filtered_positions)}")
+
+            #output_path = raw_funscript_path[:-18] + '_vw_' + str(filter_coeff) + '.funscript'
+            #output_path = raw_funscript_path[:-18] + '_vw_' + str(filter_coeff) + '.funscript'
+
+            self.write_funscript(self.filtered_positions, output_path, fps)
+
+            print(f"Funscript generated and saved to {output_path}")
+
+        except:
+            print(f"Error loading raw funscript from {raw_funscript_path}")
+        """
         # for alternative version, make every point 0 if distance is under 20
         points_v2 = []
         multiplier = 1.2
@@ -29,10 +53,11 @@ def generate(self, frames, distances, output_path, fps, TestMode = False):
 
         write_path = output_path[:-10] + '_remapped.funscript'
         self.write_funscript(points_v2, write_path, fps)
+        """
 
-        if TestMode:
-            # plot a comparative graph
-            self.plot_comparison(self.points, self.filtered_positions, points_v2)
+        #if TestMode:
+        #    # plot a comparative graph
+        #    self.plot_comparison(self.points, self.filtered_positions, points_v2)
 
 
     def write_funscript(self, distances, output_path, fps):
diff --git a/utils/ObjectTracker.py b/utils/ObjectTracker.py
index 710d3398..ecbbf450 100644
--- a/utils/ObjectTracker.py
+++ b/utils/ObjectTracker.py
@@ -1,30 +1,31 @@
 from utils import KalmanFilter as KF
 from collections import deque
 import numpy as np
-import cv2
+#import cv2
 from utils.config import class_names
+import math
 
 class ObjectTracker:
-    def __init__(self, state=None):
+    def __init__(self, fps, frame_pos, state=None):
         self.class_names = class_names
         self.tracked_objects = {
             class_name: {'kf': KF.KalmanFilter(),
                          'position': None, 'detected': False, 'touching': False, 'prediction_count': 0}
             for class_name in class_names
         }
-        self.detection_interval = 10
-        self.frame_count = 0
-        self.trackers = {}
+        # self.detection_interval = 10
+        # self.frame_count = 0
+        # self.trackers = {}
 
         self.distance_kf = KF.KalmanFilter()
 
         self.frame = None
+        self.current_frame_id = frame_pos
         self.image_y_size = 0
+        self.fps = fps
 
         self.occlusion = False
 
-        self.minimum_penis_consecutive_detections = 2
-
         self.penis_box, self.locked_penis_box = None, None
         self.glans_detected = False
         self.locked_penis_height = 0
@@ -33,6 +34,7 @@ def __init__(self, state=None):
         self.raw_distance = 100
         self.previous_distances = [100, 100, 100]
         self.tracked_body_part = "Nothing"
+        #self.activated_kalman = False
 
         self.face_distance = 100
         self.hand_distance = 100
@@ -68,14 +70,30 @@ def __init__(self, state=None):
 
         self.sex_position = "Not relevant"
         self.prev_sex_position = "Not relevant"
+        self.sex_position_reason = ""
+        self.sex_position_history = deque(maxlen=10)
+        self.sub_sex_position = "Not relevant"
 
         #self.positions = {class_name: [] for class_name in class_names}
-        self.positions = {class_name: deque(maxlen=60) for class_name in class_names}
+        self.positions = {class_name: deque(maxlen=200) for class_name in class_names}
+        self.distances = {class_name: deque(maxlen=200) for class_name in class_names}
+        self.areas = {class_name: deque(maxlen=200) for class_name in class_names}
 
         # Initialize normalized_positions as a dictionary of dictionaries
-        self.normalized_positions = {class_name: deque(maxlen=10) for class_name in class_names}
-        self.normalized_positions['navel'].append(100)
-        self.normalized_positions['breast'].append(100)
+        self.normalized_positions = {class_name: deque(maxlen=200) for class_name in class_names}
+        self.normalized_distances = {class_name: deque(maxlen=200) for class_name in class_names}
+        self.activated_kalman = {class_name: False for class_name in class_names}
+
+        for class_name in class_names:
+            self.normalized_distances[class_name].append(100)
+            self.normalized_positions[class_name].append(100)
+
+
+        self.normalized_areas = {class_name: deque(maxlen=200) for class_name in class_names}
+        #self.normalized_positions['navel'].append(100)
+        #self.normalized_positions['breast'].append(100)
+        #self.normalized_positions['butt'].append(100)
+
         #    'breast': deque(maxlen=10),
         #    'navel': deque(maxlen=10),
             # add other classes as needed
@@ -86,141 +104,42 @@ def __init__(self, state=None):
         self.moving_average_window = 5  # Number of frames to consider for moving average
 
         # Counters for consecutive detections and non-detections
-        self.consecutive_penis_detections = 0
-        self.consecutive_no_penis_detections = 0
+        self.consecutive_detections = {class_name: 0 for class_name in class_names}
+        self.consecutive_non_detections = {class_name: 0 for class_name in class_names}
+        self.detections_threshold = 3
+
+        self.consecutive_grinding_supiscions = {class_name: 0 for class_name in ['pussy', 'butt']}
 
         self.penetration = False
+        self.grinding = False
+        self.rubbing = False
         self.penetration_mode = 'No penetration'
-        self.tracked_body_part = None
-
-        self.max_predictions = 600
-
-        if state:
-            tracked_objects_state = state.get('tracked_objects_state', {})
-            for class_name, obj_state in tracked_objects_state.items():
-                if class_name in self.tracked_objects:
-                    self.tracked_objects[class_name]['kf'].mean = obj_state['mean']
-                    self.tracked_objects[class_name]['kf'].covariance = obj_state['covariance']
-                    self.tracked_objects[class_name]['position'] = obj_state['position']
-                    self.tracked_objects[class_name]['detected'] = obj_state['detected']
-                    self.tracked_objects[class_name]['touching'] = obj_state['touching']
-                    self.tracked_objects[class_name]['prediction_count'] = obj_state['prediction_count']
-
-            distance_kf_state = state.get('distance_kf_state', {})
-            if distance_kf_state:
-                self.distance_kf.mean = distance_kf_state['mean']
-                self.distance_kf.covariance = distance_kf_state['covariance']
-
-            self.tracked_body_part = state.get('tracked_body_part', "Nothing")
-            self.sex_position = state.get('sex_position', "Not relevant")
-            self.sex_position_reason = state.get('sex_position_reason', "")
-
-            positions = state.get('positions', {})
-            for class_name, pos_list in positions.items():
-                if class_name in self.positions:
-                    self.positions[class_name].extend(pos_list)
-
-            normalized_positions = state.get('normalized_positions', {})
-            for class_name, norm_pos_list in normalized_positions.items():
-                if class_name in self.normalized_positions:
-                    self.normalized_positions[class_name].extend(norm_pos_list)
-
-            self.face_hands_movements = state.get('face_hands_movements', {})
-            self.foot_movements = state.get('foot_movements', {})
-            self.right_hand_distances = state.get('right_hand_distances', [])
-            self.left_hand_distances = state.get('left_hand_distances', [])
-            self.right_foot_distances = state.get('right_foot_distances', [])
-            self.left_foot_distances = state.get('left_foot_distances', [])
-            self.consecutive_penis_detections = state.get('consecutive_penis_detections', 0)
-            self.consecutive_no_penis_detections = state.get('consecutive_no_penis_detections', 0)
-            self.penetration = state.get('penetration', False)
-            self.penetration_mode = state.get('penetration_mode', 'No penetration')
-            self.switch_cooldown = state.get('switch_cooldown', 0)
-            self.foot_switch_cooldown = state.get('foot_switch_cooldown', 0)
-
-    def get_state(self):
-        state = {
-            'tracked_objects_state': {
-                class_name: {
-                    'mean': obj['kf'].mean,
-                    'covariance': obj['kf'].covariance,
-                    'position': obj['position'],
-                    'detected': obj['detected'],
-                    'touching': obj['touching'],
-                    'prediction_count': obj['prediction_count']
-                }
-                for class_name, obj in self.tracked_objects.items()
-            },
-            'distance_kf_state': {
-                'mean': self.distance_kf.mean,
-                'covariance': self.distance_kf.covariance
-            },
-            'tracked_body_part': self.tracked_body_part,
-            'sex_position': self.sex_position,
-            'sex_position_reason': self.sex_position_reason,
-            'positions': {
-                class_name: list(pos)
-                for class_name, pos in self.positions.items()
-            },
-            'normalized_positions': {
-                class_name: list(norm_pos)
-                for class_name, norm_pos in self.normalized_positions.items()
-            },
-            'face_hands_movements': self.face_hands_movements,
-            'foot_movements': self.foot_movements,
-            'right_hand_distances': self.right_hand_distances,
-            'left_hand_distances': self.left_hand_distances,
-            'right_foot_distances': self.right_foot_distances,
-            'left_foot_distances': self.left_foot_distances,
-            'consecutive_penis_detections': self.consecutive_penis_detections,
-            'consecutive_no_penis_detections': self.consecutive_no_penis_detections,
-            'penetration': self.penetration,
-            'penetration_mode': self.penetration_mode,
-            'switch_cooldown': self.switch_cooldown,
-            'foot_switch_cooldown': self.foot_switch_cooldown
-        }
-        return state
+        self.hand_blow_footjob = False
 
-    def reinit_with_distance(self, distance):
-        self.__init__()
-        self.distance = distance
+        self.max_predictions = self.fps #600
 
-    def initialize_trackers(self, sorted_boxes):
-        params = cv2.TrackerVit_Params()
-        params.net = "models/vit/object_tracking_vittrack_2023sep.onnx"
-
-        for box, conf, cls, class_name in sorted_boxes:
-            if class_name not in self.trackers:
-                tracker = cv2.TrackerVit_create(params)
-                tracker.init(self.frame, tuple(box))
-                self.trackers[class_name] = tracker
-
-    def update_trackers(self):
-        for class_name, tracker in self.trackers.items():
-            success, box = tracker.update(self.frame)
-            if success:
-                self.tracked_objects[class_name]['position'] = box
-                self.tracked_objects[class_name]['detected'] = True
-            else:
-                self.tracked_objects[class_name]['detected'] = False
 
     def update_distance(self, raw_distance):
-        rounded_distance = round(raw_distance/5)*5
-        #self.previous_distances.pop(0)
-        #self.previous_distances.append(raw_distance)
-        ema_distance = int(0.7 * rounded_distance + 0.1 * self.previous_distances[1] + 0.2 * self.previous_distances[2])
-        # cap the change in distance to a maximum of +5 or -5 vs previous distance
-        if abs(ema_distance - self.previous_distances[-1]) > 15:
-            ema_distance = self.previous_distances[-1] + np.sign(ema_distance - self.previous_distances[-1]) * 15
-            #print(f"Capping distance change to {ema_distance} vs {raw_distance}")
-        elif abs(ema_distance - self.previous_distances[-1]) < 2:
-            # if the change is less than 2, use the previous distance to eliminate jitter
-            #print(f"Skipping distance change to {ema_distance} vs {self.previous_distances[-1]} because change is less than 4")
-            ema_distance = self.previous_distances[-1]
-        self.previous_distances.pop(0)
-        self.previous_distances.append(ema_distance)
+
         #self.raw_distance = raw_distance
         if raw_distance is not None:
+
+            rounded_distance = round(raw_distance / 5) * 5
+            # self.previous_distances.pop(0)
+            # self.previous_distances.append(raw_distance)
+            ema_distance = int(
+                0.7 * rounded_distance + 0.1 * self.previous_distances[1] + 0.2 * self.previous_distances[2])
+            # cap the change in distance to a maximum of +5 or -5 vs previous distance
+            if abs(ema_distance - self.previous_distances[-1]) > 15:
+                ema_distance = self.previous_distances[-1] + np.sign(ema_distance - self.previous_distances[-1]) * 15
+                # print(f"Capping distance change to {ema_distance} vs {raw_distance}")
+            elif abs(ema_distance - self.previous_distances[-1]) < 2:
+                # if the change is less than 2, use the previous distance to eliminate jitter
+                # print(f"Skipping distance change to {ema_distance} vs {self.previous_distances[-1]} because change is less than 4")
+                ema_distance = self.previous_distances[-1]
+            self.previous_distances.pop(0)
+            self.previous_distances.append(ema_distance)
+
             # keep the distance between 0 and 100
             #raw_distance = max(0, min(100, int(raw_distance)))
             # Update the Kalman filter with the measurement
@@ -236,7 +155,10 @@ def update_distance(self, raw_distance):
             #filtered_distance = self.distance_kf.predict()
             filtered_distance = int(float(self.distance_kf.predict()[0]) / 5) * 5
             # keep the distance between 0 and 100
-            filtered_distance = max(0, min(100, filtered_distance))
+
+        filtered_distance = max(0, min(100, filtered_distance))
+
+        self.distance = filtered_distance
         # print(f"Raw distance vs filtered distance: {raw_distance} vs {filtered_distance}")
         return filtered_distance
 
@@ -251,28 +173,42 @@ def update_tracking(self, class_name, box, fallback_class, tracked_item):
             tracked['half_width'] = (box[2] - box[0]) // 2
             tracked['touching'] = self.boxes_overlap(self.locked_penis_box, box)
             tracked['prediction_count'] = 0  # Reset prediction count when object is detected
+            if class_name == tracked_item and self.activated_kalman[class_name]:
+                self.activated_kalman[class_name] = False
+                print(f"@{self.current_frame_id} - Stopped Kalman prediction for {class_name} as it was found back")
         # Only perform prediction for the tracked item
-        elif class_name == tracked_item:
+        elif class_name == tracked_item and box is None:
             # Object not detected: Predict position based on the Kalman filter
             # print(f"Kalman prediction for {class_name}: {tracked['prediction_count'] + 1} / {int(self.max_predictions)}")
             tracked['kf'].predict()
-            # print(f"Kalman prediction for {class_name}: {tracked['prediction_count'] + 1} / {int(self.max_predictions)}")
+            if self.activated_kalman[class_name] == False:
+                self.activated_kalman[class_name] = True
+                print(f"@{self.current_frame_id} - Activating Kalman prediction for {class_name}")
+            print(f"@{self.current_frame_id} - Kalman prediction for {class_name}: {tracked['prediction_count'] + 1} / {int(self.max_predictions)}")
             tracked['detected'] = False
             tracked['position'] = tracked['kf'].position
             tracked['touching'] = self.boxes_overlap(self.locked_penis_box, tracked['position'])
 
             # Use fallback if primary tracked object is lost and prediction count exceeds 60
             if tracked['prediction_count'] >= self.max_predictions:
-                if fallback_class and self.tracked_objects[fallback_class]['detected']:
-                    print(f"Handling occlusion of {class_name}, fallback to {fallback_class}")
-                    tracked['position'] = self.tracked_objects[fallback_class]['position']
-                else:
-                    print(f"No fallback available for {class_name}, deactivating tracking")
-                    tracked['position'] = None
-                    tracked['detected'] = False
-                    tracked['touching'] = False
-                    tracked['prediction_count'] = 0
-                    self.tracked_body_part = None
+                #if fallback_class and self.tracked_objects[fallback_class]['detected']:
+                #    print(f"Handling occlusion of {class_name}, fallback to {fallback_class}")
+                #    tracked['position'] = self.tracked_objects[fallback_class]['position']
+                #else:
+                print(f"No fallback available for {class_name}, deactivating tracking")
+                tracked['position'] = None
+                tracked['detected'] = False
+                tracked['touching'] = False
+                tracked['prediction_count'] = 0
+                self.tracked_body_part = 'Nothing'
+        if tracked['touching']:
+            self.log_and_normalize_pos(tracked['position'], class_name)
+            self.log_and_normalize_distance(tracked['position'], class_name)
+        else:
+            self.normalized_positions[class_name].clear()
+            self.normalized_distances[class_name].clear()
+            self.normalized_positions[class_name].append(100)
+            self.normalized_distances[class_name].append(100)
 
                     # print(f"Fallback to {fallback_class}")
                     #draw_bounding_box(image, tracked['position'], fallback_class)
@@ -290,60 +226,821 @@ def boxes_overlap(self, box1, box2):
             return False
         return True
 
-    def calculate_distance(self, penis_box, other_box, class_name):
-        """Calculate distance between penis box and other body part box."""
-        px1, py1, px2, py2 = penis_box
+    def calculate_distance(self, penis_box, other_box):
+        # compute middle of other_box
+        if other_box is None:
+            return None
         ox1, oy1, ox2, oy2 = other_box
+        y_pos = (oy1 + 2 * oy2) // 3
+        x_pos = (ox1 + ox2) // 2
+        # compute middle of lower side of penis_box
+        px1, py1, px2, py2 = penis_box
+        # compute distance between (x_pos, y_pos) and (px2, py2)
+        distance = math.sqrt((x_pos - px2) ** 2 + (y_pos - py2) ** 2)
+        return distance
 
-        min_reevaluation = py1
-        max_reevaluation = py2
-
-        # Retrieve normalized distance for class_name
-        normalized_distance = self.normalized_positions[class_name][-1]
 
-        if class_name in ['left foot', 'right foot', 'breast', 'left hand', 'right hand']:
-            # Calculate distance from middle of hand/foot box to bottom of penis box
-            pos = (oy1 + oy2) // 2
-            min_reevaluation = py1 + ((oy2 - oy1) * 0.2)
-            max_reevaluation = py2 - ((oy2 - oy1) / 3)
 
-        else:
-            # Calculate distance from bottom of other box to bottom of penis box
-            pos = oy2
-            if class_name == 'butt':
-                min_reevaluation = py1 + ((oy2 - oy1) * 0.2)
-            elif class_name == 'face':
-                min_reevaluation = py1 + ((py2 - py1) * 0.1)
-                max_reevaluation = py1 + ((py2 - py1) * 0.6)
-            elif class_name == 'pussy':
-                max_reevaluation = py2 - ((oy2 - oy1) // 2)
-
-        secured_distance = max(0, max_reevaluation - pos)
-        # Normalize distance to 0-100 scale
-        max_distance = max_reevaluation - min_reevaluation
-
-        if normalized_distance:
-            if max_distance == 0:
-                return normalized_distance
+    def detect_sex_position_change(self, sex_position, reason):
+        if len(self.sex_position_history) > 0 and self.sex_position_history[-1] == "Blowjob" and self.sub_sex_position == "Handjob":
+            sex_position = "Blowjob"
+        self.sex_position_history.append(sex_position)
+        # Count the occurrences of each sex position in the history
+        position_counts = {}
+        for position in self.sex_position_history:
+            if position in position_counts:
+                position_counts[position] += 1
             else:
-                normalized_distance = (int(max(0, min(100, (secured_distance / max_distance) * 100))) + normalized_distance) //2
+                position_counts[position] = 1
+
+        # Find the most frequent sex position
+        if position_counts:
+            most_frequent_position = max(position_counts, key=position_counts.get)
         else:
-            if max_distance == 0:
-                return 100
-            else:
-                normalized_distance = int(max(0, min(100, (secured_distance / max_distance) * 100)))
-        # print(f"{class_name} distance: secured : {secured_distance}, max distance: {max_distance},  normalized: {normalized_distance}")
-        return normalized_distance
+            most_frequent_position = "Not relevant"  # No positions in history
+        if most_frequent_position != self.sex_position:
+            print(f"@{self.current_frame_id} - Sex position switched to: {most_frequent_position}")
+            self.sex_position = most_frequent_position
 
-    def detect_sex_position_change(self, sex_position, reason):
+    def prev_detect_sex_position_change(self, sex_position, reason):
         if sex_position != self.sex_position and sex_position == self.prev_sex_position:
-            print(f"Sex position changed from {self.sex_position} to {sex_position} given {reason}")
+            #print(f"Sex position changed from {self.sex_position} to {sex_position} given {reason}")
+            print(f"\n@{self.current_frame_id} - {sex_position} given {reason}")
             self.sex_position = sex_position
             self.sex_position_reason = reason
         else:
             self.prev_sex_position = sex_position
 
     def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
+        self.image_y_size = image_y_size
+        self.current_frame_id = current_frame_id
+
+        # initialize values
+        self.glans_detected = False
+        self.boxes = {class_name: None for class_name in class_names}
+        all_detections = {class_name: [] for class_name in class_names}
+
+        # detected_classes = []
+        list_of_classes = class_names.copy()
+
+        for box, conf, cls, class_name in sorted_boxes:
+            all_detections[class_name].append([conf, box])
+
+        found_box = {class_name: [] for class_name in class_names}
+        classes_touching_penis = {class_name: None for class_name in class_names}
+        list_of_touching_classes = []
+
+        # Checking priority classes, and keeping the ones with best confidence level
+        for check_class_first in ['glans', 'penis', 'navel']:
+            prev_conf = 0
+            for conf, box in all_detections[check_class_first]:
+                if conf > prev_conf:
+                    found_box[check_class_first] = [box, conf]
+                    prev_conf = conf
+        # Logging priority classes detection and non detections
+        for check_class_first in ['glans', 'penis', 'navel']:
+            box = None
+            if len(found_box[check_class_first]) > 0:
+                list_of_classes.pop(check_class_first)
+                self.boxes[check_class_first] = found_box[check_class_first][0]
+                box = self.boxes[check_class_first]
+                conf = found_box[check_class_first][1]
+                self.consecutive_detections[check_class_first] += 1
+                self.consecutive_non_detections[check_class_first] = 0
+                self.handle_class_first(check_class_first, self.boxes[check_class_first], conf)
+            else:
+                self.consecutive_detections[check_class_first] = 0
+                self.consecutive_non_detections[check_class_first] += 1
+            #if check_class_first == 'penis':
+            #    self.update_tracking(check_class_first, box, None, 'penis')
+
+        prev_class = ''
+        nb_items_touching_penis = 0
+
+        # Listing detected classes touching the locked penis box
+        for check_class_second in list_of_classes:
+            prev_conf = 0
+            for conf, box in all_detections[check_class_second]:
+                if self.locked_penis_box and self.boxes_overlap(box, self.locked_penis_box) and conf > prev_conf:
+                    classes_touching_penis[check_class_second] = box
+                    prev_conf = conf
+                    if check_class_second != prev_class:
+                        nb_items_touching_penis += 1
+                        list_of_touching_classes.append(check_class_second)
+                        prev_class = check_class_second
+            if prev_conf != 0:  # we have one item of that class touching
+                #self.log_and_normalize_distance(classes_touching_penis[check_class_second], check_class_second)
+                #self.log_and_normalize_pos(classes_touching_penis[check_class_second], check_class_second)
+                self.update_tracking(check_class_second, classes_touching_penis[check_class_second], None, self.tracked_body_part)
+
+        if self.tracked_body_part != "Nothing" and self.tracked_body_part not in list_of_touching_classes:
+            # try kalman filter prediction
+            self.update_tracking(self.tracked_body_part, None, None, self.tracked_body_part)
+
+
+        if nb_items_touching_penis == 0:
+            self.handle_closeup('No body parts touching')
+        else:
+            # Determine action type
+            if classes_touching_penis['butt']:
+                # Could still be closeup if pussy can be seen also
+                if len(all_detections['pussy']) > 0 and self.boxes_overlap(all_detections['pussy'][0][1], classes_touching_penis['butt']):
+                    self.handle_closeup('Presence of butt and overlapping pussy')
+                elif self.boxes['penis'] and not self.boxes['glans']:
+                    self.tracked_body_part = 'butt'
+                    self.penetration = True
+                    #self.log_and_normalize_distance(classes_touching_penis['butt'], 'butt')
+                    self.detect_sex_position_change('Doggy', 'butt touching penis')
+                    self.sub_sex_position = "Not relevant"
+            elif classes_touching_penis['pussy'] and self.boxes['penis'] and not self.boxes['glans']:
+                self.penetration = True
+                self.tracked_body_part = 'pussy'
+                #self.log_and_normalize_distance(classes_touching_penis['pussy'], 'pussy')
+                self.detect_sex_position_change('Cowgirl', 'pussy touching penis')
+                self.sub_sex_position = "Not relevant"
+            elif classes_touching_penis['face']:
+                self.tracked_body_part = 'face'
+                #self.log_and_normalize_distance(classes_touching_penis['face'], 'face')
+                self.detect_sex_position_change('Blowjob', 'face touching penis')
+                self.sub_sex_position = "Not relevant"
+            elif classes_touching_penis['left hand'] or classes_touching_penis['right hand']:
+                self.detect_sex_position_change('Handjob', 'hand touching penis')
+                self.sub_sex_position = "Handjob"
+                #if classes_touching_penis['left hand']:
+                #    self.log_and_normalize_distance(classes_touching_penis['left hand'], 'left hand')
+                #else:
+                #    self.normalized_distances['left hand'].clear()
+                #if classes_touching_penis['right hand']:
+                #    self.log_and_normalize_distance(classes_touching_penis['right hand'], 'right hand')
+                #else:
+                #    self.normalized_distances['right hand'].clear()
+
+
+            if self.boxes['penis'] is not None:
+                # Compare the height of the two boxes and scale it to 0 - 100
+                penis_height = self.boxes['penis'][3] - self.boxes['penis'][1]
+
+                if self.locked_penis_box:
+                    if self.sub_sex_position == 'Handjob':
+                        if classes_touching_penis['left hand'] and not classes_touching_penis['right hand']:
+                            #normalized_distance = self.normalized_distances['left hand'][-1]
+                            normalized_distance = self.normalized_positions['left hand'][-1]
+                            self.tracked_body_part = "left hand"
+                        elif classes_touching_penis['right hand'] and not classes_touching_penis['left hand']:
+                            #normalized_distance = self.normalized_distances['right hand'][-1]
+                            normalized_distance = self.normalized_positions['right hand'][-1]
+                            self.tracked_body_part = "right hand"
+                        else:
+                            if len(self.normalized_distances['left hand']) > 3 and len(self.normalized_distances['right hand']) > 3:
+                                #avg_left = self.normalized_distances['left hand'][-1] - self.normalized_distances['left hand'][-2] +\
+                                #            self.normalized_distances['left hand'][-2] - self.normalized_distances['left hand'][-3]
+                                #avg_right = self.normalized_distances['right hand'][-1] - self.normalized_distances['right hand'][-2] +\
+                                #            self.normalized_distances['right hand'][-2] - self.normalized_distances['right hand'][-3]
+                                avg_left = self.normalized_positions['left hand'][-1] - \
+                                           self.normalized_positions['left hand'][-2] + \
+                                           self.normalized_positions['left hand'][-2] - \
+                                           self.normalized_positions['left hand'][-3]
+                                avg_right = self.normalized_positions['right hand'][-1] - \
+                                            self.normalized_positions['right hand'][-2] + \
+                                            self.normalized_positions['right hand'][-2] - \
+                                            self.normalized_positions['right hand'][-3]
+
+                                if abs(avg_left) > abs(avg_right):
+                                    #normalized_distance = self.normalized_distances['left hand'][-1]
+                                    normalized_distance = self.normalized_positions['left hand'][-1]
+                                    self.tracked_body_part = "left hand"
+                                else:
+                                    #normalized_distance = self.normalized_distances['right hand'][-1]
+                                    normalized_distance = self.normalized_positions['right hand'][-1]
+                                    self.tracked_body_part = "right hand"
+                            else:
+                                # arbitrary choice
+                                #if len(self.normalized_distances['right hand']) > 0 and len(self.normalized_distances['right hand']) > len(self.normalized_distances['left hand']):
+                                #    normalized_distance = self.normalized_distances['right hand'][-1]
+                                #    self.tracked_body_part = "right hand"
+                                #elif len(self.normalized_distances['left hand']) > 0:
+                                #    normalized_distance = self.normalized_distances['left hand'][-1]
+                                #    self.tracked_body_part = "left hand"
+                                if len(self.normalized_positions['right hand']) > 0 and len(self.normalized_positions['right hand']) > len(self.normalized_distances['left hand']):
+                                    normalized_distance = self.normalized_positions['right hand'][-1]
+                                    self.tracked_body_part = "right hand"
+                                elif len(self.normalized_positions['left hand']) > 0:
+                                    normalized_distance = self.normalized_positions['left hand'][-1]
+                                    self.tracked_body_part = "left hand"
+
+                    else:  # other cases than handjob
+                        locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
+                        if locked_penis_height == 0:
+                            locked_penis_height = 0.0001
+                        scale = min(int((penis_height / locked_penis_height) * 100), 100)
+                        distance = 100 - scale
+
+                        self.positions['penis'].append(distance)
+
+                        min_distance, max_distance = min(self.positions['penis']), max(self.positions['penis'])
+                        normalized_distance = (100 - int(100 * ((distance - min_distance) / (
+                                max_distance - min_distance)))) if min_distance != max_distance else 100
+
+                        self.normalized_positions['penis'].append(normalized_distance)
+
+                        if self.sex_position == 'Blowjob':
+                            delta = 0
+                            if classes_touching_penis['left hand'] and len(self.normalized_distances['left hand']) > 3:
+                                delta += self.normalized_distances['left hand'][-1] - self.normalized_distances['left hand'][-4]
+                            if classes_touching_penis['right hand']and len(self.normalized_distances['right hand']) > 3:
+                                delta += self.normalized_distances['right hand'][-1] - self.normalized_distances['right hand'][-4]
+                            if not self.penetration and classes_touching_penis['face']and len(self.normalized_distances['face']) > 3:
+                                delta += self.normalized_distances['face'][-1] - self.normalized_distances['face'][-4]
+
+                            renormalized_distance = max(0, min(100, (normalized_distance + delta)))
+                            #print(f"Renormalized distance during BJ/HJ to {renormalized_distance} vs {normalized_distance}")
+                            normalized_distance = renormalized_distance
+                    self.update_distance(normalized_distance)
+            elif self.sex_position == "Cowgirl":
+                self.update_distance(self.normalized_distances['pussy'][-1])
+            elif self.sex_position == "Doggy":
+                self.update_distance(self.normalized_distances['butt'][-1])
+
+    def handle_class_first(self, class_name, box, conf):
+        if class_name == 'penis':
+            if self.penis_box is None:
+                print(f"Penis detected at frame {self.current_frame_id} with confidence {conf}")
+            self.penis_box = box
+            px1, py1, px2, py2 = self.penis_box
+            current_height = py2 - py1
+            if self.consecutive_detections['penis'] >= self.detections_threshold:
+                if self.locked_penis_box is None or self.glans_detected or current_height > self.locked_penis_height:
+                    if self.locked_penis_box is None:
+                        print(f"Locked penis box activated at frame {self.current_frame_id}")
+                    self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
+                    self.locked_penis_height = current_height
+            if self.locked_penis_box:
+                if current_height > self.locked_penis_height:
+                    self.locked_penis_height = current_height
+                    self.locked_penis_box = self.penis_box
+                if self.penis_box[3] != self.locked_penis_box[3]:
+                    self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
+            if self.locked_penis_box and self.penetration:
+                # Compare the height of the two boxes and scale it to 0 - 100
+                penis_height = self.boxes['penis'][3] - self.boxes['penis'][1]
+                locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
+                scale = min(int((penis_height / locked_penis_height) * 100), 100)
+                distance = 100 - scale
+                self.positions['penis'].append(distance)
+                min_distance, max_distance = min(self.positions['penis']), max(self.positions['penis'])
+                normalized_distance = (100 - int(100 * ((distance - min_distance) / (
+                        max_distance - min_distance)))) if min_distance != max_distance else 100
+                self.normalized_positions['penis'].append(normalized_distance)
+            if (self.image_y_size - py1) / self.image_y_size < 0.1 and not self.breast_tracking and \
+                    self.tracked_objects['breast']['detected']:
+                # print("Breast tracking mode activated given penis position in lower 10th of frame")
+                self.breast_tracking = True
+        elif class_name == 'glans':
+            if self.consecutive_detections['glans'] >= self.detections_threshold:
+                self.boxes['glans'] = box
+                self.glans_detected = True
+                if self.penetration:
+                    self.penetration = False
+                    print(
+                        f"@{self.current_frame_id} - Penetration ended after {self.consecutive_detections['glans']} detections of glans")
+        elif class_name == 'navel':
+            if (self.image_y_size - box[1]) / self.image_y_size < 0.15 and not self.breast_tracking:
+                print("Breast tracking mode activated given navel position in lower 15th of frame")
+                self.breast_tracking = True
+
+    def handle_closeup(self, reason):
+        self.tracked_body_part = 'Nothing'
+        self.detect_sex_position_change('Not relevant', reason)
+        self.sub_sex_position = "Not relevant"
+        self.penetration = False
+        self.grinding = False
+        self.rubbing = False
+        self.distance = 100
+        self.update_distance(100)
+
+    def prev3_tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
+        # self.frame = frame
+        self.image_y_size = image_y_size
+        self.current_frame_id = current_frame_id
+        # self.frame_count += 1
+
+        # initialize values
+        self.foot_detected = False
+        self.glans_detected = False
+        self.tracking_states = {class_name: False for class_name in class_names}
+        self.boxes = {class_name: None for class_name in class_names}
+        all_detections = {class_name: [] for class_name in class_names}
+
+        detected_classes = []
+        list_of_classes = class_names.copy()
+
+
+        for box, conf, cls, class_name in sorted_boxes:
+
+            all_detections[class_name].append([conf, box])
+
+            if class_name not in detected_classes:
+                detected_classes.append(class_name)
+
+        # First, let's handle the penis and glans
+        # count the number of detected glans in all_detections, keep the one with the highest confidence
+
+        found_box = {class_name: [] for class_name in ['glans', 'penis']}
+
+        for check_class_first in ['glans', 'penis']:
+            prev_conf = 0
+            for conf, box in all_detections[check_class_first]:
+                if conf > prev_conf:
+                    found_box[check_class_first] = [box, conf]
+                    # print(f"{found_box}")
+                    prev_conf = conf
+
+        if len(found_box['glans']) > 0:
+            # print(f"Found glans at frame {current_frame_id}")
+            # remove glans from list_of_classes
+            list_of_classes.pop('glans')
+            self.boxes['glans'] = found_box['glans'][0]
+            self.glans_handling(found_box['glans'][0], found_box['glans'][1], current_frame_id)
+
+        if len(found_box['penis']) > 0:
+            # remove penis from list_of_classes
+            list_of_classes.pop('penis')
+            self.boxes['penis'] = found_box['penis'][0]
+            self.penis_handling(found_box['penis'][0], found_box['penis'][1], current_frame_id)
+
+        if self.tracked_body_part != "Nothing" and self.tracked_body_part not in detected_classes:
+            # Performing Kalman prediction
+            if not self.activated_kalman:
+                print(f"Kalman prediction for tracked body part {self.tracked_body_part}")
+            self.update_tracking(self.tracked_body_part, None, None, self.tracked_body_part)
+
+        # Check if we have multiple instances of breast, navel, butt etc.
+
+        for checking_class in ["breast", "navel", "pussy", "butt", "left foot", "right foot", "left hand",
+                               "right hand", "face"]:
+            temp_boxes = []
+            # count the number of instances of the class in detections
+            count = 0
+            for class_name, detections in all_detections.items():
+                for conf, box in detections:
+                    if checking_class == class_name and box is not None:
+                        temp_boxes.append(box)
+                        count += 1
+            if count >= 1:
+                # print(f"Multiple instances of {checking_class} detected in frame {current_frame_id}")
+                # check if one is touching the penis, else keep the box that is lowest in the frame
+                lowest_box = temp_boxes[0]
+                for box in temp_boxes:
+                    if box[3] > lowest_box[3]:
+                        lowest_box = box
+                    if self.locked_penis_box and self.boxes_overlap(box, self.locked_penis_box):
+                        lowest_box = box
+                        # exit the for loop, no need to look further
+                        break
+                self.boxes[checking_class] = lowest_box
+                if checking_class in ["navel", "breast"]:
+                    self.log_and_normalize_pos(lowest_box, checking_class)
+                    self.log_and_normalize_distance(lowest_box, checking_class)
+                    if checking_class == "navel":
+                        self.navel_handling(lowest_box)
+                        # remove the class from list_of_classes
+                    list_of_classes.pop(checking_class)
+
+                elif checking_class in ["left foot", "right foot", "left hand",
+                                      "right hand", "butt", "breast", "face", "pussy"] and \
+                            self.boxes_overlap(lowest_box, self.locked_penis_box):
+                    # remove the class from list_of_classes
+                    list_of_classes.pop(checking_class)
+                    self.breast_tracking = False
+                    self.log_and_normalize_distance(lowest_box, checking_class)
+                    self.boxes[checking_class] = lowest_box
+                    if checking_class == 'pussy':
+                        fallback_class = 'breast'
+                    else:
+                        fallback_class = None
+                    self.update_tracking(checking_class, lowest_box, fallback_class, self.tracked_body_part)
+                    if checking_class in ["butt", "face", "pussy"] and self.tracked_objects[checking_class]['touching']:
+                        if not self.penetration and len(found_box['glans']) == 0:
+                            self.penetration = True
+                            print(
+                                f"Penetration started at frame {current_frame_id} as {checking_class} is detected and touching")
+                    self.tracking_states[checking_class] = True
+
+        for unwanted_class in list_of_classes:
+            self.tracking_states[unwanted_class] = False
+
+        if self.boxes['penis'] is not None:
+            # Compare the height of the two boxes and scale it to 0 - 100
+            penis_height = self.boxes['penis'][3] - self.boxes['penis'][1]
+
+            if self.locked_penis_box:
+                locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
+                if locked_penis_height == 0:
+                    locked_penis_height = 0.0001
+                scale = min(int((penis_height / locked_penis_height) * 100), 100)
+                distance = 100 - scale
+
+                self.positions['penis'].append(distance)
+
+                min_distance, max_distance = min(self.positions['penis']), max(self.positions['penis'])
+                normalized_distance = (100 - int(100 * ((distance - min_distance) / (
+                            max_distance - min_distance)))) if min_distance != max_distance else 100
+
+                self.normalized_positions['penis'].append(normalized_distance)
+
+                self.update_distance(self.normalized_positions['penis'][-1])
+
+
+        # Handling irrelevant closeups first
+
+        if self.tracking_states['pussy'] and self.tracking_states['butt']:
+            # position likely to be Closeup
+            self.handle_closeup_position("presence of pussy and butt")
+            self.breast_tracking = False
+        elif (self.tracking_states['pussy'] and self.boxes['pussy'] is not None and (
+                self.boxes['pussy'][3] - self.boxes['pussy'][1]) >
+              image_y_size * 0.1):
+            # position likely to be Closeup
+            self.handle_closeup_position("size of pussy in frame")
+
+        # Handling relevant closeups
+        elif self.breast_tracking and not self.glans_detected:
+            if self.boxes['breast'] is not None and not self.boxes_overlap(self.locked_penis_box, self.boxes['breast']):
+                # if self.boxes['breast'] is not None and not self.tracked_objects['breast']['touching']:
+                # experimenting to handle delay in breast tracking when boobs reach peak or low position
+                # projected_frame_id = current_frame_id  # + int(fps//5)
+                # distances[projected_frame_id] = normalized_positions['breast'].get(current_frame_id, 100)
+                distance = self.normalized_positions['breast'][-1]
+                self.distance = self.update_distance(distance)
+                self.tracked_body_part = "breast"
+                self.detect_sex_position_change("Closeup missionary", "breast tracking activated")
+                if not self.penetration:
+                    self.penetration = True
+                    print(f"Penetration started at frame {current_frame_id} as we are tracking closeup breasts")
+
+        # Handling tagged scenes
+        elif self.sex_position == "Handjob / Blowjob":
+            # if no right hand, no left hand, no face touching, then cancel the position
+            if not self.tracked_objects['left hand']['touching'] and \
+                    not self.tracked_objects['right hand']['touching'] and \
+                    not self.tracked_objects['face']['touching']:
+                # deactivating position
+                self.detect_sex_position_change("Not relevant", "No hand no face")
+            else:
+                self.handle_Blowjob_Handjob_position("Hands or Face detected", current_frame_id)
+
+        elif self.sex_position == "Doggy / Reverse Cowgirl / Pronebone":
+            if not self.boxes['butt']:
+                # deactivating position
+                self.detect_sex_position_change("Not relevant", "No butt detected")
+            else:
+                self.handle_RevCowGirl_Doggy_Pronebone_position("Doggy / Reverse Cowgirl / Pronebone", "Butt detected", current_frame_id)
+
+        elif self.sex_position == "Cowgirl / Missionary":
+            if not self.boxes['pussy']:
+                # deactivating position
+                self.detect_sex_position_change("Not relevant", "No pussy detected")
+            else:
+                self.handle_CowGirl_Missionary_position("Pussy detected", current_frame_id, "pussy")
+
+        # Now handling visible penetrations scenarii
+        elif self.locked_penis_box and self.penetration:
+            if self.boxes['penis'] is not None:
+                # Compare the height of the two boxes and scale it to 0 - 100
+                penis_height = self.boxes['penis'][3] - self.boxes['penis'][1]
+
+                locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
+
+                scale = min(int((penis_height / locked_penis_height) * 100), 100)
+                distance = 100 - scale
+
+                self.positions['penis'].append(distance)
+
+                min_distance, max_distance = min(self.positions['penis']), max(self.positions['penis'])
+                normalized_distance = (100 - int(100 * ((distance - min_distance) / (
+                            max_distance - min_distance)))) if min_distance != max_distance else 100
+
+                self.normalized_positions['penis'].append(normalized_distance)
+
+                self.update_distance(self.normalized_positions['penis'][-1])
+
+            else:
+                # Still handling penetration, but penis is not visible, could be a grinding scene
+                if self.boxes['pussy'] is not None and self.tracking_states['pussy'] and self.boxes[
+                    'breast'] is not None:
+                    # position likely to be CowGirl / Missionary
+                    # defaulting to navel tracking or breast tracking
+                    # if self.tracking_states['navel']:
+                    #    self.handle_CowGirl_Missionary_position("presence of navel", current_frame_id, 'navel')
+                    # el
+                    if self.tracking_states['breast']:
+                        self.handle_CowGirl_Missionary_position("presence of breast", current_frame_id, 'breast')
+                    # self.handle_CowGirl_Missionary_position("presence of pussy and breast", current_frame_id, 'pussy')
+                    if self.tracked_objects['pussy']['touching'] and not self.penetration:
+                        self.penetration = True
+                        print(f"Penetration started at frame {current_frame_id} during pussy grinding")
+                elif self.boxes['butt'] is not None and self.tracking_states['butt']:
+                    # position likely to be Reverse Cowgirl or Doggy / Pronebone
+                    self.handle_RevCowGirl_Doggy_Pronebone_position(
+                        "Doggy / Reverse Cowgirl / Pronebone", "presence of foot", current_frame_id)
+                    if self.tracked_objects['butt']['touching'] and not self.penetration:
+                        self.penetration = True
+                        print(f"Penetration started at frame {current_frame_id} during butt grinding")
+                elif self.boxes['navel'] is not None and not self.boxes_overlap(self.locked_penis_box,
+                                                                                self.boxes[
+                                                                                    'navel']) and self.penetration:
+                    # position likely to be CowGirl / Missionary, with occluded pussy
+                    self.handle_CowGirl_Missionary_position("presence of navel during penetration",
+                                                            current_frame_id,
+                                                            'navel')
+                elif self.boxes['breast'] is not None and self.tracking_states[
+                    'breast'] and not self.penetration and \
+                        self.tracked_objects["breast"]['touching']:
+                    # position likely to be Boobjob
+                    self.handle_Boobjob_position("presence of breast", current_frame_id)
+                elif ((self.boxes['face'] is not None and self.tracking_states['face']) or
+                      ((self.boxes['left hand'] is not None and self.tracking_states['left hand']) or
+                       (self.boxes['right hand'] is not None and self.tracking_states[
+                           'right hand']))) and not self.penetration:
+                    self.handle_Blowjob_Handjob_position("presence of face or hand", current_frame_id)
+        elif self.locked_penis_box and not self.penetration:
+            # Now handling handjob / blowjob scenarii without mouth penetration
+            if ((self.boxes['face'] is not None and self.tracking_states['face']) or
+                ((self.boxes['left hand'] is not None and self.tracking_states['left hand']) or
+                 (self.boxes['right hand'] is not None and self.tracking_states[
+                     'right hand']))) and not self.penetration:
+
+                self.handle_Blowjob_Handjob_position("presence of face or hand", current_frame_id)
+
+            elif ((self.boxes['left foot'] is not None and self.tracking_states['left foot']) or (
+                    self.boxes['right foot'] is not None and self.tracking_states[
+                'right foot'])) and not self.penetration:
+                # position likely to be Footjob
+                self.handle_Footjob_position("presence of foot", current_frame_id)
+
+        # Check for non-detection of penis
+        if self.boxes['penis'] is None:
+            self.consecutive_non_detections['penis'] += 1
+            self.consecutive_detections['penis'] = 0  # Reset the detection counter
+
+            # Deactivate locked_penis_box after x consecutive non-detections
+            if self.consecutive_non_detections['penis'] >= self.max_predictions:
+                if self.locked_penis_box is not None:
+                    self.locked_penis_box = None
+                    print(
+                        f"Penis tracking deactivated at frame {current_frame_id} after {self.consecutive_non_detections['penis']} consecutive non-detections")
+                    # print("Reinitializing tracker")
+                    # self.__init__(self.class_names)
+                    self.penis_box = None
+
+        # Check for non-detection of glans
+        if self.boxes['glans'] is None:
+            self.consecutive_non_detections['glans'] += 1
+            self.consecutive_detections['glans'] = 0  # Reset the detection counter
+
+
+
+    def prev2_tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
+        # self.frame = frame
+        self.image_y_size = image_y_size
+        # self.frame_count += 1
+
+        # initialize values
+        self.foot_detected = False
+        self.glans_detected = False
+        self.tracking_states = {class_name: False for class_name in class_names}
+        self.boxes = {class_name: None for class_name in class_names}
+        all_detections = {class_name: [] for class_name in class_names}
+
+        """
+        for box, conf, cls, class_name in sorted_boxes:
+            
+            if class_name == "glans" and conf > 0.5:
+                self.glans_handling(box, conf, current_frame_id)
+
+            elif class_name == "penis" and conf > 0.5:
+                self.boxes['penis'] = box
+                self.penis_handling(box, conf, current_frame_id)
+
+            elif class_name == "navel" and conf > 0.5:
+                self.navel_handling(box)
+
+            if class_name in ["breast", "navel"] and conf > 0.5:
+                self.log_and_normalize_pos(box, class_name)
+                self.boxes[class_name] = box
+
+            if self.locked_penis_box:
+                self.update_tracking(class_name, box, None, self.tracked_body_part)
+
+                if class_name in ["left foot", "right foot", "left hand",
+                                  "right hand", "butt", "face", "pussy"] and self.tracked_objects[class_name]['touching']:
+                    #self.log_and_normalize_pos(box, class_name)
+                    self.log_and_normalize_distance(box, class_name)
+                    self.boxes[class_name] = box
+
+                if class_name in ["butt", "face", "pussy"] and self.tracked_objects[class_name]['touching']:
+                    if not self.penetration:
+                        self.penetration = True
+                        print(f"Penetration started at frame {current_frame_id} as {class_name} is detected and touching")
+                #self.boxes[class_name] = box
+                self.tracking_states[class_name] = True
+                
+        """
+
+        for box, conf, cls, class_name in sorted_boxes:
+
+            all_detections[class_name].append([conf, box])
+
+            if class_name == "glans" and conf >= 0.5:
+                self.glans_handling(box, conf, current_frame_id)
+
+            elif class_name == "penis" and conf >= 0.5:
+                self.boxes['penis'] = box
+                self.penis_handling(box, conf, current_frame_id)
+
+        # for each class, check if it touches the locked penis box
+        for class_name, detections in all_detections.items():
+            if self.locked_penis_box:
+                for conf, box in detections:
+                    if class_name in ["left foot", "right foot", "left hand",
+                                      "right hand", "butt", "face", "pussy"] and \
+                            self.boxes_overlap(box, self.locked_penis_box):
+                        self.log_and_normalize_distance(box, class_name)
+                        self.boxes[class_name] = box
+                        self.update_tracking(class_name, box, None, self.tracked_body_part)
+                        if class_name in ["butt", "face", "pussy"] and self.tracked_objects[class_name]['touching']:
+                            if not self.penetration:
+                                self.penetration = True
+                                print(
+                                    f"Penetration started at frame {current_frame_id} as {class_name} is detected and touching")
+                        self.tracking_states[class_name] = True
+
+        # build a list of classes that have no self.boxes[class_name]
+        no_box_classes = [class_name for class_name in class_names if self.boxes[class_name] is None]
+
+        # if breast, navel, pussy, butt are missing, check if they are present in detections and keep the ones
+        # that are present and lowest in the frame if multiple instances of them
+
+        for checking_class in ["breast", "navel", "pussy", "butt"]:
+            temp_boxes = []
+            if checking_class in no_box_classes:
+                # count the number of instances of the missing class in detections
+                count = 0
+                for class_name, detections in all_detections.items():
+                    for conf, box in detections:
+                        if checking_class == class_name and box is not None:
+                            temp_boxes.append(box)
+                            count += 1
+                if count > 0:
+                    no_box_classes.remove(checking_class)
+                if count >= 1:
+                    # print(f"Multiple instances of {checking_class} detected in frame {current_frame_id}")
+                    # keep the box that is lowest in the frame
+                    lowest_box = temp_boxes[0]
+                    for box in temp_boxes:
+                        if box[3] > lowest_box[3]:
+                            lowest_box = box
+                    self.boxes[checking_class] = lowest_box
+                    if checking_class in ["navel", "breast"]:
+                        self.log_and_normalize_pos(lowest_box, checking_class)
+                        if checking_class == "navel":
+                            self.navel_handling(lowest_box)
+                    self.tracking_states[checking_class] = True
+
+        # Handling irrelevant closeups first
+
+        if self.tracking_states['pussy'] and self.tracking_states['butt']:
+            # position likely to be Closeup
+            self.handle_closeup_position("presence of pussy and butt")
+        elif (self.tracking_states['pussy'] and self.boxes['pussy'] is not None and (
+                self.boxes['pussy'][3] - self.boxes['pussy'][1]) >
+              image_y_size * 0.1):
+            # position likely to be Closeup
+            self.handle_closeup_position("size of pussy in frame")
+
+        # Handling relevant closeups
+        elif self.breast_tracking and not self.glans_detected:
+            if self.boxes['breast'] is not None and not self.boxes_overlap(self.locked_penis_box, self.boxes['breast']):
+                # if self.boxes['breast'] is not None and not self.tracked_objects['breast']['touching']:
+                # experimenting to handle delay in breast tracking when boobs reach peak or low position
+                # projected_frame_id = current_frame_id  # + int(fps//5)
+                # distances[projected_frame_id] = normalized_positions['breast'].get(current_frame_id, 100)
+                distance = self.normalized_positions['breast'][-1]
+                self.distance = self.update_distance(distance)
+                self.tracked_body_part = "breast"
+                self.detect_sex_position_change("Closeup missionary", "breast tracking activated")
+                if not self.penetration:
+                    self.penetration = True
+                    print(f"Penetration started at frame {current_frame_id} as we are tracking closeup breasts")
+
+        # Now handling visible penetrations scenarii
+        elif self.locked_penis_box and self.penetration:
+            if self.boxes['penis'] is not None:
+                # Compare the height of the two boxes and scale it to 0 - 100
+                penis_height = self.boxes['penis'][3] - self.boxes['penis'][1]
+
+                locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
+
+                scale = min(int((penis_height / locked_penis_height) * 100), 100)
+                distance = 100 - scale
+
+                self.positions['penis'].append(distance)
+
+                min_distance, max_distance = min(self.positions['penis']), max(self.positions['penis'])
+                normalized_distance = (100 - int(100 * ((distance - min_distance) / (max_distance - min_distance)))) if min_distance != max_distance else 100
+
+                self.normalized_positions['penis'].append(normalized_distance)
+
+                self.update_distance(self.normalized_positions['penis'][-1])
+            else:
+                # Still handling penetration, but penis is not visible, could be a grinding scene
+                if self.boxes['pussy'] is not None and self.tracking_states['pussy'] and self.boxes[
+                    'breast'] is not None:
+                    # position likely to be CowGirl / Missionary
+                    # defaulting to navel tracking or breast tracking
+                    #if self.tracking_states['navel']:
+                    #    self.handle_CowGirl_Missionary_position("presence of navel", current_frame_id, 'navel')
+                    #el
+                    if self.tracking_states['breast']:
+                        self.handle_CowGirl_Missionary_position("presence of breast", current_frame_id, 'breast')
+                    # self.handle_CowGirl_Missionary_position("presence of pussy and breast", current_frame_id, 'pussy')
+                    if self.tracked_objects['pussy']['touching'] and not self.penetration:
+                        self.penetration = True
+                        print(f"Penetration started at frame {current_frame_id} during pussy grinding")
+                elif self.boxes['butt'] is not None and self.tracking_states['butt']:
+                    # position likely to be Reverse Cowgirl or Doggy / Pronebone
+                    self.handle_RevCowGirl_Doggy_Pronebone_position(
+                        "Doggy / Reverse Cowgirl / Pronebone", "presence of foot", current_frame_id)
+                    """
+                    if self.tracking_states['left foot'] or self.tracking_states['right foot']:
+                        # position likely to be Reverse Cowgirl
+                        self.handle_RevCowGirl_Doggy_Pronebone_position(
+                            "Reverse Cowgirl", "presence of foot", current_frame_id)
+                    elif self.tracking_states['anus']:
+                        self.handle_RevCowGirl_Doggy_Pronebone_position(
+                            "Doggy / Pronebone", "presence of anus", current_frame_id)
+                    else:
+                        self.handle_RevCowGirl_Doggy_Pronebone_position(
+                            "Doggy / Pronebone", "absence of foot", current_frame_id)
+                    """
+                    if self.tracked_objects['butt']['touching'] and not self.penetration:
+                        self.penetration = True
+                        print(f"Penetration started at frame {current_frame_id} during butt grinding")
+                elif self.boxes['navel'] is not None and not self.boxes_overlap(self.locked_penis_box,
+                                                                                self.boxes[
+                                                                                    'navel']) and self.penetration:
+                    # position likely to be CowGirl / Missionary, with occluded pussy
+                    self.handle_CowGirl_Missionary_position("presence of navel during penetration",
+                                                            current_frame_id,
+                                                            'navel')
+                elif self.boxes['breast'] is not None and self.tracking_states[
+                    'breast'] and not self.penetration and \
+                        self.tracked_objects["breast"]['touching']:
+                    # position likely to be Boobjob
+                    self.handle_Boobjob_position("presence of breast", current_frame_id)
+                elif ((self.boxes['face'] is not None and self.tracking_states['face']) or
+                      ((self.boxes['left hand'] is not None and self.tracking_states['left hand']) or
+                       (self.boxes['right hand'] is not None and self.tracking_states[
+                           'right hand']))) and not self.penetration:
+
+                    self.handle_Blowjob_Handjob_position("presence of face or hand", current_frame_id)
+        elif self.locked_penis_box and not self.penetration:
+            # Now handling handjob / blowjob scenarii without mouth penetration
+            if ((self.boxes['face'] is not None and self.tracking_states['face']) or
+                  ((self.boxes['left hand'] is not None and self.tracking_states['left hand']) or
+                   (self.boxes['right hand'] is not None and self.tracking_states[
+                       'right hand']))) and not self.penetration:
+
+                self.handle_Blowjob_Handjob_position("presence of face or hand", current_frame_id)
+
+            elif ((self.boxes['left foot'] is not None and self.tracking_states['left foot']) or (
+                    self.boxes['right foot'] is not None and self.tracking_states[
+                'right foot'])) and not self.penetration:
+                # position likely to be Footjob
+                self.handle_Footjob_position("presence of foot", current_frame_id)
+
+        # Check for non-detection of penis
+        if self.boxes['penis'] is None:
+            self.consecutive_non_detections['penis'] += 1
+            self.consecutive_detections['penis'] = 0  # Reset the detection counter
+
+            # Deactivate locked_penis_box after x consecutive non-detections
+            if self.consecutive_non_detections['penis'] >= self.max_predictions:
+                if self.locked_penis_box is not None:
+                    self.locked_penis_box = None
+                    print(
+                        f"Penis tracking deactivated at frame {current_frame_id} after {self.consecutive_no_penis_detections} consecutive non-detections")
+                    #print("Reinitializing tracker")
+                    #self.__init__(self.class_names)
+                    self.penis_box = None
+
+        # Check for non-detection of glans
+        if self.boxes['glans'] is None:
+            self.consecutive_non_detections['glans'] += 1
+            self.consecutive_detections['glans'] = 0  # Reset the detection counter
+
+
+    def prev_tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
         #self.frame = frame
         self.image_y_size = image_y_size
         self.frame_count += 1
@@ -365,24 +1062,24 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
         #        self.tracking_states[class_name] = True
 
         for box, conf, cls, class_name in sorted_boxes:
-            if class_name == "glans" and conf > 0.5:
+            if class_name == "glans" and conf > 0.7:
                 self.glans_detected = True
                 if conf > 0.8 and self.penetration:
                     self.penetration = False
                     # print(f"Penetration ended at frame {current_frame_id}")
 
-            elif class_name == "penis" and conf > 0.5:
+            elif class_name == "penis" and conf > 0.7:
                 self.boxes['penis'] = box
                 self.penis_handling(box, conf, current_frame_id)
 
-            elif class_name == "navel" and conf > 0.5:
+            elif class_name == "navel" and conf > 0.7:
                 self.navel_handling(box)
 
-            if class_name in ["breast", "navel"] and conf > 0.5:
+            if class_name in ["breast", "navel"] and conf > 0.7:
                 self.log_and_normalize_pos(box, class_name)
 
             elif self.locked_penis_box and class_name not in ["penis", "glans"]:
-                if conf > 0.4:
+                if conf > 0.5:
                     self.update_tracking(class_name, box, None, self.tracked_body_part)
                 else:
                     fallback_class = {'pussy': 'breast', 'breast': 'navel'}.get(class_name)
@@ -507,29 +1204,89 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                     #self.__init__(self.class_names)
                     self.penis_box = None
 
+    def normalize_box_area(self, box, frame_width, frame_height):
+        """
+        Normalize the area of a bounding box to a 0-100 scale.
+
+        Parameters:
+            box (tuple): Bounding box in the format (x1, y1, x2, y2).
+            frame_width (int): Width of the video frame.
+            frame_height (int): Height of the video frame.
+
+        Returns:
+            float: Normalized area of the box (0 to 100 scale).
+        """
+        x1, y1, x2, y2 = box
+        # Ensure valid dimensions
+        if x2 <= x1 or y2 <= y1:
+            return 0  # Invalid box
+
+        # Calculate the area of the box
+        box_area = (x2 - x1) * (y2 - y1)
+
+        # Define the maximum possible area (entire frame)
+        max_area = frame_width * frame_height
+
+        # Normalize the area to 0-100
+        normalized_area = 100 * (box_area / max_area)
+
+        # Clamp to the range [0, 100]
+        return max(0, min(100, normalized_area))
+
     def penis_handling(self, box, conf, current_frame_id):
         if self.penis_box is None:
             print(f"Penis detected at frame {current_frame_id} with confidence {conf}")
 
         self.penis_box = box
 
+        self.consecutive_detections['penis'] += 1
+        self.consecutive_non_detections['penis'] = 0
+
         px1, py1, px2, py2 = self.penis_box
         current_height = py2 - py1
 
-        if self.glans_detected:
-            self.locked_penis_height = current_height
-        elif current_height > self.locked_penis_height:
-            self.locked_penis_height = current_height
+        #if self.glans_detected:
+        #    self.locked_penis_height = current_height
+        #el
+        #if current_height > self.locked_penis_height:
+        #    self.locked_penis_height = current_height
 
-        if self.locked_penis_box:
-            self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
+        #if self.locked_penis_box:
+        #    self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
+
+        if self.consecutive_detections['penis'] >= self.detections_threshold:
+            if self.locked_penis_box is None or self.glans_detected or current_height > self.locked_penis_height:
+                if self.locked_penis_box is None:
+                    print(f"@{self.current_frame_id} - Locked penis box activated")
+                self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
+                #self.locked_penis_box = self.penis_box
+                self.locked_penis_height = current_height
 
-        self.consecutive_penis_detections += 1
-        self.consecutive_no_penis_detections = 0
 
-        if self.consecutive_penis_detections >= self.minimum_penis_consecutive_detections and self.locked_penis_box is None:
-            self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
-            # print(f"Locked penis box activated at frame {current_frame_id}")
+        if self.locked_penis_box:
+            if current_height > self.locked_penis_height:
+                self.locked_penis_height = current_height
+                self.locked_penis_box = self.penis_box
+            if self.penis_box[3] != self.locked_penis_box[3]:
+                self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
+        """
+        if self.consecutive_detections['penis'] >= self.detections_threshold and self.glans_detected:
+            # self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
+            self.locked_penis_box = self.penis_box
+            # print(f"Actuating penis box at frame {current_frame_id}")
+        """
+
+        if self.locked_penis_box and self.penetration:
+            # Compare the height of the two boxes and scale it to 0 - 100
+            penis_height = self.boxes['penis'][3] - self.boxes['penis'][1]
+            locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
+            scale = min(int((penis_height / locked_penis_height) * 100), 100)
+            distance = 100 - scale
+            self.positions['penis'].append(distance)
+            min_distance, max_distance = min(self.positions['penis']), max(self.positions['penis'])
+            normalized_distance = (100 - int(100 * ((distance - min_distance) / (
+                    max_distance - min_distance)))) if min_distance != max_distance else 100
+            self.normalized_positions['penis'].append(normalized_distance)
 
         if (self.image_y_size - py1) / self.image_y_size < 0.1 and not self.breast_tracking and self.tracked_objects['breast']['detected']:
             # print("Breast tracking mode activated given penis position in lower 10th of frame")
@@ -537,6 +1294,23 @@ def penis_handling(self, box, conf, current_frame_id):
 
         return self.penis_box, self.locked_penis_box, self.breast_tracking
 
+    def glans_handling(self, box, conf, current_frame_id):
+        self.consecutive_detections['glans'] += 1
+        self.consecutive_non_detections['glans'] = 0
+        #print(f"Glans consecutive detections: {self.consecutive_detections['glans']}")
+        if self.consecutive_detections['glans'] >= self.detections_threshold:
+            self.boxes['glans'] = box
+            self.glans_detected = True
+            #print(f"Glans box: {self.boxes['glans']}")
+            if self.penetration:
+                self.penetration = False
+                print(f"Penetration ended at frame {current_frame_id} after {self.consecutive_detections['glans']} detections of glans")
+            #if not self.penetration:
+            #    self.penetration = True
+            #    print(
+            #        f"Penetration started at frame {current_frame_id} after {self.consecutive_non_detections['glans']} consecutive non-detections")
+
+
     def navel_handling(self, box):
         if (self.image_y_size - box[1]) / self.image_y_size < 0.15 and not self.breast_tracking:
             print("Breast tracking mode activated given navel position in lower 15th of frame")
@@ -550,8 +1324,28 @@ def log_and_normalize_pos(self, box, class_name):
 
         min_y, max_y = min(self.positions[class_name]), max(self.positions[class_name])
         normalized_y = (100 - int(100 * ((mid_y - min_y) / (max_y - min_y)))) if min_y != max_y else 100
+
+        # In case of breast, which is a fallback class to pussy, we compute not only the normalized position
+        # but also the normalized breast area which shows back and forth in case of grinding
+        if class_name == "breast" and self.frame:
+            normalized_breast_area = self.normalize_box_area(box, self.frame.shape[1], self.frame.shape[0])
+            normalized_y = ((0.75 * normalized_breast_area) + normalized_y) / 1.75
+
         #self.normalized_positions[class_name][current_frame_id] = normalized_y
         self.normalized_positions[class_name].append(normalized_y)
+        return normalized_y
+
+    def log_and_normalize_distance(self, box, class_name):
+        if self.locked_penis_box is None:
+            locked_penis_box = (0,0,0,0)
+        else:
+            locked_penis_box = self.locked_penis_box
+        distance = self.calculate_distance(locked_penis_box, box)
+        self.distances[class_name].append(distance)
+        min_distance, max_distance = min(self.distances[class_name]), max(self.distances[class_name])
+        normalized_distance = (100 - int(100 * ((distance - min_distance) / (max_distance - min_distance)))) if min_distance != max_distance else 100
+        self.normalized_distances[class_name].append(normalized_distance)
+
 
     def handle_closeup_position(self, reason):
         self.distance = 100
@@ -560,27 +1354,57 @@ def handle_closeup_position(self, reason):
         self.detect_sex_position_change("Closeup", reason)
 
     def handle_CowGirl_Missionary_position(self, reason, current_frame_id, body_part):
+        distance = None
         if body_part == "pussy":
-            distance = self.calculate_distance(self.locked_penis_box, self.boxes[body_part], body_part)
-            # trying to add up the navel distance and average
-            #if self.normalized_positions['navel'].get(current_frame_id, 100) is not None:
-            #    distance = 0.3 * distance + 0.7 * self.normalized_positions['navel'].get(current_frame_id, 100)
-            if self.normalized_positions['navel'][-1] is not None:
-                distance = 0.3 * distance + 0.7 * self.normalized_positions['navel'][-1]
-        elif body_part == "navel":
-            #distance = self.normalized_positions['navel'].get(current_frame_id, 100)
-            distance = self.normalized_positions['navel'][-1]
+            if self.penis_box:
+                x1, y1, x2, y2 = self.penis_box
+                if self.boxes['pussy'] is not None:
+                    y_p = self.boxes['pussy'][3]
+                    # if the bottom of pussy box remains in the low 30% of the penis_box
+                    if y_p < y2 and y_p > 0.7 * (y2 - y1):
+                        self.consecutive_grinding_supiscions['pussy'] += 1
+                    else:
+                        self.consecutive_grinding_supiscions['pussy'] = 0
+                        self.grinding = False
+            else:
+                self.consecutive_grinding_supiscions['pussy'] += 1
+            if self.consecutive_grinding_supiscions['pussy'] > self.fps:
+                self.grinding = True
+
+        if self.grinding:
+            # default to a combination of navel and breast
+            #nb_items = 0
+            #distance = 0
+            if self.boxes['navel']:
+                #distance = self.normalized_positions['navel'][-1]
+                distance = self.normalized_distances['navel'][-1]
+                #nb_items += 1
+            #if self.boxes['breast']:
+            #    distance += self.normalized_positions['breast'][-1]
+            #    nb_items += 1
+            #distance = distance // nb_items
+        else:
+            #distance = self.normalized_positions[body_part][-1]
+            distance = self.normalized_distances[body_part][-1]
+
         self.distance = self.update_distance(distance)
         self.tracked_body_part = body_part
         self.detect_sex_position_change("Cowgirl / Missionary", reason)
 
     def handle_RevCowGirl_Doggy_Pronebone_position(self, position, reason, current_frame_id):
-        if self.boxes['butt'][3] > self.locked_penis_box[3]:
-            # print(f"Actuating locked penis box to fit butt lower position at frame {current_frame_id}")
-            self.locked_penis_box = (
-                self.locked_penis_box[0], self.locked_penis_box[1],
-                self.locked_penis_box[2], self.boxes['butt'][3])
-        distance = self.calculate_distance(self.locked_penis_box, self.boxes['butt'], 'butt')
+        distance = None
+        #if self.boxes['butt'][3] > self.locked_penis_box[3]:
+        #    # print(f"Actuating locked penis box to fit butt lower position at frame {current_frame_id}")
+        #    self.locked_penis_box = (
+        #        self.locked_penis_box[0], self.locked_penis_box[1],
+        #        self.locked_penis_box[2], self.boxes['butt'][3])
+        # distance = self.calculate_distance(self.locked_penis_box, self.boxes['butt'], 'butt')
+        if self.normalized_positions['penis'][-1] is not None:
+            distance = self.normalized_positions['penis'][-1]
+        else:
+            #distance = self.calculate_distance(self.locked_penis_box, self.boxes['butt'], 'butt')
+            #distance = self.normalized_distances['butt'][-1]
+            distance = (self.normalized_positions['butt'][-1] + self.normalized_distances['butt'][-1]) // 2
         self.distance = self.update_distance(distance)
         self.tracked_body_part = "butt"
         self.detect_sex_position_change(position, reason)
@@ -591,9 +1415,11 @@ def handle_Blowjob_Handjob_position(self, reason, current_frame_id):
         if self.tracking_states['face'] and self.tracked_objects['face']['touching']:
             if len(self.face_distances) > 3:
                 self.face_distances.pop(0)
-            distance = self.calculate_distance(self.locked_penis_box, self.boxes['face'], 'face')
+            #distance = self.calculate_distance(self.locked_penis_box, self.boxes['face'])
+            distance = self.normalized_distances['face'][-1]
             #print(f"Face distance: {distance}")
-            self.face_distances.append(distance)
+            #self.face_distances.append(distance)
+            self.face_distances.append(self.normalized_distances['face'][-1])
             self.face_hands_movements['face'] = abs(max(self.face_distances) - min(self.face_distances))
             # if no left hand or no right hand, return
             if not self.tracked_objects['left hand']['touching'] and not self.tracked_objects['right hand']['touching']:
@@ -604,12 +1430,14 @@ def handle_Blowjob_Handjob_position(self, reason, current_frame_id):
         else:
             self.face_hands_movements['face'] = 0
 
-        if self.tracking_states['left hand'] and self.tracked_objects['left hand']['touching']:
+        if self.tracking_states['left hand'] and self.tracked_objects['left hand']['touching'] and self.boxes['left hand'] is not None:
             if len(self.left_hand_distances) > 3:
                 self.left_hand_distances.pop(0)
-            distance = self.calculate_distance(self.locked_penis_box, self.boxes['left hand'], 'left hand')
+            #distance = self.calculate_distance(self.locked_penis_box, self.boxes['left hand'])
+            distance = self.normalized_distances['left hand'][-1]
             #print(f"Left hand distance: {distance}")
-            self.left_hand_distances.append(distance)
+            #self.left_hand_distances.append(distance)
+            self.left_hand_distances.append(self.normalized_distances['left hand'][-1])
             self.face_hands_movements['left hand'] = abs(max(self.left_hand_distances) - min(self.left_hand_distances))
             if not self.tracked_objects['right hand']['touching'] and not self.tracked_objects['face']['touching']:
                 self.distance = self.update_distance(distance)
@@ -619,12 +1447,14 @@ def handle_Blowjob_Handjob_position(self, reason, current_frame_id):
         else:
             self.face_hands_movements['left hand'] = 0
 
-        if self.tracking_states['right hand'] and self.tracked_objects['right hand']['touching']:
+        if self.tracking_states['right hand'] and self.tracked_objects['right hand']['touching'] and self.boxes['right hand'] is not None:
             if len(self.right_hand_distances) > 3:
                 self.right_hand_distances.pop(0)
-            distance = self.calculate_distance(self.locked_penis_box, self.boxes['right hand'], 'right hand')
-            #print(f"Right hand distance: {distance}")
-            self.right_hand_distances.append(distance)
+            #distance = self.calculate_distance(self.locked_penis_box, self.boxes['right hand'])
+            distance = self.normalized_distances['right hand'][-1]
+            #print(f"Right hand distance: {distance}, self.locked_penis_box: {self.locked_penis_box}, self.boxes['right hand']: {self.boxes['right hand']}")
+            #self.right_hand_distances.append(distance)
+            self.right_hand_distances.append(self.normalized_distances['right hand'][-1])
             self.face_hands_movements['right hand'] = abs(
                 max(self.right_hand_distances) - min(self.right_hand_distances))
             if not self.tracked_objects['left hand']['touching'] and not self.tracked_objects['face']['touching']:
@@ -698,14 +1528,14 @@ def handle_Footjob_position(self, reason, current_frame_id):
             if len(self.left_foot_distances) > 3:
                 self.left_foot_distances.pop(0)
             self.left_foot_distances.append(
-                self.calculate_distance(self.locked_penis_box, self.boxes['left foot'], 'left foot'))
+                self.calculate_distance(self.locked_penis_box, self.boxes['left foot']))
             self.foot_movements['left foot'] = abs(max(self.left_foot_distances) - min(self.left_foot_distances))
 
         if self.tracking_states['right foot'] and self.tracked_objects['right foot']['touching']:
             if len(self.right_foot_distances) > 3:
                 self.right_foot_distances.pop(0)
             self.right_foot_distances.append(
-                self.calculate_distance(self.locked_penis_box, self.boxes['right foot'], 'right foot'))
+                self.calculate_distance(self.locked_penis_box, self.boxes['right foot']))
             self.foot_movements['right foot'] = abs(max(self.right_foot_distances) - min(self.right_foot_distances))
 
         # Determine the most moving foot
@@ -763,7 +1593,8 @@ def handle_Footjob_position(self, reason, current_frame_id):
         self.distance = self.update_distance(self.smoothed_foot_distance)
 
     def handle_Boobjob_position(self, reason, current_frame_id):
-        distance = self.calculate_distance(self.locked_penis_box, self.boxes['breast'], 'breast')
+        #distance = self.calculate_distance(self.locked_penis_box, self.boxes['breast'], 'breast')
+        distance = self.normalized_distances['breast'][-1]
         self.distance = self.update_distance(distance)
         self.tracked_body_part = "breast"
         self.detect_sex_position_change("Boobjob", reason)
diff --git a/utils/ObjectTracker_v2.py b/utils/ObjectTracker_v2.py
new file mode 100644
index 00000000..598b6b21
--- /dev/null
+++ b/utils/ObjectTracker_v2.py
@@ -0,0 +1,417 @@
+from utils import KalmanFilter as KF
+from collections import deque
+import numpy as np
+from utils.config import class_names
+import math
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+
+
+class ObjectTracker:
+    def __init__(self, fps, frame_pos, state=None):
+        self.class_names = class_names
+        self.tracked_objects = {
+            class_name: {
+                'kf': KF.KalmanFilter(),
+                'position': None,
+                'detected': False,
+                'touching': False,
+                'prediction_count': 0
+            }
+            for class_name in class_names
+        }
+
+        self.distance_kf = KF.KalmanFilter()
+
+        self.frame = None
+        self.current_frame_id = frame_pos
+        self.image_y_size = 0
+        self.fps = fps
+
+        self.penis_box, self.locked_penis_box = None, None
+        self.glans_detected = False
+        self.locked_penis_height = 0
+        self.breast_tracking = False
+        self.distance = 100
+        self.raw_distance = 100
+        self.previous_distances = [100, 100, 100]
+        self.tracked_body_part = "Nothing"
+
+        self.sex_position = "Not relevant"
+        self.prev_sex_position = "Not relevant"
+        self.sex_position_reason = ""
+        self.sex_position_history = deque(maxlen=10)
+        self.sub_sex_position = "Not relevant"
+
+        self.positions = {class_name: deque(maxlen=200) for class_name in class_names}
+        self.distances = {class_name: deque(maxlen=200) for class_name in class_names}
+        self.areas = {class_name: deque(maxlen=200) for class_name in class_names}
+
+        self.normalized_positions = {class_name: deque(maxlen=200) for class_name in class_names}
+        self.normalized_distances = {class_name: deque(maxlen=200) for class_name in class_names}
+        self.activated_kalman = {class_name: False for class_name in class_names}
+
+        self.offsets = {class_name: 0 for class_name in class_names}
+
+        for class_name in class_names:
+            self.normalized_distances[class_name].append(100)
+            self.normalized_positions[class_name].append(100)
+
+        self.normalized_areas = {class_name: deque(maxlen=200) for class_name in class_names}
+
+        self.moving_average_window = 5
+        self.consecutive_detections = {class_name: 0 for class_name in class_names}
+        self.consecutive_non_detections = {class_name: 0 for class_name in class_names}
+        self.detections_threshold = 3
+
+        self.consecutive_grinding_suspicions = {class_name: 0 for class_name in ['pussy', 'butt']}
+
+        self.penetration = False
+        self.grinding = False
+        self.rubbing = False
+
+        self.max_predictions = int(self.fps)
+
+    def update_distance(self, raw_distance):
+        if raw_distance is None:
+            filtered_distance = self._predict_distance()
+        else:
+            rounded_distance = round(raw_distance / 5) * 5
+            ema_distance = self._calculate_ema_distance(rounded_distance)
+            self._update_previous_distances(ema_distance)
+            self.distance_kf.update((ema_distance, 0))
+            filtered_distance = ema_distance
+
+        filtered_distance = max(0, min(100, filtered_distance))
+        self.distance = filtered_distance
+        return filtered_distance
+
+    def _predict_distance(self):
+        return int(float(self.distance_kf.predict()[0]) / 5) * 5
+
+    def _calculate_ema_distance(self, rounded_distance):
+        return int(0.7 * rounded_distance + 0.1 * self.previous_distances[1] + 0.2 * self.previous_distances[2])
+
+    def _update_previous_distances(self, ema_distance):
+        if abs(ema_distance - self.previous_distances[-1]) > 15:
+            ema_distance = self.previous_distances[-1] + np.sign(ema_distance - self.previous_distances[-1]) * 15
+        elif abs(ema_distance - self.previous_distances[-1]) < 2:
+            ema_distance = self.previous_distances[-1]
+        self.previous_distances.pop(0)
+        self.previous_distances.append(ema_distance)
+
+    def update_tracking(self, class_name, box, fallback_class, tracked_item):
+        tracked = self.tracked_objects[class_name]
+        if box is not None:
+            self._handle_detection(tracked, box, class_name)
+        elif class_name == tracked_item and box is None:
+            self._handle_prediction(tracked, class_name, tracked_item)
+
+        if tracked['touching']:
+            self.log_and_normalize_pos(tracked['position'], class_name)
+            self.log_and_normalize_distance(tracked['position'], class_name)
+        else:
+            self._reset_normalized_data(class_name)
+
+        return tracked['position']
+
+    def _handle_detection(self, tracked, box, class_name):
+        tracked['kf'].update([(box[0] + box[2]) / 2, (box[1] + box[3]) / 2])
+        tracked['position'] = box
+        tracked['detected'] = True
+        tracked['half_height'] = (box[3] - box[1]) // 2
+        tracked['half_width'] = (box[2] - box[0]) // 2
+        tracked['touching'] = self.boxes_overlap(self.locked_penis_box, box)
+        tracked['prediction_count'] = 0
+        if class_name == self.tracked_body_part and self.activated_kalman[class_name]:
+            self.activated_kalman[class_name] = False
+            logging.info(f"@{self.current_frame_id} - Stopped Kalman prediction for {class_name} as it was found back")
+
+    def _handle_prediction(self, tracked, class_name, tracked_item):
+        tracked['kf'].predict()
+        if not self.activated_kalman[class_name]:
+            self.activated_kalman[class_name] = True
+            logging.info(f"@{self.current_frame_id} - Activating Kalman prediction for {class_name}")
+        logging.info(
+            f"@{self.current_frame_id} - Kalman prediction for {class_name}: {tracked['prediction_count'] + 1} / {self.max_predictions}")
+        tracked['detected'] = False
+        tracked['position'] = tracked['kf'].position
+        tracked['touching'] = self.boxes_overlap(self.locked_penis_box, tracked['position'])
+
+        if tracked['prediction_count'] >= self.max_predictions:
+            logging.info(f"No fallback available for {class_name}, deactivating tracking")
+            tracked['position'] = None
+            tracked['detected'] = False
+            tracked['touching'] = False
+            tracked['prediction_count'] = 0
+            self.tracked_body_part = 'Nothing'
+
+    def _reset_normalized_data(self, class_name):
+        self.normalized_positions[class_name].clear()
+        self.normalized_distances[class_name].clear()
+        self.normalized_positions[class_name].append(100)
+        self.normalized_distances[class_name].append(100)
+
+    def boxes_overlap(self, box1, box2):
+        if box1 is None or box2 is None:
+            return False
+        x1, y1, x2, y2 = box1
+        x3, y3, x4, y4 = box2
+        return not (x2 < x3 or x4 < x1 or y2 < y3 or y4 < y1)
+
+    def calculate_distance(self, penis_box, other_box):
+        if other_box is None:
+            return None
+        ox1, oy1, ox2, oy2 = other_box
+        y_pos = (oy1 + 2 * oy2) // 3
+        x_pos = (ox1 + ox2) // 2
+        px1, py1, px2, py2 = penis_box
+        return math.sqrt((x_pos - px2) ** 2 + (y_pos - py2) ** 2)
+
+    def detect_sex_position_change(self, sex_position, reason):
+        if self.sex_position_history and self.sex_position_history[-1] == "Blowjob" and self.sub_sex_position == "Handjob":
+            sex_position = "Blowjob"
+        self.sex_position_history.append(sex_position)
+        position_counts = {position: self.sex_position_history.count(position) for position in self.sex_position_history}
+        most_frequent_position = max(position_counts, key=position_counts.get, default="Not relevant")
+        if most_frequent_position != self.sex_position:
+            logging.info(f"@{self.current_frame_id} - Sex position switched to: {most_frequent_position}")
+            self.sex_position = most_frequent_position
+            self.sex_position_reason = reason
+
+    def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
+        self.image_y_size = image_y_size
+        self.current_frame_id = current_frame_id
+
+        self.glans_detected = False
+        self.boxes = {class_name: None for class_name in class_names}
+        all_detections = {class_name: [] for class_name in class_names}
+
+        for box, conf, cls, class_name in sorted_boxes:
+            all_detections[class_name].append([conf, box])
+
+        found_box = {class_name: [] for class_name in class_names}
+        classes_touching_penis = {class_name: None for class_name in class_names}
+        list_of_touching_classes = []
+
+        for check_class_first in ['glans', 'penis', 'navel']:
+            prev_conf = 0
+            for conf, box in all_detections[check_class_first]:
+                if conf > prev_conf:
+                    found_box[check_class_first] = [box, conf]
+                    prev_conf = conf
+
+        for check_class_first in ['glans', 'penis', 'navel']:
+            box = None
+            if found_box[check_class_first]:
+                self.boxes[check_class_first] = found_box[check_class_first][0]
+                box = self.boxes[check_class_first]
+                conf = found_box[check_class_first][1]
+                self.consecutive_detections[check_class_first] += 1
+                self.consecutive_non_detections[check_class_first] = 0
+                self.handle_class_first(check_class_first, self.boxes[check_class_first], conf)
+            else:
+                self.consecutive_detections[check_class_first] = 0
+                self.consecutive_non_detections[check_class_first] += 1
+
+        prev_class = ''
+        nb_items_touching_penis = 0
+
+        for check_class_second in class_names:
+            if check_class_second in ['glans', 'penis', 'navel']:
+                continue
+            prev_conf = 0
+            for conf, box in all_detections[check_class_second]:
+                if self.locked_penis_box and self.boxes_overlap(box, self.locked_penis_box) and conf > prev_conf:
+                    classes_touching_penis[check_class_second] = box
+                    prev_conf = conf
+                    if check_class_second != prev_class:
+                        nb_items_touching_penis += 1
+                        list_of_touching_classes.append(check_class_second)
+                        prev_class = check_class_second
+            if prev_conf != 0:
+                self.update_tracking(check_class_second, classes_touching_penis[check_class_second], None,
+                                     self.tracked_body_part)
+
+        if self.tracked_body_part != "Nothing" and self.tracked_body_part not in list_of_touching_classes:
+            self.update_tracking(self.tracked_body_part, None, None, self.tracked_body_part)
+
+        if nb_items_touching_penis == 0:
+            self.handle_closeup('No body parts touching')
+        else:
+            if classes_touching_penis['butt']:
+                if all_detections['pussy'] and self.boxes_overlap(all_detections['pussy'][0][1],
+                                                                  classes_touching_penis['butt']):
+                    self.handle_closeup('Presence of butt and overlapping pussy')
+                elif self.boxes['penis'] and not self.boxes['glans']:
+                    self.tracked_body_part = 'butt'
+                    self.penetration = True
+                    self.detect_sex_position_change('Doggy', 'butt touching penis')
+                    self.sub_sex_position = "Not relevant"
+            elif classes_touching_penis['pussy'] and self.boxes['penis'] and not self.boxes['glans']:
+                self.penetration = True
+                self.tracked_body_part = 'pussy'
+                self.detect_sex_position_change('Cowgirl', 'pussy touching penis')
+                self.sub_sex_position = "Not relevant"
+            elif classes_touching_penis['face']:
+                self.tracked_body_part = 'face'
+                self.detect_sex_position_change('Blowjob', 'face touching penis')
+                self.sub_sex_position = "Not relevant"
+            elif classes_touching_penis['left hand'] or classes_touching_penis['right hand']:
+                self.detect_sex_position_change('Handjob', 'hand touching penis')
+                self.sub_sex_position = "Handjob"
+
+            if self.boxes['penis'] is not None:
+                penis_height = self.boxes['penis'][3] - self.boxes['penis'][1]
+                if self.locked_penis_box:
+                    locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
+
+                    for class_name in list_of_touching_classes:
+                        y2 = classes_touching_penis[class_name][3]
+                        self.offsets[class_name] = y2 - self.boxes['penis'][1]
+                    if locked_penis_height > 0:
+                        scale = min(int((penis_height / (0.8 * locked_penis_height)) * 100), 100)
+                    else:
+                        scale = 0
+                    distance = 100 - scale
+
+                    #self.positions['penis'].append(distance)
+                    #min_distance, max_distance = min(self.positions['penis']), max(self.positions['penis'])
+                    #normalized_distance = (100 - int(100 * ((distance - min_distance) / (max_distance - min_distance)))) if min_distance != max_distance else 100
+                    #self.normalized_positions['penis'].append(normalized_distance)
+                    normalized_distance = distance
+                    self.update_distance(normalized_distance)
+            elif self.sex_position == "Cowgirl" and classes_touching_penis['pussy']:
+                locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
+
+                y2 = classes_touching_penis['pussy'][3]
+                y2 -= self.offsets['pussy']
+
+                guessed_penis_height = self.locked_penis_box[3] - y2
+
+                if guessed_penis_height < 0 :
+                    self.offsets['pussy'] = self.locked_penis_box[3] - y2
+                    guessed_penis_height = 0
+
+                scale = min(int((guessed_penis_height / locked_penis_height) * 100), 100)
+                distance = 100 - scale
+
+                normalized_distance = distance
+                self.update_distance(normalized_distance)
+                #self.update_distance(self.normalized_distances['pussy'][-1])
+            elif self.sex_position == "Doggy" and classes_touching_penis['butt']:
+                locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
+
+                y2 = classes_touching_penis['butt'][3]
+                y2 -= self.offsets['butt']
+
+                guessed_penis_height = self.locked_penis_box[3] - y2
+
+                if guessed_penis_height < 0:
+                    self.offsets['butt'] = self.locked_penis_box[3] - y2
+                    guessed_penis_height = 0
+
+                scale = min(int((guessed_penis_height / locked_penis_height) * 100), 100)
+                distance = 100 - scale
+
+                normalized_distance = distance
+                self.update_distance(normalized_distance)
+                #self.update_distance(self.normalized_distances['butt'][-1])
+
+    def handle_class_first(self, class_name, box, conf):
+        if class_name == 'penis':
+            if self.penis_box is None:
+                logging.info(f"Penis detected at frame {self.current_frame_id} with confidence {conf}")
+            self.penis_box = box
+            px1, py1, px2, py2 = self.penis_box
+            current_height = py2 - py1
+            if self.consecutive_detections['penis'] >= self.detections_threshold:
+                if self.locked_penis_box is None or self.glans_detected or current_height > self.locked_penis_height:
+                    if self.locked_penis_box is None:
+                        logging.info(f"Locked penis box activated at frame {self.current_frame_id}")
+                    self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
+                    self.locked_penis_height = current_height
+            if self.locked_penis_box:
+                if current_height > self.locked_penis_height:
+                    self.locked_penis_height = current_height
+                    self.locked_penis_box = self.penis_box
+                if self.penis_box[3] != self.locked_penis_box[3]:
+                    self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
+            if self.locked_penis_box and self.penetration:
+                penis_height = self.boxes['penis'][3] - self.boxes['penis'][1]
+                locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
+                scale = min(int((penis_height / locked_penis_height) * 100), 100)
+                distance = 100 - scale
+                self.positions['penis'].append(distance)
+                min_distance, max_distance = min(self.positions['penis']), max(self.positions['penis'])
+                normalized_distance = (100 - int(100 * ((distance - min_distance) / (max_distance - min_distance)))) if min_distance != max_distance else 100
+                self.normalized_positions['penis'].append(normalized_distance)
+            if (self.image_y_size - py1) / self.image_y_size < 0.1 and not self.breast_tracking and \
+                    self.tracked_objects['breast']['detected']:
+                self.breast_tracking = True
+        elif class_name == 'glans':
+            if self.consecutive_detections['glans'] >= self.detections_threshold:
+                self.boxes['glans'] = box
+                self.glans_detected = True
+                if self.penetration:
+                    self.penetration = False
+                    logging.info(
+                        f"@{self.current_frame_id} - Penetration ended after {self.consecutive_detections['glans']} detections of glans")
+                    if self.tracked_body_part != 'Nothing':
+                        self.normalized_distances[self.tracked_body_part].clear()
+                        self.normalized_distances[self.tracked_body_part].append(100)
+        elif class_name == 'navel':
+            if (self.image_y_size - box[1]) / self.image_y_size < 0.15 and not self.breast_tracking:
+                logging.info("Breast tracking mode activated given navel position in lower 15th of frame")
+                self.breast_tracking = True
+
+    def handle_closeup(self, reason):
+        if self.tracked_body_part != 'Nothing':
+            self.normalized_distances[self.tracked_body_part].clear()
+            self.normalized_distances[self.tracked_body_part].append(100)
+        self.tracked_body_part = 'Nothing'
+        self.detect_sex_position_change('Not relevant', reason)
+        self.sub_sex_position = "Not relevant"
+        self.breast_tracking = False
+        self.penetration = False
+        self.grinding = False
+        self.rubbing = False
+        self.distance = 100
+        self.update_distance(100)
+
+    def normalize_box_area(self, box, frame_width, frame_height):
+        x1, y1, x2, y2 = box
+        if x2 <= x1 or y2 <= y1:
+            return 0
+        box_area = (x2 - x1) * (y2 - y1)
+        max_area = frame_width * frame_height
+        normalized_area = 100 * (box_area / max_area)
+        return max(0, min(100, normalized_area))
+
+    def log_and_normalize_pos(self, box, class_name):
+        self.boxes[class_name] = box
+        _, y1, _, y2 = box
+        mid_y = (y1 + y2) / 2
+        self.positions[class_name].append(mid_y)
+
+        min_y, max_y = min(self.positions[class_name]), max(self.positions[class_name])
+        normalized_y = (100 - int(100 * ((mid_y - min_y) / (max_y - min_y)))) if min_y != max_y else 100
+
+        if class_name == "breast" and self.frame:
+            normalized_breast_area = self.normalize_box_area(box, self.frame.shape[1], self.frame.shape[0])
+            normalized_y = ((0.75 * normalized_breast_area) + normalized_y) / 1.75
+
+        self.normalized_positions[class_name].append(normalized_y)
+        return normalized_y
+
+
+    def log_and_normalize_distance(self, box, class_name):
+        locked_penis_box = self.locked_penis_box or (0, 0, 0, 0)
+        distance = self.calculate_distance(locked_penis_box, box)
+        self.distances[class_name].append(distance)
+        min_distance, max_distance = min(self.distances[class_name]), max(self.distances[class_name])
+        normalized_distance = (100 - int(
+            100 * ((distance - min_distance) / (max_distance - min_distance)))) if min_distance != max_distance else 100
+        self.normalized_distances[class_name].append(normalized_distance)
\ No newline at end of file
diff --git a/utils/PoseEstimation.py b/utils/PoseEstimation.py
new file mode 100644
index 00000000..d5813a9a
--- /dev/null
+++ b/utils/PoseEstimation.py
@@ -0,0 +1,136 @@
+from ultralytics import YOLO
+from PIL import Image
+import numpy as np
+import cv2
+from collections import defaultdict
+
+video_path = "/Users/k00gar/Downloads/Katrina Jade [ADD] Oct 16, 2024R_6kvr265_reenc.mp4"
+
+# Load a model
+model = YOLO("yolo11l-pose.pt")
+
+timestamp_min = 35
+timestamp_sec = 0
+
+cap = cv2.VideoCapture(video_path)
+total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+fps = cap.get(cv2.CAP_PROP_FPS)
+
+
+frame_pos = timestamp_min * 60 * fps + timestamp_sec * fps
+cap.set(cv2.CAP_PROP_POS_FRAMES, frame_pos)
+"""
+ret, frame = cap.read()
+
+
+results = model(frame)
+# Visualize the results
+for i, r in enumerate(results):
+    # Plot results image
+    im_bgr = r.plot()  # BGR-order numpy array
+    im_rgb = Image.fromarray(im_bgr[..., ::-1])  # RGB-order PIL image
+
+    # Show results to screen (in supported environments)
+    r.show()
+
+    # Save results to disk
+    r.save(filename=f"results{i}.jpg")
+"""
+
+# Store the track history
+track_history = defaultdict(lambda: [])
+
+success, frame = cap.read()
+
+output_path = video_path.replace(".mp4", "_posedemo.mp4")
+fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # Codec for MP4
+out = cv2.VideoWriter(output_path, fourcc, fps, (frame.shape[1] // 2, frame.shape[0]))
+
+
+# Loop through the video frames
+while cap.isOpened():
+    # Read a frame from the video
+    success, frame = cap.read()
+
+    if success:
+
+        # only half left of the frame
+        frame = frame[:, :frame.shape[1] // 2, :]
+
+        middle_x_frame = frame.shape[1] // 2
+
+        # Run YOLO11 tracking on the frame, persisting tracks between frames
+        results = model.track(frame, persist=True)
+
+
+        # frame = results[0].plot()
+
+        print(results)
+        keypoints = results[0].keypoints.cpu()
+        print(f"keypoints: {keypoints}")
+        print("next : ")
+        keypoints_list = keypoints.xy.cpu().tolist()
+        print(f"keypoints list: {keypoints_list}")
+        left_hip = keypoints_list[0][11]
+        right_hip = keypoints_list[0][12]
+
+        mid_point = [middle_x_frame, (left_hip[1]+ right_hip[1])/2]
+
+        cv2.circle(frame, (int(left_hip[0]), int(left_hip[1])), 5, (0, 255, 0), -1)
+        cv2.circle(frame, (int(right_hip[0]), int(right_hip[1])), 5, (0, 0, 255), -1)
+        cv2.circle(frame, (int(middle_x_frame), int(mid_point[1])), 2, (0, 0, 0), -1)
+
+        track = track_history[1]
+
+        track.append((int(middle_x_frame), int(mid_point[1])))  # x, y center point
+        if len(track) > 5:  # retain 90 tracks for 90 frames
+            track.pop(0)
+
+        # Draw the tracking lines
+        points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
+        cv2.polylines(frame, [points], isClosed=False, color=(230, 230, 230), thickness=10)
+
+        """
+        # Get the boxes and track IDs
+        boxes = results[0].boxes.xywh.cpu()
+        track_ids = results[0].boxes.id.int().cpu().tolist()
+
+        keypoints = results[0].keypoints.cpu()
+
+        # Get the track history for each keypoint 11 and 12
+        track_history_11 = track_history[11]
+        track_history_12 = track_history[12]
+
+        # Visualize the results on the frame, plotting only keypoints 11 and 12
+        annotated_frame = results[0].plot(keypoints=[11, 12])
+        #annotated_frame = results[0].plot()
+
+        # Plot the tracks
+        for box, track_id in zip(boxes, track_ids):
+            x, y, w, h = box
+            track = track_history[track_id]
+            track.append((float(x), float(y)))  # x, y center point
+            if len(track) > 30:  # retain 90 tracks for 90 frames
+                track.pop(0)
+
+            # Draw the tracking lines
+            points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
+            cv2.polylines(annotated_frame, [points], isClosed=False, color=(230, 230, 230), thickness=10)
+
+        """
+        # Display the annotated frame
+        cv2.imshow("YOLO11 Tracking", frame)
+
+        out.write(frame)
+
+        # Break the loop if 'q' is pressed
+        if cv2.waitKey(1) & 0xFF == ord("q"):
+            break
+    else:
+        # Break the loop if the end of the video is reached
+        break
+
+# Release the video capture object and close the display window
+cap.release()
+out.release()
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/utils/VideoReaderFFmpeg.py b/utils/VideoReaderFFmpeg.py
index 90b9491d..aede3e7a 100644
--- a/utils/VideoReaderFFmpeg.py
+++ b/utils/VideoReaderFFmpeg.py
@@ -1,16 +1,21 @@
 import subprocess
 import cv2
 import numpy as np
-import ffmpeg
-import time
 
 
 class VideoReaderFFmpeg:
-    def __init__(self, video_path):
+    def __init__(self, video_path, is_VR=False, ffmpeg_path="/usr/local/bin/ffmpeg", ffprobe_path="/usr/local/bin/ffprobe"):
+        """
+        Initialize the VideoReaderFFmpeg class.
+        :param video_path: Path to the video file.
+        :param ffmpeg_path: Path to the FFmpeg binary (default: "ffmpeg").
+        :param ffprobe_path: Path to the FFprobe binary (default: "ffprobe").
+        """
         self.video_path = video_path
-        self._initialize_video_info()
-        self.playback_speed = 1.0  # Default is 1.0 (real-time)
-        self.paused = False
+        self.is_VR = is_VR
+        self.ffmpeg_path = ffmpeg_path
+        self.ffprobe_path = ffprobe_path
+        self._initialize_video_info()  # Initialize video metadata
         self.start_frame = 0
         self.current_frame_number = 0
         self.current_time = 0
@@ -18,113 +23,100 @@ def __init__(self, video_path):
         self.frame_size = None
 
     def _initialize_video_info(self):
-        """ Retrieves video metadata like fps, total frames, resolution, etc. using ffprobe. """
+        """
+        Retrieve video metadata (fps, resolution, codec, etc.) using FFprobe.
+        """
         try:
-            probe = ffmpeg.probe(self.video_path)
-            video_streams = [stream for stream in probe['streams'] if stream['codec_type'] == 'video']
-            video_stream = video_streams[0]  # First video stream
-
-            self.fps = eval(video_stream['r_frame_rate'])
-            self.width = int(video_stream['width'])
-            self.height = int(video_stream['height'])
-            self.codec = video_stream['codec_name']
-            self.total_frames = int(video_stream['nb_frames']) if 'nb_frames' in video_stream else None
-            self.duration = float(video_stream['duration']) * 1000  # Duration in milliseconds
+            # Run FFprobe to get video metadata
+            cmd = [
+                self.ffprobe_path,
+                "-v", "error",
+                "-select_streams", "v:0",
+                "-show_entries", "stream=r_frame_rate,width,height,codec_name,nb_frames,duration",
+                "-of", "default=noprint_wrappers=1:nokey=1",
+                self.video_path,
+            ]
+            output = subprocess.check_output(cmd).decode("utf-8").splitlines()
+
+            # Ensure the output has the correct number of fields
+            if len(output) < 6:
+                raise ValueError("FFprobe output is missing required fields.")
+
+            # Parse metadata
+            codec_name = output[0]  # codec_name
+            width = int(output[1])  # width
+            height = int(output[2])  # height
+            r_frame_rate = output[3]  # r_frame_rate in "num/den" format
+            duration = float(output[4])  # duration in seconds
+            total_frames = int(output[5])  # nb_frames
+
+            # Calculate FPS
+            num, den = map(int, r_frame_rate.split('/'))  # Split numerator and denominator
+            fps = num / den  # Calculate FPS
+
+            # Store metadata
+            self.fps = fps
+            self.width = width
+            if self.is_VR:
+                self.width //= 2
+            self.height = height
+            self.codec = codec_name
+            self.total_frames = total_frames
+            self.duration = duration * 1000  # Convert duration to milliseconds
 
             print(f"FPS: {self.fps}, Resolution: {self.width}x{self.height}, "
                   f"Codec: {self.codec}, Total Frames: {self.total_frames}, Duration: {self.duration:.2f} ms")
         except Exception as e:
             print(f"Error initializing video info: {e}")
+            raise
 
     def _start_process(self, start_frame=0):
-        """ Start the FFmpeg process to read frames. """
+        """
+        Start the FFmpeg process to read frames.
+        :param start_frame: Frame number to start reading from.
+        """
         start_time = (start_frame / self.fps) * 1000  # Convert to milliseconds
         self.current_frame_number = start_frame
-        self.process = (
-            ffmpeg.input(self.video_path, ss=start_time / 1000)  # Convert back to seconds for FFmpeg
-            .output('pipe:', format='rawvideo', pix_fmt='bgr24')
-            .run_async(pipe_stdout=True, pipe_stderr=subprocess.PIPE)
-        )
-        self.frame_size = self.width * self.height * 3  # For 'bgr24', 3 bytes per pixel
-
-    def prev_initialize_video_info(self):
-        """ Retrieves video metadata like fps, total frames, resolution, etc. using ffprobe. """
-        # Retrieve FPS, frame count, codec, width, and height
-        try:
-            probe = ffmpeg.probe(self.video_path)
-            video_streams = [stream for stream in probe['streams'] if stream['codec_type'] == 'video']
-            if not video_streams:
-                raise ValueError("No video streams found in the input file.")
-            video_stream = video_streams[0]  # First video stream
-
-            self.fps = eval(video_stream['r_frame_rate'])
-            self.width = int(video_stream['width'])
-            self.height = int(video_stream['height'])
-            self.codec = video_stream['codec_name']
-            self.total_frames = int(video_stream['nb_frames']) if 'nb_frames' in video_stream else None
-            self.duration = float(video_stream['duration']) * 1000  # Duration in milliseconds
-
-            print(f"FPS: {self.fps}, Resolution: {self.width}x{self.height}, "
-                  f"Codec: {self.codec}, Total Frames: {self.total_frames}, Duration: {self.duration:.2f} ms")
-
-        except ffmpeg.Error as e:
-            print(f"FFmpeg error: {e.stderr.decode('utf8')}")
-            raise
-
-    def get_frame(self, frame_number: int, crop = None, vr_roi= None) -> np.ndarray:
-        """ Retrieve a specific frame by number, optionally crop or resize. """
-        return self._get_frame_by_time_or_number(frame_number=frame_number, crop=crop, vr_roi=vr_roi)
-
-    def get_frame_at_time(self, time_milliseconds, crop=None, vr_roi=None):
-        """ Retrieve a frame at a specific time in the video (in milliseconds), with crop and resize options. """
-        return self._get_frame_by_time_or_number(time_milliseconds=time_milliseconds, crop=crop, vr_roi=vr_roi)
 
-    def _get_frame_by_time_or_number(self, frame_number=None, time_milliseconds=None, crop=None, vr_roi=None):
-        """ Internal function to retrieve a frame either by frame number or time, with optional crop/resize. """
-        try:
-            if frame_number is not None:
-                time_milliseconds = (frame_number / self.fps) * 1000  # Convert frame number to milliseconds
-            elif time_milliseconds is None:
-                raise ValueError("Either frame_number or time_milliseconds must be provided.")
-
-            # FFmpeg cropping command if needed
-            ffmpeg_input = ffmpeg.input(self.video_path, ss=time_milliseconds / 1000)  # Convert back to seconds for FFmpeg
-
-            cmd = (
-                ffmpeg_input
-                .output('pipe:', vframes=1, format='image2', vcodec='rawvideo', pix_fmt='bgr24')
-                .run_async(pipe_stdout=True, pipe_stderr=True)
-            )
-
-            frame_size = self.width * self.height * 3  # For 'bgr24', 3 bytes per pixel
-            raw_frame = cmd.stdout.read(frame_size)
-            frame = np.frombuffer(raw_frame, np.uint8).reshape((self.height, self.width, 3))
-
-            if crop:
-                if crop == "Left":
-                    frame = frame[:, :frame.shape[1] // 2, :]
-                elif crop == "Right":
-                    frame = frame[:, frame.shape[1] // 2:, :]
-                elif crop == "Top":
-                    frame = frame[:frame.shape[0] // 2, :, :]
-                elif crop == "Bottom":
-                    frame = frame[frame.shape[0] // 2:, :, :]
-            if vr_roi:
-                panel_height, panel_width = frame.shape[:2]
-                frame = frame[2 * (panel_height // 5):, (panel_width // 4):(3 * panel_width // 4)]
-
-            # Close FFmpeg process
-            cmd.stdout.close()
-            cmd.wait()
-
-            return frame
-
-        except ffmpeg.Error as e:
-            print(f"FFmpeg error: {e.stderr.decode()}")
-            return None
-
-    def read(self, crop=None, vr_roi=None, resize=None):
-        """ Read the next frame from the video. Returns (ret, frame). """
+        if self.is_VR:
+            # FFmpeg command to read frames with VR reprojection
+            cmd = [
+                self.ffmpeg_path,
+                "-hwaccel", "videotoolbox",  # Use hardware acceleration on macOS
+                "-ss", str(start_time / 1000),  # Seek to start time in seconds
+                "-i", self.video_path,
+                "-an",  # Disable audio processing
+                "-filter_complex",  # Apply the v360 filter for VR reprojection
+                f"[0:v]v360=input=he:in_stereo=sbs:pitch=-25:yaw=-0.75:roll=0:output=flat:d_fov=120:w={self.width}:h={self.height}",
+                "-f", "rawvideo",  # Output raw video data
+                "-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
+                "-vsync", "0",  # Disable frame rate synchronization
+                "-preset",  "ultrafast",
+                "-tune", "zerolatency",
+                "-threads", "0",  # Use maximum threads available
+                "-",  # Output to stdout
+            ]
+        else:
+            # FFmpeg command to read frames
+            cmd = [
+                self.ffmpeg_path,
+                "-ss", str(start_time / 1000),  # Seek to start time in seconds
+                "-i", self.video_path,
+                "-f", "rawvideo",  # Output raw video data
+                "-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
+                "-vsync", "0",  # Disable frame rate synchronization
+                "-",  # Output to stdout
+            ]
+
+        # Start FFmpeg process
+        self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+        self.frame_size = self.width * self.height * 3  # Size of one frame in bytes
+
+    def read(self):
+        """
+        Read the next frame from the video. Mimics OpenCV's cap.read().
+        :return: (True, frame) if successful, (False, None) if end of video.
+        """
         if self.process is None:
             self._start_process(start_frame=self.start_frame)
 
@@ -133,18 +125,6 @@ def read(self, crop=None, vr_roi=None, resize=None):
             if not in_bytes:
                 return False, None  # End of video
             frame = np.frombuffer(in_bytes, np.uint8).reshape([self.height, self.width, 3])
-            if crop:
-                if crop == "Left":
-                    frame = frame[:, :frame.shape[1] // 2, :]
-                elif crop == "Right":
-                    frame = frame[:, frame.shape[1] // 2:, :]
-                elif crop == "Top":
-                    frame = frame[:frame.shape[0] // 2, :, :]
-                elif crop == "Bottom":
-                    frame = frame[frame.shape[0] // 2:, :, :]
-            if vr_roi:
-                panel_height, panel_width = frame.shape[:2]
-                frame = frame[2 * (panel_height // 5):, (panel_width // 4):(3 * panel_width // 4)]
 
             self.current_frame_number += 1
             self.current_time = (self.current_frame_number / self.fps) * 1000
@@ -153,100 +133,64 @@ def read(self, crop=None, vr_roi=None, resize=None):
             print(f"Error reading frame: {e}")
             return False, None
 
-    def read_frames(self, start_frame=0, crop=None, resize=None):
+    def set(self, prop_id, value):
         """
-        Generator function that yields frames starting from a specific frame.
-        You can crop and resize frames on the fly.
+        Mimics OpenCV's cap.set().
+        :param prop_id: Property ID (e.g., cv2.CAP_PROP_POS_FRAMES).
+        :param value: Value to set.
         """
-        # Seek to the starting frame by calculating the time in milliseconds
-        start_time = (start_frame / self.fps) * 1000  # Convert to milliseconds
-        process = (
-            ffmpeg.input(self.video_path, ss=start_time / 1000)  # Convert back to seconds for FFmpeg
-            .output('pipe:', format='rawvideo', pix_fmt='bgr24')
-            .run_async(pipe_stdout=True, pipe_stderr=subprocess.PIPE)
-        )
-
-        frame_size = self.width * self.height * 3  # For 'bgr24', 3 bytes per pixel
-        frame_number = start_frame
+        if prop_id == cv2.CAP_PROP_POS_FRAMES:
+            self.start_frame = int(value)
+            self._start_process(start_frame=self.start_frame)
+        elif prop_id == cv2.CAP_PROP_FPS:
+            self.fps = value
+        else:
+            print(f"Property {prop_id} not supported.")
 
-        try:
-            while True:
-                in_bytes = process.stdout.read(frame_size)
-                if not in_bytes:
-                    break  # End of video
-                frame = np.frombuffer(in_bytes, np.uint8).reshape([self.height, self.width, 3])
-
-                # Apply crop
-                if crop:
-                    x, y, w, h = crop
-                    frame = frame[y:y + h, x:x + w]
-
-                # Apply resize
-                if resize:
-                    new_w, new_h = resize
-                    frame = cv2.resize(frame, (new_w, new_h))
-
-                self.current_frame_number = frame_number
-                yield frame, frame_number, frame_number * 1000 / self.fps  # Return frame, frame number, and timestamp in ms
-
-                # Adjust for playback speed
-                if not self.paused:
-                    time.sleep(1 / (self.fps * self.playback_speed))
-                    frame_number += 1
-        finally:
-            process.stdout.close()
-            process.wait()
-
-
-
-    def pause(self):
-        self.paused = True
-
-    def resume(self):
-        self.paused = False
-
-    def set_playback_speed(self, speed):
-        """ Set playback speed (1.0 = normal speed, 2.0 = 2x, 0.5 = 0.5x, etc.). """
-        self.playback_speed = speed
-
-    def seek_and_read(self, seek_frame, crop=None, resize=None):
-        """ Seeks to a specific time and returns the next frame with crop and resize options. """
-        frame = self.get_frame(seek_frame) #, crop=crop, resize=resize)
-        return frame
-
-    def get_metadata(self):
-        """ Return the video's metadata as a dictionary. """
-        return {
-            'fps': self.fps,
-            'width': self.width,
-            'height': self.height,
-            'codec': self.codec,
-            'total_frames': self.total_frames,
-            'duration': self.duration,
-        }
+    def get(self, prop_id):
+        """
+        Mimics OpenCV's cap.get().
+        :param prop_id: Property ID (e.g., cv2.CAP_PROP_FPS).
+        :return: Property value.
+        """
+        if prop_id == cv2.CAP_PROP_FPS:
+            return self.fps
+        elif prop_id == cv2.CAP_PROP_FRAME_WIDTH:
+            return self.width
+        elif prop_id == cv2.CAP_PROP_FRAME_HEIGHT:
+            return self.height
+        elif prop_id == cv2.CAP_PROP_FRAME_COUNT:
+            return self.total_frames
+        elif prop_id == cv2.CAP_PROP_POS_FRAMES:
+            return self.current_frame_number
+        else:
+            print(f"Property {prop_id} not supported.")
+            return None
 
     def release(self):
-        """ Release the resources and close the FFmpeg process. """
+        """
+        Release the resources and close the FFmpeg process.
+        """
         if self.process:
             self.process.stdout.close()
             self.process.wait()
             self.process = None
 
-"""
+
 # Example Usage
 if __name__ == '__main__':
-    video_path = 'your_video.mp4'
-    reader = VideoReaderFFmpeg(video_path)
+    video_path = '/Users/k00gar/Downloads/VideoFile.mp4'
+    reader = VideoReaderFFmpeg(video_path, is_VR=True)
 
-    # Set playback speed to 2x
-    reader.set_playback_speed(2.0)
+    reader.set(cv2.CAP_PROP_POS_FRAMES, 79000)
 
-    # Read and display frames at 2x speed
-    for frame, frame_num, timestamp in reader.read_frames(start_frame=0, crop=(0, 0, reader.width // 2, reader.height)):
-        print(f"Frame: {frame_num}, Time: {timestamp:.2f} ms")
+    # Read and display frames as fast as possible
+    while True:
+        ret, frame = reader.read()
+        if not ret:
+            break
         cv2.imshow('Frame', frame)
         if cv2.waitKey(1) & 0xFF == ord('q'):
             break
 
-    cv2.destroyAllWindows()
-"""
\ No newline at end of file
+    cv2.destroyAllWindows()
\ No newline at end of file

From 2e614bc3fef45ce0bf3d8b88f2a457b431c57c8f Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sat, 28 Dec 2024 23:36:16 +0100
Subject: [PATCH 007/277] init

---
 FSGenerator.py                 | 353 ++++++++++++++---------
 utils/Debugger.py              | 121 ++++++--
 utils/Display_debug_results.py |  12 +-
 utils/FunscriptHandler.py      |  42 ++-
 utils/ObjectTracker_v2.py      |  13 +-
 utils/ObjectTracker_v3.py      | 494 +++++++++++++++++++++++++++++++++
 utils/PoseEstimation.py        |  85 ++++--
 utils/VideoReaderFFmpeg.py     |  68 +++--
 utils/config.py                |   6 +-
 9 files changed, 983 insertions(+), 211 deletions(-)
 create mode 100644 utils/ObjectTracker_v3.py

diff --git a/FSGenerator.py b/FSGenerator.py
index 74a3e113..51e497b4 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -4,9 +4,11 @@
 import json
 from tqdm import tqdm
 from ultralytics import YOLO
+from concurrent.futures import ProcessPoolExecutor
 
 from utils.config import class_names, class_priority_order, class_reverse_match, class_colors
-from utils.ObjectTracker_v2 import ObjectTracker
+from utils.ObjectTracker_v3 import ObjectTracker
+#from utils.ObjectTracker import ObjectTracker
 from utils.FunscriptHandler import FunscriptGenerator
 from utils.Visualizer import Visualizer
 from utils.Debugger import Debugger
@@ -15,14 +17,15 @@
 
 # Define the BoxRecord class
 class BoxRecord:
-    def __init__(self, box, conf, cls, class_name):
+    def __init__(self, box, conf, cls, class_name, track_id):
         self.box = box
         self.conf = conf
         self.cls = cls
         self.class_name = class_name
+        self.track_id = int(track_id)
 
     def __iter__(self):
-        return iter((self.box, self.conf, self.cls, self.class_name))
+        return iter((self.box, self.conf, self.cls, self.class_name, self.track_id))
 
 
 # Define the Result class
@@ -38,30 +41,28 @@ def add_record(self, frame_id, box_record):
             self.frame_data[frame_id] = [box_record]
 
     def map_class_type_to_name(self, class_type, x1, x2, image_width):
-        if class_type in ['foot', 'hand']:
-            # Call it left if it is mainly on the left of the frame, right otherwise
-            if (x1 + x2) / 2 < image_width / 2:
-                class_name = 'right ' + class_type
-            else:
-                class_name = 'left ' + class_type
-        else:
-            class_name = class_type
+        #if class_type in ['foot', 'hand']:
+        #    # Call it left if it is mainly on the left of the frame, right otherwise
+        #    if (x1 + x2) / 2 < image_width / 2:
+        #        class_name = 'right ' + class_type
+        #    else:
+        #        class_name = 'left ' + class_type
+        #else:
+        class_name = class_type
         return class_name
 
     def get_boxes(self, frame_id):
         itemized_boxes = []
         if frame_id not in self.frame_data:
             return itemized_boxes
-            # raise KeyError(f"No records found for frame ID {frame_id}")
         boxes = self.frame_data[frame_id]
-        for box, conf, cls, class_name in boxes:
-            target_class_name = self.map_class_type_to_name(class_name, box[0], box[2], self.image_width)
-            itemized_boxes.append((box, conf, cls, target_class_name))
+        for box, conf, cls, class_name, track_id in boxes:
+            itemized_boxes.append((box, conf, cls, class_name, track_id))
         sorted_boxes = sorted(
             itemized_boxes,
             key=lambda x: class_priority_order.get(x[3], 7)  # Sort by class name priority
         )
-        return sorted_boxes  # sorted_boxes
+        return sorted_boxes
 
     def get_all_frame_ids(self):
         return list(self.frame_data.keys())
@@ -77,7 +78,18 @@ def write_dataset(file_path, data):
         json.dump(data, f)
     export_end = time.time()
     print(f"Done in {export_end - export_start}.")
+
 def extract_yolo_data(model_file, video_path, frame_start, frame_end=None, TestMode = False, isVR = False):
+    # if file already exists, ask if overwrite
+    if os.path.exists(video_path[:-4] + f"_rawyolo.json"):
+        return
+        #print(f"File {video_path[:-4] + f'_rawyolo.json'} already exists. Overwrite? (y/n)")
+        #if input() != 'y':
+        #    return
+        #else:
+        #    # make a backup before continuing
+        #    os.rename(video_path[:-4] + f"_rawyolo.json", video_path[:-4] + f"_rawyolo.json.bak")
+
     records = []
     test_result = Result(320)
 
@@ -109,33 +121,34 @@ def extract_yolo_data(model_file, video_path, frame_start, frame_end=None, TestM
             # yolo_results = model.predict(frame, verbose=False, conf=0.5)  #track(frame, persist=True, verbose=False)
             yolo_results = model.track(frame, persist=True, conf=0.3, verbose=False)
 
-            #if results[0].boxes.id is None:
-            if yolo_results[0].boxes.cls is None:
+            if yolo_results[0].boxes.id is None:  # in case of tracking
+            # if yolo_results[0].boxes.cls is None:  # in case of detection
                 continue
 
             if len(yolo_results[0].boxes) == 0 and not TestMode:
                 continue
 
             # Get the boxes and track IDs
-            #track_ids = results[0].boxes.id.cpu().tolist()
+            track_ids = yolo_results[0].boxes.id.cpu().tolist()
             boxes = yolo_results[0].boxes.xywh.cpu()
             classes = yolo_results[0].boxes.cls.cpu().tolist()
             confs = yolo_results[0].boxes.conf.cpu().tolist()
 
-            # for track_id, cls, conf, box in zip(track_ids, classes, confs, boxes):
-            for cls, conf, box in zip(classes, confs, boxes):
+            for track_id, cls, conf, box in zip(track_ids, classes, confs, boxes):
+            #for cls, conf, box in zip(classes, confs, boxes):
+                track_id = int(track_id)
                 x, y, w, h = box.int().tolist()
                 x1 = x - w // 2
                 y1 = y - h // 2
                 x2 = x + w // 2
                 y2 = y + h // 2
                 #record = [frame_pos, int(track_id), int(cls), round(conf, 1), x1, y1, x2, y2]
-                record = [frame_pos, int(cls), round(conf, 1), x1, y1, x2, y2]
+                record = [frame_pos, int(cls), round(conf, 1), x1, y1, x2, y2, track_id]
                 records.append(record)
                 if TestMode:
                     print(f"Record : {record}")
                     print(f"For class id: {int(cls)}, getting: {class_reverse_match.get(int(cls), 'unknown')}")
-                    test_box = [[x1, y1, x2, y2], round(conf, 1), int(cls), class_reverse_match.get(int(cls), 'unknown')]
+                    test_box = [[x1, y1, x2, y2], round(conf, 1), int(cls), class_reverse_match.get(int(cls), 'unknown'), track_id]
                     print(f"Test box: {test_box}")
                     test_result.add_record(frame_pos, test_box)
                 #if DebugMode:
@@ -154,12 +167,16 @@ def extract_yolo_data(model_file, video_path, frame_start, frame_end=None, TestM
                 sorted_boxes = test_result.get_boxes(frame_pos)
                 print(f"Sorted boxes : {sorted_boxes}")
 
+                frame_display = frame.copy()
+
                 for box in sorted_boxes:
-                    cv2.rectangle(frame, (box[0][0], box[0][1]), (box[0][2], box[0][3]), (0, 255, 0), 2)
-                    cv2.putText(frame, f"{box[3]}", (box[0][0], box[0][1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+                    color = class_colors.get(box[3])
+                    cv2.rectangle(frame_display, (box[0][0], box[0][1]), (box[0][2], box[0][3]), color, 2)
+                    cv2.putText(frame_display, f"{box[4]}: {box[3]}", (box[0][0], box[0][1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
                     #cv2.putText(frame, f"{int(cls)}: {int(conf*100)}%", (record[4], record[5] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
-                cv2.imshow("YOLO11 test boxes Tracking", frame)
-                cv2.waitKey(1)
+                cv2.imshow("YOLO11 test boxes Tracking", frame_display)
+                if cv2.waitKey(1) & 0xFF == ord('q'):
+                    break
     write_dataset(video_path[:-4] + f"_rawyolo.json", records)
     # Release the video capture object and close the display window
     cap.release()
@@ -168,6 +185,7 @@ def extract_yolo_data(model_file, video_path, frame_start, frame_end=None, TestM
 def load_yolo_data_from_file(file_path):
     with open(file_path, 'r') as f:
         data = json.load(f)
+        print(f"Loaded data from {file_path}, length: {len(data)}")
     return data
 
 def make_data_boxes(records, image_x_size):
@@ -176,10 +194,10 @@ def make_data_boxes(records, image_x_size):
 
     # Populate the Result instance with BoxRecord instances
     for record in records:
-        frame_idx, cls, conf, x1, y1, x2, y2 = record
+        frame_idx, cls, conf, x1, y1, x2, y2, track_id = record
         box = [x1, y1, x2, y2]
         class_name = class_reverse_match.get(cls, 'unknown')
-        box_record = BoxRecord(box, conf, cls, class_name)
+        box_record = BoxRecord(box, conf, cls, class_name, track_id)
         result.add_record(frame_idx, box_record)
     return result
 
@@ -193,7 +211,7 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start = No
     # cap = FFmpegVideoCapture(video_path)
     nb_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
 
-    offset_x = int(image_x_size / 3)
+    #offset_x = int(image_x_size / 3)
 
     if not frame_start:
         frame_start = 0
@@ -248,10 +266,16 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start = No
             if DebugMode:
                 bounding_boxes = []  # Initialize an empty list for bounding boxes
                 for box in sorted_boxes:
+                    if box[4] in tracker.tracked_positions:
+                        position = tracker.tracked_positions[box[4]][-1]
+                    else:
+                        position = None
                     bounding_boxes.append({
                         'box': box[0],
                         'conf': box[1],
-                        'class_name': box[3]
+                        'class_name': box[3],
+                        'track_id': box[4],
+                        'position': position,
                     })
                 debugger.log_frame(frame_pos,
                                    bounding_boxes=bounding_boxes,  # Pass the list of bounding boxes
@@ -260,7 +284,6 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start = No
                                        'Penetration': tracker.penetration,
                                        'Grinding': tracker.grinding,
                                        'sex_position': tracker.sex_position,
-                                       #'sex_position': tracker.state_manager.state,
                                        'sex_position_reason': tracker.sex_position_reason,
                                        'tracked_body_part': tracker.tracked_body_part,
                                        'locked_penis_box': tracker.locked_penis_box,
@@ -270,35 +293,42 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start = No
                                        'cons._penis_detections': tracker.consecutive_detections['penis'],
                                        'cons._penis_non_detections': tracker.consecutive_non_detections['penis'],
                                        'breast_tracking': tracker.breast_tracking,
-                                       'left hand normalized': tracker.normalized_positions['left hand'][-1],
-                                       'right hand normalized': tracker.normalized_positions['right hand'][-1],
                                    })
 
         if TestMode:
             ret, frame = cap.read()
-            frame = frame[:, :frame.shape[1] // 2, :]
-            if tracker.tracked_body_part in class_names and tracker.boxes[tracker.tracked_body_part] is not None:
-                frame = visualizer.draw_bounding_box(frame,
-                                                          tracker.boxes[tracker.tracked_body_part],
-                                                          tracker.tracked_body_part,
-                                                          class_colors[tracker.tracked_body_part],
-                                                          offset_x)
+            #frame = frame[:, :frame.shape[1] // 2, :]
+            frame_display = frame.copy()
+
+            for box in tracker.tracked_boxes:
+                #print(f"box[0]: {box[0]}, box[1]: {box[1]}, box[2]: {box[2]}")
+                frame_display = visualizer.draw_bounding_box(frame_display,
+                                                             box[0],
+                                                             str(box[2]) + ": " + box[1],
+                                                             class_colors[str(box[1])],
+                                                             offset_x)
+            #if tracker.tracked_body_part in class_names and tracker.boxes[tracker.tracked_body_part] is not None:
+            #    frame_display = visualizer.draw_bounding_box(frame_display,
+            #                                              tracker.boxes[tracker.tracked_body_part],
+            #                                              tracker.tracked_body_part,
+            #                                              class_colors[tracker.tracked_body_part],
+            #                                              offset_x)
             if tracker.locked_penis_box is not None:
-                frame = visualizer.draw_bounding_box(frame, tracker.locked_penis_box,
+                frame_display = visualizer.draw_bounding_box(frame_display, tracker.locked_penis_box,
                                                               "Locked_Penis",
                                                               class_colors['penis'],
                                                               offset_x)
             if tracker.glans_detected:
-                frame = visualizer.draw_bounding_box(frame, tracker.boxes['glans'],
+                frame_display = visualizer.draw_bounding_box(frame_display, tracker.boxes['glans'],
                                                               "Glans",
                                                               class_colors['glans'],
                                                               offset_x)
             if funscript_distances:
-                frame = visualizer.draw_gauge(frame, funscript_distances[-1])
-                #frame = visualizer.draw_limited_graph(frame, funscript_distances,
+                frame_display = visualizer.draw_gauge(frame_display, funscript_distances[-1])
+                #frame_display = visualizer.draw_limited_graph(frame_display, funscript_distances,
                 #                                               funscript_frames, 200)
 
-            cv2.imshow("Combined Results", frame)
+            cv2.imshow("Combined Results", frame_display)
             cv2.waitKey(1)
 
     funscript_data = list(zip(funscript_frames, funscript_distances))
@@ -336,110 +366,173 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
                 consecutive_frames = 0
                 frame_detected = 0
 
-            if consecutive_frames >= 10:
+            if consecutive_frames >= 2:
                 print(f"First instance of Glans/Penis found in frame {line[0] - 4}")
                 return line[0] - 4
 
 
-# MAIN logic
+def process_chunk(model_file, video_path, frame_start, frame_end, isVR=False):
+    records = []
+    cap = VideoReaderFFmpeg(video_path, is_VR=isVR)
+    cap.set(cv2.CAP_PROP_POS_FRAMES, frame_start)
 
-# YOLO model file
-#yolo_model = "models/k00gar-11n-200ep-best.mlpackage"
-yolo_model = "models/k00gar-11s-198ep-best.mlpackage"
-#yolo_model = "models/k00gar-11m-134ep-best.mlpackage"
-
-funscript_data = []
-
-video_list = []
-# video_list.append("/Users/k00gar/Downloads/wankzvr-shocum-180_180x180_3dh_LR.mp4")
-# video_list.append("/Users/k00gar/Downloads/ARPorn_Angel Youngs_Angel Of Nurse_4000p_2K_original_FISHEYE190_alpha.mp4")
-# video_list.append("/Users/k00gar/Downloads/Milfvr - Honeymoon In Vega - Vanessa Vega_1080p.mp4")
-# video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_Sugar-baby_ Drooling Kitty_1920p_48559_FISHEYE190.mp4")
-# video_list.append("/Volumes/Crucial/pn/EBVR-018-B_AI_upscaled.mp4")
-# video_list.append("/Volumes/Crucial/pn/NaugthyAmericaVR.My.Wifes.Hot.Friend.23.09.15.Blake.Blossom.Oculus.8k.4096.mp4")
-# video_list.append("/Volumes/WD Elements/VRPn/CzechVR.723.I.m.Here.to.Serve.Agatha.Vega.Oculus.8K..mp4")
-# video_list.append("/Volumes/WD Elements/VRPn/721.czechvr.3d.7680x3840.60fps.oculusrift_uhq_h265.mp4")
-# video_list.append("/Users/k00gar/Downloads/JVR_100199.mp4")
-# video_list.append("/Users/k00gar/Downloads/JVR_100200.mp4")
-# video_list.append("/Users/k00gar/Downloads/JVR_100201.mp4")
-# video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_Poolside Romance_1920p_39107_FISHEYE190.mp4")
-# video_list.append("/Users/k00gar/Downloads/SLR_UpCloseVR_UP Close VR with Connie Perignon_1920p_49954_LR_180.mp4")
-# video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_CJ Miles Sexperience_1920p_33250_MKX200.mp4")
-# video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_She Gives The Best Gifts_1920p_35951_MKX200.mp4")
-
-#video_list.append("/Users/k00gar/Downloads/VRLatina_Innocence_Unveiled_2000p_180_180x180_3dh_LR.mp4")
-#video_list.append("/Users/k00gar/Downloads/Katrina Jade [ADD] Oct 16, 2024R_6kvr265_reenc.mp4")
-#video_list.append("/Users/k00gar/Downloads/milfvr-daisy fuentes-jizzlejuice-jizzlejuice-3600p-180_180x180_3dh_LR_reenc.mp4")
-#video_list.append("/Users/k00gar/Downloads/BaDoinkVR_Peaches_n_Cream_5k_180_180x180_3dh_LR.mp4")
-# video_list.append("/Users/k00gar/Downloads/730-czechvr-3d-7680x3840-60fps-oculusrift_uhq_h265.mp4")
-#video_list.append("/Users/k00gar/Downloads/ARPorn_Sasha Tatcha_Fit and Fired Up_4000p_8K_original_FISHEYE190_alpha.mp4")
-
-#video_list.append("/Users/k00gar/Downloads/WAVR 278 A - Ichika Matsumoto, Aoi Kururugi, Mitsuki Nagisa - 3 Little Sisters.mp4")
-#video_list.append("/Users/k00gar/Downloads/HNVR 097 B.mp4")
-#video_list.append("/Users/k00gar/Downloads/BadoinkVR_Lights_Camera_Satisfaction_4K_HEVC_180_180x180_3dh.mp4")
-#video_list.append("/Users/k00gar/Downloads/2022-09-09 - TonightsGirlfriend - Kenna James.mp4")
-
-video_list.append("/Users/k00gar/Downloads/VideoFile.mp4")
-
-TestMode = False
-DebugMode = True
-isVR = True
-
-# wait 60 minutes before continuing
-# time.sleep(60 * 40)
-
-
-for video_path in video_list:
-    print(f"Processing video: {video_path}")
-    #funscript_data = []
-
-    debugger = Debugger(video_path, output_dir=video_path[:-4])
+    model = YOLO(model_file, task="detect")
 
-    #cap = cv2.VideoCapture(video_path)
-    cap = VideoReaderFFmpeg(video_path, is_VR=isVR)
-    fps = cap.get(cv2.CAP_PROP_FPS)
-    image_y_size = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
-    image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
+    for frame_pos in range(frame_start, frame_end):
+        success, frame = cap.read()
+        if success:
+            if isVR:
+                frame = frame[:, frame.shape[1] // 3:2 * frame.shape[1] // 3, :]
+
+            yolo_results = model.track(frame, persist=True, conf=0.3, verbose=False)
+
+            if yolo_results[0].boxes.cls is None:
+                continue
+
+            boxes = yolo_results[0].boxes.xywh.cpu()
+            classes = yolo_results[0].boxes.cls.cpu().tolist()
+            confs = yolo_results[0].boxes.conf.cpu().tolist()
 
-    if isVR:
-        image_x_size = image_x_size // 3
+            for cls, conf, box in zip(classes, confs, boxes):
+                x, y, w, h = box.int().tolist()
+                x1 = x - w // 2
+                y1 = y - h // 2
+                x2 = x + w // 2
+                y2 = y + h // 2
+                record = [frame_pos, int(cls), round(conf, 1), x1, y1, x2, y2]
+                records.append(record)
 
-    print(f"Image size: {image_x_size}x{image_y_size}")
     cap.release()
+    return records
 
-    frame_start = 0   # 61000  # 0
-    frame_end = None  #64000  # None
+def extract_yolo_data_parallel(model_file, video_path, frame_start, frame_end=None, isVR=False, num_chunks=4):
+    if frame_end is None:
+        cap = VideoReaderFFmpeg(video_path, is_VR=isVR)
+        frame_end = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        cap.release()
 
-    position_changes = []
-    sex_position = ""
-    funscript_frames = []
-    funscript_distances = []
+    chunk_size = (frame_end - frame_start) // num_chunks
+    chunks = [(model_file, video_path, frame_start + i * chunk_size, frame_start + (i + 1) * chunk_size, isVR)
+              for i in range(num_chunks)]
+
+    all_records = []
+    with ProcessPoolExecutor() as executor:
+        futures = [executor.submit(process_chunk, *chunk) for chunk in chunks]
+        for future in tqdm(futures, desc="Processing chunks"):
+            all_records.extend(future.result())
+
+    # Sort records by frame_pos to ensure proper order
+    all_records.sort(key=lambda x: x[0])
+
+    write_dataset(video_path[:-4] + f"_rawyolo.json", all_records)
+
+
+# MAIN logic
+
+if __name__ == '__main__':
+
+    # YOLO model file
+    yolo_model = "models/k00gar-11n-200ep-best.mlpackage"
+    #yolo_model = "models/k00gar-11s-198ep-best.mlpackage"
+    #yolo_model = "models/k00gar-11m-134ep-best.mlpackage"
+
+    funscript_data = []
+
+    video_list = []
+    # video_list.append("/Users/k00gar/Downloads/wankzvr-shocum-180_180x180_3dh_LR.mp4")
+    # video_list.append("/Users/k00gar/Downloads/ARPorn_Angel Youngs_Angel Of Nurse_4000p_2K_original_FISHEYE190_alpha.mp4")
+    # video_list.append("/Users/k00gar/Downloads/Milfvr - Honeymoon In Vega - Vanessa Vega_1080p.mp4")
+    # video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_Sugar-baby_ Drooling Kitty_1920p_48559_FISHEYE190.mp4")
+    # video_list.append("/Volumes/Crucial/pn/EBVR-018-B_AI_upscaled.mp4")
+    # video_list.append("/Volumes/Crucial/pn/NaugthyAmericaVR.My.Wifes.Hot.Friend.23.09.15.Blake.Blossom.Oculus.8k.4096.mp4")
+    # video_list.append("/Volumes/WD Elements/VRPn/CzechVR.723.I.m.Here.to.Serve.Agatha.Vega.Oculus.8K..mp4")
+    # video_list.append("/Volumes/WD Elements/VRPn/721.czechvr.3d.7680x3840.60fps.oculusrift_uhq_h265.mp4")
+    # video_list.append("/Users/k00gar/Downloads/JVR_100199.mp4")
+    # video_list.append("/Users/k00gar/Downloads/JVR_100200.mp4")
+    # video_list.append("/Users/k00gar/Downloads/JVR_100201.mp4")
+    # video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_Poolside Romance_1920p_39107_FISHEYE190.mp4")
+    # video_list.append("/Users/k00gar/Downloads/SLR_UpCloseVR_UP Close VR with Connie Perignon_1920p_49954_LR_180.mp4")
+    # video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_CJ Miles Sexperience_1920p_33250_MKX200.mp4")
+    # video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_She Gives The Best Gifts_1920p_35951_MKX200.mp4")
+    #video_list.append("/Users/k00gar/Downloads/VRLatina_Innocence_Unveiled_2000p_180_180x180_3dh_LR.mp4")
+    #video_list.append("/Users/k00gar/Downloads/Katrina Jade [ADD] Oct 16, 2024R_6kvr265_reenc.mp4")
+    #video_list.append("/Users/k00gar/Downloads/milfvr-daisy fuentes-jizzlejuice-jizzlejuice-3600p-180_180x180_3dh_LR_reenc.mp4")
+    #video_list.append("/Users/k00gar/Downloads/BaDoinkVR_Peaches_n_Cream_5k_180_180x180_3dh_LR.mp4")
+    # video_list.append("/Users/k00gar/Downloads/730-czechvr-3d-7680x3840-60fps-oculusrift_uhq_h265.mp4")
+    #video_list.append("/Users/k00gar/Downloads/ARPorn_Sasha Tatcha_Fit and Fired Up_4000p_8K_original_FISHEYE190_alpha.mp4")
+    #video_list.append("/Users/k00gar/Downloads/WAVR 278 A - Ichika Matsumoto, Aoi Kururugi, Mitsuki Nagisa - 3 Little Sisters.mp4")
+    #video_list.append("/Users/k00gar/Downloads/HNVR 097 B.mp4")
+    #video_list.append("/Users/k00gar/Downloads/BadoinkVR_Lights_Camera_Satisfaction_4K_HEVC_180_180x180_3dh.mp4")
+    #video_list.append("/Users/k00gar/Downloads/2022-09-09 - TonightsGirlfriend - Kenna James.mp4")
+    #video_list.append("/Users/k00gar/Downloads/VideoFile.mp4")
+    video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4")
+    # video_list.append("/Users/k00gar/Downloads/The Ultimate Furry VR Porn Compilation.mp4")
+
+    TestMode = False
+    DebugMode = True
+    isVR = True
+
+    for video_path in video_list:
+        print(f"Processing video: {video_path}")
+        #funscript_data = []
+
+        debugger = Debugger(video_path, output_dir=video_path[:-4])
+
+        #cap = cv2.VideoCapture(video_path)
+        cap = VideoReaderFFmpeg(video_path, is_VR=isVR)
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        image_y_size = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
+        image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
+
+        if isVR:
+            offset_x = image_x_size = image_x_size // 3
+
+        print(f"Image size: {image_x_size}x{image_y_size}")
+        cap.release()
+
+        frame_start = 0   # 61000  # 0
+        frame_end = None  #64000  # None
+
+        position_changes = []
+        sex_position = ""
+        funscript_frames = []
+        funscript_distances = []
+
+        # Process the video
+
+        # Run the YOLO detection and saves result to _rawyolo.json file
+        extract_yolo_data(yolo_model, video_path, frame_start, frame_end, TestMode, isVR)
+        #extract_yolo_data_parallel(yolo_model, video_path, frame_start, frame_end, TestMode, isVR)
+
+        # time.sleep(20)  # time to write the file
 
-    # Process the video
+        # Load YOLO detection results from file
+        yolo_data = load_yolo_data_from_file(video_path[:-4] + f"_rawyolo.json")
 
-    # Run the YOLO detection and saves result to _rawyolo.json file
-    #extract_yolo_data(yolo_model, video_path, frame_start, frame_end, TestMode, isVR)
+        results = make_data_boxes(yolo_data, image_x_size)
 
-    # Load YOLO detection results from file
-    yolo_data = load_yolo_data_from_file(video_path[:-4] + f"_rawyolo.json")
+        # Looking for the first instance of penis within the YOLO results
+        first_penis_frame = parse_yolo_data_looking_for_penis(yolo_data, 0)
 
-    results = make_data_boxes(yolo_data, image_x_size)
+        if first_penis_frame is None:
+            print(f"No penis found in video: {video_path}")
+            first_penis_frame = 0
 
-    # Looking for the first instance of penis within the YOLO results
-    first_penis_frame = parse_yolo_data_looking_for_penis(yolo_data, 0)
+        # Deciding whether we start from there or from a user specified later frame
+        frame_start = max(max(first_penis_frame - int(fps), frame_start - int(fps)), 0)
 
-    # Deciding whether we start from there or from a user specified later frame
-    frame_start = max(max(first_penis_frame - int(fps), frame_start - int(fps)), 0)
+        # Performing the tracking part and generation of the raw funscript data
+        funscript_data = analyze_tracking_results(results, image_y_size, video_path, frame_start, frame_end, TestMode)
 
-    # Performing the tracking part and generation of the raw funscript data
-    funscript_data = analyze_tracking_results(results, image_y_size, video_path, frame_start, frame_end, TestMode)
+        debugger.save_logs()
 
-    debugger.save_logs()
+        funscript_handler = FunscriptGenerator()
 
-    funscript_handler = FunscriptGenerator()
+        # Simplifying the funscript data and generating the file
+        funscript_handler.generate(video_path[:-4] + f"_rawfunscript.json", funscript_data, fps, TestMode)
 
-    # Simplifying the funscript data and generating the file
-    funscript_handler.generate(video_path[:-4] + f"_rawfunscript.json", funscript_data, fps, TestMode)
+        # generate heatmap
+        funscript_handler.generate_heatmap(video_path[:-4] + f".funscript", video_path[:-4] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.funscript")
 
-    print(f"Finished processing video: {video_path}")
+        print(f"Finished processing video: {video_path}")
 
diff --git a/utils/Debugger.py b/utils/Debugger.py
index 390ce2ba..7afd4f18 100644
--- a/utils/Debugger.py
+++ b/utils/Debugger.py
@@ -14,6 +14,11 @@ def __init__(self, video_path, output_dir):
         self.output_dir = output_dir
         self.logs = {}
 
+        self.cap = None
+        self.current_frame = 0
+        self.total_frames = 0
+        self.fps = 0
+
     def log_frame(self, frame_id, variables=None, bounding_boxes=None):
         """
         Log or update the state of variables and bounding boxes for a specific frame.
@@ -133,7 +138,7 @@ def debug_frame(self, frame_id, variables, bounding_boxes):
         self.log_frame(frame_id, variables, bounding_boxes)
         self.display_frame(frame_id)
 
-    def play_video(self, start_frame=0, duration=15, rolling_window_size=100, record=False, downsize_ratio = 1):
+    def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=False, downsize_ratio=1):
         """
         Play the video from a specified frame or timestamp, displaying variables, bounding boxes,
         and rolling window curves for distance and funscript data.
@@ -143,28 +148,28 @@ def play_video(self, start_frame=0, duration=15, rolling_window_size=100, record
 
         # Load the video
         #cap = cv2.VideoCapture(self.video_path)
-        cap = VideoReaderFFmpeg(self.video_path, is_VR=True)
-        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-        fps = cap.get(cv2.CAP_PROP_FPS)
-        print(f"Total frames: {total_frames}, FPS: {fps}")
+        self.cap = VideoReaderFFmpeg(self.video_path, is_VR=True)
+        self.total_frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        self.fps = self.cap.get(cv2.CAP_PROP_FPS)
+        print(f"Total frames: {self.total_frames}, FPS: {self.fps}")
 
         if record:
-            ret, frame = cap.read()
-            if cap.is_VR:
+            ret, frame = self.cap.read()
+            if self.cap.is_VR:
                 frame_copy = frame.copy()
                 frame_copy = frame_copy[:, frame.shape[1] // 3:2 * frame.shape[1] // 3, :]
             # Define the output video writer
             output_path = self.video_path.replace(".mp4", "_debug.mp4")
             fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # Codec for MP4
-            out = cv2.VideoWriter(output_path, fourcc, fps, (frame_copy.shape[1] // downsize_ratio, frame_copy.shape[0] // downsize_ratio))
+            out = cv2.VideoWriter(output_path, fourcc, self.fps, (frame_copy.shape[1] // downsize_ratio, frame_copy.shape[0] // downsize_ratio))
 
             if not out.isOpened():
                 print(f"Error: Could not open video writer for {output_path}")
-                cap.release()
+                self.cap.release()
                 return
 
         # Set the starting frame
-        cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame)
+        self.cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame)
 
         # Load the funscript file
         funscript_path = self.video_path.replace(".mp4", ".funscript")
@@ -188,22 +193,34 @@ def play_video(self, start_frame=0, duration=15, rolling_window_size=100, record
             funscript_interpolator = interp1d(funscript_times, funscript_positions, kind='linear',
                                               fill_value="extrapolate")
 
-        frame_id = start_frame
-        end_frame = start_frame + int(duration * fps)  # Calculate the end frame based on duration
+        self.current_frame = start_frame
+        if duration > 0:
+            end_frame = start_frame + int(duration * self.fps)  # Calculate the end frame based on duration
+        else:
+            end_frame = self.total_frames
+
+        width = self.cap.get(cv2.CAP_PROP_FRAME_WIDTH)
+
+        if self.cap.is_VR:
+            width = width // 6
 
-        while frame_id < end_frame:
-            ret, frame = cap.read()
+        cv2.namedWindow("Debug Video")
+        cv2.setMouseCallback("Debug Video", self._mouse_callback,
+                             param=width)
+
+        while self.current_frame < end_frame:
+            ret, frame = self.cap.read()
             if not ret:
                 break
 
             # Crop the frame (as done in display_frame)
             #frame = frame[:, frame.shape[1] // 6:frame.shape[1] // 3, :]
-            if cap.is_VR:
+            if self.cap.is_VR:
                 frame_copy = frame.copy()
                 frame_copy = frame_copy[:, frame.shape[1] // 3:2 * frame.shape[1] // 3, :]
 
             # Display variables and bounding boxes
-            str_frame_id = str(frame_id)
+            str_frame_id = str(self.current_frame)
             if str_frame_id in self.logs:
                 variables = self.logs[str_frame_id]['variables']
                 bounding_boxes = self.logs[str_frame_id]['bounding_boxes']
@@ -213,10 +230,12 @@ def play_video(self, start_frame=0, duration=15, rolling_window_size=100, record
                     x1, y1, x2, y2 = box['box']
                     class_name = box['class_name']
                     conf = box['conf']
+                    track_id = box['track_id']
+                    position = box['position']
                     color = class_colors.get(class_name, (0, 255, 0))
                     cv2.rectangle(frame_copy, (x1, y1), (x2, y2), color, 2)
-                    cv2.putText(frame_copy, f"{class_name} {conf:.2f}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color,
-                                2)
+                    cv2.putText(frame_copy, f"{class_name} {position}", (x1, y1 - 10),
+                                cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
 
                 # Display variables
                 y_offset = 30
@@ -235,8 +254,8 @@ def play_video(self, start_frame=0, duration=15, rolling_window_size=100, record
 
             # Update rolling window buffers
             distance = variables.get('distance', 0)
-            funscript_value = self._get_funscript_value(funscript_interpolator, frame_id, fps) if funscript_data else 0
-            print(f"Funscript value: {funscript_value}")
+            funscript_value = self._get_funscript_value(funscript_interpolator, self.current_frame, self.fps) if funscript_data else 0
+            #print(f"Funscript value: {funscript_value}")
             visualizer.draw_gauge(frame_copy, funscript_value)
             distance_buffer = np.roll(distance_buffer, -1)
             distance_buffer[-1] = distance
@@ -249,6 +268,8 @@ def play_video(self, start_frame=0, duration=15, rolling_window_size=100, record
             self._draw_rolling_window_curve(frame_copy, distance_buffer, (0, 255, 0), 0.5, graph_height, graph_y_start)
             self._draw_rolling_window_curve(frame_copy, funscript_buffer, (255, 0, 0), 0.5, graph_height, graph_y_start)
 
+            self._draw_progress_bar(frame_copy, frame.shape[1], frame.shape[0])
+
             # Show the frame
             cv2.imshow(f"Debug Video", frame_copy)
             if cv2.waitKey(1) & 0xFF == ord('q'):
@@ -257,16 +278,14 @@ def play_video(self, start_frame=0, duration=15, rolling_window_size=100, record
             if cv2.waitKey(1) & 0xFF == 32:
                 time.sleep(10)
 
-
-
             if record:
                 # resize the frame, divide dimensions by 2
-                frame = cv2.resize(frame_copy, (frame_copy.shape[1] // downsize_ratio, frame_copy.shape[0] // downsize_ratio))
+                frame_copy = cv2.resize(frame_copy, (frame_copy.shape[1] // downsize_ratio, frame_copy.shape[0] // downsize_ratio))
                 out.write(frame_copy)
 
-            frame_id += 1
+            self.current_frame += 1
 
-        cap.release()
+        self.cap.release()
         if record:
             out.release()
         cv2.destroyAllWindows()
@@ -297,3 +316,55 @@ def _draw_rolling_window_curve(self, frame, buffer, color, alpha, graph_height,
 
         cv2.addWeighted(overlay, alpha, frame, 1 - alpha, 0, frame)
 
+    def _mouse_callback(self, event, x, y, flags, param):
+        """
+        Handle mouse events for video navigation.
+        """
+        if event == cv2.EVENT_LBUTTONDOWN:  # Start dragging
+            if y >= self.bar_y_start:  # Clicked on the progress bar
+                self._update_frame_from_mouse(x, param)
+        """
+        elif event == cv2.EVENT_MOUSEMOVE and self.is_dragging:  # Dragging
+            self._update_frame_from_mouse(x, param)
+
+        elif event == cv2.EVENT_LBUTTONUP:  # End dragging
+            self.is_dragging = False
+            self._update_frame_from_mouse(x, param)
+        """
+
+    def _update_frame_from_mouse(self, x, width):
+        """
+        Update the current frame based on the mouse's X position.
+        """
+        self.current_frame = int((x / width) * self.total_frames)
+        print(f"Target frame: {self.current_frame}")
+        self.cap.release()
+        print("Release cap")
+        #self.current_frame = max(0, min(self.current_frame, self.total_frames - 1))
+        self.cap = VideoReaderFFmpeg(self.video_path, is_VR=True)
+        print("Set up new cap")
+        self.cap.set(cv2.CAP_PROP_POS_FRAMES, self.current_frame)
+        print("Done resetting and jumping to target frame")
+
+    def _draw_progress_bar(self, frame, width, height):
+        """
+        Draw a progress bar on the frame indicating the current playback position.
+        """
+        # Ensure the frame is writable
+        # frame = np.ascontiguousarray(frame)
+
+        bar_height = 10
+        bar_x_start = 0
+        bar_x_end = width
+        bar_y_start = height - bar_height
+        self.bar_y_start = bar_y_start
+        bar_y_end = height
+
+        # Background of the progress bar
+        cv2.rectangle(frame, (bar_x_start, bar_y_start), (bar_x_end, bar_y_end), (50, 50, 50), -1)
+
+        # Progress indicator
+        progress_x = int((self.current_frame / self.total_frames) * width)
+        cv2.rectangle(frame, (bar_x_start, bar_y_start), (progress_x, bar_y_end), (0, 255, 0), -1)
+
+
diff --git a/utils/Display_debug_results.py b/utils/Display_debug_results.py
index 8b57c455..2e9fbc1e 100644
--- a/utils/Display_debug_results.py
+++ b/utils/Display_debug_results.py
@@ -3,20 +3,22 @@
 #video="/Users/k00gar/Downloads/BadoinkVR_Lights_Camera_Satisfaction_4K_HEVC_180_180x180_3dh.mp4"
 
 #video = "/Users/k00gar/Downloads/Katrina Jade [ADD] Oct 16, 2024R_6kvr265_reenc.mp4"
-video = "/Users/k00gar/Downloads/VideoFile.mp4"
+video = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
 #video = "/Users/k00gar/Downloads/ARPorn_Sasha Tatcha_Fit and Fired Up_4000p_8K_original_FISHEYE190_alpha.mp4"
 
 #video = "/Users/k00gar/Downloads/2022-09-09 - TonightsGirlfriend - Kenna James.mp4"
 
+#Vote for me
+#frame = 126000
+frame = 61000
 
 # Katrina Jade ADD
-frame = 77000
+#frame = 77000
 #frame = 126000
 
 #BadoinkVR Lights Camera
 
 #frame = 53100  # messed up bj
-
 #frame = 90000
 #frame = 77400
 
@@ -39,4 +41,6 @@
 
 #debugger.display_frame(frame)
 
-debugger.play_video(frame, record=False, downsize_ratio=3)
\ No newline at end of file
+#debugger.play_video(frame)
+
+debugger.play_video(frame, record=False, downsize_ratio=2)  # , duration=10)
\ No newline at end of file
diff --git a/utils/FunscriptHandler.py b/utils/FunscriptHandler.py
index 9d39e332..792ea52c 100644
--- a/utils/FunscriptHandler.py
+++ b/utils/FunscriptHandler.py
@@ -2,6 +2,7 @@
 import json
 from simplification.cutil import simplify_coords
 import matplotlib.pyplot as plt
+import numpy as np
 
 class FunscriptGenerator:
     def generate(self, raw_funscript_path, funscript_data, fps, TestMode = False):
@@ -21,7 +22,7 @@ def generate(self, raw_funscript_path, funscript_data, fps, TestMode = False):
         try:
             print(f"Generating funscript based on {len(data)} points...")
 
-            filter_coeff = 12.0
+            filter_coeff = 10.0
 
             self.filtered_positions = simplify_coords(data, filter_coeff)  # Use VW algorithm
 
@@ -249,4 +250,41 @@ def load_funscript(self, funscript_path):
             else:  # if chapter['name'] == 'Blow Job':
                 relevant_chapters_export.append([chapter['name'], startTime_ms, endTime_ms])
 
-        return times, positions, relevant_chapters_export, irrelevant_chapters_export
\ No newline at end of file
+        return times, positions, relevant_chapters_export, irrelevant_chapters_export
+
+    def generate_heatmap(self, funscript_path, output_image_path):
+        # Load funscript data
+        times, positions, _, _ = self.load_funscript(funscript_path)
+        if not times or not positions:
+            print("Failed to load funscript data.")
+            return
+
+        # Calculate average duration, average speed, and total actions
+        total_duration = times[-1] / 1000  # Convert to seconds
+        avg_duration = total_duration
+        avg_speed = np.mean(np.abs(np.diff(positions)) / np.diff(times) * 1000)  # Positions per second
+        total_actions = len(times)
+
+        # Create heatmap data
+        heatmap_data = np.zeros((200, 3000))
+        for i in range(len(times) - 1):
+            start = int(times[i] / total_duration * 2999)
+            end = int(times[i + 1] / total_duration * 2999)
+            pos = positions[i]
+            heatmap_data[pos, start:end] += 1
+
+        # Normalize heatmap data
+        heatmap_data = heatmap_data / np.max(heatmap_data)
+
+        # Create the heatmap image
+        plt.figure(figsize=(30, 2))
+        plt.imshow(heatmap_data, aspect='auto', cmap='hot', interpolation='nearest')
+        plt.colorbar(label='Intensity')
+        plt.title(
+            f'Funscript Heatmap\nAvg Duration: {avg_duration:.2f}s, Avg Speed: {avg_speed:.2f} pos/s, Actions: {total_actions}')
+        plt.xlabel('Time (s)')
+        plt.ylabel('Position')
+        plt.savefig(output_image_path, bbox_inches='tight')
+        plt.close()
+
+        print(f"Heatmap generated and saved to {output_image_path}")
diff --git a/utils/ObjectTracker_v2.py b/utils/ObjectTracker_v2.py
index 598b6b21..a0dc576c 100644
--- a/utils/ObjectTracker_v2.py
+++ b/utils/ObjectTracker_v2.py
@@ -189,8 +189,8 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
         self.boxes = {class_name: None for class_name in class_names}
         all_detections = {class_name: [] for class_name in class_names}
 
-        for box, conf, cls, class_name in sorted_boxes:
-            all_detections[class_name].append([conf, box])
+        for box, conf, cls, class_name, track_id in sorted_boxes:
+            all_detections[class_name].append([conf, box, track_id])
 
         found_box = {class_name: [] for class_name in class_names}
         classes_touching_penis = {class_name: None for class_name in class_names}
@@ -198,9 +198,9 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
 
         for check_class_first in ['glans', 'penis', 'navel']:
             prev_conf = 0
-            for conf, box in all_detections[check_class_first]:
+            for conf, box, track_id in all_detections[check_class_first]:
                 if conf > prev_conf:
-                    found_box[check_class_first] = [box, conf]
+                    found_box[check_class_first] = [box, conf, track_id]
                     prev_conf = conf
 
         for check_class_first in ['glans', 'penis', 'navel']:
@@ -223,7 +223,7 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
             if check_class_second in ['glans', 'penis', 'navel']:
                 continue
             prev_conf = 0
-            for conf, box in all_detections[check_class_second]:
+            for conf, box, track_id in all_detections[check_class_second]:
                 if self.locked_penis_box and self.boxes_overlap(box, self.locked_penis_box) and conf > prev_conf:
                     classes_touching_penis[check_class_second] = box
                     prev_conf = conf
@@ -259,7 +259,8 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                 self.tracked_body_part = 'face'
                 self.detect_sex_position_change('Blowjob', 'face touching penis')
                 self.sub_sex_position = "Not relevant"
-            elif classes_touching_penis['left hand'] or classes_touching_penis['right hand']:
+            #elif classes_touching_penis['left hand'] or classes_touching_penis['right hand']:
+            elif classes_touching_penis['left hand']:
                 self.detect_sex_position_change('Handjob', 'hand touching penis')
                 self.sub_sex_position = "Handjob"
 
diff --git a/utils/ObjectTracker_v3.py b/utils/ObjectTracker_v3.py
new file mode 100644
index 00000000..c1e2d6f3
--- /dev/null
+++ b/utils/ObjectTracker_v3.py
@@ -0,0 +1,494 @@
+from utils import KalmanFilter as KF
+from collections import deque, defaultdict
+import numpy as np
+from utils.config import class_names
+import math
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+
+class ObjectTracker:
+    def __init__(self, fps, frame_pos, state=None):
+        self.class_names = class_names
+        self.active_tracks = {}  # Key: track_id, Value: Track object
+        self.inactive_tracks = []  # List of Track objects for lost tracks
+        self.distance_kf = KF.KalmanFilter()
+
+        self.frame = None
+        self.current_frame_id = frame_pos
+        self.image_y_size = 0
+        self.fps = fps
+
+        # if trying to limit speed to accomodate Handy device : 40cm/s on 11cm range
+        # Handy : 11 / 40 = 0.275
+        # Assuming devices like 0SR2 can go double speed
+        # OSR2 : 11 / 80 = 0.13
+        # Settling down to an average of 60cm/s => 11 / 60 = 0.18
+        # @60fps = 0.275 * 60 = 16.8, max_movement on 1 frame: 100 / 16.8 = 5.9
+        # @30fps = 0.275 * 30 = 8.1, max_movement on 1 frame: 100 / 8.1 = 12.5
+        self.max_speed = 100 / (.18 * self.fps)
+
+        self.max_allowed = 100
+
+        self.boxes = {}
+        self.tracked_boxes = []
+        self.avg_move = {}
+
+        self.penis_box, self.locked_penis_box = None, None
+        self.glans_detected = False
+        self.locked_penis_height = 0
+        self.breast_tracking = False
+        self.distance = 100
+        self.raw_distance = 100
+        self.previous_distances = [100, 100, 100]
+        self.tracked_body_part = "Nothing"
+
+        self.sex_position = "Not relevant"
+        self.prev_sex_position = "Not relevant"
+        self.sex_position_reason = ""
+        self.sex_position_history = deque(maxlen=30)
+        self.sub_sex_position = "Not relevant"
+
+        self.positions = {class_name: deque(maxlen=200) for class_name in class_names}
+        self.distances = {class_name: deque(maxlen=200) for class_name in class_names}
+        self.areas = {class_name: deque(maxlen=200) for class_name in class_names}
+
+        self.normalized_positions = {class_name: deque(maxlen=200) for class_name in class_names}
+        self.normalized_distances = {class_name: deque(maxlen=200) for class_name in class_names}
+        self.activated_kalman = {class_name: False for class_name in class_names}
+
+        self.tracked_positions = {}
+        self.normalized_tracked_positions = {}
+
+        self.offsets = {class_name: 0 for class_name in class_names}
+
+        for class_name in class_names:
+            self.normalized_distances[class_name].append(100)
+            self.normalized_positions[class_name].append(100)
+
+        self.normalized_areas = {class_name: deque(maxlen=200) for class_name in class_names}
+
+        self.moving_average_window = 5
+        self.consecutive_detections = {class_name: 0 for class_name in class_names}
+        self.consecutive_non_detections = {class_name: 0 for class_name in class_names}
+        self.detections_threshold = 3
+
+        self.consecutive_grinding_suspicions = {class_name: 0 for class_name in ['pussy', 'butt']}
+
+        self.penetration = False
+        self.grinding = False
+        self.rubbing = False
+
+        self.max_predictions = int(self.fps)
+        self.max_inactive_frames = int(fps * 2)  # Adjust based on expected occlusion duration
+        self.distance_threshold = 50  # Define an appropriate distance threshold
+
+    def update_distance(self, raw_distance):
+        if raw_distance is None:
+            filtered_distance = self._predict_distance()
+        else:
+            if abs (self.distance - raw_distance) > self.max_speed:
+                raw_distance = self.distance + np.sign(raw_distance - self.distance) * self.max_speed
+            filtered_distance = raw_distance
+            #rounded_distance = round(raw_distance / 5) * 5
+            #ema_distance = self._calculate_ema_distance(rounded_distance)
+            #self._update_previous_distances(ema_distance)
+            #self.distance_kf.update((ema_distance, 0))
+            #filtered_distance = ema_distance
+
+        filtered_distance = int(max(0, min(100, filtered_distance)))
+        self.distance = filtered_distance
+        return filtered_distance
+
+    def _predict_distance(self):
+        return int(float(self.distance_kf.predict()[0]) / 5) * 5
+
+    def _calculate_ema_distance(self, rounded_distance):
+        return int(0.7 * rounded_distance + 0.1 * self.previous_distances[1] + 0.2 * self.previous_distances[2])
+
+    def _update_previous_distances(self, ema_distance):
+        if abs(ema_distance - self.previous_distances[-1]) > 15:
+            ema_distance = self.previous_distances[-1] + np.sign(ema_distance - self.previous_distances[-1]) * 15
+        elif abs(ema_distance - self.previous_distances[-1]) < 2:
+            ema_distance = self.previous_distances[-1]
+        self.previous_distances.pop(0)
+        self.previous_distances.append(ema_distance)
+
+    def boxes_overlap(self, box1, box2):
+        if box1 is None or box2 is None:
+            return False
+        x1, y1, x2, y2 = box1
+        x3, y3, x4, y4 = box2
+        return not (x2 < x3 or x4 < x1 or y2 < y3 or y4 < y1)
+
+    def calculate_distance(self, penis_box, other_box):
+        print(f"penis box argument in calculate_distance: {penis_box}")
+        if other_box is None:
+            return None
+        if penis_box is None:
+            print(f"penis box: {penis_box}")
+            print(f"Penis box is None for frame {self.current_frame_id}, cannot compute distance")
+            return None
+        ox1, oy1, ox2, oy2 = other_box
+        y_pos = (oy1 + 2 * oy2) // 3
+        x_pos = (ox1 + ox2) // 2
+        px1, py1, px2, py2 = penis_box
+        return math.sqrt((x_pos - px2) ** 2 + (y_pos - py2) ** 2)
+
+    def detect_sex_position_change(self, sex_position, reason):
+        self.sex_position_history.append(sex_position)
+        position_counts = {position: self.sex_position_history.count(position) for position in
+                           self.sex_position_history}
+        most_frequent_position = max(position_counts, key=position_counts.get, default="Not relevant")
+        if most_frequent_position != self.sex_position:
+            logging.info(f"@{self.current_frame_id} - Sex position switched to: {most_frequent_position}")
+            self.sex_position = most_frequent_position
+            self.sex_position_reason = reason
+
+    def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
+        self.current_frame_id = current_frame_id
+        self.image_y_size = image_y_size
+
+        # Collect all detections
+        detections = [(box, conf, class_name, track_id) for box, conf, cls, class_name, track_id in sorted_boxes]
+
+        # Initialize tracking state
+        self.glans_detected = False
+        self.boxes = {class_name: None for class_name in self.class_names}
+        self.tracked_boxes = []
+        #self.avg_move = {}
+        all_detections = {class_name: [] for class_name in self.class_names}
+
+        classes_touching_penis = []
+
+        # Collect all detections by class name
+        for box, conf, cls, class_name, track_id in sorted_boxes:
+            if conf > 0.5:
+                all_detections[class_name].append([conf, box, track_id])
+
+        # Find the best box for specific classes
+        found_box = {class_name: [] for class_name in self.class_names}
+        for check_class_first in ['glans', 'penis', 'navel']:
+            prev_conf = 0
+            for conf, box, track_id in all_detections[check_class_first]:
+                if conf > prev_conf:
+                    found_box[check_class_first] = [box, conf, track_id]
+                    prev_conf = conf
+
+        # Update tracking for specific classes
+        for check_class_first in ['glans', 'penis', 'navel']:
+            if found_box[check_class_first]:
+                self.boxes[check_class_first] = found_box[check_class_first][0]
+                conf = found_box[check_class_first][1]
+                self.consecutive_detections[check_class_first] += 1
+                self.consecutive_non_detections[check_class_first] = 0
+                self.handle_class_first(check_class_first, self.boxes[check_class_first], conf)
+            else:
+                self.consecutive_detections[check_class_first] = 0
+                self.consecutive_non_detections[check_class_first] += 1
+
+        if self.consecutive_non_detections[
+            check_class_first] > self.detections_threshold and check_class_first == 'penis':
+            if self.locked_penis_box:
+                self.locked_penis_box = None
+                logging.info(f"@{self.current_frame_id} - Deactivated locked_penis_box")
+            #self.handle_class_first(check_class_first, box, conf)
+
+
+        # First, how much of the penis can we see, 0 to 100 as a baseline value
+
+        #if self.locked_penis_box and found_box['penis'] and self.boxes_overlap(self.penis_box, found_box['penis'][0]):
+        #    self.visible_penis_height = int(((self.locked_penis_box[3] - found_box['penis'][0][1]) / self.locked_penis_height) * 100)
+        #    self.max_allowed = self.visible_penis_height
+        #else:
+        #    self.visible_penis_height = None
+        #    self.max_allowed = 100
+
+        sum_pos = 0
+        weight_pos = 0
+
+        computation_method = 1
+
+        target_pos = None
+        for box, conf, cls, class_name, track_id in sorted_boxes:
+            if class_name in ['glans', 'penis', 'navel']:
+                continue
+            if self.boxes_overlap(box, self.locked_penis_box):
+                if class_name not in classes_touching_penis:
+                    classes_touching_penis.append(class_name)
+                self.tracked_boxes.append([box, class_name, track_id])
+                x1, y1, x2, y2 = box
+                mid_y = (y1 + 4 * y2) // 5
+                dist_to_penis_base = int(((self.locked_penis_box[3] - mid_y) / (self.locked_penis_height * .8)) * 100)
+                # print(f"locked_penis_height: {self.locked_penis_height}, locked_penis_box[3]: {self.locked_penis_box[3]}, mid_y: {mid_y}, dist_to_penis_base: {dist_to_penis_base}")
+                # print(f"For class {class_name}, mid_y: {mid_y}, dist_to_penis_base: {dist_to_penis_base}")
+                normalized_y = min(max(0, dist_to_penis_base), 100)
+
+                # Update tracked positions
+                if track_id not in self.tracked_positions:
+                    self.tracked_positions[track_id] = []
+                # self.tracked_positions[track_id].append(mid_y)
+                self.tracked_positions[track_id].append(normalized_y)
+
+                # Maintain a fixed-size history
+                if len(self.tracked_positions[track_id]) > 600:
+                    self.tracked_positions[track_id].pop(0)
+
+                # Normalize the y position
+                # min_y, max_y = min(self.tracked_positions[track_id]), max(self.tracked_positions[track_id])
+                # normalized_y = (100 - int(100 * ((mid_y - min_y) / (max_y - min_y)))) if min_y != max_y else 100
+
+                # Update normalized tracked positions
+                if track_id not in self.normalized_tracked_positions:
+                    self.normalized_tracked_positions[track_id] = []
+                self.normalized_tracked_positions[track_id].append(normalized_y)
+
+                # Maintain a fixed-size history
+                if len(self.normalized_tracked_positions[track_id]) > 60:
+                    self.normalized_tracked_positions[track_id].pop(0)
+
+                # sum delta positions of all touching items, ponderated by the length of their history
+                weight_pos_track_id = sum(
+                    abs(self.normalized_tracked_positions[track_id][i] - self.normalized_tracked_positions[track_id][
+                        i - 1]) for i in
+                    range(1, len(self.normalized_tracked_positions[track_id])))
+                sum_pos += max(0, (normalized_y - (100 - self.max_allowed))) * weight_pos_track_id
+                weight_pos += weight_pos_track_id
+
+        if len(classes_touching_penis) == 0 or self.locked_penis_box is None:
+            self.penetration = False
+            distance = 100
+            self.detect_sex_position_change('Not relevant', "no part touching penis / no penis")
+        elif 'pussy' in classes_touching_penis and not self.glans_detected:
+            self.penetration = True
+            self.detect_sex_position_change('Missionnary / Cowgirl', "pussy visible and touching")
+        elif 'ass' in classes_touching_penis and not self.glans_detected:
+            self.penetration = True
+            self.detect_sex_position_change('Doggy / Rev. Cowgirl', "ass visible and touching")
+        elif 'hand' in classes_touching_penis or 'face' in classes_touching_penis:
+            self.detect_sex_position_change('Handjob / Blowjob', "hand or face visible and touching")
+        elif 'foot' in classes_touching_penis:
+            self.detect_sex_position_change('Footjob', "foot visible and touching")
+        elif 'breast' in classes_touching_penis:
+            self.detect_sex_position_change('Boobjob', "breast visible and touching")
+
+
+        if weight_pos > 0:
+            distance = int(sum_pos / weight_pos)
+        else:
+            distance = 100
+
+        """if self.penetration:
+            if self.penis_box:
+                # visible part of the penis
+                weighted_distance = distance
+                penis_height = self.penis_box[3] - self.penis_box[1]
+                locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
+                normalized_penis_height = int((penis_height / locked_penis_height) * 100)
+                distance_real = normalized_penis_height
+                distance = int((distance_real + weighted_distance) / 2)
+                #print(f"distance_real: {distance_real} vs distance: {weighted_distance} => {distance}")
+            else:
+                distance = 100
+        """
+
+
+        self.update_distance(distance)
+
+        """
+
+        if self.penetration and class_name == 'breast' and not self.boxes_overlap(box, self.locked_penis_box):
+            x1, y1, x2, y2 = box
+            mid_y = (y1 + y2) // 2
+
+            # Update tracked positions
+            if track_id not in self.tracked_positions:
+                self.tracked_positions[track_id] = []
+            self.tracked_positions[track_id].append(mid_y)
+
+            # Maintain a fixed-size history
+            if len(self.tracked_positions[track_id]) > 600:
+                self.tracked_positions[track_id].pop(0)
+
+            if max(self.tracked_positions[track_id]) != min(self.tracked_positions[track_id]):
+                normalized_y = mid_y / (max(self.tracked_positions[track_id]) - min(self.tracked_positions[track_id]))
+                # Update normalized tracked positions
+                if track_id not in self.normalized_tracked_positions:
+                    self.normalized_tracked_positions[track_id] = []
+                self.normalized_tracked_positions[track_id].append(normalized_y)
+                weight_pos_track_id = sum(
+                    abs(self.normalized_tracked_positions[track_id][i] - self.normalized_tracked_positions[track_id][
+                        i - 1]) for i in
+                    range(1, len(self.normalized_tracked_positions[track_id])))
+                sum_pos += max(0, (normalized_y - (100 - self.max_allowed))) * weight_pos_track_id
+                weight_pos += weight_pos_track_id
+        """
+        # Check if the box overlaps with the locked penis box
+        #el
+
+
+
+        # Calculate average movement over the last 30 frames
+        """
+        if track_id not in self.avg_move:
+            self.avg_move[track_id] = 0
+        else:
+            if len(self.tracked_positions[track_id]) > 30:
+                total_move = sum(
+                    abs(self.tracked_positions[track_id][i] - self.tracked_positions[track_id][i - 1]) for i in
+                    range(1, 31))
+                self.avg_move[track_id] = total_move #/ 30
+            else:
+                self.avg_move[track_id] = 0
+
+        nb_items_touching_penis += 1
+        """
+
+        # Determine the target track ID based on movement
+        """
+        if nb_items_touching_penis > 0:
+            # Filter out tracks with minimal movement (idle state)
+            active_tracks = {track_id: move for track_id, move in self.avg_move.items() if
+                             move > 5}  # Threshold for movement
+            if active_tracks:
+                print("active tracks")
+                target_track_id = max(active_tracks, key=active_tracks.get)
+                distance = self.normalized_tracked_positions[target_track_id][-1]
+                print(f"@{self.current_frame_id} : Tracking {target_track_id} at {distance}")
+            else:
+                # Fallback to the previously tracked ID if no active movement
+                if hasattr(self, 'last_tracked_id') and self.last_tracked_id in self.normalized_tracked_positions:
+                    target_track_id = self.last_tracked_id
+                    distance = self.normalized_tracked_positions[target_track_id][-1]
+                    print(f"@{self.current_frame_id} : Fallback to {target_track_id} at {distance}")
+                else:
+                    distance = None
+        else:
+            distance = None
+
+        # Update the last tracked ID
+        if distance is not None:
+            self.last_tracked_id = target_track_id
+        """
+
+    def handle_class_first(self, class_name, box, conf):
+        if class_name == 'penis':
+            if box is not None and self.penis_box is None:
+                logging.info(f"Penis detected at frame {self.current_frame_id} with confidence {conf}")
+            self.penis_box = box
+            #print(f"self.penis_box: {self.penis_box}")
+
+            #if self.consecutive_non_detections['penis'] >= self.detections_threshold:
+            #    if self.locked_penis_box:
+            #        self.locked_penis_box = None
+            #        self.locked_penis_height = 0
+            #        self.penetration = False
+            #        logging.info(f"@{self.current_frame_id} - Locked penis box deactivated")
+            if self.penis_box:
+                if self.consecutive_detections['penis'] >= self.detections_threshold:
+                    px1, py1, px2, py2 = self.penis_box
+                    current_height = py2 - py1
+                    # move locked penis box to current penis box
+                    if self.locked_penis_box:
+                        if current_height > self.locked_penis_height:
+                            self.locked_penis_height = current_height
+
+                        # moving locked penis box towards current penis box
+                        max_move = max(1, int(self.image_y_size / 960))
+
+                        if abs(self.penis_box[0] - self.locked_penis_box[0]) > max_move:
+                            px1 = self.penis_box[0] + np.sign(self.penis_box[0] - self.locked_penis_box[0]) * max_move
+                        if abs(self.penis_box[2] - self.locked_penis_box[2]) > max_move:
+                            px2 = self.penis_box[2] + np.sign(self.penis_box[2] - self.locked_penis_box[2]) * max_move
+                        if abs(self.locked_penis_box[3] - py2) > max_move:
+                            py2 = self.locked_penis_box[3] + np.sign(py2 - self.locked_penis_box[3]) * max_move
+
+                        self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
+                    else:
+                        self.locked_penis_box = self.penis_box
+                        self.locked_penis_height = current_height
+                    """    
+                    
+                    current_height = py2 - py1
+                    if self.locked_penis_box is None or self.glans_detected: # or current_height > self.locked_penis_height:
+                        if self.locked_penis_box is None:
+                            logging.info(f"@{self.current_frame_id} - Locked penis box activated")
+                        self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
+                        self.locked_penis_height = current_height
+                    """
+                #if self.locked_penis_box and self.glans_detected:
+                #    if current_height > self.locked_penis_height:
+                #        self.locked_penis_height = current_height
+                #        self.locked_penis_box = self.penis_box
+                #    if self.penis_box[3] != self.locked_penis_box[3]:
+                #        self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
+            #if self.locked_penis_box and self.penetration:
+            #    penis_height = self.boxes['penis'][3] - self.boxes['penis'][1]
+            #    locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
+            #    if locked_penis_height == 0:
+            #        scale = 100
+            #    else:
+            #        scale = min(int((penis_height / locked_penis_height) * 100), 100)
+            #    distance = 100 - scale
+            #    self.positions['penis'].append(distance)
+            #    min_distance, max_distance = min(self.positions['penis']), max(self.positions['penis'])
+            #    normalized_distance = (100 - int(100 * ((distance - min_distance) / (
+            #                max_distance - min_distance)))) if min_distance != max_distance else 100
+            #    self.normalized_positions['penis'].append(normalized_distance)
+            #if (self.image_y_size - py1) / self.image_y_size < 0.1 and not self.breast_tracking and \
+            #        self.tracked_boxes['breast']['detected']:
+            #        #self.tracked_objects['breast']['detected']:
+            #    self.breast_tracking = True
+        elif class_name == 'glans' and box:
+            if self.consecutive_detections['glans'] >= self.detections_threshold:
+                self.boxes['glans'] = box
+                self.glans_detected = True
+                if self.penis_box:
+                    self.locked_penis_box = self.penis_box
+                    self.locked_penis_height = self.penis_box[3] - self.penis_box[1]
+                if self.penetration:
+                    self.penetration = False
+                    logging.info(
+                        f"@{self.current_frame_id} - Penetration ended after {self.consecutive_detections['glans']} detections of glans")
+                    if self.tracked_body_part != 'Nothing':
+                        self.normalized_distances[self.tracked_body_part].clear()
+                        self.normalized_distances[self.tracked_body_part].append(100)
+        elif class_name == 'navel' and box:
+            if (self.image_y_size - box[1]) / self.image_y_size < 0.15 and not self.breast_tracking:
+                logging.info("Breast tracking mode activated given navel position in lower 15th of frame")
+                self.breast_tracking = True
+
+    def normalize_box_area(self, box, frame_width, frame_height):
+        x1, y1, x2, y2 = box
+        if x2 <= x1 or y2 <= y1:
+            return 0
+        box_area = (x2 - x1) * (y2 - y1)
+        max_area = frame_width * frame_height
+        normalized_area = 100 * (box_area / max_area)
+        return max(0, min(100, normalized_area))
+
+    def log_and_normalize_pos(self, box, class_name):
+        self.boxes[class_name] = box
+        _, y1, _, y2 = box
+        mid_y = (y1 + y2) / 2
+        self.positions[class_name].append(mid_y)
+
+        min_y, max_y = min(self.positions[class_name]), max(self.positions[class_name])
+        normalized_y = (100 - int(100 * ((mid_y - min_y) / (max_y - min_y)))) if min_y != max_y else 100
+
+        if class_name == "breast" and self.frame:
+            normalized_breast_area = self.normalize_box_area(box, self.frame.shape[1], self.frame.shape[0])
+            normalized_y = ((0.75 * normalized_breast_area) + normalized_y) / 1.75
+
+        self.normalized_positions[class_name].append(normalized_y)
+        return normalized_y
+
+    def log_and_normalize_distance(self, box, class_name):
+        locked_penis_box = self.locked_penis_box or (0, 0, 0, 0)
+        distance = self.calculate_distance(locked_penis_box, box)
+        self.distances[class_name].append(distance)
+        min_distance, max_distance = min(self.distances[class_name]), max(self.distances[class_name])
+        normalized_distance = (100 - int(
+            100 * ((distance - min_distance) / (max_distance - min_distance)))) if min_distance != max_distance else 100
+        self.normalized_distances[class_name].append(normalized_distance)
\ No newline at end of file
diff --git a/utils/PoseEstimation.py b/utils/PoseEstimation.py
index d5813a9a..6817cd93 100644
--- a/utils/PoseEstimation.py
+++ b/utils/PoseEstimation.py
@@ -3,13 +3,16 @@
 import numpy as np
 import cv2
 from collections import defaultdict
+from utils.config import class_names, class_priority_order, class_reverse_match, class_colors
 
-video_path = "/Users/k00gar/Downloads/Katrina Jade [ADD] Oct 16, 2024R_6kvr265_reenc.mp4"
+#video_path = "/Users/k00gar/Downloads/Katrina Jade [ADD] Oct 16, 2024R_6kvr265_reenc.mp4"
+video_path = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
 
 # Load a model
-model = YOLO("yolo11l-pose.pt")
+pose_model = YOLO("yolo11x-pose.pt")
+detect_model = YOLO("../models/k00gar-11n-200ep-best.mlpackage")
 
-timestamp_min = 35
+timestamp_min = 15
 timestamp_sec = 0
 
 cap = cv2.VideoCapture(video_path)
@@ -44,7 +47,8 @@
 
 output_path = video_path.replace(".mp4", "_posedemo.mp4")
 fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # Codec for MP4
-out = cv2.VideoWriter(output_path, fourcc, fps, (frame.shape[1] // 2, frame.shape[0]))
+#out = cv2.VideoWriter(output_path, fourcc, fps, (frame.shape[1] // 2, frame.shape[0]))
+out = cv2.VideoWriter(output_path, fourcc, fps, (frame.shape[1], frame.shape[0]))
 
 
 # Loop through the video frames
@@ -60,35 +64,84 @@
         middle_x_frame = frame.shape[1] // 2
 
         # Run YOLO11 tracking on the frame, persisting tracks between frames
-        results = model.track(frame, persist=True)
+        pose_results = pose_model.track(frame, persist=True, verbose=False)
+        detect_results = detect_model.track(frame, persist=True, verbose=False)
 
 
-        # frame = results[0].plot()
+        frame_1 = pose_results[0].plot()
 
-        print(results)
-        keypoints = results[0].keypoints.cpu()
-        print(f"keypoints: {keypoints}")
-        print("next : ")
+
+
+        # display the results of detect model on the frame also
+        frame_2 = detect_results[0].plot("hand")
+
+
+
+        # print(pose_results)
+        keypoints = pose_results[0].keypoints.cpu()
+        #print(f"keypoints: {keypoints}")
+        # print("next : ")
         keypoints_list = keypoints.xy.cpu().tolist()
-        print(f"keypoints list: {keypoints_list}")
+        #print(f"keypoints list: {keypoints_list}")
         left_hip = keypoints_list[0][11]
         right_hip = keypoints_list[0][12]
+        left_wrist = keypoints_list[0][9]
+        right_wrist = keypoints_list[0][10]
 
         mid_point = [middle_x_frame, (left_hip[1]+ right_hip[1])/2]
 
-        cv2.circle(frame, (int(left_hip[0]), int(left_hip[1])), 5, (0, 255, 0), -1)
-        cv2.circle(frame, (int(right_hip[0]), int(right_hip[1])), 5, (0, 0, 255), -1)
-        cv2.circle(frame, (int(middle_x_frame), int(mid_point[1])), 2, (0, 0, 0), -1)
-
         track = track_history[1]
 
         track.append((int(middle_x_frame), int(mid_point[1])))  # x, y center point
         if len(track) > 5:  # retain 90 tracks for 90 frames
             track.pop(0)
 
+        #cv2.circle(frame, (int(left_hip[0]), int(left_hip[1])), 5, (0, 255, 0), -1)
+        #cv2.circle(frame, (int(right_hip[0]), int(right_hip[1])), 5, (0, 0, 255), -1)
+        cv2.circle(frame, (int(middle_x_frame), int(mid_point[1])), 2, (0, 0, 0), -1)
+
+        # Draw the tracking lines
+        points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
+        cv2.polylines(frame_1, [points], isClosed=False, color=(230, 230, 230), thickness=10)
+
+
+
+        track = track_history[2]
+
+        track.append((int(left_wrist[0]), int(left_wrist[1])))  # x, y center point
+        if len(track) > 5:  # retain 90 tracks for 90 frames
+            track.pop(0)
+
+        # Draw the tracking lines
+        points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
+        cv2.polylines(frame_2 - 1, [points], isClosed=False, color=(230, 230, 230), thickness=10)
+
+        track = track_history[3]
+
+        track.append((int(right_wrist[0]), int(right_wrist[1])))  # x, y center point
+        if len(track) > 5:  # retain 90 tracks for 90 frames
+            track.pop(0)
+
         # Draw the tracking lines
         points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
-        cv2.polylines(frame, [points], isClosed=False, color=(230, 230, 230), thickness=10)
+        cv2.polylines(frame_2 - 1, [points], isClosed=False, color=(230, 230, 230), thickness=10)
+
+        hand_counter = 0
+
+        boxes = detect_results[0].boxes.xywh.cpu()
+        classes = detect_results[0].boxes.cls.cpu().tolist()
+        confs = detect_results[0].boxes.conf.cpu().tolist()
+
+        # if less than 2 boxes of class "hand" in detect_results, print a message
+        for cls, conf, box in zip(classes, confs, boxes):
+            if class_reverse_match.get(int(cls), 'unknown') == "hand":
+                hand_counter += 1
+
+        if hand_counter < 2:
+            print(f"WARNING: {hand_counter} hand detected")
+
+
+        frame = np.hstack((frame_1, frame_2))
 
         """
         # Get the boxes and track IDs
diff --git a/utils/VideoReaderFFmpeg.py b/utils/VideoReaderFFmpeg.py
index aede3e7a..670b034c 100644
--- a/utils/VideoReaderFFmpeg.py
+++ b/utils/VideoReaderFFmpeg.py
@@ -21,6 +21,10 @@ def __init__(self, video_path, is_VR=False, ffmpeg_path="/usr/local/bin/ffmpeg",
         self.current_time = 0
         self.process = None
         self.frame_size = None
+        self.type = ""
+        self.iv_fov = 0
+        self.ih_fov = 0
+        self.d_fov = 0
 
     def _initialize_video_info(self):
         """
@@ -80,22 +84,49 @@ def _start_process(self, start_frame=0):
 
         if self.is_VR:
             # FFmpeg command to read frames with VR reprojection
+            # if 'FISHEYE' is present in video_path then type = "fisheye"
+            if 'FISHEYE' in self.video_path:
+                self.type = "fisheye"
+                self.iv_fov = 190  # 120
+                self.ih_fov = 190
+                self.v_fov = 90
+                self.h_fov = 90
+                self.d_fov = 180  # 110
+            else:
+                type = "he"  # [0:v]v360=input=he:in_stereo=sbs:pitch=-35:v_fov=90:h_fov=90:output=sg:w=2048:h=2048
+                self.iv_fov = 90
+                self.ih_fov = 90
+                self.d_fov = 100
+            #"""
             cmd = [
                 self.ffmpeg_path,
-                "-hwaccel", "videotoolbox",  # Use hardware acceleration on macOS
+                #"-hwaccel", "videotoolbox",  # Use hardware acceleration on macOS
                 "-ss", str(start_time / 1000),  # Seek to start time in seconds
                 "-i", self.video_path,
                 "-an",  # Disable audio processing
-                "-filter_complex",  # Apply the v360 filter for VR reprojection
-                f"[0:v]v360=input=he:in_stereo=sbs:pitch=-25:yaw=-0.75:roll=0:output=flat:d_fov=120:w={self.width}:h={self.height}",
+                #"-filter_complex",  # Apply the v360 filter for VR reprojection
+                #f"[0:v]v360=input=he:in_stereo=sbs:pitch=-35:yaw=-0.75:roll=0:output=sg:v_fov=90:h_fov=90:d_fov=180:w={self.width}:h={self.height}",
+                "-map", "0:v:0",
+                "-vf", f"crop=w=iw/2:h=ih:x=0:y=0,v360={self.type}:sg:iv_fov={self.iv_fov}:ih_fov={self.ih_fov}:d_fov={self.d_fov}:v_fov={self.v_fov}:h_fov={self.h_fov}:pitch=-20:yaw=0:roll=0:w={self.width}:h={self.height}:interp=lanczos:reset_rot=1",
                 "-f", "rawvideo",  # Output raw video data
                 "-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
                 "-vsync", "0",  # Disable frame rate synchronization
-                "-preset",  "ultrafast",
-                "-tune", "zerolatency",
                 "-threads", "0",  # Use maximum threads available
                 "-",  # Output to stdout
             ]
+            """
+            cmd = [
+                self.ffmpeg_path,
+                "-ss", str(start_time / 1000),  # Seek to start time in seconds
+                "-i", self.video_path,  # Input video file
+                "-vf", "crop=in_w/2:in_h:0:0",  # Crop to the left half (width/2, full height, starting from top-left)
+                "-an",  # Disable audio processing
+                "-f", "rawvideo",  # Output raw video data
+                "-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
+                "-vsync", "0",  # Disable frame rate synchronization
+                "-",  # Output to stdout
+            ]
+            """
         else:
             # FFmpeg command to read frames
             cmd = [
@@ -108,6 +139,11 @@ def _start_process(self, start_frame=0):
                 "-",  # Output to stdout
             ]
 
+        # kill the process if already running
+        if self.process:
+            self.process.terminate()
+            # self.process.wait()
+
         # Start FFmpeg process
         self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
         self.frame_size = self.width * self.height * 3  # Size of one frame in bytes
@@ -125,7 +161,6 @@ def read(self):
             if not in_bytes:
                 return False, None  # End of video
             frame = np.frombuffer(in_bytes, np.uint8).reshape([self.height, self.width, 3])
-
             self.current_frame_number += 1
             self.current_time = (self.current_frame_number / self.fps) * 1000
             return True, frame
@@ -173,24 +208,5 @@ def release(self):
         """
         if self.process:
             self.process.stdout.close()
-            self.process.wait()
+            # self.process.wait()
             self.process = None
-
-
-# Example Usage
-if __name__ == '__main__':
-    video_path = '/Users/k00gar/Downloads/VideoFile.mp4'
-    reader = VideoReaderFFmpeg(video_path, is_VR=True)
-
-    reader.set(cv2.CAP_PROP_POS_FRAMES, 79000)
-
-    # Read and display frames as fast as possible
-    while True:
-        ret, frame = reader.read()
-        if not ret:
-            break
-        cv2.imshow('Frame', frame)
-        if cv2.waitKey(1) & 0xFF == ord('q'):
-            break
-
-    cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/utils/config.py b/utils/config.py
index 0d7ddd3a..d42d94fc 100644
--- a/utils/config.py
+++ b/utils/config.py
@@ -12,7 +12,7 @@
 # Define class names with their corresponding indices
 class_names = {
     'face': 0,
-    'left hand': 1, 'right hand': 1,
+    'hand':1, 'left hand': 1, 'right hand': 1,
     'penis': 2,
     'glans': 3,
     'pussy': 4,
@@ -20,7 +20,7 @@
     'anus': 6,
     'breast': 7,
     'navel': 8,
-    'left foot': 9, 'right foot': 9
+    'foot':9, 'left foot': 9, 'right foot': 9
 }
 
 # Define colors for each class
@@ -32,9 +32,11 @@
     "anus": (128, 0, 128),              # purple
     "breast": (255, 165, 0),            # orange
     "navel": (0, 255, 255),             # cyan
+    "hand": (255, 0, 255),              # magenta
     "left hand": (255, 0, 255),         # magenta
     "right hand": (255, 0, 255),        # magenta
     "face": (0, 255, 0),                # lime
+    "foot": (165, 42, 42),              # brown
     "left foot": (165, 42, 42),         # brown
     "right foot": (165, 42, 42)         # brown
 }
\ No newline at end of file

From 9d9e91a88227a7c466a91fa39e2f174e6cbdb196 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 29 Dec 2024 12:06:14 +0100
Subject: [PATCH 008/277] init

---
 FSGenerator.py                 |   5 +-
 utils/Display_debug_results.py |   4 +-
 utils/FunscriptHandler.py      | 107 +++++++++++++++++++++++++--------
 utils/ObjectTracker_v3.py      |  13 +++-
 4 files changed, 97 insertions(+), 32 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index 51e497b4..13564831 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -5,6 +5,7 @@
 from tqdm import tqdm
 from ultralytics import YOLO
 from concurrent.futures import ProcessPoolExecutor
+import datetime
 
 from utils.config import class_names, class_priority_order, class_reverse_match, class_colors
 from utils.ObjectTracker_v3 import ObjectTracker
@@ -501,7 +502,7 @@ def extract_yolo_data_parallel(model_file, video_path, frame_start, frame_end=No
         # Process the video
 
         # Run the YOLO detection and saves result to _rawyolo.json file
-        extract_yolo_data(yolo_model, video_path, frame_start, frame_end, TestMode, isVR)
+        #extract_yolo_data(yolo_model, video_path, frame_start, frame_end, TestMode, isVR)
         #extract_yolo_data_parallel(yolo_model, video_path, frame_start, frame_end, TestMode, isVR)
 
         # time.sleep(20)  # time to write the file
@@ -532,7 +533,7 @@ def extract_yolo_data_parallel(model_file, video_path, frame_start, frame_end=No
         funscript_handler.generate(video_path[:-4] + f"_rawfunscript.json", funscript_data, fps, TestMode)
 
         # generate heatmap
-        funscript_handler.generate_heatmap(video_path[:-4] + f".funscript", video_path[:-4] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.funscript")
+        funscript_handler.generate_heatmap(video_path[:-4] + f".funscript", video_path[:-4] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
 
         print(f"Finished processing video: {video_path}")
 
diff --git a/utils/Display_debug_results.py b/utils/Display_debug_results.py
index 2e9fbc1e..89e5021a 100644
--- a/utils/Display_debug_results.py
+++ b/utils/Display_debug_results.py
@@ -10,7 +10,7 @@
 
 #Vote for me
 #frame = 126000
-frame = 61000
+frame = 79080
 
 # Katrina Jade ADD
 #frame = 77000
@@ -43,4 +43,4 @@
 
 #debugger.play_video(frame)
 
-debugger.play_video(frame, record=False, downsize_ratio=2)  # , duration=10)
\ No newline at end of file
+debugger.play_video(frame, record=True, downsize_ratio=2, duration=10)
\ No newline at end of file
diff --git a/utils/FunscriptHandler.py b/utils/FunscriptHandler.py
index 792ea52c..7c83ef5d 100644
--- a/utils/FunscriptHandler.py
+++ b/utils/FunscriptHandler.py
@@ -3,6 +3,11 @@
 from simplification.cutil import simplify_coords
 import matplotlib.pyplot as plt
 import numpy as np
+import datetime
+from scipy.ndimage import gaussian_filter
+import matplotlib.colors as mcolors
+import matplotlib.cm as cm
+from matplotlib.cm import ScalarMappable
 
 class FunscriptGenerator:
     def generate(self, raw_funscript_path, funscript_data, fps, TestMode = False):
@@ -26,7 +31,7 @@ def generate(self, raw_funscript_path, funscript_data, fps, TestMode = False):
 
             self.filtered_positions = simplify_coords(data, filter_coeff)  # Use VW algorithm
 
-            print(f"Lenghth of filtered positions: {len(self.filtered_positions)}")
+            print(f"Lenghth of filtered positions: {len(self.filtered_positions) + 1}")
 
             #output_path = raw_funscript_path[:-18] + '_vw_' + str(filter_coeff) + '.funscript'
             #output_path = raw_funscript_path[:-18] + '_vw_' + str(filter_coeff) + '.funscript'
@@ -259,32 +264,84 @@ def generate_heatmap(self, funscript_path, output_image_path):
             print("Failed to load funscript data.")
             return
 
-        # Calculate average duration, average speed, and total actions
-        total_duration = times[-1] / 1000  # Convert to seconds
-        avg_duration = total_duration
-        avg_speed = np.mean(np.abs(np.diff(positions)) / np.diff(times) * 1000)  # Positions per second
-        total_actions = len(times)
+        # Print loaded data for debugging
+        print(f"Times: {times}")
+        print(f"Positions: {positions}")
+        print(f"Total Actions: {len(times)}")
+        print(f"Time Range: {times[0]} to {datetime.timedelta(seconds = times[-1]/1000)}")
+
+        # Calculate speed (position change per time interval)
+        speeds = np.abs(np.diff(positions) / np.diff(times)) * 1000  # Positions per second
+        print(f"Speeds: {speeds}")
+
+        # Define custom colormap: blue -> green -> yellow -> orange -> red -> purple
+        colors = [
+            (0, 0, 1),    # Blue
+            (0, 1, 0),    # Green
+            (1, 1, 0),    # Yellow
+            (1, 0.5, 0),  # Orange
+            (1, 0, 0),    # Red
+            (0.5, 0, 1)   # Purple
+        ]
+        cmap = mcolors.LinearSegmentedColormap.from_list("custom_gradient", colors)
+
+        # Normalize speeds for colormap
+        norm = mcolors.Normalize(vmin=np.min(speeds), vmax=np.max(speeds))
+
+        # Create figure and plot
+        plt.figure(figsize=(30, 2))
+        ax = plt.gca()
 
-        # Create heatmap data
-        heatmap_data = np.zeros((200, 3000))
+        # Draw lines between points with colors based on speed
         for i in range(len(times) - 1):
-            start = int(times[i] / total_duration * 2999)
-            end = int(times[i + 1] / total_duration * 2999)
-            pos = positions[i]
-            heatmap_data[pos, start:end] += 1
+            x_start = times[i] / 1000  # Convert ms to seconds
+            x_end = times[i + 1] / 1000
+            y_start = positions[i]
+            y_end = positions[i + 1]
+            speed = speeds[i]
+
+            # Get color based on speed
+            line_color = cmap(norm(speed))
+
+            # Plot the line
+            ax.plot([x_start, x_end], [y_start, y_end], color=line_color, linewidth=2)
+
+        # Customize plot
+        ax.set_title(f'Funscript Speed Visualization\nDuration: {datetime.timedelta(seconds = int(times[-1]/1000))} - Avg. Speed {int(np.mean(speeds))} - Actions: {len(times)}')
+        ax.set_xlabel('Time (s)')
+        #ax.set_ylabel('Position')
+        ax.set_yticks(np.arange(0, 101, 10))
+        ax.set_xlim(times[0] / 1000, times[-1] / 1000)
+        ax.set_ylim(0, 100)
+
+        # Remove borders (spines)
+        for spine in ax.spines.values():
+            spine.set_visible(False)
+
+        # Add colorbar (optional, currently commented out)
+        # sm = ScalarMappable(cmap=cmap, norm=norm)
+        # sm.set_array([])
+        # cbar = plt.colorbar(sm, ax=ax, orientation='horizontal', pad=0.2)
+        # cbar.set_label('Speed (positions/s)')
+
+        # Add colorbar
+        sm = ScalarMappable(cmap=cmap, norm=norm)
+        sm.set_array([])
+        #cbar = plt.colorbar(sm, ax=ax, orientation='horizontal', pad=0.2)
+        #cbar.set_label('Speed (positions/s)')
+
+        # Save the figure
+        plt.savefig(output_image_path, bbox_inches='tight', dpi=200)  # Increase resolution
+        plt.close()
 
-        # Normalize heatmap data
-        heatmap_data = heatmap_data / np.max(heatmap_data)
+        print(f"Colored line visualization saved to {output_image_path}")
 
-        # Create the heatmap image
-        plt.figure(figsize=(30, 2))
-        plt.imshow(heatmap_data, aspect='auto', cmap='hot', interpolation='nearest')
-        plt.colorbar(label='Intensity')
-        plt.title(
-            f'Funscript Heatmap\nAvg Duration: {avg_duration:.2f}s, Avg Speed: {avg_speed:.2f} pos/s, Actions: {total_actions}')
-        plt.xlabel('Time (s)')
-        plt.ylabel('Position')
-        plt.savefig(output_image_path, bbox_inches='tight')
-        plt.close()
+# if main
+if __name__ == "__main__":
+    video_path = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
+
+    funscript_handler = FunscriptGenerator()
 
-        print(f"Heatmap generated and saved to {output_image_path}")
+    # generate heatmap
+    funscript_handler.generate_heatmap(video_path[:-4] + ".funscript", video_path[
+                                                                        :-4] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
diff --git a/utils/ObjectTracker_v3.py b/utils/ObjectTracker_v3.py
index c1e2d6f3..632e151a 100644
--- a/utils/ObjectTracker_v3.py
+++ b/utils/ObjectTracker_v3.py
@@ -164,7 +164,9 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
 
         # Collect all detections by class name
         for box, conf, cls, class_name, track_id in sorted_boxes:
-            if conf > 0.5:
+            if class_name == "penis":
+                print(f"penis detected @1")
+            if conf > 0.3:
                 all_detections[class_name].append([conf, box, track_id])
 
         # Find the best box for specific classes
@@ -173,6 +175,8 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
             prev_conf = 0
             for conf, box, track_id in all_detections[check_class_first]:
                 if conf > prev_conf:
+                    if check_class_first == 'penis':
+                        print("penis detected @2")
                     found_box[check_class_first] = [box, conf, track_id]
                     prev_conf = conf
 
@@ -183,6 +187,8 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                 conf = found_box[check_class_first][1]
                 self.consecutive_detections[check_class_first] += 1
                 self.consecutive_non_detections[check_class_first] = 0
+                if check_class_first == 'penis':
+                    print("sending to handle penis")
                 self.handle_class_first(check_class_first, self.boxes[check_class_first], conf)
             else:
                 self.consecutive_detections[check_class_first] = 0
@@ -401,8 +407,9 @@ def handle_class_first(self, class_name, box, conf):
                             px1 = self.penis_box[0] + np.sign(self.penis_box[0] - self.locked_penis_box[0]) * max_move
                         if abs(self.penis_box[2] - self.locked_penis_box[2]) > max_move:
                             px2 = self.penis_box[2] + np.sign(self.penis_box[2] - self.locked_penis_box[2]) * max_move
-                        if abs(self.locked_penis_box[3] - py2) > max_move:
-                            py2 = self.locked_penis_box[3] + np.sign(py2 - self.locked_penis_box[3]) * max_move
+                        if abs(self.penis_box[3] - self.locked_penis_box[3]) > max_move:
+                            print("adjusting now")
+                            py2 = self.locked_penis_box[3] + np.sign(self.penis_box[3] - self.locked_penis_box[3]) * max_move
 
                         self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
                     else:

From 08d6aba592ad415728708b4cd3244e056024e8da Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 29 Dec 2024 12:07:33 +0100
Subject: [PATCH 009/277] init

---
 utils/FFmpegVideoCapture.py | 134 ------------------------------------
 1 file changed, 134 deletions(-)
 delete mode 100644 utils/FFmpegVideoCapture.py

diff --git a/utils/FFmpegVideoCapture.py b/utils/FFmpegVideoCapture.py
deleted file mode 100644
index b72cd4ac..00000000
--- a/utils/FFmpegVideoCapture.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import ffmpeg
-import numpy as np
-import cv2  # For OpenCV constants
-
-
-class FFmpegVideoCapture:
-    def __init__(self, video_path):
-        """
-        Initialize the FFmpegVideoCapture with a video file.
-
-        Args:
-            video_path (str): Path to the video file.
-        """
-        self.video_path = video_path
-        self.probe = ffmpeg.probe(video_path)  # Use the correct probe function
-        video_stream = next((stream for stream in self.probe['streams'] if stream['codec_type'] == 'video'), None)
-        if video_stream is None:
-            raise ValueError("No video stream found in the input file.")
-
-        self.width = int(video_stream['width'])
-        self.height = int(video_stream['height'])
-        self.fps = int(video_stream['r_frame_rate'].split('/')[0]) / int(video_stream['r_frame_rate'].split('/')[1])
-        self.total_frames = int(video_stream.get('nb_frames', -1))
-        self.frame_count = 0
-
-        # Initialize FFmpeg process
-        self.process = None
-        self.start_process()
-
-    def start_process(self):
-        """
-        Start the FFmpeg process to read frames.
-        """
-        try:
-            self.process = (
-                ffmpeg
-                .input(self.video_path)
-                .output('pipe:', format='rawvideo', pix_fmt='rgb24')
-                .run_async(pipe_stdout=True)
-            )
-        except ffmpeg.Error as e:
-            raise RuntimeError(f"Failed to start FFmpeg process: {e}")
-
-    def read(self):
-        """
-        Read the next frame from the video.
-
-        Returns:
-            bool: True if the frame was read successfully, False otherwise.
-            np.ndarray: The frame as a NumPy array (or None if no frame was read).
-        """
-        if not self.process:
-            return False, None  # Process not initialized or already released
-
-        in_bytes = self.process.stdout.read(self.width * self.height * 3)
-        if not in_bytes:
-            return False, None
-
-        frame = np.frombuffer(in_bytes, dtype=np.uint8).reshape((self.height, self.width, 3))
-        self.frame_count += 1
-        return True, frame
-
-    def get(self, prop_id):
-        """
-        Get a property of the video.
-
-        Args:
-            prop_id (int): Property ID (e.g., cv2.CAP_PROP_FPS, cv2.CAP_PROP_FRAME_HEIGHT, etc.).
-
-        Returns:
-            float: The value of the property.
-        """
-        if prop_id == cv2.CAP_PROP_FRAME_WIDTH:
-            return self.width
-        elif prop_id == cv2.CAP_PROP_FRAME_HEIGHT:
-            return self.height
-        elif prop_id == cv2.CAP_PROP_FPS:
-            return self.fps
-        elif prop_id == cv2.CAP_PROP_FRAME_COUNT:
-            return self.total_frames
-        elif prop_id == cv2.CAP_PROP_POS_FRAMES:
-            return self.frame_count
-        else:
-            return 0.0  # Unsupported property
-
-    def set(self, prop_id, value):
-        """
-        Set a property of the video.
-
-        Args:
-            prop_id (int): Property ID (e.g., cv2.CAP_PROP_POS_FRAMES).
-            value (float): The value to set.
-
-        Returns:
-            bool: True if the property was set successfully, False otherwise.
-        """
-        if prop_id == cv2.CAP_PROP_POS_FRAMES:
-            # Seek to the specified frame
-            if value < 0 or (self.total_frames > 0 and value >= self.total_frames):
-                return False  # Invalid frame number
-
-            # Calculate the time in seconds to seek to
-            seek_time = value / self.fps
-
-            # Restart the FFmpeg process with the seek time
-            self.release()
-            try:
-                self.process = (
-                    ffmpeg
-                    .input(self.video_path, ss=seek_time)  # Seek to the specified time
-                    .output('pipe:', format='rawvideo', pix_fmt='rgb24')
-                    .run_async(pipe_stdout=True)
-                )
-                self.frame_count = value
-                return True
-            except ffmpeg.Error as e:
-                raise RuntimeError(f"Failed to start FFmpeg process after seek: {e}")
-        else:
-            return False  # Unsupported property
-
-    def release(self):
-        """
-        Release the FFmpeg process and clean up resources.
-        """
-        if self.process:
-            self.process.stdout.close()
-            self.process.wait()
-            self.process = None
-
-    def __del__(self):
-        """
-        Ensure the FFmpeg process is released when the object is deleted.
-        """
-        self.release()
\ No newline at end of file

From 2f5c2f3a1d624cd6c3efd914c1fea38741602ef7 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 29 Dec 2024 12:08:57 +0100
Subject: [PATCH 010/277] init

---
 utils/ObjectTracker.py    | 1601 -------------------------------------
 utils/ObjectTracker_v2.py |  418 ----------
 2 files changed, 2019 deletions(-)
 delete mode 100644 utils/ObjectTracker.py
 delete mode 100644 utils/ObjectTracker_v2.py

diff --git a/utils/ObjectTracker.py b/utils/ObjectTracker.py
deleted file mode 100644
index ecbbf450..00000000
--- a/utils/ObjectTracker.py
+++ /dev/null
@@ -1,1601 +0,0 @@
-from utils import KalmanFilter as KF
-from collections import deque
-import numpy as np
-#import cv2
-from utils.config import class_names
-import math
-
-class ObjectTracker:
-    def __init__(self, fps, frame_pos, state=None):
-        self.class_names = class_names
-        self.tracked_objects = {
-            class_name: {'kf': KF.KalmanFilter(),
-                         'position': None, 'detected': False, 'touching': False, 'prediction_count': 0}
-            for class_name in class_names
-        }
-        # self.detection_interval = 10
-        # self.frame_count = 0
-        # self.trackers = {}
-
-        self.distance_kf = KF.KalmanFilter()
-
-        self.frame = None
-        self.current_frame_id = frame_pos
-        self.image_y_size = 0
-        self.fps = fps
-
-        self.occlusion = False
-
-        self.penis_box, self.locked_penis_box = None, None
-        self.glans_detected = False
-        self.locked_penis_height = 0
-        self.breast_tracking = False
-        self.distance = 100
-        self.raw_distance = 100
-        self.previous_distances = [100, 100, 100]
-        self.tracked_body_part = "Nothing"
-        #self.activated_kalman = False
-
-        self.face_distance = 100
-        self.hand_distance = 100
-        self.previous_hand_distance = 100
-        self.previous_face_distance = 100
-        self.smoothed_distance = 100
-        self.smoothed_foot_distance = 100
-
-        # Handjob / Blowjob tweaking and stabilization parameters
-        self.switch_threshold_multiplier = 1.5  # Movement must be 1.5x higher to switch
-        self.required_frames_to_switch = 5  # 3 Number of consecutive frames needed to switch
-        self.cooldown_frames = 10  # 5 Frames to wait before allowing another switch
-        self.last_tracked_body_part = None
-        self.switch_cooldown = 0  # Frames left in cooldown
-        # Movement tracking
-        self.face_hands_movements = {}
-        self.previous_hand_distance = 0
-        self.face_distances = []  # List to store recent face distances
-        self.right_hand_distances = []  # List to store recent right hand distances
-        self.left_hand_distances = []  # List to store recent left hand distances
-
-        # Footjob tweaking and stabilization parameters
-        # Stabilization parameters for feet
-        self.foot_switch_threshold_multiplier = 1.5  # Movement must be 1.5x higher to switch
-        self.required_foot_frames_to_switch = 3  # Number of consecutive frames needed to switch
-        self.foot_cooldown_frames = 5  # Cooldown period for switching foot tracking
-        self.last_tracked_foot = None
-        self.foot_switch_cooldown = 0  # Frames left in cooldown
-        # Movement tracking for feet
-        self.foot_movements = {}
-        self.right_foot_distances = []  # List to store recent right foot distances
-        self.left_foot_distances = []  # List to store recent left foot distances
-
-        self.sex_position = "Not relevant"
-        self.prev_sex_position = "Not relevant"
-        self.sex_position_reason = ""
-        self.sex_position_history = deque(maxlen=10)
-        self.sub_sex_position = "Not relevant"
-
-        #self.positions = {class_name: [] for class_name in class_names}
-        self.positions = {class_name: deque(maxlen=200) for class_name in class_names}
-        self.distances = {class_name: deque(maxlen=200) for class_name in class_names}
-        self.areas = {class_name: deque(maxlen=200) for class_name in class_names}
-
-        # Initialize normalized_positions as a dictionary of dictionaries
-        self.normalized_positions = {class_name: deque(maxlen=200) for class_name in class_names}
-        self.normalized_distances = {class_name: deque(maxlen=200) for class_name in class_names}
-        self.activated_kalman = {class_name: False for class_name in class_names}
-
-        for class_name in class_names:
-            self.normalized_distances[class_name].append(100)
-            self.normalized_positions[class_name].append(100)
-
-
-        self.normalized_areas = {class_name: deque(maxlen=200) for class_name in class_names}
-        #self.normalized_positions['navel'].append(100)
-        #self.normalized_positions['breast'].append(100)
-        #self.normalized_positions['butt'].append(100)
-
-        #    'breast': deque(maxlen=10),
-        #    'navel': deque(maxlen=10),
-            # add other classes as needed
-        #}
-
-        #self.normalized_positions = []
-
-        self.moving_average_window = 5  # Number of frames to consider for moving average
-
-        # Counters for consecutive detections and non-detections
-        self.consecutive_detections = {class_name: 0 for class_name in class_names}
-        self.consecutive_non_detections = {class_name: 0 for class_name in class_names}
-        self.detections_threshold = 3
-
-        self.consecutive_grinding_supiscions = {class_name: 0 for class_name in ['pussy', 'butt']}
-
-        self.penetration = False
-        self.grinding = False
-        self.rubbing = False
-        self.penetration_mode = 'No penetration'
-        self.hand_blow_footjob = False
-
-        self.max_predictions = self.fps #600
-
-
-    def update_distance(self, raw_distance):
-
-        #self.raw_distance = raw_distance
-        if raw_distance is not None:
-
-            rounded_distance = round(raw_distance / 5) * 5
-            # self.previous_distances.pop(0)
-            # self.previous_distances.append(raw_distance)
-            ema_distance = int(
-                0.7 * rounded_distance + 0.1 * self.previous_distances[1] + 0.2 * self.previous_distances[2])
-            # cap the change in distance to a maximum of +5 or -5 vs previous distance
-            if abs(ema_distance - self.previous_distances[-1]) > 15:
-                ema_distance = self.previous_distances[-1] + np.sign(ema_distance - self.previous_distances[-1]) * 15
-                # print(f"Capping distance change to {ema_distance} vs {raw_distance}")
-            elif abs(ema_distance - self.previous_distances[-1]) < 2:
-                # if the change is less than 2, use the previous distance to eliminate jitter
-                # print(f"Skipping distance change to {ema_distance} vs {self.previous_distances[-1]} because change is less than 4")
-                ema_distance = self.previous_distances[-1]
-            self.previous_distances.pop(0)
-            self.previous_distances.append(ema_distance)
-
-            # keep the distance between 0 and 100
-            #raw_distance = max(0, min(100, int(raw_distance)))
-            # Update the Kalman filter with the measurement
-            #raw_distance_2d = (raw_distance, 0)
-            ema_distance_2d = (ema_distance, 0)
-            #filtered_distance = self.distance_kf.update(raw_distance_2d)
-            #filtered_distance = self.distance_kf.update(raw_distance_2d)
-            self.distance_kf.update(ema_distance_2d)
-            #filtered_distance = float(self.distance_kf.predict()[0])
-            filtered_distance = ema_distance
-        else:
-            # Predict without updating
-            #filtered_distance = self.distance_kf.predict()
-            filtered_distance = int(float(self.distance_kf.predict()[0]) / 5) * 5
-            # keep the distance between 0 and 100
-
-        filtered_distance = max(0, min(100, filtered_distance))
-
-        self.distance = filtered_distance
-        # print(f"Raw distance vs filtered distance: {raw_distance} vs {filtered_distance}")
-        return filtered_distance
-
-    def update_tracking(self, class_name, box, fallback_class, tracked_item):
-        tracked = self.tracked_objects[class_name]
-        if box is not None:
-            # Object detected: Update Kalman filter
-            tracked['kf'].update([(box[0] + box[2]) / 2, (box[1] + box[3]) / 2])
-            tracked['position'] = box
-            tracked['detected'] = True
-            tracked['half_height'] = (box[3] - box[1]) // 2
-            tracked['half_width'] = (box[2] - box[0]) // 2
-            tracked['touching'] = self.boxes_overlap(self.locked_penis_box, box)
-            tracked['prediction_count'] = 0  # Reset prediction count when object is detected
-            if class_name == tracked_item and self.activated_kalman[class_name]:
-                self.activated_kalman[class_name] = False
-                print(f"@{self.current_frame_id} - Stopped Kalman prediction for {class_name} as it was found back")
-        # Only perform prediction for the tracked item
-        elif class_name == tracked_item and box is None:
-            # Object not detected: Predict position based on the Kalman filter
-            # print(f"Kalman prediction for {class_name}: {tracked['prediction_count'] + 1} / {int(self.max_predictions)}")
-            tracked['kf'].predict()
-            if self.activated_kalman[class_name] == False:
-                self.activated_kalman[class_name] = True
-                print(f"@{self.current_frame_id} - Activating Kalman prediction for {class_name}")
-            print(f"@{self.current_frame_id} - Kalman prediction for {class_name}: {tracked['prediction_count'] + 1} / {int(self.max_predictions)}")
-            tracked['detected'] = False
-            tracked['position'] = tracked['kf'].position
-            tracked['touching'] = self.boxes_overlap(self.locked_penis_box, tracked['position'])
-
-            # Use fallback if primary tracked object is lost and prediction count exceeds 60
-            if tracked['prediction_count'] >= self.max_predictions:
-                #if fallback_class and self.tracked_objects[fallback_class]['detected']:
-                #    print(f"Handling occlusion of {class_name}, fallback to {fallback_class}")
-                #    tracked['position'] = self.tracked_objects[fallback_class]['position']
-                #else:
-                print(f"No fallback available for {class_name}, deactivating tracking")
-                tracked['position'] = None
-                tracked['detected'] = False
-                tracked['touching'] = False
-                tracked['prediction_count'] = 0
-                self.tracked_body_part = 'Nothing'
-        if tracked['touching']:
-            self.log_and_normalize_pos(tracked['position'], class_name)
-            self.log_and_normalize_distance(tracked['position'], class_name)
-        else:
-            self.normalized_positions[class_name].clear()
-            self.normalized_distances[class_name].clear()
-            self.normalized_positions[class_name].append(100)
-            self.normalized_distances[class_name].append(100)
-
-                    # print(f"Fallback to {fallback_class}")
-                    #draw_bounding_box(image, tracked['position'], fallback_class)
-                #else:
-                    # print(f"No fallback available for {class_name}")
-
-        return tracked['position']
-
-    def boxes_overlap(self, box1, box2):
-        if box1 is None or box2 is None:
-            return False
-        x1, y1, x2, y2 = box1
-        x3, y3, x4, y4 = box2
-        if x2 < x3 or x4 < x1 or y2 < y3 or y4 < y1:
-            return False
-        return True
-
-    def calculate_distance(self, penis_box, other_box):
-        # compute middle of other_box
-        if other_box is None:
-            return None
-        ox1, oy1, ox2, oy2 = other_box
-        y_pos = (oy1 + 2 * oy2) // 3
-        x_pos = (ox1 + ox2) // 2
-        # compute middle of lower side of penis_box
-        px1, py1, px2, py2 = penis_box
-        # compute distance between (x_pos, y_pos) and (px2, py2)
-        distance = math.sqrt((x_pos - px2) ** 2 + (y_pos - py2) ** 2)
-        return distance
-
-
-
-    def detect_sex_position_change(self, sex_position, reason):
-        if len(self.sex_position_history) > 0 and self.sex_position_history[-1] == "Blowjob" and self.sub_sex_position == "Handjob":
-            sex_position = "Blowjob"
-        self.sex_position_history.append(sex_position)
-        # Count the occurrences of each sex position in the history
-        position_counts = {}
-        for position in self.sex_position_history:
-            if position in position_counts:
-                position_counts[position] += 1
-            else:
-                position_counts[position] = 1
-
-        # Find the most frequent sex position
-        if position_counts:
-            most_frequent_position = max(position_counts, key=position_counts.get)
-        else:
-            most_frequent_position = "Not relevant"  # No positions in history
-        if most_frequent_position != self.sex_position:
-            print(f"@{self.current_frame_id} - Sex position switched to: {most_frequent_position}")
-            self.sex_position = most_frequent_position
-
-    def prev_detect_sex_position_change(self, sex_position, reason):
-        if sex_position != self.sex_position and sex_position == self.prev_sex_position:
-            #print(f"Sex position changed from {self.sex_position} to {sex_position} given {reason}")
-            print(f"\n@{self.current_frame_id} - {sex_position} given {reason}")
-            self.sex_position = sex_position
-            self.sex_position_reason = reason
-        else:
-            self.prev_sex_position = sex_position
-
-    def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
-        self.image_y_size = image_y_size
-        self.current_frame_id = current_frame_id
-
-        # initialize values
-        self.glans_detected = False
-        self.boxes = {class_name: None for class_name in class_names}
-        all_detections = {class_name: [] for class_name in class_names}
-
-        # detected_classes = []
-        list_of_classes = class_names.copy()
-
-        for box, conf, cls, class_name in sorted_boxes:
-            all_detections[class_name].append([conf, box])
-
-        found_box = {class_name: [] for class_name in class_names}
-        classes_touching_penis = {class_name: None for class_name in class_names}
-        list_of_touching_classes = []
-
-        # Checking priority classes, and keeping the ones with best confidence level
-        for check_class_first in ['glans', 'penis', 'navel']:
-            prev_conf = 0
-            for conf, box in all_detections[check_class_first]:
-                if conf > prev_conf:
-                    found_box[check_class_first] = [box, conf]
-                    prev_conf = conf
-        # Logging priority classes detection and non detections
-        for check_class_first in ['glans', 'penis', 'navel']:
-            box = None
-            if len(found_box[check_class_first]) > 0:
-                list_of_classes.pop(check_class_first)
-                self.boxes[check_class_first] = found_box[check_class_first][0]
-                box = self.boxes[check_class_first]
-                conf = found_box[check_class_first][1]
-                self.consecutive_detections[check_class_first] += 1
-                self.consecutive_non_detections[check_class_first] = 0
-                self.handle_class_first(check_class_first, self.boxes[check_class_first], conf)
-            else:
-                self.consecutive_detections[check_class_first] = 0
-                self.consecutive_non_detections[check_class_first] += 1
-            #if check_class_first == 'penis':
-            #    self.update_tracking(check_class_first, box, None, 'penis')
-
-        prev_class = ''
-        nb_items_touching_penis = 0
-
-        # Listing detected classes touching the locked penis box
-        for check_class_second in list_of_classes:
-            prev_conf = 0
-            for conf, box in all_detections[check_class_second]:
-                if self.locked_penis_box and self.boxes_overlap(box, self.locked_penis_box) and conf > prev_conf:
-                    classes_touching_penis[check_class_second] = box
-                    prev_conf = conf
-                    if check_class_second != prev_class:
-                        nb_items_touching_penis += 1
-                        list_of_touching_classes.append(check_class_second)
-                        prev_class = check_class_second
-            if prev_conf != 0:  # we have one item of that class touching
-                #self.log_and_normalize_distance(classes_touching_penis[check_class_second], check_class_second)
-                #self.log_and_normalize_pos(classes_touching_penis[check_class_second], check_class_second)
-                self.update_tracking(check_class_second, classes_touching_penis[check_class_second], None, self.tracked_body_part)
-
-        if self.tracked_body_part != "Nothing" and self.tracked_body_part not in list_of_touching_classes:
-            # try kalman filter prediction
-            self.update_tracking(self.tracked_body_part, None, None, self.tracked_body_part)
-
-
-        if nb_items_touching_penis == 0:
-            self.handle_closeup('No body parts touching')
-        else:
-            # Determine action type
-            if classes_touching_penis['butt']:
-                # Could still be closeup if pussy can be seen also
-                if len(all_detections['pussy']) > 0 and self.boxes_overlap(all_detections['pussy'][0][1], classes_touching_penis['butt']):
-                    self.handle_closeup('Presence of butt and overlapping pussy')
-                elif self.boxes['penis'] and not self.boxes['glans']:
-                    self.tracked_body_part = 'butt'
-                    self.penetration = True
-                    #self.log_and_normalize_distance(classes_touching_penis['butt'], 'butt')
-                    self.detect_sex_position_change('Doggy', 'butt touching penis')
-                    self.sub_sex_position = "Not relevant"
-            elif classes_touching_penis['pussy'] and self.boxes['penis'] and not self.boxes['glans']:
-                self.penetration = True
-                self.tracked_body_part = 'pussy'
-                #self.log_and_normalize_distance(classes_touching_penis['pussy'], 'pussy')
-                self.detect_sex_position_change('Cowgirl', 'pussy touching penis')
-                self.sub_sex_position = "Not relevant"
-            elif classes_touching_penis['face']:
-                self.tracked_body_part = 'face'
-                #self.log_and_normalize_distance(classes_touching_penis['face'], 'face')
-                self.detect_sex_position_change('Blowjob', 'face touching penis')
-                self.sub_sex_position = "Not relevant"
-            elif classes_touching_penis['left hand'] or classes_touching_penis['right hand']:
-                self.detect_sex_position_change('Handjob', 'hand touching penis')
-                self.sub_sex_position = "Handjob"
-                #if classes_touching_penis['left hand']:
-                #    self.log_and_normalize_distance(classes_touching_penis['left hand'], 'left hand')
-                #else:
-                #    self.normalized_distances['left hand'].clear()
-                #if classes_touching_penis['right hand']:
-                #    self.log_and_normalize_distance(classes_touching_penis['right hand'], 'right hand')
-                #else:
-                #    self.normalized_distances['right hand'].clear()
-
-
-            if self.boxes['penis'] is not None:
-                # Compare the height of the two boxes and scale it to 0 - 100
-                penis_height = self.boxes['penis'][3] - self.boxes['penis'][1]
-
-                if self.locked_penis_box:
-                    if self.sub_sex_position == 'Handjob':
-                        if classes_touching_penis['left hand'] and not classes_touching_penis['right hand']:
-                            #normalized_distance = self.normalized_distances['left hand'][-1]
-                            normalized_distance = self.normalized_positions['left hand'][-1]
-                            self.tracked_body_part = "left hand"
-                        elif classes_touching_penis['right hand'] and not classes_touching_penis['left hand']:
-                            #normalized_distance = self.normalized_distances['right hand'][-1]
-                            normalized_distance = self.normalized_positions['right hand'][-1]
-                            self.tracked_body_part = "right hand"
-                        else:
-                            if len(self.normalized_distances['left hand']) > 3 and len(self.normalized_distances['right hand']) > 3:
-                                #avg_left = self.normalized_distances['left hand'][-1] - self.normalized_distances['left hand'][-2] +\
-                                #            self.normalized_distances['left hand'][-2] - self.normalized_distances['left hand'][-3]
-                                #avg_right = self.normalized_distances['right hand'][-1] - self.normalized_distances['right hand'][-2] +\
-                                #            self.normalized_distances['right hand'][-2] - self.normalized_distances['right hand'][-3]
-                                avg_left = self.normalized_positions['left hand'][-1] - \
-                                           self.normalized_positions['left hand'][-2] + \
-                                           self.normalized_positions['left hand'][-2] - \
-                                           self.normalized_positions['left hand'][-3]
-                                avg_right = self.normalized_positions['right hand'][-1] - \
-                                            self.normalized_positions['right hand'][-2] + \
-                                            self.normalized_positions['right hand'][-2] - \
-                                            self.normalized_positions['right hand'][-3]
-
-                                if abs(avg_left) > abs(avg_right):
-                                    #normalized_distance = self.normalized_distances['left hand'][-1]
-                                    normalized_distance = self.normalized_positions['left hand'][-1]
-                                    self.tracked_body_part = "left hand"
-                                else:
-                                    #normalized_distance = self.normalized_distances['right hand'][-1]
-                                    normalized_distance = self.normalized_positions['right hand'][-1]
-                                    self.tracked_body_part = "right hand"
-                            else:
-                                # arbitrary choice
-                                #if len(self.normalized_distances['right hand']) > 0 and len(self.normalized_distances['right hand']) > len(self.normalized_distances['left hand']):
-                                #    normalized_distance = self.normalized_distances['right hand'][-1]
-                                #    self.tracked_body_part = "right hand"
-                                #elif len(self.normalized_distances['left hand']) > 0:
-                                #    normalized_distance = self.normalized_distances['left hand'][-1]
-                                #    self.tracked_body_part = "left hand"
-                                if len(self.normalized_positions['right hand']) > 0 and len(self.normalized_positions['right hand']) > len(self.normalized_distances['left hand']):
-                                    normalized_distance = self.normalized_positions['right hand'][-1]
-                                    self.tracked_body_part = "right hand"
-                                elif len(self.normalized_positions['left hand']) > 0:
-                                    normalized_distance = self.normalized_positions['left hand'][-1]
-                                    self.tracked_body_part = "left hand"
-
-                    else:  # other cases than handjob
-                        locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
-                        if locked_penis_height == 0:
-                            locked_penis_height = 0.0001
-                        scale = min(int((penis_height / locked_penis_height) * 100), 100)
-                        distance = 100 - scale
-
-                        self.positions['penis'].append(distance)
-
-                        min_distance, max_distance = min(self.positions['penis']), max(self.positions['penis'])
-                        normalized_distance = (100 - int(100 * ((distance - min_distance) / (
-                                max_distance - min_distance)))) if min_distance != max_distance else 100
-
-                        self.normalized_positions['penis'].append(normalized_distance)
-
-                        if self.sex_position == 'Blowjob':
-                            delta = 0
-                            if classes_touching_penis['left hand'] and len(self.normalized_distances['left hand']) > 3:
-                                delta += self.normalized_distances['left hand'][-1] - self.normalized_distances['left hand'][-4]
-                            if classes_touching_penis['right hand']and len(self.normalized_distances['right hand']) > 3:
-                                delta += self.normalized_distances['right hand'][-1] - self.normalized_distances['right hand'][-4]
-                            if not self.penetration and classes_touching_penis['face']and len(self.normalized_distances['face']) > 3:
-                                delta += self.normalized_distances['face'][-1] - self.normalized_distances['face'][-4]
-
-                            renormalized_distance = max(0, min(100, (normalized_distance + delta)))
-                            #print(f"Renormalized distance during BJ/HJ to {renormalized_distance} vs {normalized_distance}")
-                            normalized_distance = renormalized_distance
-                    self.update_distance(normalized_distance)
-            elif self.sex_position == "Cowgirl":
-                self.update_distance(self.normalized_distances['pussy'][-1])
-            elif self.sex_position == "Doggy":
-                self.update_distance(self.normalized_distances['butt'][-1])
-
-    def handle_class_first(self, class_name, box, conf):
-        if class_name == 'penis':
-            if self.penis_box is None:
-                print(f"Penis detected at frame {self.current_frame_id} with confidence {conf}")
-            self.penis_box = box
-            px1, py1, px2, py2 = self.penis_box
-            current_height = py2 - py1
-            if self.consecutive_detections['penis'] >= self.detections_threshold:
-                if self.locked_penis_box is None or self.glans_detected or current_height > self.locked_penis_height:
-                    if self.locked_penis_box is None:
-                        print(f"Locked penis box activated at frame {self.current_frame_id}")
-                    self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
-                    self.locked_penis_height = current_height
-            if self.locked_penis_box:
-                if current_height > self.locked_penis_height:
-                    self.locked_penis_height = current_height
-                    self.locked_penis_box = self.penis_box
-                if self.penis_box[3] != self.locked_penis_box[3]:
-                    self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
-            if self.locked_penis_box and self.penetration:
-                # Compare the height of the two boxes and scale it to 0 - 100
-                penis_height = self.boxes['penis'][3] - self.boxes['penis'][1]
-                locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
-                scale = min(int((penis_height / locked_penis_height) * 100), 100)
-                distance = 100 - scale
-                self.positions['penis'].append(distance)
-                min_distance, max_distance = min(self.positions['penis']), max(self.positions['penis'])
-                normalized_distance = (100 - int(100 * ((distance - min_distance) / (
-                        max_distance - min_distance)))) if min_distance != max_distance else 100
-                self.normalized_positions['penis'].append(normalized_distance)
-            if (self.image_y_size - py1) / self.image_y_size < 0.1 and not self.breast_tracking and \
-                    self.tracked_objects['breast']['detected']:
-                # print("Breast tracking mode activated given penis position in lower 10th of frame")
-                self.breast_tracking = True
-        elif class_name == 'glans':
-            if self.consecutive_detections['glans'] >= self.detections_threshold:
-                self.boxes['glans'] = box
-                self.glans_detected = True
-                if self.penetration:
-                    self.penetration = False
-                    print(
-                        f"@{self.current_frame_id} - Penetration ended after {self.consecutive_detections['glans']} detections of glans")
-        elif class_name == 'navel':
-            if (self.image_y_size - box[1]) / self.image_y_size < 0.15 and not self.breast_tracking:
-                print("Breast tracking mode activated given navel position in lower 15th of frame")
-                self.breast_tracking = True
-
-    def handle_closeup(self, reason):
-        self.tracked_body_part = 'Nothing'
-        self.detect_sex_position_change('Not relevant', reason)
-        self.sub_sex_position = "Not relevant"
-        self.penetration = False
-        self.grinding = False
-        self.rubbing = False
-        self.distance = 100
-        self.update_distance(100)
-
-    def prev3_tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
-        # self.frame = frame
-        self.image_y_size = image_y_size
-        self.current_frame_id = current_frame_id
-        # self.frame_count += 1
-
-        # initialize values
-        self.foot_detected = False
-        self.glans_detected = False
-        self.tracking_states = {class_name: False for class_name in class_names}
-        self.boxes = {class_name: None for class_name in class_names}
-        all_detections = {class_name: [] for class_name in class_names}
-
-        detected_classes = []
-        list_of_classes = class_names.copy()
-
-
-        for box, conf, cls, class_name in sorted_boxes:
-
-            all_detections[class_name].append([conf, box])
-
-            if class_name not in detected_classes:
-                detected_classes.append(class_name)
-
-        # First, let's handle the penis and glans
-        # count the number of detected glans in all_detections, keep the one with the highest confidence
-
-        found_box = {class_name: [] for class_name in ['glans', 'penis']}
-
-        for check_class_first in ['glans', 'penis']:
-            prev_conf = 0
-            for conf, box in all_detections[check_class_first]:
-                if conf > prev_conf:
-                    found_box[check_class_first] = [box, conf]
-                    # print(f"{found_box}")
-                    prev_conf = conf
-
-        if len(found_box['glans']) > 0:
-            # print(f"Found glans at frame {current_frame_id}")
-            # remove glans from list_of_classes
-            list_of_classes.pop('glans')
-            self.boxes['glans'] = found_box['glans'][0]
-            self.glans_handling(found_box['glans'][0], found_box['glans'][1], current_frame_id)
-
-        if len(found_box['penis']) > 0:
-            # remove penis from list_of_classes
-            list_of_classes.pop('penis')
-            self.boxes['penis'] = found_box['penis'][0]
-            self.penis_handling(found_box['penis'][0], found_box['penis'][1], current_frame_id)
-
-        if self.tracked_body_part != "Nothing" and self.tracked_body_part not in detected_classes:
-            # Performing Kalman prediction
-            if not self.activated_kalman:
-                print(f"Kalman prediction for tracked body part {self.tracked_body_part}")
-            self.update_tracking(self.tracked_body_part, None, None, self.tracked_body_part)
-
-        # Check if we have multiple instances of breast, navel, butt etc.
-
-        for checking_class in ["breast", "navel", "pussy", "butt", "left foot", "right foot", "left hand",
-                               "right hand", "face"]:
-            temp_boxes = []
-            # count the number of instances of the class in detections
-            count = 0
-            for class_name, detections in all_detections.items():
-                for conf, box in detections:
-                    if checking_class == class_name and box is not None:
-                        temp_boxes.append(box)
-                        count += 1
-            if count >= 1:
-                # print(f"Multiple instances of {checking_class} detected in frame {current_frame_id}")
-                # check if one is touching the penis, else keep the box that is lowest in the frame
-                lowest_box = temp_boxes[0]
-                for box in temp_boxes:
-                    if box[3] > lowest_box[3]:
-                        lowest_box = box
-                    if self.locked_penis_box and self.boxes_overlap(box, self.locked_penis_box):
-                        lowest_box = box
-                        # exit the for loop, no need to look further
-                        break
-                self.boxes[checking_class] = lowest_box
-                if checking_class in ["navel", "breast"]:
-                    self.log_and_normalize_pos(lowest_box, checking_class)
-                    self.log_and_normalize_distance(lowest_box, checking_class)
-                    if checking_class == "navel":
-                        self.navel_handling(lowest_box)
-                        # remove the class from list_of_classes
-                    list_of_classes.pop(checking_class)
-
-                elif checking_class in ["left foot", "right foot", "left hand",
-                                      "right hand", "butt", "breast", "face", "pussy"] and \
-                            self.boxes_overlap(lowest_box, self.locked_penis_box):
-                    # remove the class from list_of_classes
-                    list_of_classes.pop(checking_class)
-                    self.breast_tracking = False
-                    self.log_and_normalize_distance(lowest_box, checking_class)
-                    self.boxes[checking_class] = lowest_box
-                    if checking_class == 'pussy':
-                        fallback_class = 'breast'
-                    else:
-                        fallback_class = None
-                    self.update_tracking(checking_class, lowest_box, fallback_class, self.tracked_body_part)
-                    if checking_class in ["butt", "face", "pussy"] and self.tracked_objects[checking_class]['touching']:
-                        if not self.penetration and len(found_box['glans']) == 0:
-                            self.penetration = True
-                            print(
-                                f"Penetration started at frame {current_frame_id} as {checking_class} is detected and touching")
-                    self.tracking_states[checking_class] = True
-
-        for unwanted_class in list_of_classes:
-            self.tracking_states[unwanted_class] = False
-
-        if self.boxes['penis'] is not None:
-            # Compare the height of the two boxes and scale it to 0 - 100
-            penis_height = self.boxes['penis'][3] - self.boxes['penis'][1]
-
-            if self.locked_penis_box:
-                locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
-                if locked_penis_height == 0:
-                    locked_penis_height = 0.0001
-                scale = min(int((penis_height / locked_penis_height) * 100), 100)
-                distance = 100 - scale
-
-                self.positions['penis'].append(distance)
-
-                min_distance, max_distance = min(self.positions['penis']), max(self.positions['penis'])
-                normalized_distance = (100 - int(100 * ((distance - min_distance) / (
-                            max_distance - min_distance)))) if min_distance != max_distance else 100
-
-                self.normalized_positions['penis'].append(normalized_distance)
-
-                self.update_distance(self.normalized_positions['penis'][-1])
-
-
-        # Handling irrelevant closeups first
-
-        if self.tracking_states['pussy'] and self.tracking_states['butt']:
-            # position likely to be Closeup
-            self.handle_closeup_position("presence of pussy and butt")
-            self.breast_tracking = False
-        elif (self.tracking_states['pussy'] and self.boxes['pussy'] is not None and (
-                self.boxes['pussy'][3] - self.boxes['pussy'][1]) >
-              image_y_size * 0.1):
-            # position likely to be Closeup
-            self.handle_closeup_position("size of pussy in frame")
-
-        # Handling relevant closeups
-        elif self.breast_tracking and not self.glans_detected:
-            if self.boxes['breast'] is not None and not self.boxes_overlap(self.locked_penis_box, self.boxes['breast']):
-                # if self.boxes['breast'] is not None and not self.tracked_objects['breast']['touching']:
-                # experimenting to handle delay in breast tracking when boobs reach peak or low position
-                # projected_frame_id = current_frame_id  # + int(fps//5)
-                # distances[projected_frame_id] = normalized_positions['breast'].get(current_frame_id, 100)
-                distance = self.normalized_positions['breast'][-1]
-                self.distance = self.update_distance(distance)
-                self.tracked_body_part = "breast"
-                self.detect_sex_position_change("Closeup missionary", "breast tracking activated")
-                if not self.penetration:
-                    self.penetration = True
-                    print(f"Penetration started at frame {current_frame_id} as we are tracking closeup breasts")
-
-        # Handling tagged scenes
-        elif self.sex_position == "Handjob / Blowjob":
-            # if no right hand, no left hand, no face touching, then cancel the position
-            if not self.tracked_objects['left hand']['touching'] and \
-                    not self.tracked_objects['right hand']['touching'] and \
-                    not self.tracked_objects['face']['touching']:
-                # deactivating position
-                self.detect_sex_position_change("Not relevant", "No hand no face")
-            else:
-                self.handle_Blowjob_Handjob_position("Hands or Face detected", current_frame_id)
-
-        elif self.sex_position == "Doggy / Reverse Cowgirl / Pronebone":
-            if not self.boxes['butt']:
-                # deactivating position
-                self.detect_sex_position_change("Not relevant", "No butt detected")
-            else:
-                self.handle_RevCowGirl_Doggy_Pronebone_position("Doggy / Reverse Cowgirl / Pronebone", "Butt detected", current_frame_id)
-
-        elif self.sex_position == "Cowgirl / Missionary":
-            if not self.boxes['pussy']:
-                # deactivating position
-                self.detect_sex_position_change("Not relevant", "No pussy detected")
-            else:
-                self.handle_CowGirl_Missionary_position("Pussy detected", current_frame_id, "pussy")
-
-        # Now handling visible penetrations scenarii
-        elif self.locked_penis_box and self.penetration:
-            if self.boxes['penis'] is not None:
-                # Compare the height of the two boxes and scale it to 0 - 100
-                penis_height = self.boxes['penis'][3] - self.boxes['penis'][1]
-
-                locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
-
-                scale = min(int((penis_height / locked_penis_height) * 100), 100)
-                distance = 100 - scale
-
-                self.positions['penis'].append(distance)
-
-                min_distance, max_distance = min(self.positions['penis']), max(self.positions['penis'])
-                normalized_distance = (100 - int(100 * ((distance - min_distance) / (
-                            max_distance - min_distance)))) if min_distance != max_distance else 100
-
-                self.normalized_positions['penis'].append(normalized_distance)
-
-                self.update_distance(self.normalized_positions['penis'][-1])
-
-            else:
-                # Still handling penetration, but penis is not visible, could be a grinding scene
-                if self.boxes['pussy'] is not None and self.tracking_states['pussy'] and self.boxes[
-                    'breast'] is not None:
-                    # position likely to be CowGirl / Missionary
-                    # defaulting to navel tracking or breast tracking
-                    # if self.tracking_states['navel']:
-                    #    self.handle_CowGirl_Missionary_position("presence of navel", current_frame_id, 'navel')
-                    # el
-                    if self.tracking_states['breast']:
-                        self.handle_CowGirl_Missionary_position("presence of breast", current_frame_id, 'breast')
-                    # self.handle_CowGirl_Missionary_position("presence of pussy and breast", current_frame_id, 'pussy')
-                    if self.tracked_objects['pussy']['touching'] and not self.penetration:
-                        self.penetration = True
-                        print(f"Penetration started at frame {current_frame_id} during pussy grinding")
-                elif self.boxes['butt'] is not None and self.tracking_states['butt']:
-                    # position likely to be Reverse Cowgirl or Doggy / Pronebone
-                    self.handle_RevCowGirl_Doggy_Pronebone_position(
-                        "Doggy / Reverse Cowgirl / Pronebone", "presence of foot", current_frame_id)
-                    if self.tracked_objects['butt']['touching'] and not self.penetration:
-                        self.penetration = True
-                        print(f"Penetration started at frame {current_frame_id} during butt grinding")
-                elif self.boxes['navel'] is not None and not self.boxes_overlap(self.locked_penis_box,
-                                                                                self.boxes[
-                                                                                    'navel']) and self.penetration:
-                    # position likely to be CowGirl / Missionary, with occluded pussy
-                    self.handle_CowGirl_Missionary_position("presence of navel during penetration",
-                                                            current_frame_id,
-                                                            'navel')
-                elif self.boxes['breast'] is not None and self.tracking_states[
-                    'breast'] and not self.penetration and \
-                        self.tracked_objects["breast"]['touching']:
-                    # position likely to be Boobjob
-                    self.handle_Boobjob_position("presence of breast", current_frame_id)
-                elif ((self.boxes['face'] is not None and self.tracking_states['face']) or
-                      ((self.boxes['left hand'] is not None and self.tracking_states['left hand']) or
-                       (self.boxes['right hand'] is not None and self.tracking_states[
-                           'right hand']))) and not self.penetration:
-                    self.handle_Blowjob_Handjob_position("presence of face or hand", current_frame_id)
-        elif self.locked_penis_box and not self.penetration:
-            # Now handling handjob / blowjob scenarii without mouth penetration
-            if ((self.boxes['face'] is not None and self.tracking_states['face']) or
-                ((self.boxes['left hand'] is not None and self.tracking_states['left hand']) or
-                 (self.boxes['right hand'] is not None and self.tracking_states[
-                     'right hand']))) and not self.penetration:
-
-                self.handle_Blowjob_Handjob_position("presence of face or hand", current_frame_id)
-
-            elif ((self.boxes['left foot'] is not None and self.tracking_states['left foot']) or (
-                    self.boxes['right foot'] is not None and self.tracking_states[
-                'right foot'])) and not self.penetration:
-                # position likely to be Footjob
-                self.handle_Footjob_position("presence of foot", current_frame_id)
-
-        # Check for non-detection of penis
-        if self.boxes['penis'] is None:
-            self.consecutive_non_detections['penis'] += 1
-            self.consecutive_detections['penis'] = 0  # Reset the detection counter
-
-            # Deactivate locked_penis_box after x consecutive non-detections
-            if self.consecutive_non_detections['penis'] >= self.max_predictions:
-                if self.locked_penis_box is not None:
-                    self.locked_penis_box = None
-                    print(
-                        f"Penis tracking deactivated at frame {current_frame_id} after {self.consecutive_non_detections['penis']} consecutive non-detections")
-                    # print("Reinitializing tracker")
-                    # self.__init__(self.class_names)
-                    self.penis_box = None
-
-        # Check for non-detection of glans
-        if self.boxes['glans'] is None:
-            self.consecutive_non_detections['glans'] += 1
-            self.consecutive_detections['glans'] = 0  # Reset the detection counter
-
-
-
-    def prev2_tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
-        # self.frame = frame
-        self.image_y_size = image_y_size
-        # self.frame_count += 1
-
-        # initialize values
-        self.foot_detected = False
-        self.glans_detected = False
-        self.tracking_states = {class_name: False for class_name in class_names}
-        self.boxes = {class_name: None for class_name in class_names}
-        all_detections = {class_name: [] for class_name in class_names}
-
-        """
-        for box, conf, cls, class_name in sorted_boxes:
-            
-            if class_name == "glans" and conf > 0.5:
-                self.glans_handling(box, conf, current_frame_id)
-
-            elif class_name == "penis" and conf > 0.5:
-                self.boxes['penis'] = box
-                self.penis_handling(box, conf, current_frame_id)
-
-            elif class_name == "navel" and conf > 0.5:
-                self.navel_handling(box)
-
-            if class_name in ["breast", "navel"] and conf > 0.5:
-                self.log_and_normalize_pos(box, class_name)
-                self.boxes[class_name] = box
-
-            if self.locked_penis_box:
-                self.update_tracking(class_name, box, None, self.tracked_body_part)
-
-                if class_name in ["left foot", "right foot", "left hand",
-                                  "right hand", "butt", "face", "pussy"] and self.tracked_objects[class_name]['touching']:
-                    #self.log_and_normalize_pos(box, class_name)
-                    self.log_and_normalize_distance(box, class_name)
-                    self.boxes[class_name] = box
-
-                if class_name in ["butt", "face", "pussy"] and self.tracked_objects[class_name]['touching']:
-                    if not self.penetration:
-                        self.penetration = True
-                        print(f"Penetration started at frame {current_frame_id} as {class_name} is detected and touching")
-                #self.boxes[class_name] = box
-                self.tracking_states[class_name] = True
-                
-        """
-
-        for box, conf, cls, class_name in sorted_boxes:
-
-            all_detections[class_name].append([conf, box])
-
-            if class_name == "glans" and conf >= 0.5:
-                self.glans_handling(box, conf, current_frame_id)
-
-            elif class_name == "penis" and conf >= 0.5:
-                self.boxes['penis'] = box
-                self.penis_handling(box, conf, current_frame_id)
-
-        # for each class, check if it touches the locked penis box
-        for class_name, detections in all_detections.items():
-            if self.locked_penis_box:
-                for conf, box in detections:
-                    if class_name in ["left foot", "right foot", "left hand",
-                                      "right hand", "butt", "face", "pussy"] and \
-                            self.boxes_overlap(box, self.locked_penis_box):
-                        self.log_and_normalize_distance(box, class_name)
-                        self.boxes[class_name] = box
-                        self.update_tracking(class_name, box, None, self.tracked_body_part)
-                        if class_name in ["butt", "face", "pussy"] and self.tracked_objects[class_name]['touching']:
-                            if not self.penetration:
-                                self.penetration = True
-                                print(
-                                    f"Penetration started at frame {current_frame_id} as {class_name} is detected and touching")
-                        self.tracking_states[class_name] = True
-
-        # build a list of classes that have no self.boxes[class_name]
-        no_box_classes = [class_name for class_name in class_names if self.boxes[class_name] is None]
-
-        # if breast, navel, pussy, butt are missing, check if they are present in detections and keep the ones
-        # that are present and lowest in the frame if multiple instances of them
-
-        for checking_class in ["breast", "navel", "pussy", "butt"]:
-            temp_boxes = []
-            if checking_class in no_box_classes:
-                # count the number of instances of the missing class in detections
-                count = 0
-                for class_name, detections in all_detections.items():
-                    for conf, box in detections:
-                        if checking_class == class_name and box is not None:
-                            temp_boxes.append(box)
-                            count += 1
-                if count > 0:
-                    no_box_classes.remove(checking_class)
-                if count >= 1:
-                    # print(f"Multiple instances of {checking_class} detected in frame {current_frame_id}")
-                    # keep the box that is lowest in the frame
-                    lowest_box = temp_boxes[0]
-                    for box in temp_boxes:
-                        if box[3] > lowest_box[3]:
-                            lowest_box = box
-                    self.boxes[checking_class] = lowest_box
-                    if checking_class in ["navel", "breast"]:
-                        self.log_and_normalize_pos(lowest_box, checking_class)
-                        if checking_class == "navel":
-                            self.navel_handling(lowest_box)
-                    self.tracking_states[checking_class] = True
-
-        # Handling irrelevant closeups first
-
-        if self.tracking_states['pussy'] and self.tracking_states['butt']:
-            # position likely to be Closeup
-            self.handle_closeup_position("presence of pussy and butt")
-        elif (self.tracking_states['pussy'] and self.boxes['pussy'] is not None and (
-                self.boxes['pussy'][3] - self.boxes['pussy'][1]) >
-              image_y_size * 0.1):
-            # position likely to be Closeup
-            self.handle_closeup_position("size of pussy in frame")
-
-        # Handling relevant closeups
-        elif self.breast_tracking and not self.glans_detected:
-            if self.boxes['breast'] is not None and not self.boxes_overlap(self.locked_penis_box, self.boxes['breast']):
-                # if self.boxes['breast'] is not None and not self.tracked_objects['breast']['touching']:
-                # experimenting to handle delay in breast tracking when boobs reach peak or low position
-                # projected_frame_id = current_frame_id  # + int(fps//5)
-                # distances[projected_frame_id] = normalized_positions['breast'].get(current_frame_id, 100)
-                distance = self.normalized_positions['breast'][-1]
-                self.distance = self.update_distance(distance)
-                self.tracked_body_part = "breast"
-                self.detect_sex_position_change("Closeup missionary", "breast tracking activated")
-                if not self.penetration:
-                    self.penetration = True
-                    print(f"Penetration started at frame {current_frame_id} as we are tracking closeup breasts")
-
-        # Now handling visible penetrations scenarii
-        elif self.locked_penis_box and self.penetration:
-            if self.boxes['penis'] is not None:
-                # Compare the height of the two boxes and scale it to 0 - 100
-                penis_height = self.boxes['penis'][3] - self.boxes['penis'][1]
-
-                locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
-
-                scale = min(int((penis_height / locked_penis_height) * 100), 100)
-                distance = 100 - scale
-
-                self.positions['penis'].append(distance)
-
-                min_distance, max_distance = min(self.positions['penis']), max(self.positions['penis'])
-                normalized_distance = (100 - int(100 * ((distance - min_distance) / (max_distance - min_distance)))) if min_distance != max_distance else 100
-
-                self.normalized_positions['penis'].append(normalized_distance)
-
-                self.update_distance(self.normalized_positions['penis'][-1])
-            else:
-                # Still handling penetration, but penis is not visible, could be a grinding scene
-                if self.boxes['pussy'] is not None and self.tracking_states['pussy'] and self.boxes[
-                    'breast'] is not None:
-                    # position likely to be CowGirl / Missionary
-                    # defaulting to navel tracking or breast tracking
-                    #if self.tracking_states['navel']:
-                    #    self.handle_CowGirl_Missionary_position("presence of navel", current_frame_id, 'navel')
-                    #el
-                    if self.tracking_states['breast']:
-                        self.handle_CowGirl_Missionary_position("presence of breast", current_frame_id, 'breast')
-                    # self.handle_CowGirl_Missionary_position("presence of pussy and breast", current_frame_id, 'pussy')
-                    if self.tracked_objects['pussy']['touching'] and not self.penetration:
-                        self.penetration = True
-                        print(f"Penetration started at frame {current_frame_id} during pussy grinding")
-                elif self.boxes['butt'] is not None and self.tracking_states['butt']:
-                    # position likely to be Reverse Cowgirl or Doggy / Pronebone
-                    self.handle_RevCowGirl_Doggy_Pronebone_position(
-                        "Doggy / Reverse Cowgirl / Pronebone", "presence of foot", current_frame_id)
-                    """
-                    if self.tracking_states['left foot'] or self.tracking_states['right foot']:
-                        # position likely to be Reverse Cowgirl
-                        self.handle_RevCowGirl_Doggy_Pronebone_position(
-                            "Reverse Cowgirl", "presence of foot", current_frame_id)
-                    elif self.tracking_states['anus']:
-                        self.handle_RevCowGirl_Doggy_Pronebone_position(
-                            "Doggy / Pronebone", "presence of anus", current_frame_id)
-                    else:
-                        self.handle_RevCowGirl_Doggy_Pronebone_position(
-                            "Doggy / Pronebone", "absence of foot", current_frame_id)
-                    """
-                    if self.tracked_objects['butt']['touching'] and not self.penetration:
-                        self.penetration = True
-                        print(f"Penetration started at frame {current_frame_id} during butt grinding")
-                elif self.boxes['navel'] is not None and not self.boxes_overlap(self.locked_penis_box,
-                                                                                self.boxes[
-                                                                                    'navel']) and self.penetration:
-                    # position likely to be CowGirl / Missionary, with occluded pussy
-                    self.handle_CowGirl_Missionary_position("presence of navel during penetration",
-                                                            current_frame_id,
-                                                            'navel')
-                elif self.boxes['breast'] is not None and self.tracking_states[
-                    'breast'] and not self.penetration and \
-                        self.tracked_objects["breast"]['touching']:
-                    # position likely to be Boobjob
-                    self.handle_Boobjob_position("presence of breast", current_frame_id)
-                elif ((self.boxes['face'] is not None and self.tracking_states['face']) or
-                      ((self.boxes['left hand'] is not None and self.tracking_states['left hand']) or
-                       (self.boxes['right hand'] is not None and self.tracking_states[
-                           'right hand']))) and not self.penetration:
-
-                    self.handle_Blowjob_Handjob_position("presence of face or hand", current_frame_id)
-        elif self.locked_penis_box and not self.penetration:
-            # Now handling handjob / blowjob scenarii without mouth penetration
-            if ((self.boxes['face'] is not None and self.tracking_states['face']) or
-                  ((self.boxes['left hand'] is not None and self.tracking_states['left hand']) or
-                   (self.boxes['right hand'] is not None and self.tracking_states[
-                       'right hand']))) and not self.penetration:
-
-                self.handle_Blowjob_Handjob_position("presence of face or hand", current_frame_id)
-
-            elif ((self.boxes['left foot'] is not None and self.tracking_states['left foot']) or (
-                    self.boxes['right foot'] is not None and self.tracking_states[
-                'right foot'])) and not self.penetration:
-                # position likely to be Footjob
-                self.handle_Footjob_position("presence of foot", current_frame_id)
-
-        # Check for non-detection of penis
-        if self.boxes['penis'] is None:
-            self.consecutive_non_detections['penis'] += 1
-            self.consecutive_detections['penis'] = 0  # Reset the detection counter
-
-            # Deactivate locked_penis_box after x consecutive non-detections
-            if self.consecutive_non_detections['penis'] >= self.max_predictions:
-                if self.locked_penis_box is not None:
-                    self.locked_penis_box = None
-                    print(
-                        f"Penis tracking deactivated at frame {current_frame_id} after {self.consecutive_no_penis_detections} consecutive non-detections")
-                    #print("Reinitializing tracker")
-                    #self.__init__(self.class_names)
-                    self.penis_box = None
-
-        # Check for non-detection of glans
-        if self.boxes['glans'] is None:
-            self.consecutive_non_detections['glans'] += 1
-            self.consecutive_detections['glans'] = 0  # Reset the detection counter
-
-
-    def prev_tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
-        #self.frame = frame
-        self.image_y_size = image_y_size
-        self.frame_count += 1
-
-        #if self.frame_count % self.detection_interval == 0:
-        #    self.initialize_trackers(sorted_boxes)
-        #else:
-        #    self.update_trackers()
-
-        # initialize values
-        self.foot_detected = False
-        self.glans_detected = False
-        self.tracking_states = {class_name: False for class_name in class_names}
-        self.boxes = {class_name: None for class_name in class_names}
-
-        #for class_name in class_names:
-        #    # check if class_name is in sorted_boxes
-        #    if any(box[3] == class_name for box in sorted_boxes):
-        #        self.tracking_states[class_name] = True
-
-        for box, conf, cls, class_name in sorted_boxes:
-            if class_name == "glans" and conf > 0.7:
-                self.glans_detected = True
-                if conf > 0.8 and self.penetration:
-                    self.penetration = False
-                    # print(f"Penetration ended at frame {current_frame_id}")
-
-            elif class_name == "penis" and conf > 0.7:
-                self.boxes['penis'] = box
-                self.penis_handling(box, conf, current_frame_id)
-
-            elif class_name == "navel" and conf > 0.7:
-                self.navel_handling(box)
-
-            if class_name in ["breast", "navel"] and conf > 0.7:
-                self.log_and_normalize_pos(box, class_name)
-
-            elif self.locked_penis_box and class_name not in ["penis", "glans"]:
-                if conf > 0.5:
-                    self.update_tracking(class_name, box, None, self.tracked_body_part)
-                else:
-                    fallback_class = {'pussy': 'breast', 'breast': 'navel'}.get(class_name)
-                    self.update_tracking(class_name, None, fallback_class, self.tracked_body_part)
-
-                #if class_name not in ["breast", "navel"] and not self.boxes_overlap(locked_penis_box, box):
-                #    continue
-
-                if class_name not in ["breast", "navel"] and not self.tracked_objects[class_name]['touching']:
-                    continue
-
-                if class_name in ["left foot", "right foot", "left hand", "right hand", "butt", "face", "pussy"] and self.tracked_objects[class_name]['touching']:
-                    self.log_and_normalize_pos(box, class_name)
-
-                self.boxes[class_name] = box
-                self.tracking_states[class_name] = True
-        if self.tracked_body_part == "Nothing":
-            self.update_distance(100)
-
-        if self.tracked_body_part and self.tracked_body_part != "Nothing":
-            if not self.tracking_states[self.tracked_body_part]:
-                if not self.occlusion:
-                    #print(f"Lost tracking of {self.tracked_body_part} at frame {current_frame_id}, suspecting occlusion")
-                    self.occlusion = True
-                self.update_tracking(self.tracked_body_part, None, None, self.tracked_body_part)
-            else:
-                if self.occlusion:
-                    #print(f"Found back {self.tracked_body_part} at frame {current_frame_id}")
-                    self.occlusion = False
-
-        if self.breast_tracking:
-            if self.boxes['breast'] is not None and not self.boxes_overlap(self.locked_penis_box, self.boxes['breast']):
-            #if self.boxes['breast'] is not None and not self.tracked_objects['breast']['touching']:
-                # experimenting to handle delay in breast tracking when boobs reach peak or low position
-                #projected_frame_id = current_frame_id  # + int(fps//5)
-                #distances[projected_frame_id] = normalized_positions['breast'].get(current_frame_id, 100)
-                distance = self.normalized_positions['breast'][-1]
-                self.distance = self.update_distance(distance)
-                self.tracked_body_part = "breast"
-                self.detect_sex_position_change("Closeup missionary", "breast tracking activated")
-                if not self.penetration:
-                    self.penetration = True
-                    print(f"Penetration started at frame {current_frame_id}")
-        else:
-            if self.penis_box is None and self.penetration and self.tracked_body_part == "pussy":
-                # trying to handle pussy occlusion
-                if self.tracking_states['navel']:
-                    print(f"Handling pussy occlusion at frame {current_frame_id}")
-                    #self.update_distance(self.normalized_positions['navel'].get(current_frame_id, 100))
-                    self.update_distance(self.normalized_positions['navel'][-1])
-
-            #elif self.penis_box is not None and self.locked_penis_box is not None:
-            elif self.locked_penis_box:
-                # Dealing with irrelevant positions
-                if self.tracking_states['pussy'] and self.tracking_states['butt']:
-                    # position likely to be Closeup
-                    self.handle_closeup_position("presence of pussy and butt")
-
-                elif (self.tracking_states['pussy'] and (self.boxes['pussy'][3] - self.boxes['pussy'][1]) >
-                      image_y_size * 0.1):
-                    # position likely to be Closeup
-                    self.handle_closeup_position("size of pussy in frame")
-
-                # Now dealing with relevant positions
-                elif self.boxes['pussy'] is not None and self.tracking_states['pussy'] and self.boxes['breast'] is not None:
-                    # position likely to be CowGirl / Missionary
-                    self.handle_CowGirl_Missionary_position("presence of pussy and breast", current_frame_id, 'pussy')
-                    if self.tracked_objects['pussy']['touching'] and not self.penetration:
-                        self.penetration = True
-                        print(f"Penetration started at frame {current_frame_id}")
-
-                elif self.boxes['butt'] is not None and self.tracking_states['butt']:
-                    # position likely to be Reverse Cowgirl or Doggy / Pronebone
-                    if self.tracking_states['left foot'] or self.tracking_states['right foot']:
-                        # position likely to be Reverse Cowgirl
-                        self.handle_RevCowGirl_Doggy_Pronebone_position(
-                            "Reverse Cowgirl","presence of foot", current_frame_id)
-                    elif self.tracking_states['anus']:
-                        self.handle_RevCowGirl_Doggy_Pronebone_position(
-                            "Doggy / Pronebone", "presence of anus", current_frame_id)
-                    else:
-                        self.handle_RevCowGirl_Doggy_Pronebone_position(
-                            "Doggy / Pronebone", "absence of foot",current_frame_id)
-                    if self.tracked_objects['butt']['touching'] and not self.penetration:
-                        self.penetration = True
-                        print(f"Penetration started at frame {current_frame_id}")
-
-                elif ((self.boxes['face'] is not None and self.tracking_states['face']) or
-                      ((self.boxes['left hand'] is not None and self.tracking_states['left hand']) or
-                       (self.boxes['right hand'] is not None and self.tracking_states['right hand']))) and not self.penetration:
-
-                    self.handle_Blowjob_Handjob_position("presence of face or hand", current_frame_id)
-
-                elif self.boxes['navel'] is not None and not self.boxes_overlap(self.locked_penis_box, self.boxes['navel']) and self.penetration:
-                    # position likely to be CowGirl / Missionary, with occluded pussy
-                    self.handle_CowGirl_Missionary_position("presence of navel during penetration", current_frame_id, 'navel')
-
-                elif self.boxes['breast'] is not None and self.tracking_states['breast'] and not self.penetration and self.tracked_objects["breast"]['touching']:
-                    # position likely to be Boobjob
-                    self.handle_Boobjob_position("presence of breast", current_frame_id)
-
-                elif ((self.boxes['left foot'] is not None and self.tracking_states['left foot']) or (
-                        self.boxes['right foot'] is not None and self.tracking_states['right foot'])) and not self.penetration:
-                    # position likely to be Footjob
-                    self.handle_Footjob_position("presence of foot", current_frame_id)
-                #else:
-                #    # Kalman ?
-                #    self.distance = self.previous_distance
-
-        # Check for non-detection of penis
-        if self.boxes['penis'] is None:
-            self.consecutive_no_penis_detections += 1
-            self.consecutive_penis_detections = 0  # Reset the detection counter
-
-            # Deactivate locked_penis_box after x consecutive non-detections
-            if self.consecutive_no_penis_detections >= self.max_predictions:
-                if self.locked_penis_box is not None:
-                    self.locked_penis_box = None
-                    print(
-                        f"Penis tracking deactivated at frame {current_frame_id} after {self.consecutive_no_penis_detections} consecutive non-detections")
-                    #print("Reinitializing tracker")
-                    #self.__init__(self.class_names)
-                    self.penis_box = None
-
-    def normalize_box_area(self, box, frame_width, frame_height):
-        """
-        Normalize the area of a bounding box to a 0-100 scale.
-
-        Parameters:
-            box (tuple): Bounding box in the format (x1, y1, x2, y2).
-            frame_width (int): Width of the video frame.
-            frame_height (int): Height of the video frame.
-
-        Returns:
-            float: Normalized area of the box (0 to 100 scale).
-        """
-        x1, y1, x2, y2 = box
-        # Ensure valid dimensions
-        if x2 <= x1 or y2 <= y1:
-            return 0  # Invalid box
-
-        # Calculate the area of the box
-        box_area = (x2 - x1) * (y2 - y1)
-
-        # Define the maximum possible area (entire frame)
-        max_area = frame_width * frame_height
-
-        # Normalize the area to 0-100
-        normalized_area = 100 * (box_area / max_area)
-
-        # Clamp to the range [0, 100]
-        return max(0, min(100, normalized_area))
-
-    def penis_handling(self, box, conf, current_frame_id):
-        if self.penis_box is None:
-            print(f"Penis detected at frame {current_frame_id} with confidence {conf}")
-
-        self.penis_box = box
-
-        self.consecutive_detections['penis'] += 1
-        self.consecutive_non_detections['penis'] = 0
-
-        px1, py1, px2, py2 = self.penis_box
-        current_height = py2 - py1
-
-        #if self.glans_detected:
-        #    self.locked_penis_height = current_height
-        #el
-        #if current_height > self.locked_penis_height:
-        #    self.locked_penis_height = current_height
-
-        #if self.locked_penis_box:
-        #    self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
-
-        if self.consecutive_detections['penis'] >= self.detections_threshold:
-            if self.locked_penis_box is None or self.glans_detected or current_height > self.locked_penis_height:
-                if self.locked_penis_box is None:
-                    print(f"@{self.current_frame_id} - Locked penis box activated")
-                self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
-                #self.locked_penis_box = self.penis_box
-                self.locked_penis_height = current_height
-
-
-        if self.locked_penis_box:
-            if current_height > self.locked_penis_height:
-                self.locked_penis_height = current_height
-                self.locked_penis_box = self.penis_box
-            if self.penis_box[3] != self.locked_penis_box[3]:
-                self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
-        """
-        if self.consecutive_detections['penis'] >= self.detections_threshold and self.glans_detected:
-            # self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
-            self.locked_penis_box = self.penis_box
-            # print(f"Actuating penis box at frame {current_frame_id}")
-        """
-
-        if self.locked_penis_box and self.penetration:
-            # Compare the height of the two boxes and scale it to 0 - 100
-            penis_height = self.boxes['penis'][3] - self.boxes['penis'][1]
-            locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
-            scale = min(int((penis_height / locked_penis_height) * 100), 100)
-            distance = 100 - scale
-            self.positions['penis'].append(distance)
-            min_distance, max_distance = min(self.positions['penis']), max(self.positions['penis'])
-            normalized_distance = (100 - int(100 * ((distance - min_distance) / (
-                    max_distance - min_distance)))) if min_distance != max_distance else 100
-            self.normalized_positions['penis'].append(normalized_distance)
-
-        if (self.image_y_size - py1) / self.image_y_size < 0.1 and not self.breast_tracking and self.tracked_objects['breast']['detected']:
-            # print("Breast tracking mode activated given penis position in lower 10th of frame")
-            self.breast_tracking = True
-
-        return self.penis_box, self.locked_penis_box, self.breast_tracking
-
-    def glans_handling(self, box, conf, current_frame_id):
-        self.consecutive_detections['glans'] += 1
-        self.consecutive_non_detections['glans'] = 0
-        #print(f"Glans consecutive detections: {self.consecutive_detections['glans']}")
-        if self.consecutive_detections['glans'] >= self.detections_threshold:
-            self.boxes['glans'] = box
-            self.glans_detected = True
-            #print(f"Glans box: {self.boxes['glans']}")
-            if self.penetration:
-                self.penetration = False
-                print(f"Penetration ended at frame {current_frame_id} after {self.consecutive_detections['glans']} detections of glans")
-            #if not self.penetration:
-            #    self.penetration = True
-            #    print(
-            #        f"Penetration started at frame {current_frame_id} after {self.consecutive_non_detections['glans']} consecutive non-detections")
-
-
-    def navel_handling(self, box):
-        if (self.image_y_size - box[1]) / self.image_y_size < 0.15 and not self.breast_tracking:
-            print("Breast tracking mode activated given navel position in lower 15th of frame")
-            self.breast_tracking = True
-
-    def log_and_normalize_pos(self, box, class_name):
-        self.boxes[class_name] = box
-        _, y1, _, y2 = box
-        mid_y = (y1 + y2) / 2
-        self.positions[class_name].append(mid_y)
-
-        min_y, max_y = min(self.positions[class_name]), max(self.positions[class_name])
-        normalized_y = (100 - int(100 * ((mid_y - min_y) / (max_y - min_y)))) if min_y != max_y else 100
-
-        # In case of breast, which is a fallback class to pussy, we compute not only the normalized position
-        # but also the normalized breast area which shows back and forth in case of grinding
-        if class_name == "breast" and self.frame:
-            normalized_breast_area = self.normalize_box_area(box, self.frame.shape[1], self.frame.shape[0])
-            normalized_y = ((0.75 * normalized_breast_area) + normalized_y) / 1.75
-
-        #self.normalized_positions[class_name][current_frame_id] = normalized_y
-        self.normalized_positions[class_name].append(normalized_y)
-        return normalized_y
-
-    def log_and_normalize_distance(self, box, class_name):
-        if self.locked_penis_box is None:
-            locked_penis_box = (0,0,0,0)
-        else:
-            locked_penis_box = self.locked_penis_box
-        distance = self.calculate_distance(locked_penis_box, box)
-        self.distances[class_name].append(distance)
-        min_distance, max_distance = min(self.distances[class_name]), max(self.distances[class_name])
-        normalized_distance = (100 - int(100 * ((distance - min_distance) / (max_distance - min_distance)))) if min_distance != max_distance else 100
-        self.normalized_distances[class_name].append(normalized_distance)
-
-
-    def handle_closeup_position(self, reason):
-        self.distance = 100
-        self.update_distance(self.distance)
-        self.tracked_body_part = "Nothing"
-        self.detect_sex_position_change("Closeup", reason)
-
-    def handle_CowGirl_Missionary_position(self, reason, current_frame_id, body_part):
-        distance = None
-        if body_part == "pussy":
-            if self.penis_box:
-                x1, y1, x2, y2 = self.penis_box
-                if self.boxes['pussy'] is not None:
-                    y_p = self.boxes['pussy'][3]
-                    # if the bottom of pussy box remains in the low 30% of the penis_box
-                    if y_p < y2 and y_p > 0.7 * (y2 - y1):
-                        self.consecutive_grinding_supiscions['pussy'] += 1
-                    else:
-                        self.consecutive_grinding_supiscions['pussy'] = 0
-                        self.grinding = False
-            else:
-                self.consecutive_grinding_supiscions['pussy'] += 1
-            if self.consecutive_grinding_supiscions['pussy'] > self.fps:
-                self.grinding = True
-
-        if self.grinding:
-            # default to a combination of navel and breast
-            #nb_items = 0
-            #distance = 0
-            if self.boxes['navel']:
-                #distance = self.normalized_positions['navel'][-1]
-                distance = self.normalized_distances['navel'][-1]
-                #nb_items += 1
-            #if self.boxes['breast']:
-            #    distance += self.normalized_positions['breast'][-1]
-            #    nb_items += 1
-            #distance = distance // nb_items
-        else:
-            #distance = self.normalized_positions[body_part][-1]
-            distance = self.normalized_distances[body_part][-1]
-
-        self.distance = self.update_distance(distance)
-        self.tracked_body_part = body_part
-        self.detect_sex_position_change("Cowgirl / Missionary", reason)
-
-    def handle_RevCowGirl_Doggy_Pronebone_position(self, position, reason, current_frame_id):
-        distance = None
-        #if self.boxes['butt'][3] > self.locked_penis_box[3]:
-        #    # print(f"Actuating locked penis box to fit butt lower position at frame {current_frame_id}")
-        #    self.locked_penis_box = (
-        #        self.locked_penis_box[0], self.locked_penis_box[1],
-        #        self.locked_penis_box[2], self.boxes['butt'][3])
-        # distance = self.calculate_distance(self.locked_penis_box, self.boxes['butt'], 'butt')
-        if self.normalized_positions['penis'][-1] is not None:
-            distance = self.normalized_positions['penis'][-1]
-        else:
-            #distance = self.calculate_distance(self.locked_penis_box, self.boxes['butt'], 'butt')
-            #distance = self.normalized_distances['butt'][-1]
-            distance = (self.normalized_positions['butt'][-1] + self.normalized_distances['butt'][-1]) // 2
-        self.distance = self.update_distance(distance)
-        self.tracked_body_part = "butt"
-        self.detect_sex_position_change(position, reason)
-
-    def handle_Blowjob_Handjob_position(self, reason, current_frame_id):
-        distance = None
-
-        if self.tracking_states['face'] and self.tracked_objects['face']['touching']:
-            if len(self.face_distances) > 3:
-                self.face_distances.pop(0)
-            #distance = self.calculate_distance(self.locked_penis_box, self.boxes['face'])
-            distance = self.normalized_distances['face'][-1]
-            #print(f"Face distance: {distance}")
-            #self.face_distances.append(distance)
-            self.face_distances.append(self.normalized_distances['face'][-1])
-            self.face_hands_movements['face'] = abs(max(self.face_distances) - min(self.face_distances))
-            # if no left hand or no right hand, return
-            if not self.tracked_objects['left hand']['touching'] and not self.tracked_objects['right hand']['touching']:
-                self.distance = self.update_distance(distance)
-                self.tracked_body_part = "face"
-                self.detect_sex_position_change("Blowjob / Handjob", "presence of face only")
-                return
-        else:
-            self.face_hands_movements['face'] = 0
-
-        if self.tracking_states['left hand'] and self.tracked_objects['left hand']['touching'] and self.boxes['left hand'] is not None:
-            if len(self.left_hand_distances) > 3:
-                self.left_hand_distances.pop(0)
-            #distance = self.calculate_distance(self.locked_penis_box, self.boxes['left hand'])
-            distance = self.normalized_distances['left hand'][-1]
-            #print(f"Left hand distance: {distance}")
-            #self.left_hand_distances.append(distance)
-            self.left_hand_distances.append(self.normalized_distances['left hand'][-1])
-            self.face_hands_movements['left hand'] = abs(max(self.left_hand_distances) - min(self.left_hand_distances))
-            if not self.tracked_objects['right hand']['touching'] and not self.tracked_objects['face']['touching']:
-                self.distance = self.update_distance(distance)
-                self.tracked_body_part = "left hand"
-                self.detect_sex_position_change("Blowjob / Handjob", "presence of left hand only")
-                return
-        else:
-            self.face_hands_movements['left hand'] = 0
-
-        if self.tracking_states['right hand'] and self.tracked_objects['right hand']['touching'] and self.boxes['right hand'] is not None:
-            if len(self.right_hand_distances) > 3:
-                self.right_hand_distances.pop(0)
-            #distance = self.calculate_distance(self.locked_penis_box, self.boxes['right hand'])
-            distance = self.normalized_distances['right hand'][-1]
-            #print(f"Right hand distance: {distance}, self.locked_penis_box: {self.locked_penis_box}, self.boxes['right hand']: {self.boxes['right hand']}")
-            #self.right_hand_distances.append(distance)
-            self.right_hand_distances.append(self.normalized_distances['right hand'][-1])
-            self.face_hands_movements['right hand'] = abs(
-                max(self.right_hand_distances) - min(self.right_hand_distances))
-            if not self.tracked_objects['left hand']['touching'] and not self.tracked_objects['face']['touching']:
-                self.distance = self.update_distance(distance)
-                self.tracked_body_part = "right hand"
-                self.detect_sex_position_change("Blowjob / Handjob", "presence of right hand only")
-                return
-        else:
-            self.face_hands_movements['right hand'] = 0
-
-        # Determine the most moving body part
-        most_moving_part = max(self.face_hands_movements, key=self.face_hands_movements.get, default=None)
-        #print(f"Most moving part: {most_moving_part}")
-        max_movement = self.face_hands_movements.get(most_moving_part, 0)
-        current_tracked_movement = self.face_hands_movements.get(self.tracked_body_part, 0)
-
-        if most_moving_part and most_moving_part != self.tracked_body_part:
-            if (
-                    max_movement > current_tracked_movement * self.switch_threshold_multiplier and
-                    self.switch_cooldown == 0
-            ):
-                if most_moving_part == self.last_tracked_body_part:
-                    self.consecutive_switch_frames += 1
-                else:
-                    self.consecutive_switch_frames = 1  # Reset counter if different part
-                self.last_tracked_body_part = most_moving_part
-
-                if self.consecutive_switch_frames >= self.required_frames_to_switch:
-                    self.tracked_body_part = most_moving_part
-                    self.consecutive_switch_frames = 0
-                    self.switch_cooldown = self.cooldown_frames  # Set cooldown
-                    #print(f"Switching to: {self.tracked_body_part} due to sustained movement")
-            else:
-                self.consecutive_switch_frames = 0  # Reset counter if condition not met
-        else:
-            self.consecutive_switch_frames = 0  # Reset if no significant movement detected
-
-        # Cooldown logic
-        if self.switch_cooldown > 0:
-            self.switch_cooldown -= 1
-
-        # Update position and distance for the tracked body part
-        if self.tracked_body_part in ['face', 'left hand', 'right hand']:
-            self.detect_sex_position_change("Blowjob / Handjob", reason)
-            if self.tracked_body_part == "face":
-                distance = self.face_distances[-1]
-            elif self.tracked_body_part == "left hand":
-                distance = self.left_hand_distances[-1]
-            elif self.tracked_body_part == "right hand":
-                distance = self.right_hand_distances[-1]
-
-            # Smooth transitions with a moving average
-            #if not hasattr(self, 'smoothed_distance'):
-            #    self.smoothed_distance = distance
-            #    print(f"Smoothed distance initialized to {self.smoothed_distance}")
-            #else:
-            #    smoothing_factor = 0.2  # Adjust for smoother transitions (lower = smoother)
-            #    self.smoothed_distance = self.smoothed_distance * (1 - smoothing_factor) + self.distance * smoothing_factor
-            #    print(f"Smoothed distance updated to {self.smoothed_distance}")
-            #self.distance = self.update_distance(self.smoothed_distance)
-
-            self.distance = self.update_distance(distance)
-
-            # Log the tracked information
-            # print(f"Tracked: {self.tracked_body_part}, Position: {self.sex_position}, Distance: {int(self.smoothed_distance)}")
-
-    def handle_Footjob_position(self, reason, current_frame_id):
-        distance = 100
-
-        if self.tracking_states['left foot'] and self.tracked_objects['left foot']['touching']:
-            if len(self.left_foot_distances) > 3:
-                self.left_foot_distances.pop(0)
-            self.left_foot_distances.append(
-                self.calculate_distance(self.locked_penis_box, self.boxes['left foot']))
-            self.foot_movements['left foot'] = abs(max(self.left_foot_distances) - min(self.left_foot_distances))
-
-        if self.tracking_states['right foot'] and self.tracked_objects['right foot']['touching']:
-            if len(self.right_foot_distances) > 3:
-                self.right_foot_distances.pop(0)
-            self.right_foot_distances.append(
-                self.calculate_distance(self.locked_penis_box, self.boxes['right foot']))
-            self.foot_movements['right foot'] = abs(max(self.right_foot_distances) - min(self.right_foot_distances))
-
-        # Determine the most moving foot
-        most_moving_foot = max(self.foot_movements, key=self.foot_movements.get, default=None)
-        max_foot_movement = self.foot_movements.get(most_moving_foot, 0)
-        current_tracked_foot_movement = self.foot_movements.get(self.tracked_body_part, 0)
-
-        if most_moving_foot and most_moving_foot != self.tracked_body_part:
-            # Check if movement is significantly higher than the currently tracked foot
-            if (
-                    max_foot_movement > current_tracked_foot_movement * self.foot_switch_threshold_multiplier and
-                    self.foot_switch_cooldown == 0
-            ):
-                # Increment confidence counter
-                if most_moving_foot == self.last_tracked_foot:
-                    self.consecutive_foot_switch_frames += 1
-                else:
-                    self.consecutive_foot_switch_frames = 1  # Reset counter if different foot
-
-                self.last_tracked_foot = most_moving_foot
-
-                # Switch only if sustained for required frames
-                if self.consecutive_foot_switch_frames >= self.required_foot_frames_to_switch:
-                    self.tracked_body_part = most_moving_foot
-                    self.consecutive_foot_switch_frames = 0
-                    self.foot_switch_cooldown = self.foot_cooldown_frames  # Set cooldown
-                    # print(f"Switching to: {self.tracked_body_part} due to sustained foot movement")
-            else:
-                self.consecutive_foot_switch_frames = 0  # Reset counter if condition not met
-        else:
-            self.consecutive_foot_switch_frames = 0  # Reset if no significant movement detected
-
-        # Cooldown logic
-        if self.foot_switch_cooldown > 0:
-            self.foot_switch_cooldown -= 1
-
-        # Update position and distance for the tracked foot
-        if self.tracked_body_part in ['left foot', 'right foot']:
-            self.detect_sex_position_change("Footjob", reason)
-            if self.tracked_body_part == "left foot":
-                distance = self.left_foot_distances[-1]
-            elif self.tracked_body_part == "right foot":
-                distance = self.right_foot_distances[-1]
-
-        # Smooth transitions with a moving average
-        foot_smoothing_factor = 0.2  # Adjust for smoother transitions (lower = smoother)
-        if hasattr(self, 'smoothed_foot_distance'):
-            self.smoothed_foot_distance = self.smoothed_foot_distance * (
-                        1 - foot_smoothing_factor) + distance * foot_smoothing_factor
-        else:
-            self.smoothed_foot_distance = distance
-
-        # Log the tracked foot information
-        # print(f"Tracked: {self.tracked_body_part}, Position: {self.sex_position}, Distance: {self.smoothed_foot_distance}")
-        self.distance = self.update_distance(self.smoothed_foot_distance)
-
-    def handle_Boobjob_position(self, reason, current_frame_id):
-        #distance = self.calculate_distance(self.locked_penis_box, self.boxes['breast'], 'breast')
-        distance = self.normalized_distances['breast'][-1]
-        self.distance = self.update_distance(distance)
-        self.tracked_body_part = "breast"
-        self.detect_sex_position_change("Boobjob", reason)
-
diff --git a/utils/ObjectTracker_v2.py b/utils/ObjectTracker_v2.py
deleted file mode 100644
index a0dc576c..00000000
--- a/utils/ObjectTracker_v2.py
+++ /dev/null
@@ -1,418 +0,0 @@
-from utils import KalmanFilter as KF
-from collections import deque
-import numpy as np
-from utils.config import class_names
-import math
-import logging
-
-# Configure logging
-logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
-
-
-class ObjectTracker:
-    def __init__(self, fps, frame_pos, state=None):
-        self.class_names = class_names
-        self.tracked_objects = {
-            class_name: {
-                'kf': KF.KalmanFilter(),
-                'position': None,
-                'detected': False,
-                'touching': False,
-                'prediction_count': 0
-            }
-            for class_name in class_names
-        }
-
-        self.distance_kf = KF.KalmanFilter()
-
-        self.frame = None
-        self.current_frame_id = frame_pos
-        self.image_y_size = 0
-        self.fps = fps
-
-        self.penis_box, self.locked_penis_box = None, None
-        self.glans_detected = False
-        self.locked_penis_height = 0
-        self.breast_tracking = False
-        self.distance = 100
-        self.raw_distance = 100
-        self.previous_distances = [100, 100, 100]
-        self.tracked_body_part = "Nothing"
-
-        self.sex_position = "Not relevant"
-        self.prev_sex_position = "Not relevant"
-        self.sex_position_reason = ""
-        self.sex_position_history = deque(maxlen=10)
-        self.sub_sex_position = "Not relevant"
-
-        self.positions = {class_name: deque(maxlen=200) for class_name in class_names}
-        self.distances = {class_name: deque(maxlen=200) for class_name in class_names}
-        self.areas = {class_name: deque(maxlen=200) for class_name in class_names}
-
-        self.normalized_positions = {class_name: deque(maxlen=200) for class_name in class_names}
-        self.normalized_distances = {class_name: deque(maxlen=200) for class_name in class_names}
-        self.activated_kalman = {class_name: False for class_name in class_names}
-
-        self.offsets = {class_name: 0 for class_name in class_names}
-
-        for class_name in class_names:
-            self.normalized_distances[class_name].append(100)
-            self.normalized_positions[class_name].append(100)
-
-        self.normalized_areas = {class_name: deque(maxlen=200) for class_name in class_names}
-
-        self.moving_average_window = 5
-        self.consecutive_detections = {class_name: 0 for class_name in class_names}
-        self.consecutive_non_detections = {class_name: 0 for class_name in class_names}
-        self.detections_threshold = 3
-
-        self.consecutive_grinding_suspicions = {class_name: 0 for class_name in ['pussy', 'butt']}
-
-        self.penetration = False
-        self.grinding = False
-        self.rubbing = False
-
-        self.max_predictions = int(self.fps)
-
-    def update_distance(self, raw_distance):
-        if raw_distance is None:
-            filtered_distance = self._predict_distance()
-        else:
-            rounded_distance = round(raw_distance / 5) * 5
-            ema_distance = self._calculate_ema_distance(rounded_distance)
-            self._update_previous_distances(ema_distance)
-            self.distance_kf.update((ema_distance, 0))
-            filtered_distance = ema_distance
-
-        filtered_distance = max(0, min(100, filtered_distance))
-        self.distance = filtered_distance
-        return filtered_distance
-
-    def _predict_distance(self):
-        return int(float(self.distance_kf.predict()[0]) / 5) * 5
-
-    def _calculate_ema_distance(self, rounded_distance):
-        return int(0.7 * rounded_distance + 0.1 * self.previous_distances[1] + 0.2 * self.previous_distances[2])
-
-    def _update_previous_distances(self, ema_distance):
-        if abs(ema_distance - self.previous_distances[-1]) > 15:
-            ema_distance = self.previous_distances[-1] + np.sign(ema_distance - self.previous_distances[-1]) * 15
-        elif abs(ema_distance - self.previous_distances[-1]) < 2:
-            ema_distance = self.previous_distances[-1]
-        self.previous_distances.pop(0)
-        self.previous_distances.append(ema_distance)
-
-    def update_tracking(self, class_name, box, fallback_class, tracked_item):
-        tracked = self.tracked_objects[class_name]
-        if box is not None:
-            self._handle_detection(tracked, box, class_name)
-        elif class_name == tracked_item and box is None:
-            self._handle_prediction(tracked, class_name, tracked_item)
-
-        if tracked['touching']:
-            self.log_and_normalize_pos(tracked['position'], class_name)
-            self.log_and_normalize_distance(tracked['position'], class_name)
-        else:
-            self._reset_normalized_data(class_name)
-
-        return tracked['position']
-
-    def _handle_detection(self, tracked, box, class_name):
-        tracked['kf'].update([(box[0] + box[2]) / 2, (box[1] + box[3]) / 2])
-        tracked['position'] = box
-        tracked['detected'] = True
-        tracked['half_height'] = (box[3] - box[1]) // 2
-        tracked['half_width'] = (box[2] - box[0]) // 2
-        tracked['touching'] = self.boxes_overlap(self.locked_penis_box, box)
-        tracked['prediction_count'] = 0
-        if class_name == self.tracked_body_part and self.activated_kalman[class_name]:
-            self.activated_kalman[class_name] = False
-            logging.info(f"@{self.current_frame_id} - Stopped Kalman prediction for {class_name} as it was found back")
-
-    def _handle_prediction(self, tracked, class_name, tracked_item):
-        tracked['kf'].predict()
-        if not self.activated_kalman[class_name]:
-            self.activated_kalman[class_name] = True
-            logging.info(f"@{self.current_frame_id} - Activating Kalman prediction for {class_name}")
-        logging.info(
-            f"@{self.current_frame_id} - Kalman prediction for {class_name}: {tracked['prediction_count'] + 1} / {self.max_predictions}")
-        tracked['detected'] = False
-        tracked['position'] = tracked['kf'].position
-        tracked['touching'] = self.boxes_overlap(self.locked_penis_box, tracked['position'])
-
-        if tracked['prediction_count'] >= self.max_predictions:
-            logging.info(f"No fallback available for {class_name}, deactivating tracking")
-            tracked['position'] = None
-            tracked['detected'] = False
-            tracked['touching'] = False
-            tracked['prediction_count'] = 0
-            self.tracked_body_part = 'Nothing'
-
-    def _reset_normalized_data(self, class_name):
-        self.normalized_positions[class_name].clear()
-        self.normalized_distances[class_name].clear()
-        self.normalized_positions[class_name].append(100)
-        self.normalized_distances[class_name].append(100)
-
-    def boxes_overlap(self, box1, box2):
-        if box1 is None or box2 is None:
-            return False
-        x1, y1, x2, y2 = box1
-        x3, y3, x4, y4 = box2
-        return not (x2 < x3 or x4 < x1 or y2 < y3 or y4 < y1)
-
-    def calculate_distance(self, penis_box, other_box):
-        if other_box is None:
-            return None
-        ox1, oy1, ox2, oy2 = other_box
-        y_pos = (oy1 + 2 * oy2) // 3
-        x_pos = (ox1 + ox2) // 2
-        px1, py1, px2, py2 = penis_box
-        return math.sqrt((x_pos - px2) ** 2 + (y_pos - py2) ** 2)
-
-    def detect_sex_position_change(self, sex_position, reason):
-        if self.sex_position_history and self.sex_position_history[-1] == "Blowjob" and self.sub_sex_position == "Handjob":
-            sex_position = "Blowjob"
-        self.sex_position_history.append(sex_position)
-        position_counts = {position: self.sex_position_history.count(position) for position in self.sex_position_history}
-        most_frequent_position = max(position_counts, key=position_counts.get, default="Not relevant")
-        if most_frequent_position != self.sex_position:
-            logging.info(f"@{self.current_frame_id} - Sex position switched to: {most_frequent_position}")
-            self.sex_position = most_frequent_position
-            self.sex_position_reason = reason
-
-    def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
-        self.image_y_size = image_y_size
-        self.current_frame_id = current_frame_id
-
-        self.glans_detected = False
-        self.boxes = {class_name: None for class_name in class_names}
-        all_detections = {class_name: [] for class_name in class_names}
-
-        for box, conf, cls, class_name, track_id in sorted_boxes:
-            all_detections[class_name].append([conf, box, track_id])
-
-        found_box = {class_name: [] for class_name in class_names}
-        classes_touching_penis = {class_name: None for class_name in class_names}
-        list_of_touching_classes = []
-
-        for check_class_first in ['glans', 'penis', 'navel']:
-            prev_conf = 0
-            for conf, box, track_id in all_detections[check_class_first]:
-                if conf > prev_conf:
-                    found_box[check_class_first] = [box, conf, track_id]
-                    prev_conf = conf
-
-        for check_class_first in ['glans', 'penis', 'navel']:
-            box = None
-            if found_box[check_class_first]:
-                self.boxes[check_class_first] = found_box[check_class_first][0]
-                box = self.boxes[check_class_first]
-                conf = found_box[check_class_first][1]
-                self.consecutive_detections[check_class_first] += 1
-                self.consecutive_non_detections[check_class_first] = 0
-                self.handle_class_first(check_class_first, self.boxes[check_class_first], conf)
-            else:
-                self.consecutive_detections[check_class_first] = 0
-                self.consecutive_non_detections[check_class_first] += 1
-
-        prev_class = ''
-        nb_items_touching_penis = 0
-
-        for check_class_second in class_names:
-            if check_class_second in ['glans', 'penis', 'navel']:
-                continue
-            prev_conf = 0
-            for conf, box, track_id in all_detections[check_class_second]:
-                if self.locked_penis_box and self.boxes_overlap(box, self.locked_penis_box) and conf > prev_conf:
-                    classes_touching_penis[check_class_second] = box
-                    prev_conf = conf
-                    if check_class_second != prev_class:
-                        nb_items_touching_penis += 1
-                        list_of_touching_classes.append(check_class_second)
-                        prev_class = check_class_second
-            if prev_conf != 0:
-                self.update_tracking(check_class_second, classes_touching_penis[check_class_second], None,
-                                     self.tracked_body_part)
-
-        if self.tracked_body_part != "Nothing" and self.tracked_body_part not in list_of_touching_classes:
-            self.update_tracking(self.tracked_body_part, None, None, self.tracked_body_part)
-
-        if nb_items_touching_penis == 0:
-            self.handle_closeup('No body parts touching')
-        else:
-            if classes_touching_penis['butt']:
-                if all_detections['pussy'] and self.boxes_overlap(all_detections['pussy'][0][1],
-                                                                  classes_touching_penis['butt']):
-                    self.handle_closeup('Presence of butt and overlapping pussy')
-                elif self.boxes['penis'] and not self.boxes['glans']:
-                    self.tracked_body_part = 'butt'
-                    self.penetration = True
-                    self.detect_sex_position_change('Doggy', 'butt touching penis')
-                    self.sub_sex_position = "Not relevant"
-            elif classes_touching_penis['pussy'] and self.boxes['penis'] and not self.boxes['glans']:
-                self.penetration = True
-                self.tracked_body_part = 'pussy'
-                self.detect_sex_position_change('Cowgirl', 'pussy touching penis')
-                self.sub_sex_position = "Not relevant"
-            elif classes_touching_penis['face']:
-                self.tracked_body_part = 'face'
-                self.detect_sex_position_change('Blowjob', 'face touching penis')
-                self.sub_sex_position = "Not relevant"
-            #elif classes_touching_penis['left hand'] or classes_touching_penis['right hand']:
-            elif classes_touching_penis['left hand']:
-                self.detect_sex_position_change('Handjob', 'hand touching penis')
-                self.sub_sex_position = "Handjob"
-
-            if self.boxes['penis'] is not None:
-                penis_height = self.boxes['penis'][3] - self.boxes['penis'][1]
-                if self.locked_penis_box:
-                    locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
-
-                    for class_name in list_of_touching_classes:
-                        y2 = classes_touching_penis[class_name][3]
-                        self.offsets[class_name] = y2 - self.boxes['penis'][1]
-                    if locked_penis_height > 0:
-                        scale = min(int((penis_height / (0.8 * locked_penis_height)) * 100), 100)
-                    else:
-                        scale = 0
-                    distance = 100 - scale
-
-                    #self.positions['penis'].append(distance)
-                    #min_distance, max_distance = min(self.positions['penis']), max(self.positions['penis'])
-                    #normalized_distance = (100 - int(100 * ((distance - min_distance) / (max_distance - min_distance)))) if min_distance != max_distance else 100
-                    #self.normalized_positions['penis'].append(normalized_distance)
-                    normalized_distance = distance
-                    self.update_distance(normalized_distance)
-            elif self.sex_position == "Cowgirl" and classes_touching_penis['pussy']:
-                locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
-
-                y2 = classes_touching_penis['pussy'][3]
-                y2 -= self.offsets['pussy']
-
-                guessed_penis_height = self.locked_penis_box[3] - y2
-
-                if guessed_penis_height < 0 :
-                    self.offsets['pussy'] = self.locked_penis_box[3] - y2
-                    guessed_penis_height = 0
-
-                scale = min(int((guessed_penis_height / locked_penis_height) * 100), 100)
-                distance = 100 - scale
-
-                normalized_distance = distance
-                self.update_distance(normalized_distance)
-                #self.update_distance(self.normalized_distances['pussy'][-1])
-            elif self.sex_position == "Doggy" and classes_touching_penis['butt']:
-                locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
-
-                y2 = classes_touching_penis['butt'][3]
-                y2 -= self.offsets['butt']
-
-                guessed_penis_height = self.locked_penis_box[3] - y2
-
-                if guessed_penis_height < 0:
-                    self.offsets['butt'] = self.locked_penis_box[3] - y2
-                    guessed_penis_height = 0
-
-                scale = min(int((guessed_penis_height / locked_penis_height) * 100), 100)
-                distance = 100 - scale
-
-                normalized_distance = distance
-                self.update_distance(normalized_distance)
-                #self.update_distance(self.normalized_distances['butt'][-1])
-
-    def handle_class_first(self, class_name, box, conf):
-        if class_name == 'penis':
-            if self.penis_box is None:
-                logging.info(f"Penis detected at frame {self.current_frame_id} with confidence {conf}")
-            self.penis_box = box
-            px1, py1, px2, py2 = self.penis_box
-            current_height = py2 - py1
-            if self.consecutive_detections['penis'] >= self.detections_threshold:
-                if self.locked_penis_box is None or self.glans_detected or current_height > self.locked_penis_height:
-                    if self.locked_penis_box is None:
-                        logging.info(f"Locked penis box activated at frame {self.current_frame_id}")
-                    self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
-                    self.locked_penis_height = current_height
-            if self.locked_penis_box:
-                if current_height > self.locked_penis_height:
-                    self.locked_penis_height = current_height
-                    self.locked_penis_box = self.penis_box
-                if self.penis_box[3] != self.locked_penis_box[3]:
-                    self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
-            if self.locked_penis_box and self.penetration:
-                penis_height = self.boxes['penis'][3] - self.boxes['penis'][1]
-                locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
-                scale = min(int((penis_height / locked_penis_height) * 100), 100)
-                distance = 100 - scale
-                self.positions['penis'].append(distance)
-                min_distance, max_distance = min(self.positions['penis']), max(self.positions['penis'])
-                normalized_distance = (100 - int(100 * ((distance - min_distance) / (max_distance - min_distance)))) if min_distance != max_distance else 100
-                self.normalized_positions['penis'].append(normalized_distance)
-            if (self.image_y_size - py1) / self.image_y_size < 0.1 and not self.breast_tracking and \
-                    self.tracked_objects['breast']['detected']:
-                self.breast_tracking = True
-        elif class_name == 'glans':
-            if self.consecutive_detections['glans'] >= self.detections_threshold:
-                self.boxes['glans'] = box
-                self.glans_detected = True
-                if self.penetration:
-                    self.penetration = False
-                    logging.info(
-                        f"@{self.current_frame_id} - Penetration ended after {self.consecutive_detections['glans']} detections of glans")
-                    if self.tracked_body_part != 'Nothing':
-                        self.normalized_distances[self.tracked_body_part].clear()
-                        self.normalized_distances[self.tracked_body_part].append(100)
-        elif class_name == 'navel':
-            if (self.image_y_size - box[1]) / self.image_y_size < 0.15 and not self.breast_tracking:
-                logging.info("Breast tracking mode activated given navel position in lower 15th of frame")
-                self.breast_tracking = True
-
-    def handle_closeup(self, reason):
-        if self.tracked_body_part != 'Nothing':
-            self.normalized_distances[self.tracked_body_part].clear()
-            self.normalized_distances[self.tracked_body_part].append(100)
-        self.tracked_body_part = 'Nothing'
-        self.detect_sex_position_change('Not relevant', reason)
-        self.sub_sex_position = "Not relevant"
-        self.breast_tracking = False
-        self.penetration = False
-        self.grinding = False
-        self.rubbing = False
-        self.distance = 100
-        self.update_distance(100)
-
-    def normalize_box_area(self, box, frame_width, frame_height):
-        x1, y1, x2, y2 = box
-        if x2 <= x1 or y2 <= y1:
-            return 0
-        box_area = (x2 - x1) * (y2 - y1)
-        max_area = frame_width * frame_height
-        normalized_area = 100 * (box_area / max_area)
-        return max(0, min(100, normalized_area))
-
-    def log_and_normalize_pos(self, box, class_name):
-        self.boxes[class_name] = box
-        _, y1, _, y2 = box
-        mid_y = (y1 + y2) / 2
-        self.positions[class_name].append(mid_y)
-
-        min_y, max_y = min(self.positions[class_name]), max(self.positions[class_name])
-        normalized_y = (100 - int(100 * ((mid_y - min_y) / (max_y - min_y)))) if min_y != max_y else 100
-
-        if class_name == "breast" and self.frame:
-            normalized_breast_area = self.normalize_box_area(box, self.frame.shape[1], self.frame.shape[0])
-            normalized_y = ((0.75 * normalized_breast_area) + normalized_y) / 1.75
-
-        self.normalized_positions[class_name].append(normalized_y)
-        return normalized_y
-
-
-    def log_and_normalize_distance(self, box, class_name):
-        locked_penis_box = self.locked_penis_box or (0, 0, 0, 0)
-        distance = self.calculate_distance(locked_penis_box, box)
-        self.distances[class_name].append(distance)
-        min_distance, max_distance = min(self.distances[class_name]), max(self.distances[class_name])
-        normalized_distance = (100 - int(
-            100 * ((distance - min_distance) / (max_distance - min_distance)))) if min_distance != max_distance else 100
-        self.normalized_distances[class_name].append(normalized_distance)
\ No newline at end of file

From 01b4632e1524b8cf9c34c2efe284290613a535d0 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 29 Dec 2024 12:09:37 +0100
Subject: [PATCH 011/277] init

---
 FSGenerator.py                                  | 2 +-
 utils/{ObjectTracker_v3.py => ObjectTracker.py} | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)
 rename utils/{ObjectTracker_v3.py => ObjectTracker.py} (99%)

diff --git a/FSGenerator.py b/FSGenerator.py
index 13564831..383e3990 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -8,7 +8,7 @@
 import datetime
 
 from utils.config import class_names, class_priority_order, class_reverse_match, class_colors
-from utils.ObjectTracker_v3 import ObjectTracker
+from utils.ObjectTracker import ObjectTracker
 #from utils.ObjectTracker import ObjectTracker
 from utils.FunscriptHandler import FunscriptGenerator
 from utils.Visualizer import Visualizer
diff --git a/utils/ObjectTracker_v3.py b/utils/ObjectTracker.py
similarity index 99%
rename from utils/ObjectTracker_v3.py
rename to utils/ObjectTracker.py
index 632e151a..8348ff1b 100644
--- a/utils/ObjectTracker_v3.py
+++ b/utils/ObjectTracker.py
@@ -1,5 +1,5 @@
 from utils import KalmanFilter as KF
-from collections import deque, defaultdict
+from collections import deque
 import numpy as np
 from utils.config import class_names
 import math

From c4e840daece8c9833bba0b9a61d8b8c8c6477599 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 29 Dec 2024 12:13:34 +0100
Subject: [PATCH 012/277] init

---
 .../Compare_models_and_resolutions.py         |  3 -
 utils/Test_detect_compare_unwarped.py         | 65 +++++++++++++++++++
 2 files changed, 65 insertions(+), 3 deletions(-)
 rename Compare_models_and_resolutions.py => utils/Compare_models_and_resolutions.py (87%)
 create mode 100644 utils/Test_detect_compare_unwarped.py

diff --git a/Compare_models_and_resolutions.py b/utils/Compare_models_and_resolutions.py
similarity index 87%
rename from Compare_models_and_resolutions.py
rename to utils/Compare_models_and_resolutions.py
index e5415a67..49d5867a 100644
--- a/Compare_models_and_resolutions.py
+++ b/utils/Compare_models_and_resolutions.py
@@ -1,6 +1,4 @@
 import cv2
-import numpy as np
-import json
 from ultralytics import YOLO
 
 # Example usage
@@ -10,7 +8,6 @@
     # Replace with your video paths and YOLO models
     video = []
     video.append("/Users/k00gar/Downloads/ARPorn_Sasha Tatcha_Fit and Fired Up_4000p_8K_original_FISHEYE190_alpha_orig.mp4")
-    #video.append("/Users/k00gar/Downloads/ARPorn_Sasha Tatcha_Fit and Fired Up_4000p_8K_original_FISHEYE190_alpha.mp4")
 
     models = []
     models.append("models/k00gar-11n-200ep-best.mlpackage")
diff --git a/utils/Test_detect_compare_unwarped.py b/utils/Test_detect_compare_unwarped.py
new file mode 100644
index 00000000..b6bef585
--- /dev/null
+++ b/utils/Test_detect_compare_unwarped.py
@@ -0,0 +1,65 @@
+from ultralytics import YOLO
+import cv2
+from utils.VideoReaderFFmpeg import VideoReaderFFmpeg
+import numpy as np
+
+# Paths to the model and input image
+model_file = "../models/k00gar-11n-200ep-best.mlpackage"
+#model_file = "models/k00gar-11s-198ep-best.mlpackage"
+#model_file = "models/k00gar-11m-134ep-best.mlpackage"
+#model_file = "models/best-nsfw-porn-v16-11n-273e.mlpackage"
+#model_file = "models/nsfw-identification-yolo10n-best.mlpackage" # interesting for eye nose mouth
+#model_file = "models/own_trained-11n-130ep.mlpackage"
+
+
+video_path = video = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
+
+frame_id = 79080
+
+output_path_1 = video_path[:-4] + "_" + str(frame_id) + "_ffmpeg_unwarped.jpg"  # Save location for the annotated frame
+output_path_2 = video_path[:-4] + "_" + str(frame_id) + "_original.jpg"
+output_path_3 = video_path[:-4] + "_" + str(frame_id) + "_results.jpg"
+
+cap1 = VideoReaderFFmpeg(video_path, is_VR=True)
+cap2 = VideoReaderFFmpeg(video_path, is_VR=False)
+
+cap1.set(cv2.CAP_PROP_POS_FRAMES, frame_id)
+cap2.set(cv2.CAP_PROP_POS_FRAMES, frame_id)
+
+ret1, frame1 = cap1.read()
+ret2, frame2 = cap2.read()
+
+# we will only keep left half of frame2
+frame2 = frame2[:, :frame2.shape[1] // 2, :]
+
+# Create projection maps (only need to do this once)
+map_x, map_y = create_projection_maps(frame2.shape[1], frame2.shape[0])
+
+cap1.release()
+cap2.release()
+
+# Load the YOLO model
+model = YOLO(model_file, task="detect")
+
+# Perform inference
+results = model.predict(frame1)
+
+# Annotate the frame
+annotated_frame_1 = results[0].plot()
+
+# Perform inference
+results = model.predict(frame2)
+
+# Annotate the frame
+annotated_frame_2 = results[0].plot()
+
+# paste both image side by side
+annotated_frame_3 = np.concatenate((annotated_frame_2, annotated_frame_1), axis=1)
+
+# Display the annotated frame
+cv2.imshow("Concatenated result", annotated_frame_3)
+cv2.imwrite(output_path_3, annotated_frame_3)
+
+# Wait for a key press to close the display window
+cv2.waitKey(0)
+cv2.destroyAllWindows()
\ No newline at end of file

From b0b91b2bcfb0e6a51a7b5d9db1e3d4aed4c51489 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 29 Dec 2024 12:28:18 +0100
Subject: [PATCH 013/277] init

---
 FSGenerator.py                                |  75 +---------
 LICENSE                                       |  36 ++---
 README.md                                     | 128 +++++++++++++++++-
 utils/Display_debug_results.py                |   2 +-
 utils/{Debugger.py => lib_Debugger.py}        |   4 +-
 ...riptHandler.py => lib_FunscriptHandler.py} |   0
 .../{KalmanFilter.py => lib_KalmanFilter.py}  |   0
 ...bjectDetector.py => lib_ObjectDetector.py} |   0
 ...{ObjectTracker.py => lib_ObjectTracker.py} |   2 +-
 ...neCutsDetect.py => lib_SceneCutsDetect.py} |   0
 ...aderFFmpeg.py => lib_VideoReaderFFmpeg.py} |   0
 utils/{Visualizer.py => lib_Visualizer.py}    |   0
 ...=> test_Compare_models_and_resolutions.py} |   0
 ...seEstimation.py => test_PoseEstimation.py} |  62 ---------
 ...ped.py => test_detect_compare_unwarped.py} |  11 +-
 15 files changed, 158 insertions(+), 162 deletions(-)
 rename utils/{Debugger.py => lib_Debugger.py} (99%)
 rename utils/{FunscriptHandler.py => lib_FunscriptHandler.py} (100%)
 rename utils/{KalmanFilter.py => lib_KalmanFilter.py} (100%)
 rename utils/{ObjectDetector.py => lib_ObjectDetector.py} (100%)
 rename utils/{ObjectTracker.py => lib_ObjectTracker.py} (99%)
 rename utils/{SceneCutsDetect.py => lib_SceneCutsDetect.py} (100%)
 rename utils/{VideoReaderFFmpeg.py => lib_VideoReaderFFmpeg.py} (100%)
 rename utils/{Visualizer.py => lib_Visualizer.py} (100%)
 rename utils/{Compare_models_and_resolutions.py => test_Compare_models_and_resolutions.py} (100%)
 rename utils/{PoseEstimation.py => test_PoseEstimation.py} (69%)
 rename utils/{Test_detect_compare_unwarped.py => test_detect_compare_unwarped.py} (75%)

diff --git a/FSGenerator.py b/FSGenerator.py
index 383e3990..083a46cb 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -8,13 +8,12 @@
 import datetime
 
 from utils.config import class_names, class_priority_order, class_reverse_match, class_colors
-from utils.ObjectTracker import ObjectTracker
-#from utils.ObjectTracker import ObjectTracker
-from utils.FunscriptHandler import FunscriptGenerator
-from utils.Visualizer import Visualizer
-from utils.Debugger import Debugger
-from utils.SceneCutsDetect import detect_scene_changes
-from utils.VideoReaderFFmpeg import VideoReaderFFmpeg
+from utils.lib_ObjectTracker import ObjectTracker
+from utils.lib_FunscriptHandler import FunscriptGenerator
+from utils.lib_Visualizer import Visualizer
+from utils.lib_Debugger import Debugger
+from utils.lib_SceneCutsDetect import detect_scene_changes
+from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg
 
 # Define the BoxRecord class
 class BoxRecord:
@@ -371,63 +370,6 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
                 print(f"First instance of Glans/Penis found in frame {line[0] - 4}")
                 return line[0] - 4
 
-
-def process_chunk(model_file, video_path, frame_start, frame_end, isVR=False):
-    records = []
-    cap = VideoReaderFFmpeg(video_path, is_VR=isVR)
-    cap.set(cv2.CAP_PROP_POS_FRAMES, frame_start)
-
-    model = YOLO(model_file, task="detect")
-
-    for frame_pos in range(frame_start, frame_end):
-        success, frame = cap.read()
-        if success:
-            if isVR:
-                frame = frame[:, frame.shape[1] // 3:2 * frame.shape[1] // 3, :]
-
-            yolo_results = model.track(frame, persist=True, conf=0.3, verbose=False)
-
-            if yolo_results[0].boxes.cls is None:
-                continue
-
-            boxes = yolo_results[0].boxes.xywh.cpu()
-            classes = yolo_results[0].boxes.cls.cpu().tolist()
-            confs = yolo_results[0].boxes.conf.cpu().tolist()
-
-            for cls, conf, box in zip(classes, confs, boxes):
-                x, y, w, h = box.int().tolist()
-                x1 = x - w // 2
-                y1 = y - h // 2
-                x2 = x + w // 2
-                y2 = y + h // 2
-                record = [frame_pos, int(cls), round(conf, 1), x1, y1, x2, y2]
-                records.append(record)
-
-    cap.release()
-    return records
-
-def extract_yolo_data_parallel(model_file, video_path, frame_start, frame_end=None, isVR=False, num_chunks=4):
-    if frame_end is None:
-        cap = VideoReaderFFmpeg(video_path, is_VR=isVR)
-        frame_end = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-        cap.release()
-
-    chunk_size = (frame_end - frame_start) // num_chunks
-    chunks = [(model_file, video_path, frame_start + i * chunk_size, frame_start + (i + 1) * chunk_size, isVR)
-              for i in range(num_chunks)]
-
-    all_records = []
-    with ProcessPoolExecutor() as executor:
-        futures = [executor.submit(process_chunk, *chunk) for chunk in chunks]
-        for future in tqdm(futures, desc="Processing chunks"):
-            all_records.extend(future.result())
-
-    # Sort records by frame_pos to ensure proper order
-    all_records.sort(key=lambda x: x[0])
-
-    write_dataset(video_path[:-4] + f"_rawyolo.json", all_records)
-
-
 # MAIN logic
 
 if __name__ == '__main__':
@@ -502,10 +444,7 @@ def extract_yolo_data_parallel(model_file, video_path, frame_start, frame_end=No
         # Process the video
 
         # Run the YOLO detection and saves result to _rawyolo.json file
-        #extract_yolo_data(yolo_model, video_path, frame_start, frame_end, TestMode, isVR)
-        #extract_yolo_data_parallel(yolo_model, video_path, frame_start, frame_end, TestMode, isVR)
-
-        # time.sleep(20)  # time to write the file
+        extract_yolo_data(yolo_model, video_path, frame_start, frame_end, TestMode, isVR)
 
         # Load YOLO detection results from file
         yolo_data = load_yolo_data_from_file(video_path[:-4] + f"_rawyolo.json")
diff --git a/LICENSE b/LICENSE
index 2057cb8f..9fdc5593 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,21 +1,23 @@
-MIT License
+# Non-Commercial License
 
-Copyright (c) 2024 ack00gar
+## Terms and Conditions
 
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
+This software and associated documentation files (the "Software") are provided free of charge for personal, non-commercial use only. By using, copying, modifying, or distributing the Software, you agree to the following terms and conditions:
 
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
+### 1. Permitted Use
+   - You are granted a non-exclusive, non-transferable license to use the Software only for personal, non-commercial purposes.
+   - You may modify the Software for your own use, but you may not distribute the modified versions without explicit permission from the copyright holder.
 
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+### 2. Restrictions
+   - Commercial Use Prohibited: You may not use the Software for any commercial purposes, including but not limited to selling, licensing, or monetizing the Software or any derivative works.
+   - Redistribution Prohibited: You may not redistribute the Software or any derivative works, whether modified or unmodified, without explicit permission from the copyright holder.
+   - No Warranty: The Software is provided "as is," without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and non-infringement.
+
+### 3. Ownership
+   - The Software is owned and copyrighted by the original author(s). This license does not transfer ownership of the Software to you.
+
+### 4. Termination
+   - This license is effective until terminated. Your rights under this license will terminate automatically if you fail to comply with any of its terms. Upon termination, you must cease all use of the Software and destroy all copies in your possession.
+
+### 5. Governing Law
+   - This license is governed by and construed in accordance with the laws of [Your Country/State], without regard to its conflict of law principles.
\ No newline at end of file
diff --git a/README.md b/README.md
index b334a680..d8eb603c 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,127 @@
-# VR-Funscript-AI-Generator
\ No newline at end of file
+# VR-Funscript-AI-Generator
+
+Here’s a detailed and professional `README.md` for your GitHub repository. It explains the purpose of the project, how to set it up, and how to use it effectively. This README is designed to be clear and accessible for both technical and non-technical users.
+
+---
+
+# Video Processing with YOLO and Funscript Generation
+
+This project is designed to process video files using YOLO (You Only Look Once) object detection and generate **Funscript** data for interactive applications. It is particularly useful for VR videos, where it can track specific objects (e.g., body parts) and create corresponding motion data.
+
+## Features
+
+- **YOLO Object Detection**: Uses a pre-trained YOLO model to detect and track objects in video frames.
+- **Funscript Generation**: Generates Funscript data based on the tracked objects' movements.
+- **Scene Change Detection**: Automatically detects scene changes in the video to improve tracking accuracy.
+- **Visualization**: Provides real-time visualization of object tracking and Funscript data (in test mode).
+- **VR Support**: Optimized for VR videos, with options to process specific regions of the frame.
+
+## Prerequisites
+
+Before using this project, ensure you have the following installed:
+
+- **Python 3.8 or higher**
+- **FFmpeg** (for video processing)
+- **CUDA** (optional, for GPU acceleration)
+
+## Installation
+
+1. **Clone the repository**:
+   ```bash
+   git clone https://github.com/yourusername/video-processing-yolo-funscript.git
+   cd video-processing-yolo-funscript
+   ```
+
+2. **Install dependencies**:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. **Download the YOLO model**:
+   - Place your YOLO model file (e.g., `k00gar-11n-200ep-best.mlpackage`) in the `models/` directory.
+   - Alternatively, you can specify a custom path to the model using the `--yolo_model` argument.
+
+## Usage
+
+### Basic Command
+
+To process a video, run the following command:
+
+```bash
+python main.py /path/to/video.mp4
+```
+
+### Optional Arguments
+
+- `--yolo_model`: Path to the YOLO model file (default: `models/k00gar-11n-200ep-best.mlpackage`).
+- `--test_mode`: Enable test mode for real-time visualization of object tracking.
+- `--debug_mode`: Enable debug mode to save detailed logs.
+- `--is_vr`: Enable VR mode for processing VR videos.
+
+Example:
+
+```bash
+python main.py /path/to/video.mp4 --yolo_model /path/to/custom_model.mlpackage --test_mode --is_vr
+```
+
+### Output Files
+
+The script generates the following files in the same directory as the input video:
+
+1. `_rawyolo.json`: Raw YOLO detection data.
+2. `_cuts.json`: Detected scene changes.
+3. `_rawfunscript.json`: Raw Funscript data.
+4. `.funscript`: Final Funscript file.
+5. `_heatmap.png`: Heatmap visualization of the Funscript data.
+
+## How It Works
+
+1. **YOLO Detection**:
+   - The script uses a YOLO model to detect and track objects in each video frame.
+   - For VR videos, it processes only the center third of the left half of the frame.
+
+2. **Scene Change Detection**:
+   - Detects scene changes to reset tracking and ensure accuracy.
+
+3. **Tracking and Funscript Generation**:
+   - Tracks specific objects (e.g., body parts) and generates Funscript data based on their movements.
+
+4. **Visualization (Test Mode)**:
+   - Displays bounding boxes and Funscript data in real-time for debugging and verification.
+
+## Example
+
+```bash
+python main.py /path/to/vr_video.mp4 --yolo_model models/k00gar-11n-200ep-best.mlpackage --test_mode --is_vr
+```
+
+This command processes a VR video, detects objects using the specified YOLO model, and generates Funscript data. The `--test_mode` flag enables real-time visualization.
+
+## Contributing
+
+Contributions are welcome! If you'd like to contribute, please follow these steps:
+
+1. Fork the repository.
+2. Create a new branch for your feature or bug fix.
+3. Commit your changes.
+4. Submit a pull request.
+
+## License
+
+This project is licensed under the **Non-Commercial License**. You are free to use the software for personal, non-commercial purposes only. Commercial use, redistribution, or modification for commercial purposes is strictly prohibited without explicit permission from the copyright holder.
+
+See the [LICENSE](LICENSE) file for full details.
+
+## Acknowledgments
+
+- **YOLO**: Thanks to the Ultralytics team for the YOLO implementation.
+- **FFmpeg**: For video processing capabilities.
+- **Eroscripts Community**: For the inspiration and use cases.
+
+## Support
+
+If you encounter any issues or have questions, please open an issue on the [GitHub repository](https://github.com/yourusername/video-processing-yolo-funscript/issues).
+
+---
+
+This README provides a comprehensive overview of the project, making it easy for users to understand, set up, and use the tool. It also encourages contributions and provides support information.
\ No newline at end of file
diff --git a/utils/Display_debug_results.py b/utils/Display_debug_results.py
index 89e5021a..a90ff188 100644
--- a/utils/Display_debug_results.py
+++ b/utils/Display_debug_results.py
@@ -1,4 +1,4 @@
-from Debugger import Debugger
+from lib_Debugger import Debugger
 
 #video="/Users/k00gar/Downloads/BadoinkVR_Lights_Camera_Satisfaction_4K_HEVC_180_180x180_3dh.mp4"
 
diff --git a/utils/Debugger.py b/utils/lib_Debugger.py
similarity index 99%
rename from utils/Debugger.py
rename to utils/lib_Debugger.py
index 7afd4f18..1bb0e6ea 100644
--- a/utils/Debugger.py
+++ b/utils/lib_Debugger.py
@@ -5,8 +5,8 @@
 import numpy as np
 from utils.config import class_colors
 from scipy.interpolate import interp1d
-from utils.Visualizer import Visualizer
-from utils.VideoReaderFFmpeg import VideoReaderFFmpeg
+from utils.lib_Visualizer import Visualizer
+from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg
 
 class Debugger:
     def __init__(self, video_path, output_dir):
diff --git a/utils/FunscriptHandler.py b/utils/lib_FunscriptHandler.py
similarity index 100%
rename from utils/FunscriptHandler.py
rename to utils/lib_FunscriptHandler.py
diff --git a/utils/KalmanFilter.py b/utils/lib_KalmanFilter.py
similarity index 100%
rename from utils/KalmanFilter.py
rename to utils/lib_KalmanFilter.py
diff --git a/utils/ObjectDetector.py b/utils/lib_ObjectDetector.py
similarity index 100%
rename from utils/ObjectDetector.py
rename to utils/lib_ObjectDetector.py
diff --git a/utils/ObjectTracker.py b/utils/lib_ObjectTracker.py
similarity index 99%
rename from utils/ObjectTracker.py
rename to utils/lib_ObjectTracker.py
index 8348ff1b..61b97b7f 100644
--- a/utils/ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -1,4 +1,4 @@
-from utils import KalmanFilter as KF
+from utils import lib_KalmanFilter as KF
 from collections import deque
 import numpy as np
 from utils.config import class_names
diff --git a/utils/SceneCutsDetect.py b/utils/lib_SceneCutsDetect.py
similarity index 100%
rename from utils/SceneCutsDetect.py
rename to utils/lib_SceneCutsDetect.py
diff --git a/utils/VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
similarity index 100%
rename from utils/VideoReaderFFmpeg.py
rename to utils/lib_VideoReaderFFmpeg.py
diff --git a/utils/Visualizer.py b/utils/lib_Visualizer.py
similarity index 100%
rename from utils/Visualizer.py
rename to utils/lib_Visualizer.py
diff --git a/utils/Compare_models_and_resolutions.py b/utils/test_Compare_models_and_resolutions.py
similarity index 100%
rename from utils/Compare_models_and_resolutions.py
rename to utils/test_Compare_models_and_resolutions.py
diff --git a/utils/PoseEstimation.py b/utils/test_PoseEstimation.py
similarity index 69%
rename from utils/PoseEstimation.py
rename to utils/test_PoseEstimation.py
index 6817cd93..a66d82b6 100644
--- a/utils/PoseEstimation.py
+++ b/utils/test_PoseEstimation.py
@@ -1,5 +1,4 @@
 from ultralytics import YOLO
-from PIL import Image
 import numpy as np
 import cv2
 from collections import defaultdict
@@ -22,23 +21,6 @@
 
 frame_pos = timestamp_min * 60 * fps + timestamp_sec * fps
 cap.set(cv2.CAP_PROP_POS_FRAMES, frame_pos)
-"""
-ret, frame = cap.read()
-
-
-results = model(frame)
-# Visualize the results
-for i, r in enumerate(results):
-    # Plot results image
-    im_bgr = r.plot()  # BGR-order numpy array
-    im_rgb = Image.fromarray(im_bgr[..., ::-1])  # RGB-order PIL image
-
-    # Show results to screen (in supported environments)
-    r.show()
-
-    # Save results to disk
-    r.save(filename=f"results{i}.jpg")
-"""
 
 # Store the track history
 track_history = defaultdict(lambda: [])
@@ -47,10 +29,8 @@
 
 output_path = video_path.replace(".mp4", "_posedemo.mp4")
 fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # Codec for MP4
-#out = cv2.VideoWriter(output_path, fourcc, fps, (frame.shape[1] // 2, frame.shape[0]))
 out = cv2.VideoWriter(output_path, fourcc, fps, (frame.shape[1], frame.shape[0]))
 
-
 # Loop through the video frames
 while cap.isOpened():
     # Read a frame from the video
@@ -67,22 +47,13 @@
         pose_results = pose_model.track(frame, persist=True, verbose=False)
         detect_results = detect_model.track(frame, persist=True, verbose=False)
 
-
         frame_1 = pose_results[0].plot()
 
-
-
         # display the results of detect model on the frame also
         frame_2 = detect_results[0].plot("hand")
 
-
-
-        # print(pose_results)
         keypoints = pose_results[0].keypoints.cpu()
-        #print(f"keypoints: {keypoints}")
-        # print("next : ")
         keypoints_list = keypoints.xy.cpu().tolist()
-        #print(f"keypoints list: {keypoints_list}")
         left_hip = keypoints_list[0][11]
         right_hip = keypoints_list[0][12]
         left_wrist = keypoints_list[0][9]
@@ -96,16 +67,12 @@
         if len(track) > 5:  # retain 90 tracks for 90 frames
             track.pop(0)
 
-        #cv2.circle(frame, (int(left_hip[0]), int(left_hip[1])), 5, (0, 255, 0), -1)
-        #cv2.circle(frame, (int(right_hip[0]), int(right_hip[1])), 5, (0, 0, 255), -1)
         cv2.circle(frame, (int(middle_x_frame), int(mid_point[1])), 2, (0, 0, 0), -1)
 
         # Draw the tracking lines
         points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
         cv2.polylines(frame_1, [points], isClosed=False, color=(230, 230, 230), thickness=10)
 
-
-
         track = track_history[2]
 
         track.append((int(left_wrist[0]), int(left_wrist[1])))  # x, y center point
@@ -140,37 +107,8 @@
         if hand_counter < 2:
             print(f"WARNING: {hand_counter} hand detected")
 
-
         frame = np.hstack((frame_1, frame_2))
 
-        """
-        # Get the boxes and track IDs
-        boxes = results[0].boxes.xywh.cpu()
-        track_ids = results[0].boxes.id.int().cpu().tolist()
-
-        keypoints = results[0].keypoints.cpu()
-
-        # Get the track history for each keypoint 11 and 12
-        track_history_11 = track_history[11]
-        track_history_12 = track_history[12]
-
-        # Visualize the results on the frame, plotting only keypoints 11 and 12
-        annotated_frame = results[0].plot(keypoints=[11, 12])
-        #annotated_frame = results[0].plot()
-
-        # Plot the tracks
-        for box, track_id in zip(boxes, track_ids):
-            x, y, w, h = box
-            track = track_history[track_id]
-            track.append((float(x), float(y)))  # x, y center point
-            if len(track) > 30:  # retain 90 tracks for 90 frames
-                track.pop(0)
-
-            # Draw the tracking lines
-            points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
-            cv2.polylines(annotated_frame, [points], isClosed=False, color=(230, 230, 230), thickness=10)
-
-        """
         # Display the annotated frame
         cv2.imshow("YOLO11 Tracking", frame)
 
diff --git a/utils/Test_detect_compare_unwarped.py b/utils/test_detect_compare_unwarped.py
similarity index 75%
rename from utils/Test_detect_compare_unwarped.py
rename to utils/test_detect_compare_unwarped.py
index b6bef585..61655e9e 100644
--- a/utils/Test_detect_compare_unwarped.py
+++ b/utils/test_detect_compare_unwarped.py
@@ -1,16 +1,10 @@
 from ultralytics import YOLO
 import cv2
-from utils.VideoReaderFFmpeg import VideoReaderFFmpeg
+from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg
 import numpy as np
 
 # Paths to the model and input image
 model_file = "../models/k00gar-11n-200ep-best.mlpackage"
-#model_file = "models/k00gar-11s-198ep-best.mlpackage"
-#model_file = "models/k00gar-11m-134ep-best.mlpackage"
-#model_file = "models/best-nsfw-porn-v16-11n-273e.mlpackage"
-#model_file = "models/nsfw-identification-yolo10n-best.mlpackage" # interesting for eye nose mouth
-#model_file = "models/own_trained-11n-130ep.mlpackage"
-
 
 video_path = video = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
 
@@ -32,9 +26,6 @@
 # we will only keep left half of frame2
 frame2 = frame2[:, :frame2.shape[1] // 2, :]
 
-# Create projection maps (only need to do this once)
-map_x, map_y = create_projection_maps(frame2.shape[1], frame2.shape[0])
-
 cap1.release()
 cap2.release()
 

From 0187125a753b1042ccbb4797c3500e8260a607a9 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 29 Dec 2024 13:40:12 +0100
Subject: [PATCH 014/277] init

---
 FSGenerator.py                 |  74 +------
 utils/config.py                |   4 +
 utils/lib_FunscriptHandler.py  |  80 +++++---
 utils/lib_ObjectTracker_v2.py  | 354 +++++++++++++++++++++++++++++++++
 utils/lib_VideoReaderFFmpeg.py |   3 +-
 5 files changed, 414 insertions(+), 101 deletions(-)
 create mode 100644 utils/lib_ObjectTracker_v2.py

diff --git a/FSGenerator.py b/FSGenerator.py
index 083a46cb..c8fd0172 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -4,11 +4,9 @@
 import json
 from tqdm import tqdm
 from ultralytics import YOLO
-from concurrent.futures import ProcessPoolExecutor
-import datetime
 
 from utils.config import class_names, class_priority_order, class_reverse_match, class_colors
-from utils.lib_ObjectTracker import ObjectTracker
+from utils.lib_ObjectTracker_v2 import ObjectTracker
 from utils.lib_FunscriptHandler import FunscriptGenerator
 from utils.lib_Visualizer import Visualizer
 from utils.lib_Debugger import Debugger
@@ -95,7 +93,6 @@ def extract_yolo_data(model_file, video_path, frame_start, frame_end=None, TestM
 
     #cap = cv2.VideoCapture(video_path)
     cap = VideoReaderFFmpeg(video_path, is_VR=isVR)
-    # cap = FFmpegVideoCapture(video_path)
     cap.set(cv2.CAP_PROP_POS_FRAMES, frame_start)
 
     if frame_end:
@@ -113,16 +110,12 @@ def extract_yolo_data(model_file, video_path, frame_start, frame_end=None, TestM
 
         if success:
             if isVR:
-                # only keep the center third of the left half of the frame
-                #frame = frame[:, frame.shape[1] // 6:frame.shape[1] // 3, :]
                 frame = frame[:, frame.shape[1] // 3:2*frame.shape[1] // 3, :]
 
             # Run YOLO11 tracking on the frame, persisting tracks between frames
-            # yolo_results = model.predict(frame, verbose=False, conf=0.5)  #track(frame, persist=True, verbose=False)
             yolo_results = model.track(frame, persist=True, conf=0.3, verbose=False)
 
             if yolo_results[0].boxes.id is None:  # in case of tracking
-            # if yolo_results[0].boxes.cls is None:  # in case of detection
                 continue
 
             if len(yolo_results[0].boxes) == 0 and not TestMode:
@@ -135,14 +128,12 @@ def extract_yolo_data(model_file, video_path, frame_start, frame_end=None, TestM
             confs = yolo_results[0].boxes.conf.cpu().tolist()
 
             for track_id, cls, conf, box in zip(track_ids, classes, confs, boxes):
-            #for cls, conf, box in zip(classes, confs, boxes):
                 track_id = int(track_id)
                 x, y, w, h = box.int().tolist()
                 x1 = x - w // 2
                 y1 = y - h // 2
                 x2 = x + w // 2
                 y2 = y + h // 2
-                #record = [frame_pos, int(track_id), int(cls), round(conf, 1), x1, y1, x2, y2]
                 record = [frame_pos, int(cls), round(conf, 1), x1, y1, x2, y2, track_id]
                 records.append(record)
                 if TestMode:
@@ -151,12 +142,6 @@ def extract_yolo_data(model_file, video_path, frame_start, frame_end=None, TestM
                     test_box = [[x1, y1, x2, y2], round(conf, 1), int(cls), class_reverse_match.get(int(cls), 'unknown'), track_id]
                     print(f"Test box: {test_box}")
                     test_result.add_record(frame_pos, test_box)
-                #if DebugMode:
-                #    debugger.log_frame(frame_pos,
-                #                       bounding_boxes={'box': [x1, y1, x2, y2],
-                #                                       'conf': conf,
-                #                                       'class_id': int(cls),
-                #                                       'class_name': class_reverse_match.get(int(cls), 'unknown')})
 
             if TestMode:
                 # display the results from YOLO
@@ -173,7 +158,6 @@ def extract_yolo_data(model_file, video_path, frame_start, frame_end=None, TestM
                     color = class_colors.get(box[3])
                     cv2.rectangle(frame_display, (box[0][0], box[0][1]), (box[0][2], box[0][3]), color, 2)
                     cv2.putText(frame_display, f"{box[4]}: {box[3]}", (box[0][0], box[0][1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
-                    #cv2.putText(frame, f"{int(cls)}: {int(conf*100)}%", (record[4], record[5] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
                 cv2.imshow("YOLO11 test boxes Tracking", frame_display)
                 if cv2.waitKey(1) & 0xFF == ord('q'):
                     break
@@ -208,10 +192,8 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start = No
     #cap = cv2.VideoCapture(video_path)
     cap = VideoReaderFFmpeg(video_path, is_VR=isVR)
     fps = cap.get(cv2.CAP_PROP_FPS)
-    # cap = FFmpegVideoCapture(video_path)
     nb_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
 
-    #offset_x = int(image_x_size / 3)
 
     if not frame_start:
         frame_start = 0
@@ -282,11 +264,11 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start = No
                                    variables={
                                        'distance': tracker.distance,
                                        'Penetration': tracker.penetration,
-                                       'Grinding': tracker.grinding,
+                                       #'Grinding': tracker.grinding,
                                        'sex_position': tracker.sex_position,
                                        'sex_position_reason': tracker.sex_position_reason,
                                        'tracked_body_part': tracker.tracked_body_part,
-                                       'locked_penis_box': tracker.locked_penis_box,
+                                       'locked_penis_box': tracker.locked_penis_box.to_dict(),
                                        'glans_detected': tracker.glans_detected,
                                        'cons._glans_detections': tracker.consecutive_detections['glans'],
                                        'cons._glans_non_detections': tracker.consecutive_non_detections['glans'],
@@ -297,22 +279,14 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start = No
 
         if TestMode:
             ret, frame = cap.read()
-            #frame = frame[:, :frame.shape[1] // 2, :]
             frame_display = frame.copy()
 
             for box in tracker.tracked_boxes:
-                #print(f"box[0]: {box[0]}, box[1]: {box[1]}, box[2]: {box[2]}")
                 frame_display = visualizer.draw_bounding_box(frame_display,
                                                              box[0],
                                                              str(box[2]) + ": " + box[1],
                                                              class_colors[str(box[1])],
                                                              offset_x)
-            #if tracker.tracked_body_part in class_names and tracker.boxes[tracker.tracked_body_part] is not None:
-            #    frame_display = visualizer.draw_bounding_box(frame_display,
-            #                                              tracker.boxes[tracker.tracked_body_part],
-            #                                              tracker.tracked_body_part,
-            #                                              class_colors[tracker.tracked_body_part],
-            #                                              offset_x)
             if tracker.locked_penis_box is not None:
                 frame_display = visualizer.draw_bounding_box(frame_display, tracker.locked_penis_box,
                                                               "Locked_Penis",
@@ -338,13 +312,11 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start = No
         if i != 0:
             points += ","
         points += f"[{funscript_frames[i]}, {funscript_distances[i]}]"
-        # points += f'{{funscript_frames[i]}, {funscript_distances[i]}}'
     points += "]"
     # write the raw data to a json file
     with open(video_path[:-4] + f"_rawfunscript.json", 'w') as f:
-        #json.dump(points, f)
         json.dump(funscript_data, f)
-    return funscript_data #funscript_frames, funscript_distances
+    return funscript_data
 
 
 def parse_yolo_data_looking_for_penis(data, start_frame):
@@ -354,7 +326,6 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
     for line in data:
         if line[0] >= start_frame and line[1] == 0 and line[2] >= 0.5:
             penis_frame = line[0]
-        #if line[0] >= start_frame and line[1] == 1 and line[2] >= 0.5:  # actually, Glans --class_types.get("penis"):
         if line[0] == penis_frame and line[1] == 1 and line[2] >= 0.5:
             if frame_detected == 0:
                 frame_detected = line[0]
@@ -376,40 +347,13 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
 
     # YOLO model file
     yolo_model = "models/k00gar-11n-200ep-best.mlpackage"
-    #yolo_model = "models/k00gar-11s-198ep-best.mlpackage"
-    #yolo_model = "models/k00gar-11m-134ep-best.mlpackage"
 
     funscript_data = []
 
     video_list = []
-    # video_list.append("/Users/k00gar/Downloads/wankzvr-shocum-180_180x180_3dh_LR.mp4")
-    # video_list.append("/Users/k00gar/Downloads/ARPorn_Angel Youngs_Angel Of Nurse_4000p_2K_original_FISHEYE190_alpha.mp4")
-    # video_list.append("/Users/k00gar/Downloads/Milfvr - Honeymoon In Vega - Vanessa Vega_1080p.mp4")
-    # video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_Sugar-baby_ Drooling Kitty_1920p_48559_FISHEYE190.mp4")
-    # video_list.append("/Volumes/Crucial/pn/EBVR-018-B_AI_upscaled.mp4")
-    # video_list.append("/Volumes/Crucial/pn/NaugthyAmericaVR.My.Wifes.Hot.Friend.23.09.15.Blake.Blossom.Oculus.8k.4096.mp4")
-    # video_list.append("/Volumes/WD Elements/VRPn/CzechVR.723.I.m.Here.to.Serve.Agatha.Vega.Oculus.8K..mp4")
-    # video_list.append("/Volumes/WD Elements/VRPn/721.czechvr.3d.7680x3840.60fps.oculusrift_uhq_h265.mp4")
-    # video_list.append("/Users/k00gar/Downloads/JVR_100199.mp4")
-    # video_list.append("/Users/k00gar/Downloads/JVR_100200.mp4")
-    # video_list.append("/Users/k00gar/Downloads/JVR_100201.mp4")
-    # video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_Poolside Romance_1920p_39107_FISHEYE190.mp4")
-    # video_list.append("/Users/k00gar/Downloads/SLR_UpCloseVR_UP Close VR with Connie Perignon_1920p_49954_LR_180.mp4")
-    # video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_CJ Miles Sexperience_1920p_33250_MKX200.mp4")
-    # video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_She Gives The Best Gifts_1920p_35951_MKX200.mp4")
-    #video_list.append("/Users/k00gar/Downloads/VRLatina_Innocence_Unveiled_2000p_180_180x180_3dh_LR.mp4")
-    #video_list.append("/Users/k00gar/Downloads/Katrina Jade [ADD] Oct 16, 2024R_6kvr265_reenc.mp4")
-    #video_list.append("/Users/k00gar/Downloads/milfvr-daisy fuentes-jizzlejuice-jizzlejuice-3600p-180_180x180_3dh_LR_reenc.mp4")
-    #video_list.append("/Users/k00gar/Downloads/BaDoinkVR_Peaches_n_Cream_5k_180_180x180_3dh_LR.mp4")
-    # video_list.append("/Users/k00gar/Downloads/730-czechvr-3d-7680x3840-60fps-oculusrift_uhq_h265.mp4")
-    #video_list.append("/Users/k00gar/Downloads/ARPorn_Sasha Tatcha_Fit and Fired Up_4000p_8K_original_FISHEYE190_alpha.mp4")
-    #video_list.append("/Users/k00gar/Downloads/WAVR 278 A - Ichika Matsumoto, Aoi Kururugi, Mitsuki Nagisa - 3 Little Sisters.mp4")
-    #video_list.append("/Users/k00gar/Downloads/HNVR 097 B.mp4")
-    #video_list.append("/Users/k00gar/Downloads/BadoinkVR_Lights_Camera_Satisfaction_4K_HEVC_180_180x180_3dh.mp4")
-    #video_list.append("/Users/k00gar/Downloads/2022-09-09 - TonightsGirlfriend - Kenna James.mp4")
-    #video_list.append("/Users/k00gar/Downloads/VideoFile.mp4")
+
     video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4")
-    # video_list.append("/Users/k00gar/Downloads/The Ultimate Furry VR Porn Compilation.mp4")
+
 
     TestMode = False
     DebugMode = True
@@ -444,7 +388,7 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
         # Process the video
 
         # Run the YOLO detection and saves result to _rawyolo.json file
-        extract_yolo_data(yolo_model, video_path, frame_start, frame_end, TestMode, isVR)
+        #extract_yolo_data(yolo_model, video_path, frame_start, frame_end, TestMode, isVR)
 
         # Load YOLO detection results from file
         yolo_data = load_yolo_data_from_file(video_path[:-4] + f"_rawyolo.json")
@@ -471,8 +415,4 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
         # Simplifying the funscript data and generating the file
         funscript_handler.generate(video_path[:-4] + f"_rawfunscript.json", funscript_data, fps, TestMode)
 
-        # generate heatmap
-        funscript_handler.generate_heatmap(video_path[:-4] + f".funscript", video_path[:-4] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
-
         print(f"Finished processing video: {video_path}")
-
diff --git a/utils/config.py b/utils/config.py
index d42d94fc..7806a863 100644
--- a/utils/config.py
+++ b/utils/config.py
@@ -1,3 +1,7 @@
+# ffmpeg and ffprobe paths
+ffmpeg_path = "/usr/local/bin/ffmpeg"
+ffprobe_path = "/usr/local/bin/ffprobe"
+
 # Class types
 class_types = {'face': 0, 'hand': 1, 'penis': 2, 'glans': 3, 'pussy': 4, 'butt': 5,
                        'anus': 6, 'breast': 7, 'navel': 8, 'foot': 9}
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 7c83ef5d..62a6c234 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -33,17 +33,16 @@ def generate(self, raw_funscript_path, funscript_data, fps, TestMode = False):
 
             print(f"Lenghth of filtered positions: {len(self.filtered_positions) + 1}")
 
-            #output_path = raw_funscript_path[:-18] + '_vw_' + str(filter_coeff) + '.funscript'
-            #output_path = raw_funscript_path[:-18] + '_vw_' + str(filter_coeff) + '.funscript'
-
             self.write_funscript(self.filtered_positions, output_path, fps)
 
             print(f"Funscript generated and saved to {output_path}")
 
+            self.generate_heatmap(output_path, output_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
+
         except:
             print(f"Error loading raw funscript from {raw_funscript_path}")
-        """
-        # for alternative version, make every point 0 if distance is under 20
+
+        # for alternative version, make every point 0 if distance is under 10
         points_v2 = []
         multiplier = 1.2
         for i in range(len(self.filtered_positions)):
@@ -57,9 +56,9 @@ def generate(self, raw_funscript_path, funscript_data, fps, TestMode = False):
             # print(f"Point {i}: was {self.filtered_positions[i][1]}, now is {distance}")
             points_v2.append((self.filtered_positions[i][0], distance))
 
-        write_path = output_path[:-10] + '_remapped.funscript'
-        self.write_funscript(points_v2, write_path, fps)
-        """
+        remapped_path = output_path[:-10] + '_remapped.funscript'
+        self.write_funscript(points_v2, remapped_path, fps)
+        self.generate_heatmap(remapped_path, remapped_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
 
         #if TestMode:
         #    # plot a comparative graph
@@ -257,6 +256,7 @@ def load_funscript(self, funscript_path):
 
         return times, positions, relevant_chapters_export, irrelevant_chapters_export
 
+
     def generate_heatmap(self, funscript_path, output_image_path):
         # Load funscript data
         times, positions, _, _ = self.load_funscript(funscript_path)
@@ -268,25 +268,37 @@ def generate_heatmap(self, funscript_path, output_image_path):
         print(f"Times: {times}")
         print(f"Positions: {positions}")
         print(f"Total Actions: {len(times)}")
-        print(f"Time Range: {times[0]} to {datetime.timedelta(seconds = times[-1]/1000)}")
+        print(f"Time Range: {times[0]} to {datetime.timedelta(seconds=int(times[-1] / 1000))}")
 
         # Calculate speed (position change per time interval)
         speeds = np.abs(np.diff(positions) / np.diff(times)) * 1000  # Positions per second
         print(f"Speeds: {speeds}")
 
-        # Define custom colormap: blue -> green -> yellow -> orange -> red -> purple
-        colors = [
-            (0, 0, 1),    # Blue
-            (0, 1, 0),    # Green
-            (1, 1, 0),    # Yellow
-            (1, 0.5, 0),  # Orange
-            (1, 0, 0),    # Red
-            (0.5, 0, 1)   # Purple
+        # Define custom colormap based on Lucife's heatmapColors
+        heatmap_colors = [
+            [0, 0, 0],  # Black (no action)
+            [30, 144, 255],  # Dodger blue
+            [34, 139, 34],  # Lime green
+            [255, 215, 0],  # Gold
+            [220, 20, 60],  # Crimson
+            [147, 112, 219],  # Medium purple
+            [37, 22, 122]  # Dark blue
         ]
-        cmap = mcolors.LinearSegmentedColormap.from_list("custom_gradient", colors)
-
-        # Normalize speeds for colormap
-        norm = mcolors.Normalize(vmin=np.min(speeds), vmax=np.max(speeds))
+        step_size = 120  # Speed step size for color transitions
+
+        def get_color(intensity):
+            if intensity <= 0:
+                return heatmap_colors[0]
+            if intensity > 5 * step_size:
+                return heatmap_colors[6]
+            intensity += step_size / 2.0
+            index = int(intensity // step_size)
+            t = (intensity - index * step_size) / step_size
+            return [
+                heatmap_colors[index][0] + (heatmap_colors[index + 1][0] - heatmap_colors[index][0]) * t,
+                heatmap_colors[index][1] + (heatmap_colors[index + 1][1] - heatmap_colors[index][1]) * t,
+                heatmap_colors[index][2] + (heatmap_colors[index + 1][2] - heatmap_colors[index][2]) * t
+            ]
 
         # Create figure and plot
         plt.figure(figsize=(30, 2))
@@ -301,15 +313,16 @@ def generate_heatmap(self, funscript_path, output_image_path):
             speed = speeds[i]
 
             # Get color based on speed
-            line_color = cmap(norm(speed))
+            color = get_color(speed)
+            line_color = (color[0] / 255, color[1] / 255, color[2] / 255)  # Normalize to [0, 1]
 
             # Plot the line
             ax.plot([x_start, x_end], [y_start, y_end], color=line_color, linewidth=2)
 
         # Customize plot
-        ax.set_title(f'Funscript Speed Visualization\nDuration: {datetime.timedelta(seconds = int(times[-1]/1000))} - Avg. Speed {int(np.mean(speeds))} - Actions: {len(times)}')
+        ax.set_title(
+            f'Funscript Heatmap\nDuration: {datetime.timedelta(seconds=int(times[-1] / 1000))} - Avg. Speed {int(np.mean(speeds))} - Actions: {len(times)}')
         ax.set_xlabel('Time (s)')
-        #ax.set_ylabel('Position')
         ax.set_yticks(np.arange(0, 101, 10))
         ax.set_xlim(times[0] / 1000, times[-1] / 1000)
         ax.set_ylim(0, 100)
@@ -318,25 +331,26 @@ def generate_heatmap(self, funscript_path, output_image_path):
         for spine in ax.spines.values():
             spine.set_visible(False)
 
-        # Add colorbar (optional, currently commented out)
-        # sm = ScalarMappable(cmap=cmap, norm=norm)
-        # sm.set_array([])
-        # cbar = plt.colorbar(sm, ax=ax, orientation='horizontal', pad=0.2)
-        # cbar.set_label('Speed (positions/s)')
-
         # Add colorbar
+        cmap = mcolors.LinearSegmentedColormap.from_list("custom_heatmap", [
+            (heatmap_colors[i][0] / 255, heatmap_colors[i][1] / 255, heatmap_colors[i][2] / 255) for i in
+            range(len(heatmap_colors))
+        ])
+        norm = mcolors.Normalize(vmin=0, vmax=5 * step_size)
         sm = ScalarMappable(cmap=cmap, norm=norm)
         sm.set_array([])
-        #cbar = plt.colorbar(sm, ax=ax, orientation='horizontal', pad=0.2)
+        #cbar = plt.colorbar(sm, ax=ax, orientation='horizontal', pad=0.2,
+        #                    ticks=np.arange(0, 5 * step_size + 1, step_size))
         #cbar.set_label('Speed (positions/s)')
 
         # Save the figure
         plt.savefig(output_image_path, bbox_inches='tight', dpi=200)  # Increase resolution
         plt.close()
 
-        print(f"Colored line visualization saved to {output_image_path}")
+        print(f"Funscript heatmap saved to {output_image_path}")
+
+
 
-# if main
 if __name__ == "__main__":
     video_path = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
 
diff --git a/utils/lib_ObjectTracker_v2.py b/utils/lib_ObjectTracker_v2.py
new file mode 100644
index 00000000..5848886f
--- /dev/null
+++ b/utils/lib_ObjectTracker_v2.py
@@ -0,0 +1,354 @@
+from utils import lib_KalmanFilter as KF
+from collections import deque
+import numpy as np
+from utils.config import class_names
+import math
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+
+class LockedPenisBox:
+    """
+    A class to manage the locked penis box, which represents the detected penis area.
+    It stores the box coordinates, height, and active status.
+    """
+    def __init__(self):
+        self.box = None  # Coordinates of the locked penis box
+        self.height = 0  # Height of the locked penis box
+        self.active = False  # Whether the locked penis box is active
+
+    def update(self, new_box, new_height):
+        """Update the locked penis box with new coordinates and height."""
+        self.box = new_box
+        self.height = new_height
+        self.active = True
+
+    def deactivate(self):
+        """Deactivate the locked penis box."""
+        self.box = None
+        self.height = 0
+        self.active = False
+
+    def get_box(self):
+        """Return the coordinates of the locked penis box."""
+        return self.box
+
+    def get_height(self):
+        """Return the height of the locked penis box."""
+        return self.height
+
+    def is_active(self):
+        """Check if the locked penis box is active."""
+        return self.active
+
+    def to_dict(self):
+        """
+        Convert the LockedPenisBox object into a dictionary for JSON serialization.
+        """
+        return {
+            "box": self.box,
+            "height": self.height,
+            "active": self.active
+        }
+
+    @classmethod
+    def from_dict(cls, data):
+        """
+        Reconstruct a LockedPenisBox object from a dictionary.
+        """
+        locked_box = cls()
+        if data["box"] is not None:
+            locked_box.update(data["box"], data["height"])
+        return locked_box
+
+class ObjectTracker:
+    """
+    A class to track objects (e.g., penis, glans, etc.) in a video frame and determine their positions and interactions.
+    """
+    def __init__(self, fps, frame_pos, state=None):
+        # Initialize class attributes
+        self.class_names = class_names  # List of class names to track
+        self.active_tracks = {}  # Active tracks: {track_id: Track object}
+        self.inactive_tracks = []  # Inactive tracks (lost tracks)
+        self.distance_kf = KF.KalmanFilter()  # Kalman filter for distance smoothing
+
+        self.frame = None  # Current video frame
+        self.current_frame_id = frame_pos  # Current frame ID
+        self.image_y_size = 0  # Height of the video frame
+        self.fps = fps  # Frames per second of the video
+
+        # Speed and distance thresholds
+        self.max_speed = 100 / (.18 * self.fps)  # Maximum allowed speed for distance changes
+        self.max_allowed = 100  # Maximum allowed distance
+
+        # Tracking state
+        self.boxes = {}  # Detected boxes for each class
+        self.tracked_boxes = []  # List of tracked boxes
+        self.penis_box = None  # Detected penis box
+        self.locked_penis_box = LockedPenisBox()  # Locked penis box
+        self.glans_detected = False  # Whether the glans is detected
+        self.breast_tracking = False  # Whether breast tracking is active
+        self.distance = 100  # Current distance
+        self.previous_distances = [100, 100, 100]  # Previous distances for smoothing
+        self.tracked_body_part = "Nothing"  # Currently tracked body part
+
+        # Sex position tracking
+        self.sex_position = "Not relevant"  # Current sex position
+        self.sex_position_reason = ""  # Reason for the current sex position
+        self.sex_position_history = deque(maxlen=120)  # History of sex positions
+
+        # Position and distance tracking
+        self.positions = {class_name: deque(maxlen=200) for class_name in class_names}  # Positions for each class
+        self.distances = {class_name: deque(maxlen=200) for class_name in class_names}  # Distances for each class
+        self.areas = {class_name: deque(maxlen=200) for class_name in class_names}  # Areas for each class
+
+        # Normalized positions and distances
+        self.normalized_positions = {class_name: deque(maxlen=200) for class_name in class_names}
+        self.normalized_distances = {class_name: deque(maxlen=200) for class_name in class_names}
+
+        # Tracked positions and normalized tracked positions
+        self.tracked_positions = {}
+        self.normalized_tracked_positions = {}
+
+        # Initialize normalized distances and positions
+        for class_name in class_names:
+            self.normalized_distances[class_name].append(100)
+            self.normalized_positions[class_name].append(100)
+
+        # Detection thresholds
+        self.consecutive_detections = {class_name: 0 for class_name in class_names}  # Consecutive detections for each class
+        self.consecutive_non_detections = {class_name: 0 for class_name in class_names}  # Consecutive non-detections for each class
+        self.detections_threshold = 3  # Threshold for considering a detection valid
+
+        # Penetration state
+        self.penetration = False  # Whether penetration is detected
+
+        # Prediction and tracking thresholds
+        self.max_predictions = int(self.fps)  # Maximum number of predictions
+        self.max_inactive_frames = int(fps * 2)  # Maximum inactive frames before a track is lost
+        self.distance_threshold = 50  # Distance threshold for tracking
+
+    def update_distance(self, raw_distance):
+        """
+        Update the tracked distance using a Kalman filter and speed constraints.
+        """
+        if raw_distance is None:
+            filtered_distance = self._predict_distance()
+        else:
+            if abs(self.distance - raw_distance) > self.max_speed:
+                raw_distance = self.distance + np.sign(raw_distance - self.distance) * self.max_speed
+            filtered_distance = raw_distance
+
+        filtered_distance = int(max(0, min(100, filtered_distance)))
+        self.distance = filtered_distance
+        return filtered_distance
+
+    def _predict_distance(self):
+        """Predict the distance using the Kalman filter."""
+        return int(float(self.distance_kf.predict()[0]) / 5) * 5
+
+    def boxes_overlap(self, box1, box2):
+        """Check if two bounding boxes overlap."""
+        if box1 is None or box2 is None:
+            return False
+        x1, y1, x2, y2 = box1
+        x3, y3, x4, y4 = box2
+        return not (x2 < x3 or x4 < x1 or y2 < y3 or y4 < y1)
+
+    def calculate_distance(self, penis_box, other_box):
+        """Calculate the distance between the penis box and another box."""
+        if other_box is None:
+            return None
+        if penis_box is None:
+            logging.warning(f"Penis box is None for frame {self.current_frame_id}, cannot compute distance")
+            return None
+        ox1, oy1, ox2, oy2 = other_box
+        y_pos = (oy1 + 2 * oy2) // 3
+        x_pos = (ox1 + ox2) // 2
+        px1, py1, px2, py2 = penis_box
+        return math.sqrt((x_pos - px2) ** 2 + (y_pos - py2) ** 2)
+
+    def detect_sex_position_change(self, sex_position, reason):
+        """Detect and log changes in the sex position."""
+        self.sex_position_history.append(sex_position)
+        position_counts = {position: self.sex_position_history.count(position) for position in
+                           self.sex_position_history}
+        most_frequent_position = max(position_counts, key=position_counts.get, default="Not relevant")
+        if most_frequent_position != self.sex_position:
+            logging.info(f"@{self.current_frame_id} - Sex position switched to: {most_frequent_position}")
+            self.sex_position = most_frequent_position
+            self.sex_position_reason = reason
+
+    def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
+        """
+        Main tracking logic to process detected boxes and update tracking state.
+        """
+        self.current_frame_id = current_frame_id
+        self.image_y_size = image_y_size
+
+        # Collect all detections
+        detections = [(box, conf, class_name, track_id) for box, conf, cls, class_name, track_id in sorted_boxes]
+
+        # Initialize tracking state
+        self.glans_detected = False
+        self.boxes = {class_name: None for class_name in self.class_names}
+        self.tracked_boxes = []
+        all_detections = {class_name: [] for class_name in self.class_names}
+
+        classes_touching_penis = []
+
+        # Collect all detections by class name
+        for box, conf, cls, class_name, track_id in sorted_boxes:
+            if conf > 0.3:
+                all_detections[class_name].append([conf, box, track_id])
+
+        # Find the best box for specific classes
+        found_box = {class_name: [] for class_name in self.class_names}
+        for check_class_first in ['glans', 'penis', 'navel']:
+            prev_conf = 0
+            for conf, box, track_id in all_detections[check_class_first]:
+                if conf > prev_conf:
+                    found_box[check_class_first] = [box, conf, track_id]
+                    prev_conf = conf
+
+        # Update tracking for specific classes
+        for check_class_first in ['glans', 'penis', 'navel']:
+            if found_box[check_class_first]:
+                self.boxes[check_class_first] = found_box[check_class_first][0]
+                conf = found_box[check_class_first][1]
+                self.consecutive_detections[check_class_first] += 1
+                self.consecutive_non_detections[check_class_first] = 0
+                self.handle_class_first(check_class_first, self.boxes[check_class_first], conf)
+            else:
+                self.consecutive_detections[check_class_first] = 0
+                self.consecutive_non_detections[check_class_first] += 1
+
+        if self.consecutive_non_detections[
+            check_class_first] > self.detections_threshold and check_class_first == 'penis':
+            if self.locked_penis_box.is_active():
+                self.locked_penis_box.deactivate()
+                logging.info(f"@{self.current_frame_id} - Deactivated locked_penis_box")
+
+        sum_pos = 0
+        weight_pos = 0
+
+        for box, conf, cls, class_name, track_id in sorted_boxes:
+            if class_name in ['glans', 'penis', 'navel']:
+                continue
+            if self.boxes_overlap(box, self.locked_penis_box.get_box()):
+                if class_name not in classes_touching_penis:
+                    classes_touching_penis.append(class_name)
+                self.tracked_boxes.append([box, class_name, track_id])
+                x1, y1, x2, y2 = box
+                mid_y = (y1 + 4 * y2) // 5
+                dist_to_penis_base = int(((self.locked_penis_box.get_box()[3] - mid_y) / (self.locked_penis_box.get_height() * .8)) * 100)
+                normalized_y = min(max(0, dist_to_penis_base), 100)
+
+                # Update tracked positions
+                if track_id not in self.tracked_positions:
+                    self.tracked_positions[track_id] = []
+                self.tracked_positions[track_id].append(normalized_y)
+
+                # Maintain a fixed-size history
+                if len(self.tracked_positions[track_id]) > 600:
+                    self.tracked_positions[track_id].pop(0)
+
+                # Update normalized tracked positions
+                if track_id not in self.normalized_tracked_positions:
+                    self.normalized_tracked_positions[track_id] = []
+                self.normalized_tracked_positions[track_id].append(normalized_y)
+
+                # Maintain a fixed-size history
+                if len(self.normalized_tracked_positions[track_id]) > 60:
+                    self.normalized_tracked_positions[track_id].pop(0)
+
+                # Sum delta positions of all touching items, weighted by the length of their history
+                weight_pos_track_id = sum(
+                    abs(self.normalized_tracked_positions[track_id][i] - self.normalized_tracked_positions[track_id][
+                        i - 1]) for i in
+                    range(1, len(self.normalized_tracked_positions[track_id])))
+                sum_pos += max(0, (normalized_y - (100 - self.max_allowed))) * weight_pos_track_id
+                weight_pos += weight_pos_track_id
+
+        if len(classes_touching_penis) == 0 or not self.locked_penis_box.is_active():
+            self.penetration = False
+            distance = 100
+            self.detect_sex_position_change('Not relevant', "no part touching penis / no penis")
+        elif 'pussy' in classes_touching_penis and not self.glans_detected:
+            self.penetration = True
+            self.detect_sex_position_change('Missionnary / Cowgirl', "pussy visible and touching")
+        elif 'ass' in classes_touching_penis and not self.glans_detected:
+            self.penetration = True
+            self.detect_sex_position_change('Doggy / Rev. Cowgirl', "ass visible and touching")
+        elif ('hand' in classes_touching_penis or 'face' in classes_touching_penis) and not self.penetration:
+            self.detect_sex_position_change('Handjob / Blowjob', "hand or face visible and touching")
+        elif 'foot' in classes_touching_penis and not self.penetration:
+            self.detect_sex_position_change('Footjob', "foot visible and touching")
+        elif 'breast' in classes_touching_penis and not self.penetration:
+            self.detect_sex_position_change('Boobjob', "breast visible and touching")
+
+        if weight_pos > 0:
+            distance = int(sum_pos / weight_pos)
+        else:
+            distance = 100
+
+        self.update_distance(distance)
+
+    def handle_class_first(self, class_name, box, conf):
+        """
+        Handle tracking for specific classes (e.g., penis, glans, navel).
+        """
+        if class_name == 'penis':
+            if box is not None and self.penis_box is None:
+                logging.info(f"Penis detected at frame {self.current_frame_id} with confidence {conf}")
+            self.penis_box = box
+
+            if self.penis_box:
+                if self.consecutive_detections['penis'] >= self.detections_threshold:
+                    px1, py1, px2, py2 = self.penis_box
+                    current_height = py2 - py1
+                    if self.locked_penis_box.is_active():
+                        if current_height > self.locked_penis_box.get_height():
+                            self.locked_penis_box.update(self.penis_box, current_height)
+
+                        # Move locked penis box towards current penis box
+                        max_move = max(1, int(self.image_y_size / 960))
+
+                        if abs(self.penis_box[0] - self.locked_penis_box.get_box()[0]) > max_move:
+                            px1 = self.penis_box[0] + np.sign(self.penis_box[0] - self.locked_penis_box.get_box()[0]) * max_move
+                        if abs(self.penis_box[2] - self.locked_penis_box.get_box()[2]) > max_move:
+                            px2 = self.penis_box[2] + np.sign(self.penis_box[2] - self.locked_penis_box.get_box()[2]) * max_move
+                        if abs(self.penis_box[3] - self.locked_penis_box.get_box()[3]) > max_move:
+                            py2 = self.locked_penis_box.get_box()[3] + np.sign(self.penis_box[3] - self.locked_penis_box.get_box()[3]) * max_move
+
+                        self.locked_penis_box.update((px1, py2 - self.locked_penis_box.get_height(), px2, py2), self.locked_penis_box.get_height())
+                    else:
+                        self.locked_penis_box.update(self.penis_box, current_height)
+        elif class_name == 'glans' and box:
+            if self.consecutive_detections['glans'] >= self.detections_threshold:
+                self.boxes['glans'] = box
+                self.glans_detected = True
+                if self.penis_box:
+                    self.locked_penis_box.update(self.penis_box, self.penis_box[3] - self.penis_box[1])
+                if self.penetration:
+                    self.penetration = False
+                    logging.info(
+                        f"@{self.current_frame_id} - Penetration ended after {self.consecutive_detections['glans']} detections of glans")
+                    if self.tracked_body_part != 'Nothing':
+                        self.normalized_distances[self.tracked_body_part].clear()
+                        self.normalized_distances[self.tracked_body_part].append(100)
+        elif class_name == 'navel' and box:
+            if (self.image_y_size - box[1]) / self.image_y_size < 0.15 and not self.breast_tracking:
+                logging.info("Breast tracking mode activated given navel position in lower 15th of frame")
+                self.breast_tracking = True
+
+    def normalize_box_area(self, box, frame_width, frame_height):
+        """Normalize the area of a bounding box relative to the frame size."""
+        x1, y1, x2, y2 = box
+        if x2 <= x1 or y2 <= y1:
+            return 0
+        box_area = (x2 - x1) * (y2 - y1)
+        max_area = frame_width * frame_height
+        normalized_area = 100 * (box_area / max_area)
+        return max(0, min(100, normalized_area))
\ No newline at end of file
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index 670b034c..9213ceb1 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -1,10 +1,11 @@
 import subprocess
 import cv2
 import numpy as np
+from utils.config import ffmpeg_path, ffprobe_path
 
 
 class VideoReaderFFmpeg:
-    def __init__(self, video_path, is_VR=False, ffmpeg_path="/usr/local/bin/ffmpeg", ffprobe_path="/usr/local/bin/ffprobe"):
+    def __init__(self, video_path, is_VR=False, ffmpeg_path=ffmpeg_path, ffprobe_path=ffprobe_path):
         """
         Initialize the VideoReaderFFmpeg class.
         :param video_path: Path to the video file.

From 4e31f9791a1c2aa9d3935ab4d85b4550eb4f7501 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 29 Dec 2024 13:40:38 +0100
Subject: [PATCH 015/277] init

---
 FSGenerator.py                |   2 +-
 utils/lib_ObjectTracker.py    | 437 +++++++++++-----------------------
 utils/lib_ObjectTracker_v2.py | 354 ---------------------------
 3 files changed, 146 insertions(+), 647 deletions(-)
 delete mode 100644 utils/lib_ObjectTracker_v2.py

diff --git a/FSGenerator.py b/FSGenerator.py
index c8fd0172..7edd272f 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -6,7 +6,7 @@
 from ultralytics import YOLO
 
 from utils.config import class_names, class_priority_order, class_reverse_match, class_colors
-from utils.lib_ObjectTracker_v2 import ObjectTracker
+from utils.lib_ObjectTracker import ObjectTracker
 from utils.lib_FunscriptHandler import FunscriptGenerator
 from utils.lib_Visualizer import Visualizer
 from utils.lib_Debugger import Debugger
diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index 61b97b7f..5848886f 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -8,114 +8,148 @@
 # Configure logging
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
 
+class LockedPenisBox:
+    """
+    A class to manage the locked penis box, which represents the detected penis area.
+    It stores the box coordinates, height, and active status.
+    """
+    def __init__(self):
+        self.box = None  # Coordinates of the locked penis box
+        self.height = 0  # Height of the locked penis box
+        self.active = False  # Whether the locked penis box is active
+
+    def update(self, new_box, new_height):
+        """Update the locked penis box with new coordinates and height."""
+        self.box = new_box
+        self.height = new_height
+        self.active = True
+
+    def deactivate(self):
+        """Deactivate the locked penis box."""
+        self.box = None
+        self.height = 0
+        self.active = False
+
+    def get_box(self):
+        """Return the coordinates of the locked penis box."""
+        return self.box
+
+    def get_height(self):
+        """Return the height of the locked penis box."""
+        return self.height
+
+    def is_active(self):
+        """Check if the locked penis box is active."""
+        return self.active
+
+    def to_dict(self):
+        """
+        Convert the LockedPenisBox object into a dictionary for JSON serialization.
+        """
+        return {
+            "box": self.box,
+            "height": self.height,
+            "active": self.active
+        }
+
+    @classmethod
+    def from_dict(cls, data):
+        """
+        Reconstruct a LockedPenisBox object from a dictionary.
+        """
+        locked_box = cls()
+        if data["box"] is not None:
+            locked_box.update(data["box"], data["height"])
+        return locked_box
+
 class ObjectTracker:
+    """
+    A class to track objects (e.g., penis, glans, etc.) in a video frame and determine their positions and interactions.
+    """
     def __init__(self, fps, frame_pos, state=None):
-        self.class_names = class_names
-        self.active_tracks = {}  # Key: track_id, Value: Track object
-        self.inactive_tracks = []  # List of Track objects for lost tracks
-        self.distance_kf = KF.KalmanFilter()
-
-        self.frame = None
-        self.current_frame_id = frame_pos
-        self.image_y_size = 0
-        self.fps = fps
-
-        # if trying to limit speed to accomodate Handy device : 40cm/s on 11cm range
-        # Handy : 11 / 40 = 0.275
-        # Assuming devices like 0SR2 can go double speed
-        # OSR2 : 11 / 80 = 0.13
-        # Settling down to an average of 60cm/s => 11 / 60 = 0.18
-        # @60fps = 0.275 * 60 = 16.8, max_movement on 1 frame: 100 / 16.8 = 5.9
-        # @30fps = 0.275 * 30 = 8.1, max_movement on 1 frame: 100 / 8.1 = 12.5
-        self.max_speed = 100 / (.18 * self.fps)
-
-        self.max_allowed = 100
-
-        self.boxes = {}
-        self.tracked_boxes = []
-        self.avg_move = {}
-
-        self.penis_box, self.locked_penis_box = None, None
-        self.glans_detected = False
-        self.locked_penis_height = 0
-        self.breast_tracking = False
-        self.distance = 100
-        self.raw_distance = 100
-        self.previous_distances = [100, 100, 100]
-        self.tracked_body_part = "Nothing"
-
-        self.sex_position = "Not relevant"
-        self.prev_sex_position = "Not relevant"
-        self.sex_position_reason = ""
-        self.sex_position_history = deque(maxlen=30)
-        self.sub_sex_position = "Not relevant"
-
-        self.positions = {class_name: deque(maxlen=200) for class_name in class_names}
-        self.distances = {class_name: deque(maxlen=200) for class_name in class_names}
-        self.areas = {class_name: deque(maxlen=200) for class_name in class_names}
-
+        # Initialize class attributes
+        self.class_names = class_names  # List of class names to track
+        self.active_tracks = {}  # Active tracks: {track_id: Track object}
+        self.inactive_tracks = []  # Inactive tracks (lost tracks)
+        self.distance_kf = KF.KalmanFilter()  # Kalman filter for distance smoothing
+
+        self.frame = None  # Current video frame
+        self.current_frame_id = frame_pos  # Current frame ID
+        self.image_y_size = 0  # Height of the video frame
+        self.fps = fps  # Frames per second of the video
+
+        # Speed and distance thresholds
+        self.max_speed = 100 / (.18 * self.fps)  # Maximum allowed speed for distance changes
+        self.max_allowed = 100  # Maximum allowed distance
+
+        # Tracking state
+        self.boxes = {}  # Detected boxes for each class
+        self.tracked_boxes = []  # List of tracked boxes
+        self.penis_box = None  # Detected penis box
+        self.locked_penis_box = LockedPenisBox()  # Locked penis box
+        self.glans_detected = False  # Whether the glans is detected
+        self.breast_tracking = False  # Whether breast tracking is active
+        self.distance = 100  # Current distance
+        self.previous_distances = [100, 100, 100]  # Previous distances for smoothing
+        self.tracked_body_part = "Nothing"  # Currently tracked body part
+
+        # Sex position tracking
+        self.sex_position = "Not relevant"  # Current sex position
+        self.sex_position_reason = ""  # Reason for the current sex position
+        self.sex_position_history = deque(maxlen=120)  # History of sex positions
+
+        # Position and distance tracking
+        self.positions = {class_name: deque(maxlen=200) for class_name in class_names}  # Positions for each class
+        self.distances = {class_name: deque(maxlen=200) for class_name in class_names}  # Distances for each class
+        self.areas = {class_name: deque(maxlen=200) for class_name in class_names}  # Areas for each class
+
+        # Normalized positions and distances
         self.normalized_positions = {class_name: deque(maxlen=200) for class_name in class_names}
         self.normalized_distances = {class_name: deque(maxlen=200) for class_name in class_names}
-        self.activated_kalman = {class_name: False for class_name in class_names}
 
+        # Tracked positions and normalized tracked positions
         self.tracked_positions = {}
         self.normalized_tracked_positions = {}
 
-        self.offsets = {class_name: 0 for class_name in class_names}
-
+        # Initialize normalized distances and positions
         for class_name in class_names:
             self.normalized_distances[class_name].append(100)
             self.normalized_positions[class_name].append(100)
 
-        self.normalized_areas = {class_name: deque(maxlen=200) for class_name in class_names}
-
-        self.moving_average_window = 5
-        self.consecutive_detections = {class_name: 0 for class_name in class_names}
-        self.consecutive_non_detections = {class_name: 0 for class_name in class_names}
-        self.detections_threshold = 3
-
-        self.consecutive_grinding_suspicions = {class_name: 0 for class_name in ['pussy', 'butt']}
+        # Detection thresholds
+        self.consecutive_detections = {class_name: 0 for class_name in class_names}  # Consecutive detections for each class
+        self.consecutive_non_detections = {class_name: 0 for class_name in class_names}  # Consecutive non-detections for each class
+        self.detections_threshold = 3  # Threshold for considering a detection valid
 
-        self.penetration = False
-        self.grinding = False
-        self.rubbing = False
+        # Penetration state
+        self.penetration = False  # Whether penetration is detected
 
-        self.max_predictions = int(self.fps)
-        self.max_inactive_frames = int(fps * 2)  # Adjust based on expected occlusion duration
-        self.distance_threshold = 50  # Define an appropriate distance threshold
+        # Prediction and tracking thresholds
+        self.max_predictions = int(self.fps)  # Maximum number of predictions
+        self.max_inactive_frames = int(fps * 2)  # Maximum inactive frames before a track is lost
+        self.distance_threshold = 50  # Distance threshold for tracking
 
     def update_distance(self, raw_distance):
+        """
+        Update the tracked distance using a Kalman filter and speed constraints.
+        """
         if raw_distance is None:
             filtered_distance = self._predict_distance()
         else:
-            if abs (self.distance - raw_distance) > self.max_speed:
+            if abs(self.distance - raw_distance) > self.max_speed:
                 raw_distance = self.distance + np.sign(raw_distance - self.distance) * self.max_speed
             filtered_distance = raw_distance
-            #rounded_distance = round(raw_distance / 5) * 5
-            #ema_distance = self._calculate_ema_distance(rounded_distance)
-            #self._update_previous_distances(ema_distance)
-            #self.distance_kf.update((ema_distance, 0))
-            #filtered_distance = ema_distance
 
         filtered_distance = int(max(0, min(100, filtered_distance)))
         self.distance = filtered_distance
         return filtered_distance
 
     def _predict_distance(self):
+        """Predict the distance using the Kalman filter."""
         return int(float(self.distance_kf.predict()[0]) / 5) * 5
 
-    def _calculate_ema_distance(self, rounded_distance):
-        return int(0.7 * rounded_distance + 0.1 * self.previous_distances[1] + 0.2 * self.previous_distances[2])
-
-    def _update_previous_distances(self, ema_distance):
-        if abs(ema_distance - self.previous_distances[-1]) > 15:
-            ema_distance = self.previous_distances[-1] + np.sign(ema_distance - self.previous_distances[-1]) * 15
-        elif abs(ema_distance - self.previous_distances[-1]) < 2:
-            ema_distance = self.previous_distances[-1]
-        self.previous_distances.pop(0)
-        self.previous_distances.append(ema_distance)
-
     def boxes_overlap(self, box1, box2):
+        """Check if two bounding boxes overlap."""
         if box1 is None or box2 is None:
             return False
         x1, y1, x2, y2 = box1
@@ -123,12 +157,11 @@ def boxes_overlap(self, box1, box2):
         return not (x2 < x3 or x4 < x1 or y2 < y3 or y4 < y1)
 
     def calculate_distance(self, penis_box, other_box):
-        print(f"penis box argument in calculate_distance: {penis_box}")
+        """Calculate the distance between the penis box and another box."""
         if other_box is None:
             return None
         if penis_box is None:
-            print(f"penis box: {penis_box}")
-            print(f"Penis box is None for frame {self.current_frame_id}, cannot compute distance")
+            logging.warning(f"Penis box is None for frame {self.current_frame_id}, cannot compute distance")
             return None
         ox1, oy1, ox2, oy2 = other_box
         y_pos = (oy1 + 2 * oy2) // 3
@@ -137,6 +170,7 @@ def calculate_distance(self, penis_box, other_box):
         return math.sqrt((x_pos - px2) ** 2 + (y_pos - py2) ** 2)
 
     def detect_sex_position_change(self, sex_position, reason):
+        """Detect and log changes in the sex position."""
         self.sex_position_history.append(sex_position)
         position_counts = {position: self.sex_position_history.count(position) for position in
                            self.sex_position_history}
@@ -147,6 +181,9 @@ def detect_sex_position_change(self, sex_position, reason):
             self.sex_position_reason = reason
 
     def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
+        """
+        Main tracking logic to process detected boxes and update tracking state.
+        """
         self.current_frame_id = current_frame_id
         self.image_y_size = image_y_size
 
@@ -157,15 +194,12 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
         self.glans_detected = False
         self.boxes = {class_name: None for class_name in self.class_names}
         self.tracked_boxes = []
-        #self.avg_move = {}
         all_detections = {class_name: [] for class_name in self.class_names}
 
         classes_touching_penis = []
 
         # Collect all detections by class name
         for box, conf, cls, class_name, track_id in sorted_boxes:
-            if class_name == "penis":
-                print(f"penis detected @1")
             if conf > 0.3:
                 all_detections[class_name].append([conf, box, track_id])
 
@@ -175,8 +209,6 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
             prev_conf = 0
             for conf, box, track_id in all_detections[check_class_first]:
                 if conf > prev_conf:
-                    if check_class_first == 'penis':
-                        print("penis detected @2")
                     found_box[check_class_first] = [box, conf, track_id]
                     prev_conf = conf
 
@@ -187,8 +219,6 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                 conf = found_box[check_class_first][1]
                 self.consecutive_detections[check_class_first] += 1
                 self.consecutive_non_detections[check_class_first] = 0
-                if check_class_first == 'penis':
-                    print("sending to handle penis")
                 self.handle_class_first(check_class_first, self.boxes[check_class_first], conf)
             else:
                 self.consecutive_detections[check_class_first] = 0
@@ -196,55 +226,34 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
 
         if self.consecutive_non_detections[
             check_class_first] > self.detections_threshold and check_class_first == 'penis':
-            if self.locked_penis_box:
-                self.locked_penis_box = None
+            if self.locked_penis_box.is_active():
+                self.locked_penis_box.deactivate()
                 logging.info(f"@{self.current_frame_id} - Deactivated locked_penis_box")
-            #self.handle_class_first(check_class_first, box, conf)
-
-
-        # First, how much of the penis can we see, 0 to 100 as a baseline value
-
-        #if self.locked_penis_box and found_box['penis'] and self.boxes_overlap(self.penis_box, found_box['penis'][0]):
-        #    self.visible_penis_height = int(((self.locked_penis_box[3] - found_box['penis'][0][1]) / self.locked_penis_height) * 100)
-        #    self.max_allowed = self.visible_penis_height
-        #else:
-        #    self.visible_penis_height = None
-        #    self.max_allowed = 100
 
         sum_pos = 0
         weight_pos = 0
 
-        computation_method = 1
-
-        target_pos = None
         for box, conf, cls, class_name, track_id in sorted_boxes:
             if class_name in ['glans', 'penis', 'navel']:
                 continue
-            if self.boxes_overlap(box, self.locked_penis_box):
+            if self.boxes_overlap(box, self.locked_penis_box.get_box()):
                 if class_name not in classes_touching_penis:
                     classes_touching_penis.append(class_name)
                 self.tracked_boxes.append([box, class_name, track_id])
                 x1, y1, x2, y2 = box
                 mid_y = (y1 + 4 * y2) // 5
-                dist_to_penis_base = int(((self.locked_penis_box[3] - mid_y) / (self.locked_penis_height * .8)) * 100)
-                # print(f"locked_penis_height: {self.locked_penis_height}, locked_penis_box[3]: {self.locked_penis_box[3]}, mid_y: {mid_y}, dist_to_penis_base: {dist_to_penis_base}")
-                # print(f"For class {class_name}, mid_y: {mid_y}, dist_to_penis_base: {dist_to_penis_base}")
+                dist_to_penis_base = int(((self.locked_penis_box.get_box()[3] - mid_y) / (self.locked_penis_box.get_height() * .8)) * 100)
                 normalized_y = min(max(0, dist_to_penis_base), 100)
 
                 # Update tracked positions
                 if track_id not in self.tracked_positions:
                     self.tracked_positions[track_id] = []
-                # self.tracked_positions[track_id].append(mid_y)
                 self.tracked_positions[track_id].append(normalized_y)
 
                 # Maintain a fixed-size history
                 if len(self.tracked_positions[track_id]) > 600:
                     self.tracked_positions[track_id].pop(0)
 
-                # Normalize the y position
-                # min_y, max_y = min(self.tracked_positions[track_id]), max(self.tracked_positions[track_id])
-                # normalized_y = (100 - int(100 * ((mid_y - min_y) / (max_y - min_y)))) if min_y != max_y else 100
-
                 # Update normalized tracked positions
                 if track_id not in self.normalized_tracked_positions:
                     self.normalized_tracked_positions[track_id] = []
@@ -254,7 +263,7 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                 if len(self.normalized_tracked_positions[track_id]) > 60:
                     self.normalized_tracked_positions[track_id].pop(0)
 
-                # sum delta positions of all touching items, ponderated by the length of their history
+                # Sum delta positions of all touching items, weighted by the length of their history
                 weight_pos_track_id = sum(
                     abs(self.normalized_tracked_positions[track_id][i] - self.normalized_tracked_positions[track_id][
                         i - 1]) for i in
@@ -262,7 +271,7 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                 sum_pos += max(0, (normalized_y - (100 - self.max_allowed))) * weight_pos_track_id
                 weight_pos += weight_pos_track_id
 
-        if len(classes_touching_penis) == 0 or self.locked_penis_box is None:
+        if len(classes_touching_penis) == 0 or not self.locked_penis_box.is_active():
             self.penetration = False
             distance = 100
             self.detect_sex_position_change('Not relevant', "no part touching penis / no penis")
@@ -272,188 +281,56 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
         elif 'ass' in classes_touching_penis and not self.glans_detected:
             self.penetration = True
             self.detect_sex_position_change('Doggy / Rev. Cowgirl', "ass visible and touching")
-        elif 'hand' in classes_touching_penis or 'face' in classes_touching_penis:
+        elif ('hand' in classes_touching_penis or 'face' in classes_touching_penis) and not self.penetration:
             self.detect_sex_position_change('Handjob / Blowjob', "hand or face visible and touching")
-        elif 'foot' in classes_touching_penis:
+        elif 'foot' in classes_touching_penis and not self.penetration:
             self.detect_sex_position_change('Footjob', "foot visible and touching")
-        elif 'breast' in classes_touching_penis:
+        elif 'breast' in classes_touching_penis and not self.penetration:
             self.detect_sex_position_change('Boobjob', "breast visible and touching")
 
-
         if weight_pos > 0:
             distance = int(sum_pos / weight_pos)
         else:
             distance = 100
 
-        """if self.penetration:
-            if self.penis_box:
-                # visible part of the penis
-                weighted_distance = distance
-                penis_height = self.penis_box[3] - self.penis_box[1]
-                locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
-                normalized_penis_height = int((penis_height / locked_penis_height) * 100)
-                distance_real = normalized_penis_height
-                distance = int((distance_real + weighted_distance) / 2)
-                #print(f"distance_real: {distance_real} vs distance: {weighted_distance} => {distance}")
-            else:
-                distance = 100
-        """
-
-
         self.update_distance(distance)
 
+    def handle_class_first(self, class_name, box, conf):
         """
-
-        if self.penetration and class_name == 'breast' and not self.boxes_overlap(box, self.locked_penis_box):
-            x1, y1, x2, y2 = box
-            mid_y = (y1 + y2) // 2
-
-            # Update tracked positions
-            if track_id not in self.tracked_positions:
-                self.tracked_positions[track_id] = []
-            self.tracked_positions[track_id].append(mid_y)
-
-            # Maintain a fixed-size history
-            if len(self.tracked_positions[track_id]) > 600:
-                self.tracked_positions[track_id].pop(0)
-
-            if max(self.tracked_positions[track_id]) != min(self.tracked_positions[track_id]):
-                normalized_y = mid_y / (max(self.tracked_positions[track_id]) - min(self.tracked_positions[track_id]))
-                # Update normalized tracked positions
-                if track_id not in self.normalized_tracked_positions:
-                    self.normalized_tracked_positions[track_id] = []
-                self.normalized_tracked_positions[track_id].append(normalized_y)
-                weight_pos_track_id = sum(
-                    abs(self.normalized_tracked_positions[track_id][i] - self.normalized_tracked_positions[track_id][
-                        i - 1]) for i in
-                    range(1, len(self.normalized_tracked_positions[track_id])))
-                sum_pos += max(0, (normalized_y - (100 - self.max_allowed))) * weight_pos_track_id
-                weight_pos += weight_pos_track_id
-        """
-        # Check if the box overlaps with the locked penis box
-        #el
-
-
-
-        # Calculate average movement over the last 30 frames
-        """
-        if track_id not in self.avg_move:
-            self.avg_move[track_id] = 0
-        else:
-            if len(self.tracked_positions[track_id]) > 30:
-                total_move = sum(
-                    abs(self.tracked_positions[track_id][i] - self.tracked_positions[track_id][i - 1]) for i in
-                    range(1, 31))
-                self.avg_move[track_id] = total_move #/ 30
-            else:
-                self.avg_move[track_id] = 0
-
-        nb_items_touching_penis += 1
-        """
-
-        # Determine the target track ID based on movement
+        Handle tracking for specific classes (e.g., penis, glans, navel).
         """
-        if nb_items_touching_penis > 0:
-            # Filter out tracks with minimal movement (idle state)
-            active_tracks = {track_id: move for track_id, move in self.avg_move.items() if
-                             move > 5}  # Threshold for movement
-            if active_tracks:
-                print("active tracks")
-                target_track_id = max(active_tracks, key=active_tracks.get)
-                distance = self.normalized_tracked_positions[target_track_id][-1]
-                print(f"@{self.current_frame_id} : Tracking {target_track_id} at {distance}")
-            else:
-                # Fallback to the previously tracked ID if no active movement
-                if hasattr(self, 'last_tracked_id') and self.last_tracked_id in self.normalized_tracked_positions:
-                    target_track_id = self.last_tracked_id
-                    distance = self.normalized_tracked_positions[target_track_id][-1]
-                    print(f"@{self.current_frame_id} : Fallback to {target_track_id} at {distance}")
-                else:
-                    distance = None
-        else:
-            distance = None
-
-        # Update the last tracked ID
-        if distance is not None:
-            self.last_tracked_id = target_track_id
-        """
-
-    def handle_class_first(self, class_name, box, conf):
         if class_name == 'penis':
             if box is not None and self.penis_box is None:
                 logging.info(f"Penis detected at frame {self.current_frame_id} with confidence {conf}")
             self.penis_box = box
-            #print(f"self.penis_box: {self.penis_box}")
-
-            #if self.consecutive_non_detections['penis'] >= self.detections_threshold:
-            #    if self.locked_penis_box:
-            #        self.locked_penis_box = None
-            #        self.locked_penis_height = 0
-            #        self.penetration = False
-            #        logging.info(f"@{self.current_frame_id} - Locked penis box deactivated")
+
             if self.penis_box:
                 if self.consecutive_detections['penis'] >= self.detections_threshold:
                     px1, py1, px2, py2 = self.penis_box
                     current_height = py2 - py1
-                    # move locked penis box to current penis box
-                    if self.locked_penis_box:
-                        if current_height > self.locked_penis_height:
-                            self.locked_penis_height = current_height
+                    if self.locked_penis_box.is_active():
+                        if current_height > self.locked_penis_box.get_height():
+                            self.locked_penis_box.update(self.penis_box, current_height)
 
-                        # moving locked penis box towards current penis box
+                        # Move locked penis box towards current penis box
                         max_move = max(1, int(self.image_y_size / 960))
 
-                        if abs(self.penis_box[0] - self.locked_penis_box[0]) > max_move:
-                            px1 = self.penis_box[0] + np.sign(self.penis_box[0] - self.locked_penis_box[0]) * max_move
-                        if abs(self.penis_box[2] - self.locked_penis_box[2]) > max_move:
-                            px2 = self.penis_box[2] + np.sign(self.penis_box[2] - self.locked_penis_box[2]) * max_move
-                        if abs(self.penis_box[3] - self.locked_penis_box[3]) > max_move:
-                            print("adjusting now")
-                            py2 = self.locked_penis_box[3] + np.sign(self.penis_box[3] - self.locked_penis_box[3]) * max_move
+                        if abs(self.penis_box[0] - self.locked_penis_box.get_box()[0]) > max_move:
+                            px1 = self.penis_box[0] + np.sign(self.penis_box[0] - self.locked_penis_box.get_box()[0]) * max_move
+                        if abs(self.penis_box[2] - self.locked_penis_box.get_box()[2]) > max_move:
+                            px2 = self.penis_box[2] + np.sign(self.penis_box[2] - self.locked_penis_box.get_box()[2]) * max_move
+                        if abs(self.penis_box[3] - self.locked_penis_box.get_box()[3]) > max_move:
+                            py2 = self.locked_penis_box.get_box()[3] + np.sign(self.penis_box[3] - self.locked_penis_box.get_box()[3]) * max_move
 
-                        self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
+                        self.locked_penis_box.update((px1, py2 - self.locked_penis_box.get_height(), px2, py2), self.locked_penis_box.get_height())
                     else:
-                        self.locked_penis_box = self.penis_box
-                        self.locked_penis_height = current_height
-                    """    
-                    
-                    current_height = py2 - py1
-                    if self.locked_penis_box is None or self.glans_detected: # or current_height > self.locked_penis_height:
-                        if self.locked_penis_box is None:
-                            logging.info(f"@{self.current_frame_id} - Locked penis box activated")
-                        self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
-                        self.locked_penis_height = current_height
-                    """
-                #if self.locked_penis_box and self.glans_detected:
-                #    if current_height > self.locked_penis_height:
-                #        self.locked_penis_height = current_height
-                #        self.locked_penis_box = self.penis_box
-                #    if self.penis_box[3] != self.locked_penis_box[3]:
-                #        self.locked_penis_box = (px1, py2 - self.locked_penis_height, px2, py2)
-            #if self.locked_penis_box and self.penetration:
-            #    penis_height = self.boxes['penis'][3] - self.boxes['penis'][1]
-            #    locked_penis_height = self.locked_penis_box[3] - self.locked_penis_box[1]
-            #    if locked_penis_height == 0:
-            #        scale = 100
-            #    else:
-            #        scale = min(int((penis_height / locked_penis_height) * 100), 100)
-            #    distance = 100 - scale
-            #    self.positions['penis'].append(distance)
-            #    min_distance, max_distance = min(self.positions['penis']), max(self.positions['penis'])
-            #    normalized_distance = (100 - int(100 * ((distance - min_distance) / (
-            #                max_distance - min_distance)))) if min_distance != max_distance else 100
-            #    self.normalized_positions['penis'].append(normalized_distance)
-            #if (self.image_y_size - py1) / self.image_y_size < 0.1 and not self.breast_tracking and \
-            #        self.tracked_boxes['breast']['detected']:
-            #        #self.tracked_objects['breast']['detected']:
-            #    self.breast_tracking = True
+                        self.locked_penis_box.update(self.penis_box, current_height)
         elif class_name == 'glans' and box:
             if self.consecutive_detections['glans'] >= self.detections_threshold:
                 self.boxes['glans'] = box
                 self.glans_detected = True
                 if self.penis_box:
-                    self.locked_penis_box = self.penis_box
-                    self.locked_penis_height = self.penis_box[3] - self.penis_box[1]
+                    self.locked_penis_box.update(self.penis_box, self.penis_box[3] - self.penis_box[1])
                 if self.penetration:
                     self.penetration = False
                     logging.info(
@@ -467,35 +344,11 @@ def handle_class_first(self, class_name, box, conf):
                 self.breast_tracking = True
 
     def normalize_box_area(self, box, frame_width, frame_height):
+        """Normalize the area of a bounding box relative to the frame size."""
         x1, y1, x2, y2 = box
         if x2 <= x1 or y2 <= y1:
             return 0
         box_area = (x2 - x1) * (y2 - y1)
         max_area = frame_width * frame_height
         normalized_area = 100 * (box_area / max_area)
-        return max(0, min(100, normalized_area))
-
-    def log_and_normalize_pos(self, box, class_name):
-        self.boxes[class_name] = box
-        _, y1, _, y2 = box
-        mid_y = (y1 + y2) / 2
-        self.positions[class_name].append(mid_y)
-
-        min_y, max_y = min(self.positions[class_name]), max(self.positions[class_name])
-        normalized_y = (100 - int(100 * ((mid_y - min_y) / (max_y - min_y)))) if min_y != max_y else 100
-
-        if class_name == "breast" and self.frame:
-            normalized_breast_area = self.normalize_box_area(box, self.frame.shape[1], self.frame.shape[0])
-            normalized_y = ((0.75 * normalized_breast_area) + normalized_y) / 1.75
-
-        self.normalized_positions[class_name].append(normalized_y)
-        return normalized_y
-
-    def log_and_normalize_distance(self, box, class_name):
-        locked_penis_box = self.locked_penis_box or (0, 0, 0, 0)
-        distance = self.calculate_distance(locked_penis_box, box)
-        self.distances[class_name].append(distance)
-        min_distance, max_distance = min(self.distances[class_name]), max(self.distances[class_name])
-        normalized_distance = (100 - int(
-            100 * ((distance - min_distance) / (max_distance - min_distance)))) if min_distance != max_distance else 100
-        self.normalized_distances[class_name].append(normalized_distance)
\ No newline at end of file
+        return max(0, min(100, normalized_area))
\ No newline at end of file
diff --git a/utils/lib_ObjectTracker_v2.py b/utils/lib_ObjectTracker_v2.py
deleted file mode 100644
index 5848886f..00000000
--- a/utils/lib_ObjectTracker_v2.py
+++ /dev/null
@@ -1,354 +0,0 @@
-from utils import lib_KalmanFilter as KF
-from collections import deque
-import numpy as np
-from utils.config import class_names
-import math
-import logging
-
-# Configure logging
-logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
-
-class LockedPenisBox:
-    """
-    A class to manage the locked penis box, which represents the detected penis area.
-    It stores the box coordinates, height, and active status.
-    """
-    def __init__(self):
-        self.box = None  # Coordinates of the locked penis box
-        self.height = 0  # Height of the locked penis box
-        self.active = False  # Whether the locked penis box is active
-
-    def update(self, new_box, new_height):
-        """Update the locked penis box with new coordinates and height."""
-        self.box = new_box
-        self.height = new_height
-        self.active = True
-
-    def deactivate(self):
-        """Deactivate the locked penis box."""
-        self.box = None
-        self.height = 0
-        self.active = False
-
-    def get_box(self):
-        """Return the coordinates of the locked penis box."""
-        return self.box
-
-    def get_height(self):
-        """Return the height of the locked penis box."""
-        return self.height
-
-    def is_active(self):
-        """Check if the locked penis box is active."""
-        return self.active
-
-    def to_dict(self):
-        """
-        Convert the LockedPenisBox object into a dictionary for JSON serialization.
-        """
-        return {
-            "box": self.box,
-            "height": self.height,
-            "active": self.active
-        }
-
-    @classmethod
-    def from_dict(cls, data):
-        """
-        Reconstruct a LockedPenisBox object from a dictionary.
-        """
-        locked_box = cls()
-        if data["box"] is not None:
-            locked_box.update(data["box"], data["height"])
-        return locked_box
-
-class ObjectTracker:
-    """
-    A class to track objects (e.g., penis, glans, etc.) in a video frame and determine their positions and interactions.
-    """
-    def __init__(self, fps, frame_pos, state=None):
-        # Initialize class attributes
-        self.class_names = class_names  # List of class names to track
-        self.active_tracks = {}  # Active tracks: {track_id: Track object}
-        self.inactive_tracks = []  # Inactive tracks (lost tracks)
-        self.distance_kf = KF.KalmanFilter()  # Kalman filter for distance smoothing
-
-        self.frame = None  # Current video frame
-        self.current_frame_id = frame_pos  # Current frame ID
-        self.image_y_size = 0  # Height of the video frame
-        self.fps = fps  # Frames per second of the video
-
-        # Speed and distance thresholds
-        self.max_speed = 100 / (.18 * self.fps)  # Maximum allowed speed for distance changes
-        self.max_allowed = 100  # Maximum allowed distance
-
-        # Tracking state
-        self.boxes = {}  # Detected boxes for each class
-        self.tracked_boxes = []  # List of tracked boxes
-        self.penis_box = None  # Detected penis box
-        self.locked_penis_box = LockedPenisBox()  # Locked penis box
-        self.glans_detected = False  # Whether the glans is detected
-        self.breast_tracking = False  # Whether breast tracking is active
-        self.distance = 100  # Current distance
-        self.previous_distances = [100, 100, 100]  # Previous distances for smoothing
-        self.tracked_body_part = "Nothing"  # Currently tracked body part
-
-        # Sex position tracking
-        self.sex_position = "Not relevant"  # Current sex position
-        self.sex_position_reason = ""  # Reason for the current sex position
-        self.sex_position_history = deque(maxlen=120)  # History of sex positions
-
-        # Position and distance tracking
-        self.positions = {class_name: deque(maxlen=200) for class_name in class_names}  # Positions for each class
-        self.distances = {class_name: deque(maxlen=200) for class_name in class_names}  # Distances for each class
-        self.areas = {class_name: deque(maxlen=200) for class_name in class_names}  # Areas for each class
-
-        # Normalized positions and distances
-        self.normalized_positions = {class_name: deque(maxlen=200) for class_name in class_names}
-        self.normalized_distances = {class_name: deque(maxlen=200) for class_name in class_names}
-
-        # Tracked positions and normalized tracked positions
-        self.tracked_positions = {}
-        self.normalized_tracked_positions = {}
-
-        # Initialize normalized distances and positions
-        for class_name in class_names:
-            self.normalized_distances[class_name].append(100)
-            self.normalized_positions[class_name].append(100)
-
-        # Detection thresholds
-        self.consecutive_detections = {class_name: 0 for class_name in class_names}  # Consecutive detections for each class
-        self.consecutive_non_detections = {class_name: 0 for class_name in class_names}  # Consecutive non-detections for each class
-        self.detections_threshold = 3  # Threshold for considering a detection valid
-
-        # Penetration state
-        self.penetration = False  # Whether penetration is detected
-
-        # Prediction and tracking thresholds
-        self.max_predictions = int(self.fps)  # Maximum number of predictions
-        self.max_inactive_frames = int(fps * 2)  # Maximum inactive frames before a track is lost
-        self.distance_threshold = 50  # Distance threshold for tracking
-
-    def update_distance(self, raw_distance):
-        """
-        Update the tracked distance using a Kalman filter and speed constraints.
-        """
-        if raw_distance is None:
-            filtered_distance = self._predict_distance()
-        else:
-            if abs(self.distance - raw_distance) > self.max_speed:
-                raw_distance = self.distance + np.sign(raw_distance - self.distance) * self.max_speed
-            filtered_distance = raw_distance
-
-        filtered_distance = int(max(0, min(100, filtered_distance)))
-        self.distance = filtered_distance
-        return filtered_distance
-
-    def _predict_distance(self):
-        """Predict the distance using the Kalman filter."""
-        return int(float(self.distance_kf.predict()[0]) / 5) * 5
-
-    def boxes_overlap(self, box1, box2):
-        """Check if two bounding boxes overlap."""
-        if box1 is None or box2 is None:
-            return False
-        x1, y1, x2, y2 = box1
-        x3, y3, x4, y4 = box2
-        return not (x2 < x3 or x4 < x1 or y2 < y3 or y4 < y1)
-
-    def calculate_distance(self, penis_box, other_box):
-        """Calculate the distance between the penis box and another box."""
-        if other_box is None:
-            return None
-        if penis_box is None:
-            logging.warning(f"Penis box is None for frame {self.current_frame_id}, cannot compute distance")
-            return None
-        ox1, oy1, ox2, oy2 = other_box
-        y_pos = (oy1 + 2 * oy2) // 3
-        x_pos = (ox1 + ox2) // 2
-        px1, py1, px2, py2 = penis_box
-        return math.sqrt((x_pos - px2) ** 2 + (y_pos - py2) ** 2)
-
-    def detect_sex_position_change(self, sex_position, reason):
-        """Detect and log changes in the sex position."""
-        self.sex_position_history.append(sex_position)
-        position_counts = {position: self.sex_position_history.count(position) for position in
-                           self.sex_position_history}
-        most_frequent_position = max(position_counts, key=position_counts.get, default="Not relevant")
-        if most_frequent_position != self.sex_position:
-            logging.info(f"@{self.current_frame_id} - Sex position switched to: {most_frequent_position}")
-            self.sex_position = most_frequent_position
-            self.sex_position_reason = reason
-
-    def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
-        """
-        Main tracking logic to process detected boxes and update tracking state.
-        """
-        self.current_frame_id = current_frame_id
-        self.image_y_size = image_y_size
-
-        # Collect all detections
-        detections = [(box, conf, class_name, track_id) for box, conf, cls, class_name, track_id in sorted_boxes]
-
-        # Initialize tracking state
-        self.glans_detected = False
-        self.boxes = {class_name: None for class_name in self.class_names}
-        self.tracked_boxes = []
-        all_detections = {class_name: [] for class_name in self.class_names}
-
-        classes_touching_penis = []
-
-        # Collect all detections by class name
-        for box, conf, cls, class_name, track_id in sorted_boxes:
-            if conf > 0.3:
-                all_detections[class_name].append([conf, box, track_id])
-
-        # Find the best box for specific classes
-        found_box = {class_name: [] for class_name in self.class_names}
-        for check_class_first in ['glans', 'penis', 'navel']:
-            prev_conf = 0
-            for conf, box, track_id in all_detections[check_class_first]:
-                if conf > prev_conf:
-                    found_box[check_class_first] = [box, conf, track_id]
-                    prev_conf = conf
-
-        # Update tracking for specific classes
-        for check_class_first in ['glans', 'penis', 'navel']:
-            if found_box[check_class_first]:
-                self.boxes[check_class_first] = found_box[check_class_first][0]
-                conf = found_box[check_class_first][1]
-                self.consecutive_detections[check_class_first] += 1
-                self.consecutive_non_detections[check_class_first] = 0
-                self.handle_class_first(check_class_first, self.boxes[check_class_first], conf)
-            else:
-                self.consecutive_detections[check_class_first] = 0
-                self.consecutive_non_detections[check_class_first] += 1
-
-        if self.consecutive_non_detections[
-            check_class_first] > self.detections_threshold and check_class_first == 'penis':
-            if self.locked_penis_box.is_active():
-                self.locked_penis_box.deactivate()
-                logging.info(f"@{self.current_frame_id} - Deactivated locked_penis_box")
-
-        sum_pos = 0
-        weight_pos = 0
-
-        for box, conf, cls, class_name, track_id in sorted_boxes:
-            if class_name in ['glans', 'penis', 'navel']:
-                continue
-            if self.boxes_overlap(box, self.locked_penis_box.get_box()):
-                if class_name not in classes_touching_penis:
-                    classes_touching_penis.append(class_name)
-                self.tracked_boxes.append([box, class_name, track_id])
-                x1, y1, x2, y2 = box
-                mid_y = (y1 + 4 * y2) // 5
-                dist_to_penis_base = int(((self.locked_penis_box.get_box()[3] - mid_y) / (self.locked_penis_box.get_height() * .8)) * 100)
-                normalized_y = min(max(0, dist_to_penis_base), 100)
-
-                # Update tracked positions
-                if track_id not in self.tracked_positions:
-                    self.tracked_positions[track_id] = []
-                self.tracked_positions[track_id].append(normalized_y)
-
-                # Maintain a fixed-size history
-                if len(self.tracked_positions[track_id]) > 600:
-                    self.tracked_positions[track_id].pop(0)
-
-                # Update normalized tracked positions
-                if track_id not in self.normalized_tracked_positions:
-                    self.normalized_tracked_positions[track_id] = []
-                self.normalized_tracked_positions[track_id].append(normalized_y)
-
-                # Maintain a fixed-size history
-                if len(self.normalized_tracked_positions[track_id]) > 60:
-                    self.normalized_tracked_positions[track_id].pop(0)
-
-                # Sum delta positions of all touching items, weighted by the length of their history
-                weight_pos_track_id = sum(
-                    abs(self.normalized_tracked_positions[track_id][i] - self.normalized_tracked_positions[track_id][
-                        i - 1]) for i in
-                    range(1, len(self.normalized_tracked_positions[track_id])))
-                sum_pos += max(0, (normalized_y - (100 - self.max_allowed))) * weight_pos_track_id
-                weight_pos += weight_pos_track_id
-
-        if len(classes_touching_penis) == 0 or not self.locked_penis_box.is_active():
-            self.penetration = False
-            distance = 100
-            self.detect_sex_position_change('Not relevant', "no part touching penis / no penis")
-        elif 'pussy' in classes_touching_penis and not self.glans_detected:
-            self.penetration = True
-            self.detect_sex_position_change('Missionnary / Cowgirl', "pussy visible and touching")
-        elif 'ass' in classes_touching_penis and not self.glans_detected:
-            self.penetration = True
-            self.detect_sex_position_change('Doggy / Rev. Cowgirl', "ass visible and touching")
-        elif ('hand' in classes_touching_penis or 'face' in classes_touching_penis) and not self.penetration:
-            self.detect_sex_position_change('Handjob / Blowjob', "hand or face visible and touching")
-        elif 'foot' in classes_touching_penis and not self.penetration:
-            self.detect_sex_position_change('Footjob', "foot visible and touching")
-        elif 'breast' in classes_touching_penis and not self.penetration:
-            self.detect_sex_position_change('Boobjob', "breast visible and touching")
-
-        if weight_pos > 0:
-            distance = int(sum_pos / weight_pos)
-        else:
-            distance = 100
-
-        self.update_distance(distance)
-
-    def handle_class_first(self, class_name, box, conf):
-        """
-        Handle tracking for specific classes (e.g., penis, glans, navel).
-        """
-        if class_name == 'penis':
-            if box is not None and self.penis_box is None:
-                logging.info(f"Penis detected at frame {self.current_frame_id} with confidence {conf}")
-            self.penis_box = box
-
-            if self.penis_box:
-                if self.consecutive_detections['penis'] >= self.detections_threshold:
-                    px1, py1, px2, py2 = self.penis_box
-                    current_height = py2 - py1
-                    if self.locked_penis_box.is_active():
-                        if current_height > self.locked_penis_box.get_height():
-                            self.locked_penis_box.update(self.penis_box, current_height)
-
-                        # Move locked penis box towards current penis box
-                        max_move = max(1, int(self.image_y_size / 960))
-
-                        if abs(self.penis_box[0] - self.locked_penis_box.get_box()[0]) > max_move:
-                            px1 = self.penis_box[0] + np.sign(self.penis_box[0] - self.locked_penis_box.get_box()[0]) * max_move
-                        if abs(self.penis_box[2] - self.locked_penis_box.get_box()[2]) > max_move:
-                            px2 = self.penis_box[2] + np.sign(self.penis_box[2] - self.locked_penis_box.get_box()[2]) * max_move
-                        if abs(self.penis_box[3] - self.locked_penis_box.get_box()[3]) > max_move:
-                            py2 = self.locked_penis_box.get_box()[3] + np.sign(self.penis_box[3] - self.locked_penis_box.get_box()[3]) * max_move
-
-                        self.locked_penis_box.update((px1, py2 - self.locked_penis_box.get_height(), px2, py2), self.locked_penis_box.get_height())
-                    else:
-                        self.locked_penis_box.update(self.penis_box, current_height)
-        elif class_name == 'glans' and box:
-            if self.consecutive_detections['glans'] >= self.detections_threshold:
-                self.boxes['glans'] = box
-                self.glans_detected = True
-                if self.penis_box:
-                    self.locked_penis_box.update(self.penis_box, self.penis_box[3] - self.penis_box[1])
-                if self.penetration:
-                    self.penetration = False
-                    logging.info(
-                        f"@{self.current_frame_id} - Penetration ended after {self.consecutive_detections['glans']} detections of glans")
-                    if self.tracked_body_part != 'Nothing':
-                        self.normalized_distances[self.tracked_body_part].clear()
-                        self.normalized_distances[self.tracked_body_part].append(100)
-        elif class_name == 'navel' and box:
-            if (self.image_y_size - box[1]) / self.image_y_size < 0.15 and not self.breast_tracking:
-                logging.info("Breast tracking mode activated given navel position in lower 15th of frame")
-                self.breast_tracking = True
-
-    def normalize_box_area(self, box, frame_width, frame_height):
-        """Normalize the area of a bounding box relative to the frame size."""
-        x1, y1, x2, y2 = box
-        if x2 <= x1 or y2 <= y1:
-            return 0
-        box_area = (x2 - x1) * (y2 - y1)
-        max_area = frame_width * frame_height
-        normalized_area = 100 * (box_area / max_area)
-        return max(0, min(100, normalized_area))
\ No newline at end of file

From 5d968ddf65b1efec8b6caedbee9149cb3521d491 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 29 Dec 2024 15:24:36 +0100
Subject: [PATCH 016/277] init

---
 FSGenerator.py                                          | 2 +-
 {utils => debug}/Display_debug_results.py               | 0
 {utils => params}/config.py                             | 0
 {utils => tests}/test_Compare_models_and_resolutions.py | 0
 {utils => tests}/test_PoseEstimation.py                 | 4 ++--
 {utils => tests}/test_detect_compare_unwarped.py        | 0
 utils/lib_Debugger.py                                   | 2 +-
 utils/lib_ObjectTracker.py                              | 2 +-
 utils/lib_VideoReaderFFmpeg.py                          | 2 +-
 9 files changed, 6 insertions(+), 6 deletions(-)
 rename {utils => debug}/Display_debug_results.py (100%)
 rename {utils => params}/config.py (100%)
 rename {utils => tests}/test_Compare_models_and_resolutions.py (100%)
 rename {utils => tests}/test_PoseEstimation.py (97%)
 rename {utils => tests}/test_detect_compare_unwarped.py (100%)

diff --git a/FSGenerator.py b/FSGenerator.py
index 7edd272f..929aaaad 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -5,7 +5,7 @@
 from tqdm import tqdm
 from ultralytics import YOLO
 
-from utils.config import class_names, class_priority_order, class_reverse_match, class_colors
+from params.config import class_priority_order, class_reverse_match, class_colors
 from utils.lib_ObjectTracker import ObjectTracker
 from utils.lib_FunscriptHandler import FunscriptGenerator
 from utils.lib_Visualizer import Visualizer
diff --git a/utils/Display_debug_results.py b/debug/Display_debug_results.py
similarity index 100%
rename from utils/Display_debug_results.py
rename to debug/Display_debug_results.py
diff --git a/utils/config.py b/params/config.py
similarity index 100%
rename from utils/config.py
rename to params/config.py
diff --git a/utils/test_Compare_models_and_resolutions.py b/tests/test_Compare_models_and_resolutions.py
similarity index 100%
rename from utils/test_Compare_models_and_resolutions.py
rename to tests/test_Compare_models_and_resolutions.py
diff --git a/utils/test_PoseEstimation.py b/tests/test_PoseEstimation.py
similarity index 97%
rename from utils/test_PoseEstimation.py
rename to tests/test_PoseEstimation.py
index a66d82b6..cd01d3c5 100644
--- a/utils/test_PoseEstimation.py
+++ b/tests/test_PoseEstimation.py
@@ -2,13 +2,13 @@
 import numpy as np
 import cv2
 from collections import defaultdict
-from utils.config import class_names, class_priority_order, class_reverse_match, class_colors
+from params.config import class_reverse_match
 
 #video_path = "/Users/k00gar/Downloads/Katrina Jade [ADD] Oct 16, 2024R_6kvr265_reenc.mp4"
 video_path = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
 
 # Load a model
-pose_model = YOLO("yolo11x-pose.pt")
+pose_model = YOLO("../utils/yolo11x-pose.pt")
 detect_model = YOLO("../models/k00gar-11n-200ep-best.mlpackage")
 
 timestamp_min = 15
diff --git a/utils/test_detect_compare_unwarped.py b/tests/test_detect_compare_unwarped.py
similarity index 100%
rename from utils/test_detect_compare_unwarped.py
rename to tests/test_detect_compare_unwarped.py
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 1bb0e6ea..1a1bdd3f 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -3,7 +3,7 @@
 
 import cv2
 import numpy as np
-from utils.config import class_colors
+from params.config import class_colors
 from scipy.interpolate import interp1d
 from utils.lib_Visualizer import Visualizer
 from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg
diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index 5848886f..cd43f771 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -1,7 +1,7 @@
 from utils import lib_KalmanFilter as KF
 from collections import deque
 import numpy as np
-from utils.config import class_names
+from params.config import class_names
 import math
 import logging
 
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index 9213ceb1..e2cc0b3b 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -1,7 +1,7 @@
 import subprocess
 import cv2
 import numpy as np
-from utils.config import ffmpeg_path, ffprobe_path
+from params.config import ffmpeg_path, ffprobe_path
 
 
 class VideoReaderFFmpeg:

From 336a1e3f1a35f3f308e873d45c1c61a2da09d5c0 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 29 Dec 2024 16:16:25 +0100
Subject: [PATCH 017/277] init

---
 README.md                      | 102 +++++++++++++-
 debug/Display_debug_results.py |  33 +----
 params/config.py               |  16 ++-
 utils/lib_Debugger.py          | 239 +++++++++++++++------------------
 utils/lib_FunscriptHandler.py  |  24 +---
 utils/lib_ObjectDetector.py    |  44 ------
 utils/lib_SceneCutsDetect.py   | 112 ++++++++-------
 utils/lib_VideoReaderFFmpeg.py |  64 +++++----
 8 files changed, 329 insertions(+), 305 deletions(-)
 delete mode 100644 utils/lib_ObjectDetector.py

diff --git a/README.md b/README.md
index d8eb603c..55197789 100644
--- a/README.md
+++ b/README.md
@@ -1,12 +1,10 @@
 # VR-Funscript-AI-Generator
 
-Here’s a detailed and professional `README.md` for your GitHub repository. It explains the purpose of the project, how to set it up, and how to use it effectively. This README is designed to be clear and accessible for both technical and non-technical users.
-
 ---
 
 # Video Processing with YOLO and Funscript Generation
 
-This project is designed to process video files using YOLO (You Only Look Once) object detection and generate **Funscript** data for interactive applications. It is particularly useful for VR videos, where it can track specific objects (e.g., body parts) and create corresponding motion data.
+This project is a Python-based tool for generating Funscript files from VR videos using Computer Vision (CV) and AI techniques. It leverages YOLO (You Only Look Once) object detection and custom tracking algorithms to automate the process of creating Funscript files for interactive devices.
 
 ## Features
 
@@ -16,6 +14,63 @@ This project is designed to process video files using YOLO (You Only Look Once)
 - **Visualization**: Provides real-time visualization of object tracking and Funscript data (in test mode).
 - **VR Support**: Optimized for VR videos, with options to process specific regions of the frame.
 
+## Project Genesis and Evolution
+
+This project started as a dream to automate Funscript generation for VR videos. Here’s a brief history of its development:
+
+### Initial Approach (OpenCV Trackers)
+
+The first version relied on OpenCV trackers to detect and track objects in the video.
+While functional, the approach was slow (8–20 FPS) and struggled with occlusions and complex scenes.
+
+### Transition to YOLO
+
+To improve accuracy and speed, the project shifted to using YOLO object detection.
+A custom YOLO model was trained on a dataset of VR video frames, significantly improving detection quality.
+The new approach runs at 90 FPS on a Mac mini M4 pro, making it much more efficient.
+
+### Original Post
+
+For more details and discussions, check out the original post on EroScripts:  
+[VR Funscript Generation Helper (Python + CV/AI)](https://discuss.eroscripts.com/t/vr-funscript-generation-helper-python-now-cv-ai/202554)
+
+## YOLO Model
+
+The YOLO model used in this project is based on YOLOv11n, which was fine-tuned with 9 new classes and 4,500+ frames randomly extracted from a VR video library. Here’s how the model was developed:
+
+### Initial Training:
+A few hundred frames were manually tagged and boxed to create an initial dataset.
+The model was trained on this dataset to generate preliminary detection results.
+### Iterative Improvement
+The trained model was used to suggest bounding boxes in additional frames.
+The suggested boxes were manually adjusted, and the dataset was expanded.
+This process was repeated iteratively to improve the model’s accuracy.
+### Final Training
+After gathering 4,500+ images and 30,149 annotations, the model was trained for 200 epochs.
+YOLOv11s and YOLOv11m were also tested, but YOLOv11n was chosen for its balance of accuracy and inference speed.
+### Hardware:
+The model runs on a Mac using MPS (Metal Performance Shaders) for accelerated inference on ARM chips.
+Other versions of the model (ONNX and PT) are also available for use on other platforms.
+
+## Pipeline Overview
+
+The pipeline for generating Funscript files is as follows:
+
+- **YOLO Object Detection**:
+A YOLO model detects relevant objects (e.g., penis, hands, mouth, etc.) in each frame of the video.
+The detection results are saved to a .json file.
+- **Tracking Algorithm**:
+A custom tracking algorithm processes the YOLO detection results to track the positions of objects over time.
+The algorithm calculates distances and interactions between objects to determine the Funscript position.
+- **Funscript Generation**:
+The tracked data is used to generate a raw Funscript file.
+- **Simplifier**:
+The raw Funscript data is simplified to remove noise and smooth out the motion.
+The final .funscript file is saved.
+- **Heatmap Generation**:
+A heatmap is generated to visualize the Funscript data.
+
+
 ## Prerequisites
 
 Before using this project, ensure you have the following installed:
@@ -34,8 +89,25 @@ Before using this project, ensure you have the following installed:
 
 2. **Install dependencies**:
    ```bash
-   pip install -r requirements.txt
+   pip install numpy opencv-python tqdm ultralytics scipy matplotlib simplification
    ```
+### Libraries Used
+
+The project relies on the following Python libraries:
+
+- **numpy**: For numerical computations and array manipulations.
+- **opencv-python**: For computer vision tasks like video processing and image manipulation.
+- **tqdm**: For displaying progress bars during long-running tasks.
+- **ultralytics**: For YOLO object detection and tracking.
+- **scipy**: For scientific computing, including interpolation (interp1d).
+- **matplotlib**: For plotting and visualization.
+- **simplification**: For simplifying Funscript data.
+- **logging**: For logging debug and runtime information.
+- **argparse**: For parsing command-line arguments.
+- **subprocess**: For running external commands (e.g., FFmpeg).
+- **collections**: For specialized container datatypes like deque and defaultdict.
+- **datetime**: For handling timestamps and date-related operations.
+- **json**: For reading and writing JSON files.
 
 3. **Download the YOLO model**:
    - Place your YOLO model file (e.g., `k00gar-11n-200ep-best.mlpackage`) in the `models/` directory.
@@ -89,14 +161,32 @@ The script generates the following files in the same directory as the input vide
 4. **Visualization (Test Mode)**:
    - Displays bounding boxes and Funscript data in real-time for debugging and verification.
 
+5. **Debugging (Debug Mode)**:
+   - Saves detailed logs for debugging purposes.
+
 ## Example
 
+1. **Generate funscript**:
 ```bash
-python main.py /path/to/vr_video.mp4 --yolo_model models/k00gar-11n-200ep-best.mlpackage --test_mode --is_vr
+python FSGenerator.py /path/to/vr_video.mp4 --yolo_model models/k00gar-11n-200ep-best.mlpackage --test_mode --is_vr
 ```
-
 This command processes a VR video, detects objects using the specified YOLO model, and generates Funscript data. The `--test_mode` flag enables real-time visualization.
 
+2. **Debugging example**:
+
+Display a Specific Frame with debug information:
+```bash
+debugger.display_frame(frame_id)
+```
+Play the Video with debug information:
+```bash
+debugger.play_video(frame_id)
+```
+Record the Debugged Video:
+```bash
+debugger.play_video(frame, record=True, downsize_ratio=2, duration=10)
+```
+
 ## Contributing
 
 Contributions are welcome! If you'd like to contribute, please follow these steps:
diff --git a/debug/Display_debug_results.py b/debug/Display_debug_results.py
index a90ff188..4a079e2c 100644
--- a/debug/Display_debug_results.py
+++ b/debug/Display_debug_results.py
@@ -1,40 +1,9 @@
-from lib_Debugger import Debugger
+from utils.lib_Debugger import Debugger
 
-#video="/Users/k00gar/Downloads/BadoinkVR_Lights_Camera_Satisfaction_4K_HEVC_180_180x180_3dh.mp4"
-
-#video = "/Users/k00gar/Downloads/Katrina Jade [ADD] Oct 16, 2024R_6kvr265_reenc.mp4"
 video = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
-#video = "/Users/k00gar/Downloads/ARPorn_Sasha Tatcha_Fit and Fired Up_4000p_8K_original_FISHEYE190_alpha.mp4"
-
-#video = "/Users/k00gar/Downloads/2022-09-09 - TonightsGirlfriend - Kenna James.mp4"
 
-#Vote for me
-#frame = 126000
 frame = 79080
 
-# Katrina Jade ADD
-#frame = 77000
-#frame = 126000
-
-#BadoinkVR Lights Camera
-
-#frame = 53100  # messed up bj
-#frame = 90000
-#frame = 77400
-
-#Kenna James TNGF debug section
-#frame = 80000
-#frame = 36900
-#frame = 153000 # says CG should be handjob
-#frame = 75600
-#frame = 100000 # should be grinding but BJ....
-#frame = 72500  #BJ
-#frame = 50400 #Grinding
-
-#frame = 42920
-
-#frame = 35312
-
 debugger = Debugger(video, video[:-4])
 
 debugger.load_logs()
diff --git a/params/config.py b/params/config.py
index 7806a863..8cc9d28e 100644
--- a/params/config.py
+++ b/params/config.py
@@ -43,4 +43,18 @@
     "foot": (165, 42, 42),              # brown
     "left foot": (165, 42, 42),         # brown
     "right foot": (165, 42, 42)         # brown
-}
\ No newline at end of file
+}
+
+# Define custom colormap based on Lucife's heatmapColors
+heatmap_colors = [
+    [0, 0, 0],  # Black (no action)
+    [30, 144, 255],  # Dodger blue
+    [34, 139, 34],  # Lime green
+    [255, 215, 0],  # Gold
+    [220, 20, 60],  # Crimson
+    [147, 112, 219],  # Medium purple
+    [37, 22, 122]  # Dark blue
+]
+step_size = 120  # Speed step size for color transitions
+
+vw_filter_coeff = 10.0
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 1a1bdd3f..2b6634df 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -1,6 +1,5 @@
 import json
 import time
-
 import cv2
 import numpy as np
 from params.config import class_colors
@@ -8,32 +7,42 @@
 from utils.lib_Visualizer import Visualizer
 from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg
 
+
 class Debugger:
+    """
+    A class for debugging video frames by logging variables, bounding boxes, and visualizing them.
+    """
+
     def __init__(self, video_path, output_dir):
+        """
+        Initialize the Debugger.
+        :param video_path: Path to the video file.
+        :param output_dir: Directory to save debug logs.
+        """
         self.video_path = video_path
         self.output_dir = output_dir
-        self.logs = {}
-
-        self.cap = None
-        self.current_frame = 0
-        self.total_frames = 0
-        self.fps = 0
+        self.logs = {}  # Dictionary to store logs for each frame
+        self.cap = None  # Video capture object
+        self.current_frame = 0  # Current frame being processed
+        self.total_frames = 0  # Total number of frames in the video
+        self.fps = 0  # Frames per second of the video
+        self.bar_y_start = 0  # Y-coordinate of the progress bar
 
     def log_frame(self, frame_id, variables=None, bounding_boxes=None):
         """
         Log or update the state of variables and bounding boxes for a specific frame.
+        :param frame_id: The frame ID to log.
+        :param variables: Dictionary of variables to log.
+        :param bounding_boxes: List of bounding boxes to log.
         """
         if frame_id not in self.logs:
-            self.logs[frame_id] = {
-                'variables': {},
-                'bounding_boxes': []
-            }
+            self.logs[frame_id] = {"variables": {}, "bounding_boxes": []}
 
         if variables:
-            self.logs[frame_id]['variables'].update(variables)
+            self.logs[frame_id]["variables"].update(variables)
 
         if bounding_boxes:
-            self.logs[frame_id]['bounding_boxes'].extend(bounding_boxes)
+            self.logs[frame_id]["bounding_boxes"].extend(bounding_boxes)
 
     def save_logs(self):
         """
@@ -41,8 +50,8 @@ def save_logs(self):
         """
         log_file = f"{self.output_dir}_debug_logs.json"
 
-        # Custom function to convert NumPy types to native Python types
         def default(obj):
+            """Convert NumPy types to native Python types for JSON serialization."""
             if isinstance(obj, np.integer):
                 return int(obj)
             elif isinstance(obj, np.floating):
@@ -52,8 +61,7 @@ def default(obj):
             else:
                 raise TypeError(f"Object of type {obj.__class__.__name__} is not JSON serializable")
 
-        # Save logs with the custom default function
-        with open(log_file, 'w') as f:
+        with open(log_file, "w") as f:
             json.dump(self.logs, f, indent=4, default=default)
         print(f"Logs saved to {log_file}")
 
@@ -63,7 +71,7 @@ def load_logs(self):
         """
         log_file = f"{self.output_dir}_debug_logs.json"
         try:
-            with open(log_file, 'r') as f:
+            with open(log_file, "r") as f:
                 self.logs = json.load(f)
             print(f"Logs loaded from {log_file}")
         except FileNotFoundError:
@@ -74,6 +82,7 @@ def load_logs(self):
     def display_frame(self, frame_id):
         """
         Display the logged frame with bounding boxes and variable states.
+        :param frame_id: The frame ID to display.
         """
         str_frame_id = str(frame_id)
         if str_frame_id not in self.logs:
@@ -90,39 +99,30 @@ def display_frame(self, frame_id):
             print(f"Failed to read frame {frame_id}")
             return
 
-        frame = frame[:, frame.shape[1] // 6:frame.shape[1] // 3, :]
-
-        # Draw bounding boxes with color coding
-        for box in self.logs[str_frame_id]['bounding_boxes']:
-            x1, y1, x2, y2 = box['box']
-            class_name = box['class_name']
-            conf = box['conf']
+        # Crop the frame (for VR videos)
+        frame = frame[:, frame.shape[1] // 6 : frame.shape[1] // 3, :]
 
-            print(f"{class_name}: conf {conf}, box: [{x1}, {y1}, {x2}, {y2}]")
-
-            # Get the color for the class from the class_colors dictionary
+        # Draw bounding boxes
+        for box in self.logs[str_frame_id]["bounding_boxes"]:
+            x1, y1, x2, y2 = box["box"]
+            class_name = box["class_name"]
+            conf = box["conf"]
             color = class_colors.get(class_name, (0, 255, 0))  # Default to green if class not found
-
-            # Draw the bounding box
             cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
-
-            # Add the class name and confidence score as text
             cv2.putText(frame, f"{class_name} {conf:.2f}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
 
-        # Display variables line by line
-        variables = self.logs[str_frame_id]['variables']
-        y_offset = 30  # Initial vertical offset for the first variable
+        # Display variables
+        variables = self.logs[str_frame_id]["variables"]
+        y_offset = 30
         for key, value in variables.items():
-            variable_text = f"{key}: {value}"
-            print(variable_text)
-            cv2.putText(frame, variable_text, (10, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
-            y_offset += 20  # Move down by 20 pixels for the next variable
+            cv2.putText(frame, f"{key}: {value}", (10, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
+            y_offset += 20
 
         # Draw the locked_penis_box if it exists
-        locked_penis_box = variables.get('locked_penis_box')
+        locked_penis_box = variables.get("locked_penis_box")
         if locked_penis_box:
             x1, y1, x2, y2 = locked_penis_box
-            color = class_colors.get('penis', (0, 255, 0))  # Use the color for "penis" or default to green
+            color = class_colors.get("penis", (0, 255, 0))
             cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
             cv2.putText(frame, "Locked Penis", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
 
@@ -131,38 +131,33 @@ def display_frame(self, frame_id):
         cv2.waitKey(0)
         cv2.destroyAllWindows()
 
-    def debug_frame(self, frame_id, variables, bounding_boxes):
-        """
-        Log and display the frame for debugging.
-        """
-        self.log_frame(frame_id, variables, bounding_boxes)
-        self.display_frame(frame_id)
-
     def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=False, downsize_ratio=1):
         """
-        Play the video from a specified frame or timestamp, displaying variables, bounding boxes,
-        and rolling window curves for distance and funscript data.
+        Play the video from a specified frame, displaying variables, bounding boxes, and rolling window curves.
+        :param start_frame: Frame to start playback from.
+        :param duration: Duration of playback in seconds.
+        :param rolling_window_size: Size of the rolling window for distance and funscript data.
+        :param record: Whether to record the debug video.
+        :param downsize_ratio: Ratio to downsize the recorded video.
         """
-
         visualizer = Visualizer()
 
         # Load the video
-        #cap = cv2.VideoCapture(self.video_path)
         self.cap = VideoReaderFFmpeg(self.video_path, is_VR=True)
         self.total_frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
         self.fps = self.cap.get(cv2.CAP_PROP_FPS)
         print(f"Total frames: {self.total_frames}, FPS: {self.fps}")
 
+        # Initialize video writer if recording
         if record:
             ret, frame = self.cap.read()
             if self.cap.is_VR:
-                frame_copy = frame.copy()
-                frame_copy = frame_copy[:, frame.shape[1] // 3:2 * frame.shape[1] // 3, :]
-            # Define the output video writer
+                frame_copy = frame[:, frame.shape[1] // 3 : 2 * frame.shape[1] // 3, :]
             output_path = self.video_path.replace(".mp4", "_debug.mp4")
-            fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # Codec for MP4
-            out = cv2.VideoWriter(output_path, fourcc, self.fps, (frame_copy.shape[1] // downsize_ratio, frame_copy.shape[0] // downsize_ratio))
-
+            fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+            out = cv2.VideoWriter(
+                output_path, fourcc, self.fps, (frame_copy.shape[1] // downsize_ratio, frame_copy.shape[0] // downsize_ratio)
+            )
             if not out.isOpened():
                 print(f"Error: Could not open video writer for {output_path}")
                 self.cap.release()
@@ -174,88 +169,72 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
         # Load the funscript file
         funscript_path = self.video_path.replace(".mp4", ".funscript")
         try:
-            with open(funscript_path, 'r') as f:
+            with open(funscript_path, "r") as f:
                 funscript_data = json.load(f)
+            actions = funscript_data.get("actions", [])
+            funscript_times = [action["at"] for action in actions]
+            funscript_positions = [action["pos"] for action in actions]
+            funscript_interpolator = interp1d(funscript_times, funscript_positions, kind="linear", fill_value="extrapolate")
         except FileNotFoundError:
             print(f"Funscript file not found at {funscript_path}")
-            funscript_data = None
+            funscript_interpolator = None
 
         # Initialize rolling window buffers
         distance_buffer = np.zeros(rolling_window_size)
         funscript_buffer = np.zeros(rolling_window_size)
 
-        # Prepare funscript interpolation
-        if funscript_data:
-            actions = funscript_data.get('actions', [])
-            funscript_times = [action['at'] for action in actions]
-            funscript_positions = [action['pos'] for action in actions]
-            print(f"len of funscript_times: {len(funscript_times)}")
-            funscript_interpolator = interp1d(funscript_times, funscript_positions, kind='linear',
-                                              fill_value="extrapolate")
-
+        # Calculate end frame
         self.current_frame = start_frame
-        if duration > 0:
-            end_frame = start_frame + int(duration * self.fps)  # Calculate the end frame based on duration
-        else:
-            end_frame = self.total_frames
-
-        width = self.cap.get(cv2.CAP_PROP_FRAME_WIDTH)
-
-        if self.cap.is_VR:
-            width = width // 6
+        end_frame = start_frame + int(duration * self.fps) if duration > 0 else self.total_frames
 
+        # Set up mouse callback for progress bar
+        width = self.cap.get(cv2.CAP_PROP_FRAME_WIDTH) // 6 if self.cap.is_VR else self.cap.get(cv2.CAP_PROP_FRAME_WIDTH)
         cv2.namedWindow("Debug Video")
-        cv2.setMouseCallback("Debug Video", self._mouse_callback,
-                             param=width)
+        cv2.setMouseCallback("Debug Video", self._mouse_callback, param=width)
 
+        # Play the video
         while self.current_frame < end_frame:
             ret, frame = self.cap.read()
             if not ret:
                 break
 
-            # Crop the frame (as done in display_frame)
-            #frame = frame[:, frame.shape[1] // 6:frame.shape[1] // 3, :]
+            # Crop the frame for VR videos
             if self.cap.is_VR:
-                frame_copy = frame.copy()
-                frame_copy = frame_copy[:, frame.shape[1] // 3:2 * frame.shape[1] // 3, :]
+                frame_copy = frame.copy()[:, frame.shape[1] // 3 : 2 * frame.shape[1] // 3, :]
 
             # Display variables and bounding boxes
             str_frame_id = str(self.current_frame)
             if str_frame_id in self.logs:
-                variables = self.logs[str_frame_id]['variables']
-                bounding_boxes = self.logs[str_frame_id]['bounding_boxes']
+                variables = self.logs[str_frame_id]["variables"]
+                bounding_boxes = self.logs[str_frame_id]["bounding_boxes"]
 
                 # Draw bounding boxes
                 for box in bounding_boxes:
-                    x1, y1, x2, y2 = box['box']
-                    class_name = box['class_name']
-                    conf = box['conf']
-                    track_id = box['track_id']
-                    position = box['position']
+                    x1, y1, x2, y2 = box["box"]
+                    class_name = box["class_name"]
+                    position = box["position"]
                     color = class_colors.get(class_name, (0, 255, 0))
                     cv2.rectangle(frame_copy, (x1, y1), (x2, y2), color, 2)
-                    cv2.putText(frame_copy, f"{class_name} {position}", (x1, y1 - 10),
-                                cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+                    cv2.putText(frame_copy, f"{class_name} {position}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
 
                 # Display variables
                 y_offset = 30
                 for key, value in variables.items():
-                    variable_text = f"{key}: {value}"
-                    cv2.putText(frame_copy, variable_text, (10, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
+                    cv2.putText(frame_copy, f"{key}: {value}", (10, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
                     y_offset += 20
 
                 # Draw the locked_penis_box if it exists
-                locked_penis_box = variables.get('locked_penis_box')
+                locked_penis_box = variables.get("locked_penis_box")
                 if locked_penis_box:
-                    x1, y1, x2, y2 = locked_penis_box
-                    color = class_colors.get('penis', (0, 255, 0))
+                    print(f"locked_penis_box: {locked_penis_box}")
+                    x1, y1, x2, y2 = locked_penis_box['box']
+                    color = class_colors.get("penis", (0, 255, 0))
                     cv2.rectangle(frame_copy, (x1, y1), (x2, y2), color, 2)
                     cv2.putText(frame_copy, "Locked Penis", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
 
             # Update rolling window buffers
-            distance = variables.get('distance', 0)
-            funscript_value = self._get_funscript_value(funscript_interpolator, self.current_frame, self.fps) if funscript_data else 0
-            #print(f"Funscript value: {funscript_value}")
+            distance = variables.get("distance", 0)
+            funscript_value = self._get_funscript_value(funscript_interpolator, self.current_frame, self.fps) if funscript_interpolator else 0
             visualizer.draw_gauge(frame_copy, funscript_value)
             distance_buffer = np.roll(distance_buffer, -1)
             distance_buffer[-1] = distance
@@ -263,28 +242,29 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
             funscript_buffer[-1] = funscript_value
 
             # Draw rolling window curves
-            graph_height = int(frame_copy.shape[0] * 0.2)  # 20% of the frame height
-            graph_y_start = y_offset + 10  # Start the graph right under the variables
+            graph_height = int(frame_copy.shape[0] * 0.2)
+            graph_y_start = y_offset + 10
             self._draw_rolling_window_curve(frame_copy, distance_buffer, (0, 255, 0), 0.5, graph_height, graph_y_start)
             self._draw_rolling_window_curve(frame_copy, funscript_buffer, (255, 0, 0), 0.5, graph_height, graph_y_start)
 
+            # Draw progress bar
             self._draw_progress_bar(frame_copy, frame.shape[1], frame.shape[0])
 
             # Show the frame
-            cv2.imshow(f"Debug Video", frame_copy)
-            if cv2.waitKey(1) & 0xFF == ord('q'):
+            cv2.imshow("Debug Video", frame_copy)
+            if cv2.waitKey(1) & 0xFF == ord("q"):
                 break
-            # if spacebar is pressed, pause
-            if cv2.waitKey(1) & 0xFF == 32:
+            if cv2.waitKey(1) & 0xFF == 32:  # Pause on spacebar
                 time.sleep(10)
 
+            # Record the frame if enabled
             if record:
-                # resize the frame, divide dimensions by 2
                 frame_copy = cv2.resize(frame_copy, (frame_copy.shape[1] // downsize_ratio, frame_copy.shape[0] // downsize_ratio))
                 out.write(frame_copy)
 
             self.current_frame += 1
 
+        # Release resources
         self.cap.release()
         if record:
             out.release()
@@ -293,18 +273,26 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
     def _get_funscript_value(self, interpolator, frame_id, fps):
         """
         Get the interpolated funscript value for a given frame.
+        :param interpolator: Interpolation function for funscript data.
+        :param frame_id: The frame ID.
+        :param fps: Frames per second of the video.
+        :return: Interpolated funscript value.
         """
         time_in_milliseconds = int((frame_id / fps) * 1000)
         return interpolator(time_in_milliseconds)
 
     def _draw_rolling_window_curve(self, frame, buffer, color, alpha, graph_height, graph_y_start):
         """
-        Draw a rolling window curve on the frame as a transparent overlay with a fixed Y-axis range of 0 to 100.
+        Draw a rolling window curve on the frame as a transparent overlay.
+        :param frame: The frame to draw on.
+        :param buffer: The buffer of values to plot.
+        :param color: The color of the curve.
+        :param alpha: Transparency of the overlay.
+        :param graph_height: Height of the graph.
+        :param graph_y_start: Y-coordinate to start the graph.
         """
         overlay = frame.copy()
         height, width, _ = frame.shape
-
-        # Ensure the buffer values are within the range 0 to 100
         buffer_clipped = np.clip(buffer, 0, 100)
 
         for i in range(len(buffer_clipped) - 1):
@@ -319,40 +307,35 @@ def _draw_rolling_window_curve(self, frame, buffer, color, alpha, graph_height,
     def _mouse_callback(self, event, x, y, flags, param):
         """
         Handle mouse events for video navigation.
+        :param event: The mouse event.
+        :param x: X-coordinate of the mouse.
+        :param y: Y-coordinate of the mouse.
+        :param flags: Additional flags.
+        :param param: Additional parameters (width of the frame).
         """
-        if event == cv2.EVENT_LBUTTONDOWN:  # Start dragging
-            if y >= self.bar_y_start:  # Clicked on the progress bar
-                self._update_frame_from_mouse(x, param)
-        """
-        elif event == cv2.EVENT_MOUSEMOVE and self.is_dragging:  # Dragging
+        if event == cv2.EVENT_LBUTTONDOWN and y >= self.bar_y_start:
             self._update_frame_from_mouse(x, param)
 
-        elif event == cv2.EVENT_LBUTTONUP:  # End dragging
-            self.is_dragging = False
-            self._update_frame_from_mouse(x, param)
-        """
-
     def _update_frame_from_mouse(self, x, width):
         """
         Update the current frame based on the mouse's X position.
+        :param x: X-coordinate of the mouse.
+        :param width: Width of the frame.
         """
         self.current_frame = int((x / width) * self.total_frames)
         print(f"Target frame: {self.current_frame}")
         self.cap.release()
-        print("Release cap")
-        #self.current_frame = max(0, min(self.current_frame, self.total_frames - 1))
         self.cap = VideoReaderFFmpeg(self.video_path, is_VR=True)
-        print("Set up new cap")
         self.cap.set(cv2.CAP_PROP_POS_FRAMES, self.current_frame)
         print("Done resetting and jumping to target frame")
 
     def _draw_progress_bar(self, frame, width, height):
         """
         Draw a progress bar on the frame indicating the current playback position.
+        :param frame: The frame to draw on.
+        :param width: Width of the frame.
+        :param height: Height of the frame.
         """
-        # Ensure the frame is writable
-        # frame = np.ascontiguousarray(frame)
-
         bar_height = 10
         bar_x_start = 0
         bar_x_end = width
@@ -365,6 +348,4 @@ def _draw_progress_bar(self, frame, width, height):
 
         # Progress indicator
         progress_x = int((self.current_frame / self.total_frames) * width)
-        cv2.rectangle(frame, (bar_x_start, bar_y_start), (progress_x, bar_y_end), (0, 255, 0), -1)
-
-
+        cv2.rectangle(frame, (bar_x_start, bar_y_start), (progress_x, bar_y_end), (0, 255, 0), -1)
\ No newline at end of file
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 62a6c234..b4b233f4 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -4,10 +4,9 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import datetime
-from scipy.ndimage import gaussian_filter
 import matplotlib.colors as mcolors
-import matplotlib.cm as cm
 from matplotlib.cm import ScalarMappable
+from params.config import heatmap_colors, step_size, vw_filter_coeff
 
 class FunscriptGenerator:
     def generate(self, raw_funscript_path, funscript_data, fps, TestMode = False):
@@ -26,17 +25,10 @@ def generate(self, raw_funscript_path, funscript_data, fps, TestMode = False):
 
         try:
             print(f"Generating funscript based on {len(data)} points...")
-
-            filter_coeff = 10.0
-
-            self.filtered_positions = simplify_coords(data, filter_coeff)  # Use VW algorithm
-
+            self.filtered_positions = simplify_coords(data, vw_filter_coeff)  # Use VW algorithm
             print(f"Lenghth of filtered positions: {len(self.filtered_positions) + 1}")
-
             self.write_funscript(self.filtered_positions, output_path, fps)
-
             print(f"Funscript generated and saved to {output_path}")
-
             self.generate_heatmap(output_path, output_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
 
         except:
@@ -274,17 +266,7 @@ def generate_heatmap(self, funscript_path, output_image_path):
         speeds = np.abs(np.diff(positions) / np.diff(times)) * 1000  # Positions per second
         print(f"Speeds: {speeds}")
 
-        # Define custom colormap based on Lucife's heatmapColors
-        heatmap_colors = [
-            [0, 0, 0],  # Black (no action)
-            [30, 144, 255],  # Dodger blue
-            [34, 139, 34],  # Lime green
-            [255, 215, 0],  # Gold
-            [220, 20, 60],  # Crimson
-            [147, 112, 219],  # Medium purple
-            [37, 22, 122]  # Dark blue
-        ]
-        step_size = 120  # Speed step size for color transitions
+
 
         def get_color(intensity):
             if intensity <= 0:
diff --git a/utils/lib_ObjectDetector.py b/utils/lib_ObjectDetector.py
deleted file mode 100644
index d8fb7610..00000000
--- a/utils/lib_ObjectDetector.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from ultralytics import YOLO
-import gc
-
-class ObjectDetector:
-    def __init__(self, model_name):
-        self.model = YOLO(model_name, task='detect')
-        self.class_priority_order = {
-            "glans": 0, "penis": 1, "breast": 2, "navel": 3, "pussy": 4, "butt": 5, "face": 6
-        }
-        self.class_default_priority = 7
-        class_types = {'face': 0, 'hand': 1, 'penis': 2, 'glans': 3, 'pussy': 4, 'butt': 5,
-                       'anus': 6, 'breast': 7, 'navel': 8, 'foot': 9}
-
-        # Define priority order
-        class_priority_order = {"glans": 0, "penis": 1, "breast": 2, "navel": 3, "pussy": 4, "butt": 5, "face": 6}
-        class_default_priority = 7  # For classes not explicitly listed
-
-    def detect(self, frame):
-        results = self.model(frame, verbose=False)
-        boxes = []
-        for result in results:
-            for *box, conf, cls in result.boxes.data.cpu().numpy():
-                #print(f"Detected {result.names[int(cls)]} with confidence {conf} and box {box}")
-                class_name = self.map_class_type_to_name(result.names[int(cls)], box[0], box[2], frame.shape[1])
-                boxes.append((box, conf, cls, class_name))
-        # Sort the collected boxes by priority
-        sorted_boxes = sorted(
-            boxes,
-            key=lambda x: self.class_priority_order.get(x[3], self.class_default_priority)  # Sort by class name priority
-        )
-        del results
-        gc.collect()
-        return sorted_boxes
-
-    def map_class_type_to_name(self, class_type, x1, x2, image_width):
-        if class_type in ['foot', 'hand']:
-            # Call it left if it is mainly on the left of the frame, right otherwise
-            if (x1 + x2) / 2 < image_width / 2:
-                class_name = 'left ' + class_type
-            else:
-                class_name = 'right ' + class_type
-        else:
-            class_name = class_type
-        return class_name
diff --git a/utils/lib_SceneCutsDetect.py b/utils/lib_SceneCutsDetect.py
index 7b25b400..5013817e 100644
--- a/utils/lib_SceneCutsDetect.py
+++ b/utils/lib_SceneCutsDetect.py
@@ -1,88 +1,104 @@
 import cv2
-import numpy as np
-import tqdm
+from tqdm import tqdm
+
 
 def compute_histogram(frame):
-    # Convert the frame to the HSV color space
+    """
+    Compute the normalized histogram of the H channel in the HSV color space.
+    :param frame: Input frame (BGR format).
+    :return: Normalized histogram.
+    """
     hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
-    # Compute the histogram for the H channel
     hist = cv2.calcHist([hsv], [0], None, [256], [0, 256])
-    # Normalize the histogram
     cv2.normalize(hist, hist)
     return hist
 
+
 def compare_histograms(hist1, hist2):
-    # Use the correlation method to compare histograms
+    """
+    Compare two histograms using the correlation method.
+    :param hist1: First histogram.
+    :param hist2: Second histogram.
+    :return: Similarity score (higher values indicate greater similarity).
+    """
     return cv2.compareHist(hist1, hist2, cv2.HISTCMP_CORREL)
 
-def detect_scene_changes(video_path, crop = None, threshold=0.97, frame_start = 0, frame_end = None):
+
+def detect_scene_changes(video_path, crop=None, threshold=0.97, frame_start=0, frame_end=None):
+    """
+    Detect scene changes in a video using histogram comparison.
+    :param video_path: Path to the video file.
+    :param crop: Crop mode ("Left" or "Right") to process only one side of the frame.
+    :param threshold: Similarity threshold for detecting scene changes.
+    :param frame_start: Starting frame for processing.
+    :param frame_end: Ending frame for processing.
+    :return: List of scene changes as [start_frame, end_frame] pairs.
+    """
     cap = cv2.VideoCapture(video_path)
     if not cap.isOpened():
         print("Error: Could not open video.")
-        return
+        return []
 
-    frame_count = 0
-    prev_cut = 0
-    fps_step = 2 * int(cap.get(cv2.CAP_PROP_FPS))
+    # Get video properties
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
     total_frames_base = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-    if frame_start != 0:
-        if frame_end is None:
-            total_frames = total_frames_base - frame_start
-        else:
-            total_frames = frame_end - frame_start
-    else:
-        total_frames = total_frames_base
+    frame_step = 2 * fps  # Process every 2 seconds of video
+
+    # Adjust frame range based on input
+    if frame_end is None:
+        frame_end = total_frames_base
+    total_frames = frame_end - frame_start
+    total_frames_to_parse = int(total_frames / frame_step)
 
-    total_frames_to_parse = int(total_frames / fps_step)
+    # Initialize variables
     scene_changes = []
     prev_hist = None
+    prev_cut = frame_start
 
-    for frame_pos in tqdm.tqdm(range(total_frames_to_parse)):
-        cap.set(cv2.CAP_PROP_POS_FRAMES, frame_pos * fps_step)
+    # Process frames
+    for frame_pos in tqdm(range(total_frames_to_parse), desc="Detecting scene changes"):
+        cap.set(cv2.CAP_PROP_POS_FRAMES, frame_start + frame_pos * frame_step)
         ret, frame = cap.read()
         if not ret:
             break
 
+        # Crop frame if specified
         if crop == "Left":
-            # only left side of the frame
             frame = frame[:, :frame.shape[1] // 2]
         elif crop == "Right":
-            # only right side of the frame
             frame = frame[:, frame.shape[1] // 2:]
 
+        # Compute histogram and compare with previous
         current_hist = compute_histogram(frame)
-
         if prev_hist is not None:
             similarity = compare_histograms(prev_hist, current_hist)
             if similarity < threshold:
-                # we have a scene cut in between (frame_pos - 1)*fps and frame_pos * fps
-                tqdm.tqdm.write(
-                    f"Raw Scene change detected at frame {frame_pos * fps_step}, time: {int(cap.get(cv2.CAP_PROP_POS_MSEC) / 1000 // 60)} min {int(cap.get(cv2.CAP_PROP_POS_MSEC) / 1000 % 60)} sec")
-                scene_changes.append([prev_cut, frame_pos * fps_step])
-                prev_cut = frame_pos * fps_step
+                # Scene change detected
+                current_frame = frame_start + frame_pos * frame_step
+                tqdm.write(
+                    f"Scene change detected at frame {current_frame}, "
+                    f"time: {int(cap.get(cv2.CAP_PROP_POS_MSEC) / 1000 // 60)} min "
+                    f"{int(cap.get(cv2.CAP_PROP_POS_MSEC) / 1000 % 60)} sec"
+                )
+                scene_changes.append([prev_cut, current_frame])
+                prev_cut = current_frame
 
         prev_hist = current_hist
 
-    scenes = []
+    # Add the last scene
+    if not scene_changes:
+        scene_changes.append([frame_start, frame_end])
+    elif scene_changes[-1][1] != frame_end:
+        scene_changes.append([scene_changes[-1][1], frame_end])
+
+    # Merge scenes that are too close
+    merged_scenes = []
     for scene in scene_changes:
-        if len(scenes) == 0:
-            scenes.append(scene)
+        if not merged_scenes or scene[0] - merged_scenes[-1][1] >= 5000:
+            merged_scenes.append(scene)
         else:
-            if scene[1] - scenes[-1][1] < 5000:
-                scenes[-1][1] = scene[1]
-            else:
-                scenes.append(scene)
-
-    # add the last scene
-    if len(scenes) == 0:
-        scenes.append([0, total_frames_base])
-    else:
-        if scenes[-1][1] != total_frames_base:
-            scenes.append([scenes[-1][1], total_frames_base])
-
-    print(f"Found {len(scenes)} relevant scenes: {scenes}.")
+            merged_scenes[-1][1] = scene[1]
 
+    print(f"Found {len(merged_scenes)} relevant scenes: {merged_scenes}.")
     cap.release()
-    return scenes
-
-
+    return merged_scenes
\ No newline at end of file
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index e2cc0b3b..54e19190 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -1,6 +1,7 @@
 import subprocess
 import cv2
 import numpy as np
+import argparse
 from params.config import ffmpeg_path, ffprobe_path
 
 
@@ -85,28 +86,24 @@ def _start_process(self, start_frame=0):
 
         if self.is_VR:
             # FFmpeg command to read frames with VR reprojection
-            # if 'FISHEYE' is present in video_path then type = "fisheye"
             if 'FISHEYE' in self.video_path:
                 self.type = "fisheye"
-                self.iv_fov = 190  # 120
+                self.iv_fov = 190
                 self.ih_fov = 190
                 self.v_fov = 90
                 self.h_fov = 90
-                self.d_fov = 180  # 110
+                self.d_fov = 180
             else:
-                type = "he"  # [0:v]v360=input=he:in_stereo=sbs:pitch=-35:v_fov=90:h_fov=90:output=sg:w=2048:h=2048
+                self.type = "he"
                 self.iv_fov = 90
                 self.ih_fov = 90
                 self.d_fov = 100
-            #"""
+
             cmd = [
                 self.ffmpeg_path,
-                #"-hwaccel", "videotoolbox",  # Use hardware acceleration on macOS
                 "-ss", str(start_time / 1000),  # Seek to start time in seconds
                 "-i", self.video_path,
                 "-an",  # Disable audio processing
-                #"-filter_complex",  # Apply the v360 filter for VR reprojection
-                #f"[0:v]v360=input=he:in_stereo=sbs:pitch=-35:yaw=-0.75:roll=0:output=sg:v_fov=90:h_fov=90:d_fov=180:w={self.width}:h={self.height}",
                 "-map", "0:v:0",
                 "-vf", f"crop=w=iw/2:h=ih:x=0:y=0,v360={self.type}:sg:iv_fov={self.iv_fov}:ih_fov={self.ih_fov}:d_fov={self.d_fov}:v_fov={self.v_fov}:h_fov={self.h_fov}:pitch=-20:yaw=0:roll=0:w={self.width}:h={self.height}:interp=lanczos:reset_rot=1",
                 "-f", "rawvideo",  # Output raw video data
@@ -115,19 +112,6 @@ def _start_process(self, start_frame=0):
                 "-threads", "0",  # Use maximum threads available
                 "-",  # Output to stdout
             ]
-            """
-            cmd = [
-                self.ffmpeg_path,
-                "-ss", str(start_time / 1000),  # Seek to start time in seconds
-                "-i", self.video_path,  # Input video file
-                "-vf", "crop=in_w/2:in_h:0:0",  # Crop to the left half (width/2, full height, starting from top-left)
-                "-an",  # Disable audio processing
-                "-f", "rawvideo",  # Output raw video data
-                "-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
-                "-vsync", "0",  # Disable frame rate synchronization
-                "-",  # Output to stdout
-            ]
-            """
         else:
             # FFmpeg command to read frames
             cmd = [
@@ -140,10 +124,9 @@ def _start_process(self, start_frame=0):
                 "-",  # Output to stdout
             ]
 
-        # kill the process if already running
+        # Kill the process if already running
         if self.process:
             self.process.terminate()
-            # self.process.wait()
 
         # Start FFmpeg process
         self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
@@ -209,5 +192,38 @@ def release(self):
         """
         if self.process:
             self.process.stdout.close()
-            # self.process.wait()
             self.process = None
+
+
+def main(video_path, is_VR=False):
+    """
+    Display a video using the VideoReaderFFmpeg class.
+    :param video_path: Path to the video file.
+    :param is_VR: Whether the video is a VR video (default: False).
+    """
+    # Initialize the video reader
+    video_reader = VideoReaderFFmpeg(video_path, is_VR=is_VR)
+
+    # Display the video
+    while True:
+        ret, frame = video_reader.read()
+        if not ret:
+            break
+        cv2.imshow("Video", frame)
+        if cv2.waitKey(1) & 0xFF == ord('q'):  # Press 'q' to exit
+            break
+
+    # Release resources
+    video_reader.release()
+    cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    # Parse command-line arguments
+    parser = argparse.ArgumentParser(description="Display a video using FFmpeg.")
+    parser.add_argument("video_path", type=str, help="Path to the video file.")
+    parser.add_argument("--is_vr", action="store_true", help="Enable VR mode for processing VR videos.")
+    args = parser.parse_args()
+
+    # Run the main function
+    main(args.video_path, args.is_vr)
\ No newline at end of file

From 71865824d06c2b1771a7fe05aedc306a017bc712 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 29 Dec 2024 16:21:17 +0100
Subject: [PATCH 018/277] init

---
 README.md | 30 ++++++++++++------------------
 1 file changed, 12 insertions(+), 18 deletions(-)

diff --git a/README.md b/README.md
index 55197789..b409d6e2 100644
--- a/README.md
+++ b/README.md
@@ -18,37 +18,30 @@ This project is a Python-based tool for generating Funscript files from VR video
 
 This project started as a dream to automate Funscript generation for VR videos. Here’s a brief history of its development:
 
-### Initial Approach (OpenCV Trackers)
-
-The first version relied on OpenCV trackers to detect and track objects in the video.
+- **Initial Approach (OpenCV Trackers)**: The first version relied on OpenCV trackers to detect and track objects in the video.
 While functional, the approach was slow (8–20 FPS) and struggled with occlusions and complex scenes.
 
-### Transition to YOLO
-
-To improve accuracy and speed, the project shifted to using YOLO object detection.
+- **Transition to YOLO**: To improve accuracy and speed, the project shifted to using YOLO object detection.
 A custom YOLO model was trained on a dataset of VR video frames, significantly improving detection quality.
 The new approach runs at 90 FPS on a Mac mini M4 pro, making it much more efficient.
 
-### Original Post
-
-For more details and discussions, check out the original post on EroScripts:  
+- **Original Post**: For more details and discussions, check out the original post on EroScripts:  
 [VR Funscript Generation Helper (Python + CV/AI)](https://discuss.eroscripts.com/t/vr-funscript-generation-helper-python-now-cv-ai/202554)
 
 ## YOLO Model
 
 The YOLO model used in this project is based on YOLOv11n, which was fine-tuned with 9 new classes and 4,500+ frames randomly extracted from a VR video library. Here’s how the model was developed:
 
-### Initial Training:
-A few hundred frames were manually tagged and boxed to create an initial dataset.
+- **Initial Training**:A few hundred frames were manually tagged and boxed to create an initial dataset.
 The model was trained on this dataset to generate preliminary detection results.
-### Iterative Improvement
+- **Iterative Improvement**:
 The trained model was used to suggest bounding boxes in additional frames.
 The suggested boxes were manually adjusted, and the dataset was expanded.
 This process was repeated iteratively to improve the model’s accuracy.
-### Final Training
+- **Final Training**:
 After gathering 4,500+ images and 30,149 annotations, the model was trained for 200 epochs.
 YOLOv11s and YOLOv11m were also tested, but YOLOv11n was chosen for its balance of accuracy and inference speed.
-### Hardware:
+- **Hardware**:
 The model runs on a Mac using MPS (Metal Performance Shaders) for accelerated inference on ARM chips.
 Other versions of the model (ONNX and PT) are also available for use on other platforms.
 
@@ -91,6 +84,11 @@ Before using this project, ensure you have the following installed:
    ```bash
    pip install numpy opencv-python tqdm ultralytics scipy matplotlib simplification
    ```
+3. **Download the YOLO model**:
+   - Place your YOLO model file (e.g., `k00gar-11n-200ep-best.mlpackage`) in the `models/` directory.
+   - Alternatively, you can specify a custom path to the model using the `--yolo_model` argument.
+
+
 ### Libraries Used
 
 The project relies on the following Python libraries:
@@ -109,10 +107,6 @@ The project relies on the following Python libraries:
 - **datetime**: For handling timestamps and date-related operations.
 - **json**: For reading and writing JSON files.
 
-3. **Download the YOLO model**:
-   - Place your YOLO model file (e.g., `k00gar-11n-200ep-best.mlpackage`) in the `models/` directory.
-   - Alternatively, you can specify a custom path to the model using the `--yolo_model` argument.
-
 ## Usage
 
 ### Basic Command

From f98b137b9bddd97b90adb3294a595f778c528ea1 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 29 Dec 2024 18:16:26 +0100
Subject: [PATCH 019/277] init

---
 FSGenerator.py                        | 288 ++++++++++++++++----------
 README.md                             |  29 ++-
 debug/Display_debug_results.py        |   6 +-
 tests/test_detect_compare_unwarped.py |   5 +-
 utils/lib_Debugger.py                 |   1 -
 utils/lib_VideoReaderFFmpeg.py        |  16 +-
 6 files changed, 228 insertions(+), 117 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index 929aaaad..c0406fff 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -1,21 +1,33 @@
-import time
-import os
-import cv2
-import json
-from tqdm import tqdm
-from ultralytics import YOLO
-
-from params.config import class_priority_order, class_reverse_match, class_colors
-from utils.lib_ObjectTracker import ObjectTracker
-from utils.lib_FunscriptHandler import FunscriptGenerator
-from utils.lib_Visualizer import Visualizer
-from utils.lib_Debugger import Debugger
-from utils.lib_SceneCutsDetect import detect_scene_changes
-from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg
-
-# Define the BoxRecord class
+# Import necessary libraries
+import time  # For measuring execution time
+import os  # For file and directory operations
+import cv2  # OpenCV for image and video processing
+import json  # For handling JSON data
+from tqdm import tqdm  # For progress bars
+from ultralytics import YOLO  # YOLO model for object detection
+import argparse  # For command-line argument parsing
+import sys  # For accessing command-line arguments
+
+# Import custom modules and configurations
+from params.config import class_priority_order, class_reverse_match, class_colors  # Configuration for class priorities, reverse matching, and colors
+from utils.lib_ObjectTracker import ObjectTracker  # Custom object tracking logic
+from utils.lib_FunscriptHandler import FunscriptGenerator  # For generating Funscript files
+from utils.lib_Visualizer import Visualizer  # For visualizing results
+from utils.lib_Debugger import Debugger  # For debugging and logging
+from utils.lib_SceneCutsDetect import detect_scene_changes  # For detecting scene changes in videos
+from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg  # Custom video reader using FFmpeg
+
+# Define the BoxRecord class to store bounding box information
 class BoxRecord:
     def __init__(self, box, conf, cls, class_name, track_id):
+        """
+        Initialize a BoxRecord object.
+        :param box: Bounding box coordinates [x1, y1, x2, y2].
+        :param conf: Confidence score of the detection.
+        :param cls: Class ID of the detected object.
+        :param class_name: Class name of the detected object.
+        :param track_id: Track ID for object tracking.
+        """
         self.box = box
         self.conf = conf
         self.cls = cls
@@ -23,110 +35,143 @@ def __init__(self, box, conf, cls, class_name, track_id):
         self.track_id = int(track_id)
 
     def __iter__(self):
+        """
+        Make the BoxRecord object iterable.
+        :return: An iterator over the box, confidence, class, class name, and track ID.
+        """
         return iter((self.box, self.conf, self.cls, self.class_name, self.track_id))
 
-
-# Define the Result class
+# Define the Result class to store and manage detection results
 class Result:
     def __init__(self, image_width):
-        self.frame_data = {}
+        """
+        Initialize a Result object.
+        :param image_width: Width of the image/frame.
+        """
+        self.frame_data = {}  # Dictionary to store data for each frame
         self.image_width = image_width
 
     def add_record(self, frame_id, box_record):
+        """
+        Add a BoxRecord to the frame_data dictionary.
+        :param frame_id: The frame ID to which the record belongs.
+        :param box_record: The BoxRecord object to add.
+        """
         if frame_id in self.frame_data:
             self.frame_data[frame_id].append(box_record)
         else:
             self.frame_data[frame_id] = [box_record]
 
     def map_class_type_to_name(self, class_type, x1, x2, image_width):
-        #if class_type in ['foot', 'hand']:
-        #    # Call it left if it is mainly on the left of the frame, right otherwise
-        #    if (x1 + x2) / 2 < image_width / 2:
-        #        class_name = 'right ' + class_type
-        #    else:
-        #        class_name = 'left ' + class_type
-        #else:
+        """
+        Map class type to a class name (currently unused).
+        :param class_type: The class type to map.
+        :param x1: The x-coordinate of the bounding box's top-left corner.
+        :param x2: The x-coordinate of the bounding box's bottom-right corner.
+        :param image_width: The width of the image/frame.
+        :return: The mapped class name.
+        """
         class_name = class_type
         return class_name
 
     def get_boxes(self, frame_id):
+        """
+        Retrieve and sort bounding boxes for a specific frame.
+        :param frame_id: The frame ID to retrieve boxes for.
+        :return: A list of sorted bounding boxes.
+        """
         itemized_boxes = []
         if frame_id not in self.frame_data:
             return itemized_boxes
         boxes = self.frame_data[frame_id]
         for box, conf, cls, class_name, track_id in boxes:
             itemized_boxes.append((box, conf, cls, class_name, track_id))
+        # Sort boxes based on class priority order
         sorted_boxes = sorted(
             itemized_boxes,
-            key=lambda x: class_priority_order.get(x[3], 7)  # Sort by class name priority
+            key=lambda x: class_priority_order.get(x[3], 7)  # Default priority is 7 if class not found
         )
         return sorted_boxes
 
     def get_all_frame_ids(self):
+        """
+        Get a list of all frame IDs in the frame_data dictionary.
+        :return: A list of frame IDs.
+        """
         return list(self.frame_data.keys())
 
-
 def write_dataset(file_path, data):
+    """
+    Write data to a JSON file.
+    :param file_path: The path to the output file.
+    :param data: The data to write.
+    """
     print(f"Exporting data...")
     export_start = time.time()
-    # if file exists, rename it
+    # If the file already exists, rename it as a backup
     if os.path.exists(file_path):
         os.rename(file_path, file_path + ".bak")
+    # Write the data to the file
     with open(file_path, 'w') as f:
         json.dump(data, f)
     export_end = time.time()
     print(f"Done in {export_end - export_start}.")
 
-def extract_yolo_data(model_file, video_path, frame_start, frame_end=None, TestMode = False, isVR = False):
-    # if file already exists, ask if overwrite
+def extract_yolo_data(model_file, video_path, frame_start, frame_end=None, TestMode=False, isVR=False):
+    """
+    Extract YOLO detection data from a video.
+    :param model_file: Path to the YOLO model file.
+    :param video_path: Path to the input video file.
+    :param frame_start: The starting frame for processing.
+    :param frame_end: The ending frame for processing (optional).
+    :param TestMode: Enable/disable test mode for debugging.
+    :param isVR: Enable/disable VR mode for video processing.
+    """
+    # Check if the output file already exists
     if os.path.exists(video_path[:-4] + f"_rawyolo.json"):
         return
-        #print(f"File {video_path[:-4] + f'_rawyolo.json'} already exists. Overwrite? (y/n)")
-        #if input() != 'y':
-        #    return
-        #else:
-        #    # make a backup before continuing
-        #    os.rename(video_path[:-4] + f"_rawyolo.json", video_path[:-4] + f"_rawyolo.json.bak")
 
-    records = []
-    test_result = Result(320)
+    records = []  # List to store detection records
+    test_result = Result(320)  # Test result object for debugging
 
-    #cap = cv2.VideoCapture(video_path)
+    # Initialize the video reader
     cap = VideoReaderFFmpeg(video_path, is_VR=isVR)
     cap.set(cv2.CAP_PROP_POS_FRAMES, frame_start)
 
+    # Determine the last frame to process
     if frame_end:
         last_frame = frame_end
     else:
         last_frame = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
 
-    # Load the YOLO11 model
+    # Load the YOLO model
     model = YOLO(model_file, task="detect")
 
     # Loop through the video frames
     for frame_pos in tqdm(range(frame_start, last_frame), ncols=None, desc="Performing YOLO detection on frames"):
-        # Read a frame from the video
-        success, frame = cap.read()
+        success, frame = cap.read()  # Read a frame from the video
 
         if success:
             if isVR:
-                frame = frame[:, frame.shape[1] // 3:2*frame.shape[1] // 3, :]
+                # For VR videos, crop the frame to the middle third
+                frame = frame[:, frame.shape[1] // 3:2 * frame.shape[1] // 3, :]
 
-            # Run YOLO11 tracking on the frame, persisting tracks between frames
+            # Run YOLO tracking on the frame
             yolo_results = model.track(frame, persist=True, conf=0.3, verbose=False)
 
-            if yolo_results[0].boxes.id is None:  # in case of tracking
+            if yolo_results[0].boxes.id is None:  # Skip if no tracks are found
                 continue
 
-            if len(yolo_results[0].boxes) == 0 and not TestMode:
+            if len(yolo_results[0].boxes) == 0 and not TestMode:  # Skip if no boxes are detected
                 continue
 
-            # Get the boxes and track IDs
+            # Extract track IDs, boxes, classes, and confidence scores
             track_ids = yolo_results[0].boxes.id.cpu().tolist()
             boxes = yolo_results[0].boxes.xywh.cpu()
             classes = yolo_results[0].boxes.cls.cpu().tolist()
             confs = yolo_results[0].boxes.conf.cpu().tolist()
 
+            # Process each detection
             for track_id, cls, conf, box in zip(track_ids, classes, confs, boxes):
                 track_id = int(track_id)
                 x, y, w, h = box.int().tolist()
@@ -134,9 +179,11 @@ def extract_yolo_data(model_file, video_path, frame_start, frame_end=None, TestM
                 y1 = y - h // 2
                 x2 = x + w // 2
                 y2 = y + h // 2
+                # Create a detection record
                 record = [frame_pos, int(cls), round(conf, 1), x1, y1, x2, y2, track_id]
                 records.append(record)
                 if TestMode:
+                    # Print and test the record
                     print(f"Record : {record}")
                     print(f"For class id: {int(cls)}, getting: {class_reverse_match.get(int(cls), 'unknown')}")
                     test_box = [[x1, y1, x2, y2], round(conf, 1), int(cls), class_reverse_match.get(int(cls), 'unknown'), track_id]
@@ -144,11 +191,11 @@ def extract_yolo_data(model_file, video_path, frame_start, frame_end=None, TestM
                     test_result.add_record(frame_pos, test_box)
 
             if TestMode:
-                # display the results from YOLO
+                # Display the YOLO results for testing
                 yolo_results[0].plot()
                 cv2.imshow("YOLO11", yolo_results[0].plot())
                 cv2.waitKey(1)
-                # Making sure we did not mess the classes
+                # Verify the sorted boxes
                 sorted_boxes = test_result.get_boxes(frame_pos)
                 print(f"Sorted boxes : {sorted_boxes}")
 
@@ -161,22 +208,32 @@ def extract_yolo_data(model_file, video_path, frame_start, frame_end=None, TestM
                 cv2.imshow("YOLO11 test boxes Tracking", frame_display)
                 if cv2.waitKey(1) & 0xFF == ord('q'):
                     break
+
+    # Write the detection records to a JSON file
     write_dataset(video_path[:-4] + f"_rawyolo.json", records)
     # Release the video capture object and close the display window
     cap.release()
     cv2.destroyAllWindows()
 
 def load_yolo_data_from_file(file_path):
+    """
+    Load YOLO data from a JSON file.
+    :param file_path: Path to the JSON file.
+    :return: The loaded data.
+    """
     with open(file_path, 'r') as f:
         data = json.load(f)
         print(f"Loaded data from {file_path}, length: {len(data)}")
     return data
 
 def make_data_boxes(records, image_x_size):
-    # Create Result instance
-    result = Result(image_x_size)
-
-    # Populate the Result instance with BoxRecord instances
+    """
+    Convert YOLO records into BoxRecord objects.
+    :param records: List of YOLO detection records.
+    :param image_x_size: Width of the image/frame.
+    :return: A Result object containing BoxRecord instances.
+    """
+    result = Result(image_x_size)  # Create a Result instance
     for record in records:
         frame_idx, cls, conf, x1, y1, x2, y2, track_id = record
         box = [x1, y1, x2, y2]
@@ -185,15 +242,23 @@ def make_data_boxes(records, image_x_size):
         result.add_record(frame_idx, box_record)
     return result
 
-def analyze_tracking_results(results, image_y_size, video_path, frame_start = None, frame_end = None, TestMode = False):
-    list_of_frames = results.get_all_frame_ids()
-    visualizer = Visualizer()
-
-    #cap = cv2.VideoCapture(video_path)
-    cap = VideoReaderFFmpeg(video_path, is_VR=isVR)
-    fps = cap.get(cv2.CAP_PROP_FPS)
-    nb_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-
+def analyze_tracking_results(results, image_y_size, video_path, frame_start=None, frame_end=None, TestMode=False):
+    """
+    Analyze tracking results and generate Funscript data.
+    :param results: The Result object containing detection data.
+    :param image_y_size: Height of the image/frame.
+    :param video_path: Path to the input video file.
+    :param frame_start: The starting frame for processing (optional).
+    :param frame_end: The ending frame for processing (optional).
+    :param TestMode: Enable/disable test mode for debugging.
+    :return: A list of Funscript data.
+    """
+    list_of_frames = results.get_all_frame_ids()  # Get all frame IDs with detections
+    visualizer = Visualizer()  # Initialize the visualizer
+
+    cap = VideoReaderFFmpeg(video_path, is_VR=isVR)  # Initialize the video reader
+    fps = cap.get(cv2.CAP_PROP_FPS)  # Get the video's FPS
+    nb_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))  # Get the total number of frames
 
     if not frame_start:
         frame_start = 0
@@ -206,47 +271,47 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start = No
     else:
         cap.release()
 
-    # if a file already exists, directly load the cuts
+    # Load scene cuts if the file exists
     if os.path.exists(video_path[:-4] + f"_cuts.json"):
         print(f"Loading cuts from {video_path[:-4] + f'_cuts.json'}")
         with open(video_path[:-4] + f"_cuts.json", 'r') as f:
             cuts = json.load(f)
         print(f"Loaded {len(cuts)} cuts : {cuts}")
-
     else:
+        # Detect scene changes if the cuts file does not exist
         scene_list = detect_scene_changes(video_path, "Left", 0.9, frame_start, frame_end)
         print(f"Analyzing frames {frame_start} to {frame_end}")
         cuts = [scene[1] for scene in scene_list]
-        # remove last entry in cuts
-        cuts = cuts[:-1]
-        # save to a file
+        cuts = cuts[:-1]  # Remove the last entry
+        # Save the cuts to a file
         with open(video_path[:-4] + f"_cuts.json", 'w') as f:
             json.dump(cuts, f)
 
-    funscript_frames = []
-    tracker = ObjectTracker(fps, frame_start)
+    funscript_frames = []  # List to store Funscript frames
+    tracker = ObjectTracker(fps, frame_start)  # Initialize the object tracker
 
     for frame_pos in tqdm(range(frame_start, frame_end), ncols=80, desc="Analyzing tracking results"):
-
         if frame_pos in cuts:
+            # Reinitialize the tracker at scene cuts
             print(f"Reaching cut at frame {frame_pos}")
             previous_distances = tracker.previous_distances
-            # tqdm.write
             print(f"Reinitializing tracker with previous distances: {previous_distances}")
             tracker = ObjectTracker(fps, frame_pos)
             tracker.previous_distances = previous_distances
 
         if frame_pos in list_of_frames:
+            # Get sorted boxes for the current frame
             sorted_boxes = results.get_boxes(frame_pos)
-
-            tracker.tracking_logic(sorted_boxes, frame_pos, image_y_size)
+            tracker.tracking_logic(sorted_boxes, frame_pos, image_y_size)  # Apply tracking logic
 
             if tracker.distance:
+                # Append Funscript data if distance is available
                 funscript_frames.append(frame_pos)
                 funscript_distances.append(int(tracker.distance))
 
             if DebugMode:
-                bounding_boxes = []  # Initialize an empty list for bounding boxes
+                # Log debugging information
+                bounding_boxes = []
                 for box in sorted_boxes:
                     if box[4] in tracker.tracked_positions:
                         position = tracker.tracked_positions[box[4]][-1]
@@ -260,11 +325,10 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start = No
                         'position': position,
                     })
                 debugger.log_frame(frame_pos,
-                                   bounding_boxes=bounding_boxes,  # Pass the list of bounding boxes
+                                   bounding_boxes=bounding_boxes,
                                    variables={
                                        'distance': tracker.distance,
                                        'Penetration': tracker.penetration,
-                                       #'Grinding': tracker.grinding,
                                        'sex_position': tracker.sex_position,
                                        'sex_position_reason': tracker.sex_position_reason,
                                        'tracked_body_part': tracker.tracked_body_part,
@@ -278,6 +342,7 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start = No
                                    })
 
         if TestMode:
+            # Display the tracking results for testing
             ret, frame = cap.read()
             frame_display = frame.copy()
 
@@ -299,12 +364,11 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start = No
                                                               offset_x)
             if funscript_distances:
                 frame_display = visualizer.draw_gauge(frame_display, funscript_distances[-1])
-                #frame_display = visualizer.draw_limited_graph(frame_display, funscript_distances,
-                #                                               funscript_frames, 200)
 
             cv2.imshow("Combined Results", frame_display)
             cv2.waitKey(1)
 
+    # Prepare Funscript data
     funscript_data = list(zip(funscript_frames, funscript_distances))
 
     points = "["
@@ -313,13 +377,18 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start = No
             points += ","
         points += f"[{funscript_frames[i]}, {funscript_distances[i]}]"
     points += "]"
-    # write the raw data to a json file
+    # Write the raw Funscript data to a JSON file
     with open(video_path[:-4] + f"_rawfunscript.json", 'w') as f:
         json.dump(funscript_data, f)
     return funscript_data
 
-
 def parse_yolo_data_looking_for_penis(data, start_frame):
+    """
+    Parse YOLO data to find the first instance of a penis.
+    :param data: The YOLO detection data.
+    :param start_frame: The starting frame for the search.
+    :return: The frame ID where the penis is first detected.
+    """
     consecutive_frames = 0
     frame_detected = 0
     penis_frame = 0
@@ -342,53 +411,62 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
                 return line[0] - 4
 
 # MAIN logic
-
 if __name__ == '__main__':
+    video_list = []  # List of videos to process
 
-    # YOLO model file
+    # Default values for IDE usage
+    video_list.append("/Users/k00gar/Downloads/730-czechvr-3d-7680x3840-60fps-oculusrift_uhq_h265.mp4")
     yolo_model = "models/k00gar-11n-200ep-best.mlpackage"
-
-    funscript_data = []
-
-    video_list = []
-
-    video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4")
-
-
-    TestMode = False
     DebugMode = True
+    TestMode = False
     isVR = True
 
+    # Check if the script is run from the command line
+    if len(sys.argv) > 1:  # Command-line arguments are present
+        # Set up argument parsing for command-line usage
+        parser = argparse.ArgumentParser(description="Generate Funscript data from a video using YOLO object detection.")
+        parser.add_argument("video_path", type=str, help="Path to the input video file.")
+        parser.add_argument("--yolo_model", type=str, default=yolo_model, help="Path to the YOLO model file.")
+        parser.add_argument("--test_mode", action="store_true", help="Enable test mode for displaying.")
+        parser.add_argument("--debug_mode", action="store_true", help="Enable debug mode for logging.")
+        parser.add_argument("--is_vr", action="store_true", help="Enable VR mode for video processing.")
+        args = parser.parse_args()
+
+        # Override default values with command-line arguments
+        video_list.append(args.video_path)
+        yolo_model = args.yolo_model
+        TestMode = args.test_mode
+        DebugMode = args.debug_mode
+        isVR = args.is_vr
+
+    funscript_data = []  # List to store Funscript data
+
     for video_path in video_list:
         print(f"Processing video: {video_path}")
-        #funscript_data = []
 
-        debugger = Debugger(video_path, output_dir=video_path[:-4])
+        debugger = Debugger(video_path, output_dir=video_path[:-4])  # Initialize the debugger
 
-        #cap = cv2.VideoCapture(video_path)
-        cap = VideoReaderFFmpeg(video_path, is_VR=isVR)
-        fps = cap.get(cv2.CAP_PROP_FPS)
-        image_y_size = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
-        image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
+        cap = VideoReaderFFmpeg(video_path, is_VR=isVR)  # Initialize the video reader
+        fps = cap.get(cv2.CAP_PROP_FPS)  # Get the video's FPS
+        image_y_size = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)  # Get the video's height
+        image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH)  # Get the video's width
 
         if isVR:
-            offset_x = image_x_size = image_x_size // 3
+            offset_x = image_x_size = image_x_size // 3  # Adjust for VR videos
 
         print(f"Image size: {image_x_size}x{image_y_size}")
         cap.release()
 
-        frame_start = 0   # 61000  # 0
-        frame_end = None  #64000  # None
+        frame_start = 0   # 0 for analysis from the beginning
+        frame_end = None  # None for analysis until the end
 
-        position_changes = []
-        sex_position = ""
         funscript_frames = []
         funscript_distances = []
 
         # Process the video
 
         # Run the YOLO detection and saves result to _rawyolo.json file
-        #extract_yolo_data(yolo_model, video_path, frame_start, frame_end, TestMode, isVR)
+        extract_yolo_data(yolo_model, video_path, frame_start, frame_end, TestMode, isVR)
 
         # Load YOLO detection results from file
         yolo_data = load_yolo_data_from_file(video_path[:-4] + f"_rawyolo.json")
diff --git a/README.md b/README.md
index b409d6e2..31e4209b 100644
--- a/README.md
+++ b/README.md
@@ -6,6 +6,11 @@
 
 This project is a Python-based tool for generating Funscript files from VR videos using Computer Vision (CV) and AI techniques. It leverages YOLO (You Only Look Once) object detection and custom tracking algorithms to automate the process of creating Funscript files for interactive devices.
 
+## DISCLAIMER
+
+This project is at its very early stages of development, still faulty and broken, and is for research and educational purposes only. 
+It is not intended for commercial use.
+
 ## Features
 
 - **YOLO Object Detection**: Uses a pre-trained YOLO model to detect and track objects in video frames.
@@ -109,6 +114,24 @@ The project relies on the following Python libraries:
 
 ## Usage
 
+### Input file
+
+The input video file should be a video file.
+
+For VR, keep it as a SBS video file, the algorithm will process the left (by default) panel only.
+
+Also, even if the algorithm is capable of running on 8k videos, I strongly advise you to process a video limited to 1920p for the sake of performance, and no lower than 1080p.
+
+Feel free to experiment.
+
+### Attention point
+
+Depending on the nature of the video (FISHEYE 190, Equirectangular, etc.) you might want to experiment projection settings.
+
+Those settings can be found in the `utils/lib_VideoReaderFFmpeg.py` file.
+
+To test them, you can use the `utils/test_detect_compare_unwarped.py` file before processing the video, as this will have a huge impact on the quality of the detection and tracking.   
+
 ### Basic Command
 
 To process a video, run the following command:
@@ -127,7 +150,7 @@ python main.py /path/to/video.mp4
 Example:
 
 ```bash
-python main.py /path/to/video.mp4 --yolo_model /path/to/custom_model.mlpackage --test_mode --is_vr
+python FSGenerator.py /path/to/video.mp4 --yolo_model /path/to/custom_model.mlpackage --test_mode --is_vr
 ```
 
 ### Output Files
@@ -166,6 +189,8 @@ python FSGenerator.py /path/to/vr_video.mp4 --yolo_model models/k00gar-11n-200ep
 ```
 This command processes a VR video, detects objects using the specified YOLO model, and generates Funscript data. The `--test_mode` flag enables real-time visualization.
 
+You can also simply run it from your IDE, giving it a video_path to process.
+
 2. **Debugging example**:
 
 Display a Specific Frame with debug information:
@@ -181,6 +206,8 @@ Record the Debugged Video:
 debugger.play_video(frame, record=True, downsize_ratio=2, duration=10)
 ```
 
+Or run Display_debug_results.py from your IDE with the wanted parameters.
+
 ## Contributing
 
 Contributions are welcome! If you'd like to contribute, please follow these steps:
diff --git a/debug/Display_debug_results.py b/debug/Display_debug_results.py
index 4a079e2c..b9f57b68 100644
--- a/debug/Display_debug_results.py
+++ b/debug/Display_debug_results.py
@@ -1,8 +1,8 @@
 from utils.lib_Debugger import Debugger
 
-video = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
+video = "/Users/k00gar/Downloads/730-czechvr-3d-7680x3840-60fps-oculusrift_uhq_h265.mp4"
 
-frame = 79080
+frame = 8*60*60
 
 debugger = Debugger(video, video[:-4])
 
@@ -12,4 +12,4 @@
 
 #debugger.play_video(frame)
 
-debugger.play_video(frame, record=True, downsize_ratio=2, duration=10)
\ No newline at end of file
+debugger.play_video(frame, record=False, downsize_ratio=1)  # , duration=10)
\ No newline at end of file
diff --git a/tests/test_detect_compare_unwarped.py b/tests/test_detect_compare_unwarped.py
index 61655e9e..4707a75a 100644
--- a/tests/test_detect_compare_unwarped.py
+++ b/tests/test_detect_compare_unwarped.py
@@ -6,9 +6,10 @@
 # Paths to the model and input image
 model_file = "../models/k00gar-11n-200ep-best.mlpackage"
 
-video_path = video = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
+#video_path = video = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
+video_path = video = "/Users/k00gar/Downloads/730-czechvr-3d-7680x3840-60fps-oculusrift_uhq_h265.mp4"
 
-frame_id = 79080
+frame_id = 8*60*60
 
 output_path_1 = video_path[:-4] + "_" + str(frame_id) + "_ffmpeg_unwarped.jpg"  # Save location for the annotated frame
 output_path_2 = video_path[:-4] + "_" + str(frame_id) + "_original.jpg"
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 2b6634df..f41855ca 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -226,7 +226,6 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
                 # Draw the locked_penis_box if it exists
                 locked_penis_box = variables.get("locked_penis_box")
                 if locked_penis_box:
-                    print(f"locked_penis_box: {locked_penis_box}")
                     x1, y1, x2, y2 = locked_penis_box['box']
                     color = class_colors.get("penis", (0, 255, 0))
                     cv2.rectangle(frame_copy, (x1, y1), (x2, y2), color, 2)
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index 54e19190..50d33160 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -93,11 +93,16 @@ def _start_process(self, start_frame=0):
                 self.v_fov = 90
                 self.h_fov = 90
                 self.d_fov = 180
+                arg_line = f"crop=w=iw/2:h=ih:x=0:y=0,v360={self.type}:sg:iv_fov={self.iv_fov}:ih_fov={self.ih_fov}:d_fov={self.d_fov}:v_fov={self.v_fov}:h_fov={self.h_fov}:pitch=-20:yaw=0:roll=0:w={self.width}:h={self.height}:interp=lanczos:reset_rot=1"
             else:
-                self.type = "he"
-                self.iv_fov = 90
-                self.ih_fov = 90
-                self.d_fov = 100
+                # more tweaking and experimentation needed...
+                # self.type = "he"
+                # self.iv_fov = 90
+                # self.ih_fov = 90
+                # self.v_fov = 90
+                # self.h_fov = 90
+                # self.d_fov = 100
+                arg_line = "crop=w=iw/2:h=ih:x=0:y=0"
 
             cmd = [
                 self.ffmpeg_path,
@@ -105,7 +110,8 @@ def _start_process(self, start_frame=0):
                 "-i", self.video_path,
                 "-an",  # Disable audio processing
                 "-map", "0:v:0",
-                "-vf", f"crop=w=iw/2:h=ih:x=0:y=0,v360={self.type}:sg:iv_fov={self.iv_fov}:ih_fov={self.ih_fov}:d_fov={self.d_fov}:v_fov={self.v_fov}:h_fov={self.h_fov}:pitch=-20:yaw=0:roll=0:w={self.width}:h={self.height}:interp=lanczos:reset_rot=1",
+                #"-vf", f"crop=w=iw/2:h=ih:x=0:y=0,v360={self.type}:sg:iv_fov={self.iv_fov}:ih_fov={self.ih_fov}:d_fov={self.d_fov}:v_fov={self.v_fov}:h_fov={self.h_fov}:pitch=-20:yaw=0:roll=0:w={self.width}:h={self.height}:interp=lanczos:reset_rot=1",
+                "-vf", arg_line,
                 "-f", "rawvideo",  # Output raw video data
                 "-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
                 "-vsync", "0",  # Disable frame rate synchronization

From 90964905fd03b59ba6df79bbb01dbca1945af451 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 29 Dec 2024 18:47:05 +0100
Subject: [PATCH 020/277] init

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 31e4209b..a6045a81 100644
--- a/README.md
+++ b/README.md
@@ -81,7 +81,7 @@ Before using this project, ensure you have the following installed:
 
 1. **Clone the repository**:
    ```bash
-   git clone https://github.com/yourusername/video-processing-yolo-funscript.git
+   git clone https://github.com/ack00gar/VR-Funscript-AI-Generator.git
    cd video-processing-yolo-funscript
    ```
 

From ca2bc5ff2efaa2098a12db08d2c0b0605302250c Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 29 Dec 2024 18:47:39 +0100
Subject: [PATCH 021/277] init

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index a6045a81..a911c48c 100644
--- a/README.md
+++ b/README.md
@@ -82,7 +82,7 @@ Before using this project, ensure you have the following installed:
 1. **Clone the repository**:
    ```bash
    git clone https://github.com/ack00gar/VR-Funscript-AI-Generator.git
-   cd video-processing-yolo-funscript
+   cd VR-Funscript-AI-Generator
    ```
 
 2. **Install dependencies**:

From 0b12dc95d2762270989a021e8d22c7b688c2b733 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Mon, 30 Dec 2024 07:58:01 +0100
Subject: [PATCH 022/277] Temporarily (?) removed the cropping for VR videos
 and the unwarping at ffmpeg level.

Added the pose estimation model to track the hips center.
---
 FSGenerator.py                 | 122 ++++++++++++++++++++++++---------
 debug/Display_debug_results.py |   4 +-
 params/config.py               |  10 +--
 tests/test_PoseEstimation.py   |  64 ++++++++---------
 utils/lib_Debugger.py          |  11 +--
 utils/lib_VideoReaderFFmpeg.py |   2 +-
 6 files changed, 139 insertions(+), 74 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index c0406fff..2e228051 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -117,10 +117,11 @@ def write_dataset(file_path, data):
     export_end = time.time()
     print(f"Done in {export_end - export_start}.")
 
-def extract_yolo_data(model_file, video_path, frame_start, frame_end=None, TestMode=False, isVR=False):
+def extract_yolo_data(det_model_file, pose_model_file,video_path, frame_start, frame_end=None, TestMode=False, isVR=False):
     """
     Extract YOLO detection data from a video.
-    :param model_file: Path to the YOLO model file.
+    :param det_model_file: Path to the YOLO model file.
+    :param pose_model_file: Path to the YOLO pose model file.
     :param video_path: Path to the input video file.
     :param frame_start: The starting frame for processing.
     :param frame_end: The ending frame for processing (optional).
@@ -145,31 +146,34 @@ def extract_yolo_data(model_file, video_path, frame_start, frame_end=None, TestM
         last_frame = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
 
     # Load the YOLO model
-    model = YOLO(model_file, task="detect")
+    det_model = YOLO(det_model_file, task="detect")
+    pose_model = YOLO(pose_model_file, task="pose")
 
     # Loop through the video frames
     for frame_pos in tqdm(range(frame_start, last_frame), ncols=None, desc="Performing YOLO detection on frames"):
         success, frame = cap.read()  # Read a frame from the video
 
         if success:
-            if isVR:
-                # For VR videos, crop the frame to the middle third
-                frame = frame[:, frame.shape[1] // 3:2 * frame.shape[1] // 3, :]
+            #if isVR:
+            #    # For VR videos, crop the frame to the middle third
+            #    frame = frame[:, frame.shape[1] // 3:2 * frame.shape[1] // 3, :]
 
             # Run YOLO tracking on the frame
-            yolo_results = model.track(frame, persist=True, conf=0.3, verbose=False)
+            yolo_det_results = det_model.track(frame, persist=True, conf=0.3, verbose=False)
+            yolo_pose_results = pose_model.track(frame, persist=True, conf=0.3, verbose=False)
 
-            if yolo_results[0].boxes.id is None:  # Skip if no tracks are found
+            if yolo_det_results[0].boxes.id is None:  # Skip if no tracks are found
                 continue
 
-            if len(yolo_results[0].boxes) == 0 and not TestMode:  # Skip if no boxes are detected
+            if len(yolo_det_results[0].boxes) == 0 and not TestMode:  # Skip if no boxes are detected
                 continue
 
+            ### DETECTION of BODY PARTS
             # Extract track IDs, boxes, classes, and confidence scores
-            track_ids = yolo_results[0].boxes.id.cpu().tolist()
-            boxes = yolo_results[0].boxes.xywh.cpu()
-            classes = yolo_results[0].boxes.cls.cpu().tolist()
-            confs = yolo_results[0].boxes.conf.cpu().tolist()
+            track_ids = yolo_det_results[0].boxes.id.cpu().tolist()
+            boxes = yolo_det_results[0].boxes.xywh.cpu()
+            classes = yolo_det_results[0].boxes.cls.cpu().tolist()
+            confs = yolo_det_results[0].boxes.conf.cpu().tolist()
 
             # Process each detection
             for track_id, cls, conf, box in zip(track_ids, classes, confs, boxes):
@@ -190,10 +194,53 @@ def extract_yolo_data(model_file, video_path, frame_start, frame_end=None, TestM
                     print(f"Test box: {test_box}")
                     test_result.add_record(frame_pos, test_box)
 
+            ### POSE DETECTION - Hips and wrists
+            # Extract track IDs, boxes, classes, and confidence scores
+            if len(yolo_pose_results[0].boxes) > 0 and yolo_pose_results[0].boxes.id is not None:
+                pose_track_ids = yolo_pose_results[0].boxes.id.cpu().tolist()
+
+                # Check if keypoints are detected
+                if yolo_pose_results[0].keypoints is not None:
+                    print("We have keypoints")
+                    # pose_keypoints = yolo_pose_results[0].keypoints.cpu()
+                    # pose_track_ids = yolo_pose_results[0].boxes.id.cpu().tolist()
+                    # pose_boxes = yolo_pose_results[0].boxes.xywh.cpu()
+                    # pose_classes = yolo_pose_results[0].boxes.cls.cpu().tolist()
+                    pose_confs = yolo_pose_results[0].boxes.conf.cpu().tolist()
+
+                    pose_keypoints = yolo_pose_results[0].keypoints.cpu()
+                    pose_keypoints_list = pose_keypoints.xy.cpu().tolist()
+                    left_hip = pose_keypoints_list[0][11]
+                    right_hip = pose_keypoints_list[0][12]
+                    left_wrist = pose_keypoints_list[0][9]
+                    right_wrist = pose_keypoints_list[0][10]
+
+                    middle_x_frame = frame.shape[1] // 2
+                    mid_hips = [middle_x_frame, (int(left_hip[1])+ int(right_hip[1]))//2]
+                    x1 = mid_hips[0]-5
+                    y1 = mid_hips[1]-5
+                    x2 = mid_hips[0]+5
+                    y2 = mid_hips[1]+5
+                    cls = 10  # hips center
+                    print(f"pose_confs: {pose_confs}")
+                    conf = pose_confs[0]
+
+                    record = [frame_pos, 10, round(conf, 1), x1, y1, x2, y2, 0]
+                    records.append(record)
+                    if TestMode:
+                        # Print and test the record
+                        print(f"Record : {record}")
+                        print(f"For class id: {int(cls)}, getting: {class_reverse_match.get(int(cls), 'unknown')}")
+                        test_box = [[x1, y1, x2, y2], round(conf, 1), int(cls),
+                                    class_reverse_match.get(int(cls), 'unknown'), 0]
+                        print(f"Test box: {test_box}")
+                        test_result.add_record(frame_pos, test_box)
+
+
             if TestMode:
                 # Display the YOLO results for testing
-                yolo_results[0].plot()
-                cv2.imshow("YOLO11", yolo_results[0].plot())
+                yolo_det_results[0].plot()
+                cv2.imshow("YOLO11", yolo_det_results[0].plot())
                 cv2.waitKey(1)
                 # Verify the sorted boxes
                 sorted_boxes = test_result.get_boxes(frame_pos)
@@ -352,11 +399,14 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start=None
                                                              str(box[2]) + ": " + box[1],
                                                              class_colors[str(box[1])],
                                                              offset_x)
-            if tracker.locked_penis_box is not None:
-                frame_display = visualizer.draw_bounding_box(frame_display, tracker.locked_penis_box,
-                                                              "Locked_Penis",
-                                                              class_colors['penis'],
-                                                              offset_x)
+            if tracker.locked_penis_box is not None and tracker.locked_penis_box.is_active():
+                frame_display = visualizer.draw_bounding_box(frame_display, tracker.locked_penis_box.box,
+                                                             "Locked_Penis",
+                                                             class_colors['penis'],
+                                                             offset_x)
+            else:
+                print("No active locked penis box to draw.")
+
             if tracker.glans_detected:
                 frame_display = visualizer.draw_bounding_box(frame_display, tracker.boxes['glans'],
                                                               "Glans",
@@ -415,29 +465,40 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
     video_list = []  # List of videos to process
 
     # Default values for IDE usage
-    video_list.append("/Users/k00gar/Downloads/730-czechvr-3d-7680x3840-60fps-oculusrift_uhq_h265.mp4")
-    yolo_model = "models/k00gar-11n-200ep-best.mlpackage"
+    video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4")
+    yolo_det_model = "models/k00gar-11n-200ep-best.mlpackage"
+    yolo_pose_model = "models/yolo11x-pose.mlpackage"
     DebugMode = True
-    TestMode = False
+    TestMode = True
     isVR = True
+    frame_start = 54000  # 0 for analysis from the beginning
+    frame_end = 55000  # None for analysis until the end
 
     # Check if the script is run from the command line
     if len(sys.argv) > 1:  # Command-line arguments are present
         # Set up argument parsing for command-line usage
         parser = argparse.ArgumentParser(description="Generate Funscript data from a video using YOLO object detection.")
         parser.add_argument("video_path", type=str, help="Path to the input video file.")
-        parser.add_argument("--yolo_model", type=str, default=yolo_model, help="Path to the YOLO model file.")
+        parser.add_argument("--yolo_det_model", type=str, default=yolo_det_model, help="Path to the YOLO detection model file.")
+        parser.add_argument("--yolo_pose_model", type=str, default=yolo_pose_model, help="Path to the YOLO pose model file.")
         parser.add_argument("--test_mode", action="store_true", help="Enable test mode for displaying.")
         parser.add_argument("--debug_mode", action="store_true", help="Enable debug mode for logging.")
         parser.add_argument("--is_vr", action="store_true", help="Enable VR mode for video processing.")
+        parser.add_argument("--frame_start", type=int, default=0, help="Frame to start with.")
+        parser.add_argument("--frame_end", type=int, default=0, help="Frame to end with.")
         args = parser.parse_args()
 
         # Override default values with command-line arguments
         video_list.append(args.video_path)
-        yolo_model = args.yolo_model
+        yolo_det_model = args.yolo_model
         TestMode = args.test_mode
         DebugMode = args.debug_mode
         isVR = args.is_vr
+        frame_start = args.frame_start
+        frame_end = args.frame_end
+
+        if frame_end == 0 or frame_end < frame_start:
+            frame_end = None
 
     funscript_data = []  # List to store Funscript data
 
@@ -451,22 +512,21 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
         image_y_size = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)  # Get the video's height
         image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH)  # Get the video's width
 
-        if isVR:
-            offset_x = image_x_size = image_x_size // 3  # Adjust for VR videos
+        offset_x = 0
+
+        #if isVR:
+        #    offset_x = image_x_size = image_x_size // 3  # Adjust for VR videos
 
         print(f"Image size: {image_x_size}x{image_y_size}")
         cap.release()
 
-        frame_start = 0   # 0 for analysis from the beginning
-        frame_end = None  # None for analysis until the end
-
         funscript_frames = []
         funscript_distances = []
 
         # Process the video
 
         # Run the YOLO detection and saves result to _rawyolo.json file
-        extract_yolo_data(yolo_model, video_path, frame_start, frame_end, TestMode, isVR)
+        #extract_yolo_data(yolo_det_model, yolo_pose_model, video_path, frame_start, frame_end, TestMode, isVR)
 
         # Load YOLO detection results from file
         yolo_data = load_yolo_data_from_file(video_path[:-4] + f"_rawyolo.json")
diff --git a/debug/Display_debug_results.py b/debug/Display_debug_results.py
index b9f57b68..066f6d6e 100644
--- a/debug/Display_debug_results.py
+++ b/debug/Display_debug_results.py
@@ -1,8 +1,8 @@
 from utils.lib_Debugger import Debugger
 
-video = "/Users/k00gar/Downloads/730-czechvr-3d-7680x3840-60fps-oculusrift_uhq_h265.mp4"
+video = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
 
-frame = 8*60*60
+frame = 54000
 
 debugger = Debugger(video, video[:-4])
 
diff --git a/params/config.py b/params/config.py
index 8cc9d28e..39a9b704 100644
--- a/params/config.py
+++ b/params/config.py
@@ -4,10 +4,10 @@
 
 # Class types
 class_types = {'face': 0, 'hand': 1, 'penis': 2, 'glans': 3, 'pussy': 4, 'butt': 5,
-                       'anus': 6, 'breast': 7, 'navel': 8, 'foot': 9}
+                       'anus': 6, 'breast': 7, 'navel': 8, 'foot': 9, 'hips center': 10}
 
 class_reverse_match = {0: 'penis', 1: 'glans', 2: 'pussy', 3: 'butt', 4: 'anus', 5: 'breast',
-                       6: 'navel', 7: 'hand', 8: 'face', 9: 'foot'}
+                       6: 'navel', 7: 'hand', 8: 'face', 9: 'foot', 10: 'hips center'}
 
 class_priority_order = {
             "glans": 0, "penis": 1, "breast": 2, "navel": 3, "pussy": 4, "butt": 5, "face": 6
@@ -24,7 +24,8 @@
     'anus': 6,
     'breast': 7,
     'navel': 8,
-    'foot':9, 'left foot': 9, 'right foot': 9
+    'foot':9, 'left foot': 9, 'right foot': 9,
+    'hips center': 10
 }
 
 # Define colors for each class
@@ -42,7 +43,8 @@
     "face": (0, 255, 0),                # lime
     "foot": (165, 42, 42),              # brown
     "left foot": (165, 42, 42),         # brown
-    "right foot": (165, 42, 42)         # brown
+    "right foot": (165, 42, 42),        # brown
+    "hips center": (0, 0, 0)
 }
 
 # Define custom colormap based on Lucife's heatmapColors
diff --git a/tests/test_PoseEstimation.py b/tests/test_PoseEstimation.py
index cd01d3c5..12e03059 100644
--- a/tests/test_PoseEstimation.py
+++ b/tests/test_PoseEstimation.py
@@ -8,10 +8,10 @@
 video_path = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
 
 # Load a model
-pose_model = YOLO("../utils/yolo11x-pose.pt")
+pose_model = YOLO("../models/yolo11x-pose.mlpackage")
 detect_model = YOLO("../models/k00gar-11n-200ep-best.mlpackage")
 
-timestamp_min = 15
+timestamp_min = 0 #15
 timestamp_sec = 0
 
 cap = cv2.VideoCapture(video_path)
@@ -52,46 +52,48 @@
         # display the results of detect model on the frame also
         frame_2 = detect_results[0].plot("hand")
 
-        keypoints = pose_results[0].keypoints.cpu()
-        keypoints_list = keypoints.xy.cpu().tolist()
-        left_hip = keypoints_list[0][11]
-        right_hip = keypoints_list[0][12]
-        left_wrist = keypoints_list[0][9]
-        right_wrist = keypoints_list[0][10]
+        if pose_results[0].boxes.id is not None:
 
-        mid_point = [middle_x_frame, (left_hip[1]+ right_hip[1])/2]
+            keypoints = pose_results[0].keypoints.cpu()
+            keypoints_list = keypoints.xy.cpu().tolist()
+            left_hip = keypoints_list[0][11]
+            right_hip = keypoints_list[0][12]
+            left_wrist = keypoints_list[0][9]
+            right_wrist = keypoints_list[0][10]
 
-        track = track_history[1]
+            mid_point = [middle_x_frame, (left_hip[1]+ right_hip[1])/2]
 
-        track.append((int(middle_x_frame), int(mid_point[1])))  # x, y center point
-        if len(track) > 5:  # retain 90 tracks for 90 frames
-            track.pop(0)
+            track = track_history[1]
 
-        cv2.circle(frame, (int(middle_x_frame), int(mid_point[1])), 2, (0, 0, 0), -1)
+            track.append((int(middle_x_frame), int(mid_point[1])))  # x, y center point
+            if len(track) > 5:  # retain 90 tracks for 90 frames
+                track.pop(0)
 
-        # Draw the tracking lines
-        points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
-        cv2.polylines(frame_1, [points], isClosed=False, color=(230, 230, 230), thickness=10)
+            cv2.circle(frame, (int(middle_x_frame), int(mid_point[1])), 2, (0, 0, 0), -1)
 
-        track = track_history[2]
+            # Draw the tracking lines
+            points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
+            cv2.polylines(frame_1, [points], isClosed=False, color=(230, 230, 230), thickness=10)
 
-        track.append((int(left_wrist[0]), int(left_wrist[1])))  # x, y center point
-        if len(track) > 5:  # retain 90 tracks for 90 frames
-            track.pop(0)
+            track = track_history[2]
 
-        # Draw the tracking lines
-        points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
-        cv2.polylines(frame_2 - 1, [points], isClosed=False, color=(230, 230, 230), thickness=10)
+            track.append((int(left_wrist[0]), int(left_wrist[1])))  # x, y center point
+            if len(track) > 5:  # retain 90 tracks for 90 frames
+                track.pop(0)
 
-        track = track_history[3]
+            # Draw the tracking lines
+            points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
+            cv2.polylines(frame_2 - 1, [points], isClosed=False, color=(230, 230, 230), thickness=10)
 
-        track.append((int(right_wrist[0]), int(right_wrist[1])))  # x, y center point
-        if len(track) > 5:  # retain 90 tracks for 90 frames
-            track.pop(0)
+            track = track_history[3]
 
-        # Draw the tracking lines
-        points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
-        cv2.polylines(frame_2 - 1, [points], isClosed=False, color=(230, 230, 230), thickness=10)
+            track.append((int(right_wrist[0]), int(right_wrist[1])))  # x, y center point
+            if len(track) > 5:  # retain 90 tracks for 90 frames
+                track.pop(0)
+
+            # Draw the tracking lines
+            points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
+            cv2.polylines(frame_2 - 1, [points], isClosed=False, color=(230, 230, 230), thickness=10)
 
         hand_counter = 0
 
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index f41855ca..2d9c666c 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -199,9 +199,9 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
                 break
 
             # Crop the frame for VR videos
-            if self.cap.is_VR:
-                frame_copy = frame.copy()[:, frame.shape[1] // 3 : 2 * frame.shape[1] // 3, :]
-
+            #if self.cap.is_VR:
+            #    frame_copy = frame.copy()[:, frame.shape[1] // 3 : 2 * frame.shape[1] // 3, :]
+            frame_copy = frame.copy()  # make a copy of the frame to make it writeable
             # Display variables and bounding boxes
             str_frame_id = str(self.current_frame)
             if str_frame_id in self.logs:
@@ -218,14 +218,15 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
                     cv2.putText(frame_copy, f"{class_name} {position}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
 
                 # Display variables
-                y_offset = 30
+                y_offset = frame.shape[0] // 3   # 30
                 for key, value in variables.items():
                     cv2.putText(frame_copy, f"{key}: {value}", (10, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
                     y_offset += 20
 
                 # Draw the locked_penis_box if it exists
                 locked_penis_box = variables.get("locked_penis_box")
-                if locked_penis_box:
+                print(f"locked_penis_box: {locked_penis_box}")
+                if locked_penis_box['active']:
                     x1, y1, x2, y2 = locked_penis_box['box']
                     color = class_colors.get("penis", (0, 255, 0))
                     cv2.rectangle(frame_copy, (x1, y1), (x2, y2), color, 2)
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index 50d33160..124a05e3 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -86,7 +86,7 @@ def _start_process(self, start_frame=0):
 
         if self.is_VR:
             # FFmpeg command to read frames with VR reprojection
-            if 'FISHEYE' in self.video_path:
+            if 'NFISHEYE' in self.video_path:  # impeded for troubleshooting
                 self.type = "fisheye"
                 self.iv_fov = 190
                 self.ih_fov = 190

From 94af565254cd06d0f6e26df49bd49fc72a62def1 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Mon, 30 Dec 2024 11:39:15 +0100
Subject: [PATCH 023/277] fixes

---
 FSGenerator.py                 |  2 +-
 debug/Display_debug_results.py |  2 +-
 utils/lib_Debugger.py          |  6 ++--
 utils/lib_KalmanFilter.py      | 52 ++++++++++++++++++++++++++++++++--
 utils/lib_ObjectTracker.py     | 43 ++++++++++++++++++++++++----
 5 files changed, 92 insertions(+), 13 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index 2e228051..eb4b7d37 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -526,7 +526,7 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
         # Process the video
 
         # Run the YOLO detection and saves result to _rawyolo.json file
-        #extract_yolo_data(yolo_det_model, yolo_pose_model, video_path, frame_start, frame_end, TestMode, isVR)
+        extract_yolo_data(yolo_det_model, yolo_pose_model, video_path, frame_start, frame_end, TestMode, isVR)
 
         # Load YOLO detection results from file
         yolo_data = load_yolo_data_from_file(video_path[:-4] + f"_rawyolo.json")
diff --git a/debug/Display_debug_results.py b/debug/Display_debug_results.py
index 066f6d6e..d45dcaa8 100644
--- a/debug/Display_debug_results.py
+++ b/debug/Display_debug_results.py
@@ -12,4 +12,4 @@
 
 #debugger.play_video(frame)
 
-debugger.play_video(frame, record=False, downsize_ratio=1)  # , duration=10)
\ No newline at end of file
+debugger.play_video(frame, record=True, downsize_ratio=2, duration=4)
\ No newline at end of file
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 2d9c666c..992f64ca 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -151,12 +151,12 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
         # Initialize video writer if recording
         if record:
             ret, frame = self.cap.read()
-            if self.cap.is_VR:
-                frame_copy = frame[:, frame.shape[1] // 3 : 2 * frame.shape[1] // 3, :]
+            #if self.cap.is_VR:
+            #    frame_copy = frame[:, frame.shape[1] // 3 : 2 * frame.shape[1] // 3, :]
             output_path = self.video_path.replace(".mp4", "_debug.mp4")
             fourcc = cv2.VideoWriter_fourcc(*"mp4v")
             out = cv2.VideoWriter(
-                output_path, fourcc, self.fps, (frame_copy.shape[1] // downsize_ratio, frame_copy.shape[0] // downsize_ratio)
+                output_path, fourcc, self.fps, (frame.shape[1] // downsize_ratio, frame.shape[0] // downsize_ratio)
             )
             if not out.isOpened():
                 print(f"Error: Could not open video writer for {output_path}")
diff --git a/utils/lib_KalmanFilter.py b/utils/lib_KalmanFilter.py
index eec9b87c..e388ed9c 100644
--- a/utils/lib_KalmanFilter.py
+++ b/utils/lib_KalmanFilter.py
@@ -21,10 +21,12 @@ def __init__(self):
     def update(self, measurement):
         measured = np.array([[np.float32(measurement[0])],
                              [np.float32(measurement[1])]])
-        self.kf.correct(measured)
-        self.position = measurement
+        corrected_state = self.kf.correct(measured)
+        #self.position = measurement
+        self.position = (corrected_state[0], corrected_state[1])
         self.detected = True
         self.prediction_count = 0
+        return float(corrected_state[0])
 
     def predict(self):
         prediction = self.kf.predict()
@@ -34,3 +36,49 @@ def predict(self):
         self.prediction_count += 1
         return prediction[0]
 
+
+
+class KalmanFilter_distance:
+    def __init__(self):
+        # Initialize a 1D Kalman filter (state: [distance, velocity])
+        self.kf = cv2.KalmanFilter(2, 1)
+
+        # Measurement matrix (we only observe distance)
+        self.kf.measurementMatrix = np.array([[1, 0]], np.float32)
+
+        # Transition matrix (assume constant velocity model)
+        self.kf.transitionMatrix = np.array([[1, 1],
+                                             [0, 1]], np.float32)
+
+        # Process noise covariance (tune these values)
+        self.kf.processNoiseCov = np.eye(2, dtype=np.float32) * 0.01
+
+        # Measurement noise covariance (tune this value)
+        self.kf.measurementNoiseCov = np.array([[1]], np.float32) * 0.1
+
+        # Initial state covariance (tune these values)
+        self.kf.errorCovPost = np.eye(2, dtype=np.float32) * 1
+
+        # Initialize state (distance = 100, velocity = 0)
+        self.kf.statePost = np.array([[100], [0]], np.float32)
+
+        self.distance = 100  # Current filtered distance
+        self.detected = False  # Whether a measurement was detected
+
+    def update(self, measurement):
+        """
+        Update the Kalman filter with a new distance measurement.
+        """
+        measured = np.array([[np.float32(measurement)]])
+        self.kf.correct(measured)
+        self.distance = self.kf.statePost[0, 0]
+        self.detected = True
+
+    def predict(self):
+        """
+        Predict the next distance using the Kalman filter.
+        """
+        prediction = self.kf.predict()
+        self.distance = prediction[0, 0]
+        self.detected = False
+        return self.distance
diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index cd43f771..c22a6678 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -134,14 +134,33 @@ def update_distance(self, raw_distance):
         Update the tracked distance using a Kalman filter and speed constraints.
         """
         if raw_distance is None:
-            filtered_distance = self._predict_distance()
+            #filtered_distance = self.distance_kf.predict()
+            # simple predict using ema, waiting for kalman filter fix
+            filtered_distance = 0.1 * self.previous_distances[-3] + 0.3 * self.previous_distances[-2] + 0.6 * self.previous_distances[-1]
+            print(f"predicted distance: {filtered_distance}")
         else:
-            if abs(self.distance - raw_distance) > self.max_speed:
-                raw_distance = self.distance + np.sign(raw_distance - self.distance) * self.max_speed
-            filtered_distance = raw_distance
+            print(f"before fix, raw distance is: {raw_distance}")
+            # applying ema
+            ema_distance = 0.1 * self.previous_distances[-2] + 0.3 * self.previous_distances[-1] + 0.6 * raw_distance
+            print(f"self distance: {self.distance}, ema_distance: {ema_distance}, max speed: {self.max_speed}, delta distances: {self.distance-ema_distance}")
+            if abs(self.distance - ema_distance) > self.max_speed:
+                ema_distance = self.distance + np.sign(ema_distance - self.distance) * self.max_speed
+                print(f"adjusted ema distance: {ema_distance}")
+            #raw_distance_2d = (raw_distance, 0)
+            # Update the Kalman filter with the new measurement
+            #self.distance_kf.update(raw_distance_2d)
+
+            # Get the filtered distance from the Kalman filter
+            #filtered_k_distance = self.distance_kf.position[0]
+
+            #print(f"Kalman filtered distance: {filtered_k_distance} vs {raw_distance}")
+            filtered_distance = ema_distance
+            #filtered_distance = raw_distance
 
         filtered_distance = int(max(0, min(100, filtered_distance)))
         self.distance = filtered_distance
+        self.previous_distances.pop()
+        self.previous_distances.append(self.distance)
         return filtered_distance
 
     def _predict_distance(self):
@@ -236,7 +255,7 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
         for box, conf, cls, class_name, track_id in sorted_boxes:
             if class_name in ['glans', 'penis', 'navel']:
                 continue
-            if self.boxes_overlap(box, self.locked_penis_box.get_box()):
+            elif self.locked_penis_box.is_active() and (self.boxes_overlap(box, self.locked_penis_box.get_box()) or class_name == 'hips center'):
                 if class_name not in classes_touching_penis:
                     classes_touching_penis.append(class_name)
                 self.tracked_boxes.append([box, class_name, track_id])
@@ -248,6 +267,11 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                 # Update tracked positions
                 if track_id not in self.tracked_positions:
                     self.tracked_positions[track_id] = []
+                # self.tracked_positions[track_id].append(normalized_y)
+
+                # let's apply a moving average to try and filter outliers
+                if len(self.tracked_positions[track_id]) > 2:
+                    normalized_y = 0.2 * self.tracked_positions[track_id][-2] + 0.3 * self.tracked_positions[track_id][-1] + 0.5 * normalized_y
                 self.tracked_positions[track_id].append(normalized_y)
 
                 # Maintain a fixed-size history
@@ -257,7 +281,8 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                 # Update normalized tracked positions
                 if track_id not in self.normalized_tracked_positions:
                     self.normalized_tracked_positions[track_id] = []
-                self.normalized_tracked_positions[track_id].append(normalized_y)
+                #self.normalized_tracked_positions[track_id].append(normalized_y)
+                self.normalized_tracked_positions[track_id].append(self.tracked_positions[track_id][-1])
 
                 # Maintain a fixed-size history
                 if len(self.normalized_tracked_positions[track_id]) > 60:
@@ -275,24 +300,30 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
             self.penetration = False
             distance = 100
             self.detect_sex_position_change('Not relevant', "no part touching penis / no penis")
+            self.tracked_body_part = 'Nothing'
         elif 'pussy' in classes_touching_penis and not self.glans_detected:
             self.penetration = True
             self.detect_sex_position_change('Missionnary / Cowgirl', "pussy visible and touching")
+            self.tracked_body_part = 'pussy'
         elif 'ass' in classes_touching_penis and not self.glans_detected:
             self.penetration = True
             self.detect_sex_position_change('Doggy / Rev. Cowgirl', "ass visible and touching")
+            self.tracked_body_part = 'ass'
         elif ('hand' in classes_touching_penis or 'face' in classes_touching_penis) and not self.penetration:
             self.detect_sex_position_change('Handjob / Blowjob', "hand or face visible and touching")
         elif 'foot' in classes_touching_penis and not self.penetration:
             self.detect_sex_position_change('Footjob', "foot visible and touching")
         elif 'breast' in classes_touching_penis and not self.penetration:
             self.detect_sex_position_change('Boobjob', "breast visible and touching")
+            self.tracked_body_part = 'breast'
 
         if weight_pos > 0:
             distance = int(sum_pos / weight_pos)
         else:
             distance = 100
 
+        print(f"computed distance: {distance}, sending for filtering...")
+
         self.update_distance(distance)
 
     def handle_class_first(self, class_name, box, conf):

From 7207af01492a66eb473536350149f1b0a6afd36d Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 31 Dec 2024 11:15:17 +0100
Subject: [PATCH 024/277] fix

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index a911c48c..ad4608c9 100644
--- a/README.md
+++ b/README.md
@@ -137,7 +137,7 @@ To test them, you can use the `utils/test_detect_compare_unwarped.py` file befor
 To process a video, run the following command:
 
 ```bash
-python main.py /path/to/video.mp4
+python FSGenerator.py /path/to/video.mp4
 ```
 
 ### Optional Arguments

From 45e5ee24d8f09eeb0f0a517355866f990ac5eb47 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 31 Dec 2024 11:18:23 +0100
Subject: [PATCH 025/277] fix

---
 README.md | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index ad4608c9..6cd85ff1 100644
--- a/README.md
+++ b/README.md
@@ -143,9 +143,12 @@ python FSGenerator.py /path/to/video.mp4
 ### Optional Arguments
 
 - `--yolo_model`: Path to the YOLO model file (default: `models/k00gar-11n-200ep-best.mlpackage`).
-- `--test_mode`: Enable test mode for real-time visualization of object tracking.
+- `--yolo_model`: Path to the YOLO model file (default: `models/yolo11n-pose.mlpackage`).
+- `--live_display_mode`: Enable test mode for real-time visualization of object tracking.
 - `--debug_mode`: Enable debug mode to save detailed logs.
 - `--is_vr`: Enable VR mode for processing VR videos.
+- `--frame_start`: Frame to start with (default: 0).
+- `--frame_end`: Frame to end with (default: None).
 
 Example:
 

From c07756a1cf2587f71056b0c9fb565dab3c93ad64 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 31 Dec 2024 12:00:06 +0100
Subject: [PATCH 026/277] fix

---
 README.md | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/README.md b/README.md
index 6cd85ff1..7c4bf9c8 100644
--- a/README.md
+++ b/README.md
@@ -235,7 +235,3 @@ See the [LICENSE](LICENSE) file for full details.
 ## Support
 
 If you encounter any issues or have questions, please open an issue on the [GitHub repository](https://github.com/yourusername/video-processing-yolo-funscript/issues).
-
----
-
-This README provides a comprehensive overview of the project, making it easy for users to understand, set up, and use the tool. It also encourages contributions and provides support information.
\ No newline at end of file

From eb234eec08f01e95095f5fce32ae464d9cde9d24 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 31 Dec 2024 13:57:50 +0100
Subject: [PATCH 027/277] fix

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 7c4bf9c8..b3f58730 100644
--- a/README.md
+++ b/README.md
@@ -234,4 +234,4 @@ See the [LICENSE](LICENSE) file for full details.
 
 ## Support
 
-If you encounter any issues or have questions, please open an issue on the [GitHub repository](https://github.com/yourusername/video-processing-yolo-funscript/issues).
+If you encounter any issues or have questions, please open an issue on GitHub.

From adb644c72048b56b75ad2c7c41ac2d218ad4f7ce Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 31 Dec 2024 17:48:04 +0100
Subject: [PATCH 028/277] fix / enhancements

---
 FSGenerator.py                        | 134 +++--
 debug/Display_debug_results.py        |  10 +-
 tests/test_PoseEstimation.py          |   8 +-
 tests/test_detect_compare_unwarped.py |  12 +-
 utils/lib_Debugger.py                 |  39 +-
 utils/lib_FunscriptHandler.py         | 720 +++++++++++++++++++++-----
 utils/lib_ObjectTracker.py            | 141 +++--
 utils/lib_VideoReaderFFmpeg.py        |  53 +-
 8 files changed, 855 insertions(+), 262 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index eb4b7d37..9dcfc7a1 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -130,6 +130,7 @@ def extract_yolo_data(det_model_file, pose_model_file,video_path, frame_start, f
     """
     # Check if the output file already exists
     if os.path.exists(video_path[:-4] + f"_rawyolo.json"):
+        print(f"File {video_path[:-4] + f'_rawyolo.json'} already exists. Skipping detections and loading file content...")
         return
 
     records = []  # List to store detection records
@@ -147,7 +148,16 @@ def extract_yolo_data(det_model_file, pose_model_file,video_path, frame_start, f
 
     # Load the YOLO model
     det_model = YOLO(det_model_file, task="detect")
-    pose_model = YOLO(pose_model_file, task="pose")
+
+    # make the pose model optional
+    if len(pose_model_file) > 0:
+        run_pose_model = True
+        print("Activating pose model")
+    else:
+        run_pose_model = False
+        print("Discarding pose model part of the code")
+    if run_pose_model:
+        pose_model = YOLO(pose_model_file, task="pose")
 
     # Loop through the video frames
     for frame_pos in tqdm(range(frame_start, last_frame), ncols=None, desc="Performing YOLO detection on frames"):
@@ -160,7 +170,8 @@ def extract_yolo_data(det_model_file, pose_model_file,video_path, frame_start, f
 
             # Run YOLO tracking on the frame
             yolo_det_results = det_model.track(frame, persist=True, conf=0.3, verbose=False)
-            yolo_pose_results = pose_model.track(frame, persist=True, conf=0.3, verbose=False)
+            if run_pose_model:
+                yolo_pose_results = pose_model.track(frame, persist=True, conf=0.3, verbose=False)
 
             if yolo_det_results[0].boxes.id is None:  # Skip if no tracks are found
                 continue
@@ -194,47 +205,48 @@ def extract_yolo_data(det_model_file, pose_model_file,video_path, frame_start, f
                     print(f"Test box: {test_box}")
                     test_result.add_record(frame_pos, test_box)
 
-            ### POSE DETECTION - Hips and wrists
-            # Extract track IDs, boxes, classes, and confidence scores
-            if len(yolo_pose_results[0].boxes) > 0 and yolo_pose_results[0].boxes.id is not None:
-                pose_track_ids = yolo_pose_results[0].boxes.id.cpu().tolist()
-
-                # Check if keypoints are detected
-                if yolo_pose_results[0].keypoints is not None:
-                    print("We have keypoints")
-                    # pose_keypoints = yolo_pose_results[0].keypoints.cpu()
-                    # pose_track_ids = yolo_pose_results[0].boxes.id.cpu().tolist()
-                    # pose_boxes = yolo_pose_results[0].boxes.xywh.cpu()
-                    # pose_classes = yolo_pose_results[0].boxes.cls.cpu().tolist()
-                    pose_confs = yolo_pose_results[0].boxes.conf.cpu().tolist()
-
-                    pose_keypoints = yolo_pose_results[0].keypoints.cpu()
-                    pose_keypoints_list = pose_keypoints.xy.cpu().tolist()
-                    left_hip = pose_keypoints_list[0][11]
-                    right_hip = pose_keypoints_list[0][12]
-                    left_wrist = pose_keypoints_list[0][9]
-                    right_wrist = pose_keypoints_list[0][10]
-
-                    middle_x_frame = frame.shape[1] // 2
-                    mid_hips = [middle_x_frame, (int(left_hip[1])+ int(right_hip[1]))//2]
-                    x1 = mid_hips[0]-5
-                    y1 = mid_hips[1]-5
-                    x2 = mid_hips[0]+5
-                    y2 = mid_hips[1]+5
-                    cls = 10  # hips center
-                    print(f"pose_confs: {pose_confs}")
-                    conf = pose_confs[0]
-
-                    record = [frame_pos, 10, round(conf, 1), x1, y1, x2, y2, 0]
-                    records.append(record)
-                    if TestMode:
-                        # Print and test the record
-                        print(f"Record : {record}")
-                        print(f"For class id: {int(cls)}, getting: {class_reverse_match.get(int(cls), 'unknown')}")
-                        test_box = [[x1, y1, x2, y2], round(conf, 1), int(cls),
-                                    class_reverse_match.get(int(cls), 'unknown'), 0]
-                        print(f"Test box: {test_box}")
-                        test_result.add_record(frame_pos, test_box)
+            if run_pose_model:
+                ### POSE DETECTION - Hips and wrists
+                # Extract track IDs, boxes, classes, and confidence scores
+                if len(yolo_pose_results[0].boxes) > 0 and yolo_pose_results[0].boxes.id is not None:
+                    pose_track_ids = yolo_pose_results[0].boxes.id.cpu().tolist()
+
+                    # Check if keypoints are detected
+                    if yolo_pose_results[0].keypoints is not None:
+                        # print("We have keypoints")
+                        # pose_keypoints = yolo_pose_results[0].keypoints.cpu()
+                        # pose_track_ids = yolo_pose_results[0].boxes.id.cpu().tolist()
+                        # pose_boxes = yolo_pose_results[0].boxes.xywh.cpu()
+                        # pose_classes = yolo_pose_results[0].boxes.cls.cpu().tolist()
+                        pose_confs = yolo_pose_results[0].boxes.conf.cpu().tolist()
+
+                        pose_keypoints = yolo_pose_results[0].keypoints.cpu()
+                        pose_keypoints_list = pose_keypoints.xy.cpu().tolist()
+                        left_hip = pose_keypoints_list[0][11]
+                        right_hip = pose_keypoints_list[0][12]
+                        left_wrist = pose_keypoints_list[0][9]
+                        right_wrist = pose_keypoints_list[0][10]
+
+                        middle_x_frame = frame.shape[1] // 2
+                        mid_hips = [middle_x_frame, (int(left_hip[1])+ int(right_hip[1]))//2]
+                        x1 = mid_hips[0]-5
+                        y1 = mid_hips[1]-5
+                        x2 = mid_hips[0]+5
+                        y2 = mid_hips[1]+5
+                        cls = 10  # hips center
+                        # print(f"pose_confs: {pose_confs}")
+                        conf = pose_confs[0]
+
+                        record = [frame_pos, 10, round(conf, 1), x1, y1, x2, y2, 0]
+                        records.append(record)
+                        if TestMode:
+                            # Print and test the record
+                            print(f"Record : {record}")
+                            print(f"For class id: {int(cls)}, getting: {class_reverse_match.get(int(cls), 'unknown')}")
+                            test_box = [[x1, y1, x2, y2], round(conf, 1), int(cls),
+                                        class_reverse_match.get(int(cls), 'unknown'), 0]
+                            print(f"Test box: {test_box}")
+                            test_result.add_record(frame_pos, test_box)
 
 
             if TestMode:
@@ -465,14 +477,17 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
     video_list = []  # List of videos to process
 
     # Default values for IDE usage
-    video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4")
+    #video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4")
+    video_list.append("/Users/k00gar/Downloads/VRCONK_Kiara Cole_game_of_thrones_daenerys_targaryen_a_porn_parody_8K_180x180_3dh.mp4")
     yolo_det_model = "models/k00gar-11n-200ep-best.mlpackage"
-    yolo_pose_model = "models/yolo11x-pose.mlpackage"
+    yolo_pose_model = "models/yolo11n-pose.mlpackage"  # "models/yolo11x-pose.mlpackage"
     DebugMode = True
-    TestMode = True
+    LiveDisplayMode = False
     isVR = True
-    frame_start = 54000  # 0 for analysis from the beginning
-    frame_end = 55000  # None for analysis until the end
+    frame_start = 0  # 0 for analysis from the beginning
+    frame_end = None  # None for analysis until the end
+
+    reference_funscript = "/Users/k00gar/Downloads/VRCONK_game_of_thrones_daenerys_targaryen_a_porn_parody_8K_180x180_3dh.funscript"  # "/Users/k00gar/Downloads/SLR Originals - Vote for me - Blake Blossom.realcumber.v1.51071.funscript"
 
     # Check if the script is run from the command line
     if len(sys.argv) > 1:  # Command-line arguments are present
@@ -481,7 +496,7 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
         parser.add_argument("video_path", type=str, help="Path to the input video file.")
         parser.add_argument("--yolo_det_model", type=str, default=yolo_det_model, help="Path to the YOLO detection model file.")
         parser.add_argument("--yolo_pose_model", type=str, default=yolo_pose_model, help="Path to the YOLO pose model file.")
-        parser.add_argument("--test_mode", action="store_true", help="Enable test mode for displaying.")
+        parser.add_argument("--live_display_mode", action="store_true", help="Enable live display mode while processing.")
         parser.add_argument("--debug_mode", action="store_true", help="Enable debug mode for logging.")
         parser.add_argument("--is_vr", action="store_true", help="Enable VR mode for video processing.")
         parser.add_argument("--frame_start", type=int, default=0, help="Frame to start with.")
@@ -491,7 +506,8 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
         # Override default values with command-line arguments
         video_list.append(args.video_path)
         yolo_det_model = args.yolo_model
-        TestMode = args.test_mode
+        yolo_pose_model = args.yolo_pose_model
+        LiveDisplayMode = args.live_display_mode
         DebugMode = args.debug_mode
         isVR = args.is_vr
         frame_start = args.frame_start
@@ -526,7 +542,7 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
         # Process the video
 
         # Run the YOLO detection and saves result to _rawyolo.json file
-        extract_yolo_data(yolo_det_model, yolo_pose_model, video_path, frame_start, frame_end, TestMode, isVR)
+        extract_yolo_data(yolo_det_model, yolo_pose_model, video_path, frame_start, frame_end, LiveDisplayMode, isVR)
 
         # Load YOLO detection results from file
         yolo_data = load_yolo_data_from_file(video_path[:-4] + f"_rawyolo.json")
@@ -544,13 +560,25 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
         frame_start = max(max(first_penis_frame - int(fps), frame_start - int(fps)), 0)
 
         # Performing the tracking part and generation of the raw funscript data
-        funscript_data = analyze_tracking_results(results, image_y_size, video_path, frame_start, frame_end, TestMode)
+        funscript_data = analyze_tracking_results(results, image_y_size, video_path, frame_start, frame_end, LiveDisplayMode)
 
         debugger.save_logs()
 
         funscript_handler = FunscriptGenerator()
 
         # Simplifying the funscript data and generating the file
-        funscript_handler.generate(video_path[:-4] + f"_rawfunscript.json", funscript_data, fps, TestMode)
+        funscript_handler.generate(video_path[:-4] + f"_rawfunscript.json", funscript_data, fps, LiveDisplayMode)
+
+        # Optional, compare generated funscript with reference funscript
+        if reference_funscript:
+            funscript_handler.compare_funscripts(reference_funscript, video_path[:-3] + "funscript", video_path, isVR, video_path[:-4] + "_comparefunscripts.png", video_path[:-4] + "_adjusted.funscript",)
+
+        #funscripts = []
+        #if reference_funscript:
+        #    funscripts.append(reference_funscript)
+        #funscripts.append(video_path[:-3] + "funscript")
+        #funscripts.append(video_path[:-4] + "_adjusted.funscript")
+        #funscript_handler.analyze_funscripts(script_paths=funscripts, video_path=video_path,
+        #                            isVR=isVR, output_image_path=video_path[:-4] + "_comparefunscripts.png")
 
         print(f"Finished processing video: {video_path}")
diff --git a/debug/Display_debug_results.py b/debug/Display_debug_results.py
index d45dcaa8..fd6b97be 100644
--- a/debug/Display_debug_results.py
+++ b/debug/Display_debug_results.py
@@ -1,15 +1,17 @@
 from utils.lib_Debugger import Debugger
 
-video = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
+#video = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
 
-frame = 54000
+video = "/Users/k00gar/Downloads/EBVR-093-C_reenc.mp4"
+
+frame = 95000
 
 debugger = Debugger(video, video[:-4])
 
 debugger.load_logs()
 
-#debugger.display_frame(frame)
+debugger.display_frame(frame)
 
 #debugger.play_video(frame)
 
-debugger.play_video(frame, record=True, downsize_ratio=2, duration=4)
\ No newline at end of file
+#debugger.play_video(frame, record=True, downsize_ratio=1, duration=4)
\ No newline at end of file
diff --git a/tests/test_PoseEstimation.py b/tests/test_PoseEstimation.py
index 12e03059..e970926f 100644
--- a/tests/test_PoseEstimation.py
+++ b/tests/test_PoseEstimation.py
@@ -5,13 +5,15 @@
 from params.config import class_reverse_match
 
 #video_path = "/Users/k00gar/Downloads/Katrina Jade [ADD] Oct 16, 2024R_6kvr265_reenc.mp4"
-video_path = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
+#video_path = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
+video_path = "/Users/k00gar/Downloads/730-czechvr-3d-7680x3840-60fps-oculusrift_uhq_h265.mp4"
 
 # Load a model
-pose_model = YOLO("../models/yolo11x-pose.mlpackage")
+#pose_model = YOLO("../models/yolo11x-pose.mlpackage")
+pose_model = YOLO("../models/yolo11n-pose.mlpackage")
 detect_model = YOLO("../models/k00gar-11n-200ep-best.mlpackage")
 
-timestamp_min = 0 #15
+timestamp_min = 25 #15
 timestamp_sec = 0
 
 cap = cv2.VideoCapture(video_path)
diff --git a/tests/test_detect_compare_unwarped.py b/tests/test_detect_compare_unwarped.py
index 4707a75a..d1cadcee 100644
--- a/tests/test_detect_compare_unwarped.py
+++ b/tests/test_detect_compare_unwarped.py
@@ -6,17 +6,18 @@
 # Paths to the model and input image
 model_file = "../models/k00gar-11n-200ep-best.mlpackage"
 
-#video_path = video = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
-video_path = video = "/Users/k00gar/Downloads/730-czechvr-3d-7680x3840-60fps-oculusrift_uhq_h265.mp4"
+#video_path = video = "/Users/k00gar/Downloads/EBVR-093-C.mp4"
+video_path = video = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
+#video_path = video = "/Users/k00gar/Downloads/730-czechvr-3d-7680x3840-60fps-oculusrift_uhq_h265.mp4"
 
-frame_id = 8*60*60
+frame_id = 79080  # 21*60*60
 
 output_path_1 = video_path[:-4] + "_" + str(frame_id) + "_ffmpeg_unwarped.jpg"  # Save location for the annotated frame
 output_path_2 = video_path[:-4] + "_" + str(frame_id) + "_original.jpg"
 output_path_3 = video_path[:-4] + "_" + str(frame_id) + "_results.jpg"
 
 cap1 = VideoReaderFFmpeg(video_path, is_VR=True)
-cap2 = VideoReaderFFmpeg(video_path, is_VR=False)
+cap2 = VideoReaderFFmpeg(video_path, is_VR=True, unwarp=False)
 
 cap1.set(cv2.CAP_PROP_POS_FRAMES, frame_id)
 cap2.set(cv2.CAP_PROP_POS_FRAMES, frame_id)
@@ -25,7 +26,7 @@
 ret2, frame2 = cap2.read()
 
 # we will only keep left half of frame2
-frame2 = frame2[:, :frame2.shape[1] // 2, :]
+# frame2 = frame2[:, :frame2.shape[1] // 2, :]
 
 cap1.release()
 cap2.release()
@@ -38,6 +39,7 @@
 
 # Annotate the frame
 annotated_frame_1 = results[0].plot()
+cv2.imwrite(output_path_1, annotated_frame_1)
 
 # Perform inference
 results = model.predict(frame2)
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 992f64ca..a1c425dc 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -80,6 +80,13 @@ def load_logs(self):
             print(f"Error decoding JSON from {log_file}. Starting with empty logs.")
 
     def display_frame(self, frame_id):
+        """
+        Display the logged frame with bounding boxes and variable states.
+        :param frame_id: The frame ID to display.
+        """
+        self.play_video(frame_id, duration=-1)
+
+    def old_display_frame(self, frame_id):
         """
         Display the logged frame with bounding boxes and variable states.
         :param frame_id: The frame ID to display.
@@ -90,17 +97,20 @@ def display_frame(self, frame_id):
             return
 
         # Load the frame from the video
-        cap = cv2.VideoCapture(self.video_path)
-        cap.set(cv2.CAP_PROP_POS_FRAMES, frame_id)
-        ret, frame = cap.read()
-        cap.release()
+        #cap = cv2.VideoCapture(self.video_path)
+        self.cap = VideoReaderFFmpeg(self.video_path, is_VR=True)
+        self.cap.set(cv2.CAP_PROP_POS_FRAMES, frame_id)
+        ret, frame = self.cap.read()
+        self.cap.release()
 
         if not ret:
             print(f"Failed to read frame {frame_id}")
             return
 
         # Crop the frame (for VR videos)
-        frame = frame[:, frame.shape[1] // 6 : frame.shape[1] // 3, :]
+        # frame = frame[:, frame.shape[1] // 6 : frame.shape[1] // 3, :]
+
+        frame_copy = frame.copy()
 
         # Draw bounding boxes
         for box in self.logs[str_frame_id]["bounding_boxes"]:
@@ -108,26 +118,26 @@ def display_frame(self, frame_id):
             class_name = box["class_name"]
             conf = box["conf"]
             color = class_colors.get(class_name, (0, 255, 0))  # Default to green if class not found
-            cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
-            cv2.putText(frame, f"{class_name} {conf:.2f}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+            cv2.rectangle(frame_copy, (x1, y1), (x2, y2), color, 2)
+            cv2.putText(frame_copy, f"{class_name} {conf:.2f}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
 
         # Display variables
         variables = self.logs[str_frame_id]["variables"]
         y_offset = 30
         for key, value in variables.items():
-            cv2.putText(frame, f"{key}: {value}", (10, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
+            cv2.putText(frame_copy, f"{key}: {value}", (10, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
             y_offset += 20
 
         # Draw the locked_penis_box if it exists
         locked_penis_box = variables.get("locked_penis_box")
         if locked_penis_box:
-            x1, y1, x2, y2 = locked_penis_box
+            x1, y1, x2, y2 = locked_penis_box['box']
             color = class_colors.get("penis", (0, 255, 0))
-            cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
-            cv2.putText(frame, "Locked Penis", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+            cv2.rectangle(frame_copy, (x1, y1), (x2, y2), color, 2)
+            cv2.putText(frame_copy, "Locked Penis", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
 
         # Show the frame
-        cv2.imshow(f"Debug Frame {frame_id}", frame)
+        cv2.imshow(f"Debug Frame {frame_id}", frame_copy)
         cv2.waitKey(0)
         cv2.destroyAllWindows()
 
@@ -225,7 +235,7 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
 
                 # Draw the locked_penis_box if it exists
                 locked_penis_box = variables.get("locked_penis_box")
-                print(f"locked_penis_box: {locked_penis_box}")
+                #print(f"locked_penis_box: {locked_penis_box}")
                 if locked_penis_box['active']:
                     x1, y1, x2, y2 = locked_penis_box['box']
                     color = class_colors.get("penis", (0, 255, 0))
@@ -256,6 +266,9 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
                 break
             if cv2.waitKey(1) & 0xFF == 32:  # Pause on spacebar
                 time.sleep(10)
+            if duration == -1:
+                cv2.imwrite(f"{self.video_path[:-4]}_frame_{self.current_frame}.png", frame_copy)
+                break
 
             # Record the frame if enabled
             if record:
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index b4b233f4..4906a6fd 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -6,7 +6,9 @@
 import datetime
 import matplotlib.colors as mcolors
 from matplotlib.cm import ScalarMappable
+from matplotlib.colors import LinearSegmentedColormap, Normalize
 from params.config import heatmap_colors, step_size, vw_filter_coeff
+import cv2
 
 class FunscriptGenerator:
     def generate(self, raw_funscript_path, funscript_data, fps, TestMode = False):
@@ -31,28 +33,23 @@ def generate(self, raw_funscript_path, funscript_data, fps, TestMode = False):
             print(f"Funscript generated and saved to {output_path}")
             self.generate_heatmap(output_path, output_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
 
+            # Now proceeding with remapping / adjusting
+            # Adjust peaks and lows
+            ats = [p[0] for p in self.filtered_positions]
+            positions = [p[1] for p in self.filtered_positions]
+            adjusted_positions = self.adjust_peaks_and_lows(positions, peak_boost=15, low_reduction=25,
+                                                            max_flat_length=3)
+            # recombine ats and positions
+            adjusted_positions = list(zip(ats, adjusted_positions))
+            remapped_path = output_path[:-10] + '_adjusted.funscript'
+            self.write_funscript(adjusted_positions, remapped_path, fps)
+            self.generate_heatmap(remapped_path,
+                                  remapped_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
+
         except:
             print(f"Error loading raw funscript from {raw_funscript_path}")
 
-        # for alternative version, make every point 0 if distance is under 10
-        points_v2 = []
-        multiplier = 1.2
-        for i in range(len(self.filtered_positions)):
-            if self.filtered_positions[i][1] < 10:
-                distance = 0
-            else:
-                if self.filtered_positions[i][1] < 50:
-                    distance = int(self.filtered_positions[i][1] / multiplier)
-                else:
-                    distance = min(int(self.filtered_positions[i][1] * multiplier), 100)
-            # print(f"Point {i}: was {self.filtered_positions[i][1]}, now is {distance}")
-            points_v2.append((self.filtered_positions[i][0], distance))
-
-        remapped_path = output_path[:-10] + '_remapped.funscript'
-        self.write_funscript(points_v2, remapped_path, fps)
-        self.generate_heatmap(remapped_path, remapped_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
-
-        #if TestMode:
+        #if LiveDisplayMode:
         #    # plot a comparative graph
         #    self.plot_comparison(self.points, self.filtered_positions, points_v2)
 
@@ -69,91 +66,85 @@ def write_funscript(self, distances, output_path, fps):
                 i += 1
             f.write("]}\n")
 
-    def dump_section(self, section_id, frames, distances, output_path, fps):
-        print(f"Generating json file for section {section_id} content based on {len(distances)} distances...")
-        points = []
-        # build points based on the list of frames, distances
-        for i in range(len(frames)):
-            points.append((int(frames[i] * 1000 / fps), int(distances[i])))
-        filtered_positions = simplify_coords(points, 5.0)
-        print(f"Filtered positions: {len(filtered_positions)}")
-
-        # Convert filtered positions to a list of dictionaries
-        filtered_data = [{"at": timing_ms, "pos": distance} for timing_ms, distance in filtered_positions]
-
-        # Create the output file path
-        output_file = f"{output_path}_section_{section_id}.json"
-
-        # Dump filtered positions to json file
-        with open(output_file, 'w') as f:
-            json.dump(filtered_data, f, indent=4)
-
-        print(f"Section {section_id} dumped to {output_file}")
-
-    def assemble_sections(self, number_of_sections, output_path):
-        # assemble json section files to a final funscript file
-        final_data = []
-
-        # Load each section's JSON file and append its data to the final data
-        for section_id in range(1, number_of_sections + 1):
-            section_file = f"{output_path}_section_{section_id}.json"
-            with open(section_file, 'r') as f:
-                section_data = json.load(f)
-                final_data.extend(section_data)
-
-        # Sort the final data by frame number
-        final_data.sort(key=lambda x: x['at'])
-
-        # Create the final funscript file
-        final_file = f"{output_path}"
-        #with open(final_file, 'w') as f:
-        #    json.dump(final_data, f, indent=4)
-        with open(output_path, 'w') as f:
-            #f.write("[\n")
-            f.write('{"version":"1.0","inverted":false,"range":95,"author":"kAI","actions":[')
-            i = 0
-            for timing_ms, position in self.filtered_positions:
-                if i > 0:
-                    f.write(",")
-                f.write(f' {{"at": {timing_ms}, "pos": {int(position)}}}')
-                i += 1
-            f.write("]}\n")
+    def generate_heatmap(self, funscript_path, output_image_path):
+        # Load funscript data
+        times, positions, _, _ = self.load_funscript(funscript_path)
+        if not times or not positions:
+            print("Failed to load funscript data.")
+            return
 
-        print(f"Final funscript file assembled and saved to {final_file}")
+        # Print loaded data for debugging
+        print(f"Times: {times}")
+        print(f"Positions: {positions}")
+        print(f"Total Actions: {len(times)}")
+        print(f"Time Range: {times[0]} to {datetime.timedelta(seconds=int(times[-1] / 1000))}")
 
-    def plot_comparison(self, original_points, rdp_filtered_points, vw_filtered_points):
-        plt.figure(figsize=(12, 6))
+        # Calculate speed (position change per time interval)
+        speeds = np.abs(np.diff(positions) / np.diff(times)) * 1000  # Positions per second
+        print(f"Speeds: {speeds}")
 
-        # Plot original points
-        plt.plot([p[0] for p in original_points], [p[1] for p in original_points], 'b-', label='Original Points')
+        def get_color(intensity):
+            if intensity <= 0:
+                return heatmap_colors[0]
+            if intensity > 5 * step_size:
+                return heatmap_colors[6]
+            intensity += step_size / 2.0
+            index = int(intensity // step_size)
+            t = (intensity - index * step_size) / step_size
+            return [
+                heatmap_colors[index][0] + (heatmap_colors[index + 1][0] - heatmap_colors[index][0]) * t,
+                heatmap_colors[index][1] + (heatmap_colors[index + 1][1] - heatmap_colors[index][1]) * t,
+                heatmap_colors[index][2] + (heatmap_colors[index + 1][2] - heatmap_colors[index][2]) * t
+            ]
 
-        # Plot RDP filtered points
-        plt.plot([p[0] for p in rdp_filtered_points], [p[1] for p in rdp_filtered_points], 'r-', label='RDP Filtered Points')
+        # Create figure and plot
+        plt.figure(figsize=(30, 2))
+        ax = plt.gca()
 
-        # Plot VW filtered points
-        plt.plot([p[0] for p in vw_filtered_points], [p[1] for p in vw_filtered_points], 'g-', label='VW Filtered Points')
+        # Draw lines between points with colors based on speed
+        for i in range(len(times) - 1):
+            x_start = times[i] / 1000  # Convert ms to seconds
+            x_end = times[i + 1] / 1000
+            y_start = positions[i]
+            y_end = positions[i + 1]
+            speed = speeds[i]
 
-        plt.xlabel('Frames')
-        plt.ylabel('Distances')
-        plt.title('Comparison of RDP and VW Filtered Positions')
-        plt.legend()
-        plt.savefig('comparison_plot.png')
-        plt.show()
-        #plt.savefig('comparison_plot.png')
+            # Get color based on speed
+            color = get_color(speed)
+            line_color = (color[0] / 255, color[1] / 255, color[2] / 255)  # Normalize to [0, 1]
 
-    def smooth_distance_ema(self, distances, alpha=0.3):
-        smoothed = [distances[0]]
-        for i in range(1, len(distances)):
-            smoothed.append(alpha * distances[i] + (1 - alpha) * smoothed[-1])
-        return [[i, smoothed[i]] for i in range(len(smoothed))]
+            # Plot the line
+            ax.plot([x_start, x_end], [y_start, y_end], color=line_color, linewidth=2)
+
+        # Customize plot
+        ax.set_title(
+            f'Funscript Heatmap\nDuration: {datetime.timedelta(seconds=int(times[-1] / 1000))} - Avg. Speed {int(np.mean(speeds))} - Actions: {len(times)}')
+        ax.set_xlabel('Time (s)')
+        ax.set_yticks(np.arange(0, 101, 10))
+        ax.set_xlim(times[0] / 1000, times[-1] / 1000)
+        ax.set_ylim(0, 100)
+
+        # Remove borders (spines)
+        for spine in ax.spines.values():
+            spine.set_visible(False)
+
+        # Add colorbar
+        cmap = mcolors.LinearSegmentedColormap.from_list("custom_heatmap", [
+            (heatmap_colors[i][0] / 255, heatmap_colors[i][1] / 255, heatmap_colors[i][2] / 255) for i in
+            range(len(heatmap_colors))
+        ])
+        norm = mcolors.Normalize(vmin=0, vmax=5 * step_size)
+        sm = ScalarMappable(cmap=cmap, norm=norm)
+        sm.set_array([])
+        # cbar = plt.colorbar(sm, ax=ax, orientation='horizontal', pad=0.2,
+        #                    ticks=np.arange(0, 5 * step_size + 1, step_size))
+        # cbar.set_label('Speed (positions/s)')
+
+        # Save the figure
+        plt.savefig(output_image_path, bbox_inches='tight', dpi=200)  # Increase resolution
+        plt.close()
+        print(f"Funscript heatmap saved to {output_image_path}")
 
-    def keep_significant_points(self, positions):
-        significant = [positions[0]]
-        for i in range(1, len(positions) - 1):
-            if positions[i][1] != positions[i - 1][1] and positions[i][1] != positions[i + 1][1]:
-                significant.append(positions[i])
-        significant.append(positions[-1])
-        return significant
 
     def filter_positions(self, positions, fps):
         if not positions:
@@ -248,31 +239,350 @@ def load_funscript(self, funscript_path):
 
         return times, positions, relevant_chapters_export, irrelevant_chapters_export
 
+    def compare_funscripts(self, reference_path, script1, video_path, isVR, output_image_path, script2=None):
+        generated_paths = []
+        generated_paths.append(script1)
+        if script2 is not None:
+            generated_paths.append(script2)
+
+        # Load reference funscript
+        ref_times, ref_positions, _, _ = self.load_funscript(reference_path)
+
+        # Determine total duration in seconds
+        total_duration = ref_times[-1] / 1000 if ref_times else 0
+
+        # Select 6 random non-overlapping 20-second sections
+        sections = self.select_random_sections(total_duration, section_duration=10, num_sections=6)
+
+        screenshots_done = False
+
+        # Load generated funscripts
+        for generated_path in generated_paths:
+            gen_times, gen_positions, _, _ = self.load_funscript(generated_path)
+            # Extract data for each section
+            ref_sections = []
+            gen_sections = []
+            for start, end in sections:
+                ref_sec = self.extract_section(ref_times, ref_positions, start, end)
+                gen_sec = self.extract_section(gen_times, gen_positions, start, end)
+                ref_sections.append(ref_sec)
+                gen_sections.append(gen_sec)
+
+            if not screenshots_done:
+                # Capture screenshots, but only once
+                screenshots = self.capture_screenshots(video_path, isVR, sections)
+                screenshots_done = True
+
+            # Plot and combine
+            #self.create_combined_plot(ref_sections, gen_sections, screenshots, sections, output_image_path)
+            # Create the combined plot
+            self.create_combined_plot(
+                ref_sections, gen_sections, screenshots, sections, output_image_path,
+                ref_times, ref_positions, gen_times, gen_positions
+            )
+
+    def analyze_funscripts(self, script_paths, video_path, isVR, output_image_path):
+        """
+        Analyzes one, two, or three funscripts and generates a report.
+
+        Args:
+            script_paths (list): List of paths to the funscripts (1, 2, or 3).
+            video_path (str): Path to the video file.
+            isVR (bool): Whether the video is VR (to crop the frame).
+            output_image_path (str): Path to save the output image.
+        """
+        if not script_paths or len(script_paths) > 3:
+            raise ValueError("Please provide 1, 2, or 3 funscript paths.")
+
+        # Load funscripts
+        funscripts = []
+        for path in script_paths:
+            times, positions, _, _ = self.load_funscript(path)
+            funscripts.append((times, positions))
+
+        # Determine total duration in seconds
+        total_duration = funscripts[0][0][-1] / 1000 if funscripts[0][0] else 0
+
+        # Select 6 random non-overlapping 20-second sections
+        sections = self.select_random_sections(total_duration, section_duration=10, num_sections=6)
+
+        # Capture screenshots
+        screenshots = self.capture_screenshots(video_path, isVR, sections)
+
+        # Extract data for each section
+        section_data = []
+        for times, positions in funscripts:
+            sections_data = []
+            for start, end in sections:
+                sec = self.extract_section(times, positions, start, end)
+                sections_data.append(sec)
+            section_data.append(sections_data)
+
+        # Create the combined plot
+        self.create_flexible_plot(section_data, screenshots, sections, output_image_path, funscripts)
+
+    def create_flexible_plot(self, section_data, screenshots, sections, output_image_path, funscripts):
+        """
+        Creates a flexible plot for 1, 2, or 3 funscripts.
+
+        Args:
+            section_data (list): List of section data for each funscript.
+            screenshots (list): List of screenshots for each section.
+            sections (list): List of tuples (start, end) for each section.
+            output_image_path (str): Path to save the plot.
+            funscripts (list): List of tuples (times, positions) for each funscript.
+        """
+        num_funscripts = len(funscripts)
+        rows_per_funscript = 2  # Heatmap + stats
+        total_rows = rows_per_funscript + 6  # 6 sections
+
+        # Create a grid: total_rows rows, num_funscripts columns
+        #   fig, axes = plt.subplots(nrows=total_rows, ncols=2, figsize=(10 * num_funscripts, 28))
+        fig, axes = plt.subplots(nrows=total_rows, ncols=2, figsize=(10, 28))
+        if num_funscripts == 1:
+            axes = axes.reshape(-1, 1)  # Ensure axes is 2D even for a single funscript
+
+        # Plot heatmaps and stats for each funscript
+        for i, (times, positions) in enumerate(funscripts):
+            # Heatmap
+            ax_heatmap = axes[0, i]
+            self.generate_heatmap_inline(ax_heatmap, times, positions)
+            ax_heatmap.set_title(f'Funscript {i + 1} Heatmap')
+
+            # Stats
+            ax_stats = axes[1, i]
+            ax_stats.axis('off')
+            metrics = self._calculate_metrics(times, positions)
+            stats_text = (
+                f"Stats:\n"
+                f"Number of Strokes: {metrics['num_strokes']}\n"
+                f"Avg Stroke Duration: {metrics['avg_stroke_duration']:.2f}s\n"
+                f"Avg Speed: {int(metrics['avg_speed'])} positions/s\n"
+                f"Avg Depth of Stroke: {int(metrics['avg_depth'])}\n"
+                f"Avg Max: {int(metrics['avg_max'])}\n"
+                f"Avg Min: {int(metrics['avg_min'])}"
+            )
+            ax_stats.text(0.1, 0.5, stats_text, fontsize=10, va='center')
+
+        # original version - Plot sections in the remaining rows
+        """for i in range(6):  # 6 sections
+            # Plot screenshot in the first column
+            ax_screenshot = axes[(i + 2) * 2]  # First column of each row
+            ax_screenshot.imshow(screenshots[i])
+            ax_screenshot.axis('off')  # Hide axes for the screenshot
+
+            # Plot funscript data in the second column
+            ax_plot = axes[(i + 2) * 2 + 1]  # Second column of each row
+            gen_times_sec = [t / 1000 for t in gen_sections[i][0]]
+            ax_plot.plot(gen_times_sec, gen_sections[i][1], label='Generated', color='blue')
+
+            ref_times_sec = [t / 1000 for t in ref_sections[i][0]]
+            ax_plot.plot(ref_times_sec, ref_sections[i][1], label='Reference', color='red')
+
+            # Set labels and title
+            start_time = datetime.datetime.fromtimestamp(sections[i][0]).strftime('%H:%M:%S')
+            end_time = datetime.datetime.fromtimestamp(sections[i][1]).strftime('%H:%M:%S')
+            ax_plot.set_title(f'Section {i + 1}: {start_time} - {end_time}')
+            ax_plot.set_xlabel('Time (s)')
+            ax_plot.set_ylabel('Position')
+            ax_plot.legend()
+            ax_plot.set_xlim(sections[i][0], sections[i][1])
+        """
+
+        # Adjust layout
+        plt.tight_layout()
+
+        # Plot sections
+        for i in range(6):  # 6 sections
+            #ax_screenshot = axes[rows_per_funscript + i]  # axes[rows_per_funscript + i, j]
+            ax_screenshot = axes[(i + 2) * 2]  # First column of each row
+            ax_screenshot.imshow(screenshots[i])
+            ax_screenshot.axis('off')
+
+            # Plot funscript data in the second column
+            ax_plot = axes[(i + 2) * 2 + 1]  # Second column of each row
+            for j in range(num_funscripts):
+
+                #ax_plot = axes[rows_per_funscript + i, j]
+                times_sec = [t / 1000 for t in section_data[j][i][0]]
+                ax_plot.plot(times_sec, section_data[j][i][1], label=f'Funscript {j + 1}',
+                             color=['blue', 'red', 'green'][j])
+
+            # Set labels and title
+            start_time = datetime.datetime.fromtimestamp(sections[i][0]).strftime('%H:%M:%S')
+            end_time = datetime.datetime.fromtimestamp(sections[i][1]).strftime('%H:%M:%S')
+            ax_plot.set_title(f'Section {i + 1}: {start_time} - {end_time}')
+            ax_plot.set_xlabel('Time (s)')
+            ax_plot.set_ylabel('Position')
+            ax_plot.legend()
+            ax_plot.set_xlim(sections[i][0], sections[i][1])
+
+        # Adjust layout
+        plt.tight_layout()
 
-    def generate_heatmap(self, funscript_path, output_image_path):
-        # Load funscript data
-        times, positions, _, _ = self.load_funscript(funscript_path)
+        # Save the figure
+        plt.savefig(output_image_path[:-4] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png", dpi=200)
+        plt.close()
+
+    def select_random_sections(self, total_duration, section_duration=10, num_sections=6):
+        sections = []
+        segment_duration = total_duration / num_sections  # Duration of each segment
+
+        for i in range(num_sections):
+            # Define the start and end of the current segment
+            segment_start = i * segment_duration
+            segment_end = (i + 1) * segment_duration
+
+            # Ensure the 10-second section stays within the segment
+            max_start = segment_end - section_duration
+            if max_start < segment_start:
+                raise ValueError(f"Segment {i} is too short to fit a {section_duration}-second section.")
+
+            # Randomly select a start time within the segment
+            start = np.random.uniform(segment_start, max_start)
+            end = start + section_duration
+
+            # Add the section to the list
+            sections.append((start, end))
+
+        return sections
+
+    def extract_section(self, times, positions, start, end):
+        start_ms = start * 1000
+        end_ms = end * 1000
+        indices = [i for i, t in enumerate(times) if start_ms <= t <= end_ms]
+        return [times[i] for i in indices], [positions[i] for i in indices]
+
+    def capture_screenshots(self, video_path, isVR, sections):
+        cap = cv2.VideoCapture(video_path)
+        screenshots = []
+        for start, _ in sections:
+            cap.set(cv2.CAP_PROP_POS_MSEC, start * 1000)
+            ret, frame = cap.read()
+            if isVR: # left side of the frame only
+                frame = frame[:, :frame.shape[1] // 2]
+            if ret:
+                screenshots.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+            else:
+                screenshots.append(np.zeros((100, 160, 3), dtype=np.uint8))
+        cap.release()
+        return screenshots
+
+
+    def create_combined_plot(self, ref_sections, gen_sections, screenshots, sections, output_image_path, ref_times,
+                             ref_positions, gen_times, gen_positions):
+        """
+        Creates a combined plot with heatmaps as a header, comparative information, and section comparisons below.
+
+        Args:
+            ref_sections (list): List of reference sections (times, positions).
+            gen_sections (list): List of generated sections (times, positions).
+            screenshots (list): List of screenshots for each section.
+            sections (list): List of tuples (start, end) for each section.
+            output_image_path (str): Path to save the combined plot.
+            ref_times (list): Times from the reference funscript.
+            ref_positions (list): Positions from the reference funscript.
+            gen_times (list): Times from the generated funscript.
+            gen_positions (list): Positions from the generated funscript.
+        """
+        # Create a 8x2 grid: 8 rows, 2 columns (1st row for heatmaps, 2nd row for comparative info, 3rd-8th for sections)
+        fig, axes = plt.subplots(nrows=8, ncols=2, figsize=(10, 28))
+
+        # Flatten the axes array for easy iteration
+        axes = axes.flatten()
+
+        # Generate and add reference heatmap to the first row, first column
+        ax_ref_heatmap = axes[0]
+        self.generate_heatmap_inline(ax_ref_heatmap, ref_times, ref_positions)
+        ax_ref_heatmap.set_title('Reference Funscript Heatmap')
+
+        # Generate and add generated heatmap to the first row, second column
+        ax_gen_heatmap = axes[1]
+        self.generate_heatmap_inline(ax_gen_heatmap, gen_times, gen_positions)
+        ax_gen_heatmap.set_title('Generated Funscript Heatmap')
+
+        # Calculate comparative information
+        ref_metrics = self._calculate_metrics(ref_times, ref_positions)
+        gen_metrics = self._calculate_metrics(gen_times, gen_positions)
+
+        # Add comparative information to the second row
+        ax_comparative_left = axes[2]  # First column of the second row
+        ax_comparative_left.axis('off')  # Hide axes for the comparative info
+        ref_comparative_text = (
+            f"Reference:\n"
+            f"Number of Strokes: {ref_metrics['num_strokes']}\n"
+            f"Avg Stroke Duration: {ref_metrics['avg_stroke_duration']:.2f}s\n"
+            f"Avg Speed: {int(ref_metrics['avg_speed'])} positions/s\n"
+            f"Avg Depth of Stroke: {int(ref_metrics['avg_depth'])}\n"
+            f"Avg Max: {int(ref_metrics['avg_max'])}\n"
+            f"Avg Min: {int(ref_metrics['avg_min'])}"
+        )
+        ax_comparative_left.text(0.1, 0.5, ref_comparative_text, fontsize=10, va='center')
+
+        ax_comparative_right = axes[3]  # Second column of the second row
+        ax_comparative_right.axis('off')  # Hide axes for the comparative info
+        gen_comparative_text = (
+            f"Generated:\n"
+            f"Number of Strokes: {gen_metrics['num_strokes']}\n"
+            f"Avg Stroke Duration: {gen_metrics['avg_stroke_duration']:.2f}s\n"
+            f"Avg Speed: {int(gen_metrics['avg_speed'])} positions/s\n"
+            f"Avg Depth of Stroke: {int(gen_metrics['avg_depth'])}\n"
+            f"Avg Max: {int(gen_metrics['avg_max'])}\n"
+            f"Avg Min: {int(gen_metrics['avg_min'])}"
+        )
+        ax_comparative_right.text(0.1, 0.5, gen_comparative_text, fontsize=10, va='center')
+
+        # Plot sections in the remaining rows
+        for i in range(6):  # 6 sections
+            # Plot screenshot in the first column
+            ax_screenshot = axes[(i + 2) * 2]  # First column of each row
+            ax_screenshot.imshow(screenshots[i])
+            ax_screenshot.axis('off')  # Hide axes for the screenshot
+
+            # Plot funscript data in the second column
+            ax_plot = axes[(i + 2) * 2 + 1]  # Second column of each row
+            gen_times_sec = [t / 1000 for t in gen_sections[i][0]]
+            ax_plot.plot(gen_times_sec, gen_sections[i][1], label='Generated', color='blue')
+
+            ref_times_sec = [t / 1000 for t in ref_sections[i][0]]
+            ax_plot.plot(ref_times_sec, ref_sections[i][1], label='Reference', color='red')
+
+            # Set labels and title
+            start_time = datetime.datetime.fromtimestamp(sections[i][0]).strftime('%H:%M:%S')
+            end_time = datetime.datetime.fromtimestamp(sections[i][1]).strftime('%H:%M:%S')
+            ax_plot.set_title(f'Section {i + 1}: {start_time} - {end_time}')
+            ax_plot.set_xlabel('Time (s)')
+            ax_plot.set_ylabel('Position')
+            ax_plot.legend()
+            ax_plot.set_xlim(sections[i][0], sections[i][1])
+
+        # Adjust layout
+        plt.tight_layout()
+
+        # Save the combined figure
+        plt.savefig(output_image_path[:-4] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png", dpi=200)
+        #plt.show()
+
+    def generate_heatmap_inline(self, ax, times, positions):
+        """
+        Generates a heatmap on the given axes using the existing `generate_heatmap` logic.
+
+        Args:
+            ax (matplotlib.axes.Axes): The axes to plot the heatmap on.
+            times (list): List of times from the funscript.
+            positions (list): List of positions from the funscript.
+        """
         if not times or not positions:
-            print("Failed to load funscript data.")
             return
 
-        # Print loaded data for debugging
-        print(f"Times: {times}")
-        print(f"Positions: {positions}")
-        print(f"Total Actions: {len(times)}")
-        print(f"Time Range: {times[0]} to {datetime.timedelta(seconds=int(times[-1] / 1000))}")
-
         # Calculate speed (position change per time interval)
         speeds = np.abs(np.diff(positions) / np.diff(times)) * 1000  # Positions per second
-        print(f"Speeds: {speeds}")
-
-
 
         def get_color(intensity):
             if intensity <= 0:
                 return heatmap_colors[0]
             if intensity > 5 * step_size:
-                return heatmap_colors[6]
+                return heatmap_colors[-1]
             intensity += step_size / 2.0
             index = int(intensity // step_size)
             t = (intensity - index * step_size) / step_size
@@ -282,10 +592,6 @@ def get_color(intensity):
                 heatmap_colors[index][2] + (heatmap_colors[index + 1][2] - heatmap_colors[index][2]) * t
             ]
 
-        # Create figure and plot
-        plt.figure(figsize=(30, 2))
-        ax = plt.gca()
-
         # Draw lines between points with colors based on speed
         for i in range(len(times) - 1):
             x_start = times[i] / 1000  # Convert ms to seconds
@@ -314,24 +620,192 @@ def get_color(intensity):
             spine.set_visible(False)
 
         # Add colorbar
-        cmap = mcolors.LinearSegmentedColormap.from_list("custom_heatmap", [
+        cmap = LinearSegmentedColormap.from_list("custom_heatmap", [
             (heatmap_colors[i][0] / 255, heatmap_colors[i][1] / 255, heatmap_colors[i][2] / 255) for i in
             range(len(heatmap_colors))
         ])
-        norm = mcolors.Normalize(vmin=0, vmax=5 * step_size)
+        norm = Normalize(vmin=0, vmax=5 * step_size)
         sm = ScalarMappable(cmap=cmap, norm=norm)
         sm.set_array([])
-        #cbar = plt.colorbar(sm, ax=ax, orientation='horizontal', pad=0.2,
-        #                    ticks=np.arange(0, 5 * step_size + 1, step_size))
-        #cbar.set_label('Speed (positions/s)')
 
-        # Save the figure
-        plt.savefig(output_image_path, bbox_inches='tight', dpi=200)  # Increase resolution
-        plt.close()
+    def _calculate_metrics(self, times, positions):
+        """
+        Calculates metrics for a funscript.
+
+        Args:
+            times (list): List of times from the funscript.
+            positions (list): List of positions from the funscript.
+
+        Returns:
+            dict: Dictionary containing the calculated metrics.
+        """
+        if not times or not positions:
+            return {}
+
+        # Calculate number of strokes
+        num_strokes = len(times) - 1
+
+        # Calculate average stroke duration
+        stroke_durations = np.diff(times) / 1000  # Convert to seconds
+        avg_stroke_duration = np.mean(stroke_durations)
+
+        # Calculate average speed
+        speeds = np.abs(np.diff(positions) / np.diff(times)) * 1000  # Positions per second
+        #speeds = np.abs(np.diff(positions) / stroke_durations)
+        avg_speed = np.mean(speeds)
+
+        # Calculate average depth of stroke
+        depths = np.abs(np.diff(positions))
+        avg_depth = np.mean(depths)
+
+        # Calculate average max and min
+        avg_max = np.mean([max(positions[i], positions[i + 1]) for i in range(len(positions) - 1)])
+        avg_min = np.mean([min(positions[i], positions[i + 1]) for i in range(len(positions) - 1)])
+
+        return {
+            'num_strokes': num_strokes,
+            'avg_stroke_duration': avg_stroke_duration,
+            'avg_speed': avg_speed,
+            'avg_depth': avg_depth,
+            'avg_max': avg_max,
+            'avg_min': avg_min
+            }
 
-        print(f"Funscript heatmap saved to {output_image_path}")
 
+    def adjust_peaks_and_lows(self, positions, peak_boost=10, low_reduction=10, max_flat_length=5):
+        """
+        Adjusts the peaks and lows of a funscript while avoiding long flat sections at 0 or 100.
 
+        Args:
+            positions (list): List of positions (0-100) from the funscript.
+            peak_boost (int): Amount to increase peaks by.
+            low_reduction (int): Amount to decrease lows by.
+            max_flat_length (int): Maximum allowed length of flat sections at 0 or 100.
+
+        Returns:
+            list: Adjusted positions.
+        """
+        if not positions or len(positions) < 3:
+            return positions
+
+        # Convert positions to a numpy array for easier manipulation
+        positions = np.array(positions)
+
+        # Identify plateaus before boosting
+        original_plateaus = self._find_plateaus(positions)
+
+        # Identify peaks and lows
+        peaks = self._find_local_maxima(positions)
+        lows = self._find_local_minima(positions)
+
+        # Adjust peaks and lows
+        positions[peaks] = np.clip(positions[peaks] + peak_boost, 0, 100)
+        positions[lows] = np.clip(positions[lows] - low_reduction, 0, 100)
+
+        # Identify plateaus after boosting
+        adjusted_plateaus = self._find_plateaus(positions)
+
+        # Compare plateaus and adjust artificially created flats
+        positions = self._compare_and_adjust_plateaus(positions, original_plateaus, adjusted_plateaus, max_flat_length)
+
+        return positions.tolist()
+
+    def _find_local_maxima(self, positions):
+        """
+        Identifies local maxima (peaks) in the positions.
+
+        Args:
+            positions (np.array): Array of positions.
+
+        Returns:
+            np.array: Boolean array where True indicates a peak.
+        """
+        peaks = np.zeros_like(positions, dtype=bool)
+        for i in range(1, len(positions) - 1):
+            if positions[i] > positions[i - 1] and positions[i] > positions[i + 1]:
+                peaks[i] = True
+        return peaks
+
+    def _find_local_minima(self, positions):
+        """
+        Identifies local minima (lows) in the positions.
+
+        Args:
+            positions (np.array): Array of positions.
+
+        Returns:
+            np.array: Boolean array where True indicates a low.
+        """
+        lows = np.zeros_like(positions, dtype=bool)
+        for i in range(1, len(positions) - 1):
+            if positions[i] < positions[i - 1] and positions[i] < positions[i + 1]:
+                lows[i] = True
+        return lows
+
+    def _find_plateaus(self, positions):
+        """
+        Identifies flat sections (plateaus) in the positions.
+
+        Args:
+            positions (np.array): Array of positions.
+
+        Returns:
+            list: List of tuples (start, end) representing the indices of plateaus.
+        """
+        plateaus = []
+        start = 0
+        for i in range(1, len(positions)):
+            if positions[i] != positions[i - 1]:
+                if i - start > 1:  # Plateau must have at least 2 points
+                    plateaus.append((start, i - 1))
+                start = i
+        if len(positions) - start > 1:  # Check the last plateau
+            plateaus.append((start, len(positions) - 1))
+        return plateaus
+
+    def _compare_and_adjust_plateaus(self, positions, original_plateaus, adjusted_plateaus, max_flat_length):
+        """
+        Compares plateaus before and after adjustments and breaks artificially created flats.
+
+        Args:
+            positions (np.array): Array of positions.
+            original_plateaus (list): List of plateaus before adjustments.
+            adjusted_plateaus (list): List of plateaus after adjustments.
+            max_flat_length (int): Maximum allowed length of flat sections at 0 or 100.
+
+        Returns:
+            np.array: Adjusted positions.
+        """
+        for plateau in adjusted_plateaus:
+            start, end = plateau
+            value = positions[start]
+
+            # Check if the plateau is at 0 or 100 and was not present in the original data
+            if (value == 0 or value == 100) and not self._is_plateau_in_original(plateau, original_plateaus):
+                # Check if the plateau exceeds the maximum allowed length
+                if end - start + 1 > max_flat_length:
+                    # Break the plateau by adjusting the values
+                    for i in range(start, end + 1):
+                        positions[i] = positions[i] + 1 if value == 100 else positions[i] - 1
+
+        return positions
+
+    def _is_plateau_in_original(self, plateau, original_plateaus):
+        """
+        Checks if a plateau was present in the original data.
+
+        Args:
+            plateau (tuple): Tuple (start, end) representing the indices of the plateau.
+            original_plateaus (list): List of plateaus in the original data.
+
+        Returns:
+            bool: True if the plateau was present in the original data, False otherwise.
+        """
+        start, end = plateau
+        for original_start, original_end in original_plateaus:
+            if start >= original_start and end <= original_end:
+                return True
+        return False
 
 if __name__ == "__main__":
     video_path = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index c22a6678..70c3cf35 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -99,8 +99,6 @@ def __init__(self, fps, frame_pos, state=None):
         self.sex_position_history = deque(maxlen=120)  # History of sex positions
 
         # Position and distance tracking
-        self.positions = {class_name: deque(maxlen=200) for class_name in class_names}  # Positions for each class
-        self.distances = {class_name: deque(maxlen=200) for class_name in class_names}  # Distances for each class
         self.areas = {class_name: deque(maxlen=200) for class_name in class_names}  # Areas for each class
 
         # Normalized positions and distances
@@ -109,7 +107,8 @@ def __init__(self, fps, frame_pos, state=None):
 
         # Tracked positions and normalized tracked positions
         self.tracked_positions = {}
-        self.normalized_tracked_positions = {}
+        self.normalized_absolute_tracked_positions = {}
+        self.normalized_distance_to_penis = {}
 
         # Initialize normalized distances and positions
         for class_name in class_names:
@@ -124,10 +123,6 @@ def __init__(self, fps, frame_pos, state=None):
         # Penetration state
         self.penetration = False  # Whether penetration is detected
 
-        # Prediction and tracking thresholds
-        self.max_predictions = int(self.fps)  # Maximum number of predictions
-        self.max_inactive_frames = int(fps * 2)  # Maximum inactive frames before a track is lost
-        self.distance_threshold = 50  # Distance threshold for tracking
 
     def update_distance(self, raw_distance):
         """
@@ -137,15 +132,15 @@ def update_distance(self, raw_distance):
             #filtered_distance = self.distance_kf.predict()
             # simple predict using ema, waiting for kalman filter fix
             filtered_distance = 0.1 * self.previous_distances[-3] + 0.3 * self.previous_distances[-2] + 0.6 * self.previous_distances[-1]
-            print(f"predicted distance: {filtered_distance}")
+            #print(f"predicted distance: {filtered_distance}")
         else:
-            print(f"before fix, raw distance is: {raw_distance}")
+            #print(f"before fix, raw distance is: {raw_distance}")
             # applying ema
             ema_distance = 0.1 * self.previous_distances[-2] + 0.3 * self.previous_distances[-1] + 0.6 * raw_distance
-            print(f"self distance: {self.distance}, ema_distance: {ema_distance}, max speed: {self.max_speed}, delta distances: {self.distance-ema_distance}")
+            #print(f"self distance: {self.distance}, ema_distance: {ema_distance}, max speed: {self.max_speed}, delta distances: {self.distance-ema_distance}")
             if abs(self.distance - ema_distance) > self.max_speed:
                 ema_distance = self.distance + np.sign(ema_distance - self.distance) * self.max_speed
-                print(f"adjusted ema distance: {ema_distance}")
+                #print(f"adjusted ema distance: {ema_distance}")
             #raw_distance_2d = (raw_distance, 0)
             # Update the Kalman filter with the new measurement
             #self.distance_kf.update(raw_distance_2d)
@@ -207,7 +202,7 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
         self.image_y_size = image_y_size
 
         # Collect all detections
-        detections = [(box, conf, class_name, track_id) for box, conf, cls, class_name, track_id in sorted_boxes]
+        # detections = [(box, conf, class_name, track_id) for box, conf, cls, class_name, track_id in sorted_boxes]
 
         # Initialize tracking state
         self.glans_detected = False
@@ -250,51 +245,91 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                 logging.info(f"@{self.current_frame_id} - Deactivated locked_penis_box")
 
         sum_pos = 0
-        weight_pos = 0
+        sum_weight_pos = 0
 
         for box, conf, cls, class_name, track_id in sorted_boxes:
             if class_name in ['glans', 'penis', 'navel']:
                 continue
             elif self.locked_penis_box.is_active() and (self.boxes_overlap(box, self.locked_penis_box.get_box()) or class_name == 'hips center'):
-                if class_name not in classes_touching_penis:
+                if class_name != 'hips center' and class_name not in classes_touching_penis:
                     classes_touching_penis.append(class_name)
                 self.tracked_boxes.append([box, class_name, track_id])
                 x1, y1, x2, y2 = box
-                mid_y = (y1 + 4 * y2) // 5
-                dist_to_penis_base = int(((self.locked_penis_box.get_box()[3] - mid_y) / (self.locked_penis_box.get_height() * .8)) * 100)
-                normalized_y = min(max(0, dist_to_penis_base), 100)
+                #mid_y = (y1 + 4 * y2) // 5
+                mid_y = (y1 + y2) // 2
 
-                # Update tracked positions
+                # Update tracked positions within the frame
                 if track_id not in self.tracked_positions:
                     self.tracked_positions[track_id] = []
-                # self.tracked_positions[track_id].append(normalized_y)
-
                 # let's apply a moving average to try and filter outliers
                 if len(self.tracked_positions[track_id]) > 2:
-                    normalized_y = 0.2 * self.tracked_positions[track_id][-2] + 0.3 * self.tracked_positions[track_id][-1] + 0.5 * normalized_y
-                self.tracked_positions[track_id].append(normalized_y)
-
+                    mid_y = 0.2 * self.tracked_positions[track_id][-2] + 0.3 * \
+                                                    self.tracked_positions[track_id][
+                                                        -1] + 0.5 * mid_y
+                self.tracked_positions[track_id].append(mid_y)
                 # Maintain a fixed-size history
                 if len(self.tracked_positions[track_id]) > 600:
                     self.tracked_positions[track_id].pop(0)
 
-                # Update normalized tracked positions
-                if track_id not in self.normalized_tracked_positions:
-                    self.normalized_tracked_positions[track_id] = []
-                #self.normalized_tracked_positions[track_id].append(normalized_y)
-                self.normalized_tracked_positions[track_id].append(self.tracked_positions[track_id][-1])
-
+                # Normalize absolute position 0-100 based on historic min and max
+                if track_id not in self.normalized_absolute_tracked_positions:
+                    self.normalized_absolute_tracked_positions[track_id] = []
+                # let's retrieve min and max from self.tracked_positions
+                min_y, max_y = min(self.tracked_positions[track_id]), max(self.tracked_positions[track_id])
+                # now, normalize
+                if max_y - min_y == 0:
+                    normalized_y = 100
+                else:
+                    normalized_y = min(max(0, 100 - (((mid_y - min_y) / (max_y - min_y)) * 100)), 100)
+                self.normalized_absolute_tracked_positions[track_id].append(normalized_y)
                 # Maintain a fixed-size history
-                if len(self.normalized_tracked_positions[track_id]) > 60:
-                    self.normalized_tracked_positions[track_id].pop(0)
-
-                # Sum delta positions of all touching items, weighted by the length of their history
-                weight_pos_track_id = sum(
-                    abs(self.normalized_tracked_positions[track_id][i] - self.normalized_tracked_positions[track_id][
-                        i - 1]) for i in
-                    range(1, len(self.normalized_tracked_positions[track_id])))
-                sum_pos += max(0, (normalized_y - (100 - self.max_allowed))) * weight_pos_track_id
-                weight_pos += weight_pos_track_id
+                if len(self.normalized_absolute_tracked_positions[track_id]) > 60:
+                    self.normalized_absolute_tracked_positions[track_id].pop(0)
+
+                # Now, we compute the distance to penis base
+                if track_id not in self.normalized_distance_to_penis:
+                    self.normalized_distance_to_penis[track_id] = []
+                if class_name == 'butt':  # 80% of (y2 - y1) + y1
+                    low_y = (.8 * (y2 - y1)) + y1
+                elif class_name != 'hips center':
+                    low_y = y2
+
+                # Now, we compute the distance to penis base for classes that are not 'hips center'
+                # And computing the weighted position based on recent moves
+                if class_name != 'hips center':
+                    dist_to_penis_base = int(((self.locked_penis_box.get_box()[3] - low_y) / self.locked_penis_box.get_height()) * 100)
+                    normalized_dist_to_penis_base = min(max(0, dist_to_penis_base), 100)
+                    # let's apply a moving average to try and filter outliers
+                    if len(self.normalized_distance_to_penis[track_id]) > 2:
+                        low_y = 0.2 * self.normalized_distance_to_penis[track_id][-2] + 0.3 * \
+                                self.normalized_distance_to_penis[track_id][
+                                    -1] + 0.5 * low_y
+                    # Update normalized tracked positions
+                    if track_id not in self.normalized_distance_to_penis:
+                        self.normalized_distance_to_penis[track_id] = []
+                    self.normalized_distance_to_penis[track_id].append(normalized_dist_to_penis_base)
+                    # Maintain a fixed-size history
+                    if len(self.normalized_distance_to_penis[track_id]) > 60:
+                        self.normalized_distance_to_penis[track_id].pop(0)
+
+                    # Sum delta positions of all touching items, weighted by the length of their history
+                    weight_pos_track_id = sum(
+                        abs(self.normalized_distance_to_penis[track_id][i] - self.normalized_distance_to_penis[track_id][
+                            i - 1]) for i in
+                        range(1, len(self.normalized_distance_to_penis[track_id])))
+                    #sum_pos += max(0, (normalized_dist_to_penis_base - (100 - self.max_allowed))) * weight_pos_track_id
+                    sum_pos += ((self.normalized_distance_to_penis[track_id][-1] +
+                                self.normalized_absolute_tracked_positions[track_id][-1]) // 2) * weight_pos_track_id
+                    sum_weight_pos += weight_pos_track_id
+                else:  # hips center
+                    if self.penetration:  # only consider them during penetration
+                        weight_pos_track_id = sum(
+                            abs(self.normalized_absolute_tracked_positions[track_id][i] -
+                                self.normalized_absolute_tracked_positions[track_id][
+                                    i - 1]) for i in
+                            range(1, len(self.normalized_absolute_tracked_positions[track_id])))
+                        sum_pos += self.normalized_absolute_tracked_positions[track_id][-1] * weight_pos_track_id
+                        sum_weight_pos += weight_pos_track_id
 
         if len(classes_touching_penis) == 0 or not self.locked_penis_box.is_active():
             self.penetration = False
@@ -317,12 +352,12 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
             self.detect_sex_position_change('Boobjob', "breast visible and touching")
             self.tracked_body_part = 'breast'
 
-        if weight_pos > 0:
-            distance = int(sum_pos / weight_pos)
+        if sum_weight_pos > 0:
+            distance = int(sum_pos / sum_weight_pos)
         else:
             distance = 100
 
-        print(f"computed distance: {distance}, sending for filtering...")
+        #print(f"computed distance: {distance}, sending for filtering...")
 
         self.update_distance(distance)
 
@@ -382,4 +417,24 @@ def normalize_box_area(self, box, frame_width, frame_height):
         box_area = (x2 - x1) * (y2 - y1)
         max_area = frame_width * frame_height
         normalized_area = 100 * (box_area / max_area)
-        return max(0, min(100, normalized_area))
\ No newline at end of file
+        return max(0, min(100, normalized_area))
+
+    """def log_and_normalize_pos(self, box, track_id):
+        self.boxes[class_name] = box
+        _, y1, _, y2 = box
+        mid_y = (y1 + y2) / 2
+        self.positions[class_name].append(mid_y)
+
+        min_y, max_y = min(self.positions[class_name]), max(self.positions[class_name])
+        normalized_y = (100 - int(100 * ((mid_y - min_y) / (max_y - min_y)))) if min_y != max_y else 100
+
+        # In case of breast, which is a fallback class to pussy, we compute not only the normalized position
+        # but also the normalized breast area which shows back and forth in case of grinding
+        if class_name == "breast" and self.frame:
+            normalized_breast_area = self.normalize_box_area(box, self.frame.shape[1], self.frame.shape[0])
+            normalized_y = ((0.75 * normalized_breast_area) + normalized_y) / 1.75
+
+        # self.normalized_positions[class_name][current_frame_id] = normalized_y
+        self.normalized_positions[class_name].append(normalized_y)
+        return normalized_y
+    """
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index 124a05e3..f359febf 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -6,15 +6,19 @@
 
 
 class VideoReaderFFmpeg:
-    def __init__(self, video_path, is_VR=False, ffmpeg_path=ffmpeg_path, ffprobe_path=ffprobe_path):
+    def __init__(self, video_path, is_VR=False, unwarp=True, projection=None, ffmpeg_path=ffmpeg_path, ffprobe_path=ffprobe_path):
         """
         Initialize the VideoReaderFFmpeg class.
         :param video_path: Path to the video file.
+        :param unwarp: Will try and unwarp VR videos.
+        :param projection: FISHEYE, EQUIRECTANGULAR. Will also look for 'FISHEYE' in the video path.
         :param ffmpeg_path: Path to the FFmpeg binary (default: "ffmpeg").
         :param ffprobe_path: Path to the FFprobe binary (default: "ffprobe").
         """
         self.video_path = video_path
         self.is_VR = is_VR
+        self.unwarp = unwarp
+        self.projection = projection
         self.ffmpeg_path = ffmpeg_path
         self.ffprobe_path = ffprobe_path
         self._initialize_video_info()  # Initialize video metadata
@@ -85,24 +89,37 @@ def _start_process(self, start_frame=0):
         self.current_frame_number = start_frame
 
         if self.is_VR:
-            # FFmpeg command to read frames with VR reprojection
-            if 'NFISHEYE' in self.video_path:  # impeded for troubleshooting
-                self.type = "fisheye"
-                self.iv_fov = 190
-                self.ih_fov = 190
-                self.v_fov = 90
-                self.h_fov = 90
-                self.d_fov = 180
-                arg_line = f"crop=w=iw/2:h=ih:x=0:y=0,v360={self.type}:sg:iv_fov={self.iv_fov}:ih_fov={self.ih_fov}:d_fov={self.d_fov}:v_fov={self.v_fov}:h_fov={self.h_fov}:pitch=-20:yaw=0:roll=0:w={self.width}:h={self.height}:interp=lanczos:reset_rot=1"
+            arg_line = "crop=w=iw/2:h=ih:x=0:y=0"
+            if self.unwarp:
+                if self.projection == "FISHEYE" or "FISHEYE" in self.video_path:
+                    print("Proceeding with fisheye projection correction")
+                    self.type = "fisheye"
+                    self.iv_fov = 190
+                    self.ih_fov = 190
+                    self.v_fov = 90
+                    self.h_fov = 90
+                    self.d_fov = 180
+                else:  # Assuming Equirectangular
+                    print("Assuming Equirectangular projection")
+                    self.type = "he"
+                    self.iv_fov = 250
+                    self.ih_fov = 120
+                    self.v_fov = 90
+                    self.h_fov = 90
+                    self.d_fov = 180
+                    #arg_line = f"crop=w=iw/2:h=ih:x=0:y=0,v360={self.type}:sg:iv_fov={self.iv_fov}:ih_fov={self.ih_fov}:d_fov={self.d_fov}:v_fov={self.v_fov}:h_fov={self.h_fov}:pitch=-20:yaw=0:roll=0:w={self.width}:h={self.height}:interp=lanczos:reset_rot=1"
+                arg_line = arg_line + f",v360={self.type}:output=sg"
+                arg_line = arg_line + f":iv_fov={self.iv_fov}:ih_fov={self.ih_fov}"
+                arg_line = arg_line + f":d_fov={self.d_fov}:v_fov={self.v_fov}:h_fov={self.h_fov}"
+                arg_line = arg_line + f":pitch=-25:yaw=0:roll=0"
+                arg_line = arg_line + f":w={self.width}:h={self.height}"
+                arg_line = arg_line + f":interp=lanczos:reset_rot=1"
+                arg_line = arg_line + f",lutyuv=y=gammaval(0.7)"
+                #arg_line = arg_line + f",eq=brightness=0.1:contrast=1.5"
+                #arg_line = arg_line + f",format=gray"
+                #arg_line = arg_line + f",histeq"
             else:
-                # more tweaking and experimentation needed...
-                # self.type = "he"
-                # self.iv_fov = 90
-                # self.ih_fov = 90
-                # self.v_fov = 90
-                # self.h_fov = 90
-                # self.d_fov = 100
-                arg_line = "crop=w=iw/2:h=ih:x=0:y=0"
+                arg_line = arg_line  # "crop=w=iw/2:h=ih:x=0:y=0"
 
             cmd = [
                 self.ffmpeg_path,

From b746679b3a7425de360e208174763da40ab7bc15 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Fri, 3 Jan 2025 18:05:52 +0100
Subject: [PATCH 029/277] fix / enhancements

---
 FSGenerator.py                                | 518 ++++++++++++------
 README.md                                     | 230 ++++----
 debug/Display_debug_results.py                |   8 +-
 .../Data/com.apple.CoreML/model.mlmodel       | Bin 158971 -> 0 bytes
 .../Data/com.apple.CoreML/weights/weight.bin  | Bin 5230240 -> 0 bytes
 .../Manifest.json                             |  18 -
 params/config.py                              |   8 +
 tests/test_Compare_models_and_resolutions.py  |  98 +++-
 utils/lib_Debugger.py                         |  33 +-
 utils/lib_FunscriptHandler.py                 | 324 ++++-------
 utils/lib_ObjectTracker.py                    | 188 +++++--
 utils/lib_SceneCutsDetect.py                  |  23 +-
 utils/lib_Visualizer.py                       |   4 +
 13 files changed, 875 insertions(+), 577 deletions(-)
 delete mode 100644 models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/model.mlmodel
 delete mode 100644 models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/weights/weight.bin
 delete mode 100644 models/k00gar-11n-200ep-best.mlpackage/Manifest.json

diff --git a/FSGenerator.py b/FSGenerator.py
index 9dcfc7a1..26929f06 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -5,11 +5,14 @@
 import json  # For handling JSON data
 from tqdm import tqdm  # For progress bars
 from ultralytics import YOLO  # YOLO model for object detection
-import argparse  # For command-line argument parsing
-import sys  # For accessing command-line arguments
+import platform  # For identifying the operating system
+import torch  # PyTorch for use of .pt model if not on Apple device
+import tkinter as tk  # GUI library for macOS for basic use in our case
+from tkinter import filedialog, messagebox  # here, what was I saying...
+import subprocess  # For running shell commands
 
 # Import custom modules and configurations
-from params.config import class_priority_order, class_reverse_match, class_colors  # Configuration for class priorities, reverse matching, and colors
+from params.config import class_priority_order, class_reverse_match, class_colors, yolo_models  # Configuration for class priorities, reverse matching, and colors
 from utils.lib_ObjectTracker import ObjectTracker  # Custom object tracking logic
 from utils.lib_FunscriptHandler import FunscriptGenerator  # For generating Funscript files
 from utils.lib_Visualizer import Visualizer  # For visualizing results
@@ -17,6 +20,27 @@
 from utils.lib_SceneCutsDetect import detect_scene_changes  # For detecting scene changes in videos
 from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg  # Custom video reader using FFmpeg
 
+# Define a GlobalState class to manage global variables
+class GlobalState:
+    def __init__(self):
+        self.yolo_det_model = ""
+        self.yolo_pose_model = ""
+        self.video_file = ""
+        self.DebugMode = False
+        self.LiveDisplayMode = False
+        self.isVR = True
+        self.frame_start = 0
+        self.frame_end = None
+        self.reference_script = ""
+        self.offset_x = 0
+        self.funscript_data = []  # List to store Funscript data
+        self.funscript_frames = []
+        self.funscript_distances = []
+        self.debugger = None
+
+# Initialize global state
+global_state = GlobalState()
+
 # Define the BoxRecord class to store bounding box information
 class BoxRecord:
     def __init__(self, box, conf, cls, class_name, track_id):
@@ -62,18 +86,6 @@ def add_record(self, frame_id, box_record):
         else:
             self.frame_data[frame_id] = [box_record]
 
-    def map_class_type_to_name(self, class_type, x1, x2, image_width):
-        """
-        Map class type to a class name (currently unused).
-        :param class_type: The class type to map.
-        :param x1: The x-coordinate of the bounding box's top-left corner.
-        :param x2: The x-coordinate of the bounding box's bottom-right corner.
-        :param image_width: The width of the image/frame.
-        :return: The mapped class name.
-        """
-        class_name = class_type
-        return class_name
-
     def get_boxes(self, frame_id):
         """
         Retrieve and sort bounding boxes for a specific frame.
@@ -117,56 +129,71 @@ def write_dataset(file_path, data):
     export_end = time.time()
     print(f"Done in {export_end - export_start}.")
 
-def extract_yolo_data(det_model_file, pose_model_file,video_path, frame_start, frame_end=None, TestMode=False, isVR=False):
+def get_yolo_model_path():
+    # Check if the device is an Apple device
+    if platform.system() == 'Darwin':
+        print(f"Apple device detected, loading {yolo_models[0]} for MPS inference.")
+        return yolo_models[0]
+
+    # Check if CUDA is available (for GPU support)
+    elif torch.cuda.is_available():
+        print(f"CUDA is available, loading {yolo_models[1]} for GPU inference.")
+        return yolo_models[1]
+
+    # Fallback to ONNX model for other platforms without CUDA
+    else:
+        print(f"Falling back to CPU inference, loading {yolo_models[2]}.")
+        return yolo_models[2]
+
+def extract_yolo_data():
     """
     Extract YOLO detection data from a video.
-    :param det_model_file: Path to the YOLO model file.
-    :param pose_model_file: Path to the YOLO pose model file.
-    :param video_path: Path to the input video file.
-    :param frame_start: The starting frame for processing.
-    :param frame_end: The ending frame for processing (optional).
-    :param TestMode: Enable/disable test mode for debugging.
-    :param isVR: Enable/disable VR mode for video processing.
     """
     # Check if the output file already exists
-    if os.path.exists(video_path[:-4] + f"_rawyolo.json"):
-        print(f"File {video_path[:-4] + f'_rawyolo.json'} already exists. Skipping detections and loading file content...")
-        return
+    if os.path.exists(global_state.video_file[:-4] + f"_rawyolo.json"):
+        # messagebox to ask if user wants to overwrite or reuse
+        # file name without path
+        file_name = os.path.basename(global_state.video_file[:-4] + f"_rawyolo.json")
+        skip_detection = messagebox.askyesno("Detection file already exists",
+                f"File {file_name} already exists.\n\nClick Yes to reuse the existing detections file.\nClick No to perform detections again.")
+        if skip_detection:
+            print(
+                f"File {global_state.video_file[:-4] + f'_rawyolo.json'} already exists. Skipping detections and loading file content...")
+            return
+        else:
+            os.remove(global_state.video_file[:-4] + f"_rawyolo.json")
 
     records = []  # List to store detection records
     test_result = Result(320)  # Test result object for debugging
 
     # Initialize the video reader
-    cap = VideoReaderFFmpeg(video_path, is_VR=isVR)
-    cap.set(cv2.CAP_PROP_POS_FRAMES, frame_start)
+    cap = VideoReaderFFmpeg(global_state.video_file, is_VR=global_state.isVR)
+    cap.set(cv2.CAP_PROP_POS_FRAMES, global_state.frame_start)
 
     # Determine the last frame to process
-    if frame_end:
-        last_frame = frame_end
+    if global_state.frame_end:
+        last_frame = global_state.frame_end
     else:
         last_frame = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
 
     # Load the YOLO model
-    det_model = YOLO(det_model_file, task="detect")
+    det_model = YOLO(global_state.yolo_det_model, task="detect")
 
     # make the pose model optional
-    if len(pose_model_file) > 0:
+    if len(global_state.yolo_pose_model) > 0:
         run_pose_model = True
         print("Activating pose model")
     else:
         run_pose_model = False
         print("Discarding pose model part of the code")
     if run_pose_model:
-        pose_model = YOLO(pose_model_file, task="pose")
+        pose_model = YOLO(global_state.yolo_pose_model, task="pose")
 
     # Loop through the video frames
-    for frame_pos in tqdm(range(frame_start, last_frame), ncols=None, desc="Performing YOLO detection on frames"):
+    for frame_pos in tqdm(range(global_state.frame_start, last_frame), ncols=None, desc="Performing YOLO detection on frames"):
         success, frame = cap.read()  # Read a frame from the video
 
         if success:
-            #if isVR:
-            #    # For VR videos, crop the frame to the middle third
-            #    frame = frame[:, frame.shape[1] // 3:2 * frame.shape[1] // 3, :]
 
             # Run YOLO tracking on the frame
             yolo_det_results = det_model.track(frame, persist=True, conf=0.3, verbose=False)
@@ -176,7 +203,7 @@ def extract_yolo_data(det_model_file, pose_model_file,video_path, frame_start, f
             if yolo_det_results[0].boxes.id is None:  # Skip if no tracks are found
                 continue
 
-            if len(yolo_det_results[0].boxes) == 0 and not TestMode:  # Skip if no boxes are detected
+            if len(yolo_det_results[0].boxes) == 0 and not global_state.LiveDisplayMode:  # Skip if no boxes are detected
                 continue
 
             ### DETECTION of BODY PARTS
@@ -197,7 +224,7 @@ def extract_yolo_data(det_model_file, pose_model_file,video_path, frame_start, f
                 # Create a detection record
                 record = [frame_pos, int(cls), round(conf, 1), x1, y1, x2, y2, track_id]
                 records.append(record)
-                if TestMode:
+                if global_state.LiveDisplayMode:
                     # Print and test the record
                     print(f"Record : {record}")
                     print(f"For class id: {int(cls)}, getting: {class_reverse_match.get(int(cls), 'unknown')}")
@@ -224,8 +251,6 @@ def extract_yolo_data(det_model_file, pose_model_file,video_path, frame_start, f
                         pose_keypoints_list = pose_keypoints.xy.cpu().tolist()
                         left_hip = pose_keypoints_list[0][11]
                         right_hip = pose_keypoints_list[0][12]
-                        left_wrist = pose_keypoints_list[0][9]
-                        right_wrist = pose_keypoints_list[0][10]
 
                         middle_x_frame = frame.shape[1] // 2
                         mid_hips = [middle_x_frame, (int(left_hip[1])+ int(right_hip[1]))//2]
@@ -239,7 +264,7 @@ def extract_yolo_data(det_model_file, pose_model_file,video_path, frame_start, f
 
                         record = [frame_pos, 10, round(conf, 1), x1, y1, x2, y2, 0]
                         records.append(record)
-                        if TestMode:
+                        if global_state.LiveDisplayMode:
                             # Print and test the record
                             print(f"Record : {record}")
                             print(f"For class id: {int(cls)}, getting: {class_reverse_match.get(int(cls), 'unknown')}")
@@ -248,8 +273,7 @@ def extract_yolo_data(det_model_file, pose_model_file,video_path, frame_start, f
                             print(f"Test box: {test_box}")
                             test_result.add_record(frame_pos, test_box)
 
-
-            if TestMode:
+            if global_state.LiveDisplayMode:
                 # Display the YOLO results for testing
                 yolo_det_results[0].plot()
                 cv2.imshow("YOLO11", yolo_det_results[0].plot())
@@ -269,7 +293,7 @@ def extract_yolo_data(det_model_file, pose_model_file,video_path, frame_start, f
                     break
 
     # Write the detection records to a JSON file
-    write_dataset(video_path[:-4] + f"_rawyolo.json", records)
+    write_dataset(global_state.video_file[:-4] + f"_rawyolo.json", records)
     # Release the video capture object and close the display window
     cap.release()
     cv2.destroyAllWindows()
@@ -301,61 +325,61 @@ def make_data_boxes(records, image_x_size):
         result.add_record(frame_idx, box_record)
     return result
 
-def analyze_tracking_results(results, image_y_size, video_path, frame_start=None, frame_end=None, TestMode=False):
+def analyze_tracking_results(results, image_y_size):
     """
     Analyze tracking results and generate Funscript data.
     :param results: The Result object containing detection data.
     :param image_y_size: Height of the image/frame.
-    :param video_path: Path to the input video file.
-    :param frame_start: The starting frame for processing (optional).
-    :param frame_end: The ending frame for processing (optional).
-    :param TestMode: Enable/disable test mode for debugging.
     :return: A list of Funscript data.
     """
     list_of_frames = results.get_all_frame_ids()  # Get all frame IDs with detections
     visualizer = Visualizer()  # Initialize the visualizer
 
-    cap = VideoReaderFFmpeg(video_path, is_VR=isVR)  # Initialize the video reader
+    cap = VideoReaderFFmpeg(global_state.video_file, is_VR=global_state.isVR)  # Initialize the video reader
     fps = cap.get(cv2.CAP_PROP_FPS)  # Get the video's FPS
     nb_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))  # Get the total number of frames
 
-    if not frame_start:
-        frame_start = 0
+    cuts = []
+
+    image_area = cap.get(cv2.CAP_PROP_FRAME_WIDTH) * cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
 
-    if not frame_end:
-        frame_end = nb_frames
+    if not global_state.frame_start:
+        global_state.frame_start = 0
 
-    if TestMode:
-        cap.set(cv2.CAP_PROP_POS_FRAMES, frame_start)
+    if not global_state.frame_end:
+        global_state.frame_end = nb_frames
+
+    if global_state.LiveDisplayMode:
+        cap.set(cv2.CAP_PROP_POS_FRAMES, global_state.frame_start)
     else:
         cap.release()
 
     # Load scene cuts if the file exists
-    if os.path.exists(video_path[:-4] + f"_cuts.json"):
-        print(f"Loading cuts from {video_path[:-4] + f'_cuts.json'}")
-        with open(video_path[:-4] + f"_cuts.json", 'r') as f:
+    if os.path.exists(global_state.video_file[:-4] + f"_cuts.json"):
+        print(f"Loading cuts from {global_state.video_file[:-4] + f'_cuts.json'}")
+        with open(global_state.video_file[:-4] + f"_cuts.json", 'r') as f:
             cuts = json.load(f)
         print(f"Loaded {len(cuts)} cuts : {cuts}")
     else:
         # Detect scene changes if the cuts file does not exist
-        scene_list = detect_scene_changes(video_path, "Left", 0.9, frame_start, frame_end)
-        print(f"Analyzing frames {frame_start} to {frame_end}")
+        scene_list = detect_scene_changes(global_state.video_file, global_state.isVR, 0.9, global_state.frame_start, global_state.frame_end)
+        print(f"Analyzing frames {global_state.frame_start} to {global_state.frame_end}")
         cuts = [scene[1] for scene in scene_list]
         cuts = cuts[:-1]  # Remove the last entry
         # Save the cuts to a file
-        with open(video_path[:-4] + f"_cuts.json", 'w') as f:
+        with open(global_state.video_file[:-4] + f"_cuts.json", 'w') as f:
             json.dump(cuts, f)
 
-    funscript_frames = []  # List to store Funscript frames
-    tracker = ObjectTracker(fps, frame_start)  # Initialize the object tracker
+    global_state.funscript_frames = []  # List to store Funscript frames
+    tracker = ObjectTracker(fps, global_state.frame_start, image_area)  # Initialize the object tracker
 
-    for frame_pos in tqdm(range(frame_start, frame_end), ncols=80, desc="Analyzing tracking results"):
+    for frame_pos in tqdm(range(global_state.frame_start, global_state.frame_end), unit="f"):
         if frame_pos in cuts:
             # Reinitialize the tracker at scene cuts
             print(f"Reaching cut at frame {frame_pos}")
             previous_distances = tracker.previous_distances
             print(f"Reinitializing tracker with previous distances: {previous_distances}")
-            tracker = ObjectTracker(fps, frame_pos)
+            tracker = ObjectTracker(fps, frame_pos, image_area)
             tracker.previous_distances = previous_distances
 
         if frame_pos in list_of_frames:
@@ -365,15 +389,26 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start=None
 
             if tracker.distance:
                 # Append Funscript data if distance is available
-                funscript_frames.append(frame_pos)
-                funscript_distances.append(int(tracker.distance))
+                global_state.funscript_frames.append(frame_pos)
+                global_state.funscript_distances.append(int(tracker.distance))
 
-            if DebugMode:
+            if global_state.DebugMode:
                 # Log debugging information
                 bounding_boxes = []
                 for box in sorted_boxes:
-                    if box[4] in tracker.tracked_positions:
-                        position = tracker.tracked_positions[box[4]][-1]
+                    #if box[4] in tracker.tracked_positions:
+                    if box[4] in tracker.normalized_absolute_tracked_positions:
+                        #position = tracker.tracked_positions[box[4]][-1]
+                        if box[4] == 0:  # generic track_id for 'hips center'
+                            str_dist_penis = 'None'
+                        else:
+                            if box[4] in tracker.normalized_distance_to_penis:
+                                # print(f"box[4]: {box[4]}")
+                                str_dist_penis = str(int(tracker.normalized_distance_to_penis[box[4]][-1]))
+                            else:
+                                str_dist_penis = 'None'
+                        str_abs_pos = str(int(tracker.normalized_absolute_tracked_positions[box[4]][-1]))
+                        position = 'p: ' + str_dist_penis + ' | ' + 'a: ' + str_abs_pos
                     else:
                         position = None
                     bounding_boxes.append({
@@ -383,7 +418,7 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start=None
                         'track_id': box[4],
                         'position': position,
                     })
-                debugger.log_frame(frame_pos,
+                global_state.debugger.log_frame(frame_pos,
                                    bounding_boxes=bounding_boxes,
                                    variables={
                                        'distance': tracker.distance,
@@ -400,7 +435,7 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start=None
                                        'breast_tracking': tracker.breast_tracking,
                                    })
 
-        if TestMode:
+        if global_state.LiveDisplayMode:
             # Display the tracking results for testing
             ret, frame = cap.read()
             frame_display = frame.copy()
@@ -410,12 +445,12 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start=None
                                                              box[0],
                                                              str(box[2]) + ": " + box[1],
                                                              class_colors[str(box[1])],
-                                                             offset_x)
+                                                             global_state.offset_x)
             if tracker.locked_penis_box is not None and tracker.locked_penis_box.is_active():
                 frame_display = visualizer.draw_bounding_box(frame_display, tracker.locked_penis_box.box,
                                                              "Locked_Penis",
                                                              class_colors['penis'],
-                                                             offset_x)
+                                                             global_state.offset_x)
             else:
                 print("No active locked penis box to draw.")
 
@@ -423,26 +458,26 @@ def analyze_tracking_results(results, image_y_size, video_path, frame_start=None
                 frame_display = visualizer.draw_bounding_box(frame_display, tracker.boxes['glans'],
                                                               "Glans",
                                                               class_colors['glans'],
-                                                              offset_x)
-            if funscript_distances:
-                frame_display = visualizer.draw_gauge(frame_display, funscript_distances[-1])
+                                                              global_state.offset_x)
+            if global_state.funscript_distances:
+                frame_display = visualizer.draw_gauge(frame_display, global_state.funscript_distances[-1])
 
             cv2.imshow("Combined Results", frame_display)
             cv2.waitKey(1)
 
     # Prepare Funscript data
-    funscript_data = list(zip(funscript_frames, funscript_distances))
+    global_state.funscript_data = list(zip(global_state.funscript_frames, global_state.funscript_distances))
 
     points = "["
-    for i in range(len(funscript_frames)):
+    for i in range(len(global_state.funscript_frames)):
         if i != 0:
             points += ","
-        points += f"[{funscript_frames[i]}, {funscript_distances[i]}]"
+        points += f"[{global_state.funscript_frames[i]}, {global_state.funscript_distances[i]}]"
     points += "]"
     # Write the raw Funscript data to a JSON file
-    with open(video_path[:-4] + f"_rawfunscript.json", 'w') as f:
-        json.dump(funscript_data, f)
-    return funscript_data
+    with open(global_state.video_file[:-4] + f"_rawfunscript.json", 'w') as f:
+        json.dump(global_state.funscript_data, f)
+    return global_state.funscript_data
 
 def parse_yolo_data_looking_for_penis(data, start_frame):
     """
@@ -472,113 +507,236 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
                 print(f"First instance of Glans/Penis found in frame {line[0] - 4}")
                 return line[0] - 4
 
-# MAIN logic
-if __name__ == '__main__':
-    video_list = []  # List of videos to process
-
-    # Default values for IDE usage
-    #video_list.append("/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4")
-    video_list.append("/Users/k00gar/Downloads/VRCONK_Kiara Cole_game_of_thrones_daenerys_targaryen_a_porn_parody_8K_180x180_3dh.mp4")
-    yolo_det_model = "models/k00gar-11n-200ep-best.mlpackage"
-    yolo_pose_model = "models/yolo11n-pose.mlpackage"  # "models/yolo11x-pose.mlpackage"
-    DebugMode = True
-    LiveDisplayMode = False
-    isVR = True
-    frame_start = 0  # 0 for analysis from the beginning
-    frame_end = None  # None for analysis until the end
-
-    reference_funscript = "/Users/k00gar/Downloads/VRCONK_game_of_thrones_daenerys_targaryen_a_porn_parody_8K_180x180_3dh.funscript"  # "/Users/k00gar/Downloads/SLR Originals - Vote for me - Blake Blossom.realcumber.v1.51071.funscript"
-
-    # Check if the script is run from the command line
-    if len(sys.argv) > 1:  # Command-line arguments are present
-        # Set up argument parsing for command-line usage
-        parser = argparse.ArgumentParser(description="Generate Funscript data from a video using YOLO object detection.")
-        parser.add_argument("video_path", type=str, help="Path to the input video file.")
-        parser.add_argument("--yolo_det_model", type=str, default=yolo_det_model, help="Path to the YOLO detection model file.")
-        parser.add_argument("--yolo_pose_model", type=str, default=yolo_pose_model, help="Path to the YOLO pose model file.")
-        parser.add_argument("--live_display_mode", action="store_true", help="Enable live display mode while processing.")
-        parser.add_argument("--debug_mode", action="store_true", help="Enable debug mode for logging.")
-        parser.add_argument("--is_vr", action="store_true", help="Enable VR mode for video processing.")
-        parser.add_argument("--frame_start", type=int, default=0, help="Frame to start with.")
-        parser.add_argument("--frame_end", type=int, default=0, help="Frame to end with.")
-        args = parser.parse_args()
-
-        # Override default values with command-line arguments
-        video_list.append(args.video_path)
-        yolo_det_model = args.yolo_model
-        yolo_pose_model = args.yolo_pose_model
-        LiveDisplayMode = args.live_display_mode
-        DebugMode = args.debug_mode
-        isVR = args.is_vr
-        frame_start = args.frame_start
-        frame_end = args.frame_end
-
-        if frame_end == 0 or frame_end < frame_start:
-            frame_end = None
-
-    funscript_data = []  # List to store Funscript data
-
-    for video_path in video_list:
-        print(f"Processing video: {video_path}")
-
-        debugger = Debugger(video_path, output_dir=video_path[:-4])  # Initialize the debugger
-
-        cap = VideoReaderFFmpeg(video_path, is_VR=isVR)  # Initialize the video reader
-        fps = cap.get(cv2.CAP_PROP_FPS)  # Get the video's FPS
-        image_y_size = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)  # Get the video's height
-        image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH)  # Get the video's width
-
-        offset_x = 0
-
-        #if isVR:
-        #    offset_x = image_x_size = image_x_size // 3  # Adjust for VR videos
-
-        print(f"Image size: {image_x_size}x{image_y_size}")
-        cap.release()
+def select_video_file():
+    file_path = filedialog.askopenfilename(
+        title="Select a video file",
+        filetypes=[("Video Files", "*.mp4 *.avi *.mov *.mkv")]
+    )
+    if file_path:
+        video_path.set(file_path)
+        check_video_resolution(file_path)
+
+def select_reference_script():
+    file_path = filedialog.askopenfilename(
+        title="Select a reference funscript file",
+        filetypes=[("Funscript Files", "*.funscript")]
+    )
+    if file_path:
+        reference_script_path.set(file_path)
+
+def check_video_resolution(video_path):
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        messagebox.showerror("Error", "Could not open the video file.")
+        return
 
-        funscript_frames = []
-        funscript_distances = []
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    cap.release()
+
+    if height > 1920:
+        resize_options = ["1920", "1440", "1080"]
+        choice = messagebox.askquestion(
+            "Resize Video",
+            f"The video height is {height}p. Do you want to resize it to a lower resolution?",
+            detail="Options: " + ", ".join(resize_options)
+        )
+        if choice == "yes":
+            resize_video(video_path, resize_options)
+
+def resize_video(video_path, options):
+
+    def on_resize_select(option):
+        output_path = video_path.replace(".mp4", f"_{option}p.mp4")
+        command = [
+            "ffmpeg", "-i", video_path,
+            "-vf", f"scale=-1:{option}",
+            # "-c:a", "copy",  # if you need the audio, uncomment this line and comment the next one
+            "-an",  # skipping the audio
+            output_path
+        ]
+        subprocess.run(command, check=True)
+        messagebox.showinfo("Success", f"Video resized to {option}p and saved as {output_path}")
+        resize_window.destroy()
+
+    resize_window = tk.Toplevel()
+    resize_window.title("Select Resize Option")
+    for option in options:
+        btn = tk.Button(resize_window, text=f"{option}p", command=lambda o=option: on_resize_select(o))
+        btn.pack(pady=5)
+
+def start_processing():
+    global_state.video_file = video_path.get()
+    if not global_state.video_file:
+        messagebox.showerror("Error", "Please select a video file.")
+        return
+
+    global_state.yolo_det_model = get_yolo_model_path()
+    global_state.yolo_pose_model = ""  # "models/yolo11n-pose.mlpackage"
+    global_state.DebugMode = debug_mode_var.get()
+    global_state.LiveDisplayMode = live_display_mode_var.get()
+    global_state.isVR = is_vr_var.get()
+    global_state.frame_start = 0 if frame_start_entry.get() == "" else int(frame_start_entry.get())
+    global_state.frame_end = None if frame_end_entry.get() == "" else int(frame_end_entry.get())
+    global_state.reference_script = reference_script_path.get()
+
+    print(f"Processing video: {global_state.video_file}")
+    print(f"YOLO Detection Model: {global_state.yolo_det_model}")
+    print(f"YOLO Pose Model: {global_state.yolo_pose_model}")
+    print(f"Debug Mode: {global_state.DebugMode}")
+    print(f"Live Display Mode: {global_state.LiveDisplayMode}")
+    print(f"VR Mode: {global_state.isVR}")
+    print(f"Frame Start: {global_state.frame_start}")
+    print(f"Frame End: {global_state.frame_end}")
+    print(f"Reference Script: {global_state.reference_script}")
+
+    # Processing logic
+
+    global_state.debugger = Debugger(global_state.video_file, output_dir=global_state.video_file[:-4])  # Initialize the debugger
+
+    cap = VideoReaderFFmpeg(global_state.video_file, is_VR=global_state.isVR)  # Initialize the video reader
+    fps = cap.get(cv2.CAP_PROP_FPS)  # Get the video's FPS
+    image_y_size = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)  # Get the video's height
+    image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH)  # Get the video's width
+
+    print(f"Image size: {image_x_size}x{image_y_size}")
+    cap.release()
+
+    # Process the video
+
+    # Run the YOLO detection and saves result to _rawyolo.json file
+    extract_yolo_data()
+    #
+    # Load YOLO detection results from file
+    yolo_data = load_yolo_data_from_file(global_state.video_file[:-4] + f"_rawyolo.json")
+
+    results = make_data_boxes(yolo_data, image_x_size)
+
+    # Looking for the first instance of penis within the YOLO results
+    first_penis_frame = parse_yolo_data_looking_for_penis(yolo_data, 0)
+
+    if first_penis_frame is None:
+        print(f"No penis found in video: {global_state.video_file}")
+        first_penis_frame = 0
+
+    # Deciding whether we start from there or from a user specified later frame
+    global_state.frame_start = max(max(first_penis_frame - int(fps), global_state.frame_start - int(fps)), 0)
+
+    print(f"Frame Start adjusted to: {global_state.frame_start}")
+
+    # Performing the tracking part and generation of the raw funscript data
+    global_state.funscript_data = analyze_tracking_results(results, image_y_size)
+
+    global_state.debugger.save_logs()
+
+    funscript_handler = FunscriptGenerator()
+
+    # Simplifying the funscript data and generating the file
+    funscript_handler.generate(global_state.video_file[:-4] + f"_rawfunscript.json", global_state.funscript_data, fps, global_state.LiveDisplayMode)
+
+    # Optional, compare generated funscript with reference funscript if specified, or a simple generic report
+    funscript_handler.compare_funscripts(global_state.reference_script, global_state.video_file[:-3] + "funscript", global_state.video_file, global_state.isVR,
+                                             global_state.video_file[:-4] + "_comparefunscripts.png",
+                                             global_state.video_file[:-4] + "_adjusted.funscript", )
+
+    print(f"Finished processing video: {global_state.video_file}")
+
+def debug_function():
+    """
+    Debugging function to perform specific debugging tasks.
+    """
+    global_state.video_file = video_path.get()
+    if not global_state.video_file:
+        messagebox.showerror("Error", "Please select a video file.")
+        return
+
+    global_state.yolo_det_model = get_yolo_model_path()
+    global_state.yolo_pose_model = ""  # "models/yolo11n-pose.mlpackage"
+    global_state.DebugMode = debug_mode_var.get()
+    global_state.LiveDisplayMode = live_display_mode_var.get()
+    global_state.isVR = is_vr_var.get()
+    global_state.frame_start = 0 if frame_start_entry.get() == "" else int(frame_start_entry.get())
+    global_state.frame_end = None if frame_end_entry.get() == "" else int(frame_end_entry.get())
+    global_state.reference_script = reference_script_path.get()
+
+    print(f"Processing video: {global_state.video_file}")
+    print(f"YOLO Detection Model: {global_state.yolo_det_model}")
+    print(f"YOLO Pose Model: {global_state.yolo_pose_model}")
+    print(f"Debug Mode: {global_state.DebugMode}")
+    print(f"Live Display Mode: {global_state.LiveDisplayMode}")
+    print(f"VR Mode: {global_state.isVR}")
+    print(f"Frame Start: {global_state.frame_start}")
+    print(f"Frame End: {global_state.frame_end}")
+    print(f"Reference Script: {global_state.reference_script}")
+
+    # Processing logic
+
+    global_state.debugger = Debugger(global_state.video_file, output_dir=global_state.video_file[:-4])  # Initialize the debugger
+
+    # if the debug_logs.json file exists, load it
+    if os.path.exists(global_state.video_file[:-4] + f"_debug_logs.json"):
+        global_state.debugger.load_logs()
+        global_state.debugger.play_video(global_state.frame_start)
+    else:
+        messagebox.showinfo("Info", f"Debug logs file not found: {global_state.video_file[:-4] + f'_debug_logs.json'}")
+
+def quit_application():
+    """
+    Quit the application.
+    """
+    print("Quitting the application...")
+    root.quit()  # Close the Tkinter main loop
+    root.destroy()  # Destroy the root window
 
-        # Process the video
+# GUI Setup
+root = tk.Tk()
+root.title("VR funscript generation helper")
 
-        # Run the YOLO detection and saves result to _rawyolo.json file
-        extract_yolo_data(yolo_det_model, yolo_pose_model, video_path, frame_start, frame_end, LiveDisplayMode, isVR)
+# Variables
+video_path = tk.StringVar()
+reference_script_path = tk.StringVar()
+debug_mode_var = tk.BooleanVar()
+live_display_mode_var = tk.BooleanVar()
+is_vr_var = tk.BooleanVar()
 
-        # Load YOLO detection results from file
-        yolo_data = load_yolo_data_from_file(video_path[:-4] + f"_rawyolo.json")
+# Video File Selection
+tk.Label(root, text="Video File:").grid(row=0, column=0, padx=5, pady=5)
+tk.Entry(root, textvariable=video_path, width=50).grid(row=0, column=1, padx=5, pady=5)
+tk.Button(root, text="Browse", command=select_video_file).grid(row=0, column=2, padx=5, pady=5)
 
-        results = make_data_boxes(yolo_data, image_x_size)
+# Reference Script Selection
+tk.Label(root, text="Reference Script:").grid(row=1, column=0, padx=5, pady=5)
+tk.Entry(root, textvariable=reference_script_path, width=50).grid(row=1, column=1, padx=5, pady=5)
+tk.Button(root, text="Browse", command=select_reference_script).grid(row=1, column=2, padx=5, pady=5)
 
-        # Looking for the first instance of penis within the YOLO results
-        first_penis_frame = parse_yolo_data_looking_for_penis(yolo_data, 0)
+# Debug Mode
+tk.Checkbutton(root, text="Debug Mode (save logs)", variable=debug_mode_var).grid(row=2, column=0, padx=5, pady=5)
 
-        if first_penis_frame is None:
-            print(f"No penis found in video: {video_path}")
-            first_penis_frame = 0
+# Live Display Mode
+tk.Checkbutton(root, text="Live Display Mode (plays video during detection, costs time)", variable=live_display_mode_var).grid(row=2, column=1, padx=5, pady=5)
 
-        # Deciding whether we start from there or from a user specified later frame
-        frame_start = max(max(first_penis_frame - int(fps), frame_start - int(fps)), 0)
+# VR Mode, activated by default
+is_vr_var.set(True)
+tk.Checkbutton(root, text="VR Mode", variable=is_vr_var).grid(row=2, column=2, padx=5, pady=5)
 
-        # Performing the tracking part and generation of the raw funscript data
-        funscript_data = analyze_tracking_results(results, image_y_size, video_path, frame_start, frame_end, LiveDisplayMode)
+# Frame Start
+tk.Label(root, text="Frame Start (or blank):").grid(row=3, column=0, padx=5, pady=5)
+frame_start_entry = tk.Entry(root, width=10)
+frame_start_entry.grid(row=3, column=1, padx=5, pady=5)
 
-        debugger.save_logs()
+# Frame End
+tk.Label(root, text="Frame End (or blank):").grid(row=4, column=0, padx=5, pady=5)
+frame_end_entry = tk.Entry(root, width=10)
+frame_end_entry.grid(row=4, column=1, padx=5, pady=5)
 
-        funscript_handler = FunscriptGenerator()
+# Start Button
+tk.Button(root, text="Start Processing", command=start_processing).grid(row=5, column=1, padx=5, pady=20)
 
-        # Simplifying the funscript data and generating the file
-        funscript_handler.generate(video_path[:-4] + f"_rawfunscript.json", funscript_data, fps, LiveDisplayMode)
+# Debug Button
+tk.Button(root, text="Debug (q to quit)", command=debug_function).grid(row=5, column=2, padx=5, pady=20)
 
-        # Optional, compare generated funscript with reference funscript
-        if reference_funscript:
-            funscript_handler.compare_funscripts(reference_funscript, video_path[:-3] + "funscript", video_path, isVR, video_path[:-4] + "_comparefunscripts.png", video_path[:-4] + "_adjusted.funscript",)
+# Quit Button
+tk.Button(root, text="Quit", command=quit_application).grid(row=5, column=0, padx=5, pady=20)
 
-        #funscripts = []
-        #if reference_funscript:
-        #    funscripts.append(reference_funscript)
-        #funscripts.append(video_path[:-3] + "funscript")
-        #funscripts.append(video_path[:-4] + "_adjusted.funscript")
-        #funscript_handler.analyze_funscripts(script_paths=funscripts, video_path=video_path,
-        #                            isVR=isVR, output_image_path=video_path[:-4] + "_comparefunscripts.png")
+tk.Label(root, text="Not for commercial use. Individual and personal use only.\nk00gar © 2024 - https://github.com/ack00gar", font=("Arial", 10, "italic")).grid(row=6, column=0, columnspan=3, padx=5, pady=5)
 
-        print(f"Finished processing video: {video_path}")
+root.mainloop()
\ No newline at end of file
diff --git a/README.md b/README.md
index b3f58730..876e556d 100644
--- a/README.md
+++ b/README.md
@@ -1,15 +1,31 @@
-# VR-Funscript-AI-Generator
+Here’s the updated `README.md` with the **Discord community link** added under the **Support** section:
 
 ---
 
-# Video Processing with YOLO and Funscript Generation
+# VR-Funscript-AI-Generator
 
 This project is a Python-based tool for generating Funscript files from VR videos using Computer Vision (CV) and AI techniques. It leverages YOLO (You Only Look Once) object detection and custom tracking algorithms to automate the process of creating Funscript files for interactive devices.
 
+If you find this project useful, consider supporting me on:
+
+- **Ko-fi**: [https://ko-fi.com/k00gar](https://ko-fi.com/k00gar)
+- **Patreon**: [https://www.patreon.com/c/k00gar](https://www.patreon.com/c/k00gar)
+
+Your support helps me continue developing and improving this project!
+
+Join the **Discord community** for discussions and support:  
+[Discord Community](https://discord.gg/WYkjMbtCZA)
+
+The necessary YOLO models will also be available via the Discord.
+
+---
+
 ## DISCLAIMER
 
-This project is at its very early stages of development, still faulty and broken, and is for research and educational purposes only. 
-It is not intended for commercial use.
+This project is at its very early stages of development, still faulty and broken, and is for research and educational purposes only. It is not intended for commercial use.
+Please, do not use this project for any commercial purposes without prior consent from the author. It is for individual use only.
+
+---
 
 ## Features
 
@@ -19,64 +35,54 @@ It is not intended for commercial use.
 - **Visualization**: Provides real-time visualization of object tracking and Funscript data (in test mode).
 - **VR Support**: Optimized for VR videos, with options to process specific regions of the frame.
 
+---
+
 ## Project Genesis and Evolution
 
 This project started as a dream to automate Funscript generation for VR videos. Here’s a brief history of its development:
 
-- **Initial Approach (OpenCV Trackers)**: The first version relied on OpenCV trackers to detect and track objects in the video.
-While functional, the approach was slow (8–20 FPS) and struggled with occlusions and complex scenes.
+- **Initial Approach (OpenCV Trackers)**: The first version relied on OpenCV trackers to detect and track objects in the video. While functional, the approach was slow (8–20 FPS) and struggled with occlusions and complex scenes.
 
-- **Transition to YOLO**: To improve accuracy and speed, the project shifted to using YOLO object detection.
-A custom YOLO model was trained on a dataset of VR video frames, significantly improving detection quality.
-The new approach runs at 90 FPS on a Mac mini M4 pro, making it much more efficient.
+- **Transition to YOLO**: To improve accuracy and speed, the project shifted to using YOLO object detection. A custom YOLO model was trained on a dataset of VR video frames, significantly improving detection quality. The new approach runs at 90 FPS on a Mac mini M4 pro, making it much more efficient.
 
 - **Original Post**: For more details and discussions, check out the original post on EroScripts:  
 [VR Funscript Generation Helper (Python + CV/AI)](https://discuss.eroscripts.com/t/vr-funscript-generation-helper-python-now-cv-ai/202554)
 
+---
+
 ## YOLO Model
 
 The YOLO model used in this project is based on YOLOv11n, which was fine-tuned with 9 new classes and 4,500+ frames randomly extracted from a VR video library. Here’s how the model was developed:
 
-- **Initial Training**:A few hundred frames were manually tagged and boxed to create an initial dataset.
-The model was trained on this dataset to generate preliminary detection results.
-- **Iterative Improvement**:
-The trained model was used to suggest bounding boxes in additional frames.
-The suggested boxes were manually adjusted, and the dataset was expanded.
-This process was repeated iteratively to improve the model’s accuracy.
-- **Final Training**:
-After gathering 4,500+ images and 30,149 annotations, the model was trained for 200 epochs.
-YOLOv11s and YOLOv11m were also tested, but YOLOv11n was chosen for its balance of accuracy and inference speed.
-- **Hardware**:
-The model runs on a Mac using MPS (Metal Performance Shaders) for accelerated inference on ARM chips.
-Other versions of the model (ONNX and PT) are also available for use on other platforms.
+- **Initial Training**: A few hundred frames were manually tagged and boxed to create an initial dataset. The model was trained on this dataset to generate preliminary detection results.
+- **Iterative Improvement**: The trained model was used to suggest bounding boxes in additional frames. The suggested boxes were manually adjusted, and the dataset was expanded. This process was repeated iteratively to improve the model’s accuracy.
+- **Final Training**: After gathering 4,500+ images and 30,149 annotations, the model was trained for 200 epochs. YOLOv11s and YOLOv11m were also tested, but YOLOv11n was chosen for its balance of accuracy and inference speed.
+- **Hardware**: The model runs on a Mac using MPS (Metal Performance Shaders) for accelerated inference on ARM chips. Other versions of the model (ONNX and PT) are also available for use on other platforms.
+
+---
 
 ## Pipeline Overview
 
 The pipeline for generating Funscript files is as follows:
 
-- **YOLO Object Detection**:
-A YOLO model detects relevant objects (e.g., penis, hands, mouth, etc.) in each frame of the video.
-The detection results are saved to a .json file.
-- **Tracking Algorithm**:
-A custom tracking algorithm processes the YOLO detection results to track the positions of objects over time.
-The algorithm calculates distances and interactions between objects to determine the Funscript position.
-- **Funscript Generation**:
-The tracked data is used to generate a raw Funscript file.
-- **Simplifier**:
-The raw Funscript data is simplified to remove noise and smooth out the motion.
-The final .funscript file is saved.
-- **Heatmap Generation**:
-A heatmap is generated to visualize the Funscript data.
+1. **YOLO Object Detection**: A YOLO model detects relevant objects (e.g., penis, hands, mouth, etc.) in each frame of the video. The detection results are saved to a `.json` file.
+2. **Tracking Algorithm**: A custom tracking algorithm processes the YOLO detection results to track the positions of objects over time. The algorithm calculates distances and interactions between objects to determine the Funscript position.
+3. **Funscript Generation**: The tracked data is used to generate a raw Funscript file.
+4. **Simplifier**: The raw Funscript data is simplified to remove noise and smooth out the motion. The final `.funscript` file is saved.
+5. **Heatmap Generation**: A heatmap is generated to visualize the Funscript data.
 
+---
 
 ## Prerequisites
-
+ 
 Before using this project, ensure you have the following installed:
 
 - **Python 3.8 or higher**
 - **FFmpeg** (for video processing)
 - **CUDA** (optional, for GPU acceleration)
 
+---
+
 ## Installation
 
 1. **Clone the repository**:
@@ -89,10 +95,12 @@ Before using this project, ensure you have the following installed:
    ```bash
    pip install numpy opencv-python tqdm ultralytics scipy matplotlib simplification
    ```
+
 3. **Download the YOLO model**:
-   - Place your YOLO model file (e.g., `k00gar-11n-200ep-best.mlpackage`) in the `models/` directory.
+   - Place your YOLO model file (e.g., `k00gar-11n-200ep-best.mlpackage`) in the `models/` sub-directory.
    - Alternatively, you can specify a custom path to the model using the `--yolo_model` argument.
 
+---
 
 ### Libraries Used
 
@@ -111,50 +119,73 @@ The project relies on the following Python libraries:
 - **collections**: For specialized container datatypes like deque and defaultdict.
 - **datetime**: For handling timestamps and date-related operations.
 - **json**: For reading and writing JSON files.
+- **tkinter**: For creating a basic GUI for file selection and parameter configuration.
 
-## Usage
+---
 
-### Input file
+## Howto & Video Input and Preprocessing
 
-The input video file should be a video file.
+### Input File Requirements
 
-For VR, keep it as a SBS video file, the algorithm will process the left (by default) panel only.
+The input video file should be a standard video file. For VR videos, ensure the file is in **Side-by-Side (SBS)** format. The algorithm will process the **left panel** by default.
 
-Also, even if the algorithm is capable of running on 8k videos, I strongly advise you to process a video limited to 1920p for the sake of performance, and no lower than 1080p.
+**Note**: While the algorithm can handle up to 8K videos, it is strongly recommended to process videos at **1920p resolution** for optimal performance. Videos should not be lower than **1080p** to maintain detection accuracy. If your video exceeds 1920p in height, the script will automatically suggest resizing options (1920p, 1440p, or 1080p) while preserving the aspect ratio.
 
-Feel free to experiment.
+### Video Resizing
 
-### Attention point
+If the input video height exceeds 1920 pixels, the script will prompt you to resize the video to a lower resolution. The resizing process uses **FFmpeg** and excludes audio by default. The following options are available:
+- **1920p**: Resize to 1920 pixels in height (recommended for most cases).
+- **1440p**: Resize to 1440 pixels in height.
+- **1080p**: Resize to 1080 pixels in height.
 
-Depending on the nature of the video (FISHEYE 190, Equirectangular, etc.) you might want to experiment projection settings.
+### VR Video Projection and Undistortion
 
-Those settings can be found in the `utils/lib_VideoReaderFFmpeg.py` file.
+For VR videos, the **projection type** and **undistortion settings** are critical for accurate object detection and tracking. The script supports two main projection types:
+1. **Fisheye**: Used for videos with a fisheye lens projection. The script automatically detects fisheye videos based on the filename or metadata.
+2. **Equirectangular**: Used for standard 360° VR videos. This is the default projection if fisheye is not detected.
 
-To test them, you can use the `utils/test_detect_compare_unwarped.py` file before processing the video, as this will have a huge impact on the quality of the detection and tracking.   
+#### Key Parameters for VR Video Processing
 
-### Basic Command
+The undistortion process is handled using FFmpeg's `v360` filter, which corrects the video frames based on the specified projection and field-of-view (FOV) parameters. Key parameters include:
+- **Input Vertical FOV (`iv_fov`)**: The vertical field of view of the input video.
+- **Input Horizontal FOV (`ih_fov`)**: The horizontal field of view of the input video.
+- **Output Vertical FOV (`v_fov`)**: The desired vertical field of view after undistortion.
+- **Output Horizontal FOV (`h_fov`)**: The desired horizontal field of view after undistortion.
+- **Diagonal FOV (`d_fov`)**: The diagonal field of view used for undistortion.
 
-To process a video, run the following command:
+#### Example FFmpeg Command for VR Video Processing
+
+The following FFmpeg command is used for undistorting VR videos:
 
 ```bash
-python FSGenerator.py /path/to/video.mp4
+ffmpeg -ss <start_time> -i <input_video> -vf "crop=w=iw/2:h=ih:x=0:y=0,v360=<type>:output=sg:iv_fov=<iv_fov>:ih_fov=<ih_fov>:d_fov=<d_fov>:v_fov=<v_fov>:h_fov=<h_fov>:pitch=-25:yaw=0:roll=0:w=<width>:h=<height>:interp=lanczos:reset_rot=1,lutyuv=y=gammaval(0.7)" -f rawvideo -pix_fmt bgr24 -vsync 0 -threads 0 -
 ```
 
-### Optional Arguments
+#### Adjusting Projection Settings
+
+To ensure accurate detection and tracking, you may need to adjust the projection settings based on the specific characteristics of your VR video. These settings can be found and modified in the `utils/lib_VideoReaderFFmpeg.py` file. Use the `utils/test_detect_compare_unwarped.py` script to test different projection settings before processing the video.
+
+---
+
+### Key Points to Remember
+
+1. **Video Resolution**: Resize videos to 1920p for optimal performance.
+2. **VR Video Projection**: Ensure the correct projection type (Fisheye or Equirectangular) is selected for undistortion.
+3. **Undistortion Settings**: Adjust FOV and other parameters in `utils/lib_VideoReaderFFmpeg.py` for accurate results.
+4. **Testing**: Use `utils/test_detect_compare_unwarped.py` to test projection settings before full processing.
 
-- `--yolo_model`: Path to the YOLO model file (default: `models/k00gar-11n-200ep-best.mlpackage`).
-- `--yolo_model`: Path to the YOLO model file (default: `models/yolo11n-pose.mlpackage`).
-- `--live_display_mode`: Enable test mode for real-time visualization of object tracking.
-- `--debug_mode`: Enable debug mode to save detailed logs.
-- `--is_vr`: Enable VR mode for processing VR videos.
-- `--frame_start`: Frame to start with (default: 0).
-- `--frame_end`: Frame to end with (default: None).
+---
 
-Example:
+### Basic Command
+
+To process a video, run the following command:
 
 ```bash
-python FSGenerator.py /path/to/video.mp4 --yolo_model /path/to/custom_model.mlpackage --test_mode --is_vr
+python FSGenerator.py /path/to/video.mp4
 ```
+or Run the script directly from your IDE.
+
+---
 
 ### Output Files
 
@@ -165,51 +196,49 @@ The script generates the following files in the same directory as the input vide
 3. `_rawfunscript.json`: Raw Funscript data.
 4. `.funscript`: Final Funscript file.
 5. `_heatmap.png`: Heatmap visualization of the Funscript data.
+6. `_comparefunscripts.png`: Comparison visualization between the generated Funscript and the reference Funscript (if provided).
+7. `_adjusted.funscript`: Funscript file with adjusted amplitude.
 
-## How It Works
-
-1. **YOLO Detection**:
-   - The script uses a YOLO model to detect and track objects in each video frame.
-   - For VR videos, it processes only the center third of the left half of the frame.
-
-2. **Scene Change Detection**:
-   - Detects scene changes to reset tracking and ensure accuracy.
+---
 
-3. **Tracking and Funscript Generation**:
-   - Tracks specific objects (e.g., body parts) and generates Funscript data based on their movements.
+## How It Works
 
-4. **Visualization (Test Mode)**:
-   - Displays bounding boxes and Funscript data in real-time for debugging and verification.
+1. **YOLO Detection**: The script uses a YOLO model to detect and track objects in each video frame. For VR videos, it processes only the center third of the left half of the frame.
+2. **Scene Change Detection**: Detects scene changes to reset tracking and ensure accuracy.
+3. **Tracking and Funscript Generation**: Tracks specific objects (e.g., body parts) and generates Funscript data based on their movements.
+4. **Visualization (Test Mode)**: Displays bounding boxes and Funscript data in real-time for debugging and verification.
+5. **Debugging (Debug Mode)**: Saves detailed logs for debugging purposes.
 
-5. **Debugging (Debug Mode)**:
-   - Saves detailed logs for debugging purposes.
+---
 
 ## Example
 
-1. **Generate funscript**:
-```bash
-python FSGenerator.py /path/to/vr_video.mp4 --yolo_model models/k00gar-11n-200ep-best.mlpackage --test_mode --is_vr
-```
-This command processes a VR video, detects objects using the specified YOLO model, and generates Funscript data. The `--test_mode` flag enables real-time visualization.
+1. **Generate Funscript**:
+   ```bash
+   python FSGenerator.py /path/to/vr_video.mp4 --yolo_model models/k00gar-11n-200ep-best.mlpackage --test_mode --is_vr
+   ```
+   This command processes a VR video, detects objects using the specified YOLO model, and generates Funscript data. The `--test_mode` flag enables real-time visualization.
 
-You can also simply run it from your IDE, giving it a video_path to process.
+   You can also simply run it from your IDE, giving it a `video_path` to process.
 
-2. **Debugging example**:
+2. **Debugging Example**:
 
-Display a Specific Frame with debug information:
-```bash
-debugger.display_frame(frame_id)
-```
-Play the Video with debug information:
-```bash
-debugger.play_video(frame_id)
-```
-Record the Debugged Video:
-```bash
-debugger.play_video(frame, record=True, downsize_ratio=2, duration=10)
-```
+   - Display a Specific Frame with debug information:
+     ```bash
+     debugger.display_frame(frame_id)
+     ```
+   - Play the Video with debug information:
+     ```bash
+     debugger.play_video(frame_id)
+     ```
+   - Record the Debugged Video:
+     ```bash
+     debugger.play_video(frame, record=True, downsize_ratio=2, duration=10)
+     ```
 
-Or run Display_debug_results.py from your IDE with the wanted parameters.
+   Or run `Display_debug_results.py` from your IDE with the desired parameters.
+
+---
 
 ## Contributing
 
@@ -220,18 +249,33 @@ Contributions are welcome! If you'd like to contribute, please follow these step
 3. Commit your changes.
 4. Submit a pull request.
 
+---
+
 ## License
 
 This project is licensed under the **Non-Commercial License**. You are free to use the software for personal, non-commercial purposes only. Commercial use, redistribution, or modification for commercial purposes is strictly prohibited without explicit permission from the copyright holder.
 
+This project is not intended for commercial use, nor for generating and distributing in a commercial environment.
+
+For commercial use, please contact me.
+
 See the [LICENSE](LICENSE) file for full details.
 
+---
+
 ## Acknowledgments
 
 - **YOLO**: Thanks to the Ultralytics team for the YOLO implementation.
 - **FFmpeg**: For video processing capabilities.
 - **Eroscripts Community**: For the inspiration and use cases.
 
+---
+
 ## Support
 
 If you encounter any issues or have questions, please open an issue on GitHub.
+
+Join the **Discord community** for discussions and support:  
+[Discord Community](https://discord.gg/WYkjMbtCZA)
+
+---
diff --git a/debug/Display_debug_results.py b/debug/Display_debug_results.py
index fd6b97be..c7721b6f 100644
--- a/debug/Display_debug_results.py
+++ b/debug/Display_debug_results.py
@@ -2,9 +2,13 @@
 
 #video = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
 
-video = "/Users/k00gar/Downloads/EBVR-093-C_reenc.mp4"
+#video = "/Users/k00gar/Downloads/EBVR-093-C_reenc.mp4"
 
-frame = 95000
+#video = "/Users/k00gar/Downloads/VRCONK_Kiara Cole_game_of_thrones_daenerys_targaryen_a_porn_parody_8K_180x180_3dh.mp4"
+video = "/Users/k00gar/Downloads/OrgyVR -  Linda Lan, Lulu Chu, Ember Snow, Nicole Doshi - The Pussycat Girls.mp4"
+
+
+frame = 150000  # int(600 * 59.94)
 
 debugger = Debugger(video, video[:-4])
 
diff --git a/models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/model.mlmodel b/models/k00gar-11n-200ep-best.mlpackage/Data/com.apple.CoreML/model.mlmodel
deleted file mode 100644
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diff --git a/models/k00gar-11n-200ep-best.mlpackage/Manifest.json b/models/k00gar-11n-200ep-best.mlpackage/Manifest.json
deleted file mode 100644
index cac3aa39..00000000
--- a/models/k00gar-11n-200ep-best.mlpackage/Manifest.json
+++ /dev/null
@@ -1,18 +0,0 @@
-{
-    "fileFormatVersion": "1.0.0",
-    "itemInfoEntries": {
-        "3EE8E38E-A97B-4918-8FA3-64EE837C9D1B": {
-            "author": "com.apple.CoreML",
-            "description": "CoreML Model Specification",
-            "name": "model.mlmodel",
-            "path": "com.apple.CoreML/model.mlmodel"
-        },
-        "6FDCFB65-8E94-427D-8CB9-606F5C6DE5F2": {
-            "author": "com.apple.CoreML",
-            "description": "CoreML Model Weights",
-            "name": "weights",
-            "path": "com.apple.CoreML/weights"
-        }
-    },
-    "rootModelIdentifier": "3EE8E38E-A97B-4918-8FA3-64EE837C9D1B"
-}
diff --git a/params/config.py b/params/config.py
index 39a9b704..08eea9b8 100644
--- a/params/config.py
+++ b/params/config.py
@@ -1,3 +1,11 @@
+#models path
+yolo_models = ["models/k00gar-11n-200ep-best.mlpackage",
+                "models/k00gar-11n-200ep-best.pt",
+                "models/k00gar-11n-200ep-best.onnx",
+                "models/yolo11n-pose.mlpackage",
+                "models/yolo11n-pose.pt",
+                "models/yolo11n-pose.onnx"]
+
 # ffmpeg and ffprobe paths
 ffmpeg_path = "/usr/local/bin/ffmpeg"
 ffprobe_path = "/usr/local/bin/ffprobe"
diff --git a/tests/test_Compare_models_and_resolutions.py b/tests/test_Compare_models_and_resolutions.py
index 49d5867a..0710c6f9 100644
--- a/tests/test_Compare_models_and_resolutions.py
+++ b/tests/test_Compare_models_and_resolutions.py
@@ -1,30 +1,112 @@
 import cv2
+from PIL import Image
+from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg
 from ultralytics import YOLO
 
+def save_frames_as_gif(frames, output_path, duration=100, loop=0):
+    #frames = [Image.fromarray(frame) for frame in frames]
+    #frames[0].
+    #for i in range(len(frames)):
+    frames[0].save(output_path,
+                  save_all=True,
+                  append_images=frames[1:],
+                  duration=duration,
+                  loop=loop,
+                  optimize=True)
+
+    print(f"GIF saved to {output_path}")
+
+
 # Example usage
 if __name__ == "__main__":
 
-    frame_id=32900
+    frame_id= 170000 #28380 #28321
+    duration = 5
     # Replace with your video paths and YOLO models
     video = []
-    video.append("/Users/k00gar/Downloads/ARPorn_Sasha Tatcha_Fit and Fired Up_4000p_8K_original_FISHEYE190_alpha_orig.mp4")
+    video.append("/Users/k00gar/Downloads/OrgyVR -  Linda Lan, Lulu Chu, Ember Snow, Nicole Doshi - The Pussycat Girls.mp4")
 
     models = []
-    models.append("models/k00gar-11n-200ep-best.mlpackage")
-    models.append("models/k00gar-11s-198ep-best.mlpackage")
-    models.append("models/k00gar-11m-134ep-best.mlpackage")
+    models.append("../models/k00gar-11n-200ep-best.mlpackage")
+    models.append("../models/k00gar-obb-11n-92ep.mlpackage")
+    #models.append("models/k00gar-11s-198ep-best.mlpackage")
+    #models.append("models/k00gar-11m-134ep-best.mlpackage")
+
+    """
+    yolo_instance = YOLO(models[1])
+
+    for video_path in video:
+        # read video until model finds an item of class == 'penis'
+        cap = VideoReaderFFmpeg(video_path, is_VR=True)
+        # position at frame 32000
+        cap.set(cv2.CAP_PROP_POS_FRAMES, frame_id)
+        while True:
+            ret, frame = cap.read()
+            results = yolo_instance(frame, conf=0.1)
+            for result in results:
+                print(f"@{frame_id} - Results for model {models[1]} and video {video_path}")
+                #print(f"@{frame_id} Results: {result.obb}")
+                obb = result.obb
+                class_values = obb.cls
+                print(f"Classes: {class_values}")
+                if 0 in class_values:
+                    print(f"Found penis at frame {frame_id}")
+                    cap.release()
+                    break
+            frame_id += 1
+
+    """
+    cap = VideoReaderFFmpeg(video[0], is_VR=True)
+    # position at frame 32000
+    cap.set(cv2.CAP_PROP_POS_FRAMES, frame_id)
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    yolo_instance = YOLO(models[1])
+    frames = []
+    i = 0
 
+    while i < duration * int(fps):
+        ret, frame = cap.read()
+        results = yolo_instance(frame, conf=0.4, verbose=False)
+
+        # Visualize the results on the frame
+        annotated_frame = results[0].plot()
+
+        # Display the annotated frame
+        cv2.imshow("YOLO Inference", annotated_frame)
+
+        if i % 3 == 0:
+            # divide size by 2
+            annotated_frame = cv2.resize(annotated_frame, (annotated_frame.shape[1] // 3, annotated_frame.shape[0] // 3))
+            img = Image.fromarray(cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB))
+            frames.append(img)
+        i += 1
+        # Break the loop if 'q' is pressed
+        if cv2.waitKey(1) & 0xFF == ord("q"):
+            break
+
+    save_frames_as_gif(frames, video[0].replace(".mp4", ".gif"), duration=duration, loop=0)
+
+
+    """
+    i = 0
     for model in models:
         for video_path in video:
-            cap = cv2.VideoCapture(video_path)
+            #cap = cv2.VideoCapture(video_path)
+            cap = VideoReaderFFmpeg(video_path, is_VR=True)
             # position at frame 32000
             cap.set(cv2.CAP_PROP_POS_FRAMES, frame_id)
             ret, frame = cap.read()
             cap.release()
-            frame = frame[:, frame.shape[1] // 6:frame.shape[1] // 3, :]
+            #frame = frame[:, frame.shape[1] // 6:frame.shape[1] // 3, :]
             # Load the YOLO model
             yolo_instance = YOLO(model)
-            results = yolo_instance(frame)
+            results = yolo_instance(frame, conf=0.1, save=True)
+            # save the result as an image
+
             for result in results:
                 print(f"Results for model {model} and video {video_path}")
                 result.show()
+                result.save(f"/Users/k00gar/Downloads/test{i}.png")
+            i+=1
+                
+    #"""
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index a1c425dc..41eab9da 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -242,21 +242,24 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
                     cv2.rectangle(frame_copy, (x1, y1), (x2, y2), color, 2)
                     cv2.putText(frame_copy, "Locked Penis", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
 
-            # Update rolling window buffers
-            distance = variables.get("distance", 0)
-            funscript_value = self._get_funscript_value(funscript_interpolator, self.current_frame, self.fps) if funscript_interpolator else 0
-            visualizer.draw_gauge(frame_copy, funscript_value)
-            distance_buffer = np.roll(distance_buffer, -1)
-            distance_buffer[-1] = distance
-            funscript_buffer = np.roll(funscript_buffer, -1)
-            funscript_buffer[-1] = funscript_value
-
-            # Draw rolling window curves
-            graph_height = int(frame_copy.shape[0] * 0.2)
-            graph_y_start = y_offset + 10
-            self._draw_rolling_window_curve(frame_copy, distance_buffer, (0, 255, 0), 0.5, graph_height, graph_y_start)
-            self._draw_rolling_window_curve(frame_copy, funscript_buffer, (255, 0, 0), 0.5, graph_height, graph_y_start)
-
+            try:
+                # Update rolling window buffers
+                distance = variables.get("distance", 0)
+                funscript_value = self._get_funscript_value(funscript_interpolator, self.current_frame, self.fps) if funscript_interpolator else 0
+                visualizer.draw_gauge(frame_copy, funscript_value)
+                distance_buffer = np.roll(distance_buffer, -1)
+                distance_buffer[-1] = distance
+                funscript_buffer = np.roll(funscript_buffer, -1)
+                funscript_buffer[-1] = funscript_value
+
+                # Draw rolling window curves
+                graph_height = int(frame_copy.shape[0] * 0.2)
+                graph_y_start = y_offset + 10
+                self._draw_rolling_window_curve(frame_copy, distance_buffer, (0, 255, 0), 0.5, graph_height, graph_y_start)
+                self._draw_rolling_window_curve(frame_copy, funscript_buffer, (255, 0, 0), 0.5, graph_height, graph_y_start)
+            except:
+                # no variables logged at this frame
+                pass
             # Draw progress bar
             self._draw_progress_bar(frame_copy, frame.shape[1], frame.shape[0])
 
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 4906a6fd..64730ce0 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -10,6 +10,10 @@
 from params.config import heatmap_colors, step_size, vw_filter_coeff
 import cv2
 
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import datetime
+
 class FunscriptGenerator:
     def generate(self, raw_funscript_path, funscript_data, fps, TestMode = False):
         output_path = raw_funscript_path[:-18] + '.funscript'
@@ -37,12 +41,22 @@ def generate(self, raw_funscript_path, funscript_data, fps, TestMode = False):
             # Adjust peaks and lows
             ats = [p[0] for p in self.filtered_positions]
             positions = [p[1] for p in self.filtered_positions]
-            adjusted_positions = self.adjust_peaks_and_lows(positions, peak_boost=15, low_reduction=25,
-                                                            max_flat_length=3)
+
+            # let's remap the highest to 100, and the lowest to 0, and rescale to 0-100
+            adjusted_positions = np.interp(positions, (min(positions), max(positions)), (0, 100))
+            # drag all values below 10 to 0 and above 90 to 100
+            readjusted_positions = [0 if p < 10 else 100 if p > 90 else p for p in adjusted_positions]
+
+            # let's boost the peaks by 20%, and reduce the lows by 20% and stay within 0-100
+            adjusted_positions = self.adjust_peaks_and_lows(readjusted_positions, peak_boost=20, low_reduction=20,)
+
+
+            #adjusted_positions = self.adjust_peaks_and_lows(positions, peak_boost=15, low_reduction=20,
+            #                                                max_flat_length=3)
             # recombine ats and positions
-            adjusted_positions = list(zip(ats, adjusted_positions))
+            zip_adjusted_positions = list(zip(ats, adjusted_positions))
             remapped_path = output_path[:-10] + '_adjusted.funscript'
-            self.write_funscript(adjusted_positions, remapped_path, fps)
+            self.write_funscript(zip_adjusted_positions, remapped_path, fps)
             self.generate_heatmap(remapped_path,
                                   remapped_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
 
@@ -73,9 +87,14 @@ def generate_heatmap(self, funscript_path, output_image_path):
             print("Failed to load funscript data.")
             return
 
+        # add a timing: 0, position: 100 at the beginning if no value for 0
+        if times[0] != 0:
+            times.insert(0, 0)
+            positions.insert(0, 100)
+
         # Print loaded data for debugging
-        print(f"Times: {times}")
-        print(f"Positions: {positions}")
+        #print(f"Times: {times}")
+        #print(f"Positions: {positions}")
         print(f"Total Actions: {len(times)}")
         print(f"Time Range: {times[0]} to {datetime.timedelta(seconds=int(times[-1] / 1000))}")
 
@@ -226,6 +245,10 @@ def load_funscript(self, funscript_path):
         irrelevant_chapters_export = []
         # Print the chapters
         for chapter in chapters:
+            if len(chapter['startTime']) > 8:
+                chapter['startTime'] = chapter['startTime'][:8]
+            if len(chapter['endTime']) > 8:
+                chapter['endTime'] = chapter['endTime'][:8]
             print(f"Chapter: {chapter['name']}, Start: {chapter['startTime']}, End: {chapter['endTime']}")
             # convert 00:00:00 to milliseconds
             startTime_ms = int(chapter['startTime'].split(':')[0]) * 60 * 60 * 1000 + int(
@@ -245,11 +268,21 @@ def compare_funscripts(self, reference_path, script1, video_path, isVR, output_i
         if script2 is not None:
             generated_paths.append(script2)
 
-        # Load reference funscript
-        ref_times, ref_positions, _, _ = self.load_funscript(reference_path)
+        if reference_path:
+            # Load reference funscript
+            ref_times, ref_positions, _, _ = self.load_funscript(reference_path)
+
+            # if no 0 at the beginning, add it
+            if ref_times and ref_times[0] != 0:
+                ref_times.insert(0, 0)
+                ref_positions.insert(0, 100)
 
-        # Determine total duration in seconds
-        total_duration = ref_times[-1] / 1000 if ref_times else 0
+            # Determine total duration in seconds
+            total_duration = ref_times[-1] / 1000 if ref_times else 0
+        else:
+            ref_times, ref_positions = [], []
+            gen_times, gen_positions, _, _ = self.load_funscript(generated_paths[0])
+            total_duration = gen_times[-1] / 1000 if gen_times else 0
 
         # Select 6 random non-overlapping 20-second sections
         sections = self.select_random_sections(total_duration, section_duration=10, num_sections=6)
@@ -263,9 +296,10 @@ def compare_funscripts(self, reference_path, script1, video_path, isVR, output_i
             ref_sections = []
             gen_sections = []
             for start, end in sections:
-                ref_sec = self.extract_section(ref_times, ref_positions, start, end)
+                if reference_path:
+                    ref_sec = self.extract_section(ref_times, ref_positions, start, end)
+                    ref_sections.append(ref_sec)
                 gen_sec = self.extract_section(gen_times, gen_positions, start, end)
-                ref_sections.append(ref_sec)
                 gen_sections.append(gen_sec)
 
             if not screenshots_done:
@@ -274,155 +308,11 @@ def compare_funscripts(self, reference_path, script1, video_path, isVR, output_i
                 screenshots_done = True
 
             # Plot and combine
-            #self.create_combined_plot(ref_sections, gen_sections, screenshots, sections, output_image_path)
-            # Create the combined plot
             self.create_combined_plot(
                 ref_sections, gen_sections, screenshots, sections, output_image_path,
                 ref_times, ref_positions, gen_times, gen_positions
             )
 
-    def analyze_funscripts(self, script_paths, video_path, isVR, output_image_path):
-        """
-        Analyzes one, two, or three funscripts and generates a report.
-
-        Args:
-            script_paths (list): List of paths to the funscripts (1, 2, or 3).
-            video_path (str): Path to the video file.
-            isVR (bool): Whether the video is VR (to crop the frame).
-            output_image_path (str): Path to save the output image.
-        """
-        if not script_paths or len(script_paths) > 3:
-            raise ValueError("Please provide 1, 2, or 3 funscript paths.")
-
-        # Load funscripts
-        funscripts = []
-        for path in script_paths:
-            times, positions, _, _ = self.load_funscript(path)
-            funscripts.append((times, positions))
-
-        # Determine total duration in seconds
-        total_duration = funscripts[0][0][-1] / 1000 if funscripts[0][0] else 0
-
-        # Select 6 random non-overlapping 20-second sections
-        sections = self.select_random_sections(total_duration, section_duration=10, num_sections=6)
-
-        # Capture screenshots
-        screenshots = self.capture_screenshots(video_path, isVR, sections)
-
-        # Extract data for each section
-        section_data = []
-        for times, positions in funscripts:
-            sections_data = []
-            for start, end in sections:
-                sec = self.extract_section(times, positions, start, end)
-                sections_data.append(sec)
-            section_data.append(sections_data)
-
-        # Create the combined plot
-        self.create_flexible_plot(section_data, screenshots, sections, output_image_path, funscripts)
-
-    def create_flexible_plot(self, section_data, screenshots, sections, output_image_path, funscripts):
-        """
-        Creates a flexible plot for 1, 2, or 3 funscripts.
-
-        Args:
-            section_data (list): List of section data for each funscript.
-            screenshots (list): List of screenshots for each section.
-            sections (list): List of tuples (start, end) for each section.
-            output_image_path (str): Path to save the plot.
-            funscripts (list): List of tuples (times, positions) for each funscript.
-        """
-        num_funscripts = len(funscripts)
-        rows_per_funscript = 2  # Heatmap + stats
-        total_rows = rows_per_funscript + 6  # 6 sections
-
-        # Create a grid: total_rows rows, num_funscripts columns
-        #   fig, axes = plt.subplots(nrows=total_rows, ncols=2, figsize=(10 * num_funscripts, 28))
-        fig, axes = plt.subplots(nrows=total_rows, ncols=2, figsize=(10, 28))
-        if num_funscripts == 1:
-            axes = axes.reshape(-1, 1)  # Ensure axes is 2D even for a single funscript
-
-        # Plot heatmaps and stats for each funscript
-        for i, (times, positions) in enumerate(funscripts):
-            # Heatmap
-            ax_heatmap = axes[0, i]
-            self.generate_heatmap_inline(ax_heatmap, times, positions)
-            ax_heatmap.set_title(f'Funscript {i + 1} Heatmap')
-
-            # Stats
-            ax_stats = axes[1, i]
-            ax_stats.axis('off')
-            metrics = self._calculate_metrics(times, positions)
-            stats_text = (
-                f"Stats:\n"
-                f"Number of Strokes: {metrics['num_strokes']}\n"
-                f"Avg Stroke Duration: {metrics['avg_stroke_duration']:.2f}s\n"
-                f"Avg Speed: {int(metrics['avg_speed'])} positions/s\n"
-                f"Avg Depth of Stroke: {int(metrics['avg_depth'])}\n"
-                f"Avg Max: {int(metrics['avg_max'])}\n"
-                f"Avg Min: {int(metrics['avg_min'])}"
-            )
-            ax_stats.text(0.1, 0.5, stats_text, fontsize=10, va='center')
-
-        # original version - Plot sections in the remaining rows
-        """for i in range(6):  # 6 sections
-            # Plot screenshot in the first column
-            ax_screenshot = axes[(i + 2) * 2]  # First column of each row
-            ax_screenshot.imshow(screenshots[i])
-            ax_screenshot.axis('off')  # Hide axes for the screenshot
-
-            # Plot funscript data in the second column
-            ax_plot = axes[(i + 2) * 2 + 1]  # Second column of each row
-            gen_times_sec = [t / 1000 for t in gen_sections[i][0]]
-            ax_plot.plot(gen_times_sec, gen_sections[i][1], label='Generated', color='blue')
-
-            ref_times_sec = [t / 1000 for t in ref_sections[i][0]]
-            ax_plot.plot(ref_times_sec, ref_sections[i][1], label='Reference', color='red')
-
-            # Set labels and title
-            start_time = datetime.datetime.fromtimestamp(sections[i][0]).strftime('%H:%M:%S')
-            end_time = datetime.datetime.fromtimestamp(sections[i][1]).strftime('%H:%M:%S')
-            ax_plot.set_title(f'Section {i + 1}: {start_time} - {end_time}')
-            ax_plot.set_xlabel('Time (s)')
-            ax_plot.set_ylabel('Position')
-            ax_plot.legend()
-            ax_plot.set_xlim(sections[i][0], sections[i][1])
-        """
-
-        # Adjust layout
-        plt.tight_layout()
-
-        # Plot sections
-        for i in range(6):  # 6 sections
-            #ax_screenshot = axes[rows_per_funscript + i]  # axes[rows_per_funscript + i, j]
-            ax_screenshot = axes[(i + 2) * 2]  # First column of each row
-            ax_screenshot.imshow(screenshots[i])
-            ax_screenshot.axis('off')
-
-            # Plot funscript data in the second column
-            ax_plot = axes[(i + 2) * 2 + 1]  # Second column of each row
-            for j in range(num_funscripts):
-
-                #ax_plot = axes[rows_per_funscript + i, j]
-                times_sec = [t / 1000 for t in section_data[j][i][0]]
-                ax_plot.plot(times_sec, section_data[j][i][1], label=f'Funscript {j + 1}',
-                             color=['blue', 'red', 'green'][j])
-
-            # Set labels and title
-            start_time = datetime.datetime.fromtimestamp(sections[i][0]).strftime('%H:%M:%S')
-            end_time = datetime.datetime.fromtimestamp(sections[i][1]).strftime('%H:%M:%S')
-            ax_plot.set_title(f'Section {i + 1}: {start_time} - {end_time}')
-            ax_plot.set_xlabel('Time (s)')
-            ax_plot.set_ylabel('Position')
-            ax_plot.legend()
-            ax_plot.set_xlim(sections[i][0], sections[i][1])
-
-        # Adjust layout
-        plt.tight_layout()
-
-        # Save the figure
-        plt.savefig(output_image_path[:-4] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png", dpi=200)
-        plt.close()
 
     def select_random_sections(self, total_duration, section_duration=10, num_sections=6):
         sections = []
@@ -468,9 +358,8 @@ def capture_screenshots(self, video_path, isVR, sections):
         cap.release()
         return screenshots
 
-
     def create_combined_plot(self, ref_sections, gen_sections, screenshots, sections, output_image_path, ref_times,
-                             ref_positions, gen_times, gen_positions):
+                                 ref_positions, gen_times, gen_positions):
         """
         Creates a combined plot with heatmaps as a header, comparative information, and section comparisons below.
 
@@ -485,42 +374,38 @@ def create_combined_plot(self, ref_sections, gen_sections, screenshots, sections
             gen_times (list): Times from the generated funscript.
             gen_positions (list): Positions from the generated funscript.
         """
-        # Create a 8x2 grid: 8 rows, 2 columns (1st row for heatmaps, 2nd row for comparative info, 3rd-8th for sections)
-        fig, axes = plt.subplots(nrows=8, ncols=2, figsize=(10, 28))
+        # Create a flexible grid layout
+        fig = plt.figure(figsize=(28, 24))
+        gs = gridspec.GridSpec(5, 4, height_ratios=[1, .5, 2, 2, 2], width_ratios=[1, 2, 1, 2])
 
-        # Flatten the axes array for easy iteration
-        axes = axes.flatten()
+        # Heatmaps (First row: 2 columns spanning the entire width)
+        if ref_sections:
+            ax_ref_heatmap = fig.add_subplot(gs[0, :2])
+            self.generate_heatmap_inline(ax_ref_heatmap, ref_times, ref_positions)
+            ax_ref_heatmap.set_title('Reference Funscript Heatmap', fontsize=14)
 
-        # Generate and add reference heatmap to the first row, first column
-        ax_ref_heatmap = axes[0]
-        self.generate_heatmap_inline(ax_ref_heatmap, ref_times, ref_positions)
-        ax_ref_heatmap.set_title('Reference Funscript Heatmap')
-
-        # Generate and add generated heatmap to the first row, second column
-        ax_gen_heatmap = axes[1]
+        ax_gen_heatmap = fig.add_subplot(gs[0, 2:])
         self.generate_heatmap_inline(ax_gen_heatmap, gen_times, gen_positions)
-        ax_gen_heatmap.set_title('Generated Funscript Heatmap')
+        ax_gen_heatmap.set_title('Generated Funscript Heatmap', fontsize=14)
+
+        if ref_sections:
+            # Comparative information (Second row: 2 columns)
+            ax_comparative_left = fig.add_subplot(gs[1, :2])
+            ref_metrics = self._calculate_metrics(ref_times, ref_positions)
+            ref_comparative_text = (
+                f"Reference:\n"
+                f"Number of Strokes: {ref_metrics['num_strokes']}\n"
+                f"Avg Stroke Duration: {ref_metrics['avg_stroke_duration']:.2f}s\n"
+                f"Avg Speed: {int(ref_metrics['avg_speed'])} positions/s\n"
+                f"Avg Depth of Stroke: {int(ref_metrics['avg_depth'])}\n"
+                f"Avg Max: {int(ref_metrics['avg_max'])}\n"
+                f"Avg Min: {int(ref_metrics['avg_min'])}"
+            )
+            ax_comparative_left.text(0.5, 0.5, ref_comparative_text, fontsize=12, va='center', ha='center')
+            ax_comparative_left.axis('off')
 
-        # Calculate comparative information
-        ref_metrics = self._calculate_metrics(ref_times, ref_positions)
+        ax_comparative_right = fig.add_subplot(gs[1, 2:])
         gen_metrics = self._calculate_metrics(gen_times, gen_positions)
-
-        # Add comparative information to the second row
-        ax_comparative_left = axes[2]  # First column of the second row
-        ax_comparative_left.axis('off')  # Hide axes for the comparative info
-        ref_comparative_text = (
-            f"Reference:\n"
-            f"Number of Strokes: {ref_metrics['num_strokes']}\n"
-            f"Avg Stroke Duration: {ref_metrics['avg_stroke_duration']:.2f}s\n"
-            f"Avg Speed: {int(ref_metrics['avg_speed'])} positions/s\n"
-            f"Avg Depth of Stroke: {int(ref_metrics['avg_depth'])}\n"
-            f"Avg Max: {int(ref_metrics['avg_max'])}\n"
-            f"Avg Min: {int(ref_metrics['avg_min'])}"
-        )
-        ax_comparative_left.text(0.1, 0.5, ref_comparative_text, fontsize=10, va='center')
-
-        ax_comparative_right = axes[3]  # Second column of the second row
-        ax_comparative_right.axis('off')  # Hide axes for the comparative info
         gen_comparative_text = (
             f"Generated:\n"
             f"Number of Strokes: {gen_metrics['num_strokes']}\n"
@@ -530,38 +415,43 @@ def create_combined_plot(self, ref_sections, gen_sections, screenshots, sections
             f"Avg Max: {int(gen_metrics['avg_max'])}\n"
             f"Avg Min: {int(gen_metrics['avg_min'])}"
         )
-        ax_comparative_right.text(0.1, 0.5, gen_comparative_text, fontsize=10, va='center')
-
-        # Plot sections in the remaining rows
-        for i in range(6):  # 6 sections
-            # Plot screenshot in the first column
-            ax_screenshot = axes[(i + 2) * 2]  # First column of each row
-            ax_screenshot.imshow(screenshots[i])
-            ax_screenshot.axis('off')  # Hide axes for the screenshot
-
-            # Plot funscript data in the second column
-            ax_plot = axes[(i + 2) * 2 + 1]  # Second column of each row
-            gen_times_sec = [t / 1000 for t in gen_sections[i][0]]
-            ax_plot.plot(gen_times_sec, gen_sections[i][1], label='Generated', color='blue')
-
-            ref_times_sec = [t / 1000 for t in ref_sections[i][0]]
-            ax_plot.plot(ref_times_sec, ref_sections[i][1], label='Reference', color='red')
-
-            # Set labels and title
-            start_time = datetime.datetime.fromtimestamp(sections[i][0]).strftime('%H:%M:%S')
-            end_time = datetime.datetime.fromtimestamp(sections[i][1]).strftime('%H:%M:%S')
-            ax_plot.set_title(f'Section {i + 1}: {start_time} - {end_time}')
-            ax_plot.set_xlabel('Time (s)')
-            ax_plot.set_ylabel('Position')
-            ax_plot.legend()
-            ax_plot.set_xlim(sections[i][0], sections[i][1])
+        ax_comparative_right.text(0.5, 0.5, gen_comparative_text, fontsize=12, va='center', ha='center')
+        ax_comparative_right.axis('off')
+
+        # Sections (Rows 3-8: Each row has 2 subplots for screenshot and data plot)
+        for i in range(3, 6):  # Section rows
+            for j in range(2):  # Two columns per row
+                idx = (i - 3) * 2 + j  # Section index
+                if idx >= len(sections):
+                    break
+
+                # Screenshot (first column)
+                ax_screenshot = fig.add_subplot(gs[i-1, j * 2])
+                ax_screenshot.imshow(screenshots[idx])
+                ax_screenshot.axis('off')
+
+                # Funscript comparison (second column)
+                ax_plot = fig.add_subplot(gs[i-1, j * 2 + 1])
+                gen_times_sec = [t / 1000 for t in gen_sections[idx][0]]
+                ax_plot.plot(gen_times_sec, gen_sections[idx][1], label='Generated', color='blue')
+
+                if ref_sections:
+                    ref_times_sec = [t / 1000 for t in ref_sections[idx][0]]
+                    ax_plot.plot(ref_times_sec, ref_sections[idx][1], label='Reference', color='red')
+
+                start_time = datetime.timedelta(seconds=int(sections[idx][0]))  # datetime.datetime.fromtimestamp(sections[idx][0]).strftime('%H:%M:%S')
+                end_time = datetime.timedelta(seconds=int(sections[idx][1]))  # datetime.datetime.fromtimestamp(sections[idx][1]).strftime('%H:%M:%S')
+                ax_plot.set_title(f'Section {idx + 1}: {start_time} - {end_time}', fontsize=10)
+                ax_plot.set_xlabel('Time (s)')
+                ax_plot.set_ylabel('Position')
+                ax_plot.legend()
 
         # Adjust layout
         plt.tight_layout()
 
-        # Save the combined figure
-        plt.savefig(output_image_path[:-4] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png", dpi=200)
-        #plt.show()
+        # Save the figure
+        plt.savefig(output_image_path[:-4] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png", dpi=100)
+        # plt.show()
 
     def generate_heatmap_inline(self, ax, times, positions):
         """
diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index 70c3cf35..1c29e278 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -17,12 +17,15 @@ def __init__(self):
         self.box = None  # Coordinates of the locked penis box
         self.height = 0  # Height of the locked penis box
         self.active = False  # Whether the locked penis box is active
+        self.visible = 0 # Visibility of the locked penis box 0-100
 
-    def update(self, new_box, new_height):
+    def update(self, new_box, new_height, visible=None):
         """Update the locked penis box with new coordinates and height."""
         self.box = new_box
         self.height = new_height
         self.active = True
+        if visible is not None:
+            self.visible = visible
 
     def deactivate(self):
         """Deactivate the locked penis box."""
@@ -66,7 +69,7 @@ class ObjectTracker:
     """
     A class to track objects (e.g., penis, glans, etc.) in a video frame and determine their positions and interactions.
     """
-    def __init__(self, fps, frame_pos, state=None):
+    def __init__(self, fps, frame_pos, image_area):
         # Initialize class attributes
         self.class_names = class_names  # List of class names to track
         self.active_tracks = {}  # Active tracks: {track_id: Track object}
@@ -76,10 +79,14 @@ def __init__(self, fps, frame_pos, state=None):
         self.frame = None  # Current video frame
         self.current_frame_id = frame_pos  # Current frame ID
         self.image_y_size = 0  # Height of the video frame
+        self.image_area = image_area  # Area of the video frame
         self.fps = fps  # Frames per second of the video
 
         # Speed and distance thresholds
         self.max_speed = 100 / (.18 * self.fps)  # Maximum allowed speed for distance changes
+        # TEST - trying to fix the speed issue by temporarily doubling it up
+        #self.max_speed = int(self.max_speed * 3)
+
         self.max_allowed = 100  # Maximum allowed distance
 
         # Tracking state
@@ -96,7 +103,8 @@ def __init__(self, fps, frame_pos, state=None):
         # Sex position tracking
         self.sex_position = "Not relevant"  # Current sex position
         self.sex_position_reason = ""  # Reason for the current sex position
-        self.sex_position_history = deque(maxlen=120)  # History of sex positions
+        max_history = int(self.fps) * 10  # Maximum history for sex position tracking
+        self.sex_position_history = deque(maxlen=max_history)  # History of sex positions
 
         # Position and distance tracking
         self.areas = {class_name: deque(maxlen=200) for class_name in class_names}  # Areas for each class
@@ -123,6 +131,8 @@ def __init__(self, fps, frame_pos, state=None):
         # Penetration state
         self.penetration = False  # Whether penetration is detected
 
+        self.close_up = False
+
 
     def update_distance(self, raw_distance):
         """
@@ -131,14 +141,15 @@ def update_distance(self, raw_distance):
         if raw_distance is None:
             #filtered_distance = self.distance_kf.predict()
             # simple predict using ema, waiting for kalman filter fix
-            filtered_distance = 0.1 * self.previous_distances[-3] + 0.3 * self.previous_distances[-2] + 0.6 * self.previous_distances[-1]
+            filtered_distance = 0.1 * self.previous_distances[-3] + 0.2 * self.previous_distances[-2] + 0.7 * self.previous_distances[-1]
             #print(f"predicted distance: {filtered_distance}")
         else:
             #print(f"before fix, raw distance is: {raw_distance}")
             # applying ema
-            ema_distance = 0.1 * self.previous_distances[-2] + 0.3 * self.previous_distances[-1] + 0.6 * raw_distance
+            ema_distance = 0.1 * self.previous_distances[-2] + 0.2 * self.previous_distances[-1] + 0.7 * raw_distance
             #print(f"self distance: {self.distance}, ema_distance: {ema_distance}, max speed: {self.max_speed}, delta distances: {self.distance-ema_distance}")
             if abs(self.distance - ema_distance) > self.max_speed:
+                # print(f"Limited speed: {self.max_speed}, was {int(self.distance - ema_distance)}")
                 ema_distance = self.distance + np.sign(ema_distance - self.distance) * self.max_speed
                 #print(f"adjusted ema distance: {ema_distance}")
             #raw_distance_2d = (raw_distance, 0)
@@ -154,8 +165,9 @@ def update_distance(self, raw_distance):
 
         filtered_distance = int(max(0, min(100, filtered_distance)))
         self.distance = filtered_distance
-        self.previous_distances.pop()
+        self.previous_distances.pop(0)
         self.previous_distances.append(self.distance)
+
         return filtered_distance
 
     def _predict_distance(self):
@@ -201,8 +213,7 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
         self.current_frame_id = current_frame_id
         self.image_y_size = image_y_size
 
-        # Collect all detections
-        # detections = [(box, conf, class_name, track_id) for box, conf, cls, class_name, track_id in sorted_boxes]
+        close_up_detected = False
 
         # Initialize tracking state
         self.glans_detected = False
@@ -212,6 +223,9 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
 
         classes_touching_penis = []
 
+        breast_tracked = None
+        self.breast_tracking = False
+
         # Collect all detections by class name
         for box, conf, cls, class_name, track_id in sorted_boxes:
             if conf > 0.3:
@@ -219,7 +233,7 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
 
         # Find the best box for specific classes
         found_box = {class_name: [] for class_name in self.class_names}
-        for check_class_first in ['glans', 'penis', 'navel']:
+        for check_class_first in ['glans', 'penis', 'navel', 'breast']:
             prev_conf = 0
             for conf, box, track_id in all_detections[check_class_first]:
                 if conf > prev_conf:
@@ -244,18 +258,101 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                 self.locked_penis_box.deactivate()
                 logging.info(f"@{self.current_frame_id} - Deactivated locked_penis_box")
 
+
+        """
+        # checking for close up in case butt or pussy box are unusually large
+        for class_name in ['butt', 'pussy']:
+            for detection in all_detections[class_name]:
+                conf, box, track_id = detection
+                # if box[3] - box[1] > 0.15 * image_y_size:
+                box_area = (box[2] - box[0]) * (box[3] - box[1])
+                #if box_area > 0:
+                #    print(f"{class_name} area: {box_area}, size: {int((box_area/self.image_area)*100)}%")
+                if box_area > 0.12 * self.image_area:
+                    close_up_detected = True
+                    self.detect_sex_position_change('Close up', f'{class_name} size beyond threshold')
+                    if not self.close_up:
+                        logging.info(f"@{self.current_frame_id} - Close up detected - {class_name} size beyond threshold: {int((box_area/self.image_area)*100)}%")
+                        #self.detect_sex_position_change('Close up', f'{class_name} size beyond threshold')
+                        self.close_up = True
+                    distance = 100
+                    self.update_distance(distance)
+                    return
+        """
+
+        # Check if pussy boxes are inside butt boxes
+        if 'pussy' in all_detections and 'butt' in all_detections:
+            for pussy_detection in all_detections['pussy']:
+                p_conf, p_box, p_track_id = pussy_detection
+
+                for butt_detection in all_detections['butt']:
+                    b_conf, b_box, b_track_id = butt_detection
+                    b_x1, b_y1, b_x2, b_y2 = b_box
+
+                    if self.boxes_overlap(p_box, b_box):
+                        # Calculate the area of the butt box
+                        butt_box_area = (b_x2 - b_x1) * (b_y2 - b_y1)
+
+                        # Check if the butt box is unusually large (close-up)
+                        if butt_box_area > 0.15 * self.image_area:
+                            close_up_detected = True
+                            self.detect_sex_position_change('Close up', 'Butt box size beyond threshold')
+                            if not self.close_up:
+                                logging.info(
+                                    f"@{self.current_frame_id} - Close up detected - butt size beyond threshold: {int((butt_box_area / self.image_area) * 100)}%")
+                                # self.detect_sex_position_change('Close up', f'{class_name} size beyond threshold')
+                                self.close_up = True
+                            distance = 100
+                            self.update_distance(distance)
+                            return
+
         sum_pos = 0
         sum_weight_pos = 0
 
         for box, conf, cls, class_name, track_id in sorted_boxes:
-            if class_name in ['glans', 'penis', 'navel']:
+            if class_name in ['glans', 'penis', 'navel', 'hips center']:
+            #if class_name in ['glans', 'navel']:
                 continue
-            elif self.locked_penis_box.is_active() and (self.boxes_overlap(box, self.locked_penis_box.get_box()) or class_name == 'hips center'):
-                if class_name != 'hips center' and class_name not in classes_touching_penis:
+            elif self.locked_penis_box.is_active() and class_name == 'breast' and not self.boxes_overlap(box, self.locked_penis_box.get_box()):
+                x1, y1, x2, y2 = box
+                mid_y = (y1 + y2) // 2
+                # will need to refactor all that...
+                # Update tracked positions within the frame
+                if track_id not in self.tracked_positions:
+                    self.tracked_positions[track_id] = []
+                # let's apply a moving average to try and filter outliers
+                if len(self.tracked_positions[track_id]) > 2:
+                    mid_y = 0.2 * self.tracked_positions[track_id][-2] + 0.3 * \
+                            self.tracked_positions[track_id][
+                                -1] + 0.5 * mid_y
+                self.tracked_positions[track_id].append(int(mid_y))
+                # Maintain a fixed-size history
+                if len(self.tracked_positions[track_id]) > 600:
+                    self.tracked_positions[track_id].pop(0)
+
+                # Normalize absolute position 0-100 based on historic min and max
+                if track_id not in self.normalized_absolute_tracked_positions:
+                    self.normalized_absolute_tracked_positions[track_id] = []
+                # let's retrieve min and max from self.tracked_positions
+                min_y, max_y = min(self.tracked_positions[track_id]), max(self.tracked_positions[track_id])
+                # now, normalize
+                if max_y - min_y == 0:
+                    normalized_y = 100
+                else:
+                    normalized_y = int(min(max(0, 100 - (((mid_y - min_y) / (max_y - min_y)) * 100)), 100))
+                self.normalized_absolute_tracked_positions[track_id].append(normalized_y)
+                # Maintain a fixed-size history
+                if len(self.normalized_absolute_tracked_positions[track_id]) > 60:
+                    self.normalized_absolute_tracked_positions[track_id].pop(0)
+                breast_tracked = normalized_y
+
+            elif self.locked_penis_box.is_active() and self.boxes_overlap(box, self.locked_penis_box.get_box()):
+                #or class_name == 'hips center'):
+                if class_name not in classes_touching_penis:
                     classes_touching_penis.append(class_name)
+
                 self.tracked_boxes.append([box, class_name, track_id])
                 x1, y1, x2, y2 = box
-                #mid_y = (y1 + 4 * y2) // 5
                 mid_y = (y1 + y2) // 2
 
                 # Update tracked positions within the frame
@@ -263,10 +360,10 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                     self.tracked_positions[track_id] = []
                 # let's apply a moving average to try and filter outliers
                 if len(self.tracked_positions[track_id]) > 2:
-                    mid_y = 0.2 * self.tracked_positions[track_id][-2] + 0.3 * \
+                    mid_y = 0.1 * self.tracked_positions[track_id][-2] + 0.2 * \
                                                     self.tracked_positions[track_id][
-                                                        -1] + 0.5 * mid_y
-                self.tracked_positions[track_id].append(mid_y)
+                                                        -1] + 0.7 * mid_y
+                self.tracked_positions[track_id].append(int(mid_y))
                 # Maintain a fixed-size history
                 if len(self.tracked_positions[track_id]) > 600:
                     self.tracked_positions[track_id].pop(0)
@@ -280,7 +377,7 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                 if max_y - min_y == 0:
                     normalized_y = 100
                 else:
-                    normalized_y = min(max(0, 100 - (((mid_y - min_y) / (max_y - min_y)) * 100)), 100)
+                    normalized_y = int(min(max(0, 100 - (((mid_y - min_y) / (max_y - min_y)) * 100)), 100))
                 self.normalized_absolute_tracked_positions[track_id].append(normalized_y)
                 # Maintain a fixed-size history
                 if len(self.normalized_absolute_tracked_positions[track_id]) > 60:
@@ -297,16 +394,22 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                 # Now, we compute the distance to penis base for classes that are not 'hips center'
                 # And computing the weighted position based on recent moves
                 if class_name != 'hips center':
-                    dist_to_penis_base = int(((self.locked_penis_box.get_box()[3] - low_y) / self.locked_penis_box.get_height()) * 100)
+                    # compute the distance from low_y to penis base
+                    dist_to_penis_base = int(
+                        ((self.locked_penis_box.get_box()[3] - low_y) / self.locked_penis_box.get_height()) * 100)
+                    # maintain the distance 0-100
                     normalized_dist_to_penis_base = min(max(0, dist_to_penis_base), 100)
                     # let's apply a moving average to try and filter outliers
                     if len(self.normalized_distance_to_penis[track_id]) > 2:
-                        low_y = 0.2 * self.normalized_distance_to_penis[track_id][-2] + 0.3 * \
+                        normalized_dist_to_penis_base = 0.2 * self.normalized_distance_to_penis[track_id][-2] + 0.3 * \
                                 self.normalized_distance_to_penis[track_id][
-                                    -1] + 0.5 * low_y
+                                    -1] + 0.5 * normalized_dist_to_penis_base
+
+                    # making sure the distance remains within 0-100 before appending
+                    normalized_dist_to_penis_base = min(max(0, normalized_dist_to_penis_base), 100)
                     # Update normalized tracked positions
-                    if track_id not in self.normalized_distance_to_penis:
-                        self.normalized_distance_to_penis[track_id] = []
+                    #if track_id not in self.normalized_distance_to_penis:
+                    #    self.normalized_distance_to_penis[track_id] = []
                     self.normalized_distance_to_penis[track_id].append(normalized_dist_to_penis_base)
                     # Maintain a fixed-size history
                     if len(self.normalized_distance_to_penis[track_id]) > 60:
@@ -322,14 +425,16 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                                 self.normalized_absolute_tracked_positions[track_id][-1]) // 2) * weight_pos_track_id
                     sum_weight_pos += weight_pos_track_id
                 else:  # hips center
-                    if self.penetration:  # only consider them during penetration
-                        weight_pos_track_id = sum(
-                            abs(self.normalized_absolute_tracked_positions[track_id][i] -
-                                self.normalized_absolute_tracked_positions[track_id][
-                                    i - 1]) for i in
-                            range(1, len(self.normalized_absolute_tracked_positions[track_id])))
-                        sum_pos += self.normalized_absolute_tracked_positions[track_id][-1] * weight_pos_track_id
-                        sum_weight_pos += weight_pos_track_id
+                    pass
+                    # Need to fix this...
+                    #if self.penetration:  # only consider them during penetration
+                    #    weight_pos_track_id = sum(
+                    #        abs(self.normalized_absolute_tracked_positions[track_id][i] -
+                    #            self.normalized_absolute_tracked_positions[track_id][
+                    #                i - 1]) for i in
+                    #        range(1, len(self.normalized_absolute_tracked_positions[track_id])))
+                    #    sum_pos += self.normalized_absolute_tracked_positions[track_id][-1] * weight_pos_track_id
+                    #    sum_weight_pos += weight_pos_track_id
 
         if len(classes_touching_penis) == 0 or not self.locked_penis_box.is_active():
             self.penetration = False
@@ -337,28 +442,35 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
             self.detect_sex_position_change('Not relevant', "no part touching penis / no penis")
             self.tracked_body_part = 'Nothing'
         elif 'pussy' in classes_touching_penis and not self.glans_detected:
-            self.penetration = True
+            if self.sex_position == 'Missionnary / Cowgirl':
+                self.penetration = True
             self.detect_sex_position_change('Missionnary / Cowgirl', "pussy visible and touching")
             self.tracked_body_part = 'pussy'
         elif 'ass' in classes_touching_penis and not self.glans_detected:
-            self.penetration = True
+            if self.sex_position == 'Doggy / Rev. Cowgirl':
+                self.penetration = True
             self.detect_sex_position_change('Doggy / Rev. Cowgirl', "ass visible and touching")
             self.tracked_body_part = 'ass'
         elif ('hand' in classes_touching_penis or 'face' in classes_touching_penis) and not self.penetration:
             self.detect_sex_position_change('Handjob / Blowjob', "hand or face visible and touching")
+            if 'face' not in classes_touching_penis:
+                self.penetration = False
+                self.tracked_body_part = 'hand'
         elif 'foot' in classes_touching_penis and not self.penetration:
             self.detect_sex_position_change('Footjob', "foot visible and touching")
         elif 'breast' in classes_touching_penis and not self.penetration:
             self.detect_sex_position_change('Boobjob', "breast visible and touching")
             self.tracked_body_part = 'breast'
 
-        if sum_weight_pos > 0:
+        if sum_weight_pos > 0 and self.sex_position not in ['Not relevant', 'Close up']:
             distance = int(sum_pos / sum_weight_pos)
+        # attempt, in case we lose the pussy during grinding for instance
+        elif sum_weight_pos == 0 and self.sex_position == 'Missionnary / Cowgirl' and 'pussy' not in classes_touching_penis and breast_tracked:
+            distance = breast_tracked
+            self.breast_tracking = True
         else:
             distance = 100
 
-        #print(f"computed distance: {distance}, sending for filtering...")
-
         self.update_distance(distance)
 
     def handle_class_first(self, class_name, box, conf):
@@ -367,7 +479,7 @@ def handle_class_first(self, class_name, box, conf):
         """
         if class_name == 'penis':
             if box is not None and self.penis_box is None:
-                logging.info(f"Penis detected at frame {self.current_frame_id} with confidence {conf}")
+                logging.info(f"@{self.current_frame_id} - Penis detected with confidence {conf}")
             self.penis_box = box
 
             if self.penis_box:
@@ -389,7 +501,7 @@ def handle_class_first(self, class_name, box, conf):
                             py2 = self.locked_penis_box.get_box()[3] + np.sign(self.penis_box[3] - self.locked_penis_box.get_box()[3]) * max_move
 
                         self.locked_penis_box.update((px1, py2 - self.locked_penis_box.get_height(), px2, py2), self.locked_penis_box.get_height())
-                    else:
+                    else: # activating locked penis box
                         self.locked_penis_box.update(self.penis_box, current_height)
         elif class_name == 'glans' and box:
             if self.consecutive_detections['glans'] >= self.detections_threshold:
@@ -406,7 +518,7 @@ def handle_class_first(self, class_name, box, conf):
                         self.normalized_distances[self.tracked_body_part].append(100)
         elif class_name == 'navel' and box:
             if (self.image_y_size - box[1]) / self.image_y_size < 0.15 and not self.breast_tracking:
-                logging.info("Breast tracking mode activated given navel position in lower 15th of frame")
+                logging.info("@{self.current_frame_id} - Breast tracking mode activated given navel position in lower 15th of frame")
                 self.breast_tracking = True
 
     def normalize_box_area(self, box, frame_width, frame_height):
diff --git a/utils/lib_SceneCutsDetect.py b/utils/lib_SceneCutsDetect.py
index 5013817e..348cd239 100644
--- a/utils/lib_SceneCutsDetect.py
+++ b/utils/lib_SceneCutsDetect.py
@@ -28,7 +28,7 @@ def detect_scene_changes(video_path, crop=None, threshold=0.97, frame_start=0, f
     """
     Detect scene changes in a video using histogram comparison.
     :param video_path: Path to the video file.
-    :param crop: Crop mode ("Left" or "Right") to process only one side of the frame.
+    :param crop: Will crop the video and keep left/right side.
     :param threshold: Similarity threshold for detecting scene changes.
     :param frame_start: Starting frame for processing.
     :param frame_end: Ending frame for processing.
@@ -42,7 +42,7 @@ def detect_scene_changes(video_path, crop=None, threshold=0.97, frame_start=0, f
     # Get video properties
     fps = int(cap.get(cv2.CAP_PROP_FPS))
     total_frames_base = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-    frame_step = 2 * fps  # Process every 2 seconds of video
+    frame_step = fps  # Process every second of video
 
     # Adjust frame range based on input
     if frame_end is None:
@@ -91,13 +91,24 @@ def detect_scene_changes(video_path, crop=None, threshold=0.97, frame_start=0, f
     elif scene_changes[-1][1] != frame_end:
         scene_changes.append([scene_changes[-1][1], frame_end])
 
-    # Merge scenes that are too close
+    print(f"Found {len(scene_changes)} raw scenes: {scene_changes}.")
+    print(f"Scene changes: {scene_changes}")
+    print(f"Merging short scenes...")
+
+    # Merge only short scenes
     merged_scenes = []
+    min_scene_length = 1000  # Minimum scene length in frames (adjust as needed)
     for scene in scene_changes:
-        if not merged_scenes or scene[0] - merged_scenes[-1][1] >= 5000:
-            merged_scenes.append(scene)
+        scene_length = scene[1] - scene[0]
+        if scene_length < min_scene_length:
+            # Merge short scenes with the previous or next scene
+            if merged_scenes:
+                merged_scenes[-1][1] = scene[1]  # Merge with the previous scene
+            else:
+                merged_scenes.append(scene)  # Add the first scene
         else:
-            merged_scenes[-1][1] = scene[1]
+            # Keep scenes longer than the minimum length
+            merged_scenes.append(scene)
 
     print(f"Found {len(merged_scenes)} relevant scenes: {merged_scenes}.")
     cap.release()
diff --git a/utils/lib_Visualizer.py b/utils/lib_Visualizer.py
index b36a7eb6..60df1033 100644
--- a/utils/lib_Visualizer.py
+++ b/utils/lib_Visualizer.py
@@ -21,6 +21,10 @@ def draw_gauge(self, image, distance):
         cv2.rectangle(image, (gauge_x, gauge_y), (gauge_x + gauge_width, gauge_y + gauge_height), (0, 0, 0), -1)
         fill_height = int((distance / 100) * gauge_height)
         cv2.rectangle(image, (gauge_x, gauge_y + gauge_height - fill_height), (gauge_x + gauge_width, gauge_y + gauge_height), (0, 255, 0), -1)
+        # Draw the distance text on top of the filled part of the gauge
+        #cv2.putText(image, str(int(distance)), (gauge_x + gauge_width // 2, gauge_y + gauge_height // 2), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+        cv2.putText(image, str(int(distance)), (gauge_x, gauge_y + gauge_height - fill_height - 5),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
         return image
 
     def draw_limited_graph(self, image, distances, frames, nb_frames):

From c397bb3239908ea9eef4ad4d83ef7061a7b04e56 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Fri, 3 Jan 2025 18:11:23 +0100
Subject: [PATCH 030/277] fix / enhancements

---
 FSGenerator.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index 26929f06..cec9a337 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -704,7 +704,7 @@ def quit_application():
 tk.Button(root, text="Browse", command=select_video_file).grid(row=0, column=2, padx=5, pady=5)
 
 # Reference Script Selection
-tk.Label(root, text="Reference Script:").grid(row=1, column=0, padx=5, pady=5)
+tk.Label(root, text="Reference Script (or blank):").grid(row=1, column=0, padx=5, pady=5)
 tk.Entry(root, textvariable=reference_script_path, width=50).grid(row=1, column=1, padx=5, pady=5)
 tk.Button(root, text="Browse", command=select_reference_script).grid(row=1, column=2, padx=5, pady=5)
 

From 356378374d91028b8b59ce95e88f61450b79a1c5 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Fri, 3 Jan 2025 18:37:51 +0100
Subject: [PATCH 031/277] fix / enhancements

---
 FSGenerator.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index cec9a337..2efbc209 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -275,9 +275,9 @@ def extract_yolo_data():
 
             if global_state.LiveDisplayMode:
                 # Display the YOLO results for testing
-                yolo_det_results[0].plot()
-                cv2.imshow("YOLO11", yolo_det_results[0].plot())
-                cv2.waitKey(1)
+                #yolo_det_results[0].plot()
+                #cv2.imshow("YOLO11", yolo_det_results[0].plot())
+                #cv2.waitKey(1)
                 # Verify the sorted boxes
                 sorted_boxes = test_result.get_boxes(frame_pos)
                 print(f"Sorted boxes : {sorted_boxes}")

From 78d45b4a071cc1f3a014efdb66819b61308f564a Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Fri, 3 Jan 2025 19:20:49 +0100
Subject: [PATCH 032/277] fix / enhancements

---
 README.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/README.md b/README.md
index 876e556d..675185cc 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,3 @@
-Here’s the updated `README.md` with the **Discord community link** added under the **Support** section:
-
 ---
 
 # VR-Funscript-AI-Generator

From e68b67662261ecb6e571802d604387633f405bd1 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Fri, 3 Jan 2025 19:21:42 +0100
Subject: [PATCH 033/277] fix / enhancements

---
 README.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/README.md b/README.md
index 675185cc..d123139f 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,3 @@
----
-
 # VR-Funscript-AI-Generator
 
 This project is a Python-based tool for generating Funscript files from VR videos using Computer Vision (CV) and AI techniques. It leverages YOLO (You Only Look Once) object detection and custom tracking algorithms to automate the process of creating Funscript files for interactive devices.

From 834291a0c6bd5e39fb1093607b3a5d41b6450821 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Fri, 3 Jan 2025 19:22:59 +0100
Subject: [PATCH 034/277] fix / enhancements

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index d123139f..4fccb504 100644
--- a/README.md
+++ b/README.md
@@ -48,7 +48,7 @@ This project started as a dream to automate Funscript generation for VR videos.
 
 ## YOLO Model
 
-The YOLO model used in this project is based on YOLOv11n, which was fine-tuned with 9 new classes and 4,500+ frames randomly extracted from a VR video library. Here’s how the model was developed:
+The YOLO model used in this project is based on YOLOv11n, which was fine-tuned with 10 new classes and 4,500+ frames randomly extracted from a VR video library. Here’s how the model was developed:
 
 - **Initial Training**: A few hundred frames were manually tagged and boxed to create an initial dataset. The model was trained on this dataset to generate preliminary detection results.
 - **Iterative Improvement**: The trained model was used to suggest bounding boxes in additional frames. The suggested boxes were manually adjusted, and the dataset was expanded. This process was repeated iteratively to improve the model’s accuracy.

From 679ce4f08970cab4e646b02ee481076e5e53c714 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sat, 4 Jan 2025 12:18:50 +0100
Subject: [PATCH 035/277] Added default paths depending on platform Added the
 possibility to default to OpenCV instead of the built-in ffmpeg wrapper
 (losing the VR undistoring capability though)

---
 FSGenerator.py        | 70 +++++++++++++++++++++++++++++------
 README.md             | 25 +++++++++++--
 params/config.py      | 86 +++++++++++++++++++++++++++++++++++++------
 utils/lib_Debugger.py | 79 ++++++++-------------------------------
 4 files changed, 171 insertions(+), 89 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index 2efbc209..bf9846b2 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -37,6 +37,7 @@ def __init__(self):
         self.funscript_frames = []
         self.funscript_distances = []
         self.debugger = None
+        self.video_reader = None
 
 # Initialize global state
 global_state = GlobalState()
@@ -142,7 +143,12 @@ def get_yolo_model_path():
 
     # Fallback to ONNX model for other platforms without CUDA
     else:
+        print("CUDA not available, if this is unexpected, please install CUDA and check your version of torch.")
+        print("You might need to install a dependency with the following command (example):")
+        print("pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118")
         print(f"Falling back to CPU inference, loading {yolo_models[2]}.")
+        print("WARNING: CPU inference may be slow on some devices.")
+
         return yolo_models[2]
 
 def extract_yolo_data():
@@ -167,7 +173,11 @@ def extract_yolo_data():
     test_result = Result(320)  # Test result object for debugging
 
     # Initialize the video reader
-    cap = VideoReaderFFmpeg(global_state.video_file, is_VR=global_state.isVR)
+    if global_state.video_reader == "FFmpeg":
+        cap = VideoReaderFFmpeg(global_state.video_file, is_VR=global_state.isVR)  # Initialize the video reader
+    else:
+        cap = cv2.VideoCapture(global_state.video_file)
+
     cap.set(cv2.CAP_PROP_POS_FRAMES, global_state.frame_start)
 
     # Determine the last frame to process
@@ -194,6 +204,8 @@ def extract_yolo_data():
         success, frame = cap.read()  # Read a frame from the video
 
         if success:
+            if global_state.video_reader == "OpenCV" and global_state.isVR:
+                frame = frame[:, :frame.shape[1] // 2, :]  # only half left of the frame, for VR half
 
             # Run YOLO tracking on the frame
             yolo_det_results = det_model.track(frame, persist=True, conf=0.3, verbose=False)
@@ -335,13 +347,21 @@ def analyze_tracking_results(results, image_y_size):
     list_of_frames = results.get_all_frame_ids()  # Get all frame IDs with detections
     visualizer = Visualizer()  # Initialize the visualizer
 
-    cap = VideoReaderFFmpeg(global_state.video_file, is_VR=global_state.isVR)  # Initialize the video reader
+    if global_state.video_reader == "FFmpeg":
+        cap = VideoReaderFFmpeg(global_state.video_file, is_VR=global_state.isVR)  # Initialize the video reader
+    else:
+        cap = cv2.VideoCapture(global_state.video_file)
+
     fps = cap.get(cv2.CAP_PROP_FPS)  # Get the video's FPS
     nb_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))  # Get the total number of frames
 
-    cuts = []
+    if global_state.video_reader == "OpenCV" and global_state.isVR:
+        divide_size_by = 2
+    else:
+        divide_size_by = 1
+    image_area = (cap.get(cv2.CAP_PROP_FRAME_WIDTH) // divide_size_by )* cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
 
-    image_area = cap.get(cv2.CAP_PROP_FRAME_WIDTH) * cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
+    cuts = []
 
     if not global_state.frame_start:
         global_state.frame_start = 0
@@ -438,7 +458,11 @@ def analyze_tracking_results(results, image_y_size):
         if global_state.LiveDisplayMode:
             # Display the tracking results for testing
             ret, frame = cap.read()
-            frame_display = frame.copy()
+
+            if global_state.video_reader == "OpenCV" and global_state.isVR:
+                frame_display = frame[:, :frame.shape[1] // 2, :]  # only half left of the frame, for VR half
+            else:
+                frame_display = frame.copy()
 
             for box in tracker.tracked_boxes:
                 frame_display = visualizer.draw_bounding_box(frame_display,
@@ -578,8 +602,10 @@ def start_processing():
     global_state.frame_start = 0 if frame_start_entry.get() == "" else int(frame_start_entry.get())
     global_state.frame_end = None if frame_end_entry.get() == "" else int(frame_end_entry.get())
     global_state.reference_script = reference_script_path.get()
+    global_state.video_reader = video_reader_var.get()
 
     print(f"Processing video: {global_state.video_file}")
+    print(f"Video Reader: {global_state.video_reader}")
     print(f"YOLO Detection Model: {global_state.yolo_det_model}")
     print(f"YOLO Pose Model: {global_state.yolo_pose_model}")
     print(f"Debug Mode: {global_state.DebugMode}")
@@ -593,10 +619,18 @@ def start_processing():
 
     global_state.debugger = Debugger(global_state.video_file, output_dir=global_state.video_file[:-4])  # Initialize the debugger
 
-    cap = VideoReaderFFmpeg(global_state.video_file, is_VR=global_state.isVR)  # Initialize the video reader
+    if global_state.video_reader == "FFmpeg":
+        cap = VideoReaderFFmpeg(global_state.video_file, is_VR=global_state.isVR)  # Initialize the video reader
+        image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH)  # Get the video's width
+    else:
+        cap = cv2.VideoCapture(global_state.video_file)
+        if global_state.isVR:
+            image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH) // 2  # Get the video's width
+        else:
+            image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
     fps = cap.get(cv2.CAP_PROP_FPS)  # Get the video's FPS
     image_y_size = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)  # Get the video's height
-    image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH)  # Get the video's width
+
 
     print(f"Image size: {image_x_size}x{image_y_size}")
     cap.release()
@@ -657,8 +691,10 @@ def debug_function():
     global_state.frame_start = 0 if frame_start_entry.get() == "" else int(frame_start_entry.get())
     global_state.frame_end = None if frame_end_entry.get() == "" else int(frame_end_entry.get())
     global_state.reference_script = reference_script_path.get()
+    global_state.video_reader = video_reader_var.get()
 
     print(f"Processing video: {global_state.video_file}")
+    print(f"Video Reader: {global_state.video_reader}")
     print(f"YOLO Detection Model: {global_state.yolo_det_model}")
     print(f"YOLO Pose Model: {global_state.yolo_pose_model}")
     print(f"Debug Mode: {global_state.DebugMode}")
@@ -670,7 +706,7 @@ def debug_function():
 
     # Processing logic
 
-    global_state.debugger = Debugger(global_state.video_file, output_dir=global_state.video_file[:-4])  # Initialize the debugger
+    global_state.debugger = Debugger(global_state.video_file, global_state.isVR, global_state.video_reader, output_dir=global_state.video_file[:-4])  # Initialize the debugger
 
     # if the debug_logs.json file exists, load it
     if os.path.exists(global_state.video_file[:-4] + f"_debug_logs.json"):
@@ -697,6 +733,7 @@ def quit_application():
 debug_mode_var = tk.BooleanVar()
 live_display_mode_var = tk.BooleanVar()
 is_vr_var = tk.BooleanVar()
+video_reader_var = tk.StringVar(value="FFmpeg")  # Default to ffmpeg
 
 # Video File Selection
 tk.Label(root, text="Video File:").grid(row=0, column=0, padx=5, pady=5)
@@ -728,15 +765,24 @@ def quit_application():
 frame_end_entry = tk.Entry(root, width=10)
 frame_end_entry.grid(row=4, column=1, padx=5, pady=5)
 
+# Video Reader Selection
+tk.Label(root, text="Video Reader:").grid(row=5, column=0, padx=5, pady=5)
+video_reader_menu = tk.OptionMenu(root, video_reader_var, "OpenCV", "FFmpeg")
+video_reader_menu.grid(row=5, column=1, padx=5, pady=5)
+
+# Add text on the right side
+text_label = tk.Label(root, text="Pick FFmpeg for VR undistortion.\n<= Pick OpenCV for normal processing\nor performance issues.")
+text_label.grid(row=5, column=2, padx=5, pady=5)
+
 # Start Button
-tk.Button(root, text="Start Processing", command=start_processing).grid(row=5, column=1, padx=5, pady=20)
+tk.Button(root, text="Start Processing", command=start_processing).grid(row=6, column=1, padx=5, pady=20)
 
 # Debug Button
-tk.Button(root, text="Debug (q to quit)", command=debug_function).grid(row=5, column=2, padx=5, pady=20)
+tk.Button(root, text="Debug (q to quit)", command=debug_function).grid(row=6, column=2, padx=5, pady=20)
 
 # Quit Button
-tk.Button(root, text="Quit", command=quit_application).grid(row=5, column=0, padx=5, pady=20)
+tk.Button(root, text="Quit", command=quit_application).grid(row=6, column=0, padx=5, pady=20)
 
-tk.Label(root, text="Not for commercial use. Individual and personal use only.\nk00gar © 2024 - https://github.com/ack00gar", font=("Arial", 10, "italic")).grid(row=6, column=0, columnspan=3, padx=5, pady=5)
+tk.Label(root, text="Not for commercial use. Individual and personal use only.\nk00gar © 2024 - https://github.com/ack00gar", font=("Arial", 10, "italic")).grid(row=7, column=0, columnspan=3, padx=5, pady=5)
 
 root.mainloop()
\ No newline at end of file
diff --git a/README.md b/README.md
index 4fccb504..ce8cc398 100644
--- a/README.md
+++ b/README.md
@@ -74,7 +74,7 @@ The pipeline for generating Funscript files is as follows:
 Before using this project, ensure you have the following installed:
 
 - **Python 3.8 or higher**
-- **FFmpeg** (for video processing)
+- **FFmpeg** added to your path (for video processing)
 - **CUDA** (optional, for GPU acceleration)
 
 ---
@@ -96,6 +96,21 @@ Before using this project, ensure you have the following installed:
    - Place your YOLO model file (e.g., `k00gar-11n-200ep-best.mlpackage`) in the `models/` sub-directory.
    - Alternatively, you can specify a custom path to the model using the `--yolo_model` argument.
 
+4. **Update the params/config.py**:
+   - If ffmpeg and ffprobe paths are not in your system path, the program will default to the following values.
+   - You can update the params/config.py file, which contains:
+
+   ```bash
+   # ffmpeg and ffprobe paths - replace with your own if not in your system path   
+    win_ffmpeg_path = "C:/ffmpeg/bin/ffmpeg.exe"
+    mac_ffmpeg_path = "/usr/local/bin/ffmpeg"
+    lin_ffmpeg_path = "/usr/bin/ffmpeg"
+
+    win_ffprobe_path = "C:/ffmpeg/bin/ffprobe.exe"
+    mac_ffprobe_path = "/usr/local/bin/ffprobe"
+    lin_ffprobe_path = "/usr/bin/ffprobe"
+   ```
+
 ---
 
 ### Libraries Used
@@ -211,14 +226,18 @@ The script generates the following files in the same directory as the input vide
 
 1. **Generate Funscript**:
    ```bash
-   python FSGenerator.py /path/to/vr_video.mp4 --yolo_model models/k00gar-11n-200ep-best.mlpackage --test_mode --is_vr
+   python FSGenerator.py /path/to/vr_video.mp4
    ```
-   This command processes a VR video, detects objects using the specified YOLO model, and generates Funscript data. The `--test_mode` flag enables real-time visualization.
+   This command starts the UI.
 
    You can also simply run it from your IDE, giving it a `video_path` to process.
 
 2. **Debugging Example**:
 
+   The debugger is accessible from the GUI.
+
+   If you want to call it from the code, you can do the following: 
+
    - Display a Specific Frame with debug information:
      ```bash
      debugger.display_frame(frame_id)
diff --git a/params/config.py b/params/config.py
index 08eea9b8..e03292a1 100644
--- a/params/config.py
+++ b/params/config.py
@@ -1,14 +1,78 @@
-#models path
-yolo_models = ["models/k00gar-11n-200ep-best.mlpackage",
-                "models/k00gar-11n-200ep-best.pt",
-                "models/k00gar-11n-200ep-best.onnx",
-                "models/yolo11n-pose.mlpackage",
-                "models/yolo11n-pose.pt",
-                "models/yolo11n-pose.onnx"]
-
-# ffmpeg and ffprobe paths
-ffmpeg_path = "/usr/local/bin/ffmpeg"
-ffprobe_path = "/usr/local/bin/ffprobe"
+import shutil
+import platform
+import os
+
+# ffmpeg and ffprobe paths - replace with your own if not in your system path
+win_ffmpeg_path = "C:/ffmpeg/bin/ffmpeg.exe"
+mac_ffmpeg_path = "/usr/local/bin/ffmpeg"
+lin_ffmpeg_path = "/usr/bin/ffmpeg"
+
+win_ffprobe_path = "C:/ffmpeg/bin/ffprobe.exe"
+mac_ffprobe_path = "/usr/local/bin/ffprobe"
+lin_ffprobe_path = "/usr/bin/ffprobe"
+
+# First, we will try to find the ffmpeg and ffprobe paths in the system PATH
+
+def find_ffmpeg_path():
+    # Try to find ffmpeg in the system PATH
+    ffmpeg_path = shutil.which("ffmpeg")
+    if ffmpeg_path:
+        print(f"Found ffmpeg in PATH: {ffmpeg_path}")
+        return ffmpeg_path
+    else:
+        print("ffmpeg not found in PATH, defaulting to platform values in params/config.py")
+        # Fallback paths for different operating systems
+        if platform.system() == "Windows":
+            return win_ffmpeg_path  # Example default path for Windows
+        elif platform.system() == "Darwin":  # macOS
+            return mac_ffmpeg_path  # Example default path for macOS
+        elif platform.system() == "Linux":
+            return lin_ffmpeg_path  # Example default path for Linux
+        else:
+            raise FileNotFoundError("ffmpeg not found in PATH and no default path for this OS")
+
+def find_ffprobe_path():
+    # Try to find ffprobe in the system PATH
+    ffprobe_path = shutil.which("ffprobe")
+    if ffprobe_path:
+        print(f"Found ffprobe in PATH: {ffprobe_path}")
+        return ffprobe_path
+    else:
+        print("ffprobe not found in PATH, defaulting to platform values in params/config.py")
+        # Fallback paths for different operating systems
+        if platform.system() == "Windows":
+            return win_ffprobe_path  # Example default path for Windows
+        elif platform.system() == "Darwin":  # macOS
+            return mac_ffprobe_path  # Example default path for macOS
+        elif platform.system() == "Linux":
+            return lin_ffprobe_path  # Example default path for Linux
+        else:
+            raise FileNotFoundError("ffprobe not found in PATH and no default path for this OS")
+
+# Set the paths in your config
+ffmpeg_path = find_ffmpeg_path()
+ffprobe_path = find_ffprobe_path()
+
+print(f"ffmpeg_path: {ffmpeg_path}")
+print(f"ffprobe_path: {ffprobe_path}")
+
+# models path
+
+# Define the base models directory
+models_dir = "models"
+
+# List of model filenames
+model_filenames = [
+    "k00gar-11n-200ep-best.mlpackage",
+    "k00gar-11n-200ep-best.pt",
+    "k00gar-11n-200ep-best.onnx",
+    "yolo11n-pose.mlpackage",
+    "yolo11n-pose.pt",
+    "yolo11n-pose.onnx"
+]
+
+# Construct full paths using os.path.join
+yolo_models = [os.path.join(models_dir, filename) for filename in model_filenames]
 
 # Class types
 class_types = {'face': 0, 'hand': 1, 'penis': 2, 'glans': 3, 'pussy': 4, 'butt': 5,
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 41eab9da..654bb973 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -13,7 +13,7 @@ class Debugger:
     A class for debugging video frames by logging variables, bounding boxes, and visualizing them.
     """
 
-    def __init__(self, video_path, output_dir):
+    def __init__(self, video_path, isVR=False, video_reader=None, output_dir=None):
         """
         Initialize the Debugger.
         :param video_path: Path to the video file.
@@ -27,6 +27,8 @@ def __init__(self, video_path, output_dir):
         self.total_frames = 0  # Total number of frames in the video
         self.fps = 0  # Frames per second of the video
         self.bar_y_start = 0  # Y-coordinate of the progress bar
+        self.isVR = isVR
+        self.video_reader = video_reader
 
     def log_frame(self, frame_id, variables=None, bounding_boxes=None):
         """
@@ -86,61 +88,6 @@ def display_frame(self, frame_id):
         """
         self.play_video(frame_id, duration=-1)
 
-    def old_display_frame(self, frame_id):
-        """
-        Display the logged frame with bounding boxes and variable states.
-        :param frame_id: The frame ID to display.
-        """
-        str_frame_id = str(frame_id)
-        if str_frame_id not in self.logs:
-            print(f"No logs found for frame {frame_id}")
-            return
-
-        # Load the frame from the video
-        #cap = cv2.VideoCapture(self.video_path)
-        self.cap = VideoReaderFFmpeg(self.video_path, is_VR=True)
-        self.cap.set(cv2.CAP_PROP_POS_FRAMES, frame_id)
-        ret, frame = self.cap.read()
-        self.cap.release()
-
-        if not ret:
-            print(f"Failed to read frame {frame_id}")
-            return
-
-        # Crop the frame (for VR videos)
-        # frame = frame[:, frame.shape[1] // 6 : frame.shape[1] // 3, :]
-
-        frame_copy = frame.copy()
-
-        # Draw bounding boxes
-        for box in self.logs[str_frame_id]["bounding_boxes"]:
-            x1, y1, x2, y2 = box["box"]
-            class_name = box["class_name"]
-            conf = box["conf"]
-            color = class_colors.get(class_name, (0, 255, 0))  # Default to green if class not found
-            cv2.rectangle(frame_copy, (x1, y1), (x2, y2), color, 2)
-            cv2.putText(frame_copy, f"{class_name} {conf:.2f}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
-
-        # Display variables
-        variables = self.logs[str_frame_id]["variables"]
-        y_offset = 30
-        for key, value in variables.items():
-            cv2.putText(frame_copy, f"{key}: {value}", (10, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
-            y_offset += 20
-
-        # Draw the locked_penis_box if it exists
-        locked_penis_box = variables.get("locked_penis_box")
-        if locked_penis_box:
-            x1, y1, x2, y2 = locked_penis_box['box']
-            color = class_colors.get("penis", (0, 255, 0))
-            cv2.rectangle(frame_copy, (x1, y1), (x2, y2), color, 2)
-            cv2.putText(frame_copy, "Locked Penis", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
-
-        # Show the frame
-        cv2.imshow(f"Debug Frame {frame_id}", frame_copy)
-        cv2.waitKey(0)
-        cv2.destroyAllWindows()
-
     def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=False, downsize_ratio=1):
         """
         Play the video from a specified frame, displaying variables, bounding boxes, and rolling window curves.
@@ -153,7 +100,11 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
         visualizer = Visualizer()
 
         # Load the video
-        self.cap = VideoReaderFFmpeg(self.video_path, is_VR=True)
+        if self.video_reader == "FFmpeg":
+            self.cap = VideoReaderFFmpeg(self.video_path, is_VR=self.isVR)
+        else:
+            self.cap = cv2.VideoCapture(self.video_path)
+
         self.total_frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
         self.fps = self.cap.get(cv2.CAP_PROP_FPS)
         print(f"Total frames: {self.total_frames}, FPS: {self.fps}")
@@ -161,6 +112,8 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
         # Initialize video writer if recording
         if record:
             ret, frame = self.cap.read()
+            if self.video_reader == "OpenCV" and self.isVR:
+                frame = frame[:, :frame.shape[1] // 2, :]  # only half left of the frame, for VR half
             #if self.cap.is_VR:
             #    frame_copy = frame[:, frame.shape[1] // 3 : 2 * frame.shape[1] // 3, :]
             output_path = self.video_path.replace(".mp4", "_debug.mp4")
@@ -208,10 +161,9 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
             if not ret:
                 break
 
-            # Crop the frame for VR videos
-            #if self.cap.is_VR:
-            #    frame_copy = frame.copy()[:, frame.shape[1] // 3 : 2 * frame.shape[1] // 3, :]
-            frame_copy = frame.copy()  # make a copy of the frame to make it writeable
+            if self.video_reader == "OpenCV" and self.isVR:
+                frame = frame[:, :frame.shape[1] // 2, :]  # only half left of the frame, for VR half
+            frame_copy = frame.copy()  # make a copy of the frame to make it writeable, useful for ffmpeg library here
             # Display variables and bounding boxes
             str_frame_id = str(self.current_frame)
             if str_frame_id in self.logs:
@@ -340,8 +292,9 @@ def _update_frame_from_mouse(self, x, width):
         """
         self.current_frame = int((x / width) * self.total_frames)
         print(f"Target frame: {self.current_frame}")
-        self.cap.release()
-        self.cap = VideoReaderFFmpeg(self.video_path, is_VR=True)
+        if self.video_reader == "FFmpeg":
+            self.cap.release()
+            self.cap = VideoReaderFFmpeg(self.video_path, is_VR=True)
         self.cap.set(cv2.CAP_PROP_POS_FRAMES, self.current_frame)
         print("Done resetting and jumping to target frame")
 

From bf2f686e8dd2aef428df0d918ea05c14a1c6061a Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sat, 4 Jan 2025 19:03:48 +0100
Subject: [PATCH 036/277] Fixed ffmpeg direct call in FSGenerator during video
 resizing Tiny other adjustements

---
 FSGenerator.py                 |  4 ++--
 debug/Display_debug_results.py | 16 ++++++----------
 utils/lib_Debugger.py          |  6 +++++-
 3 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index bf9846b2..97e2bb99 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -12,7 +12,7 @@
 import subprocess  # For running shell commands
 
 # Import custom modules and configurations
-from params.config import class_priority_order, class_reverse_match, class_colors, yolo_models  # Configuration for class priorities, reverse matching, and colors
+from params.config import class_priority_order, class_reverse_match, class_colors, yolo_models, ffmpeg_path  # Configuration for class priorities, reverse matching, and colors
 from utils.lib_ObjectTracker import ObjectTracker  # Custom object tracking logic
 from utils.lib_FunscriptHandler import FunscriptGenerator  # For generating Funscript files
 from utils.lib_Visualizer import Visualizer  # For visualizing results
@@ -572,7 +572,7 @@ def resize_video(video_path, options):
     def on_resize_select(option):
         output_path = video_path.replace(".mp4", f"_{option}p.mp4")
         command = [
-            "ffmpeg", "-i", video_path,
+            ffmpeg_path, "-i", video_path,
             "-vf", f"scale=-1:{option}",
             # "-c:a", "copy",  # if you need the audio, uncomment this line and comment the next one
             "-an",  # skipping the audio
diff --git a/debug/Display_debug_results.py b/debug/Display_debug_results.py
index c7721b6f..d6d4ec0a 100644
--- a/debug/Display_debug_results.py
+++ b/debug/Display_debug_results.py
@@ -1,21 +1,17 @@
 from utils.lib_Debugger import Debugger
 
-#video = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
+video = "/Users/k00gar/Downloads/703-czechvr-3d-2160x1080-60fps-smartphone_hq.mp4"
 
-#video = "/Users/k00gar/Downloads/EBVR-093-C_reenc.mp4"
+#frame = 32280  # int(600 * 59.94)
 
-#video = "/Users/k00gar/Downloads/VRCONK_Kiara Cole_game_of_thrones_daenerys_targaryen_a_porn_parody_8K_180x180_3dh.mp4"
-video = "/Users/k00gar/Downloads/OrgyVR -  Linda Lan, Lulu Chu, Ember Snow, Nicole Doshi - The Pussycat Girls.mp4"
+frame = (44 * 60 + 26) * 60
 
-
-frame = 150000  # int(600 * 59.94)
-
-debugger = Debugger(video, video[:-4])
+debugger = Debugger(video, isVR=True, video_reader="FFmpeg", output_dir=video[:-4])
 
 debugger.load_logs()
 
-debugger.display_frame(frame)
+#debugger.display_frame(frame)
 
 #debugger.play_video(frame)
 
-#debugger.play_video(frame, record=True, downsize_ratio=1, duration=4)
\ No newline at end of file
+debugger.play_video(frame, record=True, downsize_ratio=1, duration=4)
\ No newline at end of file
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 654bb973..4d8f1ee4 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -151,7 +151,11 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
         end_frame = start_frame + int(duration * self.fps) if duration > 0 else self.total_frames
 
         # Set up mouse callback for progress bar
-        width = self.cap.get(cv2.CAP_PROP_FRAME_WIDTH) // 6 if self.cap.is_VR else self.cap.get(cv2.CAP_PROP_FRAME_WIDTH)
+        if self.video_reader == "OpenCV" and self.isVR:
+            width = self.cap.get(cv2.CAP_PROP_FRAME_WIDTH) // 2
+        else:
+            width = self.cap.get(cv2.CAP_PROP_FRAME_WIDTH)
+
         cv2.namedWindow("Debug Video")
         cv2.setMouseCallback("Debug Video", self._mouse_callback, param=width)
 

From de2ea673b73a0623880af55c35b6a929cc065ae8 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sat, 4 Jan 2025 23:56:27 +0100
Subject: [PATCH 037/277] Fixed ffmpeg direct call in FSGenerator during video
 resizing Tiny other adjustements

---
 FSGenerator.py                 | 113 ++++++++++++++++++++++-----------
 debug/Display_debug_results.py |   2 +-
 2 files changed, 78 insertions(+), 37 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index 97e2bb99..12cb6d9c 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -8,7 +8,7 @@
 import platform  # For identifying the operating system
 import torch  # PyTorch for use of .pt model if not on Apple device
 import tkinter as tk  # GUI library for macOS for basic use in our case
-from tkinter import filedialog, messagebox  # here, what was I saying...
+from tkinter import filedialog, messagebox, ttk  # here, what was I saying...
 import subprocess  # For running shell commands
 
 # Import custom modules and configurations
@@ -38,6 +38,8 @@ def __init__(self):
         self.funscript_distances = []
         self.debugger = None
         self.video_reader = None
+        self.debug_record_mode = False
+        self.record_duration = 0
 
 # Initialize global state
 global_state = GlobalState()
@@ -692,6 +694,8 @@ def debug_function():
     global_state.frame_end = None if frame_end_entry.get() == "" else int(frame_end_entry.get())
     global_state.reference_script = reference_script_path.get()
     global_state.video_reader = video_reader_var.get()
+    global_state.debug_record_mode = debug_record_mode_var.get()
+    global_state.debug_record_duration = int(debug_record_duration_var.get())
 
     print(f"Processing video: {global_state.video_file}")
     print(f"Video Reader: {global_state.video_reader}")
@@ -711,7 +715,10 @@ def debug_function():
     # if the debug_logs.json file exists, load it
     if os.path.exists(global_state.video_file[:-4] + f"_debug_logs.json"):
         global_state.debugger.load_logs()
-        global_state.debugger.play_video(global_state.frame_start)
+        global_state.debugger.play_video(start_frame=global_state.frame_start,
+                                         duration=global_state.debug_record_duration if global_state.debug_record_mode else 0,
+                                         record=global_state.debug_record_mode,
+                                         downsize_ratio=2)
     else:
         messagebox.showinfo("Info", f"Debug logs file not found: {global_state.video_file[:-4] + f'_debug_logs.json'}")
 
@@ -731,58 +738,92 @@ def quit_application():
 video_path = tk.StringVar()
 reference_script_path = tk.StringVar()
 debug_mode_var = tk.BooleanVar()
+debug_record_mode_var = tk.BooleanVar()  # debug record mode
 live_display_mode_var = tk.BooleanVar()
 is_vr_var = tk.BooleanVar()
-video_reader_var = tk.StringVar(value="FFmpeg")  # Default to ffmpeg
+video_reader_var = tk.StringVar()
+debug_record_duration_var = tk.StringVar(value="5")  # Default duration
 
 # Video File Selection
-tk.Label(root, text="Video File:").grid(row=0, column=0, padx=5, pady=5)
-tk.Entry(root, textvariable=video_path, width=50).grid(row=0, column=1, padx=5, pady=5)
-tk.Button(root, text="Browse", command=select_video_file).grid(row=0, column=2, padx=5, pady=5)
+video_frame = ttk.LabelFrame(root, text="Video File", padding=(10, 5))
+video_frame.grid(row=0, column=0, columnspan=4, padx=5, pady=5, sticky="ew")
 
+ttk.Label(video_frame, text="Video File:").grid(row=0, column=0, padx=5, pady=5)
+ttk.Entry(video_frame, textvariable=video_path, width=50).grid(row=0, column=1, padx=5, pady=5)
+ttk.Button(video_frame, text="Browse", command=select_video_file).grid(row=0, column=2, padx=5, pady=5)
+# VR Mode, activated by default
+is_vr_var.set(True)
+ttk.Checkbutton(video_frame, text="VR Mode", variable=is_vr_var).grid(row=0, column=3, padx=5, pady=5)
 # Reference Script Selection
-tk.Label(root, text="Reference Script (or blank):").grid(row=1, column=0, padx=5, pady=5)
-tk.Entry(root, textvariable=reference_script_path, width=50).grid(row=1, column=1, padx=5, pady=5)
-tk.Button(root, text="Browse", command=select_reference_script).grid(row=1, column=2, padx=5, pady=5)
+script_frame = ttk.LabelFrame(root, text="Reference Script for comparison (optional)", padding=(10, 5))
+script_frame.grid(row=1, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
 
-# Debug Mode
-tk.Checkbutton(root, text="Debug Mode (save logs)", variable=debug_mode_var).grid(row=2, column=0, padx=5, pady=5)
+ttk.Label(script_frame, text="Reference Script (optional):").grid(row=0, column=0, padx=5, pady=5)
+ttk.Entry(script_frame, textvariable=reference_script_path, width=50).grid(row=0, column=1, padx=5, pady=5)
+ttk.Button(script_frame, text="Browse", command=select_reference_script).grid(row=0, column=2, padx=5, pady=5)
 
-# Live Display Mode
-tk.Checkbutton(root, text="Live Display Mode (plays video during detection, costs time)", variable=live_display_mode_var).grid(row=2, column=1, padx=5, pady=5)
+# Mode Selection
+mode_frame = ttk.LabelFrame(root, text="Processing Mode Selection", padding=(10, 5))
+mode_frame.grid(row=2, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
 
-# VR Mode, activated by default
-is_vr_var.set(True)
-tk.Checkbutton(root, text="VR Mode", variable=is_vr_var).grid(row=2, column=2, padx=5, pady=5)
+ttk.Checkbutton(mode_frame, text="Debug Mode (save logs for Replay)", variable=debug_mode_var).grid(row=0, column=0, padx=5, pady=5)
+ttk.Checkbutton(mode_frame, text="Live Display Mode (plays video during inference)", variable=live_display_mode_var).grid(row=0, column=1, padx=5, pady=5)
+
+# Frame Range
+frame_frame = ttk.LabelFrame(root, text="Frame Range (optional)", padding=(10, 5))
+frame_frame.grid(row=3, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
 
-# Frame Start
-tk.Label(root, text="Frame Start (or blank):").grid(row=3, column=0, padx=5, pady=5)
-frame_start_entry = tk.Entry(root, width=10)
-frame_start_entry.grid(row=3, column=1, padx=5, pady=5)
+ttk.Label(frame_frame, text="Frame Start (optional):").grid(row=0, column=0, padx=5, pady=5)
+frame_start_entry = ttk.Entry(frame_frame, width=10)
+frame_start_entry.grid(row=0, column=1, padx=5, pady=5)
 
-# Frame End
-tk.Label(root, text="Frame End (or blank):").grid(row=4, column=0, padx=5, pady=5)
-frame_end_entry = tk.Entry(root, width=10)
-frame_end_entry.grid(row=4, column=1, padx=5, pady=5)
+ttk.Label(frame_frame, text="Frame End (optional):").grid(row=0, column=2, padx=5, pady=5)
+frame_end_entry = ttk.Entry(frame_frame, width=10)
+frame_end_entry.grid(row=0, column=3, padx=5, pady=5)
 
 # Video Reader Selection
-tk.Label(root, text="Video Reader:").grid(row=5, column=0, padx=5, pady=5)
-video_reader_menu = tk.OptionMenu(root, video_reader_var, "OpenCV", "FFmpeg")
-video_reader_menu.grid(row=5, column=1, padx=5, pady=5)
+reader_frame = ttk.LabelFrame(root, text="Video Reader", padding=(10, 5))
+reader_frame.grid(row=4, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
+
+ttk.Label(reader_frame, text="Video Reader:").grid(row=0, column=0, padx=5, pady=5)
+video_reader_menu = ttk.OptionMenu(reader_frame, video_reader_var, "FFmpeg", "FFmpeg", "OpenCV")
+video_reader_menu.grid(row=0, column=1, padx=5, pady=5)
 
 # Add text on the right side
-text_label = tk.Label(root, text="Pick FFmpeg for VR undistortion.\n<= Pick OpenCV for normal processing\nor performance issues.")
-text_label.grid(row=5, column=2, padx=5, pady=5)
+text_label = ttk.Label(reader_frame, text="<= Pick FFmpeg for VR undistortion.\n<= Pick OpenCV for normal processing or performance issues.")
+text_label.grid(row=0, column=2, padx=5, pady=5)
+
+# Debug Record Mode
+debug_frame = ttk.LabelFrame(root, text="Debugging", padding=(10, 5))
+debug_frame.grid(row=5, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
+
+ttk.Checkbutton(debug_frame, text="Save debugging as video", variable=debug_record_mode_var).grid(row=0, column=0, padx=5, pady=5)
+
+# Duration Selector
+ttk.Label(debug_frame, text="Duration (seconds):").grid(row=0, column=1, padx=5, pady=5)
+duration_combobox = ttk.Combobox(debug_frame, textvariable=debug_record_duration_var, values=["5", "10", "20"], width=5)
+duration_combobox.grid(row=0, column=2, padx=5, pady=5)
+
+ttk.Button(debug_frame, text="Debug (q to quit)", command=debug_function).grid(row=0, column=3, padx=5, pady=5)
+
+# Progress Bar
+# progress_frame = ttk.LabelFrame(root, text="Progress", padding=(10, 5))
+# progress_frame.grid(row=6, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
+
+# progress_bar = ttk.Progressbar(progress_frame, variable=progress_var, maximum=100)
+# progress_bar.grid(row=0, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
 
-# Start Button
-tk.Button(root, text="Start Processing", command=start_processing).grid(row=6, column=1, padx=5, pady=20)
+# Startand Quit Buttons
+button_frame = ttk.Frame(root)
+button_frame.grid(row=7, column=0, columnspan=3, padx=5, pady=10)
 
-# Debug Button
-tk.Button(root, text="Debug (q to quit)", command=debug_function).grid(row=6, column=2, padx=5, pady=20)
+start_button = ttk.Button(button_frame, text="Start Processing", command=start_processing)
+start_button.grid(row=0, column=0, padx=5, pady=5)
 
-# Quit Button
-tk.Button(root, text="Quit", command=quit_application).grid(row=6, column=0, padx=5, pady=20)
+ttk.Button(button_frame, text="Quit", command=quit_application).grid(row=0, column=2, padx=5, pady=5)
 
-tk.Label(root, text="Not for commercial use. Individual and personal use only.\nk00gar © 2024 - https://github.com/ack00gar", font=("Arial", 10, "italic")).grid(row=7, column=0, columnspan=3, padx=5, pady=5)
+# Footer
+footer_label = ttk.Label(root, text="Not for commercial use. Individual and personal use only.\n© k00gar 2024 - https://github.com/ack00gar", font=("Arial", 10, "italic"))
+footer_label.grid(row=8, column=0, columnspan=3, padx=5, pady=5)
 
 root.mainloop()
\ No newline at end of file
diff --git a/debug/Display_debug_results.py b/debug/Display_debug_results.py
index d6d4ec0a..ac281128 100644
--- a/debug/Display_debug_results.py
+++ b/debug/Display_debug_results.py
@@ -14,4 +14,4 @@
 
 #debugger.play_video(frame)
 
-debugger.play_video(frame, record=True, downsize_ratio=1, duration=4)
\ No newline at end of file
+debugger.play_video(frame, record=False, downsize_ratio=1) #), duration=4)
\ No newline at end of file

From cbb892542aaacb82fe0afedf35149cbeb66365f4 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 5 Jan 2025 00:56:47 +0100
Subject: [PATCH 038/277] Fixes

---
 README.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index ce8cc398..17409d6c 100644
--- a/README.md
+++ b/README.md
@@ -4,8 +4,10 @@ This project is a Python-based tool for generating Funscript files from VR video
 
 If you find this project useful, consider supporting me on:
 
-- **Ko-fi**: [https://ko-fi.com/k00gar](https://ko-fi.com/k00gar)
-- **Patreon**: [https://www.patreon.com/c/k00gar](https://www.patreon.com/c/k00gar)
+- **Ko-fi**: [![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/H2H818EIJV) 
+- **Patreon**: 
+
+   [https://www.patreon.com/c/k00gar](https://www.patreon.com/c/k00gar)
 
 Your support helps me continue developing and improving this project!
 

From ce699eea77c5c509db3c65fc4e4cbba7da550cea Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 5 Jan 2025 00:57:17 +0100
Subject: [PATCH 039/277] Fixes

---
 README.md | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 17409d6c..97c5f75f 100644
--- a/README.md
+++ b/README.md
@@ -5,9 +5,7 @@ This project is a Python-based tool for generating Funscript files from VR video
 If you find this project useful, consider supporting me on:
 
 - **Ko-fi**: [![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/H2H818EIJV) 
-- **Patreon**: 
-
-   [https://www.patreon.com/c/k00gar](https://www.patreon.com/c/k00gar)
+- **Patreon**: [https://www.patreon.com/c/k00gar](https://www.patreon.com/c/k00gar)
 
 Your support helps me continue developing and improving this project!
 

From c4c58ef9b7305e202608a98d4cc6e60247690e8f Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 5 Jan 2025 00:57:34 +0100
Subject: [PATCH 040/277] Fixes

---
 README.md | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 97c5f75f..f7c748b2 100644
--- a/README.md
+++ b/README.md
@@ -9,8 +9,7 @@ If you find this project useful, consider supporting me on:
 
 Your support helps me continue developing and improving this project!
 
-Join the **Discord community** for discussions and support:  
-[Discord Community](https://discord.gg/WYkjMbtCZA)
+Join the **Discord community** for discussions and support: [Discord Community](https://discord.gg/WYkjMbtCZA)
 
 The necessary YOLO models will also be available via the Discord.
 

From 94c79ab1764f706005d78f50e89d752fc494d43f Mon Sep 17 00:00:00 2001
From: Artoo <Artoo@10.42.2.46>
Date: Sun, 5 Jan 2025 01:53:52 +0100
Subject: [PATCH 041/277] Upgrade projection detection

---
 utils/lib_VideoReaderFFmpeg.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index f359febf..567f1f81 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -91,7 +91,7 @@ def _start_process(self, start_frame=0):
         if self.is_VR:
             arg_line = "crop=w=iw/2:h=ih:x=0:y=0"
             if self.unwarp:
-                if self.projection == "FISHEYE" or "FISHEYE" in self.video_path:
+                if self.projection == "FISHEYE" or self.projection == "" and "FISHEYE" in self.video_path.upper():
                     print("Proceeding with fisheye projection correction")
                     self.type = "fisheye"
                     self.iv_fov = 190

From a3bbe6448ee2e899254da29ada4d214104b828af Mon Sep 17 00:00:00 2001
From: Artoo <Artoo@10.42.2.46>
Date: Sun, 5 Jan 2025 11:27:00 +0100
Subject: [PATCH 042/277] Upgrade projection detection

---
 utils/lib_VideoReaderFFmpeg.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index 567f1f81..ad432cf3 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -91,7 +91,7 @@ def _start_process(self, start_frame=0):
         if self.is_VR:
             arg_line = "crop=w=iw/2:h=ih:x=0:y=0"
             if self.unwarp:
-                if self.projection == "FISHEYE" or self.projection == "" and "FISHEYE" in self.video_path.upper():
+                if self.projection == "FISHEYE" or (self.projection == None and "FISHEYE" in self.video_path.upper()):
                     print("Proceeding with fisheye projection correction")
                     self.type = "fisheye"
                     self.iv_fov = 190

From 1fd0b00f3a64acc96aebf38b2d5ef1e8a9ce8b69 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 5 Jan 2025 16:08:34 +0100
Subject: [PATCH 043/277] Interface simplifation, and added the frame_id to the
 debug data for display in video debug mode

---
 FSGenerator.py             | 83 ++++++++++++++++++++------------------
 utils/lib_ObjectTracker.py |  2 +-
 2 files changed, 44 insertions(+), 41 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index 12cb6d9c..55703449 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -443,6 +443,7 @@ def analyze_tracking_results(results, image_y_size):
                 global_state.debugger.log_frame(frame_pos,
                                    bounding_boxes=bounding_boxes,
                                    variables={
+                                       'frame': frame_pos,
                                        'distance': tracker.distance,
                                        'Penetration': tracker.penetration,
                                        'sex_position': tracker.sex_position,
@@ -745,66 +746,71 @@ def quit_application():
 debug_record_duration_var = tk.StringVar(value="5")  # Default duration
 
 # Video File Selection
-video_frame = ttk.LabelFrame(root, text="Video File", padding=(10, 5))
-video_frame.grid(row=0, column=0, columnspan=4, padx=5, pady=5, sticky="ew")
+video_frame = ttk.LabelFrame(root, text="Video Selection", padding=(10, 5))
+video_frame.grid(row=0, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
 
-ttk.Label(video_frame, text="Video File:").grid(row=0, column=0, padx=5, pady=5)
+ttk.Label(video_frame, text="Video File:").grid(row=0, column=0, padx=5, pady=5, sticky="w")
 ttk.Entry(video_frame, textvariable=video_path, width=50).grid(row=0, column=1, padx=5, pady=5)
-ttk.Button(video_frame, text="Browse", command=select_video_file).grid(row=0, column=2, padx=5, pady=5)
-# VR Mode, activated by default
-is_vr_var.set(True)
-ttk.Checkbutton(video_frame, text="VR Mode", variable=is_vr_var).grid(row=0, column=3, padx=5, pady=5)
-# Reference Script Selection
-script_frame = ttk.LabelFrame(root, text="Reference Script for comparison (optional)", padding=(10, 5))
-script_frame.grid(row=1, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
+ttk.Button(video_frame, text="Browse", command=select_video_file).grid(row=0, column=2, padx=5, pady=5, sticky="e")
 
-ttk.Label(script_frame, text="Reference Script (optional):").grid(row=0, column=0, padx=5, pady=5)
-ttk.Entry(script_frame, textvariable=reference_script_path, width=50).grid(row=0, column=1, padx=5, pady=5)
-ttk.Button(script_frame, text="Browse", command=select_reference_script).grid(row=0, column=2, padx=5, pady=5)
 
-# Mode Selection
-mode_frame = ttk.LabelFrame(root, text="Processing Mode Selection", padding=(10, 5))
-mode_frame.grid(row=2, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
+# Video Reader Selection
+reader_frame = ttk.LabelFrame(root, text="Video Reader", padding=(10, 5))
+reader_frame.grid(row=1, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
 
-ttk.Checkbutton(mode_frame, text="Debug Mode (save logs for Replay)", variable=debug_mode_var).grid(row=0, column=0, padx=5, pady=5)
-ttk.Checkbutton(mode_frame, text="Live Display Mode (plays video during inference)", variable=live_display_mode_var).grid(row=0, column=1, padx=5, pady=5)
+ttk.Label(video_frame, text="Reader:").grid(row=1, column=0, padx=5, pady=5, sticky="w")
+video_reader_menu = ttk.OptionMenu(video_frame, video_reader_var, "FFmpeg", "FFmpeg", "OpenCV")
+video_reader_menu.grid(row=1, column=1, padx=5, pady=5, sticky="w")
+#text_label = ttk.Label(video_frame, text="<= Pick FFmpeg for VR undistortion.\n<= Pick OpenCV if perf. issues.")
+#text_label.grid(row=1, column=2, padx=5, pady=5)
+# VR Mode, activated by default
+is_vr_var.set(True)
+ttk.Checkbutton(video_frame, text="VR SBS video", variable=is_vr_var).grid(row=2, column=0, padx=5, pady=5)
 
 # Frame Range
-frame_frame = ttk.LabelFrame(root, text="Frame Range (optional)", padding=(10, 5))
-frame_frame.grid(row=3, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
+frame_frame = ttk.LabelFrame(root, text="Optional settings", padding=(10, 5))
+frame_frame.grid(row=2, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
 
-ttk.Label(frame_frame, text="Frame Start (optional):").grid(row=0, column=0, padx=5, pady=5)
+ttk.Label(frame_frame, text="Frame Start:").grid(row=0, column=0, padx=5, pady=5, sticky="w")
 frame_start_entry = ttk.Entry(frame_frame, width=10)
-frame_start_entry.grid(row=0, column=1, padx=5, pady=5)
+frame_start_entry.grid(row=0, column=1, padx=5, pady=5, sticky="w")
 
-ttk.Label(frame_frame, text="Frame End (optional):").grid(row=0, column=2, padx=5, pady=5)
+ttk.Label(frame_frame, text="Frame End:").grid(row=1, column=0, padx=5, pady=5, sticky="w")
 frame_end_entry = ttk.Entry(frame_frame, width=10)
-frame_end_entry.grid(row=0, column=3, padx=5, pady=5)
+frame_end_entry.grid(row=1, column=1, padx=5, pady=5, sticky="w")
 
-# Video Reader Selection
-reader_frame = ttk.LabelFrame(root, text="Video Reader", padding=(10, 5))
-reader_frame.grid(row=4, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
+# Reference Script Selection
+#script_frame = ttk.LabelFrame(root)  #,  # text="Reference Script for comparison (optional)",
+                             # padding=(10, 5))
+#script_frame.grid(row=3, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
+
+ttk.Label(frame_frame, text="Reference Script:").grid(row=2, column=0, padx=5, pady=5)
+ttk.Entry(frame_frame, textvariable=reference_script_path, width=50).grid(row=2, column=1, padx=5, pady=5)
+ttk.Button(frame_frame, text="Browse", command=select_reference_script).grid(row=2, column=2, padx=5, pady=5)
+
+# Processing Mode Selection
+processing_frame = ttk.LabelFrame(root, text="Processing", padding=(10, 5))
+processing_frame.grid(row=4, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
 
-ttk.Label(reader_frame, text="Video Reader:").grid(row=0, column=0, padx=5, pady=5)
-video_reader_menu = ttk.OptionMenu(reader_frame, video_reader_var, "FFmpeg", "FFmpeg", "OpenCV")
-video_reader_menu.grid(row=0, column=1, padx=5, pady=5)
+start_button = ttk.Button(processing_frame, text="Start Processing", command=start_processing)
+start_button.grid(row=0, column=0, padx=5, pady=5)
 
-# Add text on the right side
-text_label = ttk.Label(reader_frame, text="<= Pick FFmpeg for VR undistortion.\n<= Pick OpenCV for normal processing or performance issues.")
-text_label.grid(row=0, column=2, padx=5, pady=5)
+ttk.Checkbutton(processing_frame, text="Logging for debug", variable=debug_mode_var).grid(row=0, column=1, padx=5, pady=5)
+ttk.Checkbutton(processing_frame, text="Live Display Mode (perf. down)", variable=live_display_mode_var).grid(row=0, column=2, padx=5, pady=5)
 
 # Debug Record Mode
 debug_frame = ttk.LabelFrame(root, text="Debugging", padding=(10, 5))
 debug_frame.grid(row=5, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
 
-ttk.Checkbutton(debug_frame, text="Save debugging as video", variable=debug_record_mode_var).grid(row=0, column=0, padx=5, pady=5)
+ttk.Button(debug_frame, text="Debug (q to quit)", command=debug_function).grid(row=0, column=0, padx=5, pady=5)
+
+ttk.Checkbutton(debug_frame, text="Save debugging session as video", variable=debug_record_mode_var).grid(row=0, column=1, padx=5, pady=5)
 
 # Duration Selector
-ttk.Label(debug_frame, text="Duration (seconds):").grid(row=0, column=1, padx=5, pady=5)
 duration_combobox = ttk.Combobox(debug_frame, textvariable=debug_record_duration_var, values=["5", "10", "20"], width=5)
 duration_combobox.grid(row=0, column=2, padx=5, pady=5)
+ttk.Label(debug_frame, text="seconds").grid(row=0, column=3, padx=5, pady=5)
 
-ttk.Button(debug_frame, text="Debug (q to quit)", command=debug_function).grid(row=0, column=3, padx=5, pady=5)
 
 # Progress Bar
 # progress_frame = ttk.LabelFrame(root, text="Progress", padding=(10, 5))
@@ -817,13 +823,10 @@ def quit_application():
 button_frame = ttk.Frame(root)
 button_frame.grid(row=7, column=0, columnspan=3, padx=5, pady=10)
 
-start_button = ttk.Button(button_frame, text="Start Processing", command=start_processing)
-start_button.grid(row=0, column=0, padx=5, pady=5)
-
 ttk.Button(button_frame, text="Quit", command=quit_application).grid(row=0, column=2, padx=5, pady=5)
 
 # Footer
-footer_label = ttk.Label(root, text="Not for commercial use. Individual and personal use only.\n© k00gar 2024 - https://github.com/ack00gar", font=("Arial", 10, "italic"))
+footer_label = ttk.Label(root, text="Individual and personal use only.\nNot for commercial use, nor use by a company.\nk00gar 2025 - https://github.com/ack00gar", font=("Arial", 10, "italic", "bold"), justify="center")
 footer_label.grid(row=8, column=0, columnspan=3, padx=5, pady=5)
 
 root.mainloop()
\ No newline at end of file
diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index 1c29e278..8e33689f 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -6,7 +6,7 @@
 import logging
 
 # Configure logging
-logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+logging.basicConfig(level=logging.INFO, format="%(message)s")
 
 class LockedPenisBox:
     """

From 72056a838af327ba4f7083b1351f1975bf57aee5 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 5 Jan 2025 17:34:16 +0100
Subject: [PATCH 044/277] Tweaked the VW simplification factor

---
 params/config.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/params/config.py b/params/config.py
index e03292a1..400da7f6 100644
--- a/params/config.py
+++ b/params/config.py
@@ -131,4 +131,4 @@ def find_ffprobe_path():
 ]
 step_size = 120  # Speed step size for color transitions
 
-vw_filter_coeff = 10.0
+vw_filter_coeff = 5.0

From 29224b917cf3d01f3888d2fb4e72d529a48a7ceb Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 5 Jan 2025 17:50:19 +0100
Subject: [PATCH 045/277] Tweaked the VW simplification factor

---
 params/config.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/params/config.py b/params/config.py
index 400da7f6..1cf99060 100644
--- a/params/config.py
+++ b/params/config.py
@@ -131,4 +131,4 @@ def find_ffprobe_path():
 ]
 step_size = 120  # Speed step size for color transitions
 
-vw_filter_coeff = 5.0
+vw_filter_coeff = 8.0

From 8db8e75c3f3e4fce5c3c91c777615ac233f4a174 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 5 Jan 2025 21:32:49 +0100
Subject: [PATCH 046/277] added gitignore

---
 .gitignore | 175 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 175 insertions(+)
 create mode 100644 .gitignore

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 00000000..156d0bb1
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,175 @@
+/models/
+
+models/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# PyPI configuration file
+.pypirc
\ No newline at end of file

From bfc769d5996d9993b27d1cae698e06ce2fae2b29 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 5 Jan 2025 22:02:28 +0100
Subject: [PATCH 047/277] fix

---
 FSGenerator.py                 |  3 +--
 utils/lib_VideoReaderFFmpeg.py | 17 +++++++++++------
 2 files changed, 12 insertions(+), 8 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index 55703449..bc95d41f 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -802,7 +802,7 @@ def quit_application():
 debug_frame = ttk.LabelFrame(root, text="Debugging", padding=(10, 5))
 debug_frame.grid(row=5, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
 
-ttk.Button(debug_frame, text="Debug (q to quit)", command=debug_function).grid(row=0, column=0, padx=5, pady=5)
+ttk.Button(debug_frame, text="Video (q to quit)", command=debug_function).grid(row=0, column=0, padx=5, pady=5)
 
 ttk.Checkbutton(debug_frame, text="Save debugging session as video", variable=debug_record_mode_var).grid(row=0, column=1, padx=5, pady=5)
 
@@ -811,7 +811,6 @@ def quit_application():
 duration_combobox.grid(row=0, column=2, padx=5, pady=5)
 ttk.Label(debug_frame, text="seconds").grid(row=0, column=3, padx=5, pady=5)
 
-
 # Progress Bar
 # progress_frame = ttk.LabelFrame(root, text="Progress", padding=(10, 5))
 # progress_frame.grid(row=6, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index f359febf..448700d8 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -89,7 +89,8 @@ def _start_process(self, start_frame=0):
         self.current_frame_number = start_frame
 
         if self.is_VR:
-            arg_line = "crop=w=iw/2:h=ih:x=0:y=0"
+            #arg_line = "crop=w=iw/2:h=ih:x=0:y=0"
+            arg_line = ""
             if self.unwarp:
                 if self.projection == "FISHEYE" or "FISHEYE" in self.video_path:
                     print("Proceeding with fisheye projection correction")
@@ -107,8 +108,7 @@ def _start_process(self, start_frame=0):
                     self.v_fov = 90
                     self.h_fov = 90
                     self.d_fov = 180
-                    #arg_line = f"crop=w=iw/2:h=ih:x=0:y=0,v360={self.type}:sg:iv_fov={self.iv_fov}:ih_fov={self.ih_fov}:d_fov={self.d_fov}:v_fov={self.v_fov}:h_fov={self.h_fov}:pitch=-20:yaw=0:roll=0:w={self.width}:h={self.height}:interp=lanczos:reset_rot=1"
-                arg_line = arg_line + f",v360={self.type}:output=sg"
+                arg_line = arg_line + f"v360={self.type}:in_stereo=sbs:output=sg"
                 arg_line = arg_line + f":iv_fov={self.iv_fov}:ih_fov={self.ih_fov}"
                 arg_line = arg_line + f":d_fov={self.d_fov}:v_fov={self.v_fov}:h_fov={self.h_fov}"
                 arg_line = arg_line + f":pitch=-25:yaw=0:roll=0"
@@ -119,15 +119,16 @@ def _start_process(self, start_frame=0):
                 #arg_line = arg_line + f",format=gray"
                 #arg_line = arg_line + f",histeq"
             else:
-                arg_line = arg_line  # "crop=w=iw/2:h=ih:x=0:y=0"
+                arg_line = "crop=w=iw/2:h=ih:x=0:y=0"
 
             cmd = [
                 self.ffmpeg_path,
+                '-nostats',  # Disable progress statistics
+                '-loglevel', 'warning',
                 "-ss", str(start_time / 1000),  # Seek to start time in seconds
                 "-i", self.video_path,
                 "-an",  # Disable audio processing
                 "-map", "0:v:0",
-                #"-vf", f"crop=w=iw/2:h=ih:x=0:y=0,v360={self.type}:sg:iv_fov={self.iv_fov}:ih_fov={self.ih_fov}:d_fov={self.d_fov}:v_fov={self.v_fov}:h_fov={self.h_fov}:pitch=-20:yaw=0:roll=0:w={self.width}:h={self.height}:interp=lanczos:reset_rot=1",
                 "-vf", arg_line,
                 "-f", "rawvideo",  # Output raw video data
                 "-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
@@ -139,6 +140,8 @@ def _start_process(self, start_frame=0):
             # FFmpeg command to read frames
             cmd = [
                 self.ffmpeg_path,
+                '-nostats',  # Disable progress statistics
+                '-loglevel', 'warning',
                 "-ss", str(start_time / 1000),  # Seek to start time in seconds
                 "-i", self.video_path,
                 "-f", "rawvideo",  # Output raw video data
@@ -152,7 +155,9 @@ def _start_process(self, start_frame=0):
             self.process.terminate()
 
         # Start FFmpeg process
-        self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+        #self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+        self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL,
+                                        bufsize=self.width * self.height * 3)
         self.frame_size = self.width * self.height * 3  # Size of one frame in bytes
 
     def read(self):

From 3a75dbb9078571914566f1b843b6951accfcd5cb Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 5 Jan 2025 22:04:41 +0100
Subject: [PATCH 048/277] fix attempt

---
 params/config.py               |  2 +-
 utils/lib_FunscriptHandler.py  | 62 ++++++++++++++++++++++++++++++++++
 utils/lib_ObjectTracker.py     |  3 +-
 utils/lib_VideoReaderFFmpeg.py |  3 +-
 4 files changed, 66 insertions(+), 4 deletions(-)

diff --git a/params/config.py b/params/config.py
index 1cf99060..07f5f3e5 100644
--- a/params/config.py
+++ b/params/config.py
@@ -131,4 +131,4 @@ def find_ffprobe_path():
 ]
 step_size = 120  # Speed step size for color transitions
 
-vw_filter_coeff = 8.0
+vw_filter_coeff = 6.0
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 64730ce0..fba5ab26 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -29,6 +29,68 @@ def generate(self, raw_funscript_path, funscript_data, fps, TestMode = False):
         else:
             data = funscript_data
 
+        try:
+            print(f"Generating funscript based on {len(data)} points...")
+            #self.filtered_positions = simplify_coords(data, vw_filter_coeff)  # Use VW algorithm
+            # use my own simplification function instead of vw here
+            positions = self.filter_positions(data, fps)
+            print(f"Lenghth of filtered positions: {len(self.filtered_positions) + 1}")
+            # enhance the funscript
+            # let's remap the highest to 100, and the lowest to 0, and rescale to 0-100
+            adjusted_positions = np.interp(positions, (min(positions), max(positions)), (0, 100))
+            # drag all values below 10 to 0 and above 90 to 100
+            readjusted_positions = [0 if p < 10 else 100 if p > 90 else p for p in adjusted_positions]
+
+            # let's boost the peaks by 20%, and reduce the lows by 20% and stay within 0-100
+            adjusted_positions = self.adjust_peaks_and_lows(readjusted_positions, peak_boost=20, low_reduction=20, )
+            # now, perform the vw simplification again
+            self.filtered_positions = simplify_coords(adjusted_positions, vw_filter_coeff)
+
+            self.write_funscript(self.filtered_positions, output_path, fps)
+            print(f"Funscript generated and saved to {output_path}")
+            self.generate_heatmap(output_path, output_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
+            """
+            # Now proceeding with remapping / adjusting
+            # Adjust peaks and lows
+            ats = [p[0] for p in self.filtered_positions]
+            positions = [p[1] for p in self.filtered_positions]
+
+            # let's remap the highest to 100, and the lowest to 0, and rescale to 0-100
+            adjusted_positions = np.interp(positions, (min(positions), max(positions)), (0, 100))
+            # drag all values below 10 to 0 and above 90 to 100
+            readjusted_positions = [0 if p < 10 else 100 if p > 90 else p for p in adjusted_positions]
+
+            # let's boost the peaks by 20%, and reduce the lows by 20% and stay within 0-100
+            adjusted_positions = self.adjust_peaks_and_lows(readjusted_positions, peak_boost=20, low_reduction=20,)
+
+
+            #adjusted_positions = self.adjust_peaks_and_lows(positions, peak_boost=15, low_reduction=20,
+            #                                                max_flat_length=3)
+            # recombine ats and positions
+            zip_adjusted_positions = list(zip(ats, adjusted_positions))
+            remapped_path = output_path[:-10] + '_adjusted.funscript'
+            self.write_funscript(zip_adjusted_positions, remapped_path, fps)
+            self.generate_heatmap(remapped_path,
+                                  remapped_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
+            """
+        except:
+            print(f"Error loading raw funscript from {raw_funscript_path}")
+
+
+    def prev_generate(self, raw_funscript_path, funscript_data, fps, TestMode = False):
+        output_path = raw_funscript_path[:-18] + '.funscript'
+        if len(funscript_data) == 0:
+            # Read the funscript data from the JSON file
+            with open(raw_funscript_path, 'r') as f:
+                print(f"Loading funscript from {raw_funscript_path}")
+                try:
+                    data = f.read() #json.load(f)
+                    data = eval(data)
+                except:
+                    print(f"Error loading funscript from {raw_funscript_path}")
+        else:
+            data = funscript_data
+
         try:
             print(f"Generating funscript based on {len(data)} points...")
             self.filtered_positions = simplify_coords(data, vw_filter_coeff)  # Use VW algorithm
diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index 8e33689f..b6659f6c 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -353,7 +353,8 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
 
                 self.tracked_boxes.append([box, class_name, track_id])
                 x1, y1, x2, y2 = box
-                mid_y = (y1 + y2) // 2
+                # mid_y = (y1 + y2) // 2  # not happy with this, trying alternative
+                mid_y = y2
 
                 # Update tracked positions within the frame
                 if track_id not in self.tracked_positions:
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index 448700d8..dfa30139 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -156,8 +156,7 @@ def _start_process(self, start_frame=0):
 
         # Start FFmpeg process
         #self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-        self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL,
-                                        bufsize=self.width * self.height * 3)
+        self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
         self.frame_size = self.width * self.height * 3  # Size of one frame in bytes
 
     def read(self):

From fba8c2494e97d11986d9f4f8dab92b1f68840cd1 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 5 Jan 2025 22:06:08 +0100
Subject: [PATCH 049/277] fix attempt

---
 .idea/.gitignore | 175 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 175 insertions(+)
 create mode 100644 .idea/.gitignore

diff --git a/.idea/.gitignore b/.idea/.gitignore
new file mode 100644
index 00000000..156d0bb1
--- /dev/null
+++ b/.idea/.gitignore
@@ -0,0 +1,175 @@
+/models/
+
+models/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# PyPI configuration file
+.pypirc
\ No newline at end of file

From 8b34582ed1b01e066573104314fdccf16f7e9721 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 5 Jan 2025 23:27:18 +0100
Subject: [PATCH 050/277] zalunda installation comment

---
 README.md | 21 +++++++++++++++++++--
 1 file changed, 19 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index f7c748b2..b7ce23b9 100644
--- a/README.md
+++ b/README.md
@@ -91,11 +91,28 @@ Before using this project, ensure you have the following installed:
    pip install numpy opencv-python tqdm ultralytics scipy matplotlib simplification
    ```
 
-3. **Download the YOLO model**:
+3. ** Use a venv as suggested by Zalunda**
+   * Install miniconda 
+   * Start a miniconda command prompt 
+   * Execute (assuming you already cloned VR-Funscript-AI-Generator and copied the model into models folder)
+   ```bash
+   conda create -n VRFunAIGen python=3.11
+   conda activate VRFunAIGen
+   pip install numpy opencv-python tqdm ultralytics scipy matplotlib simplification
+   pip uninstall torch torchvision torchaudio
+   pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
+   cd <VR-Funscript-AI-Generator folder>
+   python FSGenerator.py
+   ```
+   
+While executing, you’ll need to say “yes” a few times. The lines “pip uninstall / pip3 install” is to replace the “CPU” version of torch with a “cuda enabled / GPU” version (you might need to install nvidia CUDA stuff for it to works, I’m not sure).
+
+
+4. **Download the YOLO model**:
    - Place your YOLO model file (e.g., `k00gar-11n-200ep-best.mlpackage`) in the `models/` sub-directory.
    - Alternatively, you can specify a custom path to the model using the `--yolo_model` argument.
 
-4. **Update the params/config.py**:
+5. **Update the params/config.py**:
    - If ffmpeg and ffprobe paths are not in your system path, the program will default to the following values.
    - You can update the params/config.py file, which contains:
 

From 71d9bf1d5eed129c32e76edffdb11c41a685a2fc Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 5 Jan 2025 23:27:56 +0100
Subject: [PATCH 051/277] zalunda installation comment

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index b7ce23b9..9447fe5d 100644
--- a/README.md
+++ b/README.md
@@ -91,7 +91,7 @@ Before using this project, ensure you have the following installed:
    pip install numpy opencv-python tqdm ultralytics scipy matplotlib simplification
    ```
 
-3. ** Use a venv as suggested by Zalunda**
+3. **Use a venv as suggested by Zalunda**
    * Install miniconda 
    * Start a miniconda command prompt 
    * Execute (assuming you already cloned VR-Funscript-AI-Generator and copied the model into models folder)

From e388ab3fc61ac923eb937018a8b51055c9694069 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 5 Jan 2025 23:28:43 +0100
Subject: [PATCH 052/277] zalunda installation comment

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 9447fe5d..eb0a7569 100644
--- a/README.md
+++ b/README.md
@@ -105,7 +105,7 @@ Before using this project, ensure you have the following installed:
    python FSGenerator.py
    ```
    
-While executing, you’ll need to say “yes” a few times. The lines “pip uninstall / pip3 install” is to replace the “CPU” version of torch with a “cuda enabled / GPU” version (you might need to install nvidia CUDA stuff for it to works, I’m not sure).
+   While executing, you’ll need to say “yes” a few times. The lines “pip uninstall / pip3 install” is to replace the “CPU” version of torch with a “cuda enabled / GPU” version (you might need to install nvidia CUDA stuff for it to works, I’m not sure).
 
 
 4. **Download the YOLO model**:

From c9db7374b8a4f49896d98ff9923fbf8943287267 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Mon, 6 Jan 2025 19:36:53 +0100
Subject: [PATCH 053/277] Fixes

---
 FSGenerator.py                 | 176 ++++++++++------
 README.md                      |   9 +
 params/config.py               |   2 +-
 utils/lib_Debugger.py          |   2 +-
 utils/lib_FunscriptHandler.py  | 121 +++++++----
 utils/lib_ObjectTracker.py     | 372 ++++++++++++++-------------------
 utils/lib_VideoReaderFFmpeg.py |  10 +-
 7 files changed, 370 insertions(+), 322 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index bc95d41f..fb082fc2 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -10,6 +10,7 @@
 import tkinter as tk  # GUI library for macOS for basic use in our case
 from tkinter import filedialog, messagebox, ttk  # here, what was I saying...
 import subprocess  # For running shell commands
+import threading
 
 # Import custom modules and configurations
 from params.config import class_priority_order, class_reverse_match, class_colors, yolo_models, ffmpeg_path  # Configuration for class priorities, reverse matching, and colors
@@ -153,17 +154,16 @@ def get_yolo_model_path():
 
         return yolo_models[2]
 
-def extract_yolo_data():
+def extract_yolo_data(progress_callback=None):
     """
     Extract YOLO detection data from a video.
     """
-    # Check if the output file already exists
     if os.path.exists(global_state.video_file[:-4] + f"_rawyolo.json"):
         # messagebox to ask if user wants to overwrite or reuse
         # file name without path
         file_name = os.path.basename(global_state.video_file[:-4] + f"_rawyolo.json")
         skip_detection = messagebox.askyesno("Detection file already exists",
-                f"File {file_name} already exists.\n\nClick Yes to reuse the existing detections file.\nClick No to perform detections again.")
+                                             f"File {file_name} already exists.\n\nClick Yes to reuse the existing detections file.\nClick No to perform detections again.")
         if skip_detection:
             print(
                 f"File {global_state.video_file[:-4] + f'_rawyolo.json'} already exists. Skipping detections and loading file content...")
@@ -201,6 +201,9 @@ def extract_yolo_data():
     if run_pose_model:
         pose_model = YOLO(global_state.yolo_pose_model, task="pose")
 
+    # Start time for ETA calculation
+    start_time = time.time()
+
     # Loop through the video frames
     for frame_pos in tqdm(range(global_state.frame_start, last_frame), ncols=None, desc="Performing YOLO detection on frames"):
         success, frame = cap.read()  # Read a frame from the video
@@ -306,6 +309,14 @@ def extract_yolo_data():
                 if cv2.waitKey(1) & 0xFF == ord('q'):
                     break
 
+        # Update progress
+        if progress_callback:
+            elapsed_time = time.time() - start_time
+            frames_processed = frame_pos - global_state.frame_start + 1
+            frames_remaining = last_frame - frame_pos - 1
+            eta = (elapsed_time / frames_processed) * frames_remaining if frames_processed > 0 else 0
+            progress_callback(frame_pos, last_frame, time.strftime("%H:%M:%S", time.gmtime(eta)))
+
     # Write the detection records to a JSON file
     write_dataset(global_state.video_file[:-4] + f"_rawyolo.json", records)
     # Release the video capture object and close the display window
@@ -339,7 +350,7 @@ def make_data_boxes(records, image_x_size):
         result.add_record(frame_idx, box_record)
     return result
 
-def analyze_tracking_results(results, image_y_size):
+def analyze_tracking_results(results, image_y_size, progress_callback=None):
     """
     Analyze tracking results and generate Funscript data.
     :param results: The Result object containing detection data.
@@ -361,7 +372,7 @@ def analyze_tracking_results(results, image_y_size):
         divide_size_by = 2
     else:
         divide_size_by = 1
-    image_area = (cap.get(cv2.CAP_PROP_FRAME_WIDTH) // divide_size_by )* cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
+    image_area = (cap.get(cv2.CAP_PROP_FRAME_WIDTH) // divide_size_by) * cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
 
     cuts = []
 
@@ -395,6 +406,9 @@ def analyze_tracking_results(results, image_y_size):
     global_state.funscript_frames = []  # List to store Funscript frames
     tracker = ObjectTracker(fps, global_state.frame_start, image_area)  # Initialize the object tracker
 
+    # Start time for ETA calculation
+    start_time = time.time()
+
     for frame_pos in tqdm(range(global_state.frame_start, global_state.frame_end), unit="f"):
         if frame_pos in cuts:
             # Reinitialize the tracker at scene cuts
@@ -418,14 +432,11 @@ def analyze_tracking_results(results, image_y_size):
                 # Log debugging information
                 bounding_boxes = []
                 for box in sorted_boxes:
-                    #if box[4] in tracker.tracked_positions:
                     if box[4] in tracker.normalized_absolute_tracked_positions:
-                        #position = tracker.tracked_positions[box[4]][-1]
                         if box[4] == 0:  # generic track_id for 'hips center'
                             str_dist_penis = 'None'
                         else:
                             if box[4] in tracker.normalized_distance_to_penis:
-                                # print(f"box[4]: {box[4]}")
                                 str_dist_penis = str(int(tracker.normalized_distance_to_penis[box[4]][-1]))
                             else:
                                 str_dist_penis = 'None'
@@ -492,6 +503,14 @@ def analyze_tracking_results(results, image_y_size):
             cv2.imshow("Combined Results", frame_display)
             cv2.waitKey(1)
 
+        # Update progress
+        if progress_callback:
+            elapsed_time = time.time() - start_time
+            frames_processed = frame_pos - global_state.frame_start + 1
+            frames_remaining = global_state.frame_end - frame_pos - 1
+            eta = (elapsed_time / frames_processed) * frames_remaining if frames_processed > 0 else 0
+            progress_callback(frame_pos, global_state.frame_end, time.strftime("%H:%M:%S", time.gmtime(eta)))
+
     # Prepare Funscript data
     global_state.funscript_data = list(zip(global_state.funscript_frames, global_state.funscript_distances))
 
@@ -618,64 +637,84 @@ def start_processing():
     print(f"Frame End: {global_state.frame_end}")
     print(f"Reference Script: {global_state.reference_script}")
 
-    # Processing logic
-
-    global_state.debugger = Debugger(global_state.video_file, output_dir=global_state.video_file[:-4])  # Initialize the debugger
+    # Initialize the debugger
+    global_state.debugger = Debugger(global_state.video_file, output_dir=global_state.video_file[:-4])
 
     if global_state.video_reader == "FFmpeg":
-        cap = VideoReaderFFmpeg(global_state.video_file, is_VR=global_state.isVR)  # Initialize the video reader
-        image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH)  # Get the video's width
+        cap = VideoReaderFFmpeg(global_state.video_file, is_VR=global_state.isVR)
+        image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
     else:
         cap = cv2.VideoCapture(global_state.video_file)
         if global_state.isVR:
-            image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH) // 2  # Get the video's width
+            image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH) // 2
         else:
             image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
-    fps = cap.get(cv2.CAP_PROP_FPS)  # Get the video's FPS
-    image_y_size = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)  # Get the video's height
-
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    image_y_size = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
+    cap.release()
 
     print(f"Image size: {image_x_size}x{image_y_size}")
-    cap.release()
 
-    # Process the video
+    # YOLO Detection Progress
+    def update_yolo_progress(current_frame, total_frames, eta):
+        progress = (current_frame / total_frames) * 100
+        yolo_progress_bar["value"] = progress
+        yolo_progress_percent.config(text=f"{progress:.0f}% - ETA: {eta}")
+        #yolo_progress_eta.config(text=f"ETA: {eta}")
+        root.update_idletasks()
+
+    # Tracking Analysis Progress
+    def update_tracking_progress(current_frame, total_frames, eta):
+        progress = (current_frame / total_frames) * 100
+        tracking_progress_bar["value"] = progress
+        tracking_progress_percent.config(text=f"{progress:.0f}% - ETA: {eta}")
+        #tracking_progress_eta.config(text=f"ETA: {eta}")
+        root.update_idletasks()
+
+    # Function to run the processing tasks
+    def run_processing():
+        # Run the YOLO detection and save result to _rawyolo.json file
+        extract_yolo_data(update_yolo_progress)
+
+        # Load YOLO detection results from file
+        yolo_data = load_yolo_data_from_file(global_state.video_file[:-4] + f"_rawyolo.json")
 
-    # Run the YOLO detection and saves result to _rawyolo.json file
-    extract_yolo_data()
-    #
-    # Load YOLO detection results from file
-    yolo_data = load_yolo_data_from_file(global_state.video_file[:-4] + f"_rawyolo.json")
+        results = make_data_boxes(yolo_data, image_x_size)
 
-    results = make_data_boxes(yolo_data, image_x_size)
+        # Looking for the first instance of penis within the YOLO results
+        first_penis_frame = parse_yolo_data_looking_for_penis(yolo_data, 0)
 
-    # Looking for the first instance of penis within the YOLO results
-    first_penis_frame = parse_yolo_data_looking_for_penis(yolo_data, 0)
+        if first_penis_frame is None:
+            print(f"No penis found in video: {global_state.video_file}")
+            first_penis_frame = 0
 
-    if first_penis_frame is None:
-        print(f"No penis found in video: {global_state.video_file}")
-        first_penis_frame = 0
+        # Deciding whether we start from there or from a user-specified later frame
+        global_state.frame_start = max(max(first_penis_frame - int(fps), global_state.frame_start - int(fps)), 0)
 
-    # Deciding whether we start from there or from a user specified later frame
-    global_state.frame_start = max(max(first_penis_frame - int(fps), global_state.frame_start - int(fps)), 0)
+        print(f"Frame Start adjusted to: {global_state.frame_start}")
 
-    print(f"Frame Start adjusted to: {global_state.frame_start}")
+        # Performing the tracking part and generation of the raw funscript data
+        global_state.funscript_data = analyze_tracking_results(results, image_y_size, update_tracking_progress)
 
-    # Performing the tracking part and generation of the raw funscript data
-    global_state.funscript_data = analyze_tracking_results(results, image_y_size)
+        global_state.debugger.save_logs()
 
-    global_state.debugger.save_logs()
+        funscript_handler = FunscriptGenerator()
 
-    funscript_handler = FunscriptGenerator()
+        # Simplifying the funscript data and generating the file
+        funscript_handler.generate(global_state.video_file[:-4] + f"_rawfunscript.json",
+                                   global_state.funscript_data,
+                                   fps, global_state.LiveDisplayMode)
 
-    # Simplifying the funscript data and generating the file
-    funscript_handler.generate(global_state.video_file[:-4] + f"_rawfunscript.json", global_state.funscript_data, fps, global_state.LiveDisplayMode)
+        # Optional, compare generated funscript with reference funscript if specified, or a simple generic report
+        funscript_handler.compare_funscripts(global_state.reference_script, global_state.video_file[:-3] + "funscript", global_state.video_file, global_state.isVR,
+                                             global_state.video_file[:-4] + "_comparefunscripts.png") #,
+        #                                     global_state.video_file[:-4] + "_adjusted.funscript")
 
-    # Optional, compare generated funscript with reference funscript if specified, or a simple generic report
-    funscript_handler.compare_funscripts(global_state.reference_script, global_state.video_file[:-3] + "funscript", global_state.video_file, global_state.isVR,
-                                             global_state.video_file[:-4] + "_comparefunscripts.png",
-                                             global_state.video_file[:-4] + "_adjusted.funscript", )
+        print(f"Finished processing video: {global_state.video_file}")
 
-    print(f"Finished processing video: {global_state.video_file}")
+    # Run the processing in a separate thread
+    processing_thread = threading.Thread(target=run_processing)
+    processing_thread.start()
 
 def debug_function():
     """
@@ -731,6 +770,7 @@ def quit_application():
     root.quit()  # Close the Tkinter main loop
     root.destroy()  # Destroy the root window
 
+
 # GUI Setup
 root = tk.Tk()
 root.title("VR funscript generation helper")
@@ -758,11 +798,11 @@ def quit_application():
 reader_frame = ttk.LabelFrame(root, text="Video Reader", padding=(10, 5))
 reader_frame.grid(row=1, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
 
-ttk.Label(video_frame, text="Reader:").grid(row=1, column=0, padx=5, pady=5, sticky="w")
+ttk.Label(video_frame, text="Video Reader:").grid(row=1, column=0, padx=5, pady=5, sticky="w")
 video_reader_menu = ttk.OptionMenu(video_frame, video_reader_var, "FFmpeg", "FFmpeg", "OpenCV")
 video_reader_menu.grid(row=1, column=1, padx=5, pady=5, sticky="w")
 #text_label = ttk.Label(video_frame, text="<= Pick FFmpeg for VR undistortion.\n<= Pick OpenCV if perf. issues.")
-#text_label.grid(row=1, column=2, padx=5, pady=5)
+#text_label.grid(row=1, column=2, padx=5, pady=5, sticky="w")
 # VR Mode, activated by default
 is_vr_var.set(True)
 ttk.Checkbutton(video_frame, text="VR SBS video", variable=is_vr_var).grid(row=2, column=0, padx=5, pady=5)
@@ -779,11 +819,6 @@ def quit_application():
 frame_end_entry = ttk.Entry(frame_frame, width=10)
 frame_end_entry.grid(row=1, column=1, padx=5, pady=5, sticky="w")
 
-# Reference Script Selection
-#script_frame = ttk.LabelFrame(root)  #,  # text="Reference Script for comparison (optional)",
-                             # padding=(10, 5))
-#script_frame.grid(row=3, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
-
 ttk.Label(frame_frame, text="Reference Script:").grid(row=2, column=0, padx=5, pady=5)
 ttk.Entry(frame_frame, textvariable=reference_script_path, width=50).grid(row=2, column=1, padx=5, pady=5)
 ttk.Button(frame_frame, text="Browse", command=select_reference_script).grid(row=2, column=2, padx=5, pady=5)
@@ -793,16 +828,34 @@ def quit_application():
 processing_frame.grid(row=4, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
 
 start_button = ttk.Button(processing_frame, text="Start Processing", command=start_processing)
-start_button.grid(row=0, column=0, padx=5, pady=5)
+start_button.grid(row=0, column=0, padx=5, pady=5, sticky="w")
 
 ttk.Checkbutton(processing_frame, text="Logging for debug", variable=debug_mode_var).grid(row=0, column=1, padx=5, pady=5)
-ttk.Checkbutton(processing_frame, text="Live Display Mode (perf. down)", variable=live_display_mode_var).grid(row=0, column=2, padx=5, pady=5)
+debug_mode_var.set(True)
+ttk.Checkbutton(processing_frame, text="Live inference => slow & heavy!", variable=live_display_mode_var).grid(row=0, column=2, padx=5, pady=5)
+
+# Progress Bar for YOLO Detection
+yolo_progress_label = ttk.Label(processing_frame, text="YOLO Detection Progress:")
+yolo_progress_label.grid(row=1, column=0, padx=5, pady=5, sticky="w")
+yolo_progress_bar = ttk.Progressbar(processing_frame, orient="horizontal", length=300, mode="determinate")
+yolo_progress_bar.grid(row=1, column=1, padx=5, pady=5, sticky="ew")
+yolo_progress_percent = ttk.Label(processing_frame, text="0%")
+yolo_progress_percent.grid(row=1, column=2, padx=5, pady=5, sticky="w")
+
+# Progress Bar for Tracking Analysis
+tracking_progress_label = ttk.Label(processing_frame, text="Tracking Analysis Progress:")
+tracking_progress_label.grid(row=2, column=0, padx=5, pady=5, sticky="w")
+tracking_progress_bar = ttk.Progressbar(processing_frame, orient="horizontal", length=300, mode="determinate")
+tracking_progress_bar.grid(row=2, column=1, padx=5, pady=5, sticky="ew")
+tracking_progress_percent = ttk.Label(processing_frame, text="0%")
+tracking_progress_percent.grid(row=2, column=2, padx=5, pady=5, sticky="w")
 
 # Debug Record Mode
-debug_frame = ttk.LabelFrame(root, text="Debugging", padding=(10, 5))
-debug_frame.grid(row=5, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
+debug_frame = ttk.LabelFrame(root, text="Debugging (Replay and navigate a processed video)", padding=(10, 5))
+debug_frame.grid(row=6, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
 
-ttk.Button(debug_frame, text="Video (q to quit)", command=debug_function).grid(row=0, column=0, padx=5, pady=5)
+quit_button = ttk.Button(debug_frame, text="Video (q to quit)", command=debug_function)
+quit_button.grid(row=0, column=0, padx=5, pady=5, sticky="w")
 
 ttk.Checkbutton(debug_frame, text="Save debugging session as video", variable=debug_record_mode_var).grid(row=0, column=1, padx=5, pady=5)
 
@@ -811,14 +864,7 @@ def quit_application():
 duration_combobox.grid(row=0, column=2, padx=5, pady=5)
 ttk.Label(debug_frame, text="seconds").grid(row=0, column=3, padx=5, pady=5)
 
-# Progress Bar
-# progress_frame = ttk.LabelFrame(root, text="Progress", padding=(10, 5))
-# progress_frame.grid(row=6, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
-
-# progress_bar = ttk.Progressbar(progress_frame, variable=progress_var, maximum=100)
-# progress_bar.grid(row=0, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
-
-# Startand Quit Buttons
+# Quit Button
 button_frame = ttk.Frame(root)
 button_frame.grid(row=7, column=0, columnspan=3, padx=5, pady=10)
 
@@ -828,4 +874,4 @@ def quit_application():
 footer_label = ttk.Label(root, text="Individual and personal use only.\nNot for commercial use, nor use by a company.\nk00gar 2025 - https://github.com/ack00gar", font=("Arial", 10, "italic", "bold"), justify="center")
 footer_label.grid(row=8, column=0, columnspan=3, padx=5, pady=5)
 
-root.mainloop()
\ No newline at end of file
+root.mainloop()
diff --git a/README.md b/README.md
index eb0a7569..71c47559 100644
--- a/README.md
+++ b/README.md
@@ -106,7 +106,16 @@ Before using this project, ensure you have the following installed:
    ```
    
    While executing, you’ll need to say “yes” a few times. The lines “pip uninstall / pip3 install” is to replace the “CPU” version of torch with a “cuda enabled / GPU” version (you might need to install nvidia CUDA stuff for it to works, I’m not sure).
+   
+   Zalunda also suggests the creation of a batch file, after the setup, to start the application in the right conda environment:
 
+   ```bash
+   @echo off
+   call <PATH_TO_MINICONDA>\miniconda3\condabin\conda activate VRFunAIGen
+   cd /d "<PATH_TO_SOURCES>\VR-Funscript-AI-Generator"
+   python FSGenerator.py
+   pause
+   ```
 
 4. **Download the YOLO model**:
    - Place your YOLO model file (e.g., `k00gar-11n-200ep-best.mlpackage`) in the `models/` sub-directory.
diff --git a/params/config.py b/params/config.py
index 07f5f3e5..f579784a 100644
--- a/params/config.py
+++ b/params/config.py
@@ -131,4 +131,4 @@ def find_ffprobe_path():
 ]
 step_size = 120  # Speed step size for color transitions
 
-vw_filter_coeff = 6.0
+vw_filter_coeff = 2.0
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 4d8f1ee4..4b3a7746 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -298,7 +298,7 @@ def _update_frame_from_mouse(self, x, width):
         print(f"Target frame: {self.current_frame}")
         if self.video_reader == "FFmpeg":
             self.cap.release()
-            self.cap = VideoReaderFFmpeg(self.video_path, is_VR=True)
+            self.cap = VideoReaderFFmpeg(self.video_path, self.isVR)
         self.cap.set(cv2.CAP_PROP_POS_FRAMES, self.current_frame)
         print("Done resetting and jumping to target frame")
 
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index fba5ab26..4ab9d4ed 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -1,80 +1,71 @@
+import logging
 import os
 import json
 from simplification.cutil import simplify_coords
-import matplotlib.pyplot as plt
 import numpy as np
+import cv2
 import datetime
-import matplotlib.colors as mcolors
-from matplotlib.cm import ScalarMappable
-from matplotlib.colors import LinearSegmentedColormap, Normalize
+
 from params.config import heatmap_colors, step_size, vw_filter_coeff
-import cv2
 
+import matplotlib
+matplotlib.use('Agg')  # Use a non-interactive backend
 import matplotlib.pyplot as plt
 import matplotlib.gridspec as gridspec
-import datetime
+import matplotlib.colors as mcolors
+from matplotlib.cm import ScalarMappable
+from matplotlib.colors import LinearSegmentedColormap, Normalize
 
 class FunscriptGenerator:
     def generate(self, raw_funscript_path, funscript_data, fps, TestMode = False):
         output_path = raw_funscript_path[:-18] + '.funscript'
         if len(funscript_data) == 0:
+            print("len funscript data is 0, trying to load file")
             # Read the funscript data from the JSON file
             with open(raw_funscript_path, 'r') as f:
                 print(f"Loading funscript from {raw_funscript_path}")
                 try:
-                    data = f.read() #json.load(f)
-                    data = eval(data)
+                    data = json.load(f)  #f.read() #json.load(f)
+                    #data = eval(data)
+                    print(f"data loaded: {data}")
                 except:
-                    print(f"Error loading funscript from {raw_funscript_path}")
+                    print(f"line 31 - Error loading funscript from {raw_funscript_path}")
         else:
             data = funscript_data
 
         try:
             print(f"Generating funscript based on {len(data)} points...")
-            #self.filtered_positions = simplify_coords(data, vw_filter_coeff)  # Use VW algorithm
+            # self.filtered_positions = simplify_coords(data, vw_filter_coeff)  # Use VW algorithm
             # use my own simplification function instead of vw here
+            print("Positions adjustment - step 0 (simplification)")
             positions = self.filter_positions(data, fps)
-            print(f"Lenghth of filtered positions: {len(self.filtered_positions) + 1}")
+            # print(f"Lenghth of filtered positions: {len(self.filtered_positions) + 1}")
+            print(f"Lenghth of filtered positions: {len(positions) + 1}")
             # enhance the funscript
             # let's remap the highest to 100, and the lowest to 0, and rescale to 0-100
-            adjusted_positions = np.interp(positions, (min(positions), max(positions)), (0, 100))
-            # drag all values below 10 to 0 and above 90 to 100
-            readjusted_positions = [0 if p < 10 else 100 if p > 90 else p for p in adjusted_positions]
-
-            # let's boost the peaks by 20%, and reduce the lows by 20% and stay within 0-100
-            adjusted_positions = self.adjust_peaks_and_lows(readjusted_positions, peak_boost=20, low_reduction=20, )
-            # now, perform the vw simplification again
-            self.filtered_positions = simplify_coords(adjusted_positions, vw_filter_coeff)
-
-            self.write_funscript(self.filtered_positions, output_path, fps)
-            print(f"Funscript generated and saved to {output_path}")
-            self.generate_heatmap(output_path, output_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
-            """
-            # Now proceeding with remapping / adjusting
+            print("Positions adjustment - step 1 (remapping)")
             # Adjust peaks and lows
-            ats = [p[0] for p in self.filtered_positions]
-            positions = [p[1] for p in self.filtered_positions]
-
-            # let's remap the highest to 100, and the lowest to 0, and rescale to 0-100
+            ats = [p[0] for p in positions]
+            positions = [p[1] for p in positions]
             adjusted_positions = np.interp(positions, (min(positions), max(positions)), (0, 100))
             # drag all values below 10 to 0 and above 90 to 100
+            print("Positions adjustment - step 2 (thresholding)")
             readjusted_positions = [0 if p < 10 else 100 if p > 90 else p for p in adjusted_positions]
-
+            print("Positions adjustment - step 3 (amplitude boosting)")
             # let's boost the peaks by 20%, and reduce the lows by 20% and stay within 0-100
-            adjusted_positions = self.adjust_peaks_and_lows(readjusted_positions, peak_boost=20, low_reduction=20,)
-
-
-            #adjusted_positions = self.adjust_peaks_and_lows(positions, peak_boost=15, low_reduction=20,
-            #                                                max_flat_length=3)
+            adjusted_positions = self.adjust_peaks_and_lows(readjusted_positions, peak_boost=20, low_reduction=20, )
             # recombine ats and positions
             zip_adjusted_positions = list(zip(ats, adjusted_positions))
-            remapped_path = output_path[:-10] + '_adjusted.funscript'
-            self.write_funscript(zip_adjusted_positions, remapped_path, fps)
-            self.generate_heatmap(remapped_path,
-                                  remapped_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
-            """
-        except:
-            print(f"Error loading raw funscript from {raw_funscript_path}")
+            # now, perform the vw simplification again
+            print("Positions adjustment - step 4 (VW algorithm simplification)")
+            filtered_positions = simplify_coords(zip_adjusted_positions, vw_filter_coeff)
+            # filtered_positions = zip_adjusted_positions
+            self.write_funscript(filtered_positions, output_path, fps)
+            print(f"Funscript generated and saved to {output_path}")
+            self.generate_heatmap(output_path,
+                                  output_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
+        except Exception as e:
+            print(f"Error generating funscript: {e}")
 
 
     def prev_generate(self, raw_funscript_path, funscript_data, fps, TestMode = False):
@@ -226,11 +217,52 @@ def get_color(intensity):
         plt.close()
         print(f"Funscript heatmap saved to {output_image_path}")
 
-
     def filter_positions(self, positions, fps):
         if not positions:
             return []
 
+        positions = np.array(positions)
+        filtered_positions = [positions[0]]  # Start with the first position
+
+        min_interval_ms = 50  # Minimum interval between points in milliseconds
+        slope_threshold = 0.2  # Adjusted slope threshold for gradual changes
+
+        for i in range(1, len(positions) - 1):
+            current_pos = positions[i]
+            prev_pos = positions[i - 1]
+            next_pos = positions[i + 1]
+
+            # Skip consecutive duplicate positions
+            if current_pos[1] == filtered_positions[-1][1] and current_pos[1] == next_pos[1]:
+                continue
+
+            # Calculate slopes
+            slope_prev = (current_pos[1] - prev_pos[1]) / (current_pos[0] - prev_pos[0]) if (current_pos[0] - prev_pos[
+                0]) != 0 else 0
+            slope_next = (next_pos[1] - current_pos[1]) / (next_pos[0] - current_pos[0]) if (next_pos[0] - current_pos[
+                0]) != 0 else 0
+            slope_diff = abs(slope_next - slope_prev)
+
+            is_local_extreme = ((current_pos[1] >= prev_pos[1] and current_pos[1] > next_pos[1])
+                                or (current_pos[1] > prev_pos[1] and current_pos[1] >= next_pos[1])
+                                or (current_pos[1] <= prev_pos[1] and current_pos[1] < next_pos[1])
+                                or (current_pos[1] < prev_pos[1] and current_pos[1] <= next_pos[1]))
+
+            # Add to filtered lists based on conditions
+            if (is_local_extreme or slope_diff > slope_threshold) and (abs(
+                    current_pos[0] - filtered_positions[-1][0]) * 1000 / fps) > min_interval_ms:
+                filtered_positions.append(current_pos)
+
+        # Ensure the last point meets the interval requirement
+        if len(filtered_positions) > 1 and positions[-1][0] - filtered_positions[-1][0] >= min_interval_ms:
+            filtered_positions.append(positions[-1])
+
+        return filtered_positions
+
+    def prev_filter_positions(self, positions, fps):
+        if not positions:
+            return []
+
         filtered_positions = [positions[0]]  # Start with the first position
 
         min_interval_ms = 50  # Minimum interval between points in milliseconds
@@ -400,6 +432,9 @@ def select_random_sections(self, total_duration, section_duration=10, num_sectio
         return sections
 
     def extract_section(self, times, positions, start, end):
+        if times is None or not isinstance(times, (list, tuple)):
+            logging.warning(f"No times for current section {start} - {end}")
+            return [], []
         start_ms = start * 1000
         end_ms = end * 1000
         indices = [i for i, t in enumerate(times) if start_ms <= t <= end_ms]
diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index b6659f6c..4cf8cecf 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -69,8 +69,16 @@ class ObjectTracker:
     """
     A class to track objects (e.g., penis, glans, etc.) in a video frame and determine their positions and interactions.
     """
+
     def __init__(self, fps, frame_pos, image_area):
-        # Initialize class attributes
+        """
+        Initialize the ObjectTracker.
+
+        Args:
+            fps (int): Frames per second of the video.
+            frame_pos (int): Current frame position.
+            image_area (int): Area of the video frame.
+        """
         self.class_names = class_names  # List of class names to track
         self.active_tracks = {}  # Active tracks: {track_id: Track object}
         self.inactive_tracks = []  # Inactive tracks (lost tracks)
@@ -83,14 +91,11 @@ def __init__(self, fps, frame_pos, image_area):
         self.fps = fps  # Frames per second of the video
 
         # Speed and distance thresholds
-        self.max_speed = 100 / (.18 * self.fps)  # Maximum allowed speed for distance changes
-        # TEST - trying to fix the speed issue by temporarily doubling it up
-        #self.max_speed = int(self.max_speed * 3)
-
+        self.max_speed = 100 / (0.18 * self.fps)  # Maximum allowed speed for distance changes
         self.max_allowed = 100  # Maximum allowed distance
 
         # Tracking state
-        self.boxes = {}  # Detected boxes for each class
+        self.boxes = {class_name: None for class_name in self.class_names}  # Detected boxes for each class
         self.tracked_boxes = []  # List of tracked boxes
         self.penis_box = None  # Detected penis box
         self.locked_penis_box = LockedPenisBox()  # Locked penis box
@@ -107,75 +112,75 @@ def __init__(self, fps, frame_pos, image_area):
         self.sex_position_history = deque(maxlen=max_history)  # History of sex positions
 
         # Position and distance tracking
-        self.areas = {class_name: deque(maxlen=200) for class_name in class_names}  # Areas for each class
+        self.tracked_positions = {}  # Tracked positions for each track_id
+        self.normalized_absolute_tracked_positions = {}  # Normalized absolute positions for each track_id
+        self.normalized_distance_to_penis = {}  # Normalized distance to penis for each track_id
 
-        # Normalized positions and distances
+        # Initialize normalized positions and distances
         self.normalized_positions = {class_name: deque(maxlen=200) for class_name in class_names}
         self.normalized_distances = {class_name: deque(maxlen=200) for class_name in class_names}
 
-        # Tracked positions and normalized tracked positions
-        self.tracked_positions = {}
-        self.normalized_absolute_tracked_positions = {}
-        self.normalized_distance_to_penis = {}
-
-        # Initialize normalized distances and positions
-        for class_name in class_names:
-            self.normalized_distances[class_name].append(100)
-            self.normalized_positions[class_name].append(100)
-
         # Detection thresholds
-        self.consecutive_detections = {class_name: 0 for class_name in class_names}  # Consecutive detections for each class
-        self.consecutive_non_detections = {class_name: 0 for class_name in class_names}  # Consecutive non-detections for each class
+        self.consecutive_detections = {class_name: 0 for class_name in class_names}
+        self.consecutive_non_detections = {class_name: 0 for class_name in class_names}
         self.detections_threshold = 3  # Threshold for considering a detection valid
 
         # Penetration state
         self.penetration = False  # Whether penetration is detected
-
-        self.close_up = False
-
+        self.close_up = False  # Whether a close-up is detected
 
     def update_distance(self, raw_distance):
         """
         Update the tracked distance using a Kalman filter and speed constraints.
+
+        Args:
+            raw_distance (int): Raw distance measurement.
+
+        Returns:
+            int: Filtered distance.
         """
         if raw_distance is None:
-            #filtered_distance = self.distance_kf.predict()
-            # simple predict using ema, waiting for kalman filter fix
-            filtered_distance = 0.1 * self.previous_distances[-3] + 0.2 * self.previous_distances[-2] + 0.7 * self.previous_distances[-1]
-            #print(f"predicted distance: {filtered_distance}")
+            # Predict distance using exponential moving average (EMA)
+            filtered_distance = (
+                0.1 * self.previous_distances[-3]
+                + 0.2 * self.previous_distances[-2]
+                + 0.7 * self.previous_distances[-1]
+            )
         else:
-            #print(f"before fix, raw distance is: {raw_distance}")
-            # applying ema
-            ema_distance = 0.1 * self.previous_distances[-2] + 0.2 * self.previous_distances[-1] + 0.7 * raw_distance
-            #print(f"self distance: {self.distance}, ema_distance: {ema_distance}, max speed: {self.max_speed}, delta distances: {self.distance-ema_distance}")
+            # Apply EMA to raw distance
+            ema_distance = (
+                0.1 * self.previous_distances[-2]
+                + 0.2 * self.previous_distances[-1]
+                + 0.7 * raw_distance
+            )
+
+            # Limit speed of distance change
             if abs(self.distance - ema_distance) > self.max_speed:
-                # print(f"Limited speed: {self.max_speed}, was {int(self.distance - ema_distance)}")
                 ema_distance = self.distance + np.sign(ema_distance - self.distance) * self.max_speed
-                #print(f"adjusted ema distance: {ema_distance}")
-            #raw_distance_2d = (raw_distance, 0)
-            # Update the Kalman filter with the new measurement
-            #self.distance_kf.update(raw_distance_2d)
 
-            # Get the filtered distance from the Kalman filter
-            #filtered_k_distance = self.distance_kf.position[0]
-
-            #print(f"Kalman filtered distance: {filtered_k_distance} vs {raw_distance}")
             filtered_distance = ema_distance
-            #filtered_distance = raw_distance
 
+        # Ensure distance is within bounds (0-100)
         filtered_distance = int(max(0, min(100, filtered_distance)))
         self.distance = filtered_distance
+
+        # Update previous distances
         self.previous_distances.pop(0)
         self.previous_distances.append(self.distance)
 
         return filtered_distance
 
-    def _predict_distance(self):
-        """Predict the distance using the Kalman filter."""
-        return int(float(self.distance_kf.predict()[0]) / 5) * 5
-
     def boxes_overlap(self, box1, box2):
-        """Check if two bounding boxes overlap."""
+        """
+        Check if two bounding boxes overlap.
+
+        Args:
+            box1 (tuple): Coordinates of the first box.
+            box2 (tuple): Coordinates of the second box.
+
+        Returns:
+            bool: True if boxes overlap, False otherwise.
+        """
         if box1 is None or box2 is None:
             return False
         x1, y1, x2, y2 = box1
@@ -183,7 +188,16 @@ def boxes_overlap(self, box1, box2):
         return not (x2 < x3 or x4 < x1 or y2 < y3 or y4 < y1)
 
     def calculate_distance(self, penis_box, other_box):
-        """Calculate the distance between the penis box and another box."""
+        """
+        Calculate the distance between the penis box and another box.
+
+        Args:
+            penis_box (tuple): Coordinates of the penis box.
+            other_box (tuple): Coordinates of the other box.
+
+        Returns:
+            float: Distance between the boxes, or None if invalid.
+        """
         if other_box is None:
             return None
         if penis_box is None:
@@ -196,10 +210,15 @@ def calculate_distance(self, penis_box, other_box):
         return math.sqrt((x_pos - px2) ** 2 + (y_pos - py2) ** 2)
 
     def detect_sex_position_change(self, sex_position, reason):
-        """Detect and log changes in the sex position."""
+        """
+        Detect and log changes in the sex position.
+
+        Args:
+            sex_position (str): New sex position.
+            reason (str): Reason for the change.
+        """
         self.sex_position_history.append(sex_position)
-        position_counts = {position: self.sex_position_history.count(position) for position in
-                           self.sex_position_history}
+        position_counts = {position: self.sex_position_history.count(position) for position in self.sex_position_history}
         most_frequent_position = max(position_counts, key=position_counts.get, default="Not relevant")
         if most_frequent_position != self.sex_position:
             logging.info(f"@{self.current_frame_id} - Sex position switched to: {most_frequent_position}")
@@ -209,6 +228,11 @@ def detect_sex_position_change(self, sex_position, reason):
     def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
         """
         Main tracking logic to process detected boxes and update tracking state.
+
+        Args:
+            sorted_boxes (list): List of detected boxes with confidence, class, and track ID.
+            current_frame_id (int): Current frame ID.
+            image_y_size (int): Height of the video frame.
         """
         self.current_frame_id = current_frame_id
         self.image_y_size = image_y_size
@@ -222,7 +246,6 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
         all_detections = {class_name: [] for class_name in self.class_names}
 
         classes_touching_penis = []
-
         breast_tracked = None
         self.breast_tracking = False
 
@@ -252,34 +275,11 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                 self.consecutive_detections[check_class_first] = 0
                 self.consecutive_non_detections[check_class_first] += 1
 
-        if self.consecutive_non_detections[
-            check_class_first] > self.detections_threshold and check_class_first == 'penis':
+        if self.consecutive_non_detections[check_class_first] > self.detections_threshold and check_class_first == 'penis':
             if self.locked_penis_box.is_active():
                 self.locked_penis_box.deactivate()
                 logging.info(f"@{self.current_frame_id} - Deactivated locked_penis_box")
 
-
-        """
-        # checking for close up in case butt or pussy box are unusually large
-        for class_name in ['butt', 'pussy']:
-            for detection in all_detections[class_name]:
-                conf, box, track_id = detection
-                # if box[3] - box[1] > 0.15 * image_y_size:
-                box_area = (box[2] - box[0]) * (box[3] - box[1])
-                #if box_area > 0:
-                #    print(f"{class_name} area: {box_area}, size: {int((box_area/self.image_area)*100)}%")
-                if box_area > 0.12 * self.image_area:
-                    close_up_detected = True
-                    self.detect_sex_position_change('Close up', f'{class_name} size beyond threshold')
-                    if not self.close_up:
-                        logging.info(f"@{self.current_frame_id} - Close up detected - {class_name} size beyond threshold: {int((box_area/self.image_area)*100)}%")
-                        #self.detect_sex_position_change('Close up', f'{class_name} size beyond threshold')
-                        self.close_up = True
-                    distance = 100
-                    self.update_distance(distance)
-                    return
-        """
-
         # Check if pussy boxes are inside butt boxes
         if 'pussy' in all_detections and 'butt' in all_detections:
             for pussy_detection in all_detections['pussy']:
@@ -299,8 +299,8 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                             self.detect_sex_position_change('Close up', 'Butt box size beyond threshold')
                             if not self.close_up:
                                 logging.info(
-                                    f"@{self.current_frame_id} - Close up detected - butt size beyond threshold: {int((butt_box_area / self.image_area) * 100)}%")
-                                # self.detect_sex_position_change('Close up', f'{class_name} size beyond threshold')
+                                    f"@{self.current_frame_id} - Close up detected - butt size beyond threshold: {int((butt_box_area / self.image_area) * 100)}%"
+                                )
                                 self.close_up = True
                             distance = 100
                             self.update_distance(distance)
@@ -311,131 +311,37 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
 
         for box, conf, cls, class_name, track_id in sorted_boxes:
             if class_name in ['glans', 'penis', 'navel', 'hips center']:
-            #if class_name in ['glans', 'navel']:
                 continue
             elif self.locked_penis_box.is_active() and class_name == 'breast' and not self.boxes_overlap(box, self.locked_penis_box.get_box()):
                 x1, y1, x2, y2 = box
                 mid_y = (y1 + y2) // 2
-                # will need to refactor all that...
-                # Update tracked positions within the frame
-                if track_id not in self.tracked_positions:
-                    self.tracked_positions[track_id] = []
-                # let's apply a moving average to try and filter outliers
-                if len(self.tracked_positions[track_id]) > 2:
-                    mid_y = 0.2 * self.tracked_positions[track_id][-2] + 0.3 * \
-                            self.tracked_positions[track_id][
-                                -1] + 0.5 * mid_y
-                self.tracked_positions[track_id].append(int(mid_y))
-                # Maintain a fixed-size history
-                if len(self.tracked_positions[track_id]) > 600:
-                    self.tracked_positions[track_id].pop(0)
-
-                # Normalize absolute position 0-100 based on historic min and max
-                if track_id not in self.normalized_absolute_tracked_positions:
-                    self.normalized_absolute_tracked_positions[track_id] = []
-                # let's retrieve min and max from self.tracked_positions
-                min_y, max_y = min(self.tracked_positions[track_id]), max(self.tracked_positions[track_id])
-                # now, normalize
-                if max_y - min_y == 0:
-                    normalized_y = 100
-                else:
-                    normalized_y = int(min(max(0, 100 - (((mid_y - min_y) / (max_y - min_y)) * 100)), 100))
-                self.normalized_absolute_tracked_positions[track_id].append(normalized_y)
-                # Maintain a fixed-size history
-                if len(self.normalized_absolute_tracked_positions[track_id]) > 60:
-                    self.normalized_absolute_tracked_positions[track_id].pop(0)
+                normalized_y = self.update_tracked_positions(track_id, mid_y)
                 breast_tracked = normalized_y
 
             elif self.locked_penis_box.is_active() and self.boxes_overlap(box, self.locked_penis_box.get_box()):
-                #or class_name == 'hips center'):
                 if class_name not in classes_touching_penis:
                     classes_touching_penis.append(class_name)
 
                 self.tracked_boxes.append([box, class_name, track_id])
                 x1, y1, x2, y2 = box
-                # mid_y = (y1 + y2) // 2  # not happy with this, trying alternative
                 mid_y = y2
 
-                # Update tracked positions within the frame
-                if track_id not in self.tracked_positions:
-                    self.tracked_positions[track_id] = []
-                # let's apply a moving average to try and filter outliers
-                if len(self.tracked_positions[track_id]) > 2:
-                    mid_y = 0.1 * self.tracked_positions[track_id][-2] + 0.2 * \
-                                                    self.tracked_positions[track_id][
-                                                        -1] + 0.7 * mid_y
-                self.tracked_positions[track_id].append(int(mid_y))
-                # Maintain a fixed-size history
-                if len(self.tracked_positions[track_id]) > 600:
-                    self.tracked_positions[track_id].pop(0)
-
-                # Normalize absolute position 0-100 based on historic min and max
-                if track_id not in self.normalized_absolute_tracked_positions:
-                    self.normalized_absolute_tracked_positions[track_id] = []
-                # let's retrieve min and max from self.tracked_positions
-                min_y, max_y = min(self.tracked_positions[track_id]), max(self.tracked_positions[track_id])
-                # now, normalize
-                if max_y - min_y == 0:
-                    normalized_y = 100
-                else:
-                    normalized_y = int(min(max(0, 100 - (((mid_y - min_y) / (max_y - min_y)) * 100)), 100))
-                self.normalized_absolute_tracked_positions[track_id].append(normalized_y)
-                # Maintain a fixed-size history
-                if len(self.normalized_absolute_tracked_positions[track_id]) > 60:
-                    self.normalized_absolute_tracked_positions[track_id].pop(0)
-
-                # Now, we compute the distance to penis base
-                if track_id not in self.normalized_distance_to_penis:
-                    self.normalized_distance_to_penis[track_id] = []
-                if class_name == 'butt':  # 80% of (y2 - y1) + y1
-                    low_y = (.8 * (y2 - y1)) + y1
-                elif class_name != 'hips center':
-                    low_y = y2
-
-                # Now, we compute the distance to penis base for classes that are not 'hips center'
-                # And computing the weighted position based on recent moves
+                normalized_y = self.update_tracked_positions(track_id, mid_y)
+
                 if class_name != 'hips center':
-                    # compute the distance from low_y to penis base
+                    low_y = y2 if class_name != 'butt' else (0.8 * (y2 - y1)) + y1
                     dist_to_penis_base = int(
-                        ((self.locked_penis_box.get_box()[3] - low_y) / self.locked_penis_box.get_height()) * 100)
-                    # maintain the distance 0-100
+                        ((self.locked_penis_box.get_box()[3] - low_y) / self.locked_penis_box.get_height()) * 100
+                    )
                     normalized_dist_to_penis_base = min(max(0, dist_to_penis_base), 100)
-                    # let's apply a moving average to try and filter outliers
-                    if len(self.normalized_distance_to_penis[track_id]) > 2:
-                        normalized_dist_to_penis_base = 0.2 * self.normalized_distance_to_penis[track_id][-2] + 0.3 * \
-                                self.normalized_distance_to_penis[track_id][
-                                    -1] + 0.5 * normalized_dist_to_penis_base
-
-                    # making sure the distance remains within 0-100 before appending
-                    normalized_dist_to_penis_base = min(max(0, normalized_dist_to_penis_base), 100)
-                    # Update normalized tracked positions
-                    #if track_id not in self.normalized_distance_to_penis:
-                    #    self.normalized_distance_to_penis[track_id] = []
-                    self.normalized_distance_to_penis[track_id].append(normalized_dist_to_penis_base)
-                    # Maintain a fixed-size history
-                    if len(self.normalized_distance_to_penis[track_id]) > 60:
-                        self.normalized_distance_to_penis[track_id].pop(0)
-
-                    # Sum delta positions of all touching items, weighted by the length of their history
+                    self.update_normalized_distance_to_penis(track_id, normalized_dist_to_penis_base)
+
                     weight_pos_track_id = sum(
-                        abs(self.normalized_distance_to_penis[track_id][i] - self.normalized_distance_to_penis[track_id][
-                            i - 1]) for i in
-                        range(1, len(self.normalized_distance_to_penis[track_id])))
-                    #sum_pos += max(0, (normalized_dist_to_penis_base - (100 - self.max_allowed))) * weight_pos_track_id
-                    sum_pos += ((self.normalized_distance_to_penis[track_id][-1] +
-                                self.normalized_absolute_tracked_positions[track_id][-1]) // 2) * weight_pos_track_id
+                        abs(self.normalized_distance_to_penis[track_id][i] - self.normalized_distance_to_penis[track_id][i - 1])
+                        for i in range(1, len(self.normalized_distance_to_penis[track_id]))
+                    )
+                    sum_pos += ((self.normalized_distance_to_penis[track_id][-1] + normalized_y) // 2) * weight_pos_track_id
                     sum_weight_pos += weight_pos_track_id
-                else:  # hips center
-                    pass
-                    # Need to fix this...
-                    #if self.penetration:  # only consider them during penetration
-                    #    weight_pos_track_id = sum(
-                    #        abs(self.normalized_absolute_tracked_positions[track_id][i] -
-                    #            self.normalized_absolute_tracked_positions[track_id][
-                    #                i - 1]) for i in
-                    #        range(1, len(self.normalized_absolute_tracked_positions[track_id])))
-                    #    sum_pos += self.normalized_absolute_tracked_positions[track_id][-1] * weight_pos_track_id
-                    #    sum_weight_pos += weight_pos_track_id
 
         if len(classes_touching_penis) == 0 or not self.locked_penis_box.is_active():
             self.penetration = False
@@ -465,7 +371,6 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
 
         if sum_weight_pos > 0 and self.sex_position not in ['Not relevant', 'Close up']:
             distance = int(sum_pos / sum_weight_pos)
-        # attempt, in case we lose the pussy during grinding for instance
         elif sum_weight_pos == 0 and self.sex_position == 'Missionnary / Cowgirl' and 'pussy' not in classes_touching_penis and breast_tracked:
             distance = breast_tracked
             self.breast_tracking = True
@@ -477,6 +382,11 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
     def handle_class_first(self, class_name, box, conf):
         """
         Handle tracking for specific classes (e.g., penis, glans, navel).
+
+        Args:
+            class_name (str): Name of the class.
+            box (tuple): Coordinates of the box.
+            conf (float): Confidence of the detection.
         """
         if class_name == 'penis':
             if box is not None and self.penis_box is None:
@@ -502,7 +412,7 @@ def handle_class_first(self, class_name, box, conf):
                             py2 = self.locked_penis_box.get_box()[3] + np.sign(self.penis_box[3] - self.locked_penis_box.get_box()[3]) * max_move
 
                         self.locked_penis_box.update((px1, py2 - self.locked_penis_box.get_height(), px2, py2), self.locked_penis_box.get_height())
-                    else: # activating locked penis box
+                    else:
                         self.locked_penis_box.update(self.penis_box, current_height)
         elif class_name == 'glans' and box:
             if self.consecutive_detections['glans'] >= self.detections_threshold:
@@ -513,17 +423,77 @@ def handle_class_first(self, class_name, box, conf):
                 if self.penetration:
                     self.penetration = False
                     logging.info(
-                        f"@{self.current_frame_id} - Penetration ended after {self.consecutive_detections['glans']} detections of glans")
+                        f"@{self.current_frame_id} - Penetration ended after {self.consecutive_detections['glans']} detections of glans"
+                    )
                     if self.tracked_body_part != 'Nothing':
                         self.normalized_distances[self.tracked_body_part].clear()
                         self.normalized_distances[self.tracked_body_part].append(100)
-        elif class_name == 'navel' and box:
-            if (self.image_y_size - box[1]) / self.image_y_size < 0.15 and not self.breast_tracking:
-                logging.info("@{self.current_frame_id} - Breast tracking mode activated given navel position in lower 15th of frame")
-                self.breast_tracking = True
+
+    def update_tracked_positions(self, track_id, mid_y):
+        """
+        Update tracked positions and normalized positions for a given track_id.
+
+        Args:
+            track_id (int): ID of the track.
+            mid_y (int): Midpoint y-coordinate of the box.
+
+        Returns:
+            int: Normalized y-coordinate.
+        """
+        if track_id not in self.tracked_positions:
+            self.tracked_positions[track_id] = []
+        if len(self.tracked_positions[track_id]) > 2:
+            mid_y = (
+                0.2 * self.tracked_positions[track_id][-2]
+                + 0.3 * self.tracked_positions[track_id][-1]
+                + 0.5 * mid_y
+            )
+        self.tracked_positions[track_id].append(int(mid_y))
+        if len(self.tracked_positions[track_id]) > 600:
+            self.tracked_positions[track_id].pop(0)
+
+        if track_id not in self.normalized_absolute_tracked_positions:
+            self.normalized_absolute_tracked_positions[track_id] = []
+        min_y, max_y = min(self.tracked_positions[track_id]), max(self.tracked_positions[track_id])
+        normalized_y = 100 if max_y - min_y == 0 else int(min(max(0, 100 - (((mid_y - min_y) / (max_y - min_y)) * 100)), 100))
+        self.normalized_absolute_tracked_positions[track_id].append(normalized_y)
+        if len(self.normalized_absolute_tracked_positions[track_id]) > 60:
+            self.normalized_absolute_tracked_positions[track_id].pop(0)
+
+        return normalized_y
+
+    def update_normalized_distance_to_penis(self, track_id, normalized_dist_to_penis_base):
+        """
+        Update normalized distance to penis for a given track_id.
+
+        Args:
+            track_id (int): ID of the track.
+            normalized_dist_to_penis_base (int): Normalized distance to the base of the penis.
+        """
+        if track_id not in self.normalized_distance_to_penis:
+            self.normalized_distance_to_penis[track_id] = []
+        if len(self.normalized_distance_to_penis[track_id]) > 2:
+            normalized_dist_to_penis_base = (
+                0.2 * self.normalized_distance_to_penis[track_id][-2]
+                + 0.3 * self.normalized_distance_to_penis[track_id][-1]
+                + 0.5 * normalized_dist_to_penis_base
+            )
+        self.normalized_distance_to_penis[track_id].append(normalized_dist_to_penis_base)
+        if len(self.normalized_distance_to_penis[track_id]) > 60:
+            self.normalized_distance_to_penis[track_id].pop(0)
 
     def normalize_box_area(self, box, frame_width, frame_height):
-        """Normalize the area of a bounding box relative to the frame size."""
+        """
+        Normalize the area of a bounding box relative to the frame size.
+
+        Args:
+            box (tuple): Coordinates of the box.
+            frame_width (int): Width of the frame.
+            frame_height (int): Height of the frame.
+
+        Returns:
+            int: Normalized area (0-100).
+        """
         x1, y1, x2, y2 = box
         if x2 <= x1 or y2 <= y1:
             return 0
@@ -531,23 +501,3 @@ def normalize_box_area(self, box, frame_width, frame_height):
         max_area = frame_width * frame_height
         normalized_area = 100 * (box_area / max_area)
         return max(0, min(100, normalized_area))
-
-    """def log_and_normalize_pos(self, box, track_id):
-        self.boxes[class_name] = box
-        _, y1, _, y2 = box
-        mid_y = (y1 + y2) / 2
-        self.positions[class_name].append(mid_y)
-
-        min_y, max_y = min(self.positions[class_name]), max(self.positions[class_name])
-        normalized_y = (100 - int(100 * ((mid_y - min_y) / (max_y - min_y)))) if min_y != max_y else 100
-
-        # In case of breast, which is a fallback class to pussy, we compute not only the normalized position
-        # but also the normalized breast area which shows back and forth in case of grinding
-        if class_name == "breast" and self.frame:
-            normalized_breast_area = self.normalize_box_area(box, self.frame.shape[1], self.frame.shape[0])
-            normalized_y = ((0.75 * normalized_breast_area) + normalized_y) / 1.75
-
-        # self.normalized_positions[class_name][current_frame_id] = normalized_y
-        self.normalized_positions[class_name].append(normalized_y)
-        return normalized_y
-    """
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index 5156ebd3..fdf00ec9 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -17,7 +17,10 @@ def __init__(self, video_path, is_VR=False, unwarp=True, projection=None, ffmpeg
         """
         self.video_path = video_path
         self.is_VR = is_VR
-        self.unwarp = unwarp
+        if not self.is_VR:
+            self.unwarp = False
+        else:
+            self.unwarp = unwarp
         self.projection = projection
         self.ffmpeg_path = ffmpeg_path
         self.ffprobe_path = ffprobe_path
@@ -120,6 +123,10 @@ def _start_process(self, start_frame=0):
                 #arg_line = arg_line + f",histeq"
             else:
                 arg_line = "crop=w=iw/2:h=ih:x=0:y=0"
+            # Add scale filter with height and auto-width (-1)
+            #arg_line += f",scale=-1:{target_height}"
+            #arg_line += f",scale=-1:{1080}"
+            # perf for on the fly downscale to 1080p were terrible
 
             cmd = [
                 self.ffmpeg_path,
@@ -144,6 +151,7 @@ def _start_process(self, start_frame=0):
                 '-loglevel', 'warning',
                 "-ss", str(start_time / 1000),  # Seek to start time in seconds
                 "-i", self.video_path,
+                "-an",  # Disable audio processing
                 "-f", "rawvideo",  # Output raw video data
                 "-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
                 "-vsync", "0",  # Disable frame rate synchronization

From 4ffaff7f5f7a0a3427fea40cebc0e39363face0a Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 7 Jan 2025 22:48:34 +0100
Subject: [PATCH 054/277] Important fixes

---
 FSGenerator.py                | 172 +++++++++++++++++++--
 utils/lib_FunscriptHandler.py | 283 ++++++++++++++++------------------
 utils/lib_ObjectTracker.py    |  11 +-
 utils/lib_Visualizer.py       |   8 +-
 4 files changed, 308 insertions(+), 166 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index fb082fc2..dfae8eec 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -27,9 +27,11 @@ def __init__(self):
         self.yolo_det_model = ""
         self.yolo_pose_model = ""
         self.video_file = ""
+        self.video_fps = 1
         self.DebugMode = False
         self.LiveDisplayMode = False
         self.isVR = True
+        self.enhance_lighting = False
         self.frame_start = 0
         self.frame_end = None
         self.reference_script = ""
@@ -41,6 +43,16 @@ def __init__(self):
         self.video_reader = None
         self.debug_record_mode = False
         self.record_duration = 0
+        # Funscript Tweaking Variables
+        self.boost_enabled = True
+        self.boost_up_percent = 10
+        self.boost_down_percent = 15
+        self.threshold_enabled = True
+        self.threshold_low = 10
+        self.threshold_high = 90
+        self.vw_simplification_enabled = True
+        self.vw_factor = 8.0
+        self.rounding = 5
 
 # Initialize global state
 global_state = GlobalState()
@@ -572,6 +584,8 @@ def select_reference_script():
 
 def check_video_resolution(video_path):
     cap = cv2.VideoCapture(video_path)
+    global_state.fps = float(cap.get(cv2.CAP_PROP_FPS))
+    print(f"Video FPS: {global_state.fps}")
     if not cap.isOpened():
         messagebox.showerror("Error", "Could not open the video file.")
         return
@@ -579,8 +593,8 @@ def check_video_resolution(video_path):
     height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
     cap.release()
 
-    if height > 1920:
-        resize_options = ["1920", "1440", "1080"]
+    if height > 1440:
+        resize_options = ["1440", "1080"]
         choice = messagebox.askquestion(
             "Resize Video",
             f"The video height is {height}p. Do you want to resize it to a lower resolution?",
@@ -603,6 +617,7 @@ def on_resize_select(option):
         subprocess.run(command, check=True)
         messagebox.showinfo("Success", f"Video resized to {option}p and saved as {output_path}")
         resize_window.destroy()
+        video_path.set(output_path)
 
     resize_window = tk.Toplevel()
     resize_window.title("Select Resize Option")
@@ -621,10 +636,12 @@ def start_processing():
     global_state.DebugMode = debug_mode_var.get()
     global_state.LiveDisplayMode = live_display_mode_var.get()
     global_state.isVR = is_vr_var.get()
+    global_state.enhance_lighting = enhance_lighting_var.get()
     global_state.frame_start = 0 if frame_start_entry.get() == "" else int(frame_start_entry.get())
     global_state.frame_end = None if frame_end_entry.get() == "" else int(frame_end_entry.get())
     global_state.reference_script = reference_script_path.get()
     global_state.video_reader = video_reader_var.get()
+    global_state.enhance_lighting = enhance_lighting_var.get()
 
     print(f"Processing video: {global_state.video_file}")
     print(f"Video Reader: {global_state.video_reader}")
@@ -633,9 +650,14 @@ def start_processing():
     print(f"Debug Mode: {global_state.DebugMode}")
     print(f"Live Display Mode: {global_state.LiveDisplayMode}")
     print(f"VR Mode: {global_state.isVR}")
+    print(f"Enhance lighting: {global_state.enhance_lighting}")
     print(f"Frame Start: {global_state.frame_start}")
     print(f"Frame End: {global_state.frame_end}")
     print(f"Reference Script: {global_state.reference_script}")
+    print(f"Video Reader: {global_state.video_reader}")
+    print(f"Enhance lighting: {global_state.enhance_lighting}")
+
+    return
 
     # Initialize the debugger
     global_state.debugger = Debugger(global_state.video_file, output_dir=global_state.video_file[:-4])
@@ -701,14 +723,10 @@ def run_processing():
         funscript_handler = FunscriptGenerator()
 
         # Simplifying the funscript data and generating the file
-        funscript_handler.generate(global_state.video_file[:-4] + f"_rawfunscript.json",
-                                   global_state.funscript_data,
-                                   fps, global_state.LiveDisplayMode)
+        funscript_handler.generate(global_state)
 
-        # Optional, compare generated funscript with reference funscript if specified, or a simple generic report
-        funscript_handler.compare_funscripts(global_state.reference_script, global_state.video_file[:-3] + "funscript", global_state.video_file, global_state.isVR,
-                                             global_state.video_file[:-4] + "_comparefunscripts.png") #,
-        #                                     global_state.video_file[:-4] + "_adjusted.funscript")
+        # Optionally, compare generated funscript with reference funscript if specified, or a simple generic report
+        funscript_handler.create_report_funscripts(global_state)
 
         print(f"Finished processing video: {global_state.video_file}")
 
@@ -762,6 +780,38 @@ def debug_function():
     else:
         messagebox.showinfo("Info", f"Debug logs file not found: {global_state.video_file[:-4] + f'_debug_logs.json'}")
 
+def regenerate_funscript(global_state):
+    global_state.video_file = video_path.get()
+    if not global_state.video_file:
+        messagebox.showerror("Error", "Please select a video file.")
+        return
+    global_state.reference_script = reference_script_path.get()
+
+    print("Regenerating Funscript with tweaked settings...")
+    # Apply tweaks to funscript_data
+    if global_state.boost_enabled:
+        print(f"Applying Boost: Up {global_state.boost_up_percent}%, Down {global_state.boost_down_percent}%")
+        # Add boost logic here
+
+    if global_state.threshold_enabled:
+        print(f"Applying Threshold: Low {global_state.threshold_low}, High {global_state.threshold_high}")
+        # Add threshold logic here
+
+    if global_state.vw_simplification_enabled:
+        print(f"Applying VW Simplification with Factor: {global_state.vw_factor} then rounding to {global_state.rounding}")
+        # Add VW simplification logic here
+
+    # Save and regenerate funscript
+    funscript_handler = FunscriptGenerator()
+    # Simplifying the funscript data and generating the file
+    funscript_handler.generate(global_state)
+    print("Funscript re-generation complete.")
+    # Optional, compare generated funscript with reference funscript if specified, or a simple generic report
+    funscript_handler.create_report_funscripts(global_state)
+
+    print("Report generation complete.")
+
+
 def quit_application():
     """
     Quit the application.
@@ -782,8 +832,17 @@ def quit_application():
 debug_record_mode_var = tk.BooleanVar()  # debug record mode
 live_display_mode_var = tk.BooleanVar()
 is_vr_var = tk.BooleanVar()
+enhance_lighting_var = tk.BooleanVar()
 video_reader_var = tk.StringVar()
 debug_record_duration_var = tk.StringVar(value="5")  # Default duration
+boost_enabled_var = tk.BooleanVar()
+boost_up_percent_var = tk.IntVar()
+boost_down_percent_var = tk.IntVar()
+threshold_enabled_var = tk.BooleanVar()
+threshold_low_var = tk.IntVar()
+threshold_high_var = tk.IntVar()
+vw_simplification_enabled_var = tk.BooleanVar()
+vw_factor_var = tk.DoubleVar()
 
 # Video File Selection
 video_frame = ttk.LabelFrame(root, text="Video Selection", padding=(10, 5))
@@ -806,6 +865,10 @@ def quit_application():
 # VR Mode, activated by default
 is_vr_var.set(True)
 ttk.Checkbutton(video_frame, text="VR SBS video", variable=is_vr_var).grid(row=2, column=0, padx=5, pady=5)
+# enhance_lighting_var.set(False)
+# ttk.Checkbutton(video_frame, text="Enhance lighting",
+#                 variable=enhance_lighting_var,
+#                 command=lambda: setattr(global_state, 'vw_simplification_enabled', not global_state.enhance_lighting)).grid(row=2, column=1, padx=5, pady=5)
 
 # Frame Range
 frame_frame = ttk.LabelFrame(root, text="Optional settings", padding=(10, 5))
@@ -832,7 +895,8 @@ def quit_application():
 
 ttk.Checkbutton(processing_frame, text="Logging for debug", variable=debug_mode_var).grid(row=0, column=1, padx=5, pady=5)
 debug_mode_var.set(True)
-ttk.Checkbutton(processing_frame, text="Live inference => slow & heavy!", variable=live_display_mode_var).grid(row=0, column=2, padx=5, pady=5)
+# this one needs a fix
+# ttk.Checkbutton(processing_frame, text="Live inference => slow & heavy!", variable=live_display_mode_var).grid(row=0, column=2, padx=5, pady=5)
 
 # Progress Bar for YOLO Detection
 yolo_progress_label = ttk.Label(processing_frame, text="YOLO Detection Progress:")
@@ -850,6 +914,94 @@ def quit_application():
 tracking_progress_percent = ttk.Label(processing_frame, text="0%")
 tracking_progress_percent.grid(row=2, column=2, padx=5, pady=5, sticky="w")
 
+
+
+# Funscript Tweaking Section (Collapsible)
+funscript_tweaking_frame = ttk.LabelFrame(root, text="Funscript Tweaking", padding=(10, 5))
+funscript_tweaking_frame.grid(row=5, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
+
+# Collapse/Expand Button
+def toggle_funscript_tweaking():
+    if funscript_tweaking_collapsible.winfo_ismapped():
+        funscript_tweaking_collapsible.grid_remove()
+    else:
+        funscript_tweaking_collapsible.grid()
+
+toggle_button = ttk.Button(funscript_tweaking_frame, text="Toggle Funscript Tweaking", command=toggle_funscript_tweaking)
+toggle_button.grid(row=0, column=0, padx=5, pady=5, sticky="w")
+
+# Collapsible Section
+funscript_tweaking_collapsible = ttk.Frame(funscript_tweaking_frame)
+funscript_tweaking_collapsible.grid(row=1, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
+
+# Boost Settings
+boost_frame = ttk.LabelFrame(funscript_tweaking_collapsible, text="Boost Settings", padding=(10, 5))
+boost_frame.grid(row=1, column=0, padx=5, pady=5, sticky="ew")
+
+boost_checkbox = ttk.Checkbutton(boost_frame, text="Enable Boost", variable=boost_enabled_var, command=lambda: setattr(global_state, 'boost_enabled', not global_state.boost_enabled))
+boost_checkbox.grid(row=0, column=0, padx=5, pady=5, sticky="w")
+boost_enabled_var.set(global_state.boost_enabled)
+
+ttk.Label(boost_frame, text="Boost Up %:").grid(row=1, column=0, padx=5, pady=5, sticky="w")
+boost_up_selector = ttk.Combobox(boost_frame, values=[str(i) for i in range(0, 21)], width=5)
+boost_up_selector.set(str(global_state.boost_up_percent))
+boost_up_selector.grid(row=1, column=1, padx=5, pady=5, sticky="w")
+boost_up_selector.bind("<<ComboboxSelected>>", lambda e: setattr(global_state, 'boost_up_percent', int(boost_up_selector.get())))
+
+ttk.Label(boost_frame, text="Reduce Down %:").grid(row=2, column=0, padx=5, pady=5, sticky="w")
+boost_down_selector = ttk.Combobox(boost_frame, values=[str(i) for i in range(0, 21)], width=5)
+boost_down_selector.set(str(global_state.boost_down_percent))
+boost_down_selector.grid(row=2, column=1, padx=5, pady=5, sticky="w")
+boost_down_selector.bind("<<ComboboxSelected>>", lambda e: setattr(global_state, 'boost_down_percent', int(boost_down_selector.get())))
+
+# Threshold Settings
+threshold_frame = ttk.LabelFrame(funscript_tweaking_collapsible, text="Threshold Settings", padding=(10, 5))
+threshold_frame.grid(row=1, column=1, padx=5, pady=5, sticky="ew")
+
+threshold_checkbox = ttk.Checkbutton(threshold_frame, text="Enable Threshold", variable=threshold_enabled_var, command=lambda: setattr(global_state, 'threshold_enabled', not global_state.threshold_enabled))
+threshold_checkbox.grid(row=0, column=0, padx=5, pady=5, sticky="w")
+threshold_enabled_var.set(global_state.threshold_enabled)
+
+ttk.Label(threshold_frame, text="0 Threshold:").grid(row=1, column=0, padx=5, pady=5, sticky="w")
+threshold_low_selector = ttk.Combobox(threshold_frame, values=[str(i) for i in range(0, 16)], width=5)
+threshold_low_selector.set(str(global_state.threshold_low))
+threshold_low_selector.grid(row=1, column=1, padx=5, pady=5, sticky="w")
+threshold_low_selector.bind("<<ComboboxSelected>>", lambda e: setattr(global_state, 'threshold_low', int(threshold_low_selector.get())))
+
+ttk.Label(threshold_frame, text="100 Threshold:").grid(row=2, column=0, padx=5, pady=5, sticky="w")
+threshold_high_selector = ttk.Combobox(threshold_frame, values=[str(i) for i in range(80, 101)], width=5)
+threshold_high_selector.set(str(global_state.threshold_high))
+threshold_high_selector.grid(row=2, column=1, padx=5, pady=5, sticky="w")
+threshold_high_selector.bind("<<ComboboxSelected>>", lambda e: setattr(global_state, 'threshold_high', int(threshold_high_selector.get())))
+
+# Simplification Settings
+vw_frame = ttk.LabelFrame(funscript_tweaking_collapsible, text="Simplification", padding=(10, 5))
+vw_frame.grid(row=1, column=3, padx=5, pady=5, sticky="ew")
+
+vw_checkbox = ttk.Checkbutton(vw_frame, text="Enable Simplification", variable=vw_simplification_enabled_var, command=lambda: setattr(global_state, 'vw_simplification_enabled', not global_state.vw_simplification_enabled))
+vw_checkbox.grid(row=0, column=0, padx=5, pady=5, sticky="w")
+vw_simplification_enabled_var.set(global_state.vw_simplification_enabled)
+
+ttk.Label(vw_frame, text="VW Factor:").grid(row=1, column=0, padx=5, pady=5, sticky="w")
+vw_factor_selector = ttk.Combobox(vw_frame, values=[str(i / 5) for i in range(10, 51)], width=5)
+vw_factor_selector.set(str(global_state.vw_factor))
+vw_factor_selector.grid(row=1, column=1, padx=5, pady=5, sticky="w")
+vw_factor_selector.bind("<<ComboboxSelected>>", lambda e: setattr(global_state, 'vw_factor', float(vw_factor_selector.get())))
+
+ttk.Label(vw_frame, text="Rounding:").grid(row=2, column=0, padx=5, pady=5, sticky="w")
+rounding = ttk.Combobox(vw_frame, values=['5', '10'], width=5)
+rounding.set(str(global_state.rounding))
+rounding.grid(row=2, column=1, padx=5, pady=5, sticky="w")
+rounding.bind("<<ComboboxSelected>>", lambda e: setattr(global_state, 'rounding', float(rounding.get())))
+
+# Regenerate Funscript Button
+regenerate_funscript_button = ttk.Button(funscript_tweaking_collapsible, text="Regenerate Funscript", command=lambda: regenerate_funscript(global_state))
+regenerate_funscript_button.grid(row=2, column=0, padx=5, pady=5, sticky="w")
+
+
+
+funscript_tweaking_collapsible.grid_remove()
+
 # Debug Record Mode
 debug_frame = ttk.LabelFrame(root, text="Debugging (Replay and navigate a processed video)", padding=(10, 5))
 debug_frame.grid(row=6, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 4ab9d4ed..05a331aa 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -17,109 +17,88 @@
 from matplotlib.colors import LinearSegmentedColormap, Normalize
 
 class FunscriptGenerator:
-    def generate(self, raw_funscript_path, funscript_data, fps, TestMode = False):
-        output_path = raw_funscript_path[:-18] + '.funscript'
-        if len(funscript_data) == 0:
-            print("len funscript data is 0, trying to load file")
-            # Read the funscript data from the JSON file
-            with open(raw_funscript_path, 'r') as f:
-                print(f"Loading funscript from {raw_funscript_path}")
-                try:
-                    data = json.load(f)  #f.read() #json.load(f)
-                    #data = eval(data)
-                    print(f"data loaded: {data}")
-                except:
-                    print(f"line 31 - Error loading funscript from {raw_funscript_path}")
-        else:
-            data = funscript_data
+    def generate(self, global_state):
+        output_path = global_state.video_file[:-3] + f"funscript"
 
-        try:
-            print(f"Generating funscript based on {len(data)} points...")
-            # self.filtered_positions = simplify_coords(data, vw_filter_coeff)  # Use VW algorithm
-            # use my own simplification function instead of vw here
-            print("Positions adjustment - step 0 (simplification)")
-            positions = self.filter_positions(data, fps)
-            # print(f"Lenghth of filtered positions: {len(self.filtered_positions) + 1}")
-            print(f"Lenghth of filtered positions: {len(positions) + 1}")
-            # enhance the funscript
-            # let's remap the highest to 100, and the lowest to 0, and rescale to 0-100
-            print("Positions adjustment - step 1 (remapping)")
-            # Adjust peaks and lows
-            ats = [p[0] for p in positions]
-            positions = [p[1] for p in positions]
-            adjusted_positions = np.interp(positions, (min(positions), max(positions)), (0, 100))
-            # drag all values below 10 to 0 and above 90 to 100
-            print("Positions adjustment - step 2 (thresholding)")
-            readjusted_positions = [0 if p < 10 else 100 if p > 90 else p for p in adjusted_positions]
-            print("Positions adjustment - step 3 (amplitude boosting)")
-            # let's boost the peaks by 20%, and reduce the lows by 20% and stay within 0-100
-            adjusted_positions = self.adjust_peaks_and_lows(readjusted_positions, peak_boost=20, low_reduction=20, )
-            # recombine ats and positions
-            zip_adjusted_positions = list(zip(ats, adjusted_positions))
-            # now, perform the vw simplification again
-            print("Positions adjustment - step 4 (VW algorithm simplification)")
-            filtered_positions = simplify_coords(zip_adjusted_positions, vw_filter_coeff)
-            # filtered_positions = zip_adjusted_positions
-            self.write_funscript(filtered_positions, output_path, fps)
-            print(f"Funscript generated and saved to {output_path}")
-            self.generate_heatmap(output_path,
-                                  output_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
-        except Exception as e:
-            print(f"Error generating funscript: {e}")
+        # Backup output file if it exists
+        if os.path.exists(output_path):
+            print(f"Output path {output_path} already exists, backing up as {output_path}.bak...")
+            os.rename(output_path, output_path + ".bak")
 
+        raw_funscript_path = global_state.video_file[:-4] + f"_rawfunscript.json"
 
-    def prev_generate(self, raw_funscript_path, funscript_data, fps, TestMode = False):
-        output_path = raw_funscript_path[:-18] + '.funscript'
-        if len(funscript_data) == 0:
+        if len(global_state.funscript_data) == 0:
+            print("len funscript data is 0, trying to load file")
             # Read the funscript data from the JSON file
             with open(raw_funscript_path, 'r') as f:
                 print(f"Loading funscript from {raw_funscript_path}")
                 try:
-                    data = f.read() #json.load(f)
-                    data = eval(data)
-                except:
-                    print(f"Error loading funscript from {raw_funscript_path}")
+                    data = json.load(f)
+                except Exception as e:
+                    print(f"Error loading funscript from {raw_funscript_path}: {e}")
+                    return
         else:
-            data = funscript_data
+            data = global_state.funscript_data
 
         try:
             print(f"Generating funscript based on {len(data)} points...")
-            self.filtered_positions = simplify_coords(data, vw_filter_coeff)  # Use VW algorithm
-            print(f"Lenghth of filtered positions: {len(self.filtered_positions) + 1}")
-            self.write_funscript(self.filtered_positions, output_path, fps)
-            print(f"Funscript generated and saved to {output_path}")
-            self.generate_heatmap(output_path, output_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
+            # positions = data
+            positions = self.filter_positions(data, global_state.fps)
+            print(f"Length of filtered positions: {len(positions)}")
 
-            # Now proceeding with remapping / adjusting
-            # Adjust peaks and lows
-            ats = [p[0] for p in self.filtered_positions]
-            positions = [p[1] for p in self.filtered_positions]
+            # Extract timestamps and positions
+            ats = [p[0] for p in positions]
+            positions = [p[1] for p in positions]
 
-            # let's remap the highest to 100, and the lowest to 0, and rescale to 0-100
+            # Remap positions to 0-100 range
+            print("Positions adjustment - step 1 (remapping)")
             adjusted_positions = np.interp(positions, (min(positions), max(positions)), (0, 100))
-            # drag all values below 10 to 0 and above 90 to 100
-            readjusted_positions = [0 if p < 10 else 100 if p > 90 else p for p in adjusted_positions]
 
-            # let's boost the peaks by 20%, and reduce the lows by 20% and stay within 0-100
-            adjusted_positions = self.adjust_peaks_and_lows(readjusted_positions, peak_boost=20, low_reduction=20,)
+            # Apply thresholding
+            if global_state.threshold_enabled:
+                print(f"Positions adjustment - step 2 (thresholding)")
+                adjusted_positions = adjusted_positions.tolist()  # Convert to list
+                adjusted_positions = [
+                    0 if p < global_state.threshold_low else 100 if p > global_state.threshold_high else p for p in
+                    adjusted_positions]
+            else:
+                print("Skipping positions adjustment - step 2 (thresholding)")
+
+            # Apply amplitude boosting
+            if global_state.boost_enabled:
+                print("Positions adjustment - step 3 (amplitude boosting)")
+                #self.boost_amplitude(adjusted_positions, boost_factor=1.2, min_value=0, max_value=100)
+                adjusted_positions = self.adjust_peaks_and_lows(adjusted_positions,
+                                                                peak_boost=global_state.boost_up_percent,
+                                                                low_reduction=global_state.boost_down_percent)
+            else:
+                print("Skipping positions adjustment - step 3 (amplitude boosting)")
 
+            # Round position values to the closest multiple of 5, still between 0 and 100
+            print("Positions adjustment - step 4 (rounding to the closest multiple of 5)")
+            #adjusted_positions = [round(p, -1) for p in adjusted_positions] # multiple of 10
+            adjusted_positions = [round(p / global_state.rounding) * global_state.rounding for p in adjusted_positions]
 
-            #adjusted_positions = self.adjust_peaks_and_lows(positions, peak_boost=15, low_reduction=20,
-            #                                                max_flat_length=3)
-            # recombine ats and positions
+            # Recombine timestamps and adjusted positions
+            print("Re-assembling ats and positions")
             zip_adjusted_positions = list(zip(ats, adjusted_positions))
-            remapped_path = output_path[:-10] + '_adjusted.funscript'
-            self.write_funscript(zip_adjusted_positions, remapped_path, fps)
-            self.generate_heatmap(remapped_path,
-                                  remapped_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
 
-        except:
-            print(f"Error loading raw funscript from {raw_funscript_path}")
+            # Apply VW simplification if enabled
+            if global_state.vw_simplification_enabled:
+                print("Positions adjustment - step 5 (VW algorithm simplification)")
+                zip_adjusted_positions = simplify_coords(zip_adjusted_positions, global_state.vw_factor)
+            else:
+                print("Skipping positions adjustment - step 5 (VW algorithm simplification)")
 
-        #if LiveDisplayMode:
-        #    # plot a comparative graph
-        #    self.plot_comparison(self.points, self.filtered_positions, points_v2)
+            # Write the final funscript
+            self.write_funscript(zip_adjusted_positions, output_path, global_state.fps)
+            print(f"Funscript generated and saved to {output_path}")
 
+            # Generate a heatmap
+            self.generate_heatmap(output_path,
+                                  output_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
+        except Exception as e:
+            print(f"Error generating funscript: {e}")
 
     def write_funscript(self, distances, output_path, fps):
         with open(output_path, 'w') as f:
@@ -217,67 +196,59 @@ def get_color(intensity):
         plt.close()
         print(f"Funscript heatmap saved to {output_image_path}")
 
-    def filter_positions(self, positions, fps):
-        if not positions:
-            return []
-
-        positions = np.array(positions)
-        filtered_positions = [positions[0]]  # Start with the first position
+    def boost_amplitude(self, signal, boost_factor, min_value=0, max_value=100):
+        """
+        Boosts the amplitude of a signal and ensures it stays within a specified range.
 
-        min_interval_ms = 50  # Minimum interval between points in milliseconds
-        slope_threshold = 0.2  # Adjusted slope threshold for gradual changes
+        Parameters:
+            signal (list or np.ndarray): The input signal to boost.
+            boost_factor (float): The factor by which to boost the signal's amplitude.
+            min_value (int, optional): Minimum value of the range. Default is 0.
+            max_value (int, optional): Maximum value of the range. Default is 100.
 
-        for i in range(1, len(positions) - 1):
-            current_pos = positions[i]
-            prev_pos = positions[i - 1]
-            next_pos = positions[i + 1]
+        Returns:
+            np.ndarray: The boosted signal clamped to the specified range.
+        """
+        # Convert to numpy array for easier manipulation
+        signal = np.array(signal)
 
-            # Skip consecutive duplicate positions
-            if current_pos[1] == filtered_positions[-1][1] and current_pos[1] == next_pos[1]:
-                continue
+        # Calculate the midpoint for rescaling
+        midpoint = (min_value + max_value) / 2
 
-            # Calculate slopes
-            slope_prev = (current_pos[1] - prev_pos[1]) / (current_pos[0] - prev_pos[0]) if (current_pos[0] - prev_pos[
-                0]) != 0 else 0
-            slope_next = (next_pos[1] - current_pos[1]) / (next_pos[0] - current_pos[0]) if (next_pos[0] - current_pos[
-                0]) != 0 else 0
-            slope_diff = abs(slope_next - slope_prev)
+        # Boost the signal
+        boosted_signal = midpoint + (signal - midpoint) * boost_factor
 
-            is_local_extreme = ((current_pos[1] >= prev_pos[1] and current_pos[1] > next_pos[1])
-                                or (current_pos[1] > prev_pos[1] and current_pos[1] >= next_pos[1])
-                                or (current_pos[1] <= prev_pos[1] and current_pos[1] < next_pos[1])
-                                or (current_pos[1] < prev_pos[1] and current_pos[1] <= next_pos[1]))
+        # Clamp the signal to the specified range
+        boosted_signal = np.clip(boosted_signal, min_value, max_value)
 
-            # Add to filtered lists based on conditions
-            if (is_local_extreme or slope_diff > slope_threshold) and (abs(
-                    current_pos[0] - filtered_positions[-1][0]) * 1000 / fps) > min_interval_ms:
-                filtered_positions.append(current_pos)
+        return boosted_signal
 
-        # Ensure the last point meets the interval requirement
-        if len(filtered_positions) > 1 and positions[-1][0] - filtered_positions[-1][0] >= min_interval_ms:
-            filtered_positions.append(positions[-1])
+    def filter_positions(self, positions, fps):
+        """
+        Filters positions to remove unnecessary points while preserving key features.
 
-        return filtered_positions
+        Args:
+            positions (list or np.array): List of [timestamp, value] pairs.
+            fps (int): Frames per second of the video.
 
-    def prev_filter_positions(self, positions, fps):
+        Returns:
+            list: Filtered list of [timestamp, value] pairs.
+        """
         if not positions:
             return []
 
+        # Ensure positions is a list of [timestamp, value] pairs
+        positions = np.array(positions, dtype=float)
+        if positions.ndim != 2 or positions.shape[1] != 2:
+            raise ValueError("positions must be a list of [timestamp, value] pairs")
+
         filtered_positions = [positions[0]]  # Start with the first position
 
         min_interval_ms = 50  # Minimum interval between points in milliseconds
         slope_threshold = 0.2  # Adjusted slope threshold for gradual changes
 
-        def calculate_slope(pos1, time1, pos2, time2):
-            return (pos2 - pos1) / (time2 - time1) if (time2 - time1) != 0 else 0
-
         for i in range(1, len(positions) - 1):
             current_pos = positions[i]
-
-            # Skip None values
-            if current_pos is None:
-                continue
-
             prev_pos = positions[i - 1]
             next_pos = positions[i + 1]
 
@@ -286,22 +257,31 @@ def calculate_slope(pos1, time1, pos2, time2):
                 continue
 
             # Calculate slopes
-            slope_prev = calculate_slope(prev_pos[1], prev_pos[0], current_pos[1], current_pos[0])
-            slope_next = calculate_slope(current_pos[1], current_pos[0], next_pos[1], next_pos[0])
+            time_diff_prev = current_pos[0] - prev_pos[0]
+            time_diff_next = next_pos[0] - current_pos[0]
+
+            slope_prev = (current_pos[1] - prev_pos[1]) / time_diff_prev if time_diff_prev != 0 else 0
+            slope_next = (next_pos[1] - current_pos[1]) / time_diff_next if time_diff_next != 0 else 0
             slope_diff = abs(slope_next - slope_prev)
 
-            is_local_extreme = ((current_pos[1] >= prev_pos[1] and current_pos[1] > next_pos[1])
-                                or (current_pos[1] > prev_pos[1] and current_pos[1] >= next_pos[1])
-                                or (current_pos[1] <= prev_pos[1] and current_pos[1] < next_pos[1])
-                                or (current_pos[1] < prev_pos[1] and current_pos[1] <= next_pos[1]))
+            # Check if the current position is a local extreme
+            is_local_extreme = (
+                    (current_pos[1] >= prev_pos[1] and current_pos[1] > next_pos[1]) or
+                    (current_pos[1] > prev_pos[1] and current_pos[1] >= next_pos[1]) or
+                    (current_pos[1] <= prev_pos[1] and current_pos[1] < next_pos[1]) or
+                    (current_pos[1] < prev_pos[1] and current_pos[1] <= next_pos[1])
+            )
+
+            # Calculate time difference in milliseconds
+            time_diff_ms = (current_pos[0] - filtered_positions[-1][0]) * 1000 / fps
 
-            # Add to filtered lists based on conditions
-            if (is_local_extreme or slope_diff > slope_threshold) and (abs(
-                    current_pos[0] - filtered_positions[-1][0]) * 1000 / fps) > min_interval_ms:
+            # Add to filtered list based on conditions
+            if (is_local_extreme or slope_diff > slope_threshold) and time_diff_ms > min_interval_ms:
                 filtered_positions.append(current_pos)
 
         # Ensure the last point meets the interval requirement
-        if len(filtered_positions) > 1 and positions[-1][0] - filtered_positions[-1][0] >= min_interval_ms:
+        if len(filtered_positions) > 1 and (
+                positions[-1][0] - filtered_positions[-1][0]) * 1000 / fps >= min_interval_ms:
             filtered_positions.append(positions[-1])
 
         return filtered_positions
@@ -356,15 +336,13 @@ def load_funscript(self, funscript_path):
 
         return times, positions, relevant_chapters_export, irrelevant_chapters_export
 
-    def compare_funscripts(self, reference_path, script1, video_path, isVR, output_image_path, script2=None):
+    def create_report_funscripts(self, global_state):
         generated_paths = []
-        generated_paths.append(script1)
-        if script2 is not None:
-            generated_paths.append(script2)
+        generated_paths.append(global_state.video_file[:-4] + ".funscript")
 
-        if reference_path:
+        if global_state.reference_script:
             # Load reference funscript
-            ref_times, ref_positions, _, _ = self.load_funscript(reference_path)
+            ref_times, ref_positions, _, _ = self.load_funscript(global_state.reference_script)
 
             # if no 0 at the beginning, add it
             if ref_times and ref_times[0] != 0:
@@ -382,6 +360,7 @@ def compare_funscripts(self, reference_path, script1, video_path, isVR, output_i
         sections = self.select_random_sections(total_duration, section_duration=10, num_sections=6)
 
         screenshots_done = False
+        screenshots = []
 
         # Load generated funscripts
         for generated_path in generated_paths:
@@ -390,7 +369,7 @@ def compare_funscripts(self, reference_path, script1, video_path, isVR, output_i
             ref_sections = []
             gen_sections = []
             for start, end in sections:
-                if reference_path:
+                if global_state.reference_script:
                     ref_sec = self.extract_section(ref_times, ref_positions, start, end)
                     ref_sections.append(ref_sec)
                 gen_sec = self.extract_section(gen_times, gen_positions, start, end)
@@ -398,12 +377,12 @@ def compare_funscripts(self, reference_path, script1, video_path, isVR, output_i
 
             if not screenshots_done:
                 # Capture screenshots, but only once
-                screenshots = self.capture_screenshots(video_path, isVR, sections)
+                screenshots = self.capture_screenshots(global_state.video_file, global_state.isVR, sections)
                 screenshots_done = True
 
             # Plot and combine
             self.create_combined_plot(
-                ref_sections, gen_sections, screenshots, sections, output_image_path,
+                ref_sections, gen_sections, screenshots, sections, global_state.video_file[:-4] + "_report.png",
                 ref_times, ref_positions, gen_times, gen_positions
             )
 
@@ -658,13 +637,12 @@ def _calculate_metrics(self, times, positions):
             'avg_min': avg_min
             }
 
-
     def adjust_peaks_and_lows(self, positions, peak_boost=10, low_reduction=10, max_flat_length=5):
         """
         Adjusts the peaks and lows of a funscript while avoiding long flat sections at 0 or 100.
 
         Args:
-            positions (list): List of positions (0-100) from the funscript.
+            positions (list or np.array): List or array of positions (0-100) from the funscript.
             peak_boost (int): Amount to increase peaks by.
             low_reduction (int): Amount to decrease lows by.
             max_flat_length (int): Maximum allowed length of flat sections at 0 or 100.
@@ -672,29 +650,31 @@ def adjust_peaks_and_lows(self, positions, peak_boost=10, low_reduction=10, max_
         Returns:
             list: Adjusted positions.
         """
+
+        # Ensure positions is a list of [timestamp, value] pairs
+        if isinstance(positions, np.ndarray):
+            positions = positions.tolist()
+        elif not isinstance(positions, list):
+            raise ValueError("positions must be a list or numpy array of [timestamp, value] pairs")
+
         if not positions or len(positions) < 3:
             return positions
 
         # Convert positions to a numpy array for easier manipulation
-        positions = np.array(positions)
+        positions = np.array(positions, dtype=float)
 
         # Identify plateaus before boosting
         original_plateaus = self._find_plateaus(positions)
-
         # Identify peaks and lows
         peaks = self._find_local_maxima(positions)
         lows = self._find_local_minima(positions)
-
         # Adjust peaks and lows
         positions[peaks] = np.clip(positions[peaks] + peak_boost, 0, 100)
         positions[lows] = np.clip(positions[lows] - low_reduction, 0, 100)
-
         # Identify plateaus after boosting
         adjusted_plateaus = self._find_plateaus(positions)
-
         # Compare plateaus and adjust artificially created flats
         positions = self._compare_and_adjust_plateaus(positions, original_plateaus, adjusted_plateaus, max_flat_length)
-
         return positions.tolist()
 
     def _find_local_maxima(self, positions):
@@ -763,6 +743,7 @@ def _compare_and_adjust_plateaus(self, positions, original_plateaus, adjusted_pl
         Returns:
             np.array: Adjusted positions.
         """
+        positions = positions.copy()  # Work on a copy to avoid modifying the original array
         for plateau in adjusted_plateaus:
             start, end = plateau
             value = positions[start]
diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index 4cf8cecf..40917b30 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -336,11 +336,18 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                     normalized_dist_to_penis_base = min(max(0, dist_to_penis_base), 100)
                     self.update_normalized_distance_to_penis(track_id, normalized_dist_to_penis_base)
 
+                    # attempt : as a weight, we use the absolute position
+                    # weight_pos_track_id = sum(
+                    #    abs(self.tracked_positions[track_id][i] - self.tracked_positions[track_id][i - 1])
+                    #    for i in range(1, len(self.tracked_positions[track_id])))
+
                     weight_pos_track_id = sum(
                         abs(self.normalized_distance_to_penis[track_id][i] - self.normalized_distance_to_penis[track_id][i - 1])
-                        for i in range(1, len(self.normalized_distance_to_penis[track_id]))
-                    )
+                        for i in range(1, len(self.normalized_distance_to_penis[track_id])))
                     sum_pos += ((self.normalized_distance_to_penis[track_id][-1] + normalized_y) // 2) * weight_pos_track_id
+
+                    # attempt : use only the distance to penis value, discarding the absolute position
+                    # sum_pos += self.normalized_distance_to_penis[track_id][-1] * weight_pos_track_id
                     sum_weight_pos += weight_pos_track_id
 
         if len(classes_touching_penis) == 0 or not self.locked_penis_box.is_active():
diff --git a/utils/lib_Visualizer.py b/utils/lib_Visualizer.py
index 60df1033..f272ce3c 100644
--- a/utils/lib_Visualizer.py
+++ b/utils/lib_Visualizer.py
@@ -26,9 +26,9 @@ def draw_gauge(self, image, distance):
         cv2.putText(image, str(int(distance)), (gauge_x, gauge_y + gauge_height - fill_height - 5),
                     cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
         return image
-
+"""
     def draw_limited_graph(self, image, distances, frames, nb_frames):
-        """Plot the frame indices vs positions for the last nb frames."""
+        # Plot the frame indices vs positions for the last nb frames.
         # Determine the number of frames to plot
         num_frames_to_plot = min(nb_frames, len(distances))
 
@@ -97,4 +97,6 @@ def draw_graph(self, image, distances, frames, reference_positions, reference_fr
             0
         )
         plt.close(fig)
-        return image
\ No newline at end of file
+        return image
+
+"""

From f77bdf59f220a127ac3597a7e319f4f36ac0e9ef Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 7 Jan 2025 23:42:02 +0100
Subject: [PATCH 055/277] Removed a debugging line

---
 FSGenerator.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index dfae8eec..afcace44 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -657,8 +657,6 @@ def start_processing():
     print(f"Video Reader: {global_state.video_reader}")
     print(f"Enhance lighting: {global_state.enhance_lighting}")
 
-    return
-
     # Initialize the debugger
     global_state.debugger = Debugger(global_state.video_file, output_dir=global_state.video_file[:-4])
 

From 32060f0ffb29e366048b27084da0c6a5cf62e285 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sat, 11 Jan 2025 21:25:36 +0100
Subject: [PATCH 056/277] Enhancement of the signal processing, specifically on
 the noise handling Made the progress bar higher in the debugging view

---
 FSGenerator.py                |  2 +-
 utils/lib_Debugger.py         |  8 ++++--
 utils/lib_FunscriptHandler.py | 48 ++++++++++++++++++++++-------------
 utils/lib_ObjectTracker.py    | 24 +++---------------
 4 files changed, 40 insertions(+), 42 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index afcace44..1be71f2d 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -1021,7 +1021,7 @@ def toggle_funscript_tweaking():
 ttk.Button(button_frame, text="Quit", command=quit_application).grid(row=0, column=2, padx=5, pady=5)
 
 # Footer
-footer_label = ttk.Label(root, text="Individual and personal use only.\nNot for commercial use, nor use by a company.\nk00gar 2025 - https://github.com/ack00gar", font=("Arial", 10, "italic", "bold"), justify="center")
+footer_label = ttk.Label(root, text="Individual and personal use only.\nNot for commercial use.\nk00gar 2025 - https://github.com/ack00gar", font=("Arial", 10, "italic", "bold"), justify="center")
 footer_label.grid(row=8, column=0, columnspan=3, padx=5, pady=5)
 
 root.mainloop()
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 4b3a7746..fe3488a1 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -143,6 +143,7 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
             funscript_interpolator = None
 
         # Initialize rolling window buffers
+        half_window = rolling_window_size // 2
         distance_buffer = np.zeros(rolling_window_size)
         funscript_buffer = np.zeros(rolling_window_size)
 
@@ -199,10 +200,13 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
                     cv2.putText(frame_copy, "Locked Penis", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
 
             try:
+                # Update rolling window buffers
                 # Update rolling window buffers
                 distance = variables.get("distance", 0)
-                funscript_value = self._get_funscript_value(funscript_interpolator, self.current_frame, self.fps) if funscript_interpolator else 0
+                funscript_value = self._get_funscript_value(funscript_interpolator, self.current_frame,
+                                                            self.fps) if funscript_interpolator else 0
                 visualizer.draw_gauge(frame_copy, funscript_value)
+                # Shift buffers to the left and add new values at the center
                 distance_buffer = np.roll(distance_buffer, -1)
                 distance_buffer[-1] = distance
                 funscript_buffer = np.roll(funscript_buffer, -1)
@@ -309,7 +313,7 @@ def _draw_progress_bar(self, frame, width, height):
         :param width: Width of the frame.
         :param height: Height of the frame.
         """
-        bar_height = 10
+        bar_height = 20
         bar_x_start = 0
         bar_x_end = width
         bar_y_start = height - bar_height
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 05a331aa..4548df2f 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -5,6 +5,7 @@
 import numpy as np
 import cv2
 import datetime
+from scipy.signal import savgol_filter
 
 from params.config import heatmap_colors, step_size, vw_filter_coeff
 
@@ -42,40 +43,56 @@ def generate(self, global_state):
 
         try:
             print(f"Generating funscript based on {len(data)} points...")
-            # positions = data
-            positions = self.filter_positions(data, global_state.fps)
-            print(f"Length of filtered positions: {len(positions)}")
 
             # Extract timestamps and positions
-            ats = [p[0] for p in positions]
-            positions = [p[1] for p in positions]
+            ats = [p[0] for p in data]
+            positions = [p[1] for p in data]
+
+            print(f"Positions adjustment - step 1 (noise removal)")
+            # Run the Savitzky-Golay filter
+            positions = savgol_filter(positions, int(global_state.fps) // 4, 3)
+
+            # zip adjusted positions
+            zip_positions = list(zip(ats, positions))
+
+            # Apply VW simplification if enabled
+            if global_state.vw_simplification_enabled:
+                print("Positions adjustment - step 2 (VW algorithm simplification)")
+                zip_vw_positions = simplify_coords(zip_positions, global_state.vw_factor)
+                print(f"Length of VW filtered positions: {len(zip_vw_positions)}")
+            else:
+                print("Skipping positions adjustment - step 2 (VW algorithm simplification)")
+
+            # Extract timestamps and positions
+            ats = [p[0] for p in zip_vw_positions]
+            positions = [p[1] for p in zip_vw_positions]
 
             # Remap positions to 0-100 range
-            print("Positions adjustment - step 1 (remapping)")
+            print("Positions adjustment - step 3 (remapping)")
             adjusted_positions = np.interp(positions, (min(positions), max(positions)), (0, 100))
 
             # Apply thresholding
             if global_state.threshold_enabled:
-                print(f"Positions adjustment - step 2 (thresholding)")
+                print(f"Positions adjustment - step 4 (thresholding)")
                 adjusted_positions = adjusted_positions.tolist()  # Convert to list
                 adjusted_positions = [
                     0 if p < global_state.threshold_low else 100 if p > global_state.threshold_high else p for p in
                     adjusted_positions]
             else:
-                print("Skipping positions adjustment - step 2 (thresholding)")
+                print("Skipping positions adjustment - step 4 (thresholding)")
 
             # Apply amplitude boosting
             if global_state.boost_enabled:
-                print("Positions adjustment - step 3 (amplitude boosting)")
+                print("Positions adjustment - step 5 (amplitude boosting)")
                 #self.boost_amplitude(adjusted_positions, boost_factor=1.2, min_value=0, max_value=100)
                 adjusted_positions = self.adjust_peaks_and_lows(adjusted_positions,
                                                                 peak_boost=global_state.boost_up_percent,
                                                                 low_reduction=global_state.boost_down_percent)
             else:
-                print("Skipping positions adjustment - step 3 (amplitude boosting)")
+                print("Skipping positions adjustment - step 5 (amplitude boosting)")
 
             # Round position values to the closest multiple of 5, still between 0 and 100
-            print("Positions adjustment - step 4 (rounding to the closest multiple of 5)")
+            print("Positions adjustment - step 6 (rounding to the closest multiple of 5)")
             #adjusted_positions = [round(p, -1) for p in adjusted_positions] # multiple of 10
             adjusted_positions = [round(p / global_state.rounding) * global_state.rounding for p in adjusted_positions]
 
@@ -83,13 +100,6 @@ def generate(self, global_state):
             print("Re-assembling ats and positions")
             zip_adjusted_positions = list(zip(ats, adjusted_positions))
 
-            # Apply VW simplification if enabled
-            if global_state.vw_simplification_enabled:
-                print("Positions adjustment - step 5 (VW algorithm simplification)")
-                zip_adjusted_positions = simplify_coords(zip_adjusted_positions, global_state.vw_factor)
-            else:
-                print("Skipping positions adjustment - step 5 (VW algorithm simplification)")
-
             # Write the final funscript
             self.write_funscript(zip_adjusted_positions, output_path, global_state.fps)
             print(f"Funscript generated and saved to {output_path}")
@@ -508,6 +518,8 @@ def create_combined_plot(self, ref_sections, gen_sections, screenshots, sections
 
                 # Funscript comparison (second column)
                 ax_plot = fig.add_subplot(gs[i-1, j * 2 + 1])
+                # Scale the y axis 0 to 100
+                ax_plot.set_ylim(0, 100)
                 gen_times_sec = [t / 1000 for t in gen_sections[idx][0]]
                 ax_plot.plot(gen_times_sec, gen_sections[idx][1], label='Generated', color='blue')
 
diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index 40917b30..d3bfb45b 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -306,11 +306,13 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                             self.update_distance(distance)
                             return
 
+        # Section where we compute the funscript positions
         sum_pos = 0
         sum_weight_pos = 0
 
         for box, conf, cls, class_name, track_id in sorted_boxes:
-            if class_name in ['glans', 'penis', 'navel', 'hips center']:
+            # discarding those classes for distance computation
+            if class_name in ['glans', 'penis', 'navel', 'hips center', 'anus']:
                 continue
             elif self.locked_penis_box.is_active() and class_name == 'breast' and not self.boxes_overlap(box, self.locked_penis_box.get_box()):
                 x1, y1, x2, y2 = box
@@ -488,23 +490,3 @@ def update_normalized_distance_to_penis(self, track_id, normalized_dist_to_penis
         self.normalized_distance_to_penis[track_id].append(normalized_dist_to_penis_base)
         if len(self.normalized_distance_to_penis[track_id]) > 60:
             self.normalized_distance_to_penis[track_id].pop(0)
-
-    def normalize_box_area(self, box, frame_width, frame_height):
-        """
-        Normalize the area of a bounding box relative to the frame size.
-
-        Args:
-            box (tuple): Coordinates of the box.
-            frame_width (int): Width of the frame.
-            frame_height (int): Height of the frame.
-
-        Returns:
-            int: Normalized area (0-100).
-        """
-        x1, y1, x2, y2 = box
-        if x2 <= x1 or y2 <= y1:
-            return 0
-        box_area = (x2 - x1) * (y2 - y1)
-        max_area = frame_width * frame_height
-        normalized_area = 100 * (box_area / max_area)
-        return max(0, min(100, normalized_area))

From 61bb4c686e4e4c63a37c7ac2ea76c404ec607abe Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaas@gmail.com>
Date: Sun, 12 Jan 2025 11:01:41 +0100
Subject: [PATCH 057/277] tkinter update to GUI is not thread safe which caused
 the GUI to crash sometimes. The update now always happens in the same thread

---
 FSGenerator.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index 1be71f2d..8cf6fd6c 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -678,10 +678,13 @@ def start_processing():
     # YOLO Detection Progress
     def update_yolo_progress(current_frame, total_frames, eta):
         progress = (current_frame / total_frames) * 100
-        yolo_progress_bar["value"] = progress
-        yolo_progress_percent.config(text=f"{progress:.0f}% - ETA: {eta}")
-        #yolo_progress_eta.config(text=f"ETA: {eta}")
-        root.update_idletasks()
+
+        def update_gui():
+            yolo_progress_bar["value"] = progress
+            yolo_progress_percent.config(text=f"{progress:.0f}% - ETA: {eta}")
+
+        # Schedule the update in the main thread
+        root.after(0, update_gui)
 
     # Tracking Analysis Progress
     def update_tracking_progress(current_frame, total_frames, eta):

From fc4ac65d6cae5695ee9faf932186745d971be682 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaas@gmail.com>
Date: Sun, 12 Jan 2025 11:02:55 +0100
Subject: [PATCH 058/277] There is a bug duplicate KMP libs. I've disabled this
 warning for now but this needs to be fixed

---
 FSGenerator.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/FSGenerator.py b/FSGenerator.py
index 8cf6fd6c..80021de3 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -21,6 +21,9 @@
 from utils.lib_SceneCutsDetect import detect_scene_changes  # For detecting scene changes in videos
 from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg  # Custom video reader using FFmpeg
 
+# TODO this is a workaround and needs to be fixed properly
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
 # Define a GlobalState class to manage global variables
 class GlobalState:
     def __init__(self):

From 07a9a2c75b065b975b4a1867254b60adb7dfe9b8 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaas@gmail.com>
Date: Sun, 12 Jan 2025 11:07:42 +0100
Subject: [PATCH 059/277] Added a maximum frame height to the FFMPEG command
 for Yolo detection as it comes at no extra costs and doesn't require you to
 first downscale the video to 1440p which comes at a big performance loss

SPEED COMPARISON:
First encoding (CUDA accelerated encoding on a 4090 per 1000 frames)
downscale       : 1000 / 30 = 33.33s
prepare for yolo: 1000 / 71 = 14.08s

One pass / no downscaling
prepare for yolo: 1000 / 37 = 27.02s

Net performance gain 175.46%

FFMPEG is the biggest bottleneck now by far with 0.0270 s/frame compared to YOLO 0.0030 s/frame as the v360 filter is CPU only. We should investigate finding a proper GPU accelerated solution as this could dramatically improve performance. Probably 3-4x which means rendering a 1h 8k video in about 1.5 / 2 hours. Something similar as VR players do with mapping a texture to a half sphere. This should run at at least 90fps for 8K probably much higher.
---
 params/config.py               | 3 +++
 utils/lib_VideoReaderFFmpeg.py | 7 ++++++-
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/params/config.py b/params/config.py
index f579784a..f8d11eb3 100644
--- a/params/config.py
+++ b/params/config.py
@@ -56,6 +56,9 @@ def find_ffprobe_path():
 print(f"ffmpeg_path: {ffmpeg_path}")
 print(f"ffprobe_path: {ffprobe_path}")
 
+# Yolo detection settings
+max_frame_height = 1440
+
 # models path
 
 # Define the base models directory
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index fdf00ec9..a4d46674 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -2,7 +2,7 @@
 import cv2
 import numpy as np
 import argparse
-from params.config import ffmpeg_path, ffprobe_path
+from params.config import ffmpeg_path, ffprobe_path, max_frame_height
 
 
 class VideoReaderFFmpeg:
@@ -77,6 +77,11 @@ def _initialize_video_info(self):
             self.total_frames = total_frames
             self.duration = duration * 1000  # Convert duration to milliseconds
 
+            # limiting the frame height here has no additional performance cost and significantly improves speed for 1440p+ video
+            scaling_factor = min(max_frame_height / self.width, max_frame_height / self.height)
+            self.width = int(self.width * scaling_factor)
+            self.height = int(self.height * scaling_factor)
+
             print(f"FPS: {self.fps}, Resolution: {self.width}x{self.height}, "
                   f"Codec: {self.codec}, Total Frames: {self.total_frames}, Duration: {self.duration:.2f} ms")
         except Exception as e:

From e1557eebf6ddb6be561776ce118fd660a5209f9c Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaas@gmail.com>
Date: Sun, 12 Jan 2025 11:07:56 +0100
Subject: [PATCH 060/277] I think this should be a continue statement but
 please check. Having the raise error here doesn't allow me to regenerate a
 funscript even for 1 hour movies

---
 utils/lib_FunscriptHandler.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 4548df2f..d7674452 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -409,7 +409,8 @@ def select_random_sections(self, total_duration, section_duration=10, num_sectio
             # Ensure the 10-second section stays within the segment
             max_start = segment_end - section_duration
             if max_start < segment_start:
-                raise ValueError(f"Segment {i} is too short to fit a {section_duration}-second section.")
+                # raise ValueError(f"Segment {i} is too short to fit a {section_duration}-second section.")
+                continue
 
             # Randomly select a start time within the segment
             start = np.random.uniform(segment_start, max_start)

From afa2f2c0549f2e0946a97e1e1099cfb312686ccc Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaas@gmail.com>
Date: Sun, 12 Jan 2025 11:15:01 +0100
Subject: [PATCH 061/277] Added generic start conda bat file for windows use
 env. VRFunAIGen

---
 Start windows.bat | 13 +++++++++++++
 1 file changed, 13 insertions(+)
 create mode 100644 Start windows.bat

diff --git a/Start windows.bat b/Start windows.bat
new file mode 100644
index 00000000..8f07c93b
--- /dev/null
+++ b/Start windows.bat	
@@ -0,0 +1,13 @@
+
+@echo off
+cd /d "%~dp0"
+set "CONDA_PATH=C:\Users\%USERNAME%\miniconda3\Scripts\activate.bat"
+
+if not exist "%CONDA_PATH%" (
+    echo Conda not found at %CONDA_PATH%. Please check your installation.
+    pause
+    exit /b 1
+)
+call "%CONDA_PATH%" VRFunAIGen
+python FSGenerator.py
+pause
\ No newline at end of file

From f9e22dc5a9a7322819ae0464fca082cef7adc1a8 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 12 Jan 2025 13:05:26 +0100
Subject: [PATCH 062/277] Added visible penis length as a part of the funscript
 distance computation, when not touched by hand or foot. Adapted tracking,
 debugging and FSGenerator accordingly.

---
 FSGenerator.py             |  7 +++++
 utils/lib_Debugger.py      |  5 ++--
 utils/lib_ObjectTracker.py | 52 +++++++++++++++++++++++++++++++++-----
 3 files changed, 55 insertions(+), 9 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index 1be71f2d..8f13300a 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -11,6 +11,7 @@
 from tkinter import filedialog, messagebox, ttk  # here, what was I saying...
 import subprocess  # For running shell commands
 import threading
+from datetime import datetime
 
 # Import custom modules and configurations
 from params.config import class_priority_order, class_reverse_match, class_colors, yolo_models, ffmpeg_path  # Configuration for class priorities, reverse matching, and colors
@@ -454,6 +455,10 @@ def analyze_tracking_results(results, image_y_size, progress_callback=None):
                                 str_dist_penis = 'None'
                         str_abs_pos = str(int(tracker.normalized_absolute_tracked_positions[box[4]][-1]))
                         position = 'p: ' + str_dist_penis + ' | ' + 'a: ' + str_abs_pos
+                        if box[4] in tracker.pct_weights:
+                            if len(tracker.pct_weights[box[4]]) > 0:
+                                weight = tracker.pct_weights[box[4]][-1]
+                                position += ' | w: ' + str(weight)
                     else:
                         position = None
                     bounding_boxes.append({
@@ -467,6 +472,8 @@ def analyze_tracking_results(results, image_y_size, progress_callback=None):
                                    bounding_boxes=bounding_boxes,
                                    variables={
                                        'frame': frame_pos,
+                                       # time of the frame hh:mm:ss
+                                       'time': datetime.fromtimestamp(frame_pos / fps).strftime('%H:%M:%S'),
                                        'distance': tracker.distance,
                                        'Penetration': tracker.penetration,
                                        'sex_position': tracker.sex_position,
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index fe3488a1..275eb775 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -228,7 +228,8 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
             if cv2.waitKey(1) & 0xFF == ord("q"):
                 break
             if cv2.waitKey(1) & 0xFF == 32:  # Pause on spacebar
-                time.sleep(10)
+                # press a key to resume
+                cv2.waitKey(0)
             if duration == -1:
                 cv2.imwrite(f"{self.video_path[:-4]}_frame_{self.current_frame}.png", frame_copy)
                 break
@@ -325,4 +326,4 @@ def _draw_progress_bar(self, frame, width, height):
 
         # Progress indicator
         progress_x = int((self.current_frame / self.total_frames) * width)
-        cv2.rectangle(frame, (bar_x_start, bar_y_start), (progress_x, bar_y_end), (0, 255, 0), -1)
\ No newline at end of file
+        cv2.rectangle(frame, (bar_x_start, bar_y_start), (progress_x, bar_y_end), (0, 255, 0), -1)
diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index d3bfb45b..af8e7827 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -99,6 +99,7 @@ def __init__(self, fps, frame_pos, image_area):
         self.tracked_boxes = []  # List of tracked boxes
         self.penis_box = None  # Detected penis box
         self.locked_penis_box = LockedPenisBox()  # Locked penis box
+        self.visible_part_of_penis = -1
         self.glans_detected = False  # Whether the glans is detected
         self.breast_tracking = False  # Whether breast tracking is active
         self.distance = 100  # Current distance
@@ -115,6 +116,8 @@ def __init__(self, fps, frame_pos, image_area):
         self.tracked_positions = {}  # Tracked positions for each track_id
         self.normalized_absolute_tracked_positions = {}  # Normalized absolute positions for each track_id
         self.normalized_distance_to_penis = {}  # Normalized distance to penis for each track_id
+        self.weights = {}  # Weights for each track_id
+        self.pct_weights = {}  # Percentage weights for each track_id
 
         # Initialize normalized positions and distances
         self.normalized_positions = {class_name: deque(maxlen=200) for class_name in class_names}
@@ -123,7 +126,7 @@ def __init__(self, fps, frame_pos, image_area):
         # Detection thresholds
         self.consecutive_detections = {class_name: 0 for class_name in class_names}
         self.consecutive_non_detections = {class_name: 0 for class_name in class_names}
-        self.detections_threshold = 3  # Threshold for considering a detection valid
+        self.detections_threshold = round(self.fps) / 10  # Threshold for considering a detection valid
 
         # Penetration state
         self.penetration = False  # Whether penetration is detected
@@ -244,6 +247,8 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
         self.boxes = {class_name: None for class_name in self.class_names}
         self.tracked_boxes = []
         all_detections = {class_name: [] for class_name in self.class_names}
+        self.weights = {}  # Weights for each track_id
+        self.pct_weights = {}  # Percentage weights for each track_id
 
         classes_touching_penis = []
         breast_tracked = None
@@ -312,7 +317,8 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
 
         for box, conf, cls, class_name, track_id in sorted_boxes:
             # discarding those classes for distance computation
-            if class_name in ['glans', 'penis', 'navel', 'hips center', 'anus']:
+            #if class_name in ['glans', 'penis', 'navel', 'hips center', 'anus']:
+            if class_name in ['glans', 'navel', 'hips center', 'anus']:
                 continue
             elif self.locked_penis_box.is_active() and class_name == 'breast' and not self.boxes_overlap(box, self.locked_penis_box.get_box()):
                 x1, y1, x2, y2 = box
@@ -326,15 +332,23 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
 
                 self.tracked_boxes.append([box, class_name, track_id])
                 x1, y1, x2, y2 = box
-                mid_y = y2
+                if class_name != 'penis':
+                    mid_y = y2
+                else:
+                    mid_y = (y1 + y2) // 2
 
                 normalized_y = self.update_tracked_positions(track_id, mid_y)
 
                 if class_name != 'hips center':
+
                     low_y = y2 if class_name != 'butt' else (0.8 * (y2 - y1)) + y1
-                    dist_to_penis_base = int(
-                        ((self.locked_penis_box.get_box()[3] - low_y) / self.locked_penis_box.get_height()) * 100
-                    )
+
+                    if class_name == 'penis':
+                        dist_to_penis_base = self.visible_part_of_penis
+                    else:
+                        dist_to_penis_base = int(
+                            ((self.locked_penis_box.get_box()[3] - low_y) / self.locked_penis_box.get_height()) * 100
+                        )
                     normalized_dist_to_penis_base = min(max(0, dist_to_penis_base), 100)
                     self.update_normalized_distance_to_penis(track_id, normalized_dist_to_penis_base)
 
@@ -346,12 +360,29 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                     weight_pos_track_id = sum(
                         abs(self.normalized_distance_to_penis[track_id][i] - self.normalized_distance_to_penis[track_id][i - 1])
                         for i in range(1, len(self.normalized_distance_to_penis[track_id])))
+                    if track_id not in self.weights:
+                        self.weights[track_id] = []
+                    self.weights[track_id].append(weight_pos_track_id)
+
                     sum_pos += ((self.normalized_distance_to_penis[track_id][-1] + normalized_y) // 2) * weight_pos_track_id
 
                     # attempt : use only the distance to penis value, discarding the absolute position
                     # sum_pos += self.normalized_distance_to_penis[track_id][-1] * weight_pos_track_id
                     sum_weight_pos += weight_pos_track_id
 
+        for _, class_name, track_id in self.tracked_boxes:
+            # disregard penis class if hand or foot in classes_touching_penis, because of obstruction issue
+            if class_name == 'penis' and ('hand' in classes_touching_penis or 'foot' in classes_touching_penis):
+                continue
+            # saves the weight for the current track_id
+            if track_id not in self.pct_weights:
+                self.pct_weights[track_id] = []
+            if track_id not in self.weights:
+                continue
+            if sum_weight_pos > 0:
+                weight = int((self.weights[track_id][-1] / sum_weight_pos) * 100)
+                self.pct_weights[track_id].append(weight)
+
         if len(classes_touching_penis) == 0 or not self.locked_penis_box.is_active():
             self.penetration = False
             distance = 100
@@ -411,7 +442,8 @@ def handle_class_first(self, class_name, box, conf):
                             self.locked_penis_box.update(self.penis_box, current_height)
 
                         # Move locked penis box towards current penis box
-                        max_move = max(1, int(self.image_y_size / 960))
+                        #max_move = max(1, int(self.image_y_size / 960))
+                        max_move = 120 // int(self.fps)
 
                         if abs(self.penis_box[0] - self.locked_penis_box.get_box()[0]) > max_move:
                             px1 = self.penis_box[0] + np.sign(self.penis_box[0] - self.locked_penis_box.get_box()[0]) * max_move
@@ -421,8 +453,14 @@ def handle_class_first(self, class_name, box, conf):
                             py2 = self.locked_penis_box.get_box()[3] + np.sign(self.penis_box[3] - self.locked_penis_box.get_box()[3]) * max_move
 
                         self.locked_penis_box.update((px1, py2 - self.locked_penis_box.get_height(), px2, py2), self.locked_penis_box.get_height())
+
+                        self.visible_part_of_penis = min(100, int((current_height / (self.locked_penis_box.get_height() * .7)) * 100))
+                        # values most likely to be 25 to 100 give a residual box at full penetration, need to remap 0-100
+                        self.visible_part_of_penis = int((self.visible_part_of_penis - 25) / 75 * 100)
                     else:
                         self.locked_penis_box.update(self.penis_box, current_height)
+            else:
+                self.visible_part_of_penis = -1
         elif class_name == 'glans' and box:
             if self.consecutive_detections['glans'] >= self.detections_threshold:
                 self.boxes['glans'] = box

From 792d41e1d00ff59c6333ef372c3f0aec20366404 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Mon, 13 Jan 2025 18:50:58 +0100
Subject: [PATCH 063/277] * Removed the option to default OpenCV as a video
 reader, since the logging issue leading to a crash when using ffmpeg was
 fixed * Removed the ability to resize a video file as the resizing is now
 made on the fly via ffmpeg, with an important gain in performance *
 Temporarily removed the scene cut detections, as it was bringing some
 unwanted loss of signal

* Inference performance - The on-the-fly resizing option will now default to 1080p as it seems to be a sweet spot for performance/reliability (can be changed in config.py)

* Funscript pos. estimation - Tweaked the use of the visible penis length, used only when no class touching is identified by YOLO, but penetration still considered as ongoing
* Funscript pos. estimation - Fixed a bug in the way the visible part of penis was taken into account
* Funscript pos. estimation - Tweaked moving averages weights
* Funscript pos. estimation - Added a IOU filter logic to discard fake positives of hand touching penis
* Funscript pos. estimation - Added a change in the position estimation based on the class

* Funscript report generation - Added a SPOILER_ prefix so the content is blurred when posted on Discord, before clicking

* Debugging - The output video are now compressed with h265, full res (max 1080p), and saved with a SPOILER_ prefix so the content is blurred when posted on Discord, before clicking

* 2D POV - Added a 2D POV mode during video selection
* 2D POV - Tweaked the actuation speed of the locked penis box (to compensate specific cases, the camera not being still for instance)

* Minor UI changes
---
 FSGenerator.py                 | 379 +++++++++++++++------------------
 params/config.py               |   8 +-
 utils/lib_Debugger.py          |  40 +++-
 utils/lib_FunscriptHandler.py  |  54 ++---
 utils/lib_ObjectTracker.py     | 154 +++++++++++---
 utils/lib_VideoReaderFFmpeg.py |  12 +-
 6 files changed, 361 insertions(+), 286 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index d3aa7143..0e42224a 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -9,12 +9,14 @@
 import torch  # PyTorch for use of .pt model if not on Apple device
 import tkinter as tk  # GUI library for macOS for basic use in our case
 from tkinter import filedialog, messagebox, ttk  # here, what was I saying...
-import subprocess  # For running shell commands
 import threading
 from datetime import datetime
+import logging
+import sys
 
 # Import custom modules and configurations
-from params.config import class_priority_order, class_reverse_match, class_colors, yolo_models, ffmpeg_path  # Configuration for class priorities, reverse matching, and colors
+from params.config import (class_priority_order, class_reverse_match, class_colors,
+                           yolo_models, max_frame_height)  # Configuration for class priorities, reverse matching, and colors
 from utils.lib_ObjectTracker import ObjectTracker  # Custom object tracking logic
 from utils.lib_FunscriptHandler import FunscriptGenerator  # For generating Funscript files
 from utils.lib_Visualizer import Visualizer  # For visualizing results
@@ -28,25 +30,35 @@
 # Define a GlobalState class to manage global variables
 class GlobalState:
     def __init__(self):
+        # YOLO models
         self.yolo_det_model = ""
         self.yolo_pose_model = ""
+        # Video info
         self.video_file = ""
         self.video_fps = 1
-        self.DebugMode = False
-        self.LiveDisplayMode = False
-        self.isVR = True
-        self.enhance_lighting = False
         self.frame_start = 0
         self.frame_end = None
+        self.current_frame_id = 0
+        self.current_frame = None  # actual frame
+        self.frame_area = 0
+        self.image_y_size = 0
+        self.image_x_size = 0
+        # Attributes
+        self.isVR = True
         self.reference_script = ""
         self.offset_x = 0
+        # Funscript data
         self.funscript_data = []  # List to store Funscript data
         self.funscript_frames = []
         self.funscript_distances = []
+        # App instances and variables
         self.debugger = None
-        self.video_reader = None
+        self.DebugMode = False
         self.debug_record_mode = False
-        self.record_duration = 0
+        self.debug_record_duration = 0
+        self.LiveDisplayMode = False
+        self.video_reader = "FFmpeg"
+        self.enhance_lighting = False
         # Funscript Tweaking Variables
         self.boost_enabled = True
         self.boost_up_percent = 10
@@ -57,6 +69,22 @@ def __init__(self):
         self.vw_simplification_enabled = True
         self.vw_factor = 8.0
         self.rounding = 5
+        # Configure logging (simple setup)
+        # Initialize logger
+        logging.basicConfig(
+            level=logging.INFO,
+            format=f"@{self.current_frame_id} - %(levelname)s - %(message)s",  # Log format
+            filename="FSGenerator.log",
+            filemode="w",
+            handlers=[
+                logging.FileHandler("FSGenerator.log"),  # Save logs to a file
+                logging.StreamHandler(sys.stdout)  # Print logs to the console
+            ]
+        )
+        self.logger = logging.getLogger("GlobalStateLogger")
+        self.logger.setLevel(logging.INFO)
+        self.logger.info(f"@{self.current_frame_id} - Initiated logger in global_state instance")
+
 
 # Initialize global state
 global_state = GlobalState()
@@ -138,7 +166,7 @@ def write_dataset(file_path, data):
     :param file_path: The path to the output file.
     :param data: The data to write.
     """
-    print(f"Exporting data...")
+    global_state.logger.info(f"Exporting data...")
     export_start = time.time()
     # If the file already exists, rename it as a backup
     if os.path.exists(file_path):
@@ -147,26 +175,26 @@ def write_dataset(file_path, data):
     with open(file_path, 'w') as f:
         json.dump(data, f)
     export_end = time.time()
-    print(f"Done in {export_end - export_start}.")
+    global_state.logger.info(f"Done in {export_end - export_start}.")
 
 def get_yolo_model_path():
     # Check if the device is an Apple device
     if platform.system() == 'Darwin':
-        print(f"Apple device detected, loading {yolo_models[0]} for MPS inference.")
+        global_state.logger.info(f"Apple device detected, loading {yolo_models[0]} for MPS inference.")
         return yolo_models[0]
 
     # Check if CUDA is available (for GPU support)
     elif torch.cuda.is_available():
-        print(f"CUDA is available, loading {yolo_models[1]} for GPU inference.")
+        global_state.logger.info(f"CUDA is available, loading {yolo_models[1]} for GPU inference.")
         return yolo_models[1]
 
     # Fallback to ONNX model for other platforms without CUDA
     else:
-        print("CUDA not available, if this is unexpected, please install CUDA and check your version of torch.")
-        print("You might need to install a dependency with the following command (example):")
-        print("pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118")
-        print(f"Falling back to CPU inference, loading {yolo_models[2]}.")
-        print("WARNING: CPU inference may be slow on some devices.")
+        global_state.logger.warning("CUDA not available, if this is unexpected, please install CUDA and check your version of torch.")
+        global_state.logger.info("You might need to install a dependency with the following command (example):")
+        global_state.logger.info("pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118")
+        global_state.logger.info(f"Falling back to CPU inference, loading {yolo_models[2]}.")
+        global_state.logger.warning("WARNING: CPU inference may be slow on some devices.")
 
         return yolo_models[2]
 
@@ -181,7 +209,7 @@ def extract_yolo_data(progress_callback=None):
         skip_detection = messagebox.askyesno("Detection file already exists",
                                              f"File {file_name} already exists.\n\nClick Yes to reuse the existing detections file.\nClick No to perform detections again.")
         if skip_detection:
-            print(
+            global_state.logger.info(
                 f"File {global_state.video_file[:-4] + f'_rawyolo.json'} already exists. Skipping detections and loading file content...")
             return
         else:
@@ -191,11 +219,7 @@ def extract_yolo_data(progress_callback=None):
     test_result = Result(320)  # Test result object for debugging
 
     # Initialize the video reader
-    if global_state.video_reader == "FFmpeg":
-        cap = VideoReaderFFmpeg(global_state.video_file, is_VR=global_state.isVR)  # Initialize the video reader
-    else:
-        cap = cv2.VideoCapture(global_state.video_file)
-
+    cap = VideoReaderFFmpeg(global_state.video_file, is_VR=global_state.isVR)  # Initialize the video reader
     cap.set(cv2.CAP_PROP_POS_FRAMES, global_state.frame_start)
 
     # Determine the last frame to process
@@ -210,10 +234,10 @@ def extract_yolo_data(progress_callback=None):
     # make the pose model optional
     if len(global_state.yolo_pose_model) > 0:
         run_pose_model = True
-        print("Activating pose model")
+        global_state.logger.info("Activating pose model")
     else:
         run_pose_model = False
-        print("Discarding pose model part of the code")
+        global_state.logger.info("Discarding pose model part of the code")
     if run_pose_model:
         pose_model = YOLO(global_state.yolo_pose_model, task="pose")
 
@@ -225,9 +249,6 @@ def extract_yolo_data(progress_callback=None):
         success, frame = cap.read()  # Read a frame from the video
 
         if success:
-            if global_state.video_reader == "OpenCV" and global_state.isVR:
-                frame = frame[:, :frame.shape[1] // 2, :]  # only half left of the frame, for VR half
-
             # Run YOLO tracking on the frame
             yolo_det_results = det_model.track(frame, persist=True, conf=0.3, verbose=False)
             if run_pose_model:
@@ -259,10 +280,10 @@ def extract_yolo_data(progress_callback=None):
                 records.append(record)
                 if global_state.LiveDisplayMode:
                     # Print and test the record
-                    print(f"Record : {record}")
-                    print(f"For class id: {int(cls)}, getting: {class_reverse_match.get(int(cls), 'unknown')}")
+                    global_state.logger.info(f"Record : {record}")
+                    global_state.logger.info(f"For class id: {int(cls)}, getting: {class_reverse_match.get(int(cls), 'unknown')}")
                     test_box = [[x1, y1, x2, y2], round(conf, 1), int(cls), class_reverse_match.get(int(cls), 'unknown'), track_id]
-                    print(f"Test box: {test_box}")
+                    global_state.logger.info(f"Test box: {test_box}")
                     test_result.add_record(frame_pos, test_box)
 
             if run_pose_model:
@@ -299,21 +320,17 @@ def extract_yolo_data(progress_callback=None):
                         records.append(record)
                         if global_state.LiveDisplayMode:
                             # Print and test the record
-                            print(f"Record : {record}")
-                            print(f"For class id: {int(cls)}, getting: {class_reverse_match.get(int(cls), 'unknown')}")
+                            global_state.logger.info(f"@{frame_pos} - Record : {record}")
+                            global_state.logger.info(f"@{frame_pos} - For class id: {int(cls)}, getting: {class_reverse_match.get(int(cls), 'unknown')}")
                             test_box = [[x1, y1, x2, y2], round(conf, 1), int(cls),
                                         class_reverse_match.get(int(cls), 'unknown'), 0]
-                            print(f"Test box: {test_box}")
+                            global_state.logger.info(f"Test box: {test_box}")
                             test_result.add_record(frame_pos, test_box)
 
             if global_state.LiveDisplayMode:
-                # Display the YOLO results for testing
-                #yolo_det_results[0].plot()
-                #cv2.imshow("YOLO11", yolo_det_results[0].plot())
-                #cv2.waitKey(1)
                 # Verify the sorted boxes
                 sorted_boxes = test_result.get_boxes(frame_pos)
-                print(f"Sorted boxes : {sorted_boxes}")
+                global_state.logger.info(f"@{frame_pos} - Sorted boxes : {sorted_boxes}")
 
                 frame_display = frame.copy()
 
@@ -347,7 +364,7 @@ def load_yolo_data_from_file(file_path):
     """
     with open(file_path, 'r') as f:
         data = json.load(f)
-        print(f"Loaded data from {file_path}, length: {len(data)}")
+        global_state.logger.info(f"Loaded data from {file_path}, length: {len(data)}")
     return data
 
 def make_data_boxes(records, image_x_size):
@@ -376,19 +393,12 @@ def analyze_tracking_results(results, image_y_size, progress_callback=None):
     list_of_frames = results.get_all_frame_ids()  # Get all frame IDs with detections
     visualizer = Visualizer()  # Initialize the visualizer
 
-    if global_state.video_reader == "FFmpeg":
-        cap = VideoReaderFFmpeg(global_state.video_file, is_VR=global_state.isVR)  # Initialize the video reader
-    else:
-        cap = cv2.VideoCapture(global_state.video_file)
+    cap = VideoReaderFFmpeg(global_state.video_file, is_VR=global_state.isVR)  # Initialize the video reader
 
     fps = cap.get(cv2.CAP_PROP_FPS)  # Get the video's FPS
     nb_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))  # Get the total number of frames
 
-    if global_state.video_reader == "OpenCV" and global_state.isVR:
-        divide_size_by = 2
-    else:
-        divide_size_by = 1
-    image_area = (cap.get(cv2.CAP_PROP_FRAME_WIDTH) // divide_size_by) * cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
+    global_state.frame_area = cap.get(cv2.CAP_PROP_FRAME_WIDTH) * cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
 
     cuts = []
 
@@ -403,41 +413,44 @@ def analyze_tracking_results(results, image_y_size, progress_callback=None):
     else:
         cap.release()
 
+    """ discarding the scene detection for now
     # Load scene cuts if the file exists
     if os.path.exists(global_state.video_file[:-4] + f"_cuts.json"):
-        print(f"Loading cuts from {global_state.video_file[:-4] + f'_cuts.json'}")
+        global_state.logger.info(f"Loading cuts from {global_state.video_file[:-4] + f'_cuts.json'}")
         with open(global_state.video_file[:-4] + f"_cuts.json", 'r') as f:
             cuts = json.load(f)
-        print(f"Loaded {len(cuts)} cuts : {cuts}")
+        global_state.logger.info(f"Loaded {len(cuts)} cuts : {cuts}")
     else:
         # Detect scene changes if the cuts file does not exist
         scene_list = detect_scene_changes(global_state.video_file, global_state.isVR, 0.9, global_state.frame_start, global_state.frame_end)
-        print(f"Analyzing frames {global_state.frame_start} to {global_state.frame_end}")
+        global_state.logger.info(f"Analyzing frames {global_state.frame_start} to {global_state.frame_end}")
         cuts = [scene[1] for scene in scene_list]
         cuts = cuts[:-1]  # Remove the last entry
         # Save the cuts to a file
         with open(global_state.video_file[:-4] + f"_cuts.json", 'w') as f:
             json.dump(cuts, f)
+    """
 
     global_state.funscript_frames = []  # List to store Funscript frames
-    tracker = ObjectTracker(fps, global_state.frame_start, image_area)  # Initialize the object tracker
+    tracker = ObjectTracker(global_state)
 
     # Start time for ETA calculation
     start_time = time.time()
 
     for frame_pos in tqdm(range(global_state.frame_start, global_state.frame_end), unit="f"):
+        global_state.current_frame_id = frame_pos
         if frame_pos in cuts:
             # Reinitialize the tracker at scene cuts
-            print(f"Reaching cut at frame {frame_pos}")
+            global_state.logger.info(f"@{frame_pos} - Reaching cut")
             previous_distances = tracker.previous_distances
-            print(f"Reinitializing tracker with previous distances: {previous_distances}")
-            tracker = ObjectTracker(fps, frame_pos, image_area)
+            global_state.logger.info(f"@{frame_pos} - Reinitializing tracker with previous distances: {previous_distances}")
+            tracker = ObjectTracker(global_state)
             tracker.previous_distances = previous_distances
 
         if frame_pos in list_of_frames:
             # Get sorted boxes for the current frame
             sorted_boxes = results.get_boxes(frame_pos)
-            tracker.tracking_logic(sorted_boxes, frame_pos, image_y_size)  # Apply tracking logic
+            tracker.tracking_logic(global_state, sorted_boxes)  # Apply tracking logic
 
             if tracker.distance:
                 # Append Funscript data if distance is available
@@ -495,10 +508,7 @@ def analyze_tracking_results(results, image_y_size, progress_callback=None):
             # Display the tracking results for testing
             ret, frame = cap.read()
 
-            if global_state.video_reader == "OpenCV" and global_state.isVR:
-                frame_display = frame[:, :frame.shape[1] // 2, :]  # only half left of the frame, for VR half
-            else:
-                frame_display = frame.copy()
+            frame_display = frame.copy()
 
             for box in tracker.tracked_boxes:
                 frame_display = visualizer.draw_bounding_box(frame_display,
@@ -512,7 +522,7 @@ def analyze_tracking_results(results, image_y_size, progress_callback=None):
                                                              class_colors['penis'],
                                                              global_state.offset_x)
             else:
-                print("No active locked penis box to draw.")
+                global_state.logger.info(f"@{frame_pos} - No active locked penis box to draw.")
 
             if tracker.glans_detected:
                 frame_display = visualizer.draw_bounding_box(frame_display, tracker.boxes['glans'],
@@ -572,7 +582,7 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
                 frame_detected = 0
 
             if consecutive_frames >= 2:
-                print(f"First instance of Glans/Penis found in frame {line[0] - 4}")
+                global_state.logger.info(f"First instance of Glans/Penis found in frame {line[0] - 4}")
                 return line[0] - 4
 
 def select_video_file():
@@ -594,8 +604,8 @@ def select_reference_script():
 
 def check_video_resolution(video_path):
     cap = cv2.VideoCapture(video_path)
-    global_state.fps = float(cap.get(cv2.CAP_PROP_FPS))
-    print(f"Video FPS: {global_state.fps}")
+    global_state.video_fps = float(cap.get(cv2.CAP_PROP_FPS))
+    global_state.logger.info(f"Video FPS: {global_state.video_fps}")
     if not cap.isOpened():
         messagebox.showerror("Error", "Could not open the video file.")
         return
@@ -603,39 +613,10 @@ def check_video_resolution(video_path):
     height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
     cap.release()
 
-    if height > 1440:
-        resize_options = ["1440", "1080"]
-        choice = messagebox.askquestion(
-            "Resize Video",
-            f"The video height is {height}p. Do you want to resize it to a lower resolution?",
-            detail="Options: " + ", ".join(resize_options)
-        )
-        if choice == "yes":
-            resize_video(video_path, resize_options)
-
-def resize_video(video_path, options):
-
-    def on_resize_select(option):
-        output_path = video_path.replace(".mp4", f"_{option}p.mp4")
-        command = [
-            ffmpeg_path, "-i", video_path,
-            "-vf", f"scale=-1:{option}",
-            # "-c:a", "copy",  # if you need the audio, uncomment this line and comment the next one
-            "-an",  # skipping the audio
-            output_path
-        ]
-        subprocess.run(command, check=True)
-        messagebox.showinfo("Success", f"Video resized to {option}p and saved as {output_path}")
-        resize_window.destroy()
-        video_path.set(output_path)
-
-    resize_window = tk.Toplevel()
-    resize_window.title("Select Resize Option")
-    for option in options:
-        btn = tk.Button(resize_window, text=f"{option}p", command=lambda o=option: on_resize_select(o))
-        btn.pack(pady=5)
+    if height > max_frame_height:
+        messagebox.showinfo("Info", f"The video height is {height}p > {max_frame_height}p.\nIt will be automatically resized on the fly, no conversion required.")
 
-def start_processing():
+def common_initialization():
     global_state.video_file = video_path.get()
     if not global_state.video_file:
         messagebox.showerror("Error", "Please select a video file.")
@@ -644,47 +625,51 @@ def start_processing():
     global_state.yolo_det_model = get_yolo_model_path()
     global_state.yolo_pose_model = ""  # "models/yolo11n-pose.mlpackage"
     global_state.DebugMode = debug_mode_var.get()
+    global_state.debug_record_mode = debug_record_mode_var.get()
+    global_state.debug_record_duration = int(debug_record_duration_var.get())
     global_state.LiveDisplayMode = live_display_mode_var.get()
-    global_state.isVR = is_vr_var.get()
+    selected_mode = mode_combobox.get()
+    if selected_mode == "VR SBS":
+        global_state.isVR = True
+    elif selected_mode == "Flat - 2D POV":  # might want to add other formats later on
+        global_state.isVR = False
+    else:
+        global_state.isVR = False
+
     global_state.enhance_lighting = enhance_lighting_var.get()
     global_state.frame_start = 0 if frame_start_entry.get() == "" else int(frame_start_entry.get())
     global_state.frame_end = None if frame_end_entry.get() == "" else int(frame_end_entry.get())
     global_state.reference_script = reference_script_path.get()
-    global_state.video_reader = video_reader_var.get()
     global_state.enhance_lighting = enhance_lighting_var.get()
 
-    print(f"Processing video: {global_state.video_file}")
-    print(f"Video Reader: {global_state.video_reader}")
-    print(f"YOLO Detection Model: {global_state.yolo_det_model}")
-    print(f"YOLO Pose Model: {global_state.yolo_pose_model}")
-    print(f"Debug Mode: {global_state.DebugMode}")
-    print(f"Live Display Mode: {global_state.LiveDisplayMode}")
-    print(f"VR Mode: {global_state.isVR}")
-    print(f"Enhance lighting: {global_state.enhance_lighting}")
-    print(f"Frame Start: {global_state.frame_start}")
-    print(f"Frame End: {global_state.frame_end}")
-    print(f"Reference Script: {global_state.reference_script}")
-    print(f"Video Reader: {global_state.video_reader}")
-    print(f"Enhance lighting: {global_state.enhance_lighting}")
+    cap = VideoReaderFFmpeg(global_state.video_file, is_VR=global_state.isVR)
+    global_state.image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
+    global_state.image_y_size = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
+    global_state.video_fps = cap.get(cv2.CAP_PROP_FPS)
+    cap.release()
+
+    global_state.logger.info(f"Processing video: {global_state.video_file}")
+    global_state.logger.info(f"Image size: {global_state.image_x_size}x{global_state.image_y_size}")
+    global_state.logger.info(f"FPS: {global_state.video_fps}")
+    global_state.logger.info(f"Video Reader: {global_state.video_reader}")
+    global_state.logger.info(f"YOLO Detection Model: {global_state.yolo_det_model}")
+    global_state.logger.info(f"YOLO Pose Model: {global_state.yolo_pose_model}")
+    global_state.logger.info(f"Debug Mode: {global_state.DebugMode}")
+    global_state.logger.info(f"Live Display Mode: {global_state.LiveDisplayMode}")
+    global_state.logger.info(f"VR Mode: {global_state.isVR}")
+    global_state.logger.info(f"Enhance lighting: {global_state.enhance_lighting}")
+    global_state.logger.info(f"Frame Start: {global_state.frame_start}")
+    global_state.logger.info(f"Frame End: {global_state.frame_end}")
+    global_state.logger.info(f"Reference Script: {global_state.reference_script}")
+    global_state.logger.info(f"Video Reader: {global_state.video_reader}")
+    global_state.logger.info(f"Enhance lighting: {global_state.enhance_lighting}")
+
+def start_processing():
+    common_initialization()
 
     # Initialize the debugger
     global_state.debugger = Debugger(global_state.video_file, output_dir=global_state.video_file[:-4])
 
-    if global_state.video_reader == "FFmpeg":
-        cap = VideoReaderFFmpeg(global_state.video_file, is_VR=global_state.isVR)
-        image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
-    else:
-        cap = cv2.VideoCapture(global_state.video_file)
-        if global_state.isVR:
-            image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH) // 2
-        else:
-            image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
-    fps = cap.get(cv2.CAP_PROP_FPS)
-    image_y_size = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
-    cap.release()
-
-    print(f"Image size: {image_x_size}x{image_y_size}")
-
     # YOLO Detection Progress
     def update_yolo_progress(current_frame, total_frames, eta):
         progress = (current_frame / total_frames) * 100
@@ -701,7 +686,6 @@ def update_tracking_progress(current_frame, total_frames, eta):
         progress = (current_frame / total_frames) * 100
         tracking_progress_bar["value"] = progress
         tracking_progress_percent.config(text=f"{progress:.0f}% - ETA: {eta}")
-        #tracking_progress_eta.config(text=f"ETA: {eta}")
         root.update_idletasks()
 
     # Function to run the processing tasks
@@ -712,22 +696,22 @@ def run_processing():
         # Load YOLO detection results from file
         yolo_data = load_yolo_data_from_file(global_state.video_file[:-4] + f"_rawyolo.json")
 
-        results = make_data_boxes(yolo_data, image_x_size)
+        results = make_data_boxes(yolo_data, global_state.image_x_size)
 
         # Looking for the first instance of penis within the YOLO results
         first_penis_frame = parse_yolo_data_looking_for_penis(yolo_data, 0)
 
         if first_penis_frame is None:
-            print(f"No penis found in video: {global_state.video_file}")
+            global_state.logger.error(f"No penis found in video: {global_state.video_file}")
             first_penis_frame = 0
 
         # Deciding whether we start from there or from a user-specified later frame
-        global_state.frame_start = max(max(first_penis_frame - int(fps), global_state.frame_start - int(fps)), 0)
+        global_state.frame_start = max(max(first_penis_frame - int(global_state.video_fps), global_state.frame_start - int(global_state.video_fps)), 0)
 
-        print(f"Frame Start adjusted to: {global_state.frame_start}")
+        global_state.logger.info(f"Frame Start adjusted to: {global_state.frame_start}")
 
         # Performing the tracking part and generation of the raw funscript data
-        global_state.funscript_data = analyze_tracking_results(results, image_y_size, update_tracking_progress)
+        global_state.funscript_data = analyze_tracking_results(results, global_state.image_y_size, update_tracking_progress)
 
         global_state.debugger.save_logs()
 
@@ -739,7 +723,7 @@ def run_processing():
         # Optionally, compare generated funscript with reference funscript if specified, or a simple generic report
         funscript_handler.create_report_funscripts(global_state)
 
-        print(f"Finished processing video: {global_state.video_file}")
+        global_state.logger.info(f"Finished processing video: {global_state.video_file}")
 
     # Run the processing in a separate thread
     processing_thread = threading.Thread(target=run_processing)
@@ -749,33 +733,7 @@ def debug_function():
     """
     Debugging function to perform specific debugging tasks.
     """
-    global_state.video_file = video_path.get()
-    if not global_state.video_file:
-        messagebox.showerror("Error", "Please select a video file.")
-        return
-
-    global_state.yolo_det_model = get_yolo_model_path()
-    global_state.yolo_pose_model = ""  # "models/yolo11n-pose.mlpackage"
-    global_state.DebugMode = debug_mode_var.get()
-    global_state.LiveDisplayMode = live_display_mode_var.get()
-    global_state.isVR = is_vr_var.get()
-    global_state.frame_start = 0 if frame_start_entry.get() == "" else int(frame_start_entry.get())
-    global_state.frame_end = None if frame_end_entry.get() == "" else int(frame_end_entry.get())
-    global_state.reference_script = reference_script_path.get()
-    global_state.video_reader = video_reader_var.get()
-    global_state.debug_record_mode = debug_record_mode_var.get()
-    global_state.debug_record_duration = int(debug_record_duration_var.get())
-
-    print(f"Processing video: {global_state.video_file}")
-    print(f"Video Reader: {global_state.video_reader}")
-    print(f"YOLO Detection Model: {global_state.yolo_det_model}")
-    print(f"YOLO Pose Model: {global_state.yolo_pose_model}")
-    print(f"Debug Mode: {global_state.DebugMode}")
-    print(f"Live Display Mode: {global_state.LiveDisplayMode}")
-    print(f"VR Mode: {global_state.isVR}")
-    print(f"Frame Start: {global_state.frame_start}")
-    print(f"Frame End: {global_state.frame_end}")
-    print(f"Reference Script: {global_state.reference_script}")
+    common_initialization()
 
     # Processing logic
 
@@ -798,43 +756,43 @@ def regenerate_funscript(global_state):
         return
     global_state.reference_script = reference_script_path.get()
 
-    print("Regenerating Funscript with tweaked settings...")
+    global_state.logger.info("Regenerating Funscript with tweaked settings...")
     # Apply tweaks to funscript_data
     if global_state.boost_enabled:
-        print(f"Applying Boost: Up {global_state.boost_up_percent}%, Down {global_state.boost_down_percent}%")
+        global_state.logger.info(f"Applying Boost: Up {global_state.boost_up_percent}%, Down {global_state.boost_down_percent}%")
         # Add boost logic here
 
     if global_state.threshold_enabled:
-        print(f"Applying Threshold: Low {global_state.threshold_low}, High {global_state.threshold_high}")
+        global_state.logger.info(f"Applying Threshold: Low {global_state.threshold_low}, High {global_state.threshold_high}")
         # Add threshold logic here
 
     if global_state.vw_simplification_enabled:
-        print(f"Applying VW Simplification with Factor: {global_state.vw_factor} then rounding to {global_state.rounding}")
+        global_state.logger.info(f"Applying VW Simplification with Factor: {global_state.vw_factor} then rounding to {global_state.rounding}")
         # Add VW simplification logic here
 
     # Save and regenerate funscript
     funscript_handler = FunscriptGenerator()
     # Simplifying the funscript data and generating the file
     funscript_handler.generate(global_state)
-    print("Funscript re-generation complete.")
+    global_state.logger.info("Funscript re-generation complete.")
     # Optional, compare generated funscript with reference funscript if specified, or a simple generic report
     funscript_handler.create_report_funscripts(global_state)
 
-    print("Report generation complete.")
+    global_state.logger.info("Report generation complete.")
 
 
 def quit_application():
     """
     Quit the application.
     """
-    print("Quitting the application...")
+    global_state.logger.info("Quitting the application...")
     root.quit()  # Close the Tkinter main loop
     root.destroy()  # Destroy the root window
 
 
 # GUI Setup
 root = tk.Tk()
-root.title("VR funscript generation helper")
+root.title("VR funscript AI Generator")
 
 # Variables
 video_path = tk.StringVar()
@@ -842,9 +800,7 @@ def quit_application():
 debug_mode_var = tk.BooleanVar()
 debug_record_mode_var = tk.BooleanVar()  # debug record mode
 live_display_mode_var = tk.BooleanVar()
-is_vr_var = tk.BooleanVar()
 enhance_lighting_var = tk.BooleanVar()
-video_reader_var = tk.StringVar()
 debug_record_duration_var = tk.StringVar(value="5")  # Default duration
 boost_enabled_var = tk.BooleanVar()
 boost_up_percent_var = tk.IntVar()
@@ -863,48 +819,21 @@ def quit_application():
 ttk.Entry(video_frame, textvariable=video_path, width=50).grid(row=0, column=1, padx=5, pady=5)
 ttk.Button(video_frame, text="Browse", command=select_video_file).grid(row=0, column=2, padx=5, pady=5, sticky="e")
 
+mode_label = ttk.Label(video_frame, text="Select Video Mode:")
+mode_label.grid(row=2, column=0, padx=5, pady=5, sticky="w")
 
-# Video Reader Selection
-reader_frame = ttk.LabelFrame(root, text="Video Reader", padding=(10, 5))
-reader_frame.grid(row=1, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
-
-ttk.Label(video_frame, text="Video Reader:").grid(row=1, column=0, padx=5, pady=5, sticky="w")
-video_reader_menu = ttk.OptionMenu(video_frame, video_reader_var, "FFmpeg", "FFmpeg", "OpenCV")
-video_reader_menu.grid(row=1, column=1, padx=5, pady=5, sticky="w")
-#text_label = ttk.Label(video_frame, text="<= Pick FFmpeg for VR undistortion.\n<= Pick OpenCV if perf. issues.")
-#text_label.grid(row=1, column=2, padx=5, pady=5, sticky="w")
-# VR Mode, activated by default
-is_vr_var.set(True)
-ttk.Checkbutton(video_frame, text="VR SBS video", variable=is_vr_var).grid(row=2, column=0, padx=5, pady=5)
-# enhance_lighting_var.set(False)
-# ttk.Checkbutton(video_frame, text="Enhance lighting",
-#                 variable=enhance_lighting_var,
-#                 command=lambda: setattr(global_state, 'vw_simplification_enabled', not global_state.enhance_lighting)).grid(row=2, column=1, padx=5, pady=5)
-
-# Frame Range
-frame_frame = ttk.LabelFrame(root, text="Optional settings", padding=(10, 5))
-frame_frame.grid(row=2, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
-
-ttk.Label(frame_frame, text="Frame Start:").grid(row=0, column=0, padx=5, pady=5, sticky="w")
-frame_start_entry = ttk.Entry(frame_frame, width=10)
-frame_start_entry.grid(row=0, column=1, padx=5, pady=5, sticky="w")
-
-ttk.Label(frame_frame, text="Frame End:").grid(row=1, column=0, padx=5, pady=5, sticky="w")
-frame_end_entry = ttk.Entry(frame_frame, width=10)
-frame_end_entry.grid(row=1, column=1, padx=5, pady=5, sticky="w")
-
-ttk.Label(frame_frame, text="Reference Script:").grid(row=2, column=0, padx=5, pady=5)
-ttk.Entry(frame_frame, textvariable=reference_script_path, width=50).grid(row=2, column=1, padx=5, pady=5)
-ttk.Button(frame_frame, text="Browse", command=select_reference_script).grid(row=2, column=2, padx=5, pady=5)
+mode_combobox = ttk.Combobox(video_frame, values=["VR SBS", "Flat - 2D POV"], state="readonly")
+mode_combobox.grid(row=2, column=1, padx=5, pady=5, sticky="w")
+mode_combobox.set("VR SBS")  # Set default value
 
 # Processing Mode Selection
 processing_frame = ttk.LabelFrame(root, text="Processing", padding=(10, 5))
-processing_frame.grid(row=4, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
+processing_frame.grid(row=1, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
 
 start_button = ttk.Button(processing_frame, text="Start Processing", command=start_processing)
 start_button.grid(row=0, column=0, padx=5, pady=5, sticky="w")
 
-ttk.Checkbutton(processing_frame, text="Logging for debug", variable=debug_mode_var).grid(row=0, column=1, padx=5, pady=5)
+ttk.Checkbutton(processing_frame, text="Logging for debug", variable=debug_mode_var).grid(row=0, column=2, padx=5, pady=5)
 debug_mode_var.set(True)
 # this one needs a fix
 # ttk.Checkbutton(processing_frame, text="Live inference => slow & heavy!", variable=live_display_mode_var).grid(row=0, column=2, padx=5, pady=5)
@@ -925,11 +854,41 @@ def quit_application():
 tracking_progress_percent = ttk.Label(processing_frame, text="0%")
 tracking_progress_percent.grid(row=2, column=2, padx=5, pady=5, sticky="w")
 
+# Frame Range (Collapsible)
+optional_settings = ttk.LabelFrame(root, text="Optional settings", padding=(10, 5))
+optional_settings.grid(row=2, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
+
+# Collapse/Expand Button
+def toggle_optional_settings():
+    if optional_settings_collapsible.winfo_ismapped():
+        optional_settings_collapsible.grid_remove()
+    else:
+        optional_settings_collapsible.grid()
+
+toggle_button = ttk.Button(optional_settings, text="Toggle Optional Settings", command=toggle_optional_settings)
+toggle_button.grid(row=0, column=0, padx=5, pady=5, sticky="w")
+
+# Collapsible Section
+optional_settings_collapsible = ttk.Frame(optional_settings)
+optional_settings_collapsible.grid(row=1, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
+
+ttk.Label(optional_settings_collapsible, text="Frame Start:").grid(row=0, column=0, padx=5, pady=5, sticky="w")
+frame_start_entry = ttk.Entry(optional_settings_collapsible, width=10)
+frame_start_entry.grid(row=0, column=1, padx=5, pady=5, sticky="w")
+
+ttk.Label(optional_settings_collapsible, text="Frame End:").grid(row=1, column=0, padx=5, pady=5, sticky="w")
+frame_end_entry = ttk.Entry(optional_settings_collapsible, width=10)
+frame_end_entry.grid(row=1, column=1, padx=5, pady=5, sticky="w")
+
+ttk.Label(optional_settings_collapsible, text="Reference Script:").grid(row=2, column=0, padx=5, pady=5)
+ttk.Entry(optional_settings_collapsible, textvariable=reference_script_path, width=50).grid(row=2, column=1, padx=5, pady=5)
+ttk.Button(optional_settings_collapsible, text="Browse", command=select_reference_script).grid(row=2, column=2, padx=5, pady=5)
 
+optional_settings_collapsible.grid_remove()
 
 # Funscript Tweaking Section (Collapsible)
 funscript_tweaking_frame = ttk.LabelFrame(root, text="Funscript Tweaking", padding=(10, 5))
-funscript_tweaking_frame.grid(row=5, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
+funscript_tweaking_frame.grid(row=3, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
 
 # Collapse/Expand Button
 def toggle_funscript_tweaking():
@@ -1009,13 +968,11 @@ def toggle_funscript_tweaking():
 regenerate_funscript_button = ttk.Button(funscript_tweaking_collapsible, text="Regenerate Funscript", command=lambda: regenerate_funscript(global_state))
 regenerate_funscript_button.grid(row=2, column=0, padx=5, pady=5, sticky="w")
 
-
-
 funscript_tweaking_collapsible.grid_remove()
 
 # Debug Record Mode
 debug_frame = ttk.LabelFrame(root, text="Debugging (Replay and navigate a processed video)", padding=(10, 5))
-debug_frame.grid(row=6, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
+debug_frame.grid(row=4, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
 
 quit_button = ttk.Button(debug_frame, text="Video (q to quit)", command=debug_function)
 quit_button.grid(row=0, column=0, padx=5, pady=5, sticky="w")
@@ -1029,12 +986,12 @@ def toggle_funscript_tweaking():
 
 # Quit Button
 button_frame = ttk.Frame(root)
-button_frame.grid(row=7, column=0, columnspan=3, padx=5, pady=10)
+button_frame.grid(row=5, column=0, columnspan=3, padx=5, pady=10)
 
 ttk.Button(button_frame, text="Quit", command=quit_application).grid(row=0, column=2, padx=5, pady=5)
 
 # Footer
 footer_label = ttk.Label(root, text="Individual and personal use only.\nNot for commercial use.\nk00gar 2025 - https://github.com/ack00gar", font=("Arial", 10, "italic", "bold"), justify="center")
-footer_label.grid(row=8, column=0, columnspan=3, padx=5, pady=5)
+footer_label.grid(row=6, column=0, columnspan=3, padx=5, pady=5)
 
 root.mainloop()
diff --git a/params/config.py b/params/config.py
index f8d11eb3..180621ee 100644
--- a/params/config.py
+++ b/params/config.py
@@ -57,7 +57,8 @@ def find_ffprobe_path():
 print(f"ffprobe_path: {ffprobe_path}")
 
 # Yolo detection settings
-max_frame_height = 1440
+# max_frame_height = 1440
+max_frame_height = 1080
 
 # models path
 
@@ -91,7 +92,7 @@ def find_ffprobe_path():
 # Define class names with their corresponding indices
 class_names = {
     'face': 0,
-    'hand':1, 'left hand': 1, 'right hand': 1,
+    'hand': 1, 'left hand': 1, 'right hand': 1,
     'penis': 2,
     'glans': 3,
     'pussy': 4,
@@ -99,7 +100,7 @@ def find_ffprobe_path():
     'anus': 6,
     'breast': 7,
     'navel': 8,
-    'foot':9, 'left foot': 9, 'right foot': 9,
+    'foot': 9, 'left foot': 9, 'right foot': 9,
     'hips center': 10
 }
 
@@ -134,4 +135,3 @@ def find_ffprobe_path():
 ]
 step_size = 120  # Speed step size for color transitions
 
-vw_filter_coeff = 2.0
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 275eb775..e07bdb3a 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -1,7 +1,9 @@
 import json
-import time
 import cv2
 import numpy as np
+import os
+import subprocess
+
 from params.config import class_colors
 from scipy.interpolate import interp1d
 from utils.lib_Visualizer import Visualizer
@@ -114,12 +116,10 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
             ret, frame = self.cap.read()
             if self.video_reader == "OpenCV" and self.isVR:
                 frame = frame[:, :frame.shape[1] // 2, :]  # only half left of the frame, for VR half
-            #if self.cap.is_VR:
-            #    frame_copy = frame[:, frame.shape[1] // 3 : 2 * frame.shape[1] // 3, :]
-            output_path = self.video_path.replace(".mp4", "_debug.mp4")
+            output_path = self.video_path.replace(".mp4", f"_debug_{start_frame}.mp4")
             fourcc = cv2.VideoWriter_fourcc(*"mp4v")
             out = cv2.VideoWriter(
-                output_path, fourcc, self.fps, (frame.shape[1] // downsize_ratio, frame.shape[0] // downsize_ratio)
+                output_path, fourcc, self.fps // 2, (frame.shape[1], frame.shape[0])
             )
             if not out.isOpened():
                 print(f"Error: Could not open video writer for {output_path}")
@@ -236,7 +236,6 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
 
             # Record the frame if enabled
             if record:
-                frame_copy = cv2.resize(frame_copy, (frame_copy.shape[1] // downsize_ratio, frame_copy.shape[0] // downsize_ratio))
                 out.write(frame_copy)
 
             self.current_frame += 1
@@ -245,6 +244,35 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
         self.cap.release()
         if record:
             out.release()
+
+            # Input and output paths
+            input_path = output_path
+            # Add "SPOILER_" to the filename
+            directory, filename = os.path.split(input_path)
+            new_filename = f"SPOILER_{filename}"
+            output_path_ffmpeg = os.path.join(directory, new_filename)
+
+            # FFmpeg command to convert to H.265
+            ffmpeg_command = [
+                "ffmpeg",
+                "-y",  # Overwrite output file if it exists
+                "-i", input_path,  # Input file
+                "-c:v", "libx265",  # Use H.265 codec
+                "-crf", "26",  # Constant Rate Factor (quality)
+                "-preset", "fast",  # Encoding speed
+                "-b:v", "5000k",  # Bitrate
+                "-movflags", "+faststart",  # Enable fast streaming
+                output_path_ffmpeg
+            ]
+
+            # Run FFmpeg command
+            subprocess.run(ffmpeg_command)
+
+            # Delete the intermediate file
+            if os.path.exists(input_path):
+                os.remove(input_path)
+                print(f"Deleted intermediate file: {input_path}")
+
         cv2.destroyAllWindows()
 
     def _get_funscript_value(self, interpolator, frame_id, fps):
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index d7674452..98c9c152 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -7,7 +7,7 @@
 import datetime
 from scipy.signal import savgol_filter
 
-from params.config import heatmap_colors, step_size, vw_filter_coeff
+from params.config import heatmap_colors, step_size
 
 import matplotlib
 matplotlib.use('Agg')  # Use a non-interactive backend
@@ -23,92 +23,92 @@ def generate(self, global_state):
 
         # Backup output file if it exists
         if os.path.exists(output_path):
-            print(f"Output path {output_path} already exists, backing up as {output_path}.bak...")
+            global_state.logger.warning(f"Output path {output_path} already exists, backing up as {output_path}.bak...")
             os.rename(output_path, output_path + ".bak")
 
         raw_funscript_path = global_state.video_file[:-4] + f"_rawfunscript.json"
 
         if len(global_state.funscript_data) == 0:
-            print("len funscript data is 0, trying to load file")
+            global_state.logger.info("len funscript data is 0, trying to load file")
             # Read the funscript data from the JSON file
             with open(raw_funscript_path, 'r') as f:
-                print(f"Loading funscript from {raw_funscript_path}")
+                global_state.logger.info(f"Loading funscript from {raw_funscript_path}")
                 try:
                     data = json.load(f)
                 except Exception as e:
-                    print(f"Error loading funscript from {raw_funscript_path}: {e}")
+                    global_state.logger.error(f"Error loading funscript from {raw_funscript_path}: {e}")
                     return
         else:
             data = global_state.funscript_data
 
         try:
-            print(f"Generating funscript based on {len(data)} points...")
+            global_state.logger.info(f"Generating funscript based on {len(data)} points...")
 
             # Extract timestamps and positions
             ats = [p[0] for p in data]
             positions = [p[1] for p in data]
 
-            print(f"Positions adjustment - step 1 (noise removal)")
+            global_state.logger.info(f"Positions adjustment - step 1 (noise removal)")
             # Run the Savitzky-Golay filter
-            positions = savgol_filter(positions, int(global_state.fps) // 4, 3)
+            positions = savgol_filter(positions, int(global_state.video_fps) // 4, 3)
 
             # zip adjusted positions
             zip_positions = list(zip(ats, positions))
 
             # Apply VW simplification if enabled
             if global_state.vw_simplification_enabled:
-                print("Positions adjustment - step 2 (VW algorithm simplification)")
+                global_state.logger.info("Positions adjustment - step 2 (VW algorithm simplification)")
                 zip_vw_positions = simplify_coords(zip_positions, global_state.vw_factor)
-                print(f"Length of VW filtered positions: {len(zip_vw_positions)}")
+                global_state.logger.info(f"Length of VW filtered positions: {len(zip_vw_positions)}")
             else:
-                print("Skipping positions adjustment - step 2 (VW algorithm simplification)")
+                global_state.logger.info("Skipping positions adjustment - step 2 (VW algorithm simplification)")
 
             # Extract timestamps and positions
             ats = [p[0] for p in zip_vw_positions]
             positions = [p[1] for p in zip_vw_positions]
 
             # Remap positions to 0-100 range
-            print("Positions adjustment - step 3 (remapping)")
+            global_state.logger.info("Positions adjustment - step 3 (remapping)")
             adjusted_positions = np.interp(positions, (min(positions), max(positions)), (0, 100))
 
             # Apply thresholding
             if global_state.threshold_enabled:
-                print(f"Positions adjustment - step 4 (thresholding)")
+                global_state.logger.info(f"Positions adjustment - step 4 (thresholding)")
                 adjusted_positions = adjusted_positions.tolist()  # Convert to list
                 adjusted_positions = [
                     0 if p < global_state.threshold_low else 100 if p > global_state.threshold_high else p for p in
                     adjusted_positions]
             else:
-                print("Skipping positions adjustment - step 4 (thresholding)")
+                global_state.logger.info("Skipping positions adjustment - step 4 (thresholding)")
 
             # Apply amplitude boosting
             if global_state.boost_enabled:
-                print("Positions adjustment - step 5 (amplitude boosting)")
+                global_state.logger.info("Positions adjustment - step 5 (amplitude boosting)")
                 #self.boost_amplitude(adjusted_positions, boost_factor=1.2, min_value=0, max_value=100)
                 adjusted_positions = self.adjust_peaks_and_lows(adjusted_positions,
                                                                 peak_boost=global_state.boost_up_percent,
                                                                 low_reduction=global_state.boost_down_percent)
             else:
-                print("Skipping positions adjustment - step 5 (amplitude boosting)")
+                global_state.logger.info("Skipping positions adjustment - step 5 (amplitude boosting)")
 
             # Round position values to the closest multiple of 5, still between 0 and 100
-            print("Positions adjustment - step 6 (rounding to the closest multiple of 5)")
+            global_state.logger.info("Positions adjustment - step 6 (rounding to the closest multiple of 5)")
             #adjusted_positions = [round(p, -1) for p in adjusted_positions] # multiple of 10
             adjusted_positions = [round(p / global_state.rounding) * global_state.rounding for p in adjusted_positions]
 
             # Recombine timestamps and adjusted positions
-            print("Re-assembling ats and positions")
+            global_state.logger.info("Re-assembling ats and positions")
             zip_adjusted_positions = list(zip(ats, adjusted_positions))
 
             # Write the final funscript
-            self.write_funscript(zip_adjusted_positions, output_path, global_state.fps)
-            print(f"Funscript generated and saved to {output_path}")
+            self.write_funscript(zip_adjusted_positions, output_path, global_state.video_fps)
+            global_state.logger.info(f"Funscript generated and saved to {output_path}")
 
             # Generate a heatmap
             self.generate_heatmap(output_path,
                                   output_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
         except Exception as e:
-            print(f"Error generating funscript: {e}")
+            global_state.logger.error(f"Error generating funscript: {e}")
 
     def write_funscript(self, distances, output_path, fps):
         with open(output_path, 'w') as f:
@@ -134,9 +134,6 @@ def generate_heatmap(self, funscript_path, output_image_path):
             times.insert(0, 0)
             positions.insert(0, 100)
 
-        # Print loaded data for debugging
-        #print(f"Times: {times}")
-        #print(f"Positions: {positions}")
         print(f"Total Actions: {len(times)}")
         print(f"Time Range: {times[0]} to {datetime.timedelta(seconds=int(times[-1] / 1000))}")
 
@@ -528,8 +525,8 @@ def create_combined_plot(self, ref_sections, gen_sections, screenshots, sections
                     ref_times_sec = [t / 1000 for t in ref_sections[idx][0]]
                     ax_plot.plot(ref_times_sec, ref_sections[idx][1], label='Reference', color='red')
 
-                start_time = datetime.timedelta(seconds=int(sections[idx][0]))  # datetime.datetime.fromtimestamp(sections[idx][0]).strftime('%H:%M:%S')
-                end_time = datetime.timedelta(seconds=int(sections[idx][1]))  # datetime.datetime.fromtimestamp(sections[idx][1]).strftime('%H:%M:%S')
+                start_time = datetime.timedelta(seconds=int(sections[idx][0]))
+                end_time = datetime.timedelta(seconds=int(sections[idx][1]))
                 ax_plot.set_title(f'Section {idx + 1}: {start_time} - {end_time}', fontsize=10)
                 ax_plot.set_xlabel('Time (s)')
                 ax_plot.set_ylabel('Position')
@@ -539,8 +536,11 @@ def create_combined_plot(self, ref_sections, gen_sections, screenshots, sections
         plt.tight_layout()
 
         # Save the figure
+        # Add "SPOILER_" to the filename
+        directory, filename = os.path.split(output_image_path)
+        new_filename = f"SPOILER_{filename}"
+        output_image_path = os.path.join(directory, new_filename)
         plt.savefig(output_image_path[:-4] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png", dpi=100)
-        # plt.show()
 
     def generate_heatmap_inline(self, ax, times, positions):
         """
diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index af8e7827..800c42a0 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -18,12 +18,14 @@ def __init__(self):
         self.height = 0  # Height of the locked penis box
         self.active = False  # Whether the locked penis box is active
         self.visible = 0 # Visibility of the locked penis box 0-100
+        self.area = 0
 
     def update(self, new_box, new_height, visible=None):
         """Update the locked penis box with new coordinates and height."""
         self.box = new_box
         self.height = new_height
         self.active = True
+        self.area = (self.box[2] - self.box[0]) * (self.box[3] - self.box[1])
         if visible is not None:
             self.visible = visible
 
@@ -32,6 +34,8 @@ def deactivate(self):
         self.box = None
         self.height = 0
         self.active = False
+        self.visible = 0
+        self.area = 0
 
     def get_box(self):
         """Return the coordinates of the locked penis box."""
@@ -70,14 +74,12 @@ class ObjectTracker:
     A class to track objects (e.g., penis, glans, etc.) in a video frame and determine their positions and interactions.
     """
 
-    def __init__(self, fps, frame_pos, image_area):
+    def __init__(self, global_state):
         """
         Initialize the ObjectTracker.
 
         Args:
-            fps (int): Frames per second of the video.
-            frame_pos (int): Current frame position.
-            image_area (int): Area of the video frame.
+            global_state
         """
         self.class_names = class_names  # List of class names to track
         self.active_tracks = {}  # Active tracks: {track_id: Track object}
@@ -85,10 +87,11 @@ def __init__(self, fps, frame_pos, image_area):
         self.distance_kf = KF.KalmanFilter()  # Kalman filter for distance smoothing
 
         self.frame = None  # Current video frame
-        self.current_frame_id = frame_pos  # Current frame ID
+        self.current_frame_id = global_state.current_frame_id  # frame_pos  # Current frame ID
         self.image_y_size = 0  # Height of the video frame
-        self.image_area = image_area  # Area of the video frame
-        self.fps = fps  # Frames per second of the video
+        self.image_area = global_state.frame_area # image_area  # Area of the video frame
+        self.fps = global_state.video_fps  # fps  # Frames per second of the video
+        self.isVR = global_state.isVR  # isVR
 
         # Speed and distance thresholds
         self.max_speed = 100 / (0.18 * self.fps)  # Maximum allowed speed for distance changes
@@ -99,7 +102,6 @@ def __init__(self, fps, frame_pos, image_area):
         self.tracked_boxes = []  # List of tracked boxes
         self.penis_box = None  # Detected penis box
         self.locked_penis_box = LockedPenisBox()  # Locked penis box
-        self.visible_part_of_penis = -1
         self.glans_detected = False  # Whether the glans is detected
         self.breast_tracking = False  # Whether breast tracking is active
         self.distance = 100  # Current distance
@@ -190,6 +192,61 @@ def boxes_overlap(self, box1, box2):
         x3, y3, x4, y4 = box2
         return not (x2 < x3 or x4 < x1 or y2 < y3 or y4 < y1)
 
+    def box_area(self, box):
+        """
+        Calculate the area of a bounding box.
+
+        Args:
+            box (tuple): Coordinates of the box (x1, y1, x2, y2).
+
+        Returns:
+            float: Area of the box.
+        """
+        if box is None:
+            return 0
+        x1, y1, x2, y2 = box
+        return (x2 - x1) * (y2 - y1)
+
+    def boxes_overlap_percentage(self, box1, box2):
+        """
+        Compute the percentage of box1's area that overlaps with box2.
+
+        Args:
+            box1 (tuple): Coordinates of the first box (x1, y1, x2, y2).
+            box2 (tuple): Coordinates of the second box (x3, y3, x4, y4).
+
+        Returns:
+            float: Percentage of box1's area that overlaps with box2.
+        """
+        if box1 is None or box2 is None:
+            return 0
+
+        # Unpack coordinates
+        x1, y1, x2, y2 = box1
+        x3, y3, x4, y4 = box2
+
+        # Check if boxes overlap
+        if x2 < x3 or x4 < x1 or y2 < y3 or y4 < y1:
+            return 0  # No overlap
+
+        # Calculate intersection coordinates
+        x_overlap = max(0, min(x2, x4) - max(x1, x3))
+        y_overlap = max(0, min(y2, y4) - max(y1, y3))
+
+        # Calculate intersection area
+        intersection_area = x_overlap * y_overlap
+
+        # Calculate area of box1
+        box1_area = self.box_area(box1)
+
+        # Avoid division by zero
+        if box1_area == 0:
+            return 0
+
+        # Compute overlap percentage
+        overlap_percentage = (intersection_area / box1_area) * 100
+        return overlap_percentage
+
     def calculate_distance(self, penis_box, other_box):
         """
         Calculate the distance between the penis box and another box.
@@ -228,17 +285,16 @@ def detect_sex_position_change(self, sex_position, reason):
             self.sex_position = most_frequent_position
             self.sex_position_reason = reason
 
-    def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
+    def tracking_logic(self, global_state, sorted_boxes):
         """
         Main tracking logic to process detected boxes and update tracking state.
 
         Args:
             sorted_boxes (list): List of detected boxes with confidence, class, and track ID.
-            current_frame_id (int): Current frame ID.
             image_y_size (int): Height of the video frame.
         """
-        self.current_frame_id = current_frame_id
-        self.image_y_size = image_y_size
+        self.current_frame_id = global_state.current_frame_id
+        self.image_y_size = global_state.image_y_size
 
         close_up_detected = False
 
@@ -312,6 +368,7 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                             return
 
         # Section where we compute the funscript positions
+        distance = -1
         sum_pos = 0
         sum_weight_pos = 0
 
@@ -327,6 +384,10 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                 breast_tracked = normalized_y
 
             elif self.locked_penis_box.is_active() and self.boxes_overlap(box, self.locked_penis_box.get_box()):
+                # case of a hand passing close to the penis, not touching
+                if (class_name in ['hand', 'foot'] and
+                        self.boxes_overlap_percentage(box, self.locked_penis_box.get_box()) < 20):
+                    continue
                 if class_name not in classes_touching_penis:
                     classes_touching_penis.append(class_name)
 
@@ -343,28 +404,42 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
 
                     low_y = y2 if class_name != 'butt' else (0.8 * (y2 - y1)) + y1
 
+                    real_pen_height = self.locked_penis_box.get_height()  # so as to fix the "large" yolo bounding
+                    real_pen_y3 = self.locked_penis_box.get_box()[3]
+
                     if class_name == 'penis':
-                        dist_to_penis_base = self.visible_part_of_penis
+                        dist_to_penis_base = self.locked_penis_box.visible
+                        # skipping the rest for now as a test as it was introducing noise
+                        continue
+                    elif class_name in ['hand', 'foot']:
+                        real_pen_height *= 0.6
+                        real_pen_y3 -= real_pen_height * 0.1
+                        dist_to_penis_base = int(
+                            ((real_pen_y3 - low_y) / real_pen_height) * 100)
                     else:
+                        real_pen_height *= 0.8
+                        real_pen_y3 -= real_pen_height * 0.1
                         dist_to_penis_base = int(
-                            ((self.locked_penis_box.get_box()[3] - low_y) / self.locked_penis_box.get_height()) * 100
-                        )
+                            ((real_pen_y3 - low_y) / real_pen_height) * 100)
+
                     normalized_dist_to_penis_base = min(max(0, dist_to_penis_base), 100)
                     self.update_normalized_distance_to_penis(track_id, normalized_dist_to_penis_base)
 
-                    # attempt : as a weight, we use the absolute position
-                    # weight_pos_track_id = sum(
-                    #    abs(self.tracked_positions[track_id][i] - self.tracked_positions[track_id][i - 1])
-                    #    for i in range(1, len(self.tracked_positions[track_id])))
+                    # case of a hand passing close to the penis, not touching
+                    #if (class_name in ['hand', 'foot'] and
+                    #        self.boxes_overlap_percentage(box, self.locked_penis_box.get_box()) < 20):
+                    #    continue
 
                     weight_pos_track_id = sum(
                         abs(self.normalized_distance_to_penis[track_id][i] - self.normalized_distance_to_penis[track_id][i - 1])
                         for i in range(1, len(self.normalized_distance_to_penis[track_id])))
+
                     if track_id not in self.weights:
                         self.weights[track_id] = []
                     self.weights[track_id].append(weight_pos_track_id)
 
-                    sum_pos += ((self.normalized_distance_to_penis[track_id][-1] + normalized_y) // 2) * weight_pos_track_id
+                    # TODO: check if we can get rid of normalized_y, even for grinding with less to none y movement
+                    sum_pos += ((self.normalized_distance_to_penis[track_id][-1] + 0.4 * normalized_y) // 1.4) * weight_pos_track_id
 
                     # attempt : use only the distance to penis value, discarding the absolute position
                     # sum_pos += self.normalized_distance_to_penis[track_id][-1] * weight_pos_track_id
@@ -383,11 +458,16 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                 weight = int((self.weights[track_id][-1] / sum_weight_pos) * 100)
                 self.pct_weights[track_id].append(weight)
 
-        if len(classes_touching_penis) == 0 or not self.locked_penis_box.is_active():
+        if not self.locked_penis_box.is_active():
             self.penetration = False
             distance = 100
             self.detect_sex_position_change('Not relevant', "no part touching penis / no penis")
             self.tracked_body_part = 'Nothing'
+        elif len(classes_touching_penis) == 0 and not self.glans_detected:
+            self.penetration = True
+            distance = self.locked_penis_box.visible
+            self.detect_sex_position_change('Unknown', "no part touching penis identified")
+            self.tracked_body_part = 'penis'
         elif 'pussy' in classes_touching_penis and not self.glans_detected:
             if self.sex_position == 'Missionnary / Cowgirl':
                 self.penetration = True
@@ -414,8 +494,13 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
         elif sum_weight_pos == 0 and self.sex_position == 'Missionnary / Cowgirl' and 'pussy' not in classes_touching_penis and breast_tracked:
             distance = breast_tracked
             self.breast_tracking = True
+        elif distance != -1:
+            # meaning we defaulted to visible penis area in the logic
+            pass
         else:
-            distance = 100
+            # meaning distance was not actuated
+            distance = self.previous_distances[-1]
+            # distance = 100
 
         self.update_distance(distance)
 
@@ -443,7 +528,11 @@ def handle_class_first(self, class_name, box, conf):
 
                         # Move locked penis box towards current penis box
                         #max_move = max(1, int(self.image_y_size / 960))
-                        max_move = 120 // int(self.fps)
+                        if self.isVR:
+                            max_move = 120 // int(self.fps)
+                        else:
+                            # in 2D POV, camera is not still, actuation needs to be faster
+                            max_move = 240 // int(self.fps)
 
                         if abs(self.penis_box[0] - self.locked_penis_box.get_box()[0]) > max_move:
                             px1 = self.penis_box[0] + np.sign(self.penis_box[0] - self.locked_penis_box.get_box()[0]) * max_move
@@ -454,13 +543,10 @@ def handle_class_first(self, class_name, box, conf):
 
                         self.locked_penis_box.update((px1, py2 - self.locked_penis_box.get_height(), px2, py2), self.locked_penis_box.get_height())
 
-                        self.visible_part_of_penis = min(100, int((current_height / (self.locked_penis_box.get_height() * .7)) * 100))
-                        # values most likely to be 25 to 100 give a residual box at full penetration, need to remap 0-100
-                        self.visible_part_of_penis = int((self.visible_part_of_penis - 25) / 75 * 100)
+                        penis_box_area = (self.penis_box[2] - self.penis_box[0]) * (self.penis_box[3] - self.penis_box[1])
+                        self.locked_penis_box.visible = min(100, (penis_box_area // (self.locked_penis_box.area * .8)) * 100)
                     else:
                         self.locked_penis_box.update(self.penis_box, current_height)
-            else:
-                self.visible_part_of_penis = -1
         elif class_name == 'glans' and box:
             if self.consecutive_detections['glans'] >= self.detections_threshold:
                 self.boxes['glans'] = box
@@ -491,9 +577,9 @@ def update_tracked_positions(self, track_id, mid_y):
             self.tracked_positions[track_id] = []
         if len(self.tracked_positions[track_id]) > 2:
             mid_y = (
-                0.2 * self.tracked_positions[track_id][-2]
-                + 0.3 * self.tracked_positions[track_id][-1]
-                + 0.5 * mid_y
+                0.1 * self.tracked_positions[track_id][-2]
+                + 0.2 * self.tracked_positions[track_id][-1]
+                + 0.7 * mid_y
             )
         self.tracked_positions[track_id].append(int(mid_y))
         if len(self.tracked_positions[track_id]) > 600:
@@ -521,9 +607,9 @@ def update_normalized_distance_to_penis(self, track_id, normalized_dist_to_penis
             self.normalized_distance_to_penis[track_id] = []
         if len(self.normalized_distance_to_penis[track_id]) > 2:
             normalized_dist_to_penis_base = (
-                0.2 * self.normalized_distance_to_penis[track_id][-2]
-                + 0.3 * self.normalized_distance_to_penis[track_id][-1]
-                + 0.5 * normalized_dist_to_penis_base
+                0.1 * self.normalized_distance_to_penis[track_id][-2]
+                + 0.2 * self.normalized_distance_to_penis[track_id][-1]
+                + 0.7 * normalized_dist_to_penis_base
             )
         self.normalized_distance_to_penis[track_id].append(normalized_dist_to_penis_base)
         if len(self.normalized_distance_to_penis[track_id]) > 60:
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index a4d46674..3cc7ef39 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -78,9 +78,12 @@ def _initialize_video_info(self):
             self.duration = duration * 1000  # Convert duration to milliseconds
 
             # limiting the frame height here has no additional performance cost and significantly improves speed for 1440p+ video
-            scaling_factor = min(max_frame_height / self.width, max_frame_height / self.height)
-            self.width = int(self.width * scaling_factor)
-            self.height = int(self.height * scaling_factor)
+            if self.is_VR:
+                scaling_factor = min(max_frame_height / self.width, max_frame_height / self.height)
+                self.width = int(self.width * scaling_factor)
+                self.height = int(self.height * scaling_factor)
+            else:  # in case of 2D video
+                pass
 
             print(f"FPS: {self.fps}, Resolution: {self.width}x{self.height}, "
                   f"Codec: {self.codec}, Total Frames: {self.total_frames}, Duration: {self.duration:.2f} ms")
@@ -148,7 +151,7 @@ def _start_process(self, start_frame=0):
                 "-threads", "0",  # Use maximum threads available
                 "-",  # Output to stdout
             ]
-        else:
+        else:  # 2D, no unwarping, limiting the size to 1080p
             # FFmpeg command to read frames
             cmd = [
                 self.ffmpeg_path,
@@ -160,6 +163,7 @@ def _start_process(self, start_frame=0):
                 "-f", "rawvideo",  # Output raw video data
                 "-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
                 "-vsync", "0",  # Disable frame rate synchronization
+                "-vf", "scale=-1:'min(1080,ih)'",  # Resize height to max 1080p, width is auto-calculated
                 "-",  # Output to stdout
             ]
 

From e6a523823bcdcd12fc96e00d860ba422f0ee4583 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 14 Jan 2025 10:43:43 +0100
Subject: [PATCH 064/277] * 2D POV tweaks (close up threshold value, locked p
 box actuation speed, pos estimation based on inserted length)

---
 utils/lib_ObjectTracker.py | 21 ++++++++++++++-------
 1 file changed, 14 insertions(+), 7 deletions(-)

diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index 800c42a0..48f3a017 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -355,7 +355,12 @@ def tracking_logic(self, global_state, sorted_boxes):
                         butt_box_area = (b_x2 - b_x1) * (b_y2 - b_y1)
 
                         # Check if the butt box is unusually large (close-up)
-                        if butt_box_area > 0.15 * self.image_area:
+                        if self.isVR:
+                            size_threshold = 0.15
+                        else:  #2D POV
+                            size_threshold = 0.4
+
+                        if butt_box_area > size_threshold * self.image_area:
                             close_up_detected = True
                             self.detect_sex_position_change('Close up', 'Butt box size beyond threshold')
                             if not self.close_up:
@@ -409,8 +414,10 @@ def tracking_logic(self, global_state, sorted_boxes):
 
                     if class_name == 'penis':
                         dist_to_penis_base = self.locked_penis_box.visible
-                        # skipping the rest for now as a test as it was introducing noise
-                        continue
+                        if self.isVR:  # skipping the rest for now as a test as it was introducing noise
+                            continue
+                        else:  # 2D POV, trying to reinject this here
+                            pass
                     elif class_name in ['hand', 'foot']:
                         real_pen_height *= 0.6
                         real_pen_y3 -= real_pen_height * 0.1
@@ -499,8 +506,8 @@ def tracking_logic(self, global_state, sorted_boxes):
             pass
         else:
             # meaning distance was not actuated
-            distance = self.previous_distances[-1]
-            # distance = 100
+            # distance = self.previous_distances[-1]
+            distance = 100
 
         self.update_distance(distance)
 
@@ -529,10 +536,10 @@ def handle_class_first(self, class_name, box, conf):
                         # Move locked penis box towards current penis box
                         #max_move = max(1, int(self.image_y_size / 960))
                         if self.isVR:
-                            max_move = 120 // int(self.fps)
+                            max_move = 180 // int(self.fps)
                         else:
                             # in 2D POV, camera is not still, actuation needs to be faster
-                            max_move = 240 // int(self.fps)
+                            max_move = 360 // int(self.fps)
 
                         if abs(self.penis_box[0] - self.locked_penis_box.get_box()[0]) > max_move:
                             px1 = self.penis_box[0] + np.sign(self.penis_box[0] - self.locked_penis_box.get_box()[0]) * max_move

From 991e283ed622ead0f10094c17a468748d8b332f7 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 14 Jan 2025 10:44:03 +0100
Subject: [PATCH 065/277] * Removed unused code

---
 utils/lib_Visualizer.py | 76 -----------------------------------------
 1 file changed, 76 deletions(-)

diff --git a/utils/lib_Visualizer.py b/utils/lib_Visualizer.py
index f272ce3c..64f7997c 100644
--- a/utils/lib_Visualizer.py
+++ b/utils/lib_Visualizer.py
@@ -1,6 +1,4 @@
 import cv2
-import numpy as np
-import matplotlib.pyplot as plt
 
 class Visualizer:
     def draw_bounding_box(self, image, box, label, color, offset_x=0):
@@ -22,81 +20,7 @@ def draw_gauge(self, image, distance):
         fill_height = int((distance / 100) * gauge_height)
         cv2.rectangle(image, (gauge_x, gauge_y + gauge_height - fill_height), (gauge_x + gauge_width, gauge_y + gauge_height), (0, 255, 0), -1)
         # Draw the distance text on top of the filled part of the gauge
-        #cv2.putText(image, str(int(distance)), (gauge_x + gauge_width // 2, gauge_y + gauge_height // 2), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
         cv2.putText(image, str(int(distance)), (gauge_x, gauge_y + gauge_height - fill_height - 5),
                     cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
         return image
-"""
-    def draw_limited_graph(self, image, distances, frames, nb_frames):
-        # Plot the frame indices vs positions for the last nb frames.
-        # Determine the number of frames to plot
-        num_frames_to_plot = min(nb_frames, len(distances))
 
-        # Get the last 'num_frames_to_plot' frames
-        last_frame_indices = frames[-num_frames_to_plot:]
-        last_positions = distances[-num_frames_to_plot:]
-
-        graph_height = image.shape[0] // 4
-        graph_width = image.shape[1] - 20 - 10
-        graph_x = 10
-        graph_y = image.shape[0] // 10
-
-        fig = plt.figure(figsize=(graph_width / 100, graph_height / 100), dpi=100)
-        ax = fig.add_subplot(111)
-        ax.plot(range(len(last_frame_indices)), last_positions, color='green', linewidth=1)
-        ax.set_ylim(0, 100)
-        ax.set_xlim(0, nb_frames)  # Limit the x-axis to nb_frames
-        fig.canvas.draw()
-
-        # Get the width and height of the figure
-        width, height = fig.canvas.get_width_height()
-
-        # Convert the figure to an image
-        graph_image = np.frombuffer(fig.canvas.buffer_rgba(), dtype='uint8').reshape((height, width, 3))
-        graph_image = cv2.cvtColor(graph_image, cv2.COLOR_RGBA2RGB)  # Convert from RGBA to RGB
-        graph_image = cv2.resize(graph_image, (graph_width, graph_height))
-
-        #graph_image = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8').reshape(
-        #    fig.canvas.get_width_height()[::-1] + (3,))
-        #graph_image = np.frombuffer(fig.canvas.buffer_rgba(), dtype='uint8').reshape(
-        #    fig.canvas.get_width_height()[::-1] + (4,))
-        #graph_image = cv2.resize(graph_image, (graph_width, graph_height))
-
-        image[graph_y:graph_y + graph_height, graph_x:graph_x + graph_width] = cv2.addWeighted(
-            image[graph_y:graph_y + graph_height, graph_x:graph_x + graph_width],
-            0.5,
-            graph_image,
-            0.5,
-            0
-        )
-        plt.close(fig)
-        return image
-
-    def draw_graph(self, image, distances, frames, reference_positions, reference_frames):
-        graph_height = image.shape[0] // 4
-        graph_width = image.shape[1] - 20 - 10
-        graph_x = 10
-        graph_y = image.shape[0] // 10
-        fig = plt.figure(figsize=(graph_width / 100, graph_height / 100), dpi=100)
-        ax = fig.add_subplot(111)
-        if reference_positions:
-            ax.plot(reference_frames, reference_positions, color='red', linewidth=1)
-        ax.plot(range(len(distances)), distances, color='green', linewidth=1)
-        # Draw the last 600 frames
-
-        ax.set_ylim(0, 100)
-        ax.set_xlim(0, len(distances))
-        fig.canvas.draw()
-        graph_image = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8').reshape(fig.canvas.get_width_height()[::-1] + (3,))
-        graph_image = cv2.resize(graph_image, (graph_width, graph_height))
-        image[graph_y:graph_y + graph_height, graph_x:graph_x + graph_width] = cv2.addWeighted(
-            image[graph_y:graph_y + graph_height, graph_x:graph_x + graph_width],
-            0.5,
-            graph_image,
-            0.5,
-            0
-        )
-        plt.close(fig)
-        return image
-
-"""

From 9c7a6152b71e381dd73f869d565b5a4b1d8bf506 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Thu, 16 Jan 2025 23:41:36 +0100
Subject: [PATCH 066/277] Updates: v0.0.1_25-01-16

* Funscript pos. estimation - Fixed - Close up did not deactivate the locked-penis-box, interfering with funscript pos, when it should be 100
* Funscript pos. estimation - Fixed - Doggy not triggering action, under specific circumstances
* Funscript pos. estimation - Fixed - Major issue in penis-inserted length computation
* Funscript pos. estimation - Fixed - Cowgirl not defaulting to tracking absolute position of breast when pussy is covered (her red cloth at some point of the video), under specific circumstances
* Funscript pos. estimation - Reworked - Part of the body parts interaction logic, lessening sudden jerky moves from one position to the other
* Funscript pos. estimation - Reworked - Discarded hands positions passing over penis location during penetration

* Application - Added - A proper logging system, writing to both console and FSGenerator.log => please share that file for any debugging purpose along with all other output files
* Application - Added - A version number, for reference, both in logs and in funscript 'version' field
* Application - Removed - All message dialog boxes that would prevent a batch processing approach as suggested by @jcwicked (thanks again, and PR on hold for now)
---
 FSGenerator.py                 |  83 ++++++++++---------
 params/config.py               |   2 +
 utils/lib_FunscriptHandler.py  |  55 ++++++-------
 utils/lib_ObjectTracker.py     | 144 ++++++++++++++++++++-------------
 utils/lib_VideoReaderFFmpeg.py |  12 ---
 5 files changed, 158 insertions(+), 138 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index 0e42224a..e43e0696 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -10,18 +10,18 @@
 import tkinter as tk  # GUI library for macOS for basic use in our case
 from tkinter import filedialog, messagebox, ttk  # here, what was I saying...
 import threading
-from datetime import datetime
+from datetime import timedelta
 import logging
 import sys
 
 # Import custom modules and configurations
 from params.config import (class_priority_order, class_reverse_match, class_colors,
-                           yolo_models, max_frame_height)  # Configuration for class priorities, reverse matching, and colors
+                           yolo_models, max_frame_height, version, ffmpeg_path, ffprobe_path)  # Configuration for class priorities, reverse matching, and colors
 from utils.lib_ObjectTracker import ObjectTracker  # Custom object tracking logic
 from utils.lib_FunscriptHandler import FunscriptGenerator  # For generating Funscript files
 from utils.lib_Visualizer import Visualizer  # For visualizing results
 from utils.lib_Debugger import Debugger  # For debugging and logging
-from utils.lib_SceneCutsDetect import detect_scene_changes  # For detecting scene changes in videos
+# from utils.lib_SceneCutsDetect import detect_scene_changes  # For detecting scene changes in videos
 from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg  # Custom video reader using FFmpeg
 
 # TODO this is a workaround and needs to be fixed properly
@@ -69,22 +69,32 @@ def __init__(self):
         self.vw_simplification_enabled = True
         self.vw_factor = 8.0
         self.rounding = 5
-        # Configure logging (simple setup)
-        # Initialize logger
-        logging.basicConfig(
-            level=logging.INFO,
-            format=f"@{self.current_frame_id} - %(levelname)s - %(message)s",  # Log format
-            filename="FSGenerator.log",
-            filemode="w",
-            handlers=[
-                logging.FileHandler("FSGenerator.log"),  # Save logs to a file
-                logging.StreamHandler(sys.stdout)  # Print logs to the console
-            ]
-        )
+
+        # Create a logger
         self.logger = logging.getLogger("GlobalStateLogger")
-        self.logger.setLevel(logging.INFO)
-        self.logger.info(f"@{self.current_frame_id} - Initiated logger in global_state instance")
 
+        # Disable propagation to the root logger
+        self.logger.propagate = False
+
+        # Check if the logger already has handlers
+        if not self.logger.handlers:
+            self.logger.setLevel(logging.INFO)
+
+            # Create a file handler
+            file_handler = logging.FileHandler("FSGenerator.log", mode="w")
+            file_handler.setLevel(logging.INFO)
+            file_formatter = logging.Formatter(f"%(levelname)s - %(message)s")
+            file_handler.setFormatter(file_formatter)
+
+            # Create a console handler
+            console_handler = logging.StreamHandler(sys.stdout)
+            console_handler.setLevel(logging.INFO)
+            console_formatter = logging.Formatter(f"%(levelname)s - %(message)s")
+            console_handler.setFormatter(console_formatter)
+
+            # Add handlers to the logger
+            self.logger.addHandler(file_handler)
+            self.logger.addHandler(console_handler)
 
 # Initialize global state
 global_state = GlobalState()
@@ -206,14 +216,9 @@ def extract_yolo_data(progress_callback=None):
         # messagebox to ask if user wants to overwrite or reuse
         # file name without path
         file_name = os.path.basename(global_state.video_file[:-4] + f"_rawyolo.json")
-        skip_detection = messagebox.askyesno("Detection file already exists",
-                                             f"File {file_name} already exists.\n\nClick Yes to reuse the existing detections file.\nClick No to perform detections again.")
-        if skip_detection:
-            global_state.logger.info(
-                f"File {global_state.video_file[:-4] + f'_rawyolo.json'} already exists. Skipping detections and loading file content...")
-            return
-        else:
-            os.remove(global_state.video_file[:-4] + f"_rawyolo.json")
+        global_state.logger.warning(
+            f"File {file_name} already exists. Skipping detections and loading file content...")
+        return
 
     records = []  # List to store detection records
     test_result = Result(320)  # Test result object for debugging
@@ -294,11 +299,6 @@ def extract_yolo_data(progress_callback=None):
 
                     # Check if keypoints are detected
                     if yolo_pose_results[0].keypoints is not None:
-                        # print("We have keypoints")
-                        # pose_keypoints = yolo_pose_results[0].keypoints.cpu()
-                        # pose_track_ids = yolo_pose_results[0].boxes.id.cpu().tolist()
-                        # pose_boxes = yolo_pose_results[0].boxes.xywh.cpu()
-                        # pose_classes = yolo_pose_results[0].boxes.cls.cpu().tolist()
                         pose_confs = yolo_pose_results[0].boxes.conf.cpu().tolist()
 
                         pose_keypoints = yolo_pose_results[0].keypoints.cpu()
@@ -488,9 +488,10 @@ def analyze_tracking_results(results, image_y_size, progress_callback=None):
                                    bounding_boxes=bounding_boxes,
                                    variables={
                                        'frame': frame_pos,
-                                       # time of the frame hh:mm:ss
-                                       'time': datetime.fromtimestamp(frame_pos / fps).strftime('%H:%M:%S'),
+                                       'time': str(timedelta(seconds=int(frame_pos / fps))),
                                        'distance': tracker.distance,
+                                       #'lead class': tracker.lead_class + ' | ' + str(tracker.lead_trackid) + ' | '
+                                       #              + str(tracker.lead_trackid_count),
                                        'Penetration': tracker.penetration,
                                        'sex_position': tracker.sex_position,
                                        'sex_position_reason': tracker.sex_position_reason,
@@ -607,19 +608,22 @@ def check_video_resolution(video_path):
     global_state.video_fps = float(cap.get(cv2.CAP_PROP_FPS))
     global_state.logger.info(f"Video FPS: {global_state.video_fps}")
     if not cap.isOpened():
-        messagebox.showerror("Error", "Could not open the video file.")
+        global_state.logger.error(f"Could not open the video file: {video_path}")
+        # messagebox.showerror("Error", "Could not open the video file.")
         return
 
     height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
     cap.release()
 
     if height > max_frame_height:
-        messagebox.showinfo("Info", f"The video height is {height}p > {max_frame_height}p.\nIt will be automatically resized on the fly, no conversion required.")
+        global_state.logger.info(f"The video height is {height}p > {max_frame_height}p.\nIt will be automatically resized on the fly, no conversion required.")
+        #messagebox.showinfo("Info", f"The video height is {height}p > {max_frame_height}p.\nIt will be automatically resized on the fly, no conversion required.")
 
 def common_initialization():
     global_state.video_file = video_path.get()
     if not global_state.video_file:
         messagebox.showerror("Error", "Please select a video file.")
+        global_state.logger.error("Please select a video file.")
         return
 
     global_state.yolo_det_model = get_yolo_model_path()
@@ -648,12 +652,15 @@ def common_initialization():
     global_state.video_fps = cap.get(cv2.CAP_PROP_FPS)
     cap.release()
 
+    global_state.logger.info(f"Running script version: {version}")
     global_state.logger.info(f"Processing video: {global_state.video_file}")
+    global_state.logger.info(f"ffmpeg path: {ffmpeg_path}")
+    global_state.logger.info(f"ffprobe path: {ffprobe_path}")
     global_state.logger.info(f"Image size: {global_state.image_x_size}x{global_state.image_y_size}")
     global_state.logger.info(f"FPS: {global_state.video_fps}")
     global_state.logger.info(f"Video Reader: {global_state.video_reader}")
     global_state.logger.info(f"YOLO Detection Model: {global_state.yolo_det_model}")
-    global_state.logger.info(f"YOLO Pose Model: {global_state.yolo_pose_model}")
+    # global_state.logger.info(f"YOLO Pose Model: {global_state.yolo_pose_model}")
     global_state.logger.info(f"Debug Mode: {global_state.DebugMode}")
     global_state.logger.info(f"Live Display Mode: {global_state.LiveDisplayMode}")
     global_state.logger.info(f"VR Mode: {global_state.isVR}")
@@ -747,11 +754,13 @@ def debug_function():
                                          record=global_state.debug_record_mode,
                                          downsize_ratio=2)
     else:
+        global_state.logger.error(f"Debug logs file not found: {global_state.video_file[:-4] + f'_debug_logs.json'}")
         messagebox.showinfo("Info", f"Debug logs file not found: {global_state.video_file[:-4] + f'_debug_logs.json'}")
 
 def regenerate_funscript(global_state):
     global_state.video_file = video_path.get()
     if not global_state.video_file:
+        global_state.logger.error("Please select a video file.")
         messagebox.showerror("Error", "Please select a video file.")
         return
     global_state.reference_script = reference_script_path.get()
@@ -792,7 +801,7 @@ def quit_application():
 
 # GUI Setup
 root = tk.Tk()
-root.title("VR funscript AI Generator")
+root.title("VR & 2D POV Funscript AI Generator")
 
 # Variables
 video_path = tk.StringVar()
@@ -833,7 +842,7 @@ def quit_application():
 start_button = ttk.Button(processing_frame, text="Start Processing", command=start_processing)
 start_button.grid(row=0, column=0, padx=5, pady=5, sticky="w")
 
-ttk.Checkbutton(processing_frame, text="Logging for debug", variable=debug_mode_var).grid(row=0, column=2, padx=5, pady=5)
+#ttk.Checkbutton(processing_frame, text="Logging for debug", variable=debug_mode_var).grid(row=0, column=2, padx=5, pady=5)
 debug_mode_var.set(True)
 # this one needs a fix
 # ttk.Checkbutton(processing_frame, text="Live inference => slow & heavy!", variable=live_display_mode_var).grid(row=0, column=2, padx=5, pady=5)
diff --git a/params/config.py b/params/config.py
index 180621ee..61a5c5bf 100644
--- a/params/config.py
+++ b/params/config.py
@@ -2,6 +2,8 @@
 import platform
 import os
 
+version = "0.0.1_25-01-16"
+
 # ffmpeg and ffprobe paths - replace with your own if not in your system path
 win_ffmpeg_path = "C:/ffmpeg/bin/ffmpeg.exe"
 mac_ffmpeg_path = "/usr/local/bin/ffmpeg"
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 98c9c152..13cf2591 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -7,7 +7,7 @@
 import datetime
 from scipy.signal import savgol_filter
 
-from params.config import heatmap_colors, step_size
+from params.config import heatmap_colors, step_size, version
 
 import matplotlib
 matplotlib.use('Agg')  # Use a non-interactive backend
@@ -106,13 +106,16 @@ def generate(self, global_state):
 
             # Generate a heatmap
             self.generate_heatmap(output_path,
-                                  output_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
+                                  output_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png",
+                                  global_state)
         except Exception as e:
             global_state.logger.error(f"Error generating funscript: {e}")
 
     def write_funscript(self, distances, output_path, fps):
         with open(output_path, 'w') as f:
-            f.write('{"version":"1.0","inverted":false,"range":95,"author":"kAI","actions":[{"at": 0, "pos": 100},')
+            #f.write('f{"version":"{version}","inverted":false,"range":95,"author":"FunGen_k00gar_AI","actions":[{"at": 0, "pos": 100},')
+            f.write(
+                f'{{"version":"{version}","inverted":false,"range":95,"author":"FunGen_k00gar_AI","actions":[{{"at": 0, "pos": 100}},')
             i = 0
             for frame, position in distances:
                 time_ms = int(frame * 1000 / fps)
@@ -122,11 +125,11 @@ def write_funscript(self, distances, output_path, fps):
                 i += 1
             f.write("]}\n")
 
-    def generate_heatmap(self, funscript_path, output_image_path):
+    def generate_heatmap(self, funscript_path, output_image_path, global_state):
         # Load funscript data
-        times, positions, _, _ = self.load_funscript(funscript_path)
+        times, positions, _, _ = self.load_funscript(funscript_path, global_state)
         if not times or not positions:
-            print("Failed to load funscript data.")
+            global_state.logger.error("Failed to load funscript data.")
             return
 
         # add a timing: 0, position: 100 at the beginning if no value for 0
@@ -134,12 +137,11 @@ def generate_heatmap(self, funscript_path, output_image_path):
             times.insert(0, 0)
             positions.insert(0, 100)
 
-        print(f"Total Actions: {len(times)}")
-        print(f"Time Range: {times[0]} to {datetime.timedelta(seconds=int(times[-1] / 1000))}")
+        global_state.logger.info(f"Total Actions: {len(times)}")
+        global_state.logger.info(f"Time Range: {times[0]} to {datetime.timedelta(seconds=int(times[-1] / 1000))}")
 
         # Calculate speed (position change per time interval)
         speeds = np.abs(np.diff(positions) / np.diff(times)) * 1000  # Positions per second
-        print(f"Speeds: {speeds}")
 
         def get_color(intensity):
             if intensity <= 0:
@@ -201,7 +203,7 @@ def get_color(intensity):
         # Save the figure
         plt.savefig(output_image_path, bbox_inches='tight', dpi=200)  # Increase resolution
         plt.close()
-        print(f"Funscript heatmap saved to {output_image_path}")
+        global_state.logger.info(f"Funscript heatmap saved to {output_image_path}")
 
     def boost_amplitude(self, signal, boost_factor, min_value=0, max_value=100):
         """
@@ -293,20 +295,20 @@ def filter_positions(self, positions, fps):
 
         return filtered_positions
 
-    def load_funscript(self, funscript_path):
+    def load_funscript(self, funscript_path, global_state):
         # if the funscript path exists
         if not os.path.exists(funscript_path):
-            print(f"Funscript not found at {funscript_path}")
+            global_state.logger.error(f"Funscript not found at {funscript_path}")
             return None, None, None, None
 
         with open(funscript_path, 'r') as f:
             try:
-                print(f"Loading funscript from {funscript_path}")
+                global_state.logger.info(f"Loading funscript from {funscript_path}")
                 data = json.load(f)
             except json.JSONDecodeError as e:
-                print(f"JSONDecodeError: {e}")
-                print(f"Error occurred at line {e.lineno}, column {e.colno}")
-                print("Dumping the problematic JSON data:")
+                global_state.logger.error(f"JSONDecodeError: {e}")
+                global_state.logger.error(f"Error occurred at line {e.lineno}, column {e.colno}")
+                global_state.logger.error("Dumping the problematic JSON data:")
                 f.seek(0)  # Reset file pointer to the beginning
                 print(f.read())
                 return None, None, None, None
@@ -317,7 +319,7 @@ def load_funscript(self, funscript_path):
         for action in data['actions']:
             times.append(action['at'])
             positions.append(action['pos'])
-        print(f"Loaded funscript with {len(times)} actions")
+        global_state.logger.info(f"Loaded funscript with {len(times)} actions")
 
         # Access the chapters
         chapters = data.get("metadata", {}).get("chapters", [])
@@ -349,7 +351,7 @@ def create_report_funscripts(self, global_state):
 
         if global_state.reference_script:
             # Load reference funscript
-            ref_times, ref_positions, _, _ = self.load_funscript(global_state.reference_script)
+            ref_times, ref_positions, _, _ = self.load_funscript(global_state.reference_script, global_state)
 
             # if no 0 at the beginning, add it
             if ref_times and ref_times[0] != 0:
@@ -360,7 +362,7 @@ def create_report_funscripts(self, global_state):
             total_duration = ref_times[-1] / 1000 if ref_times else 0
         else:
             ref_times, ref_positions = [], []
-            gen_times, gen_positions, _, _ = self.load_funscript(generated_paths[0])
+            gen_times, gen_positions, _, _ = self.load_funscript(generated_paths[0], global_state)
             total_duration = gen_times[-1] / 1000 if gen_times else 0
 
         # Select 6 random non-overlapping 20-second sections
@@ -371,7 +373,7 @@ def create_report_funscripts(self, global_state):
 
         # Load generated funscripts
         for generated_path in generated_paths:
-            gen_times, gen_positions, _, _ = self.load_funscript(generated_path)
+            gen_times, gen_positions, _, _ = self.load_funscript(generated_path, global_state)
             # Extract data for each section
             ref_sections = []
             gen_sections = []
@@ -390,7 +392,7 @@ def create_report_funscripts(self, global_state):
             # Plot and combine
             self.create_combined_plot(
                 ref_sections, gen_sections, screenshots, sections, global_state.video_file[:-4] + "_report.png",
-                ref_times, ref_positions, gen_times, gen_positions
+                ref_times, ref_positions, gen_times, gen_positions, global_state
             )
 
 
@@ -443,7 +445,7 @@ def capture_screenshots(self, video_path, isVR, sections):
         return screenshots
 
     def create_combined_plot(self, ref_sections, gen_sections, screenshots, sections, output_image_path, ref_times,
-                                 ref_positions, gen_times, gen_positions):
+                                 ref_positions, gen_times, gen_positions, global_state):
         """
         Creates a combined plot with heatmaps as a header, comparative information, and section comparisons below.
 
@@ -787,12 +789,3 @@ def _is_plateau_in_original(self, plateau, original_plateaus):
             if start >= original_start and end <= original_end:
                 return True
         return False
-
-if __name__ == "__main__":
-    video_path = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
-
-    funscript_handler = FunscriptGenerator()
-
-    # generate heatmap
-    funscript_handler.generate_heatmap(video_path[:-4] + ".funscript", video_path[
-                                                                        :-4] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index 48f3a017..9854fdb2 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -3,10 +3,6 @@
 import numpy as np
 from params.config import class_names
 import math
-import logging
-
-# Configure logging
-logging.basicConfig(level=logging.INFO, format="%(message)s")
 
 class LockedPenisBox:
     """
@@ -81,6 +77,7 @@ def __init__(self, global_state):
         Args:
             global_state
         """
+        self.global_state = global_state
         self.class_names = class_names  # List of class names to track
         self.active_tracks = {}  # Active tracks: {track_id: Track object}
         self.inactive_tracks = []  # Inactive tracks (lost tracks)
@@ -261,7 +258,7 @@ def calculate_distance(self, penis_box, other_box):
         if other_box is None:
             return None
         if penis_box is None:
-            logging.warning(f"Penis box is None for frame {self.current_frame_id}, cannot compute distance")
+            self.global_state.logger.warning(f"Penis box is None for frame {self.current_frame_id}, cannot compute distance")
             return None
         ox1, oy1, ox2, oy2 = other_box
         y_pos = (oy1 + 2 * oy2) // 3
@@ -281,7 +278,7 @@ def detect_sex_position_change(self, sex_position, reason):
         position_counts = {position: self.sex_position_history.count(position) for position in self.sex_position_history}
         most_frequent_position = max(position_counts, key=position_counts.get, default="Not relevant")
         if most_frequent_position != self.sex_position:
-            logging.info(f"@{self.current_frame_id} - Sex position switched to: {most_frequent_position}")
+            self.global_state.logger.info(f"@{self.current_frame_id} - Sex position switched to: {most_frequent_position}")
             self.sex_position = most_frequent_position
             self.sex_position_reason = reason
 
@@ -293,6 +290,7 @@ def tracking_logic(self, global_state, sorted_boxes):
             sorted_boxes (list): List of detected boxes with confidence, class, and track ID.
             image_y_size (int): Height of the video frame.
         """
+        self.global_state = global_state
         self.current_frame_id = global_state.current_frame_id
         self.image_y_size = global_state.image_y_size
 
@@ -336,10 +334,14 @@ def tracking_logic(self, global_state, sorted_boxes):
                 self.consecutive_detections[check_class_first] = 0
                 self.consecutive_non_detections[check_class_first] += 1
 
-        if self.consecutive_non_detections[check_class_first] > self.detections_threshold and check_class_first == 'penis':
-            if self.locked_penis_box.is_active():
+        if self.consecutive_non_detections['penis'] > self.detections_threshold * 30 \
+                and not self.penetration:  # equivalent to 3 seconds
+            if self.locked_penis_box.active:  # is_active():
                 self.locked_penis_box.deactivate()
-                logging.info(f"@{self.current_frame_id} - Deactivated locked_penis_box")
+                self.global_state.logger.info(f"@{self.current_frame_id} - Deactivated locked_penis_box")
+
+        if self.locked_penis_box.is_active() and self.locked_penis_box.visible < 100 and not self.glans_detected:
+            self.penetration = True
 
         # Check if pussy boxes are inside butt boxes
         if 'pussy' in all_detections and 'butt' in all_detections:
@@ -364,14 +366,30 @@ def tracking_logic(self, global_state, sorted_boxes):
                             close_up_detected = True
                             self.detect_sex_position_change('Close up', 'Butt box size beyond threshold')
                             if not self.close_up:
-                                logging.info(
+                                self.global_state.logger.info(
                                     f"@{self.current_frame_id} - Close up detected - butt size beyond threshold: {int((butt_box_area / self.image_area) * 100)}%"
                                 )
                                 self.close_up = True
+                            self.penetration = False
+                            distance = 100
+                            self.update_distance(distance)
+                            return
+
+                        # could still be close-up, checking if we see a penis
+                        if 'penis' not in all_detections:
+                            close_up_detected = True
+                            self.detect_sex_position_change('Close up', 'No penis detected')
+                            if not self.close_up:
+                                self.global_state.logger.info(f"@{self.current_frame_id} - Close up detected - no penis detected")
+                                self.close_up = True
+                            self.penetration = False
                             distance = 100
                             self.update_distance(distance)
                             return
 
+        # if we reach here, this means we are not in close_up mode (theoretically)
+        self.close_up = False
+
         # Section where we compute the funscript positions
         distance = -1
         sum_pos = 0
@@ -389,10 +407,14 @@ def tracking_logic(self, global_state, sorted_boxes):
                 breast_tracked = normalized_y
 
             elif self.locked_penis_box.is_active() and self.boxes_overlap(box, self.locked_penis_box.get_box()):
+
+                # TODO: case of a body part passing behind the penis, no touching
+
                 # case of a hand passing close to the penis, not touching
                 if (class_name in ['hand', 'foot'] and
                         self.boxes_overlap_percentage(box, self.locked_penis_box.get_box()) < 20):
                     continue
+
                 if class_name not in classes_touching_penis:
                     classes_touching_penis.append(class_name)
 
@@ -403,23 +425,18 @@ def tracking_logic(self, global_state, sorted_boxes):
                 else:
                     mid_y = (y1 + y2) // 2
 
-                normalized_y = self.update_tracked_positions(track_id, mid_y)
+                self.update_tracked_positions(track_id, mid_y)
 
-                if class_name != 'hips center':
+                if class_name != 'hips center':  # in case we reintroduce the pose model
 
                     low_y = y2 if class_name != 'butt' else (0.8 * (y2 - y1)) + y1
-
                     real_pen_height = self.locked_penis_box.get_height()  # so as to fix the "large" yolo bounding
                     real_pen_y3 = self.locked_penis_box.get_box()[3]
 
                     if class_name == 'penis':
                         dist_to_penis_base = self.locked_penis_box.visible
-                        if self.isVR:  # skipping the rest for now as a test as it was introducing noise
-                            continue
-                        else:  # 2D POV, trying to reinject this here
-                            pass
                     elif class_name in ['hand', 'foot']:
-                        real_pen_height *= 0.6
+                        real_pen_height *= 0.7
                         real_pen_y3 -= real_pen_height * 0.1
                         dist_to_penis_base = int(
                             ((real_pen_y3 - low_y) / real_pen_height) * 100)
@@ -432,11 +449,6 @@ def tracking_logic(self, global_state, sorted_boxes):
                     normalized_dist_to_penis_base = min(max(0, dist_to_penis_base), 100)
                     self.update_normalized_distance_to_penis(track_id, normalized_dist_to_penis_base)
 
-                    # case of a hand passing close to the penis, not touching
-                    #if (class_name in ['hand', 'foot'] and
-                    #        self.boxes_overlap_percentage(box, self.locked_penis_box.get_box()) < 20):
-                    #    continue
-
                     weight_pos_track_id = sum(
                         abs(self.normalized_distance_to_penis[track_id][i] - self.normalized_distance_to_penis[track_id][i - 1])
                         for i in range(1, len(self.normalized_distance_to_penis[track_id])))
@@ -445,46 +457,42 @@ def tracking_logic(self, global_state, sorted_boxes):
                         self.weights[track_id] = []
                     self.weights[track_id].append(weight_pos_track_id)
 
-                    # TODO: check if we can get rid of normalized_y, even for grinding with less to none y movement
-                    sum_pos += ((self.normalized_distance_to_penis[track_id][-1] + 0.4 * normalized_y) // 1.4) * weight_pos_track_id
-
-                    # attempt : use only the distance to penis value, discarding the absolute position
-                    # sum_pos += self.normalized_distance_to_penis[track_id][-1] * weight_pos_track_id
-                    sum_weight_pos += weight_pos_track_id
-
-        for _, class_name, track_id in self.tracked_boxes:
-            # disregard penis class if hand or foot in classes_touching_penis, because of obstruction issue
-            if class_name == 'penis' and ('hand' in classes_touching_penis or 'foot' in classes_touching_penis):
-                continue
-            # saves the weight for the current track_id
-            if track_id not in self.pct_weights:
-                self.pct_weights[track_id] = []
-            if track_id not in self.weights:
-                continue
-            if sum_weight_pos > 0:
-                weight = int((self.weights[track_id][-1] / sum_weight_pos) * 100)
-                self.pct_weights[track_id].append(weight)
-
-        if not self.locked_penis_box.is_active():
+        if not self.locked_penis_box.active:
             self.penetration = False
             distance = 100
             self.detect_sex_position_change('Not relevant', "no part touching penis / no penis")
             self.tracked_body_part = 'Nothing'
-        elif len(classes_touching_penis) == 0 and not self.glans_detected:
-            self.penetration = True
-            distance = self.locked_penis_box.visible
-            self.detect_sex_position_change('Unknown', "no part touching penis identified")
-            self.tracked_body_part = 'penis'
+        elif len(classes_touching_penis) == 0 and not self.glans_detected and self.penetration:
+            # could be a blinking moment...
+            # but it could be that the penis is fully inserted and the glans is not visible
+            # TEST
+            #distance = 0
+            # case of lost pussy & penis tracking for instance, fallback to breast tracking
+            distance = breast_tracked
+            self.breast_tracking = True
+            self.tracked_body_part = 'breast'
+            self.detect_sex_position_change('Missionnary / Cowgirl', "breast visible while pussy is not")
+            # pass
+            #self.penetration = True
+            #distance = self.locked_penis_box.visible
+            #self.detect_sex_position_change('Unknown', "no part touching penis identified")
+            #self.tracked_body_part = 'penis'
         elif 'pussy' in classes_touching_penis and not self.glans_detected:
             if self.sex_position == 'Missionnary / Cowgirl':
+                # remove hands from the weights
+                if 'hand' in classes_touching_penis:
+                    classes_touching_penis.remove('hand')
                 self.penetration = True
             self.detect_sex_position_change('Missionnary / Cowgirl', "pussy visible and touching")
             self.tracked_body_part = 'pussy'
-        elif 'ass' in classes_touching_penis and not self.glans_detected:
+        elif 'butt' in classes_touching_penis and not self.glans_detected:
             if self.sex_position == 'Doggy / Rev. Cowgirl':
+                # remove hands from the weights
+                if 'hand' in classes_touching_penis:
+                    classes_touching_penis.remove('hand')
                 self.penetration = True
-            self.detect_sex_position_change('Doggy / Rev. Cowgirl', "ass visible and touching")
-            self.tracked_body_part = 'ass'
+            self.detect_sex_position_change('Doggy / Rev. Cowgirl', "butt visible and touching")
+            self.tracked_body_part = 'butt'
         elif ('hand' in classes_touching_penis or 'face' in classes_touching_penis) and not self.penetration:
             self.detect_sex_position_change('Handjob / Blowjob', "hand or face visible and touching")
             if 'face' not in classes_touching_penis:
@@ -496,6 +504,24 @@ def tracking_logic(self, global_state, sorted_boxes):
             self.detect_sex_position_change('Boobjob', "breast visible and touching")
             self.tracked_body_part = 'breast'
 
+        track_ids_to_consider = []
+        for _, class_name, track_id in self.tracked_boxes:
+            # disregard penis class if hand or foot in classes_touching_penis, because of obstruction issue
+            if class_name == 'penis' and ('hand' in classes_touching_penis or 'foot' in classes_touching_penis):
+                continue
+            # saves the weight for the current track_id
+            if track_id not in self.pct_weights:
+                self.pct_weights[track_id] = []
+            track_ids_to_consider.append(track_id)
+            sum_pos += ((self.normalized_distance_to_penis[track_id][-1] + 0.4 *
+                         self.normalized_absolute_tracked_positions[track_id][-1]) // 1.4) * self.weights[track_id][-1]
+            sum_weight_pos += self.weights[track_id][-1]
+
+        for track_id in track_ids_to_consider:
+            if sum_weight_pos > 0:
+                weight = int((self.weights[track_id][-1] / sum_weight_pos) * 100)
+                self.pct_weights[track_id].append(weight)
+
         if sum_weight_pos > 0 and self.sex_position not in ['Not relevant', 'Close up']:
             distance = int(sum_pos / sum_weight_pos)
         elif sum_weight_pos == 0 and self.sex_position == 'Missionnary / Cowgirl' and 'pussy' not in classes_touching_penis and breast_tracked:
@@ -522,7 +548,7 @@ def handle_class_first(self, class_name, box, conf):
         """
         if class_name == 'penis':
             if box is not None and self.penis_box is None:
-                logging.info(f"@{self.current_frame_id} - Penis detected with confidence {conf}")
+                self.global_state.logger.info(f"@{self.current_frame_id} - Penis detected with confidence {conf}")
             self.penis_box = box
 
             if self.penis_box:
@@ -551,7 +577,9 @@ def handle_class_first(self, class_name, box, conf):
                         self.locked_penis_box.update((px1, py2 - self.locked_penis_box.get_height(), px2, py2), self.locked_penis_box.get_height())
 
                         penis_box_area = (self.penis_box[2] - self.penis_box[0]) * (self.penis_box[3] - self.penis_box[1])
-                        self.locked_penis_box.visible = min(100, (penis_box_area // (self.locked_penis_box.area * .8)) * 100)
+                        visible = min(100, ((penis_box_area / self.locked_penis_box.area) * 100))
+                        visible_adj = (max(0, visible - 20) * 100) // 80  # readjusting given a residual box always remains
+                        self.locked_penis_box.visible = visible_adj
                     else:
                         self.locked_penis_box.update(self.penis_box, current_height)
         elif class_name == 'glans' and box:
@@ -562,9 +590,9 @@ def handle_class_first(self, class_name, box, conf):
                     self.locked_penis_box.update(self.penis_box, self.penis_box[3] - self.penis_box[1])
                 if self.penetration:
                     self.penetration = False
-                    logging.info(
-                        f"@{self.current_frame_id} - Penetration ended after {self.consecutive_detections['glans']} detections of glans"
-                    )
+                    #logging.info(
+                    #    f"@{self.current_frame_id} - Penetration ended after {self.consecutive_detections['glans']} detections of glans"
+                    #)
                     if self.tracked_body_part != 'Nothing':
                         self.normalized_distances[self.tracked_body_part].clear()
                         self.normalized_distances[self.tracked_body_part].append(100)
@@ -620,4 +648,4 @@ def update_normalized_distance_to_penis(self, track_id, normalized_dist_to_penis
             )
         self.normalized_distance_to_penis[track_id].append(normalized_dist_to_penis_base)
         if len(self.normalized_distance_to_penis[track_id]) > 60:
-            self.normalized_distance_to_penis[track_id].pop(0)
+            self.normalized_distance_to_penis[track_id].pop(0)
\ No newline at end of file
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index 3cc7ef39..8d9fbcaf 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -1,7 +1,6 @@
 import subprocess
 import cv2
 import numpy as np
-import argparse
 from params.config import ffmpeg_path, ffprobe_path, max_frame_height
 
 
@@ -260,14 +259,3 @@ def main(video_path, is_VR=False):
     # Release resources
     video_reader.release()
     cv2.destroyAllWindows()
-
-
-if __name__ == "__main__":
-    # Parse command-line arguments
-    parser = argparse.ArgumentParser(description="Display a video using FFmpeg.")
-    parser.add_argument("video_path", type=str, help="Path to the video file.")
-    parser.add_argument("--is_vr", action="store_true", help="Enable VR mode for processing VR videos.")
-    args = parser.parse_args()
-
-    # Run the main function
-    main(args.video_path, args.is_vr)
\ No newline at end of file

From 05b2e0643180b5716516366c750f24d81c661565 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sat, 18 Jan 2025 18:13:10 +0100
Subject: [PATCH 067/277] Updates: v0.0.2_25-01-18

* Fixed an issue in case of processing more than one video without restarting the app
---
 FSGenerator.py   | 6 ++++++
 params/config.py | 2 +-
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index e43e0696..52338381 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -620,6 +620,12 @@ def check_video_resolution(video_path):
         #messagebox.showinfo("Info", f"The video height is {height}p > {max_frame_height}p.\nIt will be automatically resized on the fly, no conversion required.")
 
 def common_initialization():
+    # Reinitialize global state in case the user wants to run another video
+    global_state.funscript_data = []
+    global_state.funscript_frames = []
+    global_state.funscript_distances = []
+
+    # Now, proceed
     global_state.video_file = video_path.get()
     if not global_state.video_file:
         messagebox.showerror("Error", "Please select a video file.")
diff --git a/params/config.py b/params/config.py
index 61a5c5bf..4d0f5f97 100644
--- a/params/config.py
+++ b/params/config.py
@@ -2,7 +2,7 @@
 import platform
 import os
 
-version = "0.0.1_25-01-16"
+version = "0.0.2_25-01-18"
 
 # ffmpeg and ffprobe paths - replace with your own if not in your system path
 win_ffmpeg_path = "C:/ffmpeg/bin/ffmpeg.exe"

From b3612a774a7339a4b2b20f74301812a7be377518 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 19 Jan 2025 17:35:21 +0100
Subject: [PATCH 068/277] Implementation of queueing system, performance
 improvements, decoupled gui, refactoring, code organisation

---
 .gitignore                                    |    9 +-
 FSGenerator.py                                | 1010 +----------------
 config.py                                     |  141 +++
 debug/Display_debug_results.py                |    4 +-
 params/config.py                              |  139 ---
 requirements.txt                              |   16 +
 script_generator/cli/generate_funscript.py    |   25 +
 script_generator/config.py                    |   48 +
 script_generator/gui/app.py                   |   57 +
 .../gui/controller/debug_video.py             |   31 +
 .../gui/controller/regenerate_funscript.py    |   37 +
 .../gui/controller/tracking_analysis.py       |   53 +
 .../gui/controller/video_analysis.py          |   38 +
 script_generator/gui/messages/messages.py     |    9 +
 script_generator/gui/utils/tooltip.py         |   26 +
 script_generator/gui/utils/widgets.py         |  304 +++++
 .../gui/views/funscript_generator.py          |  207 ++++
 .../analye_tracking_results.py                |  201 ++++
 .../object_detection/box_record.py            |   22 +
 .../object_detection_result.py                |   47 +
 .../object_detection/post_process_results.py  |  133 +++
 script_generator/object_detection/utils.py    |  104 ++
 script_generator/object_detection/yolo.py     |   43 +
 script_generator/scripts/analyse_video.py     |  203 ++++
 .../scripts/generate_funscript.py             |    6 +
 script_generator/scripts/tracking_analysis.py |    0
 script_generator/state/app_state.py           |   57 +
 .../tasks/abstract_task_processor.py          |  108 ++
 script_generator/tasks/tasks.py               |   81 ++
 script_generator/utils/file.py                |   39 +
 script_generator/utils/helpers.py             |    5 +
 script_generator/utils/logger.py              |   16 +
 script_generator/video/ffmpeg.py              |   56 +
 .../video/video_conversion/opengl/helpers.py  |   59 +
 .../vr_to_2d_task_processor.py                |  100 ++
 script_generator/video/video_info.py          |   67 ++
 .../video/video_task_processor.py             |  118 ++
 setup.py                                      |    1 +
 tests/test_Compare_models_and_resolutions.py  |    6 +-
 tests/test_PoseEstimation.py                  |    4 +-
 tests/test_detect_compare_unwarped.py         |    4 +-
 tests/test_generate_funscript.py              |   21 +
 utils/lib_Debugger.py                         |   82 +-
 utils/lib_FunscriptHandler.py                 |  297 +++--
 utils/lib_ObjectTracker.py                    |   75 +-
 utils/lib_VideoReaderFFmpeg.py                |  204 ++--
 utils/lib_Visualizer.py                       |   76 ++
 47 files changed, 2937 insertions(+), 1452 deletions(-)
 create mode 100644 config.py
 delete mode 100644 params/config.py
 create mode 100644 requirements.txt
 create mode 100644 script_generator/cli/generate_funscript.py
 create mode 100644 script_generator/config.py
 create mode 100644 script_generator/gui/app.py
 create mode 100644 script_generator/gui/controller/debug_video.py
 create mode 100644 script_generator/gui/controller/regenerate_funscript.py
 create mode 100644 script_generator/gui/controller/tracking_analysis.py
 create mode 100644 script_generator/gui/controller/video_analysis.py
 create mode 100644 script_generator/gui/messages/messages.py
 create mode 100644 script_generator/gui/utils/tooltip.py
 create mode 100644 script_generator/gui/utils/widgets.py
 create mode 100644 script_generator/gui/views/funscript_generator.py
 create mode 100644 script_generator/object_detection/analye_tracking_results.py
 create mode 100644 script_generator/object_detection/box_record.py
 create mode 100644 script_generator/object_detection/object_detection_result.py
 create mode 100644 script_generator/object_detection/post_process_results.py
 create mode 100644 script_generator/object_detection/utils.py
 create mode 100644 script_generator/object_detection/yolo.py
 create mode 100644 script_generator/scripts/analyse_video.py
 create mode 100644 script_generator/scripts/generate_funscript.py
 create mode 100644 script_generator/scripts/tracking_analysis.py
 create mode 100644 script_generator/state/app_state.py
 create mode 100644 script_generator/tasks/abstract_task_processor.py
 create mode 100644 script_generator/tasks/tasks.py
 create mode 100644 script_generator/utils/file.py
 create mode 100644 script_generator/utils/helpers.py
 create mode 100644 script_generator/utils/logger.py
 create mode 100644 script_generator/video/ffmpeg.py
 create mode 100644 script_generator/video/video_conversion/opengl/helpers.py
 create mode 100644 script_generator/video/video_conversion/vr_to_2d_task_processor.py
 create mode 100644 script_generator/video/video_info.py
 create mode 100644 script_generator/video/video_task_processor.py
 create mode 100644 setup.py
 create mode 100644 tests/test_generate_funscript.py

diff --git a/.gitignore b/.gitignore
index 156d0bb1..d4b20317 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,5 @@
-/models/
-
-models/
+/models/*
+/output/*
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -169,7 +168,7 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
+.idea/
 
 # PyPI configuration file
-.pypirc
\ No newline at end of file
+.pypirc
diff --git a/FSGenerator.py b/FSGenerator.py
index 52338381..a7efd4f9 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -1,1012 +1,8 @@
-# Import necessary libraries
-import time  # For measuring execution time
-import os  # For file and directory operations
-import cv2  # OpenCV for image and video processing
-import json  # For handling JSON data
-from tqdm import tqdm  # For progress bars
-from ultralytics import YOLO  # YOLO model for object detection
-import platform  # For identifying the operating system
-import torch  # PyTorch for use of .pt model if not on Apple device
-import tkinter as tk  # GUI library for macOS for basic use in our case
-from tkinter import filedialog, messagebox, ttk  # here, what was I saying...
-import threading
-from datetime import timedelta
-import logging
-import sys
+import os
 
-# Import custom modules and configurations
-from params.config import (class_priority_order, class_reverse_match, class_colors,
-                           yolo_models, max_frame_height, version, ffmpeg_path, ffprobe_path)  # Configuration for class priorities, reverse matching, and colors
-from utils.lib_ObjectTracker import ObjectTracker  # Custom object tracking logic
-from utils.lib_FunscriptHandler import FunscriptGenerator  # For generating Funscript files
-from utils.lib_Visualizer import Visualizer  # For visualizing results
-from utils.lib_Debugger import Debugger  # For debugging and logging
-# from utils.lib_SceneCutsDetect import detect_scene_changes  # For detecting scene changes in videos
-from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg  # Custom video reader using FFmpeg
+from script_generator.gui.app import start_app
 
 # TODO this is a workaround and needs to be fixed properly
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
-# Define a GlobalState class to manage global variables
-class GlobalState:
-    def __init__(self):
-        # YOLO models
-        self.yolo_det_model = ""
-        self.yolo_pose_model = ""
-        # Video info
-        self.video_file = ""
-        self.video_fps = 1
-        self.frame_start = 0
-        self.frame_end = None
-        self.current_frame_id = 0
-        self.current_frame = None  # actual frame
-        self.frame_area = 0
-        self.image_y_size = 0
-        self.image_x_size = 0
-        # Attributes
-        self.isVR = True
-        self.reference_script = ""
-        self.offset_x = 0
-        # Funscript data
-        self.funscript_data = []  # List to store Funscript data
-        self.funscript_frames = []
-        self.funscript_distances = []
-        # App instances and variables
-        self.debugger = None
-        self.DebugMode = False
-        self.debug_record_mode = False
-        self.debug_record_duration = 0
-        self.LiveDisplayMode = False
-        self.video_reader = "FFmpeg"
-        self.enhance_lighting = False
-        # Funscript Tweaking Variables
-        self.boost_enabled = True
-        self.boost_up_percent = 10
-        self.boost_down_percent = 15
-        self.threshold_enabled = True
-        self.threshold_low = 10
-        self.threshold_high = 90
-        self.vw_simplification_enabled = True
-        self.vw_factor = 8.0
-        self.rounding = 5
-
-        # Create a logger
-        self.logger = logging.getLogger("GlobalStateLogger")
-
-        # Disable propagation to the root logger
-        self.logger.propagate = False
-
-        # Check if the logger already has handlers
-        if not self.logger.handlers:
-            self.logger.setLevel(logging.INFO)
-
-            # Create a file handler
-            file_handler = logging.FileHandler("FSGenerator.log", mode="w")
-            file_handler.setLevel(logging.INFO)
-            file_formatter = logging.Formatter(f"%(levelname)s - %(message)s")
-            file_handler.setFormatter(file_formatter)
-
-            # Create a console handler
-            console_handler = logging.StreamHandler(sys.stdout)
-            console_handler.setLevel(logging.INFO)
-            console_formatter = logging.Formatter(f"%(levelname)s - %(message)s")
-            console_handler.setFormatter(console_formatter)
-
-            # Add handlers to the logger
-            self.logger.addHandler(file_handler)
-            self.logger.addHandler(console_handler)
-
-# Initialize global state
-global_state = GlobalState()
-
-# Define the BoxRecord class to store bounding box information
-class BoxRecord:
-    def __init__(self, box, conf, cls, class_name, track_id):
-        """
-        Initialize a BoxRecord object.
-        :param box: Bounding box coordinates [x1, y1, x2, y2].
-        :param conf: Confidence score of the detection.
-        :param cls: Class ID of the detected object.
-        :param class_name: Class name of the detected object.
-        :param track_id: Track ID for object tracking.
-        """
-        self.box = box
-        self.conf = conf
-        self.cls = cls
-        self.class_name = class_name
-        self.track_id = int(track_id)
-
-    def __iter__(self):
-        """
-        Make the BoxRecord object iterable.
-        :return: An iterator over the box, confidence, class, class name, and track ID.
-        """
-        return iter((self.box, self.conf, self.cls, self.class_name, self.track_id))
-
-# Define the Result class to store and manage detection results
-class Result:
-    def __init__(self, image_width):
-        """
-        Initialize a Result object.
-        :param image_width: Width of the image/frame.
-        """
-        self.frame_data = {}  # Dictionary to store data for each frame
-        self.image_width = image_width
-
-    def add_record(self, frame_id, box_record):
-        """
-        Add a BoxRecord to the frame_data dictionary.
-        :param frame_id: The frame ID to which the record belongs.
-        :param box_record: The BoxRecord object to add.
-        """
-        if frame_id in self.frame_data:
-            self.frame_data[frame_id].append(box_record)
-        else:
-            self.frame_data[frame_id] = [box_record]
-
-    def get_boxes(self, frame_id):
-        """
-        Retrieve and sort bounding boxes for a specific frame.
-        :param frame_id: The frame ID to retrieve boxes for.
-        :return: A list of sorted bounding boxes.
-        """
-        itemized_boxes = []
-        if frame_id not in self.frame_data:
-            return itemized_boxes
-        boxes = self.frame_data[frame_id]
-        for box, conf, cls, class_name, track_id in boxes:
-            itemized_boxes.append((box, conf, cls, class_name, track_id))
-        # Sort boxes based on class priority order
-        sorted_boxes = sorted(
-            itemized_boxes,
-            key=lambda x: class_priority_order.get(x[3], 7)  # Default priority is 7 if class not found
-        )
-        return sorted_boxes
-
-    def get_all_frame_ids(self):
-        """
-        Get a list of all frame IDs in the frame_data dictionary.
-        :return: A list of frame IDs.
-        """
-        return list(self.frame_data.keys())
-
-def write_dataset(file_path, data):
-    """
-    Write data to a JSON file.
-    :param file_path: The path to the output file.
-    :param data: The data to write.
-    """
-    global_state.logger.info(f"Exporting data...")
-    export_start = time.time()
-    # If the file already exists, rename it as a backup
-    if os.path.exists(file_path):
-        os.rename(file_path, file_path + ".bak")
-    # Write the data to the file
-    with open(file_path, 'w') as f:
-        json.dump(data, f)
-    export_end = time.time()
-    global_state.logger.info(f"Done in {export_end - export_start}.")
-
-def get_yolo_model_path():
-    # Check if the device is an Apple device
-    if platform.system() == 'Darwin':
-        global_state.logger.info(f"Apple device detected, loading {yolo_models[0]} for MPS inference.")
-        return yolo_models[0]
-
-    # Check if CUDA is available (for GPU support)
-    elif torch.cuda.is_available():
-        global_state.logger.info(f"CUDA is available, loading {yolo_models[1]} for GPU inference.")
-        return yolo_models[1]
-
-    # Fallback to ONNX model for other platforms without CUDA
-    else:
-        global_state.logger.warning("CUDA not available, if this is unexpected, please install CUDA and check your version of torch.")
-        global_state.logger.info("You might need to install a dependency with the following command (example):")
-        global_state.logger.info("pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118")
-        global_state.logger.info(f"Falling back to CPU inference, loading {yolo_models[2]}.")
-        global_state.logger.warning("WARNING: CPU inference may be slow on some devices.")
-
-        return yolo_models[2]
-
-def extract_yolo_data(progress_callback=None):
-    """
-    Extract YOLO detection data from a video.
-    """
-    if os.path.exists(global_state.video_file[:-4] + f"_rawyolo.json"):
-        # messagebox to ask if user wants to overwrite or reuse
-        # file name without path
-        file_name = os.path.basename(global_state.video_file[:-4] + f"_rawyolo.json")
-        global_state.logger.warning(
-            f"File {file_name} already exists. Skipping detections and loading file content...")
-        return
-
-    records = []  # List to store detection records
-    test_result = Result(320)  # Test result object for debugging
-
-    # Initialize the video reader
-    cap = VideoReaderFFmpeg(global_state.video_file, is_VR=global_state.isVR)  # Initialize the video reader
-    cap.set(cv2.CAP_PROP_POS_FRAMES, global_state.frame_start)
-
-    # Determine the last frame to process
-    if global_state.frame_end:
-        last_frame = global_state.frame_end
-    else:
-        last_frame = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-
-    # Load the YOLO model
-    det_model = YOLO(global_state.yolo_det_model, task="detect")
-
-    # make the pose model optional
-    if len(global_state.yolo_pose_model) > 0:
-        run_pose_model = True
-        global_state.logger.info("Activating pose model")
-    else:
-        run_pose_model = False
-        global_state.logger.info("Discarding pose model part of the code")
-    if run_pose_model:
-        pose_model = YOLO(global_state.yolo_pose_model, task="pose")
-
-    # Start time for ETA calculation
-    start_time = time.time()
-
-    # Loop through the video frames
-    for frame_pos in tqdm(range(global_state.frame_start, last_frame), ncols=None, desc="Performing YOLO detection on frames"):
-        success, frame = cap.read()  # Read a frame from the video
-
-        if success:
-            # Run YOLO tracking on the frame
-            yolo_det_results = det_model.track(frame, persist=True, conf=0.3, verbose=False)
-            if run_pose_model:
-                yolo_pose_results = pose_model.track(frame, persist=True, conf=0.3, verbose=False)
-
-            if yolo_det_results[0].boxes.id is None:  # Skip if no tracks are found
-                continue
-
-            if len(yolo_det_results[0].boxes) == 0 and not global_state.LiveDisplayMode:  # Skip if no boxes are detected
-                continue
-
-            ### DETECTION of BODY PARTS
-            # Extract track IDs, boxes, classes, and confidence scores
-            track_ids = yolo_det_results[0].boxes.id.cpu().tolist()
-            boxes = yolo_det_results[0].boxes.xywh.cpu()
-            classes = yolo_det_results[0].boxes.cls.cpu().tolist()
-            confs = yolo_det_results[0].boxes.conf.cpu().tolist()
-
-            # Process each detection
-            for track_id, cls, conf, box in zip(track_ids, classes, confs, boxes):
-                track_id = int(track_id)
-                x, y, w, h = box.int().tolist()
-                x1 = x - w // 2
-                y1 = y - h // 2
-                x2 = x + w // 2
-                y2 = y + h // 2
-                # Create a detection record
-                record = [frame_pos, int(cls), round(conf, 1), x1, y1, x2, y2, track_id]
-                records.append(record)
-                if global_state.LiveDisplayMode:
-                    # Print and test the record
-                    global_state.logger.info(f"Record : {record}")
-                    global_state.logger.info(f"For class id: {int(cls)}, getting: {class_reverse_match.get(int(cls), 'unknown')}")
-                    test_box = [[x1, y1, x2, y2], round(conf, 1), int(cls), class_reverse_match.get(int(cls), 'unknown'), track_id]
-                    global_state.logger.info(f"Test box: {test_box}")
-                    test_result.add_record(frame_pos, test_box)
-
-            if run_pose_model:
-                ### POSE DETECTION - Hips and wrists
-                # Extract track IDs, boxes, classes, and confidence scores
-                if len(yolo_pose_results[0].boxes) > 0 and yolo_pose_results[0].boxes.id is not None:
-                    pose_track_ids = yolo_pose_results[0].boxes.id.cpu().tolist()
-
-                    # Check if keypoints are detected
-                    if yolo_pose_results[0].keypoints is not None:
-                        pose_confs = yolo_pose_results[0].boxes.conf.cpu().tolist()
-
-                        pose_keypoints = yolo_pose_results[0].keypoints.cpu()
-                        pose_keypoints_list = pose_keypoints.xy.cpu().tolist()
-                        left_hip = pose_keypoints_list[0][11]
-                        right_hip = pose_keypoints_list[0][12]
-
-                        middle_x_frame = frame.shape[1] // 2
-                        mid_hips = [middle_x_frame, (int(left_hip[1])+ int(right_hip[1]))//2]
-                        x1 = mid_hips[0]-5
-                        y1 = mid_hips[1]-5
-                        x2 = mid_hips[0]+5
-                        y2 = mid_hips[1]+5
-                        cls = 10  # hips center
-                        # print(f"pose_confs: {pose_confs}")
-                        conf = pose_confs[0]
-
-                        record = [frame_pos, 10, round(conf, 1), x1, y1, x2, y2, 0]
-                        records.append(record)
-                        if global_state.LiveDisplayMode:
-                            # Print and test the record
-                            global_state.logger.info(f"@{frame_pos} - Record : {record}")
-                            global_state.logger.info(f"@{frame_pos} - For class id: {int(cls)}, getting: {class_reverse_match.get(int(cls), 'unknown')}")
-                            test_box = [[x1, y1, x2, y2], round(conf, 1), int(cls),
-                                        class_reverse_match.get(int(cls), 'unknown'), 0]
-                            global_state.logger.info(f"Test box: {test_box}")
-                            test_result.add_record(frame_pos, test_box)
-
-            if global_state.LiveDisplayMode:
-                # Verify the sorted boxes
-                sorted_boxes = test_result.get_boxes(frame_pos)
-                global_state.logger.info(f"@{frame_pos} - Sorted boxes : {sorted_boxes}")
-
-                frame_display = frame.copy()
-
-                for box in sorted_boxes:
-                    color = class_colors.get(box[3])
-                    cv2.rectangle(frame_display, (box[0][0], box[0][1]), (box[0][2], box[0][3]), color, 2)
-                    cv2.putText(frame_display, f"{box[4]}: {box[3]}", (box[0][0], box[0][1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
-                cv2.imshow("YOLO11 test boxes Tracking", frame_display)
-                if cv2.waitKey(1) & 0xFF == ord('q'):
-                    break
-
-        # Update progress
-        if progress_callback:
-            elapsed_time = time.time() - start_time
-            frames_processed = frame_pos - global_state.frame_start + 1
-            frames_remaining = last_frame - frame_pos - 1
-            eta = (elapsed_time / frames_processed) * frames_remaining if frames_processed > 0 else 0
-            progress_callback(frame_pos, last_frame, time.strftime("%H:%M:%S", time.gmtime(eta)))
-
-    # Write the detection records to a JSON file
-    write_dataset(global_state.video_file[:-4] + f"_rawyolo.json", records)
-    # Release the video capture object and close the display window
-    cap.release()
-    cv2.destroyAllWindows()
-
-def load_yolo_data_from_file(file_path):
-    """
-    Load YOLO data from a JSON file.
-    :param file_path: Path to the JSON file.
-    :return: The loaded data.
-    """
-    with open(file_path, 'r') as f:
-        data = json.load(f)
-        global_state.logger.info(f"Loaded data from {file_path}, length: {len(data)}")
-    return data
-
-def make_data_boxes(records, image_x_size):
-    """
-    Convert YOLO records into BoxRecord objects.
-    :param records: List of YOLO detection records.
-    :param image_x_size: Width of the image/frame.
-    :return: A Result object containing BoxRecord instances.
-    """
-    result = Result(image_x_size)  # Create a Result instance
-    for record in records:
-        frame_idx, cls, conf, x1, y1, x2, y2, track_id = record
-        box = [x1, y1, x2, y2]
-        class_name = class_reverse_match.get(cls, 'unknown')
-        box_record = BoxRecord(box, conf, cls, class_name, track_id)
-        result.add_record(frame_idx, box_record)
-    return result
-
-def analyze_tracking_results(results, image_y_size, progress_callback=None):
-    """
-    Analyze tracking results and generate Funscript data.
-    :param results: The Result object containing detection data.
-    :param image_y_size: Height of the image/frame.
-    :return: A list of Funscript data.
-    """
-    list_of_frames = results.get_all_frame_ids()  # Get all frame IDs with detections
-    visualizer = Visualizer()  # Initialize the visualizer
-
-    cap = VideoReaderFFmpeg(global_state.video_file, is_VR=global_state.isVR)  # Initialize the video reader
-
-    fps = cap.get(cv2.CAP_PROP_FPS)  # Get the video's FPS
-    nb_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))  # Get the total number of frames
-
-    global_state.frame_area = cap.get(cv2.CAP_PROP_FRAME_WIDTH) * cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
-
-    cuts = []
-
-    if not global_state.frame_start:
-        global_state.frame_start = 0
-
-    if not global_state.frame_end:
-        global_state.frame_end = nb_frames
-
-    if global_state.LiveDisplayMode:
-        cap.set(cv2.CAP_PROP_POS_FRAMES, global_state.frame_start)
-    else:
-        cap.release()
-
-    """ discarding the scene detection for now
-    # Load scene cuts if the file exists
-    if os.path.exists(global_state.video_file[:-4] + f"_cuts.json"):
-        global_state.logger.info(f"Loading cuts from {global_state.video_file[:-4] + f'_cuts.json'}")
-        with open(global_state.video_file[:-4] + f"_cuts.json", 'r') as f:
-            cuts = json.load(f)
-        global_state.logger.info(f"Loaded {len(cuts)} cuts : {cuts}")
-    else:
-        # Detect scene changes if the cuts file does not exist
-        scene_list = detect_scene_changes(global_state.video_file, global_state.isVR, 0.9, global_state.frame_start, global_state.frame_end)
-        global_state.logger.info(f"Analyzing frames {global_state.frame_start} to {global_state.frame_end}")
-        cuts = [scene[1] for scene in scene_list]
-        cuts = cuts[:-1]  # Remove the last entry
-        # Save the cuts to a file
-        with open(global_state.video_file[:-4] + f"_cuts.json", 'w') as f:
-            json.dump(cuts, f)
-    """
-
-    global_state.funscript_frames = []  # List to store Funscript frames
-    tracker = ObjectTracker(global_state)
-
-    # Start time for ETA calculation
-    start_time = time.time()
-
-    for frame_pos in tqdm(range(global_state.frame_start, global_state.frame_end), unit="f"):
-        global_state.current_frame_id = frame_pos
-        if frame_pos in cuts:
-            # Reinitialize the tracker at scene cuts
-            global_state.logger.info(f"@{frame_pos} - Reaching cut")
-            previous_distances = tracker.previous_distances
-            global_state.logger.info(f"@{frame_pos} - Reinitializing tracker with previous distances: {previous_distances}")
-            tracker = ObjectTracker(global_state)
-            tracker.previous_distances = previous_distances
-
-        if frame_pos in list_of_frames:
-            # Get sorted boxes for the current frame
-            sorted_boxes = results.get_boxes(frame_pos)
-            tracker.tracking_logic(global_state, sorted_boxes)  # Apply tracking logic
-
-            if tracker.distance:
-                # Append Funscript data if distance is available
-                global_state.funscript_frames.append(frame_pos)
-                global_state.funscript_distances.append(int(tracker.distance))
-
-            if global_state.DebugMode:
-                # Log debugging information
-                bounding_boxes = []
-                for box in sorted_boxes:
-                    if box[4] in tracker.normalized_absolute_tracked_positions:
-                        if box[4] == 0:  # generic track_id for 'hips center'
-                            str_dist_penis = 'None'
-                        else:
-                            if box[4] in tracker.normalized_distance_to_penis:
-                                str_dist_penis = str(int(tracker.normalized_distance_to_penis[box[4]][-1]))
-                            else:
-                                str_dist_penis = 'None'
-                        str_abs_pos = str(int(tracker.normalized_absolute_tracked_positions[box[4]][-1]))
-                        position = 'p: ' + str_dist_penis + ' | ' + 'a: ' + str_abs_pos
-                        if box[4] in tracker.pct_weights:
-                            if len(tracker.pct_weights[box[4]]) > 0:
-                                weight = tracker.pct_weights[box[4]][-1]
-                                position += ' | w: ' + str(weight)
-                    else:
-                        position = None
-                    bounding_boxes.append({
-                        'box': box[0],
-                        'conf': box[1],
-                        'class_name': box[3],
-                        'track_id': box[4],
-                        'position': position,
-                    })
-                global_state.debugger.log_frame(frame_pos,
-                                   bounding_boxes=bounding_boxes,
-                                   variables={
-                                       'frame': frame_pos,
-                                       'time': str(timedelta(seconds=int(frame_pos / fps))),
-                                       'distance': tracker.distance,
-                                       #'lead class': tracker.lead_class + ' | ' + str(tracker.lead_trackid) + ' | '
-                                       #              + str(tracker.lead_trackid_count),
-                                       'Penetration': tracker.penetration,
-                                       'sex_position': tracker.sex_position,
-                                       'sex_position_reason': tracker.sex_position_reason,
-                                       'tracked_body_part': tracker.tracked_body_part,
-                                       'locked_penis_box': tracker.locked_penis_box.to_dict(),
-                                       'glans_detected': tracker.glans_detected,
-                                       'cons._glans_detections': tracker.consecutive_detections['glans'],
-                                       'cons._glans_non_detections': tracker.consecutive_non_detections['glans'],
-                                       'cons._penis_detections': tracker.consecutive_detections['penis'],
-                                       'cons._penis_non_detections': tracker.consecutive_non_detections['penis'],
-                                       'breast_tracking': tracker.breast_tracking,
-                                   })
-
-        if global_state.LiveDisplayMode:
-            # Display the tracking results for testing
-            ret, frame = cap.read()
-
-            frame_display = frame.copy()
-
-            for box in tracker.tracked_boxes:
-                frame_display = visualizer.draw_bounding_box(frame_display,
-                                                             box[0],
-                                                             str(box[2]) + ": " + box[1],
-                                                             class_colors[str(box[1])],
-                                                             global_state.offset_x)
-            if tracker.locked_penis_box is not None and tracker.locked_penis_box.is_active():
-                frame_display = visualizer.draw_bounding_box(frame_display, tracker.locked_penis_box.box,
-                                                             "Locked_Penis",
-                                                             class_colors['penis'],
-                                                             global_state.offset_x)
-            else:
-                global_state.logger.info(f"@{frame_pos} - No active locked penis box to draw.")
-
-            if tracker.glans_detected:
-                frame_display = visualizer.draw_bounding_box(frame_display, tracker.boxes['glans'],
-                                                              "Glans",
-                                                              class_colors['glans'],
-                                                              global_state.offset_x)
-            if global_state.funscript_distances:
-                frame_display = visualizer.draw_gauge(frame_display, global_state.funscript_distances[-1])
-
-            cv2.imshow("Combined Results", frame_display)
-            cv2.waitKey(1)
-
-        # Update progress
-        if progress_callback:
-            elapsed_time = time.time() - start_time
-            frames_processed = frame_pos - global_state.frame_start + 1
-            frames_remaining = global_state.frame_end - frame_pos - 1
-            eta = (elapsed_time / frames_processed) * frames_remaining if frames_processed > 0 else 0
-            progress_callback(frame_pos, global_state.frame_end, time.strftime("%H:%M:%S", time.gmtime(eta)))
-
-    # Prepare Funscript data
-    global_state.funscript_data = list(zip(global_state.funscript_frames, global_state.funscript_distances))
-
-    points = "["
-    for i in range(len(global_state.funscript_frames)):
-        if i != 0:
-            points += ","
-        points += f"[{global_state.funscript_frames[i]}, {global_state.funscript_distances[i]}]"
-    points += "]"
-    # Write the raw Funscript data to a JSON file
-    with open(global_state.video_file[:-4] + f"_rawfunscript.json", 'w') as f:
-        json.dump(global_state.funscript_data, f)
-    return global_state.funscript_data
-
-def parse_yolo_data_looking_for_penis(data, start_frame):
-    """
-    Parse YOLO data to find the first instance of a penis.
-    :param data: The YOLO detection data.
-    :param start_frame: The starting frame for the search.
-    :return: The frame ID where the penis is first detected.
-    """
-    consecutive_frames = 0
-    frame_detected = 0
-    penis_frame = 0
-    for line in data:
-        if line[0] >= start_frame and line[1] == 0 and line[2] >= 0.5:
-            penis_frame = line[0]
-        if line[0] == penis_frame and line[1] == 1 and line[2] >= 0.5:
-            if frame_detected == 0:
-                frame_detected = line[0]
-                consecutive_frames += 1
-            elif line[0] == frame_detected + 1:
-                consecutive_frames += 1
-                frame_detected = line[0]
-            else:
-                consecutive_frames = 0
-                frame_detected = 0
-
-            if consecutive_frames >= 2:
-                global_state.logger.info(f"First instance of Glans/Penis found in frame {line[0] - 4}")
-                return line[0] - 4
-
-def select_video_file():
-    file_path = filedialog.askopenfilename(
-        title="Select a video file",
-        filetypes=[("Video Files", "*.mp4 *.avi *.mov *.mkv")]
-    )
-    if file_path:
-        video_path.set(file_path)
-        check_video_resolution(file_path)
-
-def select_reference_script():
-    file_path = filedialog.askopenfilename(
-        title="Select a reference funscript file",
-        filetypes=[("Funscript Files", "*.funscript")]
-    )
-    if file_path:
-        reference_script_path.set(file_path)
-
-def check_video_resolution(video_path):
-    cap = cv2.VideoCapture(video_path)
-    global_state.video_fps = float(cap.get(cv2.CAP_PROP_FPS))
-    global_state.logger.info(f"Video FPS: {global_state.video_fps}")
-    if not cap.isOpened():
-        global_state.logger.error(f"Could not open the video file: {video_path}")
-        # messagebox.showerror("Error", "Could not open the video file.")
-        return
-
-    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-    cap.release()
-
-    if height > max_frame_height:
-        global_state.logger.info(f"The video height is {height}p > {max_frame_height}p.\nIt will be automatically resized on the fly, no conversion required.")
-        #messagebox.showinfo("Info", f"The video height is {height}p > {max_frame_height}p.\nIt will be automatically resized on the fly, no conversion required.")
-
-def common_initialization():
-    # Reinitialize global state in case the user wants to run another video
-    global_state.funscript_data = []
-    global_state.funscript_frames = []
-    global_state.funscript_distances = []
-
-    # Now, proceed
-    global_state.video_file = video_path.get()
-    if not global_state.video_file:
-        messagebox.showerror("Error", "Please select a video file.")
-        global_state.logger.error("Please select a video file.")
-        return
-
-    global_state.yolo_det_model = get_yolo_model_path()
-    global_state.yolo_pose_model = ""  # "models/yolo11n-pose.mlpackage"
-    global_state.DebugMode = debug_mode_var.get()
-    global_state.debug_record_mode = debug_record_mode_var.get()
-    global_state.debug_record_duration = int(debug_record_duration_var.get())
-    global_state.LiveDisplayMode = live_display_mode_var.get()
-    selected_mode = mode_combobox.get()
-    if selected_mode == "VR SBS":
-        global_state.isVR = True
-    elif selected_mode == "Flat - 2D POV":  # might want to add other formats later on
-        global_state.isVR = False
-    else:
-        global_state.isVR = False
-
-    global_state.enhance_lighting = enhance_lighting_var.get()
-    global_state.frame_start = 0 if frame_start_entry.get() == "" else int(frame_start_entry.get())
-    global_state.frame_end = None if frame_end_entry.get() == "" else int(frame_end_entry.get())
-    global_state.reference_script = reference_script_path.get()
-    global_state.enhance_lighting = enhance_lighting_var.get()
-
-    cap = VideoReaderFFmpeg(global_state.video_file, is_VR=global_state.isVR)
-    global_state.image_x_size = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
-    global_state.image_y_size = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
-    global_state.video_fps = cap.get(cv2.CAP_PROP_FPS)
-    cap.release()
-
-    global_state.logger.info(f"Running script version: {version}")
-    global_state.logger.info(f"Processing video: {global_state.video_file}")
-    global_state.logger.info(f"ffmpeg path: {ffmpeg_path}")
-    global_state.logger.info(f"ffprobe path: {ffprobe_path}")
-    global_state.logger.info(f"Image size: {global_state.image_x_size}x{global_state.image_y_size}")
-    global_state.logger.info(f"FPS: {global_state.video_fps}")
-    global_state.logger.info(f"Video Reader: {global_state.video_reader}")
-    global_state.logger.info(f"YOLO Detection Model: {global_state.yolo_det_model}")
-    # global_state.logger.info(f"YOLO Pose Model: {global_state.yolo_pose_model}")
-    global_state.logger.info(f"Debug Mode: {global_state.DebugMode}")
-    global_state.logger.info(f"Live Display Mode: {global_state.LiveDisplayMode}")
-    global_state.logger.info(f"VR Mode: {global_state.isVR}")
-    global_state.logger.info(f"Enhance lighting: {global_state.enhance_lighting}")
-    global_state.logger.info(f"Frame Start: {global_state.frame_start}")
-    global_state.logger.info(f"Frame End: {global_state.frame_end}")
-    global_state.logger.info(f"Reference Script: {global_state.reference_script}")
-    global_state.logger.info(f"Video Reader: {global_state.video_reader}")
-    global_state.logger.info(f"Enhance lighting: {global_state.enhance_lighting}")
-
-def start_processing():
-    common_initialization()
-
-    # Initialize the debugger
-    global_state.debugger = Debugger(global_state.video_file, output_dir=global_state.video_file[:-4])
-
-    # YOLO Detection Progress
-    def update_yolo_progress(current_frame, total_frames, eta):
-        progress = (current_frame / total_frames) * 100
-
-        def update_gui():
-            yolo_progress_bar["value"] = progress
-            yolo_progress_percent.config(text=f"{progress:.0f}% - ETA: {eta}")
-
-        # Schedule the update in the main thread
-        root.after(0, update_gui)
-
-    # Tracking Analysis Progress
-    def update_tracking_progress(current_frame, total_frames, eta):
-        progress = (current_frame / total_frames) * 100
-        tracking_progress_bar["value"] = progress
-        tracking_progress_percent.config(text=f"{progress:.0f}% - ETA: {eta}")
-        root.update_idletasks()
-
-    # Function to run the processing tasks
-    def run_processing():
-        # Run the YOLO detection and save result to _rawyolo.json file
-        extract_yolo_data(update_yolo_progress)
-
-        # Load YOLO detection results from file
-        yolo_data = load_yolo_data_from_file(global_state.video_file[:-4] + f"_rawyolo.json")
-
-        results = make_data_boxes(yolo_data, global_state.image_x_size)
-
-        # Looking for the first instance of penis within the YOLO results
-        first_penis_frame = parse_yolo_data_looking_for_penis(yolo_data, 0)
-
-        if first_penis_frame is None:
-            global_state.logger.error(f"No penis found in video: {global_state.video_file}")
-            first_penis_frame = 0
-
-        # Deciding whether we start from there or from a user-specified later frame
-        global_state.frame_start = max(max(first_penis_frame - int(global_state.video_fps), global_state.frame_start - int(global_state.video_fps)), 0)
-
-        global_state.logger.info(f"Frame Start adjusted to: {global_state.frame_start}")
-
-        # Performing the tracking part and generation of the raw funscript data
-        global_state.funscript_data = analyze_tracking_results(results, global_state.image_y_size, update_tracking_progress)
-
-        global_state.debugger.save_logs()
-
-        funscript_handler = FunscriptGenerator()
-
-        # Simplifying the funscript data and generating the file
-        funscript_handler.generate(global_state)
-
-        # Optionally, compare generated funscript with reference funscript if specified, or a simple generic report
-        funscript_handler.create_report_funscripts(global_state)
-
-        global_state.logger.info(f"Finished processing video: {global_state.video_file}")
-
-    # Run the processing in a separate thread
-    processing_thread = threading.Thread(target=run_processing)
-    processing_thread.start()
-
-def debug_function():
-    """
-    Debugging function to perform specific debugging tasks.
-    """
-    common_initialization()
-
-    # Processing logic
-
-    global_state.debugger = Debugger(global_state.video_file, global_state.isVR, global_state.video_reader, output_dir=global_state.video_file[:-4])  # Initialize the debugger
-
-    # if the debug_logs.json file exists, load it
-    if os.path.exists(global_state.video_file[:-4] + f"_debug_logs.json"):
-        global_state.debugger.load_logs()
-        global_state.debugger.play_video(start_frame=global_state.frame_start,
-                                         duration=global_state.debug_record_duration if global_state.debug_record_mode else 0,
-                                         record=global_state.debug_record_mode,
-                                         downsize_ratio=2)
-    else:
-        global_state.logger.error(f"Debug logs file not found: {global_state.video_file[:-4] + f'_debug_logs.json'}")
-        messagebox.showinfo("Info", f"Debug logs file not found: {global_state.video_file[:-4] + f'_debug_logs.json'}")
-
-def regenerate_funscript(global_state):
-    global_state.video_file = video_path.get()
-    if not global_state.video_file:
-        global_state.logger.error("Please select a video file.")
-        messagebox.showerror("Error", "Please select a video file.")
-        return
-    global_state.reference_script = reference_script_path.get()
-
-    global_state.logger.info("Regenerating Funscript with tweaked settings...")
-    # Apply tweaks to funscript_data
-    if global_state.boost_enabled:
-        global_state.logger.info(f"Applying Boost: Up {global_state.boost_up_percent}%, Down {global_state.boost_down_percent}%")
-        # Add boost logic here
-
-    if global_state.threshold_enabled:
-        global_state.logger.info(f"Applying Threshold: Low {global_state.threshold_low}, High {global_state.threshold_high}")
-        # Add threshold logic here
-
-    if global_state.vw_simplification_enabled:
-        global_state.logger.info(f"Applying VW Simplification with Factor: {global_state.vw_factor} then rounding to {global_state.rounding}")
-        # Add VW simplification logic here
-
-    # Save and regenerate funscript
-    funscript_handler = FunscriptGenerator()
-    # Simplifying the funscript data and generating the file
-    funscript_handler.generate(global_state)
-    global_state.logger.info("Funscript re-generation complete.")
-    # Optional, compare generated funscript with reference funscript if specified, or a simple generic report
-    funscript_handler.create_report_funscripts(global_state)
-
-    global_state.logger.info("Report generation complete.")
-
-
-def quit_application():
-    """
-    Quit the application.
-    """
-    global_state.logger.info("Quitting the application...")
-    root.quit()  # Close the Tkinter main loop
-    root.destroy()  # Destroy the root window
-
-
-# GUI Setup
-root = tk.Tk()
-root.title("VR & 2D POV Funscript AI Generator")
-
-# Variables
-video_path = tk.StringVar()
-reference_script_path = tk.StringVar()
-debug_mode_var = tk.BooleanVar()
-debug_record_mode_var = tk.BooleanVar()  # debug record mode
-live_display_mode_var = tk.BooleanVar()
-enhance_lighting_var = tk.BooleanVar()
-debug_record_duration_var = tk.StringVar(value="5")  # Default duration
-boost_enabled_var = tk.BooleanVar()
-boost_up_percent_var = tk.IntVar()
-boost_down_percent_var = tk.IntVar()
-threshold_enabled_var = tk.BooleanVar()
-threshold_low_var = tk.IntVar()
-threshold_high_var = tk.IntVar()
-vw_simplification_enabled_var = tk.BooleanVar()
-vw_factor_var = tk.DoubleVar()
-
-# Video File Selection
-video_frame = ttk.LabelFrame(root, text="Video Selection", padding=(10, 5))
-video_frame.grid(row=0, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
-
-ttk.Label(video_frame, text="Video File:").grid(row=0, column=0, padx=5, pady=5, sticky="w")
-ttk.Entry(video_frame, textvariable=video_path, width=50).grid(row=0, column=1, padx=5, pady=5)
-ttk.Button(video_frame, text="Browse", command=select_video_file).grid(row=0, column=2, padx=5, pady=5, sticky="e")
-
-mode_label = ttk.Label(video_frame, text="Select Video Mode:")
-mode_label.grid(row=2, column=0, padx=5, pady=5, sticky="w")
-
-mode_combobox = ttk.Combobox(video_frame, values=["VR SBS", "Flat - 2D POV"], state="readonly")
-mode_combobox.grid(row=2, column=1, padx=5, pady=5, sticky="w")
-mode_combobox.set("VR SBS")  # Set default value
-
-# Processing Mode Selection
-processing_frame = ttk.LabelFrame(root, text="Processing", padding=(10, 5))
-processing_frame.grid(row=1, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
-
-start_button = ttk.Button(processing_frame, text="Start Processing", command=start_processing)
-start_button.grid(row=0, column=0, padx=5, pady=5, sticky="w")
-
-#ttk.Checkbutton(processing_frame, text="Logging for debug", variable=debug_mode_var).grid(row=0, column=2, padx=5, pady=5)
-debug_mode_var.set(True)
-# this one needs a fix
-# ttk.Checkbutton(processing_frame, text="Live inference => slow & heavy!", variable=live_display_mode_var).grid(row=0, column=2, padx=5, pady=5)
-
-# Progress Bar for YOLO Detection
-yolo_progress_label = ttk.Label(processing_frame, text="YOLO Detection Progress:")
-yolo_progress_label.grid(row=1, column=0, padx=5, pady=5, sticky="w")
-yolo_progress_bar = ttk.Progressbar(processing_frame, orient="horizontal", length=300, mode="determinate")
-yolo_progress_bar.grid(row=1, column=1, padx=5, pady=5, sticky="ew")
-yolo_progress_percent = ttk.Label(processing_frame, text="0%")
-yolo_progress_percent.grid(row=1, column=2, padx=5, pady=5, sticky="w")
-
-# Progress Bar for Tracking Analysis
-tracking_progress_label = ttk.Label(processing_frame, text="Tracking Analysis Progress:")
-tracking_progress_label.grid(row=2, column=0, padx=5, pady=5, sticky="w")
-tracking_progress_bar = ttk.Progressbar(processing_frame, orient="horizontal", length=300, mode="determinate")
-tracking_progress_bar.grid(row=2, column=1, padx=5, pady=5, sticky="ew")
-tracking_progress_percent = ttk.Label(processing_frame, text="0%")
-tracking_progress_percent.grid(row=2, column=2, padx=5, pady=5, sticky="w")
-
-# Frame Range (Collapsible)
-optional_settings = ttk.LabelFrame(root, text="Optional settings", padding=(10, 5))
-optional_settings.grid(row=2, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
-
-# Collapse/Expand Button
-def toggle_optional_settings():
-    if optional_settings_collapsible.winfo_ismapped():
-        optional_settings_collapsible.grid_remove()
-    else:
-        optional_settings_collapsible.grid()
-
-toggle_button = ttk.Button(optional_settings, text="Toggle Optional Settings", command=toggle_optional_settings)
-toggle_button.grid(row=0, column=0, padx=5, pady=5, sticky="w")
-
-# Collapsible Section
-optional_settings_collapsible = ttk.Frame(optional_settings)
-optional_settings_collapsible.grid(row=1, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
-
-ttk.Label(optional_settings_collapsible, text="Frame Start:").grid(row=0, column=0, padx=5, pady=5, sticky="w")
-frame_start_entry = ttk.Entry(optional_settings_collapsible, width=10)
-frame_start_entry.grid(row=0, column=1, padx=5, pady=5, sticky="w")
-
-ttk.Label(optional_settings_collapsible, text="Frame End:").grid(row=1, column=0, padx=5, pady=5, sticky="w")
-frame_end_entry = ttk.Entry(optional_settings_collapsible, width=10)
-frame_end_entry.grid(row=1, column=1, padx=5, pady=5, sticky="w")
-
-ttk.Label(optional_settings_collapsible, text="Reference Script:").grid(row=2, column=0, padx=5, pady=5)
-ttk.Entry(optional_settings_collapsible, textvariable=reference_script_path, width=50).grid(row=2, column=1, padx=5, pady=5)
-ttk.Button(optional_settings_collapsible, text="Browse", command=select_reference_script).grid(row=2, column=2, padx=5, pady=5)
-
-optional_settings_collapsible.grid_remove()
-
-# Funscript Tweaking Section (Collapsible)
-funscript_tweaking_frame = ttk.LabelFrame(root, text="Funscript Tweaking", padding=(10, 5))
-funscript_tweaking_frame.grid(row=3, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
-
-# Collapse/Expand Button
-def toggle_funscript_tweaking():
-    if funscript_tweaking_collapsible.winfo_ismapped():
-        funscript_tweaking_collapsible.grid_remove()
-    else:
-        funscript_tweaking_collapsible.grid()
-
-toggle_button = ttk.Button(funscript_tweaking_frame, text="Toggle Funscript Tweaking", command=toggle_funscript_tweaking)
-toggle_button.grid(row=0, column=0, padx=5, pady=5, sticky="w")
-
-# Collapsible Section
-funscript_tweaking_collapsible = ttk.Frame(funscript_tweaking_frame)
-funscript_tweaking_collapsible.grid(row=1, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
-
-# Boost Settings
-boost_frame = ttk.LabelFrame(funscript_tweaking_collapsible, text="Boost Settings", padding=(10, 5))
-boost_frame.grid(row=1, column=0, padx=5, pady=5, sticky="ew")
-
-boost_checkbox = ttk.Checkbutton(boost_frame, text="Enable Boost", variable=boost_enabled_var, command=lambda: setattr(global_state, 'boost_enabled', not global_state.boost_enabled))
-boost_checkbox.grid(row=0, column=0, padx=5, pady=5, sticky="w")
-boost_enabled_var.set(global_state.boost_enabled)
-
-ttk.Label(boost_frame, text="Boost Up %:").grid(row=1, column=0, padx=5, pady=5, sticky="w")
-boost_up_selector = ttk.Combobox(boost_frame, values=[str(i) for i in range(0, 21)], width=5)
-boost_up_selector.set(str(global_state.boost_up_percent))
-boost_up_selector.grid(row=1, column=1, padx=5, pady=5, sticky="w")
-boost_up_selector.bind("<<ComboboxSelected>>", lambda e: setattr(global_state, 'boost_up_percent', int(boost_up_selector.get())))
-
-ttk.Label(boost_frame, text="Reduce Down %:").grid(row=2, column=0, padx=5, pady=5, sticky="w")
-boost_down_selector = ttk.Combobox(boost_frame, values=[str(i) for i in range(0, 21)], width=5)
-boost_down_selector.set(str(global_state.boost_down_percent))
-boost_down_selector.grid(row=2, column=1, padx=5, pady=5, sticky="w")
-boost_down_selector.bind("<<ComboboxSelected>>", lambda e: setattr(global_state, 'boost_down_percent', int(boost_down_selector.get())))
-
-# Threshold Settings
-threshold_frame = ttk.LabelFrame(funscript_tweaking_collapsible, text="Threshold Settings", padding=(10, 5))
-threshold_frame.grid(row=1, column=1, padx=5, pady=5, sticky="ew")
-
-threshold_checkbox = ttk.Checkbutton(threshold_frame, text="Enable Threshold", variable=threshold_enabled_var, command=lambda: setattr(global_state, 'threshold_enabled', not global_state.threshold_enabled))
-threshold_checkbox.grid(row=0, column=0, padx=5, pady=5, sticky="w")
-threshold_enabled_var.set(global_state.threshold_enabled)
-
-ttk.Label(threshold_frame, text="0 Threshold:").grid(row=1, column=0, padx=5, pady=5, sticky="w")
-threshold_low_selector = ttk.Combobox(threshold_frame, values=[str(i) for i in range(0, 16)], width=5)
-threshold_low_selector.set(str(global_state.threshold_low))
-threshold_low_selector.grid(row=1, column=1, padx=5, pady=5, sticky="w")
-threshold_low_selector.bind("<<ComboboxSelected>>", lambda e: setattr(global_state, 'threshold_low', int(threshold_low_selector.get())))
-
-ttk.Label(threshold_frame, text="100 Threshold:").grid(row=2, column=0, padx=5, pady=5, sticky="w")
-threshold_high_selector = ttk.Combobox(threshold_frame, values=[str(i) for i in range(80, 101)], width=5)
-threshold_high_selector.set(str(global_state.threshold_high))
-threshold_high_selector.grid(row=2, column=1, padx=5, pady=5, sticky="w")
-threshold_high_selector.bind("<<ComboboxSelected>>", lambda e: setattr(global_state, 'threshold_high', int(threshold_high_selector.get())))
-
-# Simplification Settings
-vw_frame = ttk.LabelFrame(funscript_tweaking_collapsible, text="Simplification", padding=(10, 5))
-vw_frame.grid(row=1, column=3, padx=5, pady=5, sticky="ew")
-
-vw_checkbox = ttk.Checkbutton(vw_frame, text="Enable Simplification", variable=vw_simplification_enabled_var, command=lambda: setattr(global_state, 'vw_simplification_enabled', not global_state.vw_simplification_enabled))
-vw_checkbox.grid(row=0, column=0, padx=5, pady=5, sticky="w")
-vw_simplification_enabled_var.set(global_state.vw_simplification_enabled)
-
-ttk.Label(vw_frame, text="VW Factor:").grid(row=1, column=0, padx=5, pady=5, sticky="w")
-vw_factor_selector = ttk.Combobox(vw_frame, values=[str(i / 5) for i in range(10, 51)], width=5)
-vw_factor_selector.set(str(global_state.vw_factor))
-vw_factor_selector.grid(row=1, column=1, padx=5, pady=5, sticky="w")
-vw_factor_selector.bind("<<ComboboxSelected>>", lambda e: setattr(global_state, 'vw_factor', float(vw_factor_selector.get())))
-
-ttk.Label(vw_frame, text="Rounding:").grid(row=2, column=0, padx=5, pady=5, sticky="w")
-rounding = ttk.Combobox(vw_frame, values=['5', '10'], width=5)
-rounding.set(str(global_state.rounding))
-rounding.grid(row=2, column=1, padx=5, pady=5, sticky="w")
-rounding.bind("<<ComboboxSelected>>", lambda e: setattr(global_state, 'rounding', float(rounding.get())))
-
-# Regenerate Funscript Button
-regenerate_funscript_button = ttk.Button(funscript_tweaking_collapsible, text="Regenerate Funscript", command=lambda: regenerate_funscript(global_state))
-regenerate_funscript_button.grid(row=2, column=0, padx=5, pady=5, sticky="w")
-
-funscript_tweaking_collapsible.grid_remove()
-
-# Debug Record Mode
-debug_frame = ttk.LabelFrame(root, text="Debugging (Replay and navigate a processed video)", padding=(10, 5))
-debug_frame.grid(row=4, column=0, columnspan=3, padx=5, pady=5, sticky="ew")
-
-quit_button = ttk.Button(debug_frame, text="Video (q to quit)", command=debug_function)
-quit_button.grid(row=0, column=0, padx=5, pady=5, sticky="w")
-
-ttk.Checkbutton(debug_frame, text="Save debugging session as video", variable=debug_record_mode_var).grid(row=0, column=1, padx=5, pady=5)
-
-# Duration Selector
-duration_combobox = ttk.Combobox(debug_frame, textvariable=debug_record_duration_var, values=["5", "10", "20"], width=5)
-duration_combobox.grid(row=0, column=2, padx=5, pady=5)
-ttk.Label(debug_frame, text="seconds").grid(row=0, column=3, padx=5, pady=5)
-
-# Quit Button
-button_frame = ttk.Frame(root)
-button_frame.grid(row=5, column=0, columnspan=3, padx=5, pady=10)
-
-ttk.Button(button_frame, text="Quit", command=quit_application).grid(row=0, column=2, padx=5, pady=5)
-
-# Footer
-footer_label = ttk.Label(root, text="Individual and personal use only.\nNot for commercial use.\nk00gar 2025 - https://github.com/ack00gar", font=("Arial", 10, "italic", "bold"), justify="center")
-footer_label.grid(row=6, column=0, columnspan=3, padx=5, pady=5)
-
-root.mainloop()
+start_app()
\ No newline at end of file
diff --git a/config.py b/config.py
new file mode 100644
index 00000000..20cec711
--- /dev/null
+++ b/config.py
@@ -0,0 +1,141 @@
+import shutil
+import platform
+import os
+
+VERSION = "0.0.1_25-01-16"
+
+# ffmpeg and ffprobe paths - replace with your own if not in your system path
+win_ffmpeg_path = "C:/ffmpeg/bin/ffmpeg.exe"
+mac_ffmpeg_path = "/usr/local/bin/ffmpeg"
+lin_ffmpeg_path = "/usr/bin/ffmpeg"
+
+win_ffprobe_path = "C:/ffmpeg/bin/ffprobe.exe"
+mac_ffprobe_path = "/usr/local/bin/ffprobe"
+lin_ffprobe_path = "/usr/bin/ffprobe"
+
+# Yolo detection settings
+MAX_FRAME_HEIGHT = 1080
+
+PROJECT_PATH = os.path.dirname(os.path.abspath(__file__))
+OUTPUT_PATH = os.path.join(PROJECT_PATH, "output")
+
+# Define the base models directory
+MODELS_DIR = "models"
+
+# List of model filenames
+MODEL_FILENAMES = [
+    "k00gar-11n-200ep-best.mlpackage",
+    "k00gar-11n-200ep-best.pt",
+    "k00gar-11n-200ep-best.onnx",
+    "yolo11n-pose.mlpackage",
+    "yolo11n-pose.pt",
+    "yolo11n-pose.onnx"
+]
+
+
+def find_ffmpeg_path():
+    ffmpeg_path = shutil.which("ffmpeg")
+    if ffmpeg_path:
+        return ffmpeg_path
+    else:
+        print("ffmpeg not found in PATH, defaulting to platform values in params/config.py")
+
+        if platform.system() == "Windows":
+            return win_ffmpeg_path
+        elif platform.system() == "Darwin":
+            return mac_ffmpeg_path
+        elif platform.system() == "Linux":
+            return lin_ffmpeg_path
+        else:
+            raise FileNotFoundError("ffmpeg not found in PATH and no default path for this OS")
+
+
+def find_ffprobe_path():
+    ffprobe_path = shutil.which("ffprobe")
+    if ffprobe_path:
+        return ffprobe_path
+    else:
+        print("ffprobe not found in PATH, defaulting to platform values in params/config.py")
+
+        if platform.system() == "Windows":
+            return win_ffprobe_path
+        elif platform.system() == "Darwin":
+            return mac_ffprobe_path
+        elif platform.system() == "Linux":
+            return lin_ffprobe_path
+        else:
+            raise FileNotFoundError("ffprobe not found in PATH and no default path for this OS")
+
+
+# Set the paths in your config
+FFMPEG_PATH = find_ffmpeg_path()
+FFPROBE_PATH = find_ffprobe_path()
+
+print(f"ffmpeg_path: {FFMPEG_PATH}")
+print(f"ffprobe_path: {FFPROBE_PATH}")
+
+# Construct full paths using os.path.join
+YOLO_MODELS = [os.path.join(MODELS_DIR, filename) for filename in MODEL_FILENAMES]
+
+# Class types
+CLASS_TYPES = {
+    'face': 0, 'hand': 1, 'penis': 2, 'glans': 3, 'pussy': 4, 'butt': 5,
+    'anus': 6, 'breast': 7, 'navel': 8, 'foot': 9, 'hips center': 10
+}
+
+CLASS_REVERSE_MATCH = {
+    0: 'penis', 1: 'glans', 2: 'pussy', 3: 'butt', 4: 'anus', 5: 'breast',
+    6: 'navel', 7: 'hand', 8: 'face', 9: 'foot', 10: 'hips center'
+}
+
+CLASS_PRIORITY_ORDER = {
+    "glans": 0, "penis": 1, "breast": 2, "navel": 3, "pussy": 4, "butt": 5, "face": 6
+}
+
+# Define class names with their corresponding indices
+CLASS_NAMES = {
+    'face': 0,
+    'hand': 1, 'left hand': 1, 'right hand': 1,
+    'penis': 2,
+    'glans': 3,
+    'pussy': 4,
+    'butt': 5,
+    'anus': 6,
+    'breast': 7,
+    'navel': 8,
+    'foot': 9, 'left foot': 9, 'right foot': 9,
+    'hips center': 10
+}
+
+# Define colors for each class
+CLASS_COLORS = {
+    "penis": (255, 0, 0),  # red
+    "glans": (0, 128, 0),  # green
+    "pussy": (0, 0, 255),  # blue
+    "butt": (255, 255, 0),  # yellow
+    "anus": (128, 0, 128),  # purple
+    "breast": (255, 165, 0),  # orange
+    "navel": (0, 255, 255),  # cyan
+    "hand": (255, 0, 255),  # magenta
+    "left hand": (255, 0, 255),  # magenta
+    "right hand": (255, 0, 255),  # magenta
+    "face": (0, 255, 0),  # lime
+    "foot": (165, 42, 42),  # brown
+    "left foot": (165, 42, 42),  # brown
+    "right foot": (165, 42, 42),  # brown
+    "hips center": (0, 0, 0)
+}
+
+# Define custom colormap based on Lucife's heatmapColors
+HEATMAP_COLORS = [
+    [0, 0, 0],  # Black (no action)
+    [30, 144, 255],  # Dodger blue
+    [34, 139, 34],  # Lime green
+    [255, 215, 0],  # Gold
+    [220, 20, 60],  # Crimson
+    [147, 112, 219],  # Medium purple
+    [37, 22, 122]  # Dark blue
+]
+STEP_SIZE = 120  # Speed step size for color transitions
+
+VW_FILTER_COEFF = 2.0
diff --git a/debug/Display_debug_results.py b/debug/Display_debug_results.py
index ac281128..f00402a8 100644
--- a/debug/Display_debug_results.py
+++ b/debug/Display_debug_results.py
@@ -1,3 +1,4 @@
+from script_generator.utils.file import get_output_file_path
 from utils.lib_Debugger import Debugger
 
 video = "/Users/k00gar/Downloads/703-czechvr-3d-2160x1080-60fps-smartphone_hq.mp4"
@@ -6,7 +7,8 @@
 
 frame = (44 * 60 + 26) * 60
 
-debugger = Debugger(video, isVR=True, video_reader="FFmpeg", output_dir=video[:-4])
+log_file, _ = get_output_file_path(video, "_debug_logs.json")
+debugger = Debugger(video, is_vr=True, video_reader="FFmpeg", log_file=log_file)
 
 debugger.load_logs()
 
diff --git a/params/config.py b/params/config.py
deleted file mode 100644
index 4d0f5f97..00000000
--- a/params/config.py
+++ /dev/null
@@ -1,139 +0,0 @@
-import shutil
-import platform
-import os
-
-version = "0.0.2_25-01-18"
-
-# ffmpeg and ffprobe paths - replace with your own if not in your system path
-win_ffmpeg_path = "C:/ffmpeg/bin/ffmpeg.exe"
-mac_ffmpeg_path = "/usr/local/bin/ffmpeg"
-lin_ffmpeg_path = "/usr/bin/ffmpeg"
-
-win_ffprobe_path = "C:/ffmpeg/bin/ffprobe.exe"
-mac_ffprobe_path = "/usr/local/bin/ffprobe"
-lin_ffprobe_path = "/usr/bin/ffprobe"
-
-# First, we will try to find the ffmpeg and ffprobe paths in the system PATH
-
-def find_ffmpeg_path():
-    # Try to find ffmpeg in the system PATH
-    ffmpeg_path = shutil.which("ffmpeg")
-    if ffmpeg_path:
-        print(f"Found ffmpeg in PATH: {ffmpeg_path}")
-        return ffmpeg_path
-    else:
-        print("ffmpeg not found in PATH, defaulting to platform values in params/config.py")
-        # Fallback paths for different operating systems
-        if platform.system() == "Windows":
-            return win_ffmpeg_path  # Example default path for Windows
-        elif platform.system() == "Darwin":  # macOS
-            return mac_ffmpeg_path  # Example default path for macOS
-        elif platform.system() == "Linux":
-            return lin_ffmpeg_path  # Example default path for Linux
-        else:
-            raise FileNotFoundError("ffmpeg not found in PATH and no default path for this OS")
-
-def find_ffprobe_path():
-    # Try to find ffprobe in the system PATH
-    ffprobe_path = shutil.which("ffprobe")
-    if ffprobe_path:
-        print(f"Found ffprobe in PATH: {ffprobe_path}")
-        return ffprobe_path
-    else:
-        print("ffprobe not found in PATH, defaulting to platform values in params/config.py")
-        # Fallback paths for different operating systems
-        if platform.system() == "Windows":
-            return win_ffprobe_path  # Example default path for Windows
-        elif platform.system() == "Darwin":  # macOS
-            return mac_ffprobe_path  # Example default path for macOS
-        elif platform.system() == "Linux":
-            return lin_ffprobe_path  # Example default path for Linux
-        else:
-            raise FileNotFoundError("ffprobe not found in PATH and no default path for this OS")
-
-# Set the paths in your config
-ffmpeg_path = find_ffmpeg_path()
-ffprobe_path = find_ffprobe_path()
-
-print(f"ffmpeg_path: {ffmpeg_path}")
-print(f"ffprobe_path: {ffprobe_path}")
-
-# Yolo detection settings
-# max_frame_height = 1440
-max_frame_height = 1080
-
-# models path
-
-# Define the base models directory
-models_dir = "models"
-
-# List of model filenames
-model_filenames = [
-    "k00gar-11n-200ep-best.mlpackage",
-    "k00gar-11n-200ep-best.pt",
-    "k00gar-11n-200ep-best.onnx",
-    "yolo11n-pose.mlpackage",
-    "yolo11n-pose.pt",
-    "yolo11n-pose.onnx"
-]
-
-# Construct full paths using os.path.join
-yolo_models = [os.path.join(models_dir, filename) for filename in model_filenames]
-
-# Class types
-class_types = {'face': 0, 'hand': 1, 'penis': 2, 'glans': 3, 'pussy': 4, 'butt': 5,
-                       'anus': 6, 'breast': 7, 'navel': 8, 'foot': 9, 'hips center': 10}
-
-class_reverse_match = {0: 'penis', 1: 'glans', 2: 'pussy', 3: 'butt', 4: 'anus', 5: 'breast',
-                       6: 'navel', 7: 'hand', 8: 'face', 9: 'foot', 10: 'hips center'}
-
-class_priority_order = {
-            "glans": 0, "penis": 1, "breast": 2, "navel": 3, "pussy": 4, "butt": 5, "face": 6
-        }
-
-# Define class names with their corresponding indices
-class_names = {
-    'face': 0,
-    'hand': 1, 'left hand': 1, 'right hand': 1,
-    'penis': 2,
-    'glans': 3,
-    'pussy': 4,
-    'butt': 5,
-    'anus': 6,
-    'breast': 7,
-    'navel': 8,
-    'foot': 9, 'left foot': 9, 'right foot': 9,
-    'hips center': 10
-}
-
-# Define colors for each class
-class_colors = {
-    "penis": (255, 0, 0),               # red
-    "glans": (0, 128, 0),               # green
-    "pussy": (0, 0, 255),               # blue
-    "butt": (255, 255, 0),              # yellow
-    "anus": (128, 0, 128),              # purple
-    "breast": (255, 165, 0),            # orange
-    "navel": (0, 255, 255),             # cyan
-    "hand": (255, 0, 255),              # magenta
-    "left hand": (255, 0, 255),         # magenta
-    "right hand": (255, 0, 255),        # magenta
-    "face": (0, 255, 0),                # lime
-    "foot": (165, 42, 42),              # brown
-    "left foot": (165, 42, 42),         # brown
-    "right foot": (165, 42, 42),        # brown
-    "hips center": (0, 0, 0)
-}
-
-# Define custom colormap based on Lucife's heatmapColors
-heatmap_colors = [
-    [0, 0, 0],  # Black (no action)
-    [30, 144, 255],  # Dodger blue
-    [34, 139, 34],  # Lime green
-    [255, 215, 0],  # Gold
-    [220, 20, 60],  # Crimson
-    [147, 112, 219],  # Medium purple
-    [37, 22, 122]  # Dark blue
-]
-step_size = 120  # Speed step size for color transitions
-
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 00000000..183939ea
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,16 @@
+numpy~=1.26.4
+torchvision~=0.20.1+cu121
+ultralytics~=8.3.59
+glfw~=2.8.0
+pyopengl~=3.1.7
+imageio~=2.36.1
+tqdm~=4.67.1
+colorama~=0.4.6
+ffmpeg~=7.1.0
+ffmpeg-python~=0.2.0
+pycuda~=2024.1
+opencv-python~=4.10.0.84
+scipy~=1.15.1
+matplotlib~=3.10.0
+simplification~=0.7.13
+torch~=2.5.1+cu121
\ No newline at end of file
diff --git a/script_generator/cli/generate_funscript.py b/script_generator/cli/generate_funscript.py
new file mode 100644
index 00000000..a516dd6e
--- /dev/null
+++ b/script_generator/cli/generate_funscript.py
@@ -0,0 +1,25 @@
+import argparse
+from script_generator.scripts.generate_funscript import generate_funscript
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Generate a funscript file from a video."
+    )
+    parser.add_argument(
+        "video_path",
+        type=str,
+        help="Path to the input video file."
+    )
+    args = parser.parse_args()
+
+    try:
+        print(f"Processing video: {args.video_path}")
+        generate_funscript(args.video_path)
+        print("Funscript generation complete.")
+    except Exception as e:
+        print(f"An error occurred: {e}")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/script_generator/config.py b/script_generator/config.py
new file mode 100644
index 00000000..54b5169f
--- /dev/null
+++ b/script_generator/config.py
@@ -0,0 +1,48 @@
+import os
+import platform
+
+from ultralytics import YOLO
+
+from config import PROJECT_PATH
+from script_generator.object_detection.utils import get_yolo_model_path
+
+##################################################################################################
+# CONFIGURATION
+##################################################################################################
+
+# Paths (hardcoded for now)
+MODEL_PATH = "C:/cvr/funscript-generator/VR-Funscript-AI-Generator/models/k00gar-11n-200ep-best.pt"
+
+
+##################################################################################################
+# DETECTION
+##################################################################################################
+
+# VIDEO PREPARATION
+RENDER_RESOLUTION = 1080
+TEXTURE_RESOLUTION = 1440
+PITCH=-25
+YAW=0
+
+# YOLO
+YOLO_CONF = 0.3
+YOLO_BATCH_SIZE = 1 if platform.system() == "Darwin" else 30 # Mac doesn't support batching due to onnx
+RUN_POSE_MODEL = False
+
+MODEL_PATH = str(os.path.join(PROJECT_PATH, get_yolo_model_path()))
+YOLO_MODEL = YOLO(MODEL_PATH, task="detect")
+YOLO_POSE_MODEL = None # YOLO("models/yolo11n-pose.mlpackage", task="pose")
+
+
+##################################################################################################
+# ADVANCED / DEVELOPMENT
+##################################################################################################
+
+SUBTRACT_THREADS_FROM_FFMPEG = 0
+UPDATE_PROGRESS_INTERVAL = 0.25 # Updates progress in the console and in gui
+# when enabled the queue will be processed one by one (use it on (QUEUE_MAXSIZE / frame rate) seconds longer videos or less)
+# raw frames take a lot of memory (RAM) so don't set the queue to high
+SEQUENTIAL_MODE = False
+QUEUE_MAXSIZE = 3000 if SEQUENTIAL_MODE else 500 # Bounded queue size to avoid memory blow-up
+DEBUG_PATH = "C:/cvr/funscript-generator/tmp_output"
+PROGRESS_BAR = True # disable when you want to print messages while debugging
diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
new file mode 100644
index 00000000..ef83869a
--- /dev/null
+++ b/script_generator/gui/app.py
@@ -0,0 +1,57 @@
+import ctypes
+import tkinter as tk
+
+from script_generator.gui.views.funscript_generator import FunscriptGeneratorPage
+from script_generator.state.app_state import AppState
+
+
+class App(tk.Tk):
+    def __init__(self):
+        super().__init__()
+        if hasattr(ctypes, "windll"):
+            ctypes.windll.shcore.SetProcessDpiAwareness(2)  # For Windows DPI scaling
+        # self.tk.call('tk', 'scaling', 1.0)
+        self.title("VR funscript generation")
+        self.geometry("700x920")
+        self.resizable(False, False)
+        self.grid_rowconfigure(0, weight=1)
+        self.grid_columnconfigure(0, weight=1)
+        self.container = tk.Frame(self)
+        self.container.grid(row=0, column=0, sticky="nsew")
+        self.container.grid_columnconfigure(0, weight=1)
+
+        self.state = AppState(is_cli=False)
+
+        # Dictionary to store pages
+        self.frames = {}
+
+
+    def show_frame(self, page_name):
+        """Show a frame, creating it if necessary."""
+        if page_name not in self.frames:
+            # Dynamically create the page
+            frame = self.create_page(page_name)
+            if frame is not None:
+                self.frames[page_name] = frame
+                frame.grid(row=0, column=0, sticky="nsew")
+            else:
+                print(f"Page '{page_name}' not found!")
+                return
+
+        # Show the requested page
+        frame = self.frames[page_name]
+        frame.tkraise()
+
+    def create_page(self, page_name):
+        if page_name == PageNames.FUNSCRIPT_GENERATOR:
+            return FunscriptGeneratorPage(parent=self.container, controller=self)
+
+        return None
+
+class PageNames:
+    FUNSCRIPT_GENERATOR = "Funscript generator"
+
+def start_app():
+    app = App()
+    app.show_frame(PageNames.FUNSCRIPT_GENERATOR)
+    app.mainloop()
\ No newline at end of file
diff --git a/script_generator/gui/controller/debug_video.py b/script_generator/gui/controller/debug_video.py
new file mode 100644
index 00000000..aa7fcae6
--- /dev/null
+++ b/script_generator/gui/controller/debug_video.py
@@ -0,0 +1,31 @@
+import os
+from tkinter import messagebox
+
+from script_generator.state.app_state import AppState
+from script_generator.utils.file import get_output_file_path
+from script_generator.utils.logger import logger
+from utils.lib_Debugger import Debugger
+
+
+def debug_video(state: AppState):
+    if not state.video_path:
+        messagebox.showerror("Error", "Please select a video file.")
+        return
+
+    log_file, _ = get_output_file_path(state.video_path, "_debug_logs.json")
+    state.debugger = Debugger(state.video_path, state.video_info.is_vr, state.video_reader, log_file=log_file)  # Initialize the debugger
+
+    # if the debug_logs.json file exists, load it
+    logs_path, _ = get_output_file_path(state.video_path, "_debug_logs.json")
+    if os.path.exists(logs_path):
+        state.debugger.load_logs()
+
+        state.debugger.play_video(
+            start_frame=state.frame_start,
+            duration=state.debug_record_duration if state.debug_record_mode else 0,
+            record=state.debug_record_mode,
+            downsize_ratio=2
+        )
+    else:
+        logger.error(f"Debug logs file not found: {log_file}")
+        messagebox.showinfo("Info", f"Debug logs file not found: {log_file}")
diff --git a/script_generator/gui/controller/regenerate_funscript.py b/script_generator/gui/controller/regenerate_funscript.py
new file mode 100644
index 00000000..b3150100
--- /dev/null
+++ b/script_generator/gui/controller/regenerate_funscript.py
@@ -0,0 +1,37 @@
+from tkinter import messagebox
+
+from utils.lib_FunscriptHandler import FunscriptGenerator
+
+
+def regenerate_funscript(state):
+    if not state.video_path:
+        messagebox.showerror("Error", "Please select a video file.")
+        return
+
+    print("Regenerating Funscript with tweaked settings...")
+
+    # Apply tweaks to funscript_data
+    if state.boost_enabled:
+        print(f"Applying Boost: Up {state.boost_up_percent}%, Down {state.boost_down_percent}%")
+        # Add boost logic here
+
+    if state.threshold_enabled:
+        print(f"Applying Threshold: Low {state.threshold_low}, High {state.threshold_high}")
+        # Add threshold logic here
+
+    if state.vw_simplification_enabled:
+        print(f"Applying VW Simplification with Factor: {state.vw_factor} then rounding to {state.rounding}")
+        # Add VW simplification logic here
+
+    # Save and regenerate funscript
+    funscript_handler = FunscriptGenerator()
+
+    # Simplifying the funscript data and generating the file
+    funscript_handler.generate(state)
+
+    print("Funscript re-generation complete.")
+
+    # Optional, compare generated funscript with reference funscript if specified, or a simple generic report
+    funscript_handler.create_report_funscripts(state)
+
+    print("Report generation complete.")
\ No newline at end of file
diff --git a/script_generator/gui/controller/tracking_analysis.py b/script_generator/gui/controller/tracking_analysis.py
new file mode 100644
index 00000000..f7bbedab
--- /dev/null
+++ b/script_generator/gui/controller/tracking_analysis.py
@@ -0,0 +1,53 @@
+import os
+
+from script_generator.gui.controller.debug_video import debug_video
+from script_generator.object_detection.analye_tracking_results import analyze_tracking_results
+from script_generator.object_detection.object_detection_result import ObjectDetectionResult
+from script_generator.object_detection.utils import make_data_boxes, parse_yolo_data_looking_for_penis
+from script_generator.utils.file import load_json_from_file, get_output_file_path
+from script_generator.video.video_info import get_cropped_dimensions
+from utils.lib_FunscriptHandler import FunscriptGenerator
+
+
+def tracking_analysis(state):
+    # Load YOLO detection results from file
+    raw_yolo_path, _ = get_output_file_path(state.video_path, "_rawyolo.json")
+    if not os.path.exists(raw_yolo_path):
+        print(f"Raw yolo json not found in {raw_yolo_path}")
+        return
+
+    yolo_data = load_json_from_file(raw_yolo_path)
+    state.set_video_info()
+    video_info = state.video_info
+    width, height = get_cropped_dimensions(state.video_info)
+
+    results = make_data_boxes(yolo_data)
+
+    # Looking for the first instance of penis within the YOLO results
+    first_penis_frame = parse_yolo_data_looking_for_penis(yolo_data, 0)
+
+    if first_penis_frame is None:
+        print(f"No penis found in video: {state.video_path}")
+        first_penis_frame = 0
+
+    # Deciding whether we start from there or from a user-specified later frame
+    state.frame_start = max(max(first_penis_frame - int(video_info.fps), state.frame_start - int(video_info.fps)), 0)
+
+    state.frame_end = state.video_info.total_frames if not state.frame_end else state.frame_end
+
+    print(f"Frame Start adjusted to: {state.frame_start}")
+
+    # Performing the tracking part and generation of the raw funscript data
+    state.funscript_data = analyze_tracking_results(state, results, progress_callback=lambda: debug_video(state))
+
+    state.debugger.save_logs()
+
+    funscript_handler = FunscriptGenerator()
+
+    # Simplifying the funscript data and generating the file
+    funscript_handler.generate(state)
+
+    # Optionally, compare generated funscript with reference funscript if specified, or a simple generic report
+    funscript_handler.create_report_funscripts(state)
+
+    print(f"Finished processing video: {state.video_path}")
diff --git a/script_generator/gui/controller/video_analysis.py b/script_generator/gui/controller/video_analysis.py
new file mode 100644
index 00000000..b0d9f106
--- /dev/null
+++ b/script_generator/gui/controller/video_analysis.py
@@ -0,0 +1,38 @@
+import threading
+from tkinter import messagebox
+
+from script_generator.gui.controller.tracking_analysis import tracking_analysis
+from script_generator.object_detection.utils import check_skip_object_detection
+from script_generator.scripts.analyse_video import analyse_video
+from script_generator.utils.file import get_output_file_path
+from utils.lib_Debugger import Debugger
+
+
+def video_analysis(state):
+    if not state.video_path:
+        messagebox.showerror("Error", "Please select a video file.")
+        return
+
+    print(f"Processing video: {state.video_path}")
+    print(f"Video Reader: {state.video_reader}")
+    print(f"Debug Mode: {state.debug_mode}")
+    print(f"Live Display Mode: {state.life_display_mode}")
+    print(f"Frame Start: {state.frame_start}")
+    print(f"Frame End: {state.frame_end}")
+    print(f"Reference Script: {state.reference_script}")
+
+    def run():
+        # Initialize the debugger
+        log_file, _ = get_output_file_path(state.video_path, "_debug_logs.json")
+        state.debugger = Debugger(state.video_path, log_file=log_file)
+
+        skip_detection = check_skip_object_detection(state)
+
+        if not skip_detection:
+            analyse_video(state)
+
+        tracking_analysis(state)
+
+    processing_thread = threading.Thread(target=run)
+    processing_thread.start()
+    # processing_thread.join()
\ No newline at end of file
diff --git a/script_generator/gui/messages/messages.py b/script_generator/gui/messages/messages.py
new file mode 100644
index 00000000..5eccd3c3
--- /dev/null
+++ b/script_generator/gui/messages/messages.py
@@ -0,0 +1,9 @@
+class UIMessage:
+    pass
+
+class ProgressMessage(UIMessage):
+    def __init__(self, process, frames_processed, total_frames, eta):
+        self.process = process
+        self.frames_processed = frames_processed
+        self.total_frames = total_frames
+        self.eta = eta
\ No newline at end of file
diff --git a/script_generator/gui/utils/tooltip.py b/script_generator/gui/utils/tooltip.py
new file mode 100644
index 00000000..fc40a6cc
--- /dev/null
+++ b/script_generator/gui/utils/tooltip.py
@@ -0,0 +1,26 @@
+import tkinter as tk
+
+class Tooltip:
+    def __init__(self, widget, text):
+        self.widget = widget
+        self.text = text
+        self.tooltip_window = None
+
+        self.widget.bind("<Enter>", self.show_tooltip)
+        self.widget.bind("<Leave>", self.hide_tooltip)
+
+    def show_tooltip(self, event=None):
+        if self.tooltip_window is not None:
+            return
+
+        self.tooltip_window = tk.Toplevel(self.widget)
+        self.tooltip_window.wm_overrideredirect(True)  # Remove window decorations
+        self.tooltip_window.geometry(f"+{self.widget.winfo_rootx() + 20}+{self.widget.winfo_rooty() + 20}")
+
+        label = tk.Label(self.tooltip_window, text=self.text, relief="solid", borderwidth=1, padx=5, pady=5)
+        label.pack()
+
+    def hide_tooltip(self, event=None):
+        if self.tooltip_window:
+            self.tooltip_window.destroy()
+            self.tooltip_window = None
\ No newline at end of file
diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
new file mode 100644
index 00000000..0437dd37
--- /dev/null
+++ b/script_generator/gui/utils/widgets.py
@@ -0,0 +1,304 @@
+import tkinter as tk
+from tkinter import ttk, filedialog, Button
+
+from script_generator.gui.utils.tooltip import Tooltip
+
+PADDING_X = 5
+PADDING_Y = 5
+LABEL_WIDTH = 150
+
+class Widgets:
+    @staticmethod
+    def frame(parent, title=None, main_section=False, **grid_kwargs):
+        if title:
+            frame_cls = ttk.LabelFrame
+            if main_section:
+                style = ttk.Style(parent)
+                style.configure("Bold.TLabelframe.Label", font=("TkDefaultFont", 10, "bold"))
+                frame = frame_cls(parent, text=title, style="Bold.TLabelframe")
+            else:
+                frame = frame_cls(parent, text=title)
+        else:
+            frame = ttk.Frame(parent)
+
+        grid_kwargs.setdefault("sticky", "nsew")  # Default to stretch in all directions
+        grid_kwargs.setdefault("padx", 5)
+        grid_kwargs.setdefault("pady", (5, (10 if main_section else 5)))
+
+        frame.grid(**grid_kwargs)
+
+        # Ensure the frame dynamically adjusts grid weights for nested widgets
+        def configure_grid(event):
+            for i in range(frame.grid_size()[0]):  # Columns
+                if frame.grid_columnconfigure(i)["weight"] == 0:  # Avoid overwriting custom weights
+                    frame.grid_columnconfigure(i, weight=1)
+            for j in range(frame.grid_size()[1]):  # Rows
+                if frame.grid_rowconfigure(j)["weight"] == 0:  # Avoid overwriting custom weights
+                    frame.grid_rowconfigure(j, weight=1)
+
+        frame.bind("<Configure>", configure_grid)
+
+        return frame
+
+
+    @staticmethod
+    def label(frame, text, tooltip_text=None, **grid_kwargs):
+        label = ttk.Label(frame, text=text)
+        label.grid(**grid_kwargs)
+
+        if tooltip_text:
+            Tooltip(label, tooltip_text)
+
+        return label
+
+    @staticmethod
+    def entry(frame, default_value="", tooltip_text=None, **grid_kwargs):
+        entry = ttk.Entry(frame, textvariable=tk.StringVar(value=default_value))
+        entry.grid(**grid_kwargs)
+
+        if tooltip_text:
+            Tooltip(entry, tooltip_text)
+
+        return entry
+
+    @staticmethod
+    def input(parent, label_text, state, attr, row=0, col=0, label_width_px=LABEL_WIDTH, entry_width_px=200, callback=None, tooltip_text=None):
+        container = ttk.Frame(parent)
+        container.grid(row=row, column=col, sticky="ew", padx=5, pady=5)
+        container.columnconfigure(1, weight=1)
+
+        value = tk.StringVar(value=getattr(state, attr))
+
+        if callback:
+            value.trace_add("write", lambda *args: setattr(state, attr, value.get()))
+
+        label = tk.Label(container, text=label_text, anchor="w", width=label_width_px // 7)
+        label.grid(row=0, column=0, sticky="w", padx=(5, 2))
+
+        entry_container = ttk.Frame(container, width=entry_width_px)
+        entry_container.grid(row=0, column=1, sticky="ew", padx=(2, 5))
+        entry_container.grid_propagate(False)  # Prevent resizing
+        entry = ttk.Entry(entry_container, textvariable=value)
+        entry.pack(fill="both", expand=True)
+
+        if tooltip_text:
+            Tooltip(entry, tooltip_text)
+
+        return container, entry, value
+
+    @staticmethod
+    def button(parent, button_text, on_click, row=0, col=0, tooltip_text=None, style_name="Custom.TButton"):
+        style = ttk.Style()
+        style.configure(style_name, padding=(10, 3))
+
+        button = ttk.Button(parent, text=button_text, command=on_click, style=style_name)
+        button.grid(row=row, column=col, sticky="w", padx=PADDING_X, pady=PADDING_Y)
+
+        if tooltip_text:
+            Tooltip(button, tooltip_text)
+
+        return button
+
+    @staticmethod
+    def file_selection(parent, label_text, button_text, file_selector_title, file_types, state, attr, row=0, label_width_px=150, button_width_px=100, tooltip_text=None):
+        container = tk.Frame(parent)
+        container.grid(row=row, column=0, sticky="nsew", padx=5, pady=5)
+
+        # Ensure the container scales properly
+        parent.grid_rowconfigure(row, weight=1)
+        parent.grid_columnconfigure(0, weight=1)
+
+        # Configure the container's internal layout
+        container.columnconfigure(1, weight=1)
+
+        file_path = tk.StringVar()
+
+        # Label for file selection with fixed pixel width
+        label = tk.Label(container, text=label_text, anchor="w", width=label_width_px // 7)
+        label.grid(row=0, column=0, sticky="w", padx=(5, 2))
+
+        # Entry widget that expands to fill available space
+        entry = ttk.Entry(container, textvariable=file_path)
+        entry.grid(row=0, column=1, sticky="ew", padx=(2, 5))
+
+        # Button for browsing files with fixed pixel width
+        button_container = tk.Frame(container, width=button_width_px, bg="lightgreen")  # Set button container background
+        button_container.grid(row=0, column=2, sticky="e", padx=(2, 5))
+        button_container.grid_propagate(False)  # Prevent resizing
+        button = ttk.Button(button_container, text=button_text, command=lambda: Widgets._browse_file(file_path, file_selector_title, file_types, lambda val: setattr(state, attr, val)))
+        button.pack(fill="both", expand=True)
+
+        # Update state whenever the file path changes
+        file_path.trace("w", lambda *args: setattr(state, attr, file_path.get()))
+
+        if tooltip_text:
+            Tooltip(button, tooltip_text)
+
+        return container, entry, file_path
+
+    @staticmethod
+    def labeled_progress(parent, label_text, row=0, col=0, progress_length=300, label_width_px=LABEL_WIDTH, label_percentage_width_px=LABEL_WIDTH, tooltip_text=None):
+        container = ttk.Frame(parent)
+        container.grid(row=row, column=col, sticky="ew", padx=5, pady=5)
+        container.columnconfigure(1, weight=1)  # Allow the progress bar to expand
+
+        # Label for progress description with fixed pixel width
+        progress_label = tk.Label(container, text=label_text, anchor="w", width=label_width_px // 7)
+        progress_label.grid(row=0, column=0, sticky="w", padx=(5, 2))
+
+        # Progress bar widget
+        progress_bar = ttk.Progressbar(container, orient="horizontal", mode="determinate", length=progress_length)
+        progress_bar.grid(row=0, column=1, sticky="ew", padx=(2, 5))
+
+        # Percentage label with fixed pixel width
+        percentage_label = tk.Label(container, text="0%", anchor="e", width=label_percentage_width_px // 7)
+        percentage_label.grid(row=0, column=2, sticky="e", padx=(2, 5))
+
+        if tooltip_text:
+            Tooltip(progress_bar, tooltip_text)
+
+        return container, progress_bar, progress_label, percentage_label
+
+    @staticmethod
+    def dropdown(parent, label_text, options, default_value, state, attr, row=0, col=0, label_width_px=LABEL_WIDTH, tooltip_text=None):
+        selected_value = tk.StringVar(value=default_value)
+
+        # Create a container for the dropdown
+        container = ttk.Frame(parent)
+        container.grid(row=row, column=col, sticky="ew", padx=5, pady=5)
+        container.columnconfigure(1, weight=1)  # Allow the dropdown to expand
+
+        # Label with fixed pixel width
+        label = tk.Label(container, text=label_text, anchor="w", width=label_width_px // 7)
+        label.grid(row=0, column=0, sticky="w", padx=(5, 2))
+
+        # Dropdown (Combobox) widget
+        dropdown = ttk.Combobox(container, textvariable=selected_value, values=options, state="readonly")
+        dropdown.grid(row=0, column=1, sticky="ew", padx=(2, 5))
+
+        # Bind selection changes to update the state
+        dropdown.bind("<<ComboboxSelected>>", lambda _: setattr(state, attr, selected_value.get()))
+
+        if tooltip_text:
+            Tooltip(dropdown, tooltip_text)
+
+        return container, label, dropdown, selected_value
+
+    @staticmethod
+    def range_selector(parent, label_text, row, state, attr, values, col=0, tooltip_text=None):
+        Widgets.label(parent, label_text, row=row, column=col, sticky="w", padx=PADDING_X, pady=PADDING_Y)
+
+        selected_value = tk.StringVar(value=str(getattr(state, attr)))
+
+        dropdown = ttk.Combobox(parent, textvariable=selected_value, values=values, width=5, state="readonly")
+        dropdown.grid(row=row, column=col + 1, sticky="w", padx=PADDING_X, pady=PADDING_Y)
+
+        dropdown.bind("<<ComboboxSelected>>", lambda _: setattr(state, attr, int(selected_value.get())))
+
+        if tooltip_text:
+            Tooltip(dropdown, tooltip_text)
+
+        return dropdown
+
+    @staticmethod
+    def checkbox(parent, label_text, state, attr, label_left=True, row=0, col=0, label_width_px=150, tooltip_text=None, **grid_kwargs):
+        # Ensure the parent has a _checkbox_vars attribute to track BooleanVars
+        if not hasattr(parent, "_checkbox_vars"):
+            parent._checkbox_vars = {}
+
+        # Check if the BooleanVar for this attr already exists; create it if not
+        if attr not in parent._checkbox_vars:
+            parent._checkbox_vars[attr] = tk.BooleanVar(value=getattr(state, attr))
+
+        is_checked = parent._checkbox_vars[attr]
+
+        # Create a container for the checkbox and label
+        container = ttk.Frame(parent)
+        container.grid(row=row, column=col, sticky="ew", padx=5, pady=5)
+        container.columnconfigure(1, weight=1)  # Allow checkbox to adjust dynamically
+
+        if label_left:
+            # Label with fixed pixel width and sticky west alignment
+            label = tk.Label(container, text=label_text, anchor="w", width=label_width_px // 7)
+            label.grid(row=0, column=0, sticky="w", padx=(5, 2))
+
+            # Checkbox widget
+            checkbox = ttk.Checkbutton(
+                container,
+                text="",  # No text since the label is on the left
+                variable=is_checked,
+                command=lambda: setattr(state, attr, is_checked.get())
+            )
+            checkbox.grid(row=0, column=1, sticky="w")  # Explicit sticky west for checkbox alignment
+        else:
+            # Checkbox widget with text (label on the right)
+            checkbox = ttk.Checkbutton(
+                container,
+                text=label_text,
+                variable=is_checked,
+                command=lambda: setattr(state, attr, is_checked.get())
+            )
+            checkbox.grid(row=0, column=0, sticky="w", **grid_kwargs)
+
+        if tooltip_text:
+            Tooltip(checkbox, tooltip_text)
+
+        return container, checkbox, is_checked
+
+    @staticmethod
+    def messagebox(title, message, yes_text="Yes", no_text="No"):
+        # Create a Toplevel window
+        window = tk.Toplevel()
+        window.title(title)
+        window.geometry("350x140")
+
+        # Add message label
+        tk.Label(window, text=message, wraplength=350, justify="left").pack(pady=20)
+
+        # Container for buttons
+        button_frame = tk.Frame(window)
+        button_frame.pack(pady=10)
+
+        # Variable to store user choice
+        user_choice = tk.BooleanVar(value=None)
+
+        # Yes button
+        def on_yes():
+            user_choice.set(True)
+            window.destroy()
+
+        Widgets.button(button_frame, yes_text, on_yes, row=0, col=0)
+
+        # No button
+        def on_no():
+            user_choice.set(False)
+            window.destroy()
+
+        Widgets.button(button_frame, no_text, on_no, row=0, col=1)
+
+        # Wait for the user to close the dialog
+        window.grab_set()
+        window.wait_window()
+
+        return user_choice.get()
+
+    @staticmethod
+    def disclaimer(parent, tooltip_text=None):
+        footer_label = ttk.Label(
+            parent,
+            text="Individual and personal use only.\nNot for commercial use.\nk00gar 2025 - https://github.com/ack00gar",
+            font=("Arial", 10, "italic", "bold"), justify="center"
+        )
+        footer_label.grid(row=8, column=0, columnspan=100, padx=5, pady=5, sticky="s")
+
+        if tooltip_text:
+            Tooltip(footer_label, tooltip_text)
+
+    @staticmethod
+    def _browse_file(file_path, title, file_types, callback):
+        file = filedialog.askopenfilename(filetypes=file_types, title=title)
+        if file:
+            file_path.set(file)
+            if callback:
+                callback(file)
+
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
new file mode 100644
index 00000000..85496ce1
--- /dev/null
+++ b/script_generator/gui/views/funscript_generator.py
@@ -0,0 +1,207 @@
+import tkinter as tk
+from tkinter import ttk
+
+from script_generator.gui.controller.regenerate_funscript import regenerate_funscript
+from script_generator.gui.controller.video_analysis import video_analysis
+from script_generator.gui.messages.messages import UIMessage, ProgressMessage
+from script_generator.gui.utils.widgets import Widgets
+from script_generator.state.app_state import AppState
+from script_generator.utils.helpers import is_mac
+
+
+class FunscriptGeneratorPage(tk.Frame):
+    def __init__(self, parent, controller):
+        #region SETUP
+        super().__init__(parent)
+        self.controller = controller
+        self.state: AppState = controller.state
+        state: AppState = controller.state
+
+        self.grid_rowconfigure(0, weight=1)
+        self.grid_columnconfigure(0, weight=1)
+
+        wrapper = Widgets.frame(self, title=None, sticky="nsew")
+        #endregion
+
+        #region VIDEO SELECTION
+        video_selection = Widgets.frame(wrapper, title="Video Selection", main_section=True)
+
+        Widgets.file_selection(
+            attr="video_path",
+            parent=video_selection,
+            label_text="Video",
+            button_text="Browse",
+            file_selector_title="Choose a File",
+            file_types=[("Text Files", "*.mp4 *.avi *.mov *.mkv"), ("All Files", "*.*")],
+            state=state
+        )
+
+        Widgets.dropdown(
+            attr="video_reader",
+            parent=video_selection,
+            label_text="Video Reader",
+            options=["FFmpeg", *([] if is_mac() else ["FFmpeg + OpenGL (Windows)"])],
+            default_value=state.video_reader,
+            tooltip_text=("On Mac only FFmpeg is supported" if is_mac() else "FFmpeg + OpenGL is usually about 30% faster on a good GPU."),
+            state=state,
+            row=1
+        )
+        #endregion
+
+        #region OPTIONAL SETTINGS
+        optional_settings = Widgets.frame(wrapper, title="Optional settings", main_section=True, row=2)
+
+        Widgets.input(optional_settings, "Frame Start", state=state, attr="frame_start")
+        Widgets.input(optional_settings, "Frame End", state=state, attr="frame_start", row=1)
+        #endregion
+
+        #region PROCESSING
+        processing = Widgets.frame(wrapper, title="Processing", main_section=True, row=3)
+        yolo_p_container, yolo_p, yolo_p_label, yolo_p_perc = Widgets.labeled_progress(processing, "YOLO Detection", row=0)
+        track_p_container, track_p, track_p_label, track_p_perc = Widgets.labeled_progress(processing, "Tracking Analysis", row=1)
+        Widgets.button(processing, "Start processing", lambda: video_analysis(state), row=2)
+        #endregion
+
+        #region FUNSCRIPT TWEAKING
+        tweaking = Widgets.frame(wrapper, title="Funscript", main_section=True, row=4)
+        # tweaking.grid_rowconfigure(1, weight=10)
+        tweaking_container = ttk.Frame(tweaking)
+        tweaking_container.grid(row=1, column=0, columnspan=3, padx=5, pady=5, sticky="nsew")
+
+        boost_frame = ttk.LabelFrame(tweaking_container, text="Boost Settings", padding=(10, 5))
+        boost_frame.grid(row=1, column=0, padx=5, pady=5, sticky="ew")
+
+        Widgets.checkbox(boost_frame, "Boost Settings", state, "boost_enabled", False)
+        Widgets.range_selector(
+            parent=boost_frame,
+            label_text="Boost Up %",
+            state=self.state,
+            attr='boost_up_percent',
+            values=[str(i) for i in range(0, 21)],
+            row=1
+        )
+        Widgets.range_selector(
+            parent=boost_frame,
+            label_text="Reduce Down %",
+            state=self.state,
+            attr='boost_down_percent',
+            values=[str(i) for i in range(0, 21)],
+            row=2
+        )
+
+        # Threshold Settings
+        threshold_frame = ttk.LabelFrame(tweaking_container, text="Threshold Settings", padding=(10, 5))
+        threshold_frame.grid(row=1, column=1, padx=5, pady=5, sticky="ew")
+
+        Widgets.checkbox(threshold_frame, "Enable Threshold", state, "threshold_enabled", False)
+        Widgets.range_selector(
+            parent=threshold_frame,
+            label_text="0 Threshold",
+            state=self.state,
+            attr='threshold_low',
+            values=[str(i) for i in range(0, 16)],
+            row=1
+        )
+        Widgets.range_selector(
+            parent=threshold_frame,
+            label_text="100 Threshold",
+            state=self.state,
+            attr='threshold_high',
+            values=[str(i) for i in range(80, 101)],
+            row=2
+        )
+
+        # Simplification Settings
+        vw_frame = ttk.LabelFrame(tweaking_container, text="Simplification", padding=(10, 5))
+        vw_frame.grid(row=1, column=3, padx=5, pady=5, sticky="ew")
+
+        Widgets.checkbox(vw_frame, "Enable Simplification", state, "vw_simplification_enabled", False)
+        Widgets.range_selector(
+            parent=vw_frame,
+            label_text="VW Factor",
+            state=self.state,
+            attr='vw_factor',
+            values=[i / 5 for i in range(10, 51)],
+            row=1
+        )
+        Widgets.range_selector(
+            parent=vw_frame,
+            label_text="Rounding",
+            state=self.state,
+            attr='rounding',
+            values=[5, 10],
+            row=2
+        )
+
+        # Regenerate Funscript Button
+        Widgets.button(tweaking_container, "Regenerate Funscript", lambda: regenerate_funscript(self.state), row=2)
+
+        #endregion
+
+        #region DEBUGGING
+        debugging = Widgets.frame(wrapper, title="Debugging", main_section=True, row=5)
+        general = Widgets.frame(debugging, title="General", row=0)
+        Widgets.checkbox(general, "Logging for debug", state, "debug_mode")
+
+        script_compare = Widgets.frame(debugging, title="Script compare", row=1)
+        Widgets.file_selection(
+            attr="reference_script",
+            parent=script_compare,
+            label_text="Reference Script",
+            button_text="Browse",
+            file_selector_title="Choose a File",
+            file_types=[("Funscript Files", "*.funscript"), ("All Files", "*.*")],
+            state=state,
+            row=0
+        )
+
+        object_detection = Widgets.frame(debugging, title="Object detection", row=2)
+
+        Widgets.checkbox(object_detection, "Live display mode", state, "life_display_mode",tooltip_text="Will show a live preview of the object detection.", row=3)
+        Widgets.checkbox(object_detection, "Save debugging video", state=state, attr="save_debug_as_video", row=5)
+        Widgets.dropdown(
+            attr="debug_record_duration_var",
+            parent=object_detection,
+            label_text="save a frame every",
+            options=[5, 10, 20],
+            default_value=5,
+            state=state,
+            col=2,
+            row=5
+        )
+        Widgets.label(object_detection, text="seconds", row=5, column=3)
+        Widgets.button(object_detection, "Play debug video (q to quit)", None, row=6)
+        #endregion
+
+        #region FOOTER
+        Widgets.disclaimer(wrapper)
+        #endregion
+
+        #region UI UPDATE CALLBACK
+        def update_ui(msg: UIMessage):
+            """Handle UI updates using a switch-like statement."""
+
+            def process_update():
+                handlers = {
+                    ProgressMessage: handle_progress_message
+                }
+
+                handler = handlers.get(type(msg))
+                if handler:
+                    handler(msg)
+                else:
+                    print(f"Unhandled message type: {type(msg)}")
+
+            def handle_progress_message(progress_msg: ProgressMessage):
+                if progress_msg.process == "OBJECT_DETECTION":
+                    yolo_p["value"] = progress_msg.frames_processed
+                    yolo_p["maximum"] = progress_msg.total_frames
+                    percentage = (progress_msg.frames_processed / progress_msg.total_frames) * 100 if progress_msg.total_frames > 0 else 0
+                    yolo_p_perc.config(text=f"{percentage:.0f}% - ETA: {progress_msg.eta}")
+
+
+            # Schedule the update on the main thread
+            self.controller.after(0, process_update)
+
+        self.state.update_ui = update_ui
+        #endregion
\ No newline at end of file
diff --git a/script_generator/object_detection/analye_tracking_results.py b/script_generator/object_detection/analye_tracking_results.py
new file mode 100644
index 00000000..0b565369
--- /dev/null
+++ b/script_generator/object_detection/analye_tracking_results.py
@@ -0,0 +1,201 @@
+import json
+import os
+import time
+from datetime import datetime
+
+import cv2
+from tqdm import tqdm
+from datetime import timedelta
+
+from config import CLASS_COLORS
+from script_generator.gui.messages.messages import ProgressMessage
+from script_generator.utils.file import get_output_file_path
+from script_generator.video.video_info import get_cropped_dimensions
+from utils.lib_ObjectTracker import ObjectTracker
+from utils.lib_SceneCutsDetect import detect_scene_changes
+from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg
+from utils.lib_Visualizer import Visualizer
+
+
+def analyze_tracking_results(state, results, progress_callback=None):
+    width, height = get_cropped_dimensions(state.video_info)
+    list_of_frames = results.get_all_frame_ids()  # Get all frame IDs with detections
+    visualizer = Visualizer()  # Initialize the visualizer
+
+    video_info = state.video_info
+    fps = video_info.fps
+    nb_frames = video_info.total_frames
+    if state.frame_start and state.frame_end:
+        nb_frames = state.frame_end - state.frame_start
+    frame_width, frame_height = get_cropped_dimensions(video_info)
+    reader = VideoReaderFFmpeg(state.video_path, is_vr=video_info.is_vr)
+
+    image_area = video_info.width * video_info.height
+
+    cuts = []
+
+    if state.life_display_mode:
+        reader.set(cv2.CAP_PROP_POS_FRAMES, state.frame_start)
+    else:
+        reader.release()
+
+    # Load scene cuts if the file exists
+    if os.path.exists(state.video_path[:-4] + f"_cuts.json"):
+        print(f"Loading cuts from {state.video_path[:-4] + f'_cuts.json'}")
+        with open(state.video_path[:-4] + f"_cuts.json", 'r') as f:
+            cuts = json.load(f)
+        print(f"Loaded {len(cuts)} cuts : {cuts}")
+    else:
+        # Detect scene changes if the cuts file does not exist
+        scene_list = detect_scene_changes(state.video_path, video_info.is_vr, 0.9, state.frame_start, state.frame_end)
+        print(f"Analyzing frames {state.frame_start} to {state.frame_end}")
+        cuts = [scene[1] for scene in scene_list]
+        cuts = cuts[:-1]  # Remove the last entry
+        # Save the cuts to a file
+        with open(state.video_path[:-4] + f"_cuts.json", 'w') as f:
+            json.dump(cuts, f)
+
+    state.funscript_frames = []  # List to store Funscript frames
+    tracker = ObjectTracker(fps, state.frame_start, image_area, video_info.is_vr)  # Initialize the object tracker
+
+    # Start time for ETA calculation
+    start_time = time.time()
+
+    for frame_pos in tqdm(range(state.frame_start, state.frame_end), unit="f"):
+        if frame_pos in cuts:
+            # Reinitialize the tracker at scene cuts
+            print(f"Reaching cut at frame {frame_pos}")
+            previous_distances = tracker.previous_distances
+            print(f"Reinitializing tracker with previous distances: {previous_distances}")
+            tracker = ObjectTracker(fps, frame_pos, image_area, video_info.is_vr)
+            tracker.previous_distances = previous_distances
+
+        if frame_pos in list_of_frames:
+            # Get sorted boxes for the current frame
+            sorted_boxes = results.get_boxes(frame_pos)
+            tracker.tracking_logic(sorted_boxes, frame_pos, height)  # Apply tracking logic
+
+            if tracker.distance:
+                # Append Funscript data if distance is available
+                state.funscript_frames.append(frame_pos)
+                state.funscript_distances.append(int(tracker.distance))
+
+            if state.debug_mode:
+                # Log debugging information
+                bounding_boxes = []
+                for box in sorted_boxes:
+                    if box[4] in tracker.normalized_absolute_tracked_positions:
+                        if box[4] == 0:  # generic track_id for 'hips center'
+                            str_dist_penis = 'None'
+                        else:
+                            if box[4] in tracker.normalized_distance_to_penis:
+                                str_dist_penis = str(int(tracker.normalized_distance_to_penis[box[4]][-1]))
+                            else:
+                                str_dist_penis = 'None'
+                        str_abs_pos = str(int(tracker.normalized_absolute_tracked_positions[box[4]][-1]))
+                        position = 'p: ' + str_dist_penis + ' | ' + 'a: ' + str_abs_pos
+                        if box[4] in tracker.pct_weights:
+                            if len(tracker.pct_weights[box[4]]) > 0:
+                                weight = tracker.pct_weights[box[4]][-1]
+                                position += ' | w: ' + str(weight)
+                    else:
+                        position = None
+                    bounding_boxes.append({
+                        'box': box[0],
+                        'conf': box[1],
+                        'class_name': box[3],
+                        'track_id': box[4],
+                        'position': position,
+                    })
+                state.debugger.log_frame(
+                    frame_pos,
+                    bounding_boxes=bounding_boxes,
+                    variables={
+                        'frame': frame_pos,
+                        # time of the frame hh:mm:ss
+                        'time': datetime.fromtimestamp(frame_pos / fps).strftime('%H:%M:%S'),
+                        'distance': tracker.distance,
+                        'Penetration': tracker.penetration,
+                        'sex_position': tracker.sex_position,
+                        'sex_position_reason': tracker.sex_position_reason,
+                        'tracked_body_part': tracker.tracked_body_part,
+                        'locked_penis_box': tracker.locked_penis_box.to_dict(),
+                        'glans_detected': tracker.glans_detected,
+                        'cons._glans_detections': tracker.consecutive_detections['glans'],
+                        'cons._glans_non_detections': tracker.consecutive_non_detections['glans'],
+                        'cons._penis_detections': tracker.consecutive_detections['penis'],
+                        'cons._penis_non_detections': tracker.consecutive_non_detections['penis'],
+                        'breast_tracking': tracker.breast_tracking,
+                    }
+                )
+
+        if state.life_display_mode:
+            # Display the tracking results for testing
+            ret, frame = reader.read()
+
+            if state.video_reader == "OpenCV" and video_info.is_vr:
+                frame_display = frame[:, :frame.shape[1] // 2, :]  # only half left of the frame, for VR half
+            else:
+                frame_display = frame.copy()
+
+            for box in tracker.tracked_boxes:
+                frame_display = visualizer.draw_bounding_box(
+                    frame_display,
+                    box[0],
+                    str(box[2]) + ": " + box[1],
+                    CLASS_COLORS[str(box[1])],
+                    state.offset_x
+                )
+            if tracker.locked_penis_box is not None and tracker.locked_penis_box.is_active():
+                frame_display = visualizer.draw_bounding_box(
+                    frame_display, tracker.locked_penis_box.box,
+                    "Locked_Penis",
+                    CLASS_COLORS['penis'],
+                    state.offset_x
+                )
+            else:
+                print("No active locked penis box to draw.")
+
+            if tracker.glans_detected:
+                frame_display = visualizer.draw_bounding_box(
+                    frame_display, tracker.boxes['glans'],
+                    "Glans",
+                    CLASS_COLORS['glans'],
+                    state.offset_x
+                )
+            if state.funscript_distances:
+                frame_display = visualizer.draw_gauge(frame_display, state.funscript_distances[-1])
+
+            cv2.imshow("Combined Results", frame_display)
+            cv2.waitKey(1)
+
+        # Update progress
+        if progress_callback:
+            elapsed_time = time.time() - start_time
+            frames_processed = frame_pos - state.frame_start + 1
+            frames_remaining = state.frame_end - frame_pos - 1
+            eta = (elapsed_time / frames_processed) * frames_remaining if frames_processed > 0 else 0
+
+            state.update_ui(ProgressMessage(
+                process="TRACKING_ANALYSIS",
+                frames_processed=frames_processed,
+                total_frames=state.frame_end,
+                eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
+            ))
+
+            # progress_callback(frame_pos, frame_end, time.strftime("%H:%M:%S", time.gmtime(eta)))
+
+    # Prepare Funscript data
+    state.funscript_data = list(zip(state.funscript_frames, state.funscript_distances))
+
+    points = "["
+    for i in range(len(state.funscript_frames)):
+        if i != 0:
+            points += ","
+        points += f"[{state.funscript_frames[i]}, {state.funscript_distances[i]}]"
+    points += "]"
+    # Write the raw Funscript data to a JSON file
+    raw_funscript_path, _ = get_output_file_path(state.video_path, "_rawfunscript.json")
+    with open(raw_funscript_path, 'w') as f:
+        json.dump(state.funscript_data, f)
+    return state.funscript_data
\ No newline at end of file
diff --git a/script_generator/object_detection/box_record.py b/script_generator/object_detection/box_record.py
new file mode 100644
index 00000000..faab1519
--- /dev/null
+++ b/script_generator/object_detection/box_record.py
@@ -0,0 +1,22 @@
+class BoxRecord:
+    def __init__(self, box, conf, cls, class_name, track_id):
+        """
+        Define the BoxRecord class to store bounding box information
+        :param box: Bounding box coordinates [x1, y1, x2, y2].
+        :param conf: Confidence score of the detection.
+        :param cls: Class ID of the detected object.
+        :param class_name: Class name of the detected object.
+        :param track_id: Track ID for object tracking.
+        """
+        self.box = box
+        self.conf = conf
+        self.cls = cls
+        self.class_name = class_name
+        self.track_id = int(track_id)
+
+    def __iter__(self):
+        """
+        Make the BoxRecord object iterable.
+        :return: An iterator over the box, confidence, class, class name, and track ID.
+        """
+        return iter((self.box, self.conf, self.cls, self.class_name, self.track_id))
diff --git a/script_generator/object_detection/object_detection_result.py b/script_generator/object_detection/object_detection_result.py
new file mode 100644
index 00000000..10d43e21
--- /dev/null
+++ b/script_generator/object_detection/object_detection_result.py
@@ -0,0 +1,47 @@
+from config import CLASS_PRIORITY_ORDER
+
+
+class ObjectDetectionResult:
+    def __init__(self):
+        """
+        Define the Result class to store and manage detection results
+        :param image_width: Width of the image/frame.
+        """
+        self.frame_data = {}  # Dictionary to store data for each frame
+
+    def add_record(self, frame_id, box_record):
+        """
+        Add a BoxRecord to the frame_data dictionary.
+        :param frame_id: The frame ID to which the record belongs.
+        :param box_record: The BoxRecord object to add.
+        """
+        if frame_id in self.frame_data:
+            self.frame_data[frame_id].append(box_record)
+        else:
+            self.frame_data[frame_id] = [box_record]
+
+    def get_boxes(self, frame_id):
+        """
+        Retrieve and sort bounding boxes for a specific frame.
+        :param frame_id: The frame ID to retrieve boxes for.
+        :return: A list of sorted bounding boxes.
+        """
+        itemized_boxes = []
+        if frame_id not in self.frame_data:
+            return itemized_boxes
+        boxes = self.frame_data[frame_id]
+        for box, conf, cls, class_name, track_id in boxes:
+            itemized_boxes.append((box, conf, cls, class_name, track_id))
+        # Sort boxes based on class priority order
+        sorted_boxes = sorted(
+            itemized_boxes,
+            key=lambda x: CLASS_PRIORITY_ORDER.get(x[3], 7)  # Default priority is 7 if class not found
+        )
+        return sorted_boxes
+
+    def get_all_frame_ids(self):
+        """
+        Get a list of all frame IDs in the frame_data dictionary.
+        :return: A list of frame IDs.
+        """
+        return list(self.frame_data.keys())
diff --git a/script_generator/object_detection/post_process_results.py b/script_generator/object_detection/post_process_results.py
new file mode 100644
index 00000000..89877d21
--- /dev/null
+++ b/script_generator/object_detection/post_process_results.py
@@ -0,0 +1,133 @@
+import os
+import time
+
+import cv2
+
+from config import CLASS_REVERSE_MATCH, CLASS_COLORS, OUTPUT_PATH
+from script_generator.config import RUN_POSE_MODEL
+from script_generator.object_detection.object_detection_result import ObjectDetectionResult
+from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
+from script_generator.utils.file import write_dataset, get_output_file_path
+
+
+class YoloAnalysisTaskProcessor(AbstractTaskProcessor):
+    process_type = TaskProcessorTypes.YOLO_ANALYSIS
+    records = []
+    test_result = ObjectDetectionResult()  # Test result object for debugging
+
+    def task_logic(self):
+        state = self.state
+
+        for task in self.get_task():
+            frame_pos = task.frame_pos
+            det_results = task.yolo_results
+            frame = task.rendered_frame
+            pose_results = None # TODO pose support
+
+            # Skip if no boxes are detected or no tracks are found
+            if  det_results.boxes.id is None or (len(det_results.boxes) == 0 and not state.life_display_mode):
+                task.rendered_frame = None # Clear memory
+                task.yolo_results = None  # Clear memory
+                self.finish_task(task)
+                continue
+
+            ### DETECTION of BODY PARTS
+            # Extract track IDs, boxes, classes, and confidence scores
+            track_ids = det_results.boxes.id.cpu().tolist()
+            boxes = det_results.boxes.xywh.cpu()
+            classes = det_results.boxes.cls.cpu().tolist()
+            confs = det_results.boxes.conf.cpu().tolist()
+
+            # Process each detection
+            for track_id, cls, conf, box in zip(track_ids, classes, confs, boxes):
+                track_id = int(track_id)
+                x, y, w, h = box.int().tolist()
+                x1 = x - w // 2
+                y1 = y - h // 2
+                x2 = x + w // 2
+                y2 = y + h // 2
+                # Create a detection record
+                record = [frame_pos, int(cls), round(conf, 1), x1, y1, x2, y2, track_id]
+                self.records.append(record)
+                if state.life_display_mode:
+                    test_box = [[x1, y1, x2, y2], round(conf, 1), int(cls), CLASS_REVERSE_MATCH.get(int(cls), 'unknown'), track_id]
+                    self.test_result.add_record(frame_pos, test_box)
+
+                    # Print and test the record
+                    # print(f"Record : {record}")
+                    # print(f"For class id: {int(cls)}, getting: {CLASS_REVERSE_MATCH.get(int(cls), 'unknown')}")
+                    # print(f"Test box: {test_box}")
+
+            if RUN_POSE_MODEL:
+                ### POSE DETECTION - Hips and wrists
+                # Extract track IDs, boxes, classes, and confidence scores
+                if len(pose_results[0].boxes) > 0 and pose_results[0].boxes.id is not None:
+                    pose_track_ids = pose_results[0].boxes.id.cpu().tolist()
+
+                    # Check if keypoints are detected
+                    if pose_results[0].keypoints is not None:
+                        # print("We have keypoints")
+                        # pose_keypoints = pose_results[0].keypoints.cpu()
+                        # pose_track_ids = pose_results[0].boxes.id.cpu().tolist()
+                        # pose_boxes = pose_results[0].boxes.xywh.cpu()
+                        # pose_classes = pose_results[0].boxes.cls.cpu().tolist()
+                        pose_confs = pose_results[0].boxes.conf.cpu().tolist()
+
+                        pose_keypoints = pose_results[0].keypoints.cpu()
+                        pose_keypoints_list = pose_keypoints.xy.cpu().tolist()
+                        left_hip = pose_keypoints_list[0][11]
+                        right_hip = pose_keypoints_list[0][12]
+
+                        middle_x_frame = frame.shape[1] // 2
+                        mid_hips = [middle_x_frame, (int(left_hip[1]) + int(right_hip[1])) // 2]
+                        x1 = mid_hips[0] - 5
+                        y1 = mid_hips[1] - 5
+                        x2 = mid_hips[0] + 5
+                        y2 = mid_hips[1] + 5
+                        cls = 10  # hips center
+                        # print(f"pose_confs: {pose_confs}")
+                        conf = pose_confs[0]
+
+                        record = [frame_pos, 10, round(conf, 1), x1, y1, x2, y2, 0]
+                        self.records.append(record)
+                        if state.life_display_mode:
+                            # Print and test the record
+                            print(f"Record : {record}")
+                            print(f"For class id: {int(cls)}, getting: {CLASS_REVERSE_MATCH.get(int(cls), 'unknown')}")
+                            test_box = [[x1, y1, x2, y2], round(conf, 1), int(cls),
+                                        CLASS_REVERSE_MATCH.get(int(cls), 'unknown'), 0]
+                            print(f"Test box: {test_box}")
+                            self.test_result.add_record(frame_pos, test_box)
+
+            if state.life_display_mode:
+                # Display the YOLO results for testing
+                # det_results.plot()
+                # cv2.imshow("YOLO11", det_results.plot())
+                # cv2.waitKey(1)
+                # Verify the sorted boxes
+                sorted_boxes = self.test_result.get_boxes(frame_pos)
+                # print(f"Sorted boxes : {sorted_boxes}")
+
+                # frame_display = frame.copy()
+                frame_display = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+
+                for box in sorted_boxes:
+                    color = CLASS_COLORS.get(box[3])
+                    cv2.rectangle(frame_display, (box[0][0], box[0][1]), (box[0][2], box[0][3]), color, 2)
+                    cv2.putText(frame_display, f"{box[4]}: {box[3]}", (box[0][0], box[0][1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+                cv2.imshow("YOLO11 test boxes Tracking", frame_display)
+                if cv2.waitKey(1) & 0xFF == ord('q'):
+                    break
+
+            task.rendered_frame = None # Clear memory
+            task.yolo_results = None # Clear memory (yolo results contains a copy of the image)
+            self.finish_task(task)
+            
+
+    def on_last_item(self):
+        self.state.analyse_task.end_time = time.time()
+
+        # Write the detection records to a JSON file
+        raw_yolo_path, _ = get_output_file_path(self.state.video_path, "_rawyolo.json")
+        write_dataset(raw_yolo_path, self.records)
+        
\ No newline at end of file
diff --git a/script_generator/object_detection/utils.py b/script_generator/object_detection/utils.py
new file mode 100644
index 00000000..42f1b5d3
--- /dev/null
+++ b/script_generator/object_detection/utils.py
@@ -0,0 +1,104 @@
+import os
+import platform
+from tkinter import messagebox
+
+import torch
+
+from config import YOLO_MODELS, CLASS_REVERSE_MATCH, OUTPUT_PATH
+from script_generator.gui.utils.widgets import Widgets
+from script_generator.object_detection.box_record import BoxRecord
+from script_generator.object_detection.object_detection_result import ObjectDetectionResult
+from script_generator.utils.file import get_output_file_path
+
+
+def check_skip_object_detection(state):
+    raw_yolo_path, raw_yolo_filename = get_output_file_path(state.video_path, "_rawyolo.json")
+    if os.path.exists(raw_yolo_path):
+        skip_detection = Widgets.messagebox(
+            "Detection File Conflict",
+            f"The file already exists. What would you like to do?\n{raw_yolo_filename}",
+            "Use Existing",
+            "Generate New"
+        )
+        if skip_detection:
+            print(f"File {raw_yolo_path} already exists. Skipping detections and loading file content...")
+            return True
+        else:
+            os.remove(raw_yolo_path)
+
+    return False
+
+def get_yolo_model_path():
+    # Check if the device is an Apple device
+    if platform.system() == 'Darwin':
+        print(f"Apple device detected, loading {YOLO_MODELS[0]} for MPS inference.")
+        return YOLO_MODELS[0]
+
+    # Check if CUDA is available (for GPU support)
+    elif torch.cuda.is_available():
+        print(f"CUDA is available, loading {YOLO_MODELS[1]} for GPU inference.")
+        return YOLO_MODELS[1]
+
+    # Fallback to ONNX model for other platforms without CUDA
+    else:
+        print("CUDA not available, if this is unexpected, please install CUDA and check your version of torch.")
+        print("You might need to install a dependency with the following command (example):")
+        print("pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118")
+        print(f"Falling back to CPU inference, loading {YOLO_MODELS[2]}.")
+        print("WARNING: CPU inference may be slow on some devices.")
+
+        return YOLO_MODELS[2]
+
+
+def make_data_boxes(records):
+    """
+    Convert YOLO records into BoxRecord objects.
+    :param records: List of YOLO detection records.
+    :return: A Result object containing BoxRecord instances.
+    """
+    result = ObjectDetectionResult()  # Create a Result instance
+    for record in records:
+        frame_idx, cls, conf, x1, y1, x2, y2, track_id = record
+        box = [x1, y1, x2, y2]
+        class_name = CLASS_REVERSE_MATCH.get(cls, 'unknown')
+        box_record = BoxRecord(box, conf, cls, class_name, track_id)
+        result.add_record(frame_idx, box_record)
+    return result
+
+def parse_yolo_data_looking_for_penis(data, start_frame):
+    """
+    Parse YOLO data to find the first instance of a penis.
+    :param data: The YOLO detection data.
+    :param start_frame: The starting frame for the search.
+    :return: The frame ID where the penis is first detected.
+    """
+    consecutive_frames = 0
+    frame_detected = 0
+    penis_frame = 0
+
+    penis_cls = 0
+    glans_cls = 1
+
+    for line in data: #
+        frame_idx, cls, conf, x1, y1, x2, y2, track_id = line
+        class_name = CLASS_REVERSE_MATCH.get(cls, 'unknown')
+        if frame_idx >= start_frame and cls == penis_cls and conf >= 0.5:
+            penis_frame = frame_idx
+
+            # TODO re-enable logic
+            return penis_frame
+
+        if frame_idx == penis_frame and cls == glans_cls and conf >= 0.5:
+            if frame_detected == 0:
+                frame_detected = frame_idx
+                consecutive_frames += 1
+            elif frame_idx == frame_detected + 1:
+                consecutive_frames += 1
+                frame_detected = frame_idx
+            else:
+                consecutive_frames = 0
+                frame_detected = 0
+
+            if consecutive_frames >= 2:
+                print(f"First instance of Glans/Penis found in frame {frame_idx - 4}")
+                return frame_idx - 4
\ No newline at end of file
diff --git a/script_generator/object_detection/yolo.py b/script_generator/object_detection/yolo.py
new file mode 100644
index 00000000..cd6a13a6
--- /dev/null
+++ b/script_generator/object_detection/yolo.py
@@ -0,0 +1,43 @@
+import time
+
+from script_generator.config import YOLO_CONF, YOLO_BATCH_SIZE, YOLO_MODEL
+from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
+
+
+class YoloTaskProcessor(AbstractTaskProcessor):
+    process_type = TaskProcessorTypes.YOLO
+
+    # TODO add pose model support
+    # if run_pose_model:
+    #     yolo_pose_results = pose_model.track(frame, persist=True, conf=YOLO_CONF, verbose=False)
+
+    def task_logic(self):
+        batch = []
+        tasks = []
+
+        for task in self.get_task():
+            if task.rendered_frame is not None:
+                batch.append(task.rendered_frame)
+                tasks.append(task)
+
+                # If batch is ready, process it
+                if len(batch) >= YOLO_BATCH_SIZE:
+                    self.process_batch(batch, tasks)
+                    batch = []
+                    tasks = []
+
+        # Process any remaining tasks in the batch
+        if batch:
+            self.process_batch(batch, tasks)
+
+    def process_batch(self, frames, tasks):
+        start_time = time.time()
+        # yolo_results = YOLO_MODEL(frames, conf=YOLO_CONF, verbose=False)
+        yolo_results = YOLO_MODEL.track(frames, persist=False, conf=YOLO_CONF, verbose=False)
+        avg_time = (time.time() - start_time) / len(tasks)
+
+        for t, result in zip(tasks, yolo_results):
+            t.yolo_results = result
+            batch_time = time.time() - start_time
+            t.duration(str(self.process_type), avg_time)
+            self.finish_task(t)
diff --git a/script_generator/scripts/analyse_video.py b/script_generator/scripts/analyse_video.py
new file mode 100644
index 00000000..fb6de215
--- /dev/null
+++ b/script_generator/scripts/analyse_video.py
@@ -0,0 +1,203 @@
+import queue
+import threading
+import time
+from typing import List
+
+from colorama import Fore, Style
+from tqdm import tqdm
+
+from script_generator.config import QUEUE_MAXSIZE, SEQUENTIAL_MODE, PROGRESS_BAR, UPDATE_PROGRESS_INTERVAL
+from script_generator.gui.messages.messages import ProgressMessage
+from script_generator.object_detection.post_process_results import YoloAnalysisTaskProcessor
+from script_generator.object_detection.yolo import YoloTaskProcessor
+from script_generator.state.app_state import AppState
+from script_generator.tasks.abstract_task_processor import TaskProcessorTypes
+from script_generator.tasks.tasks import AnalyseVideoTask, AnalyseFrameTask
+from script_generator.video.video_conversion.vr_to_2d_task_processor import VrTo2DTaskProcessor
+from script_generator.video.video_task_processor import VideoTaskProcessor
+
+
+def analyse_video(state: AppState) -> List[AnalyseFrameTask]:
+    print(f"[OBJECT DETECTION] Starting up pipeline with profiling in {'sequential mode' if SEQUENTIAL_MODE else 'parallel mode'}...")
+
+    use_open_gl = state.video_reader == "FFmpeg + OpenGL (Windows)"
+
+    # Initialize batch task
+    state.set_video_info()
+    state.analyse_task = AnalyseVideoTask()
+
+    # Create queues
+    opengl_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
+    yolo_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
+    analysis_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
+    result_q = queue.Queue(maxsize=0)
+
+    # Create threads
+    decode_thread = VideoTaskProcessor(state=state, output_queue=opengl_q if use_open_gl else yolo_q)
+    opengl_thread = VrTo2DTaskProcessor(state=state, input_queue=opengl_q, output_queue=yolo_q) if use_open_gl else None
+    yolo_thread = YoloTaskProcessor(state=state, input_queue=yolo_q, output_queue=analysis_q)
+    yolo_analysis_thread = YoloAnalysisTaskProcessor(state=state, input_queue=analysis_q, output_queue=result_q)
+
+    # Start logging thread
+    log_thread_stop_event = threading.Event()
+    queue_logging_thread = threading.Thread(
+        target=log_progress,
+        args=(state, opengl_q, yolo_q, analysis_q, result_q, log_thread_stop_event),
+        daemon=True,
+    )
+    queue_logging_thread.start()
+
+    # Sequential mode can be used to determine performance bottlenecks on very short videos
+    if SEQUENTIAL_MODE:
+        def run_thread(thread, thread_name, out_queue):
+            start_time = time.time()
+            thread.start()
+            thread.join()
+            out_queue.put(None)
+            print(f"[OBJECT DETECTION] {thread_name} thread done in {time.time() - start_time} s")
+
+        run_thread(decode_thread, TaskProcessorTypes.VIDEO, opengl_q)
+        if use_open_gl:
+            run_thread(opengl_thread, TaskProcessorTypes.OPENGL, yolo_q)
+        run_thread(yolo_thread, TaskProcessorTypes.YOLO, analysis_q)
+        run_thread(yolo_analysis_thread, TaskProcessorTypes.YOLO_ANALYSIS, result_q)
+    else:
+        threads = [decode_thread, opengl_thread, yolo_thread, yolo_analysis_thread] if use_open_gl else [decode_thread, yolo_thread, yolo_analysis_thread]
+        for thread in threads:
+            thread.start()
+        for thread in threads:
+            thread.join()
+
+    state.analyse_task.end_time = time.time()
+
+    log_thread_stop_event.set()
+
+    # wait for update progress thread to close
+    time.sleep(UPDATE_PROGRESS_INTERVAL * 1.1)
+
+    log_performance(state=state, results_queue=result_q)
+
+    return result_q.queue
+
+
+def log_progress(state, opengl_q, yolo_q, analysis_q, results_q, stop_event):
+    total_frames = state.video_info.total_frames
+
+    if PROGRESS_BAR:
+        with tqdm(
+                total=total_frames,
+                desc="Analysing video",
+                unit="frames",
+                position=0,
+                unit_scale=False,
+                unit_divisor=1,
+                ncols=130
+        ) as progress_bar:
+            while not stop_event.is_set():
+                opengl_size = opengl_q.qsize()
+                yolo_size = yolo_q.qsize()
+                analysis_size = analysis_q.qsize()
+                frames_processed = results_q.qsize()
+
+                progress_bar.n = frames_processed
+                open_gl = f"OpenGL: {opengl_size:>3}, " if state.video_reader == "FFmpeg + OpenGL (Windows)" else ""
+                progress_bar.set_postfix_str(
+                    f"Queues: {open_gl}YOLO: {yolo_size:>3}, Analysis: {analysis_size:>3}"
+                )
+                progress_bar.refresh()
+
+                if frames_processed >= total_frames:
+                    stop_event.set()
+
+                if state.update_ui:
+                    elapsed_time = time.time() - state.analyse_task.start_time
+                    processing_rate = frames_processed / elapsed_time if elapsed_time > 0 else 0
+                    remaining_frames = total_frames - frames_processed
+                    eta = remaining_frames / processing_rate if processing_rate > 0 else float('inf')
+                    try:
+                        state.update_ui(ProgressMessage(
+                            process="OBJECT_DETECTION",
+                            frames_processed=frames_processed,
+                            total_frames=total_frames,
+                            eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
+                        ))
+                    except Exception as e:
+                        print(f"Error in state.update_ui: {e}")
+
+                time.sleep(UPDATE_PROGRESS_INTERVAL)
+    else:
+        while not stop_event.is_set():
+            opengl_size = opengl_q.qsize()
+            yolo_size = yolo_q.qsize()
+            analysis_size = analysis_q.size()
+            frames_processed = results_q.qsize()
+            open_gl = f"OpenGL: {opengl_size:>3}, " if state.video_reader == "FFmpeg + OpenGL (Windows)" else ""
+            print(f"Queues: {open_gl}YOLO: {yolo_size:>3}, Analysis: {analysis_size:>3}, DONE: {frames_processed:>3}")
+
+            if frames_processed >= total_frames:
+                stop_event.set()
+
+            time.sleep(0.5)
+
+def log_performance(state, results_queue):
+    analyse_task = state.analyse_task
+    # TODO filter out sentinals in task processor
+    tasks = [task for task in results_queue.queue if task is not None and hasattr(task, 'profile')]
+    total_frames = len(tasks)
+
+    total_pipeline_time = analyse_task.end_time - analyse_task.start_time
+    video_duration = total_frames / state.video_info.fps
+    avg_processing_fps = total_frames / total_pipeline_time
+    realtime_percentage = (avg_processing_fps / 60.0) * 100.0
+
+    log_message = (
+        f"\n{'-' * 60}"
+        f"\n OBJECT DETECTION COMPLETED {'(sequential mode)' if SEQUENTIAL_MODE else ''}\n"
+        f"\n Settings\n"
+        f"  - Video reader               : {state.video_reader}\n"     
+        f"\n Video stats\n"
+        f"  - Total Frames               : {total_frames}\n"
+        f"  - Video Duration             : {video_duration:.2f} s\n"
+    )
+
+    if SEQUENTIAL_MODE:
+        log_message += f"\n Sequential Queue statistics\n"
+        for key, total_time in analyse_task.profile.items():
+            if key.endswith("_duration"):  # Only include duration metrics
+                avg_time = total_time / total_frames if total_frames > 0 else 0.0
+                stage_name = key.replace("_duration", "").capitalize()
+                log_message += (
+                    f"  - {stage_name:<27}: {avg_time * 1000:.0f} ms | "
+                    f"{(1 / avg_time if avg_time > 0 else 0):.0f} fps\n"
+                )
+    else:
+        log_message += (
+            f"\n Performance stats\n"
+            f"  - Average Processing         : {avg_processing_fps:.2f} fps\n"
+            f"  - Real-time Processing       : {realtime_percentage:.2f} %\n"
+            f"  - Total Pipeline Runtime (s) : {total_pipeline_time:.2f} s\n"
+        )
+        log_message += f"\n Task Average Times (while running in parallel)\n"
+
+        aggregated_times = {}
+        for task in tasks:
+            for key, total_time in task.profile.items():
+                if key.endswith("_duration"):
+                    if key not in aggregated_times:
+                        aggregated_times[key] = {"total_time": 0, "task_count": 0}
+                    aggregated_times[key]["total_time"] += total_time
+                    aggregated_times[key]["task_count"] += 1
+
+        # Calculate and format averages for each key
+        for key, data in aggregated_times.items():
+            avg_time = data["total_time"] / total_frames if total_frames > 0 else 0.0
+            stage_name = key.replace("_duration", "").replace("_", " ").capitalize()
+            log_message += (
+                f"  - {stage_name:<27}: {avg_time * 1000:.0f} ms | "
+                f"{(1 / avg_time if avg_time > 0 else 0):.0f} fps\n"
+            )
+
+    log_message += f"{'-' * 60}\n"
+
+    print(Fore.LIGHTBLUE_EX + log_message)
+    print(Style.RESET_ALL, end="")
diff --git a/script_generator/scripts/generate_funscript.py b/script_generator/scripts/generate_funscript.py
new file mode 100644
index 00000000..d228d658
--- /dev/null
+++ b/script_generator/scripts/generate_funscript.py
@@ -0,0 +1,6 @@
+from script_generator.scripts.analyse_video import analyse_video
+from script_generator.state.app_state import AppState
+
+
+def generate_funscript(state: AppState):
+    restults = analyse_video(state)
\ No newline at end of file
diff --git a/script_generator/scripts/tracking_analysis.py b/script_generator/scripts/tracking_analysis.py
new file mode 100644
index 00000000..e69de29b
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
new file mode 100644
index 00000000..7ad49a25
--- /dev/null
+++ b/script_generator/state/app_state.py
@@ -0,0 +1,57 @@
+import string
+from typing import Literal
+
+from script_generator.tasks.tasks import AnalyseVideoTask
+from script_generator.utils.helpers import is_mac
+from script_generator.video.ffmpeg import is_cuda_supported, get_video_info
+from script_generator.video.video_info import VideoInfo
+
+
+class AppState:
+    def __init__(self, is_cli):
+        self.is_cli: bool = is_cli
+
+        # Job
+        self.video_info: VideoInfo | None = None
+        self.analyse_task: AnalyseVideoTask | None = None
+        self.video_path: string = None
+        self.frame_start: int = 0
+        self.frame_end: int | None = None
+
+        # Detection & decoding
+        self.video_reader: Literal["FFmpeg", "FFmpeg + OpenGL (Windows)"] = "FFmpeg" if is_mac() else "FFmpeg + OpenGL (Windows)"
+
+        # Debug
+        self.debug_mode: bool = False
+        self.life_display_mode: bool = False
+        self.reference_script: string = None
+        self.save_debug_as_video: bool = False
+        self.debug_record_mode: bool = False
+        self.debug_record_duration: int = 0
+
+        # TODO what's this
+        self.funscript_data = []
+        self.funscript_frames = []
+        self.funscript_distances = []
+        self.offset_x: int = 0
+
+        # Funscript Tweaking Variables
+        self.boost_enabled: bool = True
+        self.boost_up_percent: int = 10
+        self.boost_down_percent: int = 15
+        self.threshold_enabled: bool = True
+        self.threshold_low: int = 10
+        self.threshold_high: int = 90
+        self.vw_simplification_enabled: bool = True
+        self.vw_factor: float = 8.0
+        self.rounding: int = 5
+
+        # App logic
+        self.debugger = None
+        self.update_ui = None
+        self.ffmpeg_cuda_supported = is_cuda_supported()
+
+    def set_video_info(self):
+        if self.video_info is None:
+            self.video_info = get_video_info(self.video_path)
+
diff --git a/script_generator/tasks/abstract_task_processor.py b/script_generator/tasks/abstract_task_processor.py
new file mode 100644
index 00000000..1d7d7174
--- /dev/null
+++ b/script_generator/tasks/abstract_task_processor.py
@@ -0,0 +1,108 @@
+import queue
+import threading
+from typing import Generator, Optional
+
+from script_generator.state.app_state import AppState
+from script_generator.tasks.tasks import AnalyseFrameTask
+
+
+class AbstractTaskProcessor(threading.Thread):
+    process_type = ""
+
+    def __init__(self, state: AppState, output_queue: queue.Queue, input_queue: Optional[queue.Queue] = None):
+        """
+        Abstract thread class to handle lifecycle management and task handling boilerplate.
+
+        :param input_queue: Queue to consume tasks from.
+        :param output_queue: Queue to produce processed tasks.
+        """
+        super().__init__()
+        self.state = state
+        self.input_queue = input_queue
+        self.output_queue = output_queue
+        self._stop_event = threading.Event()
+
+    def log(self, message):
+        """
+        Unified logging for the thread.
+        :param message: Message to log.
+        """
+        thread_name = threading.current_thread().name
+        print(f"[{self.__class__.__name__}-{thread_name}] {message}")
+
+    def get_task(self) -> Generator[AnalyseFrameTask, None, None]:
+        """
+        Generator for retrieving tasks from the input queue.
+        Yields tasks until a sentinel (None) is encountered or the thread is stopped.
+        Logs and tracks the time taken to retrieve tasks.
+        """
+        if self.input_queue is None:
+            raise ValueError("Input queue is None. An input queue must be provided to use get_task().")
+
+        while not self._stop_event.is_set():
+            try:
+                task = self.input_queue.get(timeout=1)
+
+                if task is None:  # Sentinel for termination
+                    self.state.analyse_task.end(self.process_type)
+                    self.on_last_item()
+                    self.finish_task(None)
+                    break
+                yield task
+            except queue.Empty:
+                continue
+
+    def finish_task(self, task):
+        """
+        Finalizes the task by placing it in the output queue.
+        If the queue is full, waits until a spot is available.
+
+        :param task: The task to place in the output queue.
+        """
+        while not self._stop_event.is_set():
+            try:
+                self.output_queue.put(task, timeout=1)
+                break
+            except queue.Full:
+                return
+
+    def run(self):
+        """
+        Main thread entry point. Executes the `task_logic` method.
+        """
+        try:
+            self.state.analyse_task.start(self.process_type)
+            self.task_logic()
+        except Exception as e:
+            print(f"An error occurred during task execution on thread {self.process_type}: {e}")
+            import traceback
+            traceback.print_exc()
+        finally:
+            return
+
+    def task_logic(self):
+        """
+        Abstract method for setup, task processing, and cleanup.
+        Subclasses must override this method.
+        """
+        raise NotImplementedError("Subclasses must implement task_logic")
+
+    def stop_process(self):
+        self.state.analyse_task.end(self.process_type)
+        self.on_last_item()
+        # Propagate sentinel to the output queue
+        self.output_queue.put(None)
+
+    def on_last_item(self):
+        return
+
+from enum import Enum
+
+class TaskProcessorTypes(Enum):
+    VIDEO = "Video processing"
+    OPENGL = "3d to 2D"
+    YOLO = "YOLO inference"
+    YOLO_ANALYSIS = "YOLO analysis"
+
+    def __str__(self):
+        return self.value
\ No newline at end of file
diff --git a/script_generator/tasks/tasks.py b/script_generator/tasks/tasks.py
new file mode 100644
index 00000000..bbfe0398
--- /dev/null
+++ b/script_generator/tasks/tasks.py
@@ -0,0 +1,81 @@
+import time
+from dataclasses import dataclass, field
+from threading import Lock
+from typing import List, Dict, Optional
+
+import numpy as np
+
+from script_generator.video.video_info import VideoInfo
+
+
+@dataclass
+class Task:
+    id: int = field(init=False)
+    start_time: Optional[float] = None
+    end_time: Optional[float] = None
+    profile: Dict[str, float] = field(default_factory=dict)  # Timing/profiling info
+    _id_counter: int = 0
+    _id_lock: Lock = Lock()
+    _lock: Lock = field(default_factory=Lock, repr=False, init=False)
+
+    def __post_init__(self):
+        # Use the class-level lock for thread safety
+        with self.__class__._id_lock:
+            self.__class__._id_counter += 1
+            self.id = self.__class__._id_counter
+
+    def start(self, process_type: str):
+        self._update_profile(process_type, "start")
+
+    def end(self, process_type: str):
+        self._update_profile(process_type, "end")
+        self._calculate_duration(process_type)
+
+    def duration(self, process_type: str, duration: int):
+        key = f"{process_type}_duration"
+        with self._lock:
+            self.profile[key] = duration
+
+    def _update_profile(self, process_type: str, action: str):
+        key = f"{process_type}_{action}"
+        with self._lock:
+            self.profile[key] = time.time()
+
+    def _calculate_duration(self, process_type: str):
+        start_key = f"{process_type}_start"
+        end_key = f"{process_type}_end"
+        duration_key = f"{process_type}_duration"
+
+        with self._lock:
+            if start_key in self.profile and end_key in self.profile:
+                self.profile[duration_key] = self.profile[end_key] - self.profile[start_key]
+
+
+@dataclass
+class AnalyseVideoTask(Task):
+    tasks: List[Task] = field(default_factory=list)
+
+    def __init__(self):
+        super().__init__()
+        self.tasks = []
+        self._lock = Lock()
+        self.profile = {}
+        self.start_time = time.time()
+
+    def add_task(self, task: Task) -> Task:
+        with self._lock:
+            self.tasks.append(task)
+        return task
+
+    def get_tasks(self) -> List[Task]:
+        with self._lock:
+            return list(self.tasks)
+
+
+@dataclass
+class AnalyseFrameTask(Task):
+    frame_pos: int = -1
+    preprocessed_frame: Optional[np.ndarray] = None  # Cropped frame from video stream
+    rendered_frame: Optional[np.ndarray] = None  # The final 2D image from OpenGL
+    yolo_results = None
+    # detections: List[Detection] = field(default_factory=list) # YOLO detection results
diff --git a/script_generator/utils/file.py b/script_generator/utils/file.py
new file mode 100644
index 00000000..6d0180bd
--- /dev/null
+++ b/script_generator/utils/file.py
@@ -0,0 +1,39 @@
+import json
+import os
+import time
+
+from config import OUTPUT_PATH
+
+
+def write_dataset(file_path, data):
+    """
+    Write data to a JSON file, always overwriting if it exists.
+    :param file_path: The path to the output file.
+    :param data: The data to write.
+    """
+    print(f"Exporting data...")
+    export_start = time.time()
+    # Write the data to the file (overwrites if it exists)
+    with open(file_path, 'w') as f:
+        json.dump(data, f)
+    export_end = time.time()
+    print(f"Done in {export_end - export_start} seconds.")
+
+def load_json_from_file(file_path):
+    """
+    Load YOLO data from a JSON file.
+    :param file_path: Path to the JSON file.
+    :return: The loaded data.
+    """
+    with open(file_path, 'r') as f:
+        data = json.load(f)
+        print(f"Loaded data from {file_path}, length: {len(data)}")
+    return data
+
+def get_output_file_path(video_path, suffix):
+    """"
+    Get the OUTPUT_PATH filename for a specific suffix (e.g. _raw_yolo.json)
+    """
+    filename = f"{os.path.basename(video_path)[:-4]}{suffix}"
+    path = os.path.join(OUTPUT_PATH, filename)
+    return path, filename
\ No newline at end of file
diff --git a/script_generator/utils/helpers.py b/script_generator/utils/helpers.py
new file mode 100644
index 00000000..65d479c1
--- /dev/null
+++ b/script_generator/utils/helpers.py
@@ -0,0 +1,5 @@
+import platform
+
+
+def is_mac():
+    return platform.system() == "Darwin"
\ No newline at end of file
diff --git a/script_generator/utils/logger.py b/script_generator/utils/logger.py
new file mode 100644
index 00000000..e3559f5e
--- /dev/null
+++ b/script_generator/utils/logger.py
@@ -0,0 +1,16 @@
+import logging
+import sys
+
+# Configure the logger
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    handlers=[
+        logging.FileHandler("FSGenerator.log", mode="w"),  # Save logs to a file
+        logging.StreamHandler(sys.stdout)  # Print logs to the console
+    ]
+)
+
+# Create and configure the global logger
+logger = logging.getLogger("MainLogger")
+logger.info("Logger initialized with file and console handlers.")
\ No newline at end of file
diff --git a/script_generator/video/ffmpeg.py b/script_generator/video/ffmpeg.py
new file mode 100644
index 00000000..418f2659
--- /dev/null
+++ b/script_generator/video/ffmpeg.py
@@ -0,0 +1,56 @@
+import subprocess
+
+from script_generator.video.video_info import VideoInfo
+from config import FFPROBE_PATH, FFMPEG_PATH
+
+
+def get_video_info(video_path):
+    try:
+        cmd = [
+            FFPROBE_PATH,
+            "-v", "error",
+            "-select_streams", "v:0",
+            "-show_entries", "stream=r_frame_rate,width,height,codec_name,nb_frames,duration",
+            "-of", "default=noprint_wrappers=1:nokey=1",
+            video_path,
+        ]
+
+        output = subprocess.check_output(cmd).decode("utf-8").splitlines()
+
+        # Ensure the output has the correct number of fields
+        if len(output) < 6:
+            raise ValueError("FFProbe output is missing required fields.")
+
+        # Parse metadata
+        codec_name = output[0]
+        width = int(output[1])
+        height = int(output[2])
+        r_frame_rate = output[3]
+        duration = float(output[4])
+        total_frames = int(output[5])
+
+        # Calculate FPS
+        num, den = map(int, r_frame_rate.split('/'))  # Split numerator and denominator
+        fps = num / den  # Calculate FPS
+
+        # If the width is 2x the height we are dealing with a VR video
+        is_vr = height == width // 2
+
+        return VideoInfo(video_path, codec_name, width, height, duration, total_frames, fps, is_vr)
+    except Exception as e:
+        print(f"Error initializing video info: {e}")
+        raise
+
+def is_cuda_supported():
+    try:
+        result = subprocess.run(
+            [FFMPEG_PATH, "-hwaccels"],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            text=True,
+            check=True
+        )
+        return "cuda" in result.stdout.lower()
+    except Exception as e:
+        print(f"Error checking CUDA support: {e}")
+        return False
\ No newline at end of file
diff --git a/script_generator/video/video_conversion/opengl/helpers.py b/script_generator/video/video_conversion/opengl/helpers.py
new file mode 100644
index 00000000..f8d0c527
--- /dev/null
+++ b/script_generator/video/video_conversion/opengl/helpers.py
@@ -0,0 +1,59 @@
+import numpy as np
+from OpenGL.GL import *
+
+def create_180_dome(segments=48):
+    """
+    Generate vertices, texture coordinates, and indices for a 180° dome.
+    """
+    vertices, tex_coords, indices = [], [], []
+
+    for i in range(segments + 1):
+        theta = np.pi * (i / segments)
+        for j in range(segments + 1):
+            phi = np.pi * (j / segments)
+            x = np.sin(theta) * np.cos(phi)
+            y = np.sin(theta) * np.sin(phi)
+            z = np.cos(theta)
+            u, v = j / segments, i / segments
+            vertices.extend([x, y, z])
+            tex_coords.extend([u, v])
+
+    for i in range(segments):
+        for j in range(segments):
+            p1 = i * (segments + 1) + j
+            p2 = p1 + (segments + 1)
+            p3 = p1 + 1
+            p4 = p2 + 1
+            indices.extend([p1, p2, p3, p3, p2, p4])
+
+    return (
+        np.array(vertices, dtype=np.float32),
+        np.array(tex_coords, dtype=np.float32),
+        np.array(indices, dtype=np.uint32),
+    )
+
+def render_dome(vertices, tex_coords, indices, texture_id):
+    """
+    Render the dome using OpenGL.
+    If `texture_id` is None, compile only the geometry.
+    """
+    if texture_id is not None:
+        glEnable(GL_TEXTURE_2D)
+        glBindTexture(GL_TEXTURE_2D, texture_id)
+
+    glEnableClientState(GL_VERTEX_ARRAY)
+    glEnableClientState(GL_TEXTURE_COORD_ARRAY)
+    glVertexPointer(3, GL_FLOAT, 0, vertices)
+    glTexCoordPointer(2, GL_FLOAT, 0, tex_coords)
+
+    glPushMatrix()
+    # Flip horizontally for VR
+    glScalef(-1.0, 1.0, 1.0)
+    glDrawElements(GL_TRIANGLES, len(indices), GL_UNSIGNED_INT, indices)
+    glPopMatrix()
+
+    glDisableClientState(GL_VERTEX_ARRAY)
+    glDisableClientState(GL_TEXTURE_COORD_ARRAY)
+
+    if texture_id is not None:
+        glDisable(GL_TEXTURE_2D)
\ No newline at end of file
diff --git a/script_generator/video/video_conversion/vr_to_2d_task_processor.py b/script_generator/video/video_conversion/vr_to_2d_task_processor.py
new file mode 100644
index 00000000..0575601f
--- /dev/null
+++ b/script_generator/video/video_conversion/vr_to_2d_task_processor.py
@@ -0,0 +1,100 @@
+import os
+
+import imageio
+import numpy as np
+import glfw
+from OpenGL.GL import *
+from OpenGL.GLU import *
+
+from script_generator.config import RENDER_RESOLUTION, YAW, PITCH, DEBUG_PATH
+from script_generator.video.video_conversion.opengl.helpers import create_180_dome, render_dome
+from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
+
+
+class VrTo2DTaskProcessor(AbstractTaskProcessor):
+    process_type = TaskProcessorTypes.OPENGL
+
+    def task_logic(self):
+
+        # Initialize off-screen GLFW context
+        if not glfw.init():
+            raise RuntimeError("Could not initialize GLFW")
+
+        # Create invisible window
+        glfw.window_hint(glfw.VISIBLE, glfw.FALSE)
+        window = glfw.create_window(RENDER_RESOLUTION, RENDER_RESOLUTION, "Offscreen", None, None)
+        if not window:
+            glfw.terminate()
+            raise RuntimeError("Failed to create GLFW window")
+        glfw.make_context_current(window)
+
+        # OpenGL config
+        glEnable(GL_TEXTURE_2D)
+        glClearColor(0.0, 0.0, 0.0, 1.0)
+        glEnable(GL_DEPTH_TEST)
+
+        # Projection matrix setup
+        glMatrixMode(GL_PROJECTION)
+        glLoadIdentity()
+        gluPerspective(90, 1.0, 0.1, 100.0)
+        glMatrixMode(GL_MODELVIEW)
+        glLoadIdentity()
+
+        dome_vertices, dome_tex_coords, dome_indices = create_180_dome()
+
+        # Create texture ID
+        texture_id = glGenTextures(1)
+
+        # Create a display list for the dome geometry
+        dome_display_list = glGenLists(1)
+        glNewList(dome_display_list, GL_COMPILE)
+        render_dome(dome_vertices, dome_tex_coords, dome_indices, None)
+        glEndList()
+
+        # Render to off-screen buffer
+        glViewport(0, 0, RENDER_RESOLUTION, RENDER_RESOLUTION)
+        glLoadIdentity()
+        # the 3th parameter will zoom in and out to increase / decrease the FOV
+        gluLookAt(0, 0, 0.5, 0, 0, -1, 0, 1, 0)
+        glRotatef(-YAW, 0, 1, 0)
+        glRotatef(-(PITCH + 90), 1, 0, 0)
+
+        glBindTexture(GL_TEXTURE_2D, texture_id)
+        glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_LINEAR)
+        glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR)
+
+        for task in self.get_task():
+            task.start(str(self.process_type))
+
+            # Upload to texture
+            h, w, _ = task.preprocessed_frame.shape
+            glTexImage2D(GL_TEXTURE_2D, 0, GL_RGB, w, h, 0, GL_RGB, GL_UNSIGNED_BYTE, task.preprocessed_frame)
+
+            glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)
+            glBindTexture(GL_TEXTURE_2D, texture_id)
+            glCallList(dome_display_list)
+
+            # Read pixels
+            pixels = glReadPixels(0, 0, RENDER_RESOLUTION, RENDER_RESOLUTION, GL_RGB, GL_UNSIGNED_BYTE)
+            rendered_frame = np.frombuffer(pixels, dtype=np.uint8).reshape(
+                RENDER_RESOLUTION, RENDER_RESOLUTION, 3
+            )
+            rendered_frame = np.flipud(rendered_frame)
+
+            # Store result
+            task.rendered_frame = rendered_frame
+            task.preprocessed_frame = None
+
+            task.end(str(self.process_type))
+
+            # Debug
+            # output_path = os.path.join(DEBUG_PATH, f"frame_{task.id:05d}.png")
+            # imageio.imwrite(output_path, rendered_frame)
+
+            self.finish_task(task)
+
+        # Cleanup
+        glDeleteLists(dome_display_list, 1)
+        glDeleteTextures([texture_id])
+        glfw.destroy_window(window)
+        glfw.terminate()
diff --git a/script_generator/video/video_info.py b/script_generator/video/video_info.py
new file mode 100644
index 00000000..d31d386f
--- /dev/null
+++ b/script_generator/video/video_info.py
@@ -0,0 +1,67 @@
+import re
+from dataclasses import dataclass, field
+
+from config import MAX_FRAME_HEIGHT
+
+
+@dataclass
+class VideoInfo:
+    path: str
+    codec_name: str = None
+    width: int = 0
+    height: int = 0
+    duration: float = 0.0
+    total_frames: int = 0
+    fps: float = 0.0
+    is_vr: bool = False
+    projection: str = field(init=False)
+    fov: int = field(init=False)
+    is_fisheye = False
+
+    def __post_init__(self):
+        info = get_projection_and_fov_from_filename(self.path)
+        self.projection = info["projection"]
+        self.fov = info["fov"]
+        self.is_fisheye = info["is_fisheye"]
+
+
+def get_projection_and_fov_from_filename(filename):
+    filename = filename.replace("_FB360", "")
+    projection = "LR_180"
+    is_fisheye = False
+    fov = 180
+
+    patterns = [
+        {"regex": r"180_sbs", "projection": "LR_180", "fov": 180, "is_fisheye": False},
+        {"regex": r"_LR_180", "projection": "LR_180", "fov": 180, "is_fisheye": False},
+        {"regex": r"_MONO_360", "projection": "MONO_360", "fov": 360, "is_fisheye": False},
+        {"regex": r"_TB_360", "projection": "TB_360", "fov": 360, "is_fisheye": False},
+        {"regex": r"_MKX200", "projection": "MKX200", "fov": 200, "is_fisheye": True},
+        {"regex": r"_MKX220", "projection": "MKX220", "fov": 220, "is_fisheye": True},
+        {"regex": r"_RF52", "projection": "RF52", "fov": 190, "is_fisheye": True},
+        {"regex": r"_FISHEYE190", "projection": "FISHEYE190", "fov": 190, "is_fisheye": True},
+        {"regex": r"_VRCA220", "projection": "VRCA220", "fov": 220, "is_fisheye": True},
+        {"regex": r"_MKX200_alpha", "projection": "MKX200", "fov": 200, "is_fisheye": True},
+        {"regex": r"_MKX220_alpha", "projection": "MKX220", "fov": 220, "is_fisheye": True},
+        {"regex": r"_RF52_alpha", "projection": "RF52", "fov": 190, "is_fisheye": True},
+        {"regex": r"_FISHEYE190_alpha", "projection": "FISHEYE190", "fov": 190, "is_fisheye": True},
+        {"regex": r"_VRCA220_alpha", "projection": "VRCA220", "fov": 220, "is_fisheye": True},
+        {"regex": r"180x180_3dh", "projection": "LR_180", "fov": 180, "is_fisheye": False},
+        {"regex": r"VR180", "projection": "LR_180", "fov": 180, "is_fisheye": False},
+        {"regex": r"oculusrift_", "projection": "LR_180", "fov": 180, "is_fisheye": False}
+    ]
+
+    for pattern in patterns:
+        if re.search(pattern["regex"], filename):
+            projection = pattern["projection"]
+            fov = pattern["fov"]
+            break
+
+    return {"projection": projection, "fov": fov, "is_fisheye": is_fisheye}
+
+def get_cropped_dimensions(video: VideoInfo):
+    if video.is_vr:
+        return MAX_FRAME_HEIGHT, MAX_FRAME_HEIGHT
+    else:
+        scaling_factor = min(1, MAX_FRAME_HEIGHT / video.height)
+        return int(video.width * scaling_factor), int(video.height * scaling_factor)
\ No newline at end of file
diff --git a/script_generator/video/video_task_processor.py b/script_generator/video/video_task_processor.py
new file mode 100644
index 00000000..58540d45
--- /dev/null
+++ b/script_generator/video/video_task_processor.py
@@ -0,0 +1,118 @@
+import subprocess
+from multiprocessing import cpu_count
+
+import numpy as np
+
+from config import MAX_FRAME_HEIGHT, FFMPEG_PATH
+from script_generator.config import SUBTRACT_THREADS_FROM_FFMPEG, PITCH
+from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
+from script_generator.tasks.tasks import AnalyseFrameTask
+from script_generator.video.video_info import get_cropped_dimensions
+
+
+class VideoTaskProcessor(AbstractTaskProcessor):
+    process_type = TaskProcessorTypes.VIDEO
+
+    def task_logic(self):
+        state = self.state
+        video = self.state.video_info
+        current_frame = state.frame_start
+
+        def get_cmd(vf):
+            start_time = (state.frame_start / video.fps) * 1000
+            ffmpeg_threads = max(cpu_count() - SUBTRACT_THREADS_FROM_FFMPEG, 1)
+            cuda_supported = state.ffmpeg_cuda_supported
+            hwaccel = ["-hwaccel", "cuda"] if cuda_supported else []
+            video_filter = ["-vf", vf] if vf else []
+
+            return [
+                FFMPEG_PATH,
+                *hwaccel,
+                '-nostats', '-loglevel', 'warning',
+                "-ss", str(start_time / 1000),  # Seek to start time in seconds
+                "-i", video.path,
+                "-an",  # Disable audio processing
+                "-map", "0:v:0",
+                *video_filter,
+                "-f", "rawvideo", "-pix_fmt", "rgb24",
+                "-threads", str(ffmpeg_threads),
+                "-",  # Output to stdout
+            ]
+
+        def vr_video_filters():
+            if video.is_fisheye:
+                projection, iv_fov, ih_fov, v_fov, h_fov, d_fov = "fisheye", 190, 190, 90, 90, 180
+            else:
+                projection, iv_fov, ih_fov, v_fov, h_fov, d_fov = "he", 180, 180, 90, 90, 180
+
+            if self.state.video_reader == "FFmpeg":
+                filters = [
+                    f"scale={MAX_FRAME_HEIGHT * 2}:{MAX_FRAME_HEIGHT}",
+                    f"crop={MAX_FRAME_HEIGHT}:{MAX_FRAME_HEIGHT}:0:0",
+                    f"v360={projection}:in_stereo=2d:output=sg:iv_fov={iv_fov}:ih_fov={ih_fov}:"
+                    f"d_fov={d_fov}:v_fov={v_fov}:h_fov={h_fov}:pitch={PITCH}:yaw=0:roll=0:"
+                    f"w={MAX_FRAME_HEIGHT}:h={MAX_FRAME_HEIGHT}:interp=lanczos:reset_rot=1",
+                    "lutyuv=y=gammaval(0.7)"
+                ]
+            else:
+                filters = [
+                    f"scale={MAX_FRAME_HEIGHT * 2}:{MAX_FRAME_HEIGHT}",
+                    f"crop={MAX_FRAME_HEIGHT}:{MAX_FRAME_HEIGHT}:0:0"
+                ]
+
+            return ",".join(filters)
+
+        def standard_video_filters():
+            return f"scale={width}:{height}" if video.height > MAX_FRAME_HEIGHT else None
+
+        width, height = get_cropped_dimensions(video)
+
+        vf = vr_video_filters() if video.is_vr else standard_video_filters()
+        cmd = get_cmd(vf)
+
+        # Debug
+        # command = ' '.join(vf)
+
+        # Start FFmpeg process
+        self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
+        frame_size = width * height * 3   # Size of one frame in bytes
+
+        # progress_bar = tqdm(total=203856, unit="frame", desc="Processing frames")
+        while True:
+            try:
+                in_bytes = self.process.stdout.read(frame_size)
+                if not in_bytes:
+                    break
+
+                task = AnalyseFrameTask(frame_pos=current_frame)
+
+                frame = np.frombuffer(in_bytes, np.uint8).reshape([height, width, 3])
+
+                # Debug
+                # output_path = os.path.join(DEBUG_PATH, f"frame_{task.id:05d}.png")
+                # imageio.imwrite(output_path, frame)
+
+                if self.state.video_reader == "FFmpeg":
+                    task.rendered_frame = frame
+                else:
+                    task.preprocessed_frame = frame
+
+                task.end(str(self.process_type))
+
+                self.finish_task(task)
+                current_frame += 1
+
+                # progress_bar.update(1)
+
+            except Exception as e:
+                print(f"Error reading frame: {e}")
+                return False, None
+
+        self.stop_process()
+
+
+    # TODO ffmpeg interrupt
+    # def release(self):
+    #     if self.process:
+    #         self.process.stdout.close()
+    #         self.process = None
diff --git a/setup.py b/setup.py
new file mode 100644
index 00000000..f87f5c14
--- /dev/null
+++ b/setup.py
@@ -0,0 +1 @@
+# TODO
\ No newline at end of file
diff --git a/tests/test_Compare_models_and_resolutions.py b/tests/test_Compare_models_and_resolutions.py
index 0710c6f9..85fd2c90 100644
--- a/tests/test_Compare_models_and_resolutions.py
+++ b/tests/test_Compare_models_and_resolutions.py
@@ -37,7 +37,7 @@ def save_frames_as_gif(frames, output_path, duration=100, loop=0):
 
     for video_path in video:
         # read video until model finds an item of class == 'penis'
-        cap = VideoReaderFFmpeg(video_path, is_VR=True)
+        cap = VideoReaderFFmpeg(video_path, is_vr=True)
         # position at frame 32000
         cap.set(cv2.CAP_PROP_POS_FRAMES, frame_id)
         while True:
@@ -56,7 +56,7 @@ def save_frames_as_gif(frames, output_path, duration=100, loop=0):
             frame_id += 1
 
     """
-    cap = VideoReaderFFmpeg(video[0], is_VR=True)
+    cap = VideoReaderFFmpeg(video[0], is_vr=True)
     # position at frame 32000
     cap.set(cv2.CAP_PROP_POS_FRAMES, frame_id)
     fps = cap.get(cv2.CAP_PROP_FPS)
@@ -92,7 +92,7 @@ def save_frames_as_gif(frames, output_path, duration=100, loop=0):
     for model in models:
         for video_path in video:
             #cap = cv2.VideoCapture(video_path)
-            cap = VideoReaderFFmpeg(video_path, is_VR=True)
+            cap = VideoReaderFFmpeg(video_path, is_vr=True)
             # position at frame 32000
             cap.set(cv2.CAP_PROP_POS_FRAMES, frame_id)
             ret, frame = cap.read()
diff --git a/tests/test_PoseEstimation.py b/tests/test_PoseEstimation.py
index e970926f..00ed99a2 100644
--- a/tests/test_PoseEstimation.py
+++ b/tests/test_PoseEstimation.py
@@ -2,7 +2,7 @@
 import numpy as np
 import cv2
 from collections import defaultdict
-from params.config import class_reverse_match
+from config import CLASS_REVERSE_MATCH
 
 #video_path = "/Users/k00gar/Downloads/Katrina Jade [ADD] Oct 16, 2024R_6kvr265_reenc.mp4"
 #video_path = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
@@ -105,7 +105,7 @@
 
         # if less than 2 boxes of class "hand" in detect_results, print a message
         for cls, conf, box in zip(classes, confs, boxes):
-            if class_reverse_match.get(int(cls), 'unknown') == "hand":
+            if CLASS_REVERSE_MATCH.get(int(cls), 'unknown') == "hand":
                 hand_counter += 1
 
         if hand_counter < 2:
diff --git a/tests/test_detect_compare_unwarped.py b/tests/test_detect_compare_unwarped.py
index d1cadcee..47679261 100644
--- a/tests/test_detect_compare_unwarped.py
+++ b/tests/test_detect_compare_unwarped.py
@@ -16,8 +16,8 @@
 output_path_2 = video_path[:-4] + "_" + str(frame_id) + "_original.jpg"
 output_path_3 = video_path[:-4] + "_" + str(frame_id) + "_results.jpg"
 
-cap1 = VideoReaderFFmpeg(video_path, is_VR=True)
-cap2 = VideoReaderFFmpeg(video_path, is_VR=True, unwarp=False)
+cap1 = VideoReaderFFmpeg(video_path, is_vr=True)
+cap2 = VideoReaderFFmpeg(video_path, is_vr=True, unwarp=False)
 
 cap1.set(cv2.CAP_PROP_POS_FRAMES, frame_id)
 cap2.set(cv2.CAP_PROP_POS_FRAMES, frame_id)
diff --git a/tests/test_generate_funscript.py b/tests/test_generate_funscript.py
new file mode 100644
index 00000000..be768412
--- /dev/null
+++ b/tests/test_generate_funscript.py
@@ -0,0 +1,21 @@
+import os
+
+from script_generator.scripts.generate_funscript import generate_funscript
+from script_generator.state.app_state import AppState
+
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+app_state = AppState(is_cli=True)
+# app_state.video_path = "C:/cvr/funscript-generator/test_short.mp4"
+# app_state.video_path = "C:/cvr/funscript-generator/test_extra_short.mp4"
+# app_state.video_path = "C:/cvr/funscript-generator/test_medium.mp4"
+app_state.video_path = "C:/cvr/funscript-generator/test.mp4"
+
+# Warmup run
+generate_funscript(app_state)
+
+# app_state.video_reader = "FFmpeg + OpenGL (Windows)"
+# generate_funscript(app_state)
+
+# app_state.video_reader = "FFmpeg"
+# generate_funscript(app_state)
\ No newline at end of file
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index e07bdb3a..4f9517f3 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -1,11 +1,10 @@
 import json
 import cv2
 import numpy as np
-import os
-import subprocess
-
-from params.config import class_colors
+from config import CLASS_COLORS
 from scipy.interpolate import interp1d
+
+from script_generator.utils.file import get_output_file_path
 from utils.lib_Visualizer import Visualizer
 from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg
 
@@ -15,21 +14,21 @@ class Debugger:
     A class for debugging video frames by logging variables, bounding boxes, and visualizing them.
     """
 
-    def __init__(self, video_path, isVR=False, video_reader=None, output_dir=None):
+    def __init__(self, video_path, is_vr=False, video_reader=None, log_file=None):
         """
         Initialize the Debugger.
         :param video_path: Path to the video file.
-        :param output_dir: Directory to save debug logs.
+        :param log_file: Directory to save debug logs.
         """
         self.video_path = video_path
-        self.output_dir = output_dir
+        self.log_file = log_file
         self.logs = {}  # Dictionary to store logs for each frame
         self.cap = None  # Video capture object
         self.current_frame = 0  # Current frame being processed
         self.total_frames = 0  # Total number of frames in the video
         self.fps = 0  # Frames per second of the video
         self.bar_y_start = 0  # Y-coordinate of the progress bar
-        self.isVR = isVR
+        self.is_vr = is_vr
         self.video_reader = video_reader
 
     def log_frame(self, frame_id, variables=None, bounding_boxes=None):
@@ -52,7 +51,6 @@ def save_logs(self):
         """
         Save the logs to a JSON file.
         """
-        log_file = f"{self.output_dir}_debug_logs.json"
 
         def default(obj):
             """Convert NumPy types to native Python types for JSON serialization."""
@@ -65,23 +63,22 @@ def default(obj):
             else:
                 raise TypeError(f"Object of type {obj.__class__.__name__} is not JSON serializable")
 
-        with open(log_file, "w") as f:
+        with open(self.log_file, "w") as f:
             json.dump(self.logs, f, indent=4, default=default)
-        print(f"Logs saved to {log_file}")
+        print(f"Logs saved to {self.log_file}")
 
     def load_logs(self):
         """
         Load existing logs from a JSON file.
         """
-        log_file = f"{self.output_dir}_debug_logs.json"
         try:
-            with open(log_file, "r") as f:
+            with open(self.log_file, "r") as f:
                 self.logs = json.load(f)
-            print(f"Logs loaded from {log_file}")
+            print(f"Logs loaded from {self.log_file}")
         except FileNotFoundError:
-            print(f"Log file {log_file} not found. Starting with empty logs.")
+            print(f"Log file {self.log_file} not found. Starting with empty logs.")
         except json.JSONDecodeError:
-            print(f"Error decoding JSON from {log_file}. Starting with empty logs.")
+            print(f"Error decoding JSON from {self.log_file}. Starting with empty logs.")
 
     def display_frame(self, frame_id):
         """
@@ -103,7 +100,7 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
 
         # Load the video
         if self.video_reader == "FFmpeg":
-            self.cap = VideoReaderFFmpeg(self.video_path, is_VR=self.isVR)
+            self.cap = VideoReaderFFmpeg(self.video_path, is_vr=self.is_vr)
         else:
             self.cap = cv2.VideoCapture(self.video_path)
 
@@ -114,12 +111,14 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
         # Initialize video writer if recording
         if record:
             ret, frame = self.cap.read()
-            if self.video_reader == "OpenCV" and self.isVR:
+            if self.video_reader == "OpenCV" and self.is_vr:
                 frame = frame[:, :frame.shape[1] // 2, :]  # only half left of the frame, for VR half
-            output_path = self.video_path.replace(".mp4", f"_debug_{start_frame}.mp4")
+            #if self.cap.is_vr:
+            #    frame_copy = frame[:, frame.shape[1] // 3 : 2 * frame.shape[1] // 3, :]
+            output_path = self.video_path.replace(".mp4", "_debug.mp4")
             fourcc = cv2.VideoWriter_fourcc(*"mp4v")
             out = cv2.VideoWriter(
-                output_path, fourcc, self.fps // 2, (frame.shape[1], frame.shape[0])
+                output_path, fourcc, self.fps, (frame.shape[1] // downsize_ratio, frame.shape[0] // downsize_ratio)
             )
             if not out.isOpened():
                 print(f"Error: Could not open video writer for {output_path}")
@@ -130,7 +129,8 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
         self.cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame)
 
         # Load the funscript file
-        funscript_path = self.video_path.replace(".mp4", ".funscript")
+        funscript_path, _ = get_output_file_path(self.video_path, ".funscript")
+
         try:
             with open(funscript_path, "r") as f:
                 funscript_data = json.load(f)
@@ -152,7 +152,7 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
         end_frame = start_frame + int(duration * self.fps) if duration > 0 else self.total_frames
 
         # Set up mouse callback for progress bar
-        if self.video_reader == "OpenCV" and self.isVR:
+        if self.video_reader == "OpenCV" and self.is_vr:
             width = self.cap.get(cv2.CAP_PROP_FRAME_WIDTH) // 2
         else:
             width = self.cap.get(cv2.CAP_PROP_FRAME_WIDTH)
@@ -166,7 +166,7 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
             if not ret:
                 break
 
-            if self.video_reader == "OpenCV" and self.isVR:
+            if self.video_reader == "OpenCV" and self.is_vr:
                 frame = frame[:, :frame.shape[1] // 2, :]  # only half left of the frame, for VR half
             frame_copy = frame.copy()  # make a copy of the frame to make it writeable, useful for ffmpeg library here
             # Display variables and bounding boxes
@@ -180,7 +180,7 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
                     x1, y1, x2, y2 = box["box"]
                     class_name = box["class_name"]
                     position = box["position"]
-                    color = class_colors.get(class_name, (0, 255, 0))
+                    color = CLASS_COLORS.get(class_name, (0, 255, 0))
                     cv2.rectangle(frame_copy, (x1, y1), (x2, y2), color, 2)
                     cv2.putText(frame_copy, f"{class_name} {position}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
 
@@ -195,7 +195,7 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
                 #print(f"locked_penis_box: {locked_penis_box}")
                 if locked_penis_box['active']:
                     x1, y1, x2, y2 = locked_penis_box['box']
-                    color = class_colors.get("penis", (0, 255, 0))
+                    color = CLASS_COLORS.get("penis", (0, 255, 0))
                     cv2.rectangle(frame_copy, (x1, y1), (x2, y2), color, 2)
                     cv2.putText(frame_copy, "Locked Penis", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
 
@@ -236,6 +236,7 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
 
             # Record the frame if enabled
             if record:
+                frame_copy = cv2.resize(frame_copy, (frame_copy.shape[1] // downsize_ratio, frame_copy.shape[0] // downsize_ratio))
                 out.write(frame_copy)
 
             self.current_frame += 1
@@ -244,35 +245,6 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
         self.cap.release()
         if record:
             out.release()
-
-            # Input and output paths
-            input_path = output_path
-            # Add "SPOILER_" to the filename
-            directory, filename = os.path.split(input_path)
-            new_filename = f"SPOILER_{filename}"
-            output_path_ffmpeg = os.path.join(directory, new_filename)
-
-            # FFmpeg command to convert to H.265
-            ffmpeg_command = [
-                "ffmpeg",
-                "-y",  # Overwrite output file if it exists
-                "-i", input_path,  # Input file
-                "-c:v", "libx265",  # Use H.265 codec
-                "-crf", "26",  # Constant Rate Factor (quality)
-                "-preset", "fast",  # Encoding speed
-                "-b:v", "5000k",  # Bitrate
-                "-movflags", "+faststart",  # Enable fast streaming
-                output_path_ffmpeg
-            ]
-
-            # Run FFmpeg command
-            subprocess.run(ffmpeg_command)
-
-            # Delete the intermediate file
-            if os.path.exists(input_path):
-                os.remove(input_path)
-                print(f"Deleted intermediate file: {input_path}")
-
         cv2.destroyAllWindows()
 
     def _get_funscript_value(self, interpolator, frame_id, fps):
@@ -331,7 +303,7 @@ def _update_frame_from_mouse(self, x, width):
         print(f"Target frame: {self.current_frame}")
         if self.video_reader == "FFmpeg":
             self.cap.release()
-            self.cap = VideoReaderFFmpeg(self.video_path, self.isVR)
+            self.cap = VideoReaderFFmpeg(self.video_path, self.is_vr)
         self.cap.set(cv2.CAP_PROP_POS_FRAMES, self.current_frame)
         print("Done resetting and jumping to target frame")
 
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 13cf2591..4474a19d 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -1,15 +1,20 @@
 import logging
 import os
 import json
+import time
+
 from simplification.cutil import simplify_coords
 import numpy as np
 import cv2
 import datetime
 from scipy.signal import savgol_filter
 
-from params.config import heatmap_colors, step_size, version
+from config import HEATMAP_COLORS, STEP_SIZE, VERSION
 
 import matplotlib
+
+from script_generator.utils.file import get_output_file_path
+
 matplotlib.use('Agg')  # Use a non-interactive backend
 import matplotlib.pyplot as plt
 import matplotlib.gridspec as gridspec
@@ -17,105 +22,102 @@
 from matplotlib.cm import ScalarMappable
 from matplotlib.colors import LinearSegmentedColormap, Normalize
 
+
 class FunscriptGenerator:
-    def generate(self, global_state):
-        output_path = global_state.video_file[:-3] + f"funscript"
+    def generate(self, state):
+        output_path, _ = get_output_file_path(state.video_path, ".funscript")
 
         # Backup output file if it exists
         if os.path.exists(output_path):
-            global_state.logger.warning(f"Output path {output_path} already exists, backing up as {output_path}.bak...")
-            os.rename(output_path, output_path + ".bak")
+            print(f"Funscript {output_path} already exists, backing up as {output_path}_{time.time()}.bak...")
+            os.rename(output_path, output_path + f"_{time.time()}.bak")
 
-        raw_funscript_path = global_state.video_file[:-4] + f"_rawfunscript.json"
+        raw_funscript_path, _ = get_output_file_path(state.video_path, "_rawfunscript.json")
 
-        if len(global_state.funscript_data) == 0:
-            global_state.logger.info("len funscript data is 0, trying to load file")
+        if len(state.funscript_data) == 0:
+            print("len funscript data is 0, trying to load file")
             # Read the funscript data from the JSON file
             with open(raw_funscript_path, 'r') as f:
-                global_state.logger.info(f"Loading funscript from {raw_funscript_path}")
+                print(f"Loading funscript from {raw_funscript_path}")
                 try:
                     data = json.load(f)
                 except Exception as e:
-                    global_state.logger.error(f"Error loading funscript from {raw_funscript_path}: {e}")
+                    print(f"Error loading funscript from {raw_funscript_path}: {e}")
                     return
         else:
-            data = global_state.funscript_data
+            data = state.funscript_data
 
         try:
-            global_state.logger.info(f"Generating funscript based on {len(data)} points...")
+            print(f"Generating funscript based on {len(data)} points...")
 
             # Extract timestamps and positions
             ats = [p[0] for p in data]
             positions = [p[1] for p in data]
 
-            global_state.logger.info(f"Positions adjustment - step 1 (noise removal)")
+            print(f"Positions adjustment - step 1 (noise removal)")
             # Run the Savitzky-Golay filter
-            positions = savgol_filter(positions, int(global_state.video_fps) // 4, 3)
+            positions = savgol_filter(positions, int(state.video_info.fps) // 4, 3)
 
             # zip adjusted positions
             zip_positions = list(zip(ats, positions))
 
             # Apply VW simplification if enabled
-            if global_state.vw_simplification_enabled:
-                global_state.logger.info("Positions adjustment - step 2 (VW algorithm simplification)")
-                zip_vw_positions = simplify_coords(zip_positions, global_state.vw_factor)
-                global_state.logger.info(f"Length of VW filtered positions: {len(zip_vw_positions)}")
+            if state.vw_simplification_enabled:
+                print("Positions adjustment - step 2 (VW algorithm simplification)")
+                zip_vw_positions = simplify_coords(zip_positions, state.vw_factor)
+                print(f"Length of VW filtered positions: {len(zip_vw_positions)}")
             else:
-                global_state.logger.info("Skipping positions adjustment - step 2 (VW algorithm simplification)")
+                print("Skipping positions adjustment - step 2 (VW algorithm simplification)")
 
             # Extract timestamps and positions
             ats = [p[0] for p in zip_vw_positions]
             positions = [p[1] for p in zip_vw_positions]
 
             # Remap positions to 0-100 range
-            global_state.logger.info("Positions adjustment - step 3 (remapping)")
+            print("Positions adjustment - step 3 (remapping)")
             adjusted_positions = np.interp(positions, (min(positions), max(positions)), (0, 100))
 
             # Apply thresholding
-            if global_state.threshold_enabled:
-                global_state.logger.info(f"Positions adjustment - step 4 (thresholding)")
+            if state.threshold_enabled:
+                print(f"Positions adjustment - step 4 (thresholding)")
                 adjusted_positions = adjusted_positions.tolist()  # Convert to list
                 adjusted_positions = [
-                    0 if p < global_state.threshold_low else 100 if p > global_state.threshold_high else p for p in
+                    0 if p < state.threshold_low else 100 if p > state.threshold_high else p for p in
                     adjusted_positions]
             else:
-                global_state.logger.info("Skipping positions adjustment - step 4 (thresholding)")
+                print("Skipping positions adjustment - step 4 (thresholding)")
 
             # Apply amplitude boosting
-            if global_state.boost_enabled:
-                global_state.logger.info("Positions adjustment - step 5 (amplitude boosting)")
-                #self.boost_amplitude(adjusted_positions, boost_factor=1.2, min_value=0, max_value=100)
+            if state.boost_enabled:
+                print("Positions adjustment - step 5 (amplitude boosting)")
+                # self.boost_amplitude(adjusted_positions, boost_factor=1.2, min_value=0, max_value=100)
                 adjusted_positions = self.adjust_peaks_and_lows(adjusted_positions,
-                                                                peak_boost=global_state.boost_up_percent,
-                                                                low_reduction=global_state.boost_down_percent)
+                                                                peak_boost=state.boost_up_percent,
+                                                                low_reduction=state.boost_down_percent)
             else:
-                global_state.logger.info("Skipping positions adjustment - step 5 (amplitude boosting)")
+                print("Skipping positions adjustment - step 5 (amplitude boosting)")
 
             # Round position values to the closest multiple of 5, still between 0 and 100
-            global_state.logger.info("Positions adjustment - step 6 (rounding to the closest multiple of 5)")
-            #adjusted_positions = [round(p, -1) for p in adjusted_positions] # multiple of 10
-            adjusted_positions = [round(p / global_state.rounding) * global_state.rounding for p in adjusted_positions]
+            print("Positions adjustment - step 6 (rounding to the closest multiple of 5)")
+            # adjusted_positions = [round(p, -1) for p in adjusted_positions] # multiple of 10
+            adjusted_positions = [round(p / state.rounding) * state.rounding for p in adjusted_positions]
 
             # Recombine timestamps and adjusted positions
-            global_state.logger.info("Re-assembling ats and positions")
+            print("Re-assembling ats and positions")
             zip_adjusted_positions = list(zip(ats, adjusted_positions))
 
             # Write the final funscript
-            self.write_funscript(zip_adjusted_positions, output_path, global_state.video_fps)
-            global_state.logger.info(f"Funscript generated and saved to {output_path}")
+            self.write_funscript(zip_adjusted_positions, output_path, state.video_info.fps)
+            print(f"Funscript generated and saved to {output_path}")
 
             # Generate a heatmap
-            self.generate_heatmap(output_path,
-                                  output_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png",
-                                  global_state)
+            self.generate_heatmap(output_path, output_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
         except Exception as e:
-            global_state.logger.error(f"Error generating funscript: {e}")
+            print(f"Error generating funscript: {e}")
 
     def write_funscript(self, distances, output_path, fps):
         with open(output_path, 'w') as f:
-            #f.write('f{"version":"{version}","inverted":false,"range":95,"author":"FunGen_k00gar_AI","actions":[{"at": 0, "pos": 100},')
-            f.write(
-                f'{{"version":"{version}","inverted":false,"range":95,"author":"FunGen_k00gar_AI","actions":[{{"at": 0, "pos": 100}},')
+            f.write(f'{{"version":"{VERSION}","inverted":false,"range":95,"author":"FunGen_k00gar_AI","actions":[{{"at": 0, "pos": 100}},')
             i = 0
             for frame, position in distances:
                 time_ms = int(frame * 1000 / fps)
@@ -125,11 +127,11 @@ def write_funscript(self, distances, output_path, fps):
                 i += 1
             f.write("]}\n")
 
-    def generate_heatmap(self, funscript_path, output_image_path, global_state):
+    def generate_heatmap(self, funscript_path, output_image_path):
         # Load funscript data
-        times, positions, _, _ = self.load_funscript(funscript_path, global_state)
+        times, positions, _, _ = self.load_funscript(funscript_path)
         if not times or not positions:
-            global_state.logger.error("Failed to load funscript data.")
+            print("Failed to load funscript data.")
             return
 
         # add a timing: 0, position: 100 at the beginning if no value for 0
@@ -137,24 +139,34 @@ def generate_heatmap(self, funscript_path, output_image_path, global_state):
             times.insert(0, 0)
             positions.insert(0, 100)
 
-        global_state.logger.info(f"Total Actions: {len(times)}")
-        global_state.logger.info(f"Time Range: {times[0]} to {datetime.timedelta(seconds=int(times[-1] / 1000))}")
+        # Print loaded data for debugging
+        # print(f"Times: {times}")
+        # print(f"Positions: {positions}")
+        print(f"Total Actions: {len(times)}")
+        print(f"Time Range: {times[0]} to {datetime.timedelta(seconds=int(times[-1] / 1000))}")
 
         # Calculate speed (position change per time interval)
-        speeds = np.abs(np.diff(positions) / np.diff(times)) * 1000  # Positions per second
+        valid_indices = ~np.isnan(times) & ~np.isnan(positions)
+        filtered_positions = positions[valid_indices]
+        filtered_times = times[valid_indices]
+
+        # Calculate speed (position change per time interval)
+        speeds = np.abs(np.diff(filtered_positions) / np.diff(filtered_times)) * 1000  # Positions per second
+
+        print(f"Speeds: {speeds}")
 
         def get_color(intensity):
             if intensity <= 0:
-                return heatmap_colors[0]
-            if intensity > 5 * step_size:
-                return heatmap_colors[6]
-            intensity += step_size / 2.0
-            index = int(intensity // step_size)
-            t = (intensity - index * step_size) / step_size
+                return HEATMAP_COLORS[0]
+            if intensity > 5 * STEP_SIZE:
+                return HEATMAP_COLORS[6]
+            intensity += STEP_SIZE / 2.0
+            index = int(intensity // STEP_SIZE)
+            t = (intensity - index * STEP_SIZE) / STEP_SIZE
             return [
-                heatmap_colors[index][0] + (heatmap_colors[index + 1][0] - heatmap_colors[index][0]) * t,
-                heatmap_colors[index][1] + (heatmap_colors[index + 1][1] - heatmap_colors[index][1]) * t,
-                heatmap_colors[index][2] + (heatmap_colors[index + 1][2] - heatmap_colors[index][2]) * t
+                HEATMAP_COLORS[index][0] + (HEATMAP_COLORS[index + 1][0] - HEATMAP_COLORS[index][0]) * t,
+                HEATMAP_COLORS[index][1] + (HEATMAP_COLORS[index + 1][1] - HEATMAP_COLORS[index][1]) * t,
+                HEATMAP_COLORS[index][2] + (HEATMAP_COLORS[index + 1][2] - HEATMAP_COLORS[index][2]) * t
             ]
 
         # Create figure and plot
@@ -190,10 +202,10 @@ def get_color(intensity):
 
         # Add colorbar
         cmap = mcolors.LinearSegmentedColormap.from_list("custom_heatmap", [
-            (heatmap_colors[i][0] / 255, heatmap_colors[i][1] / 255, heatmap_colors[i][2] / 255) for i in
-            range(len(heatmap_colors))
+            (HEATMAP_COLORS[i][0] / 255, HEATMAP_COLORS[i][1] / 255, HEATMAP_COLORS[i][2] / 255) for i in
+            range(len(HEATMAP_COLORS))
         ])
-        norm = mcolors.Normalize(vmin=0, vmax=5 * step_size)
+        norm = mcolors.Normalize(vmin=0, vmax=5 * STEP_SIZE)
         sm = ScalarMappable(cmap=cmap, norm=norm)
         sm.set_array([])
         # cbar = plt.colorbar(sm, ax=ax, orientation='horizontal', pad=0.2,
@@ -203,7 +215,7 @@ def get_color(intensity):
         # Save the figure
         plt.savefig(output_image_path, bbox_inches='tight', dpi=200)  # Increase resolution
         plt.close()
-        global_state.logger.info(f"Funscript heatmap saved to {output_image_path}")
+        print(f"Funscript heatmap saved to {output_image_path}")
 
     def boost_amplitude(self, signal, boost_factor, min_value=0, max_value=100):
         """
@@ -295,20 +307,20 @@ def filter_positions(self, positions, fps):
 
         return filtered_positions
 
-    def load_funscript(self, funscript_path, global_state):
+    def load_funscript(self, funscript_path):
         # if the funscript path exists
         if not os.path.exists(funscript_path):
-            global_state.logger.error(f"Funscript not found at {funscript_path}")
+            print(f"Funscript not found at {funscript_path}")
             return None, None, None, None
 
         with open(funscript_path, 'r') as f:
             try:
-                global_state.logger.info(f"Loading funscript from {funscript_path}")
+                print(f"Loading funscript from {funscript_path}")
                 data = json.load(f)
             except json.JSONDecodeError as e:
-                global_state.logger.error(f"JSONDecodeError: {e}")
-                global_state.logger.error(f"Error occurred at line {e.lineno}, column {e.colno}")
-                global_state.logger.error("Dumping the problematic JSON data:")
+                print(f"JSONDecodeError: {e}")
+                print(f"Error occurred at line {e.lineno}, column {e.colno}")
+                print("Dumping the problematic JSON data:")
                 f.seek(0)  # Reset file pointer to the beginning
                 print(f.read())
                 return None, None, None, None
@@ -319,7 +331,7 @@ def load_funscript(self, funscript_path, global_state):
         for action in data['actions']:
             times.append(action['at'])
             positions.append(action['pos'])
-        global_state.logger.info(f"Loaded funscript with {len(times)} actions")
+        print(f"Loaded funscript with {len(times)} actions")
 
         # Access the chapters
         chapters = data.get("metadata", {}).get("chapters", [])
@@ -345,13 +357,14 @@ def load_funscript(self, funscript_path, global_state):
 
         return times, positions, relevant_chapters_export, irrelevant_chapters_export
 
-    def create_report_funscripts(self, global_state):
+    def create_report_funscripts(self, state):
         generated_paths = []
-        generated_paths.append(global_state.video_file[:-4] + ".funscript")
+        output_path, _ = get_output_file_path(state.video_path, ".funscript")
+        generated_paths.append(output_path)
 
-        if global_state.reference_script:
+        if state.reference_script:
             # Load reference funscript
-            ref_times, ref_positions, _, _ = self.load_funscript(global_state.reference_script, global_state)
+            ref_times, ref_positions, _, _ = self.load_funscript(state.reference_script)
 
             # if no 0 at the beginning, add it
             if ref_times and ref_times[0] != 0:
@@ -362,7 +375,7 @@ def create_report_funscripts(self, global_state):
             total_duration = ref_times[-1] / 1000 if ref_times else 0
         else:
             ref_times, ref_positions = [], []
-            gen_times, gen_positions, _, _ = self.load_funscript(generated_paths[0], global_state)
+            gen_times, gen_positions, _, _ = self.load_funscript(generated_paths[0])
             total_duration = gen_times[-1] / 1000 if gen_times else 0
 
         # Select 6 random non-overlapping 20-second sections
@@ -373,12 +386,12 @@ def create_report_funscripts(self, global_state):
 
         # Load generated funscripts
         for generated_path in generated_paths:
-            gen_times, gen_positions, _, _ = self.load_funscript(generated_path, global_state)
+            gen_times, gen_positions, _, _ = self.load_funscript(generated_path)
             # Extract data for each section
             ref_sections = []
             gen_sections = []
             for start, end in sections:
-                if global_state.reference_script:
+                if state.reference_script:
                     ref_sec = self.extract_section(ref_times, ref_positions, start, end)
                     ref_sections.append(ref_sec)
                 gen_sec = self.extract_section(gen_times, gen_positions, start, end)
@@ -386,16 +399,16 @@ def create_report_funscripts(self, global_state):
 
             if not screenshots_done:
                 # Capture screenshots, but only once
-                screenshots = self.capture_screenshots(global_state.video_file, global_state.isVR, sections)
+                screenshots = self.capture_screenshots(state.video_path, state.video_info.is_vr, sections)
                 screenshots_done = True
 
             # Plot and combine
+            report_path, _ = get_output_file_path(state.video_path, "_report.png")
             self.create_combined_plot(
-                ref_sections, gen_sections, screenshots, sections, global_state.video_file[:-4] + "_report.png",
-                ref_times, ref_positions, gen_times, gen_positions, global_state
+                ref_sections, gen_sections, screenshots, sections, report_path,
+                ref_times, ref_positions, gen_times, gen_positions
             )
 
-
     def select_random_sections(self, total_duration, section_duration=10, num_sections=6):
         sections = []
         segment_duration = total_duration / num_sections  # Duration of each segment
@@ -429,13 +442,13 @@ def extract_section(self, times, positions, start, end):
         indices = [i for i, t in enumerate(times) if start_ms <= t <= end_ms]
         return [times[i] for i in indices], [positions[i] for i in indices]
 
-    def capture_screenshots(self, video_path, isVR, sections):
+    def capture_screenshots(self, video_path, is_vr, sections):
         cap = cv2.VideoCapture(video_path)
         screenshots = []
         for start, _ in sections:
             cap.set(cv2.CAP_PROP_POS_MSEC, start * 1000)
             ret, frame = cap.read()
-            if isVR: # left side of the frame only
+            if is_vr:  # left side of the frame only
                 frame = frame[:, :frame.shape[1] // 2]
             if ret:
                 screenshots.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
@@ -444,8 +457,7 @@ def capture_screenshots(self, video_path, isVR, sections):
         cap.release()
         return screenshots
 
-    def create_combined_plot(self, ref_sections, gen_sections, screenshots, sections, output_image_path, ref_times,
-                                 ref_positions, gen_times, gen_positions, global_state):
+    def create_combined_plot(self, ref_sections, gen_sections, screenshots, sections, output_image_path, ref_times, ref_positions, gen_times, gen_positions):
         """
         Creates a combined plot with heatmaps as a header, comparative information, and section comparisons below.
 
@@ -460,6 +472,7 @@ def create_combined_plot(self, ref_sections, gen_sections, screenshots, sections
             gen_times (list): Times from the generated funscript.
             gen_positions (list): Positions from the generated funscript.
         """
+
         # Create a flexible grid layout
         fig = plt.figure(figsize=(28, 24))
         gs = gridspec.GridSpec(5, 4, height_ratios=[1, .5, 2, 2, 2], width_ratios=[1, 2, 1, 2])
@@ -496,7 +509,8 @@ def create_combined_plot(self, ref_sections, gen_sections, screenshots, sections
             f"Generated:\n"
             f"Number of Strokes: {gen_metrics['num_strokes']}\n"
             f"Avg Stroke Duration: {gen_metrics['avg_stroke_duration']:.2f}s\n"
-            f"Avg Speed: {int(gen_metrics['avg_speed'])} positions/s\n"
+            # TODO re-enable
+            # f"Avg Speed: {int(gen_metrics['avg_speed'])} positions/s\n"
             f"Avg Depth of Stroke: {int(gen_metrics['avg_depth'])}\n"
             f"Avg Max: {int(gen_metrics['avg_max'])}\n"
             f"Avg Min: {int(gen_metrics['avg_min'])}"
@@ -512,12 +526,12 @@ def create_combined_plot(self, ref_sections, gen_sections, screenshots, sections
                     break
 
                 # Screenshot (first column)
-                ax_screenshot = fig.add_subplot(gs[i-1, j * 2])
+                ax_screenshot = fig.add_subplot(gs[i - 1, j * 2])
                 ax_screenshot.imshow(screenshots[idx])
                 ax_screenshot.axis('off')
 
                 # Funscript comparison (second column)
-                ax_plot = fig.add_subplot(gs[i-1, j * 2 + 1])
+                ax_plot = fig.add_subplot(gs[i - 1, j * 2 + 1])
                 # Scale the y axis 0 to 100
                 ax_plot.set_ylim(0, 100)
                 gen_times_sec = [t / 1000 for t in gen_sections[idx][0]]
@@ -527,8 +541,8 @@ def create_combined_plot(self, ref_sections, gen_sections, screenshots, sections
                     ref_times_sec = [t / 1000 for t in ref_sections[idx][0]]
                     ax_plot.plot(ref_times_sec, ref_sections[idx][1], label='Reference', color='red')
 
-                start_time = datetime.timedelta(seconds=int(sections[idx][0]))
-                end_time = datetime.timedelta(seconds=int(sections[idx][1]))
+                start_time = datetime.timedelta(seconds=int(sections[idx][0]))  # datetime.datetime.fromtimestamp(sections[idx][0]).strftime('%H:%M:%S')
+                end_time = datetime.timedelta(seconds=int(sections[idx][1]))  # datetime.datetime.fromtimestamp(sections[idx][1]).strftime('%H:%M:%S')
                 ax_plot.set_title(f'Section {idx + 1}: {start_time} - {end_time}', fontsize=10)
                 ax_plot.set_xlabel('Time (s)')
                 ax_plot.set_ylabel('Position')
@@ -538,11 +552,8 @@ def create_combined_plot(self, ref_sections, gen_sections, screenshots, sections
         plt.tight_layout()
 
         # Save the figure
-        # Add "SPOILER_" to the filename
-        directory, filename = os.path.split(output_image_path)
-        new_filename = f"SPOILER_{filename}"
-        output_image_path = os.path.join(directory, new_filename)
         plt.savefig(output_image_path[:-4] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png", dpi=100)
+        # plt.show()
 
     def generate_heatmap_inline(self, ax, times, positions):
         """
@@ -557,20 +568,45 @@ def generate_heatmap_inline(self, ax, times, positions):
             return
 
         # Calculate speed (position change per time interval)
-        speeds = np.abs(np.diff(positions) / np.diff(times)) * 1000  # Positions per second
+        # Filter out NaN values from times and positions
+        # TODO filter nan at one logical moment
+        times = np.array(times, dtype=float)  # Ensure times is a float array
+        positions = np.array(positions, dtype=float)  # Ensure positions is a float array
+
+        # Filter out NaN values
+        valid_indices = ~np.isnan(times) & ~np.isnan(positions)
+        filtered_positions = positions[valid_indices]
+        filtered_times = times[valid_indices]
+
+        # Ensure at least two valid points for speed calculation
+        if len(filtered_times) > 1:
+            time_diffs = np.diff(filtered_times)
+            pos_diffs = np.diff(filtered_positions)
+
+            # Replace zero or negative time differences with NaN to prevent invalid division
+            time_diffs[time_diffs <= 0] = np.nan
+
+            # Calculate speeds, ignoring invalid divisions
+            speeds = np.abs(pos_diffs / time_diffs) * 1000  # Positions per second
+            speeds = np.nan_to_num(speeds, nan=0.0)  # Replace NaN values in speeds with 0
+        else:
+            speeds = np.array([])  # Empty array if not enough valid data points
+        # TODO filter nan at one logical moment END
 
         def get_color(intensity):
+            if np.isnan(intensity):
+                return [128, 128, 128]
             if intensity <= 0:
-                return heatmap_colors[0]
-            if intensity > 5 * step_size:
-                return heatmap_colors[-1]
-            intensity += step_size / 2.0
-            index = int(intensity // step_size)
-            t = (intensity - index * step_size) / step_size
+                return HEATMAP_COLORS[0]
+            if intensity > 5 * STEP_SIZE:
+                return HEATMAP_COLORS[-1]
+            intensity += STEP_SIZE / 2.0
+            index = int(intensity // STEP_SIZE)
+            t = (intensity - index * STEP_SIZE) / STEP_SIZE
             return [
-                heatmap_colors[index][0] + (heatmap_colors[index + 1][0] - heatmap_colors[index][0]) * t,
-                heatmap_colors[index][1] + (heatmap_colors[index + 1][1] - heatmap_colors[index][1]) * t,
-                heatmap_colors[index][2] + (heatmap_colors[index + 1][2] - heatmap_colors[index][2]) * t
+                HEATMAP_COLORS[index][0] + (HEATMAP_COLORS[index + 1][0] - HEATMAP_COLORS[index][0]) * t,
+                HEATMAP_COLORS[index][1] + (HEATMAP_COLORS[index + 1][1] - HEATMAP_COLORS[index][1]) * t,
+                HEATMAP_COLORS[index][2] + (HEATMAP_COLORS[index + 1][2] - HEATMAP_COLORS[index][2]) * t
             ]
 
         # Draw lines between points with colors based on speed
@@ -589,8 +625,17 @@ def get_color(intensity):
             ax.plot([x_start, x_end], [y_start, y_end], color=line_color, linewidth=2)
 
         # Customize plot
+        # Safely calculate the mean speed
+        # TODO fix nan at source
+        if len(speeds) == 0 or np.isnan(np.mean(speeds)):
+            avg_speed = 0  # Default to 0 if speeds are empty or contain NaN
+        else:
+            avg_speed = int(np.nanmean(speeds))  # Use nanmean to ignore NaN values
+
+        # Set title with safe average speed
         ax.set_title(
-            f'Funscript Heatmap\nDuration: {datetime.timedelta(seconds=int(times[-1] / 1000))} - Avg. Speed {int(np.mean(speeds))} - Actions: {len(times)}')
+            f'Funscript Heatmap\nDuration: {datetime.timedelta(seconds=int(times[-1] / 1000))} - Avg. Speed {avg_speed} - Actions: {len(times)}'
+        )
         ax.set_xlabel('Time (s)')
         ax.set_yticks(np.arange(0, 101, 10))
         ax.set_xlim(times[0] / 1000, times[-1] / 1000)
@@ -602,10 +647,10 @@ def get_color(intensity):
 
         # Add colorbar
         cmap = LinearSegmentedColormap.from_list("custom_heatmap", [
-            (heatmap_colors[i][0] / 255, heatmap_colors[i][1] / 255, heatmap_colors[i][2] / 255) for i in
-            range(len(heatmap_colors))
+            (HEATMAP_COLORS[i][0] / 255, HEATMAP_COLORS[i][1] / 255, HEATMAP_COLORS[i][2] / 255) for i in
+            range(len(HEATMAP_COLORS))
         ])
-        norm = Normalize(vmin=0, vmax=5 * step_size)
+        norm = Normalize(vmin=0, vmax=5 * STEP_SIZE)
         sm = ScalarMappable(cmap=cmap, norm=norm)
         sm.set_array([])
 
@@ -631,9 +676,32 @@ def _calculate_metrics(self, times, positions):
         avg_stroke_duration = np.mean(stroke_durations)
 
         # Calculate average speed
-        speeds = np.abs(np.diff(positions) / np.diff(times)) * 1000  # Positions per second
-        #speeds = np.abs(np.diff(positions) / stroke_durations)
+        # Filter out NaN values from times and positions
+        # TODO filter in one place find out why we have nan's
+        times = np.array(times, dtype=float)  # Ensure times is a float array
+        positions = np.array(positions, dtype=float)  # Ensure positions is a float array
+
+        valid_indices = ~np.isnan(times) & ~np.isnan(positions)
+        filtered_positions = positions[valid_indices]
+        filtered_times = times[valid_indices]
+
+        # Ensure at least two valid points for speed calculation
+        if len(filtered_times) > 1:
+            time_diffs = np.diff(filtered_times).astype(float)  # Convert to float explicitly
+            pos_diffs = np.diff(filtered_positions)
+
+            # Prevent division by zero
+            time_diffs[time_diffs == 0] = np.nan  # Replace zero time differences with NaN
+
+            # Calculate speeds, ignoring invalid divisions
+            speeds = np.abs(pos_diffs / time_diffs) * 1000  # Positions per second
+            speeds = np.nan_to_num(speeds, nan=0.0)  # Replace NaN values with 0
+        else:
+            speeds = np.array([])  # Empty array if not enough valid data points
+
+        # Calculate average speed
         avg_speed = np.mean(speeds)
+        # TODO filter in one place find out why we have nan's END
 
         # Calculate average depth of stroke
         depths = np.abs(np.diff(positions))
@@ -650,7 +718,7 @@ def _calculate_metrics(self, times, positions):
             'avg_depth': avg_depth,
             'avg_max': avg_max,
             'avg_min': avg_min
-            }
+        }
 
     def adjust_peaks_and_lows(self, positions, peak_boost=10, low_reduction=10, max_flat_length=5):
         """
@@ -789,3 +857,12 @@ def _is_plateau_in_original(self, plateau, original_plateaus):
             if start >= original_start and end <= original_end:
                 return True
         return False
+
+# if __name__ == "__main__":
+#     video_path = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
+#
+#     funscript_handler = FunscriptGenerator()
+#
+#     # generate heatmap
+#     funscript_handler.generate_heatmap(video_path[:-4] + ".funscript", video_path[
+#                                                                         :-4] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index 9854fdb2..83558738 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -1,14 +1,27 @@
+import logging
+import math
+from collections import deque
+
+
 from utils import lib_KalmanFilter as KF
 from collections import deque
-import numpy as np
-from params.config import class_names
 import math
+import numpy as np
+
+from config import CLASS_NAMES
+from script_generator.utils.logger import logger
+from utils import lib_KalmanFilter as KF
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format="%(message)s")
+
 
 class LockedPenisBox:
     """
     A class to manage the locked penis box, which represents the detected penis area.
     It stores the box coordinates, height, and active status.
     """
+
     def __init__(self):
         self.box = None  # Coordinates of the locked penis box
         self.height = 0  # Height of the locked penis box
@@ -65,30 +78,32 @@ def from_dict(cls, data):
             locked_box.update(data["box"], data["height"])
         return locked_box
 
+
 class ObjectTracker:
     """
     A class to track objects (e.g., penis, glans, etc.) in a video frame and determine their positions and interactions.
     """
 
-    def __init__(self, global_state):
+    def __init__(self, fps, frame_pos, image_area, is_vr):
         """
         Initialize the ObjectTracker.
 
         Args:
-            global_state
+            fps (int): Frames per second of the video.
+            frame_pos (int): Current frame position.
+            image_area (int): Area of the video frame.
         """
-        self.global_state = global_state
-        self.class_names = class_names  # List of class names to track
+        self.is_vr = is_vr
+        self.class_names = CLASS_NAMES  # List of class names to track
         self.active_tracks = {}  # Active tracks: {track_id: Track object}
         self.inactive_tracks = []  # Inactive tracks (lost tracks)
         self.distance_kf = KF.KalmanFilter()  # Kalman filter for distance smoothing
 
         self.frame = None  # Current video frame
-        self.current_frame_id = global_state.current_frame_id  # frame_pos  # Current frame ID
+        self.current_frame_id = frame_pos  # Current frame ID
         self.image_y_size = 0  # Height of the video frame
-        self.image_area = global_state.frame_area # image_area  # Area of the video frame
-        self.fps = global_state.video_fps  # fps  # Frames per second of the video
-        self.isVR = global_state.isVR  # isVR
+        self.image_area = image_area  # Area of the video frame
+        self.fps = fps  # Frames per second of the video
 
         # Speed and distance thresholds
         self.max_speed = 100 / (0.18 * self.fps)  # Maximum allowed speed for distance changes
@@ -119,12 +134,12 @@ def __init__(self, global_state):
         self.pct_weights = {}  # Percentage weights for each track_id
 
         # Initialize normalized positions and distances
-        self.normalized_positions = {class_name: deque(maxlen=200) for class_name in class_names}
-        self.normalized_distances = {class_name: deque(maxlen=200) for class_name in class_names}
+        self.normalized_positions = {class_name: deque(maxlen=200) for class_name in CLASS_NAMES}
+        self.normalized_distances = {class_name: deque(maxlen=200) for class_name in CLASS_NAMES}
 
         # Detection thresholds
-        self.consecutive_detections = {class_name: 0 for class_name in class_names}
-        self.consecutive_non_detections = {class_name: 0 for class_name in class_names}
+        self.consecutive_detections = {class_name: 0 for class_name in CLASS_NAMES}
+        self.consecutive_non_detections = {class_name: 0 for class_name in CLASS_NAMES}
         self.detections_threshold = round(self.fps) / 10  # Threshold for considering a detection valid
 
         # Penetration state
@@ -258,7 +273,7 @@ def calculate_distance(self, penis_box, other_box):
         if other_box is None:
             return None
         if penis_box is None:
-            self.global_state.logger.warning(f"Penis box is None for frame {self.current_frame_id}, cannot compute distance")
+            logger.warning(f"Penis box is None for frame {self.current_frame_id}, cannot compute distance")
             return None
         ox1, oy1, ox2, oy2 = other_box
         y_pos = (oy1 + 2 * oy2) // 3
@@ -278,21 +293,21 @@ def detect_sex_position_change(self, sex_position, reason):
         position_counts = {position: self.sex_position_history.count(position) for position in self.sex_position_history}
         most_frequent_position = max(position_counts, key=position_counts.get, default="Not relevant")
         if most_frequent_position != self.sex_position:
-            self.global_state.logger.info(f"@{self.current_frame_id} - Sex position switched to: {most_frequent_position}")
+            logger.info(f"@{self.current_frame_id} - Sex position switched to: {most_frequent_position}")
             self.sex_position = most_frequent_position
             self.sex_position_reason = reason
 
-    def tracking_logic(self, global_state, sorted_boxes):
+    def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
         """
         Main tracking logic to process detected boxes and update tracking state.
 
         Args:
             sorted_boxes (list): List of detected boxes with confidence, class, and track ID.
+            current_frame_id (int): Current frame ID.
             image_y_size (int): Height of the video frame.
         """
-        self.global_state = global_state
-        self.current_frame_id = global_state.current_frame_id
-        self.image_y_size = global_state.image_y_size
+        self.current_frame_id = current_frame_id
+        self.image_y_size = image_y_size
 
         close_up_detected = False
 
@@ -338,7 +353,7 @@ def tracking_logic(self, global_state, sorted_boxes):
                 and not self.penetration:  # equivalent to 3 seconds
             if self.locked_penis_box.active:  # is_active():
                 self.locked_penis_box.deactivate()
-                self.global_state.logger.info(f"@{self.current_frame_id} - Deactivated locked_penis_box")
+                logger.info(f"@{self.current_frame_id} - Deactivated locked_penis_box")
 
         if self.locked_penis_box.is_active() and self.locked_penis_box.visible < 100 and not self.glans_detected:
             self.penetration = True
@@ -357,7 +372,7 @@ def tracking_logic(self, global_state, sorted_boxes):
                         butt_box_area = (b_x2 - b_x1) * (b_y2 - b_y1)
 
                         # Check if the butt box is unusually large (close-up)
-                        if self.isVR:
+                        if self.is_vr:
                             size_threshold = 0.15
                         else:  #2D POV
                             size_threshold = 0.4
@@ -366,7 +381,7 @@ def tracking_logic(self, global_state, sorted_boxes):
                             close_up_detected = True
                             self.detect_sex_position_change('Close up', 'Butt box size beyond threshold')
                             if not self.close_up:
-                                self.global_state.logger.info(
+                                logging.info(
                                     f"@{self.current_frame_id} - Close up detected - butt size beyond threshold: {int((butt_box_area / self.image_area) * 100)}%"
                                 )
                                 self.close_up = True
@@ -380,7 +395,7 @@ def tracking_logic(self, global_state, sorted_boxes):
                             close_up_detected = True
                             self.detect_sex_position_change('Close up', 'No penis detected')
                             if not self.close_up:
-                                self.global_state.logger.info(f"@{self.current_frame_id} - Close up detected - no penis detected")
+                                logger.info(f"@{self.current_frame_id} - Close up detected - no penis detected")
                                 self.close_up = True
                             self.penetration = False
                             distance = 100
@@ -397,7 +412,7 @@ def tracking_logic(self, global_state, sorted_boxes):
 
         for box, conf, cls, class_name, track_id in sorted_boxes:
             # discarding those classes for distance computation
-            #if class_name in ['glans', 'penis', 'navel', 'hips center', 'anus']:
+            # if class_name in ['glans', 'penis', 'navel', 'hips center', 'anus']:
             if class_name in ['glans', 'navel', 'hips center', 'anus']:
                 continue
             elif self.locked_penis_box.is_active() and class_name == 'breast' and not self.boxes_overlap(box, self.locked_penis_box.get_box()):
@@ -548,7 +563,7 @@ def handle_class_first(self, class_name, box, conf):
         """
         if class_name == 'penis':
             if box is not None and self.penis_box is None:
-                self.global_state.logger.info(f"@{self.current_frame_id} - Penis detected with confidence {conf}")
+                logger.info(f"@{self.current_frame_id} - Penis detected with confidence {conf}")
             self.penis_box = box
 
             if self.penis_box:
@@ -561,7 +576,7 @@ def handle_class_first(self, class_name, box, conf):
 
                         # Move locked penis box towards current penis box
                         #max_move = max(1, int(self.image_y_size / 960))
-                        if self.isVR:
+                        if self.is_vr:
                             max_move = 180 // int(self.fps)
                         else:
                             # in 2D POV, camera is not still, actuation needs to be faster
@@ -590,9 +605,9 @@ def handle_class_first(self, class_name, box, conf):
                     self.locked_penis_box.update(self.penis_box, self.penis_box[3] - self.penis_box[1])
                 if self.penetration:
                     self.penetration = False
-                    #logging.info(
+                    # logging.info(
                     #    f"@{self.current_frame_id} - Penetration ended after {self.consecutive_detections['glans']} detections of glans"
-                    #)
+                    # )
                     if self.tracked_body_part != 'Nothing':
                         self.normalized_distances[self.tracked_body_part].clear()
                         self.normalized_distances[self.tracked_body_part].append(100)
@@ -648,4 +663,4 @@ def update_normalized_distance_to_penis(self, track_id, normalized_dist_to_penis
             )
         self.normalized_distance_to_penis[track_id].append(normalized_dist_to_penis_base)
         if len(self.normalized_distance_to_penis[track_id]) > 60:
-            self.normalized_distance_to_penis[track_id].pop(0)
\ No newline at end of file
+            self.normalized_distance_to_penis[track_id].pop(0)
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index 8d9fbcaf..3f58a033 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -1,11 +1,12 @@
 import subprocess
 import cv2
 import numpy as np
-from params.config import ffmpeg_path, ffprobe_path, max_frame_height
+import argparse
+from config import FFMPEG_PATH, FFPROBE_PATH, MAX_FRAME_HEIGHT
 
 
 class VideoReaderFFmpeg:
-    def __init__(self, video_path, is_VR=False, unwarp=True, projection=None, ffmpeg_path=ffmpeg_path, ffprobe_path=ffprobe_path):
+    def __init__(self, video_path, is_vr=False, unwarp=True, projection=None, ffmpeg_path=FFMPEG_PATH, ffprobe_path=FFPROBE_PATH):
         """
         Initialize the VideoReaderFFmpeg class.
         :param video_path: Path to the video file.
@@ -15,8 +16,8 @@ def __init__(self, video_path, is_VR=False, unwarp=True, projection=None, ffmpeg
         :param ffprobe_path: Path to the FFprobe binary (default: "ffprobe").
         """
         self.video_path = video_path
-        self.is_VR = is_VR
-        if not self.is_VR:
+        self.is_vr = is_vr
+        if not self.is_vr:
             self.unwarp = False
         else:
             self.unwarp = unwarp
@@ -69,7 +70,7 @@ def _initialize_video_info(self):
             # Store metadata
             self.fps = fps
             self.width = width
-            if self.is_VR:
+            if self.is_vr:
                 self.width //= 2
             self.height = height
             self.codec = codec_name
@@ -77,12 +78,9 @@ def _initialize_video_info(self):
             self.duration = duration * 1000  # Convert duration to milliseconds
 
             # limiting the frame height here has no additional performance cost and significantly improves speed for 1440p+ video
-            if self.is_VR:
-                scaling_factor = min(max_frame_height / self.width, max_frame_height / self.height)
-                self.width = int(self.width * scaling_factor)
-                self.height = int(self.height * scaling_factor)
-            else:  # in case of 2D video
-                pass
+            scaling_factor = min(MAX_FRAME_HEIGHT / self.width, MAX_FRAME_HEIGHT / self.height)
+            self.width = int(self.width * scaling_factor)
+            self.height = int(self.height * scaling_factor)
 
             print(f"FPS: {self.fps}, Resolution: {self.width}x{self.height}, "
                   f"Codec: {self.codec}, Total Frames: {self.total_frames}, Duration: {self.duration:.2f} ms")
@@ -90,90 +88,89 @@ def _initialize_video_info(self):
             print(f"Error initializing video info: {e}")
             raise
 
-    def _start_process(self, start_frame=0):
-        """
-        Start the FFmpeg process to read frames.
-        :param start_frame: Frame number to start reading from.
-        """
-        start_time = (start_frame / self.fps) * 1000  # Convert to milliseconds
-        self.current_frame_number = start_frame
-
-        if self.is_VR:
-            #arg_line = "crop=w=iw/2:h=ih:x=0:y=0"
-            arg_line = ""
-            if self.unwarp:
-                if self.projection == "FISHEYE" or (self.projection == None and "FISHEYE" in self.video_path.upper()):
-                    print("Proceeding with fisheye projection correction")
-                    self.type = "fisheye"
-                    self.iv_fov = 190
-                    self.ih_fov = 190
-                    self.v_fov = 90
-                    self.h_fov = 90
-                    self.d_fov = 180
-                else:  # Assuming Equirectangular
-                    print("Assuming Equirectangular projection")
-                    self.type = "he"
-                    self.iv_fov = 250
-                    self.ih_fov = 120
-                    self.v_fov = 90
-                    self.h_fov = 90
-                    self.d_fov = 180
-                arg_line = arg_line + f"v360={self.type}:in_stereo=sbs:output=sg"
-                arg_line = arg_line + f":iv_fov={self.iv_fov}:ih_fov={self.ih_fov}"
-                arg_line = arg_line + f":d_fov={self.d_fov}:v_fov={self.v_fov}:h_fov={self.h_fov}"
-                arg_line = arg_line + f":pitch=-25:yaw=0:roll=0"
-                arg_line = arg_line + f":w={self.width}:h={self.height}"
-                arg_line = arg_line + f":interp=lanczos:reset_rot=1"
-                arg_line = arg_line + f",lutyuv=y=gammaval(0.7)"
-                #arg_line = arg_line + f",eq=brightness=0.1:contrast=1.5"
-                #arg_line = arg_line + f",format=gray"
-                #arg_line = arg_line + f",histeq"
-            else:
-                arg_line = "crop=w=iw/2:h=ih:x=0:y=0"
-            # Add scale filter with height and auto-width (-1)
-            #arg_line += f",scale=-1:{target_height}"
-            #arg_line += f",scale=-1:{1080}"
-            # perf for on the fly downscale to 1080p were terrible
-
-            cmd = [
-                self.ffmpeg_path,
-                '-nostats',  # Disable progress statistics
-                '-loglevel', 'warning',
-                "-ss", str(start_time / 1000),  # Seek to start time in seconds
-                "-i", self.video_path,
-                "-an",  # Disable audio processing
-                "-map", "0:v:0",
-                "-vf", arg_line,
-                "-f", "rawvideo",  # Output raw video data
-                "-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
-                "-vsync", "0",  # Disable frame rate synchronization
-                "-threads", "0",  # Use maximum threads available
-                "-",  # Output to stdout
-            ]
-        else:  # 2D, no unwarping, limiting the size to 1080p
-            # FFmpeg command to read frames
-            cmd = [
-                self.ffmpeg_path,
-                '-nostats',  # Disable progress statistics
-                '-loglevel', 'warning',
-                "-ss", str(start_time / 1000),  # Seek to start time in seconds
-                "-i", self.video_path,
-                "-an",  # Disable audio processing
-                "-f", "rawvideo",  # Output raw video data
-                "-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
-                "-vsync", "0",  # Disable frame rate synchronization
-                "-vf", "scale=-1:'min(1080,ih)'",  # Resize height to max 1080p, width is auto-calculated
-                "-",  # Output to stdout
-            ]
-
-        # Kill the process if already running
-        if self.process:
-            self.process.terminate()
-
-        # Start FFmpeg process
-        #self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-        self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
-        self.frame_size = self.width * self.height * 3  # Size of one frame in bytes
+    # def _start_process(self, start_frame=0):
+    #     """
+    #     Start the FFmpeg process to read frames.
+    #     :param start_frame: Frame number to start reading from.
+    #     """
+    #     start_time = (start_frame / self.fps) * 1000  # Convert to milliseconds
+    #     self.current_frame_number = start_frame
+    #
+    #     if self.is_vr:
+    #         #arg_line = "crop=w=iw/2:h=ih:x=0:y=0"
+    #         arg_line = ""
+    #         if self.unwarp:
+    #             if self.projection == "FISHEYE" or (self.projection == None and "FISHEYE" in self.video_path.upper()):
+    #                 print("Proceeding with fisheye projection correction")
+    #                 self.type = "fisheye"
+    #                 self.iv_fov = 190
+    #                 self.ih_fov = 190
+    #                 self.v_fov = 90
+    #                 self.h_fov = 90
+    #                 self.d_fov = 180
+    #             else:  # Assuming Equirectangular
+    #                 print("Assuming Equirectangular projection")
+    #                 self.type = "he"
+    #                 self.iv_fov = 250
+    #                 self.ih_fov = 120
+    #                 self.v_fov = 90
+    #                 self.h_fov = 90
+    #                 self.d_fov = 180
+    #             arg_line = arg_line + f"v360={self.type}:in_stereo=sbs:output=sg"
+    #             arg_line = arg_line + f":iv_fov={self.iv_fov}:ih_fov={self.ih_fov}"
+    #             arg_line = arg_line + f":d_fov={self.d_fov}:v_fov={self.v_fov}:h_fov={self.h_fov}"
+    #             arg_line = arg_line + f":pitch=-25:yaw=0:roll=0"
+    #             arg_line = arg_line + f":w={self.width}:h={self.height}"
+    #             arg_line = arg_line + f":interp=lanczos:reset_rot=1"
+    #             arg_line = arg_line + f",lutyuv=y=gammaval(0.7)"
+    #             #arg_line = arg_line + f",eq=brightness=0.1:contrast=1.5"
+    #             #arg_line = arg_line + f",format=gray"
+    #             #arg_line = arg_line + f",histeq"
+    #         else:
+    #             arg_line = "crop=w=iw/2:h=ih:x=0:y=0"
+    #         # Add scale filter with height and auto-width (-1)
+    #         #arg_line += f",scale=-1:{target_height}"
+    #         #arg_line += f",scale=-1:{1080}"
+    #         # perf for on the fly downscale to 1080p were terrible
+    #
+    #         cmd = [
+    #             self.ffmpeg_path,
+    #             '-nostats',  # Disable progress statistics
+    #             '-loglevel', 'warning',
+    #             "-ss", str(start_time / 1000),  # Seek to start time in seconds
+    #             "-i", self.video_path,
+    #             "-an",  # Disable audio processing
+    #             "-map", "0:v:0",
+    #             "-vf", arg_line,
+    #             "-f", "rawvideo",  # Output raw video data
+    #             "-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
+    #             "-vsync", "0",  # Disable frame rate synchronization
+    #             "-threads", "0",  # Use maximum threads available
+    #             "-",  # Output to stdout
+    #         ]
+    #     else:
+    #         # FFmpeg command to read frames
+    #         cmd = [
+    #             self.ffmpeg_path,
+    #             '-nostats',  # Disable progress statistics
+    #             '-loglevel', 'warning',
+    #             "-ss", str(start_time / 1000),  # Seek to start time in seconds
+    #             "-i", self.video_path,
+    #             "-an",  # Disable audio processing
+    #             "-f", "rawvideo",  # Output raw video data
+    #             "-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
+    #             "-vsync", "0",  # Disable frame rate synchronization
+    #             "-",  # Output to stdout
+    #         ]
+    #
+    #     # Kill the process if already running
+    #     if self.process:
+    #         self.process.terminate()
+    #
+    #     # Start FFmpeg process
+    #     #self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+    #     self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
+    #     self.frame_size = self.width * self.height * 3  # Size of one frame in bytes
 
     def read(self):
         """
@@ -238,14 +235,14 @@ def release(self):
             self.process = None
 
 
-def main(video_path, is_VR=False):
+def main(video_path, is_vr=False):
     """
     Display a video using the VideoReaderFFmpeg class.
     :param video_path: Path to the video file.
-    :param is_VR: Whether the video is a VR video (default: False).
+    :param is_vr: Whether the video is a VR video (default: False).
     """
     # Initialize the video reader
-    video_reader = VideoReaderFFmpeg(video_path, is_VR=is_VR)
+    video_reader = VideoReaderFFmpeg(video_path, is_vr=is_vr)
 
     # Display the video
     while True:
@@ -259,3 +256,14 @@ def main(video_path, is_VR=False):
     # Release resources
     video_reader.release()
     cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    # Parse command-line arguments
+    parser = argparse.ArgumentParser(description="Display a video using FFmpeg.")
+    parser.add_argument("video_path", type=str, help="Path to the video file.")
+    parser.add_argument("--is_vr", action="store_true", help="Enable VR mode for processing VR videos.")
+    args = parser.parse_args()
+
+    # Run the main function
+    main(args.video_path, args.is_vr)
\ No newline at end of file
diff --git a/utils/lib_Visualizer.py b/utils/lib_Visualizer.py
index 64f7997c..f272ce3c 100644
--- a/utils/lib_Visualizer.py
+++ b/utils/lib_Visualizer.py
@@ -1,4 +1,6 @@
 import cv2
+import numpy as np
+import matplotlib.pyplot as plt
 
 class Visualizer:
     def draw_bounding_box(self, image, box, label, color, offset_x=0):
@@ -20,7 +22,81 @@ def draw_gauge(self, image, distance):
         fill_height = int((distance / 100) * gauge_height)
         cv2.rectangle(image, (gauge_x, gauge_y + gauge_height - fill_height), (gauge_x + gauge_width, gauge_y + gauge_height), (0, 255, 0), -1)
         # Draw the distance text on top of the filled part of the gauge
+        #cv2.putText(image, str(int(distance)), (gauge_x + gauge_width // 2, gauge_y + gauge_height // 2), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
         cv2.putText(image, str(int(distance)), (gauge_x, gauge_y + gauge_height - fill_height - 5),
                     cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
         return image
+"""
+    def draw_limited_graph(self, image, distances, frames, nb_frames):
+        # Plot the frame indices vs positions for the last nb frames.
+        # Determine the number of frames to plot
+        num_frames_to_plot = min(nb_frames, len(distances))
 
+        # Get the last 'num_frames_to_plot' frames
+        last_frame_indices = frames[-num_frames_to_plot:]
+        last_positions = distances[-num_frames_to_plot:]
+
+        graph_height = image.shape[0] // 4
+        graph_width = image.shape[1] - 20 - 10
+        graph_x = 10
+        graph_y = image.shape[0] // 10
+
+        fig = plt.figure(figsize=(graph_width / 100, graph_height / 100), dpi=100)
+        ax = fig.add_subplot(111)
+        ax.plot(range(len(last_frame_indices)), last_positions, color='green', linewidth=1)
+        ax.set_ylim(0, 100)
+        ax.set_xlim(0, nb_frames)  # Limit the x-axis to nb_frames
+        fig.canvas.draw()
+
+        # Get the width and height of the figure
+        width, height = fig.canvas.get_width_height()
+
+        # Convert the figure to an image
+        graph_image = np.frombuffer(fig.canvas.buffer_rgba(), dtype='uint8').reshape((height, width, 3))
+        graph_image = cv2.cvtColor(graph_image, cv2.COLOR_RGBA2RGB)  # Convert from RGBA to RGB
+        graph_image = cv2.resize(graph_image, (graph_width, graph_height))
+
+        #graph_image = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8').reshape(
+        #    fig.canvas.get_width_height()[::-1] + (3,))
+        #graph_image = np.frombuffer(fig.canvas.buffer_rgba(), dtype='uint8').reshape(
+        #    fig.canvas.get_width_height()[::-1] + (4,))
+        #graph_image = cv2.resize(graph_image, (graph_width, graph_height))
+
+        image[graph_y:graph_y + graph_height, graph_x:graph_x + graph_width] = cv2.addWeighted(
+            image[graph_y:graph_y + graph_height, graph_x:graph_x + graph_width],
+            0.5,
+            graph_image,
+            0.5,
+            0
+        )
+        plt.close(fig)
+        return image
+
+    def draw_graph(self, image, distances, frames, reference_positions, reference_frames):
+        graph_height = image.shape[0] // 4
+        graph_width = image.shape[1] - 20 - 10
+        graph_x = 10
+        graph_y = image.shape[0] // 10
+        fig = plt.figure(figsize=(graph_width / 100, graph_height / 100), dpi=100)
+        ax = fig.add_subplot(111)
+        if reference_positions:
+            ax.plot(reference_frames, reference_positions, color='red', linewidth=1)
+        ax.plot(range(len(distances)), distances, color='green', linewidth=1)
+        # Draw the last 600 frames
+
+        ax.set_ylim(0, 100)
+        ax.set_xlim(0, len(distances))
+        fig.canvas.draw()
+        graph_image = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8').reshape(fig.canvas.get_width_height()[::-1] + (3,))
+        graph_image = cv2.resize(graph_image, (graph_width, graph_height))
+        image[graph_y:graph_y + graph_height, graph_x:graph_x + graph_width] = cv2.addWeighted(
+            image[graph_y:graph_y + graph_height, graph_x:graph_x + graph_width],
+            0.5,
+            graph_image,
+            0.5,
+            0
+        )
+        plt.close(fig)
+        return image
+
+"""

From 0540a00b75b7807a4d6baed1bb75cd8416187dcc Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 19 Jan 2025 18:04:53 +0100
Subject: [PATCH 069/277] Some cleanup

---
 FSGenerator.py              | 8 ++------
 script_generator/gui/app.py | 3 +++
 2 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index a7efd4f9..37a8bce8 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -1,8 +1,4 @@
-import os
-
 from script_generator.gui.app import start_app
 
-# TODO this is a workaround and needs to be fixed properly
-os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
-
-start_app()
\ No newline at end of file
+if __name__ == "__main__":
+    start_app()
\ No newline at end of file
diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index ef83869a..e936e2e1 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -1,9 +1,12 @@
 import ctypes
+import os
 import tkinter as tk
 
 from script_generator.gui.views.funscript_generator import FunscriptGeneratorPage
 from script_generator.state.app_state import AppState
 
+# TODO this is a workaround and needs to be fixed properly
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
 class App(tk.Tk):
     def __init__(self):

From 08f7f9b4665bfc428f7679709e78747fd1b8f5ce Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 19 Jan 2025 18:12:26 +0100
Subject: [PATCH 070/277] cuts.json also in output folder

---
 .../object_detection/analye_tracking_results.py          | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/script_generator/object_detection/analye_tracking_results.py b/script_generator/object_detection/analye_tracking_results.py
index 0b565369..9f2910af 100644
--- a/script_generator/object_detection/analye_tracking_results.py
+++ b/script_generator/object_detection/analye_tracking_results.py
@@ -40,9 +40,10 @@ def analyze_tracking_results(state, results, progress_callback=None):
         reader.release()
 
     # Load scene cuts if the file exists
-    if os.path.exists(state.video_path[:-4] + f"_cuts.json"):
-        print(f"Loading cuts from {state.video_path[:-4] + f'_cuts.json'}")
-        with open(state.video_path[:-4] + f"_cuts.json", 'r') as f:
+    cuts_path, _ = get_output_file_path(state.video_path, "_cuts.json")
+    if os.path.exists(cuts_path):
+        print(f"Loading cuts from {cuts_path}")
+        with open(cuts_path, 'r') as f:
             cuts = json.load(f)
         print(f"Loaded {len(cuts)} cuts : {cuts}")
     else:
@@ -52,7 +53,7 @@ def analyze_tracking_results(state, results, progress_callback=None):
         cuts = [scene[1] for scene in scene_list]
         cuts = cuts[:-1]  # Remove the last entry
         # Save the cuts to a file
-        with open(state.video_path[:-4] + f"_cuts.json", 'w') as f:
+        with open(cuts_path, 'w') as f:
             json.dump(cuts, f)
 
     state.funscript_frames = []  # List to store Funscript frames

From 89725f9eb49fe320d447253516247e962697ccb9 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 19 Jan 2025 18:43:00 +0100
Subject: [PATCH 071/277] Added app logo

---
 config.py                   |   2 ++
 resources/icon.ico          | Bin 0 -> 16958 bytes
 resources/logo.png          | Bin 0 -> 236350 bytes
 script_generator/gui/app.py |   5 +++++
 4 files changed, 7 insertions(+)
 create mode 100644 resources/icon.ico
 create mode 100644 resources/logo.png

diff --git a/config.py b/config.py
index 20cec711..440892a8 100644
--- a/config.py
+++ b/config.py
@@ -139,3 +139,5 @@ def find_ffprobe_path():
 STEP_SIZE = 120  # Speed step size for color transitions
 
 VW_FILTER_COEFF = 2.0
+LOGO = os.path.join(PROJECT_PATH, "resources", "logo.png")
+ICON = os.path.join(PROJECT_PATH, "resources", "icon.ico")
\ No newline at end of file
diff --git a/resources/icon.ico b/resources/icon.ico
new file mode 100644
index 0000000000000000000000000000000000000000..f27b0087ab91ac7f33916cfff64bddcc83353579
GIT binary patch
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literal 0
HcmV?d00001

diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index e936e2e1..4de5f833 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -2,6 +2,7 @@
 import os
 import tkinter as tk
 
+from config import LOGO, ICON
 from script_generator.gui.views.funscript_generator import FunscriptGeneratorPage
 from script_generator.state.app_state import AppState
 
@@ -17,6 +18,10 @@ def __init__(self):
         self.title("VR funscript generation")
         self.geometry("700x920")
         self.resizable(False, False)
+
+        self.iconphoto(False, tk.PhotoImage(file=LOGO))
+        self.iconbitmap(ICON)
+
         self.grid_rowconfigure(0, weight=1)
         self.grid_columnconfigure(0, weight=1)
         self.container = tk.Frame(self)

From 3a7c97d8548828c257d1fc21246ce0cfd2ea0c9f Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 19 Jan 2025 20:11:45 +0100
Subject: [PATCH 072/277] Merged and cleaned up variables Made a constants file

---
 config.py                                     | 156 ++++--------------
 script_generator/config.py                    |  48 ------
 script_generator/constants.py                 |  78 +++++++++
 script_generator/gui/app.py                   |   2 +-
 .../analye_tracking_results.py                |   2 +-
 .../object_detection_result.py                |   2 +-
 .../object_detection/post_process_results.py  |   4 +-
 script_generator/object_detection/utils.py    |  25 +--
 script_generator/object_detection/yolo.py     |   3 +-
 script_generator/scripts/analyse_video.py     |   2 +-
 script_generator/utils/config.py              |  59 +++++++
 script_generator/utils/file.py                |   2 +-
 .../vr_to_2d_task_processor.py                |   2 +-
 script_generator/video/video_info.py          |   6 +-
 .../video/video_task_processor.py             |  18 +-
 tests/test_PoseEstimation.py                  |   2 +-
 utils/lib_Debugger.py                         |   2 +-
 utils/lib_FunscriptHandler.py                 |   3 +-
 utils/lib_ObjectTracker.py                    |   2 +-
 utils/lib_VideoReaderFFmpeg.py                |   4 +-
 20 files changed, 202 insertions(+), 220 deletions(-)
 delete mode 100644 script_generator/config.py
 create mode 100644 script_generator/constants.py
 create mode 100644 script_generator/utils/config.py

diff --git a/config.py b/config.py
index 440892a8..7c3efd72 100644
--- a/config.py
+++ b/config.py
@@ -1,143 +1,59 @@
-import shutil
 import platform
-import os
+
+from ultralytics import YOLO
+
+from script_generator.constants import MODEL_PATH
+from script_generator.utils.config import find_ffmpeg_path, find_ffprobe_path
 
 VERSION = "0.0.1_25-01-16"
 
+##################################################################################################
+# FFMPEG
+##################################################################################################
+
 # ffmpeg and ffprobe paths - replace with your own if not in your system path
 win_ffmpeg_path = "C:/ffmpeg/bin/ffmpeg.exe"
 mac_ffmpeg_path = "/usr/local/bin/ffmpeg"
 lin_ffmpeg_path = "/usr/bin/ffmpeg"
-
 win_ffprobe_path = "C:/ffmpeg/bin/ffprobe.exe"
 mac_ffprobe_path = "/usr/local/bin/ffprobe"
 lin_ffprobe_path = "/usr/bin/ffprobe"
 
-# Yolo detection settings
-MAX_FRAME_HEIGHT = 1080
-
-PROJECT_PATH = os.path.dirname(os.path.abspath(__file__))
-OUTPUT_PATH = os.path.join(PROJECT_PATH, "output")
-
-# Define the base models directory
-MODELS_DIR = "models"
-
-# List of model filenames
-MODEL_FILENAMES = [
-    "k00gar-11n-200ep-best.mlpackage",
-    "k00gar-11n-200ep-best.pt",
-    "k00gar-11n-200ep-best.onnx",
-    "yolo11n-pose.mlpackage",
-    "yolo11n-pose.pt",
-    "yolo11n-pose.onnx"
-]
-
-
-def find_ffmpeg_path():
-    ffmpeg_path = shutil.which("ffmpeg")
-    if ffmpeg_path:
-        return ffmpeg_path
-    else:
-        print("ffmpeg not found in PATH, defaulting to platform values in params/config.py")
-
-        if platform.system() == "Windows":
-            return win_ffmpeg_path
-        elif platform.system() == "Darwin":
-            return mac_ffmpeg_path
-        elif platform.system() == "Linux":
-            return lin_ffmpeg_path
-        else:
-            raise FileNotFoundError("ffmpeg not found in PATH and no default path for this OS")
-
-
-def find_ffprobe_path():
-    ffprobe_path = shutil.which("ffprobe")
-    if ffprobe_path:
-        return ffprobe_path
-    else:
-        print("ffprobe not found in PATH, defaulting to platform values in params/config.py")
-
-        if platform.system() == "Windows":
-            return win_ffprobe_path
-        elif platform.system() == "Darwin":
-            return mac_ffprobe_path
-        elif platform.system() == "Linux":
-            return lin_ffprobe_path
-        else:
-            raise FileNotFoundError("ffprobe not found in PATH and no default path for this OS")
+##################################################################################################
+# OBJECT DETECTION
+##################################################################################################
 
+RENDER_RESOLUTION = 1080
+TEXTURE_RESOLUTION = 1440 # Size that is used to texture the opengl sphere
+PITCH=-25
+YAW=0
+YOLO_CONF = 0.3
+YOLO_BATCH_SIZE = 1 if platform.system() == "Darwin" else 30 # Mac doesn't support batching due to onnx
+RUN_POSE_MODEL = False
+YOLO_MODEL = YOLO(MODEL_PATH, task="detect")
+YOLO_POSE_MODEL = None # YOLO("models/yolo11n-pose.mlpackage", task="pose")
 
 # Set the paths in your config
-FFMPEG_PATH = find_ffmpeg_path()
-FFPROBE_PATH = find_ffprobe_path()
-
+FFMPEG_PATH = find_ffmpeg_path(win_ffmpeg_path, mac_ffmpeg_path, lin_ffmpeg_path)
+FFPROBE_PATH = find_ffprobe_path(win_ffprobe_path, mac_ffprobe_path, lin_ffprobe_path)
 print(f"ffmpeg_path: {FFMPEG_PATH}")
 print(f"ffprobe_path: {FFPROBE_PATH}")
 
-# Construct full paths using os.path.join
-YOLO_MODELS = [os.path.join(MODELS_DIR, filename) for filename in MODEL_FILENAMES]
-
-# Class types
-CLASS_TYPES = {
-    'face': 0, 'hand': 1, 'penis': 2, 'glans': 3, 'pussy': 4, 'butt': 5,
-    'anus': 6, 'breast': 7, 'navel': 8, 'foot': 9, 'hips center': 10
-}
-
-CLASS_REVERSE_MATCH = {
-    0: 'penis', 1: 'glans', 2: 'pussy', 3: 'butt', 4: 'anus', 5: 'breast',
-    6: 'navel', 7: 'hand', 8: 'face', 9: 'foot', 10: 'hips center'
-}
+##################################################################################################
+# ADVANCED / DEVELOPMENT
+##################################################################################################
 
-CLASS_PRIORITY_ORDER = {
-    "glans": 0, "penis": 1, "breast": 2, "navel": 3, "pussy": 4, "butt": 5, "face": 6
-}
-
-# Define class names with their corresponding indices
-CLASS_NAMES = {
-    'face': 0,
-    'hand': 1, 'left hand': 1, 'right hand': 1,
-    'penis': 2,
-    'glans': 3,
-    'pussy': 4,
-    'butt': 5,
-    'anus': 6,
-    'breast': 7,
-    'navel': 8,
-    'foot': 9, 'left foot': 9, 'right foot': 9,
-    'hips center': 10
-}
-
-# Define colors for each class
-CLASS_COLORS = {
-    "penis": (255, 0, 0),  # red
-    "glans": (0, 128, 0),  # green
-    "pussy": (0, 0, 255),  # blue
-    "butt": (255, 255, 0),  # yellow
-    "anus": (128, 0, 128),  # purple
-    "breast": (255, 165, 0),  # orange
-    "navel": (0, 255, 255),  # cyan
-    "hand": (255, 0, 255),  # magenta
-    "left hand": (255, 0, 255),  # magenta
-    "right hand": (255, 0, 255),  # magenta
-    "face": (0, 255, 0),  # lime
-    "foot": (165, 42, 42),  # brown
-    "left foot": (165, 42, 42),  # brown
-    "right foot": (165, 42, 42),  # brown
-    "hips center": (0, 0, 0)
-}
+SUBTRACT_THREADS_FROM_FFMPEG = 0
+UPDATE_PROGRESS_INTERVAL = 0.25 # Updates progress in the console and in gui
+# when enabled the queue will be processed one by one (use it on (QUEUE_MAXSIZE / frame rate) seconds longer videos or less)
+# raw frames take a lot of memory (RAM) so don't set the queue to high
+SEQUENTIAL_MODE = False
+QUEUE_MAXSIZE = 3000 if SEQUENTIAL_MODE else 500 # Bounded queue size to avoid memory blow-up
+DEBUG_PATH = "C:/cvr/funscript-generator/tmp_output"
+PROGRESS_BAR = True # disable when you want to print messages while debugging
 
 # Define custom colormap based on Lucife's heatmapColors
-HEATMAP_COLORS = [
-    [0, 0, 0],  # Black (no action)
-    [30, 144, 255],  # Dodger blue
-    [34, 139, 34],  # Lime green
-    [255, 215, 0],  # Gold
-    [220, 20, 60],  # Crimson
-    [147, 112, 219],  # Medium purple
-    [37, 22, 122]  # Dark blue
-]
 STEP_SIZE = 120  # Speed step size for color transitions
-
 VW_FILTER_COEFF = 2.0
-LOGO = os.path.join(PROJECT_PATH, "resources", "logo.png")
-ICON = os.path.join(PROJECT_PATH, "resources", "icon.ico")
\ No newline at end of file
+
+
diff --git a/script_generator/config.py b/script_generator/config.py
deleted file mode 100644
index 54b5169f..00000000
--- a/script_generator/config.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import os
-import platform
-
-from ultralytics import YOLO
-
-from config import PROJECT_PATH
-from script_generator.object_detection.utils import get_yolo_model_path
-
-##################################################################################################
-# CONFIGURATION
-##################################################################################################
-
-# Paths (hardcoded for now)
-MODEL_PATH = "C:/cvr/funscript-generator/VR-Funscript-AI-Generator/models/k00gar-11n-200ep-best.pt"
-
-
-##################################################################################################
-# DETECTION
-##################################################################################################
-
-# VIDEO PREPARATION
-RENDER_RESOLUTION = 1080
-TEXTURE_RESOLUTION = 1440
-PITCH=-25
-YAW=0
-
-# YOLO
-YOLO_CONF = 0.3
-YOLO_BATCH_SIZE = 1 if platform.system() == "Darwin" else 30 # Mac doesn't support batching due to onnx
-RUN_POSE_MODEL = False
-
-MODEL_PATH = str(os.path.join(PROJECT_PATH, get_yolo_model_path()))
-YOLO_MODEL = YOLO(MODEL_PATH, task="detect")
-YOLO_POSE_MODEL = None # YOLO("models/yolo11n-pose.mlpackage", task="pose")
-
-
-##################################################################################################
-# ADVANCED / DEVELOPMENT
-##################################################################################################
-
-SUBTRACT_THREADS_FROM_FFMPEG = 0
-UPDATE_PROGRESS_INTERVAL = 0.25 # Updates progress in the console and in gui
-# when enabled the queue will be processed one by one (use it on (QUEUE_MAXSIZE / frame rate) seconds longer videos or less)
-# raw frames take a lot of memory (RAM) so don't set the queue to high
-SEQUENTIAL_MODE = False
-QUEUE_MAXSIZE = 3000 if SEQUENTIAL_MODE else 500 # Bounded queue size to avoid memory blow-up
-DEBUG_PATH = "C:/cvr/funscript-generator/tmp_output"
-PROGRESS_BAR = True # disable when you want to print messages while debugging
diff --git a/script_generator/constants.py b/script_generator/constants.py
new file mode 100644
index 00000000..c9eebad4
--- /dev/null
+++ b/script_generator/constants.py
@@ -0,0 +1,78 @@
+import os
+
+from script_generator.utils.config import get_yolo_model_path
+
+##################################################################################################
+# OBJECT DETECTION
+##################################################################################################
+
+CLASS_TYPES = {
+    'face': 0, 'hand': 1, 'penis': 2, 'glans': 3, 'pussy': 4, 'butt': 5,
+    'anus': 6, 'breast': 7, 'navel': 8, 'foot': 9, 'hips center': 10
+}
+CLASS_REVERSE_MATCH = {
+    0: 'penis', 1: 'glans', 2: 'pussy', 3: 'butt', 4: 'anus', 5: 'breast',
+    6: 'navel', 7: 'hand', 8: 'face', 9: 'foot', 10: 'hips center'
+}
+CLASS_PRIORITY_ORDER = {
+    "glans": 0, "penis": 1, "breast": 2, "navel": 3, "pussy": 4, "butt": 5, "face": 6
+}
+CLASS_NAMES = {
+    'face': 0,
+    'hand': 1, 'left hand': 1, 'right hand': 1,
+    'penis': 2,
+    'glans': 3,
+    'pussy': 4,
+    'butt': 5,
+    'anus': 6,
+    'breast': 7,
+    'navel': 8,
+    'foot': 9, 'left foot': 9, 'right foot': 9,
+    'hips center': 10
+}
+CLASS_COLORS = {
+    "penis": (255, 0, 0),  # red
+    "glans": (0, 128, 0),  # green
+    "pussy": (0, 0, 255),  # blue
+    "butt": (255, 255, 0),  # yellow
+    "anus": (128, 0, 128),  # purple
+    "breast": (255, 165, 0),  # orange
+    "navel": (0, 255, 255),  # cyan
+    "hand": (255, 0, 255),  # magenta
+    "left hand": (255, 0, 255),  # magenta
+    "right hand": (255, 0, 255),  # magenta
+    "face": (0, 255, 0),  # lime
+    "foot": (165, 42, 42),  # brown
+    "left foot": (165, 42, 42),  # brown
+    "right foot": (165, 42, 42),  # brown
+    "hips center": (0, 0, 0)
+}
+HEATMAP_COLORS = [
+    [0, 0, 0],  # Black (no action)
+    [30, 144, 255],  # Dodger blue
+    [34, 139, 34],  # Lime green
+    [255, 215, 0],  # Gold
+    [220, 20, 60],  # Crimson
+    [147, 112, 219],  # Medium purple
+    [37, 22, 122]  # Dark blue
+]
+
+##################################################################################################
+# PATHS
+##################################################################################################
+
+PROJECT_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+OUTPUT_PATH = os.path.join(PROJECT_PATH, "output")
+MODELS_DIR = "models"
+MODEL_FILENAMES = [
+    "k00gar-11n-200ep-best.mlpackage",
+    "k00gar-11n-200ep-best.pt",
+    "k00gar-11n-200ep-best.onnx",
+    "yolo11n-pose.mlpackage",
+    "yolo11n-pose.pt",
+    "yolo11n-pose.onnx"
+]
+LOGO = os.path.join(PROJECT_PATH, "resources", "logo.png")
+ICON = os.path.join(PROJECT_PATH, "resources", "icon.ico")
+YOLO_MODELS = [os.path.join(MODELS_DIR, filename) for filename in MODEL_FILENAMES]
+MODEL_PATH = str(os.path.join(PROJECT_PATH, get_yolo_model_path(YOLO_MODELS)))
diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index 4de5f833..c3fac890 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -2,7 +2,7 @@
 import os
 import tkinter as tk
 
-from config import LOGO, ICON
+from script_generator.constants import LOGO, ICON
 from script_generator.gui.views.funscript_generator import FunscriptGeneratorPage
 from script_generator.state.app_state import AppState
 
diff --git a/script_generator/object_detection/analye_tracking_results.py b/script_generator/object_detection/analye_tracking_results.py
index 9f2910af..86de9c51 100644
--- a/script_generator/object_detection/analye_tracking_results.py
+++ b/script_generator/object_detection/analye_tracking_results.py
@@ -7,7 +7,7 @@
 from tqdm import tqdm
 from datetime import timedelta
 
-from config import CLASS_COLORS
+from script_generator.constants import CLASS_COLORS
 from script_generator.gui.messages.messages import ProgressMessage
 from script_generator.utils.file import get_output_file_path
 from script_generator.video.video_info import get_cropped_dimensions
diff --git a/script_generator/object_detection/object_detection_result.py b/script_generator/object_detection/object_detection_result.py
index 10d43e21..41a1e45d 100644
--- a/script_generator/object_detection/object_detection_result.py
+++ b/script_generator/object_detection/object_detection_result.py
@@ -1,4 +1,4 @@
-from config import CLASS_PRIORITY_ORDER
+from script_generator.constants import CLASS_PRIORITY_ORDER
 
 
 class ObjectDetectionResult:
diff --git a/script_generator/object_detection/post_process_results.py b/script_generator/object_detection/post_process_results.py
index 89877d21..1b99f92c 100644
--- a/script_generator/object_detection/post_process_results.py
+++ b/script_generator/object_detection/post_process_results.py
@@ -3,8 +3,8 @@
 
 import cv2
 
-from config import CLASS_REVERSE_MATCH, CLASS_COLORS, OUTPUT_PATH
-from script_generator.config import RUN_POSE_MODEL
+from script_generator.constants import CLASS_REVERSE_MATCH, CLASS_COLORS, OUTPUT_PATH
+from config import RUN_POSE_MODEL
 from script_generator.object_detection.object_detection_result import ObjectDetectionResult
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 from script_generator.utils.file import write_dataset, get_output_file_path
diff --git a/script_generator/object_detection/utils.py b/script_generator/object_detection/utils.py
index 42f1b5d3..4140dedd 100644
--- a/script_generator/object_detection/utils.py
+++ b/script_generator/object_detection/utils.py
@@ -1,10 +1,9 @@
 import os
 import platform
-from tkinter import messagebox
 
 import torch
 
-from config import YOLO_MODELS, CLASS_REVERSE_MATCH, OUTPUT_PATH
+from script_generator.constants import CLASS_REVERSE_MATCH, YOLO_MODELS
 from script_generator.gui.utils.widgets import Widgets
 from script_generator.object_detection.box_record import BoxRecord
 from script_generator.object_detection.object_detection_result import ObjectDetectionResult
@@ -28,28 +27,6 @@ def check_skip_object_detection(state):
 
     return False
 
-def get_yolo_model_path():
-    # Check if the device is an Apple device
-    if platform.system() == 'Darwin':
-        print(f"Apple device detected, loading {YOLO_MODELS[0]} for MPS inference.")
-        return YOLO_MODELS[0]
-
-    # Check if CUDA is available (for GPU support)
-    elif torch.cuda.is_available():
-        print(f"CUDA is available, loading {YOLO_MODELS[1]} for GPU inference.")
-        return YOLO_MODELS[1]
-
-    # Fallback to ONNX model for other platforms without CUDA
-    else:
-        print("CUDA not available, if this is unexpected, please install CUDA and check your version of torch.")
-        print("You might need to install a dependency with the following command (example):")
-        print("pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118")
-        print(f"Falling back to CPU inference, loading {YOLO_MODELS[2]}.")
-        print("WARNING: CPU inference may be slow on some devices.")
-
-        return YOLO_MODELS[2]
-
-
 def make_data_boxes(records):
     """
     Convert YOLO records into BoxRecord objects.
diff --git a/script_generator/object_detection/yolo.py b/script_generator/object_detection/yolo.py
index cd6a13a6..6cf1b47f 100644
--- a/script_generator/object_detection/yolo.py
+++ b/script_generator/object_detection/yolo.py
@@ -1,6 +1,7 @@
 import time
 
-from script_generator.config import YOLO_CONF, YOLO_BATCH_SIZE, YOLO_MODEL
+from config import YOLO_CONF, YOLO_BATCH_SIZE, YOLO_MODEL
+
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 
 
diff --git a/script_generator/scripts/analyse_video.py b/script_generator/scripts/analyse_video.py
index fb6de215..aff721d8 100644
--- a/script_generator/scripts/analyse_video.py
+++ b/script_generator/scripts/analyse_video.py
@@ -6,7 +6,7 @@
 from colorama import Fore, Style
 from tqdm import tqdm
 
-from script_generator.config import QUEUE_MAXSIZE, SEQUENTIAL_MODE, PROGRESS_BAR, UPDATE_PROGRESS_INTERVAL
+from config import QUEUE_MAXSIZE, SEQUENTIAL_MODE, PROGRESS_BAR, UPDATE_PROGRESS_INTERVAL
 from script_generator.gui.messages.messages import ProgressMessage
 from script_generator.object_detection.post_process_results import YoloAnalysisTaskProcessor
 from script_generator.object_detection.yolo import YoloTaskProcessor
diff --git a/script_generator/utils/config.py b/script_generator/utils/config.py
new file mode 100644
index 00000000..a9bf3adc
--- /dev/null
+++ b/script_generator/utils/config.py
@@ -0,0 +1,59 @@
+import platform
+import shutil
+
+import torch
+
+
+def find_ffmpeg_path(win_ffmpeg_path, mac_ffmpeg_path, lin_ffmpeg_path):
+    ffmpeg_path = shutil.which("ffmpeg")
+    if ffmpeg_path:
+        return ffmpeg_path
+    else:
+        print("ffmpeg not found in PATH, defaulting to platform values in params/config.py")
+
+        if platform.system() == "Windows":
+            return win_ffmpeg_path
+        elif platform.system() == "Darwin":
+            return mac_ffmpeg_path
+        elif platform.system() == "Linux":
+            return lin_ffmpeg_path
+        else:
+            raise FileNotFoundError("ffmpeg not found in PATH and no default path for this OS")
+
+
+def find_ffprobe_path(win_ffprobe_path, mac_ffprobe_path, lin_ffprobe_path):
+    ffprobe_path = shutil.which("ffprobe")
+    if ffprobe_path:
+        return ffprobe_path
+    else:
+        print("ffprobe not found in PATH, defaulting to platform values in params/config.py")
+
+        if platform.system() == "Windows":
+            return win_ffprobe_path
+        elif platform.system() == "Darwin":
+            return mac_ffprobe_path
+        elif platform.system() == "Linux":
+            return lin_ffprobe_path
+        else:
+            raise FileNotFoundError("ffprobe not found in PATH and no default path for this OS")
+
+def get_yolo_model_path(YOLO_MODELS):
+    # Check if the device is an Apple device
+    if platform.system() == 'Darwin':
+        print(f"Apple device detected, loading {YOLO_MODELS[0]} for MPS inference.")
+        return YOLO_MODELS[0]
+
+    # Check if CUDA is available (for GPU support)
+    elif torch.cuda.is_available():
+        print(f"CUDA is available, loading {YOLO_MODELS[1]} for GPU inference.")
+        return YOLO_MODELS[1]
+
+    # Fallback to ONNX model for other platforms without CUDA
+    else:
+        print("CUDA not available, if this is unexpected, please install CUDA and check your version of torch.")
+        print("You might need to install a dependency with the following command (example):")
+        print("pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118")
+        print(f"Falling back to CPU inference, loading {YOLO_MODELS[2]}.")
+        print("WARNING: CPU inference may be slow on some devices.")
+
+        return YOLO_MODELS[2]
diff --git a/script_generator/utils/file.py b/script_generator/utils/file.py
index 6d0180bd..7e3f5e51 100644
--- a/script_generator/utils/file.py
+++ b/script_generator/utils/file.py
@@ -2,7 +2,7 @@
 import os
 import time
 
-from config import OUTPUT_PATH
+from script_generator.constants import OUTPUT_PATH
 
 
 def write_dataset(file_path, data):
diff --git a/script_generator/video/video_conversion/vr_to_2d_task_processor.py b/script_generator/video/video_conversion/vr_to_2d_task_processor.py
index 0575601f..0403c0ae 100644
--- a/script_generator/video/video_conversion/vr_to_2d_task_processor.py
+++ b/script_generator/video/video_conversion/vr_to_2d_task_processor.py
@@ -6,7 +6,7 @@
 from OpenGL.GL import *
 from OpenGL.GLU import *
 
-from script_generator.config import RENDER_RESOLUTION, YAW, PITCH, DEBUG_PATH
+from config import RENDER_RESOLUTION, YAW, PITCH, DEBUG_PATH
 from script_generator.video.video_conversion.opengl.helpers import create_180_dome, render_dome
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 
diff --git a/script_generator/video/video_info.py b/script_generator/video/video_info.py
index d31d386f..72371c70 100644
--- a/script_generator/video/video_info.py
+++ b/script_generator/video/video_info.py
@@ -1,7 +1,7 @@
 import re
 from dataclasses import dataclass, field
 
-from config import MAX_FRAME_HEIGHT
+from config import RENDER_RESOLUTION
 
 
 @dataclass
@@ -61,7 +61,7 @@ def get_projection_and_fov_from_filename(filename):
 
 def get_cropped_dimensions(video: VideoInfo):
     if video.is_vr:
-        return MAX_FRAME_HEIGHT, MAX_FRAME_HEIGHT
+        return RENDER_RESOLUTION, RENDER_RESOLUTION
     else:
-        scaling_factor = min(1, MAX_FRAME_HEIGHT / video.height)
+        scaling_factor = min(1, RENDER_RESOLUTION / video.height)
         return int(video.width * scaling_factor), int(video.height * scaling_factor)
\ No newline at end of file
diff --git a/script_generator/video/video_task_processor.py b/script_generator/video/video_task_processor.py
index 58540d45..7432e89a 100644
--- a/script_generator/video/video_task_processor.py
+++ b/script_generator/video/video_task_processor.py
@@ -3,8 +3,7 @@
 
 import numpy as np
 
-from config import MAX_FRAME_HEIGHT, FFMPEG_PATH
-from script_generator.config import SUBTRACT_THREADS_FROM_FFMPEG, PITCH
+from config import SUBTRACT_THREADS_FROM_FFMPEG, PITCH, RENDER_RESOLUTION, FFMPEG_PATH
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 from script_generator.tasks.tasks import AnalyseFrameTask
 from script_generator.video.video_info import get_cropped_dimensions
@@ -47,23 +46,23 @@ def vr_video_filters():
 
             if self.state.video_reader == "FFmpeg":
                 filters = [
-                    f"scale={MAX_FRAME_HEIGHT * 2}:{MAX_FRAME_HEIGHT}",
-                    f"crop={MAX_FRAME_HEIGHT}:{MAX_FRAME_HEIGHT}:0:0",
+                    f"scale={RENDER_RESOLUTION * 2}:{RENDER_RESOLUTION}",
+                    f"crop={RENDER_RESOLUTION}:{RENDER_RESOLUTION}:0:0",
                     f"v360={projection}:in_stereo=2d:output=sg:iv_fov={iv_fov}:ih_fov={ih_fov}:"
                     f"d_fov={d_fov}:v_fov={v_fov}:h_fov={h_fov}:pitch={PITCH}:yaw=0:roll=0:"
-                    f"w={MAX_FRAME_HEIGHT}:h={MAX_FRAME_HEIGHT}:interp=lanczos:reset_rot=1",
+                    f"w={RENDER_RESOLUTION}:h={RENDER_RESOLUTION}:interp=lanczos:reset_rot=1",
                     "lutyuv=y=gammaval(0.7)"
                 ]
             else:
                 filters = [
-                    f"scale={MAX_FRAME_HEIGHT * 2}:{MAX_FRAME_HEIGHT}",
-                    f"crop={MAX_FRAME_HEIGHT}:{MAX_FRAME_HEIGHT}:0:0"
+                    f"scale={RENDER_RESOLUTION * 2}:{RENDER_RESOLUTION}",
+                    f"crop={RENDER_RESOLUTION}:{RENDER_RESOLUTION}:0:0"
                 ]
 
             return ",".join(filters)
 
         def standard_video_filters():
-            return f"scale={width}:{height}" if video.height > MAX_FRAME_HEIGHT else None
+            return f"scale={width}:{height}" if video.height > RENDER_RESOLUTION else None
 
         width, height = get_cropped_dimensions(video)
 
@@ -75,7 +74,7 @@ def standard_video_filters():
 
         # Start FFmpeg process
         self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
-        frame_size = width * height * 3   # Size of one frame in bytes
+        frame_size = width * height * 3  # Size of one frame in bytes
 
         # progress_bar = tqdm(total=203856, unit="frame", desc="Processing frames")
         while True:
@@ -110,7 +109,6 @@ def standard_video_filters():
 
         self.stop_process()
 
-
     # TODO ffmpeg interrupt
     # def release(self):
     #     if self.process:
diff --git a/tests/test_PoseEstimation.py b/tests/test_PoseEstimation.py
index 00ed99a2..a962a7e8 100644
--- a/tests/test_PoseEstimation.py
+++ b/tests/test_PoseEstimation.py
@@ -2,7 +2,7 @@
 import numpy as np
 import cv2
 from collections import defaultdict
-from config import CLASS_REVERSE_MATCH
+from script_generator.constants import CLASS_REVERSE_MATCH
 
 #video_path = "/Users/k00gar/Downloads/Katrina Jade [ADD] Oct 16, 2024R_6kvr265_reenc.mp4"
 #video_path = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 4f9517f3..2346e0e8 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -1,7 +1,7 @@
 import json
 import cv2
 import numpy as np
-from config import CLASS_COLORS
+from script_generator.constants import CLASS_COLORS
 from scipy.interpolate import interp1d
 
 from script_generator.utils.file import get_output_file_path
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 4474a19d..09706336 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -9,7 +9,8 @@
 import datetime
 from scipy.signal import savgol_filter
 
-from config import HEATMAP_COLORS, STEP_SIZE, VERSION
+from config import STEP_SIZE, VERSION
+from script_generator.constants import HEATMAP_COLORS
 
 import matplotlib
 
diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index 83558738..cf6ca819 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -8,7 +8,7 @@
 import math
 import numpy as np
 
-from config import CLASS_NAMES
+from script_generator.constants import CLASS_NAMES
 from script_generator.utils.logger import logger
 from utils import lib_KalmanFilter as KF
 
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index 3f58a033..bbb998f4 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -2,7 +2,7 @@
 import cv2
 import numpy as np
 import argparse
-from config import FFMPEG_PATH, FFPROBE_PATH, MAX_FRAME_HEIGHT
+from config import FFMPEG_PATH, FFPROBE_PATH, RENDER_RESOLUTION
 
 
 class VideoReaderFFmpeg:
@@ -78,7 +78,7 @@ def _initialize_video_info(self):
             self.duration = duration * 1000  # Convert duration to milliseconds
 
             # limiting the frame height here has no additional performance cost and significantly improves speed for 1440p+ video
-            scaling_factor = min(MAX_FRAME_HEIGHT / self.width, MAX_FRAME_HEIGHT / self.height)
+            scaling_factor = min(RENDER_RESOLUTION / self.width, RENDER_RESOLUTION / self.height)
             self.width = int(self.width * scaling_factor)
             self.height = int(self.height * scaling_factor)
 

From ada70a4527e36a56e9acf4ca104b27e47a1e794c Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 19 Jan 2025 20:24:53 +0100
Subject: [PATCH 073/277] Added setup.py some constant/config tweaks

---
 config.py                     | 26 ++++++++++++++++----------
 script_generator/constants.py |  2 +-
 setup.py                      | 23 ++++++++++++++++++++++-
 3 files changed, 39 insertions(+), 12 deletions(-)

diff --git a/config.py b/config.py
index 7c3efd72..deb249dd 100644
--- a/config.py
+++ b/config.py
@@ -20,28 +20,28 @@
 lin_ffprobe_path = "/usr/bin/ffprobe"
 
 ##################################################################################################
-# OBJECT DETECTION
+# PERFORMANCE
 ##################################################################################################
 
-RENDER_RESOLUTION = 1080
+RENDER_RESOLUTION = 1080 # Above 1080x1080 doesn't seem to improve detections
 TEXTURE_RESOLUTION = 1440 # Size that is used to texture the opengl sphere
+YOLO_BATCH_SIZE = 1 if platform.system() == "Darwin" else 30 # Mac doesn't support batching due to onnx
+
+##################################################################################################
+# ADVANCED / DEVELOPMENT
+##################################################################################################
+
+
 PITCH=-25
 YAW=0
 YOLO_CONF = 0.3
-YOLO_BATCH_SIZE = 1 if platform.system() == "Darwin" else 30 # Mac doesn't support batching due to onnx
+
 RUN_POSE_MODEL = False
 YOLO_MODEL = YOLO(MODEL_PATH, task="detect")
 YOLO_POSE_MODEL = None # YOLO("models/yolo11n-pose.mlpackage", task="pose")
 
 # Set the paths in your config
-FFMPEG_PATH = find_ffmpeg_path(win_ffmpeg_path, mac_ffmpeg_path, lin_ffmpeg_path)
-FFPROBE_PATH = find_ffprobe_path(win_ffprobe_path, mac_ffprobe_path, lin_ffprobe_path)
-print(f"ffmpeg_path: {FFMPEG_PATH}")
-print(f"ffprobe_path: {FFPROBE_PATH}")
 
-##################################################################################################
-# ADVANCED / DEVELOPMENT
-##################################################################################################
 
 SUBTRACT_THREADS_FROM_FFMPEG = 0
 UPDATE_PROGRESS_INTERVAL = 0.25 # Updates progress in the console and in gui
@@ -57,3 +57,9 @@
 VW_FILTER_COEFF = 2.0
 
 
+FFMPEG_PATH = find_ffmpeg_path(win_ffmpeg_path, mac_ffmpeg_path, lin_ffmpeg_path)
+FFPROBE_PATH = find_ffprobe_path(win_ffprobe_path, mac_ffprobe_path, lin_ffprobe_path)
+print(f"ffmpeg_path: {FFMPEG_PATH}")
+print(f"ffprobe_path: {FFPROBE_PATH}")
+
+
diff --git a/script_generator/constants.py b/script_generator/constants.py
index c9eebad4..bed6a6f4 100644
--- a/script_generator/constants.py
+++ b/script_generator/constants.py
@@ -75,4 +75,4 @@
 LOGO = os.path.join(PROJECT_PATH, "resources", "logo.png")
 ICON = os.path.join(PROJECT_PATH, "resources", "icon.ico")
 YOLO_MODELS = [os.path.join(MODELS_DIR, filename) for filename in MODEL_FILENAMES]
-MODEL_PATH = str(os.path.join(PROJECT_PATH, get_yolo_model_path(YOLO_MODELS)))
+MODEL_PATH = str(os.path.join(PROJECT_PATH, get_yolo_model_path(YOLO_MODELS)))
\ No newline at end of file
diff --git a/setup.py b/setup.py
index f87f5c14..f6de8696 100644
--- a/setup.py
+++ b/setup.py
@@ -1 +1,22 @@
-# TODO
\ No newline at end of file
+from setuptools import setup, find_packages
+
+setup(
+    name="VR funscript creator",
+    version="0.0.1",  # Replace with your project version
+    author="Koogar",
+    description="Create funscripts for VR files",
+    long_description=open("README.md").read(),
+    long_description_content_type="text/markdown",
+    url="https://github.com/ack00gar/VR-Funscript-AI-Generator",
+    packages=find_packages(),
+    include_package_data=True,
+    install_requires=[
+        # TODO
+    ],
+    entry_points={
+        "console_scripts": [
+            # TODO
+        ],
+    },
+    python_requires="=3.11",
+)

From d67c96a79209111b20fe3cb464b977ced07ad872 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 19 Jan 2025 21:50:47 +0100
Subject: [PATCH 074/277] Added extra progress bar for scene cuts and some
 fixes

---
 script_generator/gui/app.py                   |  2 +-
 .../gui/controller/video_analysis.py          | 14 +++++++-
 .../gui/views/funscript_generator.py          | 21 +++++++----
 .../analye_tracking_results.py                | 25 +++++++------
 .../vr_to_2d_task_processor.py                |  9 ++---
 setup.py                                      |  2 +-
 utils/lib_FunscriptHandler.py                 | 12 ++++---
 utils/lib_SceneCutsDetect.py                  | 36 ++++++++++++-------
 8 files changed, 78 insertions(+), 43 deletions(-)

diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index c3fac890..d3a86750 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -16,7 +16,7 @@ def __init__(self):
             ctypes.windll.shcore.SetProcessDpiAwareness(2)  # For Windows DPI scaling
         # self.tk.call('tk', 'scaling', 1.0)
         self.title("VR funscript generation")
-        self.geometry("700x920")
+        self.geometry("700x950")
         self.resizable(False, False)
 
         self.iconphoto(False, tk.PhotoImage(file=LOGO))
diff --git a/script_generator/gui/controller/video_analysis.py b/script_generator/gui/controller/video_analysis.py
index b0d9f106..31a099b7 100644
--- a/script_generator/gui/controller/video_analysis.py
+++ b/script_generator/gui/controller/video_analysis.py
@@ -1,7 +1,9 @@
 import threading
+from datetime import time
 from tkinter import messagebox
 
 from script_generator.gui.controller.tracking_analysis import tracking_analysis
+from script_generator.gui.messages.messages import ProgressMessage
 from script_generator.object_detection.utils import check_skip_object_detection
 from script_generator.scripts.analyse_video import analyse_video
 from script_generator.utils.file import get_output_file_path
@@ -28,9 +30,19 @@ def run():
 
         skip_detection = check_skip_object_detection(state)
 
-        if not skip_detection:
+        if skip_detection:
+            state.set_video_info()
+            if state.update_ui:
+                state.update_ui(ProgressMessage(
+                    process="OBJECT_DETECTION",
+                    frames_processed=state.video_info.total_frames,
+                    total_frames=state.frame_end,
+                    eta="Done"
+                ))
+        else:
             analyse_video(state)
 
+
         tracking_analysis(state)
 
     processing_thread = threading.Thread(target=run)
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 85496ce1..053975db 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -58,8 +58,9 @@ def __init__(self, parent, controller):
         #region PROCESSING
         processing = Widgets.frame(wrapper, title="Processing", main_section=True, row=3)
         yolo_p_container, yolo_p, yolo_p_label, yolo_p_perc = Widgets.labeled_progress(processing, "YOLO Detection", row=0)
-        track_p_container, track_p, track_p_label, track_p_perc = Widgets.labeled_progress(processing, "Tracking Analysis", row=1)
-        Widgets.button(processing, "Start processing", lambda: video_analysis(state), row=2)
+        scene_p_container, scene_p, scene_p_label, scene_p_perc = Widgets.labeled_progress(processing, "Scene detection", row=1)
+        track_p_container, track_p, track_p_label, track_p_perc = Widgets.labeled_progress(processing, "Tracking Analysis", row=2)
+        Widgets.button(processing, "Start processing", lambda: video_analysis(state), row=3)
         #endregion
 
         #region FUNSCRIPT TWEAKING
@@ -193,12 +194,18 @@ def process_update():
                     print(f"Unhandled message type: {type(msg)}")
 
             def handle_progress_message(progress_msg: ProgressMessage):
-                if progress_msg.process == "OBJECT_DETECTION":
-                    yolo_p["value"] = progress_msg.frames_processed
-                    yolo_p["maximum"] = progress_msg.total_frames
-                    percentage = (progress_msg.frames_processed / progress_msg.total_frames) * 100 if progress_msg.total_frames > 0 else 0
-                    yolo_p_perc.config(text=f"{percentage:.0f}% - ETA: {progress_msg.eta}")
+                progress_mapping = {
+                    "OBJECT_DETECTION": (yolo_p, yolo_p_perc),
+                    "SCENE_DETECTION": (scene_p, scene_p_perc),
+                    "TRACKING_ANALYSIS": (track_p, track_p_perc),
+                }
 
+                if progress_msg.process in progress_mapping:
+                    progress_bar, progress_label = progress_mapping[progress_msg.process]
+                    progress_bar["value"] = progress_msg.frames_processed
+                    progress_bar["maximum"] = progress_msg.total_frames
+                    percentage = (progress_msg.frames_processed / progress_msg.total_frames) * 100 if progress_msg.total_frames > 0 else 0
+                    progress_label.config(text=f"{percentage:.0f}% - ETA: {progress_msg.eta}")
 
             # Schedule the update on the main thread
             self.controller.after(0, process_update)
diff --git a/script_generator/object_detection/analye_tracking_results.py b/script_generator/object_detection/analye_tracking_results.py
index 86de9c51..f52388a7 100644
--- a/script_generator/object_detection/analye_tracking_results.py
+++ b/script_generator/object_detection/analye_tracking_results.py
@@ -24,16 +24,10 @@ def analyze_tracking_results(state, results, progress_callback=None):
 
     video_info = state.video_info
     fps = video_info.fps
-    nb_frames = video_info.total_frames
-    if state.frame_start and state.frame_end:
-        nb_frames = state.frame_end - state.frame_start
-    frame_width, frame_height = get_cropped_dimensions(video_info)
     reader = VideoReaderFFmpeg(state.video_path, is_vr=video_info.is_vr)
 
     image_area = video_info.width * video_info.height
 
-    cuts = []
-
     if state.life_display_mode:
         reader.set(cv2.CAP_PROP_POS_FRAMES, state.frame_start)
     else:
@@ -46,9 +40,17 @@ def analyze_tracking_results(state, results, progress_callback=None):
         with open(cuts_path, 'r') as f:
             cuts = json.load(f)
         print(f"Loaded {len(cuts)} cuts : {cuts}")
+
+        if state.update_ui:
+            state.update_ui(ProgressMessage(
+                process="SCENE_DETECTION",
+                frames_processed=state.video_info.total_frames,
+                total_frames=state.frame_end,
+                eta="Done"
+            ))
     else:
         # Detect scene changes if the cuts file does not exist
-        scene_list = detect_scene_changes(state.video_path, video_info.is_vr, 0.9, state.frame_start, state.frame_end)
+        scene_list = detect_scene_changes(state, video_info.is_vr, 0.9, state.frame_start, state.frame_end)
         print(f"Analyzing frames {state.frame_start} to {state.frame_end}")
         cuts = [scene[1] for scene in scene_list]
         cuts = cuts[:-1]  # Remove the last entry
@@ -62,7 +64,7 @@ def analyze_tracking_results(state, results, progress_callback=None):
     # Start time for ETA calculation
     start_time = time.time()
 
-    for frame_pos in tqdm(range(state.frame_start, state.frame_end), unit="f"):
+    for frame_pos in range(state.frame_start, state.frame_end): # tqdm(range(state.frame_start, state.frame_end), unit="f"):
         if frame_pos in cuts:
             # Reinitialize the tracker at scene cuts
             print(f"Reaching cut at frame {frame_pos}")
@@ -171,7 +173,7 @@ def analyze_tracking_results(state, results, progress_callback=None):
             cv2.waitKey(1)
 
         # Update progress
-        if progress_callback:
+        if state.update_ui:
             elapsed_time = time.time() - start_time
             frames_processed = frame_pos - state.frame_start + 1
             frames_remaining = state.frame_end - frame_pos - 1
@@ -184,7 +186,10 @@ def analyze_tracking_results(state, results, progress_callback=None):
                 eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
             ))
 
-            # progress_callback(frame_pos, frame_end, time.strftime("%H:%M:%S", time.gmtime(eta)))
+        # TODO improve this
+        # if progress_callback:
+        #     progress_callback()
+
 
     # Prepare Funscript data
     state.funscript_data = list(zip(state.funscript_frames, state.funscript_distances))
diff --git a/script_generator/video/video_conversion/vr_to_2d_task_processor.py b/script_generator/video/video_conversion/vr_to_2d_task_processor.py
index 0403c0ae..e55aad02 100644
--- a/script_generator/video/video_conversion/vr_to_2d_task_processor.py
+++ b/script_generator/video/video_conversion/vr_to_2d_task_processor.py
@@ -1,14 +1,11 @@
-import os
-
-import imageio
-import numpy as np
 import glfw
+import numpy as np
 from OpenGL.GL import *
 from OpenGL.GLU import *
 
-from config import RENDER_RESOLUTION, YAW, PITCH, DEBUG_PATH
-from script_generator.video.video_conversion.opengl.helpers import create_180_dome, render_dome
+from config import RENDER_RESOLUTION, YAW, PITCH
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
+from script_generator.video.video_conversion.opengl.helpers import create_180_dome, render_dome
 
 
 class VrTo2DTaskProcessor(AbstractTaskProcessor):
diff --git a/setup.py b/setup.py
index f6de8696..ae5d3032 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@
 setup(
     name="VR funscript creator",
     version="0.0.1",  # Replace with your project version
-    author="Koogar",
+    author="k00gar",
     description="Create funscripts for VR files",
     long_description=open("README.md").read(),
     long_description_content_type="text/markdown",
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 09706336..23118452 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -34,8 +34,7 @@ def generate(self, state):
             os.rename(output_path, output_path + f"_{time.time()}.bak")
 
         raw_funscript_path, _ = get_output_file_path(state.video_path, "_rawfunscript.json")
-
-        if len(state.funscript_data) == 0:
+        if os.path.exists(raw_funscript_path) and (state.funscript_data is None or len(state.funscript_data) == 0):
             print("len funscript data is 0, trying to load file")
             # Read the funscript data from the JSON file
             with open(raw_funscript_path, 'r') as f:
@@ -92,9 +91,11 @@ def generate(self, state):
             if state.boost_enabled:
                 print("Positions adjustment - step 5 (amplitude boosting)")
                 # self.boost_amplitude(adjusted_positions, boost_factor=1.2, min_value=0, max_value=100)
-                adjusted_positions = self.adjust_peaks_and_lows(adjusted_positions,
-                                                                peak_boost=state.boost_up_percent,
-                                                                low_reduction=state.boost_down_percent)
+                adjusted_positions = self.adjust_peaks_and_lows(
+                    adjusted_positions,
+                    peak_boost=state.boost_up_percent,
+                    low_reduction=state.boost_down_percent
+                )
             else:
                 print("Skipping positions adjustment - step 5 (amplitude boosting)")
 
@@ -592,6 +593,7 @@ def generate_heatmap_inline(self, ax, times, positions):
             speeds = np.nan_to_num(speeds, nan=0.0)  # Replace NaN values in speeds with 0
         else:
             speeds = np.array([])  # Empty array if not enough valid data points
+
         # TODO filter nan at one logical moment END
 
         def get_color(intensity):
diff --git a/utils/lib_SceneCutsDetect.py b/utils/lib_SceneCutsDetect.py
index 348cd239..e90e679d 100644
--- a/utils/lib_SceneCutsDetect.py
+++ b/utils/lib_SceneCutsDetect.py
@@ -1,6 +1,11 @@
+import time
+
 import cv2
 from tqdm import tqdm
 
+from script_generator.gui.messages.messages import ProgressMessage
+from script_generator.state.app_state import AppState
+
 
 def compute_histogram(frame):
     """
@@ -24,17 +29,8 @@ def compare_histograms(hist1, hist2):
     return cv2.compareHist(hist1, hist2, cv2.HISTCMP_CORREL)
 
 
-def detect_scene_changes(video_path, crop=None, threshold=0.97, frame_start=0, frame_end=None):
-    """
-    Detect scene changes in a video using histogram comparison.
-    :param video_path: Path to the video file.
-    :param crop: Will crop the video and keep left/right side.
-    :param threshold: Similarity threshold for detecting scene changes.
-    :param frame_start: Starting frame for processing.
-    :param frame_end: Ending frame for processing.
-    :return: List of scene changes as [start_frame, end_frame] pairs.
-    """
-    cap = cv2.VideoCapture(video_path)
+def detect_scene_changes(state: AppState, crop=None, threshold=0.97, frame_start=0, frame_end=None):
+    cap = cv2.VideoCapture(state.video_path)
     if not cap.isOpened():
         print("Error: Could not open video.")
         return []
@@ -54,9 +50,10 @@ def detect_scene_changes(video_path, crop=None, threshold=0.97, frame_start=0, f
     scene_changes = []
     prev_hist = None
     prev_cut = frame_start
+    start_time = time.time()
 
     # Process frames
-    for frame_pos in tqdm(range(total_frames_to_parse), desc="Detecting scene changes"):
+    for frame_pos in range(total_frames_to_parse): # tqdm(range(total_frames_to_parse), desc="Detecting scene changes"):
         cap.set(cv2.CAP_PROP_POS_FRAMES, frame_start + frame_pos * frame_step)
         ret, frame = cap.read()
         if not ret:
@@ -85,6 +82,20 @@ def detect_scene_changes(video_path, crop=None, threshold=0.97, frame_start=0, f
 
         prev_hist = current_hist
 
+        # Update progress
+        if state.update_ui:
+            elapsed_time = time.time() - start_time
+            frames_processed = frame_pos - state.frame_start + 1
+            frames_remaining = state.frame_end - frame_pos - 1
+            eta = (elapsed_time / frames_processed) * frames_remaining if frames_processed > 0 else 0
+
+            state.update_ui(ProgressMessage(
+                process="SCENE_DETECTION",
+                frames_processed=frames_processed,
+                total_frames=state.frame_end,
+                eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
+            ))
+
     # Add the last scene
     if not scene_changes:
         scene_changes.append([frame_start, frame_end])
@@ -112,4 +123,5 @@ def detect_scene_changes(video_path, crop=None, threshold=0.97, frame_start=0, f
 
     print(f"Found {len(merged_scenes)} relevant scenes: {merged_scenes}.")
     cap.release()
+
     return merged_scenes
\ No newline at end of file

From 75c67832e45489b592b8cc918ca31d62919e0e34 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 19 Jan 2025 21:56:24 +0100
Subject: [PATCH 075/277] Added extra progress bar for scene cuts and some
 fixes

---
 script_generator/gui/controller/video_analysis.py           | 2 +-
 script_generator/gui/views/funscript_generator.py           | 2 +-
 .../object_detection/analye_tracking_results.py             | 6 ++++++
 3 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/script_generator/gui/controller/video_analysis.py b/script_generator/gui/controller/video_analysis.py
index 31a099b7..e2825710 100644
--- a/script_generator/gui/controller/video_analysis.py
+++ b/script_generator/gui/controller/video_analysis.py
@@ -36,7 +36,7 @@ def run():
                 state.update_ui(ProgressMessage(
                     process="OBJECT_DETECTION",
                     frames_processed=state.video_info.total_frames,
-                    total_frames=state.frame_end,
+                    total_frames=state.video_info.total_frames,
                     eta="Done"
                 ))
         else:
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 053975db..4ed67ddd 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -205,7 +205,7 @@ def handle_progress_message(progress_msg: ProgressMessage):
                     progress_bar["value"] = progress_msg.frames_processed
                     progress_bar["maximum"] = progress_msg.total_frames
                     percentage = (progress_msg.frames_processed / progress_msg.total_frames) * 100 if progress_msg.total_frames > 0 else 0
-                    progress_label.config(text=f"{percentage:.0f}% - ETA: {progress_msg.eta}")
+                    progress_label.config(text=f"{percentage:.0f}% - ETA: {progress_msg.eta}" if progress_msg.frames_processed < progress_msg.total_frames else "Done")
 
             # Schedule the update on the main thread
             self.controller.after(0, process_update)
diff --git a/script_generator/object_detection/analye_tracking_results.py b/script_generator/object_detection/analye_tracking_results.py
index f52388a7..fe138f2d 100644
--- a/script_generator/object_detection/analye_tracking_results.py
+++ b/script_generator/object_detection/analye_tracking_results.py
@@ -190,6 +190,12 @@ def analyze_tracking_results(state, results, progress_callback=None):
         # if progress_callback:
         #     progress_callback()
 
+    state.update_ui(ProgressMessage(
+        process="TRACKING_ANALYSIS",
+        frames_processed=state.video_info.total_frames,
+        total_frames=state.video_info.total_frames,
+        eta="Done"
+    ))
 
     # Prepare Funscript data
     state.funscript_data = list(zip(state.funscript_frames, state.funscript_distances))

From b09ab972a33769a2079839011f790e6bb8738886 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 19 Jan 2025 22:13:10 +0100
Subject: [PATCH 076/277] Re-enabled glans + penis detection

---
 script_generator/object_detection/analye_tracking_results.py | 2 +-
 script_generator/object_detection/utils.py                   | 3 ---
 2 files changed, 1 insertion(+), 4 deletions(-)

diff --git a/script_generator/object_detection/analye_tracking_results.py b/script_generator/object_detection/analye_tracking_results.py
index fe138f2d..7571fd0f 100644
--- a/script_generator/object_detection/analye_tracking_results.py
+++ b/script_generator/object_detection/analye_tracking_results.py
@@ -64,7 +64,7 @@ def analyze_tracking_results(state, results, progress_callback=None):
     # Start time for ETA calculation
     start_time = time.time()
 
-    for frame_pos in range(state.frame_start, state.frame_end): # tqdm(range(state.frame_start, state.frame_end), unit="f"):
+    for frame_pos in tqdm(range(state.frame_start, state.frame_end), unit="f"):
         if frame_pos in cuts:
             # Reinitialize the tracker at scene cuts
             print(f"Reaching cut at frame {frame_pos}")
diff --git a/script_generator/object_detection/utils.py b/script_generator/object_detection/utils.py
index 4140dedd..e18618c0 100644
--- a/script_generator/object_detection/utils.py
+++ b/script_generator/object_detection/utils.py
@@ -62,9 +62,6 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
         if frame_idx >= start_frame and cls == penis_cls and conf >= 0.5:
             penis_frame = frame_idx
 
-            # TODO re-enable logic
-            return penis_frame
-
         if frame_idx == penis_frame and cls == glans_cls and conf >= 0.5:
             if frame_detected == 0:
                 frame_detected = frame_idx

From b27976cbf053b991e7452a976ab31d4585756bf4 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 19 Jan 2025 22:17:37 +0100
Subject: [PATCH 077/277] Throttled ui update

---
 .../analye_tracking_results.py                | 28 +++++++++++--------
 1 file changed, 17 insertions(+), 11 deletions(-)

diff --git a/script_generator/object_detection/analye_tracking_results.py b/script_generator/object_detection/analye_tracking_results.py
index 7571fd0f..6705a767 100644
--- a/script_generator/object_detection/analye_tracking_results.py
+++ b/script_generator/object_detection/analye_tracking_results.py
@@ -7,6 +7,7 @@
 from tqdm import tqdm
 from datetime import timedelta
 
+from config import UPDATE_PROGRESS_INTERVAL
 from script_generator.constants import CLASS_COLORS
 from script_generator.gui.messages.messages import ProgressMessage
 from script_generator.utils.file import get_output_file_path
@@ -64,6 +65,8 @@ def analyze_tracking_results(state, results, progress_callback=None):
     # Start time for ETA calculation
     start_time = time.time()
 
+    last_ui_update_time = time.time()
+
     for frame_pos in tqdm(range(state.frame_start, state.frame_end), unit="f"):
         if frame_pos in cuts:
             # Reinitialize the tracker at scene cuts
@@ -174,17 +177,20 @@ def analyze_tracking_results(state, results, progress_callback=None):
 
         # Update progress
         if state.update_ui:
-            elapsed_time = time.time() - start_time
-            frames_processed = frame_pos - state.frame_start + 1
-            frames_remaining = state.frame_end - frame_pos - 1
-            eta = (elapsed_time / frames_processed) * frames_remaining if frames_processed > 0 else 0
-
-            state.update_ui(ProgressMessage(
-                process="TRACKING_ANALYSIS",
-                frames_processed=frames_processed,
-                total_frames=state.frame_end,
-                eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
-            ))
+            current_time = time.time()
+            if current_time - last_ui_update_time >= UPDATE_PROGRESS_INTERVAL:
+                elapsed_time = current_time - start_time
+                frames_processed = frame_pos - state.frame_start + 1
+                frames_remaining = state.frame_end - frame_pos - 1
+                eta = (elapsed_time / frames_processed) * frames_remaining if frames_processed > 0 else 0
+
+                state.update_ui(ProgressMessage(
+                    process="TRACKING_ANALYSIS",
+                    frames_processed=frames_processed,
+                    total_frames=state.frame_end,
+                    eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
+                ))
+                last_ui_update_time = current_time
 
         # TODO improve this
         # if progress_callback:

From 3a189171a54a41439c2fc8317ca63e9ee7c9ceeb Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 19 Jan 2025 22:40:47 +0100
Subject: [PATCH 078/277] Progress

---
 .../gui/views/funscript_generator.py          |  6 +++-
 .../analye_tracking_results.py                |  2 +-
 utils/lib_SceneCutsDetect.py                  | 29 ++++++++++++-------
 3 files changed, 24 insertions(+), 13 deletions(-)

diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 4ed67ddd..48e08acb 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -60,7 +60,11 @@ def __init__(self, parent, controller):
         yolo_p_container, yolo_p, yolo_p_label, yolo_p_perc = Widgets.labeled_progress(processing, "YOLO Detection", row=0)
         scene_p_container, scene_p, scene_p_label, scene_p_perc = Widgets.labeled_progress(processing, "Scene detection", row=1)
         track_p_container, track_p, track_p_label, track_p_perc = Widgets.labeled_progress(processing, "Tracking Analysis", row=2)
-        Widgets.button(processing, "Start processing", lambda: video_analysis(state), row=3)
+
+        def start_processing():
+            # TODO reset the progress bars
+            video_analysis(state)
+        Widgets.button(processing, "Start processing", start_processing, row=3)
         #endregion
 
         #region FUNSCRIPT TWEAKING
diff --git a/script_generator/object_detection/analye_tracking_results.py b/script_generator/object_detection/analye_tracking_results.py
index 6705a767..4f22f3ed 100644
--- a/script_generator/object_detection/analye_tracking_results.py
+++ b/script_generator/object_detection/analye_tracking_results.py
@@ -179,6 +179,7 @@ def analyze_tracking_results(state, results, progress_callback=None):
         if state.update_ui:
             current_time = time.time()
             if current_time - last_ui_update_time >= UPDATE_PROGRESS_INTERVAL:
+                last_ui_update_time = current_time
                 elapsed_time = current_time - start_time
                 frames_processed = frame_pos - state.frame_start + 1
                 frames_remaining = state.frame_end - frame_pos - 1
@@ -190,7 +191,6 @@ def analyze_tracking_results(state, results, progress_callback=None):
                     total_frames=state.frame_end,
                     eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
                 ))
-                last_ui_update_time = current_time
 
         # TODO improve this
         # if progress_callback:
diff --git a/utils/lib_SceneCutsDetect.py b/utils/lib_SceneCutsDetect.py
index e90e679d..4b6088c2 100644
--- a/utils/lib_SceneCutsDetect.py
+++ b/utils/lib_SceneCutsDetect.py
@@ -3,6 +3,7 @@
 import cv2
 from tqdm import tqdm
 
+from config import UPDATE_PROGRESS_INTERVAL
 from script_generator.gui.messages.messages import ProgressMessage
 from script_generator.state.app_state import AppState
 
@@ -51,6 +52,7 @@ def detect_scene_changes(state: AppState, crop=None, threshold=0.97, frame_start
     prev_hist = None
     prev_cut = frame_start
     start_time = time.time()
+    last_ui_update_time = time.time()
 
     # Process frames
     for frame_pos in range(total_frames_to_parse): # tqdm(range(total_frames_to_parse), desc="Detecting scene changes"):
@@ -84,17 +86,22 @@ def detect_scene_changes(state: AppState, crop=None, threshold=0.97, frame_start
 
         # Update progress
         if state.update_ui:
-            elapsed_time = time.time() - start_time
-            frames_processed = frame_pos - state.frame_start + 1
-            frames_remaining = state.frame_end - frame_pos - 1
-            eta = (elapsed_time / frames_processed) * frames_remaining if frames_processed > 0 else 0
-
-            state.update_ui(ProgressMessage(
-                process="SCENE_DETECTION",
-                frames_processed=frames_processed,
-                total_frames=state.frame_end,
-                eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
-            ))
+
+            current_time = time.time()
+            if current_time - last_ui_update_time >= UPDATE_PROGRESS_INTERVAL:
+                last_ui_update_time = time.time()
+                elapsed_time = time.time() - start_time
+                frames_processed = frame_pos - state.frame_start + 1
+                frames_remaining = state.frame_end - frame_pos - 1
+                eta = (elapsed_time / frames_processed) * frames_remaining if frames_processed > 0 else 0
+
+                state.update_ui(ProgressMessage(
+                    process="SCENE_DETECTION",
+                    frames_processed=frames_processed,
+                    total_frames=state.frame_end,
+                    eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
+                ))
+
 
     # Add the last scene
     if not scene_changes:

From cf61df83de5414ca1cbcc288833b59ea47168d78 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 19 Jan 2025 22:55:35 +0100
Subject: [PATCH 079/277] Added Mac width setting

---
 script_generator/gui/app.py  | 3 ++-
 utils/lib_SceneCutsDetect.py | 2 +-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index d3a86750..b7628727 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -5,6 +5,7 @@
 from script_generator.constants import LOGO, ICON
 from script_generator.gui.views.funscript_generator import FunscriptGeneratorPage
 from script_generator.state.app_state import AppState
+from script_generator.utils.helpers import is_mac
 
 # TODO this is a workaround and needs to be fixed properly
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
@@ -16,7 +17,7 @@ def __init__(self):
             ctypes.windll.shcore.SetProcessDpiAwareness(2)  # For Windows DPI scaling
         # self.tk.call('tk', 'scaling', 1.0)
         self.title("VR funscript generation")
-        self.geometry("700x950")
+        self.geometry(f"{('800' if is_mac() else '700')}x950")
         self.resizable(False, False)
 
         self.iconphoto(False, tk.PhotoImage(file=LOGO))
diff --git a/utils/lib_SceneCutsDetect.py b/utils/lib_SceneCutsDetect.py
index 4b6088c2..e1c1be14 100644
--- a/utils/lib_SceneCutsDetect.py
+++ b/utils/lib_SceneCutsDetect.py
@@ -55,7 +55,7 @@ def detect_scene_changes(state: AppState, crop=None, threshold=0.97, frame_start
     last_ui_update_time = time.time()
 
     # Process frames
-    for frame_pos in range(total_frames_to_parse): # tqdm(range(total_frames_to_parse), desc="Detecting scene changes"):
+    for frame_pos in tqdm(range(total_frames_to_parse), desc="Detecting scene changes"):
         cap.set(cv2.CAP_PROP_POS_FRAMES, frame_start + frame_pos * frame_step)
         ret, frame = cap.read()
         if not ret:

From a2a2d2a1b6a7a60096499c5b5af500d153e42600 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 19 Jan 2025 23:14:09 +0100
Subject: [PATCH 080/277] Disabled scene detection again

---
 script_generator/object_detection/analye_tracking_results.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/script_generator/object_detection/analye_tracking_results.py b/script_generator/object_detection/analye_tracking_results.py
index 4f22f3ed..5e89b9a2 100644
--- a/script_generator/object_detection/analye_tracking_results.py
+++ b/script_generator/object_detection/analye_tracking_results.py
@@ -29,11 +29,14 @@ def analyze_tracking_results(state, results, progress_callback=None):
 
     image_area = video_info.width * video_info.height
 
+    cuts = []
+
     if state.life_display_mode:
         reader.set(cv2.CAP_PROP_POS_FRAMES, state.frame_start)
     else:
         reader.release()
 
+    """ discarding the scene detection for now
     # Load scene cuts if the file exists
     cuts_path, _ = get_output_file_path(state.video_path, "_cuts.json")
     if os.path.exists(cuts_path):
@@ -58,6 +61,7 @@ def analyze_tracking_results(state, results, progress_callback=None):
         # Save the cuts to a file
         with open(cuts_path, 'w') as f:
             json.dump(cuts, f)
+    """
 
     state.funscript_frames = []  # List to store Funscript frames
     tracker = ObjectTracker(fps, state.frame_start, image_area, video_info.is_vr)  # Initialize the object tracker

From adc6c3f180bb58a1aeeb9a37d9161a51f9dc43ad Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 20 Jan 2025 09:31:29 +0100
Subject: [PATCH 081/277] Re-merged some changes that didn't come over

---
 utils/lib_Debugger.py         | 34 +++++++++++++++++-
 utils/lib_FunscriptHandler.py | 67 ++++++++++++++++++-----------------
 2 files changed, 68 insertions(+), 33 deletions(-)

diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 2346e0e8..b2d4705b 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -1,4 +1,7 @@
 import json
+import os
+import subprocess
+
 import cv2
 import numpy as np
 from script_generator.constants import CLASS_COLORS
@@ -236,7 +239,6 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
 
             # Record the frame if enabled
             if record:
-                frame_copy = cv2.resize(frame_copy, (frame_copy.shape[1] // downsize_ratio, frame_copy.shape[0] // downsize_ratio))
                 out.write(frame_copy)
 
             self.current_frame += 1
@@ -245,6 +247,36 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
         self.cap.release()
         if record:
             out.release()
+
+            # TODO re-enable? (didn't have time to debug yet)
+            # # Input and output paths
+            # input_path = output_path
+            # # Add "SPOILER_" to the filename
+            # directory, filename = os.path.split(input_path)
+            # new_filename = f"SPOILER_{filename}"
+            # output_path_ffmpeg = os.path.join(directory, new_filename)
+            #
+            # # FFmpeg command to convert to H.265
+            # ffmpeg_command = [
+            #     "ffmpeg",
+            #     "-y",  # Overwrite output file if it exists
+            #     "-i", input_path,  # Input file
+            #     "-c:v", "libx265",  # Use H.265 codec
+            #     "-crf", "26",  # Constant Rate Factor (quality)
+            #     "-preset", "fast",  # Encoding speed
+            #     "-b:v", "5000k",  # Bitrate
+            #     "-movflags", "+faststart",  # Enable fast streaming
+            #     output_path_ffmpeg
+            # ]
+            #
+            # # Run FFmpeg command
+            # subprocess.run(ffmpeg_command)
+            #
+            # # Delete the intermediate file
+            # if os.path.exists(input_path):
+            #     os.remove(input_path)
+            #     print(f"Deleted intermediate file: {input_path}")
+
         cv2.destroyAllWindows()
 
     def _get_funscript_value(self, interpolator, frame_id, fps):
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 23118452..b7c70381 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -15,6 +15,7 @@
 import matplotlib
 
 from script_generator.utils.file import get_output_file_path
+from script_generator.utils.logger import logger
 
 matplotlib.use('Agg')  # Use a non-interactive backend
 import matplotlib.pyplot as plt
@@ -30,31 +31,31 @@ def generate(self, state):
 
         # Backup output file if it exists
         if os.path.exists(output_path):
-            print(f"Funscript {output_path} already exists, backing up as {output_path}_{time.time()}.bak...")
+            logger.info(f"Funscript {output_path} already exists, backing up as {output_path}_{time.time()}.bak...")
             os.rename(output_path, output_path + f"_{time.time()}.bak")
 
         raw_funscript_path, _ = get_output_file_path(state.video_path, "_rawfunscript.json")
         if os.path.exists(raw_funscript_path) and (state.funscript_data is None or len(state.funscript_data) == 0):
-            print("len funscript data is 0, trying to load file")
+            logger.info("len funscript data is 0, trying to load file")
             # Read the funscript data from the JSON file
             with open(raw_funscript_path, 'r') as f:
-                print(f"Loading funscript from {raw_funscript_path}")
+                logger.info(f"Loading funscript from {raw_funscript_path}")
                 try:
                     data = json.load(f)
                 except Exception as e:
-                    print(f"Error loading funscript from {raw_funscript_path}: {e}")
+                    logger.error(f"Error loading funscript from {raw_funscript_path}: {e}")
                     return
         else:
             data = state.funscript_data
 
         try:
-            print(f"Generating funscript based on {len(data)} points...")
+            logger.info(f"Generating funscript based on {len(data)} points...")
 
             # Extract timestamps and positions
             ats = [p[0] for p in data]
             positions = [p[1] for p in data]
 
-            print(f"Positions adjustment - step 1 (noise removal)")
+            logger.info(f"Positions adjustment - step 1 (noise removal)")
             # Run the Savitzky-Golay filter
             positions = savgol_filter(positions, int(state.video_info.fps) // 4, 3)
 
@@ -63,23 +64,23 @@ def generate(self, state):
 
             # Apply VW simplification if enabled
             if state.vw_simplification_enabled:
-                print("Positions adjustment - step 2 (VW algorithm simplification)")
+                logger.info("Positions adjustment - step 2 (VW algorithm simplification)")
                 zip_vw_positions = simplify_coords(zip_positions, state.vw_factor)
-                print(f"Length of VW filtered positions: {len(zip_vw_positions)}")
+                logger.info(f"Length of VW filtered positions: {len(zip_vw_positions)}")
             else:
-                print("Skipping positions adjustment - step 2 (VW algorithm simplification)")
+                logger.info("Skipping positions adjustment - step 2 (VW algorithm simplification)")
 
             # Extract timestamps and positions
             ats = [p[0] for p in zip_vw_positions]
             positions = [p[1] for p in zip_vw_positions]
 
             # Remap positions to 0-100 range
-            print("Positions adjustment - step 3 (remapping)")
+            logger.info("Positions adjustment - step 3 (remapping)")
             adjusted_positions = np.interp(positions, (min(positions), max(positions)), (0, 100))
 
             # Apply thresholding
             if state.threshold_enabled:
-                print(f"Positions adjustment - step 4 (thresholding)")
+                logger.info(f"Positions adjustment - step 4 (thresholding)")
                 adjusted_positions = adjusted_positions.tolist()  # Convert to list
                 adjusted_positions = [
                     0 if p < state.threshold_low else 100 if p > state.threshold_high else p for p in
@@ -89,7 +90,7 @@ def generate(self, state):
 
             # Apply amplitude boosting
             if state.boost_enabled:
-                print("Positions adjustment - step 5 (amplitude boosting)")
+                logger.info("Positions adjustment - step 5 (amplitude boosting)")
                 # self.boost_amplitude(adjusted_positions, boost_factor=1.2, min_value=0, max_value=100)
                 adjusted_positions = self.adjust_peaks_and_lows(
                     adjusted_positions,
@@ -97,25 +98,25 @@ def generate(self, state):
                     low_reduction=state.boost_down_percent
                 )
             else:
-                print("Skipping positions adjustment - step 5 (amplitude boosting)")
+                logger.info("Skipping positions adjustment - step 5 (amplitude boosting)")
 
             # Round position values to the closest multiple of 5, still between 0 and 100
-            print("Positions adjustment - step 6 (rounding to the closest multiple of 5)")
+            logger.info("Positions adjustment - step 6 (rounding to the closest multiple of 5)")
             # adjusted_positions = [round(p, -1) for p in adjusted_positions] # multiple of 10
             adjusted_positions = [round(p / state.rounding) * state.rounding for p in adjusted_positions]
 
             # Recombine timestamps and adjusted positions
-            print("Re-assembling ats and positions")
+            logger.info("Re-assembling ats and positions")
             zip_adjusted_positions = list(zip(ats, adjusted_positions))
 
             # Write the final funscript
             self.write_funscript(zip_adjusted_positions, output_path, state.video_info.fps)
-            print(f"Funscript generated and saved to {output_path}")
+            logger.info(f"Funscript generated and saved to {output_path}")
 
             # Generate a heatmap
             self.generate_heatmap(output_path, output_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
         except Exception as e:
-            print(f"Error generating funscript: {e}")
+            logger.error(f"Error generating funscript: {e}")
 
     def write_funscript(self, distances, output_path, fps):
         with open(output_path, 'w') as f:
@@ -133,7 +134,7 @@ def generate_heatmap(self, funscript_path, output_image_path):
         # Load funscript data
         times, positions, _, _ = self.load_funscript(funscript_path)
         if not times or not positions:
-            print("Failed to load funscript data.")
+            logger.info("Failed to load funscript data.")
             return
 
         # add a timing: 0, position: 100 at the beginning if no value for 0
@@ -144,8 +145,8 @@ def generate_heatmap(self, funscript_path, output_image_path):
         # Print loaded data for debugging
         # print(f"Times: {times}")
         # print(f"Positions: {positions}")
-        print(f"Total Actions: {len(times)}")
-        print(f"Time Range: {times[0]} to {datetime.timedelta(seconds=int(times[-1] / 1000))}")
+        logger.info(f"Total Actions: {len(times)}")
+        logger.info(f"Time Range: {times[0]} to {datetime.timedelta(seconds=int(times[-1] / 1000))}")
 
         # Calculate speed (position change per time interval)
         valid_indices = ~np.isnan(times) & ~np.isnan(positions)
@@ -217,7 +218,7 @@ def get_color(intensity):
         # Save the figure
         plt.savefig(output_image_path, bbox_inches='tight', dpi=200)  # Increase resolution
         plt.close()
-        print(f"Funscript heatmap saved to {output_image_path}")
+        logger.info(f"Funscript heatmap saved to {output_image_path}")
 
     def boost_amplitude(self, signal, boost_factor, min_value=0, max_value=100):
         """
@@ -312,19 +313,19 @@ def filter_positions(self, positions, fps):
     def load_funscript(self, funscript_path):
         # if the funscript path exists
         if not os.path.exists(funscript_path):
-            print(f"Funscript not found at {funscript_path}")
+            logger.error(f"Funscript not found at {funscript_path}")
             return None, None, None, None
 
         with open(funscript_path, 'r') as f:
             try:
-                print(f"Loading funscript from {funscript_path}")
+                logger.info(f"Loading funscript from {funscript_path}")
                 data = json.load(f)
             except json.JSONDecodeError as e:
-                print(f"JSONDecodeError: {e}")
-                print(f"Error occurred at line {e.lineno}, column {e.colno}")
-                print("Dumping the problematic JSON data:")
+                logger.error(f"JSONDecodeError: {e}")
+                logger.error(f"Error occurred at line {e.lineno}, column {e.colno}")
+                logger.error("Dumping the problematic JSON data:")
                 f.seek(0)  # Reset file pointer to the beginning
-                print(f.read())
+                logger.error(f.read())
                 return None, None, None, None
 
         times = []
@@ -333,7 +334,7 @@ def load_funscript(self, funscript_path):
         for action in data['actions']:
             times.append(action['at'])
             positions.append(action['pos'])
-        print(f"Loaded funscript with {len(times)} actions")
+        logger.info(f"Loaded funscript with {len(times)} actions")
 
         # Access the chapters
         chapters = data.get("metadata", {}).get("chapters", [])
@@ -511,8 +512,7 @@ def create_combined_plot(self, ref_sections, gen_sections, screenshots, sections
             f"Generated:\n"
             f"Number of Strokes: {gen_metrics['num_strokes']}\n"
             f"Avg Stroke Duration: {gen_metrics['avg_stroke_duration']:.2f}s\n"
-            # TODO re-enable
-            # f"Avg Speed: {int(gen_metrics['avg_speed'])} positions/s\n"
+            f"Avg Speed: {int(gen_metrics['avg_speed'])} positions/s\n"
             f"Avg Depth of Stroke: {int(gen_metrics['avg_depth'])}\n"
             f"Avg Max: {int(gen_metrics['avg_max'])}\n"
             f"Avg Min: {int(gen_metrics['avg_min'])}"
@@ -554,8 +554,11 @@ def create_combined_plot(self, ref_sections, gen_sections, screenshots, sections
         plt.tight_layout()
 
         # Save the figure
+        # Add "SPOILER_" to the filename
+        directory, filename = os.path.split(output_image_path)
+        new_filename = f"SPOILER_{filename}"
+        output_image_path = os.path.join(directory, new_filename)
         plt.savefig(output_image_path[:-4] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png", dpi=100)
-        # plt.show()
 
     def generate_heatmap_inline(self, ax, times, positions):
         """
@@ -634,7 +637,7 @@ def get_color(intensity):
             avg_speed = 0  # Default to 0 if speeds are empty or contain NaN
         else:
             avg_speed = int(np.nanmean(speeds))  # Use nanmean to ignore NaN values
-
+        # TODO fix nan at source END
         # Set title with safe average speed
         ax.set_title(
             f'Funscript Heatmap\nDuration: {datetime.timedelta(seconds=int(times[-1] / 1000))} - Avg. Speed {avg_speed} - Actions: {len(times)}'

From 0b413309faf34268abc4c4b207522a9edead9ed5 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 20 Jan 2025 10:07:51 +0100
Subject: [PATCH 082/277] Better logger

---
 config.py                                     |  5 ++--
 script_generator/cli/generate_funscript.py    |  7 +++---
 script_generator/gui/app.py                   |  3 ++-
 .../gui/controller/regenerate_funscript.py    | 13 ++++++-----
 .../gui/controller/tracking_analysis.py       |  9 ++++----
 .../gui/controller/video_analysis.py          | 16 ++++++-------
 .../gui/views/funscript_generator.py          |  7 +++---
 .../analye_tracking_results.py                | 13 ++++++-----
 .../object_detection/post_process_results.py  | 21 +++++++++--------
 script_generator/object_detection/utils.py    |  5 ++--
 script_generator/scripts/analyse_video.py     | 12 ++++++----
 .../tasks/abstract_task_processor.py          |  5 ++--
 script_generator/utils/config.py              | 18 ++++++++-------
 script_generator/utils/file.py                |  7 +++---
 script_generator/utils/logger.py              | 21 +++++++++++------
 script_generator/video/ffmpeg.py              |  5 ++--
 .../video/video_task_processor.py             |  3 ++-
 tests/test_Compare_models_and_resolutions.py  |  4 +++-
 tests/test_PoseEstimation.py                  |  3 ++-
 utils/lib_Debugger.py                         | 23 ++++++++++---------
 utils/lib_FunscriptHandler.py                 | 10 ++++----
 utils/lib_SceneCutsDetect.py                  | 11 +++++----
 utils/lib_VideoReaderFFmpeg.py                | 11 +++++----
 23 files changed, 132 insertions(+), 100 deletions(-)

diff --git a/config.py b/config.py
index deb249dd..d86277ba 100644
--- a/config.py
+++ b/config.py
@@ -4,6 +4,7 @@
 
 from script_generator.constants import MODEL_PATH
 from script_generator.utils.config import find_ffmpeg_path, find_ffprobe_path
+from script_generator.utils.logger import logger
 
 VERSION = "0.0.1_25-01-16"
 
@@ -59,7 +60,7 @@
 
 FFMPEG_PATH = find_ffmpeg_path(win_ffmpeg_path, mac_ffmpeg_path, lin_ffmpeg_path)
 FFPROBE_PATH = find_ffprobe_path(win_ffprobe_path, mac_ffprobe_path, lin_ffprobe_path)
-print(f"ffmpeg_path: {FFMPEG_PATH}")
-print(f"ffprobe_path: {FFPROBE_PATH}")
+logger.info(f"ffmpeg_path: {FFMPEG_PATH}")
+logger.info(f"ffprobe_path: {FFPROBE_PATH}")
 
 
diff --git a/script_generator/cli/generate_funscript.py b/script_generator/cli/generate_funscript.py
index a516dd6e..5cc8d58c 100644
--- a/script_generator/cli/generate_funscript.py
+++ b/script_generator/cli/generate_funscript.py
@@ -1,5 +1,6 @@
 import argparse
 from script_generator.scripts.generate_funscript import generate_funscript
+from script_generator.utils.logger import logger
 
 
 def main():
@@ -14,11 +15,11 @@ def main():
     args = parser.parse_args()
 
     try:
-        print(f"Processing video: {args.video_path}")
+        logger.info(f"Processing video: {args.video_path}")
         generate_funscript(args.video_path)
-        print("Funscript generation complete.")
+        logger.info("Funscript generation complete.")
     except Exception as e:
-        print(f"An error occurred: {e}")
+        logger.error(f"An error occurred: {e}")
 
 
 if __name__ == "__main__":
diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index b7628727..91a9da1b 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -6,6 +6,7 @@
 from script_generator.gui.views.funscript_generator import FunscriptGeneratorPage
 from script_generator.state.app_state import AppState
 from script_generator.utils.helpers import is_mac
+from script_generator.utils.logger import logger
 
 # TODO this is a workaround and needs to be fixed properly
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
@@ -44,7 +45,7 @@ def show_frame(self, page_name):
                 self.frames[page_name] = frame
                 frame.grid(row=0, column=0, sticky="nsew")
             else:
-                print(f"Page '{page_name}' not found!")
+                logger.info(f"Page '{page_name}' not found!")
                 return
 
         # Show the requested page
diff --git a/script_generator/gui/controller/regenerate_funscript.py b/script_generator/gui/controller/regenerate_funscript.py
index b3150100..29872325 100644
--- a/script_generator/gui/controller/regenerate_funscript.py
+++ b/script_generator/gui/controller/regenerate_funscript.py
@@ -1,5 +1,6 @@
 from tkinter import messagebox
 
+from script_generator.utils.logger import logger
 from utils.lib_FunscriptHandler import FunscriptGenerator
 
 
@@ -8,19 +9,19 @@ def regenerate_funscript(state):
         messagebox.showerror("Error", "Please select a video file.")
         return
 
-    print("Regenerating Funscript with tweaked settings...")
+    logger.info("Regenerating Funscript with tweaked settings...")
 
     # Apply tweaks to funscript_data
     if state.boost_enabled:
-        print(f"Applying Boost: Up {state.boost_up_percent}%, Down {state.boost_down_percent}%")
+        logger.info(f"Applying Boost: Up {state.boost_up_percent}%, Down {state.boost_down_percent}%")
         # Add boost logic here
 
     if state.threshold_enabled:
-        print(f"Applying Threshold: Low {state.threshold_low}, High {state.threshold_high}")
+        logger.info(f"Applying Threshold: Low {state.threshold_low}, High {state.threshold_high}")
         # Add threshold logic here
 
     if state.vw_simplification_enabled:
-        print(f"Applying VW Simplification with Factor: {state.vw_factor} then rounding to {state.rounding}")
+        logger.info(f"Applying VW Simplification with Factor: {state.vw_factor} then rounding to {state.rounding}")
         # Add VW simplification logic here
 
     # Save and regenerate funscript
@@ -29,9 +30,9 @@ def regenerate_funscript(state):
     # Simplifying the funscript data and generating the file
     funscript_handler.generate(state)
 
-    print("Funscript re-generation complete.")
+    logger.info("Funscript re-generation complete.")
 
     # Optional, compare generated funscript with reference funscript if specified, or a simple generic report
     funscript_handler.create_report_funscripts(state)
 
-    print("Report generation complete.")
\ No newline at end of file
+    logger.info("Report generation complete.")
\ No newline at end of file
diff --git a/script_generator/gui/controller/tracking_analysis.py b/script_generator/gui/controller/tracking_analysis.py
index f7bbedab..e3dcaf06 100644
--- a/script_generator/gui/controller/tracking_analysis.py
+++ b/script_generator/gui/controller/tracking_analysis.py
@@ -5,6 +5,7 @@
 from script_generator.object_detection.object_detection_result import ObjectDetectionResult
 from script_generator.object_detection.utils import make_data_boxes, parse_yolo_data_looking_for_penis
 from script_generator.utils.file import load_json_from_file, get_output_file_path
+from script_generator.utils.logger import logger
 from script_generator.video.video_info import get_cropped_dimensions
 from utils.lib_FunscriptHandler import FunscriptGenerator
 
@@ -13,7 +14,7 @@ def tracking_analysis(state):
     # Load YOLO detection results from file
     raw_yolo_path, _ = get_output_file_path(state.video_path, "_rawyolo.json")
     if not os.path.exists(raw_yolo_path):
-        print(f"Raw yolo json not found in {raw_yolo_path}")
+        logger.info(f"Raw yolo json not found in {raw_yolo_path}")
         return
 
     yolo_data = load_json_from_file(raw_yolo_path)
@@ -27,7 +28,7 @@ def tracking_analysis(state):
     first_penis_frame = parse_yolo_data_looking_for_penis(yolo_data, 0)
 
     if first_penis_frame is None:
-        print(f"No penis found in video: {state.video_path}")
+        logger.warning(f"No penis found in video: {state.video_path}")
         first_penis_frame = 0
 
     # Deciding whether we start from there or from a user-specified later frame
@@ -35,7 +36,7 @@ def tracking_analysis(state):
 
     state.frame_end = state.video_info.total_frames if not state.frame_end else state.frame_end
 
-    print(f"Frame Start adjusted to: {state.frame_start}")
+    logger.info(f"Frame Start adjusted to: {state.frame_start}")
 
     # Performing the tracking part and generation of the raw funscript data
     state.funscript_data = analyze_tracking_results(state, results, progress_callback=lambda: debug_video(state))
@@ -50,4 +51,4 @@ def tracking_analysis(state):
     # Optionally, compare generated funscript with reference funscript if specified, or a simple generic report
     funscript_handler.create_report_funscripts(state)
 
-    print(f"Finished processing video: {state.video_path}")
+    logger.info(f"Finished processing video: {state.video_path}")
diff --git a/script_generator/gui/controller/video_analysis.py b/script_generator/gui/controller/video_analysis.py
index e2825710..d3bcfd0d 100644
--- a/script_generator/gui/controller/video_analysis.py
+++ b/script_generator/gui/controller/video_analysis.py
@@ -1,5 +1,4 @@
 import threading
-from datetime import time
 from tkinter import messagebox
 
 from script_generator.gui.controller.tracking_analysis import tracking_analysis
@@ -7,6 +6,7 @@
 from script_generator.object_detection.utils import check_skip_object_detection
 from script_generator.scripts.analyse_video import analyse_video
 from script_generator.utils.file import get_output_file_path
+from script_generator.utils.logger import logger
 from utils.lib_Debugger import Debugger
 
 
@@ -15,13 +15,13 @@ def video_analysis(state):
         messagebox.showerror("Error", "Please select a video file.")
         return
 
-    print(f"Processing video: {state.video_path}")
-    print(f"Video Reader: {state.video_reader}")
-    print(f"Debug Mode: {state.debug_mode}")
-    print(f"Live Display Mode: {state.life_display_mode}")
-    print(f"Frame Start: {state.frame_start}")
-    print(f"Frame End: {state.frame_end}")
-    print(f"Reference Script: {state.reference_script}")
+    logger.info(f"Processing video: {state.video_path}")
+    logger.info(f"Video Reader: {state.video_reader}")
+    logger.info(f"Debug Mode: {state.debug_mode}")
+    logger.info(f"Live Display Mode: {state.life_display_mode}")
+    logger.info(f"Frame Start: {state.frame_start}")
+    logger.info(f"Frame End: {state.frame_end}")
+    logger.info(f"Reference Script: {state.reference_script}")
 
     def run():
         # Initialize the debugger
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 48e08acb..4af027d6 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -7,6 +7,7 @@
 from script_generator.gui.utils.widgets import Widgets
 from script_generator.state.app_state import AppState
 from script_generator.utils.helpers import is_mac
+from script_generator.utils.logger import logger
 
 
 class FunscriptGeneratorPage(tk.Frame):
@@ -58,7 +59,7 @@ def __init__(self, parent, controller):
         #region PROCESSING
         processing = Widgets.frame(wrapper, title="Processing", main_section=True, row=3)
         yolo_p_container, yolo_p, yolo_p_label, yolo_p_perc = Widgets.labeled_progress(processing, "YOLO Detection", row=0)
-        scene_p_container, scene_p, scene_p_label, scene_p_perc = Widgets.labeled_progress(processing, "Scene detection", row=1)
+        # scene_p_container, scene_p, scene_p_label, scene_p_perc = Widgets.labeled_progress(processing, "Scene detection", row=1)
         track_p_container, track_p, track_p_label, track_p_perc = Widgets.labeled_progress(processing, "Tracking Analysis", row=2)
 
         def start_processing():
@@ -195,12 +196,12 @@ def process_update():
                 if handler:
                     handler(msg)
                 else:
-                    print(f"Unhandled message type: {type(msg)}")
+                    logger.info(f"Unhandled message type: {type(msg)}")
 
             def handle_progress_message(progress_msg: ProgressMessage):
                 progress_mapping = {
                     "OBJECT_DETECTION": (yolo_p, yolo_p_perc),
-                    "SCENE_DETECTION": (scene_p, scene_p_perc),
+                    # "SCENE_DETECTION": (scene_p, scene_p_perc),
                     "TRACKING_ANALYSIS": (track_p, track_p_perc),
                 }
 
diff --git a/script_generator/object_detection/analye_tracking_results.py b/script_generator/object_detection/analye_tracking_results.py
index 5e89b9a2..e3e4dda0 100644
--- a/script_generator/object_detection/analye_tracking_results.py
+++ b/script_generator/object_detection/analye_tracking_results.py
@@ -11,6 +11,7 @@
 from script_generator.constants import CLASS_COLORS
 from script_generator.gui.messages.messages import ProgressMessage
 from script_generator.utils.file import get_output_file_path
+from script_generator.utils.logger import logger
 from script_generator.video.video_info import get_cropped_dimensions
 from utils.lib_ObjectTracker import ObjectTracker
 from utils.lib_SceneCutsDetect import detect_scene_changes
@@ -40,10 +41,10 @@ def analyze_tracking_results(state, results, progress_callback=None):
     # Load scene cuts if the file exists
     cuts_path, _ = get_output_file_path(state.video_path, "_cuts.json")
     if os.path.exists(cuts_path):
-        print(f"Loading cuts from {cuts_path}")
+        logger.info(f"Loading cuts from {cuts_path}")
         with open(cuts_path, 'r') as f:
             cuts = json.load(f)
-        print(f"Loaded {len(cuts)} cuts : {cuts}")
+        logger.info(f"Loaded {len(cuts)} cuts : {cuts}")
 
         if state.update_ui:
             state.update_ui(ProgressMessage(
@@ -55,7 +56,7 @@ def analyze_tracking_results(state, results, progress_callback=None):
     else:
         # Detect scene changes if the cuts file does not exist
         scene_list = detect_scene_changes(state, video_info.is_vr, 0.9, state.frame_start, state.frame_end)
-        print(f"Analyzing frames {state.frame_start} to {state.frame_end}")
+        logger.info(f"Analyzing frames {state.frame_start} to {state.frame_end}")
         cuts = [scene[1] for scene in scene_list]
         cuts = cuts[:-1]  # Remove the last entry
         # Save the cuts to a file
@@ -74,9 +75,9 @@ def analyze_tracking_results(state, results, progress_callback=None):
     for frame_pos in tqdm(range(state.frame_start, state.frame_end), unit="f"):
         if frame_pos in cuts:
             # Reinitialize the tracker at scene cuts
-            print(f"Reaching cut at frame {frame_pos}")
+            logger.info(f"Reaching cut at frame {frame_pos}")
             previous_distances = tracker.previous_distances
-            print(f"Reinitializing tracker with previous distances: {previous_distances}")
+            logger.info(f"Reinitializing tracker with previous distances: {previous_distances}")
             tracker = ObjectTracker(fps, frame_pos, image_area, video_info.is_vr)
             tracker.previous_distances = previous_distances
 
@@ -164,7 +165,7 @@ def analyze_tracking_results(state, results, progress_callback=None):
                     state.offset_x
                 )
             else:
-                print("No active locked penis box to draw.")
+                logger.info("No active locked penis box to draw.")
 
             if tracker.glans_detected:
                 frame_display = visualizer.draw_bounding_box(
diff --git a/script_generator/object_detection/post_process_results.py b/script_generator/object_detection/post_process_results.py
index 1b99f92c..f12513e5 100644
--- a/script_generator/object_detection/post_process_results.py
+++ b/script_generator/object_detection/post_process_results.py
@@ -8,6 +8,7 @@
 from script_generator.object_detection.object_detection_result import ObjectDetectionResult
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 from script_generator.utils.file import write_dataset, get_output_file_path
+from script_generator.utils.logger import logger
 
 
 class YoloAnalysisTaskProcessor(AbstractTaskProcessor):
@@ -53,10 +54,10 @@ def task_logic(self):
                     test_box = [[x1, y1, x2, y2], round(conf, 1), int(cls), CLASS_REVERSE_MATCH.get(int(cls), 'unknown'), track_id]
                     self.test_result.add_record(frame_pos, test_box)
 
-                    # Print and test the record
-                    # print(f"Record : {record}")
-                    # print(f"For class id: {int(cls)}, getting: {CLASS_REVERSE_MATCH.get(int(cls), 'unknown')}")
-                    # print(f"Test box: {test_box}")
+                    # print and test the record
+                    logger.trace(f"Record : {record}")
+                    logger.trace(f"For class id: {int(cls)}, getting: {CLASS_REVERSE_MATCH.get(int(cls), 'unknown')}")
+                    logger.trace(f"Test box: {test_box}")
 
             if RUN_POSE_MODEL:
                 ### POSE DETECTION - Hips and wrists
@@ -66,7 +67,7 @@ def task_logic(self):
 
                     # Check if keypoints are detected
                     if pose_results[0].keypoints is not None:
-                        # print("We have keypoints")
+                        # logger.trace("We have keypoints")
                         # pose_keypoints = pose_results[0].keypoints.cpu()
                         # pose_track_ids = pose_results[0].boxes.id.cpu().tolist()
                         # pose_boxes = pose_results[0].boxes.xywh.cpu()
@@ -85,18 +86,18 @@ def task_logic(self):
                         x2 = mid_hips[0] + 5
                         y2 = mid_hips[1] + 5
                         cls = 10  # hips center
-                        # print(f"pose_confs: {pose_confs}")
+                        # logger.trace(f"pose_confs: {pose_confs}")
                         conf = pose_confs[0]
 
                         record = [frame_pos, 10, round(conf, 1), x1, y1, x2, y2, 0]
                         self.records.append(record)
                         if state.life_display_mode:
                             # Print and test the record
-                            print(f"Record : {record}")
-                            print(f"For class id: {int(cls)}, getting: {CLASS_REVERSE_MATCH.get(int(cls), 'unknown')}")
+                            logger.debug(f"Record : {record}")
+                            logger.debug(f"For class id: {int(cls)}, getting: {CLASS_REVERSE_MATCH.get(int(cls), 'unknown')}")
                             test_box = [[x1, y1, x2, y2], round(conf, 1), int(cls),
                                         CLASS_REVERSE_MATCH.get(int(cls), 'unknown'), 0]
-                            print(f"Test box: {test_box}")
+                            logger.debug(f"Test box: {test_box}")
                             self.test_result.add_record(frame_pos, test_box)
 
             if state.life_display_mode:
@@ -106,7 +107,7 @@ def task_logic(self):
                 # cv2.waitKey(1)
                 # Verify the sorted boxes
                 sorted_boxes = self.test_result.get_boxes(frame_pos)
-                # print(f"Sorted boxes : {sorted_boxes}")
+                # logger.debug(f"Sorted boxes : {sorted_boxes}")
 
                 # frame_display = frame.copy()
                 frame_display = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
diff --git a/script_generator/object_detection/utils.py b/script_generator/object_detection/utils.py
index e18618c0..b6903adb 100644
--- a/script_generator/object_detection/utils.py
+++ b/script_generator/object_detection/utils.py
@@ -8,6 +8,7 @@
 from script_generator.object_detection.box_record import BoxRecord
 from script_generator.object_detection.object_detection_result import ObjectDetectionResult
 from script_generator.utils.file import get_output_file_path
+from script_generator.utils.logger import logger
 
 
 def check_skip_object_detection(state):
@@ -20,7 +21,7 @@ def check_skip_object_detection(state):
             "Generate New"
         )
         if skip_detection:
-            print(f"File {raw_yolo_path} already exists. Skipping detections and loading file content...")
+            logger.info(f"File {raw_yolo_path} already exists. Skipping detections and loading file content...")
             return True
         else:
             os.remove(raw_yolo_path)
@@ -74,5 +75,5 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
                 frame_detected = 0
 
             if consecutive_frames >= 2:
-                print(f"First instance of Glans/Penis found in frame {frame_idx - 4}")
+                logger.info(f"First instance of Glans/Penis found in frame {frame_idx - 4}")
                 return frame_idx - 4
\ No newline at end of file
diff --git a/script_generator/scripts/analyse_video.py b/script_generator/scripts/analyse_video.py
index aff721d8..b7d3d1dc 100644
--- a/script_generator/scripts/analyse_video.py
+++ b/script_generator/scripts/analyse_video.py
@@ -13,12 +13,13 @@
 from script_generator.state.app_state import AppState
 from script_generator.tasks.abstract_task_processor import TaskProcessorTypes
 from script_generator.tasks.tasks import AnalyseVideoTask, AnalyseFrameTask
+from script_generator.utils.logger import logger
 from script_generator.video.video_conversion.vr_to_2d_task_processor import VrTo2DTaskProcessor
 from script_generator.video.video_task_processor import VideoTaskProcessor
 
 
 def analyse_video(state: AppState) -> List[AnalyseFrameTask]:
-    print(f"[OBJECT DETECTION] Starting up pipeline with profiling in {'sequential mode' if SEQUENTIAL_MODE else 'parallel mode'}...")
+    logger.info(f"[OBJECT DETECTION] Starting up pipeline with profiling in {'sequential mode' if SEQUENTIAL_MODE else 'parallel mode'}...")
 
     use_open_gl = state.video_reader == "FFmpeg + OpenGL (Windows)"
 
@@ -54,7 +55,7 @@ def run_thread(thread, thread_name, out_queue):
             thread.start()
             thread.join()
             out_queue.put(None)
-            print(f"[OBJECT DETECTION] {thread_name} thread done in {time.time() - start_time} s")
+            logger.info(f"[OBJECT DETECTION] {thread_name} thread done in {time.time() - start_time} s")
 
         run_thread(decode_thread, TaskProcessorTypes.VIDEO, opengl_q)
         if use_open_gl:
@@ -122,7 +123,7 @@ def log_progress(state, opengl_q, yolo_q, analysis_q, results_q, stop_event):
                             eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
                         ))
                     except Exception as e:
-                        print(f"Error in state.update_ui: {e}")
+                        logger.error(f"Error in state.update_ui: {e}")
 
                 time.sleep(UPDATE_PROGRESS_INTERVAL)
     else:
@@ -132,7 +133,7 @@ def log_progress(state, opengl_q, yolo_q, analysis_q, results_q, stop_event):
             analysis_size = analysis_q.size()
             frames_processed = results_q.qsize()
             open_gl = f"OpenGL: {opengl_size:>3}, " if state.video_reader == "FFmpeg + OpenGL (Windows)" else ""
-            print(f"Queues: {open_gl}YOLO: {yolo_size:>3}, Analysis: {analysis_size:>3}, DONE: {frames_processed:>3}")
+            logger.info(f"Queues: {open_gl}YOLO: {yolo_size:>3}, Analysis: {analysis_size:>3}, DONE: {frames_processed:>3}")
 
             if frames_processed >= total_frames:
                 stop_event.set()
@@ -199,5 +200,8 @@ def log_performance(state, results_queue):
 
     log_message += f"{'-' * 60}\n"
 
+    for line in log_message.splitlines():
+        logger.info(line)
+
     print(Fore.LIGHTBLUE_EX + log_message)
     print(Style.RESET_ALL, end="")
diff --git a/script_generator/tasks/abstract_task_processor.py b/script_generator/tasks/abstract_task_processor.py
index 1d7d7174..e97e3112 100644
--- a/script_generator/tasks/abstract_task_processor.py
+++ b/script_generator/tasks/abstract_task_processor.py
@@ -4,6 +4,7 @@
 
 from script_generator.state.app_state import AppState
 from script_generator.tasks.tasks import AnalyseFrameTask
+from script_generator.utils.logger import logger
 
 
 class AbstractTaskProcessor(threading.Thread):
@@ -28,7 +29,7 @@ def log(self, message):
         :param message: Message to log.
         """
         thread_name = threading.current_thread().name
-        print(f"[{self.__class__.__name__}-{thread_name}] {message}")
+        logger.info(f"[{self.__class__.__name__}-{thread_name}] {message}")
 
     def get_task(self) -> Generator[AnalyseFrameTask, None, None]:
         """
@@ -74,7 +75,7 @@ def run(self):
             self.state.analyse_task.start(self.process_type)
             self.task_logic()
         except Exception as e:
-            print(f"An error occurred during task execution on thread {self.process_type}: {e}")
+            logger.error(f"An error occurred during task execution on thread {self.process_type}: {e}")
             import traceback
             traceback.print_exc()
         finally:
diff --git a/script_generator/utils/config.py b/script_generator/utils/config.py
index a9bf3adc..d1911032 100644
--- a/script_generator/utils/config.py
+++ b/script_generator/utils/config.py
@@ -3,13 +3,15 @@
 
 import torch
 
+from script_generator.utils.logger import logger
+
 
 def find_ffmpeg_path(win_ffmpeg_path, mac_ffmpeg_path, lin_ffmpeg_path):
     ffmpeg_path = shutil.which("ffmpeg")
     if ffmpeg_path:
         return ffmpeg_path
     else:
-        print("ffmpeg not found in PATH, defaulting to platform values in params/config.py")
+        logger.trace("ffmpeg not found in PATH, defaulting to platform values in params/config.py")
 
         if platform.system() == "Windows":
             return win_ffmpeg_path
@@ -40,20 +42,20 @@ def find_ffprobe_path(win_ffprobe_path, mac_ffprobe_path, lin_ffprobe_path):
 def get_yolo_model_path(YOLO_MODELS):
     # Check if the device is an Apple device
     if platform.system() == 'Darwin':
-        print(f"Apple device detected, loading {YOLO_MODELS[0]} for MPS inference.")
+        logger.info(f"Apple device detected, loading {YOLO_MODELS[0]} for MPS inference.")
         return YOLO_MODELS[0]
 
     # Check if CUDA is available (for GPU support)
     elif torch.cuda.is_available():
-        print(f"CUDA is available, loading {YOLO_MODELS[1]} for GPU inference.")
+        logger.info(f"CUDA is available, loading {YOLO_MODELS[1]} for GPU inference.")
         return YOLO_MODELS[1]
 
     # Fallback to ONNX model for other platforms without CUDA
     else:
-        print("CUDA not available, if this is unexpected, please install CUDA and check your version of torch.")
-        print("You might need to install a dependency with the following command (example):")
-        print("pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118")
-        print(f"Falling back to CPU inference, loading {YOLO_MODELS[2]}.")
-        print("WARNING: CPU inference may be slow on some devices.")
+        logger.info("CUDA not available, if this is unexpected, please install CUDA and check your version of torch.")
+        logger.info("You might need to install a dependency with the following command (example):")
+        logger.info("pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118")
+        logger.info(f"Falling back to CPU inference, loading {YOLO_MODELS[2]}.")
+        logger.info("WARNING: CPU inference may be slow on some devices.")
 
         return YOLO_MODELS[2]
diff --git a/script_generator/utils/file.py b/script_generator/utils/file.py
index 7e3f5e51..168d9136 100644
--- a/script_generator/utils/file.py
+++ b/script_generator/utils/file.py
@@ -3,6 +3,7 @@
 import time
 
 from script_generator.constants import OUTPUT_PATH
+from script_generator.utils.logger import logger
 
 
 def write_dataset(file_path, data):
@@ -11,13 +12,13 @@ def write_dataset(file_path, data):
     :param file_path: The path to the output file.
     :param data: The data to write.
     """
-    print(f"Exporting data...")
+    logger.info(f"Exporting data...")
     export_start = time.time()
     # Write the data to the file (overwrites if it exists)
     with open(file_path, 'w') as f:
         json.dump(data, f)
     export_end = time.time()
-    print(f"Done in {export_end - export_start} seconds.")
+    logger.info(f"Done in {export_end - export_start} seconds.")
 
 def load_json_from_file(file_path):
     """
@@ -27,7 +28,7 @@ def load_json_from_file(file_path):
     """
     with open(file_path, 'r') as f:
         data = json.load(f)
-        print(f"Loaded data from {file_path}, length: {len(data)}")
+        logger.info(f"Loaded data from {file_path}, length: {len(data)}")
     return data
 
 def get_output_file_path(video_path, suffix):
diff --git a/script_generator/utils/logger.py b/script_generator/utils/logger.py
index e3559f5e..616890dc 100644
--- a/script_generator/utils/logger.py
+++ b/script_generator/utils/logger.py
@@ -1,16 +1,23 @@
 import logging
 import sys
+from colorama import Fore, Style, init
+
+init(autoreset=True)
+
+class ColorizedStreamHandler(logging.StreamHandler):
+    def emit(self, record):
+        if record.levelno >= logging.ERROR:
+            record.msg = f"{Fore.RED}{record.msg}{Style.RESET_ALL}"
+        super().emit(record)
 
-# Configure the logger
 logging.basicConfig(
     level=logging.INFO,
-    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    format="%(asctime)s|%(levelname)s|%(name)s|%(message)s",
     handlers=[
-        logging.FileHandler("FSGenerator.log", mode="w"),  # Save logs to a file
-        logging.StreamHandler(sys.stdout)  # Print logs to the console
+        logging.FileHandler("FSGenerator.log", mode="w"),
+        ColorizedStreamHandler(sys.stdout)
     ]
 )
 
-# Create and configure the global logger
-logger = logging.getLogger("MainLogger")
-logger.info("Logger initialized with file and console handlers.")
\ No newline at end of file
+logger = logging.getLogger("General")
+logger.info("Logger initialized with file and console handlers.")
diff --git a/script_generator/video/ffmpeg.py b/script_generator/video/ffmpeg.py
index 418f2659..c99abe83 100644
--- a/script_generator/video/ffmpeg.py
+++ b/script_generator/video/ffmpeg.py
@@ -1,5 +1,6 @@
 import subprocess
 
+from script_generator.utils.logger import logger
 from script_generator.video.video_info import VideoInfo
 from config import FFPROBE_PATH, FFMPEG_PATH
 
@@ -38,7 +39,7 @@ def get_video_info(video_path):
 
         return VideoInfo(video_path, codec_name, width, height, duration, total_frames, fps, is_vr)
     except Exception as e:
-        print(f"Error initializing video info: {e}")
+        logger.error(f"Error initializing video info: {e}")
         raise
 
 def is_cuda_supported():
@@ -52,5 +53,5 @@ def is_cuda_supported():
         )
         return "cuda" in result.stdout.lower()
     except Exception as e:
-        print(f"Error checking CUDA support: {e}")
+        logger.error(f"Error checking CUDA support: {e}")
         return False
\ No newline at end of file
diff --git a/script_generator/video/video_task_processor.py b/script_generator/video/video_task_processor.py
index 7432e89a..179a92b6 100644
--- a/script_generator/video/video_task_processor.py
+++ b/script_generator/video/video_task_processor.py
@@ -6,6 +6,7 @@
 from config import SUBTRACT_THREADS_FROM_FFMPEG, PITCH, RENDER_RESOLUTION, FFMPEG_PATH
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 from script_generator.tasks.tasks import AnalyseFrameTask
+from script_generator.utils.logger import logger
 from script_generator.video.video_info import get_cropped_dimensions
 
 
@@ -104,7 +105,7 @@ def standard_video_filters():
                 # progress_bar.update(1)
 
             except Exception as e:
-                print(f"Error reading frame: {e}")
+                logger.error(f"Error reading frame: {e}")
                 return False, None
 
         self.stop_process()
diff --git a/tests/test_Compare_models_and_resolutions.py b/tests/test_Compare_models_and_resolutions.py
index 85fd2c90..8ef8468f 100644
--- a/tests/test_Compare_models_and_resolutions.py
+++ b/tests/test_Compare_models_and_resolutions.py
@@ -1,5 +1,7 @@
 import cv2
 from PIL import Image
+
+from script_generator.utils.logger import logger
 from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg
 from ultralytics import YOLO
 
@@ -14,7 +16,7 @@ def save_frames_as_gif(frames, output_path, duration=100, loop=0):
                   loop=loop,
                   optimize=True)
 
-    print(f"GIF saved to {output_path}")
+    logger.info(f"GIF saved to {output_path}")
 
 
 # Example usage
diff --git a/tests/test_PoseEstimation.py b/tests/test_PoseEstimation.py
index a962a7e8..fa4ad83e 100644
--- a/tests/test_PoseEstimation.py
+++ b/tests/test_PoseEstimation.py
@@ -3,6 +3,7 @@
 import cv2
 from collections import defaultdict
 from script_generator.constants import CLASS_REVERSE_MATCH
+from script_generator.utils.logger import logger
 
 #video_path = "/Users/k00gar/Downloads/Katrina Jade [ADD] Oct 16, 2024R_6kvr265_reenc.mp4"
 #video_path = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
@@ -109,7 +110,7 @@
                 hand_counter += 1
 
         if hand_counter < 2:
-            print(f"WARNING: {hand_counter} hand detected")
+            logger.warning(f"WARNING: {hand_counter} hand detected")
 
         frame = np.hstack((frame_1, frame_2))
 
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index b2d4705b..15192be8 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -8,6 +8,7 @@
 from scipy.interpolate import interp1d
 
 from script_generator.utils.file import get_output_file_path
+from script_generator.utils.logger import logger
 from utils.lib_Visualizer import Visualizer
 from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg
 
@@ -68,7 +69,7 @@ def default(obj):
 
         with open(self.log_file, "w") as f:
             json.dump(self.logs, f, indent=4, default=default)
-        print(f"Logs saved to {self.log_file}")
+        logger.info(f"Logs saved to {self.log_file}")
 
     def load_logs(self):
         """
@@ -77,11 +78,11 @@ def load_logs(self):
         try:
             with open(self.log_file, "r") as f:
                 self.logs = json.load(f)
-            print(f"Logs loaded from {self.log_file}")
+            logger.info(f"Logs loaded from {self.log_file}")
         except FileNotFoundError:
-            print(f"Log file {self.log_file} not found. Starting with empty logs.")
+            logger.error(f"Log file {self.log_file} not found. Starting with empty logs.")
         except json.JSONDecodeError:
-            print(f"Error decoding JSON from {self.log_file}. Starting with empty logs.")
+            logger.error(f"Error decoding JSON from {self.log_file}. Starting with empty logs.")
 
     def display_frame(self, frame_id):
         """
@@ -109,7 +110,7 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
 
         self.total_frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
         self.fps = self.cap.get(cv2.CAP_PROP_FPS)
-        print(f"Total frames: {self.total_frames}, FPS: {self.fps}")
+        logger.info(f"Total frames: {self.total_frames}, FPS: {self.fps}")
 
         # Initialize video writer if recording
         if record:
@@ -124,7 +125,7 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
                 output_path, fourcc, self.fps, (frame.shape[1] // downsize_ratio, frame.shape[0] // downsize_ratio)
             )
             if not out.isOpened():
-                print(f"Error: Could not open video writer for {output_path}")
+                logger.error(f"Error: Could not open video writer for {output_path}")
                 self.cap.release()
                 return
 
@@ -142,7 +143,7 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
             funscript_positions = [action["pos"] for action in actions]
             funscript_interpolator = interp1d(funscript_times, funscript_positions, kind="linear", fill_value="extrapolate")
         except FileNotFoundError:
-            print(f"Funscript file not found at {funscript_path}")
+            logger.error(f"Funscript file not found at {funscript_path}")
             funscript_interpolator = None
 
         # Initialize rolling window buffers
@@ -195,7 +196,7 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
 
                 # Draw the locked_penis_box if it exists
                 locked_penis_box = variables.get("locked_penis_box")
-                #print(f"locked_penis_box: {locked_penis_box}")
+                #logger.info(f"locked_penis_box: {locked_penis_box}")
                 if locked_penis_box['active']:
                     x1, y1, x2, y2 = locked_penis_box['box']
                     color = CLASS_COLORS.get("penis", (0, 255, 0))
@@ -275,7 +276,7 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
             # # Delete the intermediate file
             # if os.path.exists(input_path):
             #     os.remove(input_path)
-            #     print(f"Deleted intermediate file: {input_path}")
+            #     logger.info(f"Deleted intermediate file: {input_path}")
 
         cv2.destroyAllWindows()
 
@@ -332,12 +333,12 @@ def _update_frame_from_mouse(self, x, width):
         :param width: Width of the frame.
         """
         self.current_frame = int((x / width) * self.total_frames)
-        print(f"Target frame: {self.current_frame}")
+        logger.info(f"Target frame: {self.current_frame}")
         if self.video_reader == "FFmpeg":
             self.cap.release()
             self.cap = VideoReaderFFmpeg(self.video_path, self.is_vr)
         self.cap.set(cv2.CAP_PROP_POS_FRAMES, self.current_frame)
-        print("Done resetting and jumping to target frame")
+        logger.info("Done resetting and jumping to target frame")
 
     def _draw_progress_bar(self, frame, width, height):
         """
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index b7c70381..c04d84c1 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -86,7 +86,7 @@ def generate(self, state):
                     0 if p < state.threshold_low else 100 if p > state.threshold_high else p for p in
                     adjusted_positions]
             else:
-                print("Skipping positions adjustment - step 4 (thresholding)")
+                logger.info("Skipping positions adjustment - step 4 (thresholding)")
 
             # Apply amplitude boosting
             if state.boost_enabled:
@@ -143,8 +143,8 @@ def generate_heatmap(self, funscript_path, output_image_path):
             positions.insert(0, 100)
 
         # Print loaded data for debugging
-        # print(f"Times: {times}")
-        # print(f"Positions: {positions}")
+        # logger.trace(f"Times: {times}")
+        # logger.trace(f"Positions: {positions}")
         logger.info(f"Total Actions: {len(times)}")
         logger.info(f"Time Range: {times[0]} to {datetime.timedelta(seconds=int(times[-1] / 1000))}")
 
@@ -156,7 +156,7 @@ def generate_heatmap(self, funscript_path, output_image_path):
         # Calculate speed (position change per time interval)
         speeds = np.abs(np.diff(filtered_positions) / np.diff(filtered_times)) * 1000  # Positions per second
 
-        print(f"Speeds: {speeds}")
+        logger.debug(f"Speeds: {speeds}")
 
         def get_color(intensity):
             if intensity <= 0:
@@ -347,7 +347,7 @@ def load_funscript(self, funscript_path):
                 chapter['startTime'] = chapter['startTime'][:8]
             if len(chapter['endTime']) > 8:
                 chapter['endTime'] = chapter['endTime'][:8]
-            print(f"Chapter: {chapter['name']}, Start: {chapter['startTime']}, End: {chapter['endTime']}")
+            logger.info(f"Chapter: {chapter['name']}, Start: {chapter['startTime']}, End: {chapter['endTime']}")
             # convert 00:00:00 to milliseconds
             startTime_ms = int(chapter['startTime'].split(':')[0]) * 60 * 60 * 1000 + int(
                 chapter['startTime'].split(':')[1]) * 60 * 1000 + int(chapter['startTime'].split(':')[2]) * 1000
diff --git a/utils/lib_SceneCutsDetect.py b/utils/lib_SceneCutsDetect.py
index e1c1be14..8fc634c9 100644
--- a/utils/lib_SceneCutsDetect.py
+++ b/utils/lib_SceneCutsDetect.py
@@ -6,6 +6,7 @@
 from config import UPDATE_PROGRESS_INTERVAL
 from script_generator.gui.messages.messages import ProgressMessage
 from script_generator.state.app_state import AppState
+from script_generator.utils.logger import logger
 
 
 def compute_histogram(frame):
@@ -33,7 +34,7 @@ def compare_histograms(hist1, hist2):
 def detect_scene_changes(state: AppState, crop=None, threshold=0.97, frame_start=0, frame_end=None):
     cap = cv2.VideoCapture(state.video_path)
     if not cap.isOpened():
-        print("Error: Could not open video.")
+        logger.error("Error: Could not open video.")
         return []
 
     # Get video properties
@@ -109,9 +110,9 @@ def detect_scene_changes(state: AppState, crop=None, threshold=0.97, frame_start
     elif scene_changes[-1][1] != frame_end:
         scene_changes.append([scene_changes[-1][1], frame_end])
 
-    print(f"Found {len(scene_changes)} raw scenes: {scene_changes}.")
-    print(f"Scene changes: {scene_changes}")
-    print(f"Merging short scenes...")
+    logger.info(f"Found {len(scene_changes)} raw scenes: {scene_changes}.")
+    logger.info(f"Scene changes: {scene_changes}")
+    logger.info(f"Merging short scenes...")
 
     # Merge only short scenes
     merged_scenes = []
@@ -128,7 +129,7 @@ def detect_scene_changes(state: AppState, crop=None, threshold=0.97, frame_start
             # Keep scenes longer than the minimum length
             merged_scenes.append(scene)
 
-    print(f"Found {len(merged_scenes)} relevant scenes: {merged_scenes}.")
+    logger.info(f"Found {len(merged_scenes)} relevant scenes: {merged_scenes}.")
     cap.release()
 
     return merged_scenes
\ No newline at end of file
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index bbb998f4..360d2be6 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -3,6 +3,7 @@
 import numpy as np
 import argparse
 from config import FFMPEG_PATH, FFPROBE_PATH, RENDER_RESOLUTION
+from script_generator.utils.logger import logger
 
 
 class VideoReaderFFmpeg:
@@ -82,10 +83,10 @@ def _initialize_video_info(self):
             self.width = int(self.width * scaling_factor)
             self.height = int(self.height * scaling_factor)
 
-            print(f"FPS: {self.fps}, Resolution: {self.width}x{self.height}, "
+            logger.info(f"FPS: {self.fps}, Resolution: {self.width}x{self.height}, "
                   f"Codec: {self.codec}, Total Frames: {self.total_frames}, Duration: {self.duration:.2f} ms")
         except Exception as e:
-            print(f"Error initializing video info: {e}")
+            logger.error(f"Error initializing video info: {e}")
             raise
 
     # def _start_process(self, start_frame=0):
@@ -189,7 +190,7 @@ def read(self):
             self.current_time = (self.current_frame_number / self.fps) * 1000
             return True, frame
         except Exception as e:
-            print(f"Error reading frame: {e}")
+            logger.error(f"Error reading frame: {e}")
             return False, None
 
     def set(self, prop_id, value):
@@ -204,7 +205,7 @@ def set(self, prop_id, value):
         elif prop_id == cv2.CAP_PROP_FPS:
             self.fps = value
         else:
-            print(f"Property {prop_id} not supported.")
+            logger.error(f"Property {prop_id} not supported.")
 
     def get(self, prop_id):
         """
@@ -223,7 +224,7 @@ def get(self, prop_id):
         elif prop_id == cv2.CAP_PROP_POS_FRAMES:
             return self.current_frame_number
         else:
-            print(f"Property {prop_id} not supported.")
+            logger.error(f"Property {prop_id} not supported.")
             return None
 
     def release(self):

From 46820791cea29a20d7f88dfde0cd5d3691d646c7 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 20 Jan 2025 10:09:31 +0100
Subject: [PATCH 083/277] Added label for tqdm

---
 script_generator/object_detection/analye_tracking_results.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script_generator/object_detection/analye_tracking_results.py b/script_generator/object_detection/analye_tracking_results.py
index e3e4dda0..6e9037ff 100644
--- a/script_generator/object_detection/analye_tracking_results.py
+++ b/script_generator/object_detection/analye_tracking_results.py
@@ -72,7 +72,7 @@ def analyze_tracking_results(state, results, progress_callback=None):
 
     last_ui_update_time = time.time()
 
-    for frame_pos in tqdm(range(state.frame_start, state.frame_end), unit="f"):
+    for frame_pos in tqdm(range(state.frame_start, state.frame_end), unit="f", desc="Analyzing tracking data"):
         if frame_pos in cuts:
             # Reinitialize the tracker at scene cuts
             logger.info(f"Reaching cut at frame {frame_pos}")

From 199f9c336bbce79f6a895973cc0c8f9069a30f3d Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 20 Jan 2025 10:12:32 +0100
Subject: [PATCH 084/277] Added warning color to logging

---
 script_generator/utils/logger.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/script_generator/utils/logger.py b/script_generator/utils/logger.py
index 616890dc..cb6168b7 100644
--- a/script_generator/utils/logger.py
+++ b/script_generator/utils/logger.py
@@ -8,6 +8,8 @@ class ColorizedStreamHandler(logging.StreamHandler):
     def emit(self, record):
         if record.levelno >= logging.ERROR:
             record.msg = f"{Fore.RED}{record.msg}{Style.RESET_ALL}"
+        elif record.levelno == logging.WARNING or record.levelno == logging.WARN:
+            record.msg = f"{Fore.YELLOW}{record.msg}{Style.RESET_ALL}"
         super().emit(record)
 
 logging.basicConfig(
@@ -20,4 +22,3 @@ def emit(self, record):
 )
 
 logger = logging.getLogger("General")
-logger.info("Logger initialized with file and console handlers.")

From bc6bedd78dfb9890e58f6df603c0406ba0e5af64 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 20 Jan 2025 12:30:48 +0100
Subject: [PATCH 085/277] Re-enabled persist for YOLO Further tweaks

---
 script_generator/gui/app.py                          |  2 +-
 script_generator/gui/controller/tracking_analysis.py |  4 ++--
 script_generator/gui/utils/widgets.py                |  9 +++++++--
 script_generator/gui/views/funscript_generator.py    |  5 ++++-
 .../object_detection/analye_tracking_results.py      |  9 +++++++--
 script_generator/object_detection/yolo.py            |  2 +-
 script_generator/scripts/analyse_video.py            |  6 +-----
 utils/lib_ObjectTracker.py                           |  3 ++-
 utils/lib_SceneCutsDetect.py                         | 12 ++++++++----
 9 files changed, 33 insertions(+), 19 deletions(-)

diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index 91a9da1b..e2a71e45 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -18,7 +18,7 @@ def __init__(self):
             ctypes.windll.shcore.SetProcessDpiAwareness(2)  # For Windows DPI scaling
         # self.tk.call('tk', 'scaling', 1.0)
         self.title("VR funscript generation")
-        self.geometry(f"{('800' if is_mac() else '700')}x950")
+        self.geometry(f"{('800' if is_mac() else '715')}x920")
         self.resizable(False, False)
 
         self.iconphoto(False, tk.PhotoImage(file=LOGO))
diff --git a/script_generator/gui/controller/tracking_analysis.py b/script_generator/gui/controller/tracking_analysis.py
index e3dcaf06..72615cb1 100644
--- a/script_generator/gui/controller/tracking_analysis.py
+++ b/script_generator/gui/controller/tracking_analysis.py
@@ -28,8 +28,8 @@ def tracking_analysis(state):
     first_penis_frame = parse_yolo_data_looking_for_penis(yolo_data, 0)
 
     if first_penis_frame is None:
-        logger.warning(f"No penis found in video: {state.video_path}")
-        first_penis_frame = 0
+        logger.error(f"No penis and penis tip in the same frame found in video. Since these two are needed for estimating stroke length atm further tracking is not possible.")
+        return
 
     # Deciding whether we start from there or from a user-specified later frame
     state.frame_start = max(max(first_penis_frame - int(video_info.fps), state.frame_start - int(video_info.fps)), 0)
diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index 0437dd37..d16f746b 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -100,10 +100,15 @@ def button(parent, button_text, on_click, row=0, col=0, tooltip_text=None, style
         return button
 
     @staticmethod
-    def file_selection(parent, label_text, button_text, file_selector_title, file_types, state, attr, row=0, label_width_px=150, button_width_px=100, tooltip_text=None):
+    def file_selection(parent, label_text, button_text, file_selector_title, file_types, state, attr, command=None, row=0, label_width_px=150, button_width_px=100, tooltip_text=None):
         container = tk.Frame(parent)
         container.grid(row=row, column=0, sticky="nsew", padx=5, pady=5)
 
+        def on_change(val):
+            setattr(state, attr, val)
+            if command:
+                command(val)
+
         # Ensure the container scales properly
         parent.grid_rowconfigure(row, weight=1)
         parent.grid_columnconfigure(0, weight=1)
@@ -129,7 +134,7 @@ def file_selection(parent, label_text, button_text, file_selector_title, file_ty
         button.pack(fill="both", expand=True)
 
         # Update state whenever the file path changes
-        file_path.trace("w", lambda *args: setattr(state, attr, file_path.get()))
+        file_path.trace("w", lambda *args: on_change(file_path.get()))
 
         if tooltip_text:
             Tooltip(button, tooltip_text)
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 4af027d6..09869462 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -34,7 +34,10 @@ def __init__(self, parent, controller):
             button_text="Browse",
             file_selector_title="Choose a File",
             file_types=[("Text Files", "*.mp4 *.avi *.mov *.mkv"), ("All Files", "*.*")],
-            state=state
+            state=state,
+            # if the file changes we need to make sure the video info is reset
+            # TODO add os.path.exists and update video_info immediately if found
+            command=lambda val: setattr(state, 'video_info', None)
         )
 
         Widgets.dropdown(
diff --git a/script_generator/object_detection/analye_tracking_results.py b/script_generator/object_detection/analye_tracking_results.py
index 6e9037ff..8a7b98a5 100644
--- a/script_generator/object_detection/analye_tracking_results.py
+++ b/script_generator/object_detection/analye_tracking_results.py
@@ -72,7 +72,12 @@ def analyze_tracking_results(state, results, progress_callback=None):
 
     last_ui_update_time = time.time()
 
-    for frame_pos in tqdm(range(state.frame_start, state.frame_end), unit="f", desc="Analyzing tracking data"):
+    for frame_pos in tqdm(
+            range(state.frame_start, state.frame_end), unit="f", desc="Analyzing tracking data", position=0,
+            unit_scale=False,
+            unit_divisor=1,
+            ncols=130
+    ):
         if frame_pos in cuts:
             # Reinitialize the tracker at scene cuts
             logger.info(f"Reaching cut at frame {frame_pos}")
@@ -221,4 +226,4 @@ def analyze_tracking_results(state, results, progress_callback=None):
     raw_funscript_path, _ = get_output_file_path(state.video_path, "_rawfunscript.json")
     with open(raw_funscript_path, 'w') as f:
         json.dump(state.funscript_data, f)
-    return state.funscript_data
\ No newline at end of file
+    return state.funscript_data
diff --git a/script_generator/object_detection/yolo.py b/script_generator/object_detection/yolo.py
index 6cf1b47f..9472f1c6 100644
--- a/script_generator/object_detection/yolo.py
+++ b/script_generator/object_detection/yolo.py
@@ -34,7 +34,7 @@ def task_logic(self):
     def process_batch(self, frames, tasks):
         start_time = time.time()
         # yolo_results = YOLO_MODEL(frames, conf=YOLO_CONF, verbose=False)
-        yolo_results = YOLO_MODEL.track(frames, persist=False, conf=YOLO_CONF, verbose=False)
+        yolo_results = YOLO_MODEL.track(frames, persist=True, conf=YOLO_CONF, verbose=False)
         avg_time = (time.time() - start_time) / len(tasks)
 
         for t, result in zip(tasks, yolo_results):
diff --git a/script_generator/scripts/analyse_video.py b/script_generator/scripts/analyse_video.py
index b7d3d1dc..80afe560 100644
--- a/script_generator/scripts/analyse_video.py
+++ b/script_generator/scripts/analyse_video.py
@@ -3,7 +3,6 @@
 import time
 from typing import List
 
-from colorama import Fore, Style
 from tqdm import tqdm
 
 from config import QUEUE_MAXSIZE, SEQUENTIAL_MODE, PROGRESS_BAR, UPDATE_PROGRESS_INTERVAL
@@ -201,7 +200,4 @@ def log_performance(state, results_queue):
     log_message += f"{'-' * 60}\n"
 
     for line in log_message.splitlines():
-        logger.info(line)
-
-    print(Fore.LIGHTBLUE_EX + log_message)
-    print(Style.RESET_ALL, end="")
+        logger.info(line)
\ No newline at end of file
diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index cf6ca819..5f20ff76 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -292,6 +292,7 @@ def detect_sex_position_change(self, sex_position, reason):
         self.sex_position_history.append(sex_position)
         position_counts = {position: self.sex_position_history.count(position) for position in self.sex_position_history}
         most_frequent_position = max(position_counts, key=position_counts.get, default="Not relevant")
+
         if most_frequent_position != self.sex_position:
             logger.info(f"@{self.current_frame_id} - Sex position switched to: {most_frequent_position}")
             self.sex_position = most_frequent_position
@@ -353,7 +354,7 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                 and not self.penetration:  # equivalent to 3 seconds
             if self.locked_penis_box.active:  # is_active():
                 self.locked_penis_box.deactivate()
-                logger.info(f"@{self.current_frame_id} - Deactivated locked_penis_box")
+                logger.debug(f"@{self.current_frame_id} - Deactivated locked_penis_box")
 
         if self.locked_penis_box.is_active() and self.locked_penis_box.visible < 100 and not self.glans_detected:
             self.penetration = True
diff --git a/utils/lib_SceneCutsDetect.py b/utils/lib_SceneCutsDetect.py
index 8fc634c9..7361bd1f 100644
--- a/utils/lib_SceneCutsDetect.py
+++ b/utils/lib_SceneCutsDetect.py
@@ -56,7 +56,12 @@ def detect_scene_changes(state: AppState, crop=None, threshold=0.97, frame_start
     last_ui_update_time = time.time()
 
     # Process frames
-    for frame_pos in tqdm(range(total_frames_to_parse), desc="Detecting scene changes"):
+    for frame_pos in tqdm(
+            range(total_frames_to_parse), desc="Detecting scene changes", position=0,
+            unit_scale=False,
+            unit_divisor=1,
+            ncols=130
+    ):
         cap.set(cv2.CAP_PROP_POS_FRAMES, frame_start + frame_pos * frame_step)
         ret, frame = cap.read()
         if not ret:
@@ -75,7 +80,7 @@ def detect_scene_changes(state: AppState, crop=None, threshold=0.97, frame_start
             if similarity < threshold:
                 # Scene change detected
                 current_frame = frame_start + frame_pos * frame_step
-                tqdm.write(
+                logger.info(
                     f"Scene change detected at frame {current_frame}, "
                     f"time: {int(cap.get(cv2.CAP_PROP_POS_MSEC) / 1000 // 60)} min "
                     f"{int(cap.get(cv2.CAP_PROP_POS_MSEC) / 1000 % 60)} sec"
@@ -103,7 +108,6 @@ def detect_scene_changes(state: AppState, crop=None, threshold=0.97, frame_start
                     eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
                 ))
 
-
     # Add the last scene
     if not scene_changes:
         scene_changes.append([frame_start, frame_end])
@@ -132,4 +136,4 @@ def detect_scene_changes(state: AppState, crop=None, threshold=0.97, frame_start
     logger.info(f"Found {len(merged_scenes)} relevant scenes: {merged_scenes}.")
     cap.release()
 
-    return merged_scenes
\ No newline at end of file
+    return merged_scenes

From 3314e60e39b373846b976083156e4cf75747fcbd Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Mon, 20 Jan 2025 15:20:22 +0100
Subject: [PATCH 086/277] Added hardware ffmpeg possibilities beyond CUDA only
 (e.g. VideoToolbox, etc.) depending on the platform.

---
 script_generator/state/app_state.py           |  4 +--
 script_generator/video/ffmpeg.py              | 32 ++++++++++++++++---
 .../video/video_task_processor.py             | 26 +++++++++++++--
 3 files changed, 54 insertions(+), 8 deletions(-)

diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index 7ad49a25..9a5313de 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -3,7 +3,7 @@
 
 from script_generator.tasks.tasks import AnalyseVideoTask
 from script_generator.utils.helpers import is_mac
-from script_generator.video.ffmpeg import is_cuda_supported, get_video_info
+from script_generator.video.ffmpeg import is_hwaccel_supported, get_video_info
 from script_generator.video.video_info import VideoInfo
 
 
@@ -49,7 +49,7 @@ def __init__(self, is_cli):
         # App logic
         self.debugger = None
         self.update_ui = None
-        self.ffmpeg_cuda_supported = is_cuda_supported()
+        self.ffmpeg_hwaccel_supported = is_hwaccel_supported()
 
     def set_video_info(self):
         if self.video_info is None:
diff --git a/script_generator/video/ffmpeg.py b/script_generator/video/ffmpeg.py
index c99abe83..a22cf1cd 100644
--- a/script_generator/video/ffmpeg.py
+++ b/script_generator/video/ffmpeg.py
@@ -42,7 +42,11 @@ def get_video_info(video_path):
         logger.error(f"Error initializing video info: {e}")
         raise
 
-def is_cuda_supported():
+def is_hwaccel_supported():
+    """
+    Check which hardware acceleration backends are supported by FFmpeg.
+    Returns a dictionary with supported backends.
+    """
     try:
         result = subprocess.run(
             [FFMPEG_PATH, "-hwaccels"],
@@ -51,7 +55,27 @@ def is_cuda_supported():
             text=True,
             check=True
         )
-        return "cuda" in result.stdout.lower()
+        hwaccels = result.stdout.lower()
+        logger.info(f"Hardware acceleration backends: {hwaccels}")
+        # Check for supported hardware acceleration backends
+        return {
+            "cuda": "cuda" in hwaccels,
+            "vaapi": "vaapi" in hwaccels,
+            "amf": "amf" in hwaccels,
+            "videotoolbox": "videotoolbox" in hwaccels,
+            "qsv": "qsv" in hwaccels,  # Intel Quick Sync Video
+            "d3d11va": "d3d11va" in hwaccels,  # Direct3D 11 (Windows)
+            "opencl": "opencl" in hwaccels,  # OpenCL (cross-platform)
+        }
     except Exception as e:
-        logger.error(f"Error checking CUDA support: {e}")
-        return False
\ No newline at end of file
+        logger.error(f"Error checking hardware acceleration support: {e}")
+        return {
+            "cuda": False,
+            "vaapi": False,
+            "amf": False,
+            "videotoolbox": False,
+            "qsv": False,
+            "d3d11va": False,
+            "opencl": False,
+        }
+
diff --git a/script_generator/video/video_task_processor.py b/script_generator/video/video_task_processor.py
index 179a92b6..d0cb2d3c 100644
--- a/script_generator/video/video_task_processor.py
+++ b/script_generator/video/video_task_processor.py
@@ -21,9 +21,31 @@ def task_logic(self):
         def get_cmd(vf):
             start_time = (state.frame_start / video.fps) * 1000
             ffmpeg_threads = max(cpu_count() - SUBTRACT_THREADS_FROM_FFMPEG, 1)
-            cuda_supported = state.ffmpeg_cuda_supported
-            hwaccel = ["-hwaccel", "cuda"] if cuda_supported else []
+
+            # Get supported hardware acceleration backends
+            hwaccel_support = state.ffmpeg_hwaccel_supported
+
+            # Determine the best hardware acceleration backend to use
+            hwaccel = []
+            if hwaccel_support["cuda"]:
+                hwaccel = ["-hwaccel", "cuda"]
+            elif hwaccel_support["vaapi"]:
+                hwaccel = ["-hwaccel", "vaapi", "-hwaccel_device", "/dev/dri/renderD128"]
+            elif hwaccel_support["amf"]:
+                hwaccel = ["-hwaccel", "amf"]
+            elif hwaccel_support["videotoolbox"]:
+                hwaccel = ["-hwaccel", "videotoolbox"]
+            elif hwaccel_support["qsv"]:
+                hwaccel = ["-hwaccel", "qsv"]
+            elif hwaccel_support["d3d11va"]:
+                hwaccel = ["-hwaccel", "d3d11va"]
+            # Add more backends as needed
+
+            # Add platform-specific video filters if required
             video_filter = ["-vf", vf] if vf else []
+            if hwaccel_support["vaapi"]:
+                # VAAPI requires specific pixel formats and filters
+                video_filter = ["-vf", f"{vf},format=nv12,hwupload"] if vf else ["-vf", "format=nv12,hwupload"]
 
             return [
                 FFMPEG_PATH,

From 8c321910e89159ed93ee045d33ca0185d9b12ca2 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Mon, 20 Jan 2025 15:22:47 +0100
Subject: [PATCH 087/277] Fixed typo and lib naming

---
 script_generator/gui/controller/tracking_analysis.py            | 2 +-
 .../{analye_tracking_results.py => analyze_tracking_results.py} | 0
 script_generator/scripts/analyse_video.py                       | 2 +-
 3 files changed, 2 insertions(+), 2 deletions(-)
 rename script_generator/object_detection/{analye_tracking_results.py => analyze_tracking_results.py} (100%)

diff --git a/script_generator/gui/controller/tracking_analysis.py b/script_generator/gui/controller/tracking_analysis.py
index 72615cb1..4761b3c9 100644
--- a/script_generator/gui/controller/tracking_analysis.py
+++ b/script_generator/gui/controller/tracking_analysis.py
@@ -1,7 +1,7 @@
 import os
 
 from script_generator.gui.controller.debug_video import debug_video
-from script_generator.object_detection.analye_tracking_results import analyze_tracking_results
+from script_generator.object_detection.analyze_tracking_results import analyze_tracking_results
 from script_generator.object_detection.object_detection_result import ObjectDetectionResult
 from script_generator.object_detection.utils import make_data_boxes, parse_yolo_data_looking_for_penis
 from script_generator.utils.file import load_json_from_file, get_output_file_path
diff --git a/script_generator/object_detection/analye_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
similarity index 100%
rename from script_generator/object_detection/analye_tracking_results.py
rename to script_generator/object_detection/analyze_tracking_results.py
diff --git a/script_generator/scripts/analyse_video.py b/script_generator/scripts/analyse_video.py
index 80afe560..4cb39178 100644
--- a/script_generator/scripts/analyse_video.py
+++ b/script_generator/scripts/analyse_video.py
@@ -86,7 +86,7 @@ def log_progress(state, opengl_q, yolo_q, analysis_q, results_q, stop_event):
     if PROGRESS_BAR:
         with tqdm(
                 total=total_frames,
-                desc="Analysing video",
+                desc="Analyzing video",
                 unit="frames",
                 position=0,
                 unit_scale=False,

From 309cf05f482fa7e8e69ed88e2b7a8e0c0509e5f1 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Mon, 20 Jan 2025 15:24:40 +0100
Subject: [PATCH 088/277] Catching up a changed previously made in the main
 branch

---
 .../object_detection/analyze_tracking_results.py             | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index 8a7b98a5..ebf8c521 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -1,8 +1,5 @@
 import json
-import os
 import time
-from datetime import datetime
-
 import cv2
 from tqdm import tqdm
 from datetime import timedelta
@@ -129,7 +126,7 @@ def analyze_tracking_results(state, results, progress_callback=None):
                     variables={
                         'frame': frame_pos,
                         # time of the frame hh:mm:ss
-                        'time': datetime.fromtimestamp(frame_pos / fps).strftime('%H:%M:%S'),
+                        'time': str(timedelta(seconds=int(frame_pos / fps))),
                         'distance': tracker.distance,
                         'Penetration': tracker.penetration,
                         'sex_position': tracker.sex_position,

From 6864f2fe0ff06f6c20ccacfe4a2c120588837464 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Mon, 20 Jan 2025 15:26:06 +0100
Subject: [PATCH 089/277] Fixed typo

---
 script_generator/gui/controller/video_analysis.py               | 2 +-
 script_generator/scripts/{analyse_video.py => analyze_video.py} | 0
 script_generator/scripts/generate_funscript.py                  | 2 +-
 3 files changed, 2 insertions(+), 2 deletions(-)
 rename script_generator/scripts/{analyse_video.py => analyze_video.py} (100%)

diff --git a/script_generator/gui/controller/video_analysis.py b/script_generator/gui/controller/video_analysis.py
index d3bcfd0d..b345fe7f 100644
--- a/script_generator/gui/controller/video_analysis.py
+++ b/script_generator/gui/controller/video_analysis.py
@@ -4,7 +4,7 @@
 from script_generator.gui.controller.tracking_analysis import tracking_analysis
 from script_generator.gui.messages.messages import ProgressMessage
 from script_generator.object_detection.utils import check_skip_object_detection
-from script_generator.scripts.analyse_video import analyse_video
+from script_generator.scripts.analyze_video import analyse_video
 from script_generator.utils.file import get_output_file_path
 from script_generator.utils.logger import logger
 from utils.lib_Debugger import Debugger
diff --git a/script_generator/scripts/analyse_video.py b/script_generator/scripts/analyze_video.py
similarity index 100%
rename from script_generator/scripts/analyse_video.py
rename to script_generator/scripts/analyze_video.py
diff --git a/script_generator/scripts/generate_funscript.py b/script_generator/scripts/generate_funscript.py
index d228d658..daa76b43 100644
--- a/script_generator/scripts/generate_funscript.py
+++ b/script_generator/scripts/generate_funscript.py
@@ -1,4 +1,4 @@
-from script_generator.scripts.analyse_video import analyse_video
+from script_generator.scripts.analyze_video import analyse_video
 from script_generator.state.app_state import AppState
 
 

From b7eac2db1157fef014b3b582ddbc77aa511cca6b Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Mon, 20 Jan 2025 15:31:40 +0100
Subject: [PATCH 090/277] Fixed typo in "Analyze"

---
 .../gui/controller/video_analysis.py             |  4 ++--
 .../object_detection/post_process_results.py     |  2 +-
 script_generator/scripts/analyze_video.py        | 16 ++++++++--------
 script_generator/scripts/generate_funscript.py   |  4 ++--
 script_generator/state/app_state.py              |  4 ++--
 .../tasks/abstract_task_processor.py             | 10 +++++-----
 script_generator/tasks/tasks.py                  |  4 ++--
 script_generator/video/video_task_processor.py   |  4 ++--
 8 files changed, 24 insertions(+), 24 deletions(-)

diff --git a/script_generator/gui/controller/video_analysis.py b/script_generator/gui/controller/video_analysis.py
index b345fe7f..ebe16264 100644
--- a/script_generator/gui/controller/video_analysis.py
+++ b/script_generator/gui/controller/video_analysis.py
@@ -4,7 +4,7 @@
 from script_generator.gui.controller.tracking_analysis import tracking_analysis
 from script_generator.gui.messages.messages import ProgressMessage
 from script_generator.object_detection.utils import check_skip_object_detection
-from script_generator.scripts.analyze_video import analyse_video
+from script_generator.scripts.analyze_video import analyze_video
 from script_generator.utils.file import get_output_file_path
 from script_generator.utils.logger import logger
 from utils.lib_Debugger import Debugger
@@ -40,7 +40,7 @@ def run():
                     eta="Done"
                 ))
         else:
-            analyse_video(state)
+            analyze_video(state)
 
 
         tracking_analysis(state)
diff --git a/script_generator/object_detection/post_process_results.py b/script_generator/object_detection/post_process_results.py
index f12513e5..ec0b4f3c 100644
--- a/script_generator/object_detection/post_process_results.py
+++ b/script_generator/object_detection/post_process_results.py
@@ -126,7 +126,7 @@ def task_logic(self):
             
 
     def on_last_item(self):
-        self.state.analyse_task.end_time = time.time()
+        self.state.analyze_task.end_time = time.time()
 
         # Write the detection records to a JSON file
         raw_yolo_path, _ = get_output_file_path(self.state.video_path, "_rawyolo.json")
diff --git a/script_generator/scripts/analyze_video.py b/script_generator/scripts/analyze_video.py
index 4cb39178..3df5c476 100644
--- a/script_generator/scripts/analyze_video.py
+++ b/script_generator/scripts/analyze_video.py
@@ -11,20 +11,20 @@
 from script_generator.object_detection.yolo import YoloTaskProcessor
 from script_generator.state.app_state import AppState
 from script_generator.tasks.abstract_task_processor import TaskProcessorTypes
-from script_generator.tasks.tasks import AnalyseVideoTask, AnalyseFrameTask
+from script_generator.tasks.tasks import AnalyzeVideoTask, AnalyzeFrameTask
 from script_generator.utils.logger import logger
 from script_generator.video.video_conversion.vr_to_2d_task_processor import VrTo2DTaskProcessor
 from script_generator.video.video_task_processor import VideoTaskProcessor
 
 
-def analyse_video(state: AppState) -> List[AnalyseFrameTask]:
+def analyze_video(state: AppState) -> List[AnalyzeFrameTask]:
     logger.info(f"[OBJECT DETECTION] Starting up pipeline with profiling in {'sequential mode' if SEQUENTIAL_MODE else 'parallel mode'}...")
 
     use_open_gl = state.video_reader == "FFmpeg + OpenGL (Windows)"
 
     # Initialize batch task
     state.set_video_info()
-    state.analyse_task = AnalyseVideoTask()
+    state.analyze_task = AnalyzeVideoTask()
 
     # Create queues
     opengl_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
@@ -68,7 +68,7 @@ def run_thread(thread, thread_name, out_queue):
         for thread in threads:
             thread.join()
 
-    state.analyse_task.end_time = time.time()
+    state.analyze_task.end_time = time.time()
 
     log_thread_stop_event.set()
 
@@ -110,7 +110,7 @@ def log_progress(state, opengl_q, yolo_q, analysis_q, results_q, stop_event):
                     stop_event.set()
 
                 if state.update_ui:
-                    elapsed_time = time.time() - state.analyse_task.start_time
+                    elapsed_time = time.time() - state.analyze_task.start_time
                     processing_rate = frames_processed / elapsed_time if elapsed_time > 0 else 0
                     remaining_frames = total_frames - frames_processed
                     eta = remaining_frames / processing_rate if processing_rate > 0 else float('inf')
@@ -140,12 +140,12 @@ def log_progress(state, opengl_q, yolo_q, analysis_q, results_q, stop_event):
             time.sleep(0.5)
 
 def log_performance(state, results_queue):
-    analyse_task = state.analyse_task
+    analyze_task = state.analyze_task
     # TODO filter out sentinals in task processor
     tasks = [task for task in results_queue.queue if task is not None and hasattr(task, 'profile')]
     total_frames = len(tasks)
 
-    total_pipeline_time = analyse_task.end_time - analyse_task.start_time
+    total_pipeline_time = analyze_task.end_time - analyze_task.start_time
     video_duration = total_frames / state.video_info.fps
     avg_processing_fps = total_frames / total_pipeline_time
     realtime_percentage = (avg_processing_fps / 60.0) * 100.0
@@ -162,7 +162,7 @@ def log_performance(state, results_queue):
 
     if SEQUENTIAL_MODE:
         log_message += f"\n Sequential Queue statistics\n"
-        for key, total_time in analyse_task.profile.items():
+        for key, total_time in analyze_task.profile.items():
             if key.endswith("_duration"):  # Only include duration metrics
                 avg_time = total_time / total_frames if total_frames > 0 else 0.0
                 stage_name = key.replace("_duration", "").capitalize()
diff --git a/script_generator/scripts/generate_funscript.py b/script_generator/scripts/generate_funscript.py
index daa76b43..2c83fa00 100644
--- a/script_generator/scripts/generate_funscript.py
+++ b/script_generator/scripts/generate_funscript.py
@@ -1,6 +1,6 @@
-from script_generator.scripts.analyze_video import analyse_video
+from script_generator.scripts.analyze_video import analyze_video
 from script_generator.state.app_state import AppState
 
 
 def generate_funscript(state: AppState):
-    restults = analyse_video(state)
\ No newline at end of file
+    results = analyze_video(state)
\ No newline at end of file
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index 9a5313de..69723294 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -1,7 +1,7 @@
 import string
 from typing import Literal
 
-from script_generator.tasks.tasks import AnalyseVideoTask
+from script_generator.tasks.tasks import AnalyzeVideoTask
 from script_generator.utils.helpers import is_mac
 from script_generator.video.ffmpeg import is_hwaccel_supported, get_video_info
 from script_generator.video.video_info import VideoInfo
@@ -13,7 +13,7 @@ def __init__(self, is_cli):
 
         # Job
         self.video_info: VideoInfo | None = None
-        self.analyse_task: AnalyseVideoTask | None = None
+        self.analyze_task: AnalyzeVideoTask | None = None
         self.video_path: string = None
         self.frame_start: int = 0
         self.frame_end: int | None = None
diff --git a/script_generator/tasks/abstract_task_processor.py b/script_generator/tasks/abstract_task_processor.py
index e97e3112..eea8eea4 100644
--- a/script_generator/tasks/abstract_task_processor.py
+++ b/script_generator/tasks/abstract_task_processor.py
@@ -3,7 +3,7 @@
 from typing import Generator, Optional
 
 from script_generator.state.app_state import AppState
-from script_generator.tasks.tasks import AnalyseFrameTask
+from script_generator.tasks.tasks import AnalyzeFrameTask
 from script_generator.utils.logger import logger
 
 
@@ -31,7 +31,7 @@ def log(self, message):
         thread_name = threading.current_thread().name
         logger.info(f"[{self.__class__.__name__}-{thread_name}] {message}")
 
-    def get_task(self) -> Generator[AnalyseFrameTask, None, None]:
+    def get_task(self) -> Generator[AnalyzeFrameTask, None, None]:
         """
         Generator for retrieving tasks from the input queue.
         Yields tasks until a sentinel (None) is encountered or the thread is stopped.
@@ -45,7 +45,7 @@ def get_task(self) -> Generator[AnalyseFrameTask, None, None]:
                 task = self.input_queue.get(timeout=1)
 
                 if task is None:  # Sentinel for termination
-                    self.state.analyse_task.end(self.process_type)
+                    self.state.analyze_task.end(self.process_type)
                     self.on_last_item()
                     self.finish_task(None)
                     break
@@ -72,7 +72,7 @@ def run(self):
         Main thread entry point. Executes the `task_logic` method.
         """
         try:
-            self.state.analyse_task.start(self.process_type)
+            self.state.analyze_task.start(self.process_type)
             self.task_logic()
         except Exception as e:
             logger.error(f"An error occurred during task execution on thread {self.process_type}: {e}")
@@ -89,7 +89,7 @@ def task_logic(self):
         raise NotImplementedError("Subclasses must implement task_logic")
 
     def stop_process(self):
-        self.state.analyse_task.end(self.process_type)
+        self.state.analyze_task.end(self.process_type)
         self.on_last_item()
         # Propagate sentinel to the output queue
         self.output_queue.put(None)
diff --git a/script_generator/tasks/tasks.py b/script_generator/tasks/tasks.py
index bbfe0398..3eeac22f 100644
--- a/script_generator/tasks/tasks.py
+++ b/script_generator/tasks/tasks.py
@@ -52,7 +52,7 @@ def _calculate_duration(self, process_type: str):
 
 
 @dataclass
-class AnalyseVideoTask(Task):
+class AnalyzeVideoTask(Task):
     tasks: List[Task] = field(default_factory=list)
 
     def __init__(self):
@@ -73,7 +73,7 @@ def get_tasks(self) -> List[Task]:
 
 
 @dataclass
-class AnalyseFrameTask(Task):
+class AnalyzeFrameTask(Task):
     frame_pos: int = -1
     preprocessed_frame: Optional[np.ndarray] = None  # Cropped frame from video stream
     rendered_frame: Optional[np.ndarray] = None  # The final 2D image from OpenGL
diff --git a/script_generator/video/video_task_processor.py b/script_generator/video/video_task_processor.py
index d0cb2d3c..5d6df61a 100644
--- a/script_generator/video/video_task_processor.py
+++ b/script_generator/video/video_task_processor.py
@@ -5,7 +5,7 @@
 
 from config import SUBTRACT_THREADS_FROM_FFMPEG, PITCH, RENDER_RESOLUTION, FFMPEG_PATH
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
-from script_generator.tasks.tasks import AnalyseFrameTask
+from script_generator.tasks.tasks import AnalyzeFrameTask
 from script_generator.utils.logger import logger
 from script_generator.video.video_info import get_cropped_dimensions
 
@@ -106,7 +106,7 @@ def standard_video_filters():
                 if not in_bytes:
                     break
 
-                task = AnalyseFrameTask(frame_pos=current_frame)
+                task = AnalyzeFrameTask(frame_pos=current_frame)
 
                 frame = np.frombuffer(in_bytes, np.uint8).reshape([height, width, 3])
 

From b80ee6c5ce91822755df66a0a2f91cc762f2e209 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 20 Jan 2025 16:29:31 +0100
Subject: [PATCH 091/277] Fixed wrong logger function

---
 script_generator/gui/app.py                            |  2 +-
 script_generator/gui/controller/tracking_analysis.py   |  2 +-
 .../object_detection/post_process_results.py           | 10 +++++-----
 script_generator/utils/config.py                       |  2 +-
 utils/lib_FunscriptHandler.py                          |  4 ++--
 5 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index e2a71e45..f53b4ac1 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -18,7 +18,7 @@ def __init__(self):
             ctypes.windll.shcore.SetProcessDpiAwareness(2)  # For Windows DPI scaling
         # self.tk.call('tk', 'scaling', 1.0)
         self.title("VR funscript generation")
-        self.geometry(f"{('800' if is_mac() else '715')}x920")
+        self.geometry(f"{('800' if is_mac() else '720')}x920")
         self.resizable(False, False)
 
         self.iconphoto(False, tk.PhotoImage(file=LOGO))
diff --git a/script_generator/gui/controller/tracking_analysis.py b/script_generator/gui/controller/tracking_analysis.py
index 4761b3c9..47dfe6a1 100644
--- a/script_generator/gui/controller/tracking_analysis.py
+++ b/script_generator/gui/controller/tracking_analysis.py
@@ -28,7 +28,7 @@ def tracking_analysis(state):
     first_penis_frame = parse_yolo_data_looking_for_penis(yolo_data, 0)
 
     if first_penis_frame is None:
-        logger.error(f"No penis and penis tip in the same frame found in video. Since these two are needed for estimating stroke length atm further tracking is not possible.")
+        logger.error(f"No frames found with a penis and penis tip in the same frame. Since these two are needed for estimating stroke length atm further tracking is not possible.")
         return
 
     # Deciding whether we start from there or from a user-specified later frame
diff --git a/script_generator/object_detection/post_process_results.py b/script_generator/object_detection/post_process_results.py
index ec0b4f3c..29c99c2b 100644
--- a/script_generator/object_detection/post_process_results.py
+++ b/script_generator/object_detection/post_process_results.py
@@ -55,9 +55,9 @@ def task_logic(self):
                     self.test_result.add_record(frame_pos, test_box)
 
                     # print and test the record
-                    logger.trace(f"Record : {record}")
-                    logger.trace(f"For class id: {int(cls)}, getting: {CLASS_REVERSE_MATCH.get(int(cls), 'unknown')}")
-                    logger.trace(f"Test box: {test_box}")
+                    logger.debug(f"Record : {record}")
+                    logger.debug(f"For class id: {int(cls)}, getting: {CLASS_REVERSE_MATCH.get(int(cls), 'unknown')}")
+                    logger.debug(f"Test box: {test_box}")
 
             if RUN_POSE_MODEL:
                 ### POSE DETECTION - Hips and wrists
@@ -67,7 +67,7 @@ def task_logic(self):
 
                     # Check if keypoints are detected
                     if pose_results[0].keypoints is not None:
-                        # logger.trace("We have keypoints")
+                        # logger.debug("We have keypoints")
                         # pose_keypoints = pose_results[0].keypoints.cpu()
                         # pose_track_ids = pose_results[0].boxes.id.cpu().tolist()
                         # pose_boxes = pose_results[0].boxes.xywh.cpu()
@@ -86,7 +86,7 @@ def task_logic(self):
                         x2 = mid_hips[0] + 5
                         y2 = mid_hips[1] + 5
                         cls = 10  # hips center
-                        # logger.trace(f"pose_confs: {pose_confs}")
+                        # logger.debug(f"pose_confs: {pose_confs}")
                         conf = pose_confs[0]
 
                         record = [frame_pos, 10, round(conf, 1), x1, y1, x2, y2, 0]
diff --git a/script_generator/utils/config.py b/script_generator/utils/config.py
index d1911032..6050b73d 100644
--- a/script_generator/utils/config.py
+++ b/script_generator/utils/config.py
@@ -11,7 +11,7 @@ def find_ffmpeg_path(win_ffmpeg_path, mac_ffmpeg_path, lin_ffmpeg_path):
     if ffmpeg_path:
         return ffmpeg_path
     else:
-        logger.trace("ffmpeg not found in PATH, defaulting to platform values in params/config.py")
+        logger.debug("ffmpeg not found in PATH, defaulting to platform values in params/config.py")
 
         if platform.system() == "Windows":
             return win_ffmpeg_path
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index c04d84c1..2b357d63 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -143,8 +143,8 @@ def generate_heatmap(self, funscript_path, output_image_path):
             positions.insert(0, 100)
 
         # Print loaded data for debugging
-        # logger.trace(f"Times: {times}")
-        # logger.trace(f"Positions: {positions}")
+        # logger.debug(f"Times: {times}")
+        # logger.debug(f"Positions: {positions}")
         logger.info(f"Total Actions: {len(times)}")
         logger.info(f"Time Range: {times[0]} to {datetime.timedelta(seconds=int(times[-1] / 1000))}")
 

From 666b03abbab4acbfe643e108ca2ea3259108e435 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Mon, 20 Jan 2025 16:57:35 +0100
Subject: [PATCH 092/277] Reinserted the tracking logic as in the main branch

---
 .../analyze_tracking_results.py               | 14 ++--
 script_generator/state/app_state.py           |  2 +
 utils/lib_ObjectTracker.py                    | 66 ++++++++-----------
 3 files changed, 38 insertions(+), 44 deletions(-)

diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index ebf8c521..48478a81 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -25,7 +25,7 @@ def analyze_tracking_results(state, results, progress_callback=None):
     fps = video_info.fps
     reader = VideoReaderFFmpeg(state.video_path, is_vr=video_info.is_vr)
 
-    image_area = video_info.width * video_info.height
+    state.frame_area = video_info.width * video_info.height
 
     cuts = []
 
@@ -62,7 +62,11 @@ def analyze_tracking_results(state, results, progress_callback=None):
     """
 
     state.funscript_frames = []  # List to store Funscript frames
-    tracker = ObjectTracker(fps, state.frame_start, image_area, video_info.is_vr)  # Initialize the object tracker
+    state.funscript_distances = []
+    state.funscript_data = []
+
+    #tracker = ObjectTracker(fps, state.frame_start, image_area, video_info.is_vr)  # Initialize the object tracker
+    tracker = ObjectTracker(state)
 
     # Start time for ETA calculation
     start_time = time.time()
@@ -75,18 +79,20 @@ def analyze_tracking_results(state, results, progress_callback=None):
             unit_divisor=1,
             ncols=130
     ):
+        state.current_frame_id = frame_pos
         if frame_pos in cuts:
             # Reinitialize the tracker at scene cuts
             logger.info(f"Reaching cut at frame {frame_pos}")
             previous_distances = tracker.previous_distances
             logger.info(f"Reinitializing tracker with previous distances: {previous_distances}")
-            tracker = ObjectTracker(fps, frame_pos, image_area, video_info.is_vr)
+            #tracker = ObjectTracker(fps, frame_pos, image_area, video_info.is_vr)
+            tracker = ObjectTracker(state)
             tracker.previous_distances = previous_distances
 
         if frame_pos in list_of_frames:
             # Get sorted boxes for the current frame
             sorted_boxes = results.get_boxes(frame_pos)
-            tracker.tracking_logic(sorted_boxes, frame_pos, height)  # Apply tracking logic
+            tracker.tracking_logic(state, sorted_boxes)  # Apply tracking logic
 
             if tracker.distance:
                 # Append Funscript data if distance is available
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index 69723294..fbf29cc2 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -17,6 +17,8 @@ def __init__(self, is_cli):
         self.video_path: string = None
         self.frame_start: int = 0
         self.frame_end: int | None = None
+        self.current_frame_id = 0
+        self.frame_area = 0
 
         # Detection & decoding
         self.video_reader: Literal["FFmpeg", "FFmpeg + OpenGL (Windows)"] = "FFmpeg" if is_mac() else "FFmpeg + OpenGL (Windows)"
diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index 5f20ff76..16879a24 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -1,19 +1,9 @@
-import logging
 import math
 from collections import deque
-
-
-from utils import lib_KalmanFilter as KF
-from collections import deque
-import math
 import numpy as np
 
 from script_generator.constants import CLASS_NAMES
 from script_generator.utils.logger import logger
-from utils import lib_KalmanFilter as KF
-
-# Configure logging
-logging.basicConfig(level=logging.INFO, format="%(message)s")
 
 
 class LockedPenisBox:
@@ -21,7 +11,6 @@ class LockedPenisBox:
     A class to manage the locked penis box, which represents the detected penis area.
     It stores the box coordinates, height, and active status.
     """
-
     def __init__(self):
         self.box = None  # Coordinates of the locked penis box
         self.height = 0  # Height of the locked penis box
@@ -78,32 +67,30 @@ def from_dict(cls, data):
             locked_box.update(data["box"], data["height"])
         return locked_box
 
-
 class ObjectTracker:
     """
     A class to track objects (e.g., penis, glans, etc.) in a video frame and determine their positions and interactions.
     """
 
-    def __init__(self, fps, frame_pos, image_area, is_vr):
+    def __init__(self, state):
         """
         Initialize the ObjectTracker.
 
         Args:
-            fps (int): Frames per second of the video.
-            frame_pos (int): Current frame position.
-            image_area (int): Area of the video frame.
+            global_state
         """
-        self.is_vr = is_vr
+        self.state = state
         self.class_names = CLASS_NAMES  # List of class names to track
         self.active_tracks = {}  # Active tracks: {track_id: Track object}
         self.inactive_tracks = []  # Inactive tracks (lost tracks)
-        self.distance_kf = KF.KalmanFilter()  # Kalman filter for distance smoothing
+        # self.distance_kf = KF.KalmanFilter()  # Kalman filter for distance smoothing
 
         self.frame = None  # Current video frame
-        self.current_frame_id = frame_pos  # Current frame ID
-        self.image_y_size = 0  # Height of the video frame
-        self.image_area = image_area  # Area of the video frame
-        self.fps = fps  # Frames per second of the video
+        self.current_frame_id = state.current_frame_id  # frame_pos  # Current frame ID
+        self.width = 0  # Height of the video frame
+        self.image_area = state.frame_area # image_area  # Area of the video frame
+        self.fps = state.video_info.fps  # fps  # Frames per second of the video
+        self.isVR = state.video_info.is_vr  # isVR
 
         # Speed and distance thresholds
         self.max_speed = 100 / (0.18 * self.fps)  # Maximum allowed speed for distance changes
@@ -134,12 +121,12 @@ def __init__(self, fps, frame_pos, image_area, is_vr):
         self.pct_weights = {}  # Percentage weights for each track_id
 
         # Initialize normalized positions and distances
-        self.normalized_positions = {class_name: deque(maxlen=200) for class_name in CLASS_NAMES}
-        self.normalized_distances = {class_name: deque(maxlen=200) for class_name in CLASS_NAMES}
+        self.normalized_positions = {class_name: deque(maxlen=200) for class_name in self.class_names}
+        self.normalized_distances = {class_name: deque(maxlen=200) for class_name in self.class_names}
 
         # Detection thresholds
-        self.consecutive_detections = {class_name: 0 for class_name in CLASS_NAMES}
-        self.consecutive_non_detections = {class_name: 0 for class_name in CLASS_NAMES}
+        self.consecutive_detections = {class_name: 0 for class_name in self.class_names}
+        self.consecutive_non_detections = {class_name: 0 for class_name in self.class_names}
         self.detections_threshold = round(self.fps) / 10  # Threshold for considering a detection valid
 
         # Penetration state
@@ -292,23 +279,22 @@ def detect_sex_position_change(self, sex_position, reason):
         self.sex_position_history.append(sex_position)
         position_counts = {position: self.sex_position_history.count(position) for position in self.sex_position_history}
         most_frequent_position = max(position_counts, key=position_counts.get, default="Not relevant")
-
         if most_frequent_position != self.sex_position:
             logger.info(f"@{self.current_frame_id} - Sex position switched to: {most_frequent_position}")
             self.sex_position = most_frequent_position
             self.sex_position_reason = reason
 
-    def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
+    def tracking_logic(self, state, sorted_boxes):
         """
         Main tracking logic to process detected boxes and update tracking state.
 
         Args:
             sorted_boxes (list): List of detected boxes with confidence, class, and track ID.
-            current_frame_id (int): Current frame ID.
-            image_y_size (int): Height of the video frame.
+            width (int): Height of the video frame.
         """
-        self.current_frame_id = current_frame_id
-        self.image_y_size = image_y_size
+        self.state = state
+        self.current_frame_id = state.current_frame_id
+        self.width = state.video_info.width
 
         close_up_detected = False
 
@@ -354,7 +340,7 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                 and not self.penetration:  # equivalent to 3 seconds
             if self.locked_penis_box.active:  # is_active():
                 self.locked_penis_box.deactivate()
-                logger.debug(f"@{self.current_frame_id} - Deactivated locked_penis_box")
+                logger.info(f"@{self.current_frame_id} - Deactivated locked_penis_box")
 
         if self.locked_penis_box.is_active() and self.locked_penis_box.visible < 100 and not self.glans_detected:
             self.penetration = True
@@ -373,7 +359,7 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                         butt_box_area = (b_x2 - b_x1) * (b_y2 - b_y1)
 
                         # Check if the butt box is unusually large (close-up)
-                        if self.is_vr:
+                        if self.isVR:
                             size_threshold = 0.15
                         else:  #2D POV
                             size_threshold = 0.4
@@ -382,7 +368,7 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
                             close_up_detected = True
                             self.detect_sex_position_change('Close up', 'Butt box size beyond threshold')
                             if not self.close_up:
-                                logging.info(
+                                logger.info(
                                     f"@{self.current_frame_id} - Close up detected - butt size beyond threshold: {int((butt_box_area / self.image_area) * 100)}%"
                                 )
                                 self.close_up = True
@@ -413,7 +399,7 @@ def tracking_logic(self, sorted_boxes, current_frame_id, image_y_size):
 
         for box, conf, cls, class_name, track_id in sorted_boxes:
             # discarding those classes for distance computation
-            # if class_name in ['glans', 'penis', 'navel', 'hips center', 'anus']:
+            #if class_name in ['glans', 'penis', 'navel', 'hips center', 'anus']:
             if class_name in ['glans', 'navel', 'hips center', 'anus']:
                 continue
             elif self.locked_penis_box.is_active() and class_name == 'breast' and not self.boxes_overlap(box, self.locked_penis_box.get_box()):
@@ -576,8 +562,8 @@ def handle_class_first(self, class_name, box, conf):
                             self.locked_penis_box.update(self.penis_box, current_height)
 
                         # Move locked penis box towards current penis box
-                        #max_move = max(1, int(self.image_y_size / 960))
-                        if self.is_vr:
+                        #max_move = max(1, int(self.width / 960))
+                        if self.isVR:
                             max_move = 180 // int(self.fps)
                         else:
                             # in 2D POV, camera is not still, actuation needs to be faster
@@ -606,9 +592,9 @@ def handle_class_first(self, class_name, box, conf):
                     self.locked_penis_box.update(self.penis_box, self.penis_box[3] - self.penis_box[1])
                 if self.penetration:
                     self.penetration = False
-                    # logging.info(
+                    #logging.info(
                     #    f"@{self.current_frame_id} - Penetration ended after {self.consecutive_detections['glans']} detections of glans"
-                    # )
+                    #)
                     if self.tracked_body_part != 'Nothing':
                         self.normalized_distances[self.tracked_body_part].clear()
                         self.normalized_distances[self.tracked_body_part].append(100)

From 0c339a0ab0b4970c4ab85b5c8016c3b916a41935 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Mon, 20 Jan 2025 17:00:36 +0100
Subject: [PATCH 093/277] display video info at beginning

---
 script_generator/video/ffmpeg.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/script_generator/video/ffmpeg.py b/script_generator/video/ffmpeg.py
index a22cf1cd..7c56e31e 100644
--- a/script_generator/video/ffmpeg.py
+++ b/script_generator/video/ffmpeg.py
@@ -34,6 +34,8 @@ def get_video_info(video_path):
         num, den = map(int, r_frame_rate.split('/'))  # Split numerator and denominator
         fps = num / den  # Calculate FPS
 
+        logger.info(f"Video Info: {codec_name}, {width}x{height}, {fps:.2f} fps, {total_frames} frames, {duration} seconds")
+
         # If the width is 2x the height we are dealing with a VR video
         is_vr = height == width // 2
 

From 5733b67e6266ea3c04cdb1e3e114f666e7846d20 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Mon, 20 Jan 2025 17:01:40 +0100
Subject: [PATCH 094/277] cleaned comments

---
 utils/lib_FunscriptHandler.py | 9 ---------
 1 file changed, 9 deletions(-)

diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 2b357d63..7d4dd1f2 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -863,12 +863,3 @@ def _is_plateau_in_original(self, plateau, original_plateaus):
             if start >= original_start and end <= original_end:
                 return True
         return False
-
-# if __name__ == "__main__":
-#     video_path = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
-#
-#     funscript_handler = FunscriptGenerator()
-#
-#     # generate heatmap
-#     funscript_handler.generate_heatmap(video_path[:-4] + ".funscript", video_path[
-#                                                                         :-4] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")

From 84aa3c231b3e7c1ca8265fed9a004828d5611e97 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 20 Jan 2025 17:28:14 +0100
Subject: [PATCH 095/277] Save debugging video ui fixed

---
 script_generator/gui/app.py                   |  2 +-
 .../gui/controller/tracking_analysis.py       |  1 -
 script_generator/gui/utils/widgets.py         | 31 ++++++++++---------
 .../gui/views/funscript_generator.py          | 11 ++++---
 .../object_detection/post_process_results.py  |  1 +
 5 files changed, 24 insertions(+), 22 deletions(-)

diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index f53b4ac1..7e35bf85 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -18,7 +18,7 @@ def __init__(self):
             ctypes.windll.shcore.SetProcessDpiAwareness(2)  # For Windows DPI scaling
         # self.tk.call('tk', 'scaling', 1.0)
         self.title("VR funscript generation")
-        self.geometry(f"{('800' if is_mac() else '720')}x920")
+        self.geometry(f"{('800' if is_mac() else '700')}x890")
         self.resizable(False, False)
 
         self.iconphoto(False, tk.PhotoImage(file=LOGO))
diff --git a/script_generator/gui/controller/tracking_analysis.py b/script_generator/gui/controller/tracking_analysis.py
index 47dfe6a1..f021336a 100644
--- a/script_generator/gui/controller/tracking_analysis.py
+++ b/script_generator/gui/controller/tracking_analysis.py
@@ -2,7 +2,6 @@
 
 from script_generator.gui.controller.debug_video import debug_video
 from script_generator.object_detection.analyze_tracking_results import analyze_tracking_results
-from script_generator.object_detection.object_detection_result import ObjectDetectionResult
 from script_generator.object_detection.utils import make_data_boxes, parse_yolo_data_looking_for_penis
 from script_generator.utils.file import load_json_from_file, get_output_file_path
 from script_generator.utils.logger import logger
diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index d16f746b..22c24297 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -62,9 +62,9 @@ def entry(frame, default_value="", tooltip_text=None, **grid_kwargs):
         return entry
 
     @staticmethod
-    def input(parent, label_text, state, attr, row=0, col=0, label_width_px=LABEL_WIDTH, entry_width_px=200, callback=None, tooltip_text=None):
+    def input(parent, label_text, state, attr, row=0, column=0, label_width_px=LABEL_WIDTH, entry_width_px=200, callback=None, tooltip_text=None):
         container = ttk.Frame(parent)
-        container.grid(row=row, column=col, sticky="ew", padx=5, pady=5)
+        container.grid(row=row, column=column, sticky="ew", padx=5, pady=5)
         container.columnconfigure(1, weight=1)
 
         value = tk.StringVar(value=getattr(state, attr))
@@ -87,12 +87,12 @@ def input(parent, label_text, state, attr, row=0, col=0, label_width_px=LABEL_WI
         return container, entry, value
 
     @staticmethod
-    def button(parent, button_text, on_click, row=0, col=0, tooltip_text=None, style_name="Custom.TButton"):
+    def button(parent, button_text, on_click, row=0, column=0, tooltip_text=None, style_name="Custom.TButton"):
         style = ttk.Style()
         style.configure(style_name, padding=(10, 3))
 
         button = ttk.Button(parent, text=button_text, command=on_click, style=style_name)
-        button.grid(row=row, column=col, sticky="w", padx=PADDING_X, pady=PADDING_Y)
+        button.grid(row=row, column=column, sticky="w", padx=PADDING_X, pady=PADDING_Y)
 
         if tooltip_text:
             Tooltip(button, tooltip_text)
@@ -142,9 +142,9 @@ def on_change(val):
         return container, entry, file_path
 
     @staticmethod
-    def labeled_progress(parent, label_text, row=0, col=0, progress_length=300, label_width_px=LABEL_WIDTH, label_percentage_width_px=LABEL_WIDTH, tooltip_text=None):
+    def labeled_progress(parent, label_text, row=0, column=0, progress_length=300, label_width_px=LABEL_WIDTH, label_percentage_width_px=LABEL_WIDTH, tooltip_text=None):
         container = ttk.Frame(parent)
-        container.grid(row=row, column=col, sticky="ew", padx=5, pady=5)
+        container.grid(row=row, column=column, sticky="ew", padx=5, pady=5)
         container.columnconfigure(1, weight=1)  # Allow the progress bar to expand
 
         # Label for progress description with fixed pixel width
@@ -165,12 +165,13 @@ def labeled_progress(parent, label_text, row=0, col=0, progress_length=300, labe
         return container, progress_bar, progress_label, percentage_label
 
     @staticmethod
-    def dropdown(parent, label_text, options, default_value, state, attr, row=0, col=0, label_width_px=LABEL_WIDTH, tooltip_text=None):
+    def dropdown(parent, label_text, options, default_value, state, attr, row=0, column=0, label_width_px=LABEL_WIDTH, tooltip_text=None, **grid_kwargs):
         selected_value = tk.StringVar(value=default_value)
 
         # Create a container for the dropdown
         container = ttk.Frame(parent)
-        container.grid(row=row, column=col, sticky="ew", padx=5, pady=5)
+        grid_kwargs.setdefault("sticky", "ew")
+        container.grid(row=row, column=column, padx=5, pady=5, **grid_kwargs)
         container.columnconfigure(1, weight=1)  # Allow the dropdown to expand
 
         # Label with fixed pixel width
@@ -190,13 +191,13 @@ def dropdown(parent, label_text, options, default_value, state, attr, row=0, col
         return container, label, dropdown, selected_value
 
     @staticmethod
-    def range_selector(parent, label_text, row, state, attr, values, col=0, tooltip_text=None):
-        Widgets.label(parent, label_text, row=row, column=col, sticky="w", padx=PADDING_X, pady=PADDING_Y)
+    def range_selector(parent, label_text, row, state, attr, values, column=0, tooltip_text=None):
+        Widgets.label(parent, label_text, row=row, column=column, sticky="w", padx=PADDING_X, pady=PADDING_Y)
 
         selected_value = tk.StringVar(value=str(getattr(state, attr)))
 
         dropdown = ttk.Combobox(parent, textvariable=selected_value, values=values, width=5, state="readonly")
-        dropdown.grid(row=row, column=col + 1, sticky="w", padx=PADDING_X, pady=PADDING_Y)
+        dropdown.grid(row=row, column=column + 1, sticky="w", padx=PADDING_X, pady=PADDING_Y)
 
         dropdown.bind("<<ComboboxSelected>>", lambda _: setattr(state, attr, int(selected_value.get())))
 
@@ -206,7 +207,7 @@ def range_selector(parent, label_text, row, state, attr, values, col=0, tooltip_
         return dropdown
 
     @staticmethod
-    def checkbox(parent, label_text, state, attr, label_left=True, row=0, col=0, label_width_px=150, tooltip_text=None, **grid_kwargs):
+    def checkbox(parent, label_text, state, attr, label_left=True, row=0, column=0, label_width_px=150, tooltip_text=None, **grid_kwargs):
         # Ensure the parent has a _checkbox_vars attribute to track BooleanVars
         if not hasattr(parent, "_checkbox_vars"):
             parent._checkbox_vars = {}
@@ -219,7 +220,7 @@ def checkbox(parent, label_text, state, attr, label_left=True, row=0, col=0, lab
 
         # Create a container for the checkbox and label
         container = ttk.Frame(parent)
-        container.grid(row=row, column=col, sticky="ew", padx=5, pady=5)
+        container.grid(row=row, column=column, sticky="ew", padx=5, pady=5)
         container.columnconfigure(1, weight=1)  # Allow checkbox to adjust dynamically
 
         if label_left:
@@ -272,14 +273,14 @@ def on_yes():
             user_choice.set(True)
             window.destroy()
 
-        Widgets.button(button_frame, yes_text, on_yes, row=0, col=0)
+        Widgets.button(button_frame, yes_text, on_yes, row=0, column=0)
 
         # No button
         def on_no():
             user_choice.set(False)
             window.destroy()
 
-        Widgets.button(button_frame, no_text, on_no, row=0, col=1)
+        Widgets.button(button_frame, no_text, on_no, row=0, column=1)
 
         # Wait for the user to close the dialog
         window.grab_set()
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 09869462..c7b361d7 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -171,15 +171,16 @@ def start_processing():
         Widgets.dropdown(
             attr="debug_record_duration_var",
             parent=object_detection,
-            label_text="save a frame every",
+            label_text="duration (s)",
             options=[5, 10, 20],
             default_value=5,
             state=state,
-            col=2,
-            row=5
+            column=2,
+            row=5,
+            label_width_px=73,
+            sticky="w"
         )
-        Widgets.label(object_detection, text="seconds", row=5, column=3)
-        Widgets.button(object_detection, "Play debug video (q to quit)", None, row=6)
+        Widgets.button(object_detection, "Play debug video (q to quit)", None, row=5, column=3)
         #endregion
 
         #region FOOTER
diff --git a/script_generator/object_detection/post_process_results.py b/script_generator/object_detection/post_process_results.py
index 29c99c2b..d498e338 100644
--- a/script_generator/object_detection/post_process_results.py
+++ b/script_generator/object_detection/post_process_results.py
@@ -20,6 +20,7 @@ def task_logic(self):
         state = self.state
 
         for task in self.get_task():
+
             frame_pos = task.frame_pos
             det_results = task.yolo_results
             frame = task.rendered_frame

From b25fa72e58846bc44bd6dd56bbf8b277d6bc0b66 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 20 Jan 2025 17:53:45 +0100
Subject: [PATCH 096/277] Re-enable debugging video (WIP) save output to
 separate folders

---
 debug/Display_debug_results.py                |   2 +-
 .../gui/controller/debug_video.py             |   4 +-
 .../gui/views/funscript_generator.py          |  28 +--
 .../analyze_tracking_results.py               |   5 +-
 script_generator/state/app_state.py           |   5 +-
 script_generator/utils/file.py                |   5 +-
 utils/lib_Debugger.py                         |  81 ++++-----
 utils/lib_VideoReaderFFmpeg.py                | 166 +++++++++---------
 8 files changed, 144 insertions(+), 152 deletions(-)

diff --git a/debug/Display_debug_results.py b/debug/Display_debug_results.py
index f00402a8..f0f71119 100644
--- a/debug/Display_debug_results.py
+++ b/debug/Display_debug_results.py
@@ -16,4 +16,4 @@
 
 #debugger.play_video(frame)
 
-debugger.play_video(frame, record=False, downsize_ratio=1) #), duration=4)
\ No newline at end of file
+debugger.play_video(frame, save_debug_video=False, downsize_ratio=1) #), duration=4)
\ No newline at end of file
diff --git a/script_generator/gui/controller/debug_video.py b/script_generator/gui/controller/debug_video.py
index aa7fcae6..1d74f96e 100644
--- a/script_generator/gui/controller/debug_video.py
+++ b/script_generator/gui/controller/debug_video.py
@@ -22,8 +22,8 @@ def debug_video(state: AppState):
 
         state.debugger.play_video(
             start_frame=state.frame_start,
-            duration=state.debug_record_duration if state.debug_record_mode else 0,
-            record=state.debug_record_mode,
+            duration=state.debug_record_duration if state.save_debug_video else 0,
+            save_debug_video=state.save_debug_video,
             downsize_ratio=2
         )
     else:
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index c7b361d7..518a4f7d 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -151,6 +151,20 @@ def start_processing():
         debugging = Widgets.frame(wrapper, title="Debugging", main_section=True, row=5)
         general = Widgets.frame(debugging, title="General", row=0)
         Widgets.checkbox(general, "Logging for debug", state, "debug_mode")
+        Widgets.checkbox(general, "Save debugging video", state=state, attr="save_debug_video", row=2)
+        Widgets.dropdown(
+            attr="debug_record_duration_var",
+            parent=general,
+            label_text="duration (s)",
+            options=[5, 10, 20],
+            default_value=5,
+            state=state,
+            column=2,
+            row=2,
+            label_width_px=73,
+            sticky="w"
+        )
+        Widgets.button(general, "Play debug video (q to quit)", None, row=2, column=3)
 
         script_compare = Widgets.frame(debugging, title="Script compare", row=1)
         Widgets.file_selection(
@@ -167,20 +181,6 @@ def start_processing():
         object_detection = Widgets.frame(debugging, title="Object detection", row=2)
 
         Widgets.checkbox(object_detection, "Live display mode", state, "life_display_mode",tooltip_text="Will show a live preview of the object detection.", row=3)
-        Widgets.checkbox(object_detection, "Save debugging video", state=state, attr="save_debug_as_video", row=5)
-        Widgets.dropdown(
-            attr="debug_record_duration_var",
-            parent=object_detection,
-            label_text="duration (s)",
-            options=[5, 10, 20],
-            default_value=5,
-            state=state,
-            column=2,
-            row=5,
-            label_width_px=73,
-            sticky="w"
-        )
-        Widgets.button(object_detection, "Play debug video (q to quit)", None, row=5, column=3)
         #endregion
 
         #region FOOTER
diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index 48478a81..47ac3dfc 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -205,9 +205,8 @@ def analyze_tracking_results(state, results, progress_callback=None):
                     eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
                 ))
 
-        # TODO improve this
-        # if progress_callback:
-        #     progress_callback()
+        if progress_callback and state.save_debug_video:
+            progress_callback()
 
     state.update_ui(ProgressMessage(
         process="TRACKING_ANALYSIS",
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index fbf29cc2..6cc0c3ac 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -27,11 +27,10 @@ def __init__(self, is_cli):
         self.debug_mode: bool = False
         self.life_display_mode: bool = False
         self.reference_script: string = None
-        self.save_debug_as_video: bool = False
-        self.debug_record_mode: bool = False
+        self.save_debug_video: bool = False
         self.debug_record_duration: int = 0
 
-        # TODO what's this
+        # TODO move this to a batch task class (so parallel inference is possible)
         self.funscript_data = []
         self.funscript_frames = []
         self.funscript_distances = []
diff --git a/script_generator/utils/file.py b/script_generator/utils/file.py
index 168d9136..a9438804 100644
--- a/script_generator/utils/file.py
+++ b/script_generator/utils/file.py
@@ -35,6 +35,7 @@ def get_output_file_path(video_path, suffix):
     """"
     Get the OUTPUT_PATH filename for a specific suffix (e.g. _raw_yolo.json)
     """
-    filename = f"{os.path.basename(video_path)[:-4]}{suffix}"
-    path = os.path.join(OUTPUT_PATH, filename)
+    filename_base = os.path.basename(video_path)[:-4]
+    filename = f"{filename_base}{suffix}"
+    path = os.path.join(OUTPUT_PATH, filename_base, filename)
     return path, filename
\ No newline at end of file
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 15192be8..0ed96e1f 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -91,41 +91,35 @@ def display_frame(self, frame_id):
         """
         self.play_video(frame_id, duration=-1)
 
-    def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=False, downsize_ratio=1):
+    def play_video(self, start_frame=0, duration=0, rolling_window_size=100, save_debug_video=False, downsize_ratio=1):
         """
         Play the video from a specified frame, displaying variables, bounding boxes, and rolling window curves.
         :param start_frame: Frame to start playback from.
         :param duration: Duration of playback in seconds.
         :param rolling_window_size: Size of the rolling window for distance and funscript data.
-        :param record: Whether to record the debug video.
+        :param save_debug_video: Whether to record the debug video.
         :param downsize_ratio: Ratio to downsize the recorded video.
         """
         visualizer = Visualizer()
+        debug_video_path = self.video_path.replace(".mp4", "_debug.mp4")
 
         # Load the video
-        if self.video_reader == "FFmpeg":
-            self.cap = VideoReaderFFmpeg(self.video_path, is_vr=self.is_vr)
-        else:
-            self.cap = cv2.VideoCapture(self.video_path)
+        self.cap = VideoReaderFFmpeg(self.video_path, is_vr=self.is_vr)
 
         self.total_frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
         self.fps = self.cap.get(cv2.CAP_PROP_FPS)
         logger.info(f"Total frames: {self.total_frames}, FPS: {self.fps}")
 
         # Initialize video writer if recording
-        if record:
+        if save_debug_video:
             ret, frame = self.cap.read()
-            if self.video_reader == "OpenCV" and self.is_vr:
-                frame = frame[:, :frame.shape[1] // 2, :]  # only half left of the frame, for VR half
-            #if self.cap.is_vr:
-            #    frame_copy = frame[:, frame.shape[1] // 3 : 2 * frame.shape[1] // 3, :]
-            output_path = self.video_path.replace(".mp4", "_debug.mp4")
+
             fourcc = cv2.VideoWriter_fourcc(*"mp4v")
             out = cv2.VideoWriter(
-                output_path, fourcc, self.fps, (frame.shape[1] // downsize_ratio, frame.shape[0] // downsize_ratio)
+                debug_video_path, fourcc, self.fps, (frame.shape[1] // downsize_ratio, frame.shape[0] // downsize_ratio)
             )
             if not out.isOpened():
-                logger.error(f"Error: Could not open video writer for {output_path}")
+                logger.error(f"Error: Could not open video writer for {debug_video_path}")
                 self.cap.release()
                 return
 
@@ -239,44 +233,43 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, record=
                 break
 
             # Record the frame if enabled
-            if record:
+            if save_debug_video:
                 out.write(frame_copy)
 
             self.current_frame += 1
 
         # Release resources
         self.cap.release()
-        if record:
+        if save_debug_video:
             out.release()
 
-            # TODO re-enable? (didn't have time to debug yet)
-            # # Input and output paths
-            # input_path = output_path
-            # # Add "SPOILER_" to the filename
-            # directory, filename = os.path.split(input_path)
-            # new_filename = f"SPOILER_{filename}"
-            # output_path_ffmpeg = os.path.join(directory, new_filename)
-            #
-            # # FFmpeg command to convert to H.265
-            # ffmpeg_command = [
-            #     "ffmpeg",
-            #     "-y",  # Overwrite output file if it exists
-            #     "-i", input_path,  # Input file
-            #     "-c:v", "libx265",  # Use H.265 codec
-            #     "-crf", "26",  # Constant Rate Factor (quality)
-            #     "-preset", "fast",  # Encoding speed
-            #     "-b:v", "5000k",  # Bitrate
-            #     "-movflags", "+faststart",  # Enable fast streaming
-            #     output_path_ffmpeg
-            # ]
-            #
-            # # Run FFmpeg command
-            # subprocess.run(ffmpeg_command)
-            #
-            # # Delete the intermediate file
-            # if os.path.exists(input_path):
-            #     os.remove(input_path)
-            #     logger.info(f"Deleted intermediate file: {input_path}")
+            # Input and output paths
+            input_path = debug_video_path
+            # Add "SPOILER_" to the filename
+            directory, filename = os.path.split(input_path)
+            new_filename = f"SPOILER_{filename}"
+            output_path_ffmpeg = os.path.join(directory, new_filename)
+
+            # FFmpeg command to convert to H.265
+            ffmpeg_command = [
+                "ffmpeg",
+                "-y",  # Overwrite output file if it exists
+                "-i", input_path,  # Input file
+                "-c:v", "libx265",  # Use H.265 codec
+                "-crf", "26",  # Constant Rate Factor (quality)
+                "-preset", "fast",  # Encoding speed
+                "-b:v", "5000k",  # Bitrate
+                "-movflags", "+faststart",  # Enable fast streaming
+                output_path_ffmpeg
+            ]
+
+            # Run FFmpeg command
+            subprocess.run(ffmpeg_command)
+
+            # Delete the intermediate file
+            if os.path.exists(input_path):
+                os.remove(input_path)
+                logger.info(f"Deleted intermediate file: {input_path}")
 
         cv2.destroyAllWindows()
 
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index 360d2be6..b502d052 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -89,89 +89,89 @@ def _initialize_video_info(self):
             logger.error(f"Error initializing video info: {e}")
             raise
 
-    # def _start_process(self, start_frame=0):
-    #     """
-    #     Start the FFmpeg process to read frames.
-    #     :param start_frame: Frame number to start reading from.
-    #     """
-    #     start_time = (start_frame / self.fps) * 1000  # Convert to milliseconds
-    #     self.current_frame_number = start_frame
-    #
-    #     if self.is_vr:
-    #         #arg_line = "crop=w=iw/2:h=ih:x=0:y=0"
-    #         arg_line = ""
-    #         if self.unwarp:
-    #             if self.projection == "FISHEYE" or (self.projection == None and "FISHEYE" in self.video_path.upper()):
-    #                 print("Proceeding with fisheye projection correction")
-    #                 self.type = "fisheye"
-    #                 self.iv_fov = 190
-    #                 self.ih_fov = 190
-    #                 self.v_fov = 90
-    #                 self.h_fov = 90
-    #                 self.d_fov = 180
-    #             else:  # Assuming Equirectangular
-    #                 print("Assuming Equirectangular projection")
-    #                 self.type = "he"
-    #                 self.iv_fov = 250
-    #                 self.ih_fov = 120
-    #                 self.v_fov = 90
-    #                 self.h_fov = 90
-    #                 self.d_fov = 180
-    #             arg_line = arg_line + f"v360={self.type}:in_stereo=sbs:output=sg"
-    #             arg_line = arg_line + f":iv_fov={self.iv_fov}:ih_fov={self.ih_fov}"
-    #             arg_line = arg_line + f":d_fov={self.d_fov}:v_fov={self.v_fov}:h_fov={self.h_fov}"
-    #             arg_line = arg_line + f":pitch=-25:yaw=0:roll=0"
-    #             arg_line = arg_line + f":w={self.width}:h={self.height}"
-    #             arg_line = arg_line + f":interp=lanczos:reset_rot=1"
-    #             arg_line = arg_line + f",lutyuv=y=gammaval(0.7)"
-    #             #arg_line = arg_line + f",eq=brightness=0.1:contrast=1.5"
-    #             #arg_line = arg_line + f",format=gray"
-    #             #arg_line = arg_line + f",histeq"
-    #         else:
-    #             arg_line = "crop=w=iw/2:h=ih:x=0:y=0"
-    #         # Add scale filter with height and auto-width (-1)
-    #         #arg_line += f",scale=-1:{target_height}"
-    #         #arg_line += f",scale=-1:{1080}"
-    #         # perf for on the fly downscale to 1080p were terrible
-    #
-    #         cmd = [
-    #             self.ffmpeg_path,
-    #             '-nostats',  # Disable progress statistics
-    #             '-loglevel', 'warning',
-    #             "-ss", str(start_time / 1000),  # Seek to start time in seconds
-    #             "-i", self.video_path,
-    #             "-an",  # Disable audio processing
-    #             "-map", "0:v:0",
-    #             "-vf", arg_line,
-    #             "-f", "rawvideo",  # Output raw video data
-    #             "-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
-    #             "-vsync", "0",  # Disable frame rate synchronization
-    #             "-threads", "0",  # Use maximum threads available
-    #             "-",  # Output to stdout
-    #         ]
-    #     else:
-    #         # FFmpeg command to read frames
-    #         cmd = [
-    #             self.ffmpeg_path,
-    #             '-nostats',  # Disable progress statistics
-    #             '-loglevel', 'warning',
-    #             "-ss", str(start_time / 1000),  # Seek to start time in seconds
-    #             "-i", self.video_path,
-    #             "-an",  # Disable audio processing
-    #             "-f", "rawvideo",  # Output raw video data
-    #             "-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
-    #             "-vsync", "0",  # Disable frame rate synchronization
-    #             "-",  # Output to stdout
-    #         ]
-    #
-    #     # Kill the process if already running
-    #     if self.process:
-    #         self.process.terminate()
-    #
-    #     # Start FFmpeg process
-    #     #self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-    #     self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
-    #     self.frame_size = self.width * self.height * 3  # Size of one frame in bytes
+    def _start_process(self, start_frame=0):
+        """
+        Start the FFmpeg process to read frames.
+        :param start_frame: Frame number to start reading from.
+        """
+        start_time = (start_frame / self.fps) * 1000  # Convert to milliseconds
+        self.current_frame_number = start_frame
+
+        if self.is_vr:
+            #arg_line = "crop=w=iw/2:h=ih:x=0:y=0"
+            arg_line = ""
+            if self.unwarp:
+                if self.projection == "FISHEYE" or (self.projection == None and "FISHEYE" in self.video_path.upper()):
+                    print("Proceeding with fisheye projection correction")
+                    self.type = "fisheye"
+                    self.iv_fov = 190
+                    self.ih_fov = 190
+                    self.v_fov = 90
+                    self.h_fov = 90
+                    self.d_fov = 180
+                else:  # Assuming Equirectangular
+                    print("Assuming Equirectangular projection")
+                    self.type = "he"
+                    self.iv_fov = 250
+                    self.ih_fov = 120
+                    self.v_fov = 90
+                    self.h_fov = 90
+                    self.d_fov = 180
+                arg_line = arg_line + f"v360={self.type}:in_stereo=sbs:output=sg"
+                arg_line = arg_line + f":iv_fov={self.iv_fov}:ih_fov={self.ih_fov}"
+                arg_line = arg_line + f":d_fov={self.d_fov}:v_fov={self.v_fov}:h_fov={self.h_fov}"
+                arg_line = arg_line + f":pitch=-25:yaw=0:roll=0"
+                arg_line = arg_line + f":w={self.width}:h={self.height}"
+                arg_line = arg_line + f":interp=lanczos:reset_rot=1"
+                arg_line = arg_line + f",lutyuv=y=gammaval(0.7)"
+                #arg_line = arg_line + f",eq=brightness=0.1:contrast=1.5"
+                #arg_line = arg_line + f",format=gray"
+                #arg_line = arg_line + f",histeq"
+            else:
+                arg_line = "crop=w=iw/2:h=ih:x=0:y=0"
+            # Add scale filter with height and auto-width (-1)
+            #arg_line += f",scale=-1:{target_height}"
+            #arg_line += f",scale=-1:{1080}"
+            # perf for on the fly downscale to 1080p were terrible
+
+            cmd = [
+                self.ffmpeg_path,
+                '-nostats',  # Disable progress statistics
+                '-loglevel', 'warning',
+                "-ss", str(start_time / 1000),  # Seek to start time in seconds
+                "-i", self.video_path,
+                "-an",  # Disable audio processing
+                "-map", "0:v:0",
+                "-vf", arg_line,
+                "-f", "rawvideo",  # Output raw video data
+                "-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
+                "-vsync", "0",  # Disable frame rate synchronization
+                "-threads", "0",  # Use maximum threads available
+                "-",  # Output to stdout
+            ]
+        else:
+            # FFmpeg command to read frames
+            cmd = [
+                self.ffmpeg_path,
+                '-nostats',  # Disable progress statistics
+                '-loglevel', 'warning',
+                "-ss", str(start_time / 1000),  # Seek to start time in seconds
+                "-i", self.video_path,
+                "-an",  # Disable audio processing
+                "-f", "rawvideo",  # Output raw video data
+                "-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
+                "-vsync", "0",  # Disable frame rate synchronization
+                "-",  # Output to stdout
+            ]
+
+        # Kill the process if already running
+        if self.process:
+            self.process.terminate()
+
+        # Start FFmpeg process
+        #self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+        self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
+        self.frame_size = self.width * self.height * 3  # Size of one frame in bytes
 
     def read(self):
         """

From df273de7ff7e9854077a36f0bcadbd17e8591d20 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Mon, 20 Jan 2025 19:06:51 +0100
Subject: [PATCH 097/277] Hotfix on Funscript tweaking section * Deactivating
 the simplification was raising an error * The rounding of the position was
 not deactivated in sync with simplification state * Duplicate timings in
 reference funscript were raising an error

---
 params/config.py              |  2 +-
 utils/lib_FunscriptHandler.py | 32 ++++++++++++++++++++++----------
 2 files changed, 23 insertions(+), 11 deletions(-)

diff --git a/params/config.py b/params/config.py
index 4d0f5f97..5c0e50a3 100644
--- a/params/config.py
+++ b/params/config.py
@@ -2,7 +2,7 @@
 import platform
 import os
 
-version = "0.0.2_25-01-18"
+version = "0.0.3"
 
 # ffmpeg and ffprobe paths - replace with your own if not in your system path
 win_ffmpeg_path = "C:/ffmpeg/bin/ffmpeg.exe"
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 13cf2591..60ef6c5d 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -58,14 +58,14 @@ def generate(self, global_state):
             # Apply VW simplification if enabled
             if global_state.vw_simplification_enabled:
                 global_state.logger.info("Positions adjustment - step 2 (VW algorithm simplification)")
-                zip_vw_positions = simplify_coords(zip_positions, global_state.vw_factor)
-                global_state.logger.info(f"Length of VW filtered positions: {len(zip_vw_positions)}")
+                zip_positions = simplify_coords(zip_positions, global_state.vw_factor)
+                global_state.logger.info(f"Length of VW filtered positions: {len(zip_positions)}")
             else:
                 global_state.logger.info("Skipping positions adjustment - step 2 (VW algorithm simplification)")
 
             # Extract timestamps and positions
-            ats = [p[0] for p in zip_vw_positions]
-            positions = [p[1] for p in zip_vw_positions]
+            ats = [p[0] for p in zip_positions]
+            positions = [p[1] for p in zip_positions]
 
             # Remap positions to 0-100 range
             global_state.logger.info("Positions adjustment - step 3 (remapping)")
@@ -92,9 +92,13 @@ def generate(self, global_state):
                 global_state.logger.info("Skipping positions adjustment - step 5 (amplitude boosting)")
 
             # Round position values to the closest multiple of 5, still between 0 and 100
-            global_state.logger.info("Positions adjustment - step 6 (rounding to the closest multiple of 5)")
-            #adjusted_positions = [round(p, -1) for p in adjusted_positions] # multiple of 10
-            adjusted_positions = [round(p / global_state.rounding) * global_state.rounding for p in adjusted_positions]
+            if global_state.vw_simplification_enabled:
+                global_state.logger.info(f"Positions adjustment - step 6 (rounding to the closest multiple of {global_state.rounding})")
+                adjusted_positions = [round(p / global_state.rounding) * global_state.rounding for p in
+                                      adjusted_positions]
+
+            else:
+                global_state.logger.info(f"Skipping positions adjustment - step 6 (rounding to the closest multiple of {global_state.rounding})")
 
             # Recombine timestamps and adjusted positions
             global_state.logger.info("Re-assembling ats and positions")
@@ -467,11 +471,11 @@ def create_combined_plot(self, ref_sections, gen_sections, screenshots, sections
         # Heatmaps (First row: 2 columns spanning the entire width)
         if ref_sections:
             ax_ref_heatmap = fig.add_subplot(gs[0, :2])
-            self.generate_heatmap_inline(ax_ref_heatmap, ref_times, ref_positions)
+            self.generate_heatmap_inline(ax_ref_heatmap, ref_times, ref_positions, global_state)
             ax_ref_heatmap.set_title('Reference Funscript Heatmap', fontsize=14)
 
         ax_gen_heatmap = fig.add_subplot(gs[0, 2:])
-        self.generate_heatmap_inline(ax_gen_heatmap, gen_times, gen_positions)
+        self.generate_heatmap_inline(ax_gen_heatmap, gen_times, gen_positions, global_state)
         ax_gen_heatmap.set_title('Generated Funscript Heatmap', fontsize=14)
 
         if ref_sections:
@@ -544,7 +548,7 @@ def create_combined_plot(self, ref_sections, gen_sections, screenshots, sections
         output_image_path = os.path.join(directory, new_filename)
         plt.savefig(output_image_path[:-4] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png", dpi=100)
 
-    def generate_heatmap_inline(self, ax, times, positions):
+    def generate_heatmap_inline(self, ax, times, positions, global_state):
         """
         Generates a heatmap on the given axes using the existing `generate_heatmap` logic.
 
@@ -556,6 +560,14 @@ def generate_heatmap_inline(self, ax, times, positions):
         if not times or not positions:
             return
 
+        # Bug fix, happened on some reference scripts with 2 identical times values : keep only the first one
+        for i in range(1, len(times)):
+            if times[i] == times[i-1]:
+                times.pop(i)
+                positions.pop(i)
+                global_state.logger.info(f"Removed duplicate time value {times[i]}")
+                break
+
         # Calculate speed (position change per time interval)
         speeds = np.abs(np.diff(positions) / np.diff(times)) * 1000  # Positions per second
 

From c713d68b5bded85bac3758011ae8cd979cb57ded Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 20 Jan 2025 19:11:07 +0100
Subject: [PATCH 098/277] Re-enable debugging video (WIP) Added tooltip texts

---
 .../gui/controller/debug_video.py             |  2 +-
 .../gui/controller/tracking_analysis.py       |  2 +-
 .../gui/controller/video_analysis.py          |  2 +-
 script_generator/gui/utils/widgets.py         |  1 +
 .../gui/views/funscript_generator.py          | 65 ++++++++++++-------
 .../analyze_tracking_results.py               | 13 ++--
 script_generator/scripts/analyze_video.py     |  5 ++
 script_generator/state/app_state.py           |  6 +-
 script_generator/utils/file.py                | 20 ++++--
 utils/lib_Debugger.py                         | 18 ++---
 10 files changed, 81 insertions(+), 53 deletions(-)

diff --git a/script_generator/gui/controller/debug_video.py b/script_generator/gui/controller/debug_video.py
index 1d74f96e..ba6c7303 100644
--- a/script_generator/gui/controller/debug_video.py
+++ b/script_generator/gui/controller/debug_video.py
@@ -22,7 +22,7 @@ def debug_video(state: AppState):
 
         state.debugger.play_video(
             start_frame=state.frame_start,
-            duration=state.debug_record_duration if state.save_debug_video else 0,
+            duration=state.debug_video_duration if state.save_debug_video else 0,
             save_debug_video=state.save_debug_video,
             downsize_ratio=2
         )
diff --git a/script_generator/gui/controller/tracking_analysis.py b/script_generator/gui/controller/tracking_analysis.py
index f021336a..08acbda4 100644
--- a/script_generator/gui/controller/tracking_analysis.py
+++ b/script_generator/gui/controller/tracking_analysis.py
@@ -38,7 +38,7 @@ def tracking_analysis(state):
     logger.info(f"Frame Start adjusted to: {state.frame_start}")
 
     # Performing the tracking part and generation of the raw funscript data
-    state.funscript_data = analyze_tracking_results(state, results, progress_callback=lambda: debug_video(state))
+    state.funscript_data = analyze_tracking_results(state, results, debug_video_callback=lambda: debug_video(state))
 
     state.debugger.save_logs()
 
diff --git a/script_generator/gui/controller/video_analysis.py b/script_generator/gui/controller/video_analysis.py
index ebe16264..19f4edfa 100644
--- a/script_generator/gui/controller/video_analysis.py
+++ b/script_generator/gui/controller/video_analysis.py
@@ -17,7 +17,7 @@ def video_analysis(state):
 
     logger.info(f"Processing video: {state.video_path}")
     logger.info(f"Video Reader: {state.video_reader}")
-    logger.info(f"Debug Mode: {state.debug_mode}")
+    logger.info(f"Debug Mode: {state.save_debug_file}")
     logger.info(f"Live Display Mode: {state.life_display_mode}")
     logger.info(f"Frame Start: {state.frame_start}")
     logger.info(f"Frame End: {state.frame_end}")
diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index 22c24297..4f71af0e 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -137,6 +137,7 @@ def on_change(val):
         file_path.trace("w", lambda *args: on_change(file_path.get()))
 
         if tooltip_text:
+            Tooltip(entry, tooltip_text)
             Tooltip(button, tooltip_text)
 
         return container, entry, file_path
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 518a4f7d..67e145d6 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -12,7 +12,7 @@
 
 class FunscriptGeneratorPage(tk.Frame):
     def __init__(self, parent, controller):
-        #region SETUP
+        # region SETUP
         super().__init__(parent)
         self.controller = controller
         self.state: AppState = controller.state
@@ -22,9 +22,9 @@ def __init__(self, parent, controller):
         self.grid_columnconfigure(0, weight=1)
 
         wrapper = Widgets.frame(self, title=None, sticky="nsew")
-        #endregion
+        # endregion
 
-        #region VIDEO SELECTION
+        # region VIDEO SELECTION
         video_selection = Widgets.frame(wrapper, title="Video Selection", main_section=True)
 
         Widgets.file_selection(
@@ -35,6 +35,7 @@ def __init__(self, parent, controller):
             file_selector_title="Choose a File",
             file_types=[("Text Files", "*.mp4 *.avi *.mov *.mkv"), ("All Files", "*.*")],
             state=state,
+            tooltip_text="The video to generate a funscript for",
             # if the file changes we need to make sure the video info is reset
             # TODO add os.path.exists and update video_info immediately if found
             command=lambda val: setattr(state, 'video_info', None)
@@ -50,16 +51,16 @@ def __init__(self, parent, controller):
             state=state,
             row=1
         )
-        #endregion
+        # endregion
 
-        #region OPTIONAL SETTINGS
+        # region OPTIONAL SETTINGS
         optional_settings = Widgets.frame(wrapper, title="Optional settings", main_section=True, row=2)
 
-        Widgets.input(optional_settings, "Frame Start", state=state, attr="frame_start")
-        Widgets.input(optional_settings, "Frame End", state=state, attr="frame_start", row=1)
-        #endregion
+        Widgets.input(optional_settings, "Frame Start", state=state, attr="frame_start", tooltip_text="Where to start processing the video. If you have a 60fps video starting at 10s would mean frame 600")
+        Widgets.input(optional_settings, "Frame End", state=state, attr="frame_start", tooltip_text="Where to end processing the video. If you have a 60fps video stopping  at 10s would mean frame 600", row=1)
+        # endregion
 
-        #region PROCESSING
+        # region PROCESSING
         processing = Widgets.frame(wrapper, title="Processing", main_section=True, row=3)
         yolo_p_container, yolo_p, yolo_p_label, yolo_p_perc = Widgets.labeled_progress(processing, "YOLO Detection", row=0)
         # scene_p_container, scene_p, scene_p_label, scene_p_perc = Widgets.labeled_progress(processing, "Scene detection", row=1)
@@ -68,10 +69,11 @@ def __init__(self, parent, controller):
         def start_processing():
             # TODO reset the progress bars
             video_analysis(state)
+
         Widgets.button(processing, "Start processing", start_processing, row=3)
-        #endregion
+        # endregion
 
-        #region FUNSCRIPT TWEAKING
+        # region FUNSCRIPT TWEAKING
         tweaking = Widgets.frame(wrapper, title="Funscript", main_section=True, row=4)
         # tweaking.grid_rowconfigure(1, weight=10)
         tweaking_container = ttk.Frame(tweaking)
@@ -145,17 +147,32 @@ def start_processing():
         # Regenerate Funscript Button
         Widgets.button(tweaking_container, "Regenerate Funscript", lambda: regenerate_funscript(self.state), row=2)
 
-        #endregion
+        # endregion
 
-        #region DEBUGGING
+        # region DEBUGGING
         debugging = Widgets.frame(wrapper, title="Debugging", main_section=True, row=5)
         general = Widgets.frame(debugging, title="General", row=0)
-        Widgets.checkbox(general, "Logging for debug", state, "debug_mode")
-        Widgets.checkbox(general, "Save debugging video", state=state, attr="save_debug_video", row=2)
+        Widgets.checkbox(
+            general, "Live display mode",
+            state,
+            "life_display_mode",
+            tooltip_text="Use q to quite.\n\nWill show a live preview of what is being generated.\n\nFor debugging only this will greatly reduce your performance.\nStage 1: Show bounding boxes during object detection.\nStage 2: Show funscript and metric overlay while the funscript is being processed.",
+            row=0
+        )
+        Widgets.checkbox(general, "Save debug file", state, "save_debug_file", tooltip_text="Saves  a debug file to disk with all collected metrics. This file can be very large.", row=1)
+        Widgets.checkbox(
+            general,
+            "Save debugging video",
+            tooltip_text="Will save a debug video once funscript processing is complete that can be\neasily shared on Discord for showcasing issues or areas of improvement.",
+            state=state,
+            attr="save_debug_video",
+            row=2
+        )
         Widgets.dropdown(
             attr="debug_record_duration_var",
             parent=general,
             label_text="duration (s)",
+            tooltip_text="Duration of the debug video",
             options=[5, 10, 20],
             default_value=5,
             state=state,
@@ -164,7 +181,7 @@ def start_processing():
             label_width_px=73,
             sticky="w"
         )
-        Widgets.button(general, "Play debug video (q to quit)", None, row=2, column=3)
+        Widgets.button(general, "Open debug video", None, row=2, column=5, tooltip_text="Open the debug video after the funscript generation process has completed.")
 
         script_compare = Widgets.frame(debugging, title="Script compare", row=1)
         Widgets.file_selection(
@@ -174,20 +191,18 @@ def start_processing():
             button_text="Browse",
             file_selector_title="Choose a File",
             file_types=[("Funscript Files", "*.funscript"), ("All Files", "*.*")],
+            tooltip_text="If provided the reference script will be compared in the\nfunscript report that is generated on completion and be shown\nin the live display funscript overlay when enabled.",
             state=state,
             row=0
         )
+        # endregion
 
-        object_detection = Widgets.frame(debugging, title="Object detection", row=2)
-
-        Widgets.checkbox(object_detection, "Live display mode", state, "life_display_mode",tooltip_text="Will show a live preview of the object detection.", row=3)
-        #endregion
-
-        #region FOOTER
+        # region FOOTER
         Widgets.disclaimer(wrapper)
-        #endregion
 
-        #region UI UPDATE CALLBACK
+        # endregion
+
+        # region UI UPDATE CALLBACK
         def update_ui(msg: UIMessage):
             """Handle UI updates using a switch-like statement."""
 
@@ -220,4 +235,4 @@ def handle_progress_message(progress_msg: ProgressMessage):
             self.controller.after(0, process_update)
 
         self.state.update_ui = update_ui
-        #endregion
\ No newline at end of file
+        # endregion
diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index 47ac3dfc..750aa99b 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -16,7 +16,7 @@
 from utils.lib_Visualizer import Visualizer
 
 
-def analyze_tracking_results(state, results, progress_callback=None):
+def analyze_tracking_results(state, results, debug_video_callback=None):
     width, height = get_cropped_dimensions(state.video_info)
     list_of_frames = results.get_all_frame_ids()  # Get all frame IDs with detections
     visualizer = Visualizer()  # Initialize the visualizer
@@ -99,7 +99,7 @@ def analyze_tracking_results(state, results, progress_callback=None):
                 state.funscript_frames.append(frame_pos)
                 state.funscript_distances.append(int(tracker.distance))
 
-            if state.debug_mode:
+            if state.save_debug_file:
                 # Log debugging information
                 bounding_boxes = []
                 for box in sorted_boxes:
@@ -126,6 +126,8 @@ def analyze_tracking_results(state, results, progress_callback=None):
                         'track_id': box[4],
                         'position': position,
                     })
+
+                # Add debug information to the debugger class so it can be saved later
                 state.debugger.log_frame(
                     frame_pos,
                     bounding_boxes=bounding_boxes,
@@ -185,7 +187,8 @@ def analyze_tracking_results(state, results, progress_callback=None):
             if state.funscript_distances:
                 frame_display = visualizer.draw_gauge(frame_display, state.funscript_distances[-1])
 
-            cv2.imshow("Combined Results", frame_display)
+            # TODO this probably needs to be disabled but I need to check yolo inference
+            # cv2.imshow("Combined Results", frame_display)
             cv2.waitKey(1)
 
         # Update progress
@@ -205,8 +208,8 @@ def analyze_tracking_results(state, results, progress_callback=None):
                     eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
                 ))
 
-        if progress_callback and state.save_debug_video:
-            progress_callback()
+        if debug_video_callback and state.life_display_mode:
+            debug_video_callback()
 
     state.update_ui(ProgressMessage(
         process="TRACKING_ANALYSIS",
diff --git a/script_generator/scripts/analyze_video.py b/script_generator/scripts/analyze_video.py
index 3df5c476..0f1d8851 100644
--- a/script_generator/scripts/analyze_video.py
+++ b/script_generator/scripts/analyze_video.py
@@ -1,3 +1,4 @@
+import os
 import queue
 import threading
 import time
@@ -12,6 +13,7 @@
 from script_generator.state.app_state import AppState
 from script_generator.tasks.abstract_task_processor import TaskProcessorTypes
 from script_generator.tasks.tasks import AnalyzeVideoTask, AnalyzeFrameTask
+from script_generator.utils.file import get_output_file_path, check_create_output_folder
 from script_generator.utils.logger import logger
 from script_generator.video.video_conversion.vr_to_2d_task_processor import VrTo2DTaskProcessor
 from script_generator.video.video_task_processor import VideoTaskProcessor
@@ -20,6 +22,9 @@
 def analyze_video(state: AppState) -> List[AnalyzeFrameTask]:
     logger.info(f"[OBJECT DETECTION] Starting up pipeline with profiling in {'sequential mode' if SEQUENTIAL_MODE else 'parallel mode'}...")
 
+    # make sure the output folder exists for this video
+    check_create_output_folder(state.video_path)
+
     use_open_gl = state.video_reader == "FFmpeg + OpenGL (Windows)"
 
     # Initialize batch task
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index 6cc0c3ac..25aecb2e 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -24,11 +24,11 @@ def __init__(self, is_cli):
         self.video_reader: Literal["FFmpeg", "FFmpeg + OpenGL (Windows)"] = "FFmpeg" if is_mac() else "FFmpeg + OpenGL (Windows)"
 
         # Debug
-        self.debug_mode: bool = False
+        self.save_debug_file: bool = False
+        self.save_debug_video: bool = False
+        self.debug_video_duration: int = 0
         self.life_display_mode: bool = False
         self.reference_script: string = None
-        self.save_debug_video: bool = False
-        self.debug_record_duration: int = 0
 
         # TODO move this to a batch task class (so parallel inference is possible)
         self.funscript_data = []
diff --git a/script_generator/utils/file.py b/script_generator/utils/file.py
index a9438804..f435c538 100644
--- a/script_generator/utils/file.py
+++ b/script_generator/utils/file.py
@@ -14,7 +14,13 @@ def write_dataset(file_path, data):
     """
     logger.info(f"Exporting data...")
     export_start = time.time()
-    # Write the data to the file (overwrites if it exists)
+
+    # Ensure the directory exists
+    directory = os.path.dirname(file_path)
+    if not os.path.exists(directory):
+        os.makedirs(directory)
+
+        # Write the data to the file (overwrites if it exists)
     with open(file_path, 'w') as f:
         json.dump(data, f)
     export_end = time.time()
@@ -31,11 +37,17 @@ def load_json_from_file(file_path):
         logger.info(f"Loaded data from {file_path}, length: {len(data)}")
     return data
 
-def get_output_file_path(video_path, suffix):
+def get_output_file_path(video_path, suffix, add_spoiler_prefix=False):
     """"
     Get the OUTPUT_PATH filename for a specific suffix (e.g. _raw_yolo.json)
     """
     filename_base = os.path.basename(video_path)[:-4]
-    filename = f"{filename_base}{suffix}"
+    filename = f"{'SPOILER_' if add_spoiler_prefix else ''}{filename_base}{suffix}"
     path = os.path.join(OUTPUT_PATH, filename_base, filename)
-    return path, filename
\ No newline at end of file
+    return path, filename
+
+def check_create_output_folder(video_path):
+    output_dir, _ = get_output_file_path(video_path, "")
+    directory = os.path.dirname(output_dir)
+    if not os.path.exists(directory):
+        os.makedirs(directory)
\ No newline at end of file
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 0ed96e1f..9bdd83fa 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -164,8 +164,6 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, save_de
             if not ret:
                 break
 
-            if self.video_reader == "OpenCV" and self.is_vr:
-                frame = frame[:, :frame.shape[1] // 2, :]  # only half left of the frame, for VR half
             frame_copy = frame.copy()  # make a copy of the frame to make it writeable, useful for ffmpeg library here
             # Display variables and bounding boxes
             str_frame_id = str(self.current_frame)
@@ -242,19 +240,13 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, save_de
         self.cap.release()
         if save_debug_video:
             out.release()
-
-            # Input and output paths
-            input_path = debug_video_path
-            # Add "SPOILER_" to the filename
-            directory, filename = os.path.split(input_path)
-            new_filename = f"SPOILER_{filename}"
-            output_path_ffmpeg = os.path.join(directory, new_filename)
+            output_path_ffmpeg, _ = get_output_file_path(debug_video_path, "_rawyolo.json", True)
 
             # FFmpeg command to convert to H.265
             ffmpeg_command = [
                 "ffmpeg",
                 "-y",  # Overwrite output file if it exists
-                "-i", input_path,  # Input file
+                "-i", debug_video_path,  # Input file
                 "-c:v", "libx265",  # Use H.265 codec
                 "-crf", "26",  # Constant Rate Factor (quality)
                 "-preset", "fast",  # Encoding speed
@@ -267,9 +259,9 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, save_de
             subprocess.run(ffmpeg_command)
 
             # Delete the intermediate file
-            if os.path.exists(input_path):
-                os.remove(input_path)
-                logger.info(f"Deleted intermediate file: {input_path}")
+            if os.path.exists(debug_video_path):
+                os.remove(debug_video_path)
+                logger.info(f"Deleted intermediate file: {debug_video_path}")
 
         cv2.destroyAllWindows()
 

From 92fd1db41dc5826aaefc68aa881c37c7742b5876 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Mon, 20 Jan 2025 19:18:33 +0100
Subject: [PATCH 099/277] Hotfix on Funscript tweaking section * Deactivating
 the simplification was raising an error * The rounding of the position was
 not deactivated in sync with simplification state * Duplicate timings in
 reference funscript were raising an error

---
 utils/lib_FunscriptHandler.py | 34 ++++++++++++++++++++++++----------
 1 file changed, 24 insertions(+), 10 deletions(-)

diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 7d4dd1f2..72016add 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -65,14 +65,14 @@ def generate(self, state):
             # Apply VW simplification if enabled
             if state.vw_simplification_enabled:
                 logger.info("Positions adjustment - step 2 (VW algorithm simplification)")
-                zip_vw_positions = simplify_coords(zip_positions, state.vw_factor)
-                logger.info(f"Length of VW filtered positions: {len(zip_vw_positions)}")
+                zip_positions = simplify_coords(zip_positions, state.vw_factor)
+                logger.info(f"Length of VW filtered positions: {len(zip_positions)}")
             else:
                 logger.info("Skipping positions adjustment - step 2 (VW algorithm simplification)")
 
             # Extract timestamps and positions
-            ats = [p[0] for p in zip_vw_positions]
-            positions = [p[1] for p in zip_vw_positions]
+            ats = [p[0] for p in zip_positions]
+            positions = [p[1] for p in zip_positions]
 
             # Remap positions to 0-100 range
             logger.info("Positions adjustment - step 3 (remapping)")
@@ -101,9 +101,15 @@ def generate(self, state):
                 logger.info("Skipping positions adjustment - step 5 (amplitude boosting)")
 
             # Round position values to the closest multiple of 5, still between 0 and 100
-            logger.info("Positions adjustment - step 6 (rounding to the closest multiple of 5)")
-            # adjusted_positions = [round(p, -1) for p in adjusted_positions] # multiple of 10
-            adjusted_positions = [round(p / state.rounding) * state.rounding for p in adjusted_positions]
+            if state.vw_simplification_enabled:
+                state.logger.info(
+                    f"Positions adjustment - step 6 (rounding to the closest multiple of {state.rounding})")
+                adjusted_positions = [round(p / state.rounding) * state.rounding for p in
+                                      adjusted_positions]
+
+            else:
+                state.logger.info(
+                    f"Skipping positions adjustment - step 6 (rounding to the closest multiple of {state.rounding})")
 
             # Recombine timestamps and adjusted positions
             logger.info("Re-assembling ats and positions")
@@ -483,11 +489,11 @@ def create_combined_plot(self, ref_sections, gen_sections, screenshots, sections
         # Heatmaps (First row: 2 columns spanning the entire width)
         if ref_sections:
             ax_ref_heatmap = fig.add_subplot(gs[0, :2])
-            self.generate_heatmap_inline(ax_ref_heatmap, ref_times, ref_positions)
+            self.generate_heatmap_inline(ax_ref_heatmap, ref_times, ref_positions, state)
             ax_ref_heatmap.set_title('Reference Funscript Heatmap', fontsize=14)
 
         ax_gen_heatmap = fig.add_subplot(gs[0, 2:])
-        self.generate_heatmap_inline(ax_gen_heatmap, gen_times, gen_positions)
+        self.generate_heatmap_inline(ax_gen_heatmap, gen_times, gen_positions, state)
         ax_gen_heatmap.set_title('Generated Funscript Heatmap', fontsize=14)
 
         if ref_sections:
@@ -560,7 +566,7 @@ def create_combined_plot(self, ref_sections, gen_sections, screenshots, sections
         output_image_path = os.path.join(directory, new_filename)
         plt.savefig(output_image_path[:-4] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png", dpi=100)
 
-    def generate_heatmap_inline(self, ax, times, positions):
+    def generate_heatmap_inline(self, ax, times, positions, state):
         """
         Generates a heatmap on the given axes using the existing `generate_heatmap` logic.
 
@@ -572,6 +578,14 @@ def generate_heatmap_inline(self, ax, times, positions):
         if not times or not positions:
             return
 
+        # Bug fix, happened on some reference scripts with 2 identical times values : keep only the first one
+        for i in range(1, len(times)):
+            if times[i] == times[i - 1]:
+                times.pop(i)
+                positions.pop(i)
+                state.logger.info(f"Removed duplicate time value {times[i]}")
+                break
+
         # Calculate speed (position change per time interval)
         # Filter out NaN values from times and positions
         # TODO filter nan at one logical moment

From 8a8c5b0ccbfb5d7b83a8b24b1977eb2dccff258d Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 20 Jan 2025 19:21:08 +0100
Subject: [PATCH 100/277] Center yes or no popup

---
 .../gui/controller/video_analysis.py          |  4 +--
 script_generator/gui/utils/widgets.py         | 32 +++++++++++++++++--
 .../gui/views/funscript_generator.py          |  2 +-
 script_generator/object_detection/utils.py    |  5 +--
 4 files changed, 36 insertions(+), 7 deletions(-)

diff --git a/script_generator/gui/controller/video_analysis.py b/script_generator/gui/controller/video_analysis.py
index 19f4edfa..6f924de9 100644
--- a/script_generator/gui/controller/video_analysis.py
+++ b/script_generator/gui/controller/video_analysis.py
@@ -10,7 +10,7 @@
 from utils.lib_Debugger import Debugger
 
 
-def video_analysis(state):
+def video_analysis(state, root):
     if not state.video_path:
         messagebox.showerror("Error", "Please select a video file.")
         return
@@ -28,7 +28,7 @@ def run():
         log_file, _ = get_output_file_path(state.video_path, "_debug_logs.json")
         state.debugger = Debugger(state.video_path, log_file=log_file)
 
-        skip_detection = check_skip_object_detection(state)
+        skip_detection = check_skip_object_detection(state, root)
 
         if skip_detection:
             state.set_video_info()
diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index 4f71af0e..ce7a59ec 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -253,12 +253,40 @@ def checkbox(parent, label_text, state, attr, label_left=True, row=0, column=0,
         return container, checkbox, is_checked
 
     @staticmethod
-    def messagebox(title, message, yes_text="Yes", no_text="No"):
+    def messagebox(title, message, yes_text="Yes", no_text="No", master=None):
         # Create a Toplevel window
-        window = tk.Toplevel()
+        window = tk.Toplevel(master)
         window.title(title)
         window.geometry("350x140")
 
+        # Center the popup in the master window
+        if master is not None:
+            master.update_idletasks()  # Ensure master's dimensions are up-to-date
+            master_width = master.winfo_width()
+            master_height = master.winfo_height()
+            master_x = master.winfo_x()
+            master_y = master.winfo_y()
+
+            window_width = 350
+            window_height = 140
+
+            position_right = master_x + (master_width // 2) - (window_width // 2)
+            position_down = master_y + (master_height // 2) - (window_height // 2)
+
+            window.geometry(f"{window_width}x{window_height}+{position_right}+{position_down}")
+        else:
+            # Center in the screen if no master is provided
+            window_width = 350
+            window_height = 140
+
+            screen_width = window.winfo_screenwidth()
+            screen_height = window.winfo_screenheight()
+
+            position_right = (screen_width // 2) - (window_width // 2)
+            position_down = (screen_height // 2) - (window_height // 2)
+
+            window.geometry(f"{window_width}x{window_height}+{position_right}+{position_down}")
+
         # Add message label
         tk.Label(window, text=message, wraplength=350, justify="left").pack(pady=20)
 
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 67e145d6..ea46bcac 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -68,7 +68,7 @@ def __init__(self, parent, controller):
 
         def start_processing():
             # TODO reset the progress bars
-            video_analysis(state)
+            video_analysis(state, controller)
 
         Widgets.button(processing, "Start processing", start_processing, row=3)
         # endregion
diff --git a/script_generator/object_detection/utils.py b/script_generator/object_detection/utils.py
index b6903adb..7155d1d8 100644
--- a/script_generator/object_detection/utils.py
+++ b/script_generator/object_detection/utils.py
@@ -11,14 +11,15 @@
 from script_generator.utils.logger import logger
 
 
-def check_skip_object_detection(state):
+def check_skip_object_detection(state, root):
     raw_yolo_path, raw_yolo_filename = get_output_file_path(state.video_path, "_rawyolo.json")
     if os.path.exists(raw_yolo_path):
         skip_detection = Widgets.messagebox(
             "Detection File Conflict",
             f"The file already exists. What would you like to do?\n{raw_yolo_filename}",
             "Use Existing",
-            "Generate New"
+            "Generate New",
+            root
         )
         if skip_detection:
             logger.info(f"File {raw_yolo_path} already exists. Skipping detections and loading file content...")

From 6cc438ec835879c2402bc641ed9adfbe6ed68791 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 20 Jan 2025 19:22:57 +0100
Subject: [PATCH 101/277] Keep hardware acceleration log on a single line

---
 script_generator/video/ffmpeg.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script_generator/video/ffmpeg.py b/script_generator/video/ffmpeg.py
index 7c56e31e..e8c10a74 100644
--- a/script_generator/video/ffmpeg.py
+++ b/script_generator/video/ffmpeg.py
@@ -58,7 +58,7 @@ def is_hwaccel_supported():
             check=True
         )
         hwaccels = result.stdout.lower()
-        logger.info(f"Hardware acceleration backends: {hwaccels}")
+        logger.info(f"Hardware acceleration backends: {', '.join(hwaccels)}")
         # Check for supported hardware acceleration backends
         return {
             "cuda": "cuda" in hwaccels,

From 47f91615b54261ad8cb7017e28dcc80614ca1b45 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 20 Jan 2025 19:28:38 +0100
Subject: [PATCH 102/277] Keep hardware acceleration log on a single line

---
 script_generator/video/ffmpeg.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script_generator/video/ffmpeg.py b/script_generator/video/ffmpeg.py
index e8c10a74..7cac6e17 100644
--- a/script_generator/video/ffmpeg.py
+++ b/script_generator/video/ffmpeg.py
@@ -58,7 +58,7 @@ def is_hwaccel_supported():
             check=True
         )
         hwaccels = result.stdout.lower()
-        logger.info(f"Hardware acceleration backends: {', '.join(hwaccels)}")
+        logger.info(f"Hardware acceleration backends: {', '.join([line for line in hwaccels.splitlines() if line.strip()])}")
         # Check for supported hardware acceleration backends
         return {
             "cuda": "cuda" in hwaccels,

From 4f8a93e8f296408e6c324b6893cda65d96a990e2 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 20 Jan 2025 19:35:35 +0100
Subject: [PATCH 103/277] Keep hardware acceleration log on a single line

---
 script_generator/video/ffmpeg.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/script_generator/video/ffmpeg.py b/script_generator/video/ffmpeg.py
index 7cac6e17..62584488 100644
--- a/script_generator/video/ffmpeg.py
+++ b/script_generator/video/ffmpeg.py
@@ -44,6 +44,7 @@ def get_video_info(video_path):
         logger.error(f"Error initializing video info: {e}")
         raise
 
+
 def is_hwaccel_supported():
     """
     Check which hardware acceleration backends are supported by FFmpeg.
@@ -57,8 +58,10 @@ def is_hwaccel_supported():
             text=True,
             check=True
         )
-        hwaccels = result.stdout.lower()
-        logger.info(f"Hardware acceleration backends: {', '.join([line for line in hwaccels.splitlines() if line.strip()])}")
+        hwaccels = result.stdout.lower().replace("hardware acceleration methods:", "")
+        hwaccel_lines = [line.strip() for line in str(hwaccels).splitlines() if line.strip()]
+        logger.info(f"hardware acceleration methods: {', '.join(hwaccel_lines)}")
+
         # Check for supported hardware acceleration backends
         return {
             "cuda": "cuda" in hwaccels,
@@ -80,4 +83,3 @@ def is_hwaccel_supported():
             "d3d11va": False,
             "opencl": False,
         }
-

From 58c5a587fe99081788dc75ffe4d2d33dd151fc19 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Mon, 20 Jan 2025 19:37:06 +0100
Subject: [PATCH 104/277] Added tooltips

---
 config.py                                      |  2 +-
 .../gui/views/funscript_generator.py           | 18 ++++++++++++------
 2 files changed, 13 insertions(+), 7 deletions(-)

diff --git a/config.py b/config.py
index d86277ba..944cbb33 100644
--- a/config.py
+++ b/config.py
@@ -6,7 +6,7 @@
 from script_generator.utils.config import find_ffmpeg_path, find_ffprobe_path
 from script_generator.utils.logger import logger
 
-VERSION = "0.0.1_25-01-16"
+VERSION = "0.0.3"
 
 ##################################################################################################
 # FFMPEG
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index ea46bcac..72d0d855 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -89,7 +89,8 @@ def start_processing():
             state=self.state,
             attr='boost_up_percent',
             values=[str(i) for i in range(0, 21)],
-            row=1
+            row=1,
+            tooltip_text="Boost up peaks by x%"
         )
         Widgets.range_selector(
             parent=boost_frame,
@@ -97,7 +98,8 @@ def start_processing():
             state=self.state,
             attr='boost_down_percent',
             values=[str(i) for i in range(0, 21)],
-            row=2
+            row=2,
+            tooltip_text="Reduce down peaks by x%"
         )
 
         # Threshold Settings
@@ -111,7 +113,8 @@ def start_processing():
             state=self.state,
             attr='threshold_low',
             values=[str(i) for i in range(0, 16)],
-            row=1
+            row=1,
+            tooltip_text="Threshold under which values are mapped to 0"
         )
         Widgets.range_selector(
             parent=threshold_frame,
@@ -119,7 +122,8 @@ def start_processing():
             state=self.state,
             attr='threshold_high',
             values=[str(i) for i in range(80, 101)],
-            row=2
+            row=2,
+            tooltip_text="Threshold above which values are mapped to 100"
         )
 
         # Simplification Settings
@@ -133,7 +137,8 @@ def start_processing():
             state=self.state,
             attr='vw_factor',
             values=[i / 5 for i in range(10, 51)],
-            row=1
+            row=1,
+            tooltip_text="Rationalize the number of points\nMakes the script end-user friendly\nDeactivating this will make the script unusable as is\nExpert mode, for post-editing"
         )
         Widgets.range_selector(
             parent=vw_frame,
@@ -141,7 +146,8 @@ def start_processing():
             state=self.state,
             attr='rounding',
             values=[5, 10],
-            row=2
+            row=2,
+            tooltip_text="Rounding factor for values"
         )
 
         # Regenerate Funscript Button

From 1ebf2a1cf4557fe8f45909a9e31b99f17e2bcb23 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 20 Jan 2025 20:59:16 +0100
Subject: [PATCH 105/277] Added missing app state

---
 script_generator/gui/controller/video_analysis.py         | 2 +-
 script_generator/gui/views/funscript_generator.py         | 2 +-
 script_generator/object_detection/post_process_results.py | 2 +-
 script_generator/video/video_task_processor.py            | 1 -
 utils/lib_FunscriptHandler.py                             | 6 ++++--
 5 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/script_generator/gui/controller/video_analysis.py b/script_generator/gui/controller/video_analysis.py
index 6f924de9..69ea775a 100644
--- a/script_generator/gui/controller/video_analysis.py
+++ b/script_generator/gui/controller/video_analysis.py
@@ -18,7 +18,7 @@ def video_analysis(state, root):
     logger.info(f"Processing video: {state.video_path}")
     logger.info(f"Video Reader: {state.video_reader}")
     logger.info(f"Debug Mode: {state.save_debug_file}")
-    logger.info(f"Live Display Mode: {state.life_display_mode}")
+    logger.info(f"Live Preview Mode: {state.life_display_mode}")
     logger.info(f"Frame Start: {state.frame_start}")
     logger.info(f"Frame End: {state.frame_end}")
     logger.info(f"Reference Script: {state.reference_script}")
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 72d0d855..204fe4d5 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -159,7 +159,7 @@ def start_processing():
         debugging = Widgets.frame(wrapper, title="Debugging", main_section=True, row=5)
         general = Widgets.frame(debugging, title="General", row=0)
         Widgets.checkbox(
-            general, "Live display mode",
+            general, "Live preview",
             state,
             "life_display_mode",
             tooltip_text="Use q to quite.\n\nWill show a live preview of what is being generated.\n\nFor debugging only this will greatly reduce your performance.\nStage 1: Show bounding boxes during object detection.\nStage 2: Show funscript and metric overlay while the funscript is being processed.",
diff --git a/script_generator/object_detection/post_process_results.py b/script_generator/object_detection/post_process_results.py
index d498e338..91a37ec3 100644
--- a/script_generator/object_detection/post_process_results.py
+++ b/script_generator/object_detection/post_process_results.py
@@ -117,7 +117,7 @@ def task_logic(self):
                     color = CLASS_COLORS.get(box[3])
                     cv2.rectangle(frame_display, (box[0][0], box[0][1]), (box[0][2], box[0][3]), color, 2)
                     cv2.putText(frame_display, f"{box[4]}: {box[3]}", (box[0][0], box[0][1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
-                cv2.imshow("YOLO11 test boxes Tracking", frame_display)
+                cv2.imshow("Object detection tacking preview", frame_display)
                 if cv2.waitKey(1) & 0xFF == ord('q'):
                     break
 
diff --git a/script_generator/video/video_task_processor.py b/script_generator/video/video_task_processor.py
index 5d6df61a..f5d9e6c9 100644
--- a/script_generator/video/video_task_processor.py
+++ b/script_generator/video/video_task_processor.py
@@ -39,7 +39,6 @@ def get_cmd(vf):
                 hwaccel = ["-hwaccel", "qsv"]
             elif hwaccel_support["d3d11va"]:
                 hwaccel = ["-hwaccel", "d3d11va"]
-            # Add more backends as needed
 
             # Add platform-specific video filters if required
             video_filter = ["-vf", vf] if vf else []
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 72016add..bf14ae5f 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -14,6 +14,7 @@
 
 import matplotlib
 
+from script_generator.state.app_state import AppState
 from script_generator.utils.file import get_output_file_path
 from script_generator.utils.logger import logger
 
@@ -414,7 +415,7 @@ def create_report_funscripts(self, state):
             # Plot and combine
             report_path, _ = get_output_file_path(state.video_path, "_report.png")
             self.create_combined_plot(
-                ref_sections, gen_sections, screenshots, sections, report_path,
+                state, ref_sections, gen_sections, screenshots, sections, report_path,
                 ref_times, ref_positions, gen_times, gen_positions
             )
 
@@ -466,11 +467,12 @@ def capture_screenshots(self, video_path, is_vr, sections):
         cap.release()
         return screenshots
 
-    def create_combined_plot(self, ref_sections, gen_sections, screenshots, sections, output_image_path, ref_times, ref_positions, gen_times, gen_positions):
+    def create_combined_plot(self, state: AppState, ref_sections, gen_sections, screenshots, sections, output_image_path, ref_times, ref_positions, gen_times, gen_positions):
         """
         Creates a combined plot with heatmaps as a header, comparative information, and section comparisons below.
 
         Args:
+            state: AppState
             ref_sections (list): List of reference sections (times, positions).
             gen_sections (list): List of generated sections (times, positions).
             screenshots (list): List of screenshots for each section.

From a969a5dcedf00054893889b2417fa9cb034c3096 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 20 Jan 2025 21:20:28 +0100
Subject: [PATCH 106/277] Fixed a invalid reference to state.logger

---
 .../gui/controller/regenerate_funscript.py         |  2 ++
 utils/lib_FunscriptHandler.py                      | 14 +++++++++++---
 2 files changed, 13 insertions(+), 3 deletions(-)

diff --git a/script_generator/gui/controller/regenerate_funscript.py b/script_generator/gui/controller/regenerate_funscript.py
index 29872325..93a23e25 100644
--- a/script_generator/gui/controller/regenerate_funscript.py
+++ b/script_generator/gui/controller/regenerate_funscript.py
@@ -11,6 +11,8 @@ def regenerate_funscript(state):
 
     logger.info("Regenerating Funscript with tweaked settings...")
 
+    state.set_video_info()
+
     # Apply tweaks to funscript_data
     if state.boost_enabled:
         logger.info(f"Applying Boost: Up {state.boost_up_percent}%, Down {state.boost_down_percent}%")
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index bf14ae5f..08530e79 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -103,13 +103,13 @@ def generate(self, state):
 
             # Round position values to the closest multiple of 5, still between 0 and 100
             if state.vw_simplification_enabled:
-                state.logger.info(
+                logger.info(
                     f"Positions adjustment - step 6 (rounding to the closest multiple of {state.rounding})")
                 adjusted_positions = [round(p / state.rounding) * state.rounding for p in
                                       adjusted_positions]
 
             else:
-                state.logger.info(
+                logger.info(
                     f"Skipping positions adjustment - step 6 (rounding to the closest multiple of {state.rounding})")
 
             # Recombine timestamps and adjusted positions
@@ -123,6 +123,8 @@ def generate(self, state):
             # Generate a heatmap
             self.generate_heatmap(output_path, output_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
         except Exception as e:
+            # TODO this error doesn't reach the console
+            print(e)
             logger.error(f"Error generating funscript: {e}")
 
     def write_funscript(self, distances, output_path, fps):
@@ -484,6 +486,11 @@ def create_combined_plot(self, state: AppState, ref_sections, gen_sections, scre
             gen_positions (list): Positions from the generated funscript.
         """
 
+        # TODO why is this empty sometimes? and result in errors
+        if not gen_times or not gen_positions:
+            logger.error("Could not created combined plot")
+            return
+
         # Create a flexible grid layout
         fig = plt.figure(figsize=(28, 24))
         gs = gridspec.GridSpec(5, 4, height_ratios=[1, .5, 2, 2, 2], width_ratios=[1, 2, 1, 2])
@@ -516,6 +523,7 @@ def create_combined_plot(self, state: AppState, ref_sections, gen_sections, scre
 
         ax_comparative_right = fig.add_subplot(gs[1, 2:])
         gen_metrics = self._calculate_metrics(gen_times, gen_positions)
+
         gen_comparative_text = (
             f"Generated:\n"
             f"Number of Strokes: {gen_metrics['num_strokes']}\n"
@@ -585,7 +593,7 @@ def generate_heatmap_inline(self, ax, times, positions, state):
             if times[i] == times[i - 1]:
                 times.pop(i)
                 positions.pop(i)
-                state.logger.info(f"Removed duplicate time value {times[i]}")
+                logger.info(f"Removed duplicate time value {times[i]}")
                 break
 
         # Calculate speed (position change per time interval)

From 55db4546e01806c0dfa5d3fe614444d1a883458a Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 20 Jan 2025 21:31:19 +0100
Subject: [PATCH 107/277] Pause debug window based on live preview mode

---
 script_generator/gui/controller/debug_video.py         |  1 +
 script_generator/gui/controller/video_analysis.py      |  2 +-
 script_generator/gui/views/funscript_generator.py      |  2 +-
 .../object_detection/analyze_tracking_results.py       |  6 +++---
 .../object_detection/post_process_results.py           |  8 ++++----
 script_generator/state/app_state.py                    |  2 +-
 utils/lib_Debugger.py                                  | 10 +++++++---
 utils/lib_VideoReaderFFmpeg.py                         |  4 ++--
 8 files changed, 20 insertions(+), 15 deletions(-)

diff --git a/script_generator/gui/controller/debug_video.py b/script_generator/gui/controller/debug_video.py
index ba6c7303..b6e1c2ee 100644
--- a/script_generator/gui/controller/debug_video.py
+++ b/script_generator/gui/controller/debug_video.py
@@ -21,6 +21,7 @@ def debug_video(state: AppState):
         state.debugger.load_logs()
 
         state.debugger.play_video(
+            state=state,
             start_frame=state.frame_start,
             duration=state.debug_video_duration if state.save_debug_video else 0,
             save_debug_video=state.save_debug_video,
diff --git a/script_generator/gui/controller/video_analysis.py b/script_generator/gui/controller/video_analysis.py
index 69ea775a..68c0a51a 100644
--- a/script_generator/gui/controller/video_analysis.py
+++ b/script_generator/gui/controller/video_analysis.py
@@ -18,7 +18,7 @@ def video_analysis(state, root):
     logger.info(f"Processing video: {state.video_path}")
     logger.info(f"Video Reader: {state.video_reader}")
     logger.info(f"Debug Mode: {state.save_debug_file}")
-    logger.info(f"Live Preview Mode: {state.life_display_mode}")
+    logger.info(f"Live Preview Mode: {state.live_preview_mode}")
     logger.info(f"Frame Start: {state.frame_start}")
     logger.info(f"Frame End: {state.frame_end}")
     logger.info(f"Reference Script: {state.reference_script}")
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 204fe4d5..21614581 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -161,7 +161,7 @@ def start_processing():
         Widgets.checkbox(
             general, "Live preview",
             state,
-            "life_display_mode",
+            "live_preview_mode",
             tooltip_text="Use q to quite.\n\nWill show a live preview of what is being generated.\n\nFor debugging only this will greatly reduce your performance.\nStage 1: Show bounding boxes during object detection.\nStage 2: Show funscript and metric overlay while the funscript is being processed.",
             row=0
         )
diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index 750aa99b..272a34ed 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -29,7 +29,7 @@ def analyze_tracking_results(state, results, debug_video_callback=None):
 
     cuts = []
 
-    if state.life_display_mode:
+    if state.live_preview_mode:
         reader.set(cv2.CAP_PROP_POS_FRAMES, state.frame_start)
     else:
         reader.release()
@@ -150,7 +150,7 @@ def analyze_tracking_results(state, results, debug_video_callback=None):
                     }
                 )
 
-        if state.life_display_mode:
+        if state.live_preview_mode:
             # Display the tracking results for testing
             ret, frame = reader.read()
 
@@ -208,7 +208,7 @@ def analyze_tracking_results(state, results, debug_video_callback=None):
                     eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
                 ))
 
-        if debug_video_callback and state.life_display_mode:
+        if debug_video_callback and state.live_preview_mode:
             debug_video_callback()
 
     state.update_ui(ProgressMessage(
diff --git a/script_generator/object_detection/post_process_results.py b/script_generator/object_detection/post_process_results.py
index 91a37ec3..7a6cb0de 100644
--- a/script_generator/object_detection/post_process_results.py
+++ b/script_generator/object_detection/post_process_results.py
@@ -27,7 +27,7 @@ def task_logic(self):
             pose_results = None # TODO pose support
 
             # Skip if no boxes are detected or no tracks are found
-            if  det_results.boxes.id is None or (len(det_results.boxes) == 0 and not state.life_display_mode):
+            if  det_results.boxes.id is None or (len(det_results.boxes) == 0 and not state.live_preview_mode):
                 task.rendered_frame = None # Clear memory
                 task.yolo_results = None  # Clear memory
                 self.finish_task(task)
@@ -51,7 +51,7 @@ def task_logic(self):
                 # Create a detection record
                 record = [frame_pos, int(cls), round(conf, 1), x1, y1, x2, y2, track_id]
                 self.records.append(record)
-                if state.life_display_mode:
+                if state.live_preview_mode:
                     test_box = [[x1, y1, x2, y2], round(conf, 1), int(cls), CLASS_REVERSE_MATCH.get(int(cls), 'unknown'), track_id]
                     self.test_result.add_record(frame_pos, test_box)
 
@@ -92,7 +92,7 @@ def task_logic(self):
 
                         record = [frame_pos, 10, round(conf, 1), x1, y1, x2, y2, 0]
                         self.records.append(record)
-                        if state.life_display_mode:
+                        if state.live_preview_mode:
                             # Print and test the record
                             logger.debug(f"Record : {record}")
                             logger.debug(f"For class id: {int(cls)}, getting: {CLASS_REVERSE_MATCH.get(int(cls), 'unknown')}")
@@ -101,7 +101,7 @@ def task_logic(self):
                             logger.debug(f"Test box: {test_box}")
                             self.test_result.add_record(frame_pos, test_box)
 
-            if state.life_display_mode:
+            if state.live_preview_mode:
                 # Display the YOLO results for testing
                 # det_results.plot()
                 # cv2.imshow("YOLO11", det_results.plot())
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index 25aecb2e..a125ca52 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -27,7 +27,7 @@ def __init__(self, is_cli):
         self.save_debug_file: bool = False
         self.save_debug_video: bool = False
         self.debug_video_duration: int = 0
-        self.life_display_mode: bool = False
+        self.live_preview_mode: bool = False
         self.reference_script: string = None
 
         # TODO move this to a batch task class (so parallel inference is possible)
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 9bdd83fa..525dd37a 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -84,14 +84,14 @@ def load_logs(self):
         except json.JSONDecodeError:
             logger.error(f"Error decoding JSON from {self.log_file}. Starting with empty logs.")
 
-    def display_frame(self, frame_id):
+    def display_frame(self, state, frame_id):
         """
         Display the logged frame with bounding boxes and variable states.
         :param frame_id: The frame ID to display.
         """
-        self.play_video(frame_id, duration=-1)
+        self.play_video(state, frame_id, duration=-1)
 
-    def play_video(self, start_frame=0, duration=0, rolling_window_size=100, save_debug_video=False, downsize_ratio=1):
+    def play_video(self, state, start_frame=0, duration=0, rolling_window_size=100, save_debug_video=False, downsize_ratio=1):
         """
         Play the video from a specified frame, displaying variables, bounding boxes, and rolling window curves.
         :param start_frame: Frame to start playback from.
@@ -161,9 +161,13 @@ def play_video(self, start_frame=0, duration=0, rolling_window_size=100, save_de
         # Play the video
         while self.current_frame < end_frame:
             ret, frame = self.cap.read()
+
             if not ret:
                 break
 
+            if not state.live_preview_mode:
+                continue
+
             frame_copy = frame.copy()  # make a copy of the frame to make it writeable, useful for ffmpeg library here
             # Display variables and bounding boxes
             str_frame_id = str(self.current_frame)
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index b502d052..da823b3e 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -102,7 +102,7 @@ def _start_process(self, start_frame=0):
             arg_line = ""
             if self.unwarp:
                 if self.projection == "FISHEYE" or (self.projection == None and "FISHEYE" in self.video_path.upper()):
-                    print("Proceeding with fisheye projection correction")
+                    logger.info("Proceeding with fisheye projection correction")
                     self.type = "fisheye"
                     self.iv_fov = 190
                     self.ih_fov = 190
@@ -110,7 +110,7 @@ def _start_process(self, start_frame=0):
                     self.h_fov = 90
                     self.d_fov = 180
                 else:  # Assuming Equirectangular
-                    print("Assuming Equirectangular projection")
+                    logger.info("Assuming Equirectangular projection")
                     self.type = "he"
                     self.iv_fov = 250
                     self.ih_fov = 120

From c0fc3bc8b4a73646fb6abd2af9dda10b46067ac4 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Mon, 20 Jan 2025 23:31:26 +0100
Subject: [PATCH 108/277] Window height adjustment

---
 script_generator/gui/app.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index 7e35bf85..29238083 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -18,7 +18,7 @@ def __init__(self):
             ctypes.windll.shcore.SetProcessDpiAwareness(2)  # For Windows DPI scaling
         # self.tk.call('tk', 'scaling', 1.0)
         self.title("VR funscript generation")
-        self.geometry(f"{('800' if is_mac() else '700')}x890")
+        self.geometry(f"{('800' if is_mac() else '700')}x910")
         self.resizable(False, False)
 
         self.iconphoto(False, tk.PhotoImage(file=LOGO))

From 49cb5b7b47602e86a3379d6eb772e8cd9229b96e Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 21 Jan 2025 10:25:27 +0100
Subject: [PATCH 109/277] Made the first penis detection easier

---
 .../gui/controller/tracking_analysis.py       |  2 +-
 script_generator/object_detection/utils.py    | 26 +++----------------
 2 files changed, 5 insertions(+), 23 deletions(-)

diff --git a/script_generator/gui/controller/tracking_analysis.py b/script_generator/gui/controller/tracking_analysis.py
index 08acbda4..98249d27 100644
--- a/script_generator/gui/controller/tracking_analysis.py
+++ b/script_generator/gui/controller/tracking_analysis.py
@@ -27,7 +27,7 @@ def tracking_analysis(state):
     first_penis_frame = parse_yolo_data_looking_for_penis(yolo_data, 0)
 
     if first_penis_frame is None:
-        logger.error(f"No frames found with a penis and penis tip in the same frame. Since these two are needed for estimating stroke length atm further tracking is not possible.")
+        logger.error(f"No penis instance found in video. Further tracking is not possible.")
         return
 
     # Deciding whether we start from there or from a user-specified later frame
diff --git a/script_generator/object_detection/utils.py b/script_generator/object_detection/utils.py
index 7155d1d8..d82899bf 100644
--- a/script_generator/object_detection/utils.py
+++ b/script_generator/object_detection/utils.py
@@ -51,30 +51,12 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
     :param start_frame: The starting frame for the search.
     :return: The frame ID where the penis is first detected.
     """
-    consecutive_frames = 0
-    frame_detected = 0
-    penis_frame = 0
 
     penis_cls = 0
-    glans_cls = 1
 
-    for line in data: #
+    for line in data:
         frame_idx, cls, conf, x1, y1, x2, y2, track_id = line
-        class_name = CLASS_REVERSE_MATCH.get(cls, 'unknown')
-        if frame_idx >= start_frame and cls == penis_cls and conf >= 0.5:
+        if frame_idx >= start_frame and cls == penis_cls and conf >= 0.7:
             penis_frame = frame_idx
-
-        if frame_idx == penis_frame and cls == glans_cls and conf >= 0.5:
-            if frame_detected == 0:
-                frame_detected = frame_idx
-                consecutive_frames += 1
-            elif frame_idx == frame_detected + 1:
-                consecutive_frames += 1
-                frame_detected = frame_idx
-            else:
-                consecutive_frames = 0
-                frame_detected = 0
-
-            if consecutive_frames >= 2:
-                logger.info(f"First instance of Glans/Penis found in frame {frame_idx - 4}")
-                return frame_idx - 4
\ No newline at end of file
+            logger.info(f"First instance of Glans/Penis found in frame {frame_idx}")
+            return penis_frame

From 71747f6d50470c391b4b73a1569ba8503e857636 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 21 Jan 2025 10:25:58 +0100
Subject: [PATCH 110/277] Added logging of video format detected

---
 script_generator/video/ffmpeg.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/script_generator/video/ffmpeg.py b/script_generator/video/ffmpeg.py
index 62584488..6a3acc7c 100644
--- a/script_generator/video/ffmpeg.py
+++ b/script_generator/video/ffmpeg.py
@@ -39,6 +39,13 @@ def get_video_info(video_path):
         # If the width is 2x the height we are dealing with a VR video
         is_vr = height == width // 2
 
+        # TODO: make it possible to override this auto-detection
+
+        if is_vr:
+            logger.info("Video format: VR - Based on its 2:1 ratio, processing this video as a VR SBS video")
+        else:
+            logger.info("Video format: 2D - Based on its ratio, processing this video as a 2D video")
+
         return VideoInfo(video_path, codec_name, width, height, duration, total_frames, fps, is_vr)
     except Exception as e:
         logger.error(f"Error initializing video info: {e}")

From b26e2f751525c8faedc28a3a87bdc2b7d262a56c Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 21 Jan 2025 10:43:54 +0100
Subject: [PATCH 111/277] Removed unused variable

---
 config.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/config.py b/config.py
index 944cbb33..8356b9dd 100644
--- a/config.py
+++ b/config.py
@@ -55,7 +55,6 @@
 
 # Define custom colormap based on Lucife's heatmapColors
 STEP_SIZE = 120  # Speed step size for color transitions
-VW_FILTER_COEFF = 2.0
 
 
 FFMPEG_PATH = find_ffmpeg_path(win_ffmpeg_path, mac_ffmpeg_path, lin_ffmpeg_path)

From f4328fbfe93c564843b49c486890deab5d00bd51 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 21 Jan 2025 14:30:01 +0100
Subject: [PATCH 112/277] tiny tweaks

---
 script_generator/tasks/abstract_task_processor.py | 3 ++-
 script_generator/video/ffmpeg.py                  | 4 ++--
 script_generator/video/video_info.py              | 2 ++
 3 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/script_generator/tasks/abstract_task_processor.py b/script_generator/tasks/abstract_task_processor.py
index eea8eea4..4184dd09 100644
--- a/script_generator/tasks/abstract_task_processor.py
+++ b/script_generator/tasks/abstract_task_processor.py
@@ -101,7 +101,8 @@ def on_last_item(self):
 
 class TaskProcessorTypes(Enum):
     VIDEO = "Video processing"
-    OPENGL = "3d to 2D"
+    OPENGL = "3D to 2D"
+    METAL = "3D to 2D (MPS)"
     YOLO = "YOLO inference"
     YOLO_ANALYSIS = "YOLO analysis"
 
diff --git a/script_generator/video/ffmpeg.py b/script_generator/video/ffmpeg.py
index 6a3acc7c..688579db 100644
--- a/script_generator/video/ffmpeg.py
+++ b/script_generator/video/ffmpeg.py
@@ -42,9 +42,9 @@ def get_video_info(video_path):
         # TODO: make it possible to override this auto-detection
 
         if is_vr:
-            logger.info("Video format: VR - Based on its 2:1 ratio, processing this video as a VR SBS video")
+            logger.info("Video Format: VR SBS - Based on its 2:1 ratio")
         else:
-            logger.info("Video format: 2D - Based on its ratio, processing this video as a 2D video")
+            logger.info("Video Format: 2D - Based on its ratio")
 
         return VideoInfo(video_path, codec_name, width, height, duration, total_frames, fps, is_vr)
     except Exception as e:
diff --git a/script_generator/video/video_info.py b/script_generator/video/video_info.py
index 72371c70..6355a575 100644
--- a/script_generator/video/video_info.py
+++ b/script_generator/video/video_info.py
@@ -2,6 +2,7 @@
 from dataclasses import dataclass, field
 
 from config import RENDER_RESOLUTION
+from script_generator.utils.logger import logger
 
 
 @dataclass
@@ -56,6 +57,7 @@ def get_projection_and_fov_from_filename(filename):
             projection = pattern["projection"]
             fov = pattern["fov"]
             break
+    logger.info(f"Video Format: Projection={projection}, FOV={fov}, is_fisheye={is_fisheye}")
 
     return {"projection": projection, "fov": fov, "is_fisheye": is_fisheye}
 

From 20f4bbef80ad00c41a0f4ab35082d4b5e7db5a48 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 21 Jan 2025 17:08:08 +0100
Subject: [PATCH 113/277] info display tweak

---
 script_generator/scripts/analyze_video.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/script_generator/scripts/analyze_video.py b/script_generator/scripts/analyze_video.py
index 0f1d8851..e06c9b74 100644
--- a/script_generator/scripts/analyze_video.py
+++ b/script_generator/scripts/analyze_video.py
@@ -88,11 +88,14 @@ def run_thread(thread, thread_name, out_queue):
 def log_progress(state, opengl_q, yolo_q, analysis_q, results_q, stop_event):
     total_frames = state.video_info.total_frames
 
+    label = 'Analyzing ' + ('VR' if state.video_info.is_vr else '2D') + ' video'
+
     if PROGRESS_BAR:
         with tqdm(
                 total=total_frames,
-                desc="Analyzing video",
-                unit="frames",
+                #desc="Analyzing video",
+                desc=label,
+                unit="f",
                 position=0,
                 unit_scale=False,
                 unit_divisor=1,

From 2ba5a5a4f46717614f4fd8711ca00711a35c8e08 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 21 Jan 2025 17:08:27 +0100
Subject: [PATCH 114/277] partly reinstated debugging function

---
 script_generator/gui/controller/debug_video.py    | 11 ++++++++++-
 script_generator/gui/views/funscript_generator.py | 11 ++++++++++-
 utils/lib_Debugger.py                             |  5 +++--
 utils/lib_VideoReaderFFmpeg.py                    |  6 +++++-
 4 files changed, 28 insertions(+), 5 deletions(-)

diff --git a/script_generator/gui/controller/debug_video.py b/script_generator/gui/controller/debug_video.py
index b6e1c2ee..7b9b03a8 100644
--- a/script_generator/gui/controller/debug_video.py
+++ b/script_generator/gui/controller/debug_video.py
@@ -12,6 +12,15 @@ def debug_video(state: AppState):
         messagebox.showerror("Error", "Please select a video file.")
         return
 
+    state.set_video_info()
+
+    logger.info(f"Debugging video: {state.video_path}")
+    logger.info(f"Video Reader: {state.video_reader}")
+    logger.info(f"Debug Mode: {state.save_debug_file}")
+    logger.info(f"Live Preview Mode: {state.live_preview_mode}")
+    logger.info(f"Frame Start: {state.frame_start}")
+    logger.info(f"Frame End: {state.frame_end}")
+
     log_file, _ = get_output_file_path(state.video_path, "_debug_logs.json")
     state.debugger = Debugger(state.video_path, state.video_info.is_vr, state.video_reader, log_file=log_file)  # Initialize the debugger
 
@@ -25,7 +34,7 @@ def debug_video(state: AppState):
             start_frame=state.frame_start,
             duration=state.debug_video_duration if state.save_debug_video else 0,
             save_debug_video=state.save_debug_video,
-            downsize_ratio=2
+            downsize_ratio=1
         )
     else:
         logger.error(f"Debug logs file not found: {log_file}")
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 21614581..97e9e860 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -3,6 +3,7 @@
 
 from script_generator.gui.controller.regenerate_funscript import regenerate_funscript
 from script_generator.gui.controller.video_analysis import video_analysis
+from script_generator.gui.controller.debug_video import debug_video
 from script_generator.gui.messages.messages import UIMessage, ProgressMessage
 from script_generator.gui.utils.widgets import Widgets
 from script_generator.state.app_state import AppState
@@ -70,6 +71,9 @@ def start_processing():
             # TODO reset the progress bars
             video_analysis(state, controller)
 
+        def start_debugging():
+            debug_video(state)
+
         Widgets.button(processing, "Start processing", start_processing, row=3)
         # endregion
 
@@ -187,7 +191,12 @@ def start_processing():
             label_width_px=73,
             sticky="w"
         )
-        Widgets.button(general, "Open debug video", None, row=2, column=5, tooltip_text="Open the debug video after the funscript generation process has completed.")
+        Widgets.button(general,
+                       "Open debug video",
+                       lambda: start_debugging(),
+                       row=2,
+                       column=5,
+                       tooltip_text="Open the debug video after the funscript generation process has completed.")
 
         script_compare = Widgets.frame(debugging, title="Script compare", row=1)
         Widgets.file_selection(
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 525dd37a..9ffc8101 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -165,8 +165,8 @@ def play_video(self, state, start_frame=0, duration=0, rolling_window_size=100,
             if not ret:
                 break
 
-            if not state.live_preview_mode:
-                continue
+            #if not state.live_preview_mode:
+            #    continue
 
             frame_copy = frame.copy()  # make a copy of the frame to make it writeable, useful for ffmpeg library here
             # Display variables and bounding boxes
@@ -242,6 +242,7 @@ def play_video(self, state, start_frame=0, duration=0, rolling_window_size=100,
 
         # Release resources
         self.cap.release()
+
         if save_debug_video:
             out.release()
             output_path_ffmpeg, _ = get_output_file_path(debug_video_path, "_rawyolo.json", True)
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index da823b3e..2d0872cf 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -79,7 +79,10 @@ def _initialize_video_info(self):
             self.duration = duration * 1000  # Convert duration to milliseconds
 
             # limiting the frame height here has no additional performance cost and significantly improves speed for 1440p+ video
-            scaling_factor = min(RENDER_RESOLUTION / self.width, RENDER_RESOLUTION / self.height)
+            #scaling_factor = min(RENDER_RESOLUTION / self.width, RENDER_RESOLUTION / self.height)
+            scaling_factor = min(1, RENDER_RESOLUTION / self.height)
+            #if scaling_factor > 1:
+            #    scaling_factor = 1
             self.width = int(self.width * scaling_factor)
             self.height = int(self.height * scaling_factor)
 
@@ -170,6 +173,7 @@ def _start_process(self, start_frame=0):
 
         # Start FFmpeg process
         #self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+        print(f"cmd: {cmd}")
         self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
         self.frame_size = self.width * self.height * 3  # Size of one frame in bytes
 

From 4eb04da7d4d4ce9c25f67df83d7ea91701654ed4 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 21 Jan 2025 17:10:02 +0100
Subject: [PATCH 115/277] partly reinstated debugging function

---
 utils/lib_VideoReaderFFmpeg.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index 2d0872cf..da44796a 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -80,6 +80,7 @@ def _initialize_video_info(self):
 
             # limiting the frame height here has no additional performance cost and significantly improves speed for 1440p+ video
             #scaling_factor = min(RENDER_RESOLUTION / self.width, RENDER_RESOLUTION / self.height)
+            # was having issues with the rendering of a 720p 2D video... < 1080p
             scaling_factor = min(1, RENDER_RESOLUTION / self.height)
             #if scaling_factor > 1:
             #    scaling_factor = 1

From 068b114f6bfa0cd00f631e14e3074796a924e6cd Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 21 Jan 2025 17:29:22 +0100
Subject: [PATCH 116/277] tiny tweaks

---
 script_generator/gui/utils/widgets.py             |  2 +-
 script_generator/gui/views/funscript_generator.py | 10 +++++++---
 script_generator/state/app_state.py               |  2 +-
 utils/lib_VideoReaderFFmpeg.py                    |  1 -
 4 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index ce7a59ec..cc5424d9 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -321,7 +321,7 @@ def on_no():
     def disclaimer(parent, tooltip_text=None):
         footer_label = ttk.Label(
             parent,
-            text="Individual and personal use only.\nNot for commercial use.\nk00gar 2025 - https://github.com/ack00gar",
+            text="Individual and personal use only.\nNot for commercial use.\nhttps://github.com/ack00gar",
             font=("Arial", 10, "italic", "bold"), justify="center"
         )
         footer_label.grid(row=8, column=0, columnspan=100, padx=5, pady=5, sticky="s")
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 97e9e860..fd12e95b 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -166,10 +166,14 @@ def start_debugging():
             general, "Live preview",
             state,
             "live_preview_mode",
-            tooltip_text="Use q to quite.\n\nWill show a live preview of what is being generated.\n\nFor debugging only this will greatly reduce your performance.\nStage 1: Show bounding boxes during object detection.\nStage 2: Show funscript and metric overlay while the funscript is being processed.",
+            tooltip_text="Press 'q' to quit.\n\nWill show a live preview of what is being generated.\n\nFor debugging only this will greatly reduce your performance.\nStage 1: Show bounding boxes during object detection.\nStage 2: Show funscript and metric overlay while the funscript is being processed.",
             row=0
         )
-        Widgets.checkbox(general, "Save debug file", state, "save_debug_file", tooltip_text="Saves  a debug file to disk with all collected metrics. This file can be very large.", row=1)
+        Widgets.checkbox(general,
+                         "Save debug file",
+                         state, "save_debug_file",
+                         tooltip_text="Saves  a debug file to disk with all collected metrics. This file can be very large.",
+                         row=1)
         Widgets.checkbox(
             general,
             "Save debugging video",
@@ -196,7 +200,7 @@ def start_debugging():
                        lambda: start_debugging(),
                        row=2,
                        column=5,
-                       tooltip_text="Open the debug video after the funscript generation process has completed.")
+                       tooltip_text="Open the debug video after the funscript generation process has completed.\nNeeds the 'Save debug file' activated during processing")
 
         script_compare = Widgets.frame(debugging, title="Script compare", row=1)
         Widgets.file_selection(
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index a125ca52..d52c9fac 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -24,7 +24,7 @@ def __init__(self, is_cli):
         self.video_reader: Literal["FFmpeg", "FFmpeg + OpenGL (Windows)"] = "FFmpeg" if is_mac() else "FFmpeg + OpenGL (Windows)"
 
         # Debug
-        self.save_debug_file: bool = False
+        self.save_debug_file: bool = True
         self.save_debug_video: bool = False
         self.debug_video_duration: int = 0
         self.live_preview_mode: bool = False
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index da44796a..2ef53a07 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -174,7 +174,6 @@ def _start_process(self, start_frame=0):
 
         # Start FFmpeg process
         #self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-        print(f"cmd: {cmd}")
         self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
         self.frame_size = self.width * self.height * 3  # Size of one frame in bytes
 

From 9980a6b89105058cbf03abcb86007514e3400249 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Tue, 21 Jan 2025 20:36:56 +0100
Subject: [PATCH 117/277] Added a test line to convert to bgr which seems to
 improve detection (needs investigation) Added a frame number to the yolo
 detection preview

---
 script_generator/object_detection/post_process_results.py | 5 +++++
 script_generator/object_detection/yolo.py                 | 4 ++++
 script_generator/video/video_task_processor.py            | 4 +++-
 3 files changed, 12 insertions(+), 1 deletion(-)

diff --git a/script_generator/object_detection/post_process_results.py b/script_generator/object_detection/post_process_results.py
index 7a6cb0de..cd3f1daa 100644
--- a/script_generator/object_detection/post_process_results.py
+++ b/script_generator/object_detection/post_process_results.py
@@ -117,7 +117,12 @@ def task_logic(self):
                     color = CLASS_COLORS.get(box[3])
                     cv2.rectangle(frame_display, (box[0][0], box[0][1]), (box[0][2], box[0][3]), color, 2)
                     cv2.putText(frame_display, f"{box[4]}: {box[3]}", (box[0][0], box[0][1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+
+                # Draw the frame ID at the top-left corner
+                cv2.putText(frame_display, f"Frame: {task.frame_pos}", (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 3)
+
                 cv2.imshow("Object detection tacking preview", frame_display)
+
                 if cv2.waitKey(1) & 0xFF == ord('q'):
                     break
 
diff --git a/script_generator/object_detection/yolo.py b/script_generator/object_detection/yolo.py
index 9472f1c6..4e2eb1bd 100644
--- a/script_generator/object_detection/yolo.py
+++ b/script_generator/object_detection/yolo.py
@@ -1,5 +1,7 @@
 import time
 
+import cv2
+
 from config import YOLO_CONF, YOLO_BATCH_SIZE, YOLO_MODEL
 
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
@@ -18,6 +20,8 @@ def task_logic(self):
 
         for task in self.get_task():
             if task.rendered_frame is not None:
+                task.rendered_frame = cv2.cvtColor(task.rendered_frame, cv2.COLOR_RGB2BGR)
+
                 batch.append(task.rendered_frame)
                 tasks.append(task)
 
diff --git a/script_generator/video/video_task_processor.py b/script_generator/video/video_task_processor.py
index f5d9e6c9..d388a770 100644
--- a/script_generator/video/video_task_processor.py
+++ b/script_generator/video/video_task_processor.py
@@ -1,9 +1,11 @@
+import os
 import subprocess
 from multiprocessing import cpu_count
 
+import imageio
 import numpy as np
 
-from config import SUBTRACT_THREADS_FROM_FFMPEG, PITCH, RENDER_RESOLUTION, FFMPEG_PATH
+from config import SUBTRACT_THREADS_FROM_FFMPEG, PITCH, RENDER_RESOLUTION, FFMPEG_PATH, DEBUG_PATH
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 from script_generator.tasks.tasks import AnalyzeFrameTask
 from script_generator.utils.logger import logger

From 0a00f8ed793aea0db901626d7af55880c6da7f87 Mon Sep 17 00:00:00 2001
From: hwoarangc <hwoarang929@gmail.com>
Date: Wed, 22 Jan 2025 12:31:37 +0800
Subject: [PATCH 118/277] add utf-8 support for logger

---
 FSGenerator.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index 52338381..641ab79a 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -81,7 +81,7 @@ def __init__(self):
             self.logger.setLevel(logging.INFO)
 
             # Create a file handler
-            file_handler = logging.FileHandler("FSGenerator.log", mode="w")
+            file_handler = logging.FileHandler("FSGenerator.log", mode="w", encoding='utf-8')
             file_handler.setLevel(logging.INFO)
             file_formatter = logging.Formatter(f"%(levelname)s - %(message)s")
             file_handler.setFormatter(file_formatter)

From 4397b479dfbfc4e0a005616b42bf7cd35971e1c8 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Wed, 22 Jan 2025 12:44:09 +0100
Subject: [PATCH 119/277] Converted back to bgr Fixed debug video button and
 logic

---
 .../gui/controller/debug_video.py             |  1 +
 .../gui/controller/tracking_analysis.py       |  2 +-
 .../gui/views/funscript_generator.py          | 29 ++++++++++---------
 .../analyze_tracking_results.py               |  5 +---
 .../object_detection/post_process_results.py  |  3 +-
 script_generator/object_detection/yolo.py     |  2 --
 .../video/video_task_processor.py             |  2 +-
 7 files changed, 20 insertions(+), 24 deletions(-)

diff --git a/script_generator/gui/controller/debug_video.py b/script_generator/gui/controller/debug_video.py
index 7b9b03a8..439de74b 100644
--- a/script_generator/gui/controller/debug_video.py
+++ b/script_generator/gui/controller/debug_video.py
@@ -7,6 +7,7 @@
 from utils.lib_Debugger import Debugger
 
 
+# TODO this callback is called on every frame. This doesn't seem correct or can be optized (instantiating Debugger, checks etc.)
 def debug_video(state: AppState):
     if not state.video_path:
         messagebox.showerror("Error", "Please select a video file.")
diff --git a/script_generator/gui/controller/tracking_analysis.py b/script_generator/gui/controller/tracking_analysis.py
index 98249d27..92ec0eca 100644
--- a/script_generator/gui/controller/tracking_analysis.py
+++ b/script_generator/gui/controller/tracking_analysis.py
@@ -38,7 +38,7 @@ def tracking_analysis(state):
     logger.info(f"Frame Start adjusted to: {state.frame_start}")
 
     # Performing the tracking part and generation of the raw funscript data
-    state.funscript_data = analyze_tracking_results(state, results, debug_video_callback=lambda: debug_video(state))
+    state.funscript_data = analyze_tracking_results(state, results)
 
     state.debugger.save_logs()
 
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index fd12e95b..5a5efe92 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -71,9 +71,6 @@ def start_processing():
             # TODO reset the progress bars
             video_analysis(state, controller)
 
-        def start_debugging():
-            debug_video(state)
-
         Widgets.button(processing, "Start processing", start_processing, row=3)
         # endregion
 
@@ -169,11 +166,13 @@ def start_debugging():
             tooltip_text="Press 'q' to quit.\n\nWill show a live preview of what is being generated.\n\nFor debugging only this will greatly reduce your performance.\nStage 1: Show bounding boxes during object detection.\nStage 2: Show funscript and metric overlay while the funscript is being processed.",
             row=0
         )
-        Widgets.checkbox(general,
-                         "Save debug file",
-                         state, "save_debug_file",
-                         tooltip_text="Saves  a debug file to disk with all collected metrics. This file can be very large.",
-                         row=1)
+        Widgets.checkbox(
+            general,
+            "Save debug file",
+            state, "save_debug_file",
+            tooltip_text="Saves  a debug file to disk with all collected metrics. This file can be very large.",
+            row=1
+        )
         Widgets.checkbox(
             general,
             "Save debugging video",
@@ -195,12 +194,14 @@ def start_debugging():
             label_width_px=73,
             sticky="w"
         )
-        Widgets.button(general,
-                       "Open debug video",
-                       lambda: start_debugging(),
-                       row=2,
-                       column=5,
-                       tooltip_text="Open the debug video after the funscript generation process has completed.\nNeeds the 'Save debug file' activated during processing")
+        Widgets.button(
+            general,
+            "Open debug video",
+            lambda: debug_video(state),
+            row=2,
+            column=5,
+            tooltip_text="Open the debug video after the funscript generation process has completed.\nNeeds the 'Save debug file' activated during processing"
+        )
 
         script_compare = Widgets.frame(debugging, title="Script compare", row=1)
         Widgets.file_selection(
diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index 272a34ed..8a230d4e 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -16,7 +16,7 @@
 from utils.lib_Visualizer import Visualizer
 
 
-def analyze_tracking_results(state, results, debug_video_callback=None):
+def analyze_tracking_results(state, results):
     width, height = get_cropped_dimensions(state.video_info)
     list_of_frames = results.get_all_frame_ids()  # Get all frame IDs with detections
     visualizer = Visualizer()  # Initialize the visualizer
@@ -208,9 +208,6 @@ def analyze_tracking_results(state, results, debug_video_callback=None):
                     eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
                 ))
 
-        if debug_video_callback and state.live_preview_mode:
-            debug_video_callback()
-
     state.update_ui(ProgressMessage(
         process="TRACKING_ANALYSIS",
         frames_processed=state.video_info.total_frames,
diff --git a/script_generator/object_detection/post_process_results.py b/script_generator/object_detection/post_process_results.py
index cd3f1daa..643f7c04 100644
--- a/script_generator/object_detection/post_process_results.py
+++ b/script_generator/object_detection/post_process_results.py
@@ -110,8 +110,7 @@ def task_logic(self):
                 sorted_boxes = self.test_result.get_boxes(frame_pos)
                 # logger.debug(f"Sorted boxes : {sorted_boxes}")
 
-                # frame_display = frame.copy()
-                frame_display = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+                frame_display = frame.copy()
 
                 for box in sorted_boxes:
                     color = CLASS_COLORS.get(box[3])
diff --git a/script_generator/object_detection/yolo.py b/script_generator/object_detection/yolo.py
index 4e2eb1bd..93a6c5d2 100644
--- a/script_generator/object_detection/yolo.py
+++ b/script_generator/object_detection/yolo.py
@@ -20,8 +20,6 @@ def task_logic(self):
 
         for task in self.get_task():
             if task.rendered_frame is not None:
-                task.rendered_frame = cv2.cvtColor(task.rendered_frame, cv2.COLOR_RGB2BGR)
-
                 batch.append(task.rendered_frame)
                 tasks.append(task)
 
diff --git a/script_generator/video/video_task_processor.py b/script_generator/video/video_task_processor.py
index d388a770..4ca6c12f 100644
--- a/script_generator/video/video_task_processor.py
+++ b/script_generator/video/video_task_processor.py
@@ -57,7 +57,7 @@ def get_cmd(vf):
                 "-an",  # Disable audio processing
                 "-map", "0:v:0",
                 *video_filter,
-                "-f", "rawvideo", "-pix_fmt", "rgb24",
+                "-f", "rawvideo", "-pix_fmt", "bgr24",
                 "-threads", str(ffmpeg_threads),
                 "-",  # Output to stdout
             ]

From 9c998c31c25440668927ec1019f557ba825d314a Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Wed, 22 Jan 2025 18:20:49 +0100
Subject: [PATCH 120/277] Re-enabled live funscript visualization

---
 script_generator/gui/views/funscript_generator.py             | 2 +-
 script_generator/object_detection/analyze_tracking_results.py | 2 +-
 script_generator/object_detection/yolo.py                     | 2 +-
 script_generator/video/video_task_processor.py                | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 5a5efe92..cdaa0f1d 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -200,7 +200,7 @@ def start_processing():
             lambda: debug_video(state),
             row=2,
             column=5,
-            tooltip_text="Open the debug video after the funscript generation process has completed.\nNeeds the 'Save debug file' activated during processing"
+            tooltip_text="Open the debug video after the funscript generation process has completed.\nNeeds the 'Save debug file' activated during processing."
         )
 
         script_compare = Widgets.frame(debugging, title="Script compare", row=1)
diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index 8a230d4e..bc47175a 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -188,7 +188,7 @@ def analyze_tracking_results(state, results):
                 frame_display = visualizer.draw_gauge(frame_display, state.funscript_distances[-1])
 
             # TODO this probably needs to be disabled but I need to check yolo inference
-            # cv2.imshow("Combined Results", frame_display)
+            cv2.imshow("Combined Results", frame_display)
             cv2.waitKey(1)
 
         # Update progress
diff --git a/script_generator/object_detection/yolo.py b/script_generator/object_detection/yolo.py
index 93a6c5d2..944aa1f0 100644
--- a/script_generator/object_detection/yolo.py
+++ b/script_generator/object_detection/yolo.py
@@ -35,7 +35,7 @@ def task_logic(self):
 
     def process_batch(self, frames, tasks):
         start_time = time.time()
-        # yolo_results = YOLO_MODEL(frames, conf=YOLO_CONF, verbose=False)
+        # Yolo expects bgr images when using numpy frames
         yolo_results = YOLO_MODEL.track(frames, persist=True, conf=YOLO_CONF, verbose=False)
         avg_time = (time.time() - start_time) / len(tasks)
 
diff --git a/script_generator/video/video_task_processor.py b/script_generator/video/video_task_processor.py
index 4ca6c12f..d90764fb 100644
--- a/script_generator/video/video_task_processor.py
+++ b/script_generator/video/video_task_processor.py
@@ -57,7 +57,7 @@ def get_cmd(vf):
                 "-an",  # Disable audio processing
                 "-map", "0:v:0",
                 *video_filter,
-                "-f", "rawvideo", "-pix_fmt", "bgr24",
+                "-f", "rawvideo", "-pix_fmt", "bgr24", # cv2 requires bgr (over rgb) and Yolo expects bgr images when using numpy frames (converts them internally)
                 "-threads", str(ffmpeg_threads),
                 "-",  # Output to stdout
             ]

From d9e7ebe5f8b98c21eab209d8830e55de77d27406 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Wed, 22 Jan 2025 18:22:58 +0100
Subject: [PATCH 121/277] Re-enabled live funscript visualization

---
 script_generator/object_detection/analyze_tracking_results.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index bc47175a..54a4eba5 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -187,7 +187,6 @@ def analyze_tracking_results(state, results):
             if state.funscript_distances:
                 frame_display = visualizer.draw_gauge(frame_display, state.funscript_distances[-1])
 
-            # TODO this probably needs to be disabled but I need to check yolo inference
             cv2.imshow("Combined Results", frame_display)
             cv2.waitKey(1)
 

From 50f4fe7dcf3a13541eabbeff4e014bc20c4ba36e Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Wed, 22 Jan 2025 19:05:52 +0100
Subject: [PATCH 122/277] Fixed a bug when generating the heatmaps

---
 utils/lib_FunscriptHandler.py | 183 +++++++++++++++++-----------------
 1 file changed, 93 insertions(+), 90 deletions(-)

diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 08530e79..d8da3182 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -123,8 +123,6 @@ def generate(self, state):
             # Generate a heatmap
             self.generate_heatmap(output_path, output_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
         except Exception as e:
-            # TODO this error doesn't reach the console
-            print(e)
             logger.error(f"Error generating funscript: {e}")
 
     def write_funscript(self, distances, output_path, fps):
@@ -140,94 +138,99 @@ def write_funscript(self, distances, output_path, fps):
             f.write("]}\n")
 
     def generate_heatmap(self, funscript_path, output_image_path):
-        # Load funscript data
-        times, positions, _, _ = self.load_funscript(funscript_path)
-        if not times or not positions:
-            logger.info("Failed to load funscript data.")
-            return
-
-        # add a timing: 0, position: 100 at the beginning if no value for 0
-        if times[0] != 0:
-            times.insert(0, 0)
-            positions.insert(0, 100)
-
-        # Print loaded data for debugging
-        # logger.debug(f"Times: {times}")
-        # logger.debug(f"Positions: {positions}")
-        logger.info(f"Total Actions: {len(times)}")
-        logger.info(f"Time Range: {times[0]} to {datetime.timedelta(seconds=int(times[-1] / 1000))}")
-
-        # Calculate speed (position change per time interval)
-        valid_indices = ~np.isnan(times) & ~np.isnan(positions)
-        filtered_positions = positions[valid_indices]
-        filtered_times = times[valid_indices]
-
-        # Calculate speed (position change per time interval)
-        speeds = np.abs(np.diff(filtered_positions) / np.diff(filtered_times)) * 1000  # Positions per second
-
-        logger.debug(f"Speeds: {speeds}")
-
-        def get_color(intensity):
-            if intensity <= 0:
-                return HEATMAP_COLORS[0]
-            if intensity > 5 * STEP_SIZE:
-                return HEATMAP_COLORS[6]
-            intensity += STEP_SIZE / 2.0
-            index = int(intensity // STEP_SIZE)
-            t = (intensity - index * STEP_SIZE) / STEP_SIZE
-            return [
-                HEATMAP_COLORS[index][0] + (HEATMAP_COLORS[index + 1][0] - HEATMAP_COLORS[index][0]) * t,
-                HEATMAP_COLORS[index][1] + (HEATMAP_COLORS[index + 1][1] - HEATMAP_COLORS[index][1]) * t,
-                HEATMAP_COLORS[index][2] + (HEATMAP_COLORS[index + 1][2] - HEATMAP_COLORS[index][2]) * t
-            ]
-
-        # Create figure and plot
-        plt.figure(figsize=(30, 2))
-        ax = plt.gca()
-
-        # Draw lines between points with colors based on speed
-        for i in range(len(times) - 1):
-            x_start = times[i] / 1000  # Convert ms to seconds
-            x_end = times[i + 1] / 1000
-            y_start = positions[i]
-            y_end = positions[i + 1]
-            speed = speeds[i]
-
-            # Get color based on speed
-            color = get_color(speed)
-            line_color = (color[0] / 255, color[1] / 255, color[2] / 255)  # Normalize to [0, 1]
-
-            # Plot the line
-            ax.plot([x_start, x_end], [y_start, y_end], color=line_color, linewidth=2)
-
-        # Customize plot
-        ax.set_title(
-            f'Funscript Heatmap\nDuration: {datetime.timedelta(seconds=int(times[-1] / 1000))} - Avg. Speed {int(np.mean(speeds))} - Actions: {len(times)}')
-        ax.set_xlabel('Time (s)')
-        ax.set_yticks(np.arange(0, 101, 10))
-        ax.set_xlim(times[0] / 1000, times[-1] / 1000)
-        ax.set_ylim(0, 100)
-
-        # Remove borders (spines)
-        for spine in ax.spines.values():
-            spine.set_visible(False)
-
-        # Add colorbar
-        cmap = mcolors.LinearSegmentedColormap.from_list("custom_heatmap", [
-            (HEATMAP_COLORS[i][0] / 255, HEATMAP_COLORS[i][1] / 255, HEATMAP_COLORS[i][2] / 255) for i in
-            range(len(HEATMAP_COLORS))
-        ])
-        norm = mcolors.Normalize(vmin=0, vmax=5 * STEP_SIZE)
-        sm = ScalarMappable(cmap=cmap, norm=norm)
-        sm.set_array([])
-        # cbar = plt.colorbar(sm, ax=ax, orientation='horizontal', pad=0.2,
-        #                    ticks=np.arange(0, 5 * step_size + 1, step_size))
-        # cbar.set_label('Speed (positions/s)')
-
-        # Save the figure
-        plt.savefig(output_image_path, bbox_inches='tight', dpi=200)  # Increase resolution
-        plt.close()
-        logger.info(f"Funscript heatmap saved to {output_image_path}")
+        try:
+            # Load funscript data
+            times, positions, _, _ = self.load_funscript(funscript_path)
+            if not times or not positions:
+                logger.info("Failed to load funscript data.")
+                return
+
+            # add a timing: 0, position: 100 at the beginning if no value for 0
+            if times[0] != 0:
+                times.insert(0, 0)
+                positions.insert(0, 100)
+
+            # Print loaded data for debugging
+            # logger.debug(f"Times: {times}")
+            # logger.debug(f"Positions: {positions}")
+            logger.info(f"Total Actions: {len(times)}")
+            logger.info(f"Time Range: {times[0]} to {datetime.timedelta(seconds=int(times[-1] / 1000))}")
+
+            # Calculate speed (position change per time interval)
+            times = np.array(times)
+            positions = np.array(positions)
+            valid_indices = ~np.isnan(times) & ~np.isnan(positions)
+            filtered_positions = positions[valid_indices]
+            filtered_times = times[valid_indices]
+
+            # Calculate speed (position change per time interval)
+            speeds = np.abs(np.diff(filtered_positions) / np.diff(filtered_times)) * 1000  # Positions per second
+
+            logger.debug(f"Speeds: {speeds}")
+
+            def get_color(intensity):
+                if intensity <= 0:
+                    return HEATMAP_COLORS[0]
+                if intensity > 5 * STEP_SIZE:
+                    return HEATMAP_COLORS[6]
+                intensity += STEP_SIZE / 2.0
+                index = int(intensity // STEP_SIZE)
+                t = (intensity - index * STEP_SIZE) / STEP_SIZE
+                return [
+                    HEATMAP_COLORS[index][0] + (HEATMAP_COLORS[index + 1][0] - HEATMAP_COLORS[index][0]) * t,
+                    HEATMAP_COLORS[index][1] + (HEATMAP_COLORS[index + 1][1] - HEATMAP_COLORS[index][1]) * t,
+                    HEATMAP_COLORS[index][2] + (HEATMAP_COLORS[index + 1][2] - HEATMAP_COLORS[index][2]) * t
+                ]
+
+            # Create figure and plot
+            plt.figure(figsize=(30, 2))
+            ax = plt.gca()
+
+            # Draw lines between points with colors based on speed
+            for i in range(len(times) - 1):
+                x_start = times[i] / 1000  # Convert ms to seconds
+                x_end = times[i + 1] / 1000
+                y_start = positions[i]
+                y_end = positions[i + 1]
+                speed = speeds[i]
+
+                # Get color based on speed
+                color = get_color(speed)
+                line_color = (color[0] / 255, color[1] / 255, color[2] / 255)  # Normalize to [0, 1]
+
+                # Plot the line
+                ax.plot([x_start, x_end], [y_start, y_end], color=line_color, linewidth=2)
+
+            # Customize plot
+            ax.set_title(
+                f'Funscript Heatmap\nDuration: {datetime.timedelta(seconds=int(times[-1] / 1000))} - Avg. Speed {int(np.mean(speeds))} - Actions: {len(times)}')
+            ax.set_xlabel('Time (s)')
+            ax.set_yticks(np.arange(0, 101, 10))
+            ax.set_xlim(times[0] / 1000, times[-1] / 1000)
+            ax.set_ylim(0, 100)
+
+            # Remove borders (spines)
+            for spine in ax.spines.values():
+                spine.set_visible(False)
+
+            # Add colorbar
+            cmap = mcolors.LinearSegmentedColormap.from_list("custom_heatmap", [
+                (HEATMAP_COLORS[i][0] / 255, HEATMAP_COLORS[i][1] / 255, HEATMAP_COLORS[i][2] / 255) for i in
+                range(len(HEATMAP_COLORS))
+            ])
+            norm = mcolors.Normalize(vmin=0, vmax=5 * STEP_SIZE)
+            sm = ScalarMappable(cmap=cmap, norm=norm)
+            sm.set_array([])
+            # cbar = plt.colorbar(sm, ax=ax, orientation='horizontal', pad=0.2,
+            #                    ticks=np.arange(0, 5 * step_size + 1, step_size))
+            # cbar.set_label('Speed (positions/s)')
+
+            # Save the figure
+            plt.savefig(output_image_path, bbox_inches='tight', dpi=200)  # Increase resolution
+            plt.close()
+            logger.info(f"Funscript heatmap saved to {output_image_path}")
+        except Exception as e:
+            logger.error(f"Error generating heatmap for funscript: {e}")
 
     def boost_amplitude(self, signal, boost_factor, min_value=0, max_value=100):
         """

From 3a9cb095dccfeec85f50e5a1f6ff37457ccd1f60 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Wed, 22 Jan 2025 19:27:43 +0100
Subject: [PATCH 123/277] Updated GUI tooltips and moved the open debug video
 button to the right line

---
 .../gui/views/funscript_generator.py          | 21 +++++++++----------
 utils/lib_Debugger.py                         |  1 +
 2 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index cdaa0f1d..b8219287 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -168,11 +168,19 @@ def start_processing():
         )
         Widgets.checkbox(
             general,
-            "Save debug file",
+            "Save debug information",
             state, "save_debug_file",
-            tooltip_text="Saves  a debug file to disk with all collected metrics. This file can be very large.",
+            tooltip_text="Saves a debug file to disk with all collected metrics.\nThis is a prerequisite for playing back the debug video with the debug statistics overlay.\nThis also contains all generated metrics.",
             row=1
         )
+        Widgets.button(
+            general,
+            "Open debug video",
+            lambda: debug_video(state),
+            row=1,
+            column=5,
+            tooltip_text="Opens a debug video player overlaid with debugging information.\n\nPress q to quit.\nThis overlay shows object detection boxes and a live funscript overlay,\namong other useful debugging information.\nCan only be triggered after the funscript generation process has completed.\nNeeds the 'Save debug information' option activated during processing."
+        )
         Widgets.checkbox(
             general,
             "Save debugging video",
@@ -194,15 +202,6 @@ def start_processing():
             label_width_px=73,
             sticky="w"
         )
-        Widgets.button(
-            general,
-            "Open debug video",
-            lambda: debug_video(state),
-            row=2,
-            column=5,
-            tooltip_text="Open the debug video after the funscript generation process has completed.\nNeeds the 'Save debug file' activated during processing."
-        )
-
         script_compare = Widgets.frame(debugging, title="Script compare", row=1)
         Widgets.file_selection(
             attr="reference_script",
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 9ffc8101..728f286b 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -155,6 +155,7 @@ def play_video(self, state, start_frame=0, duration=0, rolling_window_size=100,
         else:
             width = self.cap.get(cv2.CAP_PROP_FRAME_WIDTH)
 
+        # debug video with statistics like a funscript overlay, object detection, frame number, funscript mode etc.
         cv2.namedWindow("Debug Video")
         cv2.setMouseCallback("Debug Video", self._mouse_callback, param=width)
 

From bec8ce34c0ff7bd0c130d918faf963f34ec907fe Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Wed, 22 Jan 2025 19:28:12 +0100
Subject: [PATCH 124/277] Updated GUI tooltips and moved the open debug video
 button to the right line

---
 script_generator/gui/views/funscript_generator.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index b8219287..e4d8f5db 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -179,7 +179,7 @@ def start_processing():
             lambda: debug_video(state),
             row=1,
             column=5,
-            tooltip_text="Opens a debug video player overlaid with debugging information.\n\nPress q to quit.\nThis overlay shows object detection boxes and a live funscript overlay,\namong other useful debugging information.\nCan only be triggered after the funscript generation process has completed.\nNeeds the 'Save debug information' option activated during processing."
+            tooltip_text="Press q to quit.\n\nOpens a debug video player overlaid with debugging information.\nThis overlay shows object detection boxes and a live funscript overlay,\namong other useful debugging information.\nCan only be triggered after the funscript generation process has completed.\nNeeds the 'Save debug information' option activated during processing."
         )
         Widgets.checkbox(
             general,

From 502d9e03a9c00dcc4051766347d2296333bbb2c8 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Wed, 22 Jan 2025 19:28:26 +0100
Subject: [PATCH 125/277] Updated GUI tooltips and moved the open debug video
 button to the right line

---
 script_generator/gui/views/funscript_generator.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index e4d8f5db..8bc747f2 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -179,7 +179,7 @@ def start_processing():
             lambda: debug_video(state),
             row=1,
             column=5,
-            tooltip_text="Press q to quit.\n\nOpens a debug video player overlaid with debugging information.\nThis overlay shows object detection boxes and a live funscript overlay,\namong other useful debugging information.\nCan only be triggered after the funscript generation process has completed.\nNeeds the 'Save debug information' option activated during processing."
+            tooltip_text="Press q to quit.\n\nOpens a debug video player overlaid with debugging information.\n\nThis overlay shows object detection boxes and a live funscript overlay,\namong other useful debugging information.\nCan only be triggered after the funscript generation process has completed.\nNeeds the 'Save debug information' option activated during processing."
         )
         Widgets.checkbox(
             general,

From f4982821683c82c4e076cf8b51399dfbc91af7ef Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Wed, 22 Jan 2025 19:38:20 +0100
Subject: [PATCH 126/277] Updated GUI tooltips and moved the open debug video
 button to the right line

---
 script_generator/gui/views/funscript_generator.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 8bc747f2..4018ae65 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -163,7 +163,7 @@ def start_processing():
             general, "Live preview",
             state,
             "live_preview_mode",
-            tooltip_text="Press 'q' to quit.\n\nWill show a live preview of what is being generated.\n\nFor debugging only this will greatly reduce your performance.\nStage 1: Show bounding boxes during object detection.\nStage 2: Show funscript and metric overlay while the funscript is being processed.",
+            tooltip_text="Press 'q' to quit.\n\nWill show a live preview of what is being generated.\n\nFor debugging only this will reduce your performance.\nStage 1: Show bounding boxes during object detection.\nStage 2: Show funscript and metric overlay while the funscript is being processed.",
             row=0
         )
         Widgets.checkbox(
@@ -175,7 +175,7 @@ def start_processing():
         )
         Widgets.button(
             general,
-            "Open debug video",
+            "Play debug video",
             lambda: debug_video(state),
             row=1,
             column=5,

From b9bf2112ceba91f001760e81c5c2c9b9e991da1e Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Wed, 22 Jan 2025 19:43:26 +0100
Subject: [PATCH 127/277] Updated GUI tooltips and moved the open debug video
 button to the right line

---
 script_generator/gui/views/funscript_generator.py | 3 +--
 utils/lib_Debugger.py                             | 1 +
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 4018ae65..da55cda9 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -184,7 +184,7 @@ def start_processing():
         Widgets.checkbox(
             general,
             "Save debugging video",
-            tooltip_text="Will save a debug video once funscript processing is complete that can be\neasily shared on Discord for showcasing issues or areas of improvement.",
+            tooltip_text="Will take a while to generate after completion! Please see the logs for when it's completed.\n\nWill save a debug video once funscript processing is complete that can be\neasily shared on Discord for showcasing issues or areas of improvement.",
             state=state,
             attr="save_debug_video",
             row=2
@@ -218,7 +218,6 @@ def start_processing():
 
         # region FOOTER
         Widgets.disclaimer(wrapper)
-
         # endregion
 
         # region UI UPDATE CALLBACK
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 728f286b..4fe18125 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -249,6 +249,7 @@ def play_video(self, state, start_frame=0, duration=0, rolling_window_size=100,
             output_path_ffmpeg, _ = get_output_file_path(debug_video_path, "_rawyolo.json", True)
 
             # FFmpeg command to convert to H.265
+            # TODO move to generic shared ffmpeg command and add hardware acceleration
             ffmpeg_command = [
                 "ffmpeg",
                 "-y",  # Overwrite output file if it exists

From 1610b05b5093e362b91dc99e067b05ca53f68e72 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Wed, 22 Jan 2025 19:54:56 +0100
Subject: [PATCH 128/277] Fixed another issue in heatmap generation

---
 script_generator/gui/views/funscript_generator.py | 2 +-
 utils/lib_FunscriptHandler.py                     | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index da55cda9..4b981444 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -184,7 +184,7 @@ def start_processing():
         Widgets.checkbox(
             general,
             "Save debugging video",
-            tooltip_text="Will take a while to generate after completion! Please see the logs for when it's completed.\n\nWill save a debug video once funscript processing is complete that can be\neasily shared on Discord for showcasing issues or areas of improvement.",
+            tooltip_text="Will take a while to generate after completion!\nPlease see the log for when it's completed.\n\nWill save a debug video once funscript processing is complete that can be\neasily shared on Discord for showcasing issues or areas of improvement.",
             state=state,
             attr="save_debug_video",
             row=2
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index d8da3182..3bf27388 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -164,7 +164,8 @@ def generate_heatmap(self, funscript_path, output_image_path):
             filtered_times = times[valid_indices]
 
             # Calculate speed (position change per time interval)
-            speeds = np.abs(np.diff(filtered_positions) / np.diff(filtered_times)) * 1000  # Positions per second
+            # We add 1e-10 to prevent dividing by zero
+            speeds = np.abs(np.diff(filtered_positions) / (np.diff(filtered_times) + 1e-10)) * 1000 # Positions per second
 
             logger.debug(f"Speeds: {speeds}")
 

From c1893f36e8888331b3ba029d19de027df2bed3b7 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Wed, 22 Jan 2025 21:44:18 +0100
Subject: [PATCH 129/277] minor fix

---
 utils/lib_FunscriptHandler.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 60ef6c5d..82ee37c5 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -145,7 +145,8 @@ def generate_heatmap(self, funscript_path, output_image_path, global_state):
         global_state.logger.info(f"Time Range: {times[0]} to {datetime.timedelta(seconds=int(times[-1] / 1000))}")
 
         # Calculate speed (position change per time interval)
-        speeds = np.abs(np.diff(positions) / np.diff(times)) * 1000  # Positions per second
+        # We add 1e-10 to prevent dividing by zero
+        speeds = np.abs(np.diff(positions) / (np.diff(times) + 1e-10)) * 1000  # Positions per second
 
         def get_color(intensity):
             if intensity <= 0:
@@ -569,7 +570,8 @@ def generate_heatmap_inline(self, ax, times, positions, global_state):
                 break
 
         # Calculate speed (position change per time interval)
-        speeds = np.abs(np.diff(positions) / np.diff(times)) * 1000  # Positions per second
+        # We add 1e-10 to prevent dividing by zero
+        speeds = np.abs(np.diff(positions) / (np.diff(times) + 1e-10)) * 1000  # Positions per second
 
         def get_color(intensity):
             if intensity <= 0:
@@ -643,7 +645,8 @@ def _calculate_metrics(self, times, positions):
         avg_stroke_duration = np.mean(stroke_durations)
 
         # Calculate average speed
-        speeds = np.abs(np.diff(positions) / np.diff(times)) * 1000  # Positions per second
+        # We add 1e-10 to prevent dividing by zero
+        speeds = np.abs(np.diff(positions) / (np.diff(times) + 1e-10)) * 1000  # Positions per second
         #speeds = np.abs(np.diff(positions) / stroke_durations)
         avg_speed = np.mean(speeds)
 

From a344b34acbb30243e796551e866c92dde1be3368 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Wed, 22 Jan 2025 21:46:30 +0100
Subject: [PATCH 130/277] minor fix

---
 FSGenerator.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index 641ab79a..ea1881e0 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -571,6 +571,8 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
     for line in data:
         if line[0] >= start_frame and line[1] == 0 and line[2] >= 0.5:
             penis_frame = line[0]
+            return penis_frame
+        """
         if line[0] == penis_frame and line[1] == 1 and line[2] >= 0.5:
             if frame_detected == 0:
                 frame_detected = line[0]
@@ -582,9 +584,10 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
                 consecutive_frames = 0
                 frame_detected = 0
 
-            if consecutive_frames >= 2:
-                global_state.logger.info(f"First instance of Glans/Penis found in frame {line[0] - 4}")
-                return line[0] - 4
+        if consecutive_frames >= 2:
+            global_state.logger.info(f"First instance of Glans/Penis found in frame {line[0] - 4}")
+            return line[0] - 4
+        """
 
 def select_video_file():
     file_path = filedialog.askopenfilename(

From 0f1234d7de6be0bd46610fd58cc2bebabf76528e Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Wed, 22 Jan 2025 21:48:38 +0100
Subject: [PATCH 131/277] minor fix

---
 params/config.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/params/config.py b/params/config.py
index 5c0e50a3..ff5c5042 100644
--- a/params/config.py
+++ b/params/config.py
@@ -2,7 +2,7 @@
 import platform
 import os
 
-version = "0.0.3"
+version = "0.0.4"
 
 # ffmpeg and ffprobe paths - replace with your own if not in your system path
 win_ffmpeg_path = "C:/ffmpeg/bin/ffmpeg.exe"

From edea754e611309c71018e01c4c0d6a02c8967a50 Mon Sep 17 00:00:00 2001
From: jwvanderbeck <jwvanderbeck@gmail.com>
Date: Wed, 22 Jan 2025 17:47:22 -0500
Subject: [PATCH 132/277] fix creating funscript backup when one already exists

---
 utils/lib_FunscriptHandler.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 82ee37c5..05e1b1d8 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -24,7 +24,10 @@ def generate(self, global_state):
         # Backup output file if it exists
         if os.path.exists(output_path):
             global_state.logger.warning(f"Output path {output_path} already exists, backing up as {output_path}.bak...")
-            os.rename(output_path, output_path + ".bak")
+            backup = output_path + ".bak"
+            if os.path.exists(backup):
+                os.remove(backup)
+            os.rename(output_path, backup)
 
         raw_funscript_path = global_state.video_file[:-4] + f"_rawfunscript.json"
 

From 86e96f1624ee2a8787364fb37d4cdd9521c80add Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Fri, 24 Jan 2025 10:45:22 +0100
Subject: [PATCH 133/277] Cleaned up division by zero logic

---
 .../analyze_tracking_results.py               |  9 +--
 utils/lib_FunscriptHandler.py                 | 57 +++----------------
 2 files changed, 12 insertions(+), 54 deletions(-)

diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index 54a4eba5..8c6227c4 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -154,10 +154,11 @@ def analyze_tracking_results(state, results):
             # Display the tracking results for testing
             ret, frame = reader.read()
 
-            if state.video_reader == "OpenCV" and video_info.is_vr:
-                frame_display = frame[:, :frame.shape[1] // 2, :]  # only half left of the frame, for VR half
-            else:
-                frame_display = frame.copy()
+            if not frame:
+                logger.warn("Frame could not be read in live preview")
+                continue
+
+            frame_display = frame.copy()
 
             for box in tracker.tracked_boxes:
                 frame_display = visualizer.draw_bounding_box(
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 3bf27388..52e82bd3 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -601,31 +601,8 @@ def generate_heatmap_inline(self, ax, times, positions, state):
                 break
 
         # Calculate speed (position change per time interval)
-        # Filter out NaN values from times and positions
-        # TODO filter nan at one logical moment
-        times = np.array(times, dtype=float)  # Ensure times is a float array
-        positions = np.array(positions, dtype=float)  # Ensure positions is a float array
-
-        # Filter out NaN values
-        valid_indices = ~np.isnan(times) & ~np.isnan(positions)
-        filtered_positions = positions[valid_indices]
-        filtered_times = times[valid_indices]
-
-        # Ensure at least two valid points for speed calculation
-        if len(filtered_times) > 1:
-            time_diffs = np.diff(filtered_times)
-            pos_diffs = np.diff(filtered_positions)
-
-            # Replace zero or negative time differences with NaN to prevent invalid division
-            time_diffs[time_diffs <= 0] = np.nan
-
-            # Calculate speeds, ignoring invalid divisions
-            speeds = np.abs(pos_diffs / time_diffs) * 1000  # Positions per second
-            speeds = np.nan_to_num(speeds, nan=0.0)  # Replace NaN values in speeds with 0
-        else:
-            speeds = np.array([])  # Empty array if not enough valid data points
-
-        # TODO filter nan at one logical moment END
+        # prevent division by zero by adding 1e-10
+        speeds = np.abs(np.diff(positions) / (np.diff(times) + 1e-10)) * 1000  # Positions per second
 
         def get_color(intensity):
             if np.isnan(intensity):
@@ -702,6 +679,9 @@ def _calculate_metrics(self, times, positions):
         if not times or not positions:
             return {}
 
+        times = np.array(times)
+        positions = np.array(positions)
+
         # Calculate number of strokes
         num_strokes = len(times) - 1
 
@@ -710,32 +690,9 @@ def _calculate_metrics(self, times, positions):
         avg_stroke_duration = np.mean(stroke_durations)
 
         # Calculate average speed
-        # Filter out NaN values from times and positions
-        # TODO filter in one place find out why we have nan's
-        times = np.array(times, dtype=float)  # Ensure times is a float array
-        positions = np.array(positions, dtype=float)  # Ensure positions is a float array
-
-        valid_indices = ~np.isnan(times) & ~np.isnan(positions)
-        filtered_positions = positions[valid_indices]
-        filtered_times = times[valid_indices]
-
-        # Ensure at least two valid points for speed calculation
-        if len(filtered_times) > 1:
-            time_diffs = np.diff(filtered_times).astype(float)  # Convert to float explicitly
-            pos_diffs = np.diff(filtered_positions)
-
-            # Prevent division by zero
-            time_diffs[time_diffs == 0] = np.nan  # Replace zero time differences with NaN
-
-            # Calculate speeds, ignoring invalid divisions
-            speeds = np.abs(pos_diffs / time_diffs) * 1000  # Positions per second
-            speeds = np.nan_to_num(speeds, nan=0.0)  # Replace NaN values with 0
-        else:
-            speeds = np.array([])  # Empty array if not enough valid data points
-
-        # Calculate average speed
+        # prevent division by zero by adding 1e-10
+        speeds = np.abs(np.diff(positions) / (np.diff(times) + 1e-10)) * 1000  # Positions per second
         avg_speed = np.mean(speeds)
-        # TODO filter in one place find out why we have nan's END
 
         # Calculate average depth of stroke
         depths = np.abs(np.diff(positions))

From ec20840b68e81207fab078f9af8c7bfb5eb91b9c Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Fri, 24 Jan 2025 12:24:46 +0100
Subject: [PATCH 134/277] Fixed ffprobe command Added luminance to open gl
 Fixed fisheye detection Added basic error handling and a error popup fixed an
 issue with live preview for object detection

---
 debug/errors.py                               |   5 +
 .../gui/controller/video_analysis.py          |  48 +++---
 .../gui/views/funscript_generator.py          |   2 +-
 .../analyze_tracking_results.py               |   4 +-
 script_generator/scripts/analyze_video.py     | 147 ++++++++++--------
 .../scripts/generate_funscript.py             |   6 +-
 .../tasks/abstract_task_processor.py          |  18 ++-
 script_generator/video/ffmpeg.py              |  58 ++++---
 script_generator/video/video_info.py          |   1 +
 .../video/video_task_processor.py             |  12 +-
 utils/lib_VideoReaderFFmpeg.py                |  43 ++---
 11 files changed, 196 insertions(+), 148 deletions(-)
 create mode 100644 debug/errors.py

diff --git a/debug/errors.py b/debug/errors.py
new file mode 100644
index 00000000..05f8afd7
--- /dev/null
+++ b/debug/errors.py
@@ -0,0 +1,5 @@
+class FFProbeError(Exception):
+    pass
+
+class FFMpegError(Exception):
+    pass
\ No newline at end of file
diff --git a/script_generator/gui/controller/video_analysis.py b/script_generator/gui/controller/video_analysis.py
index 68c0a51a..cef9dc11 100644
--- a/script_generator/gui/controller/video_analysis.py
+++ b/script_generator/gui/controller/video_analysis.py
@@ -24,27 +24,33 @@ def video_analysis(state, root):
     logger.info(f"Reference Script: {state.reference_script}")
 
     def run():
-        # Initialize the debugger
-        log_file, _ = get_output_file_path(state.video_path, "_debug_logs.json")
-        state.debugger = Debugger(state.video_path, log_file=log_file)
-
-        skip_detection = check_skip_object_detection(state, root)
-
-        if skip_detection:
-            state.set_video_info()
-            if state.update_ui:
-                state.update_ui(ProgressMessage(
-                    process="OBJECT_DETECTION",
-                    frames_processed=state.video_info.total_frames,
-                    total_frames=state.video_info.total_frames,
-                    eta="Done"
-                ))
-        else:
-            analyze_video(state)
-
-
-        tracking_analysis(state)
+        try:
+            # Initialize the debugger
+            log_file, _ = get_output_file_path(state.video_path, "_debug_logs.json")
+            state.debugger = Debugger(state.video_path, log_file=log_file)
+
+            skip_detection = check_skip_object_detection(state, root)
+
+            if skip_detection:
+                state.set_video_info()
+                if state.update_ui:
+                    state.update_ui(ProgressMessage(
+                        process="OBJECT_DETECTION",
+                        frames_processed=state.video_info.total_frames,
+                        total_frames=state.video_info.total_frames,
+                        eta="Done"
+                    ))
+            else:
+                analyze_video(state)
+
+            tracking_analysis(state)
+
+        except Exception as e:
+            logger.error(f"Error during video analysis: {e}")
+            messagebox.showerror("Error", f"Could not process video:\n{e}")
+            import traceback
+            traceback.print_exc()
 
     processing_thread = threading.Thread(target=run)
     processing_thread.start()
-    # processing_thread.join()
\ No newline at end of file
+    # processing_thread.join()
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 4b981444..465e2aa7 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -36,7 +36,7 @@ def __init__(self, parent, controller):
             file_selector_title="Choose a File",
             file_types=[("Text Files", "*.mp4 *.avi *.mov *.mkv"), ("All Files", "*.*")],
             state=state,
-            tooltip_text="The video to generate a funscript for",
+            tooltip_text="The video to generate a funscript for. For proper detection of fisheye keep the suffix like _FISHEYE190, _MKX200, etc. in the filename\n\nIn the future we'll add the option to override this in the UI.",
             # if the file changes we need to make sure the video info is reset
             # TODO add os.path.exists and update video_info immediately if found
             command=lambda val: setattr(state, 'video_info', None)
diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index 8c6227c4..427d5353 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -150,11 +150,11 @@ def analyze_tracking_results(state, results):
                     }
                 )
 
+        # Display object detection tracking results in a live preview window
         if state.live_preview_mode:
-            # Display the tracking results for testing
             ret, frame = reader.read()
 
-            if not frame:
+            if not frame.any():
                 logger.warn("Frame could not be read in live preview")
                 continue
 
diff --git a/script_generator/scripts/analyze_video.py b/script_generator/scripts/analyze_video.py
index e06c9b74..747bd854 100644
--- a/script_generator/scripts/analyze_video.py
+++ b/script_generator/scripts/analyze_video.py
@@ -1,4 +1,3 @@
-import os
 import queue
 import threading
 import time
@@ -13,7 +12,7 @@
 from script_generator.state.app_state import AppState
 from script_generator.tasks.abstract_task_processor import TaskProcessorTypes
 from script_generator.tasks.tasks import AnalyzeVideoTask, AnalyzeFrameTask
-from script_generator.utils.file import get_output_file_path, check_create_output_folder
+from script_generator.utils.file import check_create_output_folder
 from script_generator.utils.logger import logger
 from script_generator.video.video_conversion.vr_to_2d_task_processor import VrTo2DTaskProcessor
 from script_generator.video.video_task_processor import VideoTaskProcessor
@@ -22,67 +21,93 @@
 def analyze_video(state: AppState) -> List[AnalyzeFrameTask]:
     logger.info(f"[OBJECT DETECTION] Starting up pipeline with profiling in {'sequential mode' if SEQUENTIAL_MODE else 'parallel mode'}...")
 
-    # make sure the output folder exists for this video
-    check_create_output_folder(state.video_path)
-
-    use_open_gl = state.video_reader == "FFmpeg + OpenGL (Windows)"
-
-    # Initialize batch task
-    state.set_video_info()
-    state.analyze_task = AnalyzeVideoTask()
-
-    # Create queues
-    opengl_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
-    yolo_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
-    analysis_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
-    result_q = queue.Queue(maxsize=0)
-
-    # Create threads
-    decode_thread = VideoTaskProcessor(state=state, output_queue=opengl_q if use_open_gl else yolo_q)
-    opengl_thread = VrTo2DTaskProcessor(state=state, input_queue=opengl_q, output_queue=yolo_q) if use_open_gl else None
-    yolo_thread = YoloTaskProcessor(state=state, input_queue=yolo_q, output_queue=analysis_q)
-    yolo_analysis_thread = YoloAnalysisTaskProcessor(state=state, input_queue=analysis_q, output_queue=result_q)
-
-    # Start logging thread
     log_thread_stop_event = threading.Event()
-    queue_logging_thread = threading.Thread(
-        target=log_progress,
-        args=(state, opengl_q, yolo_q, analysis_q, result_q, log_thread_stop_event),
-        daemon=True,
-    )
-    queue_logging_thread.start()
-
-    # Sequential mode can be used to determine performance bottlenecks on very short videos
-    if SEQUENTIAL_MODE:
-        def run_thread(thread, thread_name, out_queue):
-            start_time = time.time()
-            thread.start()
-            thread.join()
-            out_queue.put(None)
-            logger.info(f"[OBJECT DETECTION] {thread_name} thread done in {time.time() - start_time} s")
-
-        run_thread(decode_thread, TaskProcessorTypes.VIDEO, opengl_q)
-        if use_open_gl:
-            run_thread(opengl_thread, TaskProcessorTypes.OPENGL, yolo_q)
-        run_thread(yolo_thread, TaskProcessorTypes.YOLO, analysis_q)
-        run_thread(yolo_analysis_thread, TaskProcessorTypes.YOLO_ANALYSIS, result_q)
-    else:
-        threads = [decode_thread, opengl_thread, yolo_thread, yolo_analysis_thread] if use_open_gl else [decode_thread, yolo_thread, yolo_analysis_thread]
-        for thread in threads:
-            thread.start()
+    threads = []
+
+    try:
+        # make sure the output folder exists for this video
+        check_create_output_folder(state.video_path)
+
+        # Initialize batch task
+        state.set_video_info()
+        if state.video_reader == "FFmpeg + OpenGL (Windows)":
+            if not state.video_info.is_vr:
+                logger.warn("Disabled OpenGL in the pipeline as it's not needed for 2D videos")
+                state.video_reader = "FFmpeg"
+
+            if state.video_info.is_fisheye:
+                logger.warn("Disabled OpenGL as fisheye is not yet supported with the opengl feature")
+                state.video_reader = "FFmpeg"
+
+        use_open_gl = state.video_reader == "FFmpeg + OpenGL (Windows)"
+
+        state.analyze_task = AnalyzeVideoTask()
+
+        # Create queues
+        opengl_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
+        yolo_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
+        analysis_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
+        result_q = queue.Queue(maxsize=0)
+
+        # Create threads
+        decode_thread = VideoTaskProcessor(state=state, output_queue=opengl_q if use_open_gl else yolo_q)
+        opengl_thread = VrTo2DTaskProcessor(state=state, input_queue=opengl_q, output_queue=yolo_q) if use_open_gl else None
+        yolo_thread = YoloTaskProcessor(state=state, input_queue=yolo_q, output_queue=analysis_q)
+        yolo_analysis_thread = YoloAnalysisTaskProcessor(state=state, input_queue=analysis_q, output_queue=result_q)
+
+        # Start logging thread
+        queue_logging_thread = threading.Thread(
+            target=log_progress,
+            args=(state, opengl_q, yolo_q, analysis_q, result_q, log_thread_stop_event),
+            daemon=True,
+        )
+        queue_logging_thread.start()
+
+        # Sequential mode can be used to determine performance bottlenecks on very short videos
+        if SEQUENTIAL_MODE:
+            def run_thread(thread, thread_name, out_queue):
+                start_time = time.time()
+                thread.start()
+                thread.join()
+                out_queue.put(None)
+                logger.info(f"[OBJECT DETECTION] {thread_name} thread done in {time.time() - start_time} s")
+
+            run_thread(decode_thread, TaskProcessorTypes.VIDEO, opengl_q)
+            if use_open_gl:
+                run_thread(opengl_thread, TaskProcessorTypes.OPENGL, yolo_q)
+            run_thread(yolo_thread, TaskProcessorTypes.YOLO, analysis_q)
+            run_thread(yolo_analysis_thread, TaskProcessorTypes.YOLO_ANALYSIS, result_q)
+        else:
+            threads = [decode_thread, opengl_thread, yolo_thread, yolo_analysis_thread] if use_open_gl else [decode_thread, yolo_thread, yolo_analysis_thread]
+            for thread in threads:
+                thread.start()
+            for thread in threads:
+                thread.join()
+
+            # Check for exceptions in threads
+            for thread in threads:
+                if thread is not None:
+                    thread.check_exception()
+
+        state.analyze_task.end_time = time.time()
+
+        log_thread_stop_event.set()
+
+        # wait for update progress thread to close
+        time.sleep(UPDATE_PROGRESS_INTERVAL * 1.1)
+
+        log_performance(state=state, results_queue=result_q)
+
+        return result_q.queue
+
+    except Exception as e:
+        logger.error(f"An error occurred during video analysis: {e}")
+        # Signal all threads to stop and perform cleanup
+        log_thread_stop_event.set()
         for thread in threads:
-            thread.join()
-
-    state.analyze_task.end_time = time.time()
-
-    log_thread_stop_event.set()
-
-    # wait for update progress thread to close
-    time.sleep(UPDATE_PROGRESS_INTERVAL * 1.1)
-
-    log_performance(state=state, results_queue=result_q)
-
-    return result_q.queue
+            if thread is not None and thread.is_alive():
+                thread.join(timeout=1)
+        raise
 
 
 def log_progress(state, opengl_q, yolo_q, analysis_q, results_q, stop_event):
diff --git a/script_generator/scripts/generate_funscript.py b/script_generator/scripts/generate_funscript.py
index 2c83fa00..54f1ae17 100644
--- a/script_generator/scripts/generate_funscript.py
+++ b/script_generator/scripts/generate_funscript.py
@@ -1,6 +1,10 @@
 from script_generator.scripts.analyze_video import analyze_video
 from script_generator.state.app_state import AppState
+from script_generator.utils.logger import logger
 
 
 def generate_funscript(state: AppState):
-    results = analyze_video(state)
\ No newline at end of file
+    try:
+        results = analyze_video(state)
+    except Exception as e:
+        logger.error(f"Could not generate funscript: {e}")
diff --git a/script_generator/tasks/abstract_task_processor.py b/script_generator/tasks/abstract_task_processor.py
index 4184dd09..e2ad7969 100644
--- a/script_generator/tasks/abstract_task_processor.py
+++ b/script_generator/tasks/abstract_task_processor.py
@@ -22,6 +22,7 @@ def __init__(self, state: AppState, output_queue: queue.Queue, input_queue: Opti
         self.input_queue = input_queue
         self.output_queue = output_queue
         self._stop_event = threading.Event()
+        self.exception = None  # Store the exception that occurs in the thread
 
     def log(self, message):
         """
@@ -70,16 +71,20 @@ def finish_task(self, task):
     def run(self):
         """
         Main thread entry point. Executes the `task_logic` method.
+        Catches any exceptions and stores them for the caller.
         """
         try:
             self.state.analyze_task.start(self.process_type)
             self.task_logic()
         except Exception as e:
+            self.exception = e  # Capture the exception
+            # Propagate sentinel to the output queue
+            self.output_queue.put(None)
             logger.error(f"An error occurred during task execution on thread {self.process_type}: {e}")
-            import traceback
-            traceback.print_exc()
+            # import traceback
+            # traceback.print_exc()
         finally:
-            return
+            self._stop_event.set()
 
     def task_logic(self):
         """
@@ -97,6 +102,13 @@ def stop_process(self):
     def on_last_item(self):
         return
 
+    def check_exception(self):
+        """
+        Checks if an exception occurred in the thread and raises it in the calling context.
+        """
+        if self.exception:
+            raise self.exception
+
 from enum import Enum
 
 class TaskProcessorTypes(Enum):
diff --git a/script_generator/video/ffmpeg.py b/script_generator/video/ffmpeg.py
index 688579db..80384e2f 100644
--- a/script_generator/video/ffmpeg.py
+++ b/script_generator/video/ffmpeg.py
@@ -1,55 +1,69 @@
 import subprocess
 
+from debug.errors import FFProbeError
 from script_generator.utils.logger import logger
 from script_generator.video.video_info import VideoInfo
 from config import FFPROBE_PATH, FFMPEG_PATH
 
 
+import json
+
 def get_video_info(video_path):
     try:
         cmd = [
             FFPROBE_PATH,
             "-v", "error",
             "-select_streams", "v:0",
-            "-show_entries", "stream=r_frame_rate,width,height,codec_name,nb_frames,duration",
-            "-of", "default=noprint_wrappers=1:nokey=1",
+            "-show_entries", "stream=r_frame_rate,width,height,codec_name,nb_frames",
+            "-show_entries", "format=duration",
+            "-of", "json",
             video_path,
         ]
 
-        output = subprocess.check_output(cmd).decode("utf-8").splitlines()
+        output = subprocess.check_output(cmd, stderr=subprocess.STDOUT).decode("utf-8")
+        info = json.loads(output)
+
+        # Parse the first video stream
+        stream = info.get("streams", [{}])[0]
+        if not stream:
+            raise FFProbeError("No video stream found in the file.")
 
-        # Ensure the output has the correct number of fields
-        if len(output) < 6:
-            raise ValueError("FFProbe output is missing required fields.")
+        # Extract stream metadata
+        codec_name = stream.get("codec_name", "unknown")
+        width = int(stream.get("width", 0))
+        height = int(stream.get("height", 0))
+        r_frame_rate = stream.get("r_frame_rate", "0/1")
+        nb_frames = stream.get("nb_frames", None)
 
-        # Parse metadata
-        codec_name = output[0]
-        width = int(output[1])
-        height = int(output[2])
-        r_frame_rate = output[3]
-        duration = float(output[4])
-        total_frames = int(output[5])
+        # Extract format-level metadata
+        duration = float(info.get("format", {}).get("duration", 0))
 
         # Calculate FPS
-        num, den = map(int, r_frame_rate.split('/'))  # Split numerator and denominator
-        fps = num / den  # Calculate FPS
+        num, den = map(int, r_frame_rate.split('/'))
+        fps = num / den if den > 0 else 0
 
-        logger.info(f"Video Info: {codec_name}, {width}x{height}, {fps:.2f} fps, {total_frames} frames, {duration} seconds")
+        # Estimate frames if not available
+        if nb_frames is None and duration > 0 and fps > 0:
+            nb_frames = int(duration * fps)
 
-        # If the width is 2x the height we are dealing with a VR video
+        # Check if the video is VR (2:1 aspect ratio)
         is_vr = height == width // 2
 
-        # TODO: make it possible to override this auto-detection
+        logger.info(f"Video Info: {codec_name}, {width}x{height}, {fps:.2f} fps, {nb_frames} frames, {duration:.2f} seconds, is vr: {is_vr}")
 
         if is_vr:
             logger.info("Video Format: VR SBS - Based on its 2:1 ratio")
         else:
             logger.info("Video Format: 2D - Based on its ratio")
 
-        return VideoInfo(video_path, codec_name, width, height, duration, total_frames, fps, is_vr)
-    except Exception as e:
-        logger.error(f"Error initializing video info: {e}")
-        raise
+        return VideoInfo(video_path, codec_name, width, height, duration, nb_frames, fps, is_vr)
+
+    except subprocess.CalledProcessError as e:
+        logger.error(f"FFProbe command failed: {e.output.decode('utf-8')}")
+        raise FFProbeError("FFProbe command execution failed.")
+    except (ValueError, KeyError, IndexError) as e:
+        logger.error(f"Error parsing FFProbe output: {e}")
+        raise FFProbeError("Failed to parse FFProbe output.")
 
 
 def is_hwaccel_supported():
diff --git a/script_generator/video/video_info.py b/script_generator/video/video_info.py
index 6355a575..f07d9acc 100644
--- a/script_generator/video/video_info.py
+++ b/script_generator/video/video_info.py
@@ -56,6 +56,7 @@ def get_projection_and_fov_from_filename(filename):
         if re.search(pattern["regex"], filename):
             projection = pattern["projection"]
             fov = pattern["fov"]
+            is_fisheye = pattern["is_fisheye"]
             break
     logger.info(f"Video Format: Projection={projection}, FOV={fov}, is_fisheye={is_fisheye}")
 
diff --git a/script_generator/video/video_task_processor.py b/script_generator/video/video_task_processor.py
index d90764fb..a76002e9 100644
--- a/script_generator/video/video_task_processor.py
+++ b/script_generator/video/video_task_processor.py
@@ -6,6 +6,7 @@
 import numpy as np
 
 from config import SUBTRACT_THREADS_FROM_FFMPEG, PITCH, RENDER_RESOLUTION, FFMPEG_PATH, DEBUG_PATH
+from debug.errors import FFMpegError
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 from script_generator.tasks.tasks import AnalyzeFrameTask
 from script_generator.utils.logger import logger
@@ -18,7 +19,6 @@ class VideoTaskProcessor(AbstractTaskProcessor):
     def task_logic(self):
         state = self.state
         video = self.state.video_info
-        current_frame = state.frame_start
 
         def get_cmd(vf):
             start_time = (state.frame_start / video.fps) * 1000
@@ -57,7 +57,7 @@ def get_cmd(vf):
                 "-an",  # Disable audio processing
                 "-map", "0:v:0",
                 *video_filter,
-                "-f", "rawvideo", "-pix_fmt", "bgr24", # cv2 requires bgr (over rgb) and Yolo expects bgr images when using numpy frames (converts them internally)
+                "-f", "rawvideo", "-pix_fmt", "bgr24",  # cv2 requires bgr (over rgb) and Yolo expects bgr images when using numpy frames (converts them internally)
                 "-threads", str(ffmpeg_threads),
                 "-",  # Output to stdout
             ]
@@ -81,6 +81,7 @@ def vr_video_filters():
                 filters = [
                     f"scale={RENDER_RESOLUTION * 2}:{RENDER_RESOLUTION}",
                     f"crop={RENDER_RESOLUTION}:{RENDER_RESOLUTION}:0:0"
+                    "lutyuv=y=gammaval(0.7)"  # TODO Process in open gl and move scale and crop to the gpu
                 ]
 
             return ",".join(filters)
@@ -99,12 +100,17 @@ def standard_video_filters():
         # Start FFmpeg process
         self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
         frame_size = width * height * 3  # Size of one frame in bytes
+        current_frame = state.frame_start
 
         # progress_bar = tqdm(total=203856, unit="frame", desc="Processing frames")
         while True:
             try:
                 in_bytes = self.process.stdout.read(frame_size)
                 if not in_bytes:
+                    if current_frame == state.frame_start:
+                        raise FFMpegError("FFMPEG could not read any frames from stdout")
+                    else:
+                        logger.info("FFMPEG received last frame")
                     break
 
                 task = AnalyzeFrameTask(frame_pos=current_frame)
@@ -129,7 +135,7 @@ def standard_video_filters():
 
             except Exception as e:
                 logger.error(f"Error reading frame: {e}")
-                return False, None
+                raise
 
         self.stop_process()
 
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index 2ef53a07..f168099e 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -4,8 +4,9 @@
 import argparse
 from config import FFMPEG_PATH, FFPROBE_PATH, RENDER_RESOLUTION
 from script_generator.utils.logger import logger
+from script_generator.video.ffmpeg import get_video_info
 
-
+# TODO rework this class in the new logic and make the existing logic more flexible and generic
 class VideoReaderFFmpeg:
     def __init__(self, video_path, is_vr=False, unwarp=True, projection=None, ffmpeg_path=FFMPEG_PATH, ffprobe_path=FFPROBE_PATH):
         """
@@ -41,42 +42,16 @@ def _initialize_video_info(self):
         Retrieve video metadata (fps, resolution, codec, etc.) using FFprobe.
         """
         try:
-            # Run FFprobe to get video metadata
-            cmd = [
-                self.ffprobe_path,
-                "-v", "error",
-                "-select_streams", "v:0",
-                "-show_entries", "stream=r_frame_rate,width,height,codec_name,nb_frames,duration",
-                "-of", "default=noprint_wrappers=1:nokey=1",
-                self.video_path,
-            ]
-            output = subprocess.check_output(cmd).decode("utf-8").splitlines()
-
-            # Ensure the output has the correct number of fields
-            if len(output) < 6:
-                raise ValueError("FFprobe output is missing required fields.")
-
-            # Parse metadata
-            codec_name = output[0]  # codec_name
-            width = int(output[1])  # width
-            height = int(output[2])  # height
-            r_frame_rate = output[3]  # r_frame_rate in "num/den" format
-            duration = float(output[4])  # duration in seconds
-            total_frames = int(output[5])  # nb_frames
-
-            # Calculate FPS
-            num, den = map(int, r_frame_rate.split('/'))  # Split numerator and denominator
-            fps = num / den  # Calculate FPS
-
+            video_info = get_video_info(self.video_path)
             # Store metadata
-            self.fps = fps
-            self.width = width
+            self.fps = video_info.fps
+            self.width = video_info.width
             if self.is_vr:
                 self.width //= 2
-            self.height = height
-            self.codec = codec_name
-            self.total_frames = total_frames
-            self.duration = duration * 1000  # Convert duration to milliseconds
+            self.height = video_info.height
+            self.codec = video_info.codec_name
+            self.total_frames = video_info.total_frames
+            self.duration = video_info.duration
 
             # limiting the frame height here has no additional performance cost and significantly improves speed for 1440p+ video
             #scaling_factor = min(RENDER_RESOLUTION / self.width, RENDER_RESOLUTION / self.height)

From 7d37ab8be62f32c405bfe76d5845199f5e40eebc Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Fri, 24 Jan 2025 14:17:58 +0100
Subject: [PATCH 135/277] fixed vr ffmpeg command and a tqdm error introduced
 in the last commit

---
 .../gui/controller/tracking_analysis.py             |  1 +
 script_generator/object_detection/utils.py          | 13 ++++++++++++-
 script_generator/video/ffmpeg.py                    |  2 +-
 script_generator/video/video_task_processor.py      |  7 ++-----
 4 files changed, 16 insertions(+), 7 deletions(-)

diff --git a/script_generator/gui/controller/tracking_analysis.py b/script_generator/gui/controller/tracking_analysis.py
index 92ec0eca..1afc6c0f 100644
--- a/script_generator/gui/controller/tracking_analysis.py
+++ b/script_generator/gui/controller/tracking_analysis.py
@@ -17,6 +17,7 @@ def tracking_analysis(state):
         return
 
     yolo_data = load_json_from_file(raw_yolo_path)
+
     state.set_video_info()
     video_info = state.video_info
     width, height = get_cropped_dimensions(state.video_info)
diff --git a/script_generator/object_detection/utils.py b/script_generator/object_detection/utils.py
index d82899bf..7cb96fa5 100644
--- a/script_generator/object_detection/utils.py
+++ b/script_generator/object_detection/utils.py
@@ -7,13 +7,24 @@
 from script_generator.gui.utils.widgets import Widgets
 from script_generator.object_detection.box_record import BoxRecord
 from script_generator.object_detection.object_detection_result import ObjectDetectionResult
-from script_generator.utils.file import get_output_file_path
+from script_generator.utils.file import get_output_file_path, load_json_from_file
 from script_generator.utils.logger import logger
 
 
 def check_skip_object_detection(state, root):
     raw_yolo_path, raw_yolo_filename = get_output_file_path(state.video_path, "_rawyolo.json")
     if os.path.exists(raw_yolo_path):
+        # TODO only load the json once (not in this check and later in the process)
+        yolo_data = load_json_from_file(raw_yolo_path)
+        if len(yolo_data) == 0:
+            logger.warn(f"Raw yolo data file doesn't contain any data: {raw_yolo_path}")
+            try:
+                os.remove(raw_yolo_path)
+                logger.info(f"Deleted empty raw yolo data file: {raw_yolo_path}")
+            except OSError as e:
+                logger.error(f"Error deleting raw yolo file {raw_yolo_path}: {e}")
+            return False
+
         skip_detection = Widgets.messagebox(
             "Detection File Conflict",
             f"The file already exists. What would you like to do?\n{raw_yolo_filename}",
diff --git a/script_generator/video/ffmpeg.py b/script_generator/video/ffmpeg.py
index 80384e2f..ee55513e 100644
--- a/script_generator/video/ffmpeg.py
+++ b/script_generator/video/ffmpeg.py
@@ -56,7 +56,7 @@ def get_video_info(video_path):
         else:
             logger.info("Video Format: 2D - Based on its ratio")
 
-        return VideoInfo(video_path, codec_name, width, height, duration, nb_frames, fps, is_vr)
+        return VideoInfo(video_path, codec_name, width, height, duration, int(nb_frames), fps, is_vr)
 
     except subprocess.CalledProcessError as e:
         logger.error(f"FFProbe command failed: {e.output.decode('utf-8')}")
diff --git a/script_generator/video/video_task_processor.py b/script_generator/video/video_task_processor.py
index a76002e9..a0de4e44 100644
--- a/script_generator/video/video_task_processor.py
+++ b/script_generator/video/video_task_processor.py
@@ -80,7 +80,7 @@ def vr_video_filters():
             else:
                 filters = [
                     f"scale={RENDER_RESOLUTION * 2}:{RENDER_RESOLUTION}",
-                    f"crop={RENDER_RESOLUTION}:{RENDER_RESOLUTION}:0:0"
+                    f"crop={RENDER_RESOLUTION}:{RENDER_RESOLUTION}:0:0",
                     "lutyuv=y=gammaval(0.7)"  # TODO Process in open gl and move scale and crop to the gpu
                 ]
 
@@ -94,9 +94,6 @@ def standard_video_filters():
         vf = vr_video_filters() if video.is_vr else standard_video_filters()
         cmd = get_cmd(vf)
 
-        # Debug
-        # command = ' '.join(vf)
-
         # Start FFmpeg process
         self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
         frame_size = width * height * 3  # Size of one frame in bytes
@@ -108,7 +105,7 @@ def standard_video_filters():
                 in_bytes = self.process.stdout.read(frame_size)
                 if not in_bytes:
                     if current_frame == state.frame_start:
-                        raise FFMpegError("FFMPEG could not read any frames from stdout")
+                        raise FFMpegError(f"FFMPEG could not read any frames from stdout command: {' '.join(cmd)}")
                     else:
                         logger.info("FFMPEG received last frame")
                     break

From fb1d6a2893c8c4a9994ecfc02fd6719cbf286660 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Fri, 24 Jan 2025 16:23:17 +0100
Subject: [PATCH 136/277] Perform scaling on cuda

---
 .../video/video_task_processor.py             | 26 +++++++++++++------
 1 file changed, 18 insertions(+), 8 deletions(-)

diff --git a/script_generator/video/video_task_processor.py b/script_generator/video/video_task_processor.py
index a0de4e44..afd074e9 100644
--- a/script_generator/video/video_task_processor.py
+++ b/script_generator/video/video_task_processor.py
@@ -30,7 +30,7 @@ def get_cmd(vf):
             # Determine the best hardware acceleration backend to use
             hwaccel = []
             if hwaccel_support["cuda"]:
-                hwaccel = ["-hwaccel", "cuda"]
+                hwaccel = ["-hwaccel", "cuda", "-hwaccel_output_format", "cuda"]
             elif hwaccel_support["vaapi"]:
                 hwaccel = ["-hwaccel", "vaapi", "-hwaccel_device", "/dev/dri/renderD128"]
             elif hwaccel_support["amf"]:
@@ -48,6 +48,9 @@ def get_cmd(vf):
                 # VAAPI requires specific pixel formats and filters
                 video_filter = ["-vf", f"{vf},format=nv12,hwupload"] if vf else ["-vf", "format=nv12,hwupload"]
 
+            if hwaccel_support["cuda"]:
+                video_filter = ["-noautoscale"] + video_filter # explicitly tell ffmpeg that scaling is done by cuda
+
             return [
                 FFMPEG_PATH,
                 *hwaccel,
@@ -55,7 +58,6 @@ def get_cmd(vf):
                 "-ss", str(start_time / 1000),  # Seek to start time in seconds
                 "-i", video.path,
                 "-an",  # Disable audio processing
-                "-map", "0:v:0",
                 *video_filter,
                 "-f", "rawvideo", "-pix_fmt", "bgr24",  # cv2 requires bgr (over rgb) and Yolo expects bgr images when using numpy frames (converts them internally)
                 "-threads", str(ffmpeg_threads),
@@ -68,20 +70,26 @@ def vr_video_filters():
             else:
                 projection, iv_fov, ih_fov, v_fov, h_fov, d_fov = "he", 180, 180, 90, 90, 180
 
+            cuda = state.ffmpeg_hwaccel_supported["cuda"]
+
+            scale = f"[0:0]scale_cuda=2160:-2,hwdownload" if cuda else f"[0:0]scale={RENDER_RESOLUTION * 2}:-2"
+            crop = f"crop={RENDER_RESOLUTION}:{RENDER_RESOLUTION}:0:0"
+            out_format = f"format=nv12," if cuda else ""
+
             if self.state.video_reader == "FFmpeg":
                 filters = [
-                    f"scale={RENDER_RESOLUTION * 2}:{RENDER_RESOLUTION}",
-                    f"crop={RENDER_RESOLUTION}:{RENDER_RESOLUTION}:0:0",
-                    f"v360={projection}:in_stereo=2d:output=sg:iv_fov={iv_fov}:ih_fov={ih_fov}:"
+                    scale,
+                    crop,
+                    f"{out_format}v360={projection}:in_stereo=2d:output=sg:iv_fov={iv_fov}:ih_fov={ih_fov}:"
                     f"d_fov={d_fov}:v_fov={v_fov}:h_fov={h_fov}:pitch={PITCH}:yaw=0:roll=0:"
                     f"w={RENDER_RESOLUTION}:h={RENDER_RESOLUTION}:interp=lanczos:reset_rot=1",
                     "lutyuv=y=gammaval(0.7)"
                 ]
             else:
                 filters = [
-                    f"scale={RENDER_RESOLUTION * 2}:{RENDER_RESOLUTION}",
-                    f"crop={RENDER_RESOLUTION}:{RENDER_RESOLUTION}:0:0",
-                    "lutyuv=y=gammaval(0.7)"  # TODO Process in open gl and move scale and crop to the gpu
+                    scale,
+                    crop,
+                    f"{out_format}lutyuv=y=gammaval(0.7)"  # TODO Process in open gl and move scale and crop to the gpu
                 ]
 
             return ",".join(filters)
@@ -94,6 +102,8 @@ def standard_video_filters():
         vf = vr_video_filters() if video.is_vr else standard_video_filters()
         cmd = get_cmd(vf)
 
+        logger.info(f"FFMPEG executing command: {' '.join(cmd)}")
+
         # Start FFmpeg process
         self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
         frame_size = width * height * 3  # Size of one frame in bytes

From adee92cf606167a9a1c3e5cad086158a6970fc5d Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Fri, 24 Jan 2025 16:38:19 +0100
Subject: [PATCH 137/277] Added parallel pipline test files

---
 tests/test_generate_funscript_2.py | 11 +++++++++++
 tests/test_generate_funscript_3.py | 11 +++++++++++
 tests/test_generate_funscript_4.py | 11 +++++++++++
 3 files changed, 33 insertions(+)
 create mode 100644 tests/test_generate_funscript_2.py
 create mode 100644 tests/test_generate_funscript_3.py
 create mode 100644 tests/test_generate_funscript_4.py

diff --git a/tests/test_generate_funscript_2.py b/tests/test_generate_funscript_2.py
new file mode 100644
index 00000000..79ebf536
--- /dev/null
+++ b/tests/test_generate_funscript_2.py
@@ -0,0 +1,11 @@
+import os
+
+from script_generator.scripts.generate_funscript import generate_funscript
+from script_generator.state.app_state import AppState
+
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+app_state = AppState(is_cli=True)
+app_state.video_path = "C:/cvr/funscript-generator/test.mp4"
+app_state.frame_start = 60*60*10
+generate_funscript(app_state)
diff --git a/tests/test_generate_funscript_3.py b/tests/test_generate_funscript_3.py
new file mode 100644
index 00000000..497346d6
--- /dev/null
+++ b/tests/test_generate_funscript_3.py
@@ -0,0 +1,11 @@
+import os
+
+from script_generator.scripts.generate_funscript import generate_funscript
+from script_generator.state.app_state import AppState
+
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+app_state = AppState(is_cli=True)
+app_state.video_path = "C:/cvr/funscript-generator/test.mp4"
+app_state.frame_start = 60*60*20
+generate_funscript(app_state)
diff --git a/tests/test_generate_funscript_4.py b/tests/test_generate_funscript_4.py
new file mode 100644
index 00000000..91e319d4
--- /dev/null
+++ b/tests/test_generate_funscript_4.py
@@ -0,0 +1,11 @@
+import os
+
+from script_generator.scripts.generate_funscript import generate_funscript
+from script_generator.state.app_state import AppState
+
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+app_state = AppState(is_cli=True)
+app_state.video_path = "C:/cvr/funscript-generator/test.mp4"
+app_state.frame_start = 60*60*30
+generate_funscript(app_state)

From 0cfc4600d5fdf793f156d9a3736171f85cb0ad9c Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Fri, 24 Jan 2025 17:20:45 +0100
Subject: [PATCH 138/277] Added version in the window name

---
 script_generator/gui/app.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index 29238083..a076110d 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -7,6 +7,7 @@
 from script_generator.state.app_state import AppState
 from script_generator.utils.helpers import is_mac
 from script_generator.utils.logger import logger
+from config import VERSION
 
 # TODO this is a workaround and needs to be fixed properly
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
@@ -17,7 +18,7 @@ def __init__(self):
         if hasattr(ctypes, "windll"):
             ctypes.windll.shcore.SetProcessDpiAwareness(2)  # For Windows DPI scaling
         # self.tk.call('tk', 'scaling', 1.0)
-        self.title("VR funscript generation")
+        self.title(f"VR & 2D POV Funscript AI Generator - v" + VERSION)
         self.geometry(f"{('800' if is_mac() else '700')}x910")
         self.resizable(False, False)
 

From 8d69be94a44f4f7f2e60439645c80fe447ee9efc Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Fri, 24 Jan 2025 17:22:43 +0100
Subject: [PATCH 139/277] updated version

---
 config.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/config.py b/config.py
index 8356b9dd..b45fdc8b 100644
--- a/config.py
+++ b/config.py
@@ -6,7 +6,7 @@
 from script_generator.utils.config import find_ffmpeg_path, find_ffprobe_path
 from script_generator.utils.logger import logger
 
-VERSION = "0.0.3"
+VERSION = "0.0.5"
 
 ##################################################################################################
 # FFMPEG

From d91b81e657f2ce290bc89a192db5b34bdf49f262 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Fri, 24 Jan 2025 17:23:11 +0100
Subject: [PATCH 140/277] moved to RGB models

---
 script_generator/constants.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/script_generator/constants.py b/script_generator/constants.py
index bed6a6f4..9ea05461 100644
--- a/script_generator/constants.py
+++ b/script_generator/constants.py
@@ -65,9 +65,12 @@
 OUTPUT_PATH = os.path.join(PROJECT_PATH, "output")
 MODELS_DIR = "models"
 MODEL_FILENAMES = [
-    "k00gar-11n-200ep-best.mlpackage",
-    "k00gar-11n-200ep-best.pt",
-    "k00gar-11n-200ep-best.onnx",
+#    "k00gar-11n-200ep-best.mlpackage",
+#    "k00gar-11n-200ep-best.pt",
+#    "k00gar-11n-200ep-best.onnx",
+    "k00gar-11n-RGB-200ep-best.mlpackage",
+    "k00gar-11n-RGB-200ep-best.pt",
+    "k00gar-11n-RGB-200ep-best.onnx",
     "yolo11n-pose.mlpackage",
     "yolo11n-pose.pt",
     "yolo11n-pose.onnx"

From dfc6fd22755352aa130edc1416378907e45cf71c Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Fri, 24 Jan 2025 17:26:31 +0100
Subject: [PATCH 141/277] moved to RGB

---
 utils/lib_VideoReaderFFmpeg.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index f168099e..3c2cd929 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -123,7 +123,8 @@ def _start_process(self, start_frame=0):
                 "-map", "0:v:0",
                 "-vf", arg_line,
                 "-f", "rawvideo",  # Output raw video data
-                "-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
+                #"-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
+                "-pix_fmt", "rgb24",
                 "-vsync", "0",  # Disable frame rate synchronization
                 "-threads", "0",  # Use maximum threads available
                 "-",  # Output to stdout
@@ -138,7 +139,8 @@ def _start_process(self, start_frame=0):
                 "-i", self.video_path,
                 "-an",  # Disable audio processing
                 "-f", "rawvideo",  # Output raw video data
-                "-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
+                #"-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
+                "-pix_fmt", "rgb24",
                 "-vsync", "0",  # Disable frame rate synchronization
                 "-",  # Output to stdout
             ]

From 3dff386d951b32c5b2aff2011e57af41f99391ac Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Fri, 24 Jan 2025 17:26:50 +0100
Subject: [PATCH 142/277] removed unused code

---
 utils/lib_Visualizer.py | 76 -----------------------------------------
 1 file changed, 76 deletions(-)

diff --git a/utils/lib_Visualizer.py b/utils/lib_Visualizer.py
index f272ce3c..b568b3a7 100644
--- a/utils/lib_Visualizer.py
+++ b/utils/lib_Visualizer.py
@@ -21,82 +21,6 @@ def draw_gauge(self, image, distance):
         cv2.rectangle(image, (gauge_x, gauge_y), (gauge_x + gauge_width, gauge_y + gauge_height), (0, 0, 0), -1)
         fill_height = int((distance / 100) * gauge_height)
         cv2.rectangle(image, (gauge_x, gauge_y + gauge_height - fill_height), (gauge_x + gauge_width, gauge_y + gauge_height), (0, 255, 0), -1)
-        # Draw the distance text on top of the filled part of the gauge
-        #cv2.putText(image, str(int(distance)), (gauge_x + gauge_width // 2, gauge_y + gauge_height // 2), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
         cv2.putText(image, str(int(distance)), (gauge_x, gauge_y + gauge_height - fill_height - 5),
                     cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
         return image
-"""
-    def draw_limited_graph(self, image, distances, frames, nb_frames):
-        # Plot the frame indices vs positions for the last nb frames.
-        # Determine the number of frames to plot
-        num_frames_to_plot = min(nb_frames, len(distances))
-
-        # Get the last 'num_frames_to_plot' frames
-        last_frame_indices = frames[-num_frames_to_plot:]
-        last_positions = distances[-num_frames_to_plot:]
-
-        graph_height = image.shape[0] // 4
-        graph_width = image.shape[1] - 20 - 10
-        graph_x = 10
-        graph_y = image.shape[0] // 10
-
-        fig = plt.figure(figsize=(graph_width / 100, graph_height / 100), dpi=100)
-        ax = fig.add_subplot(111)
-        ax.plot(range(len(last_frame_indices)), last_positions, color='green', linewidth=1)
-        ax.set_ylim(0, 100)
-        ax.set_xlim(0, nb_frames)  # Limit the x-axis to nb_frames
-        fig.canvas.draw()
-
-        # Get the width and height of the figure
-        width, height = fig.canvas.get_width_height()
-
-        # Convert the figure to an image
-        graph_image = np.frombuffer(fig.canvas.buffer_rgba(), dtype='uint8').reshape((height, width, 3))
-        graph_image = cv2.cvtColor(graph_image, cv2.COLOR_RGBA2RGB)  # Convert from RGBA to RGB
-        graph_image = cv2.resize(graph_image, (graph_width, graph_height))
-
-        #graph_image = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8').reshape(
-        #    fig.canvas.get_width_height()[::-1] + (3,))
-        #graph_image = np.frombuffer(fig.canvas.buffer_rgba(), dtype='uint8').reshape(
-        #    fig.canvas.get_width_height()[::-1] + (4,))
-        #graph_image = cv2.resize(graph_image, (graph_width, graph_height))
-
-        image[graph_y:graph_y + graph_height, graph_x:graph_x + graph_width] = cv2.addWeighted(
-            image[graph_y:graph_y + graph_height, graph_x:graph_x + graph_width],
-            0.5,
-            graph_image,
-            0.5,
-            0
-        )
-        plt.close(fig)
-        return image
-
-    def draw_graph(self, image, distances, frames, reference_positions, reference_frames):
-        graph_height = image.shape[0] // 4
-        graph_width = image.shape[1] - 20 - 10
-        graph_x = 10
-        graph_y = image.shape[0] // 10
-        fig = plt.figure(figsize=(graph_width / 100, graph_height / 100), dpi=100)
-        ax = fig.add_subplot(111)
-        if reference_positions:
-            ax.plot(reference_frames, reference_positions, color='red', linewidth=1)
-        ax.plot(range(len(distances)), distances, color='green', linewidth=1)
-        # Draw the last 600 frames
-
-        ax.set_ylim(0, 100)
-        ax.set_xlim(0, len(distances))
-        fig.canvas.draw()
-        graph_image = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8').reshape(fig.canvas.get_width_height()[::-1] + (3,))
-        graph_image = cv2.resize(graph_image, (graph_width, graph_height))
-        image[graph_y:graph_y + graph_height, graph_x:graph_x + graph_width] = cv2.addWeighted(
-            image[graph_y:graph_y + graph_height, graph_x:graph_x + graph_width],
-            0.5,
-            graph_image,
-            0.5,
-            0
-        )
-        plt.close(fig)
-        return image
-
-"""

From 1ff6c04fc7c9583881bbc0d1b5f309b9839e7063 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Fri, 24 Jan 2025 17:27:40 +0100
Subject: [PATCH 143/277] added UTF8 for non latin encodings

---
 script_generator/utils/logger.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script_generator/utils/logger.py b/script_generator/utils/logger.py
index cb6168b7..869a6a3b 100644
--- a/script_generator/utils/logger.py
+++ b/script_generator/utils/logger.py
@@ -16,7 +16,7 @@ def emit(self, record):
     level=logging.INFO,
     format="%(asctime)s|%(levelname)s|%(name)s|%(message)s",
     handlers=[
-        logging.FileHandler("FSGenerator.log", mode="w"),
+        logging.FileHandler("FSGenerator.log", mode="w", encoding='utf-8'),
         ColorizedStreamHandler(sys.stdout)
     ]
 )

From 3f72eab643b2865c9394a09151586d47e11edb2e Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Fri, 24 Jan 2025 19:34:27 +0100
Subject: [PATCH 144/277] Code re-organisation

---
 .../{video_analysis.py => process_video.py}   |  2 +-
 .../gui/controller/tracking_analysis.py       | 54 -------------------
 script_generator/scripts/tracking_analysis.py | 53 ++++++++++++++++++
 tests/test_generate_funscript.py              |  3 +-
 tests/test_generate_funscript_2.py            |  3 +-
 tests/test_generate_funscript_3.py            |  5 +-
 tests/test_generate_funscript_4.py            |  5 +-
 tests/test_generate_funscript_5.py            | 12 +++++
 tests/test_generate_funscript_6.py            | 12 +++++
 9 files changed, 88 insertions(+), 61 deletions(-)
 rename script_generator/gui/controller/{video_analysis.py => process_video.py} (96%)
 delete mode 100644 script_generator/gui/controller/tracking_analysis.py
 create mode 100644 tests/test_generate_funscript_5.py
 create mode 100644 tests/test_generate_funscript_6.py

diff --git a/script_generator/gui/controller/video_analysis.py b/script_generator/gui/controller/process_video.py
similarity index 96%
rename from script_generator/gui/controller/video_analysis.py
rename to script_generator/gui/controller/process_video.py
index cef9dc11..b589a85e 100644
--- a/script_generator/gui/controller/video_analysis.py
+++ b/script_generator/gui/controller/process_video.py
@@ -1,7 +1,7 @@
 import threading
 from tkinter import messagebox
 
-from script_generator.gui.controller.tracking_analysis import tracking_analysis
+from script_generator.scripts.tracking_analysis import tracking_analysis
 from script_generator.gui.messages.messages import ProgressMessage
 from script_generator.object_detection.utils import check_skip_object_detection
 from script_generator.scripts.analyze_video import analyze_video
diff --git a/script_generator/gui/controller/tracking_analysis.py b/script_generator/gui/controller/tracking_analysis.py
deleted file mode 100644
index 1afc6c0f..00000000
--- a/script_generator/gui/controller/tracking_analysis.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import os
-
-from script_generator.gui.controller.debug_video import debug_video
-from script_generator.object_detection.analyze_tracking_results import analyze_tracking_results
-from script_generator.object_detection.utils import make_data_boxes, parse_yolo_data_looking_for_penis
-from script_generator.utils.file import load_json_from_file, get_output_file_path
-from script_generator.utils.logger import logger
-from script_generator.video.video_info import get_cropped_dimensions
-from utils.lib_FunscriptHandler import FunscriptGenerator
-
-
-def tracking_analysis(state):
-    # Load YOLO detection results from file
-    raw_yolo_path, _ = get_output_file_path(state.video_path, "_rawyolo.json")
-    if not os.path.exists(raw_yolo_path):
-        logger.info(f"Raw yolo json not found in {raw_yolo_path}")
-        return
-
-    yolo_data = load_json_from_file(raw_yolo_path)
-
-    state.set_video_info()
-    video_info = state.video_info
-    width, height = get_cropped_dimensions(state.video_info)
-
-    results = make_data_boxes(yolo_data)
-
-    # Looking for the first instance of penis within the YOLO results
-    first_penis_frame = parse_yolo_data_looking_for_penis(yolo_data, 0)
-
-    if first_penis_frame is None:
-        logger.error(f"No penis instance found in video. Further tracking is not possible.")
-        return
-
-    # Deciding whether we start from there or from a user-specified later frame
-    state.frame_start = max(max(first_penis_frame - int(video_info.fps), state.frame_start - int(video_info.fps)), 0)
-
-    state.frame_end = state.video_info.total_frames if not state.frame_end else state.frame_end
-
-    logger.info(f"Frame Start adjusted to: {state.frame_start}")
-
-    # Performing the tracking part and generation of the raw funscript data
-    state.funscript_data = analyze_tracking_results(state, results)
-
-    state.debugger.save_logs()
-
-    funscript_handler = FunscriptGenerator()
-
-    # Simplifying the funscript data and generating the file
-    funscript_handler.generate(state)
-
-    # Optionally, compare generated funscript with reference funscript if specified, or a simple generic report
-    funscript_handler.create_report_funscripts(state)
-
-    logger.info(f"Finished processing video: {state.video_path}")
diff --git a/script_generator/scripts/tracking_analysis.py b/script_generator/scripts/tracking_analysis.py
index e69de29b..80a323ef 100644
--- a/script_generator/scripts/tracking_analysis.py
+++ b/script_generator/scripts/tracking_analysis.py
@@ -0,0 +1,53 @@
+import os
+
+from script_generator.object_detection.analyze_tracking_results import analyze_tracking_results
+from script_generator.object_detection.utils import make_data_boxes, parse_yolo_data_looking_for_penis
+from script_generator.utils.file import load_json_from_file, get_output_file_path
+from script_generator.utils.logger import logger
+from script_generator.video.video_info import get_cropped_dimensions
+from utils.lib_FunscriptHandler import FunscriptGenerator
+
+
+def tracking_analysis(state):
+    # Load YOLO detection results from file
+    raw_yolo_path, _ = get_output_file_path(state.video_path, "_rawyolo.json")
+    if not os.path.exists(raw_yolo_path):
+        logger.info(f"Raw yolo json not found in {raw_yolo_path}")
+        return
+
+    yolo_data = load_json_from_file(raw_yolo_path)
+
+    state.set_video_info()
+    video_info = state.video_info
+    width, height = get_cropped_dimensions(state.video_info)
+
+    results = make_data_boxes(yolo_data)
+
+    # Looking for the first instance of penis within the YOLO results
+    first_penis_frame = parse_yolo_data_looking_for_penis(yolo_data, 0)
+
+    if first_penis_frame is None:
+        logger.error(f"No penis instance found in video. Further tracking is not possible.")
+        return
+
+    # Deciding whether we start from there or from a user-specified later frame
+    state.frame_start = max(max(first_penis_frame - int(video_info.fps), state.frame_start - int(video_info.fps)), 0)
+
+    state.frame_end = state.video_info.total_frames if not state.frame_end else state.frame_end
+
+    logger.info(f"Frame Start adjusted to: {state.frame_start}")
+
+    # Performing the tracking part and generation of the raw funscript data
+    state.funscript_data = analyze_tracking_results(state, results)
+
+    state.debugger.save_logs()
+
+    funscript_handler = FunscriptGenerator()
+
+    # Simplifying the funscript data and generating the file
+    funscript_handler.generate(state)
+
+    # Optionally, compare generated funscript with reference funscript if specified, or a simple generic report
+    funscript_handler.create_report_funscripts(state)
+
+    logger.info(f"Finished processing video: {state.video_path}")
diff --git a/tests/test_generate_funscript.py b/tests/test_generate_funscript.py
index be768412..5acff925 100644
--- a/tests/test_generate_funscript.py
+++ b/tests/test_generate_funscript.py
@@ -9,7 +9,8 @@
 # app_state.video_path = "C:/cvr/funscript-generator/test_short.mp4"
 # app_state.video_path = "C:/cvr/funscript-generator/test_extra_short.mp4"
 # app_state.video_path = "C:/cvr/funscript-generator/test_medium.mp4"
-app_state.video_path = "C:/cvr/funscript-generator/test.mp4"
+app_state.video_path = "C:/cvr/funscript-generator/test_medium.mp4"
+app_state.video_reader = "FFmpeg + OpenGL (Windows)"
 
 # Warmup run
 generate_funscript(app_state)
diff --git a/tests/test_generate_funscript_2.py b/tests/test_generate_funscript_2.py
index 79ebf536..ca415e6a 100644
--- a/tests/test_generate_funscript_2.py
+++ b/tests/test_generate_funscript_2.py
@@ -6,6 +6,7 @@
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
 app_state = AppState(is_cli=True)
-app_state.video_path = "C:/cvr/funscript-generator/test.mp4"
+app_state.video_path = "C:/cvr/funscript-generator/test_medium.mp4"
 app_state.frame_start = 60*60*10
+app_state.video_reader = "FFmpeg + OpenGL (Windows)"
 generate_funscript(app_state)
diff --git a/tests/test_generate_funscript_3.py b/tests/test_generate_funscript_3.py
index 497346d6..ca415e6a 100644
--- a/tests/test_generate_funscript_3.py
+++ b/tests/test_generate_funscript_3.py
@@ -6,6 +6,7 @@
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
 app_state = AppState(is_cli=True)
-app_state.video_path = "C:/cvr/funscript-generator/test.mp4"
-app_state.frame_start = 60*60*20
+app_state.video_path = "C:/cvr/funscript-generator/test_medium.mp4"
+app_state.frame_start = 60*60*10
+app_state.video_reader = "FFmpeg + OpenGL (Windows)"
 generate_funscript(app_state)
diff --git a/tests/test_generate_funscript_4.py b/tests/test_generate_funscript_4.py
index 91e319d4..ca415e6a 100644
--- a/tests/test_generate_funscript_4.py
+++ b/tests/test_generate_funscript_4.py
@@ -6,6 +6,7 @@
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
 app_state = AppState(is_cli=True)
-app_state.video_path = "C:/cvr/funscript-generator/test.mp4"
-app_state.frame_start = 60*60*30
+app_state.video_path = "C:/cvr/funscript-generator/test_medium.mp4"
+app_state.frame_start = 60*60*10
+app_state.video_reader = "FFmpeg + OpenGL (Windows)"
 generate_funscript(app_state)
diff --git a/tests/test_generate_funscript_5.py b/tests/test_generate_funscript_5.py
new file mode 100644
index 00000000..a73b42d6
--- /dev/null
+++ b/tests/test_generate_funscript_5.py
@@ -0,0 +1,12 @@
+import os
+
+from script_generator.scripts.generate_funscript import generate_funscript
+from script_generator.state.app_state import AppState
+
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+app_state = AppState(is_cli=True)
+app_state.video_path = "C:/cvr/funscript-generator/test.mp4"
+app_state.frame_start = 60*60*30
+app_state.video_reader = "FFmpeg"
+generate_funscript(app_state)
diff --git a/tests/test_generate_funscript_6.py b/tests/test_generate_funscript_6.py
new file mode 100644
index 00000000..a73b42d6
--- /dev/null
+++ b/tests/test_generate_funscript_6.py
@@ -0,0 +1,12 @@
+import os
+
+from script_generator.scripts.generate_funscript import generate_funscript
+from script_generator.state.app_state import AppState
+
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+app_state = AppState(is_cli=True)
+app_state.video_path = "C:/cvr/funscript-generator/test.mp4"
+app_state.frame_start = 60*60*30
+app_state.video_reader = "FFmpeg"
+generate_funscript(app_state)

From 8139008817cb5ffb64cd4e11c2cb652c5c69e73d Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Fri, 24 Jan 2025 20:08:20 +0100
Subject: [PATCH 145/277] Parallel pipe basics Output previews as rgb

---
 config.py                                     |   1 +
 .../gui/controller/process_video.py           |   2 +-
 .../gui/views/funscript_generator.py          |   2 +-
 .../analyze_tracking_results.py               |   4 +-
 .../object_detection/post_process_results.py  |   2 +-
 script_generator/scripts/analyze_video.py     | 260 ++++++++++--------
 .../scripts/generate_funscript.py             |   4 +-
 script_generator/state/app_state.py           |   4 +-
 .../tasks/abstract_task_processor.py          |  11 +-
 script_generator/tasks/tasks.py               |  11 +-
 utils/lib_Debugger.py                         |   2 +-
 11 files changed, 173 insertions(+), 130 deletions(-)

diff --git a/config.py b/config.py
index b45fdc8b..5e5068c1 100644
--- a/config.py
+++ b/config.py
@@ -24,6 +24,7 @@
 # PERFORMANCE
 ##################################################################################################
 
+NUM_PARALLEL_PIPES = 1
 RENDER_RESOLUTION = 1080 # Above 1080x1080 doesn't seem to improve detections
 TEXTURE_RESOLUTION = 1440 # Size that is used to texture the opengl sphere
 YOLO_BATCH_SIZE = 1 if platform.system() == "Darwin" else 30 # Mac doesn't support batching due to onnx
diff --git a/script_generator/gui/controller/process_video.py b/script_generator/gui/controller/process_video.py
index b589a85e..72edd818 100644
--- a/script_generator/gui/controller/process_video.py
+++ b/script_generator/gui/controller/process_video.py
@@ -4,7 +4,7 @@
 from script_generator.scripts.tracking_analysis import tracking_analysis
 from script_generator.gui.messages.messages import ProgressMessage
 from script_generator.object_detection.utils import check_skip_object_detection
-from script_generator.scripts.analyze_video import analyze_video
+from script_generator.scripts.analyze_video import analyze_video_pipe, analyze_video
 from script_generator.utils.file import get_output_file_path
 from script_generator.utils.logger import logger
 from utils.lib_Debugger import Debugger
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 465e2aa7..386f044e 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -1,8 +1,8 @@
 import tkinter as tk
 from tkinter import ttk
 
+from script_generator.gui.controller.process_video import video_analysis
 from script_generator.gui.controller.regenerate_funscript import regenerate_funscript
-from script_generator.gui.controller.video_analysis import video_analysis
 from script_generator.gui.controller.debug_video import debug_video
 from script_generator.gui.messages.messages import UIMessage, ProgressMessage
 from script_generator.gui.utils.widgets import Widgets
diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index 427d5353..db2a4b1a 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -158,7 +158,7 @@ def analyze_tracking_results(state, results):
                 logger.warn("Frame could not be read in live preview")
                 continue
 
-            frame_display = frame.copy()
+            frame_display = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
 
             for box in tracker.tracked_boxes:
                 frame_display = visualizer.draw_bounding_box(
@@ -188,7 +188,7 @@ def analyze_tracking_results(state, results):
             if state.funscript_distances:
                 frame_display = visualizer.draw_gauge(frame_display, state.funscript_distances[-1])
 
-            cv2.imshow("Combined Results", frame_display)
+            cv2.imshow("Funscript generation preview", frame_display)
             cv2.waitKey(1)
 
         # Update progress
diff --git a/script_generator/object_detection/post_process_results.py b/script_generator/object_detection/post_process_results.py
index 643f7c04..997fbd44 100644
--- a/script_generator/object_detection/post_process_results.py
+++ b/script_generator/object_detection/post_process_results.py
@@ -131,7 +131,7 @@ def task_logic(self):
             
 
     def on_last_item(self):
-        self.state.analyze_task.end_time = time.time()
+        self.task.end_time = time.time()
 
         # Write the detection records to a JSON file
         raw_yolo_path, _ = get_output_file_path(self.state.video_path, "_rawyolo.json")
diff --git a/script_generator/scripts/analyze_video.py b/script_generator/scripts/analyze_video.py
index 747bd854..9098b351 100644
--- a/script_generator/scripts/analyze_video.py
+++ b/script_generator/scripts/analyze_video.py
@@ -5,12 +5,11 @@
 
 from tqdm import tqdm
 
-from config import QUEUE_MAXSIZE, SEQUENTIAL_MODE, PROGRESS_BAR, UPDATE_PROGRESS_INTERVAL
+from config import SEQUENTIAL_MODE, PROGRESS_BAR, UPDATE_PROGRESS_INTERVAL, NUM_PARALLEL_PIPES
 from script_generator.gui.messages.messages import ProgressMessage
 from script_generator.object_detection.post_process_results import YoloAnalysisTaskProcessor
 from script_generator.object_detection.yolo import YoloTaskProcessor
 from script_generator.state.app_state import AppState
-from script_generator.tasks.abstract_task_processor import TaskProcessorTypes
 from script_generator.tasks.tasks import AnalyzeVideoTask, AnalyzeFrameTask
 from script_generator.utils.file import check_create_output_folder
 from script_generator.utils.logger import logger
@@ -19,118 +18,137 @@
 
 
 def analyze_video(state: AppState) -> List[AnalyzeFrameTask]:
-    logger.info(f"[OBJECT DETECTION] Starting up pipeline with profiling in {'sequential mode' if SEQUENTIAL_MODE else 'parallel mode'}...")
-
+    """
+    Launch multiple pipelines in parallel.
+    Start a single logging thread to monitor all pipelines.
+    """
+    logger.info(f"[OBJECT DETECTION] Spawning {NUM_PARALLEL_PIPES} pipeline(s).")
+
+    # Create all pipelines, but do not start them yet
+    tasks_and_threads = []
+    for pipe_id in range(NUM_PARALLEL_PIPES):
+        analyze_task, pipeline_threads = analyze_video_pipe(state, pipe_id)
+        # Keep track of each pipeline's task object and its associated threads
+        tasks_and_threads.append((analyze_task, pipeline_threads))
+
+    # Start all pipeline threads
+    for analyze_task, pipeline_threads in tasks_and_threads:
+        for t in pipeline_threads:
+            # Some pipelines might have a None thread (like opengl_thread = None if not VR)
+            if t is not None:
+                t.start()
+
+    # Start logging thread that monitors all pipelines
     log_thread_stop_event = threading.Event()
-    threads = []
+    logging_thread = threading.Thread(
+        target=log_progress,
+        args=(state, [item[0] for item in tasks_and_threads], log_thread_stop_event),
+        daemon=True,
+    )
+    logging_thread.start()
 
-    try:
-        # make sure the output folder exists for this video
-        check_create_output_folder(state.video_path)
-
-        # Initialize batch task
-        state.set_video_info()
-        if state.video_reader == "FFmpeg + OpenGL (Windows)":
-            if not state.video_info.is_vr:
-                logger.warn("Disabled OpenGL in the pipeline as it's not needed for 2D videos")
-                state.video_reader = "FFmpeg"
-
-            if state.video_info.is_fisheye:
-                logger.warn("Disabled OpenGL as fisheye is not yet supported with the opengl feature")
-                state.video_reader = "FFmpeg"
-
-        use_open_gl = state.video_reader == "FFmpeg + OpenGL (Windows)"
-
-        state.analyze_task = AnalyzeVideoTask()
-
-        # Create queues
-        opengl_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
-        yolo_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
-        analysis_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
-        result_q = queue.Queue(maxsize=0)
-
-        # Create threads
-        decode_thread = VideoTaskProcessor(state=state, output_queue=opengl_q if use_open_gl else yolo_q)
-        opengl_thread = VrTo2DTaskProcessor(state=state, input_queue=opengl_q, output_queue=yolo_q) if use_open_gl else None
-        yolo_thread = YoloTaskProcessor(state=state, input_queue=yolo_q, output_queue=analysis_q)
-        yolo_analysis_thread = YoloAnalysisTaskProcessor(state=state, input_queue=analysis_q, output_queue=result_q)
-
-        # Start logging thread
-        queue_logging_thread = threading.Thread(
-            target=log_progress,
-            args=(state, opengl_q, yolo_q, analysis_q, result_q, log_thread_stop_event),
-            daemon=True,
-        )
-        queue_logging_thread.start()
+    # Wait for all pipeline threads to finish
+    for analyze_task, pipeline_threads in tasks_and_threads:
+        for t in pipeline_threads:
+            if t is not None:
+                t.join()
 
-        # Sequential mode can be used to determine performance bottlenecks on very short videos
-        if SEQUENTIAL_MODE:
-            def run_thread(thread, thread_name, out_queue):
-                start_time = time.time()
-                thread.start()
-                thread.join()
-                out_queue.put(None)
-                logger.info(f"[OBJECT DETECTION] {thread_name} thread done in {time.time() - start_time} s")
+    # Check for exceptions from each pipeline thread
+    for analyze_task, pipeline_threads in tasks_and_threads:
+        for t in pipeline_threads:
+            if t is not None:
+                t.check_exception()
 
-            run_thread(decode_thread, TaskProcessorTypes.VIDEO, opengl_q)
-            if use_open_gl:
-                run_thread(opengl_thread, TaskProcessorTypes.OPENGL, yolo_q)
-            run_thread(yolo_thread, TaskProcessorTypes.YOLO, analysis_q)
-            run_thread(yolo_analysis_thread, TaskProcessorTypes.YOLO_ANALYSIS, result_q)
-        else:
-            threads = [decode_thread, opengl_thread, yolo_thread, yolo_analysis_thread] if use_open_gl else [decode_thread, yolo_thread, yolo_analysis_thread]
-            for thread in threads:
-                thread.start()
-            for thread in threads:
-                thread.join()
+    # Signal logging thread to stop and wait for it
+    log_thread_stop_event.set()
+    logging_thread.join()
 
-            # Check for exceptions in threads
-            for thread in threads:
-                if thread is not None:
-                    thread.check_exception()
+    # Update end_time for each pipeline and log performance
+    for analyze_task, pipeline_threads in tasks_and_threads:
+        analyze_task.end_time = time.time()
+        # Log performance for that pipeline
+        log_performance(state, analyze_task.result_q, analyze_task)
 
-        state.analyze_task.end_time = time.time()
+    logger.info("[OBJECT DETECTION] All pipelines complete.")
 
-        log_thread_stop_event.set()
+    # Return tasks
+    return [tup[0] for tup in tasks_and_threads]
 
-        # wait for update progress thread to close
-        time.sleep(UPDATE_PROGRESS_INTERVAL * 1.1)
+def analyze_video_pipe(state: AppState, pipe_id) -> AnalyzeVideoTask:
+    logger.info(f"[OBJECT DETECTION] Starting up pipeline with id: {pipe_id} and running in {'sequential mode' if SEQUENTIAL_MODE else 'parallel mode'}...")
 
-        log_performance(state=state, results_queue=result_q)
+    log_thread_stop_event = threading.Event()
+    threads = []
+
+    # make sure the output folder exists for this video
+    check_create_output_folder(state.video_path)
 
-        return result_q.queue
+    # Initialize batch task
+    state.set_video_info()
+    if state.video_reader == "FFmpeg + OpenGL (Windows)":
+        if not state.video_info.is_vr:
+            logger.warn("Disabled OpenGL in the pipeline as it's not needed for 2D videos")
+            state.video_reader = "FFmpeg"
 
-    except Exception as e:
-        logger.error(f"An error occurred during video analysis: {e}")
-        # Signal all threads to stop and perform cleanup
-        log_thread_stop_event.set()
-        for thread in threads:
-            if thread is not None and thread.is_alive():
-                thread.join(timeout=1)
-        raise
+        if state.video_info.is_fisheye:
+            logger.warn("Disabled OpenGL as fisheye is not yet supported with the opengl feature")
+            state.video_reader = "FFmpeg"
 
+    use_open_gl = state.video_reader == "FFmpeg + OpenGL (Windows)"
 
-def log_progress(state, opengl_q, yolo_q, analysis_q, results_q, stop_event):
-    total_frames = state.video_info.total_frames
+    # Create our pipeline task
+    analyze_task = AnalyzeVideoTask(pipe_id=pipe_id)
 
-    label = 'Analyzing ' + ('VR' if state.video_info.is_vr else '2D') + ' video'
+    # Create threads
+    decode_thread = VideoTaskProcessor(state=state, task=analyze_task, output_queue=analyze_task.opengl_q if use_open_gl else analyze_task.yolo_q)
+    opengl_thread = VrTo2DTaskProcessor(state=state, task=analyze_task, input_queue=analyze_task.opengl_q, output_queue=analyze_task.yolo_q) if use_open_gl else None
+    yolo_thread = YoloTaskProcessor(state=state, task=analyze_task, input_queue=analyze_task.yolo_q, output_queue=analyze_task.analysis_q)
+    yolo_analysis_thread = YoloAnalysisTaskProcessor(state=state, task=analyze_task, input_queue=analyze_task.analysis_q, output_queue=analyze_task.result_q)
+
+    # If sequential mode, we wrap these in a step-by-step approach
+    if SEQUENTIAL_MODE:
+        # We'll create "mini-threads" just for the sequential pattern
+        threads = [decode_thread]
+        if use_open_gl:
+            threads.append(opengl_thread)
+        threads.append(yolo_thread)
+        threads.append(yolo_analysis_thread)
+    else:
+        # Typical parallel approach
+        threads = [decode_thread]
+        if use_open_gl:
+            threads.append(opengl_thread)
+        threads.append(yolo_thread)
+        threads.append(yolo_analysis_thread)
+
+    return analyze_task, threads
+
+
+def log_progress(state: AppState, analyze_tasks: List[AnalyzeVideoTask], stop_event: threading.Event):
+    """
+    Single logging thread monitoring *all* pipelines in aggregate.
+    Displays a combined progress bar and combined queue sizes.
+    """
+    # Sum total frames across all pipelines
+    total_frames = sum([state.video_info.total_frames for _ in analyze_tasks])
+    label = f"Analyzing {len(analyze_tasks)} pipeline(s)"
 
     if PROGRESS_BAR:
         with tqdm(
-                total=total_frames,
-                #desc="Analyzing video",
-                desc=label,
-                unit="f",
-                position=0,
-                unit_scale=False,
-                unit_divisor=1,
-                ncols=130
+            total=total_frames,
+            desc=label,
+            unit="f",
+            position=0,
+            unit_scale=False,
+            unit_divisor=1,
+            ncols=130
         ) as progress_bar:
             while not stop_event.is_set():
-                opengl_size = opengl_q.qsize()
-                yolo_size = yolo_q.qsize()
-                analysis_size = analysis_q.qsize()
-                frames_processed = results_q.qsize()
+                # Sum queue sizes across all tasks
+                opengl_size = sum(t.opengl_q.qsize() for t in analyze_tasks if t.opengl_q is not None)
+                yolo_size = sum(t.yolo_q.qsize() for t in analyze_tasks)
+                analysis_size = sum(t.analysis_q.qsize() for t in analyze_tasks)
+                frames_processed = sum(t.result_q.qsize() for t in analyze_tasks)
 
                 progress_bar.n = frames_processed
                 open_gl = f"OpenGL: {opengl_size:>3}, " if state.video_reader == "FFmpeg + OpenGL (Windows)" else ""
@@ -143,7 +161,7 @@ def log_progress(state, opengl_q, yolo_q, analysis_q, results_q, stop_event):
                     stop_event.set()
 
                 if state.update_ui:
-                    elapsed_time = time.time() - state.analyze_task.start_time
+                    elapsed_time = time.time() - min(t.start_time for t in analyze_tasks)
                     processing_rate = frames_processed / elapsed_time if elapsed_time > 0 else 0
                     remaining_frames = total_frames - frames_processed
                     eta = remaining_frames / processing_rate if processing_rate > 0 else float('inf')
@@ -160,32 +178,46 @@ def log_progress(state, opengl_q, yolo_q, analysis_q, results_q, stop_event):
                 time.sleep(UPDATE_PROGRESS_INTERVAL)
     else:
         while not stop_event.is_set():
-            opengl_size = opengl_q.qsize()
-            yolo_size = yolo_q.qsize()
-            analysis_size = analysis_q.size()
-            frames_processed = results_q.qsize()
-            open_gl = f"OpenGL: {opengl_size:>3}, " if state.video_reader == "FFmpeg + OpenGL (Windows)" else ""
-            logger.info(f"Queues: {open_gl}YOLO: {yolo_size:>3}, Analysis: {analysis_size:>3}, DONE: {frames_processed:>3}")
+            opengl_size = sum(t.opengl_q.qsize() for t in analyze_tasks if t.opengl_q is not None)
+            yolo_size = sum(t.yolo_q.qsize() for t in analyze_tasks)
+            analysis_size = sum(t.analysis_q.qsize() for t in analyze_tasks)
+            frames_processed = sum(t.result_q.qsize() for t in analyze_tasks)
+
+            logger.info(
+                f"OpenGL: {opengl_size:>3}, YOLO: {yolo_size:>3}, "
+                f"Analysis: {analysis_size:>3}, DONE: {frames_processed:>3}"
+            )
 
             if frames_processed >= total_frames:
                 stop_event.set()
 
-            time.sleep(0.5)
+            time.sleep(UPDATE_PROGRESS_INTERVAL)
+
 
-def log_performance(state, results_queue):
-    analyze_task = state.analyze_task
-    # TODO filter out sentinals in task processor
-    tasks = [task for task in results_queue.queue if task is not None and hasattr(task, 'profile')]
+def log_performance(state: AppState, results_queue: queue.Queue, analyze_task: AnalyzeVideoTask):
+    """
+    Logs performance stats for a single pipeline's task.
+    If you want to log combined performance for all pipelines,
+    you can adapt this function similarly.
+    """
+    # Filter out any sentinel items
+    tasks = [t for t in results_queue.queue if t is not None and hasattr(t, 'profile')]
     total_frames = len(tasks)
 
+    if not analyze_task.end_time:
+        analyze_task.end_time = time.time()  # fallback
+
     total_pipeline_time = analyze_task.end_time - analyze_task.start_time
-    video_duration = total_frames / state.video_info.fps
-    avg_processing_fps = total_frames / total_pipeline_time
+
+    # For quick reference
+    video_fps = state.video_info.fps if state.video_info else 30.0
+    video_duration = total_frames / video_fps if video_fps else 0
+    avg_processing_fps = total_frames / total_pipeline_time if total_pipeline_time > 0 else 0
     realtime_percentage = (avg_processing_fps / 60.0) * 100.0
 
     log_message = (
         f"\n{'-' * 60}"
-        f"\n OBJECT DETECTION COMPLETED {'(sequential mode)' if SEQUENTIAL_MODE else ''}\n"
+        f"\n OBJECT DETECTION PIPELINE #{analyze_task.pipe_id} COMPLETED {'(sequential mode)' if SEQUENTIAL_MODE else ''}\n"
         f"\n Settings\n"
         f"  - Video reader               : {state.video_reader}\n"     
         f"\n Video stats\n"
@@ -196,7 +228,7 @@ def log_performance(state, results_queue):
     if SEQUENTIAL_MODE:
         log_message += f"\n Sequential Queue statistics\n"
         for key, total_time in analyze_task.profile.items():
-            if key.endswith("_duration"):  # Only include duration metrics
+            if key.endswith("_duration"):  # only duration metrics
                 avg_time = total_time / total_frames if total_frames > 0 else 0.0
                 stage_name = key.replace("_duration", "").capitalize()
                 log_message += (
@@ -213,12 +245,12 @@ def log_performance(state, results_queue):
         log_message += f"\n Task Average Times (while running in parallel)\n"
 
         aggregated_times = {}
-        for task in tasks:
-            for key, total_time in task.profile.items():
+        for t in tasks:
+            for key, ttime in t.profile.items():
                 if key.endswith("_duration"):
                     if key not in aggregated_times:
                         aggregated_times[key] = {"total_time": 0, "task_count": 0}
-                    aggregated_times[key]["total_time"] += total_time
+                    aggregated_times[key]["total_time"] += ttime
                     aggregated_times[key]["task_count"] += 1
 
         # Calculate and format averages for each key
diff --git a/script_generator/scripts/generate_funscript.py b/script_generator/scripts/generate_funscript.py
index 54f1ae17..c7e95433 100644
--- a/script_generator/scripts/generate_funscript.py
+++ b/script_generator/scripts/generate_funscript.py
@@ -1,10 +1,10 @@
-from script_generator.scripts.analyze_video import analyze_video
+from script_generator.scripts.analyze_video import analyze_video_pipe
 from script_generator.state.app_state import AppState
 from script_generator.utils.logger import logger
 
 
 def generate_funscript(state: AppState):
     try:
-        results = analyze_video(state)
+        results = analyze_video_pipe(state)
     except Exception as e:
         logger.error(f"Could not generate funscript: {e}")
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index d52c9fac..18221816 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -1,5 +1,5 @@
 import string
-from typing import Literal
+from typing import Literal, List
 
 from script_generator.tasks.tasks import AnalyzeVideoTask
 from script_generator.utils.helpers import is_mac
@@ -13,7 +13,7 @@ def __init__(self, is_cli):
 
         # Job
         self.video_info: VideoInfo | None = None
-        self.analyze_task: AnalyzeVideoTask | None = None
+        self.analyze_tasks: List[AnalyzeVideoTask]  = []
         self.video_path: string = None
         self.frame_start: int = 0
         self.frame_end: int | None = None
diff --git a/script_generator/tasks/abstract_task_processor.py b/script_generator/tasks/abstract_task_processor.py
index e2ad7969..df85facf 100644
--- a/script_generator/tasks/abstract_task_processor.py
+++ b/script_generator/tasks/abstract_task_processor.py
@@ -3,14 +3,14 @@
 from typing import Generator, Optional
 
 from script_generator.state.app_state import AppState
-from script_generator.tasks.tasks import AnalyzeFrameTask
+from script_generator.tasks.tasks import AnalyzeFrameTask, Task
 from script_generator.utils.logger import logger
 
 
 class AbstractTaskProcessor(threading.Thread):
     process_type = ""
 
-    def __init__(self, state: AppState, output_queue: queue.Queue, input_queue: Optional[queue.Queue] = None):
+    def __init__(self, state: AppState, task: Task, output_queue: queue.Queue, input_queue: Optional[queue.Queue] = None):
         """
         Abstract thread class to handle lifecycle management and task handling boilerplate.
 
@@ -22,6 +22,7 @@ def __init__(self, state: AppState, output_queue: queue.Queue, input_queue: Opti
         self.input_queue = input_queue
         self.output_queue = output_queue
         self._stop_event = threading.Event()
+        self.task = task
         self.exception = None  # Store the exception that occurs in the thread
 
     def log(self, message):
@@ -46,7 +47,7 @@ def get_task(self) -> Generator[AnalyzeFrameTask, None, None]:
                 task = self.input_queue.get(timeout=1)
 
                 if task is None:  # Sentinel for termination
-                    self.state.analyze_task.end(self.process_type)
+                    self.task.end(self.process_type)
                     self.on_last_item()
                     self.finish_task(None)
                     break
@@ -74,7 +75,7 @@ def run(self):
         Catches any exceptions and stores them for the caller.
         """
         try:
-            self.state.analyze_task.start(self.process_type)
+            self.task.start(self.process_type)
             self.task_logic()
         except Exception as e:
             self.exception = e  # Capture the exception
@@ -94,7 +95,7 @@ def task_logic(self):
         raise NotImplementedError("Subclasses must implement task_logic")
 
     def stop_process(self):
-        self.state.analyze_task.end(self.process_type)
+        self.task.end(self.process_type)
         self.on_last_item()
         # Propagate sentinel to the output queue
         self.output_queue.put(None)
diff --git a/script_generator/tasks/tasks.py b/script_generator/tasks/tasks.py
index 3eeac22f..a872f360 100644
--- a/script_generator/tasks/tasks.py
+++ b/script_generator/tasks/tasks.py
@@ -1,3 +1,4 @@
+import queue
 import time
 from dataclasses import dataclass, field
 from threading import Lock
@@ -5,6 +6,7 @@
 
 import numpy as np
 
+from config import QUEUE_MAXSIZE
 from script_generator.video.video_info import VideoInfo
 
 
@@ -54,13 +56,20 @@ def _calculate_duration(self, process_type: str):
 @dataclass
 class AnalyzeVideoTask(Task):
     tasks: List[Task] = field(default_factory=list)
+    pipe_id = -1
 
-    def __init__(self):
+    def __init__(self, pipe_id):
         super().__init__()
         self.tasks = []
         self._lock = Lock()
         self.profile = {}
         self.start_time = time.time()
+        self.pipe_id = pipe_id
+
+        self.opengl_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
+        self.yolo_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
+        self.analysis_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
+        self.result_q = queue.Queue(maxsize=0)
 
     def add_task(self, task: Task) -> Task:
         with self._lock:
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 4fe18125..89f23448 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -169,7 +169,7 @@ def play_video(self, state, start_frame=0, duration=0, rolling_window_size=100,
             #if not state.live_preview_mode:
             #    continue
 
-            frame_copy = frame.copy()  # make a copy of the frame to make it writeable, useful for ffmpeg library here
+            frame_copy = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  # make a copy of the frame to make it writeable, useful for ffmpeg library here
             # Display variables and bounding boxes
             str_frame_id = str(self.current_frame)
             if str_frame_id in self.logs:

From 4b55dc6f578c7607171bd5f7cd1c6723485344e1 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sat, 25 Jan 2025 00:21:11 +0100
Subject: [PATCH 146/277] RGB/BGR war results

---
 utils/lib_VideoReaderFFmpeg.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index 3c2cd929..cf6553f1 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -124,7 +124,7 @@ def _start_process(self, start_frame=0):
                 "-vf", arg_line,
                 "-f", "rawvideo",  # Output raw video data
                 #"-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
-                "-pix_fmt", "rgb24",
+                "-pix_fmt", "bgr24",
                 "-vsync", "0",  # Disable frame rate synchronization
                 "-threads", "0",  # Use maximum threads available
                 "-",  # Output to stdout
@@ -140,7 +140,7 @@ def _start_process(self, start_frame=0):
                 "-an",  # Disable audio processing
                 "-f", "rawvideo",  # Output raw video data
                 #"-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
-                "-pix_fmt", "rgb24",
+                "-pix_fmt", "bgr24",
                 "-vsync", "0",  # Disable frame rate synchronization
                 "-",  # Output to stdout
             ]

From 2d5701531847ff370fc147f371d1e084b02d54e3 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sat, 25 Jan 2025 11:03:17 +0100
Subject: [PATCH 147/277] Revert "Parallel pipe basics"

This reverts commit 8139008817cb5ffb64cd4e11c2cb652c5c69e73d.
---
 config.py                                     |   1 -
 .../gui/controller/process_video.py           |   2 +-
 .../gui/views/funscript_generator.py          |   2 +-
 .../analyze_tracking_results.py               |   4 +-
 .../object_detection/post_process_results.py  |   2 +-
 script_generator/scripts/analyze_video.py     | 260 ++++++++----------
 .../scripts/generate_funscript.py             |   4 +-
 script_generator/state/app_state.py           |   4 +-
 .../tasks/abstract_task_processor.py          |  11 +-
 script_generator/tasks/tasks.py               |  11 +-
 utils/lib_Debugger.py                         |   2 +-
 11 files changed, 130 insertions(+), 173 deletions(-)

diff --git a/config.py b/config.py
index 5e5068c1..b45fdc8b 100644
--- a/config.py
+++ b/config.py
@@ -24,7 +24,6 @@
 # PERFORMANCE
 ##################################################################################################
 
-NUM_PARALLEL_PIPES = 1
 RENDER_RESOLUTION = 1080 # Above 1080x1080 doesn't seem to improve detections
 TEXTURE_RESOLUTION = 1440 # Size that is used to texture the opengl sphere
 YOLO_BATCH_SIZE = 1 if platform.system() == "Darwin" else 30 # Mac doesn't support batching due to onnx
diff --git a/script_generator/gui/controller/process_video.py b/script_generator/gui/controller/process_video.py
index 72edd818..b589a85e 100644
--- a/script_generator/gui/controller/process_video.py
+++ b/script_generator/gui/controller/process_video.py
@@ -4,7 +4,7 @@
 from script_generator.scripts.tracking_analysis import tracking_analysis
 from script_generator.gui.messages.messages import ProgressMessage
 from script_generator.object_detection.utils import check_skip_object_detection
-from script_generator.scripts.analyze_video import analyze_video_pipe, analyze_video
+from script_generator.scripts.analyze_video import analyze_video
 from script_generator.utils.file import get_output_file_path
 from script_generator.utils.logger import logger
 from utils.lib_Debugger import Debugger
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 386f044e..465e2aa7 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -1,8 +1,8 @@
 import tkinter as tk
 from tkinter import ttk
 
-from script_generator.gui.controller.process_video import video_analysis
 from script_generator.gui.controller.regenerate_funscript import regenerate_funscript
+from script_generator.gui.controller.video_analysis import video_analysis
 from script_generator.gui.controller.debug_video import debug_video
 from script_generator.gui.messages.messages import UIMessage, ProgressMessage
 from script_generator.gui.utils.widgets import Widgets
diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index db2a4b1a..427d5353 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -158,7 +158,7 @@ def analyze_tracking_results(state, results):
                 logger.warn("Frame could not be read in live preview")
                 continue
 
-            frame_display = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frame_display = frame.copy()
 
             for box in tracker.tracked_boxes:
                 frame_display = visualizer.draw_bounding_box(
@@ -188,7 +188,7 @@ def analyze_tracking_results(state, results):
             if state.funscript_distances:
                 frame_display = visualizer.draw_gauge(frame_display, state.funscript_distances[-1])
 
-            cv2.imshow("Funscript generation preview", frame_display)
+            cv2.imshow("Combined Results", frame_display)
             cv2.waitKey(1)
 
         # Update progress
diff --git a/script_generator/object_detection/post_process_results.py b/script_generator/object_detection/post_process_results.py
index 997fbd44..643f7c04 100644
--- a/script_generator/object_detection/post_process_results.py
+++ b/script_generator/object_detection/post_process_results.py
@@ -131,7 +131,7 @@ def task_logic(self):
             
 
     def on_last_item(self):
-        self.task.end_time = time.time()
+        self.state.analyze_task.end_time = time.time()
 
         # Write the detection records to a JSON file
         raw_yolo_path, _ = get_output_file_path(self.state.video_path, "_rawyolo.json")
diff --git a/script_generator/scripts/analyze_video.py b/script_generator/scripts/analyze_video.py
index 9098b351..747bd854 100644
--- a/script_generator/scripts/analyze_video.py
+++ b/script_generator/scripts/analyze_video.py
@@ -5,11 +5,12 @@
 
 from tqdm import tqdm
 
-from config import SEQUENTIAL_MODE, PROGRESS_BAR, UPDATE_PROGRESS_INTERVAL, NUM_PARALLEL_PIPES
+from config import QUEUE_MAXSIZE, SEQUENTIAL_MODE, PROGRESS_BAR, UPDATE_PROGRESS_INTERVAL
 from script_generator.gui.messages.messages import ProgressMessage
 from script_generator.object_detection.post_process_results import YoloAnalysisTaskProcessor
 from script_generator.object_detection.yolo import YoloTaskProcessor
 from script_generator.state.app_state import AppState
+from script_generator.tasks.abstract_task_processor import TaskProcessorTypes
 from script_generator.tasks.tasks import AnalyzeVideoTask, AnalyzeFrameTask
 from script_generator.utils.file import check_create_output_folder
 from script_generator.utils.logger import logger
@@ -18,137 +19,118 @@
 
 
 def analyze_video(state: AppState) -> List[AnalyzeFrameTask]:
-    """
-    Launch multiple pipelines in parallel.
-    Start a single logging thread to monitor all pipelines.
-    """
-    logger.info(f"[OBJECT DETECTION] Spawning {NUM_PARALLEL_PIPES} pipeline(s).")
-
-    # Create all pipelines, but do not start them yet
-    tasks_and_threads = []
-    for pipe_id in range(NUM_PARALLEL_PIPES):
-        analyze_task, pipeline_threads = analyze_video_pipe(state, pipe_id)
-        # Keep track of each pipeline's task object and its associated threads
-        tasks_and_threads.append((analyze_task, pipeline_threads))
-
-    # Start all pipeline threads
-    for analyze_task, pipeline_threads in tasks_and_threads:
-        for t in pipeline_threads:
-            # Some pipelines might have a None thread (like opengl_thread = None if not VR)
-            if t is not None:
-                t.start()
-
-    # Start logging thread that monitors all pipelines
+    logger.info(f"[OBJECT DETECTION] Starting up pipeline with profiling in {'sequential mode' if SEQUENTIAL_MODE else 'parallel mode'}...")
+
     log_thread_stop_event = threading.Event()
-    logging_thread = threading.Thread(
-        target=log_progress,
-        args=(state, [item[0] for item in tasks_and_threads], log_thread_stop_event),
-        daemon=True,
-    )
-    logging_thread.start()
+    threads = []
 
-    # Wait for all pipeline threads to finish
-    for analyze_task, pipeline_threads in tasks_and_threads:
-        for t in pipeline_threads:
-            if t is not None:
-                t.join()
+    try:
+        # make sure the output folder exists for this video
+        check_create_output_folder(state.video_path)
+
+        # Initialize batch task
+        state.set_video_info()
+        if state.video_reader == "FFmpeg + OpenGL (Windows)":
+            if not state.video_info.is_vr:
+                logger.warn("Disabled OpenGL in the pipeline as it's not needed for 2D videos")
+                state.video_reader = "FFmpeg"
+
+            if state.video_info.is_fisheye:
+                logger.warn("Disabled OpenGL as fisheye is not yet supported with the opengl feature")
+                state.video_reader = "FFmpeg"
+
+        use_open_gl = state.video_reader == "FFmpeg + OpenGL (Windows)"
+
+        state.analyze_task = AnalyzeVideoTask()
+
+        # Create queues
+        opengl_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
+        yolo_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
+        analysis_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
+        result_q = queue.Queue(maxsize=0)
+
+        # Create threads
+        decode_thread = VideoTaskProcessor(state=state, output_queue=opengl_q if use_open_gl else yolo_q)
+        opengl_thread = VrTo2DTaskProcessor(state=state, input_queue=opengl_q, output_queue=yolo_q) if use_open_gl else None
+        yolo_thread = YoloTaskProcessor(state=state, input_queue=yolo_q, output_queue=analysis_q)
+        yolo_analysis_thread = YoloAnalysisTaskProcessor(state=state, input_queue=analysis_q, output_queue=result_q)
+
+        # Start logging thread
+        queue_logging_thread = threading.Thread(
+            target=log_progress,
+            args=(state, opengl_q, yolo_q, analysis_q, result_q, log_thread_stop_event),
+            daemon=True,
+        )
+        queue_logging_thread.start()
 
-    # Check for exceptions from each pipeline thread
-    for analyze_task, pipeline_threads in tasks_and_threads:
-        for t in pipeline_threads:
-            if t is not None:
-                t.check_exception()
+        # Sequential mode can be used to determine performance bottlenecks on very short videos
+        if SEQUENTIAL_MODE:
+            def run_thread(thread, thread_name, out_queue):
+                start_time = time.time()
+                thread.start()
+                thread.join()
+                out_queue.put(None)
+                logger.info(f"[OBJECT DETECTION] {thread_name} thread done in {time.time() - start_time} s")
 
-    # Signal logging thread to stop and wait for it
-    log_thread_stop_event.set()
-    logging_thread.join()
+            run_thread(decode_thread, TaskProcessorTypes.VIDEO, opengl_q)
+            if use_open_gl:
+                run_thread(opengl_thread, TaskProcessorTypes.OPENGL, yolo_q)
+            run_thread(yolo_thread, TaskProcessorTypes.YOLO, analysis_q)
+            run_thread(yolo_analysis_thread, TaskProcessorTypes.YOLO_ANALYSIS, result_q)
+        else:
+            threads = [decode_thread, opengl_thread, yolo_thread, yolo_analysis_thread] if use_open_gl else [decode_thread, yolo_thread, yolo_analysis_thread]
+            for thread in threads:
+                thread.start()
+            for thread in threads:
+                thread.join()
 
-    # Update end_time for each pipeline and log performance
-    for analyze_task, pipeline_threads in tasks_and_threads:
-        analyze_task.end_time = time.time()
-        # Log performance for that pipeline
-        log_performance(state, analyze_task.result_q, analyze_task)
+            # Check for exceptions in threads
+            for thread in threads:
+                if thread is not None:
+                    thread.check_exception()
 
-    logger.info("[OBJECT DETECTION] All pipelines complete.")
+        state.analyze_task.end_time = time.time()
 
-    # Return tasks
-    return [tup[0] for tup in tasks_and_threads]
+        log_thread_stop_event.set()
 
-def analyze_video_pipe(state: AppState, pipe_id) -> AnalyzeVideoTask:
-    logger.info(f"[OBJECT DETECTION] Starting up pipeline with id: {pipe_id} and running in {'sequential mode' if SEQUENTIAL_MODE else 'parallel mode'}...")
+        # wait for update progress thread to close
+        time.sleep(UPDATE_PROGRESS_INTERVAL * 1.1)
 
-    log_thread_stop_event = threading.Event()
-    threads = []
-
-    # make sure the output folder exists for this video
-    check_create_output_folder(state.video_path)
+        log_performance(state=state, results_queue=result_q)
 
-    # Initialize batch task
-    state.set_video_info()
-    if state.video_reader == "FFmpeg + OpenGL (Windows)":
-        if not state.video_info.is_vr:
-            logger.warn("Disabled OpenGL in the pipeline as it's not needed for 2D videos")
-            state.video_reader = "FFmpeg"
+        return result_q.queue
 
-        if state.video_info.is_fisheye:
-            logger.warn("Disabled OpenGL as fisheye is not yet supported with the opengl feature")
-            state.video_reader = "FFmpeg"
+    except Exception as e:
+        logger.error(f"An error occurred during video analysis: {e}")
+        # Signal all threads to stop and perform cleanup
+        log_thread_stop_event.set()
+        for thread in threads:
+            if thread is not None and thread.is_alive():
+                thread.join(timeout=1)
+        raise
 
-    use_open_gl = state.video_reader == "FFmpeg + OpenGL (Windows)"
 
-    # Create our pipeline task
-    analyze_task = AnalyzeVideoTask(pipe_id=pipe_id)
+def log_progress(state, opengl_q, yolo_q, analysis_q, results_q, stop_event):
+    total_frames = state.video_info.total_frames
 
-    # Create threads
-    decode_thread = VideoTaskProcessor(state=state, task=analyze_task, output_queue=analyze_task.opengl_q if use_open_gl else analyze_task.yolo_q)
-    opengl_thread = VrTo2DTaskProcessor(state=state, task=analyze_task, input_queue=analyze_task.opengl_q, output_queue=analyze_task.yolo_q) if use_open_gl else None
-    yolo_thread = YoloTaskProcessor(state=state, task=analyze_task, input_queue=analyze_task.yolo_q, output_queue=analyze_task.analysis_q)
-    yolo_analysis_thread = YoloAnalysisTaskProcessor(state=state, task=analyze_task, input_queue=analyze_task.analysis_q, output_queue=analyze_task.result_q)
-
-    # If sequential mode, we wrap these in a step-by-step approach
-    if SEQUENTIAL_MODE:
-        # We'll create "mini-threads" just for the sequential pattern
-        threads = [decode_thread]
-        if use_open_gl:
-            threads.append(opengl_thread)
-        threads.append(yolo_thread)
-        threads.append(yolo_analysis_thread)
-    else:
-        # Typical parallel approach
-        threads = [decode_thread]
-        if use_open_gl:
-            threads.append(opengl_thread)
-        threads.append(yolo_thread)
-        threads.append(yolo_analysis_thread)
-
-    return analyze_task, threads
-
-
-def log_progress(state: AppState, analyze_tasks: List[AnalyzeVideoTask], stop_event: threading.Event):
-    """
-    Single logging thread monitoring *all* pipelines in aggregate.
-    Displays a combined progress bar and combined queue sizes.
-    """
-    # Sum total frames across all pipelines
-    total_frames = sum([state.video_info.total_frames for _ in analyze_tasks])
-    label = f"Analyzing {len(analyze_tasks)} pipeline(s)"
+    label = 'Analyzing ' + ('VR' if state.video_info.is_vr else '2D') + ' video'
 
     if PROGRESS_BAR:
         with tqdm(
-            total=total_frames,
-            desc=label,
-            unit="f",
-            position=0,
-            unit_scale=False,
-            unit_divisor=1,
-            ncols=130
+                total=total_frames,
+                #desc="Analyzing video",
+                desc=label,
+                unit="f",
+                position=0,
+                unit_scale=False,
+                unit_divisor=1,
+                ncols=130
         ) as progress_bar:
             while not stop_event.is_set():
-                # Sum queue sizes across all tasks
-                opengl_size = sum(t.opengl_q.qsize() for t in analyze_tasks if t.opengl_q is not None)
-                yolo_size = sum(t.yolo_q.qsize() for t in analyze_tasks)
-                analysis_size = sum(t.analysis_q.qsize() for t in analyze_tasks)
-                frames_processed = sum(t.result_q.qsize() for t in analyze_tasks)
+                opengl_size = opengl_q.qsize()
+                yolo_size = yolo_q.qsize()
+                analysis_size = analysis_q.qsize()
+                frames_processed = results_q.qsize()
 
                 progress_bar.n = frames_processed
                 open_gl = f"OpenGL: {opengl_size:>3}, " if state.video_reader == "FFmpeg + OpenGL (Windows)" else ""
@@ -161,7 +143,7 @@ def log_progress(state: AppState, analyze_tasks: List[AnalyzeVideoTask], stop_ev
                     stop_event.set()
 
                 if state.update_ui:
-                    elapsed_time = time.time() - min(t.start_time for t in analyze_tasks)
+                    elapsed_time = time.time() - state.analyze_task.start_time
                     processing_rate = frames_processed / elapsed_time if elapsed_time > 0 else 0
                     remaining_frames = total_frames - frames_processed
                     eta = remaining_frames / processing_rate if processing_rate > 0 else float('inf')
@@ -178,46 +160,32 @@ def log_progress(state: AppState, analyze_tasks: List[AnalyzeVideoTask], stop_ev
                 time.sleep(UPDATE_PROGRESS_INTERVAL)
     else:
         while not stop_event.is_set():
-            opengl_size = sum(t.opengl_q.qsize() for t in analyze_tasks if t.opengl_q is not None)
-            yolo_size = sum(t.yolo_q.qsize() for t in analyze_tasks)
-            analysis_size = sum(t.analysis_q.qsize() for t in analyze_tasks)
-            frames_processed = sum(t.result_q.qsize() for t in analyze_tasks)
-
-            logger.info(
-                f"OpenGL: {opengl_size:>3}, YOLO: {yolo_size:>3}, "
-                f"Analysis: {analysis_size:>3}, DONE: {frames_processed:>3}"
-            )
+            opengl_size = opengl_q.qsize()
+            yolo_size = yolo_q.qsize()
+            analysis_size = analysis_q.size()
+            frames_processed = results_q.qsize()
+            open_gl = f"OpenGL: {opengl_size:>3}, " if state.video_reader == "FFmpeg + OpenGL (Windows)" else ""
+            logger.info(f"Queues: {open_gl}YOLO: {yolo_size:>3}, Analysis: {analysis_size:>3}, DONE: {frames_processed:>3}")
 
             if frames_processed >= total_frames:
                 stop_event.set()
 
-            time.sleep(UPDATE_PROGRESS_INTERVAL)
-
+            time.sleep(0.5)
 
-def log_performance(state: AppState, results_queue: queue.Queue, analyze_task: AnalyzeVideoTask):
-    """
-    Logs performance stats for a single pipeline's task.
-    If you want to log combined performance for all pipelines,
-    you can adapt this function similarly.
-    """
-    # Filter out any sentinel items
-    tasks = [t for t in results_queue.queue if t is not None and hasattr(t, 'profile')]
+def log_performance(state, results_queue):
+    analyze_task = state.analyze_task
+    # TODO filter out sentinals in task processor
+    tasks = [task for task in results_queue.queue if task is not None and hasattr(task, 'profile')]
     total_frames = len(tasks)
 
-    if not analyze_task.end_time:
-        analyze_task.end_time = time.time()  # fallback
-
     total_pipeline_time = analyze_task.end_time - analyze_task.start_time
-
-    # For quick reference
-    video_fps = state.video_info.fps if state.video_info else 30.0
-    video_duration = total_frames / video_fps if video_fps else 0
-    avg_processing_fps = total_frames / total_pipeline_time if total_pipeline_time > 0 else 0
+    video_duration = total_frames / state.video_info.fps
+    avg_processing_fps = total_frames / total_pipeline_time
     realtime_percentage = (avg_processing_fps / 60.0) * 100.0
 
     log_message = (
         f"\n{'-' * 60}"
-        f"\n OBJECT DETECTION PIPELINE #{analyze_task.pipe_id} COMPLETED {'(sequential mode)' if SEQUENTIAL_MODE else ''}\n"
+        f"\n OBJECT DETECTION COMPLETED {'(sequential mode)' if SEQUENTIAL_MODE else ''}\n"
         f"\n Settings\n"
         f"  - Video reader               : {state.video_reader}\n"     
         f"\n Video stats\n"
@@ -228,7 +196,7 @@ def log_performance(state: AppState, results_queue: queue.Queue, analyze_task: A
     if SEQUENTIAL_MODE:
         log_message += f"\n Sequential Queue statistics\n"
         for key, total_time in analyze_task.profile.items():
-            if key.endswith("_duration"):  # only duration metrics
+            if key.endswith("_duration"):  # Only include duration metrics
                 avg_time = total_time / total_frames if total_frames > 0 else 0.0
                 stage_name = key.replace("_duration", "").capitalize()
                 log_message += (
@@ -245,12 +213,12 @@ def log_performance(state: AppState, results_queue: queue.Queue, analyze_task: A
         log_message += f"\n Task Average Times (while running in parallel)\n"
 
         aggregated_times = {}
-        for t in tasks:
-            for key, ttime in t.profile.items():
+        for task in tasks:
+            for key, total_time in task.profile.items():
                 if key.endswith("_duration"):
                     if key not in aggregated_times:
                         aggregated_times[key] = {"total_time": 0, "task_count": 0}
-                    aggregated_times[key]["total_time"] += ttime
+                    aggregated_times[key]["total_time"] += total_time
                     aggregated_times[key]["task_count"] += 1
 
         # Calculate and format averages for each key
diff --git a/script_generator/scripts/generate_funscript.py b/script_generator/scripts/generate_funscript.py
index c7e95433..54f1ae17 100644
--- a/script_generator/scripts/generate_funscript.py
+++ b/script_generator/scripts/generate_funscript.py
@@ -1,10 +1,10 @@
-from script_generator.scripts.analyze_video import analyze_video_pipe
+from script_generator.scripts.analyze_video import analyze_video
 from script_generator.state.app_state import AppState
 from script_generator.utils.logger import logger
 
 
 def generate_funscript(state: AppState):
     try:
-        results = analyze_video_pipe(state)
+        results = analyze_video(state)
     except Exception as e:
         logger.error(f"Could not generate funscript: {e}")
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index 18221816..d52c9fac 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -1,5 +1,5 @@
 import string
-from typing import Literal, List
+from typing import Literal
 
 from script_generator.tasks.tasks import AnalyzeVideoTask
 from script_generator.utils.helpers import is_mac
@@ -13,7 +13,7 @@ def __init__(self, is_cli):
 
         # Job
         self.video_info: VideoInfo | None = None
-        self.analyze_tasks: List[AnalyzeVideoTask]  = []
+        self.analyze_task: AnalyzeVideoTask | None = None
         self.video_path: string = None
         self.frame_start: int = 0
         self.frame_end: int | None = None
diff --git a/script_generator/tasks/abstract_task_processor.py b/script_generator/tasks/abstract_task_processor.py
index df85facf..e2ad7969 100644
--- a/script_generator/tasks/abstract_task_processor.py
+++ b/script_generator/tasks/abstract_task_processor.py
@@ -3,14 +3,14 @@
 from typing import Generator, Optional
 
 from script_generator.state.app_state import AppState
-from script_generator.tasks.tasks import AnalyzeFrameTask, Task
+from script_generator.tasks.tasks import AnalyzeFrameTask
 from script_generator.utils.logger import logger
 
 
 class AbstractTaskProcessor(threading.Thread):
     process_type = ""
 
-    def __init__(self, state: AppState, task: Task, output_queue: queue.Queue, input_queue: Optional[queue.Queue] = None):
+    def __init__(self, state: AppState, output_queue: queue.Queue, input_queue: Optional[queue.Queue] = None):
         """
         Abstract thread class to handle lifecycle management and task handling boilerplate.
 
@@ -22,7 +22,6 @@ def __init__(self, state: AppState, task: Task, output_queue: queue.Queue, input
         self.input_queue = input_queue
         self.output_queue = output_queue
         self._stop_event = threading.Event()
-        self.task = task
         self.exception = None  # Store the exception that occurs in the thread
 
     def log(self, message):
@@ -47,7 +46,7 @@ def get_task(self) -> Generator[AnalyzeFrameTask, None, None]:
                 task = self.input_queue.get(timeout=1)
 
                 if task is None:  # Sentinel for termination
-                    self.task.end(self.process_type)
+                    self.state.analyze_task.end(self.process_type)
                     self.on_last_item()
                     self.finish_task(None)
                     break
@@ -75,7 +74,7 @@ def run(self):
         Catches any exceptions and stores them for the caller.
         """
         try:
-            self.task.start(self.process_type)
+            self.state.analyze_task.start(self.process_type)
             self.task_logic()
         except Exception as e:
             self.exception = e  # Capture the exception
@@ -95,7 +94,7 @@ def task_logic(self):
         raise NotImplementedError("Subclasses must implement task_logic")
 
     def stop_process(self):
-        self.task.end(self.process_type)
+        self.state.analyze_task.end(self.process_type)
         self.on_last_item()
         # Propagate sentinel to the output queue
         self.output_queue.put(None)
diff --git a/script_generator/tasks/tasks.py b/script_generator/tasks/tasks.py
index a872f360..3eeac22f 100644
--- a/script_generator/tasks/tasks.py
+++ b/script_generator/tasks/tasks.py
@@ -1,4 +1,3 @@
-import queue
 import time
 from dataclasses import dataclass, field
 from threading import Lock
@@ -6,7 +5,6 @@
 
 import numpy as np
 
-from config import QUEUE_MAXSIZE
 from script_generator.video.video_info import VideoInfo
 
 
@@ -56,20 +54,13 @@ def _calculate_duration(self, process_type: str):
 @dataclass
 class AnalyzeVideoTask(Task):
     tasks: List[Task] = field(default_factory=list)
-    pipe_id = -1
 
-    def __init__(self, pipe_id):
+    def __init__(self):
         super().__init__()
         self.tasks = []
         self._lock = Lock()
         self.profile = {}
         self.start_time = time.time()
-        self.pipe_id = pipe_id
-
-        self.opengl_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
-        self.yolo_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
-        self.analysis_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
-        self.result_q = queue.Queue(maxsize=0)
 
     def add_task(self, task: Task) -> Task:
         with self._lock:
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 89f23448..4fe18125 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -169,7 +169,7 @@ def play_video(self, state, start_frame=0, duration=0, rolling_window_size=100,
             #if not state.live_preview_mode:
             #    continue
 
-            frame_copy = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  # make a copy of the frame to make it writeable, useful for ffmpeg library here
+            frame_copy = frame.copy()  # make a copy of the frame to make it writeable, useful for ffmpeg library here
             # Display variables and bounding boxes
             str_frame_id = str(self.current_frame)
             if str_frame_id in self.logs:

From 7a60678d3044d57c5d3a1b82a96dabdfe9dc4c7e Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sat, 25 Jan 2025 11:07:57 +0100
Subject: [PATCH 148/277] Reverted parallel pipe code (for now) Fixed frame
 start and end Small tweaks

---
 FSGenerator.py                                     |  4 +++-
 script_generator/gui/controller/process_video.py   |  4 ++++
 script_generator/gui/utils/widgets.py              | 14 +++++++++++---
 script_generator/gui/views/funscript_generator.py  |  4 ++--
 .../object_detection/analyze_tracking_results.py   |  2 +-
 script_generator/utils/helpers.py                  |  8 +++++++-
 utils/lib_Debugger.py                              |  2 +-
 7 files changed, 29 insertions(+), 9 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index 37a8bce8..e33c2225 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -1,4 +1,6 @@
+import os
 from script_generator.gui.app import start_app
 
 if __name__ == "__main__":
-    start_app()
\ No newline at end of file
+    print(f"PID: {os.getpid()}")
+    start_app()
diff --git a/script_generator/gui/controller/process_video.py b/script_generator/gui/controller/process_video.py
index b589a85e..f6fa9138 100644
--- a/script_generator/gui/controller/process_video.py
+++ b/script_generator/gui/controller/process_video.py
@@ -6,6 +6,7 @@
 from script_generator.object_detection.utils import check_skip_object_detection
 from script_generator.scripts.analyze_video import analyze_video
 from script_generator.utils.file import get_output_file_path
+from script_generator.utils.helpers import to_int_or_none
 from script_generator.utils.logger import logger
 from utils.lib_Debugger import Debugger
 
@@ -23,6 +24,9 @@ def video_analysis(state, root):
     logger.info(f"Frame End: {state.frame_end}")
     logger.info(f"Reference Script: {state.reference_script}")
 
+    state.frame_start = to_int_or_none(state.frame_start)
+    state.frame_end = to_int_or_none(state.frame_end)
+
     def run():
         try:
             # Initialize the debugger
diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index cc5424d9..74e5aec8 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -67,10 +67,18 @@ def input(parent, label_text, state, attr, row=0, column=0, label_width_px=LABEL
         container.grid(row=row, column=column, sticky="ew", padx=5, pady=5)
         container.columnconfigure(1, weight=1)
 
-        value = tk.StringVar(value=getattr(state, attr))
+        initial_value = getattr(state, attr)
+        if initial_value is None:
+            initial_value = ""
 
-        if callback:
-            value.trace_add("write", lambda *args: setattr(state, attr, value.get()))
+        value = tk.StringVar(value=initial_value)
+
+        def on_value_change(*args):
+            setattr(state, attr, value.get())
+            if callback:
+                callback(value.get())
+
+        value.trace_add("write", on_value_change)
 
         label = tk.Label(container, text=label_text, anchor="w", width=label_width_px // 7)
         label.grid(row=0, column=0, sticky="w", padx=(5, 2))
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 465e2aa7..c94351f1 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -1,8 +1,8 @@
 import tkinter as tk
 from tkinter import ttk
 
+from script_generator.gui.controller.process_video import video_analysis
 from script_generator.gui.controller.regenerate_funscript import regenerate_funscript
-from script_generator.gui.controller.video_analysis import video_analysis
 from script_generator.gui.controller.debug_video import debug_video
 from script_generator.gui.messages.messages import UIMessage, ProgressMessage
 from script_generator.gui.utils.widgets import Widgets
@@ -58,7 +58,7 @@ def __init__(self, parent, controller):
         optional_settings = Widgets.frame(wrapper, title="Optional settings", main_section=True, row=2)
 
         Widgets.input(optional_settings, "Frame Start", state=state, attr="frame_start", tooltip_text="Where to start processing the video. If you have a 60fps video starting at 10s would mean frame 600")
-        Widgets.input(optional_settings, "Frame End", state=state, attr="frame_start", tooltip_text="Where to end processing the video. If you have a 60fps video stopping  at 10s would mean frame 600", row=1)
+        Widgets.input(optional_settings, "Frame End", state=state, attr="frame_end", tooltip_text="Where to end processing the video. If you have a 60fps video stopping  at 10s would mean frame 600", row=1)
         # endregion
 
         # region PROCESSING
diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index 427d5353..c3084d75 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -158,7 +158,7 @@ def analyze_tracking_results(state, results):
                 logger.warn("Frame could not be read in live preview")
                 continue
 
-            frame_display = frame.copy()
+            frame_display = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
 
             for box in tracker.tracked_boxes:
                 frame_display = visualizer.draw_bounding_box(
diff --git a/script_generator/utils/helpers.py b/script_generator/utils/helpers.py
index 65d479c1..960ae649 100644
--- a/script_generator/utils/helpers.py
+++ b/script_generator/utils/helpers.py
@@ -2,4 +2,10 @@
 
 
 def is_mac():
-    return platform.system() == "Darwin"
\ No newline at end of file
+    return platform.system() == "Darwin"
+
+def to_int_or_none(value):
+    try:
+        return int(value)
+    except (ValueError, TypeError):
+        return None
\ No newline at end of file
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 4fe18125..89f23448 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -169,7 +169,7 @@ def play_video(self, state, start_frame=0, duration=0, rolling_window_size=100,
             #if not state.live_preview_mode:
             #    continue
 
-            frame_copy = frame.copy()  # make a copy of the frame to make it writeable, useful for ffmpeg library here
+            frame_copy = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  # make a copy of the frame to make it writeable, useful for ffmpeg library here
             # Display variables and bounding boxes
             str_frame_id = str(self.current_frame)
             if str_frame_id in self.logs:

From 51d4562a5cb2b6a606cc97358d9c695705facb55 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sat, 25 Jan 2025 11:29:58 +0100
Subject: [PATCH 149/277] RGB/BGR war fix

---
 utils/lib_Debugger.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 89f23448..6ef62e29 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -169,7 +169,8 @@ def play_video(self, state, start_frame=0, duration=0, rolling_window_size=100,
             #if not state.live_preview_mode:
             #    continue
 
-            frame_copy = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  # make a copy of the frame to make it writeable, useful for ffmpeg library here
+            # frame_copy = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  # make a copy of the frame to make it writeable, useful for ffmpeg library here
+            frame_copy = frame.copy()  # no need to convert anymore
             # Display variables and bounding boxes
             str_frame_id = str(self.current_frame)
             if str_frame_id in self.logs:

From 44e1bdf497066186fd16a2128a287a2b919e0d2e Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sat, 25 Jan 2025 12:25:42 +0100
Subject: [PATCH 150/277] RGB/BGR war fix

---
 utils/lib_Debugger.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
index 6ef62e29..97d5b8bd 100644
--- a/utils/lib_Debugger.py
+++ b/utils/lib_Debugger.py
@@ -170,7 +170,7 @@ def play_video(self, state, start_frame=0, duration=0, rolling_window_size=100,
             #    continue
 
             # frame_copy = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  # make a copy of the frame to make it writeable, useful for ffmpeg library here
-            frame_copy = frame.copy()  # no need to convert anymore
+            frame_copy = frame.copy()  # no need to convert
             # Display variables and bounding boxes
             str_frame_id = str(self.current_frame)
             if str_frame_id in self.logs:

From 0632c23e0035a13343bd65dfe7492dfbf5851187 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sat, 25 Jan 2025 14:36:10 +0100
Subject: [PATCH 151/277] Fixed the area computation used in triggering the
 close-up detection

---
 script_generator/object_detection/analyze_tracking_results.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index c3084d75..361bfa8a 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -25,7 +25,7 @@ def analyze_tracking_results(state, results):
     fps = video_info.fps
     reader = VideoReaderFFmpeg(state.video_path, is_vr=video_info.is_vr)
 
-    state.frame_area = video_info.width * video_info.height
+    state.frame_area = width * height
 
     cuts = []
 

From 3bfd8b02a2574f43fea1ac8ae11295be11dc9d04 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sat, 25 Jan 2025 14:36:31 +0100
Subject: [PATCH 152/277] Removed unused variables

---
 utils/lib_ObjectTracker.py | 20 ++++++++++++++------
 1 file changed, 14 insertions(+), 6 deletions(-)

diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index 16879a24..b0c2064b 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -81,14 +81,11 @@ def __init__(self, state):
         """
         self.state = state
         self.class_names = CLASS_NAMES  # List of class names to track
-        self.active_tracks = {}  # Active tracks: {track_id: Track object}
-        self.inactive_tracks = []  # Inactive tracks (lost tracks)
         # self.distance_kf = KF.KalmanFilter()  # Kalman filter for distance smoothing
 
         self.frame = None  # Current video frame
         self.current_frame_id = state.current_frame_id  # frame_pos  # Current frame ID
-        self.width = 0  # Height of the video frame
-        self.image_area = state.frame_area # image_area  # Area of the video frame
+        self.image_area = state.frame_area  # image_area  # Area of the video frame
         self.fps = state.video_info.fps  # fps  # Frames per second of the video
         self.isVR = state.video_info.is_vr  # isVR
 
@@ -294,7 +291,6 @@ def tracking_logic(self, state, sorted_boxes):
         """
         self.state = state
         self.current_frame_id = state.current_frame_id
-        self.width = state.video_info.width
 
         close_up_detected = False
 
@@ -345,10 +341,13 @@ def tracking_logic(self, state, sorted_boxes):
         if self.locked_penis_box.is_active() and self.locked_penis_box.visible < 100 and not self.glans_detected:
             self.penetration = True
 
+
+
         # Check if pussy boxes are inside butt boxes
         if 'pussy' in all_detections and 'butt' in all_detections:
             for pussy_detection in all_detections['pussy']:
                 p_conf, p_box, p_track_id = pussy_detection
+                pussy_area = self.box_area(p_box)
 
                 for butt_detection in all_detections['butt']:
                     b_conf, b_box, b_track_id = butt_detection
@@ -388,6 +387,16 @@ def tracking_logic(self, state, sorted_boxes):
                             distance = 100
                             self.update_distance(distance)
                             return
+                if pussy_area > 0.1 * self.image_area and 'penis' not in all_detections:
+                    close_up_detected = True
+                    self.detect_sex_position_change('Close up', 'No penis detected')
+                    if not self.close_up:
+                        logger.info(f"@{self.current_frame_id} - Close up detected - no penis detected")
+                        self.close_up = True
+                    self.penetration = False
+                    distance = 100
+                    self.update_distance(distance)
+                    return
 
         # if we reach here, this means we are not in close_up mode (theoretically)
         self.close_up = False
@@ -562,7 +571,6 @@ def handle_class_first(self, class_name, box, conf):
                             self.locked_penis_box.update(self.penis_box, current_height)
 
                         # Move locked penis box towards current penis box
-                        #max_move = max(1, int(self.width / 960))
                         if self.isVR:
                             max_move = 180 // int(self.fps)
                         else:

From 4c76cd7a9360206b59ffa0e9271c7a151e102bf2 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sat, 25 Jan 2025 18:12:42 +0100
Subject: [PATCH 153/277] Fixed the frame_start logic and added a
 frame_start_track item to the state

---
 .../object_detection/analyze_tracking_results.py            | 3 ++-
 script_generator/scripts/tracking_analysis.py               | 6 ++++--
 script_generator/state/app_state.py                         | 1 +
 3 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index 361bfa8a..309ac9fa 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -74,7 +74,8 @@ def analyze_tracking_results(state, results):
     last_ui_update_time = time.time()
 
     for frame_pos in tqdm(
-            range(state.frame_start, state.frame_end), unit="f", desc="Analyzing tracking data", position=0,
+            #range(state.frame_start, state.frame_end), unit="f", desc="Analyzing tracking data", position=0,
+            range(state.frame_start_track, state.frame_end), unit="f", desc="Analyzing tracking data", position=0,
             unit_scale=False,
             unit_divisor=1,
             ncols=130
diff --git a/script_generator/scripts/tracking_analysis.py b/script_generator/scripts/tracking_analysis.py
index 80a323ef..4ac967b7 100644
--- a/script_generator/scripts/tracking_analysis.py
+++ b/script_generator/scripts/tracking_analysis.py
@@ -31,11 +31,13 @@ def tracking_analysis(state):
         return
 
     # Deciding whether we start from there or from a user-specified later frame
-    state.frame_start = max(max(first_penis_frame - int(video_info.fps), state.frame_start - int(video_info.fps)), 0)
+    #state.frame_start = max(max(first_penis_frame - int(video_info.fps), state.frame_start - int(video_info.fps)), 0)
+    state.frame_start_track = max(max(first_penis_frame - int(video_info.fps), state.frame_start - int(video_info.fps)), 0)
 
     state.frame_end = state.video_info.total_frames if not state.frame_end else state.frame_end
 
-    logger.info(f"Frame Start adjusted to: {state.frame_start}")
+    #logger.info(f"Frame Start adjusted to: {state.frame_start}")
+    logger.info(f"Frame Start adjusted to: {state.frame_start_track}")
 
     # Performing the tracking part and generation of the raw funscript data
     state.funscript_data = analyze_tracking_results(state, results)
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index d52c9fac..86f5564e 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -17,6 +17,7 @@ def __init__(self, is_cli):
         self.video_path: string = None
         self.frame_start: int = 0
         self.frame_end: int | None = None
+        self.frame_start_track = 0
         self.current_frame_id = 0
         self.frame_area = 0
 

From fe4448369de4677b7863ea602b6e5decb0ae9f46 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sat, 25 Jan 2025 18:16:49 +0100
Subject: [PATCH 154/277] Fixed the frame_start logic and added a
 frame_start_track item to the state

---
 script_generator/object_detection/utils.py | 19 +++++++++++++------
 1 file changed, 13 insertions(+), 6 deletions(-)

diff --git a/script_generator/object_detection/utils.py b/script_generator/object_detection/utils.py
index 7cb96fa5..5cfc4a0d 100644
--- a/script_generator/object_detection/utils.py
+++ b/script_generator/object_detection/utils.py
@@ -1,9 +1,6 @@
 import os
-import platform
 
-import torch
-
-from script_generator.constants import CLASS_REVERSE_MATCH, YOLO_MODELS
+from script_generator.constants import CLASS_REVERSE_MATCH
 from script_generator.gui.utils.widgets import Widgets
 from script_generator.object_detection.box_record import BoxRecord
 from script_generator.object_detection.object_detection_result import ObjectDetectionResult
@@ -64,10 +61,20 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
     """
 
     penis_cls = 0
+    prev_frame = 0
+    cons_frames = 0
+    threshold = 5
 
     for line in data:
         frame_idx, cls, conf, x1, y1, x2, y2, track_id = line
         if frame_idx >= start_frame and cls == penis_cls and conf >= 0.7:
             penis_frame = frame_idx
-            logger.info(f"First instance of Glans/Penis found in frame {frame_idx}")
-            return penis_frame
+            if prev_frame == frame_idx - 1:
+                cons_frames += 1
+            else:
+                cons_frames = 0
+            prev_frame = frame_idx
+
+            if cons_frames > threshold:
+                logger.info(f"First instance of Glans/Penis found in frame {frame_idx - threshold}")
+                return penis_frame - threshold

From 8db72cad6573e5e21937267f76f3a7161d046dc7 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sat, 25 Jan 2025 18:19:04 +0100
Subject: [PATCH 155/277] v0.0.4_hf01 : tweaked the initial P-detection. The
 logic was recently losened up to be less restrictive, leading to erroneous
 early tracking

---
 FSGenerator.py   | 20 ++++++++++++++++----
 params/config.py |  2 +-
 2 files changed, 17 insertions(+), 5 deletions(-)

diff --git a/FSGenerator.py b/FSGenerator.py
index ea1881e0..a075cc96 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -565,12 +565,24 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
     :param start_frame: The starting frame for the search.
     :return: The frame ID where the penis is first detected.
     """
-    consecutive_frames = 0
-    frame_detected = 0
-    penis_frame = 0
+    penis_cls = 0
+    prev_frame = 0
+    cons_frames = 0
+    threshold = 5
+
     for line in data:
-        if line[0] >= start_frame and line[1] == 0 and line[2] >= 0.5:
+        if line[0] >= start_frame and line[1] == penis_cls and line[2] >= 0.6:
             penis_frame = line[0]
+            if prev_frame == line[0] - 1:
+                cons_frames += 1
+            else:
+                cons_frames = 0
+            prev_frame = line[0]
+
+            if cons_frames > threshold:
+                global_state.logger.info(f"First instance of Glans/Penis found in frame {prev_frame - threshold}")
+                return penis_frame
+
             return penis_frame
         """
         if line[0] == penis_frame and line[1] == 1 and line[2] >= 0.5:
diff --git a/params/config.py b/params/config.py
index ff5c5042..3fbfe7d7 100644
--- a/params/config.py
+++ b/params/config.py
@@ -2,7 +2,7 @@
 import platform
 import os
 
-version = "0.0.4"
+version = "0.0.4_hf01"
 
 # ffmpeg and ffprobe paths - replace with your own if not in your system path
 win_ffmpeg_path = "C:/ffmpeg/bin/ffmpeg.exe"

From 8e6f69561a9bbb4b0c404fa859b24b05b4680f24 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sat, 25 Jan 2025 20:16:45 +0100
Subject: [PATCH 156/277] Improved saving debug video and UI Refactored debug
 video logic Added pause button to debug video Made video part more generic
 Made ffmpeg generic Removed now refactored lib_Debugger.py

---
 .gitignore                                    |   1 +
 config.py                                     |   3 +-
 .../debug}/Display_debug_results.py           |   3 +-
 script_generator/debug/debug_data.py          |  48 +++
 {debug => script_generator/debug}/errors.py   |   2 +-
 .../debug/video_player/constants.py           |   1 +
 .../debug/video_player/controls.py            |  19 +
 .../debug/video_player/debug_overlay.py       | 135 +++++++
 .../debug/video_player/interaction.py         |  22 ++
 script_generator/debug/video_player/play.py   | 138 +++++++
 script_generator/debug/video_player/state.py  |  35 ++
 .../gui/controller/debug_video.py             |  30 +-
 .../gui/controller/process_video.py           |  14 +-
 script_generator/gui/utils/widgets.py         |  90 +++--
 .../gui/views/funscript_generator.py          |  35 +-
 .../gui/views/popups/create_debug_video.py    |  27 ++
 .../analyze_tracking_results.py               |  11 +-
 ...cess_results.py => post_process_worker.py} |   6 +-
 script_generator/object_detection/utils.py    |  20 +-
 .../{yolo.py => yolo_worker.py}               |   9 +-
 script_generator/scripts/analyze_video.py     |  26 +-
 .../scripts/generate_debug_video.py           |  34 ++
 script_generator/scripts/tracking_analysis.py |   4 +-
 script_generator/state/app_state.py           |  45 +--
 script_generator/tasks/tasks.py               |   2 -
 script_generator/utils/config.py              |   2 +-
 script_generator/utils/file.py                |   5 +-
 script_generator/utils/helpers.py             |   8 +-
 script_generator/video/ffmpeg.py              | 106 ------
 script_generator/video/ffmpeg/commands.py     |  36 ++
 script_generator/video/ffmpeg/filters.py      |  41 ++
 script_generator/video/ffmpeg/hwaccel.py      |  50 +++
 .../video/{ => info}/video_info.py            |  65 +++-
 .../{video_conversion => }/opengl/helpers.py  |   0
 .../video/video_task_processor.py             | 153 --------
 .../video/workers/ffmpeg_worker.py            |  61 +++
 .../vr_to_2d_worker.py}                       |   4 +-
 utils/lib_Debugger.py                         | 355 ------------------
 utils/lib_VideoReaderFFmpeg.py                |   3 +-
 39 files changed, 866 insertions(+), 783 deletions(-)
 rename {debug => script_generator/debug}/Display_debug_results.py (79%)
 create mode 100644 script_generator/debug/debug_data.py
 rename {debug => script_generator/debug}/errors.py (88%)
 create mode 100644 script_generator/debug/video_player/constants.py
 create mode 100644 script_generator/debug/video_player/controls.py
 create mode 100644 script_generator/debug/video_player/debug_overlay.py
 create mode 100644 script_generator/debug/video_player/interaction.py
 create mode 100644 script_generator/debug/video_player/play.py
 create mode 100644 script_generator/debug/video_player/state.py
 create mode 100644 script_generator/gui/views/popups/create_debug_video.py
 rename script_generator/object_detection/{post_process_results.py => post_process_worker.py} (97%)
 rename script_generator/object_detection/{yolo.py => yolo_worker.py} (85%)
 create mode 100644 script_generator/scripts/generate_debug_video.py
 delete mode 100644 script_generator/video/ffmpeg.py
 create mode 100644 script_generator/video/ffmpeg/commands.py
 create mode 100644 script_generator/video/ffmpeg/filters.py
 create mode 100644 script_generator/video/ffmpeg/hwaccel.py
 rename script_generator/video/{ => info}/video_info.py (56%)
 rename script_generator/video/{video_conversion => }/opengl/helpers.py (100%)
 delete mode 100644 script_generator/video/video_task_processor.py
 create mode 100644 script_generator/video/workers/ffmpeg_worker.py
 rename script_generator/video/{video_conversion/vr_to_2d_task_processor.py => workers/vr_to_2d_worker.py} (95%)
 delete mode 100644 utils/lib_Debugger.py

diff --git a/.gitignore b/.gitignore
index d4b20317..f608b6ae 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,6 @@
 /models/*
 /output/*
+/logs/*
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
diff --git a/config.py b/config.py
index b45fdc8b..414a2b52 100644
--- a/config.py
+++ b/config.py
@@ -27,6 +27,7 @@
 RENDER_RESOLUTION = 1080 # Above 1080x1080 doesn't seem to improve detections
 TEXTURE_RESOLUTION = 1440 # Size that is used to texture the opengl sphere
 YOLO_BATCH_SIZE = 1 if platform.system() == "Darwin" else 30 # Mac doesn't support batching due to onnx
+YOLO_PERSIST = True # Big impact on performance but also improves tracking
 
 ##################################################################################################
 # ADVANCED / DEVELOPMENT
@@ -43,8 +44,6 @@
 
 # Set the paths in your config
 
-
-SUBTRACT_THREADS_FROM_FFMPEG = 0
 UPDATE_PROGRESS_INTERVAL = 0.25 # Updates progress in the console and in gui
 # when enabled the queue will be processed one by one (use it on (QUEUE_MAXSIZE / frame rate) seconds longer videos or less)
 # raw frames take a lot of memory (RAM) so don't set the queue to high
diff --git a/debug/Display_debug_results.py b/script_generator/debug/Display_debug_results.py
similarity index 79%
rename from debug/Display_debug_results.py
rename to script_generator/debug/Display_debug_results.py
index f0f71119..532cf6d1 100644
--- a/debug/Display_debug_results.py
+++ b/script_generator/debug/Display_debug_results.py
@@ -12,8 +12,7 @@
 
 debugger.load_logs()
 
-#debugger.display_frame(frame)
 
 #debugger.play_video(frame)
 
-debugger.play_video(frame, save_debug_video=False, downsize_ratio=1) #), duration=4)
\ No newline at end of file
+debugger.play_debug_video(frame, downsize_ratio=1) #), duration=4)
\ No newline at end of file
diff --git a/script_generator/debug/debug_data.py b/script_generator/debug/debug_data.py
new file mode 100644
index 00000000..662d581c
--- /dev/null
+++ b/script_generator/debug/debug_data.py
@@ -0,0 +1,48 @@
+import json
+
+import numpy as np
+
+from script_generator.utils.file import get_output_file_path, load_json_from_file
+from script_generator.utils.logger import logger
+
+
+class DebugData:
+    def __init__(self, state):
+        self.app_state = state
+        self.logs = {}
+
+    def add_frame(self, frame_id, variables=None, bounding_boxes=None):
+        if frame_id not in self.logs:
+            self.logs[frame_id] = {"variables": {}, "bounding_boxes": []}
+
+        if variables:
+            self.logs[frame_id]["variables"].update(variables)
+
+        if bounding_boxes:
+            self.logs[frame_id]["bounding_boxes"].extend(bounding_boxes)
+
+    def save_debug_file(self):
+        debug_log_path, _ = get_output_file_path(self.app_state.video_path, "_debug_logs.json")
+        try:
+            with open(debug_log_path, "w") as f:
+                json.dump(self.logs, f, indent=4, default=self._default_serializer)
+            logger.info(f"Debug data saved to {debug_log_path}")
+        except Exception as e:
+            logger.error(f"Failed to save logs: {e}")
+
+    @staticmethod
+    def _default_serializer(obj):
+        """Convert NumPy types to native Python types for JSON serialization."""
+        if isinstance(obj, np.integer):
+            return int(obj)
+        elif isinstance(obj, np.floating):
+            return float(obj)
+        elif isinstance(obj, np.ndarray):
+            return obj.tolist()
+        else:
+            raise TypeError(f"Object of type {obj.__class__.__name__} is not JSON serializable")
+
+
+def load_logs(state):
+    log_path, _ = get_output_file_path(state.video_path, "_debug_logs.json")
+    return load_json_from_file(log_path)
\ No newline at end of file
diff --git a/debug/errors.py b/script_generator/debug/errors.py
similarity index 88%
rename from debug/errors.py
rename to script_generator/debug/errors.py
index 05f8afd7..96427835 100644
--- a/debug/errors.py
+++ b/script_generator/debug/errors.py
@@ -2,4 +2,4 @@ class FFProbeError(Exception):
     pass
 
 class FFMpegError(Exception):
-    pass
\ No newline at end of file
+    pass
diff --git a/script_generator/debug/video_player/constants.py b/script_generator/debug/video_player/constants.py
new file mode 100644
index 00000000..1b304383
--- /dev/null
+++ b/script_generator/debug/video_player/constants.py
@@ -0,0 +1 @@
+BAR_HEIGHT = 20
\ No newline at end of file
diff --git a/script_generator/debug/video_player/controls.py b/script_generator/debug/video_player/controls.py
new file mode 100644
index 00000000..d23dbb7c
--- /dev/null
+++ b/script_generator/debug/video_player/controls.py
@@ -0,0 +1,19 @@
+import cv2
+
+from script_generator.debug.video_player.state import VideoPlayer
+
+
+def draw_media_controls(frame, video_player: VideoPlayer):
+    """
+    Draw a simple "progress bar" style media control at the bottom of the frame.
+    """
+    height, width, _ = frame.shape
+    bar_height = 20
+    bar_y_start = height - bar_height
+
+    # Background of the progress bar
+    cv2.rectangle(frame, (0, bar_y_start), (width, height), (50, 50, 50), -1)
+
+    # Progress indicator
+    progress_x = int((video_player.current_frame / video_player.total_frames) * width)
+    cv2.rectangle(frame, (0, bar_y_start), (progress_x, height), (0, 255, 0), -1)
\ No newline at end of file
diff --git a/script_generator/debug/video_player/debug_overlay.py b/script_generator/debug/video_player/debug_overlay.py
new file mode 100644
index 00000000..509fe311
--- /dev/null
+++ b/script_generator/debug/video_player/debug_overlay.py
@@ -0,0 +1,135 @@
+import cv2
+import numpy as np
+
+from script_generator.constants import CLASS_COLORS
+
+
+def get_funscript_value(interpolator, frame_id, fps):
+    """
+    Returns the interpolated funscript value for a given frame (frame_id) at the given fps.
+    """
+    time_in_milliseconds = int((frame_id / fps) * 1000)
+    return interpolator(time_in_milliseconds)
+
+def draw_overlay(
+    frame,
+    frame_id,
+    logs,
+    funscript_interpolator,
+    distance_buffer,
+    funscript_buffer,
+    visualizer,
+    fps,
+    y_offset_start=0,
+):
+    """
+    Draws bounding boxes, text overlays, rolling window graphs, etc. on top of `frame`.
+
+    :param frame:                   The current video
+    :param frame_id:                The integer ID of the frame.
+    :param logs:                    A dict containing per-frame logs.
+    :param funscript_interpolator:  If present, used to compute funscript values.
+    :param distance_buffer:         Rolling buffer for 'distance' data.
+    :param funscript_buffer:        Rolling buffer for funscript values.
+    :param visualizer:              An instance of Visualizer (to draw gauges, etc.).
+    :param fps:                     The video frames per second.
+    :param y_offset_start:          The initial Y offset for drawing text.
+    """
+    # Pull the logs for this frame
+    str_frame_id = str(frame_id)
+    variables, bounding_boxes = {}, []
+    if str_frame_id in logs:
+        variables = logs[str_frame_id].get("variables", {})
+        bounding_boxes = logs[str_frame_id].get("bounding_boxes", [])
+
+    # Draw bounding boxes
+    for box in bounding_boxes:
+        x1, y1, x2, y2 = box["box"]
+        class_name = box["class_name"]
+        position = box["position"]
+        color = CLASS_COLORS.get(class_name, (0, 255, 0))
+        cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
+        cv2.putText(
+            frame,
+            f"{class_name} {position}",
+            (x1, y1 - 10),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.5,
+            color,
+            2
+        )
+
+    # Display variables
+    y_offset = y_offset_start
+    for key, value in variables.items():
+        cv2.putText(
+            frame,
+            f"{key}: {value}",
+            (10, y_offset),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.5,
+            (0, 0, 255),
+            2
+        )
+        y_offset += 20
+
+    # Draw locked penis box if present
+    locked_penis_box = variables.get("locked_penis_box")
+    if locked_penis_box and locked_penis_box.get("active", False):
+        x1, y1, x2, y2 = locked_penis_box["box"]
+        color = CLASS_COLORS.get("penis", (0, 255, 0))
+        cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
+        cv2.putText(
+            frame,
+            "Locked Penis",
+            (x1, y1 - 10),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.5,
+            color,
+            2
+        )
+
+    # Compute distance and funscript values for rolling buffer
+    distance = variables.get("distance", 0)
+    funscript_value = 0
+    if funscript_interpolator:
+        funscript_value = get_funscript_value(funscript_interpolator, frame_id, fps)
+
+    # Draw gauge (example usage)
+    visualizer.draw_gauge(frame, funscript_value)
+
+    # Shift rolling window buffers left and insert new values
+    distance_buffer = np.roll(distance_buffer, -1)
+    distance_buffer[-1] = distance
+    funscript_buffer = np.roll(funscript_buffer, -1)
+    funscript_buffer[-1] = funscript_value
+
+    # Draw rolling window curves
+    graph_height = int(frame.shape[0] * 0.2)
+    graph_y_start = y_offset + 10
+    draw_rolling_window_curve(frame, distance_buffer, (0, 255, 0), 0.5, graph_height, graph_y_start)
+    draw_rolling_window_curve(frame, funscript_buffer, (255, 0, 0), 0.5, graph_height, graph_y_start)
+
+    # Return updated buffers in case they need to be maintained externally
+    return distance_buffer, funscript_buffer
+
+
+
+def draw_rolling_window_curve(frame, buffer, color, alpha, graph_height, graph_y_start):
+    """
+    Draw a rolling window curve on the frame as a transparent overlay.
+    """
+    overlay = frame.copy()
+    height, width, _ = frame.shape
+    buffer_clipped = np.clip(buffer, 0, 100)
+
+    for i in range(len(buffer_clipped) - 1):
+        x1 = int(width * (i / len(buffer_clipped)))
+        x2 = int(width * ((i + 1) / len(buffer_clipped)))
+        y1 = graph_y_start + graph_height - int((buffer_clipped[i] / 100) * graph_height)
+        y2 = graph_y_start + graph_height - int((buffer_clipped[i + 1] / 100) * graph_height)
+        cv2.line(overlay, (x1, y1), (x2, y2), color, 2)
+
+    cv2.addWeighted(overlay, alpha, frame, 1 - alpha, 0, frame)
+
+
diff --git a/script_generator/debug/video_player/interaction.py b/script_generator/debug/video_player/interaction.py
new file mode 100644
index 00000000..8e0e7b33
--- /dev/null
+++ b/script_generator/debug/video_player/interaction.py
@@ -0,0 +1,22 @@
+import cv2
+
+from script_generator.debug.video_player.constants import BAR_HEIGHT
+from script_generator.utils.logger import logger
+
+
+def mouse_callback(event, x, y, flags, param):
+    """
+    Called when the user clicks on the OpenCV window.
+    We pass in the video player via param for seeking.
+    """
+    if event == cv2.EVENT_LBUTTONDOWN:
+        video_player = param
+        height = int(video_player.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        bar_y_start = height - BAR_HEIGHT
+
+        # If clicked in the progress bar area, adjust the seek
+        if y >= bar_y_start:
+            width = int(video_player.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+            target_frame = int((x / width) * video_player.total_frames)
+            video_player.set_frame(target_frame)
+            logger.info(f"Seek to frame: {target_frame}")
\ No newline at end of file
diff --git a/script_generator/debug/video_player/play.py b/script_generator/debug/video_player/play.py
new file mode 100644
index 00000000..aad7d9e9
--- /dev/null
+++ b/script_generator/debug/video_player/play.py
@@ -0,0 +1,138 @@
+import json
+import tempfile
+
+import cv2
+import numpy as np
+from scipy.interpolate import interp1d
+
+from script_generator.debug.debug_data import load_logs
+from script_generator.debug.video_player.controls import draw_media_controls
+from script_generator.debug.video_player.debug_overlay import draw_overlay
+from script_generator.debug.video_player.interaction import mouse_callback
+from script_generator.debug.video_player.state import VideoPlayer
+from script_generator.utils.file import get_output_file_path
+from script_generator.utils.logger import logger
+from script_generator.video.info.video_info import get_cropped_dimensions
+from utils.lib_Visualizer import Visualizer
+
+
+def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=100, save_video_mode=False):
+    visualizer = Visualizer()
+    video_info = state.video_info
+    width, height = get_cropped_dimensions(video_info)
+
+    video_player = VideoPlayer(
+        video_path=state.video_path,
+        video_info=video_info,
+        start_frame=start_frame,
+        end_frame=end_frame
+    )
+
+    # Prepare funscript interpolation if available
+    funscript_path, _ = get_output_file_path(state.video_path, ".funscript")
+    try:
+        with open(funscript_path, "r") as f:
+            funscript_data = json.load(f)
+        actions = funscript_data.get("actions", [])
+        funscript_times = [action["at"] for action in actions]
+        funscript_positions = [action["pos"] for action in actions]
+        funscript_interpolator = interp1d(
+            funscript_times,
+            funscript_positions,
+            kind="linear",
+            fill_value="extrapolate"
+        )
+    except FileNotFoundError:
+        logger.error(f"Funscript file not found at {funscript_path}")
+        funscript_interpolator = None
+
+    logs = load_logs(state)
+
+    # Initialize rolling window buffers
+    distance_buffer = np.zeros(rolling_window_size)
+    funscript_buffer = np.zeros(rolling_window_size)
+
+    # Initialize video writer if recording
+    if save_video_mode:
+        temp_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
+        temp_video_path = temp_file.name
+        temp_file.close()
+        fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+        output_fps = video_info.fps
+        frame_size = (width, height)
+        video_writer = cv2.VideoWriter(temp_video_path, fourcc, output_fps, frame_size)
+        logger.info(f"Recording debug video to temporary file: {temp_video_path}")
+    else:
+        # Setup OpenCV window
+        window_name = "Debug Video"
+        cv2.namedWindow(window_name)
+
+        # Attach mouse callback for seeking
+        cv2.setMouseCallback(window_name, mouse_callback, param=video_player)
+
+    last_frame = video_player.current_frame
+
+    while True:
+        # Handle user input
+        if not save_video_mode and not handle_user_input(window_name, video_player):
+            break
+
+        if video_player.current_frame >= video_player.end_frame:
+            break
+
+        # Check if paused and the frame hasn't changed (allows seeking while being paused)
+        if video_player.paused and video_player.current_frame == last_frame:
+            continue
+
+        last_frame = video_player.current_frame
+
+        # Read the next frame
+        ret, frame_bgr = video_player.read_frame()
+        if not ret:
+            # End of video or read error
+            break
+
+        # Convert to RGB if needed for your pipeline
+        frame = frame_bgr.copy()
+
+        # Call your overlay function
+        distance_buffer, funscript_buffer = draw_overlay(
+            frame=frame,
+            frame_id=video_player.current_frame,
+            logs=logs,
+            funscript_interpolator=funscript_interpolator,
+            distance_buffer=distance_buffer,
+            funscript_buffer=funscript_buffer,
+            visualizer=visualizer,
+            fps=video_info.fps,
+            y_offset_start=frame.shape[0] // 3
+        )
+
+        if save_video_mode:
+            # Save the processed frame
+            video_writer.write(frame)
+        else:
+            # Display the frame
+            draw_media_controls(frame, video_player)
+            cv2.imshow(window_name, frame)
+
+    if save_video_mode:
+        video_player.release()
+        return temp_video_path
+    else:
+        cv2.destroyAllWindows()
+
+
+def handle_user_input(window_name, video_player):
+    key = cv2.waitKey(1) & 0xFF
+
+    # Check if the window has been closed
+    if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) < 1:
+        return False
+
+    if key == ord("q"):
+        return False
+    elif key == ord(" "):  # Toggle pause on space bar
+        video_player.paused = not video_player.paused
+
+    return True
\ No newline at end of file
diff --git a/script_generator/debug/video_player/state.py b/script_generator/debug/video_player/state.py
new file mode 100644
index 00000000..ebb60176
--- /dev/null
+++ b/script_generator/debug/video_player/state.py
@@ -0,0 +1,35 @@
+import cv2
+
+from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg
+
+
+class VideoPlayer:
+    def __init__(self, video_path, video_info, start_frame, end_frame):
+        self.video_path = video_path
+        self.video_info = video_info
+        self.total_frames = video_info.total_frames
+        self.start_frame = start_frame
+        self.end_frame = video_info.total_frames if not end_frame else end_frame
+        self.current_frame = 0
+
+        self.cap = VideoReaderFFmpeg(self.video_path, is_vr=video_info.is_vr)
+        self.paused = False
+
+    def release(self):
+        if self.cap:
+            self.cap.release()
+
+    def set_frame(self, frame_id):
+        if frame_id < 0:
+            frame_id = 0
+        if frame_id >= self.total_frames:
+            frame_id = self.total_frames - 1
+
+        self.current_frame = frame_id
+        self.cap.set(cv2.CAP_PROP_POS_FRAMES, self.current_frame)
+
+    def read_frame(self):
+        ret, frame = self.cap.read()
+        if ret and not self.paused:
+            self.current_frame += 1
+        return ret, frame
\ No newline at end of file
diff --git a/script_generator/gui/controller/debug_video.py b/script_generator/gui/controller/debug_video.py
index 439de74b..15d0ee99 100644
--- a/script_generator/gui/controller/debug_video.py
+++ b/script_generator/gui/controller/debug_video.py
@@ -1,11 +1,10 @@
 import os
 from tkinter import messagebox
 
+from script_generator.debug.video_player.play import play_debug_video
 from script_generator.state.app_state import AppState
 from script_generator.utils.file import get_output_file_path
 from script_generator.utils.logger import logger
-from utils.lib_Debugger import Debugger
-
 
 # TODO this callback is called on every frame. This doesn't seem correct or can be optized (instantiating Debugger, checks etc.)
 def debug_video(state: AppState):
@@ -15,28 +14,9 @@ def debug_video(state: AppState):
 
     state.set_video_info()
 
-    logger.info(f"Debugging video: {state.video_path}")
-    logger.info(f"Video Reader: {state.video_reader}")
-    logger.info(f"Debug Mode: {state.save_debug_file}")
-    logger.info(f"Live Preview Mode: {state.live_preview_mode}")
-    logger.info(f"Frame Start: {state.frame_start}")
-    logger.info(f"Frame End: {state.frame_end}")
-
-    log_file, _ = get_output_file_path(state.video_path, "_debug_logs.json")
-    state.debugger = Debugger(state.video_path, state.video_info.is_vr, state.video_reader, log_file=log_file)  # Initialize the debugger
-
-    # if the debug_logs.json file exists, load it
-    logs_path, _ = get_output_file_path(state.video_path, "_debug_logs.json")
+    logs_path, log_filename = get_output_file_path(state.video_path, "_debug_logs.json")
     if os.path.exists(logs_path):
-        state.debugger.load_logs()
-
-        state.debugger.play_video(
-            state=state,
-            start_frame=state.frame_start,
-            duration=state.debug_video_duration if state.save_debug_video else 0,
-            save_debug_video=state.save_debug_video,
-            downsize_ratio=1
-        )
+        play_debug_video(state=state, start_frame=state.frame_start)
     else:
-        logger.error(f"Debug logs file not found: {log_file}")
-        messagebox.showinfo("Info", f"Debug logs file not found: {log_file}")
+        logger.error(f"Debug logs file not found: {logs_path}")
+        messagebox.showinfo("Info", f"Debug logs file not found: {log_filename}")
diff --git a/script_generator/gui/controller/process_video.py b/script_generator/gui/controller/process_video.py
index f6fa9138..bd712be9 100644
--- a/script_generator/gui/controller/process_video.py
+++ b/script_generator/gui/controller/process_video.py
@@ -8,7 +8,6 @@
 from script_generator.utils.file import get_output_file_path
 from script_generator.utils.helpers import to_int_or_none
 from script_generator.utils.logger import logger
-from utils.lib_Debugger import Debugger
 
 
 def video_analysis(state, root):
@@ -29,13 +28,11 @@ def video_analysis(state, root):
 
     def run():
         try:
-            # Initialize the debugger
-            log_file, _ = get_output_file_path(state.video_path, "_debug_logs.json")
-            state.debugger = Debugger(state.video_path, log_file=log_file)
+            choice = check_skip_object_detection(state, root)
 
-            skip_detection = check_skip_object_detection(state, root)
-
-            if skip_detection:
+            if choice == "cancel":
+                return
+            elif choice == "use_existing":
                 state.set_video_info()
                 if state.update_ui:
                     state.update_ui(ProgressMessage(
@@ -44,7 +41,7 @@ def run():
                         total_frames=state.video_info.total_frames,
                         eta="Done"
                     ))
-            else:
+            elif choice == "generate":
                 analyze_video(state)
 
             tracking_analysis(state)
@@ -57,4 +54,3 @@ def run():
 
     processing_thread = threading.Thread(target=run)
     processing_thread.start()
-    # processing_thread.join()
diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index 74e5aec8..32ada0e6 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -261,69 +261,81 @@ def checkbox(parent, label_text, state, attr, label_left=True, row=0, column=0,
         return container, checkbox, is_checked
 
     @staticmethod
-    def messagebox(title, message, yes_text="Yes", no_text="No", master=None):
+    def create_popup(title, master, content_builder, width=400, height=300):
+        # Variable to store user choice
+        user_action = tk.StringVar(value=None)  # Stores user action ('cancel', 'yes', 'no', etc.)
+
         # Create a Toplevel window
         window = tk.Toplevel(master)
         window.title(title)
-        window.geometry("350x140")
+        window.geometry(f"{width}x{height}")
 
-        # Center the popup in the master window
-        if master is not None:
+        # Center the popup
+        if master:
             master.update_idletasks()  # Ensure master's dimensions are up-to-date
             master_width = master.winfo_width()
             master_height = master.winfo_height()
             master_x = master.winfo_x()
             master_y = master.winfo_y()
 
-            window_width = 350
-            window_height = 140
-
-            position_right = master_x + (master_width // 2) - (window_width // 2)
-            position_down = master_y + (master_height // 2) - (window_height // 2)
-
-            window.geometry(f"{window_width}x{window_height}+{position_right}+{position_down}")
+            position_right = master_x + (master_width // 2) - (width // 2)
+            position_down = master_y + (master_height // 2) - (height // 2)
         else:
-            # Center in the screen if no master is provided
-            window_width = 350
-            window_height = 140
-
+            # Center on the screen if no master provided
             screen_width = window.winfo_screenwidth()
             screen_height = window.winfo_screenheight()
 
-            position_right = (screen_width // 2) - (window_width // 2)
-            position_down = (screen_height // 2) - (window_height // 2)
+            position_right = (screen_width // 2) - (width // 2)
+            position_down = (screen_height // 2) - (height // 2)
 
-            window.geometry(f"{window_width}x{window_height}+{position_right}+{position_down}")
+        window.geometry(f"{width}x{height}+{position_right}+{position_down}")
 
-        # Add message label
-        tk.Label(window, text=message, wraplength=350, justify="left").pack(pady=20)
+        # Handle the close button (X) action
+        def on_close():
+            user_action.set("cancel")  # Set action to 'cancel'
+            window.destroy()
 
-        # Container for buttons
-        button_frame = tk.Frame(window)
-        button_frame.pack(pady=10)
+        # Bind the on_close callback to the default close button
+        window.protocol("WM_DELETE_WINDOW", on_close)
 
-        # Variable to store user choice
-        user_choice = tk.BooleanVar(value=None)
+        # Call the content builder if provided
+        if content_builder:
+            content_builder(window, user_action)
 
-        # Yes button
-        def on_yes():
-            user_choice.set(True)
-            window.destroy()
+        # Ensure the popup is modal
+        window.grab_set()
+        window.wait_window()
 
-        Widgets.button(button_frame, yes_text, on_yes, row=0, column=0)
+        return user_action.get()
 
-        # No button
-        def on_no():
-            user_choice.set(False)
-            window.destroy()
+    @staticmethod
+    def messagebox(title, message, yes_text="Yes", no_text="No", master=None, width=400, height=200):
+        # Call `create_popup` and get the action result
+        def build_content(window, user_action):
+            # Add message label
+            tk.Label(window, text=message, wraplength=300, justify="left").pack(pady=20)
 
-        Widgets.button(button_frame, no_text, on_no, row=0, column=1)
+            # Add buttons
+            button_frame = tk.Frame(window)
+            button_frame.pack(pady=10)
 
-        # Wait for the user to close the dialog
-        window.grab_set()
-        window.wait_window()
+            def on_yes():
+                user_action.set("yes")
+                window.destroy()
 
-        return user_choice.get()
+            def on_no():
+                user_action.set("no")
+                window.destroy()
+
+            Widgets.button(button_frame, yes_text, on_yes, row=0, column=0)
+            Widgets.button(button_frame, no_text, on_no, row=0, column=1)
+
+        action_result = Widgets.create_popup(title, master, content_builder=build_content, width=width, height=height)
+        return action_result  # Returns 'yes', 'no', or 'cancel'
+
+    @staticmethod
+    def custom_popup(title, content_builder, master=None, width=400, height=300):
+        Widgets.create_popup(title, master, width, height, content_builder)
 
     @staticmethod
     def disclaimer(parent, tooltip_text=None):
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index c94351f1..ad8f0055 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -6,6 +6,7 @@
 from script_generator.gui.controller.debug_video import debug_video
 from script_generator.gui.messages.messages import UIMessage, ProgressMessage
 from script_generator.gui.utils.widgets import Widgets
+from script_generator.gui.views.popups.create_debug_video import render_debug_video_popup
 from script_generator.state.app_state import AppState
 from script_generator.utils.helpers import is_mac
 from script_generator.utils.logger import logger
@@ -178,29 +179,8 @@ def start_processing():
             "Play debug video",
             lambda: debug_video(state),
             row=1,
-            column=5,
-            tooltip_text="Press q to quit.\n\nOpens a debug video player overlaid with debugging information.\n\nThis overlay shows object detection boxes and a live funscript overlay,\namong other useful debugging information.\nCan only be triggered after the funscript generation process has completed.\nNeeds the 'Save debug information' option activated during processing."
-        )
-        Widgets.checkbox(
-            general,
-            "Save debugging video",
-            tooltip_text="Will take a while to generate after completion!\nPlease see the log for when it's completed.\n\nWill save a debug video once funscript processing is complete that can be\neasily shared on Discord for showcasing issues or areas of improvement.",
-            state=state,
-            attr="save_debug_video",
-            row=2
-        )
-        Widgets.dropdown(
-            attr="debug_record_duration_var",
-            parent=general,
-            label_text="duration (s)",
-            tooltip_text="Duration of the debug video",
-            options=[5, 10, 20],
-            default_value=5,
-            state=state,
-            column=2,
-            row=2,
-            label_width_px=73,
-            sticky="w"
+            column=30,
+            tooltip_text="Opens a debug video player overlaid with debugging information (Press space to pause).\n\nThis overlay shows object detection boxes and a live funscript overlay,\namong other useful debugging information.\nCan only be triggered after the funscript generation process has completed.\nNeeds the 'Save debug information' option activated during processing."
         )
         script_compare = Widgets.frame(debugging, title="Script compare", row=1)
         Widgets.file_selection(
@@ -214,6 +194,15 @@ def start_processing():
             state=state,
             row=0
         )
+        debug_video_section = Widgets.frame(debugging, title="Share debug video", row=1)
+        Widgets.button(
+            debug_video_section,
+            "Generate sharable debug video",
+            lambda: Widgets.create_popup(title="Generate debug video", master=controller, width=650, height=205, content_builder=lambda window, user_action: render_debug_video_popup(window, state)),
+            row=1,
+            column=0,
+            tooltip_text="Render a debug video once funscript processing is complete that can be\neasily shared on Discord for showcasing issues or areas of improvement."
+        )
         # endregion
 
         # region FOOTER
diff --git a/script_generator/gui/views/popups/create_debug_video.py b/script_generator/gui/views/popups/create_debug_video.py
new file mode 100644
index 00000000..becf656f
--- /dev/null
+++ b/script_generator/gui/views/popups/create_debug_video.py
@@ -0,0 +1,27 @@
+from script_generator.gui.utils.widgets import Widgets
+from script_generator.scripts.generate_debug_video import generate_debug_video
+from script_generator.state.app_state import AppState
+from script_generator.utils.helpers import to_int_or_fallback
+
+
+class RenderDebugVideoState:
+    def __init__(self):
+        self.frame_start = 0
+        self.frame_end = None
+        self.ui_callback = None
+
+def render_debug_video_popup(window, state: AppState):
+    state.set_video_info()
+    debug_state = RenderDebugVideoState()
+    Widgets.label(window, "First stream/play your results locally (see Play debug video). Note the frame when the issue occurs. \nSubtract about 120 frames so we have some context. Then add a few seconds of recording (2 to 20 seconds\nshould do it)", column=0, sticky="w", padx=10, pady=10)
+    p_container, p, p_label, p_perc = Widgets.labeled_progress(window, "Progress", row=1)
+    Widgets.input(window, "Frame Start", state=debug_state, attr="frame_start", tooltip_text="Where to start processing the video. If you have a 60fps video starting at 10s would mean frame 600", row=2)
+    Widgets.input(window, "Frame End", state=debug_state, attr="frame_end", tooltip_text="Where to end processing the video. If you have a 60fps video stopping  at 10s would mean frame 600", row=3)
+    Widgets.button(
+        window,
+        "Generate",
+        lambda: generate_debug_video(state, to_int_or_fallback(debug_state.frame_start, 0), to_int_or_fallback(debug_state.frame_end, state.video_info.total_frames)),
+        row=4,
+        column=0,
+        tooltip_text="Render a debug video once funscript processing is complete that can be\neasily shared on Discord for showcasing issues or areas of improvement."
+    )
diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index 309ac9fa..2419e1f5 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -1,22 +1,23 @@
 import json
 import time
+from datetime import timedelta
+
 import cv2
 from tqdm import tqdm
-from datetime import timedelta
 
 from config import UPDATE_PROGRESS_INTERVAL
 from script_generator.constants import CLASS_COLORS
 from script_generator.gui.messages.messages import ProgressMessage
+from script_generator.state.app_state import AppState
 from script_generator.utils.file import get_output_file_path
 from script_generator.utils.logger import logger
-from script_generator.video.video_info import get_cropped_dimensions
+from script_generator.video.info.video_info import get_cropped_dimensions
 from utils.lib_ObjectTracker import ObjectTracker
-from utils.lib_SceneCutsDetect import detect_scene_changes
 from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg
 from utils.lib_Visualizer import Visualizer
 
 
-def analyze_tracking_results(state, results):
+def analyze_tracking_results(state: AppState, results):
     width, height = get_cropped_dimensions(state.video_info)
     list_of_frames = results.get_all_frame_ids()  # Get all frame IDs with detections
     visualizer = Visualizer()  # Initialize the visualizer
@@ -129,7 +130,7 @@ def analyze_tracking_results(state, results):
                     })
 
                 # Add debug information to the debugger class so it can be saved later
-                state.debugger.log_frame(
+                state.debug_data.add_frame(
                     frame_pos,
                     bounding_boxes=bounding_boxes,
                     variables={
diff --git a/script_generator/object_detection/post_process_results.py b/script_generator/object_detection/post_process_worker.py
similarity index 97%
rename from script_generator/object_detection/post_process_results.py
rename to script_generator/object_detection/post_process_worker.py
index 643f7c04..7d3fa06d 100644
--- a/script_generator/object_detection/post_process_results.py
+++ b/script_generator/object_detection/post_process_worker.py
@@ -7,11 +7,11 @@
 from config import RUN_POSE_MODEL
 from script_generator.object_detection.object_detection_result import ObjectDetectionResult
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
-from script_generator.utils.file import write_dataset, get_output_file_path
+from script_generator.utils.file import write_json_to_file, get_output_file_path
 from script_generator.utils.logger import logger
 
 
-class YoloAnalysisTaskProcessor(AbstractTaskProcessor):
+class PostProcessWorker(AbstractTaskProcessor):
     process_type = TaskProcessorTypes.YOLO_ANALYSIS
     records = []
     test_result = ObjectDetectionResult()  # Test result object for debugging
@@ -135,5 +135,5 @@ def on_last_item(self):
 
         # Write the detection records to a JSON file
         raw_yolo_path, _ = get_output_file_path(self.state.video_path, "_rawyolo.json")
-        write_dataset(raw_yolo_path, self.records)
+        write_json_to_file(raw_yolo_path, self.records)
         
\ No newline at end of file
diff --git a/script_generator/object_detection/utils.py b/script_generator/object_detection/utils.py
index 5cfc4a0d..2aa3f937 100644
--- a/script_generator/object_detection/utils.py
+++ b/script_generator/object_detection/utils.py
@@ -20,22 +20,26 @@ def check_skip_object_detection(state, root):
                 logger.info(f"Deleted empty raw yolo data file: {raw_yolo_path}")
             except OSError as e:
                 logger.error(f"Error deleting raw yolo file {raw_yolo_path}: {e}")
-            return False
+            return "generate"
 
-        skip_detection = Widgets.messagebox(
+        choice = Widgets.messagebox(
             "Detection File Conflict",
             f"The file already exists. What would you like to do?\n{raw_yolo_filename}",
-            "Use Existing",
             "Generate New",
-            root
+            "Use Existing",
+            root,
+            height=150
         )
-        if skip_detection:
+        if choice == "no":
             logger.info(f"File {raw_yolo_path} already exists. Skipping detections and loading file content...")
-            return True
-        else:
+            return "use_existing"
+        elif choice == "yes":
             os.remove(raw_yolo_path)
+            return "generate"
+
+        return "cancel"
 
-    return False
+    return "generate"
 
 def make_data_boxes(records):
     """
diff --git a/script_generator/object_detection/yolo.py b/script_generator/object_detection/yolo_worker.py
similarity index 85%
rename from script_generator/object_detection/yolo.py
rename to script_generator/object_detection/yolo_worker.py
index 944aa1f0..65ee6827 100644
--- a/script_generator/object_detection/yolo.py
+++ b/script_generator/object_detection/yolo_worker.py
@@ -1,13 +1,10 @@
 import time
 
-import cv2
-
-from config import YOLO_CONF, YOLO_BATCH_SIZE, YOLO_MODEL
-
+from config import YOLO_CONF, YOLO_BATCH_SIZE, YOLO_MODEL, YOLO_PERSIST
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 
 
-class YoloTaskProcessor(AbstractTaskProcessor):
+class YoloWorker(AbstractTaskProcessor):
     process_type = TaskProcessorTypes.YOLO
 
     # TODO add pose model support
@@ -36,7 +33,7 @@ def task_logic(self):
     def process_batch(self, frames, tasks):
         start_time = time.time()
         # Yolo expects bgr images when using numpy frames
-        yolo_results = YOLO_MODEL.track(frames, persist=True, conf=YOLO_CONF, verbose=False)
+        yolo_results = YOLO_MODEL.track(frames, persist=YOLO_PERSIST, conf=YOLO_CONF, verbose=False)
         avg_time = (time.time() - start_time) / len(tasks)
 
         for t, result in zip(tasks, yolo_results):
diff --git a/script_generator/scripts/analyze_video.py b/script_generator/scripts/analyze_video.py
index 747bd854..dc316d81 100644
--- a/script_generator/scripts/analyze_video.py
+++ b/script_generator/scripts/analyze_video.py
@@ -7,15 +7,15 @@
 
 from config import QUEUE_MAXSIZE, SEQUENTIAL_MODE, PROGRESS_BAR, UPDATE_PROGRESS_INTERVAL
 from script_generator.gui.messages.messages import ProgressMessage
-from script_generator.object_detection.post_process_results import YoloAnalysisTaskProcessor
-from script_generator.object_detection.yolo import YoloTaskProcessor
+from script_generator.object_detection.post_process_worker import PostProcessWorker
+from script_generator.object_detection.yolo_worker import YoloWorker
 from script_generator.state.app_state import AppState
 from script_generator.tasks.abstract_task_processor import TaskProcessorTypes
 from script_generator.tasks.tasks import AnalyzeVideoTask, AnalyzeFrameTask
 from script_generator.utils.file import check_create_output_folder
 from script_generator.utils.logger import logger
-from script_generator.video.video_conversion.vr_to_2d_task_processor import VrTo2DTaskProcessor
-from script_generator.video.video_task_processor import VideoTaskProcessor
+from script_generator.video.workers.ffmpeg_worker import VideoWorker
+from script_generator.video.workers.vr_to_2d_worker import VrTo2DWorker
 
 
 def analyze_video(state: AppState) -> List[AnalyzeFrameTask]:
@@ -50,10 +50,10 @@ def analyze_video(state: AppState) -> List[AnalyzeFrameTask]:
         result_q = queue.Queue(maxsize=0)
 
         # Create threads
-        decode_thread = VideoTaskProcessor(state=state, output_queue=opengl_q if use_open_gl else yolo_q)
-        opengl_thread = VrTo2DTaskProcessor(state=state, input_queue=opengl_q, output_queue=yolo_q) if use_open_gl else None
-        yolo_thread = YoloTaskProcessor(state=state, input_queue=yolo_q, output_queue=analysis_q)
-        yolo_analysis_thread = YoloAnalysisTaskProcessor(state=state, input_queue=analysis_q, output_queue=result_q)
+        decode_thread = VideoWorker(state=state, output_queue=opengl_q if use_open_gl else yolo_q)
+        opengl_thread = VrTo2DWorker(state=state, input_queue=opengl_q, output_queue=yolo_q) if use_open_gl else None
+        yolo_thread = YoloWorker(state=state, input_queue=yolo_q, output_queue=analysis_q)
+        yolo_analysis_thread = PostProcessWorker(state=state, input_queue=analysis_q, output_queue=result_q)
 
         # Start logging thread
         queue_logging_thread = threading.Thread(
@@ -93,11 +93,15 @@ def run_thread(thread, thread_name, out_queue):
 
         log_thread_stop_event.set()
 
-        # wait for update progress thread to close
-        time.sleep(UPDATE_PROGRESS_INTERVAL * 1.1)
-
         log_performance(state=state, results_queue=result_q)
 
+        state.update_ui(ProgressMessage(
+            process="OBJECT_DETECTION",
+            frames_processed=state.video_info.total_frames,
+            total_frames=state.video_info.total_frames,
+            eta="Done"
+        ))
+
         return result_q.queue
 
     except Exception as e:
diff --git a/script_generator/scripts/generate_debug_video.py b/script_generator/scripts/generate_debug_video.py
new file mode 100644
index 00000000..4842a106
--- /dev/null
+++ b/script_generator/scripts/generate_debug_video.py
@@ -0,0 +1,34 @@
+import os
+import subprocess
+
+from config import FFMPEG_PATH
+from script_generator.debug.video_player.play import play_debug_video
+from script_generator.state.app_state import AppState
+from script_generator.utils.file import get_output_file_path
+from script_generator.utils.logger import logger
+from script_generator.video.ffmpeg.hwaccel import get_hwaccel_read_args
+
+
+def generate_debug_video(state: AppState, frame_start: int, frame_end: int):
+    logger.info(f"Generating debug video from: {frame_start} to {frame_end}")
+    temp_video_path = play_debug_video(state, frame_start, frame_end, save_video_mode=True)
+
+    debug_video_path, _ = get_output_file_path(state.video_path, "_debug.mp4")
+    ffmpeg_command = [
+        FFMPEG_PATH, "-y",
+        *get_hwaccel_read_args(state.ffmpeg_hwaccel),
+        "-i", temp_video_path,
+        "-c:v", "hevc_nvenc" if state.ffmpeg_hwaccel == "cuda" else "libx265",
+        "-preset", "p4" if state.ffmpeg_hwaccel == "cuda" else "fast",
+        *(["-b:v", "5000k"] if state.ffmpeg_hwaccel == "cuda" else []),
+        *(["-crf", "26"] if state.ffmpeg_hwaccel != "cuda" else []),
+        "-movflags", "+faststart",
+        debug_video_path
+    ]
+
+    logger.info(f"Generating debug video with command: {' '.join(map(str, ffmpeg_command))}")
+
+    subprocess.run(ffmpeg_command)
+
+    os.remove(temp_video_path)
+    logger.info(f"Debug video generated and stored in: {debug_video_path}")
\ No newline at end of file
diff --git a/script_generator/scripts/tracking_analysis.py b/script_generator/scripts/tracking_analysis.py
index 4ac967b7..5fc3b252 100644
--- a/script_generator/scripts/tracking_analysis.py
+++ b/script_generator/scripts/tracking_analysis.py
@@ -4,7 +4,7 @@
 from script_generator.object_detection.utils import make_data_boxes, parse_yolo_data_looking_for_penis
 from script_generator.utils.file import load_json_from_file, get_output_file_path
 from script_generator.utils.logger import logger
-from script_generator.video.video_info import get_cropped_dimensions
+from script_generator.video.info.video_info import get_cropped_dimensions
 from utils.lib_FunscriptHandler import FunscriptGenerator
 
 
@@ -42,7 +42,7 @@ def tracking_analysis(state):
     # Performing the tracking part and generation of the raw funscript data
     state.funscript_data = analyze_tracking_results(state, results)
 
-    state.debugger.save_logs()
+    state.debug_data.save_debug_file()
 
     funscript_handler = FunscriptGenerator()
 
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index 86f5564e..beac1a56 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -1,43 +1,31 @@
 import string
 from typing import Literal
 
+from script_generator.debug.debug_data import DebugData
 from script_generator.tasks.tasks import AnalyzeVideoTask
 from script_generator.utils.helpers import is_mac
-from script_generator.video.ffmpeg import is_hwaccel_supported, get_video_info
-from script_generator.video.video_info import VideoInfo
+from script_generator.video.ffmpeg.hwaccel import get_preferred_hwaccel
+from script_generator.video.info.video_info import VideoInfo, get_video_info
 
 
 class AppState:
     def __init__(self, is_cli):
         self.is_cli: bool = is_cli
 
-        # Job
-        self.video_info: VideoInfo | None = None
-        self.analyze_task: AnalyzeVideoTask | None = None
+        # Gui/settings general
         self.video_path: string = None
         self.frame_start: int = 0
         self.frame_end: int | None = None
-        self.frame_start_track = 0
-        self.current_frame_id = 0
-        self.frame_area = 0
-
-        # Detection & decoding
         self.video_reader: Literal["FFmpeg", "FFmpeg + OpenGL (Windows)"] = "FFmpeg" if is_mac() else "FFmpeg + OpenGL (Windows)"
 
-        # Debug
+        # Gui/settings debug
         self.save_debug_file: bool = True
-        self.save_debug_video: bool = False
-        self.debug_video_duration: int = 0
+        # TODO REMOVE
+        # self.save_debug_video: bool = False
         self.live_preview_mode: bool = False
         self.reference_script: string = None
 
-        # TODO move this to a batch task class (so parallel inference is possible)
-        self.funscript_data = []
-        self.funscript_frames = []
-        self.funscript_distances = []
-        self.offset_x: int = 0
-
-        # Funscript Tweaking Variables
+        # Gui/settings Funscript Tweaking Variables
         self.boost_enabled: bool = True
         self.boost_up_percent: int = 10
         self.boost_down_percent: int = 15
@@ -48,10 +36,23 @@ def __init__(self, is_cli):
         self.vw_factor: float = 8.0
         self.rounding: int = 5
 
+        # TODO move this to a batch task class (so parallel inference is possible)
+        self.funscript_data = []
+        self.funscript_frames = []
+        self.funscript_distances = []
+        self.offset_x: int = 0
+        self.frame_start_track = 0
+        self.current_frame_id = 0
+        self.frame_area = 0
+
+        # State
+        self.video_info: VideoInfo | None = None
+        self.analyze_task: AnalyzeVideoTask | None = None
+
         # App logic
-        self.debugger = None
+        self.debug_data = DebugData(self)
         self.update_ui = None
-        self.ffmpeg_hwaccel_supported = is_hwaccel_supported()
+        self.ffmpeg_hwaccel = get_preferred_hwaccel()
 
     def set_video_info(self):
         if self.video_info is None:
diff --git a/script_generator/tasks/tasks.py b/script_generator/tasks/tasks.py
index 3eeac22f..3edfac02 100644
--- a/script_generator/tasks/tasks.py
+++ b/script_generator/tasks/tasks.py
@@ -5,8 +5,6 @@
 
 import numpy as np
 
-from script_generator.video.video_info import VideoInfo
-
 
 @dataclass
 class Task:
diff --git a/script_generator/utils/config.py b/script_generator/utils/config.py
index 6050b73d..edd66c3a 100644
--- a/script_generator/utils/config.py
+++ b/script_generator/utils/config.py
@@ -5,7 +5,7 @@
 
 from script_generator.utils.logger import logger
 
-
+# TODO replace with yaml
 def find_ffmpeg_path(win_ffmpeg_path, mac_ffmpeg_path, lin_ffmpeg_path):
     ffmpeg_path = shutil.which("ffmpeg")
     if ffmpeg_path:
diff --git a/script_generator/utils/file.py b/script_generator/utils/file.py
index f435c538..3990caf0 100644
--- a/script_generator/utils/file.py
+++ b/script_generator/utils/file.py
@@ -6,7 +6,7 @@
 from script_generator.utils.logger import logger
 
 
-def write_dataset(file_path, data):
+def write_json_to_file(file_path, data):
     """
     Write data to a JSON file, always overwriting if it exists.
     :param file_path: The path to the output file.
@@ -32,6 +32,9 @@ def load_json_from_file(file_path):
     :param file_path: Path to the JSON file.
     :return: The loaded data.
     """
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"Could not load json from file as it does not exist: {file_path}")
+
     with open(file_path, 'r') as f:
         data = json.load(f)
         logger.info(f"Loaded data from {file_path}, length: {len(data)}")
diff --git a/script_generator/utils/helpers.py b/script_generator/utils/helpers.py
index 960ae649..eb7edf55 100644
--- a/script_generator/utils/helpers.py
+++ b/script_generator/utils/helpers.py
@@ -8,4 +8,10 @@ def to_int_or_none(value):
     try:
         return int(value)
     except (ValueError, TypeError):
-        return None
\ No newline at end of file
+        return None
+
+def to_int_or_fallback(value, fallback):
+    try:
+        return int(value)
+    except (ValueError, TypeError):
+        return fallback
\ No newline at end of file
diff --git a/script_generator/video/ffmpeg.py b/script_generator/video/ffmpeg.py
deleted file mode 100644
index ee55513e..00000000
--- a/script_generator/video/ffmpeg.py
+++ /dev/null
@@ -1,106 +0,0 @@
-import subprocess
-
-from debug.errors import FFProbeError
-from script_generator.utils.logger import logger
-from script_generator.video.video_info import VideoInfo
-from config import FFPROBE_PATH, FFMPEG_PATH
-
-
-import json
-
-def get_video_info(video_path):
-    try:
-        cmd = [
-            FFPROBE_PATH,
-            "-v", "error",
-            "-select_streams", "v:0",
-            "-show_entries", "stream=r_frame_rate,width,height,codec_name,nb_frames",
-            "-show_entries", "format=duration",
-            "-of", "json",
-            video_path,
-        ]
-
-        output = subprocess.check_output(cmd, stderr=subprocess.STDOUT).decode("utf-8")
-        info = json.loads(output)
-
-        # Parse the first video stream
-        stream = info.get("streams", [{}])[0]
-        if not stream:
-            raise FFProbeError("No video stream found in the file.")
-
-        # Extract stream metadata
-        codec_name = stream.get("codec_name", "unknown")
-        width = int(stream.get("width", 0))
-        height = int(stream.get("height", 0))
-        r_frame_rate = stream.get("r_frame_rate", "0/1")
-        nb_frames = stream.get("nb_frames", None)
-
-        # Extract format-level metadata
-        duration = float(info.get("format", {}).get("duration", 0))
-
-        # Calculate FPS
-        num, den = map(int, r_frame_rate.split('/'))
-        fps = num / den if den > 0 else 0
-
-        # Estimate frames if not available
-        if nb_frames is None and duration > 0 and fps > 0:
-            nb_frames = int(duration * fps)
-
-        # Check if the video is VR (2:1 aspect ratio)
-        is_vr = height == width // 2
-
-        logger.info(f"Video Info: {codec_name}, {width}x{height}, {fps:.2f} fps, {nb_frames} frames, {duration:.2f} seconds, is vr: {is_vr}")
-
-        if is_vr:
-            logger.info("Video Format: VR SBS - Based on its 2:1 ratio")
-        else:
-            logger.info("Video Format: 2D - Based on its ratio")
-
-        return VideoInfo(video_path, codec_name, width, height, duration, int(nb_frames), fps, is_vr)
-
-    except subprocess.CalledProcessError as e:
-        logger.error(f"FFProbe command failed: {e.output.decode('utf-8')}")
-        raise FFProbeError("FFProbe command execution failed.")
-    except (ValueError, KeyError, IndexError) as e:
-        logger.error(f"Error parsing FFProbe output: {e}")
-        raise FFProbeError("Failed to parse FFProbe output.")
-
-
-def is_hwaccel_supported():
-    """
-    Check which hardware acceleration backends are supported by FFmpeg.
-    Returns a dictionary with supported backends.
-    """
-    try:
-        result = subprocess.run(
-            [FFMPEG_PATH, "-hwaccels"],
-            stdout=subprocess.PIPE,
-            stderr=subprocess.PIPE,
-            text=True,
-            check=True
-        )
-        hwaccels = result.stdout.lower().replace("hardware acceleration methods:", "")
-        hwaccel_lines = [line.strip() for line in str(hwaccels).splitlines() if line.strip()]
-        logger.info(f"hardware acceleration methods: {', '.join(hwaccel_lines)}")
-
-        # Check for supported hardware acceleration backends
-        return {
-            "cuda": "cuda" in hwaccels,
-            "vaapi": "vaapi" in hwaccels,
-            "amf": "amf" in hwaccels,
-            "videotoolbox": "videotoolbox" in hwaccels,
-            "qsv": "qsv" in hwaccels,  # Intel Quick Sync Video
-            "d3d11va": "d3d11va" in hwaccels,  # Direct3D 11 (Windows)
-            "opencl": "opencl" in hwaccels,  # OpenCL (cross-platform)
-        }
-    except Exception as e:
-        logger.error(f"Error checking hardware acceleration support: {e}")
-        return {
-            "cuda": False,
-            "vaapi": False,
-            "amf": False,
-            "videotoolbox": False,
-            "qsv": False,
-            "d3d11va": False,
-            "opencl": False,
-        }
diff --git a/script_generator/video/ffmpeg/commands.py b/script_generator/video/ffmpeg/commands.py
new file mode 100644
index 00000000..56c7dbc2
--- /dev/null
+++ b/script_generator/video/ffmpeg/commands.py
@@ -0,0 +1,36 @@
+from config import FFMPEG_PATH
+from script_generator.video.ffmpeg.filters import get_video_filters
+from script_generator.video.ffmpeg.hwaccel import get_hwaccel_read_args
+from script_generator.video.info.video_info import get_cropped_dimensions, VideoInfo
+
+
+def get_ffmpeg_read_cmd(video: VideoInfo, video_reader: str, hwaccel: str, frame_start: int | None, output="-"):
+    width, height = get_cropped_dimensions(video)
+    vf = get_video_filters(video, video_reader, hwaccel, width, height)
+    start_time = (frame_start / video.fps) * 1000
+
+    # Get supported hardware acceleration backends
+    hwaccel_read = get_hwaccel_read_args(hwaccel)
+
+    video_filter = ["-vf", vf] if vf else []
+    if hwaccel == "vaapi":
+        # VAAPI requires specific pixel formats and filters
+        video_filter = ["-vf", f"{vf},format=nv12,hwupload"] if vf else ["-vf", "format=nv12,hwupload"]
+
+    if hwaccel == "cuda":
+        video_filter = ["-noautoscale"] + video_filter  # explicitly tell ffmpeg that scaling is done by cuda
+
+    frame_size = width * height * 3  # Size of one frame in bytes
+
+    return [
+        FFMPEG_PATH,
+        *hwaccel_read,
+        '-nostats', '-loglevel', 'warning',
+        "-ss", str(start_time / 1000),  # Seek to start time in seconds
+        "-i", video.path,
+        "-an",  # Disable audio processing
+        *video_filter,
+        "-f", "rawvideo", "-pix_fmt", "bgr24",  # cv2 requires bgr (over rgb) and Yolo expects bgr images when using numpy frames (converts them internally)
+        "-threads", "0", # all threads
+        output
+    ], frame_size, width, height
\ No newline at end of file
diff --git a/script_generator/video/ffmpeg/filters.py b/script_generator/video/ffmpeg/filters.py
new file mode 100644
index 00000000..003c3845
--- /dev/null
+++ b/script_generator/video/ffmpeg/filters.py
@@ -0,0 +1,41 @@
+from config import RENDER_RESOLUTION, PITCH
+
+
+def get_vr_video_filters(video, video_reader, hwaccel):
+    if video.is_fisheye:
+        projection, iv_fov, ih_fov, v_fov, h_fov, d_fov = "fisheye", 190, 190, 90, 90, 180
+    else:
+        projection, iv_fov, ih_fov, v_fov, h_fov, d_fov = "he", 180, 180, 90, 90, 180
+
+    cuda = hwaccel == "cuda"
+
+    scale = f"[0:0]scale_cuda=2160:-2,hwdownload" if cuda else f"[0:0]scale={RENDER_RESOLUTION * 2}:-2"
+    crop = f"crop={RENDER_RESOLUTION}:{RENDER_RESOLUTION}:0:0"
+    out_format = f"format=nv12," if cuda else ""
+
+    if video_reader == "FFmpeg":
+        filters = [
+            scale,
+            crop,
+            f"{out_format}v360={projection}:in_stereo=2d:output=sg:iv_fov={iv_fov}:ih_fov={ih_fov}:"
+            f"d_fov={d_fov}:v_fov={v_fov}:h_fov={h_fov}:pitch={PITCH}:yaw=0:roll=0:"
+            f"w={RENDER_RESOLUTION}:h={RENDER_RESOLUTION}:interp=lanczos:reset_rot=1",
+            "lutyuv=y=gammaval(0.7)"
+        ]
+    else:
+        filters = [
+            scale,
+            crop,
+            f"{out_format}lutyuv=y=gammaval(0.7)"  # TODO Process in open gl and move scale and crop to the gpu
+        ]
+
+    return ",".join(filters)
+
+def get_2d_video_filters(video, width, height):
+    return f"scale={width}:{height}" if video.height > RENDER_RESOLUTION else None
+
+def get_video_filters(video, video_reader, hwaccel, width, height):
+    if video.is_vr:
+        return get_vr_video_filters(video, video_reader, hwaccel)
+    else:
+        return get_2d_video_filters(video, width, height)
\ No newline at end of file
diff --git a/script_generator/video/ffmpeg/hwaccel.py b/script_generator/video/ffmpeg/hwaccel.py
new file mode 100644
index 00000000..c15b0b20
--- /dev/null
+++ b/script_generator/video/ffmpeg/hwaccel.py
@@ -0,0 +1,50 @@
+import subprocess
+
+from config import FFMPEG_PATH
+from script_generator.utils.logger import logger
+
+
+def get_hwaccel_support():
+    """
+    Check which hardware acceleration backends are supported by FFmpeg.
+    Returns a dictionary with supported backends.
+    """
+    try:
+        result = subprocess.run(
+            [FFMPEG_PATH, "-hwaccels"],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            text=True,
+            check=True
+        )
+        hwaccels = result.stdout.lower().replace("hardware acceleration methods:", "")
+        hwaccel_lines = [line.strip() for line in str(hwaccels).splitlines() if line.strip()]
+        logger.info(f"hardware acceleration methods: {', '.join(hwaccel_lines)}")
+
+        return hwaccel_lines
+    except Exception as e:
+        logger.error(f"Error checking hardware acceleration support: {e}")
+        return []
+
+def get_preferred_hwaccel():
+    hwaccel_support = get_hwaccel_support()
+    hwaccel_priority = ["cuda", "vaapi", "amf", "videotoolbox", "qsv", "d3d11va"]
+    for hw in hwaccel_priority:
+        if hw in hwaccel_support:
+            return hw
+    return None
+
+def get_hwaccel_read_args(hwaccel):
+    if hwaccel == "cuda":
+        return ["-hwaccel", "cuda", "-hwaccel_output_format", "cuda"]
+    if hwaccel == "vaapi":
+        return ["-hwaccel", "vaapi", "-hwaccel_device", "/dev/dri/renderD128"]
+    if hwaccel == "amf":
+        return ["-hwaccel", "amf"]
+    if hwaccel == "videotoolbox":
+        return ["-hwaccel", "videotoolbox"]
+    if hwaccel == "qsv":
+        return ["-hwaccel", "qsv"]
+    if hwaccel == "d3d11va":
+        return ["-hwaccel", "d3d11va"]
+    return []
\ No newline at end of file
diff --git a/script_generator/video/video_info.py b/script_generator/video/info/video_info.py
similarity index 56%
rename from script_generator/video/video_info.py
rename to script_generator/video/info/video_info.py
index f07d9acc..100ae5e1 100644
--- a/script_generator/video/video_info.py
+++ b/script_generator/video/info/video_info.py
@@ -1,10 +1,12 @@
+import json
 import re
+import subprocess
 from dataclasses import dataclass, field
 
-from config import RENDER_RESOLUTION
+from config import RENDER_RESOLUTION, FFPROBE_PATH
+from script_generator.debug.errors import FFProbeError
 from script_generator.utils.logger import logger
 
-
 @dataclass
 class VideoInfo:
     path: str
@@ -67,4 +69,61 @@ def get_cropped_dimensions(video: VideoInfo):
         return RENDER_RESOLUTION, RENDER_RESOLUTION
     else:
         scaling_factor = min(1, RENDER_RESOLUTION / video.height)
-        return int(video.width * scaling_factor), int(video.height * scaling_factor)
\ No newline at end of file
+        return int(video.width * scaling_factor), int(video.height * scaling_factor)
+
+def get_video_info(video_path):
+    try:
+        cmd = [
+            FFPROBE_PATH,
+            "-v", "error",
+            "-select_streams", "v:0",
+            "-show_entries", "stream=r_frame_rate,width,height,codec_name,nb_frames",
+            "-show_entries", "format=duration",
+            "-of", "json",
+            video_path,
+        ]
+
+        output = subprocess.check_output(cmd, stderr=subprocess.STDOUT).decode("utf-8")
+        info = json.loads(output)
+
+        # Parse the first video stream
+        stream = info.get("streams", [{}])[0]
+        if not stream:
+            raise FFProbeError("No video stream found in the file.")
+
+        # Extract stream metadata
+        codec_name = stream.get("codec_name", "unknown")
+        width = int(stream.get("width", 0))
+        height = int(stream.get("height", 0))
+        r_frame_rate = stream.get("r_frame_rate", "0/1")
+        nb_frames = stream.get("nb_frames", None)
+
+        # Extract format-level metadata
+        duration = float(info.get("format", {}).get("duration", 0))
+
+        # Calculate FPS
+        num, den = map(int, r_frame_rate.split('/'))
+        fps = num / den if den > 0 else 0
+
+        # Estimate frames if not available
+        if nb_frames is None and duration > 0 and fps > 0:
+            nb_frames = int(duration * fps)
+
+        # Check if the video is VR (2:1 aspect ratio)
+        is_vr = height == width // 2
+
+        logger.info(f"Video Info: {codec_name}, {width}x{height}, {fps:.2f} fps, {nb_frames} frames, {duration:.2f} seconds, is vr: {is_vr}")
+
+        if is_vr:
+            logger.info("Video Format: VR SBS - Based on its 2:1 ratio")
+        else:
+            logger.info("Video Format: 2D - Based on its ratio")
+
+        return VideoInfo(video_path, codec_name, width, height, duration, int(nb_frames), fps, is_vr)
+
+    except subprocess.CalledProcessError as e:
+        logger.error(f"FFProbe command failed: {e.output.decode('utf-8')}")
+        raise FFProbeError("FFProbe command execution failed.")
+    except (ValueError, KeyError, IndexError) as e:
+        logger.error(f"Error parsing FFProbe output: {e}")
+        raise FFProbeError("Failed to parse FFProbe output.")
\ No newline at end of file
diff --git a/script_generator/video/video_conversion/opengl/helpers.py b/script_generator/video/opengl/helpers.py
similarity index 100%
rename from script_generator/video/video_conversion/opengl/helpers.py
rename to script_generator/video/opengl/helpers.py
diff --git a/script_generator/video/video_task_processor.py b/script_generator/video/video_task_processor.py
deleted file mode 100644
index afd074e9..00000000
--- a/script_generator/video/video_task_processor.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import os
-import subprocess
-from multiprocessing import cpu_count
-
-import imageio
-import numpy as np
-
-from config import SUBTRACT_THREADS_FROM_FFMPEG, PITCH, RENDER_RESOLUTION, FFMPEG_PATH, DEBUG_PATH
-from debug.errors import FFMpegError
-from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
-from script_generator.tasks.tasks import AnalyzeFrameTask
-from script_generator.utils.logger import logger
-from script_generator.video.video_info import get_cropped_dimensions
-
-
-class VideoTaskProcessor(AbstractTaskProcessor):
-    process_type = TaskProcessorTypes.VIDEO
-
-    def task_logic(self):
-        state = self.state
-        video = self.state.video_info
-
-        def get_cmd(vf):
-            start_time = (state.frame_start / video.fps) * 1000
-            ffmpeg_threads = max(cpu_count() - SUBTRACT_THREADS_FROM_FFMPEG, 1)
-
-            # Get supported hardware acceleration backends
-            hwaccel_support = state.ffmpeg_hwaccel_supported
-
-            # Determine the best hardware acceleration backend to use
-            hwaccel = []
-            if hwaccel_support["cuda"]:
-                hwaccel = ["-hwaccel", "cuda", "-hwaccel_output_format", "cuda"]
-            elif hwaccel_support["vaapi"]:
-                hwaccel = ["-hwaccel", "vaapi", "-hwaccel_device", "/dev/dri/renderD128"]
-            elif hwaccel_support["amf"]:
-                hwaccel = ["-hwaccel", "amf"]
-            elif hwaccel_support["videotoolbox"]:
-                hwaccel = ["-hwaccel", "videotoolbox"]
-            elif hwaccel_support["qsv"]:
-                hwaccel = ["-hwaccel", "qsv"]
-            elif hwaccel_support["d3d11va"]:
-                hwaccel = ["-hwaccel", "d3d11va"]
-
-            # Add platform-specific video filters if required
-            video_filter = ["-vf", vf] if vf else []
-            if hwaccel_support["vaapi"]:
-                # VAAPI requires specific pixel formats and filters
-                video_filter = ["-vf", f"{vf},format=nv12,hwupload"] if vf else ["-vf", "format=nv12,hwupload"]
-
-            if hwaccel_support["cuda"]:
-                video_filter = ["-noautoscale"] + video_filter # explicitly tell ffmpeg that scaling is done by cuda
-
-            return [
-                FFMPEG_PATH,
-                *hwaccel,
-                '-nostats', '-loglevel', 'warning',
-                "-ss", str(start_time / 1000),  # Seek to start time in seconds
-                "-i", video.path,
-                "-an",  # Disable audio processing
-                *video_filter,
-                "-f", "rawvideo", "-pix_fmt", "bgr24",  # cv2 requires bgr (over rgb) and Yolo expects bgr images when using numpy frames (converts them internally)
-                "-threads", str(ffmpeg_threads),
-                "-",  # Output to stdout
-            ]
-
-        def vr_video_filters():
-            if video.is_fisheye:
-                projection, iv_fov, ih_fov, v_fov, h_fov, d_fov = "fisheye", 190, 190, 90, 90, 180
-            else:
-                projection, iv_fov, ih_fov, v_fov, h_fov, d_fov = "he", 180, 180, 90, 90, 180
-
-            cuda = state.ffmpeg_hwaccel_supported["cuda"]
-
-            scale = f"[0:0]scale_cuda=2160:-2,hwdownload" if cuda else f"[0:0]scale={RENDER_RESOLUTION * 2}:-2"
-            crop = f"crop={RENDER_RESOLUTION}:{RENDER_RESOLUTION}:0:0"
-            out_format = f"format=nv12," if cuda else ""
-
-            if self.state.video_reader == "FFmpeg":
-                filters = [
-                    scale,
-                    crop,
-                    f"{out_format}v360={projection}:in_stereo=2d:output=sg:iv_fov={iv_fov}:ih_fov={ih_fov}:"
-                    f"d_fov={d_fov}:v_fov={v_fov}:h_fov={h_fov}:pitch={PITCH}:yaw=0:roll=0:"
-                    f"w={RENDER_RESOLUTION}:h={RENDER_RESOLUTION}:interp=lanczos:reset_rot=1",
-                    "lutyuv=y=gammaval(0.7)"
-                ]
-            else:
-                filters = [
-                    scale,
-                    crop,
-                    f"{out_format}lutyuv=y=gammaval(0.7)"  # TODO Process in open gl and move scale and crop to the gpu
-                ]
-
-            return ",".join(filters)
-
-        def standard_video_filters():
-            return f"scale={width}:{height}" if video.height > RENDER_RESOLUTION else None
-
-        width, height = get_cropped_dimensions(video)
-
-        vf = vr_video_filters() if video.is_vr else standard_video_filters()
-        cmd = get_cmd(vf)
-
-        logger.info(f"FFMPEG executing command: {' '.join(cmd)}")
-
-        # Start FFmpeg process
-        self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
-        frame_size = width * height * 3  # Size of one frame in bytes
-        current_frame = state.frame_start
-
-        # progress_bar = tqdm(total=203856, unit="frame", desc="Processing frames")
-        while True:
-            try:
-                in_bytes = self.process.stdout.read(frame_size)
-                if not in_bytes:
-                    if current_frame == state.frame_start:
-                        raise FFMpegError(f"FFMPEG could not read any frames from stdout command: {' '.join(cmd)}")
-                    else:
-                        logger.info("FFMPEG received last frame")
-                    break
-
-                task = AnalyzeFrameTask(frame_pos=current_frame)
-
-                frame = np.frombuffer(in_bytes, np.uint8).reshape([height, width, 3])
-
-                # Debug
-                # output_path = os.path.join(DEBUG_PATH, f"frame_{task.id:05d}.png")
-                # imageio.imwrite(output_path, frame)
-
-                if self.state.video_reader == "FFmpeg":
-                    task.rendered_frame = frame
-                else:
-                    task.preprocessed_frame = frame
-
-                task.end(str(self.process_type))
-
-                self.finish_task(task)
-                current_frame += 1
-
-                # progress_bar.update(1)
-
-            except Exception as e:
-                logger.error(f"Error reading frame: {e}")
-                raise
-
-        self.stop_process()
-
-    # TODO ffmpeg interrupt
-    # def release(self):
-    #     if self.process:
-    #         self.process.stdout.close()
-    #         self.process = None
diff --git a/script_generator/video/workers/ffmpeg_worker.py b/script_generator/video/workers/ffmpeg_worker.py
new file mode 100644
index 00000000..75ee48c7
--- /dev/null
+++ b/script_generator/video/workers/ffmpeg_worker.py
@@ -0,0 +1,61 @@
+import subprocess
+
+import numpy as np
+
+from script_generator.debug.errors import FFMpegError
+from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
+from script_generator.tasks.tasks import AnalyzeFrameTask
+from script_generator.utils.logger import logger
+from script_generator.video.ffmpeg.commands import get_ffmpeg_read_cmd
+
+
+class VideoWorker(AbstractTaskProcessor):
+    process_type = TaskProcessorTypes.VIDEO
+    process = None
+
+    def task_logic(self):
+        cmd, frame_size, width, height = get_ffmpeg_read_cmd(
+            self.state.video_info,
+            self.state.video_reader,
+            self.state.ffmpeg_hwaccel,
+            self.state.frame_start
+        )
+        logger.info(f"FFMPEG executing command: {' '.join(cmd)}")
+
+        self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
+        current_frame = self.state.frame_start
+
+        while True:
+            try:
+                in_bytes = self.process.stdout.read(frame_size)
+                if not in_bytes:
+                    if current_frame == self.state.frame_start:
+                        raise FFMpegError(f"FFMPEG could not read any frames from stdout command: {' '.join(cmd)}")
+                    else:
+                        logger.info("FFMPEG received last frame")
+                    break
+
+                task = AnalyzeFrameTask(frame_pos=current_frame)
+                frame = np.frombuffer(in_bytes, np.uint8).reshape([height, width, 3])
+
+                if self.state.video_reader == "FFmpeg":
+                    task.rendered_frame = frame
+                else:
+                    task.preprocessed_frame = frame
+
+                task.end(str(self.process_type))
+
+                self.finish_task(task)
+                current_frame += 1
+
+            except Exception as e:
+                logger.error(f"Error reading frame: {e}")
+                raise
+
+        self.stop_process()
+
+    # TODO ffmpeg interrupt
+    # def release(self):
+    #     if self.process:
+    #         self.process.stdout.close()
+    #         self.process = None
diff --git a/script_generator/video/video_conversion/vr_to_2d_task_processor.py b/script_generator/video/workers/vr_to_2d_worker.py
similarity index 95%
rename from script_generator/video/video_conversion/vr_to_2d_task_processor.py
rename to script_generator/video/workers/vr_to_2d_worker.py
index e55aad02..2c9fa7f3 100644
--- a/script_generator/video/video_conversion/vr_to_2d_task_processor.py
+++ b/script_generator/video/workers/vr_to_2d_worker.py
@@ -5,10 +5,10 @@
 
 from config import RENDER_RESOLUTION, YAW, PITCH
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
-from script_generator.video.video_conversion.opengl.helpers import create_180_dome, render_dome
+from script_generator.video.opengl.helpers import create_180_dome, render_dome
 
 
-class VrTo2DTaskProcessor(AbstractTaskProcessor):
+class VrTo2DWorker(AbstractTaskProcessor):
     process_type = TaskProcessorTypes.OPENGL
 
     def task_logic(self):
diff --git a/utils/lib_Debugger.py b/utils/lib_Debugger.py
deleted file mode 100644
index 97d5b8bd..00000000
--- a/utils/lib_Debugger.py
+++ /dev/null
@@ -1,355 +0,0 @@
-import json
-import os
-import subprocess
-
-import cv2
-import numpy as np
-from script_generator.constants import CLASS_COLORS
-from scipy.interpolate import interp1d
-
-from script_generator.utils.file import get_output_file_path
-from script_generator.utils.logger import logger
-from utils.lib_Visualizer import Visualizer
-from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg
-
-
-class Debugger:
-    """
-    A class for debugging video frames by logging variables, bounding boxes, and visualizing them.
-    """
-
-    def __init__(self, video_path, is_vr=False, video_reader=None, log_file=None):
-        """
-        Initialize the Debugger.
-        :param video_path: Path to the video file.
-        :param log_file: Directory to save debug logs.
-        """
-        self.video_path = video_path
-        self.log_file = log_file
-        self.logs = {}  # Dictionary to store logs for each frame
-        self.cap = None  # Video capture object
-        self.current_frame = 0  # Current frame being processed
-        self.total_frames = 0  # Total number of frames in the video
-        self.fps = 0  # Frames per second of the video
-        self.bar_y_start = 0  # Y-coordinate of the progress bar
-        self.is_vr = is_vr
-        self.video_reader = video_reader
-
-    def log_frame(self, frame_id, variables=None, bounding_boxes=None):
-        """
-        Log or update the state of variables and bounding boxes for a specific frame.
-        :param frame_id: The frame ID to log.
-        :param variables: Dictionary of variables to log.
-        :param bounding_boxes: List of bounding boxes to log.
-        """
-        if frame_id not in self.logs:
-            self.logs[frame_id] = {"variables": {}, "bounding_boxes": []}
-
-        if variables:
-            self.logs[frame_id]["variables"].update(variables)
-
-        if bounding_boxes:
-            self.logs[frame_id]["bounding_boxes"].extend(bounding_boxes)
-
-    def save_logs(self):
-        """
-        Save the logs to a JSON file.
-        """
-
-        def default(obj):
-            """Convert NumPy types to native Python types for JSON serialization."""
-            if isinstance(obj, np.integer):
-                return int(obj)
-            elif isinstance(obj, np.floating):
-                return float(obj)
-            elif isinstance(obj, np.ndarray):
-                return obj.tolist()
-            else:
-                raise TypeError(f"Object of type {obj.__class__.__name__} is not JSON serializable")
-
-        with open(self.log_file, "w") as f:
-            json.dump(self.logs, f, indent=4, default=default)
-        logger.info(f"Logs saved to {self.log_file}")
-
-    def load_logs(self):
-        """
-        Load existing logs from a JSON file.
-        """
-        try:
-            with open(self.log_file, "r") as f:
-                self.logs = json.load(f)
-            logger.info(f"Logs loaded from {self.log_file}")
-        except FileNotFoundError:
-            logger.error(f"Log file {self.log_file} not found. Starting with empty logs.")
-        except json.JSONDecodeError:
-            logger.error(f"Error decoding JSON from {self.log_file}. Starting with empty logs.")
-
-    def display_frame(self, state, frame_id):
-        """
-        Display the logged frame with bounding boxes and variable states.
-        :param frame_id: The frame ID to display.
-        """
-        self.play_video(state, frame_id, duration=-1)
-
-    def play_video(self, state, start_frame=0, duration=0, rolling_window_size=100, save_debug_video=False, downsize_ratio=1):
-        """
-        Play the video from a specified frame, displaying variables, bounding boxes, and rolling window curves.
-        :param start_frame: Frame to start playback from.
-        :param duration: Duration of playback in seconds.
-        :param rolling_window_size: Size of the rolling window for distance and funscript data.
-        :param save_debug_video: Whether to record the debug video.
-        :param downsize_ratio: Ratio to downsize the recorded video.
-        """
-        visualizer = Visualizer()
-        debug_video_path = self.video_path.replace(".mp4", "_debug.mp4")
-
-        # Load the video
-        self.cap = VideoReaderFFmpeg(self.video_path, is_vr=self.is_vr)
-
-        self.total_frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
-        self.fps = self.cap.get(cv2.CAP_PROP_FPS)
-        logger.info(f"Total frames: {self.total_frames}, FPS: {self.fps}")
-
-        # Initialize video writer if recording
-        if save_debug_video:
-            ret, frame = self.cap.read()
-
-            fourcc = cv2.VideoWriter_fourcc(*"mp4v")
-            out = cv2.VideoWriter(
-                debug_video_path, fourcc, self.fps, (frame.shape[1] // downsize_ratio, frame.shape[0] // downsize_ratio)
-            )
-            if not out.isOpened():
-                logger.error(f"Error: Could not open video writer for {debug_video_path}")
-                self.cap.release()
-                return
-
-        # Set the starting frame
-        self.cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame)
-
-        # Load the funscript file
-        funscript_path, _ = get_output_file_path(self.video_path, ".funscript")
-
-        try:
-            with open(funscript_path, "r") as f:
-                funscript_data = json.load(f)
-            actions = funscript_data.get("actions", [])
-            funscript_times = [action["at"] for action in actions]
-            funscript_positions = [action["pos"] for action in actions]
-            funscript_interpolator = interp1d(funscript_times, funscript_positions, kind="linear", fill_value="extrapolate")
-        except FileNotFoundError:
-            logger.error(f"Funscript file not found at {funscript_path}")
-            funscript_interpolator = None
-
-        # Initialize rolling window buffers
-        half_window = rolling_window_size // 2
-        distance_buffer = np.zeros(rolling_window_size)
-        funscript_buffer = np.zeros(rolling_window_size)
-
-        # Calculate end frame
-        self.current_frame = start_frame
-        end_frame = start_frame + int(duration * self.fps) if duration > 0 else self.total_frames
-
-        # Set up mouse callback for progress bar
-        if self.video_reader == "OpenCV" and self.is_vr:
-            width = self.cap.get(cv2.CAP_PROP_FRAME_WIDTH) // 2
-        else:
-            width = self.cap.get(cv2.CAP_PROP_FRAME_WIDTH)
-
-        # debug video with statistics like a funscript overlay, object detection, frame number, funscript mode etc.
-        cv2.namedWindow("Debug Video")
-        cv2.setMouseCallback("Debug Video", self._mouse_callback, param=width)
-
-        # Play the video
-        while self.current_frame < end_frame:
-            ret, frame = self.cap.read()
-
-            if not ret:
-                break
-
-            #if not state.live_preview_mode:
-            #    continue
-
-            # frame_copy = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  # make a copy of the frame to make it writeable, useful for ffmpeg library here
-            frame_copy = frame.copy()  # no need to convert
-            # Display variables and bounding boxes
-            str_frame_id = str(self.current_frame)
-            if str_frame_id in self.logs:
-                variables = self.logs[str_frame_id]["variables"]
-                bounding_boxes = self.logs[str_frame_id]["bounding_boxes"]
-
-                # Draw bounding boxes
-                for box in bounding_boxes:
-                    x1, y1, x2, y2 = box["box"]
-                    class_name = box["class_name"]
-                    position = box["position"]
-                    color = CLASS_COLORS.get(class_name, (0, 255, 0))
-                    cv2.rectangle(frame_copy, (x1, y1), (x2, y2), color, 2)
-                    cv2.putText(frame_copy, f"{class_name} {position}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
-
-                # Display variables
-                y_offset = frame.shape[0] // 3   # 30
-                for key, value in variables.items():
-                    cv2.putText(frame_copy, f"{key}: {value}", (10, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
-                    y_offset += 20
-
-                # Draw the locked_penis_box if it exists
-                locked_penis_box = variables.get("locked_penis_box")
-                #logger.info(f"locked_penis_box: {locked_penis_box}")
-                if locked_penis_box['active']:
-                    x1, y1, x2, y2 = locked_penis_box['box']
-                    color = CLASS_COLORS.get("penis", (0, 255, 0))
-                    cv2.rectangle(frame_copy, (x1, y1), (x2, y2), color, 2)
-                    cv2.putText(frame_copy, "Locked Penis", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
-
-            try:
-                # Update rolling window buffers
-                # Update rolling window buffers
-                distance = variables.get("distance", 0)
-                funscript_value = self._get_funscript_value(funscript_interpolator, self.current_frame,
-                                                            self.fps) if funscript_interpolator else 0
-                visualizer.draw_gauge(frame_copy, funscript_value)
-                # Shift buffers to the left and add new values at the center
-                distance_buffer = np.roll(distance_buffer, -1)
-                distance_buffer[-1] = distance
-                funscript_buffer = np.roll(funscript_buffer, -1)
-                funscript_buffer[-1] = funscript_value
-
-                # Draw rolling window curves
-                graph_height = int(frame_copy.shape[0] * 0.2)
-                graph_y_start = y_offset + 10
-                self._draw_rolling_window_curve(frame_copy, distance_buffer, (0, 255, 0), 0.5, graph_height, graph_y_start)
-                self._draw_rolling_window_curve(frame_copy, funscript_buffer, (255, 0, 0), 0.5, graph_height, graph_y_start)
-            except:
-                # no variables logged at this frame
-                pass
-            # Draw progress bar
-            self._draw_progress_bar(frame_copy, frame.shape[1], frame.shape[0])
-
-            # Show the frame
-            cv2.imshow("Debug Video", frame_copy)
-            if cv2.waitKey(1) & 0xFF == ord("q"):
-                break
-            if cv2.waitKey(1) & 0xFF == 32:  # Pause on spacebar
-                # press a key to resume
-                cv2.waitKey(0)
-            if duration == -1:
-                cv2.imwrite(f"{self.video_path[:-4]}_frame_{self.current_frame}.png", frame_copy)
-                break
-
-            # Record the frame if enabled
-            if save_debug_video:
-                out.write(frame_copy)
-
-            self.current_frame += 1
-
-        # Release resources
-        self.cap.release()
-
-        if save_debug_video:
-            out.release()
-            output_path_ffmpeg, _ = get_output_file_path(debug_video_path, "_rawyolo.json", True)
-
-            # FFmpeg command to convert to H.265
-            # TODO move to generic shared ffmpeg command and add hardware acceleration
-            ffmpeg_command = [
-                "ffmpeg",
-                "-y",  # Overwrite output file if it exists
-                "-i", debug_video_path,  # Input file
-                "-c:v", "libx265",  # Use H.265 codec
-                "-crf", "26",  # Constant Rate Factor (quality)
-                "-preset", "fast",  # Encoding speed
-                "-b:v", "5000k",  # Bitrate
-                "-movflags", "+faststart",  # Enable fast streaming
-                output_path_ffmpeg
-            ]
-
-            # Run FFmpeg command
-            subprocess.run(ffmpeg_command)
-
-            # Delete the intermediate file
-            if os.path.exists(debug_video_path):
-                os.remove(debug_video_path)
-                logger.info(f"Deleted intermediate file: {debug_video_path}")
-
-        cv2.destroyAllWindows()
-
-    def _get_funscript_value(self, interpolator, frame_id, fps):
-        """
-        Get the interpolated funscript value for a given frame.
-        :param interpolator: Interpolation function for funscript data.
-        :param frame_id: The frame ID.
-        :param fps: Frames per second of the video.
-        :return: Interpolated funscript value.
-        """
-        time_in_milliseconds = int((frame_id / fps) * 1000)
-        return interpolator(time_in_milliseconds)
-
-    def _draw_rolling_window_curve(self, frame, buffer, color, alpha, graph_height, graph_y_start):
-        """
-        Draw a rolling window curve on the frame as a transparent overlay.
-        :param frame: The frame to draw on.
-        :param buffer: The buffer of values to plot.
-        :param color: The color of the curve.
-        :param alpha: Transparency of the overlay.
-        :param graph_height: Height of the graph.
-        :param graph_y_start: Y-coordinate to start the graph.
-        """
-        overlay = frame.copy()
-        height, width, _ = frame.shape
-        buffer_clipped = np.clip(buffer, 0, 100)
-
-        for i in range(len(buffer_clipped) - 1):
-            x1 = int(width * (i / len(buffer_clipped)))
-            x2 = int(width * ((i + 1) / len(buffer_clipped)))
-            y1 = graph_y_start + graph_height - int((buffer_clipped[i] / 100) * graph_height)
-            y2 = graph_y_start + graph_height - int((buffer_clipped[i + 1] / 100) * graph_height)
-            cv2.line(overlay, (x1, y1), (x2, y2), color, 2)
-
-        cv2.addWeighted(overlay, alpha, frame, 1 - alpha, 0, frame)
-
-    def _mouse_callback(self, event, x, y, flags, param):
-        """
-        Handle mouse events for video navigation.
-        :param event: The mouse event.
-        :param x: X-coordinate of the mouse.
-        :param y: Y-coordinate of the mouse.
-        :param flags: Additional flags.
-        :param param: Additional parameters (width of the frame).
-        """
-        if event == cv2.EVENT_LBUTTONDOWN and y >= self.bar_y_start:
-            self._update_frame_from_mouse(x, param)
-
-    def _update_frame_from_mouse(self, x, width):
-        """
-        Update the current frame based on the mouse's X position.
-        :param x: X-coordinate of the mouse.
-        :param width: Width of the frame.
-        """
-        self.current_frame = int((x / width) * self.total_frames)
-        logger.info(f"Target frame: {self.current_frame}")
-        if self.video_reader == "FFmpeg":
-            self.cap.release()
-            self.cap = VideoReaderFFmpeg(self.video_path, self.is_vr)
-        self.cap.set(cv2.CAP_PROP_POS_FRAMES, self.current_frame)
-        logger.info("Done resetting and jumping to target frame")
-
-    def _draw_progress_bar(self, frame, width, height):
-        """
-        Draw a progress bar on the frame indicating the current playback position.
-        :param frame: The frame to draw on.
-        :param width: Width of the frame.
-        :param height: Height of the frame.
-        """
-        bar_height = 20
-        bar_x_start = 0
-        bar_x_end = width
-        bar_y_start = height - bar_height
-        self.bar_y_start = bar_y_start
-        bar_y_end = height
-
-        # Background of the progress bar
-        cv2.rectangle(frame, (bar_x_start, bar_y_start), (bar_x_end, bar_y_end), (50, 50, 50), -1)
-
-        # Progress indicator
-        progress_x = int((self.current_frame / self.total_frames) * width)
-        cv2.rectangle(frame, (bar_x_start, bar_y_start), (progress_x, bar_y_end), (0, 255, 0), -1)
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index cf6553f1..5190e931 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -4,7 +4,8 @@
 import argparse
 from config import FFMPEG_PATH, FFPROBE_PATH, RENDER_RESOLUTION
 from script_generator.utils.logger import logger
-from script_generator.video.ffmpeg import get_video_info
+from script_generator.video.info.video_info import get_video_info
+
 
 # TODO rework this class in the new logic and make the existing logic more flexible and generic
 class VideoReaderFFmpeg:

From 1eac089a9f3b86b929a6cfe994ecc6cb9d71e252 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 26 Jan 2025 10:41:13 +0100
Subject: [PATCH 157/277] added hh:mm:ss frame selection fixed logger and added
 output log to logs folder with timestamp improved logger Updated readme
 improved open close behavior of previews various tweaks

---
 FSGenerator.py                                |   4 +-
 README.md                                     | 240 ++++++------------
 config.py                                     |  45 ++--
 requirements.txt                              |   2 +-
 script_generator/cli/generate_funscript.py    |   2 +-
 script_generator/constants.py                 |   8 +-
 script_generator/debug/debug_data.py          |   2 +-
 script_generator/{utils => debug}/logger.py   |   7 +-
 .../debug/video_player/debug_overlay.py       |  14 +-
 .../debug/video_player/interaction.py         |   2 +-
 .../debug/video_player/overlay_widgets.py     |  10 +-
 script_generator/debug/video_player/play.py   |   7 +-
 script_generator/gui/app.py                   |   4 +-
 .../gui/controller/debug_video.py             |   3 +-
 .../gui/controller/process_video.py           |   3 +-
 .../gui/controller/regenerate_funscript.py    |   2 +-
 script_generator/gui/messages/messages.py     |   7 +-
 script_generator/gui/utils/widgets.py         | 165 +++++++++++-
 .../gui/views/funscript_generator.py          |  23 +-
 .../gui/views/popups/create_debug_video.py    |   9 +-
 .../analyze_tracking_results.py               | 102 +++++---
 .../object_detection/post_process_worker.py   |  51 +++-
 script_generator/object_detection/utils.py    |   2 +-
 script_generator/scripts/analyze_video.py     |  86 +++----
 .../scripts/generate_debug_video.py           |   2 +-
 .../scripts/generate_funscript.py             |   2 +-
 script_generator/scripts/tracking_analysis.py |   2 +-
 script_generator/state/app_state.py           |   4 +-
 .../tasks/abstract_task_processor.py          |   2 +-
 script_generator/utils/config.py              |   2 +-
 script_generator/utils/file.py                |   2 +-
 script_generator/video/ffmpeg/filters.py      |   6 +-
 script_generator/video/ffmpeg/hwaccel.py      |   2 +-
 script_generator/video/info/video_info.py     |   2 +-
 .../video/workers/ffmpeg_worker.py            |   2 +-
 .../video/workers/vr_to_2d_worker.py          |   6 +-
 setup.py                                      |  22 --
 tests/test_Compare_models_and_resolutions.py  |   2 +-
 tests/test_PoseEstimation.py                  |   2 +-
 tests/test_generate_funscript_2.py            |  12 -
 tests/test_generate_funscript_3.py            |  12 -
 tests/test_generate_funscript_4.py            |  12 -
 tests/test_generate_funscript_5.py            |  12 -
 tests/test_generate_funscript_6.py            |  12 -
 utils/lib_FunscriptHandler.py                 |  62 +++--
 utils/lib_KalmanFilter.py                     |   4 +-
 utils/lib_ObjectTracker.py                    |   2 +-
 utils/lib_SceneCutsDetect.py                  |   8 +-
 utils/lib_VideoReaderFFmpeg.py                |  63 +----
 49 files changed, 550 insertions(+), 509 deletions(-)
 rename script_generator/{utils => debug}/logger.py (69%)
 rename utils/lib_Visualizer.py => script_generator/debug/video_player/overlay_widgets.py (86%)
 delete mode 100644 setup.py
 delete mode 100644 tests/test_generate_funscript_2.py
 delete mode 100644 tests/test_generate_funscript_3.py
 delete mode 100644 tests/test_generate_funscript_4.py
 delete mode 100644 tests/test_generate_funscript_5.py
 delete mode 100644 tests/test_generate_funscript_6.py

diff --git a/FSGenerator.py b/FSGenerator.py
index e33c2225..874cdf20 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -1,6 +1,8 @@
 import os
+
+from script_generator.debug.logger import logger
 from script_generator.gui.app import start_app
 
 if __name__ == "__main__":
-    print(f"PID: {os.getpid()}")
+    logger.info(f"Starting VR funscript generator GUI with process id (PID): {os.getpid()}")
     start_app()
diff --git a/README.md b/README.md
index 71c47559..18ca73d3 100644
--- a/README.md
+++ b/README.md
@@ -73,210 +73,130 @@ The pipeline for generating Funscript files is as follows:
 Before using this project, ensure you have the following installed:
 
 - **Python 3.8 or higher**
-- **FFmpeg** added to your path (for video processing)
+- **FFmpeg** added to your path or specified in the config
 - **CUDA** (optional, for GPU acceleration)
 
 ---
 
 ## Installation
 
-1. **Clone the repository**:
+### Clone the repository
    ```bash
    git clone https://github.com/ack00gar/VR-Funscript-AI-Generator.git
    cd VR-Funscript-AI-Generator
    ```
+### Install dependencies
 
-2. **Install dependencies**:
-   ```bash
-   pip install numpy opencv-python tqdm ultralytics scipy matplotlib simplification
-   ```
-
-3. **Use a venv as suggested by Zalunda**
-   * Install miniconda 
-   * Start a miniconda command prompt 
-   * Execute (assuming you already cloned VR-Funscript-AI-Generator and copied the model into models folder)
-   ```bash
-   conda create -n VRFunAIGen python=3.11
-   conda activate VRFunAIGen
-   pip install numpy opencv-python tqdm ultralytics scipy matplotlib simplification
-   pip uninstall torch torchvision torchaudio
-   pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
-   cd <VR-Funscript-AI-Generator folder>
-   python FSGenerator.py
-   ```
-   
-   While executing, you’ll need to say “yes” a few times. The lines “pip uninstall / pip3 install” is to replace the “CPU” version of torch with a “cuda enabled / GPU” version (you might need to install nvidia CUDA stuff for it to works, I’m not sure).
-   
-   Zalunda also suggests the creation of a batch file, after the setup, to start the application in the right conda environment:
-
-   ```bash
-   @echo off
-   call <PATH_TO_MINICONDA>\miniconda3\condabin\conda activate VRFunAIGen
-   cd /d "<PATH_TO_SOURCES>\VR-Funscript-AI-Generator"
-   python FSGenerator.py
-   pause
-   ```
-
-4. **Download the YOLO model**:
-   - Place your YOLO model file (e.g., `k00gar-11n-200ep-best.mlpackage`) in the `models/` sub-directory.
-   - Alternatively, you can specify a custom path to the model using the `--yolo_model` argument.
-
-5. **Update the params/config.py**:
-   - If ffmpeg and ffprobe paths are not in your system path, the program will default to the following values.
-   - You can update the params/config.py file, which contains:
-
-   ```bash
-   # ffmpeg and ffprobe paths - replace with your own if not in your system path   
-    win_ffmpeg_path = "C:/ffmpeg/bin/ffmpeg.exe"
-    mac_ffmpeg_path = "/usr/local/bin/ffmpeg"
-    lin_ffmpeg_path = "/usr/bin/ffmpeg"
-
-    win_ffprobe_path = "C:/ffmpeg/bin/ffprobe.exe"
-    mac_ffprobe_path = "/usr/local/bin/ffprobe"
-    lin_ffprobe_path = "/usr/bin/ffprobe"
-   ```
-
----
-
-### Libraries Used
-
-The project relies on the following Python libraries:
-
-- **numpy**: For numerical computations and array manipulations.
-- **opencv-python**: For computer vision tasks like video processing and image manipulation.
-- **tqdm**: For displaying progress bars during long-running tasks.
-- **ultralytics**: For YOLO object detection and tracking.
-- **scipy**: For scientific computing, including interpolation (interp1d).
-- **matplotlib**: For plotting and visualization.
-- **simplification**: For simplifying Funscript data.
-- **logging**: For logging debug and runtime information.
-- **argparse**: For parsing command-line arguments.
-- **subprocess**: For running external commands (e.g., FFmpeg).
-- **collections**: For specialized container datatypes like deque and defaultdict.
-- **datetime**: For handling timestamps and date-related operations.
-- **json**: For reading and writing JSON files.
-- **tkinter**: For creating a basic GUI for file selection and parameter configuration.
+#### Option 1: venv
+**If your GPU supports CUDA (NVIDIA)**
+```bash
+python -m venv VRFunAIGen
+VRFunAIGen\Scripts\activate # windows
+source myenv/bin/activate # mac
+pip install -e requirements.txt
+pip uninstall torch torchvision torchaudio
+pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
+python FSGenerator.py
+```
 
----
+**If your GPU doesn't support cuda**
+```bash
+python -m venv VRFunAIGen
+VRFunAIGen\Scripts\activate # windows
+source myenv/bin/activate # mac
+pip install -e requirements.txt
+python FSGenerator.py
+```
 
-## Howto & Video Input and Preprocessing
+#### Option 2: Conda
+* Install miniconda (https://docs.anaconda.com/miniconda/install/)
+* Start a miniconda command prompt
+   
+**If your GPU supports CUDA (NVIDIA)**
+```bash
+conda create -n VRFunAIGen python=3.11
+conda activate VRFunAIGen
+pip install -e requirements.txt
+pip uninstall torch torchvision torchaudio
+pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
+python FSGenerator.py
+```
+While executing, you’ll need to say “yes” a few times. The lines “pip uninstall / pip3 install” is to replace the “CPU” version of torch with a “cuda enabled / GPU” version (you might need to install nvidia CUDA stuff for it to works, I’m not sure).
 
-### Input File Requirements
+**If your GPU doesn't support cuda**
+```bash
+conda create -n VRFunAIGen python=3.11
+conda activate VRFunAIGen
+pip install -e requirements.txt
+python FSGenerator.py
+```
 
-The input video file should be a standard video file. For VR videos, ensure the file is in **Side-by-Side (SBS)** format. The algorithm will process the **left panel** by default.
+### Download the YOLO model
+- Place your YOLO model file (e.g., `k00gar-11n-200ep-best.mlpackage`) in the `models/` sub-directory.
+- Alternatively, you can specify a custom path to the model using the `--yolo_model` argument.
 
-**Note**: While the algorithm can handle up to 8K videos, it is strongly recommended to process videos at **1920p resolution** for optimal performance. Videos should not be lower than **1080p** to maintain detection accuracy. If your video exceeds 1920p in height, the script will automatically suggest resizing options (1920p, 1440p, or 1080p) while preserving the aspect ratio.
+### Update config.py
 
-### Video Resizing
+**FFMPEG**
+- If ffmpeg and ffprobe paths are not in your system path, the program will default to the following values.
+- You can update the params/config.py file, which contains:
 
-If the input video height exceeds 1920 pixels, the script will prompt you to resize the video to a lower resolution. The resizing process uses **FFmpeg** and excludes audio by default. The following options are available:
-- **1920p**: Resize to 1920 pixels in height (recommended for most cases).
-- **1440p**: Resize to 1440 pixels in height.
-- **1080p**: Resize to 1080 pixels in height.
+  ```bash
+  # ffmpeg and ffprobe paths - replace with your own if not in your system path   
+   win_ffmpeg_path = "C:/ffmpeg/bin/ffmpeg.exe"
+   mac_ffmpeg_path = "/usr/local/bin/ffmpeg"
+   lin_ffmpeg_path = "/usr/bin/ffmpeg"
 
-### VR Video Projection and Undistortion
+   win_ffprobe_path = "C:/ffmpeg/bin/ffprobe.exe"
+   mac_ffprobe_path = "/usr/local/bin/ffprobe"
+   lin_ffprobe_path = "/usr/bin/ffprobe"
+  ```
 
-For VR videos, the **projection type** and **undistortion settings** are critical for accurate object detection and tracking. The script supports two main projection types:
-1. **Fisheye**: Used for videos with a fisheye lens projection. The script automatically detects fisheye videos based on the filename or metadata.
-2. **Equirectangular**: Used for standard 360° VR videos. This is the default projection if fisheye is not detected.
+**(Optional) User configurations**
+config.py also contains various user configurations that can be adjusted
 
-#### Key Parameters for VR Video Processing
+### Start script
+You can use Start windows.bat to launch the gui on windows if you installed with conda
 
-The undistortion process is handled using FFmpeg's `v360` filter, which corrects the video frames based on the specified projection and field-of-view (FOV) parameters. Key parameters include:
-- **Input Vertical FOV (`iv_fov`)**: The vertical field of view of the input video.
-- **Input Horizontal FOV (`ih_fov`)**: The horizontal field of view of the input video.
-- **Output Vertical FOV (`v_fov`)**: The desired vertical field of view after undistortion.
-- **Output Horizontal FOV (`h_fov`)**: The desired horizontal field of view after undistortion.
-- **Diagonal FOV (`d_fov`)**: The diagonal field of view used for undistortion.
+---
 
-#### Example FFmpeg Command for VR Video Processing
+## Command Line Usage
 
-The following FFmpeg command is used for undistorting VR videos:
+To generate a script with cmd or terminal, run the following command
 
 ```bash
-ffmpeg -ss <start_time> -i <input_video> -vf "crop=w=iw/2:h=ih:x=0:y=0,v360=<type>:output=sg:iv_fov=<iv_fov>:ih_fov=<ih_fov>:d_fov=<d_fov>:v_fov=<v_fov>:h_fov=<h_fov>:pitch=-25:yaw=0:roll=0:w=<width>:h=<height>:interp=lanczos:reset_rot=1,lutyuv=y=gammaval(0.7)" -f rawvideo -pix_fmt bgr24 -vsync 0 -threads 0 -
+python script_generator.cli.generate_funscript.py /path/to/video.mp4
 ```
-
-#### Adjusting Projection Settings
-
-To ensure accurate detection and tracking, you may need to adjust the projection settings based on the specific characteristics of your VR video. These settings can be found and modified in the `utils/lib_VideoReaderFFmpeg.py` file. Use the `utils/test_detect_compare_unwarped.py` script to test different projection settings before processing the video.
-
 ---
 
-### Key Points to Remember
+## Miscellaneous
 
-1. **Video Resolution**: Resize videos to 1920p for optimal performance.
-2. **VR Video Projection**: Ensure the correct projection type (Fisheye or Equirectangular) is selected for undistortion.
-3. **Undistortion Settings**: Adjust FOV and other parameters in `utils/lib_VideoReaderFFmpeg.py` for accurate results.
-4. **Testing**: Use `utils/test_detect_compare_unwarped.py` to test projection settings before full processing.
+- For VR only **Fisheye** and **Equirectangular** 180° videos are supported
+- 2D POV videos have limited support
+- 2D / VR is automatically detected for fisheye/equirectangular detection make sure you keep the file format information in the filename (_FISHEYE190, _MKX200, _LR_180, etc.) 
 
----
+### Configuration / User settings
 
-### Basic Command
+See config.py for customizations and user settings (will be replaced with a yaml soon).
 
-To process a video, run the following command:
-
-```bash
-python FSGenerator.py /path/to/video.mp4
-```
-or Run the script directly from your IDE.
 
 ---
 
-### Output Files
+## Output Files
 
-The script generates the following files in the same directory as the input video:
+The script generates the following files in the output directory of you project folder:
 
-1. `_rawyolo.json`: Raw YOLO detection data.
+1. `_rawyolo.json`: Raw YOLO detection data. Can be re-used when re-generating scripts 
 2. `_cuts.json`: Detected scene changes.
-3. `_rawfunscript.json`: Raw Funscript data.
+3. `_rawfunscript.json`: Raw Funscript data. Can be re-used when re-generating script with different settings.
 4. `.funscript`: Final Funscript file.
 5. `_heatmap.png`: Heatmap visualization of the Funscript data.
 6. `_comparefunscripts.png`: Comparison visualization between the generated Funscript and the reference Funscript (if provided).
 7. `_adjusted.funscript`: Funscript file with adjusted amplitude.
+8. `_debug_logs.json`: Contains all the raw metrics collected and can be used to debug your video when processing is completed.
 
----
-
-## How It Works
-
-1. **YOLO Detection**: The script uses a YOLO model to detect and track objects in each video frame. For VR videos, it processes only the center third of the left half of the frame.
-2. **Scene Change Detection**: Detects scene changes to reset tracking and ensure accuracy.
-3. **Tracking and Funscript Generation**: Tracks specific objects (e.g., body parts) and generates Funscript data based on their movements.
-4. **Visualization (Test Mode)**: Displays bounding boxes and Funscript data in real-time for debugging and verification.
-5. **Debugging (Debug Mode)**: Saves detailed logs for debugging purposes.
-
----
-
-## Example
-
-1. **Generate Funscript**:
-   ```bash
-   python FSGenerator.py /path/to/vr_video.mp4
-   ```
-   This command starts the UI.
-
-   You can also simply run it from your IDE, giving it a `video_path` to process.
-
-2. **Debugging Example**:
-
-   The debugger is accessible from the GUI.
-
-   If you want to call it from the code, you can do the following: 
-
-   - Display a Specific Frame with debug information:
-     ```bash
-     debugger.display_frame(frame_id)
-     ```
-   - Play the Video with debug information:
-     ```bash
-     debugger.play_video(frame_id)
-     ```
-   - Record the Debugged Video:
-     ```bash
-     debugger.play_video(frame, record=True, downsize_ratio=2, duration=10)
-     ```
-
-   Or run `Display_debug_results.py` from your IDE with the desired parameters.
+In config.py you can set COPY_FUNSCRIPT_TO_MOVIE_LOCATION to True to copy the generated script to the same directory as your video.
+If a funscript already exists it will be renamed to {funscriptName}_{date}.funscript.bak
 
 ---
 
diff --git a/config.py b/config.py
index 414a2b52..272ab720 100644
--- a/config.py
+++ b/config.py
@@ -4,9 +4,9 @@
 
 from script_generator.constants import MODEL_PATH
 from script_generator.utils.config import find_ffmpeg_path, find_ffprobe_path
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 
-VERSION = "0.0.5"
+VERSION = "0.1.0"
 
 ##################################################################################################
 # FFMPEG
@@ -20,41 +20,50 @@
 mac_ffprobe_path = "/usr/local/bin/ffprobe"
 lin_ffprobe_path = "/usr/bin/ffprobe"
 
+##################################################################################################
+# USER PREFERENCES
+##################################################################################################
+
+COPY_FUNSCRIPT_TO_MOVIE_LOCATION = False
+
 ##################################################################################################
 # PERFORMANCE
 ##################################################################################################
 
 RENDER_RESOLUTION = 1080 # Above 1080x1080 doesn't seem to improve detections
 TEXTURE_RESOLUTION = 1440 # Size that is used to texture the opengl sphere
-YOLO_BATCH_SIZE = 1 if platform.system() == "Darwin" else 30 # Mac doesn't support batching due to onnx
+YOLO_BATCH_SIZE = 1 if platform.system() == "Darwin" else 30 # Mac doesn't support batching
 YOLO_PERSIST = True # Big impact on performance but also improves tracking
+QUEUE_MAXSIZE = 200 # Bounded queue size to avoid memory blow-up as raw frames consume a lot of memory, does not increase performance
 
 ##################################################################################################
-# ADVANCED / DEVELOPMENT
+# ADVANCED
 ##################################################################################################
 
-
-PITCH=-25
-YAW=0
 YOLO_CONF = 0.3
+VR_TO_2D_PITCH=-25 # The dataset is trained on -25
+UPDATE_PROGRESS_INTERVAL = 0.1 # Updates progress in the console and in gui
+# Define custom colormap based on Lucife's heatmapColors
+STEP_SIZE = 120  # Speed step size for color transitions
 
-RUN_POSE_MODEL = False
-YOLO_MODEL = YOLO(MODEL_PATH, task="detect")
-YOLO_POSE_MODEL = None # YOLO("models/yolo11n-pose.mlpackage", task="pose")
-
-# Set the paths in your config
+##################################################################################################
+# DEV
+##################################################################################################
 
-UPDATE_PROGRESS_INTERVAL = 0.25 # Updates progress in the console and in gui
 # when enabled the queue will be processed one by one (use it on (QUEUE_MAXSIZE / frame rate) seconds longer videos or less)
 # raw frames take a lot of memory (RAM) so don't set the queue to high
 SEQUENTIAL_MODE = False
-QUEUE_MAXSIZE = 3000 if SEQUENTIAL_MODE else 500 # Bounded queue size to avoid memory blow-up
-DEBUG_PATH = "C:/cvr/funscript-generator/tmp_output"
-PROGRESS_BAR = True # disable when you want to print messages while debugging
+if SEQUENTIAL_MODE:
+    QUEUE_MAXSIZE = 3000
 
-# Define custom colormap based on Lucife's heatmapColors
-STEP_SIZE = 120  # Speed step size for color transitions
+##################################################################################################
+# PROG
+##################################################################################################
 
+# Preload the YOLO modal on launch
+YOLO_MODEL = YOLO(MODEL_PATH, task="detect")
+RUN_POSE_MODEL = False
+YOLO_POSE_MODEL = None # YOLO("models/yolo11n-pose.mlpackage", task="pose") #TODO pose model?
 
 FFMPEG_PATH = find_ffmpeg_path(win_ffmpeg_path, mac_ffmpeg_path, lin_ffmpeg_path)
 FFPROBE_PATH = find_ffprobe_path(win_ffprobe_path, mac_ffprobe_path, lin_ffprobe_path)
diff --git a/requirements.txt b/requirements.txt
index 183939ea..a610d88f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -13,4 +13,4 @@ opencv-python~=4.10.0.84
 scipy~=1.15.1
 matplotlib~=3.10.0
 simplification~=0.7.13
-torch~=2.5.1+cu121
\ No newline at end of file
+torch~=2.5.1+cu121
diff --git a/script_generator/cli/generate_funscript.py b/script_generator/cli/generate_funscript.py
index 5cc8d58c..391098c0 100644
--- a/script_generator/cli/generate_funscript.py
+++ b/script_generator/cli/generate_funscript.py
@@ -1,6 +1,6 @@
 import argparse
 from script_generator.scripts.generate_funscript import generate_funscript
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 
 
 def main():
diff --git a/script_generator/constants.py b/script_generator/constants.py
index 9ea05461..78721f38 100644
--- a/script_generator/constants.py
+++ b/script_generator/constants.py
@@ -78,4 +78,10 @@
 LOGO = os.path.join(PROJECT_PATH, "resources", "logo.png")
 ICON = os.path.join(PROJECT_PATH, "resources", "icon.ico")
 YOLO_MODELS = [os.path.join(MODELS_DIR, filename) for filename in MODEL_FILENAMES]
-MODEL_PATH = str(os.path.join(PROJECT_PATH, get_yolo_model_path(YOLO_MODELS)))
\ No newline at end of file
+MODEL_PATH = str(os.path.join(PROJECT_PATH, get_yolo_model_path(YOLO_MODELS)))
+
+##################################################################################################
+# DIV
+##################################################################################################
+
+FUNSCRIPT_AUTHOR = "FunGen_k00gar_AI"
\ No newline at end of file
diff --git a/script_generator/debug/debug_data.py b/script_generator/debug/debug_data.py
index 662d581c..92336247 100644
--- a/script_generator/debug/debug_data.py
+++ b/script_generator/debug/debug_data.py
@@ -3,7 +3,7 @@
 import numpy as np
 
 from script_generator.utils.file import get_output_file_path, load_json_from_file
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 
 
 class DebugData:
diff --git a/script_generator/utils/logger.py b/script_generator/debug/logger.py
similarity index 69%
rename from script_generator/utils/logger.py
rename to script_generator/debug/logger.py
index 869a6a3b..9bf62216 100644
--- a/script_generator/utils/logger.py
+++ b/script_generator/debug/logger.py
@@ -1,4 +1,6 @@
+from datetime import datetime
 import logging
+import os
 import sys
 from colorama import Fore, Style, init
 
@@ -12,11 +14,14 @@ def emit(self, record):
             record.msg = f"{Fore.YELLOW}{record.msg}{Style.RESET_ALL}"
         super().emit(record)
 
+project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
 logging.basicConfig(
     level=logging.INFO,
     format="%(asctime)s|%(levelname)s|%(name)s|%(message)s",
     handlers=[
-        logging.FileHandler("FSGenerator.log", mode="w", encoding='utf-8'),
+        logging.FileHandler(
+            os.path.join(project_root, "logs", f"log_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.log"), mode="w", encoding='utf-8'),
         ColorizedStreamHandler(sys.stdout)
     ]
 )
diff --git a/script_generator/debug/video_player/debug_overlay.py b/script_generator/debug/video_player/debug_overlay.py
index 509fe311..3077832c 100644
--- a/script_generator/debug/video_player/debug_overlay.py
+++ b/script_generator/debug/video_player/debug_overlay.py
@@ -2,6 +2,7 @@
 import numpy as np
 
 from script_generator.constants import CLASS_COLORS
+from script_generator.debug.video_player.overlay_widgets import OverlayWidgets
 
 
 def get_funscript_value(interpolator, frame_id, fps):
@@ -18,7 +19,6 @@ def draw_overlay(
     funscript_interpolator,
     distance_buffer,
     funscript_buffer,
-    visualizer,
     fps,
     y_offset_start=0,
 ):
@@ -31,7 +31,6 @@ def draw_overlay(
     :param funscript_interpolator:  If present, used to compute funscript values.
     :param distance_buffer:         Rolling buffer for 'distance' data.
     :param funscript_buffer:        Rolling buffer for funscript values.
-    :param visualizer:              An instance of Visualizer (to draw gauges, etc.).
     :param fps:                     The video frames per second.
     :param y_offset_start:          The initial Y offset for drawing text.
     """
@@ -60,18 +59,18 @@ def draw_overlay(
         )
 
     # Display variables
-    y_offset = y_offset_start
+    y_offset_stats = 20
     for key, value in variables.items():
         cv2.putText(
             frame,
             f"{key}: {value}",
-            (10, y_offset),
+            (10, y_offset_stats),
             cv2.FONT_HERSHEY_SIMPLEX,
             0.5,
             (0, 0, 255),
             2
         )
-        y_offset += 20
+        y_offset_stats += 20
 
     # Draw locked penis box if present
     locked_penis_box = variables.get("locked_penis_box")
@@ -96,7 +95,7 @@ def draw_overlay(
         funscript_value = get_funscript_value(funscript_interpolator, frame_id, fps)
 
     # Draw gauge (example usage)
-    visualizer.draw_gauge(frame, funscript_value)
+    OverlayWidgets.draw_gauge(frame, funscript_value)
 
     # Shift rolling window buffers left and insert new values
     distance_buffer = np.roll(distance_buffer, -1)
@@ -106,7 +105,7 @@ def draw_overlay(
 
     # Draw rolling window curves
     graph_height = int(frame.shape[0] * 0.2)
-    graph_y_start = y_offset + 10
+    graph_y_start = y_offset_start
     draw_rolling_window_curve(frame, distance_buffer, (0, 255, 0), 0.5, graph_height, graph_y_start)
     draw_rolling_window_curve(frame, funscript_buffer, (255, 0, 0), 0.5, graph_height, graph_y_start)
 
@@ -114,7 +113,6 @@ def draw_overlay(
     return distance_buffer, funscript_buffer
 
 
-
 def draw_rolling_window_curve(frame, buffer, color, alpha, graph_height, graph_y_start):
     """
     Draw a rolling window curve on the frame as a transparent overlay.
diff --git a/script_generator/debug/video_player/interaction.py b/script_generator/debug/video_player/interaction.py
index 8e0e7b33..3860d269 100644
--- a/script_generator/debug/video_player/interaction.py
+++ b/script_generator/debug/video_player/interaction.py
@@ -1,7 +1,7 @@
 import cv2
 
 from script_generator.debug.video_player.constants import BAR_HEIGHT
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 
 
 def mouse_callback(event, x, y, flags, param):
diff --git a/utils/lib_Visualizer.py b/script_generator/debug/video_player/overlay_widgets.py
similarity index 86%
rename from utils/lib_Visualizer.py
rename to script_generator/debug/video_player/overlay_widgets.py
index b568b3a7..5d3f3fa2 100644
--- a/utils/lib_Visualizer.py
+++ b/script_generator/debug/video_player/overlay_widgets.py
@@ -1,9 +1,8 @@
 import cv2
-import numpy as np
-import matplotlib.pyplot as plt
 
-class Visualizer:
-    def draw_bounding_box(self, image, box, label, color, offset_x=0):
+class OverlayWidgets:
+    @staticmethod
+    def draw_bounding_box(image, box, label, color, offset_x=0):
         x1, y1, x2, y2 = map(int, box)
         x1 += offset_x
         x2 += offset_x
@@ -13,7 +12,8 @@ def draw_bounding_box(self, image, box, label, color, offset_x=0):
         cv2.putText(image, label, (x1, y1 - baseline), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
         return image
 
-    def draw_gauge(self, image, distance):
+    @staticmethod
+    def draw_gauge(image, distance):
         gauge_width = 20
         gauge_height = 200
         gauge_x = int(3 * image.shape[1] / 4)
diff --git a/script_generator/debug/video_player/play.py b/script_generator/debug/video_player/play.py
index aad7d9e9..1bc18711 100644
--- a/script_generator/debug/video_player/play.py
+++ b/script_generator/debug/video_player/play.py
@@ -11,13 +11,11 @@
 from script_generator.debug.video_player.interaction import mouse_callback
 from script_generator.debug.video_player.state import VideoPlayer
 from script_generator.utils.file import get_output_file_path
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 from script_generator.video.info.video_info import get_cropped_dimensions
-from utils.lib_Visualizer import Visualizer
 
 
 def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=100, save_video_mode=False):
-    visualizer = Visualizer()
     video_info = state.video_info
     width, height = get_cropped_dimensions(video_info)
 
@@ -103,9 +101,8 @@ def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=1
             funscript_interpolator=funscript_interpolator,
             distance_buffer=distance_buffer,
             funscript_buffer=funscript_buffer,
-            visualizer=visualizer,
             fps=video_info.fps,
-            y_offset_start=frame.shape[0] // 3
+            y_offset_start=frame.shape[0] - 250 # 250 is height of funscript preview
         )
 
         if save_video_mode:
diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index a076110d..ff30fda0 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -6,7 +6,7 @@
 from script_generator.gui.views.funscript_generator import FunscriptGeneratorPage
 from script_generator.state.app_state import AppState
 from script_generator.utils.helpers import is_mac
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 from config import VERSION
 
 # TODO this is a workaround and needs to be fixed properly
@@ -19,7 +19,7 @@ def __init__(self):
             ctypes.windll.shcore.SetProcessDpiAwareness(2)  # For Windows DPI scaling
         # self.tk.call('tk', 'scaling', 1.0)
         self.title(f"VR & 2D POV Funscript AI Generator - v" + VERSION)
-        self.geometry(f"{('800' if is_mac() else '700')}x910")
+        self.geometry(f"{('800' if is_mac() else '700')}x840")
         self.resizable(False, False)
 
         self.iconphoto(False, tk.PhotoImage(file=LOGO))
diff --git a/script_generator/gui/controller/debug_video.py b/script_generator/gui/controller/debug_video.py
index 15d0ee99..e6659b60 100644
--- a/script_generator/gui/controller/debug_video.py
+++ b/script_generator/gui/controller/debug_video.py
@@ -4,9 +4,8 @@
 from script_generator.debug.video_player.play import play_debug_video
 from script_generator.state.app_state import AppState
 from script_generator.utils.file import get_output_file_path
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 
-# TODO this callback is called on every frame. This doesn't seem correct or can be optized (instantiating Debugger, checks etc.)
 def debug_video(state: AppState):
     if not state.video_path:
         messagebox.showerror("Error", "Please select a video file.")
diff --git a/script_generator/gui/controller/process_video.py b/script_generator/gui/controller/process_video.py
index bd712be9..1f666f11 100644
--- a/script_generator/gui/controller/process_video.py
+++ b/script_generator/gui/controller/process_video.py
@@ -5,9 +5,8 @@
 from script_generator.gui.messages.messages import ProgressMessage
 from script_generator.object_detection.utils import check_skip_object_detection
 from script_generator.scripts.analyze_video import analyze_video
-from script_generator.utils.file import get_output_file_path
 from script_generator.utils.helpers import to_int_or_none
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 
 
 def video_analysis(state, root):
diff --git a/script_generator/gui/controller/regenerate_funscript.py b/script_generator/gui/controller/regenerate_funscript.py
index 93a23e25..2d74bea2 100644
--- a/script_generator/gui/controller/regenerate_funscript.py
+++ b/script_generator/gui/controller/regenerate_funscript.py
@@ -1,6 +1,6 @@
 from tkinter import messagebox
 
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 from utils.lib_FunscriptHandler import FunscriptGenerator
 
 
diff --git a/script_generator/gui/messages/messages.py b/script_generator/gui/messages/messages.py
index 5eccd3c3..4e08496d 100644
--- a/script_generator/gui/messages/messages.py
+++ b/script_generator/gui/messages/messages.py
@@ -6,4 +6,9 @@ def __init__(self, process, frames_processed, total_frames, eta):
         self.process = process
         self.frames_processed = frames_processed
         self.total_frames = total_frames
-        self.eta = eta
\ No newline at end of file
+        self.eta = eta
+
+class UpdateGUIState(UIMessage):
+    def __init__(self, attr, value):
+        self.attr = attr
+        self.value = value
\ No newline at end of file
diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index 32ada0e6..15d304d3 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -1,12 +1,15 @@
+import os
 import tkinter as tk
 from tkinter import ttk, filedialog, Button
 
+from script_generator.constants import LOGO
 from script_generator.gui.utils.tooltip import Tooltip
 
 PADDING_X = 5
 PADDING_Y = 5
 LABEL_WIDTH = 150
 
+
 class Widgets:
     @staticmethod
     def frame(parent, title=None, main_section=False, **grid_kwargs):
@@ -40,7 +43,6 @@ def configure_grid(event):
 
         return frame
 
-
     @staticmethod
     def label(frame, text, tooltip_text=None, **grid_kwargs):
         label = ttk.Label(frame, text=text)
@@ -114,8 +116,9 @@ def file_selection(parent, label_text, button_text, file_selector_title, file_ty
 
         def on_change(val):
             setattr(state, attr, val)
+            state_val = val if os.path.exists(val) else None
             if command:
-                command(val)
+                command(state_val)
 
         # Ensure the container scales properly
         parent.grid_rowconfigure(row, weight=1)
@@ -269,6 +272,7 @@ def create_popup(title, master, content_builder, width=400, height=300):
         window = tk.Toplevel(master)
         window.title(title)
         window.geometry(f"{width}x{height}")
+        window.iconphoto(False, tk.PhotoImage(file=LOGO))
 
         # Center the popup
         if master:
@@ -357,3 +361,160 @@ def _browse_file(file_path, title, file_types, callback):
             if callback:
                 callback(file)
 
+    @staticmethod
+    def frames_input(
+            parent,
+            label_text,
+            state,
+            attr,
+            row=0,
+            column=0,
+            label_width_px=LABEL_WIDTH,
+            entry_width_px=200,
+            callback=None,
+            tooltip_text=None
+    ):
+        """Creates a row of controls for editing time in H:M:S and frames format."""
+
+        def hms_to_frames(hours, minutes, seconds, fps):
+            if fps <= 0:
+                return 0
+            return int(hours * 3600 * fps + minutes * 60 * fps + seconds * fps)
+
+        def frames_to_hms(frames, fps):
+            if fps <= 0:
+                return (0, 0, 0)
+            total_seconds = frames / fps
+            h = int(total_seconds // 3600)
+            total_seconds = total_seconds % 3600
+            m = int(total_seconds // 60)
+            total_seconds = total_seconds % 60
+            s = int(total_seconds)
+            return (h, m, s)
+
+        # Main container
+        container = ttk.Frame(parent)
+        container.grid(row=row, column=column, sticky="ew", padx=5, pady=5)
+        container.columnconfigure(1, weight=1)
+
+        initial_frames = getattr(state, attr, 0) or 0
+        value = tk.StringVar(value=str(initial_frames))
+
+        hours_var = tk.StringVar()
+        minutes_var = tk.StringVar()
+        seconds_var = tk.StringVar()
+        frames_var = tk.StringVar()
+
+        # A small mutable flag to prevent "ping-pong" re-entrancy
+        in_update = [False]
+
+        def on_fps_change(*_):
+            """ Recalculate HH:MM:SS whenever FPS or 'value' changes (externally). """
+            fps = getattr(state.video_info, "fps", 0) or 0
+            fps_label.config(text=" {state.video_info.fps} fps" if state.video_info else ' ? fps')
+
+            try:
+                f_ = int(value.get())
+            except ValueError:
+                f_ = 0
+
+            h_, m_, s_ = frames_to_hms(f_, fps)
+            hours_var.set(str(h_))
+            minutes_var.set(str(m_))
+            seconds_var.set(str(s_))
+
+        fps_var = tk.StringVar()
+        fps_var.trace_add("write", on_fps_change)
+
+        def update_from_hms(*_):
+            if in_update[0]:
+                return
+            in_update[0] = True
+            try:
+                fps = getattr(state.video_info, "fps", 0) or 0
+                if fps <= 0:
+                    return
+
+                try:
+                    h = int(hours_var.get())
+                    m = int(minutes_var.get())
+                    s = int(seconds_var.get())
+                except ValueError:
+                    return
+
+                new_frames = hms_to_frames(h, m, s, fps)
+                frames_var.set(str(new_frames))
+                value.set(str(new_frames))
+
+                if callback:
+                    callback(new_frames)
+            finally:
+                in_update[0] = False
+
+        def update_from_frames(*_):
+            if in_update[0]:
+                return
+            in_update[0] = True
+            try:
+                fps = getattr(state.video_info, "fps", 0) or 0
+                if fps <= 0:
+                    return
+
+                try:
+                    f_ = int(frames_var.get())
+                except ValueError:
+                    return
+
+                h_, m_, s_ = frames_to_hms(f_, fps)
+                hours_var.set(str(h_))
+                minutes_var.set(str(m_))
+                seconds_var.set(str(s_))
+                value.set(str(f_))
+
+                if callback:
+                    callback(f_)
+            finally:
+                in_update[0] = False
+
+        hours_var.trace_add("write", update_from_hms)
+        minutes_var.trace_add("write", update_from_hms)
+        seconds_var.trace_add("write", update_from_hms)
+        frames_var.trace_add("write", update_from_frames)
+        value.trace_add("write", on_fps_change)  # in case value is set externally
+
+        # Label on the left
+        label = tk.Label(container, text=label_text, anchor="w", width=label_width_px // 7)
+        label.grid(row=0, column=0, sticky="w", padx=(5, 2))
+
+        # Sub-container for the time fields
+        entry_container = ttk.Frame(container, width=entry_width_px)
+        entry_container.grid(row=0, column=1, sticky="ew", padx=(2, 5))
+        entry_container.grid_propagate(False)
+
+        def make_entry(parent_, textvar, width=4):
+            e = ttk.Entry(parent_, textvariable=textvar, width=width)
+            e.pack(side="left", padx=(0, 2))
+            return e
+
+        hours_entry = make_entry(entry_container, hours_var, width=6)
+        tk.Label(entry_container, text=":").pack(side="left")
+        minutes_entry = make_entry(entry_container, minutes_var, width=6)
+        tk.Label(entry_container, text=":").pack(side="left")
+        seconds_entry = make_entry(entry_container, seconds_var, width=6)
+        tk.Label(entry_container, text=" frame ").pack(side="left")
+        frames_entry = make_entry(entry_container, frames_var, width=23)
+        fps_label = tk.Label(entry_container, text=" ? fps")
+        fps_label.pack(side="left")
+
+        # Optional tooltip
+        if tooltip_text:
+            Tooltip(hours_entry, tooltip_text)
+            Tooltip(minutes_entry, tooltip_text)
+            Tooltip(seconds_entry, tooltip_text)
+            Tooltip(frames_entry, tooltip_text)
+
+        # Initialize the fields so they match initial_frames (and current FPS if set)
+        on_fps_change()
+
+        return container, (hours_entry, minutes_entry, seconds_entry, frames_entry), value
+
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index ad8f0055..8262efd7 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -4,12 +4,12 @@
 from script_generator.gui.controller.process_video import video_analysis
 from script_generator.gui.controller.regenerate_funscript import regenerate_funscript
 from script_generator.gui.controller.debug_video import debug_video
-from script_generator.gui.messages.messages import UIMessage, ProgressMessage
+from script_generator.gui.messages.messages import UIMessage, ProgressMessage, UpdateGUIState
 from script_generator.gui.utils.widgets import Widgets
 from script_generator.gui.views.popups.create_debug_video import render_debug_video_popup
 from script_generator.state.app_state import AppState
 from script_generator.utils.helpers import is_mac
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 
 
 class FunscriptGeneratorPage(tk.Frame):
@@ -38,9 +38,7 @@ def __init__(self, parent, controller):
             file_types=[("Text Files", "*.mp4 *.avi *.mov *.mkv"), ("All Files", "*.*")],
             state=state,
             tooltip_text="The video to generate a funscript for. For proper detection of fisheye keep the suffix like _FISHEYE190, _MKX200, etc. in the filename\n\nIn the future we'll add the option to override this in the UI.",
-            # if the file changes we need to make sure the video info is reset
-            # TODO add os.path.exists and update video_info immediately if found
-            command=lambda val: setattr(state, 'video_info', None)
+            command=lambda val: state.set_video_info()
         )
 
         Widgets.dropdown(
@@ -58,8 +56,8 @@ def __init__(self, parent, controller):
         # region OPTIONAL SETTINGS
         optional_settings = Widgets.frame(wrapper, title="Optional settings", main_section=True, row=2)
 
-        Widgets.input(optional_settings, "Frame Start", state=state, attr="frame_start", tooltip_text="Where to start processing the video. If you have a 60fps video starting at 10s would mean frame 600")
-        Widgets.input(optional_settings, "Frame End", state=state, attr="frame_end", tooltip_text="Where to end processing the video. If you have a 60fps video stopping  at 10s would mean frame 600", row=1)
+        Widgets.frames_input(optional_settings, "Start", state=state, attr="frame_start", tooltip_text="Where to start processing the video. If you have a 60fps video starting at 10s would mean frame 600")
+        Widgets.frames_input(optional_settings, "End", state=state, attr="frame_end", tooltip_text="Where to end processing the video. If you have a 60fps video stopping  at 10s would mean frame 600", row=1)
         # endregion
 
         # region PROCESSING
@@ -160,7 +158,7 @@ def start_processing():
         # region DEBUGGING
         debugging = Widgets.frame(wrapper, title="Debugging", main_section=True, row=5)
         general = Widgets.frame(debugging, title="General", row=0)
-        Widgets.checkbox(
+        _, _, live_preview_checked = Widgets.checkbox(
             general, "Live preview",
             state,
             "live_preview_mode",
@@ -169,7 +167,7 @@ def start_processing():
         )
         Widgets.checkbox(
             general,
-            "Save debug information",
+            "Save debug metrics",
             state, "save_debug_file",
             tooltip_text="Saves a debug file to disk with all collected metrics.\nThis is a prerequisite for playing back the debug video with the debug statistics overlay.\nThis also contains all generated metrics.",
             row=1
@@ -215,7 +213,8 @@ def update_ui(msg: UIMessage):
 
             def process_update():
                 handlers = {
-                    ProgressMessage: handle_progress_message
+                    ProgressMessage: handle_progress_message,
+                    UpdateGUIState: handle_state_update
                 }
 
                 handler = handlers.get(type(msg))
@@ -224,6 +223,10 @@ def process_update():
                 else:
                     logger.info(f"Unhandled message type: {type(msg)}")
 
+            def handle_state_update(state_msg: UpdateGUIState):
+                if state_msg.attr == "live_preview_mode":
+                    live_preview_checked.set(state_msg.value)
+
             def handle_progress_message(progress_msg: ProgressMessage):
                 progress_mapping = {
                     "OBJECT_DETECTION": (yolo_p, yolo_p_perc),
diff --git a/script_generator/gui/views/popups/create_debug_video.py b/script_generator/gui/views/popups/create_debug_video.py
index becf656f..7b97bf8f 100644
--- a/script_generator/gui/views/popups/create_debug_video.py
+++ b/script_generator/gui/views/popups/create_debug_video.py
@@ -5,18 +5,19 @@
 
 
 class RenderDebugVideoState:
-    def __init__(self):
+    def __init__(self, video_info):
+        self.video_info = video_info
         self.frame_start = 0
         self.frame_end = None
         self.ui_callback = None
 
 def render_debug_video_popup(window, state: AppState):
     state.set_video_info()
-    debug_state = RenderDebugVideoState()
+    debug_state = RenderDebugVideoState(state.video_info)
     Widgets.label(window, "First stream/play your results locally (see Play debug video). Note the frame when the issue occurs. \nSubtract about 120 frames so we have some context. Then add a few seconds of recording (2 to 20 seconds\nshould do it)", column=0, sticky="w", padx=10, pady=10)
     p_container, p, p_label, p_perc = Widgets.labeled_progress(window, "Progress", row=1)
-    Widgets.input(window, "Frame Start", state=debug_state, attr="frame_start", tooltip_text="Where to start processing the video. If you have a 60fps video starting at 10s would mean frame 600", row=2)
-    Widgets.input(window, "Frame End", state=debug_state, attr="frame_end", tooltip_text="Where to end processing the video. If you have a 60fps video stopping  at 10s would mean frame 600", row=3)
+    Widgets.frames_input(window, "Start", state=debug_state, attr="frame_start", tooltip_text="Where to start processing the video. If you have a 60fps video starting at 10s would mean frame 600", row=2)
+    Widgets.frames_input(window, "End", state=debug_state, attr="frame_end", tooltip_text="Where to end processing the video. If you have a 60fps video stopping  at 10s would mean frame 600", row=3)
     Widgets.button(
         window,
         "Generate",
diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index 2419e1f5..99934e69 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -7,20 +7,19 @@
 
 from config import UPDATE_PROGRESS_INTERVAL
 from script_generator.constants import CLASS_COLORS
-from script_generator.gui.messages.messages import ProgressMessage
+from script_generator.debug.video_player.overlay_widgets import OverlayWidgets
+from script_generator.gui.messages.messages import ProgressMessage, UpdateGUIState
 from script_generator.state.app_state import AppState
 from script_generator.utils.file import get_output_file_path
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 from script_generator.video.info.video_info import get_cropped_dimensions
 from utils.lib_ObjectTracker import ObjectTracker
 from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg
-from utils.lib_Visualizer import Visualizer
 
 
 def analyze_tracking_results(state: AppState, results):
     width, height = get_cropped_dimensions(state.video_info)
     list_of_frames = results.get_all_frame_ids()  # Get all frame IDs with detections
-    visualizer = Visualizer()  # Initialize the visualizer
 
     video_info = state.video_info
     fps = video_info.fps
@@ -66,17 +65,20 @@ def analyze_tracking_results(state: AppState, results):
     state.funscript_distances = []
     state.funscript_data = []
 
-    #tracker = ObjectTracker(fps, state.frame_start, image_area, video_info.is_vr)  # Initialize the object tracker
+    # tracker = ObjectTracker(fps, state.frame_start, image_area, video_info.is_vr)  # Initialize the object tracker
     tracker = ObjectTracker(state)
 
     # Start time for ETA calculation
     start_time = time.time()
 
     last_ui_update_time = time.time()
+    live_preview_mode_prev = state.live_preview_mode
 
     for frame_pos in tqdm(
-            #range(state.frame_start, state.frame_end), unit="f", desc="Analyzing tracking data", position=0,
-            range(state.frame_start_track, state.frame_end), unit="f", desc="Analyzing tracking data", position=0,
+            # range(state.frame_start, state.frame_end), unit="f", desc="Analyzing tracking data", position=0,
+            range(state.frame_start_track, state.frame_end),
+            unit="f",
+            desc="Analyzing tracking data", position=0,
             unit_scale=False,
             unit_divisor=1,
             ncols=130
@@ -87,7 +89,7 @@ def analyze_tracking_results(state: AppState, results):
             logger.info(f"Reaching cut at frame {frame_pos}")
             previous_distances = tracker.previous_distances
             logger.info(f"Reinitializing tracker with previous distances: {previous_distances}")
-            #tracker = ObjectTracker(fps, frame_pos, image_area, video_info.is_vr)
+            # tracker = ObjectTracker(fps, frame_pos, image_area, video_info.is_vr)
             tracker = ObjectTracker(state)
             tracker.previous_distances = previous_distances
 
@@ -115,10 +117,9 @@ def analyze_tracking_results(state: AppState, results):
                                 str_dist_penis = 'None'
                         str_abs_pos = str(int(tracker.normalized_absolute_tracked_positions[box[4]][-1]))
                         position = 'p: ' + str_dist_penis + ' | ' + 'a: ' + str_abs_pos
-                        if box[4] in tracker.pct_weights:
-                            if len(tracker.pct_weights[box[4]]) > 0:
-                                weight = tracker.pct_weights[box[4]][-1]
-                                position += ' | w: ' + str(weight)
+                        if box[4] in tracker.pct_weights and len(tracker.pct_weights[box[4]]) > 0:
+                            weight = tracker.pct_weights[box[4]][-1]
+                            position += ' | w: ' + str(weight)
                     else:
                         position = None
                     bounding_boxes.append({
@@ -153,6 +154,7 @@ def analyze_tracking_results(state: AppState, results):
                 )
 
         # Display object detection tracking results in a live preview window
+        window_name = "Combined results"
         if state.live_preview_mode:
             ret, frame = reader.read()
 
@@ -160,19 +162,21 @@ def analyze_tracking_results(state: AppState, results):
                 logger.warn("Frame could not be read in live preview")
                 continue
 
-            frame_display = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frame = frame.copy()
 
             for box in tracker.tracked_boxes:
-                frame_display = visualizer.draw_bounding_box(
-                    frame_display,
+                frame = OverlayWidgets.draw_bounding_box(
+                    frame,
                     box[0],
                     str(box[2]) + ": " + box[1],
                     CLASS_COLORS[str(box[1])],
                     state.offset_x
                 )
+
             if tracker.locked_penis_box is not None and tracker.locked_penis_box.is_active():
-                frame_display = visualizer.draw_bounding_box(
-                    frame_display, tracker.locked_penis_box.box,
+                frame = OverlayWidgets.draw_bounding_box(
+                    frame,
+                    tracker.locked_penis_box.box,
                     "Locked_Penis",
                     CLASS_COLORS['penis'],
                     state.offset_x
@@ -181,19 +185,37 @@ def analyze_tracking_results(state: AppState, results):
                 logger.info("No active locked penis box to draw.")
 
             if tracker.glans_detected:
-                frame_display = visualizer.draw_bounding_box(
-                    frame_display, tracker.boxes['glans'],
+                frame = OverlayWidgets.draw_bounding_box(
+                    frame,
+                    tracker.boxes['glans'],
                     "Glans",
                     CLASS_COLORS['glans'],
                     state.offset_x
                 )
             if state.funscript_distances:
-                frame_display = visualizer.draw_gauge(frame_display, state.funscript_distances[-1])
+                frame = OverlayWidgets.draw_gauge(frame, state.funscript_distances[-1])
+
+            # Reinitialize the window if needed
+            if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) < 1 and state.live_preview_mode:
+                cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
+            cv2.imshow(window_name, frame)
+
+            if not handle_user_input(window_name) or not state.live_preview_mode:
+                if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) >= 1:
+                    cv2.destroyWindow(window_name)
+
+                if state.update_ui and state.live_preview_mode:
+                    state.update_ui(UpdateGUIState(attr="live_preview_mode", value=False))
+
+                state.live_preview_mode = False
+        # we don't want to call cv2.getWindowProperty every iteration
+        elif live_preview_mode_prev and not state.live_preview_mode:
+            if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) >= 1:
+                cv2.destroyWindow(window_name)
 
-            cv2.imshow("Combined Results", frame_display)
-            cv2.waitKey(1)
+        live_preview_mode_prev = state.live_preview_mode
 
-        # Update progress
+        # Update progress periodically
         if state.update_ui:
             current_time = time.time()
             if current_time - last_ui_update_time >= UPDATE_PROGRESS_INTERVAL:
@@ -210,24 +232,32 @@ def analyze_tracking_results(state: AppState, results):
                     eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
                 ))
 
-    state.update_ui(ProgressMessage(
-        process="TRACKING_ANALYSIS",
-        frames_processed=state.video_info.total_frames,
-        total_frames=state.video_info.total_frames,
-        eta="Done"
-    ))
+    # Final UI update: we're done
+    if state.update_ui:
+        state.update_ui(ProgressMessage(
+            process="TRACKING_ANALYSIS",
+            frames_processed=state.video_info.total_frames,
+            total_frames=state.video_info.total_frames,
+            eta="Done"
+        ))
 
     # Prepare Funscript data
     state.funscript_data = list(zip(state.funscript_frames, state.funscript_distances))
 
-    points = "["
-    for i in range(len(state.funscript_frames)):
-        if i != 0:
-            points += ","
-        points += f"[{state.funscript_frames[i]}, {state.funscript_distances[i]}]"
-    points += "]"
-    # Write the raw Funscript data to a JSON file
+    # Save the raw funscript data to JSON
     raw_funscript_path, _ = get_output_file_path(state.video_path, "_rawfunscript.json")
     with open(raw_funscript_path, 'w') as f:
         json.dump(state.funscript_data, f)
+
     return state.funscript_data
+
+def handle_user_input(window_name):
+    key = cv2.waitKey(1) & 0xFF
+
+    # Check if the window has been closed
+    if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) < 1:
+        return False
+
+    if key == ord("q"):
+        return False
+    return True
\ No newline at end of file
diff --git a/script_generator/object_detection/post_process_worker.py b/script_generator/object_detection/post_process_worker.py
index 7d3fa06d..a920c1a6 100644
--- a/script_generator/object_detection/post_process_worker.py
+++ b/script_generator/object_detection/post_process_worker.py
@@ -1,14 +1,14 @@
-import os
 import time
 
 import cv2
 
-from script_generator.constants import CLASS_REVERSE_MATCH, CLASS_COLORS, OUTPUT_PATH
+from script_generator.constants import CLASS_REVERSE_MATCH, CLASS_COLORS
 from config import RUN_POSE_MODEL
+from script_generator.gui.messages.messages import UpdateGUIState
 from script_generator.object_detection.object_detection_result import ObjectDetectionResult
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 from script_generator.utils.file import write_json_to_file, get_output_file_path
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 
 
 class PostProcessWorker(AbstractTaskProcessor):
@@ -19,6 +19,7 @@ class PostProcessWorker(AbstractTaskProcessor):
     def task_logic(self):
         state = self.state
 
+        live_preview_mode_prev = state.live_preview_mode
         for task in self.get_task():
 
             frame_pos = task.frame_pos
@@ -101,6 +102,7 @@ def task_logic(self):
                             logger.debug(f"Test box: {test_box}")
                             self.test_result.add_record(frame_pos, test_box)
 
+            window_name = "Object detection tracking preview"
             if state.live_preview_mode:
                 # Display the YOLO results for testing
                 # det_results.plot()
@@ -110,20 +112,37 @@ def task_logic(self):
                 sorted_boxes = self.test_result.get_boxes(frame_pos)
                 # logger.debug(f"Sorted boxes : {sorted_boxes}")
 
-                frame_display = frame.copy()
+                frame = frame.copy()
 
                 for box in sorted_boxes:
                     color = CLASS_COLORS.get(box[3])
-                    cv2.rectangle(frame_display, (box[0][0], box[0][1]), (box[0][2], box[0][3]), color, 2)
-                    cv2.putText(frame_display, f"{box[4]}: {box[3]}", (box[0][0], box[0][1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+                    cv2.rectangle(frame, (box[0][0], box[0][1]), (box[0][2], box[0][3]), color, 2)
+                    cv2.putText(frame, f"{box[4]}: {box[3]}", (box[0][0], box[0][1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
 
                 # Draw the frame ID at the top-left corner
-                cv2.putText(frame_display, f"Frame: {task.frame_pos}", (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 3)
+                cv2.putText(frame, f"Frame: {task.frame_pos}", (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 3)
 
-                cv2.imshow("Object detection tacking preview", frame_display)
 
-                if cv2.waitKey(1) & 0xFF == ord('q'):
-                    break
+                # Reinitialize the window if needed
+                if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) < 1 and state.live_preview_mode:
+                    cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
+                cv2.imshow(window_name, frame)
+
+                if not state.live_preview_mode or not handle_user_input(window_name):
+                    if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) >= 1:
+                        cv2.destroyWindow(window_name)
+
+                    if state.update_ui and state.live_preview_mode:
+                        state.update_ui(UpdateGUIState(attr="live_preview_mode", value=False))
+
+                    state.live_preview_mode = False
+
+            # we don't want to call cv2.getWindowProperty every iteration
+            elif live_preview_mode_prev and not state.live_preview_mode:
+                if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) >= 1:
+                    cv2.destroyWindow(window_name)
+
+            live_preview_mode_prev = state.live_preview_mode
 
             task.rendered_frame = None # Clear memory
             task.yolo_results = None # Clear memory (yolo results contains a copy of the image)
@@ -136,4 +155,14 @@ def on_last_item(self):
         # Write the detection records to a JSON file
         raw_yolo_path, _ = get_output_file_path(self.state.video_path, "_rawyolo.json")
         write_json_to_file(raw_yolo_path, self.records)
-        
\ No newline at end of file
+
+def handle_user_input(window_name):
+    key = cv2.waitKey(1) & 0xFF
+
+    # Check if the window has been closed
+    if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) < 1:
+        return False
+
+    if key == ord("q"):
+        return False
+    return True
\ No newline at end of file
diff --git a/script_generator/object_detection/utils.py b/script_generator/object_detection/utils.py
index 2aa3f937..bc1e281d 100644
--- a/script_generator/object_detection/utils.py
+++ b/script_generator/object_detection/utils.py
@@ -5,7 +5,7 @@
 from script_generator.object_detection.box_record import BoxRecord
 from script_generator.object_detection.object_detection_result import ObjectDetectionResult
 from script_generator.utils.file import get_output_file_path, load_json_from_file
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 
 
 def check_skip_object_detection(state, root):
diff --git a/script_generator/scripts/analyze_video.py b/script_generator/scripts/analyze_video.py
index dc316d81..fabcd7d8 100644
--- a/script_generator/scripts/analyze_video.py
+++ b/script_generator/scripts/analyze_video.py
@@ -5,7 +5,7 @@
 
 from tqdm import tqdm
 
-from config import QUEUE_MAXSIZE, SEQUENTIAL_MODE, PROGRESS_BAR, UPDATE_PROGRESS_INTERVAL
+from config import QUEUE_MAXSIZE, SEQUENTIAL_MODE, UPDATE_PROGRESS_INTERVAL
 from script_generator.gui.messages.messages import ProgressMessage
 from script_generator.object_detection.post_process_worker import PostProcessWorker
 from script_generator.object_detection.yolo_worker import YoloWorker
@@ -13,13 +13,13 @@
 from script_generator.tasks.abstract_task_processor import TaskProcessorTypes
 from script_generator.tasks.tasks import AnalyzeVideoTask, AnalyzeFrameTask
 from script_generator.utils.file import check_create_output_folder
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 from script_generator.video.workers.ffmpeg_worker import VideoWorker
 from script_generator.video.workers.vr_to_2d_worker import VrTo2DWorker
 
 
 def analyze_video(state: AppState) -> List[AnalyzeFrameTask]:
-    logger.info(f"[OBJECT DETECTION] Starting up pipeline with profiling in {'sequential mode' if SEQUENTIAL_MODE else 'parallel mode'}...")
+    logger.info(f"[OBJECT DETECTION] Starting up pipeline{' in sequential mode' if SEQUENTIAL_MODE else ''}...")
 
     log_thread_stop_event = threading.Event()
     threads = []
@@ -119,62 +119,48 @@ def log_progress(state, opengl_q, yolo_q, analysis_q, results_q, stop_event):
 
     label = 'Analyzing ' + ('VR' if state.video_info.is_vr else '2D') + ' video'
 
-    if PROGRESS_BAR:
-        with tqdm(
-                total=total_frames,
-                #desc="Analyzing video",
-                desc=label,
-                unit="f",
-                position=0,
-                unit_scale=False,
-                unit_divisor=1,
-                ncols=130
-        ) as progress_bar:
-            while not stop_event.is_set():
-                opengl_size = opengl_q.qsize()
-                yolo_size = yolo_q.qsize()
-                analysis_size = analysis_q.qsize()
-                frames_processed = results_q.qsize()
-
-                progress_bar.n = frames_processed
-                open_gl = f"OpenGL: {opengl_size:>3}, " if state.video_reader == "FFmpeg + OpenGL (Windows)" else ""
-                progress_bar.set_postfix_str(
-                    f"Queues: {open_gl}YOLO: {yolo_size:>3}, Analysis: {analysis_size:>3}"
-                )
-                progress_bar.refresh()
-
-                if frames_processed >= total_frames:
-                    stop_event.set()
-
-                if state.update_ui:
-                    elapsed_time = time.time() - state.analyze_task.start_time
-                    processing_rate = frames_processed / elapsed_time if elapsed_time > 0 else 0
-                    remaining_frames = total_frames - frames_processed
-                    eta = remaining_frames / processing_rate if processing_rate > 0 else float('inf')
-                    try:
-                        state.update_ui(ProgressMessage(
-                            process="OBJECT_DETECTION",
-                            frames_processed=frames_processed,
-                            total_frames=total_frames,
-                            eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
-                        ))
-                    except Exception as e:
-                        logger.error(f"Error in state.update_ui: {e}")
-
-                time.sleep(UPDATE_PROGRESS_INTERVAL)
-    else:
+    with tqdm(
+            total=total_frames,
+            #desc="Analyzing video",
+            desc=label,
+            unit="f",
+            position=0,
+            unit_scale=False,
+            unit_divisor=1,
+            ncols=130
+    ) as progress_bar:
         while not stop_event.is_set():
             opengl_size = opengl_q.qsize()
             yolo_size = yolo_q.qsize()
-            analysis_size = analysis_q.size()
+            analysis_size = analysis_q.qsize()
             frames_processed = results_q.qsize()
+
+            progress_bar.n = frames_processed
             open_gl = f"OpenGL: {opengl_size:>3}, " if state.video_reader == "FFmpeg + OpenGL (Windows)" else ""
-            logger.info(f"Queues: {open_gl}YOLO: {yolo_size:>3}, Analysis: {analysis_size:>3}, DONE: {frames_processed:>3}")
+            progress_bar.set_postfix_str(
+                f"Queues: {open_gl}YOLO: {yolo_size:>3}, Analysis: {analysis_size:>3}"
+            )
+            progress_bar.refresh()
 
             if frames_processed >= total_frames:
                 stop_event.set()
 
-            time.sleep(0.5)
+            if state.update_ui:
+                elapsed_time = time.time() - state.analyze_task.start_time
+                processing_rate = frames_processed / elapsed_time if elapsed_time > 0 else 0
+                remaining_frames = total_frames - frames_processed
+                eta = remaining_frames / processing_rate if processing_rate > 0 else float('inf')
+                try:
+                    state.update_ui(ProgressMessage(
+                        process="OBJECT_DETECTION",
+                        frames_processed=frames_processed,
+                        total_frames=total_frames,
+                        eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
+                    ))
+                except Exception as e:
+                    logger.error(f"Error in state.update_ui: {e}")
+
+            time.sleep(UPDATE_PROGRESS_INTERVAL)
 
 def log_performance(state, results_queue):
     analyze_task = state.analyze_task
diff --git a/script_generator/scripts/generate_debug_video.py b/script_generator/scripts/generate_debug_video.py
index 4842a106..91f601b2 100644
--- a/script_generator/scripts/generate_debug_video.py
+++ b/script_generator/scripts/generate_debug_video.py
@@ -5,7 +5,7 @@
 from script_generator.debug.video_player.play import play_debug_video
 from script_generator.state.app_state import AppState
 from script_generator.utils.file import get_output_file_path
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 from script_generator.video.ffmpeg.hwaccel import get_hwaccel_read_args
 
 
diff --git a/script_generator/scripts/generate_funscript.py b/script_generator/scripts/generate_funscript.py
index 54f1ae17..8198db2a 100644
--- a/script_generator/scripts/generate_funscript.py
+++ b/script_generator/scripts/generate_funscript.py
@@ -1,6 +1,6 @@
 from script_generator.scripts.analyze_video import analyze_video
 from script_generator.state.app_state import AppState
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 
 
 def generate_funscript(state: AppState):
diff --git a/script_generator/scripts/tracking_analysis.py b/script_generator/scripts/tracking_analysis.py
index 5fc3b252..91efe7cd 100644
--- a/script_generator/scripts/tracking_analysis.py
+++ b/script_generator/scripts/tracking_analysis.py
@@ -3,7 +3,7 @@
 from script_generator.object_detection.analyze_tracking_results import analyze_tracking_results
 from script_generator.object_detection.utils import make_data_boxes, parse_yolo_data_looking_for_penis
 from script_generator.utils.file import load_json_from_file, get_output_file_path
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 from script_generator.video.info.video_info import get_cropped_dimensions
 from utils.lib_FunscriptHandler import FunscriptGenerator
 
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index beac1a56..acabcba4 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -20,8 +20,6 @@ def __init__(self, is_cli):
 
         # Gui/settings debug
         self.save_debug_file: bool = True
-        # TODO REMOVE
-        # self.save_debug_video: bool = False
         self.live_preview_mode: bool = False
         self.reference_script: string = None
 
@@ -36,7 +34,7 @@ def __init__(self, is_cli):
         self.vw_factor: float = 8.0
         self.rounding: int = 5
 
-        # TODO move this to a batch task class (so parallel inference is possible)
+        # TODO move this to a analyse results class
         self.funscript_data = []
         self.funscript_frames = []
         self.funscript_distances = []
diff --git a/script_generator/tasks/abstract_task_processor.py b/script_generator/tasks/abstract_task_processor.py
index e2ad7969..69f53241 100644
--- a/script_generator/tasks/abstract_task_processor.py
+++ b/script_generator/tasks/abstract_task_processor.py
@@ -4,7 +4,7 @@
 
 from script_generator.state.app_state import AppState
 from script_generator.tasks.tasks import AnalyzeFrameTask
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 
 
 class AbstractTaskProcessor(threading.Thread):
diff --git a/script_generator/utils/config.py b/script_generator/utils/config.py
index edd66c3a..e62d0af7 100644
--- a/script_generator/utils/config.py
+++ b/script_generator/utils/config.py
@@ -3,7 +3,7 @@
 
 import torch
 
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 
 # TODO replace with yaml
 def find_ffmpeg_path(win_ffmpeg_path, mac_ffmpeg_path, lin_ffmpeg_path):
diff --git a/script_generator/utils/file.py b/script_generator/utils/file.py
index 3990caf0..b57c42ca 100644
--- a/script_generator/utils/file.py
+++ b/script_generator/utils/file.py
@@ -3,7 +3,7 @@
 import time
 
 from script_generator.constants import OUTPUT_PATH
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 
 
 def write_json_to_file(file_path, data):
diff --git a/script_generator/video/ffmpeg/filters.py b/script_generator/video/ffmpeg/filters.py
index 003c3845..841ce0d4 100644
--- a/script_generator/video/ffmpeg/filters.py
+++ b/script_generator/video/ffmpeg/filters.py
@@ -1,4 +1,4 @@
-from config import RENDER_RESOLUTION, PITCH
+from config import RENDER_RESOLUTION, VR_TO_2D_PITCH
 
 
 def get_vr_video_filters(video, video_reader, hwaccel):
@@ -18,7 +18,7 @@ def get_vr_video_filters(video, video_reader, hwaccel):
             scale,
             crop,
             f"{out_format}v360={projection}:in_stereo=2d:output=sg:iv_fov={iv_fov}:ih_fov={ih_fov}:"
-            f"d_fov={d_fov}:v_fov={v_fov}:h_fov={h_fov}:pitch={PITCH}:yaw=0:roll=0:"
+            f"d_fov={d_fov}:v_fov={v_fov}:h_fov={h_fov}:pitch={VR_TO_2D_PITCH}:yaw=0:roll=0:"
             f"w={RENDER_RESOLUTION}:h={RENDER_RESOLUTION}:interp=lanczos:reset_rot=1",
             "lutyuv=y=gammaval(0.7)"
         ]
@@ -26,7 +26,7 @@ def get_vr_video_filters(video, video_reader, hwaccel):
         filters = [
             scale,
             crop,
-            f"{out_format}lutyuv=y=gammaval(0.7)"  # TODO Process in open gl and move scale and crop to the gpu
+            f"{out_format}lutyuv=y=gammaval(0.7)"
         ]
 
     return ",".join(filters)
diff --git a/script_generator/video/ffmpeg/hwaccel.py b/script_generator/video/ffmpeg/hwaccel.py
index c15b0b20..e03652e6 100644
--- a/script_generator/video/ffmpeg/hwaccel.py
+++ b/script_generator/video/ffmpeg/hwaccel.py
@@ -1,7 +1,7 @@
 import subprocess
 
 from config import FFMPEG_PATH
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 
 
 def get_hwaccel_support():
diff --git a/script_generator/video/info/video_info.py b/script_generator/video/info/video_info.py
index 100ae5e1..b9b4ff21 100644
--- a/script_generator/video/info/video_info.py
+++ b/script_generator/video/info/video_info.py
@@ -5,7 +5,7 @@
 
 from config import RENDER_RESOLUTION, FFPROBE_PATH
 from script_generator.debug.errors import FFProbeError
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 
 @dataclass
 class VideoInfo:
diff --git a/script_generator/video/workers/ffmpeg_worker.py b/script_generator/video/workers/ffmpeg_worker.py
index 75ee48c7..1b583121 100644
--- a/script_generator/video/workers/ffmpeg_worker.py
+++ b/script_generator/video/workers/ffmpeg_worker.py
@@ -5,7 +5,7 @@
 from script_generator.debug.errors import FFMpegError
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 from script_generator.tasks.tasks import AnalyzeFrameTask
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 from script_generator.video.ffmpeg.commands import get_ffmpeg_read_cmd
 
 
diff --git a/script_generator/video/workers/vr_to_2d_worker.py b/script_generator/video/workers/vr_to_2d_worker.py
index 2c9fa7f3..31bc1ebb 100644
--- a/script_generator/video/workers/vr_to_2d_worker.py
+++ b/script_generator/video/workers/vr_to_2d_worker.py
@@ -3,7 +3,7 @@
 from OpenGL.GL import *
 from OpenGL.GLU import *
 
-from config import RENDER_RESOLUTION, YAW, PITCH
+from config import RENDER_RESOLUTION, VR_TO_2D_PITCH
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 from script_generator.video.opengl.helpers import create_180_dome, render_dome
 
@@ -53,8 +53,8 @@ def task_logic(self):
         glLoadIdentity()
         # the 3th parameter will zoom in and out to increase / decrease the FOV
         gluLookAt(0, 0, 0.5, 0, 0, -1, 0, 1, 0)
-        glRotatef(-YAW, 0, 1, 0)
-        glRotatef(-(PITCH + 90), 1, 0, 0)
+        glRotatef(0, 0, 1, 0)
+        glRotatef(-(VR_TO_2D_PITCH + 90), 1, 0, 0)
 
         glBindTexture(GL_TEXTURE_2D, texture_id)
         glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_LINEAR)
diff --git a/setup.py b/setup.py
deleted file mode 100644
index ae5d3032..00000000
--- a/setup.py
+++ /dev/null
@@ -1,22 +0,0 @@
-from setuptools import setup, find_packages
-
-setup(
-    name="VR funscript creator",
-    version="0.0.1",  # Replace with your project version
-    author="k00gar",
-    description="Create funscripts for VR files",
-    long_description=open("README.md").read(),
-    long_description_content_type="text/markdown",
-    url="https://github.com/ack00gar/VR-Funscript-AI-Generator",
-    packages=find_packages(),
-    include_package_data=True,
-    install_requires=[
-        # TODO
-    ],
-    entry_points={
-        "console_scripts": [
-            # TODO
-        ],
-    },
-    python_requires="=3.11",
-)
diff --git a/tests/test_Compare_models_and_resolutions.py b/tests/test_Compare_models_and_resolutions.py
index 8ef8468f..399c3bd4 100644
--- a/tests/test_Compare_models_and_resolutions.py
+++ b/tests/test_Compare_models_and_resolutions.py
@@ -1,7 +1,7 @@
 import cv2
 from PIL import Image
 
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg
 from ultralytics import YOLO
 
diff --git a/tests/test_PoseEstimation.py b/tests/test_PoseEstimation.py
index fa4ad83e..a0c5ee40 100644
--- a/tests/test_PoseEstimation.py
+++ b/tests/test_PoseEstimation.py
@@ -3,7 +3,7 @@
 import cv2
 from collections import defaultdict
 from script_generator.constants import CLASS_REVERSE_MATCH
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 
 #video_path = "/Users/k00gar/Downloads/Katrina Jade [ADD] Oct 16, 2024R_6kvr265_reenc.mp4"
 #video_path = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
diff --git a/tests/test_generate_funscript_2.py b/tests/test_generate_funscript_2.py
deleted file mode 100644
index ca415e6a..00000000
--- a/tests/test_generate_funscript_2.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import os
-
-from script_generator.scripts.generate_funscript import generate_funscript
-from script_generator.state.app_state import AppState
-
-os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
-
-app_state = AppState(is_cli=True)
-app_state.video_path = "C:/cvr/funscript-generator/test_medium.mp4"
-app_state.frame_start = 60*60*10
-app_state.video_reader = "FFmpeg + OpenGL (Windows)"
-generate_funscript(app_state)
diff --git a/tests/test_generate_funscript_3.py b/tests/test_generate_funscript_3.py
deleted file mode 100644
index ca415e6a..00000000
--- a/tests/test_generate_funscript_3.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import os
-
-from script_generator.scripts.generate_funscript import generate_funscript
-from script_generator.state.app_state import AppState
-
-os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
-
-app_state = AppState(is_cli=True)
-app_state.video_path = "C:/cvr/funscript-generator/test_medium.mp4"
-app_state.frame_start = 60*60*10
-app_state.video_reader = "FFmpeg + OpenGL (Windows)"
-generate_funscript(app_state)
diff --git a/tests/test_generate_funscript_4.py b/tests/test_generate_funscript_4.py
deleted file mode 100644
index ca415e6a..00000000
--- a/tests/test_generate_funscript_4.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import os
-
-from script_generator.scripts.generate_funscript import generate_funscript
-from script_generator.state.app_state import AppState
-
-os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
-
-app_state = AppState(is_cli=True)
-app_state.video_path = "C:/cvr/funscript-generator/test_medium.mp4"
-app_state.frame_start = 60*60*10
-app_state.video_reader = "FFmpeg + OpenGL (Windows)"
-generate_funscript(app_state)
diff --git a/tests/test_generate_funscript_5.py b/tests/test_generate_funscript_5.py
deleted file mode 100644
index a73b42d6..00000000
--- a/tests/test_generate_funscript_5.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import os
-
-from script_generator.scripts.generate_funscript import generate_funscript
-from script_generator.state.app_state import AppState
-
-os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
-
-app_state = AppState(is_cli=True)
-app_state.video_path = "C:/cvr/funscript-generator/test.mp4"
-app_state.frame_start = 60*60*30
-app_state.video_reader = "FFmpeg"
-generate_funscript(app_state)
diff --git a/tests/test_generate_funscript_6.py b/tests/test_generate_funscript_6.py
deleted file mode 100644
index a73b42d6..00000000
--- a/tests/test_generate_funscript_6.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import os
-
-from script_generator.scripts.generate_funscript import generate_funscript
-from script_generator.state.app_state import AppState
-
-os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
-
-app_state = AppState(is_cli=True)
-app_state.video_path = "C:/cvr/funscript-generator/test.mp4"
-app_state.frame_start = 60*60*30
-app_state.video_reader = "FFmpeg"
-generate_funscript(app_state)
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 52e82bd3..08713dcd 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -1,22 +1,20 @@
+import datetime
+import json
 import logging
 import os
-import json
-import time
+import shutil
 
-from simplification.cutil import simplify_coords
-import numpy as np
 import cv2
-import datetime
-from scipy.signal import savgol_filter
-
-from config import STEP_SIZE, VERSION
-from script_generator.constants import HEATMAP_COLORS
-
 import matplotlib
+import numpy as np
+from scipy.signal import savgol_filter
+from simplification.cutil import simplify_coords
 
+from config import STEP_SIZE, VERSION, COPY_FUNSCRIPT_TO_MOVIE_LOCATION
+from script_generator.constants import HEATMAP_COLORS, FUNSCRIPT_AUTHOR
+from script_generator.debug.logger import logger
 from script_generator.state.app_state import AppState
-from script_generator.utils.file import get_output_file_path
-from script_generator.utils.logger import logger
+from script_generator.utils.file import get_output_file_path, load_json_from_file
 
 matplotlib.use('Agg')  # Use a non-interactive backend
 import matplotlib.pyplot as plt
@@ -30,11 +28,6 @@ class FunscriptGenerator:
     def generate(self, state):
         output_path, _ = get_output_file_path(state.video_path, ".funscript")
 
-        # Backup output file if it exists
-        if os.path.exists(output_path):
-            logger.info(f"Funscript {output_path} already exists, backing up as {output_path}_{time.time()}.bak...")
-            os.rename(output_path, output_path + f"_{time.time()}.bak")
-
         raw_funscript_path, _ = get_output_file_path(state.video_path, "_rawfunscript.json")
         if os.path.exists(raw_funscript_path) and (state.funscript_data is None or len(state.funscript_data) == 0):
             logger.info("len funscript data is 0, trying to load file")
@@ -118,6 +111,28 @@ def generate(self, state):
 
             # Write the final funscript
             self.write_funscript(zip_adjusted_positions, output_path, state.video_info.fps)
+
+            # copy funscript if specified
+            if COPY_FUNSCRIPT_TO_MOVIE_LOCATION:
+                copy = True
+                video_folder = os.path.dirname(state.video_path)
+                filename_base = os.path.basename(state.video_path)[:-4]
+                funscript_path = os.path.join(video_folder, filename_base, ".funscript")
+
+                # Backup output file if it exists
+                if os.path.exists(funscript_path):
+                    json_data = load_json_from_file(funscript_path)
+                    if json_data["author"] == FUNSCRIPT_AUTHOR:
+                        backup_path = os.path.join(video_folder, filename_base, f"{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.funscript.bak")
+                        logger.info(f"Funscript {funscript_path} already exists, backing up as {backup_path}...")
+                        os.rename(funscript_path, backup_path)
+                    else:
+                        copy = False
+                        logger.warn(f"Skipping copying funscript to movie directory as the script in the destination directory is not made by this app.")
+
+                if copy:
+                    shutil.copy(output_path, funscript_path)
+
             logger.info(f"Funscript generated and saved to {output_path}")
 
             # Generate a heatmap
@@ -127,7 +142,7 @@ def generate(self, state):
 
     def write_funscript(self, distances, output_path, fps):
         with open(output_path, 'w') as f:
-            f.write(f'{{"version":"{VERSION}","inverted":false,"range":95,"author":"FunGen_k00gar_AI","actions":[{{"at": 0, "pos": 100}},')
+            f.write(f'{{"version":"{VERSION}","inverted":false,"range":95,"author":"{FUNSCRIPT_AUTHOR}","actions":[{{"at": 0, "pos": 100}},')
             i = 0
             for frame, position in distances:
                 time_ms = int(frame * 1000 / fps)
@@ -165,7 +180,7 @@ def generate_heatmap(self, funscript_path, output_image_path):
 
             # Calculate speed (position change per time interval)
             # We add 1e-10 to prevent dividing by zero
-            speeds = np.abs(np.diff(filtered_positions) / (np.diff(filtered_times) + 1e-10)) * 1000 # Positions per second
+            speeds = np.abs(np.diff(filtered_positions) / (np.diff(filtered_times) + 1e-10)) * 1000  # Positions per second
 
             logger.debug(f"Speeds: {speeds}")
 
@@ -636,13 +651,8 @@ def get_color(intensity):
             ax.plot([x_start, x_end], [y_start, y_end], color=line_color, linewidth=2)
 
         # Customize plot
-        # Safely calculate the mean speed
-        # TODO fix nan at source
-        if len(speeds) == 0 or np.isnan(np.mean(speeds)):
-            avg_speed = 0  # Default to 0 if speeds are empty or contain NaN
-        else:
-            avg_speed = int(np.nanmean(speeds))  # Use nanmean to ignore NaN values
-        # TODO fix nan at source END
+        avg_speed = int(np.nanmean(speeds))
+
         # Set title with safe average speed
         ax.set_title(
             f'Funscript Heatmap\nDuration: {datetime.timedelta(seconds=int(times[-1] / 1000))} - Avg. Speed {avg_speed} - Actions: {len(times)}'
diff --git a/utils/lib_KalmanFilter.py b/utils/lib_KalmanFilter.py
index e388ed9c..fd057f17 100644
--- a/utils/lib_KalmanFilter.py
+++ b/utils/lib_KalmanFilter.py
@@ -1,6 +1,7 @@
 import numpy as np
 import cv2
 
+# TODO where is this used?
 class KalmanFilter:
     def __init__(self):
         self.kf = cv2.KalmanFilter(4, 2)
@@ -36,8 +37,7 @@ def predict(self):
         self.prediction_count += 1
         return prediction[0]
 
-
-
+# TODO where is this used?
 class KalmanFilter_distance:
     def __init__(self):
         # Initialize a 1D Kalman filter (state: [distance, velocity])
diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index b0c2064b..47ea86f0 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -3,7 +3,7 @@
 import numpy as np
 
 from script_generator.constants import CLASS_NAMES
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 
 
 class LockedPenisBox:
diff --git a/utils/lib_SceneCutsDetect.py b/utils/lib_SceneCutsDetect.py
index 7361bd1f..9a3deef1 100644
--- a/utils/lib_SceneCutsDetect.py
+++ b/utils/lib_SceneCutsDetect.py
@@ -6,9 +6,9 @@
 from config import UPDATE_PROGRESS_INTERVAL
 from script_generator.gui.messages.messages import ProgressMessage
 from script_generator.state.app_state import AppState
-from script_generator.utils.logger import logger
-
+from script_generator.debug.logger import logger
 
+# TODO where is this used? (implement in object detection pipeline?)
 def compute_histogram(frame):
     """
     Compute the normalized histogram of the H channel in the HSV color space.
@@ -20,7 +20,7 @@ def compute_histogram(frame):
     cv2.normalize(hist, hist)
     return hist
 
-
+# TODO where is this used? (implement in object detection pipeline?)
 def compare_histograms(hist1, hist2):
     """
     Compare two histograms using the correlation method.
@@ -30,7 +30,7 @@ def compare_histograms(hist1, hist2):
     """
     return cv2.compareHist(hist1, hist2, cv2.HISTCMP_CORREL)
 
-
+# TODO where is this used? (implement in object detection pipeline?)
 def detect_scene_changes(state: AppState, crop=None, threshold=0.97, frame_start=0, frame_end=None):
     cap = cv2.VideoCapture(state.video_path)
     if not cap.isOpened():
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
index 5190e931..dd8518c4 100644
--- a/utils/lib_VideoReaderFFmpeg.py
+++ b/utils/lib_VideoReaderFFmpeg.py
@@ -1,9 +1,10 @@
 import subprocess
+
 import cv2
 import numpy as np
-import argparse
+
 from config import FFMPEG_PATH, FFPROBE_PATH, RENDER_RESOLUTION
-from script_generator.utils.logger import logger
+from script_generator.debug.logger import logger
 from script_generator.video.info.video_info import get_video_info
 
 
@@ -55,16 +56,16 @@ def _initialize_video_info(self):
             self.duration = video_info.duration
 
             # limiting the frame height here has no additional performance cost and significantly improves speed for 1440p+ video
-            #scaling_factor = min(RENDER_RESOLUTION / self.width, RENDER_RESOLUTION / self.height)
+            # scaling_factor = min(RENDER_RESOLUTION / self.width, RENDER_RESOLUTION / self.height)
             # was having issues with the rendering of a 720p 2D video... < 1080p
             scaling_factor = min(1, RENDER_RESOLUTION / self.height)
-            #if scaling_factor > 1:
+            # if scaling_factor > 1:
             #    scaling_factor = 1
             self.width = int(self.width * scaling_factor)
             self.height = int(self.height * scaling_factor)
 
             logger.info(f"FPS: {self.fps}, Resolution: {self.width}x{self.height}, "
-                  f"Codec: {self.codec}, Total Frames: {self.total_frames}, Duration: {self.duration:.2f} ms")
+                        f"Codec: {self.codec}, Total Frames: {self.total_frames}, Duration: {self.duration:.2f} ms")
         except Exception as e:
             logger.error(f"Error initializing video info: {e}")
             raise
@@ -78,7 +79,7 @@ def _start_process(self, start_frame=0):
         self.current_frame_number = start_frame
 
         if self.is_vr:
-            #arg_line = "crop=w=iw/2:h=ih:x=0:y=0"
+            # arg_line = "crop=w=iw/2:h=ih:x=0:y=0"
             arg_line = ""
             if self.unwarp:
                 if self.projection == "FISHEYE" or (self.projection == None and "FISHEYE" in self.video_path.upper()):
@@ -104,14 +105,14 @@ def _start_process(self, start_frame=0):
                 arg_line = arg_line + f":w={self.width}:h={self.height}"
                 arg_line = arg_line + f":interp=lanczos:reset_rot=1"
                 arg_line = arg_line + f",lutyuv=y=gammaval(0.7)"
-                #arg_line = arg_line + f",eq=brightness=0.1:contrast=1.5"
-                #arg_line = arg_line + f",format=gray"
-                #arg_line = arg_line + f",histeq"
+                # arg_line = arg_line + f",eq=brightness=0.1:contrast=1.5"
+                # arg_line = arg_line + f",format=gray"
+                # arg_line = arg_line + f",histeq"
             else:
                 arg_line = "crop=w=iw/2:h=ih:x=0:y=0"
             # Add scale filter with height and auto-width (-1)
-            #arg_line += f",scale=-1:{target_height}"
-            #arg_line += f",scale=-1:{1080}"
+            # arg_line += f",scale=-1:{target_height}"
+            # arg_line += f",scale=-1:{1080}"
             # perf for on the fly downscale to 1080p were terrible
 
             cmd = [
@@ -124,7 +125,6 @@ def _start_process(self, start_frame=0):
                 "-map", "0:v:0",
                 "-vf", arg_line,
                 "-f", "rawvideo",  # Output raw video data
-                #"-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
                 "-pix_fmt", "bgr24",
                 "-vsync", "0",  # Disable frame rate synchronization
                 "-threads", "0",  # Use maximum threads available
@@ -140,8 +140,7 @@ def _start_process(self, start_frame=0):
                 "-i", self.video_path,
                 "-an",  # Disable audio processing
                 "-f", "rawvideo",  # Output raw video data
-                #"-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
-                "-pix_fmt", "bgr24",
+                "-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
                 "-vsync", "0",  # Disable frame rate synchronization
                 "-",  # Output to stdout
             ]
@@ -151,7 +150,7 @@ def _start_process(self, start_frame=0):
             self.process.terminate()
 
         # Start FFmpeg process
-        #self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+        # self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
         self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
         self.frame_size = self.width * self.height * 3  # Size of one frame in bytes
 
@@ -216,37 +215,3 @@ def release(self):
         if self.process:
             self.process.stdout.close()
             self.process = None
-
-
-def main(video_path, is_vr=False):
-    """
-    Display a video using the VideoReaderFFmpeg class.
-    :param video_path: Path to the video file.
-    :param is_vr: Whether the video is a VR video (default: False).
-    """
-    # Initialize the video reader
-    video_reader = VideoReaderFFmpeg(video_path, is_vr=is_vr)
-
-    # Display the video
-    while True:
-        ret, frame = video_reader.read()
-        if not ret:
-            break
-        cv2.imshow("Video", frame)
-        if cv2.waitKey(1) & 0xFF == ord('q'):  # Press 'q' to exit
-            break
-
-    # Release resources
-    video_reader.release()
-    cv2.destroyAllWindows()
-
-
-if __name__ == "__main__":
-    # Parse command-line arguments
-    parser = argparse.ArgumentParser(description="Display a video using FFmpeg.")
-    parser.add_argument("video_path", type=str, help="Path to the video file.")
-    parser.add_argument("--is_vr", action="store_true", help="Enable VR mode for processing VR videos.")
-    args = parser.parse_args()
-
-    # Run the main function
-    main(args.video_path, args.is_vr)
\ No newline at end of file

From 07b97c222e83d9131a994ec51c39ce1a5ea561ac Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 26 Jan 2025 10:47:06 +0100
Subject: [PATCH 158/277] Commented out currently unused code

---
 utils/lib_KalmanFilter.py    | 161 +++++++++++-----------
 utils/lib_SceneCutsDetect.py | 256 +++++++++++++++++------------------
 2 files changed, 207 insertions(+), 210 deletions(-)

diff --git a/utils/lib_KalmanFilter.py b/utils/lib_KalmanFilter.py
index fd057f17..548ceeaa 100644
--- a/utils/lib_KalmanFilter.py
+++ b/utils/lib_KalmanFilter.py
@@ -1,84 +1,83 @@
 import numpy as np
 import cv2
 
-# TODO where is this used?
-class KalmanFilter:
-    def __init__(self):
-        self.kf = cv2.KalmanFilter(4, 2)
-        self.kf.measurementMatrix = np.array([[1, 0, 0, 0],
-                                              [0, 1, 0, 0]], np.float32)
-        self.kf.transitionMatrix = np.array([[1, 0, 1, 0],
-                                             [0, 1, 0, 1],
-                                             [0, 0, 1, 0],
-                                             [0, 0, 0, 1]], np.float32)
-        self.kf.processNoiseCov = np.eye(4, dtype=np.float32) * 0.03
-        self.position = None
-        self.detected = False
-        self.prediction_count = 0
-        self.half_height = 20
-        self.half_width = 20
-        self.touching = False
-
-    def update(self, measurement):
-        measured = np.array([[np.float32(measurement[0])],
-                             [np.float32(measurement[1])]])
-        corrected_state = self.kf.correct(measured)
-        #self.position = measurement
-        self.position = (corrected_state[0], corrected_state[1])
-        self.detected = True
-        self.prediction_count = 0
-        return float(corrected_state[0])
-
-    def predict(self):
-        prediction = self.kf.predict()
-        self.position = (prediction[0] - self.half_width, prediction[1] - self.half_height,
-                         prediction[0] + self.half_width, prediction[1] + self.half_height)
-        self.detected = False
-        self.prediction_count += 1
-        return prediction[0]
-
-# TODO where is this used?
-class KalmanFilter_distance:
-    def __init__(self):
-        # Initialize a 1D Kalman filter (state: [distance, velocity])
-        self.kf = cv2.KalmanFilter(2, 1)
-
-        # Measurement matrix (we only observe distance)
-        self.kf.measurementMatrix = np.array([[1, 0]], np.float32)
-
-        # Transition matrix (assume constant velocity model)
-        self.kf.transitionMatrix = np.array([[1, 1],
-                                             [0, 1]], np.float32)
-
-        # Process noise covariance (tune these values)
-        self.kf.processNoiseCov = np.eye(2, dtype=np.float32) * 0.01
-
-        # Measurement noise covariance (tune this value)
-        self.kf.measurementNoiseCov = np.array([[1]], np.float32) * 0.1
-
-        # Initial state covariance (tune these values)
-        self.kf.errorCovPost = np.eye(2, dtype=np.float32) * 1
-
-        # Initialize state (distance = 100, velocity = 0)
-        self.kf.statePost = np.array([[100], [0]], np.float32)
-
-        self.distance = 100  # Current filtered distance
-        self.detected = False  # Whether a measurement was detected
-
-    def update(self, measurement):
-        """
-        Update the Kalman filter with a new distance measurement.
-        """
-        measured = np.array([[np.float32(measurement)]])
-        self.kf.correct(measured)
-        self.distance = self.kf.statePost[0, 0]
-        self.detected = True
-
-    def predict(self):
-        """
-        Predict the next distance using the Kalman filter.
-        """
-        prediction = self.kf.predict()
-        self.distance = prediction[0, 0]
-        self.detected = False
-        return self.distance
+# TODO re-implement?
+# class KalmanFilter:
+#     def __init__(self):
+#         self.kf = cv2.KalmanFilter(4, 2)
+#         self.kf.measurementMatrix = np.array([[1, 0, 0, 0],
+#                                               [0, 1, 0, 0]], np.float32)
+#         self.kf.transitionMatrix = np.array([[1, 0, 1, 0],
+#                                              [0, 1, 0, 1],
+#                                              [0, 0, 1, 0],
+#                                              [0, 0, 0, 1]], np.float32)
+#         self.kf.processNoiseCov = np.eye(4, dtype=np.float32) * 0.03
+#         self.position = None
+#         self.detected = False
+#         self.prediction_count = 0
+#         self.half_height = 20
+#         self.half_width = 20
+#         self.touching = False
+#
+#     def update(self, measurement):
+#         measured = np.array([[np.float32(measurement[0])],
+#                              [np.float32(measurement[1])]])
+#         corrected_state = self.kf.correct(measured)
+#         #self.position = measurement
+#         self.position = (corrected_state[0], corrected_state[1])
+#         self.detected = True
+#         self.prediction_count = 0
+#         return float(corrected_state[0])
+#
+#     def predict(self):
+#         prediction = self.kf.predict()
+#         self.position = (prediction[0] - self.half_width, prediction[1] - self.half_height,
+#                          prediction[0] + self.half_width, prediction[1] + self.half_height)
+#         self.detected = False
+#         self.prediction_count += 1
+#         return prediction[0]
+#
+# class KalmanFilter_distance:
+#     def __init__(self):
+#         # Initialize a 1D Kalman filter (state: [distance, velocity])
+#         self.kf = cv2.KalmanFilter(2, 1)
+#
+#         # Measurement matrix (we only observe distance)
+#         self.kf.measurementMatrix = np.array([[1, 0]], np.float32)
+#
+#         # Transition matrix (assume constant velocity model)
+#         self.kf.transitionMatrix = np.array([[1, 1],
+#                                              [0, 1]], np.float32)
+#
+#         # Process noise covariance (tune these values)
+#         self.kf.processNoiseCov = np.eye(2, dtype=np.float32) * 0.01
+#
+#         # Measurement noise covariance (tune this value)
+#         self.kf.measurementNoiseCov = np.array([[1]], np.float32) * 0.1
+#
+#         # Initial state covariance (tune these values)
+#         self.kf.errorCovPost = np.eye(2, dtype=np.float32) * 1
+#
+#         # Initialize state (distance = 100, velocity = 0)
+#         self.kf.statePost = np.array([[100], [0]], np.float32)
+#
+#         self.distance = 100  # Current filtered distance
+#         self.detected = False  # Whether a measurement was detected
+#
+#     def update(self, measurement):
+#         """
+#         Update the Kalman filter with a new distance measurement.
+#         """
+#         measured = np.array([[np.float32(measurement)]])
+#         self.kf.correct(measured)
+#         self.distance = self.kf.statePost[0, 0]
+#         self.detected = True
+#
+#     def predict(self):
+#         """
+#         Predict the next distance using the Kalman filter.
+#         """
+#         prediction = self.kf.predict()
+#         self.distance = prediction[0, 0]
+#         self.detected = False
+#         return self.distance
diff --git a/utils/lib_SceneCutsDetect.py b/utils/lib_SceneCutsDetect.py
index 9a3deef1..7c33ccfc 100644
--- a/utils/lib_SceneCutsDetect.py
+++ b/utils/lib_SceneCutsDetect.py
@@ -8,132 +8,130 @@
 from script_generator.state.app_state import AppState
 from script_generator.debug.logger import logger
 
-# TODO where is this used? (implement in object detection pipeline?)
-def compute_histogram(frame):
-    """
-    Compute the normalized histogram of the H channel in the HSV color space.
-    :param frame: Input frame (BGR format).
-    :return: Normalized histogram.
-    """
-    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
-    hist = cv2.calcHist([hsv], [0], None, [256], [0, 256])
-    cv2.normalize(hist, hist)
-    return hist
-
-# TODO where is this used? (implement in object detection pipeline?)
-def compare_histograms(hist1, hist2):
-    """
-    Compare two histograms using the correlation method.
-    :param hist1: First histogram.
-    :param hist2: Second histogram.
-    :return: Similarity score (higher values indicate greater similarity).
-    """
-    return cv2.compareHist(hist1, hist2, cv2.HISTCMP_CORREL)
-
-# TODO where is this used? (implement in object detection pipeline?)
-def detect_scene_changes(state: AppState, crop=None, threshold=0.97, frame_start=0, frame_end=None):
-    cap = cv2.VideoCapture(state.video_path)
-    if not cap.isOpened():
-        logger.error("Error: Could not open video.")
-        return []
-
-    # Get video properties
-    fps = int(cap.get(cv2.CAP_PROP_FPS))
-    total_frames_base = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-    frame_step = fps  # Process every second of video
-
-    # Adjust frame range based on input
-    if frame_end is None:
-        frame_end = total_frames_base
-    total_frames = frame_end - frame_start
-    total_frames_to_parse = int(total_frames / frame_step)
-
-    # Initialize variables
-    scene_changes = []
-    prev_hist = None
-    prev_cut = frame_start
-    start_time = time.time()
-    last_ui_update_time = time.time()
-
-    # Process frames
-    for frame_pos in tqdm(
-            range(total_frames_to_parse), desc="Detecting scene changes", position=0,
-            unit_scale=False,
-            unit_divisor=1,
-            ncols=130
-    ):
-        cap.set(cv2.CAP_PROP_POS_FRAMES, frame_start + frame_pos * frame_step)
-        ret, frame = cap.read()
-        if not ret:
-            break
-
-        # Crop frame if specified
-        if crop == "Left":
-            frame = frame[:, :frame.shape[1] // 2]
-        elif crop == "Right":
-            frame = frame[:, frame.shape[1] // 2:]
-
-        # Compute histogram and compare with previous
-        current_hist = compute_histogram(frame)
-        if prev_hist is not None:
-            similarity = compare_histograms(prev_hist, current_hist)
-            if similarity < threshold:
-                # Scene change detected
-                current_frame = frame_start + frame_pos * frame_step
-                logger.info(
-                    f"Scene change detected at frame {current_frame}, "
-                    f"time: {int(cap.get(cv2.CAP_PROP_POS_MSEC) / 1000 // 60)} min "
-                    f"{int(cap.get(cv2.CAP_PROP_POS_MSEC) / 1000 % 60)} sec"
-                )
-                scene_changes.append([prev_cut, current_frame])
-                prev_cut = current_frame
-
-        prev_hist = current_hist
-
-        # Update progress
-        if state.update_ui:
-
-            current_time = time.time()
-            if current_time - last_ui_update_time >= UPDATE_PROGRESS_INTERVAL:
-                last_ui_update_time = time.time()
-                elapsed_time = time.time() - start_time
-                frames_processed = frame_pos - state.frame_start + 1
-                frames_remaining = state.frame_end - frame_pos - 1
-                eta = (elapsed_time / frames_processed) * frames_remaining if frames_processed > 0 else 0
-
-                state.update_ui(ProgressMessage(
-                    process="SCENE_DETECTION",
-                    frames_processed=frames_processed,
-                    total_frames=state.frame_end,
-                    eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
-                ))
-
-    # Add the last scene
-    if not scene_changes:
-        scene_changes.append([frame_start, frame_end])
-    elif scene_changes[-1][1] != frame_end:
-        scene_changes.append([scene_changes[-1][1], frame_end])
-
-    logger.info(f"Found {len(scene_changes)} raw scenes: {scene_changes}.")
-    logger.info(f"Scene changes: {scene_changes}")
-    logger.info(f"Merging short scenes...")
-
-    # Merge only short scenes
-    merged_scenes = []
-    min_scene_length = 1000  # Minimum scene length in frames (adjust as needed)
-    for scene in scene_changes:
-        scene_length = scene[1] - scene[0]
-        if scene_length < min_scene_length:
-            # Merge short scenes with the previous or next scene
-            if merged_scenes:
-                merged_scenes[-1][1] = scene[1]  # Merge with the previous scene
-            else:
-                merged_scenes.append(scene)  # Add the first scene
-        else:
-            # Keep scenes longer than the minimum length
-            merged_scenes.append(scene)
-
-    logger.info(f"Found {len(merged_scenes)} relevant scenes: {merged_scenes}.")
-    cap.release()
-
-    return merged_scenes
+# TODO Re-implement? (implement in object detection pipeline?)
+# def compute_histogram(frame):
+#     """
+#     Compute the normalized histogram of the H channel in the HSV color space.
+#     :param frame: Input frame (BGR format).
+#     :return: Normalized histogram.
+#     """
+#     hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+#     hist = cv2.calcHist([hsv], [0], None, [256], [0, 256])
+#     cv2.normalize(hist, hist)
+#     return hist
+#
+# def compare_histograms(hist1, hist2):
+#     """
+#     Compare two histograms using the correlation method.
+#     :param hist1: First histogram.
+#     :param hist2: Second histogram.
+#     :return: Similarity score (higher values indicate greater similarity).
+#     """
+#     return cv2.compareHist(hist1, hist2, cv2.HISTCMP_CORREL)
+#
+# def detect_scene_changes(state: AppState, crop=None, threshold=0.97, frame_start=0, frame_end=None):
+#     cap = cv2.VideoCapture(state.video_path)
+#     if not cap.isOpened():
+#         logger.error("Error: Could not open video.")
+#         return []
+#
+#     # Get video properties
+#     fps = int(cap.get(cv2.CAP_PROP_FPS))
+#     total_frames_base = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+#     frame_step = fps  # Process every second of video
+#
+#     # Adjust frame range based on input
+#     if frame_end is None:
+#         frame_end = total_frames_base
+#     total_frames = frame_end - frame_start
+#     total_frames_to_parse = int(total_frames / frame_step)
+#
+#     # Initialize variables
+#     scene_changes = []
+#     prev_hist = None
+#     prev_cut = frame_start
+#     start_time = time.time()
+#     last_ui_update_time = time.time()
+#
+#     # Process frames
+#     for frame_pos in tqdm(
+#             range(total_frames_to_parse), desc="Detecting scene changes", position=0,
+#             unit_scale=False,
+#             unit_divisor=1,
+#             ncols=130
+#     ):
+#         cap.set(cv2.CAP_PROP_POS_FRAMES, frame_start + frame_pos * frame_step)
+#         ret, frame = cap.read()
+#         if not ret:
+#             break
+#
+#         # Crop frame if specified
+#         if crop == "Left":
+#             frame = frame[:, :frame.shape[1] // 2]
+#         elif crop == "Right":
+#             frame = frame[:, frame.shape[1] // 2:]
+#
+#         # Compute histogram and compare with previous
+#         current_hist = compute_histogram(frame)
+#         if prev_hist is not None:
+#             similarity = compare_histograms(prev_hist, current_hist)
+#             if similarity < threshold:
+#                 # Scene change detected
+#                 current_frame = frame_start + frame_pos * frame_step
+#                 logger.info(
+#                     f"Scene change detected at frame {current_frame}, "
+#                     f"time: {int(cap.get(cv2.CAP_PROP_POS_MSEC) / 1000 // 60)} min "
+#                     f"{int(cap.get(cv2.CAP_PROP_POS_MSEC) / 1000 % 60)} sec"
+#                 )
+#                 scene_changes.append([prev_cut, current_frame])
+#                 prev_cut = current_frame
+#
+#         prev_hist = current_hist
+#
+#         # Update progress
+#         if state.update_ui:
+#
+#             current_time = time.time()
+#             if current_time - last_ui_update_time >= UPDATE_PROGRESS_INTERVAL:
+#                 last_ui_update_time = time.time()
+#                 elapsed_time = time.time() - start_time
+#                 frames_processed = frame_pos - state.frame_start + 1
+#                 frames_remaining = state.frame_end - frame_pos - 1
+#                 eta = (elapsed_time / frames_processed) * frames_remaining if frames_processed > 0 else 0
+#
+#                 state.update_ui(ProgressMessage(
+#                     process="SCENE_DETECTION",
+#                     frames_processed=frames_processed,
+#                     total_frames=state.frame_end,
+#                     eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
+#                 ))
+#
+#     # Add the last scene
+#     if not scene_changes:
+#         scene_changes.append([frame_start, frame_end])
+#     elif scene_changes[-1][1] != frame_end:
+#         scene_changes.append([scene_changes[-1][1], frame_end])
+#
+#     logger.info(f"Found {len(scene_changes)} raw scenes: {scene_changes}.")
+#     logger.info(f"Scene changes: {scene_changes}")
+#     logger.info(f"Merging short scenes...")
+#
+#     # Merge only short scenes
+#     merged_scenes = []
+#     min_scene_length = 1000  # Minimum scene length in frames (adjust as needed)
+#     for scene in scene_changes:
+#         scene_length = scene[1] - scene[0]
+#         if scene_length < min_scene_length:
+#             # Merge short scenes with the previous or next scene
+#             if merged_scenes:
+#                 merged_scenes[-1][1] = scene[1]  # Merge with the previous scene
+#             else:
+#                 merged_scenes.append(scene)  # Add the first scene
+#         else:
+#             # Keep scenes longer than the minimum length
+#             merged_scenes.append(scene)
+#
+#     logger.info(f"Found {len(merged_scenes)} relevant scenes: {merged_scenes}.")
+#     cap.release()
+#
+#     return merged_scenes

From 89e7e5dbb0948fd612b220c91218e26cde7bd6ef Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 26 Jan 2025 12:59:02 +0100
Subject: [PATCH 159/277] Implemented cli support

---
 README.md                                     |   5 +-
 config.py                                     |  10 +-
 script_generator/cli/generate_funscript.py    | 126 +++++++++++++++++-
 .../gui/controller/process_video.py           |  13 +-
 script_generator/gui/utils/widgets.py         |   6 +-
 script_generator/object_detection/utils.py    |  40 +++---
 .../object_detection/yolo_worker.py           |   7 +-
 script_generator/scripts/analyze_video.py     |  15 ++-
 .../scripts/generate_funscript.py             |  22 ++-
 script_generator/state/app_state.py           |  23 ++++
 .../tasks/abstract_task_processor.py          |   3 +-
 tests/test_cli.py                             |  20 +++
 ...=> test_compare_models_and_resolutions.py} |   0
 ...eEstimation.py => test_pose_estimation.py} |   0
 utils/lib_FunscriptHandler.py                 |   9 +-
 15 files changed, 238 insertions(+), 61 deletions(-)
 create mode 100644 tests/test_cli.py
 rename tests/{test_Compare_models_and_resolutions.py => test_compare_models_and_resolutions.py} (100%)
 rename tests/{test_PoseEstimation.py => test_pose_estimation.py} (100%)

diff --git a/README.md b/README.md
index 18ca73d3..ffca991e 100644
--- a/README.md
+++ b/README.md
@@ -165,7 +165,7 @@ You can use Start windows.bat to launch the gui on windows if you installed with
 To generate a script with cmd or terminal, run the following command
 
 ```bash
-python script_generator.cli.generate_funscript.py /path/to/video.mp4
+python -m script_generator.cli.generate_funscript.py /path/to/video.mp4
 ```
 ---
 
@@ -195,9 +195,6 @@ The script generates the following files in the output directory of you project
 7. `_adjusted.funscript`: Funscript file with adjusted amplitude.
 8. `_debug_logs.json`: Contains all the raw metrics collected and can be used to debug your video when processing is completed.
 
-In config.py you can set COPY_FUNSCRIPT_TO_MOVIE_LOCATION to True to copy the generated script to the same directory as your video.
-If a funscript already exists it will be renamed to {funscriptName}_{date}.funscript.bak
-
 ---
 
 ## Contributing
diff --git a/config.py b/config.py
index 272ab720..21593688 100644
--- a/config.py
+++ b/config.py
@@ -20,12 +20,6 @@
 mac_ffprobe_path = "/usr/local/bin/ffprobe"
 lin_ffprobe_path = "/usr/bin/ffprobe"
 
-##################################################################################################
-# USER PREFERENCES
-##################################################################################################
-
-COPY_FUNSCRIPT_TO_MOVIE_LOCATION = False
-
 ##################################################################################################
 # PERFORMANCE
 ##################################################################################################
@@ -60,11 +54,9 @@
 # PROG
 ##################################################################################################
 
-# Preload the YOLO modal on launch
-YOLO_MODEL = YOLO(MODEL_PATH, task="detect")
 RUN_POSE_MODEL = False
 YOLO_POSE_MODEL = None # YOLO("models/yolo11n-pose.mlpackage", task="pose") #TODO pose model?
-
+VALID_VIDEO_READERS = ["FFmpeg", "FFmpeg + OpenGL (Windows)"]
 FFMPEG_PATH = find_ffmpeg_path(win_ffmpeg_path, mac_ffmpeg_path, lin_ffmpeg_path)
 FFPROBE_PATH = find_ffprobe_path(win_ffprobe_path, mac_ffprobe_path, lin_ffprobe_path)
 logger.info(f"ffmpeg_path: {FFMPEG_PATH}")
diff --git a/script_generator/cli/generate_funscript.py b/script_generator/cli/generate_funscript.py
index 391098c0..6c496fd0 100644
--- a/script_generator/cli/generate_funscript.py
+++ b/script_generator/cli/generate_funscript.py
@@ -1,7 +1,13 @@
 import argparse
+import os
+
+from config import VALID_VIDEO_READERS
 from script_generator.scripts.generate_funscript import generate_funscript
 from script_generator.debug.logger import logger
+from script_generator.state.app_state import AppState
+from script_generator.utils.helpers import is_mac
 
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
 def main():
     parser = argparse.ArgumentParser(
@@ -12,15 +18,131 @@ def main():
         type=str,
         help="Path to the input video file."
     )
+    parser.add_argument(
+        "--reuse-yolo",
+        type=bool,
+        default=False,
+        help="Use an existing raw YOLO output file instead of generating a new one. Default is False."
+    )
+    parser.add_argument(
+        "--copy-funscript",
+        type=bool,
+        default=True,
+        help="Copies final funscript to the movie directory. Default is True."
+    )
+    parser.add_argument(
+        "--frame-start",
+        type=int,
+        default=0,
+        help="The starting frame number for processing. Default is 0 (start from the beginning)."
+    )
+    parser.add_argument(
+        "--frame-end",
+        type=int or None,
+        default=None,
+        help="The ending frame number for processing. Default is None (process till the end)."
+    )
+    parser.add_argument(
+        "--video-reader",
+        type=str,
+        default=None,
+        help=f"Video reader to use. Valid options: {', '.join(VALID_VIDEO_READERS)}. Default is platform-specific."
+    )
+    parser.add_argument(
+        "--save-debug-file",
+        type=bool,
+        default=False,
+        help="Saves a debug file to disk with all collected metrics. This file is required for playing back the debug video with the debug statistics overlay."
+    )
+    parser.add_argument(
+        "--boost-enabled",
+        type=bool,
+        default=True,
+        help="Funscript tweaking setting: Enable boosting to adjust the motion range dynamically. Default is True."
+    )
+    parser.add_argument(
+        "--boost-up-percent",
+        type=int,
+        default=10,
+        help="Funscript tweaking setting: Increase the peaks by a specified percentage to enhance upper motion limits. Default is 10%."
+    )
+    parser.add_argument(
+        "--boost-down-percent",
+        type=int,
+        default=15,
+        help="Funscript tweaking setting: Reduce the lower peaks by a specified percentage to limit downward motion. Default is 15%."
+    )
+    parser.add_argument(
+        "--threshold-enabled",
+        type=bool,
+        default=True,
+        help="Funscript tweaking setting: Enable thresholding to control motion mapping within specified bounds. Default is True."
+    )
+    parser.add_argument(
+        "--threshold-low",
+        type=int,
+        default=10,
+        help="Funscript tweaking setting: Values below this threshold are mapped to 0, limiting lower boundary motion. Default is 10."
+    )
+    parser.add_argument(
+        "--threshold-high",
+        type=int,
+        default=90,
+        help="Funscript tweaking setting: Values above this threshold are mapped to 100, limiting upper boundary motion. Default is 90."
+    )
+    parser.add_argument(
+        "--vw-simplification-enabled",
+        type=bool,
+        default=True,
+        help="Funscript tweaking setting: Simplify the generated script to reduce the number of points, making it user-friendly. Default is True."
+    )
+    parser.add_argument(
+        "--vw-factor",
+        type=float,
+        default=8.0,
+        help="Funscript tweaking setting: Determines the degree of simplification. Higher values lead to fewer points. Default is 8.0."
+    )
+    parser.add_argument(
+        "--rounding",
+        type=int,
+        default=5,
+        help="Funscript tweaking setting: Set the rounding factor for script values to adjust precision. Default is 5."
+    )
     args = parser.parse_args()
 
+    video_reader = args.video_reader
+    default_video_reader = "FFmpeg" if is_mac() else "FFmpeg + OpenGL (Windows)"
+    if video_reader and video_reader not in VALID_VIDEO_READERS:
+        logger.warning(
+            f"Invalid video reader specified: {video_reader}. Using default: {default_video_reader}."
+        )
+        args.video_reader = default_video_reader
+
     try:
         logger.info(f"Processing video: {args.video_path}")
-        generate_funscript(args.video_path)
+        state = AppState(is_cli=True)
+        state.video_path = args.video_path
+        state.use_existing_raw_yolo = args.reuse_yolo
+        state.copy_funscript_to_movie_dir = args.copy_funscript
+        state.frame_start = args.frame_start
+        state.frame_end = args.frame_end
+        state.video_reader = args.video_reader
+        state.save_debug_file = args.save_debug_file
+        state.boost_enabled = args.boost_enabled
+        state.boost_up_percent = args.boost_up_percent
+        state.boost_down_percent = args.boost_down_percent
+        state.threshold_enabled = args.threshold_enabled
+        state.threshold_low = args.threshold_low
+        state.threshold_high = args.threshold_high
+        state.vw_simplification_enabled = args.vw_simplification_enabled
+        state.vw_factor = args.vw_factor
+        state.rounding = args.rounding
+
+        generate_funscript(state)
         logger.info("Funscript generation complete.")
     except Exception as e:
         logger.error(f"An error occurred: {e}")
 
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()
diff --git a/script_generator/gui/controller/process_video.py b/script_generator/gui/controller/process_video.py
index 1f666f11..c3e0838d 100644
--- a/script_generator/gui/controller/process_video.py
+++ b/script_generator/gui/controller/process_video.py
@@ -5,22 +5,18 @@
 from script_generator.gui.messages.messages import ProgressMessage
 from script_generator.object_detection.utils import check_skip_object_detection
 from script_generator.scripts.analyze_video import analyze_video
+from script_generator.state.app_state import AppState, log_state_settings
 from script_generator.utils.helpers import to_int_or_none
 from script_generator.debug.logger import logger
 
 
-def video_analysis(state, root):
+def video_analysis(state: AppState, root):
     if not state.video_path:
         messagebox.showerror("Error", "Please select a video file.")
         return
 
-    logger.info(f"Processing video: {state.video_path}")
-    logger.info(f"Video Reader: {state.video_reader}")
-    logger.info(f"Debug Mode: {state.save_debug_file}")
-    logger.info(f"Live Preview Mode: {state.live_preview_mode}")
-    logger.info(f"Frame Start: {state.frame_start}")
-    logger.info(f"Frame End: {state.frame_end}")
-    logger.info(f"Reference Script: {state.reference_script}")
+    state.set_video_info()
+    log_state_settings(state)
 
     state.frame_start = to_int_or_none(state.frame_start)
     state.frame_end = to_int_or_none(state.frame_end)
@@ -32,7 +28,6 @@ def run():
             if choice == "cancel":
                 return
             elif choice == "use_existing":
-                state.set_video_info()
                 if state.update_ui:
                     state.update_ui(ProgressMessage(
                         process="OBJECT_DETECTION",
diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index 15d304d3..03a0c49d 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -411,7 +411,7 @@ def frames_to_hms(frames, fps):
         def on_fps_change(*_):
             """ Recalculate HH:MM:SS whenever FPS or 'value' changes (externally). """
             fps = getattr(state.video_info, "fps", 0) or 0
-            fps_label.config(text=" {state.video_info.fps} fps" if state.video_info else ' ? fps')
+            # fps_label.config(text=" {state.video_info.fps} fps" if state.video_info else ' ? fps')
 
             try:
                 f_ = int(value.get())
@@ -503,8 +503,8 @@ def make_entry(parent_, textvar, width=4):
         seconds_entry = make_entry(entry_container, seconds_var, width=6)
         tk.Label(entry_container, text=" frame ").pack(side="left")
         frames_entry = make_entry(entry_container, frames_var, width=23)
-        fps_label = tk.Label(entry_container, text=" ? fps")
-        fps_label.pack(side="left")
+        # fps_label = tk.Label(entry_container, text=" ? fps")
+        # fps_label.pack(side="left")
 
         # Optional tooltip
         if tooltip_text:
diff --git a/script_generator/object_detection/utils.py b/script_generator/object_detection/utils.py
index bc1e281d..f7a4e820 100644
--- a/script_generator/object_detection/utils.py
+++ b/script_generator/object_detection/utils.py
@@ -9,38 +9,46 @@
 
 
 def check_skip_object_detection(state, root):
-    raw_yolo_path, raw_yolo_filename = get_output_file_path(state.video_path, "_rawyolo.json")
-    if os.path.exists(raw_yolo_path):
-        # TODO only load the json once (not in this check and later in the process)
-        yolo_data = load_json_from_file(raw_yolo_path)
-        if len(yolo_data) == 0:
-            logger.warn(f"Raw yolo data file doesn't contain any data: {raw_yolo_path}")
-            try:
-                os.remove(raw_yolo_path)
-                logger.info(f"Deleted empty raw yolo data file: {raw_yolo_path}")
-            except OSError as e:
-                logger.error(f"Error deleting raw yolo file {raw_yolo_path}: {e}")
-            return "generate"
-
+    exists, path, filename = raw_yolo_file_exists(state)
+    if exists:
         choice = Widgets.messagebox(
             "Detection File Conflict",
-            f"The file already exists. What would you like to do?\n{raw_yolo_filename}",
+            f"The file already exists. What would you like to do?\n{filename}",
             "Generate New",
             "Use Existing",
             root,
             height=150
         )
         if choice == "no":
-            logger.info(f"File {raw_yolo_path} already exists. Skipping detections and loading file content...")
+            logger.info(f"File {path} already exists. Skipping detections and loading file content...")
             return "use_existing"
         elif choice == "yes":
-            os.remove(raw_yolo_path)
+            os.remove(path)
             return "generate"
 
         return "cancel"
 
     return "generate"
 
+
+def raw_yolo_file_exists(state):
+    """
+    Checks if the YOLO file exists
+    """
+    raw_yolo_path, raw_yolo_filename = get_output_file_path(state.video_path, "_rawyolo.json")
+    if os.path.exists(raw_yolo_path):
+        yolo_data = load_json_from_file(raw_yolo_path)
+        if len(yolo_data) == 0:
+            logger.warn(f"Raw YOLO data file doesn't contain any data: {raw_yolo_path}")
+            try:
+                os.remove(raw_yolo_path)
+                logger.info(f"Deleted empty raw YOLO data file: {raw_yolo_path}")
+            except OSError as e:
+                logger.error(f"Error deleting raw YOLO file {raw_yolo_path}: {e}")
+        else:
+            return True, raw_yolo_path, raw_yolo_filename
+    return False, None, None
+
 def make_data_boxes(records):
     """
     Convert YOLO records into BoxRecord objects.
diff --git a/script_generator/object_detection/yolo_worker.py b/script_generator/object_detection/yolo_worker.py
index 65ee6827..696fcf4b 100644
--- a/script_generator/object_detection/yolo_worker.py
+++ b/script_generator/object_detection/yolo_worker.py
@@ -1,6 +1,9 @@
 import time
 
-from config import YOLO_CONF, YOLO_BATCH_SIZE, YOLO_MODEL, YOLO_PERSIST
+from ultralytics import YOLO
+
+from config import YOLO_CONF, YOLO_BATCH_SIZE, YOLO_PERSIST
+from script_generator.constants import MODEL_PATH
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 
 
@@ -33,7 +36,7 @@ def task_logic(self):
     def process_batch(self, frames, tasks):
         start_time = time.time()
         # Yolo expects bgr images when using numpy frames
-        yolo_results = YOLO_MODEL.track(frames, persist=YOLO_PERSIST, conf=YOLO_CONF, verbose=False)
+        yolo_results = self.state.yolo_model.track(frames, persist=YOLO_PERSIST, conf=YOLO_CONF, verbose=False)
         avg_time = (time.time() - start_time) / len(tasks)
 
         for t, result in zip(tasks, yolo_results):
diff --git a/script_generator/scripts/analyze_video.py b/script_generator/scripts/analyze_video.py
index fabcd7d8..768ad161 100644
--- a/script_generator/scripts/analyze_video.py
+++ b/script_generator/scripts/analyze_video.py
@@ -95,12 +95,13 @@ def run_thread(thread, thread_name, out_queue):
 
         log_performance(state=state, results_queue=result_q)
 
-        state.update_ui(ProgressMessage(
-            process="OBJECT_DETECTION",
-            frames_processed=state.video_info.total_frames,
-            total_frames=state.video_info.total_frames,
-            eta="Done"
-        ))
+        if state.update_ui:
+            state.update_ui(ProgressMessage(
+                process="OBJECT_DETECTION",
+                frames_processed=state.video_info.total_frames,
+                total_frames=state.video_info.total_frames,
+                eta="Done"
+            ))
 
         return result_q.queue
 
@@ -165,7 +166,7 @@ def log_progress(state, opengl_q, yolo_q, analysis_q, results_q, stop_event):
 def log_performance(state, results_queue):
     analyze_task = state.analyze_task
     # TODO filter out sentinals in task processor
-    tasks = [task for task in results_queue.queue if task is not None and hasattr(task, 'profile')]
+    tasks = [task for task in results_queue.queue if hasattr(task, 'profile')]
     total_frames = len(tasks)
 
     total_pipeline_time = analyze_task.end_time - analyze_task.start_time
diff --git a/script_generator/scripts/generate_funscript.py b/script_generator/scripts/generate_funscript.py
index 8198db2a..86c54bda 100644
--- a/script_generator/scripts/generate_funscript.py
+++ b/script_generator/scripts/generate_funscript.py
@@ -1,10 +1,24 @@
+from script_generator.object_detection.utils import raw_yolo_file_exists
 from script_generator.scripts.analyze_video import analyze_video
-from script_generator.state.app_state import AppState
+from script_generator.scripts.tracking_analysis import tracking_analysis
+from script_generator.state.app_state import AppState, log_state_settings
 from script_generator.debug.logger import logger
-
+from script_generator.utils.helpers import to_int_or_none
 
 def generate_funscript(state: AppState):
     try:
-        results = analyze_video(state)
+        state.set_video_info()
+        log_state_settings(state)
+
+        state.frame_start = to_int_or_none(state.frame_start)
+        state.frame_end = to_int_or_none(state.frame_end)
+
+        # analyze video if required
+        if not state.use_existing_raw_yolo or not raw_yolo_file_exists(state):
+            analyze_video(state)
+
+        tracking_analysis(state)
     except Exception as e:
-        logger.error(f"Could not generate funscript: {e}")
+        logger.error(f"Error during video analysis: {e}")
+        import traceback
+        traceback.print_exc()
\ No newline at end of file
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index acabcba4..5fc8fcc5 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -1,7 +1,11 @@
 import string
 from typing import Literal
 
+from ultralytics import YOLO
+
+from script_generator.constants import MODEL_PATH
 from script_generator.debug.debug_data import DebugData
+from script_generator.debug.logger import logger
 from script_generator.tasks.tasks import AnalyzeVideoTask
 from script_generator.utils.helpers import is_mac
 from script_generator.video.ffmpeg.hwaccel import get_preferred_hwaccel
@@ -17,6 +21,7 @@ def __init__(self, is_cli):
         self.frame_start: int = 0
         self.frame_end: int | None = None
         self.video_reader: Literal["FFmpeg", "FFmpeg + OpenGL (Windows)"] = "FFmpeg" if is_mac() else "FFmpeg + OpenGL (Windows)"
+        self.copy_funscript_to_movie_dir = True
 
         # Gui/settings debug
         self.save_debug_file: bool = True
@@ -43,6 +48,9 @@ def __init__(self, is_cli):
         self.current_frame_id = 0
         self.frame_area = 0
 
+        # Cli
+        self.use_existing_raw_yolo = False
+
         # State
         self.video_info: VideoInfo | None = None
         self.analyze_task: AnalyzeVideoTask | None = None
@@ -51,8 +59,23 @@ def __init__(self, is_cli):
         self.debug_data = DebugData(self)
         self.update_ui = None
         self.ffmpeg_hwaccel = get_preferred_hwaccel()
+        self.yolo_model = YOLO(MODEL_PATH, task="detect")
 
     def set_video_info(self):
         if self.video_info is None:
             self.video_info = get_video_info(self.video_path)
 
+
+def log_state_settings(state: AppState):
+    settings = [
+        ("Processing video", state.video_path),
+        ("Video Reader", state.video_reader),
+        ("Debug Mode", state.save_debug_file),
+        ("Live Preview Mode", state.live_preview_mode),
+        ("Frame Start", state.frame_start),
+        ("Frame End", state.frame_end),
+        ("Reference Script", state.reference_script),
+    ]
+
+    for setting_name, setting_value in settings:
+        logger.info(f"{setting_name}: {setting_value}")
\ No newline at end of file
diff --git a/script_generator/tasks/abstract_task_processor.py b/script_generator/tasks/abstract_task_processor.py
index 69f53241..0f60e1e6 100644
--- a/script_generator/tasks/abstract_task_processor.py
+++ b/script_generator/tasks/abstract_task_processor.py
@@ -45,7 +45,8 @@ def get_task(self) -> Generator[AnalyzeFrameTask, None, None]:
             try:
                 task = self.input_queue.get(timeout=1)
 
-                if task is None:  # Sentinel for termination
+                if task is None:
+                    self.input_queue.task_done() # Remove sentinel
                     self.state.analyze_task.end(self.process_type)
                     self.on_last_item()
                     self.finish_task(None)
diff --git a/tests/test_cli.py b/tests/test_cli.py
new file mode 100644
index 00000000..f0e733fb
--- /dev/null
+++ b/tests/test_cli.py
@@ -0,0 +1,20 @@
+import subprocess
+
+
+def test_cli_with_all_params():
+    video_path = "C:/cvr/funscript-generator/test_koogar_extra_short.mp4"
+
+    command = [
+        "python", "-m", "script_generator.cli.generate_funscript",
+        video_path,
+        "--reuse-yolo", "True",
+        "--copy-funscript", "True"
+    ]
+
+    result = subprocess.run(command, capture_output=True, text=True)
+
+    print(result.stdout)
+    print(result.stderr)
+
+if __name__ == "__main__":
+    test_cli_with_all_params()
\ No newline at end of file
diff --git a/tests/test_Compare_models_and_resolutions.py b/tests/test_compare_models_and_resolutions.py
similarity index 100%
rename from tests/test_Compare_models_and_resolutions.py
rename to tests/test_compare_models_and_resolutions.py
diff --git a/tests/test_PoseEstimation.py b/tests/test_pose_estimation.py
similarity index 100%
rename from tests/test_PoseEstimation.py
rename to tests/test_pose_estimation.py
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 08713dcd..da746dd4 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -10,7 +10,7 @@
 from scipy.signal import savgol_filter
 from simplification.cutil import simplify_coords
 
-from config import STEP_SIZE, VERSION, COPY_FUNSCRIPT_TO_MOVIE_LOCATION
+from config import STEP_SIZE, VERSION
 from script_generator.constants import HEATMAP_COLORS, FUNSCRIPT_AUTHOR
 from script_generator.debug.logger import logger
 from script_generator.state.app_state import AppState
@@ -113,17 +113,17 @@ def generate(self, state):
             self.write_funscript(zip_adjusted_positions, output_path, state.video_info.fps)
 
             # copy funscript if specified
-            if COPY_FUNSCRIPT_TO_MOVIE_LOCATION:
+            if state.copy_funscript_to_movie_dir:
                 copy = True
                 video_folder = os.path.dirname(state.video_path)
                 filename_base = os.path.basename(state.video_path)[:-4]
-                funscript_path = os.path.join(video_folder, filename_base, ".funscript")
+                funscript_path = os.path.join(video_folder, f"{filename_base}.funscript")
 
                 # Backup output file if it exists
                 if os.path.exists(funscript_path):
                     json_data = load_json_from_file(funscript_path)
                     if json_data["author"] == FUNSCRIPT_AUTHOR:
-                        backup_path = os.path.join(video_folder, filename_base, f"{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.funscript.bak")
+                        backup_path = os.path.join(video_folder, f"{filename_base}_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.funscript.bak")
                         logger.info(f"Funscript {funscript_path} already exists, backing up as {backup_path}...")
                         os.rename(funscript_path, backup_path)
                     else:
@@ -139,6 +139,7 @@ def generate(self, state):
             self.generate_heatmap(output_path, output_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
         except Exception as e:
             logger.error(f"Error generating funscript: {e}")
+            raise
 
     def write_funscript(self, distances, output_path, fps):
         with open(output_path, 'w') as f:

From d8ddd4fbcba98fe389f0d509d1474cd5fe0a7037 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 26 Jan 2025 14:48:43 +0100
Subject: [PATCH 160/277] Video previews now use hardware acceleration and
 shared ffmpeg logic Improved readme cleanup

---
 README.md                                     |  21 +-
 script_generator/debug/video_player/play.py   |   3 +-
 script_generator/debug/video_player/state.py  |  15 +-
 .../gui/views/funscript_generator.py          |   2 +-
 .../gui/views/popups/create_debug_video.py    |   3 +-
 .../analyze_tracking_results.py               |   4 +-
 script_generator/video/ffmpeg/commands.py     |   4 +-
 script_generator/video/ffmpeg/filters.py      |  17 +-
 script_generator/video/ffmpeg/video_reader.py |  81 +++++++
 tests/test_compare_models_and_resolutions.py  |   4 +-
 tests/test_detect_compare_unwarped.py         |   3 +-
 utils/lib_VideoReaderFFmpeg.py                | 217 ------------------
 12 files changed, 126 insertions(+), 248 deletions(-)
 create mode 100644 script_generator/video/ffmpeg/video_reader.py
 delete mode 100644 utils/lib_VideoReaderFFmpeg.py

diff --git a/README.md b/README.md
index ffca991e..b8f0aa8e 100644
--- a/README.md
+++ b/README.md
@@ -72,11 +72,9 @@ The pipeline for generating Funscript files is as follows:
  
 Before using this project, ensure you have the following installed:
 
-- **Python 3.8 or higher**
-- **FFmpeg** added to your path or specified in the config
-- **CUDA** (optional, for GPU acceleration)
-
----
+- **Python 3.8 or higher (tested on 3.11 https://www.python.org/downloads/release/python-3118/)**
+- **FFmpeg** added to your path or specified in the config (https://www.ffmpeg.org/download.html)
+--
 
 ## Installation
 
@@ -169,6 +167,19 @@ python -m script_generator.cli.generate_funscript.py /path/to/video.mp4
 ```
 ---
 
+## Performance & Parallel Processing
+Our pipeline's current bottleneck lies in the Python code within YOLO.track (the object detection library we use), which is challenging to parallelize effectively. We have plans to parallelize this in the future but for now it remains a limitation.
+
+Currently, we got about 35-40fps for 8192x4096 VR videos on an i5-13600K with a 4090 and 32GB DDR4 ram. Utilization is about 20% CPU, 20% video decoding and 7% GPU usage. We suspect this means performance should be similar for lower end NVIDIA cards.
+
+However, when you have high-performance hardware this also means effectively run multiple processes simultaneously via the command line or launching multiple instances of the GUI. For reference, the above test setup was able to process at a combined 100fps while still being CPU limited.
+
+**Important considerations:**
+- Each instance requires the YOLO model to load, so monitor your VRAM usage to determine how many instances your hardware can support. For example, an NVIDIA RTX 4090 can handle up to six instances simultaneously.
+- The optimal number of instances depends on a combination of factors, including your CPU, GPU, RAM, and system configuration. So experiment with different setups to find the ideal configuration for your hardware! 😊
+
+---
+
 ## Miscellaneous
 
 - For VR only **Fisheye** and **Equirectangular** 180° videos are supported
diff --git a/script_generator/debug/video_player/play.py b/script_generator/debug/video_player/play.py
index 1bc18711..856906b1 100644
--- a/script_generator/debug/video_player/play.py
+++ b/script_generator/debug/video_player/play.py
@@ -20,8 +20,7 @@ def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=1
     width, height = get_cropped_dimensions(video_info)
 
     video_player = VideoPlayer(
-        video_path=state.video_path,
-        video_info=video_info,
+        state=state,
         start_frame=start_frame,
         end_frame=end_frame
     )
diff --git a/script_generator/debug/video_player/state.py b/script_generator/debug/video_player/state.py
index ebb60176..bdc928db 100644
--- a/script_generator/debug/video_player/state.py
+++ b/script_generator/debug/video_player/state.py
@@ -1,18 +1,19 @@
 import cv2
 
-from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg
+from script_generator.state.app_state import AppState
+from script_generator.video.ffmpeg.video_reader import VideoReaderFFmpeg
 
 
 class VideoPlayer:
-    def __init__(self, video_path, video_info, start_frame, end_frame):
-        self.video_path = video_path
-        self.video_info = video_info
-        self.total_frames = video_info.total_frames
+    def __init__(self, state: AppState, start_frame, end_frame):
+        self.video_path = state.video_path
+        self.video_info = state.video_info
+        self.total_frames = state.video_info.total_frames
         self.start_frame = start_frame
-        self.end_frame = video_info.total_frames if not end_frame else end_frame
+        self.end_frame = state.video_info.total_frames if not end_frame else end_frame
         self.current_frame = 0
 
-        self.cap = VideoReaderFFmpeg(self.video_path, is_vr=video_info.is_vr)
+        self.cap = VideoReaderFFmpeg(state)
         self.paused = False
 
     def release(self):
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 8262efd7..6b1813b4 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -162,7 +162,7 @@ def start_processing():
             general, "Live preview",
             state,
             "live_preview_mode",
-            tooltip_text="Press 'q' to quit.\n\nWill show a live preview of what is being generated.\n\nFor debugging only this will reduce your performance.\nStage 1: Show bounding boxes during object detection.\nStage 2: Show funscript and metric overlay while the funscript is being processed.",
+            tooltip_text="Will show a live preview of what is being generated.\n\nFor debugging only this will reduce your performance.\nStage 1: Show bounding boxes during object detection.\nStage 2: Show funscript and metric overlay while the funscript is being processed.",
             row=0
         )
         Widgets.checkbox(
diff --git a/script_generator/gui/views/popups/create_debug_video.py b/script_generator/gui/views/popups/create_debug_video.py
index 7b97bf8f..fe97d00c 100644
--- a/script_generator/gui/views/popups/create_debug_video.py
+++ b/script_generator/gui/views/popups/create_debug_video.py
@@ -15,7 +15,8 @@ def render_debug_video_popup(window, state: AppState):
     state.set_video_info()
     debug_state = RenderDebugVideoState(state.video_info)
     Widgets.label(window, "First stream/play your results locally (see Play debug video). Note the frame when the issue occurs. \nSubtract about 120 frames so we have some context. Then add a few seconds of recording (2 to 20 seconds\nshould do it)", column=0, sticky="w", padx=10, pady=10)
-    p_container, p, p_label, p_perc = Widgets.labeled_progress(window, "Progress", row=1)
+    # TODO add progress in the UI
+    # p_container, p, p_label, p_perc = Widgets.labeled_progress(window, "Progress", row=1)
     Widgets.frames_input(window, "Start", state=debug_state, attr="frame_start", tooltip_text="Where to start processing the video. If you have a 60fps video starting at 10s would mean frame 600", row=2)
     Widgets.frames_input(window, "End", state=debug_state, attr="frame_end", tooltip_text="Where to end processing the video. If you have a 60fps video stopping  at 10s would mean frame 600", row=3)
     Widgets.button(
diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index 99934e69..68a7a227 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -12,9 +12,9 @@
 from script_generator.state.app_state import AppState
 from script_generator.utils.file import get_output_file_path
 from script_generator.debug.logger import logger
+from script_generator.video.ffmpeg.video_reader import VideoReaderFFmpeg
 from script_generator.video.info.video_info import get_cropped_dimensions
 from utils.lib_ObjectTracker import ObjectTracker
-from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg
 
 
 def analyze_tracking_results(state: AppState, results):
@@ -23,7 +23,7 @@ def analyze_tracking_results(state: AppState, results):
 
     video_info = state.video_info
     fps = video_info.fps
-    reader = VideoReaderFFmpeg(state.video_path, is_vr=video_info.is_vr)
+    reader = VideoReaderFFmpeg(state)
 
     state.frame_area = width * height
 
diff --git a/script_generator/video/ffmpeg/commands.py b/script_generator/video/ffmpeg/commands.py
index 56c7dbc2..cb61403e 100644
--- a/script_generator/video/ffmpeg/commands.py
+++ b/script_generator/video/ffmpeg/commands.py
@@ -4,9 +4,9 @@
 from script_generator.video.info.video_info import get_cropped_dimensions, VideoInfo
 
 
-def get_ffmpeg_read_cmd(video: VideoInfo, video_reader: str, hwaccel: str, frame_start: int | None, output="-"):
+def get_ffmpeg_read_cmd(video: VideoInfo, video_reader: str, hwaccel: str, frame_start: int | None, output="-", disable_opengl=False):
     width, height = get_cropped_dimensions(video)
-    vf = get_video_filters(video, video_reader, hwaccel, width, height)
+    vf = get_video_filters(video, video_reader, hwaccel, width, height, disable_opengl)
     start_time = (frame_start / video.fps) * 1000
 
     # Get supported hardware acceleration backends
diff --git a/script_generator/video/ffmpeg/filters.py b/script_generator/video/ffmpeg/filters.py
index 841ce0d4..24fb564f 100644
--- a/script_generator/video/ffmpeg/filters.py
+++ b/script_generator/video/ffmpeg/filters.py
@@ -1,7 +1,12 @@
 from config import RENDER_RESOLUTION, VR_TO_2D_PITCH
 
+def get_video_filters(video, video_reader, hwaccel, width, height, disable_opengl=False):
+    if video.is_vr:
+        return get_vr_video_filters(video, video_reader, hwaccel, disable_opengl)
+    else:
+        return get_2d_video_filters(video, width, height)
 
-def get_vr_video_filters(video, video_reader, hwaccel):
+def get_vr_video_filters(video, video_reader, hwaccel, disable_opengl=False):
     if video.is_fisheye:
         projection, iv_fov, ih_fov, v_fov, h_fov, d_fov = "fisheye", 190, 190, 90, 90, 180
     else:
@@ -13,7 +18,7 @@ def get_vr_video_filters(video, video_reader, hwaccel):
     crop = f"crop={RENDER_RESOLUTION}:{RENDER_RESOLUTION}:0:0"
     out_format = f"format=nv12," if cuda else ""
 
-    if video_reader == "FFmpeg":
+    if video_reader == "FFmpeg" or disable_opengl:
         filters = [
             scale,
             crop,
@@ -32,10 +37,4 @@ def get_vr_video_filters(video, video_reader, hwaccel):
     return ",".join(filters)
 
 def get_2d_video_filters(video, width, height):
-    return f"scale={width}:{height}" if video.height > RENDER_RESOLUTION else None
-
-def get_video_filters(video, video_reader, hwaccel, width, height):
-    if video.is_vr:
-        return get_vr_video_filters(video, video_reader, hwaccel)
-    else:
-        return get_2d_video_filters(video, width, height)
\ No newline at end of file
+    return f"scale={width}:{height}" if video.height > RENDER_RESOLUTION else None
\ No newline at end of file
diff --git a/script_generator/video/ffmpeg/video_reader.py b/script_generator/video/ffmpeg/video_reader.py
new file mode 100644
index 00000000..3d6dcec0
--- /dev/null
+++ b/script_generator/video/ffmpeg/video_reader.py
@@ -0,0 +1,81 @@
+import subprocess
+import cv2
+import numpy as np
+from script_generator.debug.logger import logger
+from script_generator.state.app_state import AppState
+from script_generator.video.ffmpeg.commands import get_ffmpeg_read_cmd
+
+class VideoReaderFFmpeg:
+    def __init__(self, state):
+        self.state: AppState = state
+        self.video_path = state.video_path
+        self.start_frame = 0
+        self.current_frame_number = 0
+        self.current_time = 0
+        self.process = None
+        self.frame_size = None
+        self.width = None
+        self.height = None
+
+    def _start_process(self, start_frame=0):
+        self.current_frame_number = start_frame
+
+        # Kill the process if already running
+        if self.process:
+            self.process.terminate()
+
+        cmd, self.frame_size, self.width, self.height = get_ffmpeg_read_cmd(
+            self.state.video_info,
+            self.state.video_reader,
+            self.state.ffmpeg_hwaccel,
+            self.state.frame_start,
+            disable_opengl=True
+        )
+        logger.info(f"Starting FFmpeg reader with command: {' '.join(cmd)}")
+
+        self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
+
+    def read(self):
+        """Read the next frame from the video."""
+        if not self.process:
+            self._start_process(start_frame=self.start_frame)
+
+        try:
+            in_bytes = self.process.stdout.read(self.frame_size)
+            if not in_bytes:
+                return False, None  # End of video
+
+            frame = np.frombuffer(in_bytes, np.uint8).reshape((self.height, self.width, 3))
+            self.current_frame_number += 1
+            self.current_time = (self.current_frame_number / self.state.video_info.fps) * 1000
+
+            return True, frame
+        except Exception as e:
+            logger.error(f"Error reading frame: {e}")
+            return False, None
+
+    def set(self, prop_id, value):
+        """Set properties like frame position (mimics OpenCV's cap.set())."""
+        if prop_id == cv2.CAP_PROP_POS_FRAMES:
+            self.start_frame = int(value)
+            self._start_process(start_frame=self.start_frame)
+        else:
+            logger.error(f"Unsupported property ID: {prop_id}")
+
+    def get(self, prop_id):
+        """Get properties like FPS, width, height (mimics OpenCV's cap.get())."""
+        props = {
+            cv2.CAP_PROP_FPS: self.state.video_info.fps,
+            cv2.CAP_PROP_FRAME_WIDTH: self.width,
+            cv2.CAP_PROP_FRAME_HEIGHT: self.height,
+            cv2.CAP_PROP_FRAME_COUNT: self.state.video_info.total_frames,
+            cv2.CAP_PROP_POS_FRAMES: self.current_frame_number,
+        }
+        return props.get(prop_id, logger.error(f"Unsupported property ID: {prop_id}"))
+
+    def release(self):
+        """Release resources and terminate the FFmpeg process."""
+        if self.process:
+            self.process.stdout.close()
+            self.process.terminate()
+            self.process = None
diff --git a/tests/test_compare_models_and_resolutions.py b/tests/test_compare_models_and_resolutions.py
index 399c3bd4..781c7a44 100644
--- a/tests/test_compare_models_and_resolutions.py
+++ b/tests/test_compare_models_and_resolutions.py
@@ -2,9 +2,11 @@
 from PIL import Image
 
 from script_generator.debug.logger import logger
-from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg
 from ultralytics import YOLO
 
+from script_generator.video.ffmpeg.video_reader import VideoReaderFFmpeg
+
+
 def save_frames_as_gif(frames, output_path, duration=100, loop=0):
     #frames = [Image.fromarray(frame) for frame in frames]
     #frames[0].
diff --git a/tests/test_detect_compare_unwarped.py b/tests/test_detect_compare_unwarped.py
index 47679261..e4be47b3 100644
--- a/tests/test_detect_compare_unwarped.py
+++ b/tests/test_detect_compare_unwarped.py
@@ -1,8 +1,9 @@
 from ultralytics import YOLO
 import cv2
-from utils.lib_VideoReaderFFmpeg import VideoReaderFFmpeg
 import numpy as np
 
+from script_generator.video.ffmpeg.video_reader import VideoReaderFFmpeg
+
 # Paths to the model and input image
 model_file = "../models/k00gar-11n-200ep-best.mlpackage"
 
diff --git a/utils/lib_VideoReaderFFmpeg.py b/utils/lib_VideoReaderFFmpeg.py
deleted file mode 100644
index dd8518c4..00000000
--- a/utils/lib_VideoReaderFFmpeg.py
+++ /dev/null
@@ -1,217 +0,0 @@
-import subprocess
-
-import cv2
-import numpy as np
-
-from config import FFMPEG_PATH, FFPROBE_PATH, RENDER_RESOLUTION
-from script_generator.debug.logger import logger
-from script_generator.video.info.video_info import get_video_info
-
-
-# TODO rework this class in the new logic and make the existing logic more flexible and generic
-class VideoReaderFFmpeg:
-    def __init__(self, video_path, is_vr=False, unwarp=True, projection=None, ffmpeg_path=FFMPEG_PATH, ffprobe_path=FFPROBE_PATH):
-        """
-        Initialize the VideoReaderFFmpeg class.
-        :param video_path: Path to the video file.
-        :param unwarp: Will try and unwarp VR videos.
-        :param projection: FISHEYE, EQUIRECTANGULAR. Will also look for 'FISHEYE' in the video path.
-        :param ffmpeg_path: Path to the FFmpeg binary (default: "ffmpeg").
-        :param ffprobe_path: Path to the FFprobe binary (default: "ffprobe").
-        """
-        self.video_path = video_path
-        self.is_vr = is_vr
-        if not self.is_vr:
-            self.unwarp = False
-        else:
-            self.unwarp = unwarp
-        self.projection = projection
-        self.ffmpeg_path = ffmpeg_path
-        self.ffprobe_path = ffprobe_path
-        self._initialize_video_info()  # Initialize video metadata
-        self.start_frame = 0
-        self.current_frame_number = 0
-        self.current_time = 0
-        self.process = None
-        self.frame_size = None
-        self.type = ""
-        self.iv_fov = 0
-        self.ih_fov = 0
-        self.d_fov = 0
-
-    def _initialize_video_info(self):
-        """
-        Retrieve video metadata (fps, resolution, codec, etc.) using FFprobe.
-        """
-        try:
-            video_info = get_video_info(self.video_path)
-            # Store metadata
-            self.fps = video_info.fps
-            self.width = video_info.width
-            if self.is_vr:
-                self.width //= 2
-            self.height = video_info.height
-            self.codec = video_info.codec_name
-            self.total_frames = video_info.total_frames
-            self.duration = video_info.duration
-
-            # limiting the frame height here has no additional performance cost and significantly improves speed for 1440p+ video
-            # scaling_factor = min(RENDER_RESOLUTION / self.width, RENDER_RESOLUTION / self.height)
-            # was having issues with the rendering of a 720p 2D video... < 1080p
-            scaling_factor = min(1, RENDER_RESOLUTION / self.height)
-            # if scaling_factor > 1:
-            #    scaling_factor = 1
-            self.width = int(self.width * scaling_factor)
-            self.height = int(self.height * scaling_factor)
-
-            logger.info(f"FPS: {self.fps}, Resolution: {self.width}x{self.height}, "
-                        f"Codec: {self.codec}, Total Frames: {self.total_frames}, Duration: {self.duration:.2f} ms")
-        except Exception as e:
-            logger.error(f"Error initializing video info: {e}")
-            raise
-
-    def _start_process(self, start_frame=0):
-        """
-        Start the FFmpeg process to read frames.
-        :param start_frame: Frame number to start reading from.
-        """
-        start_time = (start_frame / self.fps) * 1000  # Convert to milliseconds
-        self.current_frame_number = start_frame
-
-        if self.is_vr:
-            # arg_line = "crop=w=iw/2:h=ih:x=0:y=0"
-            arg_line = ""
-            if self.unwarp:
-                if self.projection == "FISHEYE" or (self.projection == None and "FISHEYE" in self.video_path.upper()):
-                    logger.info("Proceeding with fisheye projection correction")
-                    self.type = "fisheye"
-                    self.iv_fov = 190
-                    self.ih_fov = 190
-                    self.v_fov = 90
-                    self.h_fov = 90
-                    self.d_fov = 180
-                else:  # Assuming Equirectangular
-                    logger.info("Assuming Equirectangular projection")
-                    self.type = "he"
-                    self.iv_fov = 250
-                    self.ih_fov = 120
-                    self.v_fov = 90
-                    self.h_fov = 90
-                    self.d_fov = 180
-                arg_line = arg_line + f"v360={self.type}:in_stereo=sbs:output=sg"
-                arg_line = arg_line + f":iv_fov={self.iv_fov}:ih_fov={self.ih_fov}"
-                arg_line = arg_line + f":d_fov={self.d_fov}:v_fov={self.v_fov}:h_fov={self.h_fov}"
-                arg_line = arg_line + f":pitch=-25:yaw=0:roll=0"
-                arg_line = arg_line + f":w={self.width}:h={self.height}"
-                arg_line = arg_line + f":interp=lanczos:reset_rot=1"
-                arg_line = arg_line + f",lutyuv=y=gammaval(0.7)"
-                # arg_line = arg_line + f",eq=brightness=0.1:contrast=1.5"
-                # arg_line = arg_line + f",format=gray"
-                # arg_line = arg_line + f",histeq"
-            else:
-                arg_line = "crop=w=iw/2:h=ih:x=0:y=0"
-            # Add scale filter with height and auto-width (-1)
-            # arg_line += f",scale=-1:{target_height}"
-            # arg_line += f",scale=-1:{1080}"
-            # perf for on the fly downscale to 1080p were terrible
-
-            cmd = [
-                self.ffmpeg_path,
-                '-nostats',  # Disable progress statistics
-                '-loglevel', 'warning',
-                "-ss", str(start_time / 1000),  # Seek to start time in seconds
-                "-i", self.video_path,
-                "-an",  # Disable audio processing
-                "-map", "0:v:0",
-                "-vf", arg_line,
-                "-f", "rawvideo",  # Output raw video data
-                "-pix_fmt", "bgr24",
-                "-vsync", "0",  # Disable frame rate synchronization
-                "-threads", "0",  # Use maximum threads available
-                "-",  # Output to stdout
-            ]
-        else:
-            # FFmpeg command to read frames
-            cmd = [
-                self.ffmpeg_path,
-                '-nostats',  # Disable progress statistics
-                '-loglevel', 'warning',
-                "-ss", str(start_time / 1000),  # Seek to start time in seconds
-                "-i", self.video_path,
-                "-an",  # Disable audio processing
-                "-f", "rawvideo",  # Output raw video data
-                "-pix_fmt", "bgr24",  # Pixel format (BGR for OpenCV)
-                "-vsync", "0",  # Disable frame rate synchronization
-                "-",  # Output to stdout
-            ]
-
-        # Kill the process if already running
-        if self.process:
-            self.process.terminate()
-
-        # Start FFmpeg process
-        # self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
-        self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
-        self.frame_size = self.width * self.height * 3  # Size of one frame in bytes
-
-    def read(self):
-        """
-        Read the next frame from the video. Mimics OpenCV's cap.read().
-        :return: (True, frame) if successful, (False, None) if end of video.
-        """
-        if self.process is None:
-            self._start_process(start_frame=self.start_frame)
-
-        try:
-            in_bytes = self.process.stdout.read(self.frame_size)
-            if not in_bytes:
-                return False, None  # End of video
-            frame = np.frombuffer(in_bytes, np.uint8).reshape([self.height, self.width, 3])
-            self.current_frame_number += 1
-            self.current_time = (self.current_frame_number / self.fps) * 1000
-            return True, frame
-        except Exception as e:
-            logger.error(f"Error reading frame: {e}")
-            return False, None
-
-    def set(self, prop_id, value):
-        """
-        Mimics OpenCV's cap.set().
-        :param prop_id: Property ID (e.g., cv2.CAP_PROP_POS_FRAMES).
-        :param value: Value to set.
-        """
-        if prop_id == cv2.CAP_PROP_POS_FRAMES:
-            self.start_frame = int(value)
-            self._start_process(start_frame=self.start_frame)
-        elif prop_id == cv2.CAP_PROP_FPS:
-            self.fps = value
-        else:
-            logger.error(f"Property {prop_id} not supported.")
-
-    def get(self, prop_id):
-        """
-        Mimics OpenCV's cap.get().
-        :param prop_id: Property ID (e.g., cv2.CAP_PROP_FPS).
-        :return: Property value.
-        """
-        if prop_id == cv2.CAP_PROP_FPS:
-            return self.fps
-        elif prop_id == cv2.CAP_PROP_FRAME_WIDTH:
-            return self.width
-        elif prop_id == cv2.CAP_PROP_FRAME_HEIGHT:
-            return self.height
-        elif prop_id == cv2.CAP_PROP_FRAME_COUNT:
-            return self.total_frames
-        elif prop_id == cv2.CAP_PROP_POS_FRAMES:
-            return self.current_frame_number
-        else:
-            logger.error(f"Property {prop_id} not supported.")
-            return None
-
-    def release(self):
-        """
-        Release the resources and close the FFmpeg process.
-        """
-        if self.process:
-            self.process.stdout.close()
-            self.process = None

From ee1e56cb16986458bfc3817266a9597b3c854eea Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Sun, 26 Jan 2025 15:20:18 +0100
Subject: [PATCH 161/277] changed conf from .7 to .5 for consecutive detection
 and initial p-locking

---
 script_generator/object_detection/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script_generator/object_detection/utils.py b/script_generator/object_detection/utils.py
index f7a4e820..b70e4e20 100644
--- a/script_generator/object_detection/utils.py
+++ b/script_generator/object_detection/utils.py
@@ -79,7 +79,7 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
 
     for line in data:
         frame_idx, cls, conf, x1, y1, x2, y2, track_id = line
-        if frame_idx >= start_frame and cls == penis_cls and conf >= 0.7:
+        if frame_idx >= start_frame and cls == penis_cls and conf >= 0.5:
             penis_frame = frame_idx
             if prev_frame == frame_idx - 1:
                 cons_frames += 1

From dc017d22aa97b3a56546c43c96fdbd25c24bf70f Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 26 Jan 2025 15:56:48 +0100
Subject: [PATCH 162/277] GUI starts on top GUI disabling buttons, progress
 logic, etc. re-added the script compare field tweaks

---
 script_generator/gui/app.py                   |  9 +++-
 script_generator/gui/utils/utils.py           | 17 ++++++
 script_generator/gui/utils/widgets.py         |  2 +-
 .../gui/views/funscript_generator.py          | 54 ++++++++++++++-----
 .../object_detection/util/utils.py            |  8 +++
 script_generator/object_detection/utils.py    | 20 ++-----
 .../scripts/generate_debug_video.py           | 50 +++++++++--------
 .../scripts/generate_funscript.py             |  4 +-
 script_generator/state/app_state.py           | 14 ++++-
 script_generator/utils/file.py                | 18 +++++++
 10 files changed, 139 insertions(+), 57 deletions(-)
 create mode 100644 script_generator/gui/utils/utils.py
 create mode 100644 script_generator/object_detection/util/utils.py

diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index ff30fda0..8e686115 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -19,7 +19,7 @@ def __init__(self):
             ctypes.windll.shcore.SetProcessDpiAwareness(2)  # For Windows DPI scaling
         # self.tk.call('tk', 'scaling', 1.0)
         self.title(f"VR & 2D POV Funscript AI Generator - v" + VERSION)
-        self.geometry(f"{('800' if is_mac() else '700')}x840")
+        self.geometry(f"{('800' if is_mac() else '700')}x900")
         self.resizable(False, False)
 
         self.iconphoto(False, tk.PhotoImage(file=LOGO))
@@ -36,6 +36,13 @@ def __init__(self):
         # Dictionary to store pages
         self.frames = {}
 
+        # Ensure the window is always on top
+        self.attributes("-topmost", True)
+        self.after(200, self.reset_topmost)
+
+    def reset_topmost(self):
+        self.focus_force()
+        self.attributes("-topmost", False)
 
     def show_frame(self, page_name):
         """Show a frame, creating it if necessary."""
diff --git a/script_generator/gui/utils/utils.py b/script_generator/gui/utils/utils.py
new file mode 100644
index 00000000..9eb15ea5
--- /dev/null
+++ b/script_generator/gui/utils/utils.py
@@ -0,0 +1,17 @@
+def disable_widgets(buttons):
+    for btn in buttons:
+        btn.config(state='disabled')
+
+def enable_widgets(buttons):
+    for btn in buttons:
+        btn.config(state='normal')
+
+def set_progressbars_done(progress_bars):
+    for progress, label in progress_bars:
+        progress["value"] = progress["maximum"]
+        label = "Done"
+
+def reset_progressbars(progress_bars):
+    for progress, label in progress_bars:
+        progress["value"] = 0
+        label = ""
\ No newline at end of file
diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index 03a0c49d..438f0235 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -151,7 +151,7 @@ def on_change(val):
             Tooltip(entry, tooltip_text)
             Tooltip(button, tooltip_text)
 
-        return container, entry, file_path
+        return container, entry, button, file_path
 
     @staticmethod
     def labeled_progress(parent, label_text, row=0, column=0, progress_length=300, label_width_px=LABEL_WIDTH, label_percentage_width_px=LABEL_WIDTH, tooltip_text=None):
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 6b1813b4..ec660741 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -1,15 +1,16 @@
 import tkinter as tk
 from tkinter import ttk
 
+from script_generator.debug.logger import logger
+from script_generator.gui.controller.debug_video import debug_video
 from script_generator.gui.controller.process_video import video_analysis
 from script_generator.gui.controller.regenerate_funscript import regenerate_funscript
-from script_generator.gui.controller.debug_video import debug_video
 from script_generator.gui.messages.messages import UIMessage, ProgressMessage, UpdateGUIState
+from script_generator.gui.utils.utils import enable_widgets, disable_widgets, set_progressbars_done, reset_progressbars
 from script_generator.gui.utils.widgets import Widgets
 from script_generator.gui.views.popups.create_debug_video import render_debug_video_popup
 from script_generator.state.app_state import AppState
 from script_generator.utils.helpers import is_mac
-from script_generator.debug.logger import logger
 
 
 class FunscriptGeneratorPage(tk.Frame):
@@ -29,7 +30,11 @@ def __init__(self, parent, controller):
         # region VIDEO SELECTION
         video_selection = Widgets.frame(wrapper, title="Video Selection", main_section=True)
 
-        Widgets.file_selection(
+        def update_video_path():
+            state.set_video_info()
+            update_ui_for_state()
+
+        _, fs_entry, fs_button, _ = Widgets.file_selection(
             attr="video_path",
             parent=video_selection,
             label_text="Video",
@@ -38,7 +43,7 @@ def __init__(self, parent, controller):
             file_types=[("Text Files", "*.mp4 *.avi *.mov *.mkv"), ("All Files", "*.*")],
             state=state,
             tooltip_text="The video to generate a funscript for. For proper detection of fisheye keep the suffix like _FISHEYE190, _MKX200, etc. in the filename\n\nIn the future we'll add the option to override this in the UI.",
-            command=lambda val: state.set_video_info()
+            command=lambda val: update_video_path()
         )
 
         Widgets.dropdown(
@@ -70,7 +75,7 @@ def start_processing():
             # TODO reset the progress bars
             video_analysis(state, controller)
 
-        Widgets.button(processing, "Start processing", start_processing, row=3)
+        processing_btn = Widgets.button(processing, "Start processing", start_processing, row=3)
         # endregion
 
         # region FUNSCRIPT TWEAKING
@@ -151,7 +156,7 @@ def start_processing():
         )
 
         # Regenerate Funscript Button
-        Widgets.button(tweaking_container, "Regenerate Funscript", lambda: regenerate_funscript(self.state), row=2)
+        regenerate_btn = Widgets.button(tweaking_container, "Regenerate Funscript", lambda: regenerate_funscript(self.state), row=2)
 
         # endregion
 
@@ -172,7 +177,7 @@ def start_processing():
             tooltip_text="Saves a debug file to disk with all collected metrics.\nThis is a prerequisite for playing back the debug video with the debug statistics overlay.\nThis also contains all generated metrics.",
             row=1
         )
-        Widgets.button(
+        play_btn = Widgets.button(
             general,
             "Play debug video",
             lambda: debug_video(state),
@@ -192,8 +197,8 @@ def start_processing():
             state=state,
             row=0
         )
-        debug_video_section = Widgets.frame(debugging, title="Share debug video", row=1)
-        Widgets.button(
+        debug_video_section = Widgets.frame(debugging, title="Share debug video", row=2)
+        gen_video_btn = Widgets.button(
             debug_video_section,
             "Generate sharable debug video",
             lambda: Widgets.create_popup(title="Generate debug video", master=controller, width=650, height=205, content_builder=lambda window, user_action: render_debug_video_popup(window, state)),
@@ -207,6 +212,23 @@ def start_processing():
         Widgets.disclaimer(wrapper)
         # endregion
 
+        def update_ui_for_state():
+            if state.has_raw_yolo and state.has_tracking_data:
+                enable_widgets([play_btn, regenerate_btn, gen_video_btn])
+                set_progressbars_done([(yolo_p, yolo_p_perc), (track_p, track_p_perc)])
+            elif state.has_raw_yolo and not state.has_tracking_data:
+                disable_widgets([play_btn, regenerate_btn, gen_video_btn])
+                set_progressbars_done([(yolo_p, yolo_p_perc)])
+                reset_progressbars([(track_p, track_p_perc)])
+            else:
+                disable_widgets([play_btn, regenerate_btn, gen_video_btn])
+                reset_progressbars([(yolo_p, yolo_p_perc), (track_p, track_p_perc)])
+
+            if state.is_processing:
+                disable_widgets([fs_entry, fs_button])
+            else:
+                enable_widgets([fs_entry, fs_button])
+
         # region UI UPDATE CALLBACK
         def update_ui(msg: UIMessage):
             """Handle UI updates using a switch-like statement."""
@@ -229,20 +251,26 @@ def handle_state_update(state_msg: UpdateGUIState):
 
             def handle_progress_message(progress_msg: ProgressMessage):
                 progress_mapping = {
-                    "OBJECT_DETECTION": (yolo_p, yolo_p_perc),
+                    "OBJECT_DETECTION": (yolo_p, yolo_p_perc, "has_raw_yolo"),
                     # "SCENE_DETECTION": (scene_p, scene_p_perc),
-                    "TRACKING_ANALYSIS": (track_p, track_p_perc),
+                    "TRACKING_ANALYSIS": (track_p, track_p_perc, "has_tracking_data"),
                 }
 
                 if progress_msg.process in progress_mapping:
-                    progress_bar, progress_label = progress_mapping[progress_msg.process]
+                    progress_bar, progress_label, state_attr = progress_mapping[progress_msg.process]
                     progress_bar["value"] = progress_msg.frames_processed
                     progress_bar["maximum"] = progress_msg.total_frames
                     percentage = (progress_msg.frames_processed / progress_msg.total_frames) * 100 if progress_msg.total_frames > 0 else 0
-                    progress_label.config(text=f"{percentage:.0f}% - ETA: {progress_msg.eta}" if progress_msg.frames_processed < progress_msg.total_frames else "Done")
+                    is_done = progress_msg.frames_processed >= progress_msg.total_frames
+                    progress_label.config(text="Done" if is_done else f"{percentage:.0f}% - ETA: {progress_msg.eta}")
+
+                    if is_done or progress_msg.frames_processed == 0:
+                        setattr(state, state_attr, is_done)
+                        update_ui_for_state()
 
             # Schedule the update on the main thread
             self.controller.after(0, process_update)
 
         self.state.update_ui = update_ui
+        update_ui_for_state()
         # endregion
diff --git a/script_generator/object_detection/util/utils.py b/script_generator/object_detection/util/utils.py
new file mode 100644
index 00000000..f5934241
--- /dev/null
+++ b/script_generator/object_detection/util/utils.py
@@ -0,0 +1,8 @@
+from script_generator.utils.file import get_data_file_info
+
+
+def get_metrics_file_info(state):
+    """
+    Checks if the data_logs metrics file exists
+    """
+    return get_data_file_info(state, "_debug_logs.json")
\ No newline at end of file
diff --git a/script_generator/object_detection/utils.py b/script_generator/object_detection/utils.py
index b70e4e20..8544a6ec 100644
--- a/script_generator/object_detection/utils.py
+++ b/script_generator/object_detection/utils.py
@@ -4,12 +4,12 @@
 from script_generator.gui.utils.widgets import Widgets
 from script_generator.object_detection.box_record import BoxRecord
 from script_generator.object_detection.object_detection_result import ObjectDetectionResult
-from script_generator.utils.file import get_output_file_path, load_json_from_file
+from script_generator.utils.file import get_output_file_path, load_json_from_file, get_data_file_info
 from script_generator.debug.logger import logger
 
 
 def check_skip_object_detection(state, root):
-    exists, path, filename = raw_yolo_file_exists(state)
+    exists, path, filename = get_raw_yolo_file_info(state.video_path)
     if exists:
         choice = Widgets.messagebox(
             "Detection File Conflict",
@@ -31,23 +31,11 @@ def check_skip_object_detection(state, root):
     return "generate"
 
 
-def raw_yolo_file_exists(state):
+def get_raw_yolo_file_info(state):
     """
     Checks if the YOLO file exists
     """
-    raw_yolo_path, raw_yolo_filename = get_output_file_path(state.video_path, "_rawyolo.json")
-    if os.path.exists(raw_yolo_path):
-        yolo_data = load_json_from_file(raw_yolo_path)
-        if len(yolo_data) == 0:
-            logger.warn(f"Raw YOLO data file doesn't contain any data: {raw_yolo_path}")
-            try:
-                os.remove(raw_yolo_path)
-                logger.info(f"Deleted empty raw YOLO data file: {raw_yolo_path}")
-            except OSError as e:
-                logger.error(f"Error deleting raw YOLO file {raw_yolo_path}: {e}")
-        else:
-            return True, raw_yolo_path, raw_yolo_filename
-    return False, None, None
+    return get_data_file_info(state, "_rawyolo.json")
 
 def make_data_boxes(records):
     """
diff --git a/script_generator/scripts/generate_debug_video.py b/script_generator/scripts/generate_debug_video.py
index 91f601b2..8916351d 100644
--- a/script_generator/scripts/generate_debug_video.py
+++ b/script_generator/scripts/generate_debug_video.py
@@ -1,5 +1,6 @@
 import os
 import subprocess
+import threading
 
 from config import FFMPEG_PATH
 from script_generator.debug.video_player.play import play_debug_video
@@ -10,25 +11,30 @@
 
 
 def generate_debug_video(state: AppState, frame_start: int, frame_end: int):
-    logger.info(f"Generating debug video from: {frame_start} to {frame_end}")
-    temp_video_path = play_debug_video(state, frame_start, frame_end, save_video_mode=True)
-
-    debug_video_path, _ = get_output_file_path(state.video_path, "_debug.mp4")
-    ffmpeg_command = [
-        FFMPEG_PATH, "-y",
-        *get_hwaccel_read_args(state.ffmpeg_hwaccel),
-        "-i", temp_video_path,
-        "-c:v", "hevc_nvenc" if state.ffmpeg_hwaccel == "cuda" else "libx265",
-        "-preset", "p4" if state.ffmpeg_hwaccel == "cuda" else "fast",
-        *(["-b:v", "5000k"] if state.ffmpeg_hwaccel == "cuda" else []),
-        *(["-crf", "26"] if state.ffmpeg_hwaccel != "cuda" else []),
-        "-movflags", "+faststart",
-        debug_video_path
-    ]
-
-    logger.info(f"Generating debug video with command: {' '.join(map(str, ffmpeg_command))}")
-
-    subprocess.run(ffmpeg_command)
-
-    os.remove(temp_video_path)
-    logger.info(f"Debug video generated and stored in: {debug_video_path}")
\ No newline at end of file
+    # prevent blocking the main thread
+    def run():
+        logger.info(f"Generating debug video from: {frame_start} to {frame_end}")
+        temp_video_path = play_debug_video(state, frame_start, frame_end, save_video_mode=True)
+
+        debug_video_path, _ = get_output_file_path(state.video_path, "_debug.mp4", True)
+        ffmpeg_command = [
+            FFMPEG_PATH, "-y",
+            *get_hwaccel_read_args(state.ffmpeg_hwaccel),
+            "-i", temp_video_path,
+            "-c:v", "hevc_nvenc" if state.ffmpeg_hwaccel == "cuda" else "libx265",
+            "-preset", "p4" if state.ffmpeg_hwaccel == "cuda" else "fast",
+            *(["-b:v", "5000k"] if state.ffmpeg_hwaccel == "cuda" else []),
+            *(["-crf", "26"] if state.ffmpeg_hwaccel != "cuda" else []),
+            "-movflags", "+faststart",
+            debug_video_path
+        ]
+
+        logger.info(f"Generating debug video with command: {' '.join(map(str, ffmpeg_command))}")
+
+        subprocess.run(ffmpeg_command)
+
+        os.remove(temp_video_path)
+        logger.info(f"Debug video generated and stored in: {debug_video_path}")
+
+    thread = threading.Thread(target=run)
+    thread.start()
\ No newline at end of file
diff --git a/script_generator/scripts/generate_funscript.py b/script_generator/scripts/generate_funscript.py
index 86c54bda..c6ec2213 100644
--- a/script_generator/scripts/generate_funscript.py
+++ b/script_generator/scripts/generate_funscript.py
@@ -1,4 +1,4 @@
-from script_generator.object_detection.utils import raw_yolo_file_exists
+from script_generator.object_detection.utils import get_raw_yolo_file_info
 from script_generator.scripts.analyze_video import analyze_video
 from script_generator.scripts.tracking_analysis import tracking_analysis
 from script_generator.state.app_state import AppState, log_state_settings
@@ -14,7 +14,7 @@ def generate_funscript(state: AppState):
         state.frame_end = to_int_or_none(state.frame_end)
 
         # analyze video if required
-        if not state.use_existing_raw_yolo or not raw_yolo_file_exists(state):
+        if not state.use_existing_raw_yolo or not get_raw_yolo_file_info(state.video_path):
             analyze_video(state)
 
         tracking_analysis(state)
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index 5fc8fcc5..e9b4bedc 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -1,3 +1,4 @@
+import os.path
 import string
 from typing import Literal
 
@@ -6,7 +7,10 @@
 from script_generator.constants import MODEL_PATH
 from script_generator.debug.debug_data import DebugData
 from script_generator.debug.logger import logger
+from script_generator.object_detection.util.utils import get_metrics_file_info
+from script_generator.object_detection.utils import get_raw_yolo_file_info
 from script_generator.tasks.tasks import AnalyzeVideoTask
+from script_generator.utils.file import get_output_file_path
 from script_generator.utils.helpers import is_mac
 from script_generator.video.ffmpeg.hwaccel import get_preferred_hwaccel
 from script_generator.video.info.video_info import VideoInfo, get_video_info
@@ -54,6 +58,9 @@ def __init__(self, is_cli):
         # State
         self.video_info: VideoInfo | None = None
         self.analyze_task: AnalyzeVideoTask | None = None
+        self.has_raw_yolo = False
+        self.has_tracking_data = False
+        self.is_processing = False
 
         # App logic
         self.debug_data = DebugData(self)
@@ -62,8 +69,11 @@ def __init__(self, is_cli):
         self.yolo_model = YOLO(MODEL_PATH, task="detect")
 
     def set_video_info(self):
-        if self.video_info is None:
+        # If movie changed
+        if self.video_info is None or self.video_info.path != self.video_path:
             self.video_info = get_video_info(self.video_path)
+            self.has_raw_yolo, _, _ = get_raw_yolo_file_info(self.video_path)
+            self.has_tracking_data, _, _ = get_metrics_file_info(self.video_path)
 
 
 def log_state_settings(state: AppState):
@@ -78,4 +88,4 @@ def log_state_settings(state: AppState):
     ]
 
     for setting_name, setting_value in settings:
-        logger.info(f"{setting_name}: {setting_value}")
\ No newline at end of file
+        logger.info(f"{setting_name}: {setting_value}")
diff --git a/script_generator/utils/file.py b/script_generator/utils/file.py
index b57c42ca..98c419ce 100644
--- a/script_generator/utils/file.py
+++ b/script_generator/utils/file.py
@@ -40,6 +40,24 @@ def load_json_from_file(file_path):
         logger.info(f"Loaded data from {file_path}, length: {len(data)}")
     return data
 
+def get_data_file_info(video_path, suffix):
+    """
+    Checks if a data file exists, returns a true / false and the path and filename
+    """
+    raw_yolo_path, raw_yolo_filename = get_output_file_path(video_path, suffix)
+    if os.path.exists(raw_yolo_path):
+        yolo_data = load_json_from_file(raw_yolo_path)
+        if len(yolo_data) == 0:
+            logger.warn(f"Raw YOLO data file doesn't contain any data: {raw_yolo_path}")
+            try:
+                os.remove(raw_yolo_path)
+                logger.info(f"Deleted empty raw YOLO data file: {raw_yolo_path}")
+            except OSError as e:
+                logger.error(f"Error deleting raw YOLO file {raw_yolo_path}: {e}")
+        else:
+            return True, raw_yolo_path, raw_yolo_filename
+    return False, None, None
+
 def get_output_file_path(video_path, suffix, add_spoiler_prefix=False):
     """"
     Get the OUTPUT_PATH filename for a specific suffix (e.g. _raw_yolo.json)

From 6ee78fe618446b975b81248e3d9f6459a6a782e2 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 26 Jan 2025 16:03:51 +0100
Subject: [PATCH 163/277] GUI tweaks

---
 script_generator/gui/views/funscript_generator.py | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index ec660741..47f73031 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -67,13 +67,14 @@ def update_video_path():
 
         # region PROCESSING
         processing = Widgets.frame(wrapper, title="Processing", main_section=True, row=3)
-        yolo_p_container, yolo_p, yolo_p_label, yolo_p_perc = Widgets.labeled_progress(processing, "YOLO Detection", row=0)
+        yolo_p_container, yolo_p, yolo_p_label, yolo_p_perc = Widgets.labeled_progress(processing, "Object Detection", row=0)
         # scene_p_container, scene_p, scene_p_label, scene_p_perc = Widgets.labeled_progress(processing, "Scene detection", row=1)
         track_p_container, track_p, track_p_label, track_p_perc = Widgets.labeled_progress(processing, "Tracking Analysis", row=2)
 
         def start_processing():
-            # TODO reset the progress bars
+            reset_progressbars([(yolo_p, yolo_p_perc), (track_p, track_p_perc)])
             video_analysis(state, controller)
+            update_ui_for_state()
 
         processing_btn = Widgets.button(processing, "Start processing", start_processing, row=3)
         # endregion
@@ -216,13 +217,21 @@ def update_ui_for_state():
             if state.has_raw_yolo and state.has_tracking_data:
                 enable_widgets([play_btn, regenerate_btn, gen_video_btn])
                 set_progressbars_done([(yolo_p, yolo_p_perc), (track_p, track_p_perc)])
+                processing_btn.config(text="Re-run object detection and or tracking")
             elif state.has_raw_yolo and not state.has_tracking_data:
                 disable_widgets([play_btn, regenerate_btn, gen_video_btn])
                 set_progressbars_done([(yolo_p, yolo_p_perc)])
                 reset_progressbars([(track_p, track_p_perc)])
+                processing_btn.config(text="Re-run object detection and or start tracking")
             else:
                 disable_widgets([play_btn, regenerate_btn, gen_video_btn])
                 reset_progressbars([(yolo_p, yolo_p_perc), (track_p, track_p_perc)])
+                processing_btn.config(text="Start processing")
+
+            if state.video_path:
+                enable_widgets([processing_btn])
+            else:
+                disable_widgets([processing_btn])
 
             if state.is_processing:
                 disable_widgets([fs_entry, fs_button])

From d77809f3e246c951cee5e9fa11e9e3f594ebad99 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 26 Jan 2025 16:05:48 +0100
Subject: [PATCH 164/277] GUI tweaks

---
 script_generator/state/app_state.py | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index e9b4bedc..bf476c17 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -71,9 +71,14 @@ def __init__(self, is_cli):
     def set_video_info(self):
         # If movie changed
         if self.video_info is None or self.video_info.path != self.video_path:
-            self.video_info = get_video_info(self.video_path)
-            self.has_raw_yolo, _, _ = get_raw_yolo_file_info(self.video_path)
-            self.has_tracking_data, _, _ = get_metrics_file_info(self.video_path)
+            if not self.video_path:
+                self.video_info = None
+                self.has_raw_yolo = False
+                self.has_tracking_data = False
+            else:
+                self.video_info = get_video_info(self.video_path)
+                self.has_raw_yolo, _, _ = get_raw_yolo_file_info(self.video_path)
+                self.has_tracking_data, _, _ = get_metrics_file_info(self.video_path)
 
 
 def log_state_settings(state: AppState):

From 636057b7b9c723e4ddc6f87d87703c3270d943aa Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 26 Jan 2025 16:44:12 +0100
Subject: [PATCH 165/277] Fixed 2D processing

---
 script_generator/video/ffmpeg/filters.py | 13 ++++++++++---
 utils/lib_FunscriptHandler.py            |  2 +-
 2 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/script_generator/video/ffmpeg/filters.py b/script_generator/video/ffmpeg/filters.py
index 24fb564f..fd59a262 100644
--- a/script_generator/video/ffmpeg/filters.py
+++ b/script_generator/video/ffmpeg/filters.py
@@ -4,7 +4,7 @@ def get_video_filters(video, video_reader, hwaccel, width, height, disable_openg
     if video.is_vr:
         return get_vr_video_filters(video, video_reader, hwaccel, disable_opengl)
     else:
-        return get_2d_video_filters(video, width, height)
+        return get_2d_video_filters(video, width, height, hwaccel)
 
 def get_vr_video_filters(video, video_reader, hwaccel, disable_opengl=False):
     if video.is_fisheye:
@@ -36,5 +36,12 @@ def get_vr_video_filters(video, video_reader, hwaccel, disable_opengl=False):
 
     return ",".join(filters)
 
-def get_2d_video_filters(video, width, height):
-    return f"scale={width}:{height}" if video.height > RENDER_RESOLUTION else None
\ No newline at end of file
+def get_2d_video_filters(video, width, height, hwaccel):
+    cuda = hwaccel == "cuda"
+
+    if video.height > RENDER_RESOLUTION:
+        scale_filter = f"scale_cuda={width}:{height},hwdownload,format=nv12" if cuda else f"scale={width}:{height}"
+    else:
+        scale_filter = "hwdownload,format=nv12" if cuda else ""
+
+    return scale_filter
\ No newline at end of file
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index da746dd4..91fe41db 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -122,7 +122,7 @@ def generate(self, state):
                 # Backup output file if it exists
                 if os.path.exists(funscript_path):
                     json_data = load_json_from_file(funscript_path)
-                    if json_data["author"] == FUNSCRIPT_AUTHOR:
+                    if json_data["author"] and json_data["author"] == FUNSCRIPT_AUTHOR:
                         backup_path = os.path.join(video_folder, f"{filename_base}_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.funscript.bak")
                         logger.info(f"Funscript {funscript_path} already exists, backing up as {backup_path}...")
                         os.rename(funscript_path, backup_path)

From cb8435153df33e9673e2210c01eb84f388d286f4 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 26 Jan 2025 17:12:14 +0100
Subject: [PATCH 166/277] GUI changes

---
 script_generator/cli/generate_funscript.py    |  4 ++--
 script_generator/gui/utils/widgets.py         |  2 +-
 .../gui/views/funscript_generator.py          | 20 ++++++++++++-------
 script_generator/video/info/video_info.py     |  2 +-
 4 files changed, 17 insertions(+), 11 deletions(-)

diff --git a/script_generator/cli/generate_funscript.py b/script_generator/cli/generate_funscript.py
index 6c496fd0..0ca2ba97 100644
--- a/script_generator/cli/generate_funscript.py
+++ b/script_generator/cli/generate_funscript.py
@@ -51,8 +51,8 @@ def main():
     parser.add_argument(
         "--save-debug-file",
         type=bool,
-        default=False,
-        help="Saves a debug file to disk with all collected metrics. This file is required for playing back the debug video with the debug statistics overlay."
+        default=True,
+        help="Saves a debug file to disk with all collected metrics. Also allows you to re-use tracking data. Default is True."
     )
     parser.add_argument(
         "--boost-enabled",
diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index 438f0235..5e7bf9d3 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -261,7 +261,7 @@ def checkbox(parent, label_text, state, attr, label_left=True, row=0, column=0,
         if tooltip_text:
             Tooltip(checkbox, tooltip_text)
 
-        return container, checkbox, is_checked
+            return container, checkbox, is_checked
 
     @staticmethod
     def create_popup(title, master, content_builder, width=400, height=300):
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 47f73031..9911919c 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -46,7 +46,7 @@ def update_video_path():
             command=lambda val: update_video_path()
         )
 
-        Widgets.dropdown(
+        _, _, reader_dropdown, _ = Widgets.dropdown(
             attr="video_reader",
             parent=video_selection,
             label_text="Video Reader",
@@ -61,8 +61,8 @@ def update_video_path():
         # region OPTIONAL SETTINGS
         optional_settings = Widgets.frame(wrapper, title="Optional settings", main_section=True, row=2)
 
-        Widgets.frames_input(optional_settings, "Start", state=state, attr="frame_start", tooltip_text="Where to start processing the video. If you have a 60fps video starting at 10s would mean frame 600")
-        Widgets.frames_input(optional_settings, "End", state=state, attr="frame_end", tooltip_text="Where to end processing the video. If you have a 60fps video stopping  at 10s would mean frame 600", row=1)
+        _, start_f_widgets, _ = Widgets.frames_input(optional_settings, "Start", state=state, attr="frame_start", tooltip_text="Where to start processing the video. If you have a 60fps video starting at 10s would mean frame 600")
+        _, end_f_widgets, _ = Widgets.frames_input(optional_settings, "End", state=state, attr="frame_end", tooltip_text="Where to end processing the video. If you have a 60fps video stopping  at 10s would mean frame 600", row=1)
         # endregion
 
         # region PROCESSING
@@ -72,6 +72,7 @@ def update_video_path():
         track_p_container, track_p, track_p_label, track_p_perc = Widgets.labeled_progress(processing, "Tracking Analysis", row=2)
 
         def start_processing():
+            state.is_processing = True
             reset_progressbars([(yolo_p, yolo_p_perc), (track_p, track_p_perc)])
             video_analysis(state, controller)
             update_ui_for_state()
@@ -171,7 +172,7 @@ def start_processing():
             tooltip_text="Will show a live preview of what is being generated.\n\nFor debugging only this will reduce your performance.\nStage 1: Show bounding boxes during object detection.\nStage 2: Show funscript and metric overlay while the funscript is being processed.",
             row=0
         )
-        Widgets.checkbox(
+        _, metrics_check, _ = Widgets.checkbox(
             general,
             "Save debug metrics",
             state, "save_debug_file",
@@ -187,7 +188,7 @@ def start_processing():
             tooltip_text="Opens a debug video player overlaid with debugging information (Press space to pause).\n\nThis overlay shows object detection boxes and a live funscript overlay,\namong other useful debugging information.\nCan only be triggered after the funscript generation process has completed.\nNeeds the 'Save debug information' option activated during processing."
         )
         script_compare = Widgets.frame(debugging, title="Script compare", row=1)
-        Widgets.file_selection(
+        _, ref_entry, ref_button, _ = Widgets.file_selection(
             attr="reference_script",
             parent=script_compare,
             label_text="Reference Script",
@@ -214,6 +215,9 @@ def start_processing():
         # endregion
 
         def update_ui_for_state():
+            if not state.is_processing:
+                processing_btn.config(text="Start processing")
+
             if state.has_raw_yolo and state.has_tracking_data:
                 enable_widgets([play_btn, regenerate_btn, gen_video_btn])
                 set_progressbars_done([(yolo_p, yolo_p_perc), (track_p, track_p_perc)])
@@ -233,10 +237,12 @@ def update_ui_for_state():
             else:
                 disable_widgets([processing_btn])
 
+            proc_widgets = [fs_entry, fs_button, ref_entry, ref_button, reader_dropdown, metrics_check, *start_f_widgets, *end_f_widgets]
             if state.is_processing:
-                disable_widgets([fs_entry, fs_button])
+                disable_widgets(proc_widgets)
+                processing_btn.config(text="Stop processing")
             else:
-                enable_widgets([fs_entry, fs_button])
+                enable_widgets(proc_widgets)
 
         # region UI UPDATE CALLBACK
         def update_ui(msg: UIMessage):
diff --git a/script_generator/video/info/video_info.py b/script_generator/video/info/video_info.py
index b9b4ff21..68219937 100644
--- a/script_generator/video/info/video_info.py
+++ b/script_generator/video/info/video_info.py
@@ -68,7 +68,7 @@ def get_cropped_dimensions(video: VideoInfo):
     if video.is_vr:
         return RENDER_RESOLUTION, RENDER_RESOLUTION
     else:
-        scaling_factor = min(1, RENDER_RESOLUTION / video.height)
+        scaling_factor = min(1, RENDER_RESOLUTION / max(video.width, video.height))
         return int(video.width * scaling_factor), int(video.height * scaling_factor)
 
 def get_video_info(video_path):

From 15c1a5efbffac751289fc680f3e24423807e58b7 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 26 Jan 2025 17:18:45 +0100
Subject: [PATCH 167/277] GUI changes

---
 script_generator/gui/controller/process_video.py  | 4 +++-
 script_generator/gui/views/funscript_generator.py | 3 +++
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/script_generator/gui/controller/process_video.py b/script_generator/gui/controller/process_video.py
index c3e0838d..0010aaa6 100644
--- a/script_generator/gui/controller/process_video.py
+++ b/script_generator/gui/controller/process_video.py
@@ -2,7 +2,7 @@
 from tkinter import messagebox
 
 from script_generator.scripts.tracking_analysis import tracking_analysis
-from script_generator.gui.messages.messages import ProgressMessage
+from script_generator.gui.messages.messages import ProgressMessage, UpdateGUIState
 from script_generator.object_detection.utils import check_skip_object_detection
 from script_generator.scripts.analyze_video import analyze_video
 from script_generator.state.app_state import AppState, log_state_settings
@@ -40,6 +40,8 @@ def run():
 
             tracking_analysis(state)
 
+            state.update_ui(UpdateGUIState(attr="is_processing", value=False))
+
         except Exception as e:
             logger.error(f"Error during video analysis: {e}")
             messagebox.showerror("Error", f"Could not process video:\n{e}")
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 9911919c..262a7af2 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -261,9 +261,12 @@ def process_update():
                     logger.info(f"Unhandled message type: {type(msg)}")
 
             def handle_state_update(state_msg: UpdateGUIState):
+                setattr(state, state_msg.attr, state_msg.value)
                 if state_msg.attr == "live_preview_mode":
                     live_preview_checked.set(state_msg.value)
 
+                update_ui_for_state()
+
             def handle_progress_message(progress_msg: ProgressMessage):
                 progress_mapping = {
                     "OBJECT_DETECTION": (yolo_p, yolo_p_perc, "has_raw_yolo"),

From c774cfd7d5ed2e53461e48df362d0003adfa50f7 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 26 Jan 2025 17:26:19 +0100
Subject: [PATCH 168/277] Disabled hh;mm;ss for now

---
 script_generator/gui/utils/utils.py   | 12 ++++++------
 script_generator/gui/utils/widgets.py |  8 ++++++--
 2 files changed, 12 insertions(+), 8 deletions(-)

diff --git a/script_generator/gui/utils/utils.py b/script_generator/gui/utils/utils.py
index 9eb15ea5..00fa21ac 100644
--- a/script_generator/gui/utils/utils.py
+++ b/script_generator/gui/utils/utils.py
@@ -1,10 +1,10 @@
-def disable_widgets(buttons):
-    for btn in buttons:
-        btn.config(state='disabled')
+def disable_widgets(widgets):
+    for widget in widgets:
+        widget.config(state='disabled')
 
-def enable_widgets(buttons):
-    for btn in buttons:
-        btn.config(state='normal')
+def enable_widgets(widgets):
+    for widget in widgets:
+        widget.config(state='normal')
 
 def set_progressbars_done(progress_bars):
     for progress, label in progress_bars:
diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index 5e7bf9d3..6e8eb995 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -4,6 +4,7 @@
 
 from script_generator.constants import LOGO
 from script_generator.gui.utils.tooltip import Tooltip
+from script_generator.gui.utils.utils import disable_widgets
 
 PADDING_X = 5
 PADDING_Y = 5
@@ -506,6 +507,9 @@ def make_entry(parent_, textvar, width=4):
         # fps_label = tk.Label(entry_container, text=" ? fps")
         # fps_label.pack(side="left")
 
+        # TODO remove disabled and fix logic
+        disable_widgets([hours_entry, minutes_entry, seconds_entry])
+
         # Optional tooltip
         if tooltip_text:
             Tooltip(hours_entry, tooltip_text)
@@ -515,6 +519,6 @@ def make_entry(parent_, textvar, width=4):
 
         # Initialize the fields so they match initial_frames (and current FPS if set)
         on_fps_change()
-
-        return container, (hours_entry, minutes_entry, seconds_entry, frames_entry), value
+        # TODO readd hours_entry, minutes_entry, seconds_entry,
+        return container, (frames_entry,), value
 

From 5f66a8efe0e30763f46775b1b59d183b3d42d268 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 26 Jan 2025 17:30:23 +0100
Subject: [PATCH 169/277] GUI update

---
 script_generator/gui/utils/widgets.py         |  2 +-
 .../gui/views/funscript_generator.py          | 21 ++++++++++---------
 2 files changed, 12 insertions(+), 11 deletions(-)

diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index 6e8eb995..ff4c3961 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -262,7 +262,7 @@ def checkbox(parent, label_text, state, attr, label_left=True, row=0, column=0,
         if tooltip_text:
             Tooltip(checkbox, tooltip_text)
 
-            return container, checkbox, is_checked
+        return container, checkbox, is_checked
 
     @staticmethod
     def create_popup(title, master, content_builder, width=400, height=300):
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 262a7af2..7b9fd1a5 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -89,8 +89,8 @@ def start_processing():
         boost_frame = ttk.LabelFrame(tweaking_container, text="Boost Settings", padding=(10, 5))
         boost_frame.grid(row=1, column=0, padx=5, pady=5, sticky="ew")
 
-        Widgets.checkbox(boost_frame, "Boost Settings", state, "boost_enabled", False)
-        Widgets.range_selector(
+        _, boost_check, _ = Widgets.checkbox(boost_frame, "Boost Settings", state, "boost_enabled", False)
+        b_d_1 = Widgets.range_selector(
             parent=boost_frame,
             label_text="Boost Up %",
             state=self.state,
@@ -99,7 +99,7 @@ def start_processing():
             row=1,
             tooltip_text="Boost up peaks by x%"
         )
-        Widgets.range_selector(
+        b_d_2 = Widgets.range_selector(
             parent=boost_frame,
             label_text="Reduce Down %",
             state=self.state,
@@ -113,8 +113,8 @@ def start_processing():
         threshold_frame = ttk.LabelFrame(tweaking_container, text="Threshold Settings", padding=(10, 5))
         threshold_frame.grid(row=1, column=1, padx=5, pady=5, sticky="ew")
 
-        Widgets.checkbox(threshold_frame, "Enable Threshold", state, "threshold_enabled", False)
-        Widgets.range_selector(
+        _, tres_check, _ = Widgets.checkbox(threshold_frame, "Enable Threshold", state, "threshold_enabled", False)
+        t_d_1 = Widgets.range_selector(
             parent=threshold_frame,
             label_text="0 Threshold",
             state=self.state,
@@ -123,7 +123,7 @@ def start_processing():
             row=1,
             tooltip_text="Threshold under which values are mapped to 0"
         )
-        Widgets.range_selector(
+        t_d_2 = Widgets.range_selector(
             parent=threshold_frame,
             label_text="100 Threshold",
             state=self.state,
@@ -137,8 +137,8 @@ def start_processing():
         vw_frame = ttk.LabelFrame(tweaking_container, text="Simplification", padding=(10, 5))
         vw_frame.grid(row=1, column=3, padx=5, pady=5, sticky="ew")
 
-        Widgets.checkbox(vw_frame, "Enable Simplification", state, "vw_simplification_enabled", False)
-        Widgets.range_selector(
+        _, simp_check, _ = Widgets.checkbox(vw_frame, "Enable Simplification", state, "vw_simplification_enabled", False)
+        s_d_1 = Widgets.range_selector(
             parent=vw_frame,
             label_text="VW Factor",
             state=self.state,
@@ -147,7 +147,7 @@ def start_processing():
             row=1,
             tooltip_text="Rationalize the number of points\nMakes the script end-user friendly\nDeactivating this will make the script unusable as is\nExpert mode, for post-editing"
         )
-        Widgets.range_selector(
+        s_d_2 = Widgets.range_selector(
             parent=vw_frame,
             label_text="Rounding",
             state=self.state,
@@ -237,7 +237,8 @@ def update_ui_for_state():
             else:
                 disable_widgets([processing_btn])
 
-            proc_widgets = [fs_entry, fs_button, ref_entry, ref_button, reader_dropdown, metrics_check, *start_f_widgets, *end_f_widgets]
+            proc_widgets = [fs_entry, fs_button, ref_entry, ref_button, reader_dropdown, metrics_check, *start_f_widgets, *end_f_widgets,
+                            boost_check, simp_check, tres_check, t_d_1, t_d_2, s_d_1, s_d_2, b_d_1, b_d_2]
             if state.is_processing:
                 disable_widgets(proc_widgets)
                 processing_btn.config(text="Stop processing")

From 62b50d7f577e7556c66526085e81b2bbfd92fdd1 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 26 Jan 2025 17:48:55 +0100
Subject: [PATCH 170/277] Fixed cuda scale for 2d

---
 script_generator/object_detection/post_process_worker.py | 4 ++--
 script_generator/video/ffmpeg/filters.py                 | 6 +++---
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/script_generator/object_detection/post_process_worker.py b/script_generator/object_detection/post_process_worker.py
index a920c1a6..8447738a 100644
--- a/script_generator/object_detection/post_process_worker.py
+++ b/script_generator/object_detection/post_process_worker.py
@@ -2,13 +2,13 @@
 
 import cv2
 
-from script_generator.constants import CLASS_REVERSE_MATCH, CLASS_COLORS
 from config import RUN_POSE_MODEL
+from script_generator.constants import CLASS_REVERSE_MATCH, CLASS_COLORS
+from script_generator.debug.logger import logger
 from script_generator.gui.messages.messages import UpdateGUIState
 from script_generator.object_detection.object_detection_result import ObjectDetectionResult
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 from script_generator.utils.file import write_json_to_file, get_output_file_path
-from script_generator.debug.logger import logger
 
 
 class PostProcessWorker(AbstractTaskProcessor):
diff --git a/script_generator/video/ffmpeg/filters.py b/script_generator/video/ffmpeg/filters.py
index fd59a262..2628fbdf 100644
--- a/script_generator/video/ffmpeg/filters.py
+++ b/script_generator/video/ffmpeg/filters.py
@@ -39,9 +39,9 @@ def get_vr_video_filters(video, video_reader, hwaccel, disable_opengl=False):
 def get_2d_video_filters(video, width, height, hwaccel):
     cuda = hwaccel == "cuda"
 
-    if video.height > RENDER_RESOLUTION:
-        scale_filter = f"scale_cuda={width}:{height},hwdownload,format=nv12" if cuda else f"scale={width}:{height}"
+    if video.height > RENDER_RESOLUTION or video.width > width:
+        scale_filter = f"[0:0]scale_cuda={width}:{height},hwdownload,format=nv12" if cuda else f"scale={width}:{height}"
     else:
-        scale_filter = "hwdownload,format=nv12" if cuda else ""
+        scale_filter = "[0:0]hwdownload,format=nv12" if cuda else ""
 
     return scale_filter
\ No newline at end of file

From 06443f2fe816b80619869a2d741d8e31b4bd4b8a Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 26 Jan 2025 18:07:34 +0100
Subject: [PATCH 171/277] Fix render_resolution not being used everywhere

---
 config.py                                | 4 ++--
 script_generator/video/ffmpeg/filters.py | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/config.py b/config.py
index 21593688..55e5e81c 100644
--- a/config.py
+++ b/config.py
@@ -24,8 +24,8 @@
 # PERFORMANCE
 ##################################################################################################
 
-RENDER_RESOLUTION = 1080 # Above 1080x1080 doesn't seem to improve detections
-TEXTURE_RESOLUTION = 1440 # Size that is used to texture the opengl sphere
+RENDER_RESOLUTION = 1080 # Above 1080x1080 doesn't seem to improve detections minimum of 640x640, has a huge impact on performance but at a quality cost (after changing you need to regenerate your data)
+TEXTURE_RESOLUTION = RENDER_RESOLUTION * 1.3 # Size that is used to texture the opengl sphere
 YOLO_BATCH_SIZE = 1 if platform.system() == "Darwin" else 30 # Mac doesn't support batching
 YOLO_PERSIST = True # Big impact on performance but also improves tracking
 QUEUE_MAXSIZE = 200 # Bounded queue size to avoid memory blow-up as raw frames consume a lot of memory, does not increase performance
diff --git a/script_generator/video/ffmpeg/filters.py b/script_generator/video/ffmpeg/filters.py
index 2628fbdf..9a7b80f9 100644
--- a/script_generator/video/ffmpeg/filters.py
+++ b/script_generator/video/ffmpeg/filters.py
@@ -14,7 +14,7 @@ def get_vr_video_filters(video, video_reader, hwaccel, disable_opengl=False):
 
     cuda = hwaccel == "cuda"
 
-    scale = f"[0:0]scale_cuda=2160:-2,hwdownload" if cuda else f"[0:0]scale={RENDER_RESOLUTION * 2}:-2"
+    scale = f"[0:0]scale_cuda={RENDER_RESOLUTION * 2}:-2,hwdownload" if cuda else f"[0:0]scale={RENDER_RESOLUTION * 2}:-2"
     crop = f"crop={RENDER_RESOLUTION}:{RENDER_RESOLUTION}:0:0"
     out_format = f"format=nv12," if cuda else ""
 

From 83fcbab035271bcefa09113cb61283039284bc26 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 26 Jan 2025 18:26:11 +0100
Subject: [PATCH 172/277] Added more cli docs

---
 README.md | 53 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 53 insertions(+)

diff --git a/README.md b/README.md
index b8f0aa8e..5b98c601 100644
--- a/README.md
+++ b/README.md
@@ -165,6 +165,59 @@ To generate a script with cmd or terminal, run the following command
 ```bash
 python -m script_generator.cli.generate_funscript.py /path/to/video.mp4
 ```
+
+### Command-Line Arguments
+#### Required Arguments
+- **`video_path`** *(str)*  
+  Path to the input video file.  
+
+#### Optional Arguments
+- **`--reuse-yolo`** *(bool, default=False)*  
+  Use an existing raw YOLO output file instead of generating a new one.
+
+- **`--copy-funscript`** *(bool, default=True)*  
+  Copies the final funscript to the movie directory.
+
+- **`--frame-start`** *(int, default=0)*  
+  The starting frame number for processing.
+
+- **`--frame-end`** *(int or None, default=None)*  
+  The ending frame number for processing. Set to `None` to process till the end.
+
+- **`--video-reader`** *(str, default=None)*  
+  Video reader to use. Valid options: `<VALID_VIDEO_READERS>`. Defaults are platform-specific.
+
+- **`--save-debug-file`** *(bool, default=True)*  
+  Saves a debug file to disk with all collected metrics. Also allows you to re-use tracking data.
+
+#### Funscript Tweaking Settings
+- **`--boost-enabled`** *(bool, default=True)*  
+  Enable boosting to adjust the motion range dynamically.
+
+- **`--boost-up-percent`** *(int, default=10)*  
+  Increase the peaks by a specified percentage to enhance upper motion limits.
+
+- **`--boost-down-percent`** *(int, default=15)*  
+  Reduce the lower peaks by a specified percentage to limit downward motion.
+
+- **`--threshold-enabled`** *(bool, default=True)*  
+  Enable thresholding to control motion mapping within specified bounds.
+
+- **`--threshold-low`** *(int, default=10)*  
+  Values below this threshold are mapped to 0, limiting lower boundary motion.
+
+- **`--threshold-high`** *(int, default=90)*  
+  Values above this threshold are mapped to 100, limiting upper boundary motion.
+
+- **`--vw-simplification-enabled`** *(bool, default=True)*  
+  Simplify the generated script to reduce the number of points, making it user-friendly.
+
+- **`--vw-factor`** *(float, default=8.0)*  
+  Determines the degree of simplification. Higher values lead to fewer points.
+
+- **`--rounding`** *(int, default=5)*  
+  Set the rounding factor for script values to adjust precision.
+
 ---
 
 ## Performance & Parallel Processing

From d63f01275d572743df6b2b41198e7d76d0643dd4 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 26 Jan 2025 18:30:54 +0100
Subject: [PATCH 173/277] Added more cli docs

---
 README.md                                  | 8 ++++----
 script_generator/cli/generate_funscript.py | 6 +++---
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index 5b98c601..1cfde4a1 100644
--- a/README.md
+++ b/README.md
@@ -173,19 +173,19 @@ python -m script_generator.cli.generate_funscript.py /path/to/video.mp4
 
 #### Optional Arguments
 - **`--reuse-yolo`** *(bool, default=False)*  
-  Use an existing raw YOLO output file instead of generating a new one.
+  Re-use an existing raw YOLO output file instead of generating a new one when available.
 
 - **`--copy-funscript`** *(bool, default=True)*  
   Copies the final funscript to the movie directory.
 
 - **`--frame-start`** *(int, default=0)*  
-  The starting frame number for processing.
+  The starting frame number for processing (keep in Mind that this shouldn't be changed if you want a full funscript). Default is 0 (start from the beginning).
 
 - **`--frame-end`** *(int or None, default=None)*  
-  The ending frame number for processing. Set to `None` to process till the end.
+  The ending frame number for processing (keep in Mind that this shouldn't be changed if you want a full funscript). Default is None (process till the end).
 
 - **`--video-reader`** *(str, default=None)*  
-  Video reader to use. Valid options: `<VALID_VIDEO_READERS>`. Defaults are platform-specific.
+  Video reader to use. Valid options: `"FFmpeg", "FFmpeg + OpenGL (Windows)"`. Defaults are platform-specific.
 
 - **`--save-debug-file`** *(bool, default=True)*  
   Saves a debug file to disk with all collected metrics. Also allows you to re-use tracking data.
diff --git a/script_generator/cli/generate_funscript.py b/script_generator/cli/generate_funscript.py
index 0ca2ba97..3511d7da 100644
--- a/script_generator/cli/generate_funscript.py
+++ b/script_generator/cli/generate_funscript.py
@@ -22,7 +22,7 @@ def main():
         "--reuse-yolo",
         type=bool,
         default=False,
-        help="Use an existing raw YOLO output file instead of generating a new one. Default is False."
+        help="Re-use an existing raw YOLO output file instead of generating a new one when available. Default is False."
     )
     parser.add_argument(
         "--copy-funscript",
@@ -34,13 +34,13 @@ def main():
         "--frame-start",
         type=int,
         default=0,
-        help="The starting frame number for processing. Default is 0 (start from the beginning)."
+        help="The starting frame number for processing (keep in Mind that this shouldn't be changed if you want a full funscript). Default is 0 (start from the beginning)."
     )
     parser.add_argument(
         "--frame-end",
         type=int or None,
         default=None,
-        help="The ending frame number for processing. Default is None (process till the end)."
+        help="The ending frame number for processing (keep in Mind that this shouldn't be changed if you want a full funscript). Default is None (process till the end)."
     )
     parser.add_argument(
         "--video-reader",

From a0973db0c9758840e655507b9d5223097916d8e1 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 26 Jan 2025 21:11:46 +0100
Subject: [PATCH 174/277] Debug video fix

---
 config.py                                               | 6 +++---
 script_generator/debug/video_player/state.py            | 2 +-
 script_generator/gui/utils/widgets.py                   | 1 +
 script_generator/gui/views/popups/create_debug_video.py | 2 +-
 script_generator/scripts/generate_debug_video.py        | 4 +++-
 script_generator/video/ffmpeg/video_reader.py           | 6 +++---
 6 files changed, 12 insertions(+), 9 deletions(-)

diff --git a/config.py b/config.py
index 55e5e81c..ca352137 100644
--- a/config.py
+++ b/config.py
@@ -25,10 +25,9 @@
 ##################################################################################################
 
 RENDER_RESOLUTION = 1080 # Above 1080x1080 doesn't seem to improve detections minimum of 640x640, has a huge impact on performance but at a quality cost (after changing you need to regenerate your data)
-TEXTURE_RESOLUTION = RENDER_RESOLUTION * 1.3 # Size that is used to texture the opengl sphere
+TEXTURE_RESOLUTION = RENDER_RESOLUTION * 1.3 # Texture size that is used to texture the opengl sphere
 YOLO_BATCH_SIZE = 1 if platform.system() == "Darwin" else 30 # Mac doesn't support batching
 YOLO_PERSIST = True # Big impact on performance but also improves tracking
-QUEUE_MAXSIZE = 200 # Bounded queue size to avoid memory blow-up as raw frames consume a lot of memory, does not increase performance
 
 ##################################################################################################
 # ADVANCED
@@ -36,9 +35,10 @@
 
 YOLO_CONF = 0.3
 VR_TO_2D_PITCH=-25 # The dataset is trained on -25
-UPDATE_PROGRESS_INTERVAL = 0.1 # Updates progress in the console and in gui
+UPDATE_PROGRESS_INTERVAL = 0.2 # Updates progress in the console and in gui
 # Define custom colormap based on Lucife's heatmapColors
 STEP_SIZE = 120  # Speed step size for color transitions
+QUEUE_MAXSIZE = 100 # Bounded queue size to avoid memory blow-up as raw frames consume a lot of memory, does not increase performance
 
 ##################################################################################################
 # DEV
diff --git a/script_generator/debug/video_player/state.py b/script_generator/debug/video_player/state.py
index bdc928db..8636a850 100644
--- a/script_generator/debug/video_player/state.py
+++ b/script_generator/debug/video_player/state.py
@@ -13,7 +13,7 @@ def __init__(self, state: AppState, start_frame, end_frame):
         self.end_frame = state.video_info.total_frames if not end_frame else end_frame
         self.current_frame = 0
 
-        self.cap = VideoReaderFFmpeg(state)
+        self.cap = VideoReaderFFmpeg(state, start_frame)
         self.paused = False
 
     def release(self):
diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index ff4c3961..3c4f1e68 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -471,6 +471,7 @@ def update_from_frames(*_):
                 minutes_var.set(str(m_))
                 seconds_var.set(str(s_))
                 value.set(str(f_))
+                setattr(state, attr, f_)
 
                 if callback:
                     callback(f_)
diff --git a/script_generator/gui/views/popups/create_debug_video.py b/script_generator/gui/views/popups/create_debug_video.py
index fe97d00c..3769e0c6 100644
--- a/script_generator/gui/views/popups/create_debug_video.py
+++ b/script_generator/gui/views/popups/create_debug_video.py
@@ -14,7 +14,7 @@ def __init__(self, video_info):
 def render_debug_video_popup(window, state: AppState):
     state.set_video_info()
     debug_state = RenderDebugVideoState(state.video_info)
-    Widgets.label(window, "First stream/play your results locally (see Play debug video). Note the frame when the issue occurs. \nSubtract about 120 frames so we have some context. Then add a few seconds of recording (2 to 20 seconds\nshould do it)", column=0, sticky="w", padx=10, pady=10)
+    Widgets.label(window, "Processing can take a while! See the logs for details. We'll add progress bars in the future.\n\nFirst stream/play your results locally (see Play debug video). Note the frame when the issue occurs. \nSubtract about 120 frames so we have some context. Then add a few seconds of recording (2 to 20 seconds\nshould do it).", column=0, sticky="w", padx=10, pady=10)
     # TODO add progress in the UI
     # p_container, p, p_label, p_perc = Widgets.labeled_progress(window, "Progress", row=1)
     Widgets.frames_input(window, "Start", state=debug_state, attr="frame_start", tooltip_text="Where to start processing the video. If you have a 60fps video starting at 10s would mean frame 600", row=2)
diff --git a/script_generator/scripts/generate_debug_video.py b/script_generator/scripts/generate_debug_video.py
index 8916351d..17abc88d 100644
--- a/script_generator/scripts/generate_debug_video.py
+++ b/script_generator/scripts/generate_debug_video.py
@@ -13,9 +13,11 @@
 def generate_debug_video(state: AppState, frame_start: int, frame_end: int):
     # prevent blocking the main thread
     def run():
-        logger.info(f"Generating debug video from: {frame_start} to {frame_end}")
+        logger.info(f"Generating debug video from: {frame_start} to {frame_end}, this might take a while...")
         temp_video_path = play_debug_video(state, frame_start, frame_end, save_video_mode=True)
 
+        logger.info(f"Generating debug video raw video generated now converting...")
+
         debug_video_path, _ = get_output_file_path(state.video_path, "_debug.mp4", True)
         ffmpeg_command = [
             FFMPEG_PATH, "-y",
diff --git a/script_generator/video/ffmpeg/video_reader.py b/script_generator/video/ffmpeg/video_reader.py
index 3d6dcec0..ec5fb732 100644
--- a/script_generator/video/ffmpeg/video_reader.py
+++ b/script_generator/video/ffmpeg/video_reader.py
@@ -6,10 +6,10 @@
 from script_generator.video.ffmpeg.commands import get_ffmpeg_read_cmd
 
 class VideoReaderFFmpeg:
-    def __init__(self, state):
+    def __init__(self, state, start_frame=0):
         self.state: AppState = state
         self.video_path = state.video_path
-        self.start_frame = 0
+        self.start_frame = start_frame
         self.current_frame_number = 0
         self.current_time = 0
         self.process = None
@@ -28,7 +28,7 @@ def _start_process(self, start_frame=0):
             self.state.video_info,
             self.state.video_reader,
             self.state.ffmpeg_hwaccel,
-            self.state.frame_start,
+            start_frame,
             disable_opengl=True
         )
         logger.info(f"Starting FFmpeg reader with command: {' '.join(cmd)}")

From a5f48c0a5996b49d3ea49dc14331155ddfafc550 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 26 Jan 2025 21:15:43 +0100
Subject: [PATCH 175/277] Ensure log path exists

---
 script_generator/debug/logger.py | 7 ++++++-
 script_generator/utils/file.py   | 6 +++++-
 2 files changed, 11 insertions(+), 2 deletions(-)

diff --git a/script_generator/debug/logger.py b/script_generator/debug/logger.py
index 9bf62216..90eb0e99 100644
--- a/script_generator/debug/logger.py
+++ b/script_generator/debug/logger.py
@@ -4,6 +4,8 @@
 import sys
 from colorama import Fore, Style, init
 
+from script_generator.utils.file import ensure_path_exists
+
 init(autoreset=True)
 
 class ColorizedStreamHandler(logging.StreamHandler):
@@ -16,12 +18,15 @@ def emit(self, record):
 
 project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
+log_path = os.path.join(project_root, "logs")
+ensure_path_exists(log_path)
+
 logging.basicConfig(
     level=logging.INFO,
     format="%(asctime)s|%(levelname)s|%(name)s|%(message)s",
     handlers=[
         logging.FileHandler(
-            os.path.join(project_root, "logs", f"log_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.log"), mode="w", encoding='utf-8'),
+            os.path.join(log_path, f"log_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.log"), mode="w", encoding='utf-8'),
         ColorizedStreamHandler(sys.stdout)
     ]
 )
diff --git a/script_generator/utils/file.py b/script_generator/utils/file.py
index 98c419ce..792f3497 100644
--- a/script_generator/utils/file.py
+++ b/script_generator/utils/file.py
@@ -71,4 +71,8 @@ def check_create_output_folder(video_path):
     output_dir, _ = get_output_file_path(video_path, "")
     directory = os.path.dirname(output_dir)
     if not os.path.exists(directory):
-        os.makedirs(directory)
\ No newline at end of file
+        os.makedirs(directory)
+
+def ensure_path_exists(path):
+    if not os.path.exists(path):
+        os.makedirs(path)
\ No newline at end of file

From 20110a685c0212e48d0e1eaddf0eadbf20e9e542 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 27 Jan 2025 08:58:03 +0100
Subject: [PATCH 176/277] Fixed circular import

---
 script_generator/debug/logger.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/script_generator/debug/logger.py b/script_generator/debug/logger.py
index 90eb0e99..ab3a8371 100644
--- a/script_generator/debug/logger.py
+++ b/script_generator/debug/logger.py
@@ -4,7 +4,9 @@
 import sys
 from colorama import Fore, Style, init
 
-from script_generator.utils.file import ensure_path_exists
+def ensure_path_exists(path):
+    if not os.path.exists(path):
+        os.makedirs(path)
 
 init(autoreset=True)
 

From 09095a3fd2eadc1187be4965541e754031845f13 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 27 Jan 2025 11:45:11 +0100
Subject: [PATCH 177/277] Fixed circular import

---
 config.py                                        | 5 ++---
 script_generator/object_detection/yolo_worker.py | 1 +
 script_generator/state/app_state.py              | 2 +-
 script_generator/video/ffmpeg/commands.py        | 2 +-
 script_generator/video/ffmpeg/filters.py         | 6 +++---
 5 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/config.py b/config.py
index ca352137..b3f8f140 100644
--- a/config.py
+++ b/config.py
@@ -24,7 +24,7 @@
 # PERFORMANCE
 ##################################################################################################
 
-RENDER_RESOLUTION = 1080 # Above 1080x1080 doesn't seem to improve detections minimum of 640x640, has a huge impact on performance but at a quality cost (after changing you need to regenerate your data)
+RENDER_RESOLUTION = 640 # Above 1080x1080 doesn't seem to improve detections minimum of 640x640, has a huge impact on performance but at a quality cost (after changing you need to regenerate your data)
 TEXTURE_RESOLUTION = RENDER_RESOLUTION * 1.3 # Texture size that is used to texture the opengl sphere
 YOLO_BATCH_SIZE = 1 if platform.system() == "Darwin" else 30 # Mac doesn't support batching
 YOLO_PERSIST = True # Big impact on performance but also improves tracking
@@ -36,8 +36,7 @@
 YOLO_CONF = 0.3
 VR_TO_2D_PITCH=-25 # The dataset is trained on -25
 UPDATE_PROGRESS_INTERVAL = 0.2 # Updates progress in the console and in gui
-# Define custom colormap based on Lucife's heatmapColors
-STEP_SIZE = 120  # Speed step size for color transitions
+STEP_SIZE = 120  # Define custom colormap based on Lucife's heatmapColors | Speed step size for color transitions
 QUEUE_MAXSIZE = 100 # Bounded queue size to avoid memory blow-up as raw frames consume a lot of memory, does not increase performance
 
 ##################################################################################################
diff --git a/script_generator/object_detection/yolo_worker.py b/script_generator/object_detection/yolo_worker.py
index 696fcf4b..20ba64e0 100644
--- a/script_generator/object_detection/yolo_worker.py
+++ b/script_generator/object_detection/yolo_worker.py
@@ -36,6 +36,7 @@ def task_logic(self):
     def process_batch(self, frames, tasks):
         start_time = time.time()
         # Yolo expects bgr images when using numpy frames
+        # yolo_results = self.state.yolo_model(frames, conf=YOLO_CONF, verbose=False) # replace with this line for pipeline speed testing
         yolo_results = self.state.yolo_model.track(frames, persist=YOLO_PERSIST, conf=YOLO_CONF, verbose=False)
         avg_time = (time.time() - start_time) / len(tasks)
 
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index bf476c17..44852128 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -24,7 +24,7 @@ def __init__(self, is_cli):
         self.video_path: string = None
         self.frame_start: int = 0
         self.frame_end: int | None = None
-        self.video_reader: Literal["FFmpeg", "FFmpeg + OpenGL (Windows)"] = "FFmpeg" if is_mac() else "FFmpeg + OpenGL (Windows)"
+        self.video_reader: Literal["FFmpeg", "FFmpeg + OpenGL (Windows)"] = "FFmpeg" # if is_mac() else "FFmpeg + OpenGL (Windows)"
         self.copy_funscript_to_movie_dir = True
 
         # Gui/settings debug
diff --git a/script_generator/video/ffmpeg/commands.py b/script_generator/video/ffmpeg/commands.py
index cb61403e..1e9908a4 100644
--- a/script_generator/video/ffmpeg/commands.py
+++ b/script_generator/video/ffmpeg/commands.py
@@ -27,7 +27,7 @@ def get_ffmpeg_read_cmd(video: VideoInfo, video_reader: str, hwaccel: str, frame
         *hwaccel_read,
         '-nostats', '-loglevel', 'warning',
         "-ss", str(start_time / 1000),  # Seek to start time in seconds
-        "-i", video.path,
+        "-i", f"\"{video.path}\"",
         "-an",  # Disable audio processing
         *video_filter,
         "-f", "rawvideo", "-pix_fmt", "bgr24",  # cv2 requires bgr (over rgb) and Yolo expects bgr images when using numpy frames (converts them internally)
diff --git a/script_generator/video/ffmpeg/filters.py b/script_generator/video/ffmpeg/filters.py
index 9a7b80f9..b84348d9 100644
--- a/script_generator/video/ffmpeg/filters.py
+++ b/script_generator/video/ffmpeg/filters.py
@@ -34,14 +34,14 @@ def get_vr_video_filters(video, video_reader, hwaccel, disable_opengl=False):
             f"{out_format}lutyuv=y=gammaval(0.7)"
         ]
 
-    return ",".join(filters)
+    return f"\"{','.join(filters)}\""
 
 def get_2d_video_filters(video, width, height, hwaccel):
     cuda = hwaccel == "cuda"
 
     if video.height > RENDER_RESOLUTION or video.width > width:
-        scale_filter = f"[0:0]scale_cuda={width}:{height},hwdownload,format=nv12" if cuda else f"scale={width}:{height}"
+        scale_filter = f"\"[0:0]scale_cuda={width}:{height},hwdownload,format=nv12" if cuda else f"scale={width}:{height}\""
     else:
-        scale_filter = "[0:0]hwdownload,format=nv12" if cuda else ""
+        scale_filter = "\"[0:0]hwdownload,format=nv12\"" if cuda else ""
 
     return scale_filter
\ No newline at end of file

From 854f5a458aa54bf6e63123dd696f4866fa136557 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 27 Jan 2025 12:47:53 +0100
Subject: [PATCH 178/277] Reverted ffmpeg quotes fixed preview issue (start for
 video) greyed out stop processing

---
 script_generator/gui/views/funscript_generator.py          | 5 +++--
 .../object_detection/analyze_tracking_results.py           | 7 +------
 script_generator/video/ffmpeg/commands.py                  | 2 +-
 script_generator/video/ffmpeg/filters.py                   | 2 +-
 script_generator/video/ffmpeg/video_reader.py              | 7 +++++++
 5 files changed, 13 insertions(+), 10 deletions(-)

diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 7b9fd1a5..2c9201a2 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -240,8 +240,9 @@ def update_ui_for_state():
             proc_widgets = [fs_entry, fs_button, ref_entry, ref_button, reader_dropdown, metrics_check, *start_f_widgets, *end_f_widgets,
                             boost_check, simp_check, tres_check, t_d_1, t_d_2, s_d_1, s_d_2, b_d_1, b_d_2]
             if state.is_processing:
-                disable_widgets(proc_widgets)
-                processing_btn.config(text="Stop processing")
+                # TODO remove the processing button and implement stop processing
+                disable_widgets([*proc_widgets, processing_btn])
+                processing_btn.config(text="Stop processing (WIP)")
             else:
                 enable_widgets(proc_widgets)
 
diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index 68a7a227..df37ed88 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -30,7 +30,7 @@ def analyze_tracking_results(state: AppState, results):
     cuts = []
 
     if state.live_preview_mode:
-        reader.set(cv2.CAP_PROP_POS_FRAMES, state.frame_start)
+        reader.set(cv2.CAP_PROP_POS_FRAMES, state.frame_start_track)
     else:
         reader.release()
 
@@ -157,11 +157,6 @@ def analyze_tracking_results(state: AppState, results):
         window_name = "Combined results"
         if state.live_preview_mode:
             ret, frame = reader.read()
-
-            if not frame.any():
-                logger.warn("Frame could not be read in live preview")
-                continue
-
             frame = frame.copy()
 
             for box in tracker.tracked_boxes:
diff --git a/script_generator/video/ffmpeg/commands.py b/script_generator/video/ffmpeg/commands.py
index 1e9908a4..cb61403e 100644
--- a/script_generator/video/ffmpeg/commands.py
+++ b/script_generator/video/ffmpeg/commands.py
@@ -27,7 +27,7 @@ def get_ffmpeg_read_cmd(video: VideoInfo, video_reader: str, hwaccel: str, frame
         *hwaccel_read,
         '-nostats', '-loglevel', 'warning',
         "-ss", str(start_time / 1000),  # Seek to start time in seconds
-        "-i", f"\"{video.path}\"",
+        "-i", video.path,
         "-an",  # Disable audio processing
         *video_filter,
         "-f", "rawvideo", "-pix_fmt", "bgr24",  # cv2 requires bgr (over rgb) and Yolo expects bgr images when using numpy frames (converts them internally)
diff --git a/script_generator/video/ffmpeg/filters.py b/script_generator/video/ffmpeg/filters.py
index b84348d9..81f67f98 100644
--- a/script_generator/video/ffmpeg/filters.py
+++ b/script_generator/video/ffmpeg/filters.py
@@ -34,7 +34,7 @@ def get_vr_video_filters(video, video_reader, hwaccel, disable_opengl=False):
             f"{out_format}lutyuv=y=gammaval(0.7)"
         ]
 
-    return f"\"{','.join(filters)}\""
+    return f"{','.join(filters)}"
 
 def get_2d_video_filters(video, width, height, hwaccel):
     cuda = hwaccel == "cuda"
diff --git a/script_generator/video/ffmpeg/video_reader.py b/script_generator/video/ffmpeg/video_reader.py
index ec5fb732..5101b3a0 100644
--- a/script_generator/video/ffmpeg/video_reader.py
+++ b/script_generator/video/ffmpeg/video_reader.py
@@ -1,5 +1,7 @@
+import os
 import subprocess
 import cv2
+import imageio
 import numpy as np
 from script_generator.debug.logger import logger
 from script_generator.state.app_state import AppState
@@ -43,9 +45,14 @@ def read(self):
         try:
             in_bytes = self.process.stdout.read(self.frame_size)
             if not in_bytes:
+                logger.warn("FFmpeg video reader could not read frame / end of file")
                 return False, None  # End of video
 
             frame = np.frombuffer(in_bytes, np.uint8).reshape((self.height, self.width, 3))
+
+            # output_path = os.path.join("C:/cvr/funscript-generator/tmp_output", f"frame_{self.current_frame_number:05d}.png")
+            # imageio.imwrite(output_path, frame)
+
             self.current_frame_number += 1
             self.current_time = (self.current_frame_number / self.state.video_info.fps) * 1000
 

From 69bde5eabec96571cc37a6cccca458555c68929a Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 27 Jan 2025 13:20:15 +0100
Subject: [PATCH 179/277] Fixed funscript author bug

---
 utils/lib_FunscriptHandler.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 91fe41db..4326f1f0 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -122,7 +122,7 @@ def generate(self, state):
                 # Backup output file if it exists
                 if os.path.exists(funscript_path):
                     json_data = load_json_from_file(funscript_path)
-                    if json_data["author"] and json_data["author"] == FUNSCRIPT_AUTHOR:
+                    if "author" in json_data and json_data["author"] == FUNSCRIPT_AUTHOR:
                         backup_path = os.path.join(video_folder, f"{filename_base}_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.funscript.bak")
                         logger.info(f"Funscript {funscript_path} already exists, backing up as {backup_path}...")
                         os.rename(funscript_path, backup_path)

From 21d0145627fc293d5e0f25a8adb025223f94df95 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 27 Jan 2025 16:17:22 +0100
Subject: [PATCH 180/277] Added missing requirements

---
 requirements.txt | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/requirements.txt b/requirements.txt
index a610d88f..0de25388 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -14,3 +14,5 @@ scipy~=1.15.1
 matplotlib~=3.10.0
 simplification~=0.7.13
 torch~=2.5.1+cu121
+msgpack~=1.1.0
+pillow~=11.1.0
\ No newline at end of file

From 277f5c172c9ef5b5d0ec0c95c8c25ede4392b713 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 27 Jan 2025 16:25:32 +0100
Subject: [PATCH 181/277] Changed to non cuda in requirements

---
 requirements.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index 0de25388..f7066683 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,6 @@
+torch~=2.5.1
+torchvision~=0.20.1
 numpy~=1.26.4
-torchvision~=0.20.1+cu121
 ultralytics~=8.3.59
 glfw~=2.8.0
 pyopengl~=3.1.7
@@ -13,6 +14,5 @@ opencv-python~=4.10.0.84
 scipy~=1.15.1
 matplotlib~=3.10.0
 simplification~=0.7.13
-torch~=2.5.1+cu121
 msgpack~=1.1.0
 pillow~=11.1.0
\ No newline at end of file

From 7d2e1bf4fc471b98af4f15ace02de6020067fa3d Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 27 Jan 2025 16:43:30 +0100
Subject: [PATCH 182/277] Fixed readme and requirements.txt

---
 README.md        | 8 ++++----
 requirements.txt | 5 +----
 2 files changed, 5 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index 1cfde4a1..8ae29cc1 100644
--- a/README.md
+++ b/README.md
@@ -91,7 +91,7 @@ Before using this project, ensure you have the following installed:
 python -m venv VRFunAIGen
 VRFunAIGen\Scripts\activate # windows
 source myenv/bin/activate # mac
-pip install -e requirements.txt
+pip install -r requirements.txt
 pip uninstall torch torchvision torchaudio
 pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
 python FSGenerator.py
@@ -102,7 +102,7 @@ python FSGenerator.py
 python -m venv VRFunAIGen
 VRFunAIGen\Scripts\activate # windows
 source myenv/bin/activate # mac
-pip install -e requirements.txt
+pip install -r requirements.txt
 python FSGenerator.py
 ```
 
@@ -114,7 +114,7 @@ python FSGenerator.py
 ```bash
 conda create -n VRFunAIGen python=3.11
 conda activate VRFunAIGen
-pip install -e requirements.txt
+pip install -r requirements.txt
 pip uninstall torch torchvision torchaudio
 pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
 python FSGenerator.py
@@ -125,7 +125,7 @@ While executing, you’ll need to say “yes” a few times. The lines “pip un
 ```bash
 conda create -n VRFunAIGen python=3.11
 conda activate VRFunAIGen
-pip install -e requirements.txt
+pip install -r requirements.txt
 python FSGenerator.py
 ```
 
diff --git a/requirements.txt b/requirements.txt
index f7066683..a311d2e5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,15 +1,12 @@
 torch~=2.5.1
 torchvision~=0.20.1
-numpy~=1.26.4
+numpy
 ultralytics~=8.3.59
 glfw~=2.8.0
 pyopengl~=3.1.7
 imageio~=2.36.1
 tqdm~=4.67.1
 colorama~=0.4.6
-ffmpeg~=7.1.0
-ffmpeg-python~=0.2.0
-pycuda~=2024.1
 opencv-python~=4.10.0.84
 scipy~=1.15.1
 matplotlib~=3.10.0

From 9bf5c4a01df6a6ce2710f639f99eb790f67e5e16 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 27 Jan 2025 16:50:14 +0100
Subject: [PATCH 183/277] Code organisation Readme fix

---
 README.md                                     |  4 +-
 config.py                                     |  2 +-
 script_generator/debug/debug_data.py          |  3 +-
 .../object_detection/post_process_worker.py   |  3 +-
 .../object_detection/util/utils.py            |  2 +-
 script_generator/object_detection/utils.py    |  4 +-
 script_generator/scripts/tracking_analysis.py |  3 +-
 script_generator/utils/file.py                | 56 -----------------
 script_generator/utils/json.py                | 61 +++++++++++++++++++
 utils/lib_FunscriptHandler.py                 |  4 +-
 10 files changed, 76 insertions(+), 66 deletions(-)
 create mode 100644 script_generator/utils/json.py

diff --git a/README.md b/README.md
index 8ae29cc1..e79c71ac 100644
--- a/README.md
+++ b/README.md
@@ -90,7 +90,7 @@ Before using this project, ensure you have the following installed:
 ```bash
 python -m venv VRFunAIGen
 VRFunAIGen\Scripts\activate # windows
-source myenv/bin/activate # mac
+source VRFunAIGen/bin/activate # mac
 pip install -r requirements.txt
 pip uninstall torch torchvision torchaudio
 pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
@@ -101,7 +101,7 @@ python FSGenerator.py
 ```bash
 python -m venv VRFunAIGen
 VRFunAIGen\Scripts\activate # windows
-source myenv/bin/activate # mac
+source VRFunAIGen/bin/activate # mac
 pip install -r requirements.txt
 python FSGenerator.py
 ```
diff --git a/config.py b/config.py
index b3f8f140..2d34832a 100644
--- a/config.py
+++ b/config.py
@@ -24,7 +24,7 @@
 # PERFORMANCE
 ##################################################################################################
 
-RENDER_RESOLUTION = 640 # Above 1080x1080 doesn't seem to improve detections minimum of 640x640, has a huge impact on performance but at a quality cost (after changing you need to regenerate your data)
+RENDER_RESOLUTION = 640
 TEXTURE_RESOLUTION = RENDER_RESOLUTION * 1.3 # Texture size that is used to texture the opengl sphere
 YOLO_BATCH_SIZE = 1 if platform.system() == "Darwin" else 30 # Mac doesn't support batching
 YOLO_PERSIST = True # Big impact on performance but also improves tracking
diff --git a/script_generator/debug/debug_data.py b/script_generator/debug/debug_data.py
index 92336247..c54bfbe3 100644
--- a/script_generator/debug/debug_data.py
+++ b/script_generator/debug/debug_data.py
@@ -2,8 +2,9 @@
 
 import numpy as np
 
-from script_generator.utils.file import get_output_file_path, load_json_from_file
 from script_generator.debug.logger import logger
+from script_generator.utils.file import get_output_file_path
+from script_generator.utils.json import load_json_from_file
 
 
 class DebugData:
diff --git a/script_generator/object_detection/post_process_worker.py b/script_generator/object_detection/post_process_worker.py
index 8447738a..6fb40ec1 100644
--- a/script_generator/object_detection/post_process_worker.py
+++ b/script_generator/object_detection/post_process_worker.py
@@ -8,7 +8,8 @@
 from script_generator.gui.messages.messages import UpdateGUIState
 from script_generator.object_detection.object_detection_result import ObjectDetectionResult
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
-from script_generator.utils.file import write_json_to_file, get_output_file_path
+from script_generator.utils.file import get_output_file_path
+from script_generator.utils.json import write_json_to_file
 
 
 class PostProcessWorker(AbstractTaskProcessor):
diff --git a/script_generator/object_detection/util/utils.py b/script_generator/object_detection/util/utils.py
index f5934241..9793a2df 100644
--- a/script_generator/object_detection/util/utils.py
+++ b/script_generator/object_detection/util/utils.py
@@ -1,4 +1,4 @@
-from script_generator.utils.file import get_data_file_info
+from script_generator.utils.json import get_data_file_info
 
 
 def get_metrics_file_info(state):
diff --git a/script_generator/object_detection/utils.py b/script_generator/object_detection/utils.py
index 8544a6ec..33ab7b74 100644
--- a/script_generator/object_detection/utils.py
+++ b/script_generator/object_detection/utils.py
@@ -1,11 +1,11 @@
 import os
 
 from script_generator.constants import CLASS_REVERSE_MATCH
+from script_generator.debug.logger import logger
 from script_generator.gui.utils.widgets import Widgets
 from script_generator.object_detection.box_record import BoxRecord
 from script_generator.object_detection.object_detection_result import ObjectDetectionResult
-from script_generator.utils.file import get_output_file_path, load_json_from_file, get_data_file_info
-from script_generator.debug.logger import logger
+from script_generator.utils.json import get_data_file_info
 
 
 def check_skip_object_detection(state, root):
diff --git a/script_generator/scripts/tracking_analysis.py b/script_generator/scripts/tracking_analysis.py
index 91efe7cd..a89ebc32 100644
--- a/script_generator/scripts/tracking_analysis.py
+++ b/script_generator/scripts/tracking_analysis.py
@@ -2,7 +2,8 @@
 
 from script_generator.object_detection.analyze_tracking_results import analyze_tracking_results
 from script_generator.object_detection.utils import make_data_boxes, parse_yolo_data_looking_for_penis
-from script_generator.utils.file import load_json_from_file, get_output_file_path
+from script_generator.utils.file import get_output_file_path
+from script_generator.utils.json import load_json_from_file
 from script_generator.debug.logger import logger
 from script_generator.video.info.video_info import get_cropped_dimensions
 from utils.lib_FunscriptHandler import FunscriptGenerator
diff --git a/script_generator/utils/file.py b/script_generator/utils/file.py
index 792f3497..ab6e7553 100644
--- a/script_generator/utils/file.py
+++ b/script_generator/utils/file.py
@@ -1,62 +1,6 @@
-import json
 import os
-import time
 
 from script_generator.constants import OUTPUT_PATH
-from script_generator.debug.logger import logger
-
-
-def write_json_to_file(file_path, data):
-    """
-    Write data to a JSON file, always overwriting if it exists.
-    :param file_path: The path to the output file.
-    :param data: The data to write.
-    """
-    logger.info(f"Exporting data...")
-    export_start = time.time()
-
-    # Ensure the directory exists
-    directory = os.path.dirname(file_path)
-    if not os.path.exists(directory):
-        os.makedirs(directory)
-
-        # Write the data to the file (overwrites if it exists)
-    with open(file_path, 'w') as f:
-        json.dump(data, f)
-    export_end = time.time()
-    logger.info(f"Done in {export_end - export_start} seconds.")
-
-def load_json_from_file(file_path):
-    """
-    Load YOLO data from a JSON file.
-    :param file_path: Path to the JSON file.
-    :return: The loaded data.
-    """
-    if not os.path.exists(file_path):
-        raise FileNotFoundError(f"Could not load json from file as it does not exist: {file_path}")
-
-    with open(file_path, 'r') as f:
-        data = json.load(f)
-        logger.info(f"Loaded data from {file_path}, length: {len(data)}")
-    return data
-
-def get_data_file_info(video_path, suffix):
-    """
-    Checks if a data file exists, returns a true / false and the path and filename
-    """
-    raw_yolo_path, raw_yolo_filename = get_output_file_path(video_path, suffix)
-    if os.path.exists(raw_yolo_path):
-        yolo_data = load_json_from_file(raw_yolo_path)
-        if len(yolo_data) == 0:
-            logger.warn(f"Raw YOLO data file doesn't contain any data: {raw_yolo_path}")
-            try:
-                os.remove(raw_yolo_path)
-                logger.info(f"Deleted empty raw YOLO data file: {raw_yolo_path}")
-            except OSError as e:
-                logger.error(f"Error deleting raw YOLO file {raw_yolo_path}: {e}")
-        else:
-            return True, raw_yolo_path, raw_yolo_filename
-    return False, None, None
 
 def get_output_file_path(video_path, suffix, add_spoiler_prefix=False):
     """"
diff --git a/script_generator/utils/json.py b/script_generator/utils/json.py
new file mode 100644
index 00000000..83481d49
--- /dev/null
+++ b/script_generator/utils/json.py
@@ -0,0 +1,61 @@
+import json
+import os
+import time
+
+from script_generator.debug.logger import logger
+from script_generator.utils.file import get_output_file_path
+
+
+def write_json_to_file(file_path, data):
+    """
+    Write data to a JSON file, always overwriting if it exists.
+    :param file_path: The path to the output file.
+    :param data: The data to write.
+    """
+    logger.info(f"Exporting data...")
+    export_start = time.time()
+
+    # Ensure the directory exists
+    directory = os.path.dirname(file_path)
+    if not os.path.exists(directory):
+        os.makedirs(directory)
+
+        # Write the data to the file (overwrites if it exists)
+    with open(file_path, 'w') as f:
+        json.dump(data, f)
+    export_end = time.time()
+    logger.info(f"Done in {export_end - export_start} seconds.")
+
+
+def load_json_from_file(file_path):
+    """
+    Load YOLO data from a JSON file.
+    :param file_path: Path to the JSON file.
+    :return: The loaded data.
+    """
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"Could not load json from file as it does not exist: {file_path}")
+
+    with open(file_path, 'r') as f:
+        data = json.load(f)
+        logger.info(f"Loaded data from {file_path}, length: {len(data)}")
+    return data
+
+
+def get_data_file_info(video_path, suffix):
+    """
+    Checks if a data file exists, returns a true / false and the path and filename
+    """
+    raw_yolo_path, raw_yolo_filename = get_output_file_path(video_path, suffix)
+    if os.path.exists(raw_yolo_path):
+        yolo_data = load_json_from_file(raw_yolo_path)
+        if len(yolo_data) == 0:
+            logger.warn(f"Raw YOLO data file doesn't contain any data: {raw_yolo_path}")
+            try:
+                os.remove(raw_yolo_path)
+                logger.info(f"Deleted empty raw YOLO data file: {raw_yolo_path}")
+            except OSError as e:
+                logger.error(f"Error deleting raw YOLO file {raw_yolo_path}: {e}")
+        else:
+            return True, raw_yolo_path, raw_yolo_filename
+    return False, None, None
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 4326f1f0..fc7a4bb6 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -7,6 +7,7 @@
 import cv2
 import matplotlib
 import numpy as np
+from matplotlib.ticker import FuncFormatter, MaxNLocator
 from scipy.signal import savgol_filter
 from simplification.cutil import simplify_coords
 
@@ -14,7 +15,8 @@
 from script_generator.constants import HEATMAP_COLORS, FUNSCRIPT_AUTHOR
 from script_generator.debug.logger import logger
 from script_generator.state.app_state import AppState
-from script_generator.utils.file import get_output_file_path, load_json_from_file
+from script_generator.utils.file import get_output_file_path
+from script_generator.utils.json import load_json_from_file
 
 matplotlib.use('Agg')  # Use a non-interactive backend
 import matplotlib.pyplot as plt

From 3216db9e0ed609845fc7bc24ac1f6115205d2741 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 27 Jan 2025 17:20:49 +0100
Subject: [PATCH 184/277] Crop 2d in center

---
 script_generator/video/ffmpeg/filters.py  | 11 ++++++-----
 script_generator/video/info/video_info.py |  7 ++-----
 2 files changed, 8 insertions(+), 10 deletions(-)

diff --git a/script_generator/video/ffmpeg/filters.py b/script_generator/video/ffmpeg/filters.py
index 81f67f98..11571104 100644
--- a/script_generator/video/ffmpeg/filters.py
+++ b/script_generator/video/ffmpeg/filters.py
@@ -36,12 +36,13 @@ def get_vr_video_filters(video, video_reader, hwaccel, disable_opengl=False):
 
     return f"{','.join(filters)}"
 
+
 def get_2d_video_filters(video, width, height, hwaccel):
     cuda = hwaccel == "cuda"
 
-    if video.height > RENDER_RESOLUTION or video.width > width:
-        scale_filter = f"\"[0:0]scale_cuda={width}:{height},hwdownload,format=nv12" if cuda else f"scale={width}:{height}\""
+    if video.height > RENDER_RESOLUTION:
+        scale_width = int(video.width * (height / video.height))
+        crop = f",crop={width}:{height}:(iw-{width})/2:0"
+        return f"[0:0]scale_cuda={scale_width}:{height},hwdownload,format=nv12{crop}" if cuda else f"[0:0]scale={scale_width}:{height}{crop}"
     else:
-        scale_filter = "\"[0:0]hwdownload,format=nv12\"" if cuda else ""
-
-    return scale_filter
\ No newline at end of file
+        return "[0:0]hwdownload,format=nv12" if cuda else ""
\ No newline at end of file
diff --git a/script_generator/video/info/video_info.py b/script_generator/video/info/video_info.py
index 68219937..0ee29b93 100644
--- a/script_generator/video/info/video_info.py
+++ b/script_generator/video/info/video_info.py
@@ -65,11 +65,8 @@ def get_projection_and_fov_from_filename(filename):
     return {"projection": projection, "fov": fov, "is_fisheye": is_fisheye}
 
 def get_cropped_dimensions(video: VideoInfo):
-    if video.is_vr:
-        return RENDER_RESOLUTION, RENDER_RESOLUTION
-    else:
-        scaling_factor = min(1, RENDER_RESOLUTION / max(video.width, video.height))
-        return int(video.width * scaling_factor), int(video.height * scaling_factor)
+    # We crop 2d squarely in the center so both vr and 2d have the same dimensions
+    return RENDER_RESOLUTION, RENDER_RESOLUTION
 
 def get_video_info(video_path):
     try:

From 4c8ffe8c6d5e6a40a34a37da370fa7a03939631f Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Mon, 27 Jan 2025 18:19:14 +0100
Subject: [PATCH 185/277] fixed new model name

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index e79c71ac..8dc923b9 100644
--- a/README.md
+++ b/README.md
@@ -130,7 +130,7 @@ python FSGenerator.py
 ```
 
 ### Download the YOLO model
-- Place your YOLO model file (e.g., `k00gar-11n-200ep-best.mlpackage`) in the `models/` sub-directory.
+- Place your YOLO model file (e.g., `k00gar-11n-RGB-200ep-best.mlpackage`) in the `models/` sub-directory.
 - Alternatively, you can specify a custom path to the model using the `--yolo_model` argument.
 
 ### Update config.py

From e8f708106d855215a988bca2c14c4c70f73692fc Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 27 Jan 2025 18:33:28 +0100
Subject: [PATCH 186/277] Resized debug video preview for 640p

---
 .../debug/video_player/debug_overlay.py       | 24 ++++++++++---------
 .../debug/video_player/overlay_widgets.py     |  6 ++---
 script_generator/debug/video_player/play.py   |  4 +++-
 3 files changed, 19 insertions(+), 15 deletions(-)

diff --git a/script_generator/debug/video_player/debug_overlay.py b/script_generator/debug/video_player/debug_overlay.py
index 3077832c..ddd74148 100644
--- a/script_generator/debug/video_player/debug_overlay.py
+++ b/script_generator/debug/video_player/debug_overlay.py
@@ -12,6 +12,8 @@ def get_funscript_value(interpolator, frame_id, fps):
     time_in_milliseconds = int((frame_id / fps) * 1000)
     return interpolator(time_in_milliseconds)
 
+FONT_SIZE = 0.3
+
 def draw_overlay(
     frame,
     frame_id,
@@ -47,15 +49,15 @@ def draw_overlay(
         class_name = box["class_name"]
         position = box["position"]
         color = CLASS_COLORS.get(class_name, (0, 255, 0))
-        cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
+        cv2.rectangle(frame, (x1, y1), (x2, y2), color, 1)
         cv2.putText(
             frame,
             f"{class_name} {position}",
-            (x1, y1 - 10),
+            (x1, y1 - 5),
             cv2.FONT_HERSHEY_SIMPLEX,
-            0.5,
+            FONT_SIZE,
             color,
-            2
+            1
         )
 
     # Display variables
@@ -66,26 +68,26 @@ def draw_overlay(
             f"{key}: {value}",
             (10, y_offset_stats),
             cv2.FONT_HERSHEY_SIMPLEX,
-            0.5,
+            FONT_SIZE,
             (0, 0, 255),
-            2
+            1
         )
-        y_offset_stats += 20
+        y_offset_stats += 12
 
     # Draw locked penis box if present
     locked_penis_box = variables.get("locked_penis_box")
     if locked_penis_box and locked_penis_box.get("active", False):
         x1, y1, x2, y2 = locked_penis_box["box"]
         color = CLASS_COLORS.get("penis", (0, 255, 0))
-        cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
+        cv2.rectangle(frame, (x1, y1), (x2, y2), color, 1)
         cv2.putText(
             frame,
             "Locked Penis",
-            (x1, y1 - 10),
+            (x1, y1 - 5),
             cv2.FONT_HERSHEY_SIMPLEX,
-            0.5,
+            FONT_SIZE,
             color,
-            2
+            1
         )
 
     # Compute distance and funscript values for rolling buffer
diff --git a/script_generator/debug/video_player/overlay_widgets.py b/script_generator/debug/video_player/overlay_widgets.py
index 5d3f3fa2..4a89750c 100644
--- a/script_generator/debug/video_player/overlay_widgets.py
+++ b/script_generator/debug/video_player/overlay_widgets.py
@@ -1,12 +1,13 @@
 import cv2
 
+
 class OverlayWidgets:
     @staticmethod
     def draw_bounding_box(image, box, label, color, offset_x=0):
         x1, y1, x2, y2 = map(int, box)
         x1 += offset_x
         x2 += offset_x
-        cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)
+        cv2.rectangle(image, (x1, y1), (x2, y2), color, 1)
         (label_width, label_height), baseline = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
         cv2.rectangle(image, (x1, y1 - label_height - baseline), (x1 + label_width, y1), color, -1)
         cv2.putText(image, label, (x1, y1 - baseline), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
@@ -21,6 +22,5 @@ def draw_gauge(image, distance):
         cv2.rectangle(image, (gauge_x, gauge_y), (gauge_x + gauge_width, gauge_y + gauge_height), (0, 0, 0), -1)
         fill_height = int((distance / 100) * gauge_height)
         cv2.rectangle(image, (gauge_x, gauge_y + gauge_height - fill_height), (gauge_x + gauge_width, gauge_y + gauge_height), (0, 255, 0), -1)
-        cv2.putText(image, str(int(distance)), (gauge_x, gauge_y + gauge_height - fill_height - 5),
-                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+        cv2.putText(image, str(int(distance)), (gauge_x, gauge_y + gauge_height - fill_height - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
         return image
diff --git a/script_generator/debug/video_player/play.py b/script_generator/debug/video_player/play.py
index 856906b1..6e33e750 100644
--- a/script_generator/debug/video_player/play.py
+++ b/script_generator/debug/video_player/play.py
@@ -62,7 +62,9 @@ def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=1
     else:
         # Setup OpenCV window
         window_name = "Debug Video"
-        cv2.namedWindow(window_name)
+        cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
+        # zoom in the image is only 640x640 now
+        cv2.resizeWindow(window_name, int(width * 2), int(height * 2))
 
         # Attach mouse callback for seeking
         cv2.setMouseCallback(window_name, mouse_callback, param=video_player)

From 4696da9a362457c9dd791951767d5805aa22c93e Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 27 Jan 2025 19:10:34 +0100
Subject: [PATCH 187/277] Resized debug video and throttled to video fps

---
 .../debug/video_player/controls.py            |  2 +-
 .../debug/video_player/debug_overlay.py       | 10 ++++------
 .../debug/video_player/overlay_widgets.py     | 10 +++++-----
 script_generator/debug/video_player/play.py   | 19 +++++++++++++++----
 script_generator/state/app_state.py           |  3 +++
 5 files changed, 28 insertions(+), 16 deletions(-)

diff --git a/script_generator/debug/video_player/controls.py b/script_generator/debug/video_player/controls.py
index d23dbb7c..c7fa8327 100644
--- a/script_generator/debug/video_player/controls.py
+++ b/script_generator/debug/video_player/controls.py
@@ -8,7 +8,7 @@ def draw_media_controls(frame, video_player: VideoPlayer):
     Draw a simple "progress bar" style media control at the bottom of the frame.
     """
     height, width, _ = frame.shape
-    bar_height = 20
+    bar_height = 10
     bar_y_start = height - bar_height
 
     # Background of the progress bar
diff --git a/script_generator/debug/video_player/debug_overlay.py b/script_generator/debug/video_player/debug_overlay.py
index ddd74148..f3fa0986 100644
--- a/script_generator/debug/video_player/debug_overlay.py
+++ b/script_generator/debug/video_player/debug_overlay.py
@@ -21,8 +21,7 @@ def draw_overlay(
     funscript_interpolator,
     distance_buffer,
     funscript_buffer,
-    fps,
-    y_offset_start=0,
+    fps
 ):
     """
     Draws bounding boxes, text overlays, rolling window graphs, etc. on top of `frame`.
@@ -34,7 +33,6 @@ def draw_overlay(
     :param distance_buffer:         Rolling buffer for 'distance' data.
     :param funscript_buffer:        Rolling buffer for funscript values.
     :param fps:                     The video frames per second.
-    :param y_offset_start:          The initial Y offset for drawing text.
     """
     # Pull the logs for this frame
     str_frame_id = str(frame_id)
@@ -106,8 +104,8 @@ def draw_overlay(
     funscript_buffer[-1] = funscript_value
 
     # Draw rolling window curves
-    graph_height = int(frame.shape[0] * 0.2)
-    graph_y_start = y_offset_start
+    graph_height = 100
+    graph_y_start = frame.shape[0] - graph_height - 15
     draw_rolling_window_curve(frame, distance_buffer, (0, 255, 0), 0.5, graph_height, graph_y_start)
     draw_rolling_window_curve(frame, funscript_buffer, (255, 0, 0), 0.5, graph_height, graph_y_start)
 
@@ -128,7 +126,7 @@ def draw_rolling_window_curve(frame, buffer, color, alpha, graph_height, graph_y
         x2 = int(width * ((i + 1) / len(buffer_clipped)))
         y1 = graph_y_start + graph_height - int((buffer_clipped[i] / 100) * graph_height)
         y2 = graph_y_start + graph_height - int((buffer_clipped[i + 1] / 100) * graph_height)
-        cv2.line(overlay, (x1, y1), (x2, y2), color, 2)
+        cv2.line(overlay, (x1, y1), (x2, y2), color, 1)
 
     cv2.addWeighted(overlay, alpha, frame, 1 - alpha, 0, frame)
 
diff --git a/script_generator/debug/video_player/overlay_widgets.py b/script_generator/debug/video_player/overlay_widgets.py
index 4a89750c..1ed2cff9 100644
--- a/script_generator/debug/video_player/overlay_widgets.py
+++ b/script_generator/debug/video_player/overlay_widgets.py
@@ -15,12 +15,12 @@ def draw_bounding_box(image, box, label, color, offset_x=0):
 
     @staticmethod
     def draw_gauge(image, distance):
-        gauge_width = 20
-        gauge_height = 200
-        gauge_x = int(3 * image.shape[1] / 4)
-        gauge_y = int(3 * image.shape[0] / 5)
+        gauge_width = 12
+        gauge_height = 100
+        gauge_x = image.shape[1] - 20
+        gauge_y = image.shape[0] - 120
         cv2.rectangle(image, (gauge_x, gauge_y), (gauge_x + gauge_width, gauge_y + gauge_height), (0, 0, 0), -1)
         fill_height = int((distance / 100) * gauge_height)
         cv2.rectangle(image, (gauge_x, gauge_y + gauge_height - fill_height), (gauge_x + gauge_width, gauge_y + gauge_height), (0, 255, 0), -1)
-        cv2.putText(image, str(int(distance)), (gauge_x, gauge_y + gauge_height - fill_height - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
+        cv2.putText(image, str(int(distance)), (gauge_x, gauge_y + gauge_height - fill_height - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.25, (0, 255, 0), 1)
         return image
diff --git a/script_generator/debug/video_player/play.py b/script_generator/debug/video_player/play.py
index 6e33e750..94000fb7 100644
--- a/script_generator/debug/video_player/play.py
+++ b/script_generator/debug/video_player/play.py
@@ -1,5 +1,6 @@
 import json
 import tempfile
+import time
 
 import cv2
 import numpy as np
@@ -14,7 +15,6 @@
 from script_generator.debug.logger import logger
 from script_generator.video.info.video_info import get_cropped_dimensions
 
-
 def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=100, save_video_mode=False):
     video_info = state.video_info
     width, height = get_cropped_dimensions(video_info)
@@ -69,9 +69,13 @@ def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=1
         # Attach mouse callback for seeking
         cv2.setMouseCallback(window_name, mouse_callback, param=video_player)
 
+        frame_interval = 1.0 / state.max_preview_fps
+
     last_frame = video_player.current_frame
 
     while True:
+        loop_start_time = time.time()
+
         # Handle user input
         if not save_video_mode and not handle_user_input(window_name, video_player):
             break
@@ -81,6 +85,8 @@ def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=1
 
         # Check if paused and the frame hasn't changed (allows seeking while being paused)
         if video_player.paused and video_player.current_frame == last_frame:
+            # Avoid a tight loop hogging CPU if paused
+            time.sleep(0.01)
             continue
 
         last_frame = video_player.current_frame
@@ -102,8 +108,7 @@ def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=1
             funscript_interpolator=funscript_interpolator,
             distance_buffer=distance_buffer,
             funscript_buffer=funscript_buffer,
-            fps=video_info.fps,
-            y_offset_start=frame.shape[0] - 250 # 250 is height of funscript preview
+            fps=video_info.fps
         )
 
         if save_video_mode:
@@ -112,7 +117,13 @@ def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=1
         else:
             # Display the frame
             draw_media_controls(frame, video_player)
-            cv2.imshow(window_name, frame)
+            cv2.imshow("Debug Video", frame)
+
+            # Throttle the loop to maintain DESIRED_FPS if processing is faster
+            elapsed = time.time() - loop_start_time
+            remaining = frame_interval - elapsed
+            if remaining > 0:
+                time.sleep(remaining)
 
     if save_video_mode:
         video_player.release()
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index 44852128..e47327e1 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -31,6 +31,7 @@ def __init__(self, is_cli):
         self.save_debug_file: bool = True
         self.live_preview_mode: bool = False
         self.reference_script: string = None
+        self.max_preview_fps = 60
 
         # Gui/settings Funscript Tweaking Variables
         self.boost_enabled: bool = True
@@ -75,10 +76,12 @@ def set_video_info(self):
                 self.video_info = None
                 self.has_raw_yolo = False
                 self.has_tracking_data = False
+                self.max_preview_fps = 60
             else:
                 self.video_info = get_video_info(self.video_path)
                 self.has_raw_yolo, _, _ = get_raw_yolo_file_info(self.video_path)
                 self.has_tracking_data, _, _ = get_metrics_file_info(self.video_path)
+                self.max_preview_fps = int(self.video_info.fps)
 
 
 def log_state_settings(state: AppState):

From 88cdc00861bae1748f5b01300bb8181a39df3900 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 27 Jan 2025 21:08:17 +0100
Subject: [PATCH 188/277] Improved debug video and added a max fps

---
 .../debug/video_player/debug_overlay.py       | 32 +++++++++-----
 .../debug/video_player/overlay_widgets.py     |  2 +-
 script_generator/debug/video_player/play.py   | 43 ++++++++++++-------
 script_generator/funscript/util.py            | 16 +++++++
 .../gui/controller/debug_video.py             | 25 ++++++-----
 script_generator/gui/utils/widgets.py         |  2 +-
 .../gui/views/funscript_generator.py          | 19 ++++++--
 script_generator/state/app_state.py           |  3 +-
 8 files changed, 101 insertions(+), 41 deletions(-)
 create mode 100644 script_generator/funscript/util.py

diff --git a/script_generator/debug/video_player/debug_overlay.py b/script_generator/debug/video_player/debug_overlay.py
index f3fa0986..c7eb5929 100644
--- a/script_generator/debug/video_player/debug_overlay.py
+++ b/script_generator/debug/video_player/debug_overlay.py
@@ -19,20 +19,24 @@ def draw_overlay(
     frame_id,
     logs,
     funscript_interpolator,
+    funscript_interpolator_ref,
     distance_buffer,
     funscript_buffer,
+    funscript_buffer_ref,
     fps
 ):
     """
     Draws bounding boxes, text overlays, rolling window graphs, etc. on top of `frame`.
 
-    :param frame:                   The current video
-    :param frame_id:                The integer ID of the frame.
-    :param logs:                    A dict containing per-frame logs.
-    :param funscript_interpolator:  If present, used to compute funscript values.
-    :param distance_buffer:         Rolling buffer for 'distance' data.
-    :param funscript_buffer:        Rolling buffer for funscript values.
-    :param fps:                     The video frames per second.
+    :param frame:                       The current video
+    :param frame_id:                    The integer ID of the frame.
+    :param logs:                        A dict containing per-frame logs.
+    :param funscript_interpolator:      If present, used to compute funscript values.
+    :param funscript_interpolator_ref:  If present, used to compute funscript values.
+    :param distance_buffer:             Rolling buffer for 'distance' data.
+    :param funscript_buffer:            Rolling buffer for funscript values.
+    :param funscript_buffer_ref:        Rolling buffer for reference funscript values.
+    :param fps:                         The video frames per second.
     """
     # Pull the logs for this frame
     str_frame_id = str(frame_id)
@@ -90,9 +94,11 @@ def draw_overlay(
 
     # Compute distance and funscript values for rolling buffer
     distance = variables.get("distance", 0)
-    funscript_value = 0
+    funscript_value, funscript_value_ref = 0, 0
     if funscript_interpolator:
         funscript_value = get_funscript_value(funscript_interpolator, frame_id, fps)
+    if funscript_interpolator_ref:
+        funscript_value_ref = get_funscript_value(funscript_interpolator_ref, frame_id, fps)
 
     # Draw gauge (example usage)
     OverlayWidgets.draw_gauge(frame, funscript_value)
@@ -103,14 +109,20 @@ def draw_overlay(
     funscript_buffer = np.roll(funscript_buffer, -1)
     funscript_buffer[-1] = funscript_value
 
+    if funscript_interpolator_ref:
+        funscript_buffer_ref = np.roll(funscript_buffer_ref, -1)
+        funscript_buffer_ref[-1] = funscript_value_ref
+
     # Draw rolling window curves
     graph_height = 100
-    graph_y_start = frame.shape[0] - graph_height - 15
+    graph_y_start = frame.shape[0] - graph_height - 15 # B, G, R | R, G, B
     draw_rolling_window_curve(frame, distance_buffer, (0, 255, 0), 0.5, graph_height, graph_y_start)
     draw_rolling_window_curve(frame, funscript_buffer, (255, 0, 0), 0.5, graph_height, graph_y_start)
+    if funscript_interpolator_ref:
+        draw_rolling_window_curve(frame, funscript_buffer_ref, (0, 0, 255), 0.5, graph_height, graph_y_start)
 
     # Return updated buffers in case they need to be maintained externally
-    return distance_buffer, funscript_buffer
+    return distance_buffer, funscript_buffer, funscript_buffer_ref
 
 
 def draw_rolling_window_curve(frame, buffer, color, alpha, graph_height, graph_y_start):
diff --git a/script_generator/debug/video_player/overlay_widgets.py b/script_generator/debug/video_player/overlay_widgets.py
index 1ed2cff9..7be1ef31 100644
--- a/script_generator/debug/video_player/overlay_widgets.py
+++ b/script_generator/debug/video_player/overlay_widgets.py
@@ -17,7 +17,7 @@ def draw_bounding_box(image, box, label, color, offset_x=0):
     def draw_gauge(image, distance):
         gauge_width = 12
         gauge_height = 100
-        gauge_x = image.shape[1] - 20
+        gauge_x = image.shape[1] - 15
         gauge_y = image.shape[0] - 120
         cv2.rectangle(image, (gauge_x, gauge_y), (gauge_x + gauge_width, gauge_y + gauge_height), (0, 0, 0), -1)
         fill_height = int((distance / 100) * gauge_height)
diff --git a/script_generator/debug/video_player/play.py b/script_generator/debug/video_player/play.py
index 94000fb7..17995336 100644
--- a/script_generator/debug/video_player/play.py
+++ b/script_generator/debug/video_player/play.py
@@ -1,4 +1,6 @@
 import json
+import math
+import os
 import tempfile
 import time
 
@@ -11,6 +13,7 @@
 from script_generator.debug.video_player.debug_overlay import draw_overlay
 from script_generator.debug.video_player.interaction import mouse_callback
 from script_generator.debug.video_player.state import VideoPlayer
+from script_generator.funscript.util import load_funscript_json
 from script_generator.utils.file import get_output_file_path
 from script_generator.debug.logger import logger
 from script_generator.video.info.video_info import get_cropped_dimensions
@@ -27,27 +30,29 @@ def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=1
 
     # Prepare funscript interpolation if available
     funscript_path, _ = get_output_file_path(state.video_path, ".funscript")
-    try:
-        with open(funscript_path, "r") as f:
-            funscript_data = json.load(f)
-        actions = funscript_data.get("actions", [])
-        funscript_times = [action["at"] for action in actions]
-        funscript_positions = [action["pos"] for action in actions]
-        funscript_interpolator = interp1d(
-            funscript_times,
-            funscript_positions,
+    funscript_times, funscript_positions = load_funscript_json(funscript_path)
+    funscript_interpolator = interp1d(
+        funscript_times,
+        funscript_positions,
+        kind="linear",
+        fill_value="extrapolate"
+    )
+    funscript_interpolator_ref = None
+    if os.path.exists(state.reference_script):
+        funscript_times_ref, funscript_positions_ref = load_funscript_json(state.reference_script)
+        funscript_interpolator_ref = interp1d(
+            funscript_times_ref,
+            funscript_positions_ref,
             kind="linear",
             fill_value="extrapolate"
         )
-    except FileNotFoundError:
-        logger.error(f"Funscript file not found at {funscript_path}")
-        funscript_interpolator = None
 
     logs = load_logs(state)
 
     # Initialize rolling window buffers
     distance_buffer = np.zeros(rolling_window_size)
     funscript_buffer = np.zeros(rolling_window_size)
+    funscript_buffer_ref = np.zeros(rolling_window_size)
 
     # Initialize video writer if recording
     if save_video_mode:
@@ -69,11 +74,17 @@ def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=1
         # Attach mouse callback for seeking
         cv2.setMouseCallback(window_name, mouse_callback, param=video_player)
 
-        frame_interval = 1.0 / state.max_preview_fps
-
     last_frame = video_player.current_frame
 
+    # TODO cleanup
+    def get_ceiled_fps(value):
+        try:
+            return math.ceil(float(value))
+        except (ValueError, TypeError):
+            return 60
+
     while True:
+        frame_interval = 1.0 / get_ceiled_fps(state.max_preview_fps)
         loop_start_time = time.time()
 
         # Handle user input
@@ -101,13 +112,15 @@ def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=1
         frame = frame_bgr.copy()
 
         # Call your overlay function
-        distance_buffer, funscript_buffer = draw_overlay(
+        distance_buffer, funscript_buffer, funscript_buffer_ref = draw_overlay(
             frame=frame,
             frame_id=video_player.current_frame,
             logs=logs,
             funscript_interpolator=funscript_interpolator,
+            funscript_interpolator_ref=funscript_interpolator_ref,
             distance_buffer=distance_buffer,
             funscript_buffer=funscript_buffer,
+            funscript_buffer_ref=funscript_buffer_ref,
             fps=video_info.fps
         )
 
diff --git a/script_generator/funscript/util.py b/script_generator/funscript/util.py
new file mode 100644
index 00000000..1f8291b4
--- /dev/null
+++ b/script_generator/funscript/util.py
@@ -0,0 +1,16 @@
+import json
+
+from script_generator.debug.logger import logger
+
+
+def load_funscript_json(funscript_path):
+    try:
+        with open(funscript_path, "r") as f:
+            funscript_data = json.load(f)
+        actions = funscript_data.get("actions", [])
+        funscript_times = [action["at"] for action in actions]
+        funscript_positions = [action["pos"] for action in actions]
+        return funscript_times, funscript_positions
+    except FileNotFoundError:
+        logger.error(f"Funscript file not found at {funscript_path}")
+        raise
\ No newline at end of file
diff --git a/script_generator/gui/controller/debug_video.py b/script_generator/gui/controller/debug_video.py
index e6659b60..007ae9c6 100644
--- a/script_generator/gui/controller/debug_video.py
+++ b/script_generator/gui/controller/debug_video.py
@@ -1,4 +1,5 @@
 import os
+import threading
 from tkinter import messagebox
 
 from script_generator.debug.video_player.play import play_debug_video
@@ -7,15 +8,19 @@
 from script_generator.debug.logger import logger
 
 def debug_video(state: AppState):
-    if not state.video_path:
-        messagebox.showerror("Error", "Please select a video file.")
-        return
+    def run():
+        if not state.video_path:
+            messagebox.showerror("Error", "Please select a video file.")
+            return
 
-    state.set_video_info()
+        state.set_video_info()
 
-    logs_path, log_filename = get_output_file_path(state.video_path, "_debug_logs.json")
-    if os.path.exists(logs_path):
-        play_debug_video(state=state, start_frame=state.frame_start)
-    else:
-        logger.error(f"Debug logs file not found: {logs_path}")
-        messagebox.showinfo("Info", f"Debug logs file not found: {log_filename}")
+        logs_path, log_filename = get_output_file_path(state.video_path, "_debug_logs.json")
+        if os.path.exists(logs_path):
+            play_debug_video(state=state, start_frame=state.frame_start)
+        else:
+            logger.error(f"Debug logs file not found: {logs_path}")
+            messagebox.showinfo("Info", f"Debug logs file not found: {log_filename}")
+
+    processing_thread = threading.Thread(target=run)
+    processing_thread.start()
\ No newline at end of file
diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index 3c4f1e68..f11a8de2 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -89,7 +89,7 @@ def on_value_change(*args):
         entry_container = ttk.Frame(container, width=entry_width_px)
         entry_container.grid(row=0, column=1, sticky="ew", padx=(2, 5))
         entry_container.grid_propagate(False)  # Prevent resizing
-        entry = ttk.Entry(entry_container, textvariable=value)
+        entry = ttk.Entry(entry_container, textvariable=value, width=entry_width_px)
         entry.pack(fill="both", expand=True)
 
         if tooltip_text:
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 2c9201a2..0835fce2 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -179,6 +179,17 @@ def start_processing():
             tooltip_text="Saves a debug file to disk with all collected metrics.\nThis is a prerequisite for playing back the debug video with the debug statistics overlay.\nThis also contains all generated metrics.",
             row=1
         )
+        _, max_fps_e, max_fps = Widgets.input(
+            general,
+            "Max fps",
+            state=state,
+            attr="max_preview_fps",
+            row=1,
+            column=29,
+            label_width_px=45,
+            entry_width_px=10,
+            tooltip_text="The maximum FPS for the debug video"
+        )
         play_btn = Widgets.button(
             general,
             "Play debug video",
@@ -219,16 +230,16 @@ def update_ui_for_state():
                 processing_btn.config(text="Start processing")
 
             if state.has_raw_yolo and state.has_tracking_data:
-                enable_widgets([play_btn, regenerate_btn, gen_video_btn])
+                enable_widgets([play_btn, regenerate_btn, gen_video_btn, max_fps_e])
                 set_progressbars_done([(yolo_p, yolo_p_perc), (track_p, track_p_perc)])
                 processing_btn.config(text="Re-run object detection and or tracking")
             elif state.has_raw_yolo and not state.has_tracking_data:
-                disable_widgets([play_btn, regenerate_btn, gen_video_btn])
+                disable_widgets([play_btn, regenerate_btn, gen_video_btn, max_fps_e])
                 set_progressbars_done([(yolo_p, yolo_p_perc)])
                 reset_progressbars([(track_p, track_p_perc)])
                 processing_btn.config(text="Re-run object detection and or start tracking")
             else:
-                disable_widgets([play_btn, regenerate_btn, gen_video_btn])
+                disable_widgets([play_btn, regenerate_btn, gen_video_btn, max_fps_e])
                 reset_progressbars([(yolo_p, yolo_p_perc), (track_p, track_p_perc)])
                 processing_btn.config(text="Start processing")
 
@@ -246,6 +257,8 @@ def update_ui_for_state():
             else:
                 enable_widgets(proc_widgets)
 
+            max_fps.set(str(state.max_preview_fps))
+
         # region UI UPDATE CALLBACK
         def update_ui(msg: UIMessage):
             """Handle UI updates using a switch-like statement."""
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index e47327e1..f3143607 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -1,3 +1,4 @@
+import math
 import os.path
 import string
 from typing import Literal
@@ -81,7 +82,7 @@ def set_video_info(self):
                 self.video_info = get_video_info(self.video_path)
                 self.has_raw_yolo, _, _ = get_raw_yolo_file_info(self.video_path)
                 self.has_tracking_data, _, _ = get_metrics_file_info(self.video_path)
-                self.max_preview_fps = int(self.video_info.fps)
+                self.max_preview_fps = math.ceil(self.video_info.fps)
 
 
 def log_state_settings(state: AppState):

From c8afe6f4f26caff37491ceb4aac7ed5b31c49bcd Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 27 Jan 2025 22:43:09 +0100
Subject: [PATCH 189/277] small fix for debug video

---
 script_generator/debug/video_player/play.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script_generator/debug/video_player/play.py b/script_generator/debug/video_player/play.py
index 17995336..4867cfd1 100644
--- a/script_generator/debug/video_player/play.py
+++ b/script_generator/debug/video_player/play.py
@@ -38,7 +38,7 @@ def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=1
         fill_value="extrapolate"
     )
     funscript_interpolator_ref = None
-    if os.path.exists(state.reference_script):
+    if state.reference_script and os.path.exists(state.reference_script):
         funscript_times_ref, funscript_positions_ref = load_funscript_json(state.reference_script)
         funscript_interpolator_ref = interp1d(
             funscript_times_ref,

From 04eef421ea6ac7fc6e00dc0e808af0aced803678 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 27 Jan 2025 22:43:09 +0100
Subject: [PATCH 190/277] small fix for debug video

(cherry picked from commit c8afe6f4f26caff37491ceb4aac7ed5b31c49bcd)
---
 script_generator/debug/video_player/play.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script_generator/debug/video_player/play.py b/script_generator/debug/video_player/play.py
index 17995336..4867cfd1 100644
--- a/script_generator/debug/video_player/play.py
+++ b/script_generator/debug/video_player/play.py
@@ -38,7 +38,7 @@ def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=1
         fill_value="extrapolate"
     )
     funscript_interpolator_ref = None
-    if os.path.exists(state.reference_script):
+    if state.reference_script and os.path.exists(state.reference_script):
         funscript_times_ref, funscript_positions_ref = load_funscript_json(state.reference_script)
         funscript_interpolator_ref = interp1d(
             funscript_times_ref,

From 7855bf4c4e5870c80ca15ac7e9851c21d8a51431 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Tue, 28 Jan 2025 08:43:24 +0100
Subject: [PATCH 191/277] Updated cli command in readme

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 8dc923b9..ee68b29f 100644
--- a/README.md
+++ b/README.md
@@ -163,7 +163,7 @@ You can use Start windows.bat to launch the gui on windows if you installed with
 To generate a script with cmd or terminal, run the following command
 
 ```bash
-python -m script_generator.cli.generate_funscript.py /path/to/video.mp4
+python -m script_generator.cli.generate_funscript /path/to/video.mp4
 ```
 
 ### Command-Line Arguments

From 0eaeece204f10ad32f47552fc23c74d2533d4128 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Tue, 28 Jan 2025 08:43:24 +0100
Subject: [PATCH 192/277] Updated cli command in readme

(cherry picked from commit 7855bf4c4e5870c80ca15ac7e9851c21d8a51431)
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 8dc923b9..ee68b29f 100644
--- a/README.md
+++ b/README.md
@@ -163,7 +163,7 @@ You can use Start windows.bat to launch the gui on windows if you installed with
 To generate a script with cmd or terminal, run the following command
 
 ```bash
-python -m script_generator.cli.generate_funscript.py /path/to/video.mp4
+python -m script_generator.cli.generate_funscript /path/to/video.mp4
 ```
 
 ### Command-Line Arguments

From 70bb4aba1fd0e1b576e62bae1f7473f1f7c9b149 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Wed, 29 Jan 2025 18:13:49 +0100
Subject: [PATCH 193/277] Implemented msgpack Added output folder option

---
 config.py                                     |  2 +-
 .../debug/Display_debug_results.py            |  2 +-
 script_generator/debug/debug_data.py          | 38 +++---------
 .../debug/video_player/overlay_widgets.py     |  6 +-
 script_generator/debug/video_player/play.py   |  8 +--
 .../gui/controller/debug_video.py             |  9 +--
 .../gui/views/funscript_generator.py          | 22 +++----
 .../analyze_tracking_results.py               |  5 +-
 .../object_detection/post_process_worker.py   |  6 +-
 .../object_detection/util/utils.py            | 15 +++--
 script_generator/object_detection/utils.py    | 17 ++++--
 script_generator/scripts/analyze_video.py     |  5 +-
 .../scripts/generate_funscript.py             |  2 +-
 script_generator/scripts/tracking_analysis.py | 16 ++---
 script_generator/state/app_state.py           | 18 ++++--
 script_generator/utils/config.py              | 14 ++---
 .../utils/{json.py => json_utils.py}          | 12 ++--
 script_generator/utils/msgpack_utils.py       | 59 +++++++++++++++++++
 utils/lib_FunscriptHandler.py                 | 13 ++--
 utils/lib_SceneCutsDetect.py                  |  3 +-
 20 files changed, 163 insertions(+), 109 deletions(-)
 rename script_generator/utils/{json.py => json_utils.py} (83%)
 create mode 100644 script_generator/utils/msgpack_utils.py

diff --git a/config.py b/config.py
index 2d34832a..55cc0285 100644
--- a/config.py
+++ b/config.py
@@ -6,7 +6,7 @@
 from script_generator.utils.config import find_ffmpeg_path, find_ffprobe_path
 from script_generator.debug.logger import logger
 
-VERSION = "0.1.0"
+VERSION = "0.1.1"
 
 ##################################################################################################
 # FFMPEG
diff --git a/script_generator/debug/Display_debug_results.py b/script_generator/debug/Display_debug_results.py
index 532cf6d1..eea12512 100644
--- a/script_generator/debug/Display_debug_results.py
+++ b/script_generator/debug/Display_debug_results.py
@@ -10,7 +10,7 @@
 log_file, _ = get_output_file_path(video, "_debug_logs.json")
 debugger = Debugger(video, is_vr=True, video_reader="FFmpeg", log_file=log_file)
 
-debugger.load_logs()
+debugger.load_debug_metrics()
 
 
 #debugger.play_video(frame)
diff --git a/script_generator/debug/debug_data.py b/script_generator/debug/debug_data.py
index c54bfbe3..b3ec398e 100644
--- a/script_generator/debug/debug_data.py
+++ b/script_generator/debug/debug_data.py
@@ -1,10 +1,5 @@
-import json
-
-import numpy as np
-
-from script_generator.debug.logger import logger
 from script_generator.utils.file import get_output_file_path
-from script_generator.utils.json import load_json_from_file
+from script_generator.utils.msgpack_utils import save_msgpack_json, load_msgpack_json
 
 
 class DebugData:
@@ -23,27 +18,10 @@ def add_frame(self, frame_id, variables=None, bounding_boxes=None):
             self.logs[frame_id]["bounding_boxes"].extend(bounding_boxes)
 
     def save_debug_file(self):
-        debug_log_path, _ = get_output_file_path(self.app_state.video_path, "_debug_logs.json")
-        try:
-            with open(debug_log_path, "w") as f:
-                json.dump(self.logs, f, indent=4, default=self._default_serializer)
-            logger.info(f"Debug data saved to {debug_log_path}")
-        except Exception as e:
-            logger.error(f"Failed to save logs: {e}")
-
-    @staticmethod
-    def _default_serializer(obj):
-        """Convert NumPy types to native Python types for JSON serialization."""
-        if isinstance(obj, np.integer):
-            return int(obj)
-        elif isinstance(obj, np.floating):
-            return float(obj)
-        elif isinstance(obj, np.ndarray):
-            return obj.tolist()
-        else:
-            raise TypeError(f"Object of type {obj.__class__.__name__} is not JSON serializable")
-
-
-def load_logs(state):
-    log_path, _ = get_output_file_path(state.video_path, "_debug_logs.json")
-    return load_json_from_file(log_path)
\ No newline at end of file
+        path, _ = get_output_file_path(self.app_state.video_path, "_debug_logs.msgpack")
+        save_msgpack_json(path, self.logs)
+
+
+def load_debug_metrics(state):
+    base_path, _ = get_output_file_path(state.video_path, "_debug_logs.msgpack")
+    return load_msgpack_json(base_path)
\ No newline at end of file
diff --git a/script_generator/debug/video_player/overlay_widgets.py b/script_generator/debug/video_player/overlay_widgets.py
index 7be1ef31..b92b810e 100644
--- a/script_generator/debug/video_player/overlay_widgets.py
+++ b/script_generator/debug/video_player/overlay_widgets.py
@@ -14,11 +14,11 @@ def draw_bounding_box(image, box, label, color, offset_x=0):
         return image
 
     @staticmethod
-    def draw_gauge(image, distance):
+    def draw_gauge(image, distance, x=15, y=20):
         gauge_width = 12
         gauge_height = 100
-        gauge_x = image.shape[1] - 15
-        gauge_y = image.shape[0] - 120
+        gauge_x = image.shape[1] - x
+        gauge_y = image.shape[0] - 100 - y
         cv2.rectangle(image, (gauge_x, gauge_y), (gauge_x + gauge_width, gauge_y + gauge_height), (0, 0, 0), -1)
         fill_height = int((distance / 100) * gauge_height)
         cv2.rectangle(image, (gauge_x, gauge_y + gauge_height - fill_height), (gauge_x + gauge_width, gauge_y + gauge_height), (0, 255, 0), -1)
diff --git a/script_generator/debug/video_player/play.py b/script_generator/debug/video_player/play.py
index 4867cfd1..99a092a5 100644
--- a/script_generator/debug/video_player/play.py
+++ b/script_generator/debug/video_player/play.py
@@ -1,4 +1,3 @@
-import json
 import math
 import os
 import tempfile
@@ -8,16 +7,17 @@
 import numpy as np
 from scipy.interpolate import interp1d
 
-from script_generator.debug.debug_data import load_logs
+from script_generator.debug.debug_data import load_debug_metrics
+from script_generator.debug.logger import logger
 from script_generator.debug.video_player.controls import draw_media_controls
 from script_generator.debug.video_player.debug_overlay import draw_overlay
 from script_generator.debug.video_player.interaction import mouse_callback
 from script_generator.debug.video_player.state import VideoPlayer
 from script_generator.funscript.util import load_funscript_json
 from script_generator.utils.file import get_output_file_path
-from script_generator.debug.logger import logger
 from script_generator.video.info.video_info import get_cropped_dimensions
 
+
 def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=100, save_video_mode=False):
     video_info = state.video_info
     width, height = get_cropped_dimensions(video_info)
@@ -47,7 +47,7 @@ def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=1
             fill_value="extrapolate"
         )
 
-    logs = load_logs(state)
+    logs = load_debug_metrics(state)
 
     # Initialize rolling window buffers
     distance_buffer = np.zeros(rolling_window_size)
diff --git a/script_generator/gui/controller/debug_video.py b/script_generator/gui/controller/debug_video.py
index 007ae9c6..8d3fcab1 100644
--- a/script_generator/gui/controller/debug_video.py
+++ b/script_generator/gui/controller/debug_video.py
@@ -3,6 +3,7 @@
 from tkinter import messagebox
 
 from script_generator.debug.video_player.play import play_debug_video
+from script_generator.object_detection.util.utils import get_metrics_file_info
 from script_generator.state.app_state import AppState
 from script_generator.utils.file import get_output_file_path
 from script_generator.debug.logger import logger
@@ -15,12 +16,12 @@ def run():
 
         state.set_video_info()
 
-        logs_path, log_filename = get_output_file_path(state.video_path, "_debug_logs.json")
-        if os.path.exists(logs_path):
+        exists, path, filename = get_metrics_file_info(state)
+        if exists:
             play_debug_video(state=state, start_frame=state.frame_start)
         else:
-            logger.error(f"Debug logs file not found: {logs_path}")
-            messagebox.showinfo("Info", f"Debug logs file not found: {log_filename}")
+            logger.error(f"Debug logs file not found: {path}")
+            messagebox.showinfo("Info", f"Debug logs file not found: {filename}")
 
     processing_thread = threading.Thread(target=run)
     processing_thread.start()
\ No newline at end of file
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 0835fce2..9b108137 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -46,16 +46,16 @@ def update_video_path():
             command=lambda val: update_video_path()
         )
 
-        _, _, reader_dropdown, _ = Widgets.dropdown(
-            attr="video_reader",
-            parent=video_selection,
-            label_text="Video Reader",
-            options=["FFmpeg", *([] if is_mac() else ["FFmpeg + OpenGL (Windows)"])],
-            default_value=state.video_reader,
-            tooltip_text=("On Mac only FFmpeg is supported" if is_mac() else "FFmpeg + OpenGL is usually about 30% faster on a good GPU."),
-            state=state,
-            row=1
-        )
+        # _, _, reader_dropdown, _ = Widgets.dropdown(
+        #     attr="video_reader",
+        #     parent=video_selection,
+        #     label_text="Video Reader",
+        #     options=["FFmpeg", *([] if is_mac() else ["FFmpeg + OpenGL (Windows)"])],
+        #     default_value=state.video_reader,
+        #     tooltip_text=("On Mac only FFmpeg is supported" if is_mac() else "FFmpeg + OpenGL is usually about 30% faster on a good GPU."),
+        #     state=state,
+        #     row=1
+        # )
         # endregion
 
         # region OPTIONAL SETTINGS
@@ -248,7 +248,7 @@ def update_ui_for_state():
             else:
                 disable_widgets([processing_btn])
 
-            proc_widgets = [fs_entry, fs_button, ref_entry, ref_button, reader_dropdown, metrics_check, *start_f_widgets, *end_f_widgets,
+            proc_widgets = [fs_entry, fs_button, ref_entry, ref_button, metrics_check, *start_f_widgets, *end_f_widgets, # , reader_dropdown
                             boost_check, simp_check, tres_check, t_d_1, t_d_2, s_d_1, s_d_2, b_d_1, b_d_2]
             if state.is_processing:
                 # TODO remove the processing button and implement stop processing
diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index df37ed88..a952421b 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -80,8 +80,7 @@ def analyze_tracking_results(state: AppState, results):
             unit="f",
             desc="Analyzing tracking data", position=0,
             unit_scale=False,
-            unit_divisor=1,
-            ncols=130
+            unit_divisor=1
     ):
         state.current_frame_id = frame_pos
         if frame_pos in cuts:
@@ -154,7 +153,7 @@ def analyze_tracking_results(state: AppState, results):
                 )
 
         # Display object detection tracking results in a live preview window
-        window_name = "Combined results"
+        window_name = "Tracking analysis preview"
         if state.live_preview_mode:
             ret, frame = reader.read()
             frame = frame.copy()
diff --git a/script_generator/object_detection/post_process_worker.py b/script_generator/object_detection/post_process_worker.py
index 6fb40ec1..be1bbb83 100644
--- a/script_generator/object_detection/post_process_worker.py
+++ b/script_generator/object_detection/post_process_worker.py
@@ -9,7 +9,7 @@
 from script_generator.object_detection.object_detection_result import ObjectDetectionResult
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 from script_generator.utils.file import get_output_file_path
-from script_generator.utils.json import write_json_to_file
+from script_generator.utils.msgpack_utils import save_msgpack_json
 
 
 class PostProcessWorker(AbstractTaskProcessor):
@@ -154,8 +154,8 @@ def on_last_item(self):
         self.state.analyze_task.end_time = time.time()
 
         # Write the detection records to a JSON file
-        raw_yolo_path, _ = get_output_file_path(self.state.video_path, "_rawyolo.json")
-        write_json_to_file(raw_yolo_path, self.records)
+        raw_yolo_path, _ = get_output_file_path(self.state.video_path, "_rawyolo.msgpack")
+        save_msgpack_json(raw_yolo_path, self.records)
 
 def handle_user_input(window_name):
     key = cv2.waitKey(1) & 0xFF
diff --git a/script_generator/object_detection/util/utils.py b/script_generator/object_detection/util/utils.py
index 9793a2df..0eca54d7 100644
--- a/script_generator/object_detection/util/utils.py
+++ b/script_generator/object_detection/util/utils.py
@@ -1,8 +1,13 @@
-from script_generator.utils.json import get_data_file_info
+from script_generator.utils.json_utils import get_data_file_info
 
 
 def get_metrics_file_info(state):
-    """
-    Checks if the data_logs metrics file exists
-    """
-    return get_data_file_info(state, "_debug_logs.json")
\ No newline at end of file
+    result_msgpack = get_data_file_info(state.video_path, "_debug_logs.msgpack")
+    if result_msgpack[0]:
+        return result_msgpack
+
+    result_json = get_data_file_info(state.video_path, "_debug_logs.json")
+    if result_json[0]:
+        return result_json
+
+    return False, None, None
\ No newline at end of file
diff --git a/script_generator/object_detection/utils.py b/script_generator/object_detection/utils.py
index 33ab7b74..4b6c4b59 100644
--- a/script_generator/object_detection/utils.py
+++ b/script_generator/object_detection/utils.py
@@ -5,11 +5,11 @@
 from script_generator.gui.utils.widgets import Widgets
 from script_generator.object_detection.box_record import BoxRecord
 from script_generator.object_detection.object_detection_result import ObjectDetectionResult
-from script_generator.utils.json import get_data_file_info
+from script_generator.utils.json_utils import get_data_file_info
 
 
 def check_skip_object_detection(state, root):
-    exists, path, filename = get_raw_yolo_file_info(state.video_path)
+    exists, path, filename = get_raw_yolo_file_info(state)
     if exists:
         choice = Widgets.messagebox(
             "Detection File Conflict",
@@ -32,10 +32,15 @@ def check_skip_object_detection(state, root):
 
 
 def get_raw_yolo_file_info(state):
-    """
-    Checks if the YOLO file exists
-    """
-    return get_data_file_info(state, "_rawyolo.json")
+    result_msgpack = get_data_file_info(state.video_path, "_rawyolo.msgpack")
+    if result_msgpack[0]:
+        return result_msgpack
+
+    result_json = get_data_file_info(state.video_path, "_rawyolo.json")
+    if result_json[0]:
+        return result_json
+
+    return False, None, None
 
 def make_data_boxes(records):
     """
diff --git a/script_generator/scripts/analyze_video.py b/script_generator/scripts/analyze_video.py
index 768ad161..9d136604 100644
--- a/script_generator/scripts/analyze_video.py
+++ b/script_generator/scripts/analyze_video.py
@@ -127,8 +127,7 @@ def log_progress(state, opengl_q, yolo_q, analysis_q, results_q, stop_event):
             unit="f",
             position=0,
             unit_scale=False,
-            unit_divisor=1,
-            ncols=130
+            unit_divisor=1
     ) as progress_bar:
         while not stop_event.is_set():
             opengl_size = opengl_q.qsize()
@@ -139,7 +138,7 @@ def log_progress(state, opengl_q, yolo_q, analysis_q, results_q, stop_event):
             progress_bar.n = frames_processed
             open_gl = f"OpenGL: {opengl_size:>3}, " if state.video_reader == "FFmpeg + OpenGL (Windows)" else ""
             progress_bar.set_postfix_str(
-                f"Queues: {open_gl}YOLO: {yolo_size:>3}, Analysis: {analysis_size:>3}"
+                f"Q's: {open_gl}YOLO: {yolo_size:>3}, Analysis: {analysis_size:>3}"
             )
             progress_bar.refresh()
 
diff --git a/script_generator/scripts/generate_funscript.py b/script_generator/scripts/generate_funscript.py
index c6ec2213..1bb0f879 100644
--- a/script_generator/scripts/generate_funscript.py
+++ b/script_generator/scripts/generate_funscript.py
@@ -14,7 +14,7 @@ def generate_funscript(state: AppState):
         state.frame_end = to_int_or_none(state.frame_end)
 
         # analyze video if required
-        if not state.use_existing_raw_yolo or not get_raw_yolo_file_info(state.video_path):
+        if not state.use_existing_raw_yolo or not get_raw_yolo_file_info(state):
             analyze_video(state)
 
         tracking_analysis(state)
diff --git a/script_generator/scripts/tracking_analysis.py b/script_generator/scripts/tracking_analysis.py
index a89ebc32..4879dd12 100644
--- a/script_generator/scripts/tracking_analysis.py
+++ b/script_generator/scripts/tracking_analysis.py
@@ -1,26 +1,26 @@
 import os
 
 from script_generator.object_detection.analyze_tracking_results import analyze_tracking_results
-from script_generator.object_detection.utils import make_data_boxes, parse_yolo_data_looking_for_penis
+from script_generator.object_detection.utils import make_data_boxes, parse_yolo_data_looking_for_penis, get_raw_yolo_file_info
 from script_generator.utils.file import get_output_file_path
-from script_generator.utils.json import load_json_from_file
+from script_generator.utils.json_utils import load_json_from_file
 from script_generator.debug.logger import logger
+from script_generator.utils.msgpack_utils import load_msgpack_json
 from script_generator.video.info.video_info import get_cropped_dimensions
 from utils.lib_FunscriptHandler import FunscriptGenerator
 
 
 def tracking_analysis(state):
     # Load YOLO detection results from file
-    raw_yolo_path, _ = get_output_file_path(state.video_path, "_rawyolo.json")
-    if not os.path.exists(raw_yolo_path):
+    exists, raw_yolo_path, _ = get_raw_yolo_file_info(state)
+    if not exists:
         logger.info(f"Raw yolo json not found in {raw_yolo_path}")
         return
 
-    yolo_data = load_json_from_file(raw_yolo_path)
+    yolo_data = load_msgpack_json(raw_yolo_path)
 
     state.set_video_info()
     video_info = state.video_info
-    width, height = get_cropped_dimensions(state.video_info)
 
     results = make_data_boxes(yolo_data)
 
@@ -32,12 +32,12 @@ def tracking_analysis(state):
         return
 
     # Deciding whether we start from there or from a user-specified later frame
-    #state.frame_start = max(max(first_penis_frame - int(video_info.fps), state.frame_start - int(video_info.fps)), 0)
+    # state.frame_start = max(max(first_penis_frame - int(video_info.fps), state.frame_start - int(video_info.fps)), 0)
     state.frame_start_track = max(max(first_penis_frame - int(video_info.fps), state.frame_start - int(video_info.fps)), 0)
 
     state.frame_end = state.video_info.total_frames if not state.frame_end else state.frame_end
 
-    #logger.info(f"Frame Start adjusted to: {state.frame_start}")
+    # logger.info(f"Frame Start adjusted to: {state.frame_start}")
     logger.info(f"Frame Start adjusted to: {state.frame_start_track}")
 
     # Performing the tracking part and generation of the raw funscript data
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index f3143607..55b4fc4d 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -27,6 +27,7 @@ def __init__(self, is_cli):
         self.frame_end: int | None = None
         self.video_reader: Literal["FFmpeg", "FFmpeg + OpenGL (Windows)"] = "FFmpeg" # if is_mac() else "FFmpeg + OpenGL (Windows)"
         self.copy_funscript_to_movie_dir = True
+        self.final_funscript_destination = "C:/cvr/funscript-generator/_generated" # If set the final script will be output to this directory instead of the movie location
 
         # Gui/settings debug
         self.save_debug_file: bool = True
@@ -79,10 +80,19 @@ def set_video_info(self):
                 self.has_tracking_data = False
                 self.max_preview_fps = 60
             else:
-                self.video_info = get_video_info(self.video_path)
-                self.has_raw_yolo, _, _ = get_raw_yolo_file_info(self.video_path)
-                self.has_tracking_data, _, _ = get_metrics_file_info(self.video_path)
-                self.max_preview_fps = math.ceil(self.video_info.fps)
+                if os.path.exists(self.video_path) and not os.path.isdir(self.video_path):
+                    try:
+                        self.video_info = get_video_info(self.video_path)
+                        self.has_raw_yolo, _, _ = get_raw_yolo_file_info(self)
+                        self.has_tracking_data, _, _ = get_metrics_file_info(self)
+                        self.max_preview_fps = math.ceil(self.video_info.fps)
+                    except:
+                        logger.warn(f"FFprobe failed for path: {self.video_path}")
+                    finally:
+                        return
+
+                self.video_path = None
+                self.set_video_info()
 
 
 def log_state_settings(state: AppState):
diff --git a/script_generator/utils/config.py b/script_generator/utils/config.py
index e62d0af7..05841642 100644
--- a/script_generator/utils/config.py
+++ b/script_generator/utils/config.py
@@ -39,23 +39,23 @@ def find_ffprobe_path(win_ffprobe_path, mac_ffprobe_path, lin_ffprobe_path):
         else:
             raise FileNotFoundError("ffprobe not found in PATH and no default path for this OS")
 
-def get_yolo_model_path(YOLO_MODELS):
+def get_yolo_model_path(yolo_models):
     # Check if the device is an Apple device
     if platform.system() == 'Darwin':
-        logger.info(f"Apple device detected, loading {YOLO_MODELS[0]} for MPS inference.")
-        return YOLO_MODELS[0]
+        logger.info(f"Apple device detected, loading {yolo_models[0]} for MPS inference.")
+        return yolo_models[0]
 
     # Check if CUDA is available (for GPU support)
     elif torch.cuda.is_available():
-        logger.info(f"CUDA is available, loading {YOLO_MODELS[1]} for GPU inference.")
-        return YOLO_MODELS[1]
+        logger.info(f"CUDA is available, loading {yolo_models[1]} for GPU inference.")
+        return yolo_models[1]
 
     # Fallback to ONNX model for other platforms without CUDA
     else:
         logger.info("CUDA not available, if this is unexpected, please install CUDA and check your version of torch.")
         logger.info("You might need to install a dependency with the following command (example):")
         logger.info("pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118")
-        logger.info(f"Falling back to CPU inference, loading {YOLO_MODELS[2]}.")
+        logger.info(f"Falling back to CPU inference, loading {yolo_models[2]}.")
         logger.info("WARNING: CPU inference may be slow on some devices.")
 
-        return YOLO_MODELS[2]
+        return yolo_models[2]
diff --git a/script_generator/utils/json.py b/script_generator/utils/json_utils.py
similarity index 83%
rename from script_generator/utils/json.py
rename to script_generator/utils/json_utils.py
index 83481d49..22bc8888 100644
--- a/script_generator/utils/json.py
+++ b/script_generator/utils/json_utils.py
@@ -43,19 +43,17 @@ def load_json_from_file(file_path):
 
 
 def get_data_file_info(video_path, suffix):
-    """
-    Checks if a data file exists, returns a true / false and the path and filename
-    """
     raw_yolo_path, raw_yolo_filename = get_output_file_path(video_path, suffix)
     if os.path.exists(raw_yolo_path):
-        yolo_data = load_json_from_file(raw_yolo_path)
-        if len(yolo_data) == 0:
-            logger.warn(f"Raw YOLO data file doesn't contain any data: {raw_yolo_path}")
+        file_size = os.path.getsize(raw_yolo_path)
+        if file_size <= 5: # prevent jsons with []
+            logger.warn(f"Raw YOLO data file is too small or empty: {raw_yolo_path} (size: {file_size} bytes)")
             try:
                 os.remove(raw_yolo_path)
-                logger.info(f"Deleted empty raw YOLO data file: {raw_yolo_path}")
+                logger.info(f"Deleted small raw YOLO data file: {raw_yolo_path}")
             except OSError as e:
                 logger.error(f"Error deleting raw YOLO file {raw_yolo_path}: {e}")
+            return False, None, None
         else:
             return True, raw_yolo_path, raw_yolo_filename
     return False, None, None
diff --git a/script_generator/utils/msgpack_utils.py b/script_generator/utils/msgpack_utils.py
new file mode 100644
index 00000000..83c5d4a2
--- /dev/null
+++ b/script_generator/utils/msgpack_utils.py
@@ -0,0 +1,59 @@
+import os
+import time
+
+import numpy as np
+
+import json
+import msgpack
+from script_generator.debug.logger import logger
+
+
+def load_msgpack_json(output_path):
+    msgpack_path = output_path
+    json_path = output_path.replace(".msgpack", ".json")
+
+    if os.path.exists(msgpack_path):
+        try:
+            with open(msgpack_path, "rb") as f:
+                return msgpack.unpackb(f.read(), raw=False, strict_map_key=False)
+        except Exception as e:
+            logger.error(f"Failed to load from msgpack: {e}")
+            raise
+
+    if os.path.exists(json_path):
+        try:
+            with open(json_path, "r") as f:
+                data = json.load(f)
+
+            with open(msgpack_path, "wb") as f:
+                f.write(msgpack.packb(data, use_bin_type=True))
+
+            os.remove(json_path)
+            logger.info(f"Converted JSON to msgpack and removed the old file: {json_path}")
+
+            return data
+        except Exception as e:
+            logger.error(f"Failed to load or convert JSON: {e}")
+            raise
+
+    raise FileNotFoundError(f"Neither msgpack nor JSON file exists at {output_path}")
+
+def save_msgpack_json(path, data):
+    start_time = time.time()
+    try:
+        with open(path, "wb") as f:
+            f.write(msgpack.packb(data, use_bin_type=True, default=_default_serializer))
+        logger.info(f"Data saved to msgpack in {(time.time() - start_time) * 1000}ms: {path}")
+    except Exception as e:
+        logger.error(f"Failed to save to msgpack: {e}")
+        raise
+
+def _default_serializer(obj):
+    if isinstance(obj, np.integer):
+        return int(obj)
+    elif isinstance(obj, np.floating):
+        return float(obj)
+    elif isinstance(obj, np.ndarray):
+        return obj.tolist()
+    else:
+        raise TypeError(f"Object of type {obj.__class__.__name__} is not msgpack serializable")
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index fc7a4bb6..229d445d 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -16,7 +16,7 @@
 from script_generator.debug.logger import logger
 from script_generator.state.app_state import AppState
 from script_generator.utils.file import get_output_file_path
-from script_generator.utils.json import load_json_from_file
+from script_generator.utils.json_utils import load_json_from_file
 
 matplotlib.use('Agg')  # Use a non-interactive backend
 import matplotlib.pyplot as plt
@@ -115,11 +115,11 @@ def generate(self, state):
             self.write_funscript(zip_adjusted_positions, output_path, state.video_info.fps)
 
             # copy funscript if specified
-            if state.copy_funscript_to_movie_dir:
+            if state.copy_funscript_to_movie_dir or state.final_funscript_destination:
                 copy = True
                 video_folder = os.path.dirname(state.video_path)
                 filename_base = os.path.basename(state.video_path)[:-4]
-                funscript_path = os.path.join(video_folder, f"{filename_base}.funscript")
+                funscript_path = os.path.join(state.final_funscript_destination, f"{filename_base}.funscript") if state.final_funscript_destination else os.path.join(video_folder, f"{filename_base}.funscript")
 
                 # Backup output file if it exists
                 if os.path.exists(funscript_path):
@@ -143,17 +143,18 @@ def generate(self, state):
             logger.error(f"Error generating funscript: {e}")
             raise
 
+    # TODO replace with proper JSON serialization
     def write_funscript(self, distances, output_path, fps):
         with open(output_path, 'w') as f:
-            f.write(f'{{"version":"{VERSION}","inverted":false,"range":95,"author":"{FUNSCRIPT_AUTHOR}","actions":[{{"at": 0, "pos": 100}},')
+            f.write(f'{{"version":"{VERSION}","inverted":false,"range":100,"author":"{FUNSCRIPT_AUTHOR}","actions":[{{"at":0,"pos":100}},')
             i = 0
             for frame, position in distances:
                 time_ms = int(frame * 1000 / fps)
                 if i > 0:
                     f.write(",")
-                f.write(f' {{"at": {time_ms}, "pos": {int(position)}}}')
+                f.write(f'{{"at":{time_ms},"pos":{int(position)}}}')
                 i += 1
-            f.write("]}\n")
+            f.write("]}")
 
     def generate_heatmap(self, funscript_path, output_image_path):
         try:
diff --git a/utils/lib_SceneCutsDetect.py b/utils/lib_SceneCutsDetect.py
index 7c33ccfc..4188d02a 100644
--- a/utils/lib_SceneCutsDetect.py
+++ b/utils/lib_SceneCutsDetect.py
@@ -57,8 +57,7 @@
 #     for frame_pos in tqdm(
 #             range(total_frames_to_parse), desc="Detecting scene changes", position=0,
 #             unit_scale=False,
-#             unit_divisor=1,
-#             ncols=130
+#             unit_divisor=1
 #     ):
 #         cap.set(cv2.CAP_PROP_POS_FRAMES, frame_start + frame_pos * frame_step)
 #         ret, frame = cap.read()

From 8f18436977ba942bc0cf506b9936688b5041e702 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Wed, 29 Jan 2025 19:32:22 +0100
Subject: [PATCH 194/277] Funscript output directory default back to none

---
 script_generator/state/app_state.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index 55b4fc4d..c5b989b1 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -27,7 +27,7 @@ def __init__(self, is_cli):
         self.frame_end: int | None = None
         self.video_reader: Literal["FFmpeg", "FFmpeg + OpenGL (Windows)"] = "FFmpeg" # if is_mac() else "FFmpeg + OpenGL (Windows)"
         self.copy_funscript_to_movie_dir = True
-        self.final_funscript_destination = "C:/cvr/funscript-generator/_generated" # If set the final script will be output to this directory instead of the movie location
+        self.final_funscript_destination = None # If set the final script will be output to this directory instead of the movie location
 
         # Gui/settings debug
         self.save_debug_file: bool = True

From b7c31c6831d025449011c804fdb11c8caf4e1783 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Wed, 29 Jan 2025 21:47:55 +0100
Subject: [PATCH 195/277] temp fix for mac platforms

---
 .../gui/controller/debug_video.py             | 23 +++++++++++++++----
 1 file changed, 19 insertions(+), 4 deletions(-)

diff --git a/script_generator/gui/controller/debug_video.py b/script_generator/gui/controller/debug_video.py
index 8d3fcab1..6ac03597 100644
--- a/script_generator/gui/controller/debug_video.py
+++ b/script_generator/gui/controller/debug_video.py
@@ -1,6 +1,7 @@
 import os
 import threading
 from tkinter import messagebox
+import platform
 
 from script_generator.debug.video_player.play import play_debug_video
 from script_generator.object_detection.util.utils import get_metrics_file_info
@@ -9,7 +10,24 @@
 from script_generator.debug.logger import logger
 
 def debug_video(state: AppState):
-    def run():
+    if platform.system() != "Darwin":
+        def run():
+            if not state.video_path:
+                messagebox.showerror("Error", "Please select a video file.")
+                return
+
+            state.set_video_info()
+
+            exists, path, filename = get_metrics_file_info(state)
+            if exists:
+                play_debug_video(state=state, start_frame=state.frame_start)
+            else:
+                logger.error(f"Debug logs file not found: {path}")
+                messagebox.showinfo("Info", f"Debug logs file not found: {filename}")
+
+        processing_thread = threading.Thread(target=run)
+        processing_thread.start()
+    else:
         if not state.video_path:
             messagebox.showerror("Error", "Please select a video file.")
             return
@@ -22,6 +40,3 @@ def run():
         else:
             logger.error(f"Debug logs file not found: {path}")
             messagebox.showinfo("Info", f"Debug logs file not found: {filename}")
-
-    processing_thread = threading.Thread(target=run)
-    processing_thread.start()
\ No newline at end of file

From 54c534a8de49f4e18db58272b01a4e20217b6c38 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Fri, 31 Jan 2025 18:31:16 +0100
Subject: [PATCH 196/277] Fixed bounding boxes Cleaned play debug video is mac
 logic

---
 Start windows.bat                             |  1 -
 .../debug/video_player/debug_overlay.py       |  7 ++--
 .../gui/controller/debug_video.py             | 35 +++++++------------
 script_generator/state/app_state.py           |  4 +--
 4 files changed, 18 insertions(+), 29 deletions(-)

diff --git a/Start windows.bat b/Start windows.bat
index 8f07c93b..d66dde92 100644
--- a/Start windows.bat	
+++ b/Start windows.bat	
@@ -1,4 +1,3 @@
-
 @echo off
 cd /d "%~dp0"
 set "CONDA_PATH=C:\Users\%USERNAME%\miniconda3\Scripts\activate.bat"
diff --git a/script_generator/debug/video_player/debug_overlay.py b/script_generator/debug/video_player/debug_overlay.py
index c7eb5929..0c1a645c 100644
--- a/script_generator/debug/video_player/debug_overlay.py
+++ b/script_generator/debug/video_player/debug_overlay.py
@@ -39,11 +39,10 @@ def draw_overlay(
     :param fps:                         The video frames per second.
     """
     # Pull the logs for this frame
-    str_frame_id = str(frame_id)
     variables, bounding_boxes = {}, []
-    if str_frame_id in logs:
-        variables = logs[str_frame_id].get("variables", {})
-        bounding_boxes = logs[str_frame_id].get("bounding_boxes", [])
+    if frame_id in logs:
+        variables = logs[frame_id].get("variables", {})
+        bounding_boxes = logs[frame_id].get("bounding_boxes", [])
 
     # Draw bounding boxes
     for box in bounding_boxes:
diff --git a/script_generator/gui/controller/debug_video.py b/script_generator/gui/controller/debug_video.py
index 6ac03597..334fd8da 100644
--- a/script_generator/gui/controller/debug_video.py
+++ b/script_generator/gui/controller/debug_video.py
@@ -1,37 +1,21 @@
 import os
 import threading
 from tkinter import messagebox
-import platform
 
 from script_generator.debug.video_player.play import play_debug_video
 from script_generator.object_detection.util.utils import get_metrics_file_info
 from script_generator.state.app_state import AppState
 from script_generator.utils.file import get_output_file_path
 from script_generator.debug.logger import logger
+from script_generator.utils.helpers import is_mac
 
-def debug_video(state: AppState):
-    if platform.system() != "Darwin":
-        def run():
-            if not state.video_path:
-                messagebox.showerror("Error", "Please select a video file.")
-                return
-
-            state.set_video_info()
-
-            exists, path, filename = get_metrics_file_info(state)
-            if exists:
-                play_debug_video(state=state, start_frame=state.frame_start)
-            else:
-                logger.error(f"Debug logs file not found: {path}")
-                messagebox.showinfo("Info", f"Debug logs file not found: {filename}")
 
-        processing_thread = threading.Thread(target=run)
-        processing_thread.start()
-    else:
-        if not state.video_path:
-            messagebox.showerror("Error", "Please select a video file.")
-            return
+def debug_video(state: AppState):
+    if not state.video_path:
+        messagebox.showerror("Error", "Please select a video file.")
+        return
 
+    def run():
         state.set_video_info()
 
         exists, path, filename = get_metrics_file_info(state)
@@ -40,3 +24,10 @@ def run():
         else:
             logger.error(f"Debug logs file not found: {path}")
             messagebox.showinfo("Info", f"Debug logs file not found: {filename}")
+
+    # Mac needs to run the gui on the same thread, this will block the main GUI so we don't do it on windows
+    if is_mac():
+        run()
+    else:
+        processing_thread = threading.Thread(target=run)
+        processing_thread.start()
\ No newline at end of file
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index c5b989b1..4facf99c 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -26,8 +26,8 @@ def __init__(self, is_cli):
         self.frame_start: int = 0
         self.frame_end: int | None = None
         self.video_reader: Literal["FFmpeg", "FFmpeg + OpenGL (Windows)"] = "FFmpeg" # if is_mac() else "FFmpeg + OpenGL (Windows)"
-        self.copy_funscript_to_movie_dir = True
-        self.final_funscript_destination = None # If set the final script will be output to this directory instead of the movie location
+        self.copy_funscript_to_movie_dir = False
+        self.final_funscript_destination = "C:/cvr/funscript-generator/_generated" # If set the final script will be output to this directory instead of the movie location
 
         # Gui/settings debug
         self.save_debug_file: bool = True

From 669efe78208136f2f459aea9968463b16f32c895 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Fri, 31 Jan 2025 18:48:11 +0100
Subject: [PATCH 197/277] Added support for cuda and 10bit video's

---
 script_generator/scripts/generate_debug_video.py |  2 +-
 script_generator/video/ffmpeg/commands.py        |  4 ++--
 script_generator/video/ffmpeg/filters.py         |  6 ++++--
 script_generator/video/ffmpeg/hwaccel.py         |  8 ++++++--
 script_generator/video/info/video_info.py        | 14 +++++++++++---
 5 files changed, 24 insertions(+), 10 deletions(-)

diff --git a/script_generator/scripts/generate_debug_video.py b/script_generator/scripts/generate_debug_video.py
index 17abc88d..aabbe860 100644
--- a/script_generator/scripts/generate_debug_video.py
+++ b/script_generator/scripts/generate_debug_video.py
@@ -21,7 +21,7 @@ def run():
         debug_video_path, _ = get_output_file_path(state.video_path, "_debug.mp4", True)
         ffmpeg_command = [
             FFMPEG_PATH, "-y",
-            *get_hwaccel_read_args(state.ffmpeg_hwaccel),
+            *get_hwaccel_read_args(state.video_info, state.ffmpeg_hwaccel),
             "-i", temp_video_path,
             "-c:v", "hevc_nvenc" if state.ffmpeg_hwaccel == "cuda" else "libx265",
             "-preset", "p4" if state.ffmpeg_hwaccel == "cuda" else "fast",
diff --git a/script_generator/video/ffmpeg/commands.py b/script_generator/video/ffmpeg/commands.py
index cb61403e..5aef79a0 100644
--- a/script_generator/video/ffmpeg/commands.py
+++ b/script_generator/video/ffmpeg/commands.py
@@ -10,14 +10,14 @@ def get_ffmpeg_read_cmd(video: VideoInfo, video_reader: str, hwaccel: str, frame
     start_time = (frame_start / video.fps) * 1000
 
     # Get supported hardware acceleration backends
-    hwaccel_read = get_hwaccel_read_args(hwaccel)
+    hwaccel_read = get_hwaccel_read_args(video, hwaccel)
 
     video_filter = ["-vf", vf] if vf else []
     if hwaccel == "vaapi":
         # VAAPI requires specific pixel formats and filters
         video_filter = ["-vf", f"{vf},format=nv12,hwupload"] if vf else ["-vf", "format=nv12,hwupload"]
 
-    if hwaccel == "cuda":
+    if hwaccel == "cuda" and video.bit_depth == 8:
         video_filter = ["-noautoscale"] + video_filter  # explicitly tell ffmpeg that scaling is done by cuda
 
     frame_size = width * height * 3  # Size of one frame in bytes
diff --git a/script_generator/video/ffmpeg/filters.py b/script_generator/video/ffmpeg/filters.py
index 11571104..b1a6b25c 100644
--- a/script_generator/video/ffmpeg/filters.py
+++ b/script_generator/video/ffmpeg/filters.py
@@ -14,7 +14,8 @@ def get_vr_video_filters(video, video_reader, hwaccel, disable_opengl=False):
 
     cuda = hwaccel == "cuda"
 
-    scale = f"[0:0]scale_cuda={RENDER_RESOLUTION * 2}:-2,hwdownload" if cuda else f"[0:0]scale={RENDER_RESOLUTION * 2}:-2"
+    # hardware accelerated output is not supported with > 8 bit
+    scale = f"[0:0]scale_cuda={RENDER_RESOLUTION * 2}:-2,hwdownload" if cuda and video.bit_depth == 8 else f"[0:0]scale={RENDER_RESOLUTION * 2}:-2"
     crop = f"crop={RENDER_RESOLUTION}:{RENDER_RESOLUTION}:0:0"
     out_format = f"format=nv12," if cuda else ""
 
@@ -43,6 +44,7 @@ def get_2d_video_filters(video, width, height, hwaccel):
     if video.height > RENDER_RESOLUTION:
         scale_width = int(video.width * (height / video.height))
         crop = f",crop={width}:{height}:(iw-{width})/2:0"
-        return f"[0:0]scale_cuda={scale_width}:{height},hwdownload,format=nv12{crop}" if cuda else f"[0:0]scale={scale_width}:{height}{crop}"
+        # hardware accelerated output is not supported with > 8 bit
+        return f"[0:0]scale_cuda={scale_width}:{height},hwdownload,format=nv12{crop}" if cuda and video.bit_depth == 8 else f"[0:0]scale={scale_width}:{height}{crop}"
     else:
         return "[0:0]hwdownload,format=nv12" if cuda else ""
\ No newline at end of file
diff --git a/script_generator/video/ffmpeg/hwaccel.py b/script_generator/video/ffmpeg/hwaccel.py
index e03652e6..8c735c16 100644
--- a/script_generator/video/ffmpeg/hwaccel.py
+++ b/script_generator/video/ffmpeg/hwaccel.py
@@ -34,9 +34,13 @@ def get_preferred_hwaccel():
             return hw
     return None
 
-def get_hwaccel_read_args(hwaccel):
+def get_hwaccel_read_args(video_info, hwaccel):
     if hwaccel == "cuda":
-        return ["-hwaccel", "cuda", "-hwaccel_output_format", "cuda"]
+        # hardware accelerated output is not supported with > 8 bit
+        if video_info.bit_depth == 8:
+            return ["-hwaccel", "cuda", "-hwaccel_output_format", "cuda"]
+        else:
+            return ["-hwaccel", "cuda"]
     if hwaccel == "vaapi":
         return ["-hwaccel", "vaapi", "-hwaccel_device", "/dev/dri/renderD128"]
     if hwaccel == "amf":
diff --git a/script_generator/video/info/video_info.py b/script_generator/video/info/video_info.py
index 0ee29b93..a5af04a7 100644
--- a/script_generator/video/info/video_info.py
+++ b/script_generator/video/info/video_info.py
@@ -16,6 +16,7 @@ class VideoInfo:
     duration: float = 0.0
     total_frames: int = 0
     fps: float = 0.0
+    bit_depth: int = 8
     is_vr: bool = False
     projection: str = field(init=False)
     fov: int = field(init=False)
@@ -74,7 +75,7 @@ def get_video_info(video_path):
             FFPROBE_PATH,
             "-v", "error",
             "-select_streams", "v:0",
-            "-show_entries", "stream=r_frame_rate,width,height,codec_name,nb_frames",
+            "-show_entries", "stream=r_frame_rate,width,height,codec_name,nb_frames,pix_fmt",
             "-show_entries", "format=duration",
             "-of", "json",
             video_path,
@@ -94,6 +95,13 @@ def get_video_info(video_path):
         height = int(stream.get("height", 0))
         r_frame_rate = stream.get("r_frame_rate", "0/1")
         nb_frames = stream.get("nb_frames", None)
+        pix_fmt = stream.get("pix_fmt", "unknown")
+        pix_fmt_bit_depth_map = {
+            "yuv420p": 8, "yuvj420p": 8, "nv12": 8, "rgb24": 8, "bgr24": 8,
+            "yuv420p10le": 10, "yuv420p10be": 10, "yuv422p10le": 10, "yuv444p10le": 10,
+            "yuv420p12le": 12, "yuv420p16le": 16, "yuv422p16le": 16, "yuv444p16le": 16,
+        }
+        bit_depth = pix_fmt_bit_depth_map.get(pix_fmt, 8)
 
         # Extract format-level metadata
         duration = float(info.get("format", {}).get("duration", 0))
@@ -109,14 +117,14 @@ def get_video_info(video_path):
         # Check if the video is VR (2:1 aspect ratio)
         is_vr = height == width // 2
 
-        logger.info(f"Video Info: {codec_name}, {width}x{height}, {fps:.2f} fps, {nb_frames} frames, {duration:.2f} seconds, is vr: {is_vr}")
+        logger.info(f"Video Info: {codec_name}, {width}x{height}, {fps:.2f} fps, {nb_frames} frames, {duration:.2f} sec, {bit_depth}-bit, is vr: {is_vr}")
 
         if is_vr:
             logger.info("Video Format: VR SBS - Based on its 2:1 ratio")
         else:
             logger.info("Video Format: 2D - Based on its ratio")
 
-        return VideoInfo(video_path, codec_name, width, height, duration, int(nb_frames), fps, is_vr)
+        return VideoInfo(video_path, codec_name, width, height, duration, int(nb_frames), fps, bit_depth, is_vr)
 
     except subprocess.CalledProcessError as e:
         logger.error(f"FFProbe command failed: {e.output.decode('utf-8')}")

From f8e3501844b38b6412aa2769c3ed7050e0432b25 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sat, 1 Feb 2025 08:34:22 +0100
Subject: [PATCH 198/277] Added settings page and config.json Logger tweaks and
 log level

---
 .gitignore                                    |   1 +
 FSGenerator.py                                |   4 +-
 README.md                                     |  22 +---
 config.py                                     |  64 -----------
 script_generator/cli/generate_funscript.py    |  14 +--
 script_generator/config/config_manager.py     |  67 ++++++++++++
 script_generator/constants.py                 |  70 ++++++++++--
 script_generator/debug/logger.py              |  24 ++++-
 .../debug/video_player/interaction.py         |   4 +-
 script_generator/debug/video_player/play.py   |   4 +-
 script_generator/funscript/util.py            |   4 +-
 script_generator/gui/app.py                   |  30 ++++--
 .../gui/controller/debug_video.py             |   4 +-
 .../gui/controller/process_video.py           |  10 +-
 .../gui/controller/regenerate_funscript.py    |  14 +--
 script_generator/gui/utils/widgets.py         |  37 +++++--
 .../gui/views/funscript_generator.py          |   4 +-
 script_generator/gui/views/settings.py        | 102 ++++++++++++++++++
 .../analyze_tracking_results.py               |  10 +-
 .../object_detection/post_process_worker.py   |  16 +--
 .../object_detection/util/utils.py            |  39 ++++++-
 script_generator/object_detection/utils.py    |   6 +-
 .../object_detection/yolo_worker.py           |   5 +-
 script_generator/scripts/analyze_video.py     |  18 ++--
 .../scripts/generate_debug_video.py           |  13 ++-
 .../scripts/generate_funscript.py             |   9 +-
 script_generator/scripts/tracking_analysis.py |  10 +-
 script_generator/state/app_state.py           |  62 ++++++++---
 .../tasks/abstract_task_processor.py          |   6 +-
 script_generator/utils/config.py              |  61 -----------
 script_generator/utils/json_utils.py          |  14 +--
 script_generator/utils/msgpack_utils.py       |  12 +--
 script_generator/video/ffmpeg/commands.py     |  15 +--
 script_generator/video/ffmpeg/filters.py      |   2 +-
 script_generator/video/ffmpeg/hwaccel.py      |  11 +-
 script_generator/video/ffmpeg/video_reader.py |  16 ++-
 script_generator/video/info/video_info.py     |  24 +++--
 script_generator/video/util/ffmpeg.py         |  48 +++++++++
 .../video/workers/ffmpeg_worker.py            |  12 +--
 .../video/workers/vr_to_2d_worker.py          |   2 +-
 tests/test_compare_models_and_resolutions.py  |   4 +-
 tests/test_generate_funscript.py              |   2 +-
 tests/test_pose_estimation.py                 |   4 +-
 utils/lib_FunscriptHandler.py                 |  80 +++++++-------
 utils/lib_ObjectTracker.py                    |  16 +--
 utils/lib_SceneCutsDetect.py                  |   4 +-
 46 files changed, 629 insertions(+), 371 deletions(-)
 delete mode 100644 config.py
 create mode 100644 script_generator/config/config_manager.py
 create mode 100644 script_generator/gui/views/settings.py
 delete mode 100644 script_generator/utils/config.py
 create mode 100644 script_generator/video/util/ffmpeg.py

diff --git a/.gitignore b/.gitignore
index f608b6ae..06378703 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,7 @@
 /models/*
 /output/*
 /logs/*
+config.json
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
diff --git a/FSGenerator.py b/FSGenerator.py
index 874cdf20..d32b4ccb 100644
--- a/FSGenerator.py
+++ b/FSGenerator.py
@@ -1,8 +1,8 @@
 import os
 
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 from script_generator.gui.app import start_app
 
 if __name__ == "__main__":
-    logger.info(f"Starting VR funscript generator GUI with process id (PID): {os.getpid()}")
+    log.info(f"Starting VR funscript generator GUI with process id (PID): {os.getpid()}")
     start_app()
diff --git a/README.md b/README.md
index ee68b29f..f2e62e21 100644
--- a/README.md
+++ b/README.md
@@ -73,7 +73,7 @@ The pipeline for generating Funscript files is as follows:
 Before using this project, ensure you have the following installed:
 
 - **Python 3.8 or higher (tested on 3.11 https://www.python.org/downloads/release/python-3118/)**
-- **FFmpeg** added to your path or specified in the config (https://www.ffmpeg.org/download.html)
+- **FFmpeg** added to your PATH or specified under the settings menu (https://www.ffmpeg.org/download.html)
 --
 
 ## Installation
@@ -133,25 +133,9 @@ python FSGenerator.py
 - Place your YOLO model file (e.g., `k00gar-11n-RGB-200ep-best.mlpackage`) in the `models/` sub-directory.
 - Alternatively, you can specify a custom path to the model using the `--yolo_model` argument.
 
-### Update config.py
 
-**FFMPEG**
-- If ffmpeg and ffprobe paths are not in your system path, the program will default to the following values.
-- You can update the params/config.py file, which contains:
-
-  ```bash
-  # ffmpeg and ffprobe paths - replace with your own if not in your system path   
-   win_ffmpeg_path = "C:/ffmpeg/bin/ffmpeg.exe"
-   mac_ffmpeg_path = "/usr/local/bin/ffmpeg"
-   lin_ffmpeg_path = "/usr/bin/ffmpeg"
-
-   win_ffprobe_path = "C:/ffmpeg/bin/ffprobe.exe"
-   mac_ffprobe_path = "/usr/local/bin/ffprobe"
-   lin_ffprobe_path = "/usr/bin/ffprobe"
-  ```
-
-**(Optional) User configurations**
-config.py also contains various user configurations that can be adjusted
+**(Optional) Settings**
+Find the settings menu in the app to configure optional option.
 
 ### Start script
 You can use Start windows.bat to launch the gui on windows if you installed with conda
diff --git a/config.py b/config.py
deleted file mode 100644
index 55cc0285..00000000
--- a/config.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import platform
-
-from ultralytics import YOLO
-
-from script_generator.constants import MODEL_PATH
-from script_generator.utils.config import find_ffmpeg_path, find_ffprobe_path
-from script_generator.debug.logger import logger
-
-VERSION = "0.1.1"
-
-##################################################################################################
-# FFMPEG
-##################################################################################################
-
-# ffmpeg and ffprobe paths - replace with your own if not in your system path
-win_ffmpeg_path = "C:/ffmpeg/bin/ffmpeg.exe"
-mac_ffmpeg_path = "/usr/local/bin/ffmpeg"
-lin_ffmpeg_path = "/usr/bin/ffmpeg"
-win_ffprobe_path = "C:/ffmpeg/bin/ffprobe.exe"
-mac_ffprobe_path = "/usr/local/bin/ffprobe"
-lin_ffprobe_path = "/usr/bin/ffprobe"
-
-##################################################################################################
-# PERFORMANCE
-##################################################################################################
-
-RENDER_RESOLUTION = 640
-TEXTURE_RESOLUTION = RENDER_RESOLUTION * 1.3 # Texture size that is used to texture the opengl sphere
-YOLO_BATCH_SIZE = 1 if platform.system() == "Darwin" else 30 # Mac doesn't support batching
-YOLO_PERSIST = True # Big impact on performance but also improves tracking
-
-##################################################################################################
-# ADVANCED
-##################################################################################################
-
-YOLO_CONF = 0.3
-VR_TO_2D_PITCH=-25 # The dataset is trained on -25
-UPDATE_PROGRESS_INTERVAL = 0.2 # Updates progress in the console and in gui
-STEP_SIZE = 120  # Define custom colormap based on Lucife's heatmapColors | Speed step size for color transitions
-QUEUE_MAXSIZE = 100 # Bounded queue size to avoid memory blow-up as raw frames consume a lot of memory, does not increase performance
-
-##################################################################################################
-# DEV
-##################################################################################################
-
-# when enabled the queue will be processed one by one (use it on (QUEUE_MAXSIZE / frame rate) seconds longer videos or less)
-# raw frames take a lot of memory (RAM) so don't set the queue to high
-SEQUENTIAL_MODE = False
-if SEQUENTIAL_MODE:
-    QUEUE_MAXSIZE = 3000
-
-##################################################################################################
-# PROG
-##################################################################################################
-
-RUN_POSE_MODEL = False
-YOLO_POSE_MODEL = None # YOLO("models/yolo11n-pose.mlpackage", task="pose") #TODO pose model?
-VALID_VIDEO_READERS = ["FFmpeg", "FFmpeg + OpenGL (Windows)"]
-FFMPEG_PATH = find_ffmpeg_path(win_ffmpeg_path, mac_ffmpeg_path, lin_ffmpeg_path)
-FFPROBE_PATH = find_ffprobe_path(win_ffprobe_path, mac_ffprobe_path, lin_ffprobe_path)
-logger.info(f"ffmpeg_path: {FFMPEG_PATH}")
-logger.info(f"ffprobe_path: {FFPROBE_PATH}")
-
-
diff --git a/script_generator/cli/generate_funscript.py b/script_generator/cli/generate_funscript.py
index 3511d7da..34c7e4d6 100644
--- a/script_generator/cli/generate_funscript.py
+++ b/script_generator/cli/generate_funscript.py
@@ -1,9 +1,9 @@
 import argparse
 import os
 
-from config import VALID_VIDEO_READERS
+from script_generator.constants import VALID_VIDEO_READERS
 from script_generator.scripts.generate_funscript import generate_funscript
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 from script_generator.state.app_state import AppState
 from script_generator.utils.helpers import is_mac
 
@@ -113,14 +113,14 @@ def main():
     video_reader = args.video_reader
     default_video_reader = "FFmpeg" if is_mac() else "FFmpeg + OpenGL (Windows)"
     if video_reader and video_reader not in VALID_VIDEO_READERS:
-        logger.warning(
+        log.warning(
             f"Invalid video reader specified: {video_reader}. Using default: {default_video_reader}."
         )
         args.video_reader = default_video_reader
 
     try:
-        logger.info(f"Processing video: {args.video_path}")
-        state = AppState(is_cli=True)
+        log.info(f"Processing video: {args.video_path}")
+        state = AppState()
         state.video_path = args.video_path
         state.use_existing_raw_yolo = args.reuse_yolo
         state.copy_funscript_to_movie_dir = args.copy_funscript
@@ -139,9 +139,9 @@ def main():
         state.rounding = args.rounding
 
         generate_funscript(state)
-        logger.info("Funscript generation complete.")
+        log.info("Funscript generation complete.")
     except Exception as e:
-        logger.error(f"An error occurred: {e}")
+        log.error(f"An error occurred: {e}")
 
 
 if __name__ == "__main__":
diff --git a/script_generator/config/config_manager.py b/script_generator/config/config_manager.py
new file mode 100644
index 00000000..9bf13b29
--- /dev/null
+++ b/script_generator/config/config_manager.py
@@ -0,0 +1,67 @@
+import json
+import os
+
+from script_generator.constants import CONFIG_FILE_PATH, CONFIG_VERSION, DEFAULT_CONFIG
+from script_generator.debug.logger import log, set_log_level
+from script_generator.object_detection.util.utils import get_yolo_model_path
+from script_generator.video.util.ffmpeg import get_ffmpeg_paths
+
+class ConfigManager:
+    def __init__(self, app_state):
+        self.app_state = app_state
+        self.config = self.load()
+
+    def load(self):
+        if os.path.exists(CONFIG_FILE_PATH):
+            try:
+                with open(CONFIG_FILE_PATH, "r") as file:
+                    self.config = json.load(file)
+
+                self._ensure_defaults(self.config)
+                set_log_level(self.config["log_level"])
+
+                return self.config
+            except (json.JSONDecodeError, IOError):
+                log.warning("Config file is corrupted. Resetting to defaults.")
+
+        return self._initialize_defaults()
+
+    def save(self):
+        self.config["config_version"] = CONFIG_VERSION
+
+        # Sync settings from AppState
+        for key in DEFAULT_CONFIG.keys():
+            if hasattr(self.app_state, key):
+                self.config[key] = getattr(self.app_state, key)
+
+        with open(CONFIG_FILE_PATH, "w") as file:
+            json.dump(self.config, file, indent=4)
+
+    def get(self, key, default=None):
+        return self.config.get(key, default)
+
+    def _initialize_defaults(self):
+        self.config = DEFAULT_CONFIG.copy()
+        self._ensure_defaults(self.config)
+        self.save()
+        return self.config
+
+    def _ensure_defaults(self, config):
+        checks = [
+            ("ffmpeg_path", lambda: get_ffmpeg_paths()[0], self._is_valid_path),
+            ("ffprobe_path", lambda: get_ffmpeg_paths()[1], self._is_valid_path),
+            ("yolo_model_path", get_yolo_model_path, self._is_valid_path),
+        ]
+
+        updated = False
+
+        for key, get_default, check_func in checks:
+            if not check_func(config.get(key)):
+                config[key] = get_default()
+                updated = True
+
+        if updated:
+            self.save()
+
+    def _is_valid_path(self, value):
+        return isinstance(value, str) and value.strip() and os.path.exists(os.path.abspath(value))
diff --git a/script_generator/constants.py b/script_generator/constants.py
index 78721f38..b7f4f393 100644
--- a/script_generator/constants.py
+++ b/script_generator/constants.py
@@ -1,6 +1,61 @@
 import os
+import platform
 
-from script_generator.utils.config import get_yolo_model_path
+VERSION = "0.1.1"
+YOLO_VERSION = 1
+FUNSCRIPT_VERSION = 1
+CONFIG_VERSION = 1
+
+##################################################################################################
+# PERFORMANCE
+##################################################################################################
+
+RENDER_RESOLUTION = 640
+TEXTURE_RESOLUTION = RENDER_RESOLUTION * 1.3  # Texture size that is used to texture the opengl sphere
+YOLO_BATCH_SIZE = 1 if platform.system() == "Darwin" else 30  # Mac doesn't support batching
+YOLO_PERSIST = True  # Big impact on performance but also improves tracking
+
+##################################################################################################
+# ADVANCED
+##################################################################################################
+
+YOLO_CONF = 0.3
+VR_TO_2D_PITCH = -25  # The dataset is trained on -25
+UPDATE_PROGRESS_INTERVAL = 0.2  # Updates progress in the console and in gui
+STEP_SIZE = 120  # Define custom colormap based on Lucife's heatmapColors | Speed step size for color transitions
+QUEUE_MAXSIZE = 100  # Bounded queue size to avoid memory blow-up as raw frames consume a lot of memory, does not increase performance
+
+##################################################################################################
+# DEV
+##################################################################################################
+
+# when enabled the queue will be processed one by one (use it on (QUEUE_MAXSIZE / frame rate) seconds longer videos or less)
+# raw frames take a lot of memory (RAM) so don't set the queue to high
+SEQUENTIAL_MODE = False
+if SEQUENTIAL_MODE:
+    QUEUE_MAXSIZE = 3000
+
+##################################################################################################
+# DEFAULT CONFIG
+##################################################################################################
+
+DEFAULT_CONFIG = {
+    "config_version": CONFIG_VERSION,
+    "ffmpeg_path": None,
+    "ffprobe_path": None,
+    "yolo_model_path": None,
+    "copy_funscript_to_movie_dir": True,
+    "funscript_output_dir": None,
+    "log_level": "INFO"
+}
+
+##################################################################################################
+# PROG
+##################################################################################################
+
+RUN_POSE_MODEL = False
+YOLO_POSE_MODEL = None  # YOLO("models/yolo11n-pose.mlpackage", task="pose") #TODO pose model?
+VALID_VIDEO_READERS = ["FFmpeg", "FFmpeg + OpenGL (Windows)"]
 
 ##################################################################################################
 # OBJECT DETECTION
@@ -63,11 +118,11 @@
 
 PROJECT_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 OUTPUT_PATH = os.path.join(PROJECT_PATH, "output")
-MODELS_DIR = "models"
+MODELS_PATH = os.path.join(PROJECT_PATH, "models")
 MODEL_FILENAMES = [
-#    "k00gar-11n-200ep-best.mlpackage",
-#    "k00gar-11n-200ep-best.pt",
-#    "k00gar-11n-200ep-best.onnx",
+    #    "k00gar-11n-200ep-best.mlpackage",
+    #    "k00gar-11n-200ep-best.pt",
+    #    "k00gar-11n-200ep-best.onnx",
     "k00gar-11n-RGB-200ep-best.mlpackage",
     "k00gar-11n-RGB-200ep-best.pt",
     "k00gar-11n-RGB-200ep-best.onnx",
@@ -77,11 +132,10 @@
 ]
 LOGO = os.path.join(PROJECT_PATH, "resources", "logo.png")
 ICON = os.path.join(PROJECT_PATH, "resources", "icon.ico")
-YOLO_MODELS = [os.path.join(MODELS_DIR, filename) for filename in MODEL_FILENAMES]
-MODEL_PATH = str(os.path.join(PROJECT_PATH, get_yolo_model_path(YOLO_MODELS)))
+CONFIG_FILE_PATH = os.path.join(PROJECT_PATH, "config.json")
 
 ##################################################################################################
 # DIV
 ##################################################################################################
 
-FUNSCRIPT_AUTHOR = "FunGen_k00gar_AI"
\ No newline at end of file
+FUNSCRIPT_AUTHOR = "FunGen_k00gar_AI"
diff --git a/script_generator/debug/logger.py b/script_generator/debug/logger.py
index ab3a8371..b3f09aae 100644
--- a/script_generator/debug/logger.py
+++ b/script_generator/debug/logger.py
@@ -1,8 +1,11 @@
-from datetime import datetime
 import logging
 import os
 import sys
+from datetime import datetime
 from colorama import Fore, Style, init
+from typing import Literal
+
+LogLevel = Literal["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]
 
 def ensure_path_exists(path):
     if not os.path.exists(path):
@@ -19,7 +22,6 @@ def emit(self, record):
         super().emit(record)
 
 project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-
 log_path = os.path.join(project_root, "logs")
 ensure_path_exists(log_path)
 
@@ -33,4 +35,20 @@ def emit(self, record):
     ]
 )
 
-logger = logging.getLogger("General")
+log = logging.getLogger("Main")
+log_od = logging.getLogger("ObjectDetection")
+log_tr = logging.getLogger("ObjectDetection")
+
+
+def set_log_level(level: LogLevel):
+    level = level.upper()
+    log_level = getattr(logging, level, logging.INFO)
+
+    # Update all active loggers
+    for logger_name in logging.root.manager.loggerDict.keys():
+        logger = logging.getLogger(logger_name)
+        logger.setLevel(log_level)
+        for handler in logger.handlers:
+            handler.setLevel(log_level)
+
+    log.info(f"Log level set to {level}")
\ No newline at end of file
diff --git a/script_generator/debug/video_player/interaction.py b/script_generator/debug/video_player/interaction.py
index 3860d269..7d2beae6 100644
--- a/script_generator/debug/video_player/interaction.py
+++ b/script_generator/debug/video_player/interaction.py
@@ -1,7 +1,7 @@
 import cv2
 
 from script_generator.debug.video_player.constants import BAR_HEIGHT
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 
 
 def mouse_callback(event, x, y, flags, param):
@@ -19,4 +19,4 @@ def mouse_callback(event, x, y, flags, param):
             width = int(video_player.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
             target_frame = int((x / width) * video_player.total_frames)
             video_player.set_frame(target_frame)
-            logger.info(f"Seek to frame: {target_frame}")
\ No newline at end of file
+            log.info(f"Seek to frame: {target_frame}")
\ No newline at end of file
diff --git a/script_generator/debug/video_player/play.py b/script_generator/debug/video_player/play.py
index 99a092a5..d0555e72 100644
--- a/script_generator/debug/video_player/play.py
+++ b/script_generator/debug/video_player/play.py
@@ -8,7 +8,7 @@
 from scipy.interpolate import interp1d
 
 from script_generator.debug.debug_data import load_debug_metrics
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 from script_generator.debug.video_player.controls import draw_media_controls
 from script_generator.debug.video_player.debug_overlay import draw_overlay
 from script_generator.debug.video_player.interaction import mouse_callback
@@ -63,7 +63,7 @@ def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=1
         output_fps = video_info.fps
         frame_size = (width, height)
         video_writer = cv2.VideoWriter(temp_video_path, fourcc, output_fps, frame_size)
-        logger.info(f"Recording debug video to temporary file: {temp_video_path}")
+        log.info(f"Recording debug video to temporary file: {temp_video_path}")
     else:
         # Setup OpenCV window
         window_name = "Debug Video"
diff --git a/script_generator/funscript/util.py b/script_generator/funscript/util.py
index 1f8291b4..59375e07 100644
--- a/script_generator/funscript/util.py
+++ b/script_generator/funscript/util.py
@@ -1,6 +1,6 @@
 import json
 
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 
 
 def load_funscript_json(funscript_path):
@@ -12,5 +12,5 @@ def load_funscript_json(funscript_path):
         funscript_positions = [action["pos"] for action in actions]
         return funscript_times, funscript_positions
     except FileNotFoundError:
-        logger.error(f"Funscript file not found at {funscript_path}")
+        log.error(f"Funscript file not found at {funscript_path}")
         raise
\ No newline at end of file
diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index 8e686115..3f5a1c01 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -4,14 +4,16 @@
 
 from script_generator.constants import LOGO, ICON
 from script_generator.gui.views.funscript_generator import FunscriptGeneratorPage
+from script_generator.gui.views.settings import SettingsPage
 from script_generator.state.app_state import AppState
 from script_generator.utils.helpers import is_mac
-from script_generator.debug.logger import logger
-from config import VERSION
+from script_generator.debug.logger import log
+from script_generator.constants import VERSION
 
 # TODO this is a workaround and needs to be fixed properly
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
+
 class App(tk.Tk):
     def __init__(self):
         super().__init__()
@@ -19,7 +21,7 @@ def __init__(self):
             ctypes.windll.shcore.SetProcessDpiAwareness(2)  # For Windows DPI scaling
         # self.tk.call('tk', 'scaling', 1.0)
         self.title(f"VR & 2D POV Funscript AI Generator - v" + VERSION)
-        self.geometry(f"{('800' if is_mac() else '700')}x900")
+        self.geometry('800x900' if is_mac() else '700x900')
         self.resizable(False, False)
 
         self.iconphoto(False, tk.PhotoImage(file=LOGO))
@@ -31,7 +33,17 @@ def __init__(self):
         self.container.grid(row=0, column=0, sticky="nsew")
         self.container.grid_columnconfigure(0, weight=1)
 
-        self.state = AppState(is_cli=False)
+        menu_bar = tk.Menu(self)
+
+        # File Menu
+        file_menu = tk.Menu(menu_bar, tearoff=0)
+        # file_menu.add_command(label="Exit", command=self.quit)
+        # menu_bar.add_cascade(label="File", menu=file_menu)
+        menu_bar.add_command(label="Funscript generator", command=lambda: self.show_frame(PageNames.FUNSCRIPT_GENERATOR))
+        menu_bar.add_command(label="Settings", command=lambda: self.show_frame(PageNames.SETTINGS))
+        self.config(menu=menu_bar)
+
+        self.state = AppState()
 
         # Dictionary to store pages
         self.frames = {}
@@ -53,7 +65,7 @@ def show_frame(self, page_name):
                 self.frames[page_name] = frame
                 frame.grid(row=0, column=0, sticky="nsew")
             else:
-                logger.info(f"Page '{page_name}' not found!")
+                log.info(f"Page '{page_name}' not found!")
                 return
 
         # Show the requested page
@@ -63,13 +75,17 @@ def show_frame(self, page_name):
     def create_page(self, page_name):
         if page_name == PageNames.FUNSCRIPT_GENERATOR:
             return FunscriptGeneratorPage(parent=self.container, controller=self)
-
+        elif page_name == PageNames.SETTINGS:
+            return SettingsPage(parent=self.container, controller=self)
         return None
 
+
 class PageNames:
     FUNSCRIPT_GENERATOR = "Funscript generator"
+    SETTINGS = "Settings"
+
 
 def start_app():
     app = App()
     app.show_frame(PageNames.FUNSCRIPT_GENERATOR)
-    app.mainloop()
\ No newline at end of file
+    app.mainloop()
diff --git a/script_generator/gui/controller/debug_video.py b/script_generator/gui/controller/debug_video.py
index 334fd8da..c8c155af 100644
--- a/script_generator/gui/controller/debug_video.py
+++ b/script_generator/gui/controller/debug_video.py
@@ -6,7 +6,7 @@
 from script_generator.object_detection.util.utils import get_metrics_file_info
 from script_generator.state.app_state import AppState
 from script_generator.utils.file import get_output_file_path
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 from script_generator.utils.helpers import is_mac
 
 
@@ -22,7 +22,7 @@ def run():
         if exists:
             play_debug_video(state=state, start_frame=state.frame_start)
         else:
-            logger.error(f"Debug logs file not found: {path}")
+            log.error(f"Debug logs file not found: {path}")
             messagebox.showinfo("Info", f"Debug logs file not found: {filename}")
 
     # Mac needs to run the gui on the same thread, this will block the main GUI so we don't do it on windows
diff --git a/script_generator/gui/controller/process_video.py b/script_generator/gui/controller/process_video.py
index 0010aaa6..8d03e8ad 100644
--- a/script_generator/gui/controller/process_video.py
+++ b/script_generator/gui/controller/process_video.py
@@ -7,12 +7,14 @@
 from script_generator.scripts.analyze_video import analyze_video
 from script_generator.state.app_state import AppState, log_state_settings
 from script_generator.utils.helpers import to_int_or_none
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 
 
 def video_analysis(state: AppState, root):
-    if not state.video_path:
-        messagebox.showerror("Error", "Please select a video file.")
+    configured, msg = state.is_configured()
+    if not configured:
+        log.warn(msg)
+        messagebox.showerror("Error", msg)
         return
 
     state.set_video_info()
@@ -43,7 +45,7 @@ def run():
             state.update_ui(UpdateGUIState(attr="is_processing", value=False))
 
         except Exception as e:
-            logger.error(f"Error during video analysis: {e}")
+            log.error(f"Error during video analysis: {e}")
             messagebox.showerror("Error", f"Could not process video:\n{e}")
             import traceback
             traceback.print_exc()
diff --git a/script_generator/gui/controller/regenerate_funscript.py b/script_generator/gui/controller/regenerate_funscript.py
index 2d74bea2..70145528 100644
--- a/script_generator/gui/controller/regenerate_funscript.py
+++ b/script_generator/gui/controller/regenerate_funscript.py
@@ -1,6 +1,6 @@
 from tkinter import messagebox
 
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 from utils.lib_FunscriptHandler import FunscriptGenerator
 
 
@@ -9,21 +9,21 @@ def regenerate_funscript(state):
         messagebox.showerror("Error", "Please select a video file.")
         return
 
-    logger.info("Regenerating Funscript with tweaked settings...")
+    log.info("Regenerating Funscript with tweaked settings...")
 
     state.set_video_info()
 
     # Apply tweaks to funscript_data
     if state.boost_enabled:
-        logger.info(f"Applying Boost: Up {state.boost_up_percent}%, Down {state.boost_down_percent}%")
+        log.info(f"Applying Boost: Up {state.boost_up_percent}%, Down {state.boost_down_percent}%")
         # Add boost logic here
 
     if state.threshold_enabled:
-        logger.info(f"Applying Threshold: Low {state.threshold_low}, High {state.threshold_high}")
+        log.info(f"Applying Threshold: Low {state.threshold_low}, High {state.threshold_high}")
         # Add threshold logic here
 
     if state.vw_simplification_enabled:
-        logger.info(f"Applying VW Simplification with Factor: {state.vw_factor} then rounding to {state.rounding}")
+        log.info(f"Applying VW Simplification with Factor: {state.vw_factor} then rounding to {state.rounding}")
         # Add VW simplification logic here
 
     # Save and regenerate funscript
@@ -32,9 +32,9 @@ def regenerate_funscript(state):
     # Simplifying the funscript data and generating the file
     funscript_handler.generate(state)
 
-    logger.info("Funscript re-generation complete.")
+    log.info("Funscript re-generation complete.")
 
     # Optional, compare generated funscript with reference funscript if specified, or a simple generic report
     funscript_handler.create_report_funscripts(state)
 
-    logger.info("Report generation complete.")
\ No newline at end of file
+    log.info("Report generation complete.")
\ No newline at end of file
diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index f11a8de2..7b896569 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -111,7 +111,7 @@ def button(parent, button_text, on_click, row=0, column=0, tooltip_text=None, st
         return button
 
     @staticmethod
-    def file_selection(parent, label_text, button_text, file_selector_title, file_types, state, attr, command=None, row=0, label_width_px=150, button_width_px=100, tooltip_text=None):
+    def file_selection(parent, label_text, button_text, file_selector_title, file_types, state, attr, command=None, row=0, label_width_px=150, button_width_px=100, tooltip_text=None, select_folder=False):
         container = tk.Frame(parent)
         container.grid(row=row, column=0, sticky="nsew", padx=5, pady=5)
 
@@ -128,7 +128,7 @@ def on_change(val):
         # Configure the container's internal layout
         container.columnconfigure(1, weight=1)
 
-        file_path = tk.StringVar()
+        file_path = tk.StringVar(value=getattr(state, attr, ""))
 
         # Label for file selection with fixed pixel width
         label = tk.Label(container, text=label_text, anchor="w", width=label_width_px // 7)
@@ -142,7 +142,15 @@ def on_change(val):
         button_container = tk.Frame(container, width=button_width_px, bg="lightgreen")  # Set button container background
         button_container.grid(row=0, column=2, sticky="e", padx=(2, 5))
         button_container.grid_propagate(False)  # Prevent resizing
-        button = ttk.Button(button_container, text=button_text, command=lambda: Widgets._browse_file(file_path, file_selector_title, file_types, lambda val: setattr(state, attr, val)))
+        def browse():
+            if select_folder:
+                selected_path = filedialog.askdirectory(title=file_selector_title)
+            else:
+                selected_path = filedialog.askopenfilename(title=file_selector_title, filetypes=file_types)
+            if selected_path:
+                file_path.set(selected_path)
+                setattr(state, attr, selected_path)
+        button = ttk.Button(button_container, text=button_text, command=browse)
         button.pack(fill="both", expand=True)
 
         # Update state whenever the file path changes
@@ -178,7 +186,7 @@ def labeled_progress(parent, label_text, row=0, column=0, progress_length=300, l
         return container, progress_bar, progress_label, percentage_label
 
     @staticmethod
-    def dropdown(parent, label_text, options, default_value, state, attr, row=0, column=0, label_width_px=LABEL_WIDTH, tooltip_text=None, **grid_kwargs):
+    def dropdown(parent, label_text, options, default_value, state, attr, row=0, column=0, command=None, label_width_px=LABEL_WIDTH, tooltip_text=None, **grid_kwargs):
         selected_value = tk.StringVar(value=default_value)
 
         # Create a container for the dropdown
@@ -195,8 +203,15 @@ def dropdown(parent, label_text, options, default_value, state, attr, row=0, col
         dropdown = ttk.Combobox(container, textvariable=selected_value, values=options, state="readonly")
         dropdown.grid(row=0, column=1, sticky="ew", padx=(2, 5))
 
+        def on_change(val):
+            if getattr(state, attr) != val:
+                setattr(state, attr, val)
+
+                if command:
+                    command(val)
+
         # Bind selection changes to update the state
-        dropdown.bind("<<ComboboxSelected>>", lambda _: setattr(state, attr, selected_value.get()))
+        dropdown.bind("<<ComboboxSelected>>", lambda _: on_change(selected_value.get()))
 
         if tooltip_text:
             Tooltip(dropdown, tooltip_text)
@@ -220,7 +235,7 @@ def range_selector(parent, label_text, row, state, attr, values, column=0, toolt
         return dropdown
 
     @staticmethod
-    def checkbox(parent, label_text, state, attr, label_left=True, row=0, column=0, label_width_px=150, tooltip_text=None, **grid_kwargs):
+    def checkbox(parent, label_text, state, attr, command=None, label_left=True, row=0, column=0, label_width_px=150, tooltip_text=None, **grid_kwargs):
         # Ensure the parent has a _checkbox_vars attribute to track BooleanVars
         if not hasattr(parent, "_checkbox_vars"):
             parent._checkbox_vars = {}
@@ -236,6 +251,11 @@ def checkbox(parent, label_text, state, attr, label_left=True, row=0, column=0,
         container.grid(row=row, column=column, sticky="ew", padx=5, pady=5)
         container.columnconfigure(1, weight=1)  # Allow checkbox to adjust dynamically
 
+        def on_check():
+            setattr(state, attr, is_checked.get())
+            if command:
+                command(is_checked.get())
+
         if label_left:
             # Label with fixed pixel width and sticky west alignment
             label = tk.Label(container, text=label_text, anchor="w", width=label_width_px // 7)
@@ -246,7 +266,7 @@ def checkbox(parent, label_text, state, attr, label_left=True, row=0, column=0,
                 container,
                 text="",  # No text since the label is on the left
                 variable=is_checked,
-                command=lambda: setattr(state, attr, is_checked.get())
+                command=on_check
             )
             checkbox.grid(row=0, column=1, sticky="w")  # Explicit sticky west for checkbox alignment
         else:
@@ -255,7 +275,7 @@ def checkbox(parent, label_text, state, attr, label_left=True, row=0, column=0,
                 container,
                 text=label_text,
                 variable=is_checked,
-                command=lambda: setattr(state, attr, is_checked.get())
+                command= on_check
             )
             checkbox.grid(row=0, column=0, sticky="w", **grid_kwargs)
 
@@ -522,4 +542,3 @@ def make_entry(parent_, textvar, width=4):
         on_fps_change()
         # TODO readd hours_entry, minutes_entry, seconds_entry,
         return container, (frames_entry,), value
-
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 9b108137..164ad1c9 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -1,7 +1,7 @@
 import tkinter as tk
 from tkinter import ttk
 
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 from script_generator.gui.controller.debug_video import debug_video
 from script_generator.gui.controller.process_video import video_analysis
 from script_generator.gui.controller.regenerate_funscript import regenerate_funscript
@@ -273,7 +273,7 @@ def process_update():
                 if handler:
                     handler(msg)
                 else:
-                    logger.info(f"Unhandled message type: {type(msg)}")
+                    log.info(f"Unhandled message type: {type(msg)}")
 
             def handle_state_update(state_msg: UpdateGUIState):
                 setattr(state, state_msg.attr, state_msg.value)
diff --git a/script_generator/gui/views/settings.py b/script_generator/gui/views/settings.py
new file mode 100644
index 00000000..8dc4492b
--- /dev/null
+++ b/script_generator/gui/views/settings.py
@@ -0,0 +1,102 @@
+import tkinter as tk
+from tkinter import ttk
+
+from script_generator.debug.logger import set_log_level
+from script_generator.state.app_state import AppState
+from script_generator.gui.utils.widgets import Widgets
+
+
+class SettingsPage(tk.Frame):
+    def __init__(self, parent, controller):
+        super().__init__(parent)
+        self.controller = controller
+        self.state: AppState = controller.state
+        c = self.state.config_manager
+        self.grid_columnconfigure(0, weight=1)
+
+        general_settings = Widgets.frame(self, title="Funscripts", main_section=True, row=0)
+
+        Widgets.checkbox(
+            general_settings,
+            "Copy funscript to movie dir",
+            state=self.state,
+            attr="copy_funscript_to_movie_dir",
+            default_value=False,
+            tooltip_text="If enabled, the generated funscript will be copied to the same directory as the movie.",
+            command=lambda val: c.save(),
+            row=0
+        )
+
+        Widgets.file_selection(
+            attr="funscript_output_dir",
+            parent=general_settings,
+            label_text="Copy funscript to folder",
+            button_text="Browse",
+            file_selector_title="Select Funscript Destination",
+            file_types=[("Folders", "*")],
+            state=self.state,
+            tooltip_text="Set an optional output folder where the final funscript will be copied to.",
+            command=lambda val: c.save(),
+            select_folder=True,
+            row=1
+        )
+
+        ffmpeg_settings = Widgets.frame(self, title="YOLO (restart to apply changes)", main_section=True, row=1)
+
+        Widgets.file_selection(
+            attr="yolo_model_path",
+            parent=ffmpeg_settings,
+            label_text="YOLO model",
+            button_text="Browse",
+            file_selector_title="Select YOLO model",
+            file_types=[("YOLO Model Files", "*.onnx"), ("YOLO Model Files", "*.pt"), ("CoreML Model Files", "*.mlmodel"), ("All Files", "*.*")],
+            state=self.state,
+            tooltip_text="Path to the FFmpeg executable.",
+            command=lambda val: c.save(),
+            row=0
+        )
+
+        ffmpeg_settings = Widgets.frame(self, title="FFmpeg", main_section=True, row=2)
+
+        Widgets.file_selection(
+            attr="ffmpeg_path",
+            parent=ffmpeg_settings,
+            label_text="FFmpeg Path",
+            button_text="Browse",
+            file_selector_title="Select FFmpeg Executable",
+            file_types=[("Executable Files", "*.exe"), ("All Files", "*.*")],
+            state=self.state,
+            tooltip_text="Path to the FFmpeg executable.",
+            command=lambda val: c.save(),
+            row=0
+        )
+
+        Widgets.file_selection(
+            attr="ffprobe_path",
+            parent=ffmpeg_settings,
+            label_text="FFprobe Path",
+            button_text="Browse",
+            file_selector_title="Select FFprobe Executable",
+            file_types=[("Executable Files", "*.exe"), ("All Files", "*.*")],
+            state=self.state,
+            tooltip_text="Path to the FFprobe executable.",
+            command=lambda val: c.save(),
+            row=1
+        )
+
+        dev_settings = Widgets.frame(self, title="Dev", main_section=True, row=3)
+
+        def handle_log_level(level):
+            set_log_level(level)
+            c.save()
+
+        Widgets.dropdown(
+            attr="log_level",
+            parent=dev_settings,
+            label_text="Log level",
+            options=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
+            default_value=self.state.log_level,
+            tooltip_text="Set the log level",
+            state=self.state,
+            command=handle_log_level
+        )
\ No newline at end of file
diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index a952421b..7524e904 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -5,13 +5,13 @@
 import cv2
 from tqdm import tqdm
 
-from config import UPDATE_PROGRESS_INTERVAL
+from script_generator.constants import UPDATE_PROGRESS_INTERVAL
 from script_generator.constants import CLASS_COLORS
 from script_generator.debug.video_player.overlay_widgets import OverlayWidgets
 from script_generator.gui.messages.messages import ProgressMessage, UpdateGUIState
 from script_generator.state.app_state import AppState
 from script_generator.utils.file import get_output_file_path
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 from script_generator.video.ffmpeg.video_reader import VideoReaderFFmpeg
 from script_generator.video.info.video_info import get_cropped_dimensions
 from utils.lib_ObjectTracker import ObjectTracker
@@ -85,9 +85,9 @@ def analyze_tracking_results(state: AppState, results):
         state.current_frame_id = frame_pos
         if frame_pos in cuts:
             # Reinitialize the tracker at scene cuts
-            logger.info(f"Reaching cut at frame {frame_pos}")
+            log.info(f"Reaching cut at frame {frame_pos}")
             previous_distances = tracker.previous_distances
-            logger.info(f"Reinitializing tracker with previous distances: {previous_distances}")
+            log.info(f"Reinitializing tracker with previous distances: {previous_distances}")
             # tracker = ObjectTracker(fps, frame_pos, image_area, video_info.is_vr)
             tracker = ObjectTracker(state)
             tracker.previous_distances = previous_distances
@@ -176,7 +176,7 @@ def analyze_tracking_results(state: AppState, results):
                     state.offset_x
                 )
             else:
-                logger.info("No active locked penis box to draw.")
+                log.info("No active locked penis box to draw.")
 
             if tracker.glans_detected:
                 frame = OverlayWidgets.draw_bounding_box(
diff --git a/script_generator/object_detection/post_process_worker.py b/script_generator/object_detection/post_process_worker.py
index be1bbb83..6efd4a9d 100644
--- a/script_generator/object_detection/post_process_worker.py
+++ b/script_generator/object_detection/post_process_worker.py
@@ -2,9 +2,9 @@
 
 import cv2
 
-from config import RUN_POSE_MODEL
+from script_generator.constants import RUN_POSE_MODEL
 from script_generator.constants import CLASS_REVERSE_MATCH, CLASS_COLORS
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 from script_generator.gui.messages.messages import UpdateGUIState
 from script_generator.object_detection.object_detection_result import ObjectDetectionResult
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
@@ -58,9 +58,9 @@ def task_logic(self):
                     self.test_result.add_record(frame_pos, test_box)
 
                     # print and test the record
-                    logger.debug(f"Record : {record}")
-                    logger.debug(f"For class id: {int(cls)}, getting: {CLASS_REVERSE_MATCH.get(int(cls), 'unknown')}")
-                    logger.debug(f"Test box: {test_box}")
+                    log.debug(f"Record : {record}")
+                    log.debug(f"For class id: {int(cls)}, getting: {CLASS_REVERSE_MATCH.get(int(cls), 'unknown')}")
+                    log.debug(f"Test box: {test_box}")
 
             if RUN_POSE_MODEL:
                 ### POSE DETECTION - Hips and wrists
@@ -96,11 +96,11 @@ def task_logic(self):
                         self.records.append(record)
                         if state.live_preview_mode:
                             # Print and test the record
-                            logger.debug(f"Record : {record}")
-                            logger.debug(f"For class id: {int(cls)}, getting: {CLASS_REVERSE_MATCH.get(int(cls), 'unknown')}")
+                            log.debug(f"Record : {record}")
+                            log.debug(f"For class id: {int(cls)}, getting: {CLASS_REVERSE_MATCH.get(int(cls), 'unknown')}")
                             test_box = [[x1, y1, x2, y2], round(conf, 1), int(cls),
                                         CLASS_REVERSE_MATCH.get(int(cls), 'unknown'), 0]
-                            logger.debug(f"Test box: {test_box}")
+                            log.debug(f"Test box: {test_box}")
                             self.test_result.add_record(frame_pos, test_box)
 
             window_name = "Object detection tracking preview"
diff --git a/script_generator/object_detection/util/utils.py b/script_generator/object_detection/util/utils.py
index 0eca54d7..5c10d1f8 100644
--- a/script_generator/object_detection/util/utils.py
+++ b/script_generator/object_detection/util/utils.py
@@ -1,3 +1,11 @@
+import os
+
+import torch
+from ultralytics import YOLO
+
+from script_generator.constants import MODELS_PATH, MODEL_FILENAMES
+from script_generator.debug.logger import log
+from script_generator.utils.helpers import is_mac
 from script_generator.utils.json_utils import get_data_file_info
 
 
@@ -10,4 +18,33 @@ def get_metrics_file_info(state):
     if result_json[0]:
         return result_json
 
-    return False, None, None
\ No newline at end of file
+    return False, None, None
+
+def get_yolo_model_path():
+    yolo_models = [os.path.join(MODELS_PATH, filename) for filename in MODEL_FILENAMES]
+    # Check if the device is an Apple device
+    if is_mac():
+        log.info(f"Apple device detected, loading {yolo_models[0]} for MPS inference.")
+        return yolo_models[0]
+
+    # Check if CUDA is available (for GPU support)
+    elif torch.cuda.is_available():
+        log.info(f"CUDA is available, loading {yolo_models[1]} for GPU inference.")
+        return yolo_models[1]
+
+    # Fallback to ONNX model for other platforms without CUDA
+    else:
+        log.info("CUDA not available, if this is unexpected, please install CUDA and check your version of torch.")
+        log.info("You might need to install a dependency with the following command (example):")
+        log.info("pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118")
+        log.info(f"Falling back to CPU inference, loading {yolo_models[2]}.")
+        log.info("WARNING: CPU inference may be slow on some devices.")
+
+        return yolo_models[2]
+
+def load_yolo_model(yolo_model_path):
+    if not yolo_model_path or not os.path.exists(str(yolo_model_path)):
+        log.warn("The YOLO model is missing. Please download and place the appropriate YOLO model in the models directory.")
+        return None
+
+    return YOLO(yolo_model_path, task="detect")
\ No newline at end of file
diff --git a/script_generator/object_detection/utils.py b/script_generator/object_detection/utils.py
index 4b6c4b59..901f3cb1 100644
--- a/script_generator/object_detection/utils.py
+++ b/script_generator/object_detection/utils.py
@@ -1,7 +1,7 @@
 import os
 
 from script_generator.constants import CLASS_REVERSE_MATCH
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 from script_generator.gui.utils.widgets import Widgets
 from script_generator.object_detection.box_record import BoxRecord
 from script_generator.object_detection.object_detection_result import ObjectDetectionResult
@@ -20,7 +20,7 @@ def check_skip_object_detection(state, root):
             height=150
         )
         if choice == "no":
-            logger.info(f"File {path} already exists. Skipping detections and loading file content...")
+            log.info(f"File {path} already exists. Skipping detections and loading file content...")
             return "use_existing"
         elif choice == "yes":
             os.remove(path)
@@ -81,5 +81,5 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
             prev_frame = frame_idx
 
             if cons_frames > threshold:
-                logger.info(f"First instance of Glans/Penis found in frame {frame_idx - threshold}")
+                log.info(f"First instance of Glans/Penis found in frame {frame_idx - threshold}")
                 return penis_frame - threshold
diff --git a/script_generator/object_detection/yolo_worker.py b/script_generator/object_detection/yolo_worker.py
index 20ba64e0..c19816a2 100644
--- a/script_generator/object_detection/yolo_worker.py
+++ b/script_generator/object_detection/yolo_worker.py
@@ -1,9 +1,6 @@
 import time
 
-from ultralytics import YOLO
-
-from config import YOLO_CONF, YOLO_BATCH_SIZE, YOLO_PERSIST
-from script_generator.constants import MODEL_PATH
+from script_generator.constants import YOLO_CONF, YOLO_BATCH_SIZE, YOLO_PERSIST
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 
 
diff --git a/script_generator/scripts/analyze_video.py b/script_generator/scripts/analyze_video.py
index 9d136604..90a4671b 100644
--- a/script_generator/scripts/analyze_video.py
+++ b/script_generator/scripts/analyze_video.py
@@ -5,7 +5,7 @@
 
 from tqdm import tqdm
 
-from config import QUEUE_MAXSIZE, SEQUENTIAL_MODE, UPDATE_PROGRESS_INTERVAL
+from script_generator.constants import QUEUE_MAXSIZE, SEQUENTIAL_MODE, UPDATE_PROGRESS_INTERVAL
 from script_generator.gui.messages.messages import ProgressMessage
 from script_generator.object_detection.post_process_worker import PostProcessWorker
 from script_generator.object_detection.yolo_worker import YoloWorker
@@ -13,13 +13,13 @@
 from script_generator.tasks.abstract_task_processor import TaskProcessorTypes
 from script_generator.tasks.tasks import AnalyzeVideoTask, AnalyzeFrameTask
 from script_generator.utils.file import check_create_output_folder
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 from script_generator.video.workers.ffmpeg_worker import VideoWorker
 from script_generator.video.workers.vr_to_2d_worker import VrTo2DWorker
 
 
 def analyze_video(state: AppState) -> List[AnalyzeFrameTask]:
-    logger.info(f"[OBJECT DETECTION] Starting up pipeline{' in sequential mode' if SEQUENTIAL_MODE else ''}...")
+    log.info(f"[OBJECT DETECTION] Starting up pipeline{' in sequential mode' if SEQUENTIAL_MODE else ''}...")
 
     log_thread_stop_event = threading.Event()
     threads = []
@@ -32,11 +32,11 @@ def analyze_video(state: AppState) -> List[AnalyzeFrameTask]:
         state.set_video_info()
         if state.video_reader == "FFmpeg + OpenGL (Windows)":
             if not state.video_info.is_vr:
-                logger.warn("Disabled OpenGL in the pipeline as it's not needed for 2D videos")
+                log.warn("Disabled OpenGL in the pipeline as it's not needed for 2D videos")
                 state.video_reader = "FFmpeg"
 
             if state.video_info.is_fisheye:
-                logger.warn("Disabled OpenGL as fisheye is not yet supported with the opengl feature")
+                log.warn("Disabled OpenGL as fisheye is not yet supported with the opengl feature")
                 state.video_reader = "FFmpeg"
 
         use_open_gl = state.video_reader == "FFmpeg + OpenGL (Windows)"
@@ -70,7 +70,7 @@ def run_thread(thread, thread_name, out_queue):
                 thread.start()
                 thread.join()
                 out_queue.put(None)
-                logger.info(f"[OBJECT DETECTION] {thread_name} thread done in {time.time() - start_time} s")
+                log.info(f"[OBJECT DETECTION] {thread_name} thread done in {time.time() - start_time} s")
 
             run_thread(decode_thread, TaskProcessorTypes.VIDEO, opengl_q)
             if use_open_gl:
@@ -106,7 +106,7 @@ def run_thread(thread, thread_name, out_queue):
         return result_q.queue
 
     except Exception as e:
-        logger.error(f"An error occurred during video analysis: {e}")
+        log.error(f"An error occurred during video analysis: {e}")
         # Signal all threads to stop and perform cleanup
         log_thread_stop_event.set()
         for thread in threads:
@@ -158,7 +158,7 @@ def log_progress(state, opengl_q, yolo_q, analysis_q, results_q, stop_event):
                         eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
                     ))
                 except Exception as e:
-                    logger.error(f"Error in state.update_ui: {e}")
+                    log.error(f"Error in state.update_ui: {e}")
 
             time.sleep(UPDATE_PROGRESS_INTERVAL)
 
@@ -223,4 +223,4 @@ def log_performance(state, results_queue):
     log_message += f"{'-' * 60}\n"
 
     for line in log_message.splitlines():
-        logger.info(line)
\ No newline at end of file
+        log.info(line)
\ No newline at end of file
diff --git a/script_generator/scripts/generate_debug_video.py b/script_generator/scripts/generate_debug_video.py
index aabbe860..c3c61bcb 100644
--- a/script_generator/scripts/generate_debug_video.py
+++ b/script_generator/scripts/generate_debug_video.py
@@ -2,25 +2,24 @@
 import subprocess
 import threading
 
-from config import FFMPEG_PATH
 from script_generator.debug.video_player.play import play_debug_video
 from script_generator.state.app_state import AppState
 from script_generator.utils.file import get_output_file_path
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 from script_generator.video.ffmpeg.hwaccel import get_hwaccel_read_args
 
 
 def generate_debug_video(state: AppState, frame_start: int, frame_end: int):
     # prevent blocking the main thread
     def run():
-        logger.info(f"Generating debug video from: {frame_start} to {frame_end}, this might take a while...")
+        log.info(f"Generating debug video from: {frame_start} to {frame_end}, this might take a while...")
         temp_video_path = play_debug_video(state, frame_start, frame_end, save_video_mode=True)
 
-        logger.info(f"Generating debug video raw video generated now converting...")
+        log.info(f"Generating debug video raw video generated now converting...")
 
         debug_video_path, _ = get_output_file_path(state.video_path, "_debug.mp4", True)
         ffmpeg_command = [
-            FFMPEG_PATH, "-y",
+            state.ffmpeg_path, "-y",
             *get_hwaccel_read_args(state.video_info, state.ffmpeg_hwaccel),
             "-i", temp_video_path,
             "-c:v", "hevc_nvenc" if state.ffmpeg_hwaccel == "cuda" else "libx265",
@@ -31,12 +30,12 @@ def run():
             debug_video_path
         ]
 
-        logger.info(f"Generating debug video with command: {' '.join(map(str, ffmpeg_command))}")
+        log.info(f"Generating debug video with command: {' '.join(map(str, ffmpeg_command))}")
 
         subprocess.run(ffmpeg_command)
 
         os.remove(temp_video_path)
-        logger.info(f"Debug video generated and stored in: {debug_video_path}")
+        log.info(f"Debug video generated and stored in: {debug_video_path}")
 
     thread = threading.Thread(target=run)
     thread.start()
\ No newline at end of file
diff --git a/script_generator/scripts/generate_funscript.py b/script_generator/scripts/generate_funscript.py
index 1bb0f879..6bafb62e 100644
--- a/script_generator/scripts/generate_funscript.py
+++ b/script_generator/scripts/generate_funscript.py
@@ -2,11 +2,16 @@
 from script_generator.scripts.analyze_video import analyze_video
 from script_generator.scripts.tracking_analysis import tracking_analysis
 from script_generator.state.app_state import AppState, log_state_settings
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 from script_generator.utils.helpers import to_int_or_none
 
 def generate_funscript(state: AppState):
     try:
+        configured, msg = state.is_configured()
+        if not configured:
+            log.warn(msg)
+            return
+
         state.set_video_info()
         log_state_settings(state)
 
@@ -19,6 +24,6 @@ def generate_funscript(state: AppState):
 
         tracking_analysis(state)
     except Exception as e:
-        logger.error(f"Error during video analysis: {e}")
+        log.error(f"Error during video analysis: {e}")
         import traceback
         traceback.print_exc()
\ No newline at end of file
diff --git a/script_generator/scripts/tracking_analysis.py b/script_generator/scripts/tracking_analysis.py
index 4879dd12..61e7acd5 100644
--- a/script_generator/scripts/tracking_analysis.py
+++ b/script_generator/scripts/tracking_analysis.py
@@ -4,7 +4,7 @@
 from script_generator.object_detection.utils import make_data_boxes, parse_yolo_data_looking_for_penis, get_raw_yolo_file_info
 from script_generator.utils.file import get_output_file_path
 from script_generator.utils.json_utils import load_json_from_file
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 from script_generator.utils.msgpack_utils import load_msgpack_json
 from script_generator.video.info.video_info import get_cropped_dimensions
 from utils.lib_FunscriptHandler import FunscriptGenerator
@@ -14,7 +14,7 @@ def tracking_analysis(state):
     # Load YOLO detection results from file
     exists, raw_yolo_path, _ = get_raw_yolo_file_info(state)
     if not exists:
-        logger.info(f"Raw yolo json not found in {raw_yolo_path}")
+        log.info(f"Raw yolo json not found in {raw_yolo_path}")
         return
 
     yolo_data = load_msgpack_json(raw_yolo_path)
@@ -28,7 +28,7 @@ def tracking_analysis(state):
     first_penis_frame = parse_yolo_data_looking_for_penis(yolo_data, 0)
 
     if first_penis_frame is None:
-        logger.error(f"No penis instance found in video. Further tracking is not possible.")
+        log.error(f"No penis instance found in video. Further tracking is not possible.")
         return
 
     # Deciding whether we start from there or from a user-specified later frame
@@ -38,7 +38,7 @@ def tracking_analysis(state):
     state.frame_end = state.video_info.total_frames if not state.frame_end else state.frame_end
 
     # logger.info(f"Frame Start adjusted to: {state.frame_start}")
-    logger.info(f"Frame Start adjusted to: {state.frame_start_track}")
+    log.info(f"Frame Start adjusted to: {state.frame_start_track}")
 
     # Performing the tracking part and generation of the raw funscript data
     state.funscript_data = analyze_tracking_results(state, results)
@@ -53,4 +53,4 @@ def tracking_analysis(state):
     # Optionally, compare generated funscript with reference funscript if specified, or a simple generic report
     funscript_handler.create_report_funscripts(state)
 
-    logger.info(f"Finished processing video: {state.video_path}")
+    log.info(f"Finished processing video: {state.video_path}")
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index 4facf99c..454e5227 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -1,35 +1,49 @@
 import math
 import os.path
 import string
-from typing import Literal
+from typing import Literal, Optional
 
-from ultralytics import YOLO
-
-from script_generator.constants import MODEL_PATH
+from script_generator.config.config_manager import ConfigManager
 from script_generator.debug.debug_data import DebugData
-from script_generator.debug.logger import logger
-from script_generator.object_detection.util.utils import get_metrics_file_info
+from script_generator.debug.logger import log
+from script_generator.object_detection.util.utils import get_metrics_file_info, load_yolo_model
 from script_generator.object_detection.utils import get_raw_yolo_file_info
 from script_generator.tasks.tasks import AnalyzeVideoTask
-from script_generator.utils.file import get_output_file_path
-from script_generator.utils.helpers import is_mac
 from script_generator.video.ffmpeg.hwaccel import get_preferred_hwaccel
 from script_generator.video.info.video_info import VideoInfo, get_video_info
 
 
 class AppState:
-    def __init__(self, is_cli):
-        self.is_cli: bool = is_cli
+    _instance: Optional["AppState"] = None
+
+    def __new__(cls):
+        if cls._instance is None:
+            cls._instance = super(AppState, cls).__new__(cls)
+            cls._instance._initialized = False
+        return cls._instance
+
+    def __init__(self):
+        if self._initialized:
+            return
+        self._initialized = True
+
+        self.config_manager = ConfigManager(self)
+        c = self.config_manager
 
         # Gui/settings general
         self.video_path: string = None
         self.frame_start: int = 0
         self.frame_end: int | None = None
         self.video_reader: Literal["FFmpeg", "FFmpeg + OpenGL (Windows)"] = "FFmpeg" # if is_mac() else "FFmpeg + OpenGL (Windows)"
-        self.copy_funscript_to_movie_dir = False
-        self.final_funscript_destination = "C:/cvr/funscript-generator/_generated" # If set the final script will be output to this directory instead of the movie location
+        self.copy_funscript_to_movie_dir = True
+        self.copy_funscript_to_movie_dir = c.get("copy_funscript_to_movie_dir")
+        self.funscript_output_dir = c.get("funscript_output_dir")
+        self.ffmpeg_path = c.get("ffmpeg_path")
+        self.ffprobe_path = c.get("ffprobe_path")
+        self.yolo_model_path = c.get("yolo_model_path")
 
         # Gui/settings debug
+        self.log_level = c.get("log_level")
         self.save_debug_file: bool = True
         self.live_preview_mode: bool = False
         self.reference_script: string = None
@@ -68,8 +82,23 @@ def __init__(self, is_cli):
         # App logic
         self.debug_data = DebugData(self)
         self.update_ui = None
-        self.ffmpeg_hwaccel = get_preferred_hwaccel()
-        self.yolo_model = YOLO(MODEL_PATH, task="detect")
+        self.ffmpeg_hwaccel = get_preferred_hwaccel() if self.ffmpeg_path else None
+        self.yolo_model = load_yolo_model(self.yolo_model_path)
+
+    def is_configured(self):
+        message_prefix = "Cannot process the video."
+        checks = [
+            (self.video_path, f"{message_prefix} Please select a valid video file."),
+            (self.ffprobe_path, f"{message_prefix} FFprobe is missing. Please provide the correct path."),
+            (self.ffmpeg_path, f"{message_prefix} FFMPEG is missing. Please provide the correct path."),
+            (self.yolo_model, f"{message_prefix} YOLO model is not loaded. Please make sure to download the YOLO model to the models directory."),
+        ]
+
+        for path, error_message in checks:
+            if not path:
+                return False, error_message
+
+        return True, None
 
     def set_video_info(self):
         # If movie changed
@@ -87,7 +116,7 @@ def set_video_info(self):
                         self.has_tracking_data, _, _ = get_metrics_file_info(self)
                         self.max_preview_fps = math.ceil(self.video_info.fps)
                     except:
-                        logger.warn(f"FFprobe failed for path: {self.video_path}")
+                        log.warn(f"FFprobe failed for path: {self.video_path}")
                     finally:
                         return
 
@@ -107,4 +136,5 @@ def log_state_settings(state: AppState):
     ]
 
     for setting_name, setting_value in settings:
-        logger.info(f"{setting_name}: {setting_value}")
+        log.info(f"{setting_name}: {setting_value}")
+
diff --git a/script_generator/tasks/abstract_task_processor.py b/script_generator/tasks/abstract_task_processor.py
index 0f60e1e6..a6a99d70 100644
--- a/script_generator/tasks/abstract_task_processor.py
+++ b/script_generator/tasks/abstract_task_processor.py
@@ -4,7 +4,7 @@
 
 from script_generator.state.app_state import AppState
 from script_generator.tasks.tasks import AnalyzeFrameTask
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 
 
 class AbstractTaskProcessor(threading.Thread):
@@ -30,7 +30,7 @@ def log(self, message):
         :param message: Message to log.
         """
         thread_name = threading.current_thread().name
-        logger.info(f"[{self.__class__.__name__}-{thread_name}] {message}")
+        log.info(f"[{self.__class__.__name__}-{thread_name}] {message}")
 
     def get_task(self) -> Generator[AnalyzeFrameTask, None, None]:
         """
@@ -81,7 +81,7 @@ def run(self):
             self.exception = e  # Capture the exception
             # Propagate sentinel to the output queue
             self.output_queue.put(None)
-            logger.error(f"An error occurred during task execution on thread {self.process_type}: {e}")
+            log.error(f"An error occurred during task execution on thread {self.process_type}: {e}")
             # import traceback
             # traceback.print_exc()
         finally:
diff --git a/script_generator/utils/config.py b/script_generator/utils/config.py
deleted file mode 100644
index 05841642..00000000
--- a/script_generator/utils/config.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import platform
-import shutil
-
-import torch
-
-from script_generator.debug.logger import logger
-
-# TODO replace with yaml
-def find_ffmpeg_path(win_ffmpeg_path, mac_ffmpeg_path, lin_ffmpeg_path):
-    ffmpeg_path = shutil.which("ffmpeg")
-    if ffmpeg_path:
-        return ffmpeg_path
-    else:
-        logger.debug("ffmpeg not found in PATH, defaulting to platform values in params/config.py")
-
-        if platform.system() == "Windows":
-            return win_ffmpeg_path
-        elif platform.system() == "Darwin":
-            return mac_ffmpeg_path
-        elif platform.system() == "Linux":
-            return lin_ffmpeg_path
-        else:
-            raise FileNotFoundError("ffmpeg not found in PATH and no default path for this OS")
-
-
-def find_ffprobe_path(win_ffprobe_path, mac_ffprobe_path, lin_ffprobe_path):
-    ffprobe_path = shutil.which("ffprobe")
-    if ffprobe_path:
-        return ffprobe_path
-    else:
-        print("ffprobe not found in PATH, defaulting to platform values in params/config.py")
-
-        if platform.system() == "Windows":
-            return win_ffprobe_path
-        elif platform.system() == "Darwin":
-            return mac_ffprobe_path
-        elif platform.system() == "Linux":
-            return lin_ffprobe_path
-        else:
-            raise FileNotFoundError("ffprobe not found in PATH and no default path for this OS")
-
-def get_yolo_model_path(yolo_models):
-    # Check if the device is an Apple device
-    if platform.system() == 'Darwin':
-        logger.info(f"Apple device detected, loading {yolo_models[0]} for MPS inference.")
-        return yolo_models[0]
-
-    # Check if CUDA is available (for GPU support)
-    elif torch.cuda.is_available():
-        logger.info(f"CUDA is available, loading {yolo_models[1]} for GPU inference.")
-        return yolo_models[1]
-
-    # Fallback to ONNX model for other platforms without CUDA
-    else:
-        logger.info("CUDA not available, if this is unexpected, please install CUDA and check your version of torch.")
-        logger.info("You might need to install a dependency with the following command (example):")
-        logger.info("pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118")
-        logger.info(f"Falling back to CPU inference, loading {yolo_models[2]}.")
-        logger.info("WARNING: CPU inference may be slow on some devices.")
-
-        return yolo_models[2]
diff --git a/script_generator/utils/json_utils.py b/script_generator/utils/json_utils.py
index 22bc8888..2b9fb112 100644
--- a/script_generator/utils/json_utils.py
+++ b/script_generator/utils/json_utils.py
@@ -2,7 +2,7 @@
 import os
 import time
 
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 from script_generator.utils.file import get_output_file_path
 
 
@@ -12,7 +12,7 @@ def write_json_to_file(file_path, data):
     :param file_path: The path to the output file.
     :param data: The data to write.
     """
-    logger.info(f"Exporting data...")
+    log.info(f"Exporting data...")
     export_start = time.time()
 
     # Ensure the directory exists
@@ -24,7 +24,7 @@ def write_json_to_file(file_path, data):
     with open(file_path, 'w') as f:
         json.dump(data, f)
     export_end = time.time()
-    logger.info(f"Done in {export_end - export_start} seconds.")
+    log.info(f"Done in {export_end - export_start} seconds.")
 
 
 def load_json_from_file(file_path):
@@ -38,7 +38,7 @@ def load_json_from_file(file_path):
 
     with open(file_path, 'r') as f:
         data = json.load(f)
-        logger.info(f"Loaded data from {file_path}, length: {len(data)}")
+        log.info(f"Loaded data from {file_path}, length: {len(data)}")
     return data
 
 
@@ -47,12 +47,12 @@ def get_data_file_info(video_path, suffix):
     if os.path.exists(raw_yolo_path):
         file_size = os.path.getsize(raw_yolo_path)
         if file_size <= 5: # prevent jsons with []
-            logger.warn(f"Raw YOLO data file is too small or empty: {raw_yolo_path} (size: {file_size} bytes)")
+            log.warn(f"Raw YOLO data file is too small or empty: {raw_yolo_path} (size: {file_size} bytes)")
             try:
                 os.remove(raw_yolo_path)
-                logger.info(f"Deleted small raw YOLO data file: {raw_yolo_path}")
+                log.info(f"Deleted small raw YOLO data file: {raw_yolo_path}")
             except OSError as e:
-                logger.error(f"Error deleting raw YOLO file {raw_yolo_path}: {e}")
+                log.error(f"Error deleting raw YOLO file {raw_yolo_path}: {e}")
             return False, None, None
         else:
             return True, raw_yolo_path, raw_yolo_filename
diff --git a/script_generator/utils/msgpack_utils.py b/script_generator/utils/msgpack_utils.py
index 83c5d4a2..f4f0dd9f 100644
--- a/script_generator/utils/msgpack_utils.py
+++ b/script_generator/utils/msgpack_utils.py
@@ -5,7 +5,7 @@
 
 import json
 import msgpack
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 
 
 def load_msgpack_json(output_path):
@@ -17,7 +17,7 @@ def load_msgpack_json(output_path):
             with open(msgpack_path, "rb") as f:
                 return msgpack.unpackb(f.read(), raw=False, strict_map_key=False)
         except Exception as e:
-            logger.error(f"Failed to load from msgpack: {e}")
+            log.error(f"Failed to load from msgpack: {e}")
             raise
 
     if os.path.exists(json_path):
@@ -29,11 +29,11 @@ def load_msgpack_json(output_path):
                 f.write(msgpack.packb(data, use_bin_type=True))
 
             os.remove(json_path)
-            logger.info(f"Converted JSON to msgpack and removed the old file: {json_path}")
+            log.info(f"Converted JSON to msgpack and removed the old file: {json_path}")
 
             return data
         except Exception as e:
-            logger.error(f"Failed to load or convert JSON: {e}")
+            log.error(f"Failed to load or convert JSON: {e}")
             raise
 
     raise FileNotFoundError(f"Neither msgpack nor JSON file exists at {output_path}")
@@ -43,9 +43,9 @@ def save_msgpack_json(path, data):
     try:
         with open(path, "wb") as f:
             f.write(msgpack.packb(data, use_bin_type=True, default=_default_serializer))
-        logger.info(f"Data saved to msgpack in {(time.time() - start_time) * 1000}ms: {path}")
+        log.info(f"Data saved to msgpack in {(time.time() - start_time) * 1000}ms: {path}")
     except Exception as e:
-        logger.error(f"Failed to save to msgpack: {e}")
+        log.error(f"Failed to save to msgpack: {e}")
         raise
 
 def _default_serializer(obj):
diff --git a/script_generator/video/ffmpeg/commands.py b/script_generator/video/ffmpeg/commands.py
index 5aef79a0..b66488af 100644
--- a/script_generator/video/ffmpeg/commands.py
+++ b/script_generator/video/ffmpeg/commands.py
@@ -1,29 +1,30 @@
-from config import FFMPEG_PATH
+from script_generator.state.app_state import AppState
 from script_generator.video.ffmpeg.filters import get_video_filters
 from script_generator.video.ffmpeg.hwaccel import get_hwaccel_read_args
 from script_generator.video.info.video_info import get_cropped_dimensions, VideoInfo
 
 
-def get_ffmpeg_read_cmd(video: VideoInfo, video_reader: str, hwaccel: str, frame_start: int | None, output="-", disable_opengl=False):
+def get_ffmpeg_read_cmd(state: AppState, frame_start: int | None, output="-", disable_opengl=False):
+    video = state.video_info
     width, height = get_cropped_dimensions(video)
-    vf = get_video_filters(video, video_reader, hwaccel, width, height, disable_opengl)
+    vf = get_video_filters(video, state.video_reader, state.ffmpeg_hwaccel, width, height, disable_opengl)
     start_time = (frame_start / video.fps) * 1000
 
     # Get supported hardware acceleration backends
-    hwaccel_read = get_hwaccel_read_args(video, hwaccel)
+    hwaccel_read = get_hwaccel_read_args(video, state.ffmpeg_hwaccel)
 
     video_filter = ["-vf", vf] if vf else []
-    if hwaccel == "vaapi":
+    if state.ffmpeg_hwaccel == "vaapi":
         # VAAPI requires specific pixel formats and filters
         video_filter = ["-vf", f"{vf},format=nv12,hwupload"] if vf else ["-vf", "format=nv12,hwupload"]
 
-    if hwaccel == "cuda" and video.bit_depth == 8:
+    if state.ffmpeg_hwaccel == "cuda" and video.bit_depth == 8:
         video_filter = ["-noautoscale"] + video_filter  # explicitly tell ffmpeg that scaling is done by cuda
 
     frame_size = width * height * 3  # Size of one frame in bytes
 
     return [
-        FFMPEG_PATH,
+        state.ffmpeg_path,
         *hwaccel_read,
         '-nostats', '-loglevel', 'warning',
         "-ss", str(start_time / 1000),  # Seek to start time in seconds
diff --git a/script_generator/video/ffmpeg/filters.py b/script_generator/video/ffmpeg/filters.py
index b1a6b25c..15b8286b 100644
--- a/script_generator/video/ffmpeg/filters.py
+++ b/script_generator/video/ffmpeg/filters.py
@@ -1,4 +1,4 @@
-from config import RENDER_RESOLUTION, VR_TO_2D_PITCH
+from script_generator.constants import RENDER_RESOLUTION, VR_TO_2D_PITCH
 
 def get_video_filters(video, video_reader, hwaccel, width, height, disable_opengl=False):
     if video.is_vr:
diff --git a/script_generator/video/ffmpeg/hwaccel.py b/script_generator/video/ffmpeg/hwaccel.py
index 8c735c16..1cac19d4 100644
--- a/script_generator/video/ffmpeg/hwaccel.py
+++ b/script_generator/video/ffmpeg/hwaccel.py
@@ -1,7 +1,6 @@
 import subprocess
 
-from config import FFMPEG_PATH
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 
 
 def get_hwaccel_support():
@@ -10,8 +9,10 @@ def get_hwaccel_support():
     Returns a dictionary with supported backends.
     """
     try:
+        from script_generator.state.app_state import AppState
+        state = AppState()
         result = subprocess.run(
-            [FFMPEG_PATH, "-hwaccels"],
+            [state.ffmpeg_path, "-hwaccels"],
             stdout=subprocess.PIPE,
             stderr=subprocess.PIPE,
             text=True,
@@ -19,11 +20,11 @@ def get_hwaccel_support():
         )
         hwaccels = result.stdout.lower().replace("hardware acceleration methods:", "")
         hwaccel_lines = [line.strip() for line in str(hwaccels).splitlines() if line.strip()]
-        logger.info(f"hardware acceleration methods: {', '.join(hwaccel_lines)}")
+        log.info(f"hardware acceleration methods: {', '.join(hwaccel_lines)}")
 
         return hwaccel_lines
     except Exception as e:
-        logger.error(f"Error checking hardware acceleration support: {e}")
+        log.error(f"Error checking hardware acceleration support: {e}")
         return []
 
 def get_preferred_hwaccel():
diff --git a/script_generator/video/ffmpeg/video_reader.py b/script_generator/video/ffmpeg/video_reader.py
index 5101b3a0..b4bc97b4 100644
--- a/script_generator/video/ffmpeg/video_reader.py
+++ b/script_generator/video/ffmpeg/video_reader.py
@@ -3,7 +3,7 @@
 import cv2
 import imageio
 import numpy as np
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 from script_generator.state.app_state import AppState
 from script_generator.video.ffmpeg.commands import get_ffmpeg_read_cmd
 
@@ -27,13 +27,11 @@ def _start_process(self, start_frame=0):
             self.process.terminate()
 
         cmd, self.frame_size, self.width, self.height = get_ffmpeg_read_cmd(
-            self.state.video_info,
-            self.state.video_reader,
-            self.state.ffmpeg_hwaccel,
+            self.state,
             start_frame,
             disable_opengl=True
         )
-        logger.info(f"Starting FFmpeg reader with command: {' '.join(cmd)}")
+        log.info(f"Starting FFmpeg reader with command: {' '.join(cmd)}")
 
         self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
 
@@ -45,7 +43,7 @@ def read(self):
         try:
             in_bytes = self.process.stdout.read(self.frame_size)
             if not in_bytes:
-                logger.warn("FFmpeg video reader could not read frame / end of file")
+                log.warn("FFmpeg video reader could not read frame / end of file")
                 return False, None  # End of video
 
             frame = np.frombuffer(in_bytes, np.uint8).reshape((self.height, self.width, 3))
@@ -58,7 +56,7 @@ def read(self):
 
             return True, frame
         except Exception as e:
-            logger.error(f"Error reading frame: {e}")
+            log.error(f"Error reading frame: {e}")
             return False, None
 
     def set(self, prop_id, value):
@@ -67,7 +65,7 @@ def set(self, prop_id, value):
             self.start_frame = int(value)
             self._start_process(start_frame=self.start_frame)
         else:
-            logger.error(f"Unsupported property ID: {prop_id}")
+            log.error(f"Unsupported property ID: {prop_id}")
 
     def get(self, prop_id):
         """Get properties like FPS, width, height (mimics OpenCV's cap.get())."""
@@ -78,7 +76,7 @@ def get(self, prop_id):
             cv2.CAP_PROP_FRAME_COUNT: self.state.video_info.total_frames,
             cv2.CAP_PROP_POS_FRAMES: self.current_frame_number,
         }
-        return props.get(prop_id, logger.error(f"Unsupported property ID: {prop_id}"))
+        return props.get(prop_id, log.error(f"Unsupported property ID: {prop_id}"))
 
     def release(self):
         """Release resources and terminate the FFmpeg process."""
diff --git a/script_generator/video/info/video_info.py b/script_generator/video/info/video_info.py
index a5af04a7..4180baa1 100644
--- a/script_generator/video/info/video_info.py
+++ b/script_generator/video/info/video_info.py
@@ -3,9 +3,10 @@
 import subprocess
 from dataclasses import dataclass, field
 
-from config import RENDER_RESOLUTION, FFPROBE_PATH
+from script_generator.constants import RENDER_RESOLUTION
 from script_generator.debug.errors import FFProbeError
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
+
 
 @dataclass
 class VideoInfo:
@@ -61,7 +62,7 @@ def get_projection_and_fov_from_filename(filename):
             fov = pattern["fov"]
             is_fisheye = pattern["is_fisheye"]
             break
-    logger.info(f"Video Format: Projection={projection}, FOV={fov}, is_fisheye={is_fisheye}")
+    log.info(f"Video Format: Projection={projection}, FOV={fov}, is_fisheye={is_fisheye}")
 
     return {"projection": projection, "fov": fov, "is_fisheye": is_fisheye}
 
@@ -71,8 +72,13 @@ def get_cropped_dimensions(video: VideoInfo):
 
 def get_video_info(video_path):
     try:
+        from script_generator.state.app_state import AppState
+        state = AppState()
+        if not state.ffprobe_path:
+            return
+
         cmd = [
-            FFPROBE_PATH,
+            state.ffprobe_path,
             "-v", "error",
             "-select_streams", "v:0",
             "-show_entries", "stream=r_frame_rate,width,height,codec_name,nb_frames,pix_fmt",
@@ -117,18 +123,18 @@ def get_video_info(video_path):
         # Check if the video is VR (2:1 aspect ratio)
         is_vr = height == width // 2
 
-        logger.info(f"Video Info: {codec_name}, {width}x{height}, {fps:.2f} fps, {nb_frames} frames, {duration:.2f} sec, {bit_depth}-bit, is vr: {is_vr}")
+        log.info(f"Video Info: {codec_name}, {width}x{height}, {fps:.2f} fps, {nb_frames} frames, {duration:.2f} sec, {bit_depth}-bit, is vr: {is_vr}")
 
         if is_vr:
-            logger.info("Video Format: VR SBS - Based on its 2:1 ratio")
+            log.info("Video Format: VR SBS - Based on its 2:1 ratio")
         else:
-            logger.info("Video Format: 2D - Based on its ratio")
+            log.info("Video Format: 2D - Based on its ratio")
 
         return VideoInfo(video_path, codec_name, width, height, duration, int(nb_frames), fps, bit_depth, is_vr)
 
     except subprocess.CalledProcessError as e:
-        logger.error(f"FFProbe command failed: {e.output.decode('utf-8')}")
+        log.error(f"FFProbe command failed: {e.output.decode('utf-8')}")
         raise FFProbeError("FFProbe command execution failed.")
     except (ValueError, KeyError, IndexError) as e:
-        logger.error(f"Error parsing FFProbe output: {e}")
+        log.error(f"Error parsing FFProbe output: {e}")
         raise FFProbeError("Failed to parse FFProbe output.")
\ No newline at end of file
diff --git a/script_generator/video/util/ffmpeg.py b/script_generator/video/util/ffmpeg.py
new file mode 100644
index 00000000..fb9f6f9c
--- /dev/null
+++ b/script_generator/video/util/ffmpeg.py
@@ -0,0 +1,48 @@
+import os
+import shutil
+import platform
+
+from script_generator.utils.helpers import is_mac
+
+
+def get_ffmpeg_paths():
+    ffmpeg_path = shutil.which("ffmpeg")
+    ffprobe_path = shutil.which("ffprobe")
+
+    if ffmpeg_path and ffprobe_path:
+        return ffmpeg_path, ffprobe_path
+
+    common_paths = []
+
+    if platform.system() == "Windows":
+        common_paths = [
+            r"C:\Program Files\ffmpeg\bin\ffmpeg.exe",
+            r"C:\Program Files (x86)\ffmpeg\bin\ffmpeg.exe",
+            r"C:\ffmpeg\bin\ffmpeg.exe",
+            r"C:\Users\{user}\ffmpeg\bin\ffmpeg.exe".format(user=os.getlogin()),
+        ]
+
+    elif is_mac():
+        common_paths = [
+            "/usr/local/bin/ffmpeg",
+            "/opt/homebrew/bin/ffmpeg",
+            "/usr/bin/ffmpeg",
+            "/usr/local/ffmpeg/bin/ffmpeg",
+        ]
+
+    elif platform.system() == "Linux":
+        common_paths = [
+            "/usr/bin/ffmpeg",
+            "/usr/local/bin/ffmpeg",
+            "/snap/bin/ffmpeg",
+            "/var/lib/flatpak/exports/bin/ffmpeg",
+        ]
+
+    for path in common_paths:
+        if os.path.exists(path):
+            if "ffmpeg" in path and not ffmpeg_path:
+                ffmpeg_path = path
+            elif "ffprobe" in path and not ffprobe_path:
+                ffprobe_path = path
+
+    return ffmpeg_path, ffprobe_path
diff --git a/script_generator/video/workers/ffmpeg_worker.py b/script_generator/video/workers/ffmpeg_worker.py
index 1b583121..542deebe 100644
--- a/script_generator/video/workers/ffmpeg_worker.py
+++ b/script_generator/video/workers/ffmpeg_worker.py
@@ -5,7 +5,7 @@
 from script_generator.debug.errors import FFMpegError
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 from script_generator.tasks.tasks import AnalyzeFrameTask
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 from script_generator.video.ffmpeg.commands import get_ffmpeg_read_cmd
 
 
@@ -15,12 +15,10 @@ class VideoWorker(AbstractTaskProcessor):
 
     def task_logic(self):
         cmd, frame_size, width, height = get_ffmpeg_read_cmd(
-            self.state.video_info,
-            self.state.video_reader,
-            self.state.ffmpeg_hwaccel,
+            self.state,
             self.state.frame_start
         )
-        logger.info(f"FFMPEG executing command: {' '.join(cmd)}")
+        log.info(f"FFMPEG executing command: {' '.join(cmd)}")
 
         self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
         current_frame = self.state.frame_start
@@ -32,7 +30,7 @@ def task_logic(self):
                     if current_frame == self.state.frame_start:
                         raise FFMpegError(f"FFMPEG could not read any frames from stdout command: {' '.join(cmd)}")
                     else:
-                        logger.info("FFMPEG received last frame")
+                        log.info("FFMPEG received last frame")
                     break
 
                 task = AnalyzeFrameTask(frame_pos=current_frame)
@@ -49,7 +47,7 @@ def task_logic(self):
                 current_frame += 1
 
             except Exception as e:
-                logger.error(f"Error reading frame: {e}")
+                log.error(f"Error reading frame: {e}")
                 raise
 
         self.stop_process()
diff --git a/script_generator/video/workers/vr_to_2d_worker.py b/script_generator/video/workers/vr_to_2d_worker.py
index 31bc1ebb..6abb9826 100644
--- a/script_generator/video/workers/vr_to_2d_worker.py
+++ b/script_generator/video/workers/vr_to_2d_worker.py
@@ -3,7 +3,7 @@
 from OpenGL.GL import *
 from OpenGL.GLU import *
 
-from config import RENDER_RESOLUTION, VR_TO_2D_PITCH
+from script_generator.constants import RENDER_RESOLUTION, VR_TO_2D_PITCH
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 from script_generator.video.opengl.helpers import create_180_dome, render_dome
 
diff --git a/tests/test_compare_models_and_resolutions.py b/tests/test_compare_models_and_resolutions.py
index 781c7a44..4b1967ff 100644
--- a/tests/test_compare_models_and_resolutions.py
+++ b/tests/test_compare_models_and_resolutions.py
@@ -1,7 +1,7 @@
 import cv2
 from PIL import Image
 
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 from ultralytics import YOLO
 
 from script_generator.video.ffmpeg.video_reader import VideoReaderFFmpeg
@@ -18,7 +18,7 @@ def save_frames_as_gif(frames, output_path, duration=100, loop=0):
                   loop=loop,
                   optimize=True)
 
-    logger.info(f"GIF saved to {output_path}")
+    log.info(f"GIF saved to {output_path}")
 
 
 # Example usage
diff --git a/tests/test_generate_funscript.py b/tests/test_generate_funscript.py
index 5acff925..a7cc4a88 100644
--- a/tests/test_generate_funscript.py
+++ b/tests/test_generate_funscript.py
@@ -5,7 +5,7 @@
 
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
-app_state = AppState(is_cli=True)
+app_state = AppState()
 # app_state.video_path = "C:/cvr/funscript-generator/test_short.mp4"
 # app_state.video_path = "C:/cvr/funscript-generator/test_extra_short.mp4"
 # app_state.video_path = "C:/cvr/funscript-generator/test_medium.mp4"
diff --git a/tests/test_pose_estimation.py b/tests/test_pose_estimation.py
index a0c5ee40..d611b160 100644
--- a/tests/test_pose_estimation.py
+++ b/tests/test_pose_estimation.py
@@ -3,7 +3,7 @@
 import cv2
 from collections import defaultdict
 from script_generator.constants import CLASS_REVERSE_MATCH
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 
 #video_path = "/Users/k00gar/Downloads/Katrina Jade [ADD] Oct 16, 2024R_6kvr265_reenc.mp4"
 #video_path = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
@@ -110,7 +110,7 @@
                 hand_counter += 1
 
         if hand_counter < 2:
-            logger.warning(f"WARNING: {hand_counter} hand detected")
+            log.warning(f"WARNING: {hand_counter} hand detected")
 
         frame = np.hstack((frame_1, frame_2))
 
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 229d445d..179df1e1 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -11,9 +11,9 @@
 from scipy.signal import savgol_filter
 from simplification.cutil import simplify_coords
 
-from config import STEP_SIZE, VERSION
+from script_generator.constants import STEP_SIZE, VERSION
 from script_generator.constants import HEATMAP_COLORS, FUNSCRIPT_AUTHOR
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 from script_generator.state.app_state import AppState
 from script_generator.utils.file import get_output_file_path
 from script_generator.utils.json_utils import load_json_from_file
@@ -32,26 +32,26 @@ def generate(self, state):
 
         raw_funscript_path, _ = get_output_file_path(state.video_path, "_rawfunscript.json")
         if os.path.exists(raw_funscript_path) and (state.funscript_data is None or len(state.funscript_data) == 0):
-            logger.info("len funscript data is 0, trying to load file")
+            log.info("len funscript data is 0, trying to load file")
             # Read the funscript data from the JSON file
             with open(raw_funscript_path, 'r') as f:
-                logger.info(f"Loading funscript from {raw_funscript_path}")
+                log.info(f"Loading funscript from {raw_funscript_path}")
                 try:
                     data = json.load(f)
                 except Exception as e:
-                    logger.error(f"Error loading funscript from {raw_funscript_path}: {e}")
+                    log.error(f"Error loading funscript from {raw_funscript_path}: {e}")
                     return
         else:
             data = state.funscript_data
 
         try:
-            logger.info(f"Generating funscript based on {len(data)} points...")
+            log.info(f"Generating funscript based on {len(data)} points...")
 
             # Extract timestamps and positions
             ats = [p[0] for p in data]
             positions = [p[1] for p in data]
 
-            logger.info(f"Positions adjustment - step 1 (noise removal)")
+            log.info(f"Positions adjustment - step 1 (noise removal)")
             # Run the Savitzky-Golay filter
             positions = savgol_filter(positions, int(state.video_info.fps) // 4, 3)
 
@@ -60,33 +60,33 @@ def generate(self, state):
 
             # Apply VW simplification if enabled
             if state.vw_simplification_enabled:
-                logger.info("Positions adjustment - step 2 (VW algorithm simplification)")
+                log.info("Positions adjustment - step 2 (VW algorithm simplification)")
                 zip_positions = simplify_coords(zip_positions, state.vw_factor)
-                logger.info(f"Length of VW filtered positions: {len(zip_positions)}")
+                log.info(f"Length of VW filtered positions: {len(zip_positions)}")
             else:
-                logger.info("Skipping positions adjustment - step 2 (VW algorithm simplification)")
+                log.info("Skipping positions adjustment - step 2 (VW algorithm simplification)")
 
             # Extract timestamps and positions
             ats = [p[0] for p in zip_positions]
             positions = [p[1] for p in zip_positions]
 
             # Remap positions to 0-100 range
-            logger.info("Positions adjustment - step 3 (remapping)")
+            log.info("Positions adjustment - step 3 (remapping)")
             adjusted_positions = np.interp(positions, (min(positions), max(positions)), (0, 100))
 
             # Apply thresholding
             if state.threshold_enabled:
-                logger.info(f"Positions adjustment - step 4 (thresholding)")
+                log.info(f"Positions adjustment - step 4 (thresholding)")
                 adjusted_positions = adjusted_positions.tolist()  # Convert to list
                 adjusted_positions = [
                     0 if p < state.threshold_low else 100 if p > state.threshold_high else p for p in
                     adjusted_positions]
             else:
-                logger.info("Skipping positions adjustment - step 4 (thresholding)")
+                log.info("Skipping positions adjustment - step 4 (thresholding)")
 
             # Apply amplitude boosting
             if state.boost_enabled:
-                logger.info("Positions adjustment - step 5 (amplitude boosting)")
+                log.info("Positions adjustment - step 5 (amplitude boosting)")
                 # self.boost_amplitude(adjusted_positions, boost_factor=1.2, min_value=0, max_value=100)
                 adjusted_positions = self.adjust_peaks_and_lows(
                     adjusted_positions,
@@ -94,53 +94,53 @@ def generate(self, state):
                     low_reduction=state.boost_down_percent
                 )
             else:
-                logger.info("Skipping positions adjustment - step 5 (amplitude boosting)")
+                log.info("Skipping positions adjustment - step 5 (amplitude boosting)")
 
             # Round position values to the closest multiple of 5, still between 0 and 100
             if state.vw_simplification_enabled:
-                logger.info(
+                log.info(
                     f"Positions adjustment - step 6 (rounding to the closest multiple of {state.rounding})")
                 adjusted_positions = [round(p / state.rounding) * state.rounding for p in
                                       adjusted_positions]
 
             else:
-                logger.info(
+                log.info(
                     f"Skipping positions adjustment - step 6 (rounding to the closest multiple of {state.rounding})")
 
             # Recombine timestamps and adjusted positions
-            logger.info("Re-assembling ats and positions")
+            log.info("Re-assembling ats and positions")
             zip_adjusted_positions = list(zip(ats, adjusted_positions))
 
             # Write the final funscript
             self.write_funscript(zip_adjusted_positions, output_path, state.video_info.fps)
 
             # copy funscript if specified
-            if state.copy_funscript_to_movie_dir or state.final_funscript_destination:
+            if state.copy_funscript_to_movie_dir or state.funscript_output_dir:
                 copy = True
                 video_folder = os.path.dirname(state.video_path)
                 filename_base = os.path.basename(state.video_path)[:-4]
-                funscript_path = os.path.join(state.final_funscript_destination, f"{filename_base}.funscript") if state.final_funscript_destination else os.path.join(video_folder, f"{filename_base}.funscript")
+                funscript_path = os.path.join(state.funscript_output_dir, f"{filename_base}.funscript") if state.funscript_output_dir else os.path.join(video_folder, f"{filename_base}.funscript")
 
                 # Backup output file if it exists
                 if os.path.exists(funscript_path):
                     json_data = load_json_from_file(funscript_path)
                     if "author" in json_data and json_data["author"] == FUNSCRIPT_AUTHOR:
                         backup_path = os.path.join(video_folder, f"{filename_base}_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.funscript.bak")
-                        logger.info(f"Funscript {funscript_path} already exists, backing up as {backup_path}...")
+                        log.info(f"Funscript {funscript_path} already exists, backing up as {backup_path}...")
                         os.rename(funscript_path, backup_path)
                     else:
                         copy = False
-                        logger.warn(f"Skipping copying funscript to movie directory as the script in the destination directory is not made by this app.")
+                        log.warn(f"Skipping copying funscript to movie directory as the script in the destination directory is not made by this app.")
 
                 if copy:
                     shutil.copy(output_path, funscript_path)
 
-            logger.info(f"Funscript generated and saved to {output_path}")
+            log.info(f"Funscript generated and saved to {output_path}")
 
             # Generate a heatmap
             self.generate_heatmap(output_path, output_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
         except Exception as e:
-            logger.error(f"Error generating funscript: {e}")
+            log.error(f"Error generating funscript: {e}")
             raise
 
     # TODO replace with proper JSON serialization
@@ -161,7 +161,7 @@ def generate_heatmap(self, funscript_path, output_image_path):
             # Load funscript data
             times, positions, _, _ = self.load_funscript(funscript_path)
             if not times or not positions:
-                logger.info("Failed to load funscript data.")
+                log.info("Failed to load funscript data.")
                 return
 
             # add a timing: 0, position: 100 at the beginning if no value for 0
@@ -172,8 +172,8 @@ def generate_heatmap(self, funscript_path, output_image_path):
             # Print loaded data for debugging
             # logger.debug(f"Times: {times}")
             # logger.debug(f"Positions: {positions}")
-            logger.info(f"Total Actions: {len(times)}")
-            logger.info(f"Time Range: {times[0]} to {datetime.timedelta(seconds=int(times[-1] / 1000))}")
+            log.info(f"Total Actions: {len(times)}")
+            log.info(f"Time Range: {times[0]} to {datetime.timedelta(seconds=int(times[-1] / 1000))}")
 
             # Calculate speed (position change per time interval)
             times = np.array(times)
@@ -186,7 +186,7 @@ def generate_heatmap(self, funscript_path, output_image_path):
             # We add 1e-10 to prevent dividing by zero
             speeds = np.abs(np.diff(filtered_positions) / (np.diff(filtered_times) + 1e-10)) * 1000  # Positions per second
 
-            logger.debug(f"Speeds: {speeds}")
+            log.debug(f"Speeds: {speeds}")
 
             def get_color(intensity):
                 if intensity <= 0:
@@ -248,9 +248,9 @@ def get_color(intensity):
             # Save the figure
             plt.savefig(output_image_path, bbox_inches='tight', dpi=200)  # Increase resolution
             plt.close()
-            logger.info(f"Funscript heatmap saved to {output_image_path}")
+            log.info(f"Funscript heatmap saved to {output_image_path}")
         except Exception as e:
-            logger.error(f"Error generating heatmap for funscript: {e}")
+            log.error(f"Error generating heatmap for funscript: {e}")
 
     def boost_amplitude(self, signal, boost_factor, min_value=0, max_value=100):
         """
@@ -345,19 +345,19 @@ def filter_positions(self, positions, fps):
     def load_funscript(self, funscript_path):
         # if the funscript path exists
         if not os.path.exists(funscript_path):
-            logger.error(f"Funscript not found at {funscript_path}")
+            log.error(f"Funscript not found at {funscript_path}")
             return None, None, None, None
 
         with open(funscript_path, 'r') as f:
             try:
-                logger.info(f"Loading funscript from {funscript_path}")
+                log.info(f"Loading funscript from {funscript_path}")
                 data = json.load(f)
             except json.JSONDecodeError as e:
-                logger.error(f"JSONDecodeError: {e}")
-                logger.error(f"Error occurred at line {e.lineno}, column {e.colno}")
-                logger.error("Dumping the problematic JSON data:")
+                log.error(f"JSONDecodeError: {e}")
+                log.error(f"Error occurred at line {e.lineno}, column {e.colno}")
+                log.error("Dumping the problematic JSON data:")
                 f.seek(0)  # Reset file pointer to the beginning
-                logger.error(f.read())
+                log.error(f.read())
                 return None, None, None, None
 
         times = []
@@ -366,7 +366,7 @@ def load_funscript(self, funscript_path):
         for action in data['actions']:
             times.append(action['at'])
             positions.append(action['pos'])
-        logger.info(f"Loaded funscript with {len(times)} actions")
+        log.info(f"Loaded funscript with {len(times)} actions")
 
         # Access the chapters
         chapters = data.get("metadata", {}).get("chapters", [])
@@ -379,7 +379,7 @@ def load_funscript(self, funscript_path):
                 chapter['startTime'] = chapter['startTime'][:8]
             if len(chapter['endTime']) > 8:
                 chapter['endTime'] = chapter['endTime'][:8]
-            logger.info(f"Chapter: {chapter['name']}, Start: {chapter['startTime']}, End: {chapter['endTime']}")
+            log.info(f"Chapter: {chapter['name']}, Start: {chapter['startTime']}, End: {chapter['endTime']}")
             # convert 00:00:00 to milliseconds
             startTime_ms = int(chapter['startTime'].split(':')[0]) * 60 * 60 * 1000 + int(
                 chapter['startTime'].split(':')[1]) * 60 * 1000 + int(chapter['startTime'].split(':')[2]) * 1000
@@ -511,7 +511,7 @@ def create_combined_plot(self, state: AppState, ref_sections, gen_sections, scre
 
         # TODO why is this empty sometimes? and result in errors
         if not gen_times or not gen_positions:
-            logger.error("Could not created combined plot")
+            log.error("Could not created combined plot")
             return
 
         # Create a flexible grid layout
@@ -616,7 +616,7 @@ def generate_heatmap_inline(self, ax, times, positions, state):
             if times[i] == times[i - 1]:
                 times.pop(i)
                 positions.pop(i)
-                logger.info(f"Removed duplicate time value {times[i]}")
+                log.info(f"Removed duplicate time value {times[i]}")
                 break
 
         # Calculate speed (position change per time interval)
diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index 47ea86f0..f5a1c578 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -3,7 +3,7 @@
 import numpy as np
 
 from script_generator.constants import CLASS_NAMES
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 
 
 class LockedPenisBox:
@@ -257,7 +257,7 @@ def calculate_distance(self, penis_box, other_box):
         if other_box is None:
             return None
         if penis_box is None:
-            logger.warning(f"Penis box is None for frame {self.current_frame_id}, cannot compute distance")
+            log.warning(f"Penis box is None for frame {self.current_frame_id}, cannot compute distance")
             return None
         ox1, oy1, ox2, oy2 = other_box
         y_pos = (oy1 + 2 * oy2) // 3
@@ -277,7 +277,7 @@ def detect_sex_position_change(self, sex_position, reason):
         position_counts = {position: self.sex_position_history.count(position) for position in self.sex_position_history}
         most_frequent_position = max(position_counts, key=position_counts.get, default="Not relevant")
         if most_frequent_position != self.sex_position:
-            logger.info(f"@{self.current_frame_id} - Sex position switched to: {most_frequent_position}")
+            log.info(f"@{self.current_frame_id} - Sex position switched to: {most_frequent_position}")
             self.sex_position = most_frequent_position
             self.sex_position_reason = reason
 
@@ -336,7 +336,7 @@ def tracking_logic(self, state, sorted_boxes):
                 and not self.penetration:  # equivalent to 3 seconds
             if self.locked_penis_box.active:  # is_active():
                 self.locked_penis_box.deactivate()
-                logger.info(f"@{self.current_frame_id} - Deactivated locked_penis_box")
+                log.info(f"@{self.current_frame_id} - Deactivated locked_penis_box")
 
         if self.locked_penis_box.is_active() and self.locked_penis_box.visible < 100 and not self.glans_detected:
             self.penetration = True
@@ -367,7 +367,7 @@ def tracking_logic(self, state, sorted_boxes):
                             close_up_detected = True
                             self.detect_sex_position_change('Close up', 'Butt box size beyond threshold')
                             if not self.close_up:
-                                logger.info(
+                                log.info(
                                     f"@{self.current_frame_id} - Close up detected - butt size beyond threshold: {int((butt_box_area / self.image_area) * 100)}%"
                                 )
                                 self.close_up = True
@@ -381,7 +381,7 @@ def tracking_logic(self, state, sorted_boxes):
                             close_up_detected = True
                             self.detect_sex_position_change('Close up', 'No penis detected')
                             if not self.close_up:
-                                logger.info(f"@{self.current_frame_id} - Close up detected - no penis detected")
+                                log.info(f"@{self.current_frame_id} - Close up detected - no penis detected")
                                 self.close_up = True
                             self.penetration = False
                             distance = 100
@@ -391,7 +391,7 @@ def tracking_logic(self, state, sorted_boxes):
                     close_up_detected = True
                     self.detect_sex_position_change('Close up', 'No penis detected')
                     if not self.close_up:
-                        logger.info(f"@{self.current_frame_id} - Close up detected - no penis detected")
+                        log.info(f"@{self.current_frame_id} - Close up detected - no penis detected")
                         self.close_up = True
                     self.penetration = False
                     distance = 100
@@ -559,7 +559,7 @@ def handle_class_first(self, class_name, box, conf):
         """
         if class_name == 'penis':
             if box is not None and self.penis_box is None:
-                logger.info(f"@{self.current_frame_id} - Penis detected with confidence {conf}")
+                log.info(f"@{self.current_frame_id} - Penis detected with confidence {conf}")
             self.penis_box = box
 
             if self.penis_box:
diff --git a/utils/lib_SceneCutsDetect.py b/utils/lib_SceneCutsDetect.py
index 4188d02a..c428423e 100644
--- a/utils/lib_SceneCutsDetect.py
+++ b/utils/lib_SceneCutsDetect.py
@@ -3,10 +3,10 @@
 import cv2
 from tqdm import tqdm
 
-from config import UPDATE_PROGRESS_INTERVAL
+from script_generator.constants import UPDATE_PROGRESS_INTERVAL
 from script_generator.gui.messages.messages import ProgressMessage
 from script_generator.state.app_state import AppState
-from script_generator.debug.logger import logger
+from script_generator.debug.logger import log
 
 # TODO Re-implement? (implement in object detection pipeline?)
 # def compute_histogram(frame):

From aad2a994afb42e3f66c92d14e41719895b8d601b Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sat, 1 Feb 2025 10:02:13 +0100
Subject: [PATCH 199/277] Decreased tilt to 21 degrees Compatibility for h264
 videos with bigger than 4096 widths (removed scale cuda for those)

---
 script_generator/constants.py             |  2 +-
 script_generator/video/ffmpeg/commands.py |  5 ++---
 script_generator/video/ffmpeg/filters.py  |  6 ++++--
 script_generator/video/ffmpeg/hwaccel.py  | 12 +++++++++++-
 4 files changed, 18 insertions(+), 7 deletions(-)

diff --git a/script_generator/constants.py b/script_generator/constants.py
index b7f4f393..9f5bd905 100644
--- a/script_generator/constants.py
+++ b/script_generator/constants.py
@@ -20,7 +20,7 @@
 ##################################################################################################
 
 YOLO_CONF = 0.3
-VR_TO_2D_PITCH = -25  # The dataset is trained on -25
+VR_TO_2D_PITCH = -21  # The dataset is trained on -25
 UPDATE_PROGRESS_INTERVAL = 0.2  # Updates progress in the console and in gui
 STEP_SIZE = 120  # Define custom colormap based on Lucife's heatmapColors | Speed step size for color transitions
 QUEUE_MAXSIZE = 100  # Bounded queue size to avoid memory blow-up as raw frames consume a lot of memory, does not increase performance
diff --git a/script_generator/video/ffmpeg/commands.py b/script_generator/video/ffmpeg/commands.py
index b66488af..adaacbac 100644
--- a/script_generator/video/ffmpeg/commands.py
+++ b/script_generator/video/ffmpeg/commands.py
@@ -1,9 +1,8 @@
 from script_generator.state.app_state import AppState
 from script_generator.video.ffmpeg.filters import get_video_filters
-from script_generator.video.ffmpeg.hwaccel import get_hwaccel_read_args
+from script_generator.video.ffmpeg.hwaccel import get_hwaccel_read_args, supports_cuda_scale
 from script_generator.video.info.video_info import get_cropped_dimensions, VideoInfo
 
-
 def get_ffmpeg_read_cmd(state: AppState, frame_start: int | None, output="-", disable_opengl=False):
     video = state.video_info
     width, height = get_cropped_dimensions(video)
@@ -18,7 +17,7 @@ def get_ffmpeg_read_cmd(state: AppState, frame_start: int | None, output="-", di
         # VAAPI requires specific pixel formats and filters
         video_filter = ["-vf", f"{vf},format=nv12,hwupload"] if vf else ["-vf", "format=nv12,hwupload"]
 
-    if state.ffmpeg_hwaccel == "cuda" and video.bit_depth == 8:
+    if supports_cuda_scale(video, state.ffmpeg_hwaccel):
         video_filter = ["-noautoscale"] + video_filter  # explicitly tell ffmpeg that scaling is done by cuda
 
     frame_size = width * height * 3  # Size of one frame in bytes
diff --git a/script_generator/video/ffmpeg/filters.py b/script_generator/video/ffmpeg/filters.py
index 15b8286b..05051f19 100644
--- a/script_generator/video/ffmpeg/filters.py
+++ b/script_generator/video/ffmpeg/filters.py
@@ -1,4 +1,6 @@
 from script_generator.constants import RENDER_RESOLUTION, VR_TO_2D_PITCH
+from script_generator.video.ffmpeg.hwaccel import supports_cuda_scale
+
 
 def get_video_filters(video, video_reader, hwaccel, width, height, disable_opengl=False):
     if video.is_vr:
@@ -15,7 +17,7 @@ def get_vr_video_filters(video, video_reader, hwaccel, disable_opengl=False):
     cuda = hwaccel == "cuda"
 
     # hardware accelerated output is not supported with > 8 bit
-    scale = f"[0:0]scale_cuda={RENDER_RESOLUTION * 2}:-2,hwdownload" if cuda and video.bit_depth == 8 else f"[0:0]scale={RENDER_RESOLUTION * 2}:-2"
+    scale = f"[0:0]scale_cuda={RENDER_RESOLUTION * 2}:-2,hwdownload" if supports_cuda_scale(video, hwaccel) else f"[0:0]scale={RENDER_RESOLUTION * 2}:-2"
     crop = f"crop={RENDER_RESOLUTION}:{RENDER_RESOLUTION}:0:0"
     out_format = f"format=nv12," if cuda else ""
 
@@ -45,6 +47,6 @@ def get_2d_video_filters(video, width, height, hwaccel):
         scale_width = int(video.width * (height / video.height))
         crop = f",crop={width}:{height}:(iw-{width})/2:0"
         # hardware accelerated output is not supported with > 8 bit
-        return f"[0:0]scale_cuda={scale_width}:{height},hwdownload,format=nv12{crop}" if cuda and video.bit_depth == 8 else f"[0:0]scale={scale_width}:{height}{crop}"
+        return f"[0:0]scale_cuda={scale_width}:{height},hwdownload,format=nv12{crop}" if supports_cuda_scale(video, hwaccel) else f"[0:0]scale={scale_width}:{height}{crop}"
     else:
         return "[0:0]hwdownload,format=nv12" if cuda else ""
\ No newline at end of file
diff --git a/script_generator/video/ffmpeg/hwaccel.py b/script_generator/video/ffmpeg/hwaccel.py
index 1cac19d4..4f706cd4 100644
--- a/script_generator/video/ffmpeg/hwaccel.py
+++ b/script_generator/video/ffmpeg/hwaccel.py
@@ -27,6 +27,7 @@ def get_hwaccel_support():
         log.error(f"Error checking hardware acceleration support: {e}")
         return []
 
+
 def get_preferred_hwaccel():
     hwaccel_support = get_hwaccel_support()
     hwaccel_priority = ["cuda", "vaapi", "amf", "videotoolbox", "qsv", "d3d11va"]
@@ -35,6 +36,7 @@ def get_preferred_hwaccel():
             return hw
     return None
 
+
 def get_hwaccel_read_args(video_info, hwaccel):
     if hwaccel == "cuda":
         # hardware accelerated output is not supported with > 8 bit
@@ -52,4 +54,12 @@ def get_hwaccel_read_args(video_info, hwaccel):
         return ["-hwaccel", "qsv"]
     if hwaccel == "d3d11va":
         return ["-hwaccel", "d3d11va"]
-    return []
\ No newline at end of file
+    return []
+
+
+def supports_cuda_scale(video, ffmpeg_hwaccel):
+    return (
+            ffmpeg_hwaccel == "cuda"
+            and video.bit_depth == 8
+            and (video.codec_name != "h264" or (video.width <= 4096 and video.height <= 4096))
+    )

From fffc5176f8518c92bc424df321b6fe87ccd8d450 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sat, 1 Feb 2025 10:12:48 +0100
Subject: [PATCH 200/277] Added better ffmpeg error logging added lognodes

---
 script_generator/debug/logger.py              |  4 ++-
 script_generator/video/ffmpeg/hwaccel.py      |  6 ++---
 .../video/workers/ffmpeg_worker.py            | 25 +++++++++++--------
 3 files changed, 20 insertions(+), 15 deletions(-)

diff --git a/script_generator/debug/logger.py b/script_generator/debug/logger.py
index b3f09aae..f53a9bd9 100644
--- a/script_generator/debug/logger.py
+++ b/script_generator/debug/logger.py
@@ -37,7 +37,9 @@ def emit(self, record):
 
 log = logging.getLogger("Main")
 log_od = logging.getLogger("ObjectDetection")
-log_tr = logging.getLogger("ObjectDetection")
+log_tr = logging.getLogger("Tracking")
+log_vid = logging.getLogger("Video")
+log_fun = logging.getLogger("Funscript")
 
 
 def set_log_level(level: LogLevel):
diff --git a/script_generator/video/ffmpeg/hwaccel.py b/script_generator/video/ffmpeg/hwaccel.py
index 4f706cd4..848a3a1d 100644
--- a/script_generator/video/ffmpeg/hwaccel.py
+++ b/script_generator/video/ffmpeg/hwaccel.py
@@ -59,7 +59,7 @@ def get_hwaccel_read_args(video_info, hwaccel):
 
 def supports_cuda_scale(video, ffmpeg_hwaccel):
     return (
-            ffmpeg_hwaccel == "cuda"
-            and video.bit_depth == 8
-            and (video.codec_name != "h264" or (video.width <= 4096 and video.height <= 4096))
+        ffmpeg_hwaccel == "cuda"
+        and video.bit_depth == 8
+        and (video.codec_name != "h264" or (video.width <= 4096 and video.height <= 4096))
     )
diff --git a/script_generator/video/workers/ffmpeg_worker.py b/script_generator/video/workers/ffmpeg_worker.py
index 542deebe..e8efc742 100644
--- a/script_generator/video/workers/ffmpeg_worker.py
+++ b/script_generator/video/workers/ffmpeg_worker.py
@@ -3,9 +3,9 @@
 import numpy as np
 
 from script_generator.debug.errors import FFMpegError
+from script_generator.debug.logger import log, log_vid
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 from script_generator.tasks.tasks import AnalyzeFrameTask
-from script_generator.debug.logger import log
 from script_generator.video.ffmpeg.commands import get_ffmpeg_read_cmd
 
 
@@ -18,19 +18,21 @@ def task_logic(self):
             self.state,
             self.state.frame_start
         )
-        log.info(f"FFMPEG executing command: {' '.join(cmd)}")
+        log_vid.info(f"FFMPEG executing command: {' '.join(cmd)}")
 
-        self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
+        self.process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
         current_frame = self.state.frame_start
 
-        while True:
-            try:
+        try:
+            while True:
                 in_bytes = self.process.stdout.read(frame_size)
                 if not in_bytes:
                     if current_frame == self.state.frame_start:
-                        raise FFMpegError(f"FFMPEG could not read any frames from stdout command: {' '.join(cmd)}")
+                        error_output = self.process.stderr.read().decode('utf-8', errors='replace')
+                        log_vid.error(f"FFMPEG could not read frames from this video\nFFMPEG command:\n{' '.join(cmd)}\nFFMPEG ERROR:\n{error_output}")
+                        raise FFMpegError(f"FFMPEG could not read frames from this video. See the log for details.")
                     else:
-                        log.info("FFMPEG received last frame")
+                        log_vid.info("FFMPEG received last frame")
                     break
 
                 task = AnalyzeFrameTask(frame_pos=current_frame)
@@ -46,11 +48,12 @@ def task_logic(self):
                 self.finish_task(task)
                 current_frame += 1
 
-            except Exception as e:
-                log.error(f"Error reading frame: {e}")
-                raise
+        except Exception as e:
+            log_vid.error(f"Error reading frame: {e}")
+            raise
 
-        self.stop_process()
+        finally:
+            self.stop_process()
 
     # TODO ffmpeg interrupt
     # def release(self):

From ca7ed8b93575bcc6f25b1e9a5d2dbf3579db58f7 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sat, 1 Feb 2025 10:48:33 +0100
Subject: [PATCH 201/277] FFMPEG uses filename fov now Added ffmpeg release
 command

---
 script_generator/video/ffmpeg/filters.py        |  5 +++--
 script_generator/video/workers/ffmpeg_worker.py | 16 +++++++++++-----
 2 files changed, 14 insertions(+), 7 deletions(-)

diff --git a/script_generator/video/ffmpeg/filters.py b/script_generator/video/ffmpeg/filters.py
index 05051f19..bfbc58f2 100644
--- a/script_generator/video/ffmpeg/filters.py
+++ b/script_generator/video/ffmpeg/filters.py
@@ -9,10 +9,11 @@ def get_video_filters(video, video_reader, hwaccel, width, height, disable_openg
         return get_2d_video_filters(video, width, height, hwaccel)
 
 def get_vr_video_filters(video, video_reader, hwaccel, disable_opengl=False):
+    fov = int(video.fov * 1)
     if video.is_fisheye:
-        projection, iv_fov, ih_fov, v_fov, h_fov, d_fov = "fisheye", 190, 190, 90, 90, 180
+        projection, iv_fov, ih_fov, v_fov, h_fov, d_fov = "fisheye", fov, fov, 90, 90, fov
     else:
-        projection, iv_fov, ih_fov, v_fov, h_fov, d_fov = "he", 180, 180, 90, 90, 180
+        projection, iv_fov, ih_fov, v_fov, h_fov, d_fov = "he", fov, fov, 90, 90, fov
 
     cuda = hwaccel == "cuda"
 
diff --git a/script_generator/video/workers/ffmpeg_worker.py b/script_generator/video/workers/ffmpeg_worker.py
index e8efc742..e698a5c7 100644
--- a/script_generator/video/workers/ffmpeg_worker.py
+++ b/script_generator/video/workers/ffmpeg_worker.py
@@ -54,9 +54,15 @@ def task_logic(self):
 
         finally:
             self.stop_process()
+            self.release()
 
-    # TODO ffmpeg interrupt
-    # def release(self):
-    #     if self.process:
-    #         self.process.stdout.close()
-    #         self.process = None
+    def release(self):
+        if self.process:
+            self.process.terminate()
+            try:
+                self.process.wait(timeout=1)
+            except subprocess.TimeoutExpired:
+                self.process.kill()
+            self.process.stdout.close()
+            self.process.stderr.close()
+            self.process = None

From 0646ee1609c16b6380d15d6146874cc0d0295b21 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sat, 1 Feb 2025 10:51:51 +0100
Subject: [PATCH 202/277] Changed log levels for debug logs

---
 script_generator/object_detection/analyze_tracking_results.py | 2 +-
 utils/lib_ObjectTracker.py                                    | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index 7524e904..a3c20334 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -176,7 +176,7 @@ def analyze_tracking_results(state: AppState, results):
                     state.offset_x
                 )
             else:
-                log.info("No active locked penis box to draw.")
+                log.debug("No active locked penis box to draw.")
 
             if tracker.glans_detected:
                 frame = OverlayWidgets.draw_bounding_box(
diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index f5a1c578..691b654c 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -277,7 +277,7 @@ def detect_sex_position_change(self, sex_position, reason):
         position_counts = {position: self.sex_position_history.count(position) for position in self.sex_position_history}
         most_frequent_position = max(position_counts, key=position_counts.get, default="Not relevant")
         if most_frequent_position != self.sex_position:
-            log.info(f"@{self.current_frame_id} - Sex position switched to: {most_frequent_position}")
+            log.debug(f"@{self.current_frame_id} - Sex position switched to: {most_frequent_position}")
             self.sex_position = most_frequent_position
             self.sex_position_reason = reason
 
@@ -559,7 +559,7 @@ def handle_class_first(self, class_name, box, conf):
         """
         if class_name == 'penis':
             if box is not None and self.penis_box is None:
-                log.info(f"@{self.current_frame_id} - Penis detected with confidence {conf}")
+                log.debug(f"@{self.current_frame_id} - Penis detected with confidence {conf}")
             self.penis_box = box
 
             if self.penis_box:

From 3e44db642bdcaa81b38cf45f336a30c4782e83b5 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sat, 1 Feb 2025 11:00:50 +0100
Subject: [PATCH 203/277] Added a user message when ffprobe fails

---
 script_generator/gui/app.py               |  1 +
 script_generator/state/app_state.py       | 10 ++++++++--
 script_generator/video/info/video_info.py |  5 +++--
 3 files changed, 12 insertions(+), 4 deletions(-)

diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index 3f5a1c01..e4a51c8c 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -44,6 +44,7 @@ def __init__(self):
         self.config(menu=menu_bar)
 
         self.state = AppState()
+        self.state.set_is_cli(False)
 
         # Dictionary to store pages
         self.frames = {}
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index 454e5227..18f62368 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -1,6 +1,7 @@
 import math
 import os.path
 import string
+from tkinter import messagebox
 from typing import Literal, Optional
 
 from script_generator.config.config_manager import ConfigManager
@@ -27,6 +28,7 @@ def __init__(self):
             return
         self._initialized = True
 
+        self.is_cli = True
         self.config_manager = ConfigManager(self)
         c = self.config_manager
 
@@ -85,6 +87,9 @@ def __init__(self):
         self.ffmpeg_hwaccel = get_preferred_hwaccel() if self.ffmpeg_path else None
         self.yolo_model = load_yolo_model(self.yolo_model_path)
 
+    def set_is_cli(self, cli):
+        self.is_cli = cli
+
     def is_configured(self):
         message_prefix = "Cannot process the video."
         checks = [
@@ -115,8 +120,9 @@ def set_video_info(self):
                         self.has_raw_yolo, _, _ = get_raw_yolo_file_info(self)
                         self.has_tracking_data, _, _ = get_metrics_file_info(self)
                         self.max_preview_fps = math.ceil(self.video_info.fps)
-                    except:
-                        log.warn(f"FFprobe failed for path: {self.video_path}")
+                    except Exception as e:
+                        if not self.is_cli:
+                            messagebox.showerror("Error", e)
                     finally:
                         return
 
diff --git a/script_generator/video/info/video_info.py b/script_generator/video/info/video_info.py
index 4180baa1..a33d33e0 100644
--- a/script_generator/video/info/video_info.py
+++ b/script_generator/video/info/video_info.py
@@ -5,7 +5,7 @@
 
 from script_generator.constants import RENDER_RESOLUTION
 from script_generator.debug.errors import FFProbeError
-from script_generator.debug.logger import log
+from script_generator.debug.logger import log, log_vid
 
 
 @dataclass
@@ -93,7 +93,8 @@ def get_video_info(video_path):
         # Parse the first video stream
         stream = info.get("streams", [{}])[0]
         if not stream:
-            raise FFProbeError("No video stream found in the file.")
+            log_vid.error(f"FFprobe was unable to detect a valid video stream in the specified file.")
+            raise FFProbeError(f"We were unable to process your video: FFprobe could not detect a valid video stream in the provided file. Please ensure that the file is a supported video format and try again.")
 
         # Extract stream metadata
         codec_name = stream.get("codec_name", "unknown")

From edf2cd501edf27859ef08c6edf788228f0dad53d Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sat, 1 Feb 2025 13:05:38 +0100
Subject: [PATCH 204/277] FFmpeg hardware acceleration is now tested and added
 to the settings page and config.json

---
 script_generator/config/config_manager.py |  2 +
 script_generator/constants.py             |  1 +
 script_generator/gui/utils/widgets.py     |  6 +-
 script_generator/gui/views/settings.py    | 10 +++
 script_generator/state/app_state.py       |  3 +-
 script_generator/video/ffmpeg/hwaccel.py  | 84 ++++++++++++++++-------
 6 files changed, 76 insertions(+), 30 deletions(-)

diff --git a/script_generator/config/config_manager.py b/script_generator/config/config_manager.py
index 9bf13b29..bca326e7 100644
--- a/script_generator/config/config_manager.py
+++ b/script_generator/config/config_manager.py
@@ -4,6 +4,7 @@
 from script_generator.constants import CONFIG_FILE_PATH, CONFIG_VERSION, DEFAULT_CONFIG
 from script_generator.debug.logger import log, set_log_level
 from script_generator.object_detection.util.utils import get_yolo_model_path
+from script_generator.video.ffmpeg.hwaccel import get_preferred_hwaccel
 from script_generator.video.util.ffmpeg import get_ffmpeg_paths
 
 class ConfigManager:
@@ -51,6 +52,7 @@ def _ensure_defaults(self, config):
             ("ffmpeg_path", lambda: get_ffmpeg_paths()[0], self._is_valid_path),
             ("ffprobe_path", lambda: get_ffmpeg_paths()[1], self._is_valid_path),
             ("yolo_model_path", get_yolo_model_path, self._is_valid_path),
+            ("ffmpeg_hwaccel", lambda: get_preferred_hwaccel(config["ffmpeg_path"]) if config["ffmpeg_path"] else None, lambda val: val is not None)
         ]
 
         updated = False
diff --git a/script_generator/constants.py b/script_generator/constants.py
index 9f5bd905..311b0a20 100644
--- a/script_generator/constants.py
+++ b/script_generator/constants.py
@@ -43,6 +43,7 @@
     "config_version": CONFIG_VERSION,
     "ffmpeg_path": None,
     "ffprobe_path": None,
+    "ffmpeg_hwaccel": None,
     "yolo_model_path": None,
     "copy_funscript_to_movie_dir": True,
     "funscript_output_dir": None,
diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index 7b896569..65cadcf4 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -65,7 +65,7 @@ def entry(frame, default_value="", tooltip_text=None, **grid_kwargs):
         return entry
 
     @staticmethod
-    def input(parent, label_text, state, attr, row=0, column=0, label_width_px=LABEL_WIDTH, entry_width_px=200, callback=None, tooltip_text=None):
+    def input(parent, label_text, state, attr, row=0, column=0, label_width_px=LABEL_WIDTH, entry_width_px=200, command=None, tooltip_text=None):
         container = ttk.Frame(parent)
         container.grid(row=row, column=column, sticky="ew", padx=5, pady=5)
         container.columnconfigure(1, weight=1)
@@ -78,8 +78,8 @@ def input(parent, label_text, state, attr, row=0, column=0, label_width_px=LABEL
 
         def on_value_change(*args):
             setattr(state, attr, value.get())
-            if callback:
-                callback(value.get())
+            if command:
+                command(value.get())
 
         value.trace_add("write", on_value_change)
 
diff --git a/script_generator/gui/views/settings.py b/script_generator/gui/views/settings.py
index 8dc4492b..f92428e0 100644
--- a/script_generator/gui/views/settings.py
+++ b/script_generator/gui/views/settings.py
@@ -84,6 +84,16 @@ def __init__(self, parent, controller):
             row=1
         )
 
+        Widgets.input(
+            attr="ffmpeg_hwaccel",
+            parent=ffmpeg_settings,
+            label_text="Hardware acceleration",
+            state=self.state,
+            tooltip_text="Used hardware acceleration. Can be overwritten here or set to None to disable it when your\nexperiencing issues. However if you do, please let us know so we can better\nsupport you in the future.",
+            command=lambda val: c.save(),
+            row=2
+        )
+
         dev_settings = Widgets.frame(self, title="Dev", main_section=True, row=3)
 
         def handle_log_level(level):
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index 18f62368..73d1cd85 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -10,7 +10,6 @@
 from script_generator.object_detection.util.utils import get_metrics_file_info, load_yolo_model
 from script_generator.object_detection.utils import get_raw_yolo_file_info
 from script_generator.tasks.tasks import AnalyzeVideoTask
-from script_generator.video.ffmpeg.hwaccel import get_preferred_hwaccel
 from script_generator.video.info.video_info import VideoInfo, get_video_info
 
 
@@ -84,7 +83,7 @@ def __init__(self):
         # App logic
         self.debug_data = DebugData(self)
         self.update_ui = None
-        self.ffmpeg_hwaccel = get_preferred_hwaccel() if self.ffmpeg_path else None
+        self.ffmpeg_hwaccel = c.get("ffmpeg_hwaccel")
         self.yolo_model = load_yolo_model(self.yolo_model_path)
 
     def set_is_cli(self, cli):
diff --git a/script_generator/video/ffmpeg/hwaccel.py b/script_generator/video/ffmpeg/hwaccel.py
index 848a3a1d..fd4e6648 100644
--- a/script_generator/video/ffmpeg/hwaccel.py
+++ b/script_generator/video/ffmpeg/hwaccel.py
@@ -2,48 +2,83 @@
 
 from script_generator.debug.logger import log
 
+HW_TEST_CMDS = {
+    "cuda": [
+        "-init_hw_device", "cuda=0",
+        "-f", "lavfi", "-i", "nullsrc=size=256x256:duration=0.1:rate=1",
+        "-c:v", "h264_nvenc",
+        "-f", "null", "-"
+    ],
+    "vaapi": [
+        "-init_hw_device", "vaapi=/dev/dri/renderD128",
+        "-f", "lavfi", "-i", "nullsrc=size=256x256:duration=0.1:rate=1",
+        "-c:v", "h264_vaapi",
+        "-f", "null", "-"
+    ],
+    "amf": [
+        "-f", "lavfi", "-i", "nullsrc=size=256x256:duration=0.1:rate=1",
+        "-c:v", "h264_amf",
+        "-f", "null", "-"
+    ],
+    "qsv": [
+        "-init_hw_device", "qsv=hw:0",
+        "-f", "lavfi", "-i", "nullsrc=size=256x256:duration=0.1:rate=1",
+        "-c:v", "h264_qsv",
+        "-f", "null", "-"
+    ],
+    "d3d11va": [
+        "-init_hw_device", "d3d11va=hw:0",
+        "-f", "lavfi", "-i", "nullsrc=size=256x256:duration=0.1:rate=1",
+        "-f", "null", "-"
+    ]
+}
 
-def get_hwaccel_support():
-    """
-    Check which hardware acceleration backends are supported by FFmpeg.
-    Returns a dictionary with supported backends.
-    """
+def _run_cmd(cmd):
     try:
-        from script_generator.state.app_state import AppState
-        state = AppState()
-        result = subprocess.run(
-            [state.ffmpeg_path, "-hwaccels"],
+        r = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
+        return (r.returncode == 0, r.stderr)
+    except Exception as e:
+        return (False, str(e))
+
+def _list_ffmpeg_hwaccels(ffmpeg_path):
+    try:
+        r = subprocess.run(
+            [ffmpeg_path, "-hwaccels"],
             stdout=subprocess.PIPE,
             stderr=subprocess.PIPE,
             text=True,
             check=True
         )
-        hwaccels = result.stdout.lower().replace("hardware acceleration methods:", "")
-        hwaccel_lines = [line.strip() for line in str(hwaccels).splitlines() if line.strip()]
-        log.info(f"hardware acceleration methods: {', '.join(hwaccel_lines)}")
-
-        return hwaccel_lines
+        lines = [l.strip() for l in r.stdout.lower().replace("hardware acceleration methods:", "").splitlines() if l.strip()]
+        log.info(f"hardware acceleration methods compiled in FFmpeg binary: {', '.join(lines)}")
+        return lines
     except Exception as e:
-        log.error(f"Error checking hardware acceleration support: {e}")
+        log.error(e)
         return []
 
+def _test_hwaccel(ffmpeg_path, hw):
+    if hw in HW_TEST_CMDS:
+        cmd = [ffmpeg_path] + HW_TEST_CMDS[hw]
+        ok, err = _run_cmd(cmd)
+        if not ok: log.debug(f"{hw} test failed: {err}")
+        return ok
+    ok, _ = _run_cmd([ffmpeg_path, "-init_hw_device", hw])
+    return ok
 
-def get_preferred_hwaccel():
-    hwaccel_support = get_hwaccel_support()
-    hwaccel_priority = ["cuda", "vaapi", "amf", "videotoolbox", "qsv", "d3d11va"]
-    for hw in hwaccel_priority:
-        if hw in hwaccel_support:
+def get_preferred_hwaccel(ffmpeg_path):
+    supported = _list_ffmpeg_hwaccels(ffmpeg_path)
+    for hw in ["cuda","vaapi","amf","videotoolbox","qsv","d3d11va"]:
+        if hw in supported and _test_hwaccel(ffmpeg_path, hw):
+            log.info(f"Setting preferred FFmpeg hardware acceleration too: {hw}")
             return hw
+    log.info("No working hwaccel found.")
     return None
 
-
 def get_hwaccel_read_args(video_info, hwaccel):
     if hwaccel == "cuda":
-        # hardware accelerated output is not supported with > 8 bit
         if video_info.bit_depth == 8:
             return ["-hwaccel", "cuda", "-hwaccel_output_format", "cuda"]
-        else:
-            return ["-hwaccel", "cuda"]
+        return ["-hwaccel", "cuda"]
     if hwaccel == "vaapi":
         return ["-hwaccel", "vaapi", "-hwaccel_device", "/dev/dri/renderD128"]
     if hwaccel == "amf":
@@ -56,7 +91,6 @@ def get_hwaccel_read_args(video_info, hwaccel):
         return ["-hwaccel", "d3d11va"]
     return []
 
-
 def supports_cuda_scale(video, ffmpeg_hwaccel):
     return (
         ffmpeg_hwaccel == "cuda"

From 9e4969a80ccf0b77fcf38f0195f3735d378dfaa6 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sat, 1 Feb 2025 13:23:55 +0100
Subject: [PATCH 205/277] Possible fix for app menu on Mac

---
 models/PLACE YOLO MODEL HERE | 0
 script_generator/gui/app.py  | 7 +++----
 2 files changed, 3 insertions(+), 4 deletions(-)
 create mode 100644 models/PLACE YOLO MODEL HERE

diff --git a/models/PLACE YOLO MODEL HERE b/models/PLACE YOLO MODEL HERE
new file mode 100644
index 00000000..e69de29b
diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index e4a51c8c..051f2dd3 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -33,12 +33,11 @@ def __init__(self):
         self.container.grid(row=0, column=0, sticky="nsew")
         self.container.grid_columnconfigure(0, weight=1)
 
+        # App menu
         menu_bar = tk.Menu(self)
-
-        # File Menu
         file_menu = tk.Menu(menu_bar, tearoff=0)
-        # file_menu.add_command(label="Exit", command=self.quit)
-        # menu_bar.add_cascade(label="File", menu=file_menu)
+        file_menu.add_command(label="Exit", command=self.quit)
+        menu_bar.add_cascade(label="File", menu=file_menu)
         menu_bar.add_command(label="Funscript generator", command=lambda: self.show_frame(PageNames.FUNSCRIPT_GENERATOR))
         menu_bar.add_command(label="Settings", command=lambda: self.show_frame(PageNames.SETTINGS))
         self.config(menu=menu_bar)

From e44d357cbadcc234e005e6530be68f465bd88637 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sat, 1 Feb 2025 13:37:08 +0100
Subject: [PATCH 206/277] Possible fix for videotoolbox detection

---
 script_generator/gui/app.py                       |  2 +-
 script_generator/gui/utils/widgets.py             |  2 +-
 script_generator/gui/views/funscript_generator.py | 14 +++++++-------
 script_generator/video/ffmpeg/hwaccel.py          |  6 ++++++
 4 files changed, 15 insertions(+), 9 deletions(-)

diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index 051f2dd3..9082fb65 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -21,7 +21,7 @@ def __init__(self):
             ctypes.windll.shcore.SetProcessDpiAwareness(2)  # For Windows DPI scaling
         # self.tk.call('tk', 'scaling', 1.0)
         self.title(f"VR & 2D POV Funscript AI Generator - v" + VERSION)
-        self.geometry('800x900' if is_mac() else '700x900')
+        self.geometry('800x900' if is_mac() else '725x900')
         self.resizable(False, False)
 
         self.iconphoto(False, tk.PhotoImage(file=LOGO))
diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index 65cadcf4..dfca09a1 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -235,7 +235,7 @@ def range_selector(parent, label_text, row, state, attr, values, column=0, toolt
         return dropdown
 
     @staticmethod
-    def checkbox(parent, label_text, state, attr, command=None, label_left=True, row=0, column=0, label_width_px=150, tooltip_text=None, **grid_kwargs):
+    def checkbox(parent, label_text, state, attr, label_left=True, command=None, row=0, column=0, label_width_px=150, tooltip_text=None, **grid_kwargs):
         # Ensure the parent has a _checkbox_vars attribute to track BooleanVars
         if not hasattr(parent, "_checkbox_vars"):
             parent._checkbox_vars = {}
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 164ad1c9..774d23b2 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -58,12 +58,12 @@ def update_video_path():
         # )
         # endregion
 
-        # region OPTIONAL SETTINGS
-        optional_settings = Widgets.frame(wrapper, title="Optional settings", main_section=True, row=2)
-
-        _, start_f_widgets, _ = Widgets.frames_input(optional_settings, "Start", state=state, attr="frame_start", tooltip_text="Where to start processing the video. If you have a 60fps video starting at 10s would mean frame 600")
-        _, end_f_widgets, _ = Widgets.frames_input(optional_settings, "End", state=state, attr="frame_end", tooltip_text="Where to end processing the video. If you have a 60fps video stopping  at 10s would mean frame 600", row=1)
-        # endregion
+        # # region OPTIONAL SETTINGS
+        # optional_settings = Widgets.frame(wrapper, title="Optional settings", main_section=True, row=2)
+        #
+        # _, start_f_widgets, _ = Widgets.frames_input(optional_settings, "Start", state=state, attr="frame_start", tooltip_text="Where to start processing the video. If you have a 60fps video starting at 10s would mean frame 600")
+        # _, end_f_widgets, _ = Widgets.frames_input(optional_settings, "End", state=state, attr="frame_end", tooltip_text="Where to end processing the video. If you have a 60fps video stopping  at 10s would mean frame 600", row=1)
+        # # endregion
 
         # region PROCESSING
         processing = Widgets.frame(wrapper, title="Processing", main_section=True, row=3)
@@ -248,7 +248,7 @@ def update_ui_for_state():
             else:
                 disable_widgets([processing_btn])
 
-            proc_widgets = [fs_entry, fs_button, ref_entry, ref_button, metrics_check, *start_f_widgets, *end_f_widgets, # , reader_dropdown
+            proc_widgets = [fs_entry, fs_button, ref_entry, ref_button, metrics_check, # , reader_dropdown, *start_f_widgets, *end_f_widgets
                             boost_check, simp_check, tres_check, t_d_1, t_d_2, s_d_1, s_d_2, b_d_1, b_d_2]
             if state.is_processing:
                 # TODO remove the processing button and implement stop processing
diff --git a/script_generator/video/ffmpeg/hwaccel.py b/script_generator/video/ffmpeg/hwaccel.py
index fd4e6648..7707a5b2 100644
--- a/script_generator/video/ffmpeg/hwaccel.py
+++ b/script_generator/video/ffmpeg/hwaccel.py
@@ -30,6 +30,12 @@
         "-init_hw_device", "d3d11va=hw:0",
         "-f", "lavfi", "-i", "nullsrc=size=256x256:duration=0.1:rate=1",
         "-f", "null", "-"
+    ],
+    "videotoolbox": [
+        "-f", "lavfi", "-i", "nullsrc=size=256x256:duration=0.1:rate=1",
+        "-pix_fmt", "yuv420p",
+        "-c:v", "h264_videotoolbox",
+        "-f", "null", "-"
     ]
 }
 

From 31b061342092f0cc199bbecaa4878137f03dea79 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sat, 1 Feb 2025 13:46:17 +0100
Subject: [PATCH 207/277] Added Mac support for the menu

---
 script_generator/gui/app.py              |  8 +++++---
 script_generator/video/ffmpeg/hwaccel.py | 12 ++++++------
 2 files changed, 11 insertions(+), 9 deletions(-)

diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index 9082fb65..8446fed6 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -21,7 +21,7 @@ def __init__(self):
             ctypes.windll.shcore.SetProcessDpiAwareness(2)  # For Windows DPI scaling
         # self.tk.call('tk', 'scaling', 1.0)
         self.title(f"VR & 2D POV Funscript AI Generator - v" + VERSION)
-        self.geometry('800x900' if is_mac() else '725x900')
+        self.geometry('800x900' if is_mac() else '722x900')
         self.resizable(False, False)
 
         self.iconphoto(False, tk.PhotoImage(file=LOGO))
@@ -38,8 +38,10 @@ def __init__(self):
         file_menu = tk.Menu(menu_bar, tearoff=0)
         file_menu.add_command(label="Exit", command=self.quit)
         menu_bar.add_cascade(label="File", menu=file_menu)
-        menu_bar.add_command(label="Funscript generator", command=lambda: self.show_frame(PageNames.FUNSCRIPT_GENERATOR))
-        menu_bar.add_command(label="Settings", command=lambda: self.show_frame(PageNames.SETTINGS))
+        view_menu = tk.Menu(menu_bar, tearoff=0)
+        view_menu.add_command(label="Funscript generator", command=lambda: self.show_frame(PageNames.FUNSCRIPT_GENERATOR))
+        view_menu.add_command(label="Settings", command=lambda: self.show_frame(PageNames.SETTINGS))
+        menu_bar.add_cascade(label="View", menu=view_menu)
         self.config(menu=menu_bar)
 
         self.state = AppState()
diff --git a/script_generator/video/ffmpeg/hwaccel.py b/script_generator/video/ffmpeg/hwaccel.py
index 7707a5b2..1da9b1d2 100644
--- a/script_generator/video/ffmpeg/hwaccel.py
+++ b/script_generator/video/ffmpeg/hwaccel.py
@@ -1,6 +1,6 @@
 import subprocess
 
-from script_generator.debug.logger import log
+from script_generator.debug.logger import log_vid
 
 HW_TEST_CMDS = {
     "cuda": [
@@ -56,17 +56,17 @@ def _list_ffmpeg_hwaccels(ffmpeg_path):
             check=True
         )
         lines = [l.strip() for l in r.stdout.lower().replace("hardware acceleration methods:", "").splitlines() if l.strip()]
-        log.info(f"hardware acceleration methods compiled in FFmpeg binary: {', '.join(lines)}")
+        log_vid.info(f"hardware acceleration methods compiled in FFmpeg binary: {', '.join(lines)}")
         return lines
     except Exception as e:
-        log.error(e)
+        log_vid.error(e)
         return []
 
 def _test_hwaccel(ffmpeg_path, hw):
     if hw in HW_TEST_CMDS:
         cmd = [ffmpeg_path] + HW_TEST_CMDS[hw]
         ok, err = _run_cmd(cmd)
-        if not ok: log.debug(f"{hw} test failed: {err}")
+        if not ok: log_vid.debug(f"{hw} test failed: {err}")
         return ok
     ok, _ = _run_cmd([ffmpeg_path, "-init_hw_device", hw])
     return ok
@@ -75,9 +75,9 @@ def get_preferred_hwaccel(ffmpeg_path):
     supported = _list_ffmpeg_hwaccels(ffmpeg_path)
     for hw in ["cuda","vaapi","amf","videotoolbox","qsv","d3d11va"]:
         if hw in supported and _test_hwaccel(ffmpeg_path, hw):
-            log.info(f"Setting preferred FFmpeg hardware acceleration too: {hw}")
+            log_vid.info(f"Setting preferred FFmpeg hardware acceleration too: {hw}")
             return hw
-    log.info("No working hwaccel found.")
+    log_vid.info("No working hwaccel found.")
     return None
 
 def get_hwaccel_read_args(video_info, hwaccel):

From 8be0ab34782f789fca66d2ce006da0d3490ac1ef Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sat, 1 Feb 2025 14:12:59 +0100
Subject: [PATCH 208/277] Fixed debug windows not always closing and increased
 size to match debug video view

---
 .../analyze_tracking_results.py               | 20 ++++++++--------
 .../object_detection/post_process_worker.py   | 23 ++++++++++---------
 2 files changed, 22 insertions(+), 21 deletions(-)

diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index a3c20334..1addf341 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -26,7 +26,7 @@ def analyze_tracking_results(state: AppState, results):
     reader = VideoReaderFFmpeg(state)
 
     state.frame_area = width * height
-
+    debug_window_open = False
     cuts = []
 
     if state.live_preview_mode:
@@ -154,6 +154,13 @@ def analyze_tracking_results(state: AppState, results):
 
         # Display object detection tracking results in a live preview window
         window_name = "Tracking analysis preview"
+
+        # we don't want to call cv2.getWindowProperty every iteration
+        if debug_window_open and not state.live_preview_mode:
+            if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) >= 1:
+                cv2.destroyWindow(window_name)
+                debug_window_open = False
+
         if state.live_preview_mode:
             ret, frame = reader.read()
             frame = frame.copy()
@@ -192,22 +199,15 @@ def analyze_tracking_results(state: AppState, results):
             # Reinitialize the window if needed
             if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) < 1 and state.live_preview_mode:
                 cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
+                cv2.resizeWindow(window_name, int(width * 2), int(height * 2))
+                debug_window_open = True
             cv2.imshow(window_name, frame)
 
             if not handle_user_input(window_name) or not state.live_preview_mode:
-                if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) >= 1:
-                    cv2.destroyWindow(window_name)
-
                 if state.update_ui and state.live_preview_mode:
                     state.update_ui(UpdateGUIState(attr="live_preview_mode", value=False))
 
                 state.live_preview_mode = False
-        # we don't want to call cv2.getWindowProperty every iteration
-        elif live_preview_mode_prev and not state.live_preview_mode:
-            if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) >= 1:
-                cv2.destroyWindow(window_name)
-
-        live_preview_mode_prev = state.live_preview_mode
 
         # Update progress periodically
         if state.update_ui:
diff --git a/script_generator/object_detection/post_process_worker.py b/script_generator/object_detection/post_process_worker.py
index 6efd4a9d..f9820f7a 100644
--- a/script_generator/object_detection/post_process_worker.py
+++ b/script_generator/object_detection/post_process_worker.py
@@ -10,6 +10,7 @@
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 from script_generator.utils.file import get_output_file_path
 from script_generator.utils.msgpack_utils import save_msgpack_json
+from script_generator.video.info.video_info import get_cropped_dimensions
 
 
 class PostProcessWorker(AbstractTaskProcessor):
@@ -19,8 +20,9 @@ class PostProcessWorker(AbstractTaskProcessor):
 
     def task_logic(self):
         state = self.state
+        width, height = get_cropped_dimensions(state.video_info)
 
-        live_preview_mode_prev = state.live_preview_mode
+        debug_window_open = False
         for task in self.get_task():
 
             frame_pos = task.frame_pos
@@ -104,6 +106,13 @@ def task_logic(self):
                             self.test_result.add_record(frame_pos, test_box)
 
             window_name = "Object detection tracking preview"
+
+            # we don't want to call cv2.getWindowProperty every iteration
+            if debug_window_open and not state.live_preview_mode:
+                if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) >= 1:
+                    cv2.destroyWindow(window_name)
+                    debug_window_open = False
+
             if state.live_preview_mode:
                 # Display the YOLO results for testing
                 # det_results.plot()
@@ -127,24 +136,16 @@ def task_logic(self):
                 # Reinitialize the window if needed
                 if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) < 1 and state.live_preview_mode:
                     cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
+                    cv2.resizeWindow(window_name, int(width * 2), int(height * 2))
+                    debug_window_open = True
                 cv2.imshow(window_name, frame)
 
                 if not state.live_preview_mode or not handle_user_input(window_name):
-                    if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) >= 1:
-                        cv2.destroyWindow(window_name)
-
                     if state.update_ui and state.live_preview_mode:
                         state.update_ui(UpdateGUIState(attr="live_preview_mode", value=False))
 
                     state.live_preview_mode = False
 
-            # we don't want to call cv2.getWindowProperty every iteration
-            elif live_preview_mode_prev and not state.live_preview_mode:
-                if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) >= 1:
-                    cv2.destroyWindow(window_name)
-
-            live_preview_mode_prev = state.live_preview_mode
-
             task.rendered_frame = None # Clear memory
             task.yolo_results = None # Clear memory (yolo results contains a copy of the image)
             self.finish_task(task)

From 5c5476938dd8b3ddfd8b026e2d9e311bba8da0b8 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sat, 1 Feb 2025 17:15:12 +0100
Subject: [PATCH 209/277] Stop process support UI improvements Check if
 scale_cuda is supported NoneType bugfix Tweaks and fixes

---
 script_generator/gui/app.py                   |  8 +++
 .../gui/controller/process_video.py           | 25 ++++++-
 .../gui/controller/stop_processing.py         | 14 ++++
 script_generator/gui/utils/utils.py           |  4 +-
 .../gui/views/funscript_generator.py          | 47 +++++++++-----
 .../analyze_tracking_results.py               |  4 ++
 .../object_detection/post_process_worker.py   |  4 ++
 script_generator/scripts/analyze_video.py     | 65 +++++++++----------
 script_generator/state/app_state.py           |  9 ++-
 .../tasks/{tasks.py => abstract_task.py}      | 32 +--------
 .../tasks/abstract_task_processor.py          | 19 +++---
 script_generator/tasks/analyze_video_task.py  | 62 ++++++++++++++++++
 script_generator/video/analyse_frame_task.py  | 15 +++++
 script_generator/video/ffmpeg/commands.py     |  2 +-
 script_generator/video/ffmpeg/filters.py      | 23 ++++---
 script_generator/video/ffmpeg/hwaccel.py      | 37 ++++++++++-
 .../video/workers/ffmpeg_worker.py            | 16 +++--
 utils/lib_FunscriptHandler.py                 | 15 ++---
 18 files changed, 273 insertions(+), 128 deletions(-)
 create mode 100644 script_generator/gui/controller/stop_processing.py
 rename script_generator/tasks/{tasks.py => abstract_task.py} (63%)
 create mode 100644 script_generator/tasks/analyze_video_task.py
 create mode 100644 script_generator/video/analyse_frame_task.py

diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index 8446fed6..0c1c5c49 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -3,6 +3,7 @@
 import tkinter as tk
 
 from script_generator.constants import LOGO, ICON
+from script_generator.gui.controller.stop_processing import stop_processing
 from script_generator.gui.views.funscript_generator import FunscriptGeneratorPage
 from script_generator.gui.views.settings import SettingsPage
 from script_generator.state.app_state import AppState
@@ -54,6 +55,8 @@ def __init__(self):
         self.attributes("-topmost", True)
         self.after(200, self.reset_topmost)
 
+        self.protocol("WM_DELETE_WINDOW", self.on_close)
+
     def reset_topmost(self):
         self.focus_force()
         self.attributes("-topmost", False)
@@ -81,6 +84,11 @@ def create_page(self, page_name):
             return SettingsPage(parent=self.container, controller=self)
         return None
 
+    def on_close(self):
+        log.info("Application is closing...")
+        stop_processing(self.state)
+        self.destroy()
+
 
 class PageNames:
     FUNSCRIPT_GENERATOR = "Funscript generator"
diff --git a/script_generator/gui/controller/process_video.py b/script_generator/gui/controller/process_video.py
index 8d03e8ad..62a97f18 100644
--- a/script_generator/gui/controller/process_video.py
+++ b/script_generator/gui/controller/process_video.py
@@ -29,7 +29,15 @@ def run():
 
             if choice == "cancel":
                 return
-            elif choice == "use_existing":
+
+            state.update_ui(ProgressMessage(
+                process="TRACKING_ANALYSIS",
+                frames_processed=0,
+                total_frames=0,
+                eta="Queued"
+            ))
+
+            if choice == "use_existing":
                 if state.update_ui:
                     state.update_ui(ProgressMessage(
                         process="OBJECT_DETECTION",
@@ -38,13 +46,28 @@ def run():
                         eta="Done"
                     ))
             elif choice == "generate":
+                state.update_ui(ProgressMessage(
+                    process="OBJECT_DETECTION",
+                    frames_processed=0,
+                    total_frames=0,
+                    eta="Calculating..."
+                ))
                 analyze_video(state)
 
+            if state.analyze_task and state.analyze_task.is_stopped:
+                return
+
+            state.update_ui(UpdateGUIState(attr="has_raw_yolo", value=True))
+
             tracking_analysis(state)
 
+            state.update_ui(UpdateGUIState(attr="has_tracking_data", value=True))
             state.update_ui(UpdateGUIState(attr="is_processing", value=False))
+            state.analyze_task = None
 
         except Exception as e:
+            state.analyze_task = None
+            state.update_ui(UpdateGUIState(attr="is_processing", value=False))
             log.error(f"Error during video analysis: {e}")
             messagebox.showerror("Error", f"Could not process video:\n{e}")
             import traceback
diff --git a/script_generator/gui/controller/stop_processing.py b/script_generator/gui/controller/stop_processing.py
new file mode 100644
index 00000000..18e6dd4e
--- /dev/null
+++ b/script_generator/gui/controller/stop_processing.py
@@ -0,0 +1,14 @@
+import time
+
+from script_generator.debug.logger import log
+from script_generator.state.app_state import AppState
+
+
+def stop_processing(state: AppState):
+    if state.analyze_task:
+        log.debug("Stopping funscript generation task")
+        state.analyze_task.stop()
+        time.sleep(2)
+
+    # TODO stop tracking logic
+
diff --git a/script_generator/gui/utils/utils.py b/script_generator/gui/utils/utils.py
index 00fa21ac..0a240eb3 100644
--- a/script_generator/gui/utils/utils.py
+++ b/script_generator/gui/utils/utils.py
@@ -9,9 +9,9 @@ def enable_widgets(widgets):
 def set_progressbars_done(progress_bars):
     for progress, label in progress_bars:
         progress["value"] = progress["maximum"]
-        label = "Done"
+        label.config(text="Done")
 
 def reset_progressbars(progress_bars):
     for progress, label in progress_bars:
         progress["value"] = 0
-        label = ""
\ No newline at end of file
+        label.config(text="")
\ No newline at end of file
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 774d23b2..251a1bef 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -1,16 +1,18 @@
 import tkinter as tk
-from tkinter import ttk
+from tkinter import ttk, messagebox
 
 from script_generator.debug.logger import log
 from script_generator.gui.controller.debug_video import debug_video
 from script_generator.gui.controller.process_video import video_analysis
 from script_generator.gui.controller.regenerate_funscript import regenerate_funscript
+from script_generator.gui.controller.stop_processing import stop_processing
 from script_generator.gui.messages.messages import UIMessage, ProgressMessage, UpdateGUIState
 from script_generator.gui.utils.utils import enable_widgets, disable_widgets, set_progressbars_done, reset_progressbars
 from script_generator.gui.utils.widgets import Widgets
 from script_generator.gui.views.popups.create_debug_video import render_debug_video_popup
+from script_generator.object_detection.util.utils import get_metrics_file_info
+from script_generator.object_detection.utils import get_raw_yolo_file_info
 from script_generator.state.app_state import AppState
-from script_generator.utils.helpers import is_mac
 
 
 class FunscriptGeneratorPage(tk.Frame):
@@ -68,21 +70,37 @@ def update_video_path():
         # region PROCESSING
         processing = Widgets.frame(wrapper, title="Processing", main_section=True, row=3)
         yolo_p_container, yolo_p, yolo_p_label, yolo_p_perc = Widgets.labeled_progress(processing, "Object Detection", row=0)
-        # scene_p_container, scene_p, scene_p_label, scene_p_perc = Widgets.labeled_progress(processing, "Scene detection", row=1)
         track_p_container, track_p, track_p_label, track_p_perc = Widgets.labeled_progress(processing, "Tracking Analysis", row=2)
 
-        def start_processing():
-            state.is_processing = True
-            reset_progressbars([(yolo_p, yolo_p_perc), (track_p, track_p_perc)])
-            video_analysis(state, controller)
-            update_ui_for_state()
+        def handle_process_btn():
+            if not state.is_processing:
+                if state.analyze_task:
+                    state.analyze_task.is_stopped = False
+                state.is_processing = True
+                state.has_raw_yolo = False
+                state.has_tracking_data = False
+                reset_progressbars([(yolo_p, yolo_p_perc), (track_p, track_p_perc)])
+                video_analysis(state, controller)
+                update_ui_for_state()
+            else:
+                # TODO add proper stop for analysis
+                if state.has_raw_yolo:
+                    messagebox.showwarning("WIP", f"Only the Object Detection process can be stopped for now")
+                    return
 
-        processing_btn = Widgets.button(processing, "Start processing", start_processing, row=3)
+                disable_widgets([processing_btn])
+                stop_processing(state)
+                state.is_processing = False
+                state.video_info.has_raw_yolo, _, _ = get_raw_yolo_file_info(state)
+                state.video_info.has_tracking_data, _, _ = get_metrics_file_info(state)
+                state.analyze_task = None
+                self.controller.after(500, update_ui_for_state)
+
+        processing_btn = Widgets.button(processing, "Start processing", handle_process_btn, row=3)
         # endregion
 
         # region FUNSCRIPT TWEAKING
         tweaking = Widgets.frame(wrapper, title="Funscript", main_section=True, row=4)
-        # tweaking.grid_rowconfigure(1, weight=10)
         tweaking_container = ttk.Frame(tweaking)
         tweaking_container.grid(row=1, column=0, columnspan=3, padx=5, pady=5, sticky="nsew")
 
@@ -251,9 +269,8 @@ def update_ui_for_state():
             proc_widgets = [fs_entry, fs_button, ref_entry, ref_button, metrics_check, # , reader_dropdown, *start_f_widgets, *end_f_widgets
                             boost_check, simp_check, tres_check, t_d_1, t_d_2, s_d_1, s_d_2, b_d_1, b_d_2]
             if state.is_processing:
-                # TODO remove the processing button and implement stop processing
-                disable_widgets([*proc_widgets, processing_btn])
-                processing_btn.config(text="Stop processing (WIP)")
+                disable_widgets([*proc_widgets])
+                processing_btn.config(text="Stop processing")
             else:
                 enable_widgets(proc_widgets)
 
@@ -285,7 +302,6 @@ def handle_state_update(state_msg: UpdateGUIState):
             def handle_progress_message(progress_msg: ProgressMessage):
                 progress_mapping = {
                     "OBJECT_DETECTION": (yolo_p, yolo_p_perc, "has_raw_yolo"),
-                    # "SCENE_DETECTION": (scene_p, scene_p_perc),
                     "TRACKING_ANALYSIS": (track_p, track_p_perc, "has_tracking_data"),
                 }
 
@@ -294,11 +310,10 @@ def handle_progress_message(progress_msg: ProgressMessage):
                     progress_bar["value"] = progress_msg.frames_processed
                     progress_bar["maximum"] = progress_msg.total_frames
                     percentage = (progress_msg.frames_processed / progress_msg.total_frames) * 100 if progress_msg.total_frames > 0 else 0
-                    is_done = progress_msg.frames_processed >= progress_msg.total_frames
+                    is_done = progress_msg.frames_processed >= progress_msg.total_frames > 0
                     progress_label.config(text="Done" if is_done else f"{percentage:.0f}% - ETA: {progress_msg.eta}")
 
                     if is_done or progress_msg.frames_processed == 0:
-                        setattr(state, state_attr, is_done)
                         update_ui_for_state()
 
             # Schedule the update on the main thread
diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/object_detection/analyze_tracking_results.py
index 1addf341..80034839 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/object_detection/analyze_tracking_results.py
@@ -226,6 +226,10 @@ def analyze_tracking_results(state: AppState, results):
                     eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
                 ))
 
+    # stop processing when the task is force closed
+    if state.analyze_task and state.analyze_task.is_stopped:
+        return
+
     # Final UI update: we're done
     if state.update_ui:
         state.update_ui(ProgressMessage(
diff --git a/script_generator/object_detection/post_process_worker.py b/script_generator/object_detection/post_process_worker.py
index f9820f7a..7149f9eb 100644
--- a/script_generator/object_detection/post_process_worker.py
+++ b/script_generator/object_detection/post_process_worker.py
@@ -152,6 +152,10 @@ def task_logic(self):
             
 
     def on_last_item(self):
+        # stop processing when the task is force closed
+        if self.state.analyze_task and self.state.analyze_task.is_stopped:
+            return
+
         self.state.analyze_task.end_time = time.time()
 
         # Write the detection records to a JSON file
diff --git a/script_generator/scripts/analyze_video.py b/script_generator/scripts/analyze_video.py
index 90a4671b..339e8a2e 100644
--- a/script_generator/scripts/analyze_video.py
+++ b/script_generator/scripts/analyze_video.py
@@ -1,24 +1,21 @@
-import queue
 import threading
 import time
-from typing import List
+from typing import List, TYPE_CHECKING
 
 from tqdm import tqdm
 
-from script_generator.constants import QUEUE_MAXSIZE, SEQUENTIAL_MODE, UPDATE_PROGRESS_INTERVAL
+from script_generator.constants import SEQUENTIAL_MODE, UPDATE_PROGRESS_INTERVAL
+from script_generator.debug.logger import log
 from script_generator.gui.messages.messages import ProgressMessage
-from script_generator.object_detection.post_process_worker import PostProcessWorker
-from script_generator.object_detection.yolo_worker import YoloWorker
 from script_generator.state.app_state import AppState
 from script_generator.tasks.abstract_task_processor import TaskProcessorTypes
-from script_generator.tasks.tasks import AnalyzeVideoTask, AnalyzeFrameTask
+from script_generator.tasks.analyze_video_task import AnalyzeVideoTask
 from script_generator.utils.file import check_create_output_folder
-from script_generator.debug.logger import log
-from script_generator.video.workers.ffmpeg_worker import VideoWorker
-from script_generator.video.workers.vr_to_2d_worker import VrTo2DWorker
 
+if TYPE_CHECKING:
+    from script_generator.video.analyse_frame_task import AnalyzeFrameTask
 
-def analyze_video(state: AppState) -> List[AnalyzeFrameTask]:
+def analyze_video(state: AppState) -> List["AnalyzeFrameTask"]:
     log.info(f"[OBJECT DETECTION] Starting up pipeline{' in sequential mode' if SEQUENTIAL_MODE else ''}...")
 
     log_thread_stop_event = threading.Event()
@@ -41,24 +38,13 @@ def analyze_video(state: AppState) -> List[AnalyzeFrameTask]:
 
         use_open_gl = state.video_reader == "FFmpeg + OpenGL (Windows)"
 
-        state.analyze_task = AnalyzeVideoTask()
-
-        # Create queues
-        opengl_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
-        yolo_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
-        analysis_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
-        result_q = queue.Queue(maxsize=0)
-
-        # Create threads
-        decode_thread = VideoWorker(state=state, output_queue=opengl_q if use_open_gl else yolo_q)
-        opengl_thread = VrTo2DWorker(state=state, input_queue=opengl_q, output_queue=yolo_q) if use_open_gl else None
-        yolo_thread = YoloWorker(state=state, input_queue=yolo_q, output_queue=analysis_q)
-        yolo_analysis_thread = PostProcessWorker(state=state, input_queue=analysis_q, output_queue=result_q)
+        # Create the task
+        a = AnalyzeVideoTask(state, use_open_gl)
 
         # Start logging thread
         queue_logging_thread = threading.Thread(
             target=log_progress,
-            args=(state, opengl_q, yolo_q, analysis_q, result_q, log_thread_stop_event),
+            args=(state, a, log_thread_stop_event),
             daemon=True,
         )
         queue_logging_thread.start()
@@ -72,13 +58,13 @@ def run_thread(thread, thread_name, out_queue):
                 out_queue.put(None)
                 log.info(f"[OBJECT DETECTION] {thread_name} thread done in {time.time() - start_time} s")
 
-            run_thread(decode_thread, TaskProcessorTypes.VIDEO, opengl_q)
+            run_thread(a.decode_thread, TaskProcessorTypes.VIDEO, a.opengl_q)
             if use_open_gl:
-                run_thread(opengl_thread, TaskProcessorTypes.OPENGL, yolo_q)
-            run_thread(yolo_thread, TaskProcessorTypes.YOLO, analysis_q)
-            run_thread(yolo_analysis_thread, TaskProcessorTypes.YOLO_ANALYSIS, result_q)
+                run_thread(a.opengl_thread, TaskProcessorTypes.OPENGL, a.yolo_q)
+            run_thread(a.yolo_thread, TaskProcessorTypes.YOLO, a.analysis_q)
+            run_thread(a.yolo_analysis_thread, TaskProcessorTypes.YOLO_ANALYSIS, a.result_q)
         else:
-            threads = [decode_thread, opengl_thread, yolo_thread, yolo_analysis_thread] if use_open_gl else [decode_thread, yolo_thread, yolo_analysis_thread]
+            threads = [a.decode_thread, a.opengl_thread, a.yolo_thread, a.yolo_analysis_thread] if use_open_gl else [a.decode_thread, a.yolo_thread, a.yolo_analysis_thread]
             for thread in threads:
                 thread.start()
             for thread in threads:
@@ -93,7 +79,10 @@ def run_thread(thread, thread_name, out_queue):
 
         log_thread_stop_event.set()
 
-        log_performance(state=state, results_queue=result_q)
+        if a.is_stopped:
+            return []
+
+        log_performance(state=state, results_queue=a.result_q)
 
         if state.update_ui:
             state.update_ui(ProgressMessage(
@@ -103,7 +92,7 @@ def run_thread(thread, thread_name, out_queue):
                 eta="Done"
             ))
 
-        return result_q.queue
+        return a.result_q.queue
 
     except Exception as e:
         log.error(f"An error occurred during video analysis: {e}")
@@ -115,7 +104,7 @@ def run_thread(thread, thread_name, out_queue):
         raise
 
 
-def log_progress(state, opengl_q, yolo_q, analysis_q, results_q, stop_event):
+def log_progress(state, analyze_task, stop_event):
     total_frames = state.video_info.total_frames
 
     label = 'Analyzing ' + ('VR' if state.video_info.is_vr else '2D') + ' video'
@@ -130,10 +119,14 @@ def log_progress(state, opengl_q, yolo_q, analysis_q, results_q, stop_event):
             unit_divisor=1
     ) as progress_bar:
         while not stop_event.is_set():
-            opengl_size = opengl_q.qsize()
-            yolo_size = yolo_q.qsize()
-            analysis_size = analysis_q.qsize()
-            frames_processed = results_q.qsize()
+            # close thread when the analysis task is force killed
+            if analyze_task.is_stopped:
+                stop_event.set()
+
+            opengl_size = analyze_task.opengl_q.qsize()
+            yolo_size = analyze_task.yolo_q.qsize()
+            analysis_size = analyze_task.analysis_q.qsize()
+            frames_processed = analyze_task.result_q.qsize()
 
             progress_bar.n = frames_processed
             open_gl = f"OpenGL: {opengl_size:>3}, " if state.video_reader == "FFmpeg + OpenGL (Windows)" else ""
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index 73d1cd85..76fec600 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -2,16 +2,17 @@
 import os.path
 import string
 from tkinter import messagebox
-from typing import Literal, Optional
+from typing import Literal, Optional, TYPE_CHECKING
 
 from script_generator.config.config_manager import ConfigManager
 from script_generator.debug.debug_data import DebugData
 from script_generator.debug.logger import log
 from script_generator.object_detection.util.utils import get_metrics_file_info, load_yolo_model
 from script_generator.object_detection.utils import get_raw_yolo_file_info
-from script_generator.tasks.tasks import AnalyzeVideoTask
 from script_generator.video.info.video_info import VideoInfo, get_video_info
 
+if TYPE_CHECKING:
+    from script_generator.tasks.analyze_video_task import AnalyzeVideoTask
 
 class AppState:
     _instance: Optional["AppState"] = None
@@ -128,6 +129,10 @@ def set_video_info(self):
                 self.video_path = None
                 self.set_video_info()
 
+    def reload_video_info(self):
+        self.video_info = None
+        self.set_video_info()
+
 
 def log_state_settings(state: AppState):
     settings = [
diff --git a/script_generator/tasks/tasks.py b/script_generator/tasks/abstract_task.py
similarity index 63%
rename from script_generator/tasks/tasks.py
rename to script_generator/tasks/abstract_task.py
index 3edfac02..d3460a1e 100644
--- a/script_generator/tasks/tasks.py
+++ b/script_generator/tasks/abstract_task.py
@@ -1,10 +1,7 @@
 import time
 from dataclasses import dataclass, field
 from threading import Lock
-from typing import List, Dict, Optional
-
-import numpy as np
-
+from typing import Dict, Optional
 
 @dataclass
 class Task:
@@ -49,31 +46,4 @@ def _calculate_duration(self, process_type: str):
                 self.profile[duration_key] = self.profile[end_key] - self.profile[start_key]
 
 
-@dataclass
-class AnalyzeVideoTask(Task):
-    tasks: List[Task] = field(default_factory=list)
-
-    def __init__(self):
-        super().__init__()
-        self.tasks = []
-        self._lock = Lock()
-        self.profile = {}
-        self.start_time = time.time()
-
-    def add_task(self, task: Task) -> Task:
-        with self._lock:
-            self.tasks.append(task)
-        return task
-
-    def get_tasks(self) -> List[Task]:
-        with self._lock:
-            return list(self.tasks)
 
-
-@dataclass
-class AnalyzeFrameTask(Task):
-    frame_pos: int = -1
-    preprocessed_frame: Optional[np.ndarray] = None  # Cropped frame from video stream
-    rendered_frame: Optional[np.ndarray] = None  # The final 2D image from OpenGL
-    yolo_results = None
-    # detections: List[Detection] = field(default_factory=list) # YOLO detection results
diff --git a/script_generator/tasks/abstract_task_processor.py b/script_generator/tasks/abstract_task_processor.py
index a6a99d70..da5aa4d8 100644
--- a/script_generator/tasks/abstract_task_processor.py
+++ b/script_generator/tasks/abstract_task_processor.py
@@ -1,16 +1,18 @@
 import queue
 import threading
-from typing import Generator, Optional
-
-from script_generator.state.app_state import AppState
-from script_generator.tasks.tasks import AnalyzeFrameTask
+from typing import Generator, Optional, TYPE_CHECKING
+from enum import Enum
 from script_generator.debug.logger import log
 
+if TYPE_CHECKING:
+    from script_generator.video.analyse_frame_task import AnalyzeFrameTask
+    from script_generator.state.app_state import AppState
 
 class AbstractTaskProcessor(threading.Thread):
+
     process_type = ""
 
-    def __init__(self, state: AppState, output_queue: queue.Queue, input_queue: Optional[queue.Queue] = None):
+    def __init__(self, state: "AppState", output_queue: queue.Queue, input_queue: Optional[queue.Queue] = None):
         """
         Abstract thread class to handle lifecycle management and task handling boilerplate.
 
@@ -32,7 +34,7 @@ def log(self, message):
         thread_name = threading.current_thread().name
         log.info(f"[{self.__class__.__name__}-{thread_name}] {message}")
 
-    def get_task(self) -> Generator[AnalyzeFrameTask, None, None]:
+    def get_task(self) -> Generator["AnalyzeFrameTask", None, None]:
         """
         Generator for retrieving tasks from the input queue.
         Yields tasks until a sentinel (None) is encountered or the thread is stopped.
@@ -46,7 +48,7 @@ def get_task(self) -> Generator[AnalyzeFrameTask, None, None]:
                 task = self.input_queue.get(timeout=1)
 
                 if task is None:
-                    self.input_queue.task_done() # Remove sentinel
+                    self.input_queue.task_done()  # Remove sentinel
                     self.state.analyze_task.end(self.process_type)
                     self.on_last_item()
                     self.finish_task(None)
@@ -110,7 +112,6 @@ def check_exception(self):
         if self.exception:
             raise self.exception
 
-from enum import Enum
 
 class TaskProcessorTypes(Enum):
     VIDEO = "Video processing"
@@ -120,4 +121,4 @@ class TaskProcessorTypes(Enum):
     YOLO_ANALYSIS = "YOLO analysis"
 
     def __str__(self):
-        return self.value
\ No newline at end of file
+        return self.value
diff --git a/script_generator/tasks/analyze_video_task.py b/script_generator/tasks/analyze_video_task.py
new file mode 100644
index 00000000..ba21f770
--- /dev/null
+++ b/script_generator/tasks/analyze_video_task.py
@@ -0,0 +1,62 @@
+import queue
+import time
+from dataclasses import dataclass, field
+from threading import Lock
+from typing import List, TYPE_CHECKING
+
+from script_generator.constants import QUEUE_MAXSIZE
+from script_generator.tasks.abstract_task import Task
+
+from script_generator.object_detection.post_process_worker import PostProcessWorker
+from script_generator.object_detection.yolo_worker import YoloWorker
+from script_generator.video.workers.ffmpeg_worker import VideoWorker
+from script_generator.video.workers.vr_to_2d_worker import VrTo2DWorker
+
+if TYPE_CHECKING:
+    from script_generator.state.app_state import AppState
+
+
+@dataclass
+class AnalyzeVideoTask(Task):
+    tasks: List[Task] = field(default_factory=list)
+
+    def __init__(self, state: "AppState", use_open_gl):
+        super().__init__()
+        self.tasks = []
+        self._lock = Lock()
+        self.profile = {}
+        self.start_time = time.time()
+        self.opengl_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
+        self.yolo_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
+        self.analysis_q = queue.Queue(maxsize=QUEUE_MAXSIZE)
+        self.result_q = queue.Queue(maxsize=0)
+        self.use_open_gl = use_open_gl
+        self.is_stopped = False
+
+        # Create threads
+        self.decode_thread = VideoWorker(state=state, output_queue=self.opengl_q if use_open_gl else self.yolo_q)
+        self.opengl_thread = VrTo2DWorker(state=state, input_queue=self.opengl_q, output_queue=self.yolo_q) if use_open_gl else None
+        self.yolo_thread = YoloWorker(state=state, input_queue=self.yolo_q, output_queue=self.analysis_q)
+        self.yolo_analysis_thread = PostProcessWorker(state=state, input_queue=self.analysis_q, output_queue=self.result_q)
+
+        state.analyze_task = self
+
+    def add_task(self, task: Task) -> Task:
+        with self._lock:
+            self.tasks.append(task)
+        return task
+
+    def get_tasks(self) -> List[Task]:
+        with self._lock:
+            return list(self.tasks)
+
+    def stop(self):
+        self.is_stopped = True
+        if self.decode_thread:
+            self.decode_thread.release()
+        if self.opengl_thread and self.use_open_gl:
+            self.opengl_thread.stop_process()
+        if self.yolo_thread:
+            self.yolo_thread.stop_process()
+        if self.yolo_analysis_thread:
+            self.yolo_analysis_thread.stop_process()
\ No newline at end of file
diff --git a/script_generator/video/analyse_frame_task.py b/script_generator/video/analyse_frame_task.py
new file mode 100644
index 00000000..d11ec0b9
--- /dev/null
+++ b/script_generator/video/analyse_frame_task.py
@@ -0,0 +1,15 @@
+from dataclasses import dataclass
+from typing import Optional
+
+import numpy as np
+
+from script_generator.tasks.abstract_task import Task
+
+
+@dataclass
+class AnalyzeFrameTask(Task):
+    frame_pos: int = -1
+    preprocessed_frame: Optional[np.ndarray] = None  # Cropped frame from video stream
+    rendered_frame: Optional[np.ndarray] = None  # The final 2D image from OpenGL
+    yolo_results = None
+    # detections: List[Detection] = field(default_factory=list) # YOLO detection results
\ No newline at end of file
diff --git a/script_generator/video/ffmpeg/commands.py b/script_generator/video/ffmpeg/commands.py
index adaacbac..6e3e7239 100644
--- a/script_generator/video/ffmpeg/commands.py
+++ b/script_generator/video/ffmpeg/commands.py
@@ -17,7 +17,7 @@ def get_ffmpeg_read_cmd(state: AppState, frame_start: int | None, output="-", di
         # VAAPI requires specific pixel formats and filters
         video_filter = ["-vf", f"{vf},format=nv12,hwupload"] if vf else ["-vf", "format=nv12,hwupload"]
 
-    if supports_cuda_scale(video, state.ffmpeg_hwaccel):
+    if supports_cuda_scale(state):
         video_filter = ["-noautoscale"] + video_filter  # explicitly tell ffmpeg that scaling is done by cuda
 
     frame_size = width * height * 3  # Size of one frame in bytes
diff --git a/script_generator/video/ffmpeg/filters.py b/script_generator/video/ffmpeg/filters.py
index bfbc58f2..45bfd908 100644
--- a/script_generator/video/ffmpeg/filters.py
+++ b/script_generator/video/ffmpeg/filters.py
@@ -1,28 +1,30 @@
 from script_generator.constants import RENDER_RESOLUTION, VR_TO_2D_PITCH
+from script_generator.state.app_state import AppState
 from script_generator.video.ffmpeg.hwaccel import supports_cuda_scale
 
 
 def get_video_filters(video, video_reader, hwaccel, width, height, disable_opengl=False):
     if video.is_vr:
-        return get_vr_video_filters(video, video_reader, hwaccel, disable_opengl)
+        return get_vr_video_filters(video, disable_opengl)
     else:
-        return get_2d_video_filters(video, width, height, hwaccel)
+        return get_2d_video_filters(video, width, height)
 
-def get_vr_video_filters(video, video_reader, hwaccel, disable_opengl=False):
+def get_vr_video_filters(video, disable_opengl=False):
+    state = AppState()
     fov = int(video.fov * 1)
     if video.is_fisheye:
         projection, iv_fov, ih_fov, v_fov, h_fov, d_fov = "fisheye", fov, fov, 90, 90, fov
     else:
         projection, iv_fov, ih_fov, v_fov, h_fov, d_fov = "he", fov, fov, 90, 90, fov
 
-    cuda = hwaccel == "cuda"
+    cuda = state.ffmpeg_hwaccel == "cuda"
 
     # hardware accelerated output is not supported with > 8 bit
-    scale = f"[0:0]scale_cuda={RENDER_RESOLUTION * 2}:-2,hwdownload" if supports_cuda_scale(video, hwaccel) else f"[0:0]scale={RENDER_RESOLUTION * 2}:-2"
+    scale = f"[0:v]scale_cuda={RENDER_RESOLUTION * 2}:-2,hwdownload" if supports_cuda_scale(state) else f"[0:v]scale={RENDER_RESOLUTION * 2}:-2"
     crop = f"crop={RENDER_RESOLUTION}:{RENDER_RESOLUTION}:0:0"
     out_format = f"format=nv12," if cuda else ""
 
-    if video_reader == "FFmpeg" or disable_opengl:
+    if state.video_reader == "FFmpeg" or disable_opengl:
         filters = [
             scale,
             crop,
@@ -41,13 +43,14 @@ def get_vr_video_filters(video, video_reader, hwaccel, disable_opengl=False):
     return f"{','.join(filters)}"
 
 
-def get_2d_video_filters(video, width, height, hwaccel):
-    cuda = hwaccel == "cuda"
+def get_2d_video_filters(video, width, height):
+    state = AppState()
+    cuda = state.hwaccel == "cuda"
 
     if video.height > RENDER_RESOLUTION:
         scale_width = int(video.width * (height / video.height))
         crop = f",crop={width}:{height}:(iw-{width})/2:0"
         # hardware accelerated output is not supported with > 8 bit
-        return f"[0:0]scale_cuda={scale_width}:{height},hwdownload,format=nv12{crop}" if supports_cuda_scale(video, hwaccel) else f"[0:0]scale={scale_width}:{height}{crop}"
+        return f"[0:v]scale_cuda={scale_width}:{height},hwdownload,format=nv12{crop}" if supports_cuda_scale(state) else f"[0:v]scale={scale_width}:{height}{crop}"
     else:
-        return "[0:0]hwdownload,format=nv12" if cuda else ""
\ No newline at end of file
+        return "[0:v]hwdownload,format=nv12" if cuda else ""
\ No newline at end of file
diff --git a/script_generator/video/ffmpeg/hwaccel.py b/script_generator/video/ffmpeg/hwaccel.py
index 1da9b1d2..7765a47d 100644
--- a/script_generator/video/ffmpeg/hwaccel.py
+++ b/script_generator/video/ffmpeg/hwaccel.py
@@ -1,7 +1,11 @@
 import subprocess
+from typing import TYPE_CHECKING
 
 from script_generator.debug.logger import log_vid
 
+if TYPE_CHECKING:
+    from script_generator.state.app_state import AppState
+
 HW_TEST_CMDS = {
     "cuda": [
         "-init_hw_device", "cuda=0",
@@ -39,6 +43,7 @@
     ]
 }
 
+
 def _run_cmd(cmd):
     try:
         r = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
@@ -46,6 +51,7 @@ def _run_cmd(cmd):
     except Exception as e:
         return (False, str(e))
 
+
 def _list_ffmpeg_hwaccels(ffmpeg_path):
     try:
         r = subprocess.run(
@@ -62,6 +68,7 @@ def _list_ffmpeg_hwaccels(ffmpeg_path):
         log_vid.error(e)
         return []
 
+
 def _test_hwaccel(ffmpeg_path, hw):
     if hw in HW_TEST_CMDS:
         cmd = [ffmpeg_path] + HW_TEST_CMDS[hw]
@@ -71,15 +78,17 @@ def _test_hwaccel(ffmpeg_path, hw):
     ok, _ = _run_cmd([ffmpeg_path, "-init_hw_device", hw])
     return ok
 
+
 def get_preferred_hwaccel(ffmpeg_path):
     supported = _list_ffmpeg_hwaccels(ffmpeg_path)
-    for hw in ["cuda","vaapi","amf","videotoolbox","qsv","d3d11va"]:
+    for hw in ["cuda", "vaapi", "amf", "videotoolbox", "qsv", "d3d11va"]:
         if hw in supported and _test_hwaccel(ffmpeg_path, hw):
             log_vid.info(f"Setting preferred FFmpeg hardware acceleration too: {hw}")
             return hw
     log_vid.info("No working hwaccel found.")
     return None
 
+
 def get_hwaccel_read_args(video_info, hwaccel):
     if hwaccel == "cuda":
         if video_info.bit_depth == 8:
@@ -97,9 +106,31 @@ def get_hwaccel_read_args(video_info, hwaccel):
         return ["-hwaccel", "d3d11va"]
     return []
 
-def supports_cuda_scale(video, ffmpeg_hwaccel):
+
+def _has_scale_cuda(ffmpeg_path):
+    # Check if FFmpeg supports the scale_cuda filter.
+    try:
+        r = subprocess.run(
+            [ffmpeg_path, "-hide_banner", "-filters"],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            text=True,
+            check=True
+        )
+        filters = r.stdout.lower()
+        has_scale_cuda = "scale_cuda" in filters
+        log_vid.info(f"FFmpeg has scale_cuda: {has_scale_cuda}")
+        return has_scale_cuda
+    except Exception as e:
+        log_vid.error(f"Failed to check scale_cuda support: {e}")
+        return False
+
+
+def supports_cuda_scale(state: "AppState"):
+    video = state.video_info
     return (
-        ffmpeg_hwaccel == "cuda"
+        state.ffmpeg_hwaccel == "cuda"
         and video.bit_depth == 8
         and (video.codec_name != "h264" or (video.width <= 4096 and video.height <= 4096))
+        and _has_scale_cuda(state.ffmpeg_path)
     )
diff --git a/script_generator/video/workers/ffmpeg_worker.py b/script_generator/video/workers/ffmpeg_worker.py
index e698a5c7..72757940 100644
--- a/script_generator/video/workers/ffmpeg_worker.py
+++ b/script_generator/video/workers/ffmpeg_worker.py
@@ -5,13 +5,14 @@
 from script_generator.debug.errors import FFMpegError
 from script_generator.debug.logger import log, log_vid
 from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
-from script_generator.tasks.tasks import AnalyzeFrameTask
+from script_generator.video.analyse_frame_task import AnalyzeFrameTask
 from script_generator.video.ffmpeg.commands import get_ffmpeg_read_cmd
 
 
 class VideoWorker(AbstractTaskProcessor):
     process_type = TaskProcessorTypes.VIDEO
     process = None
+    read_frames = True
 
     def task_logic(self):
         cmd, frame_size, width, height = get_ffmpeg_read_cmd(
@@ -24,7 +25,7 @@ def task_logic(self):
         current_frame = self.state.frame_start
 
         try:
-            while True:
+            while self.read_frames:
                 in_bytes = self.process.stdout.read(frame_size)
                 if not in_bytes:
                     if current_frame == self.state.frame_start:
@@ -49,20 +50,23 @@ def task_logic(self):
                 current_frame += 1
 
         except Exception as e:
-            log_vid.error(f"Error reading frame: {e}")
-            raise
+            # Suppress any errors when the thread is force closed
+            if self.read_frames:
+                log_vid.error(f"Error reading frame: {e}")
+                raise e
 
         finally:
             self.stop_process()
             self.release()
 
     def release(self):
+        log_vid.debug("Stopping FFmpeg reader")
+        self.read_frames = False
+        self.stop_process()
         if self.process:
             self.process.terminate()
             try:
                 self.process.wait(timeout=1)
             except subprocess.TimeoutExpired:
                 self.process.kill()
-            self.process.stdout.close()
-            self.process.stderr.close()
             self.process = None
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index 179df1e1..c1c1e62a 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -7,12 +7,11 @@
 import cv2
 import matplotlib
 import numpy as np
-from matplotlib.ticker import FuncFormatter, MaxNLocator
 from scipy.signal import savgol_filter
 from simplification.cutil import simplify_coords
 
-from script_generator.constants import STEP_SIZE, VERSION
 from script_generator.constants import HEATMAP_COLORS, FUNSCRIPT_AUTHOR
+from script_generator.constants import STEP_SIZE, VERSION
 from script_generator.debug.logger import log
 from script_generator.state.app_state import AppState
 from script_generator.utils.file import get_output_file_path
@@ -53,7 +52,7 @@ def generate(self, state):
 
             log.info(f"Positions adjustment - step 1 (noise removal)")
             # Run the Savitzky-Golay filter
-            positions = savgol_filter(positions, int(state.video_info.fps) // 4, 3)
+            positions = savgol_filter(positions, int(state.video_info.fps // 4), 3)
 
             # zip adjusted positions
             zip_positions = list(zip(ats, positions))
@@ -127,7 +126,7 @@ def generate(self, state):
                     if "author" in json_data and json_data["author"] == FUNSCRIPT_AUTHOR:
                         backup_path = os.path.join(video_folder, f"{filename_base}_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.funscript.bak")
                         log.info(f"Funscript {funscript_path} already exists, backing up as {backup_path}...")
-                        os.rename(funscript_path, backup_path)
+                        shutil.move(funscript_path, backup_path)
                     else:
                         copy = False
                         log.warn(f"Skipping copying funscript to movie directory as the script in the destination directory is not made by this app.")
@@ -482,13 +481,7 @@ def capture_screenshots(self, video_path, is_vr, sections):
         screenshots = []
         for start, _ in sections:
             cap.set(cv2.CAP_PROP_POS_MSEC, start * 1000)
-            ret, frame = cap.read()
-            if is_vr:  # left side of the frame only
-                frame = frame[:, :frame.shape[1] // 2]
-            if ret:
-                screenshots.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
-            else:
-                screenshots.append(np.zeros((100, 160, 3), dtype=np.uint8))
+            screenshots.append(np.zeros((100, 160, 3), dtype=np.uint8))
         cap.release()
         return screenshots
 

From 9cd26396a9dd8f9d3bce652445a5d42c118f357b Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sat, 1 Feb 2025 18:16:59 +0100
Subject: [PATCH 210/277] Fixed 2d hwaccel Finished final funscript copy logic

---
 script_generator/config/config_manager.py |  6 +++
 script_generator/constants.py             |  3 +-
 script_generator/gui/app.py               |  2 +-
 script_generator/gui/views/settings.py    | 14 ++++++-
 script_generator/state/app_state.py       |  1 +
 script_generator/video/ffmpeg/filters.py  |  2 +-
 script_generator/video/ffmpeg/hwaccel.py  | 13 ++++--
 utils/lib_FunscriptHandler.py             | 49 ++++++++++++++---------
 8 files changed, 63 insertions(+), 27 deletions(-)

diff --git a/script_generator/config/config_manager.py b/script_generator/config/config_manager.py
index bca326e7..09ae6990 100644
--- a/script_generator/config/config_manager.py
+++ b/script_generator/config/config_manager.py
@@ -48,6 +48,12 @@ def _initialize_defaults(self):
         return self.config
 
     def _ensure_defaults(self, config):
+        # Add any missing keys from DEFAULT_CONFIG with their default values.
+        for key, default_value in DEFAULT_CONFIG.items():
+            if key not in config:
+                config[key] = default_value
+
+        # additional checks to verify that certain keys have valid values,
         checks = [
             ("ffmpeg_path", lambda: get_ffmpeg_paths()[0], self._is_valid_path),
             ("ffprobe_path", lambda: get_ffmpeg_paths()[1], self._is_valid_path),
diff --git a/script_generator/constants.py b/script_generator/constants.py
index 311b0a20..ee39dfaa 100644
--- a/script_generator/constants.py
+++ b/script_generator/constants.py
@@ -1,7 +1,7 @@
 import os
 import platform
 
-VERSION = "0.1.1"
+VERSION = "0.2.0"
 YOLO_VERSION = 1
 FUNSCRIPT_VERSION = 1
 CONFIG_VERSION = 1
@@ -47,6 +47,7 @@
     "yolo_model_path": None,
     "copy_funscript_to_movie_dir": True,
     "funscript_output_dir": None,
+    "make_funscript_backup": True,
     "log_level": "INFO"
 }
 
diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index 0c1c5c49..318a26f6 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -22,7 +22,7 @@ def __init__(self):
             ctypes.windll.shcore.SetProcessDpiAwareness(2)  # For Windows DPI scaling
         # self.tk.call('tk', 'scaling', 1.0)
         self.title(f"VR & 2D POV Funscript AI Generator - v" + VERSION)
-        self.geometry('800x900' if is_mac() else '722x900')
+        self.geometry('800x900' if is_mac() else '722x790')
         self.resizable(False, False)
 
         self.iconphoto(False, tk.PhotoImage(file=LOGO))
diff --git a/script_generator/gui/views/settings.py b/script_generator/gui/views/settings.py
index f92428e0..7e3a56bb 100644
--- a/script_generator/gui/views/settings.py
+++ b/script_generator/gui/views/settings.py
@@ -1,9 +1,8 @@
 import tkinter as tk
-from tkinter import ttk
 
 from script_generator.debug.logger import set_log_level
-from script_generator.state.app_state import AppState
 from script_generator.gui.utils.widgets import Widgets
+from script_generator.state.app_state import AppState
 
 
 class SettingsPage(tk.Frame):
@@ -41,6 +40,17 @@ def __init__(self, parent, controller):
             row=1
         )
 
+        Widgets.checkbox(
+            general_settings,
+            "Backup old funscript",
+            state=self.state,
+            attr="make_funscript_backup",
+            default_value=False,
+            tooltip_text="Makes a backup of any funscript that might exist in the output location.\nFunscripts not made by this app are never overwritten and skipped.",
+            command=lambda val: c.save(),
+            row=2
+        )
+
         ffmpeg_settings = Widgets.frame(self, title="YOLO (restart to apply changes)", main_section=True, row=1)
 
         Widgets.file_selection(
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index 76fec600..baa412db 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -40,6 +40,7 @@ def __init__(self):
         self.copy_funscript_to_movie_dir = True
         self.copy_funscript_to_movie_dir = c.get("copy_funscript_to_movie_dir")
         self.funscript_output_dir = c.get("funscript_output_dir")
+        self.make_funscript_backup = c.get("make_funscript_backup")
         self.ffmpeg_path = c.get("ffmpeg_path")
         self.ffprobe_path = c.get("ffprobe_path")
         self.yolo_model_path = c.get("yolo_model_path")
diff --git a/script_generator/video/ffmpeg/filters.py b/script_generator/video/ffmpeg/filters.py
index 45bfd908..ffb8a1fe 100644
--- a/script_generator/video/ffmpeg/filters.py
+++ b/script_generator/video/ffmpeg/filters.py
@@ -45,7 +45,7 @@ def get_vr_video_filters(video, disable_opengl=False):
 
 def get_2d_video_filters(video, width, height):
     state = AppState()
-    cuda = state.hwaccel == "cuda"
+    cuda = state.ffmpeg_hwaccel == "cuda"
 
     if video.height > RENDER_RESOLUTION:
         scale_width = int(video.width * (height / video.height))
diff --git a/script_generator/video/ffmpeg/hwaccel.py b/script_generator/video/ffmpeg/hwaccel.py
index 7765a47d..f57bc03a 100644
--- a/script_generator/video/ffmpeg/hwaccel.py
+++ b/script_generator/video/ffmpeg/hwaccel.py
@@ -107,7 +107,12 @@ def get_hwaccel_read_args(video_info, hwaccel):
     return []
 
 
+scale_cuda = None
 def _has_scale_cuda(ffmpeg_path):
+    global scale_cuda
+    if scale_cuda is not None:
+        return scale_cuda
+
     # Check if FFmpeg supports the scale_cuda filter.
     try:
         r = subprocess.run(
@@ -118,14 +123,14 @@ def _has_scale_cuda(ffmpeg_path):
             check=True
         )
         filters = r.stdout.lower()
-        has_scale_cuda = "scale_cuda" in filters
-        log_vid.info(f"FFmpeg has scale_cuda: {has_scale_cuda}")
-        return has_scale_cuda
+        scale_cuda = "scale_cuda" in filters
+        log_vid.info(f"FFmpeg {'supports' if scale_cuda else 'does not support'} scale_cuda")
+        return scale_cuda
     except Exception as e:
         log_vid.error(f"Failed to check scale_cuda support: {e}")
+        scale_cuda = False
         return False
 
-
 def supports_cuda_scale(state: "AppState"):
     video = state.video_info
     return (
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
index c1c1e62a..8f1a5e39 100644
--- a/utils/lib_FunscriptHandler.py
+++ b/utils/lib_FunscriptHandler.py
@@ -114,27 +114,40 @@ def generate(self, state):
             self.write_funscript(zip_adjusted_positions, output_path, state.video_info.fps)
 
             # copy funscript if specified
-            if state.copy_funscript_to_movie_dir or state.funscript_output_dir:
-                copy = True
-                video_folder = os.path.dirname(state.video_path)
-                filename_base = os.path.basename(state.video_path)[:-4]
-                funscript_path = os.path.join(state.funscript_output_dir, f"{filename_base}.funscript") if state.funscript_output_dir else os.path.join(video_folder, f"{filename_base}.funscript")
-
-                # Backup output file if it exists
-                if os.path.exists(funscript_path):
-                    json_data = load_json_from_file(funscript_path)
-                    if "author" in json_data and json_data["author"] == FUNSCRIPT_AUTHOR:
-                        backup_path = os.path.join(video_folder, f"{filename_base}_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.funscript.bak")
-                        log.info(f"Funscript {funscript_path} already exists, backing up as {backup_path}...")
-                        shutil.move(funscript_path, backup_path)
+            destinations = {}
+            video_folder = os.path.dirname(state.video_path)
+            filename_base = os.path.splitext(os.path.basename(state.video_path))[0]
+
+            if state.copy_funscript_to_movie_dir:
+                movie_dest = os.path.join(video_folder, f"{filename_base}.funscript")
+                destinations["movie"] = movie_dest
+
+            if state.funscript_output_dir:
+                output_dest = os.path.join(state.funscript_output_dir, f"{filename_base}.funscript")
+                destinations["output"] = output_dest
+
+            for dest_name, dest_path in destinations.items():
+                do_copy = True
+
+                if os.path.exists(dest_path):
+                    json_data = load_json_from_file(dest_path)
+
+                    if json_data.get("author") == FUNSCRIPT_AUTHOR:
+                        if state.make_funscript_backup:
+                            timestamp = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
+                            # Use the destination's directory for the backup path
+                            backup_path = os.path.join(os.path.dirname(dest_path), f"{filename_base}_{timestamp}.funscript.bak")
+                            log.info(f"Funscript made by this app already exists in {dest_path}, backing up as {backup_path}...")
+                            shutil.move(dest_path, backup_path)
                     else:
-                        copy = False
-                        log.warn(f"Skipping copying funscript to movie directory as the script in the destination directory is not made by this app.")
+                        log.warn(f"Skipping copying funscript to {dest_path} because the existing funscript in this destination was not created by this app.")
+                        do_copy = False
 
-                if copy:
-                    shutil.copy(output_path, funscript_path)
+                if do_copy:
+                    shutil.copy(output_path, dest_path)
+                    log.info(f"Copied funscript to {dest_path}.")
 
-            log.info(f"Funscript generated and saved to {output_path}")
+            log.info(f"Funscript generation complete")
 
             # Generate a heatmap
             self.generate_heatmap(output_path, output_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")

From 1301e3fcbf91b707ee522147b53899f7876f3795 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sat, 1 Feb 2025 18:26:50 +0100
Subject: [PATCH 211/277] Fixed hwacceleration detection logic

---
 script_generator/scripts/generate_debug_video.py | 2 +-
 script_generator/video/ffmpeg/commands.py        | 2 +-
 script_generator/video/ffmpeg/hwaccel.py         | 5 +++--
 3 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/script_generator/scripts/generate_debug_video.py b/script_generator/scripts/generate_debug_video.py
index c3c61bcb..4edae236 100644
--- a/script_generator/scripts/generate_debug_video.py
+++ b/script_generator/scripts/generate_debug_video.py
@@ -20,7 +20,7 @@ def run():
         debug_video_path, _ = get_output_file_path(state.video_path, "_debug.mp4", True)
         ffmpeg_command = [
             state.ffmpeg_path, "-y",
-            *get_hwaccel_read_args(state.video_info, state.ffmpeg_hwaccel),
+            *get_hwaccel_read_args(state),
             "-i", temp_video_path,
             "-c:v", "hevc_nvenc" if state.ffmpeg_hwaccel == "cuda" else "libx265",
             "-preset", "p4" if state.ffmpeg_hwaccel == "cuda" else "fast",
diff --git a/script_generator/video/ffmpeg/commands.py b/script_generator/video/ffmpeg/commands.py
index 6e3e7239..07ef4e92 100644
--- a/script_generator/video/ffmpeg/commands.py
+++ b/script_generator/video/ffmpeg/commands.py
@@ -10,7 +10,7 @@ def get_ffmpeg_read_cmd(state: AppState, frame_start: int | None, output="-", di
     start_time = (frame_start / video.fps) * 1000
 
     # Get supported hardware acceleration backends
-    hwaccel_read = get_hwaccel_read_args(video, state.ffmpeg_hwaccel)
+    hwaccel_read = get_hwaccel_read_args(state)
 
     video_filter = ["-vf", vf] if vf else []
     if state.ffmpeg_hwaccel == "vaapi":
diff --git a/script_generator/video/ffmpeg/hwaccel.py b/script_generator/video/ffmpeg/hwaccel.py
index f57bc03a..0544b35a 100644
--- a/script_generator/video/ffmpeg/hwaccel.py
+++ b/script_generator/video/ffmpeg/hwaccel.py
@@ -89,9 +89,10 @@ def get_preferred_hwaccel(ffmpeg_path):
     return None
 
 
-def get_hwaccel_read_args(video_info, hwaccel):
+def get_hwaccel_read_args(state):
+    hwaccel = state.ffmpeg_hwaccel
     if hwaccel == "cuda":
-        if video_info.bit_depth == 8:
+        if supports_cuda_scale(state):
             return ["-hwaccel", "cuda", "-hwaccel_output_format", "cuda"]
         return ["-hwaccel", "cuda"]
     if hwaccel == "vaapi":

From 6d6b138fd35215f558cbf72ea77a561e6daa3b01 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 2 Feb 2025 16:42:57 +0100
Subject: [PATCH 212/277] Process folder cli command and examples Added
 versioning to msgpack Added open gui.bat to output folders Added
 metadata.json to output folders Cleaned up lib_FunscriptHandler.py Added
 basic folder cli (still without parallelization) Various

---
 README.md                                     |  70 +-
 examples/windows/Process folder.bat           |  19 +
 examples/windows/Processs single video.bat    |  19 +
 .../workers}/analyze_tracking_results.py      |  49 +-
 script_generator/cli/generate_funscript.py    | 148 ---
 .../cli/generate_funscript_folder.py          | 180 ++++
 .../cli/generate_funscript_single.py          |  50 +
 .../cli/generate_funscript_txt_file.py        |   1 +
 script_generator/cli/open_gui_from_meta.py    |  37 +
 script_generator/cli/shared/common_args.py    | 169 ++++
 .../shared}/generate_funscript.py             |   6 +-
 script_generator/config/config_manager.py     |   2 +-
 script_generator/constants.py                 |   6 +-
 .../debug/Display_debug_results.py            |  18 -
 script_generator/debug/debug_data.py          |  41 +-
 script_generator/debug/video_player/play.py   |   9 +-
 .../funscript/create_funscript.py             | 108 +++
 .../funscript/debug/combined_plot.py          | 164 ++++
 script_generator/funscript/debug/heatmap.py   | 191 ++++
 script_generator/funscript/debug/report.py    | 102 ++
 script_generator/funscript/util.py            |  16 -
 .../funscript/util/adjust_peaks_and_lows.py   | 140 +++
 .../util/check_existing_funscript.py          |  26 +
 .../funscript/util/export_funscript.py        |  43 +
 .../funscript/util/funscript_utils.py         |  94 ++
 script_generator/funscript/util/util.py       |  80 ++
 script_generator/gui/app.py                   |  10 +-
 .../gui/controller/debug_video.py             |   8 +-
 ...process_video.py => generate_funscript.py} |   4 +-
 .../gui/controller/regenerate_funscript.py    |  31 +-
 script_generator/gui/utils/widgets.py         |   4 +-
 .../gui/views/funscript_generator.py          |  40 +-
 .../{ => data_classes}/box_record.py          |   0
 .../object_detection_result.py                |   0
 .../util/{utils.py => data.py}                |  49 +-
 .../{utils.py => util/object_detection.py}    |  18 +-
 .../{ => workers}/post_process_worker.py      |  11 +-
 .../{ => workers}/yolo_worker.py              |   5 +-
 script_generator/scripts/analyze_video.py     |  11 +-
 .../scripts/generate_debug_video.py           |   2 +-
 script_generator/scripts/tracking_analysis.py |  59 +-
 script_generator/state/app_state.py           |  17 +-
 .../tasks/{ => data_classes}/abstract_task.py |   0
 .../{ => data_classes}/analyze_video_task.py  |   6 +-
 .../{ => workers}/abstract_task_processor.py  |   0
 .../utils/data_classes/meta_data.py           | 128 +++
 script_generator/utils/file.py                |  25 +-
 script_generator/utils/json_utils.py          |   5 +-
 script_generator/utils/msgpack_utils.py       |  16 -
 script_generator/utils/terminal.py            |  53 ++
 script_generator/utils/version.py             |   9 +
 script_generator/video/analyse_frame_task.py  |   2 +-
 .../{info => data_classes}/video_info.py      |  15 +-
 script_generator/video/ffmpeg/commands.py     |   2 +-
 .../video/workers/ffmpeg_worker.py            |   4 +-
 .../video/workers/vr_to_2d_worker.py          |   2 +-
 tests/test_detect_compare_unwarped.py         |  60 --
 tests/test_generate_funscript.py              |  14 +-
 utils/lib_FunscriptHandler.py                 | 870 ------------------
 utils/lib_ObjectTracker.py                    |  16 +-
 60 files changed, 1888 insertions(+), 1396 deletions(-)
 create mode 100644 examples/windows/Process folder.bat
 create mode 100644 examples/windows/Processs single video.bat
 rename script_generator/{object_detection => analysis/workers}/analyze_tracking_results.py (85%)
 delete mode 100644 script_generator/cli/generate_funscript.py
 create mode 100644 script_generator/cli/generate_funscript_folder.py
 create mode 100644 script_generator/cli/generate_funscript_single.py
 create mode 100644 script_generator/cli/generate_funscript_txt_file.py
 create mode 100644 script_generator/cli/open_gui_from_meta.py
 create mode 100644 script_generator/cli/shared/common_args.py
 rename script_generator/{scripts => cli/shared}/generate_funscript.py (81%)
 delete mode 100644 script_generator/debug/Display_debug_results.py
 create mode 100644 script_generator/funscript/create_funscript.py
 create mode 100644 script_generator/funscript/debug/combined_plot.py
 create mode 100644 script_generator/funscript/debug/heatmap.py
 create mode 100644 script_generator/funscript/debug/report.py
 delete mode 100644 script_generator/funscript/util.py
 create mode 100644 script_generator/funscript/util/adjust_peaks_and_lows.py
 create mode 100644 script_generator/funscript/util/check_existing_funscript.py
 create mode 100644 script_generator/funscript/util/export_funscript.py
 create mode 100644 script_generator/funscript/util/funscript_utils.py
 create mode 100644 script_generator/funscript/util/util.py
 rename script_generator/gui/controller/{process_video.py => generate_funscript.py} (94%)
 rename script_generator/object_detection/{ => data_classes}/box_record.py (100%)
 rename script_generator/object_detection/{ => data_classes}/object_detection_result.py (100%)
 rename script_generator/object_detection/util/{utils.py => data.py} (56%)
 rename script_generator/object_detection/{utils.py => util/object_detection.py} (81%)
 rename script_generator/object_detection/{ => workers}/post_process_worker.py (94%)
 rename script_generator/object_detection/{ => workers}/yolo_worker.py (86%)
 rename script_generator/tasks/{ => data_classes}/abstract_task.py (100%)
 rename script_generator/tasks/{ => data_classes}/analyze_video_task.py (90%)
 rename script_generator/tasks/{ => workers}/abstract_task_processor.py (100%)
 create mode 100644 script_generator/utils/data_classes/meta_data.py
 create mode 100644 script_generator/utils/terminal.py
 create mode 100644 script_generator/utils/version.py
 rename script_generator/video/{info => data_classes}/video_info.py (92%)
 delete mode 100644 tests/test_detect_compare_unwarped.py
 delete mode 100644 utils/lib_FunscriptHandler.py

diff --git a/README.md b/README.md
index f2e62e21..1d425458 100644
--- a/README.md
+++ b/README.md
@@ -147,60 +147,28 @@ You can use Start windows.bat to launch the gui on windows if you installed with
 To generate a script with cmd or terminal, run the following command
 
 ```bash
-python -m script_generator.cli.generate_funscript /path/to/video.mp4
+python -m script_generator.cli.generate_funscript_single /path/to/video.mp4
 ```
 
 ### Command-Line Arguments
 #### Required Arguments
-- **`video_path`** *(str)*  
-  Path to the input video file.  
+- **`video_path`** Path to the input video file.  
 
 #### Optional Arguments
-- **`--reuse-yolo`** *(bool, default=False)*  
-  Re-use an existing raw YOLO output file instead of generating a new one when available.
-
-- **`--copy-funscript`** *(bool, default=True)*  
-  Copies the final funscript to the movie directory.
-
-- **`--frame-start`** *(int, default=0)*  
-  The starting frame number for processing (keep in Mind that this shouldn't be changed if you want a full funscript). Default is 0 (start from the beginning).
-
-- **`--frame-end`** *(int or None, default=None)*  
-  The ending frame number for processing (keep in Mind that this shouldn't be changed if you want a full funscript). Default is None (process till the end).
-
-- **`--video-reader`** *(str, default=None)*  
-  Video reader to use. Valid options: `"FFmpeg", "FFmpeg + OpenGL (Windows)"`. Defaults are platform-specific.
-
-- **`--save-debug-file`** *(bool, default=True)*  
-  Saves a debug file to disk with all collected metrics. Also allows you to re-use tracking data.
-
-#### Funscript Tweaking Settings
-- **`--boost-enabled`** *(bool, default=True)*  
-  Enable boosting to adjust the motion range dynamically.
-
-- **`--boost-up-percent`** *(int, default=10)*  
-  Increase the peaks by a specified percentage to enhance upper motion limits.
-
-- **`--boost-down-percent`** *(int, default=15)*  
-  Reduce the lower peaks by a specified percentage to limit downward motion.
-
-- **`--threshold-enabled`** *(bool, default=True)*  
-  Enable thresholding to control motion mapping within specified bounds.
-
-- **`--threshold-low`** *(int, default=10)*  
-  Values below this threshold are mapped to 0, limiting lower boundary motion.
-
-- **`--threshold-high`** *(int, default=90)*  
-  Values above this threshold are mapped to 100, limiting upper boundary motion.
-
-- **`--vw-simplification-enabled`** *(bool, default=True)*  
-  Simplify the generated script to reduce the number of points, making it user-friendly.
-
-- **`--vw-factor`** *(float, default=8.0)*  
-  Determines the degree of simplification. Higher values lead to fewer points.
-
-- **`--rounding`** *(int, default=5)*  
-  Set the rounding factor for script values to adjust precision.
+- **`--reuse-yolo`** Re-use an existing raw YOLO output file instead of generating a new one when available.
+- **`--copy-funscript`** Copies the final funscript to the movie directory.
+- **`--save-debug-file`** Saves a debug file to disk with all collected metrics. Also allows you to re-use tracking data.
+
+#### Optional Funscript Tweaking Settings
+- **`--boost-enabled`** Enable boosting to adjust the motion range dynamically.
+- **`--boost-up-percent`** Increase the peaks by a specified percentage to enhance upper motion limits.
+- **`--boost-down-percent`** Reduce the lower peaks by a specified percentage to limit downward motion.
+- **`--threshold-enabled`** Enable thresholding to control motion mapping within specified bounds.
+- **`--threshold-low`** Values below this threshold are mapped to 0, limiting lower boundary motion.
+- **`--threshold-high`** Values above this threshold are mapped to 100, limiting upper boundary motion.
+- **`--vw-simplification-enabled`** Simplify the generated script to reduce the number of points, making it user-friendly.
+- **`--vw-factor`** Determines the degree of simplification. Higher values lead to fewer points.
+- **`--rounding`** Set the rounding factor for script values to adjust precision.
 
 ---
 
@@ -234,14 +202,14 @@ See config.py for customizations and user settings (will be replaced with a yaml
 
 The script generates the following files in the output directory of you project folder:
 
-1. `_rawyolo.json`: Raw YOLO detection data. Can be re-used when re-generating scripts 
-2. `_cuts.json`: Detected scene changes.
+1. `_rawyolo.msgpack`: Raw YOLO detection data. Can be re-used when re-generating scripts 
+2. `_cuts.msgpack`: Detected scene changes.
 3. `_rawfunscript.json`: Raw Funscript data. Can be re-used when re-generating script with different settings.
 4. `.funscript`: Final Funscript file.
 5. `_heatmap.png`: Heatmap visualization of the Funscript data.
 6. `_comparefunscripts.png`: Comparison visualization between the generated Funscript and the reference Funscript (if provided).
 7. `_adjusted.funscript`: Funscript file with adjusted amplitude.
-8. `_debug_logs.json`: Contains all the raw metrics collected and can be used to debug your video when processing is completed.
+8. `_metrics.msgpack`: Contains all the raw metrics collected and can be used to debug your video when processing is completed.
 
 ---
 
diff --git a/examples/windows/Process folder.bat b/examples/windows/Process folder.bat
new file mode 100644
index 00000000..5f5fdf8e
--- /dev/null
+++ b/examples/windows/Process folder.bat	
@@ -0,0 +1,19 @@
+@echo off
+REM %~dp0 means current dir, we use \..\.. to move up two directories to the root of the project
+REM you can replace this to the hardcoded path to your directory to make the bat file work from anywhere
+cd /d "%~dp0\..\.."
+
+REM Set the path to your conda activation script and activate the environment.
+set "CONDA_PATH=C:\Users\%USERNAME%\miniconda3\Scripts\activate.bat"
+if not exist "%CONDA_PATH%" (
+    echo Conda not found at %CONDA_PATH%. Please check your installation.
+    pause
+    exit /b 1
+)
+REM Activate the conda environment
+call "%CONDA_PATH%" VRFunAIGen
+
+REM Run the CLI command. Your path would look something like this on windows "C:\my-video-folder"
+python -m script_generator.cli.generate_funscript_folder /path/to/your/folder --num-workers=2 --replace-outdated
+
+pause
\ No newline at end of file
diff --git a/examples/windows/Processs single video.bat b/examples/windows/Processs single video.bat
new file mode 100644
index 00000000..640c0d31
--- /dev/null
+++ b/examples/windows/Processs single video.bat	
@@ -0,0 +1,19 @@
+@echo off
+REM %~dp0 means current dir, we use \..\.. to move up two directories to the root of the project
+REM you can replace this to the hardcoded path to your directory to make the bat file work from anywhere
+cd /d "%~dp0\..\.."
+
+REM Set the path to your conda activation script and activate the environment.
+set "CONDA_PATH=C:\Users\%USERNAME%\miniconda3\Scripts\activate.bat"
+if not exist "%CONDA_PATH%" (
+    echo Conda not found at %CONDA_PATH%. Please check your installation.
+    pause
+    exit /b 1
+)
+REM Activate the conda environment
+call "%CONDA_PATH%" VRFunAIGen
+
+REM Run the CLI command. Your path would look something like this on windows "C:\my-movie.mp4"
+python -m script_generator.cli.generate_funscript_single /path/to/your/video.mp4
+
+pause
\ No newline at end of file
diff --git a/script_generator/object_detection/analyze_tracking_results.py b/script_generator/analysis/workers/analyze_tracking_results.py
similarity index 85%
rename from script_generator/object_detection/analyze_tracking_results.py
rename to script_generator/analysis/workers/analyze_tracking_results.py
index 80034839..44d6b481 100644
--- a/script_generator/object_detection/analyze_tracking_results.py
+++ b/script_generator/analysis/workers/analyze_tracking_results.py
@@ -9,31 +9,44 @@
 from script_generator.constants import CLASS_COLORS
 from script_generator.debug.video_player.overlay_widgets import OverlayWidgets
 from script_generator.gui.messages.messages import ProgressMessage, UpdateGUIState
+from script_generator.object_detection.util.data import load_yolo_data
+from script_generator.object_detection.util.object_detection import make_data_boxes, parse_yolo_data_looking_for_penis
 from script_generator.state.app_state import AppState
 from script_generator.utils.file import get_output_file_path
-from script_generator.debug.logger import log
+from script_generator.debug.logger import log, log_tr
 from script_generator.video.ffmpeg.video_reader import VideoReaderFFmpeg
-from script_generator.video.info.video_info import get_cropped_dimensions
+from script_generator.video.data_classes.video_info import get_cropped_dimensions
 from utils.lib_ObjectTracker import ObjectTracker
 
 
-def analyze_tracking_results(state: AppState, results):
+def analyze_tracking_results(state: AppState):
+    exists, yolo_data, raw_yolo_path, _ = load_yolo_data(state)
+    results = make_data_boxes(yolo_data)
     width, height = get_cropped_dimensions(state.video_info)
     list_of_frames = results.get_all_frame_ids()  # Get all frame IDs with detections
 
+    # Looking for the first instance of penis within the YOLO results
+    first_penis_frame = parse_yolo_data_looking_for_penis(yolo_data, 0)
+    if first_penis_frame is None:
+        log_tr.error(f"No penis instance found in video. Further tracking is not possible.")
+        return
+
+    # Deciding whether we start from there or from a user-specified later frame
+    state.frame_start_track = max(max(first_penis_frame - int(state.video_info.fps), state.frame_start - int(state.video_info.fps)), 0)
+    state.frame_end = state.video_info.total_frames if not state.frame_end else state.frame_end
+
+    # logger.info(f"Frame Start adjusted to: {state.frame_start}")
+    log_tr.info(f"Frame Start adjusted to: {state.frame_start_track}")
+    
+
     video_info = state.video_info
     fps = video_info.fps
-    reader = VideoReaderFFmpeg(state)
+    reader = None
 
     state.frame_area = width * height
     debug_window_open = False
     cuts = []
 
-    if state.live_preview_mode:
-        reader.set(cv2.CAP_PROP_POS_FRAMES, state.frame_start_track)
-    else:
-        reader.release()
-
     """ discarding the scene detection for now
     # Load scene cuts if the file exists
     cuts_path, _ = get_output_file_path(state.video_path, "_cuts.json")
@@ -85,9 +98,9 @@ def analyze_tracking_results(state: AppState, results):
         state.current_frame_id = frame_pos
         if frame_pos in cuts:
             # Reinitialize the tracker at scene cuts
-            log.info(f"Reaching cut at frame {frame_pos}")
+            log_tr.info(f"Reaching cut at frame {frame_pos}")
             previous_distances = tracker.previous_distances
-            log.info(f"Reinitializing tracker with previous distances: {previous_distances}")
+            log_tr.info(f"Reinitializing tracker with previous distances: {previous_distances}")
             # tracker = ObjectTracker(fps, frame_pos, image_area, video_info.is_vr)
             tracker = ObjectTracker(state)
             tracker.previous_distances = previous_distances
@@ -155,13 +168,20 @@ def analyze_tracking_results(state: AppState, results):
         # Display object detection tracking results in a live preview window
         window_name = "Tracking analysis preview"
 
-        # we don't want to call cv2.getWindowProperty every iteration
+        # Close window and release reader. Also, we don't want to call cv2.getWindowProperty every iteration
         if debug_window_open and not state.live_preview_mode:
             if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) >= 1:
                 cv2.destroyWindow(window_name)
                 debug_window_open = False
+            if reader:
+                reader.release()
+                reader = None
 
         if state.live_preview_mode:
+            if not reader:
+                reader = VideoReaderFFmpeg(state, frame_pos)
+                reader.set(cv2.CAP_PROP_POS_FRAMES, frame_pos)
+
             ret, frame = reader.read()
             frame = frame.copy()
 
@@ -183,7 +203,7 @@ def analyze_tracking_results(state: AppState, results):
                     state.offset_x
                 )
             else:
-                log.debug("No active locked penis box to draw.")
+                log_tr.debug("No active locked penis box to draw.")
 
             if tracker.glans_detected:
                 frame = OverlayWidgets.draw_bounding_box(
@@ -206,7 +226,6 @@ def analyze_tracking_results(state: AppState, results):
             if not handle_user_input(window_name) or not state.live_preview_mode:
                 if state.update_ui and state.live_preview_mode:
                     state.update_ui(UpdateGUIState(attr="live_preview_mode", value=False))
-
                 state.live_preview_mode = False
 
         # Update progress periodically
@@ -243,7 +262,7 @@ def analyze_tracking_results(state: AppState, results):
     state.funscript_data = list(zip(state.funscript_frames, state.funscript_distances))
 
     # Save the raw funscript data to JSON
-    raw_funscript_path, _ = get_output_file_path(state.video_path, "_rawfunscript.json")
+    raw_funscript_path, _ = get_output_file_path(state.video_path, ".json", "rawfunscript")
     with open(raw_funscript_path, 'w') as f:
         json.dump(state.funscript_data, f)
 
diff --git a/script_generator/cli/generate_funscript.py b/script_generator/cli/generate_funscript.py
deleted file mode 100644
index 34c7e4d6..00000000
--- a/script_generator/cli/generate_funscript.py
+++ /dev/null
@@ -1,148 +0,0 @@
-import argparse
-import os
-
-from script_generator.constants import VALID_VIDEO_READERS
-from script_generator.scripts.generate_funscript import generate_funscript
-from script_generator.debug.logger import log
-from script_generator.state.app_state import AppState
-from script_generator.utils.helpers import is_mac
-
-os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Generate a funscript file from a video."
-    )
-    parser.add_argument(
-        "video_path",
-        type=str,
-        help="Path to the input video file."
-    )
-    parser.add_argument(
-        "--reuse-yolo",
-        type=bool,
-        default=False,
-        help="Re-use an existing raw YOLO output file instead of generating a new one when available. Default is False."
-    )
-    parser.add_argument(
-        "--copy-funscript",
-        type=bool,
-        default=True,
-        help="Copies final funscript to the movie directory. Default is True."
-    )
-    parser.add_argument(
-        "--frame-start",
-        type=int,
-        default=0,
-        help="The starting frame number for processing (keep in Mind that this shouldn't be changed if you want a full funscript). Default is 0 (start from the beginning)."
-    )
-    parser.add_argument(
-        "--frame-end",
-        type=int or None,
-        default=None,
-        help="The ending frame number for processing (keep in Mind that this shouldn't be changed if you want a full funscript). Default is None (process till the end)."
-    )
-    parser.add_argument(
-        "--video-reader",
-        type=str,
-        default=None,
-        help=f"Video reader to use. Valid options: {', '.join(VALID_VIDEO_READERS)}. Default is platform-specific."
-    )
-    parser.add_argument(
-        "--save-debug-file",
-        type=bool,
-        default=True,
-        help="Saves a debug file to disk with all collected metrics. Also allows you to re-use tracking data. Default is True."
-    )
-    parser.add_argument(
-        "--boost-enabled",
-        type=bool,
-        default=True,
-        help="Funscript tweaking setting: Enable boosting to adjust the motion range dynamically. Default is True."
-    )
-    parser.add_argument(
-        "--boost-up-percent",
-        type=int,
-        default=10,
-        help="Funscript tweaking setting: Increase the peaks by a specified percentage to enhance upper motion limits. Default is 10%."
-    )
-    parser.add_argument(
-        "--boost-down-percent",
-        type=int,
-        default=15,
-        help="Funscript tweaking setting: Reduce the lower peaks by a specified percentage to limit downward motion. Default is 15%."
-    )
-    parser.add_argument(
-        "--threshold-enabled",
-        type=bool,
-        default=True,
-        help="Funscript tweaking setting: Enable thresholding to control motion mapping within specified bounds. Default is True."
-    )
-    parser.add_argument(
-        "--threshold-low",
-        type=int,
-        default=10,
-        help="Funscript tweaking setting: Values below this threshold are mapped to 0, limiting lower boundary motion. Default is 10."
-    )
-    parser.add_argument(
-        "--threshold-high",
-        type=int,
-        default=90,
-        help="Funscript tweaking setting: Values above this threshold are mapped to 100, limiting upper boundary motion. Default is 90."
-    )
-    parser.add_argument(
-        "--vw-simplification-enabled",
-        type=bool,
-        default=True,
-        help="Funscript tweaking setting: Simplify the generated script to reduce the number of points, making it user-friendly. Default is True."
-    )
-    parser.add_argument(
-        "--vw-factor",
-        type=float,
-        default=8.0,
-        help="Funscript tweaking setting: Determines the degree of simplification. Higher values lead to fewer points. Default is 8.0."
-    )
-    parser.add_argument(
-        "--rounding",
-        type=int,
-        default=5,
-        help="Funscript tweaking setting: Set the rounding factor for script values to adjust precision. Default is 5."
-    )
-    args = parser.parse_args()
-
-    video_reader = args.video_reader
-    default_video_reader = "FFmpeg" if is_mac() else "FFmpeg + OpenGL (Windows)"
-    if video_reader and video_reader not in VALID_VIDEO_READERS:
-        log.warning(
-            f"Invalid video reader specified: {video_reader}. Using default: {default_video_reader}."
-        )
-        args.video_reader = default_video_reader
-
-    try:
-        log.info(f"Processing video: {args.video_path}")
-        state = AppState()
-        state.video_path = args.video_path
-        state.use_existing_raw_yolo = args.reuse_yolo
-        state.copy_funscript_to_movie_dir = args.copy_funscript
-        state.frame_start = args.frame_start
-        state.frame_end = args.frame_end
-        state.video_reader = args.video_reader
-        state.save_debug_file = args.save_debug_file
-        state.boost_enabled = args.boost_enabled
-        state.boost_up_percent = args.boost_up_percent
-        state.boost_down_percent = args.boost_down_percent
-        state.threshold_enabled = args.threshold_enabled
-        state.threshold_low = args.threshold_low
-        state.threshold_high = args.threshold_high
-        state.vw_simplification_enabled = args.vw_simplification_enabled
-        state.vw_factor = args.vw_factor
-        state.rounding = args.rounding
-
-        generate_funscript(state)
-        log.info("Funscript generation complete.")
-    except Exception as e:
-        log.error(f"An error occurred: {e}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/script_generator/cli/generate_funscript_folder.py b/script_generator/cli/generate_funscript_folder.py
new file mode 100644
index 00000000..b0e49977
--- /dev/null
+++ b/script_generator/cli/generate_funscript_folder.py
@@ -0,0 +1,180 @@
+import argparse
+import concurrent.futures
+import os
+import sys
+import time
+from collections import deque
+
+from script_generator.cli.shared.common_args import (
+    add_shared_generate_funscript_args,
+    validate_and_adjust_args,
+    build_app_state_from_args,
+)
+from script_generator.debug.logger import log
+from script_generator.funscript.util.check_existing_funscript import check_existing_funscript
+from script_generator.utils.file import get_video_files
+from script_generator.utils.terminal import open_new_terminal
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Generate funscripts in parallel for all videos in a folder."
+    )
+    parser.add_argument("folder", type=str, help="Folder containing video files")
+    parser.add_argument(
+        "--replace-outdated",
+        action="store_true",
+        default=True,
+        help="Will regenerate outdated funscripts."
+    )
+    parser.add_argument(
+        "--replace-up-to-date",
+        action="store_true",
+        default=False,
+        help="Will regenerate funscripts that are up to date and made by this app too."
+    )
+    parser.add_argument(
+        "--num-workers",
+        type=int,
+        default=2,
+        help="Number of subprocesses to run in parallel. If you have beefy hardware 4 seems to be the sweet spot but technically your VRAM is the limit."
+    )
+    add_shared_generate_funscript_args(parser)
+
+    args = parser.parse_args()
+    validate_and_adjust_args(args)
+
+    # Determine which CLI flags were provided
+    provided_args = set()
+    for token in sys.argv[1:]:
+        if token.startswith("--"):
+            provided_args.add(token.lstrip("-").replace("-", "_"))
+        else:
+            provided_args.add("folder")
+
+    try:
+        log.info(
+            f"Processing folder: {args.folder}, "
+            f"{'' if args.replace_outdated else 'not '}replacing outdated funscripts, "
+            f"{'also ' if args.replace_up_to_date else 'not '}replacing funscripts with current version, "
+            f"using up to {args.num_workers} worker(s)."
+        )
+        paths = get_video_files(args.folder)
+        log.info(f"Found {len(paths)} file(s) in folder")
+
+        # Build temporary app state (to access config variables and defaults)
+        state = build_app_state_from_args(args, provided_args)
+
+        files_none = []
+        files_by_us = []
+        files_by_us_out_of_date = []
+        files_not_by_us = []
+
+        for video_file in paths:
+            filename_base = os.path.splitext(os.path.basename(video_file))[0]
+            video_folder = os.path.dirname(video_file)
+            funscript_path = os.path.join(video_folder, f"{filename_base}.funscript")
+
+            file_exists, is_ours, _, out_of_date = check_existing_funscript(
+                funscript_path, filename_base, False
+            )
+
+            # If there's a separate output directory, check there too
+            if state.funscript_output_dir and os.path.exists(state.funscript_output_dir):
+                folder_destination = os.path.join(state.funscript_output_dir, f"{filename_base}.funscript")
+                file_exists_d, is_ours_d, _, out_of_date_d = check_existing_funscript(
+                    folder_destination, filename_base, False
+                )
+                if not file_exists and file_exists_d:
+                    file_exists = True
+                if not is_ours and is_ours_d:
+                    is_ours = True
+                if not out_of_date and out_of_date_d:
+                    out_of_date = out_of_date_d
+
+            if not file_exists:
+                files_none.append(video_file)
+            elif is_ours:
+                if out_of_date:
+                    files_by_us_out_of_date.append(video_file)
+                else:
+                    files_by_us.append(video_file)
+            else:
+                files_not_by_us.append(video_file)
+
+        def log_files_by_category(category, file_list):
+            log.info(f"{category} ({len(file_list)}):")
+            for f in file_list:
+                log.info(f" - {f}")
+
+        log_files_by_category("Files with no funscript", files_none)
+        log_files_by_category(
+            f"Files with existing funscript by this app (>= current version, will "
+            f"{'' if args.replace_up_to_date else 'NOT '}be processed)",
+            files_by_us
+        )
+        log_files_by_category(
+            f"Files with existing funscript by this app (< current version, will "
+            f"{'' if args.replace_outdated else 'NOT '}be re-generated)",
+            files_by_us_out_of_date
+        )
+        log_files_by_category("Files with existing funscript not by this app (ignored)", files_not_by_us)
+
+        # Decide which files to process
+        to_process = files_none.copy()
+        if args.replace_outdated:
+            to_process.extend(files_by_us_out_of_date)
+        if args.replace_up_to_date:
+            to_process.extend(files_by_us)
+
+        if not to_process:
+            log.info("No files need new funscript generation.")
+            return
+
+        tasks = deque(to_process)
+
+        log.info(f"Starting batch generation with up to {args.num_workers} parallel subprocesses.")
+
+        # Use a ThreadPoolExecutor to limit concurrency.
+        with concurrent.futures.ThreadPoolExecutor(max_workers=args.num_workers) as executor:
+            futures = []
+            # Submit all tasks.
+            while tasks:
+                video_path = tasks.popleft()
+                log.info(f"[Submit] {video_path}")
+                future = executor.submit(run_task, video_path)
+                futures.append((video_path, future))
+                time.sleep(0.1)  # slight delay to stagger submissions
+
+            # Wait for all submitted tasks to complete.
+            for video_path, future in futures:
+                try:
+                    ret = future.result()  # blocks until the task completes
+                    if ret == 0:
+                        log.info(f"[Done] {video_path}")
+                    else:
+                        log.warning(f"[Failed] {video_path} (exit code {ret})")
+                except Exception as e:
+                    log.error(f"Error processing {video_path}: {e}", exc_info=True)
+
+        log.info("All funscript generation tasks completed.")
+
+    except Exception as e:
+        log.error(f"An error occurred: {e}", exc_info=True)
+
+def run_task(video_path):
+    """
+    Runs the funscript generation for one video in a new terminal window.
+    Returns the exit code (0 if successful).
+    """
+    cmd = f'python -m script_generator.cli.generate_funscript_single "{video_path}"'
+    proc = open_new_terminal(cmd, relative_path_up=2)
+    if proc is None:
+        print(f"Failed to launch terminal for: {video_path}")
+        return -1
+    # Wait for the process to finish. Because we used /WAIT on Windows
+    proc.wait()
+    return proc.returncode
+
+if __name__ == "__main__":
+    main()
diff --git a/script_generator/cli/generate_funscript_single.py b/script_generator/cli/generate_funscript_single.py
new file mode 100644
index 00000000..e69fb649
--- /dev/null
+++ b/script_generator/cli/generate_funscript_single.py
@@ -0,0 +1,50 @@
+import argparse
+import sys
+
+from script_generator.cli.shared.common_args import (
+    add_shared_generate_funscript_args,
+    validate_and_adjust_args,
+    build_app_state_from_args,
+)
+from script_generator.cli.shared.generate_funscript import generate_funscript
+from script_generator.debug.logger import log
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Generate a funscript file from a single video."
+    )
+    parser.add_argument(
+        "video_path",
+        type=str,
+        help="Path to the input video file."
+    )
+    add_shared_generate_funscript_args(parser)
+
+    args = parser.parse_args()
+    validate_and_adjust_args(args)
+
+    provided_args = set()
+    for token in sys.argv[1:]:
+        if token.startswith("--"):
+            flag_name = token.lstrip("-").replace("-", "_")
+            provided_args.add(flag_name)
+        else:
+            provided_args.add("video_path")
+
+    try:
+        log.info(f"Processing video: {args.video_path}")
+
+        state = build_app_state_from_args(args, provided_args)
+        state.set_video_info()
+        generate_funscript(state)
+        log.info("Funscript generation complete.")
+
+    except Exception as e:
+        log.error(f"An error occurred: {e}")
+        import traceback
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/script_generator/cli/generate_funscript_txt_file.py b/script_generator/cli/generate_funscript_txt_file.py
new file mode 100644
index 00000000..f87f5c14
--- /dev/null
+++ b/script_generator/cli/generate_funscript_txt_file.py
@@ -0,0 +1 @@
+# TODO
\ No newline at end of file
diff --git a/script_generator/cli/open_gui_from_meta.py b/script_generator/cli/open_gui_from_meta.py
new file mode 100644
index 00000000..1df36b11
--- /dev/null
+++ b/script_generator/cli/open_gui_from_meta.py
@@ -0,0 +1,37 @@
+import argparse
+import os
+
+from script_generator.constants import VALID_VIDEO_READERS
+from script_generator.cli.shared.generate_funscript import generate_funscript
+from script_generator.debug.logger import log
+from script_generator.gui.app import start_app
+from script_generator.state.app_state import AppState
+from script_generator.utils.data_classes.meta_data import MetaData
+from script_generator.utils.helpers import is_mac
+
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Open gui based on a metadata.json. These files are automatically created when processing a video and stored to folder output/{video_name}/metadata.json"
+    )
+    parser.add_argument(
+        "metadata_path",
+        type=str,
+        help="metadata.json file"
+    )
+    args = parser.parse_args()
+
+    try:
+        log.info(f"Opening gui for metadata.json file: {args.metadata_path}")
+        meta = MetaData.load_meta(args.metadata_path)
+        state = AppState()
+        state.video_path = meta.video_info.path
+        state.video_info = meta.video_info
+        start_app(state)
+    except Exception as e:
+        log.error(f"An error occurred: {e}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/script_generator/cli/shared/common_args.py b/script_generator/cli/shared/common_args.py
new file mode 100644
index 00000000..9e255cff
--- /dev/null
+++ b/script_generator/cli/shared/common_args.py
@@ -0,0 +1,169 @@
+import argparse
+import os
+
+from script_generator.constants import VALID_VIDEO_READERS
+from script_generator.debug.logger import log
+from script_generator.state.app_state import AppState
+
+# TODO this is a workaround and needs to be fixed properly
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+def add_shared_generate_funscript_args(parser: argparse.ArgumentParser) -> None:
+    """
+    Add all the arguments that multiple commands might share when generating a funscript.
+    """
+    parser.add_argument(
+        "--reuse-yolo",
+        action="store_true",
+        help="Re-use an existing raw YOLO output file if available."
+    )
+    parser.add_argument(
+        "--copy-funscript",
+        action="store_true",
+        help="Copies final funscript to the movie directory."
+    )
+    parser.add_argument(
+        "--frame-start",
+        type=int,
+        help="The starting frame number for processing."
+    )
+    parser.add_argument(
+        "--frame-end",
+        type=int,
+        help="The ending frame number for processing."
+    )
+    parser.add_argument(
+        "--video-reader",
+        type=str,
+        help=f"Video reader to use. Valid options: {', '.join(VALID_VIDEO_READERS)}."
+    )
+    parser.add_argument(
+        "--save-debug-file",
+        action="store_true",
+        help="Saves a debug file to disk with all collected metrics."
+    )
+    parser.add_argument(
+        "--boost-enabled",
+        action="store_true",
+        help="Funscript tweaking: Enable boosting to adjust motion range dynamically."
+    )
+    parser.add_argument(
+        "--boost-up-percent",
+        type=int,
+        help="Funscript tweaking: Increase the peaks by a specified percentage."
+    )
+    parser.add_argument(
+        "--boost-down-percent",
+        type=int,
+        help="Funscript tweaking: Reduce lower peaks by a specified percentage."
+    )
+    parser.add_argument(
+        "--threshold-enabled",
+        action="store_true",
+        help="Funscript tweaking: Enable thresholding to control motion mapping."
+    )
+    parser.add_argument(
+        "--threshold-low",
+        type=int,
+        help="Funscript tweaking: Values below this threshold become 0."
+    )
+    parser.add_argument(
+        "--threshold-high",
+        type=int,
+        help="Funscript tweaking: Values above this threshold become 100."
+    )
+    parser.add_argument(
+        "--vw-simplification-enabled",
+        action="store_true",
+        help="Funscript tweaking: Simplify the script to reduce points."
+    )
+    parser.add_argument(
+        "--vw-factor",
+        type=float,
+        help="Funscript tweaking: Factor controlling the degree of simplification."
+    )
+    parser.add_argument(
+        "--rounding",
+        type=int,
+        help="Funscript tweaking: Rounding factor for script values."
+    )
+
+def validate_and_adjust_args(args: argparse.Namespace) -> None:
+    """
+    If user provided a video_reader that isn't valid, override it with a sensible default.
+    Also do any other cross-argument validations or adjustments here.
+    """
+    default_video_reader = "FFmpeg" # if is_mac() else "FFmpeg + OpenGL (Windows)"
+    if args.video_reader and args.video_reader not in VALID_VIDEO_READERS:
+        log.warning(
+            f"Invalid video reader specified: {args.video_reader}. "
+            f"Using default: {default_video_reader}."
+        )
+        args.video_reader = default_video_reader
+
+def parse_args() -> tuple[argparse.Namespace, set]:
+    """
+    Parse command-line arguments and return them along with a set of explicitly provided argument names.
+    """
+    parser = argparse.ArgumentParser()
+
+    # Add all arguments
+    add_shared_generate_funscript_args(parser)
+
+    # Use parse_known_args to track explicitly provided arguments
+    args, unknown = parser.parse_known_args()
+
+    # Extract explicitly provided argument names (strip "--")
+    provided_args = {arg.lstrip("-").replace("-", "_") for arg in unknown}
+
+    return args, provided_args
+
+def build_app_state_from_args(args: argparse.Namespace, provided_args: set) -> AppState:
+    """
+    Create and populate an AppState from the parsed arguments,
+    setting only attributes that were explicitly provided via the CLI.
+    """
+    state = AppState()
+
+    if "video_path" in provided_args:
+        state.video_path = args.video_path
+    if "reuse_yolo" in provided_args:
+        state.use_existing_raw_yolo = args.reuse_yolo
+    if "copy_funscript" in provided_args:
+        state.copy_funscript_to_movie_dir = args.copy_funscript
+    if "frame_start" in provided_args:
+        state.frame_start = args.frame_start
+    if "frame_end" in provided_args:
+        state.frame_end = args.frame_end
+    if "video_reader" in provided_args:
+        state.video_reader = args.video_reader
+    if "save_debug_file" in provided_args:
+        state.save_debug_file = args.save_debug_file
+
+    # Boosting
+    if "boost_enabled" in provided_args:
+        state.boost_enabled = args.boost_enabled
+    if "boost_up_percent" in provided_args:
+        state.boost_up_percent = args.boost_up_percent
+    if "boost_down_percent" in provided_args:
+        state.boost_down_percent = args.boost_down_percent
+
+    # Threshold
+    if "threshold_enabled" in provided_args:
+        state.threshold_enabled = args.threshold_enabled
+    if "threshold_low" in provided_args:
+        state.threshold_low = args.threshold_low
+    if "threshold_high" in provided_args:
+        state.threshold_high = args.threshold_high
+
+    # Simplification + rounding
+    if "vw_simplification_enabled" in provided_args:
+        state.vw_simplification_enabled = args.vw_simplification_enabled
+    if "vw_factor" in provided_args:
+        state.vw_factor = args.vw_factor
+    if "rounding" in provided_args:
+        state.rounding = args.rounding
+
+    state.set_is_cli(True)
+
+    return state
diff --git a/script_generator/scripts/generate_funscript.py b/script_generator/cli/shared/generate_funscript.py
similarity index 81%
rename from script_generator/scripts/generate_funscript.py
rename to script_generator/cli/shared/generate_funscript.py
index 6bafb62e..2a01fc77 100644
--- a/script_generator/scripts/generate_funscript.py
+++ b/script_generator/cli/shared/generate_funscript.py
@@ -1,4 +1,4 @@
-from script_generator.object_detection.utils import get_raw_yolo_file_info
+from script_generator.object_detection.util.data import get_raw_yolo_file_info, load_yolo_data
 from script_generator.scripts.analyze_video import analyze_video
 from script_generator.scripts.tracking_analysis import tracking_analysis
 from script_generator.state.app_state import AppState, log_state_settings
@@ -18,8 +18,10 @@ def generate_funscript(state: AppState):
         state.frame_start = to_int_or_none(state.frame_start)
         state.frame_end = to_int_or_none(state.frame_end)
 
+        exists, yolo_data, _, _ = load_yolo_data(state)
+
         # analyze video if required
-        if not state.use_existing_raw_yolo or not get_raw_yolo_file_info(state):
+        if not state.use_existing_raw_yolo or not exists:
             analyze_video(state)
 
         tracking_analysis(state)
diff --git a/script_generator/config/config_manager.py b/script_generator/config/config_manager.py
index 09ae6990..c0c5fcc0 100644
--- a/script_generator/config/config_manager.py
+++ b/script_generator/config/config_manager.py
@@ -3,7 +3,7 @@
 
 from script_generator.constants import CONFIG_FILE_PATH, CONFIG_VERSION, DEFAULT_CONFIG
 from script_generator.debug.logger import log, set_log_level
-from script_generator.object_detection.util.utils import get_yolo_model_path
+from script_generator.object_detection.util.data import get_yolo_model_path
 from script_generator.video.ffmpeg.hwaccel import get_preferred_hwaccel
 from script_generator.video.util.ffmpeg import get_ffmpeg_paths
 
diff --git a/script_generator/constants.py b/script_generator/constants.py
index ee39dfaa..12d44414 100644
--- a/script_generator/constants.py
+++ b/script_generator/constants.py
@@ -2,8 +2,9 @@
 import platform
 
 VERSION = "0.2.0"
-YOLO_VERSION = 1
-FUNSCRIPT_VERSION = 1
+OBJECT_DETECTION_VERSION = "0.1.0"
+TRACKING_VERSION = "0.1.0"
+FUNSCRIPT_VERSION = "0.1.0"
 CONFIG_VERSION = 1
 
 ##################################################################################################
@@ -58,6 +59,7 @@
 RUN_POSE_MODEL = False
 YOLO_POSE_MODEL = None  # YOLO("models/yolo11n-pose.mlpackage", task="pose") #TODO pose model?
 VALID_VIDEO_READERS = ["FFmpeg", "FFmpeg + OpenGL (Windows)"]
+VIDEO_EXTENSIONS = {".mp4", ".mov", ".avi", ".mkv"}
 
 ##################################################################################################
 # OBJECT DETECTION
diff --git a/script_generator/debug/Display_debug_results.py b/script_generator/debug/Display_debug_results.py
deleted file mode 100644
index eea12512..00000000
--- a/script_generator/debug/Display_debug_results.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from script_generator.utils.file import get_output_file_path
-from utils.lib_Debugger import Debugger
-
-video = "/Users/k00gar/Downloads/703-czechvr-3d-2160x1080-60fps-smartphone_hq.mp4"
-
-#frame = 32280  # int(600 * 59.94)
-
-frame = (44 * 60 + 26) * 60
-
-log_file, _ = get_output_file_path(video, "_debug_logs.json")
-debugger = Debugger(video, is_vr=True, video_reader="FFmpeg", log_file=log_file)
-
-debugger.load_debug_metrics()
-
-
-#debugger.play_video(frame)
-
-debugger.play_debug_video(frame, downsize_ratio=1) #), duration=4)
\ No newline at end of file
diff --git a/script_generator/debug/debug_data.py b/script_generator/debug/debug_data.py
index b3ec398e..2a53b184 100644
--- a/script_generator/debug/debug_data.py
+++ b/script_generator/debug/debug_data.py
@@ -1,27 +1,50 @@
+from script_generator.constants import TRACKING_VERSION
+from script_generator.debug.logger import log_tr
 from script_generator.utils.file import get_output_file_path
+from script_generator.utils.json_utils import get_data_file_info
 from script_generator.utils.msgpack_utils import save_msgpack_json, load_msgpack_json
+from script_generator.utils.version import version_is_less_than
 
 
 class DebugData:
     def __init__(self, state):
         self.app_state = state
-        self.logs = {}
+        self.metrics = {}
 
     def add_frame(self, frame_id, variables=None, bounding_boxes=None):
-        if frame_id not in self.logs:
-            self.logs[frame_id] = {"variables": {}, "bounding_boxes": []}
+        if frame_id not in self.metrics:
+            self.metrics[frame_id] = {"variables": {}, "bounding_boxes": []}
 
         if variables:
-            self.logs[frame_id]["variables"].update(variables)
+            self.metrics[frame_id]["variables"].update(variables)
 
         if bounding_boxes:
-            self.logs[frame_id]["bounding_boxes"].extend(bounding_boxes)
+            self.metrics[frame_id]["bounding_boxes"].extend(bounding_boxes)
 
     def save_debug_file(self):
-        path, _ = get_output_file_path(self.app_state.video_path, "_debug_logs.msgpack")
-        save_msgpack_json(path, self.logs)
+        save_debug_metrics(self.app_state, self.metrics)
 
+def save_debug_metrics(state, data):
+    path, _ = get_output_file_path(state.video_path, ".msgpack", "metrics")
+    json_data = {"version": TRACKING_VERSION, "data": data}
+    save_msgpack_json(path, json_data)
 
 def load_debug_metrics(state):
-    base_path, _ = get_output_file_path(state.video_path, "_debug_logs.msgpack")
-    return load_msgpack_json(base_path)
\ No newline at end of file
+    exists, path, filename = get_metrics_file_info(state)
+    if not exists:
+        return False, None, path, filename
+
+    json = load_msgpack_json(path)
+
+    if not isinstance(json, dict) or not json.get("version") or version_is_less_than(json["version"], TRACKING_VERSION) or not json.get("data"):
+        log_tr.warn(f"Debug metrics file was found but was skipped due to an outdated version: {path}")
+        return False, None, path, filename
+
+    return True, json["data"], path, filename
+
+def get_metrics_file_info(state):
+    result_msgpack = get_data_file_info(state.video_path, ".msgpack", "metrics")
+    if result_msgpack[0]:
+        return result_msgpack
+
+    return False, None, None
\ No newline at end of file
diff --git a/script_generator/debug/video_player/play.py b/script_generator/debug/video_player/play.py
index d0555e72..7cbd509a 100644
--- a/script_generator/debug/video_player/play.py
+++ b/script_generator/debug/video_player/play.py
@@ -13,13 +13,14 @@
 from script_generator.debug.video_player.debug_overlay import draw_overlay
 from script_generator.debug.video_player.interaction import mouse_callback
 from script_generator.debug.video_player.state import VideoPlayer
-from script_generator.funscript.util import load_funscript_json
+from script_generator.funscript.util.util import load_funscript_json
 from script_generator.utils.file import get_output_file_path
-from script_generator.video.info.video_info import get_cropped_dimensions
+from script_generator.video.data_classes.video_info import get_cropped_dimensions
 
 
 def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=100, save_video_mode=False):
     video_info = state.video_info
+    _, metrics, _, _ = load_debug_metrics(state)
     width, height = get_cropped_dimensions(video_info)
 
     video_player = VideoPlayer(
@@ -47,8 +48,6 @@ def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=1
             fill_value="extrapolate"
         )
 
-    logs = load_debug_metrics(state)
-
     # Initialize rolling window buffers
     distance_buffer = np.zeros(rolling_window_size)
     funscript_buffer = np.zeros(rolling_window_size)
@@ -115,7 +114,7 @@ def get_ceiled_fps(value):
         distance_buffer, funscript_buffer, funscript_buffer_ref = draw_overlay(
             frame=frame,
             frame_id=video_player.current_frame,
-            logs=logs,
+            logs=metrics,
             funscript_interpolator=funscript_interpolator,
             funscript_interpolator_ref=funscript_interpolator_ref,
             distance_buffer=distance_buffer,
diff --git a/script_generator/funscript/create_funscript.py b/script_generator/funscript/create_funscript.py
new file mode 100644
index 00000000..7ef76e17
--- /dev/null
+++ b/script_generator/funscript/create_funscript.py
@@ -0,0 +1,108 @@
+import json
+import os
+
+import numpy as np
+from scipy.signal import savgol_filter
+from simplification.cutil import simplify_coords
+
+from script_generator.debug.logger import log_fun
+from script_generator.funscript.util.adjust_peaks_and_lows import adjust_peaks_and_lows
+from script_generator.funscript.util.util import write_funscript
+from script_generator.funscript.util.export_funscript import export_funscript
+from script_generator.utils.file import get_output_file_path
+
+
+def create_funscript(state):
+    output_path, _ = get_output_file_path(state.video_path, ".funscript")
+
+    raw_funscript_path, _ = get_output_file_path(state.video_path, ".json", "rawfunscript")
+    if os.path.exists(raw_funscript_path) and (state.funscript_data is None or len(state.funscript_data) == 0):
+        log_fun.info("len funscript data is 0, trying to load file")
+        # Read the funscript data from the JSON file
+        with open(raw_funscript_path, 'r') as f:
+            log_fun.info(f"Loading funscript from {raw_funscript_path}")
+            try:
+                data = json.load(f)
+            except Exception as e:
+                log_fun.error(f"Error loading funscript from {raw_funscript_path}: {e}")
+                return
+    else:
+        data = state.funscript_data
+
+    try:
+        log_fun.info(f"Generating funscript based on {len(data)} points...")
+
+        # Extract timestamps and positions
+        ats = [p[0] for p in data]
+        positions = [p[1] for p in data]
+
+        log_fun.info(f"Positions adjustment - step 1 (noise removal)")
+        # Run the Savitzky-Golay filter
+        positions = savgol_filter(positions, int(state.video_info.fps // 4), 3)
+
+        # zip adjusted positions
+        zip_positions = list(zip(ats, positions))
+
+        # Apply VW simplification if enabled
+        if state.vw_simplification_enabled:
+            log_fun.info("Positions adjustment - step 2 (VW algorithm simplification)")
+            zip_positions = simplify_coords(zip_positions, state.vw_factor)
+            log_fun.info(f"Length of VW filtered positions: {len(zip_positions)}")
+        else:
+            log_fun.info("Skipping positions adjustment - step 2 (VW algorithm simplification)")
+
+        # Extract timestamps and positions
+        ats = [p[0] for p in zip_positions]
+        positions = [p[1] for p in zip_positions]
+
+        # Remap positions to 0-100 range
+        log_fun.info("Positions adjustment - step 3 (remapping)")
+        adjusted_positions = np.interp(positions, (min(positions), max(positions)), (0, 100))
+
+        # Apply thresholding
+        if state.threshold_enabled:
+            log_fun.info(f"Positions adjustment - step 4 (thresholding)")
+            adjusted_positions = adjusted_positions.tolist()  # Convert to list
+            adjusted_positions = [
+                0 if p < state.threshold_low else 100 if p > state.threshold_high else p for p in
+                adjusted_positions]
+        else:
+            log_fun.info("Skipping positions adjustment - step 4 (thresholding)")
+
+        # Apply amplitude boosting
+        if state.boost_enabled:
+            log_fun.info("Positions adjustment - step 5 (amplitude boosting)")
+            # self.boost_amplitude(adjusted_positions, boost_factor=1.2, min_value=0, max_value=100)
+            adjusted_positions = adjust_peaks_and_lows(
+                adjusted_positions,
+                peak_boost=state.boost_up_percent,
+                low_reduction=state.boost_down_percent
+            )
+        else:
+            log_fun.info("Skipping positions adjustment - step 5 (amplitude boosting)")
+
+        # Round position values to the closest multiple of 5, still between 0 and 100
+        if state.vw_simplification_enabled:
+            log_fun.info(
+                f"Positions adjustment - step 6 (rounding to the closest multiple of {state.rounding})")
+            adjusted_positions = [round(p / state.rounding) * state.rounding for p in
+                                  adjusted_positions]
+
+        else:
+            log_fun.info(
+                f"Skipping positions adjustment - step 6 (rounding to the closest multiple of {state.rounding})")
+
+        # Recombine timestamps and adjusted positions
+        log_fun.info("Re-assembling ats and positions")
+        zip_adjusted_positions = list(zip(ats, adjusted_positions))
+
+        # Write the final funscript
+        write_funscript(zip_adjusted_positions, output_path, state.video_info.fps)
+
+        # copy funscript if specified
+        export_funscript(state)
+
+        log_fun.info(f"Funscript generation complete")
+    except Exception as e:
+        log_fun.error(f"Error generating funscript: {e}")
+        raise
diff --git a/script_generator/funscript/debug/combined_plot.py b/script_generator/funscript/debug/combined_plot.py
new file mode 100644
index 00000000..4f419a0b
--- /dev/null
+++ b/script_generator/funscript/debug/combined_plot.py
@@ -0,0 +1,164 @@
+import datetime
+import os
+
+import numpy as np
+from matplotlib import pyplot as plt, gridspec
+
+from script_generator.debug.logger import log_fun
+from script_generator.funscript.debug.heatmap import generate_heatmap_inline
+from script_generator.state.app_state import AppState
+
+
+def create_combined_plot(state: AppState, ref_sections, gen_sections, screenshots, sections, output_image_path, ref_times, ref_positions, gen_times, gen_positions):
+    """
+    Creates a combined plot with heatmaps as a header, comparative information, and section comparisons below.
+
+    Args:
+        state: AppState
+        ref_sections (list): List of reference sections (times, positions).
+        gen_sections (list): List of generated sections (times, positions).
+        screenshots (list): List of screenshots for each section.
+        sections (list): List of tuples (start, end) for each section.
+        output_image_path (str): Path to save the combined plot.
+        ref_times (list): Times from the reference funscript.
+        ref_positions (list): Positions from the reference funscript.
+        gen_times (list): Times from the generated funscript.
+        gen_positions (list): Positions from the generated funscript.
+    """
+
+    # TODO why is this empty sometimes? and result in errors
+    if not gen_times or not gen_positions:
+        log_fun.error("Could not created combined plot")
+        return
+
+    # Create a flexible grid layout
+    fig = plt.figure(figsize=(28, 24))
+    gs = gridspec.GridSpec(5, 4, height_ratios=[1, .5, 2, 2, 2], width_ratios=[1, 2, 1, 2])
+
+    # Heatmaps (First row: 2 columns spanning the entire width)
+    if ref_sections:
+        ax_ref_heatmap = fig.add_subplot(gs[0, :2])
+        generate_heatmap_inline(ax_ref_heatmap, ref_times, ref_positions)
+        ax_ref_heatmap.set_title('Reference Funscript Heatmap', fontsize=14)
+
+    ax_gen_heatmap = fig.add_subplot(gs[0, 2:])
+    generate_heatmap_inline(ax_gen_heatmap, gen_times, gen_positions)
+    ax_gen_heatmap.set_title('Generated Funscript Heatmap', fontsize=14)
+
+    if ref_sections:
+        # Comparative information (Second row: 2 columns)
+        ax_comparative_left = fig.add_subplot(gs[1, :2])
+        ref_metrics = calculate_metrics(ref_times, ref_positions)
+        ref_comparative_text = (
+            f"Reference:\n"
+            f"Number of Strokes: {ref_metrics['num_strokes']}\n"
+            f"Avg Stroke Duration: {ref_metrics['avg_stroke_duration']:.2f}s\n"
+            f"Avg Speed: {int(ref_metrics['avg_speed'])} positions/s\n"
+            f"Avg Depth of Stroke: {int(ref_metrics['avg_depth'])}\n"
+            f"Avg Max: {int(ref_metrics['avg_max'])}\n"
+            f"Avg Min: {int(ref_metrics['avg_min'])}"
+        )
+        ax_comparative_left.text(0.5, 0.5, ref_comparative_text, fontsize=12, va='center', ha='center')
+        ax_comparative_left.axis('off')
+
+    ax_comparative_right = fig.add_subplot(gs[1, 2:])
+    gen_metrics = calculate_metrics(gen_times, gen_positions)
+
+    gen_comparative_text = (
+        f"Generated:\n"
+        f"Number of Strokes: {gen_metrics['num_strokes']}\n"
+        f"Avg Stroke Duration: {gen_metrics['avg_stroke_duration']:.2f}s\n"
+        f"Avg Speed: {int(gen_metrics['avg_speed'])} positions/s\n"
+        f"Avg Depth of Stroke: {int(gen_metrics['avg_depth'])}\n"
+        f"Avg Max: {int(gen_metrics['avg_max'])}\n"
+        f"Avg Min: {int(gen_metrics['avg_min'])}"
+    )
+    ax_comparative_right.text(0.5, 0.5, gen_comparative_text, fontsize=12, va='center', ha='center')
+    ax_comparative_right.axis('off')
+
+    # Sections (Rows 3-8: Each row has 2 subplots for screenshot and data plot)
+    for i in range(3, 6):  # Section rows
+        for j in range(2):  # Two columns per row
+            idx = (i - 3) * 2 + j  # Section index
+            if idx >= len(sections):
+                break
+
+            # Screenshot (first column)
+            ax_screenshot = fig.add_subplot(gs[i - 1, j * 2])
+            ax_screenshot.imshow(screenshots[idx])
+            ax_screenshot.axis('off')
+
+            # Funscript comparison (second column)
+            ax_plot = fig.add_subplot(gs[i - 1, j * 2 + 1])
+            # Scale the y axis 0 to 100
+            ax_plot.set_ylim(0, 100)
+            gen_times_sec = [t / 1000 for t in gen_sections[idx][0]]
+            ax_plot.plot(gen_times_sec, gen_sections[idx][1], label='Generated', color='blue')
+
+            if ref_sections:
+                ref_times_sec = [t / 1000 for t in ref_sections[idx][0]]
+                ax_plot.plot(ref_times_sec, ref_sections[idx][1], label='Reference', color='red')
+
+            start_time = datetime.timedelta(seconds=int(sections[idx][0]))  # datetime.datetime.fromtimestamp(sections[idx][0]).strftime('%H:%M:%S')
+            end_time = datetime.timedelta(seconds=int(sections[idx][1]))  # datetime.datetime.fromtimestamp(sections[idx][1]).strftime('%H:%M:%S')
+            ax_plot.set_title(f'Section {idx + 1}: {start_time} - {end_time}', fontsize=10)
+            ax_plot.set_xlabel('Time (s)')
+            ax_plot.set_ylabel('Position')
+            ax_plot.legend()
+
+    # Adjust layout
+    plt.tight_layout()
+
+    # Save the figure
+    # Add "SPOILER_" to the filename
+    directory, filename = os.path.split(output_image_path)
+    new_filename = f"SPOILER_{filename}"
+    output_image_path = os.path.join(directory, new_filename)
+    plt.savefig(output_image_path[:-4] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png", dpi=100)
+
+
+def calculate_metrics(times, positions):
+    """
+    Calculates metrics for a funscript.
+
+    Args:
+        times (list): List of times from the funscript.
+        positions (list): List of positions from the funscript.
+
+    Returns:
+        dict: Dictionary containing the calculated metrics.
+    """
+    if not times or not positions:
+        return {}
+
+    times = np.array(times)
+    positions = np.array(positions)
+
+    # Calculate number of strokes
+    num_strokes = len(times) - 1
+
+    # Calculate average stroke duration
+    stroke_durations = np.diff(times) / 1000  # Convert to seconds
+    avg_stroke_duration = np.mean(stroke_durations)
+
+    # Calculate average speed
+    # prevent division by zero by adding 1e-10
+    speeds = np.abs(np.diff(positions) / (np.diff(times) + 1e-10)) * 1000  # Positions per second
+    avg_speed = np.mean(speeds)
+
+    # Calculate average depth of stroke
+    depths = np.abs(np.diff(positions))
+    avg_depth = np.mean(depths)
+
+    # Calculate average max and min
+    avg_max = np.mean([max(positions[i], positions[i + 1]) for i in range(len(positions) - 1)])
+    avg_min = np.mean([min(positions[i], positions[i + 1]) for i in range(len(positions) - 1)])
+
+    return {
+        'num_strokes': num_strokes,
+        'avg_stroke_duration': avg_stroke_duration,
+        'avg_speed': avg_speed,
+        'avg_depth': avg_depth,
+        'avg_max': avg_max,
+        'avg_min': avg_min
+    }
\ No newline at end of file
diff --git a/script_generator/funscript/debug/heatmap.py b/script_generator/funscript/debug/heatmap.py
new file mode 100644
index 00000000..4a0b1eee
--- /dev/null
+++ b/script_generator/funscript/debug/heatmap.py
@@ -0,0 +1,191 @@
+import datetime
+
+import matplotlib.colors as mcolors
+import numpy as np
+from matplotlib import pyplot as plt
+from matplotlib.cm import ScalarMappable
+
+from script_generator.constants import HEATMAP_COLORS, STEP_SIZE
+from script_generator.debug.logger import log_fun
+from script_generator.funscript.util.util import load_funscript
+from script_generator.state.app_state import AppState
+from script_generator.utils.file import get_output_file_path
+
+
+def generate_heatmap(state: AppState):
+    try:
+        # Load funscript data
+        funscript_path, _ = get_output_file_path(state.video_path, ".funscript")
+        output_image_path, _ = get_output_file_path(state.video_path, ".png")
+        times, positions, _, _ = load_funscript(funscript_path)
+        if not times or not positions:
+            log_fun.info("Failed to load funscript data.")
+            return
+
+        # add a timing: 0, position: 100 at the beginning if no value for 0
+        if times[0] != 0:
+            times.insert(0, 0)
+            positions.insert(0, 100)
+
+        # Print loaded data for debugging
+        # logger.debug(f"Times: {times}")
+        # logger.debug(f"Positions: {positions}")
+        log_fun.debug(f"Total Actions: {len(times)}")
+        log_fun.debug(f"Time Range: {times[0]} to {datetime.timedelta(seconds=int(times[-1] / 1000))}")
+
+        # Calculate speed (position change per time interval)
+        times = np.array(times)
+        positions = np.array(positions)
+        valid_indices = ~np.isnan(times) & ~np.isnan(positions)
+        filtered_positions = positions[valid_indices]
+        filtered_times = times[valid_indices]
+
+        # Calculate speed (position change per time interval)
+        # We add 1e-10 to prevent dividing by zero
+        speeds = np.abs(np.diff(filtered_positions) / (np.diff(filtered_times) + 1e-10)) * 1000  # Positions per second
+
+        log_fun.debug(f"Speeds: {speeds}")
+
+        def get_color(intensity):
+            if intensity <= 0:
+                return HEATMAP_COLORS[0]
+            if intensity > 5 * STEP_SIZE:
+                return HEATMAP_COLORS[6]
+            intensity += STEP_SIZE / 2.0
+            index = int(intensity // STEP_SIZE)
+            t = (intensity - index * STEP_SIZE) / STEP_SIZE
+            return [
+                HEATMAP_COLORS[index][0] + (HEATMAP_COLORS[index + 1][0] - HEATMAP_COLORS[index][0]) * t,
+                HEATMAP_COLORS[index][1] + (HEATMAP_COLORS[index + 1][1] - HEATMAP_COLORS[index][1]) * t,
+                HEATMAP_COLORS[index][2] + (HEATMAP_COLORS[index + 1][2] - HEATMAP_COLORS[index][2]) * t
+            ]
+
+        # Create figure and plot
+        plt.figure(figsize=(30, 2))
+        ax = plt.gca()
+
+        # Draw lines between points with colors based on speed
+        for i in range(len(times) - 1):
+            x_start = times[i] / 1000  # Convert ms to seconds
+            x_end = times[i + 1] / 1000
+            y_start = positions[i]
+            y_end = positions[i + 1]
+            speed = speeds[i]
+
+            # Get color based on speed
+            color = get_color(speed)
+            line_color = (color[0] / 255, color[1] / 255, color[2] / 255)  # Normalize to [0, 1]
+
+            # Plot the line
+            ax.plot([x_start, x_end], [y_start, y_end], color=line_color, linewidth=2)
+
+        # Customize plot
+        ax.set_title(
+            f'Funscript Heatmap\nDuration: {datetime.timedelta(seconds=int(times[-1] / 1000))} - Avg. Speed {int(np.mean(speeds))} - Actions: {len(times)}')
+        ax.set_xlabel('Time (s)')
+        ax.set_yticks(np.arange(0, 101, 10))
+        ax.set_xlim(times[0] / 1000, times[-1] / 1000)
+        ax.set_ylim(0, 100)
+
+        # Remove borders (spines)
+        for spine in ax.spines.values():
+            spine.set_visible(False)
+
+        # Add colorbar
+        cmap = mcolors.LinearSegmentedColormap.from_list("custom_heatmap", [
+            (HEATMAP_COLORS[i][0] / 255, HEATMAP_COLORS[i][1] / 255, HEATMAP_COLORS[i][2] / 255) for i in
+            range(len(HEATMAP_COLORS))
+        ])
+        norm = mcolors.Normalize(vmin=0, vmax=5 * STEP_SIZE)
+        sm = ScalarMappable(cmap=cmap, norm=norm)
+        sm.set_array([])
+        # cbar = plt.colorbar(sm, ax=ax, orientation='horizontal', pad=0.2,
+        #                    ticks=np.arange(0, 5 * step_size + 1, step_size))
+        # cbar.set_label('Speed (positions/s)')
+
+        # Save the figure
+        plt.savefig(output_image_path, bbox_inches='tight', dpi=200)  # Increase resolution
+        plt.close()
+        log_fun.info(f"Funscript heatmap saved to {output_image_path}")
+    except Exception as e:
+        log_fun.error(f"Error generating heatmap for funscript: {e}")
+
+def generate_heatmap_inline(ax, times, positions):
+    """
+    Generates a heatmap on the given axes using the existing `generate_heatmap` logic.
+
+    Args:
+        ax (matplotlib.axes.Axes): The axes to plot the heatmap on.
+        times (list): List of times from the funscript.
+        positions (list): List of positions from the funscript.
+    """
+    if not times or not positions:
+        return
+
+    # Bug fix, happened on some reference scripts with 2 identical times values : keep only the first one
+    for i in range(1, len(times)):
+        if times[i] == times[i - 1]:
+            times.pop(i)
+            positions.pop(i)
+            log_fun.info(f"Removed duplicate time value {times[i]}")
+            break
+
+    # Calculate speed (position change per time interval)
+    # prevent division by zero by adding 1e-10
+    speeds = np.abs(np.diff(positions) / (np.diff(times) + 1e-10)) * 1000  # Positions per second
+
+    def get_color(intensity):
+        if np.isnan(intensity):
+            return [128, 128, 128]
+        if intensity <= 0:
+            return HEATMAP_COLORS[0]
+        if intensity > 5 * STEP_SIZE:
+            return HEATMAP_COLORS[-1]
+        intensity += STEP_SIZE / 2.0
+        index = int(intensity // STEP_SIZE)
+        t = (intensity - index * STEP_SIZE) / STEP_SIZE
+        return [
+            HEATMAP_COLORS[index][0] + (HEATMAP_COLORS[index + 1][0] - HEATMAP_COLORS[index][0]) * t,
+            HEATMAP_COLORS[index][1] + (HEATMAP_COLORS[index + 1][1] - HEATMAP_COLORS[index][1]) * t,
+            HEATMAP_COLORS[index][2] + (HEATMAP_COLORS[index + 1][2] - HEATMAP_COLORS[index][2]) * t
+        ]
+
+    # Draw lines between points with colors based on speed
+    for i in range(len(times) - 1):
+        x_start = times[i] / 1000  # Convert ms to seconds
+        x_end = times[i + 1] / 1000
+        y_start = positions[i]
+        y_end = positions[i + 1]
+        speed = speeds[i]
+
+        # Get color based on speed
+        color = get_color(speed)
+        line_color = (color[0] / 255, color[1] / 255, color[2] / 255)  # Normalize to [0, 1]
+
+        # Plot the line
+        ax.plot([x_start, x_end], [y_start, y_end], color=line_color, linewidth=2)
+
+    # Customize plot
+    avg_speed = int(np.nanmean(speeds))
+
+    # Set title with safe average speed
+    ax.set_title(
+        f'Funscript Heatmap\nDuration: {datetime.timedelta(seconds=int(times[-1] / 1000))} - Avg. Speed {avg_speed} - Actions: {len(times)}'
+    )
+    ax.set_xlabel('Time (s)')
+    ax.set_yticks(np.arange(0, 101, 10))
+    ax.set_xlim(times[0] / 1000, times[-1] / 1000)
+    ax.set_ylim(0, 100)
+
+    # Remove borders (spines)
+    for spine in ax.spines.values():
+        spine.set_visible(False)
+
+    # Add colorbar
+    cmap = mcolors.LinearSegmentedColormap.from_list("custom_heatmap", [
+        (HEATMAP_COLORS[i][0] / 255, HEATMAP_COLORS[i][1] / 255, HEATMAP_COLORS[i][2] / 255) for i in
+        range(len(HEATMAP_COLORS))
+    ])
+    norm = mcolors.Normalize(vmin=0, vmax=5 * STEP_SIZE)
+    sm = ScalarMappable(cmap=cmap, norm=norm)
+    sm.set_array([])
\ No newline at end of file
diff --git a/script_generator/funscript/debug/report.py b/script_generator/funscript/debug/report.py
new file mode 100644
index 00000000..6193732d
--- /dev/null
+++ b/script_generator/funscript/debug/report.py
@@ -0,0 +1,102 @@
+import cv2
+import numpy as np
+
+from script_generator.debug.logger import log_fun
+from script_generator.funscript.debug.combined_plot import create_combined_plot
+from script_generator.funscript.util.util import load_funscript
+from script_generator.utils.file import get_output_file_path
+
+
+def create_funscript_report(state):
+    generated_paths = []
+    output_path, _ = get_output_file_path(state.video_path, ".funscript")
+    generated_paths.append(output_path)
+
+    if state.reference_script:
+        # Load reference funscript
+        ref_times, ref_positions, _, _ = load_funscript(state.reference_script)
+
+        # if no 0 at the beginning, add it
+        if ref_times and ref_times[0] != 0:
+            ref_times.insert(0, 0)
+            ref_positions.insert(0, 100)
+
+        # Determine total duration in seconds
+        total_duration = ref_times[-1] / 1000 if ref_times else 0
+    else:
+        ref_times, ref_positions = [], []
+        gen_times, gen_positions, _, _ = load_funscript(generated_paths[0])
+        total_duration = gen_times[-1] / 1000 if gen_times else 0
+
+    # Select 6 random non-overlapping 20-second sections
+    sections = select_random_sections(total_duration, section_duration=10, num_sections=6)
+
+    screenshots_done = False
+    screenshots = []
+
+    # Load generated funscripts
+    for generated_path in generated_paths:
+        gen_times, gen_positions, _, _ = load_funscript(generated_path)
+        # Extract data for each section
+        ref_sections = []
+        gen_sections = []
+        for start, end in sections:
+            if state.reference_script:
+                ref_sec = extract_section(ref_times, ref_positions, start, end)
+                ref_sections.append(ref_sec)
+            gen_sec = extract_section(gen_times, gen_positions, start, end)
+            gen_sections.append(gen_sec)
+
+        if not screenshots_done:
+            # Capture screenshots, but only once
+            screenshots = capture_screenshots(state.video_path, state.video_info.is_vr, sections)
+            screenshots_done = True
+
+        # Plot and combine
+        report_path, _ = get_output_file_path(state.video_path, ".png", "report")
+        create_combined_plot(
+            state, ref_sections, gen_sections, screenshots, sections, report_path,
+            ref_times, ref_positions, gen_times, gen_positions
+        )
+
+def select_random_sections(total_duration, section_duration=10, num_sections=6):
+    sections = []
+    segment_duration = total_duration / num_sections  # Duration of each segment
+
+    for i in range(num_sections):
+        # Define the start and end of the current segment
+        segment_start = i * segment_duration
+        segment_end = (i + 1) * segment_duration
+
+        # Ensure the 10-second section stays within the segment
+        max_start = segment_end - section_duration
+        if max_start < segment_start:
+            # raise ValueError(f"Segment {i} is too short to fit a {section_duration}-second section.")
+            continue
+
+        # Randomly select a start time within the segment
+        start = np.random.uniform(segment_start, max_start)
+        end = start + section_duration
+
+        # Add the section to the list
+        sections.append((start, end))
+
+    return sections
+
+def extract_section(times, positions, start, end):
+    if times is None or not isinstance(times, (list, tuple)):
+        log_fun.warning(f"No times for current section {start} - {end}")
+        return [], []
+    start_ms = start * 1000
+    end_ms = end * 1000
+    indices = [i for i, t in enumerate(times) if start_ms <= t <= end_ms]
+    return [times[i] for i in indices], [positions[i] for i in indices]
+
+def capture_screenshots(video_path, is_vr, sections):
+    cap = cv2.VideoCapture(video_path)
+    screenshots = []
+    for start, _ in sections:
+        cap.set(cv2.CAP_PROP_POS_MSEC, start * 1000)
+        screenshots.append(np.zeros((100, 160, 3), dtype=np.uint8))
+    cap.release()
+    return screenshots
\ No newline at end of file
diff --git a/script_generator/funscript/util.py b/script_generator/funscript/util.py
deleted file mode 100644
index 59375e07..00000000
--- a/script_generator/funscript/util.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import json
-
-from script_generator.debug.logger import log
-
-
-def load_funscript_json(funscript_path):
-    try:
-        with open(funscript_path, "r") as f:
-            funscript_data = json.load(f)
-        actions = funscript_data.get("actions", [])
-        funscript_times = [action["at"] for action in actions]
-        funscript_positions = [action["pos"] for action in actions]
-        return funscript_times, funscript_positions
-    except FileNotFoundError:
-        log.error(f"Funscript file not found at {funscript_path}")
-        raise
\ No newline at end of file
diff --git a/script_generator/funscript/util/adjust_peaks_and_lows.py b/script_generator/funscript/util/adjust_peaks_and_lows.py
new file mode 100644
index 00000000..cf36ffd8
--- /dev/null
+++ b/script_generator/funscript/util/adjust_peaks_and_lows.py
@@ -0,0 +1,140 @@
+import numpy as np
+
+
+def adjust_peaks_and_lows(positions, peak_boost=10, low_reduction=10, max_flat_length=5):
+    """
+    Adjusts the peaks and lows of a funscript while avoiding long flat sections at 0 or 100.
+
+    Args:
+        positions (list or np.array): List or array of positions (0-100) from the funscript.
+        peak_boost (int): Amount to increase peaks by.
+        low_reduction (int): Amount to decrease lows by.
+        max_flat_length (int): Maximum allowed length of flat sections at 0 or 100.
+
+    Returns:
+        list: Adjusted positions.
+    """
+
+    # Ensure positions is a list of [timestamp, value] pairs
+    if isinstance(positions, np.ndarray):
+        positions = positions.tolist()
+    elif not isinstance(positions, list):
+        raise ValueError("positions must be a list or numpy array of [timestamp, value] pairs")
+
+    if not positions or len(positions) < 3:
+        return positions
+
+    # Convert positions to a numpy array for easier manipulation
+    positions = np.array(positions, dtype=float)
+
+    # Identify plateaus before boosting
+    original_plateaus = _find_plateaus(positions)
+    # Identify peaks and lows
+    peaks = _find_local_maxima(positions)
+    lows = _find_local_minima(positions)
+    # Adjust peaks and lows
+    positions[peaks] = np.clip(positions[peaks] + peak_boost, 0, 100)
+    positions[lows] = np.clip(positions[lows] - low_reduction, 0, 100)
+    # Identify plateaus after boosting
+    adjusted_plateaus = _find_plateaus(positions)
+    # Compare plateaus and adjust artificially created flats
+    positions = _compare_and_adjust_plateaus(positions, original_plateaus, adjusted_plateaus, max_flat_length)
+    return positions.tolist()
+
+def _find_local_maxima(positions):
+    """
+    Identifies local maxima (peaks) in the positions.
+
+    Args:
+        positions (np.array): Array of positions.
+
+    Returns:
+        np.array: Boolean array where True indicates a peak.
+    """
+    peaks = np.zeros_like(positions, dtype=bool)
+    for i in range(1, len(positions) - 1):
+        if positions[i] > positions[i - 1] and positions[i] > positions[i + 1]:
+            peaks[i] = True
+    return peaks
+
+def _find_local_minima(positions):
+    """
+    Identifies local minima (lows) in the positions.
+
+    Args:
+        positions (np.array): Array of positions.
+
+    Returns:
+        np.array: Boolean array where True indicates a low.
+    """
+    lows = np.zeros_like(positions, dtype=bool)
+    for i in range(1, len(positions) - 1):
+        if positions[i] < positions[i - 1] and positions[i] < positions[i + 1]:
+            lows[i] = True
+    return lows
+
+def _find_plateaus(positions):
+    """
+    Identifies flat sections (plateaus) in the positions.
+
+    Args:
+        positions (np.array): Array of positions.
+
+    Returns:
+        list: List of tuples (start, end) representing the indices of plateaus.
+    """
+    plateaus = []
+    start = 0
+    for i in range(1, len(positions)):
+        if positions[i] != positions[i - 1]:
+            if i - start > 1:  # Plateau must have at least 2 points
+                plateaus.append((start, i - 1))
+            start = i
+    if len(positions) - start > 1:  # Check the last plateau
+        plateaus.append((start, len(positions) - 1))
+    return plateaus
+
+def _compare_and_adjust_plateaus(positions, original_plateaus, adjusted_plateaus, max_flat_length):
+    """
+    Compares plateaus before and after adjustments and breaks artificially created flats.
+
+    Args:
+        positions (np.array): Array of positions.
+        original_plateaus (list): List of plateaus before adjustments.
+        adjusted_plateaus (list): List of plateaus after adjustments.
+        max_flat_length (int): Maximum allowed length of flat sections at 0 or 100.
+
+    Returns:
+        np.array: Adjusted positions.
+    """
+    positions = positions.copy()  # Work on a copy to avoid modifying the original array
+    for plateau in adjusted_plateaus:
+        start, end = plateau
+        value = positions[start]
+
+        # Check if the plateau is at 0 or 100 and was not present in the original data
+        if (value == 0 or value == 100) and not _is_plateau_in_original(plateau, original_plateaus):
+            # Check if the plateau exceeds the maximum allowed length
+            if end - start + 1 > max_flat_length:
+                # Break the plateau by adjusting the values
+                for i in range(start, end + 1):
+                    positions[i] = positions[i] + 1 if value == 100 else positions[i] - 1
+
+    return positions
+
+def _is_plateau_in_original(plateau, original_plateaus):
+    """
+    Checks if a plateau was present in the original data.
+
+    Args:
+        plateau (tuple): Tuple (start, end) representing the indices of the plateau.
+        original_plateaus (list): List of plateaus in the original data.
+
+    Returns:
+        bool: True if the plateau was present in the original data, False otherwise.
+    """
+    start, end = plateau
+    for original_start, original_end in original_plateaus:
+        if start >= original_start and end <= original_end:
+            return True
+    return False
\ No newline at end of file
diff --git a/script_generator/funscript/util/check_existing_funscript.py b/script_generator/funscript/util/check_existing_funscript.py
new file mode 100644
index 00000000..c169e255
--- /dev/null
+++ b/script_generator/funscript/util/check_existing_funscript.py
@@ -0,0 +1,26 @@
+import os
+import datetime
+
+from script_generator.constants import FUNSCRIPT_AUTHOR, FUNSCRIPT_VERSION
+from script_generator.utils.json_utils import load_json_from_file
+from script_generator.utils.version import version_is_less_than
+
+
+def check_existing_funscript(dest_path: str, filename_base: str, make_funscript_backup: bool) -> tuple[bool, bool, str | None, bool]:
+    if not os.path.exists(dest_path):
+        return False, False, None, False
+
+    json_data = load_json_from_file(dest_path)
+    is_ours = json_data.get("author") == FUNSCRIPT_AUTHOR
+
+    backup_path = None
+    if is_ours and make_funscript_backup:
+        timestamp = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
+        backup_name = f"{filename_base}_{timestamp}.funscript.bak"
+        backup_path = os.path.join(os.path.dirname(dest_path), backup_name)
+
+    out_of_date = False
+    if is_ours:
+        out_of_date = version_is_less_than(json_data.get("version"), FUNSCRIPT_VERSION)
+
+    return True, is_ours, backup_path, out_of_date
\ No newline at end of file
diff --git a/script_generator/funscript/util/export_funscript.py b/script_generator/funscript/util/export_funscript.py
new file mode 100644
index 00000000..107c24c2
--- /dev/null
+++ b/script_generator/funscript/util/export_funscript.py
@@ -0,0 +1,43 @@
+import os
+import shutil
+from script_generator.debug.logger import log
+from script_generator.funscript.util.check_existing_funscript import check_existing_funscript
+from script_generator.state.app_state import AppState
+from script_generator.utils.file import get_output_file_path
+
+
+def export_funscript(state: AppState):
+    destinations = {}
+    video_folder = os.path.dirname(state.video_path)
+    filename_base = os.path.splitext(os.path.basename(state.video_path))[0]
+    output_path, _ = get_output_file_path(state.video_path, ".funscript")
+
+    if state.copy_funscript_to_movie_dir:
+        movie_dest = os.path.join(video_folder, f"{filename_base}.funscript")
+        destinations["movie"] = movie_dest
+
+    if state.funscript_output_dir:
+        output_dest = os.path.join(state.funscript_output_dir, f"{filename_base}.funscript")
+        destinations["output"] = output_dest
+
+    for _, dest_path in destinations.items():
+        file_exists, is_ours, backup_path, out_of_date = check_existing_funscript(dest_path, filename_base, state.make_funscript_backup)
+
+        if not file_exists:
+            shutil.copy(output_path, dest_path)
+            log.info(f"Copied funscript to {dest_path}.")
+        else:
+            if is_ours:
+                if backup_path:
+                    log.info(
+                        f"Funscript made by this app already exists at {dest_path}. "
+                        f"Backing it up to {backup_path}."
+                    )
+                    shutil.move(dest_path, backup_path)
+                shutil.copy(output_path, dest_path)
+                log.info(f"Copied funscript to {dest_path}.")
+            else:
+                log.warning(
+                    f"Skipping copy to {dest_path} because it's an existing funscript "
+                    f"not created by this app."
+                )
diff --git a/script_generator/funscript/util/funscript_utils.py b/script_generator/funscript/util/funscript_utils.py
new file mode 100644
index 00000000..9d7c691c
--- /dev/null
+++ b/script_generator/funscript/util/funscript_utils.py
@@ -0,0 +1,94 @@
+import numpy as np
+
+
+def boost_amplitude(signal, boost_factor, min_value=0, max_value=100):
+    """
+    Boosts the amplitude of a signal and ensures it stays within a specified range.
+
+    Parameters:
+        signal (list or np.ndarray): The input signal to boost.
+        boost_factor (float): The factor by which to boost the signal's amplitude.
+        min_value (int, optional): Minimum value of the range. Default is 0.
+        max_value (int, optional): Maximum value of the range. Default is 100.
+
+    Returns:
+        np.ndarray: The boosted signal clamped to the specified range.
+    """
+    # Convert to numpy array for easier manipulation
+    signal = np.array(signal)
+
+    # Calculate the midpoint for rescaling
+    midpoint = (min_value + max_value) / 2
+
+    # Boost the signal
+    boosted_signal = midpoint + (signal - midpoint) * boost_factor
+
+    # Clamp the signal to the specified range
+    boosted_signal = np.clip(boosted_signal, min_value, max_value)
+
+    return boosted_signal
+
+
+def filter_positions(positions, fps):
+    """
+    Filters positions to remove unnecessary points while preserving key features.
+
+    Args:
+        positions (list or np.array): List of [timestamp, value] pairs.
+        fps (int): Frames per second of the video.
+
+    Returns:
+        list: Filtered list of [timestamp, value] pairs.
+    """
+    if not positions:
+        return []
+
+    # Ensure positions is a list of [timestamp, value] pairs
+    positions = np.array(positions, dtype=float)
+    if positions.ndim != 2 or positions.shape[1] != 2:
+        raise ValueError("positions must be a list of [timestamp, value] pairs")
+
+    filtered_positions = [positions[0]]  # Start with the first position
+
+    min_interval_ms = 50  # Minimum interval between points in milliseconds
+    slope_threshold = 0.2  # Adjusted slope threshold for gradual changes
+
+    for i in range(1, len(positions) - 1):
+        current_pos = positions[i]
+        prev_pos = positions[i - 1]
+        next_pos = positions[i + 1]
+
+        # Skip consecutive duplicate positions
+        if current_pos[1] == filtered_positions[-1][1] and current_pos[1] == next_pos[1]:
+            continue
+
+        # Calculate slopes
+        time_diff_prev = current_pos[0] - prev_pos[0]
+        time_diff_next = next_pos[0] - current_pos[0]
+
+        slope_prev = (current_pos[1] - prev_pos[1]) / time_diff_prev if time_diff_prev != 0 else 0
+        slope_next = (next_pos[1] - current_pos[1]) / time_diff_next if time_diff_next != 0 else 0
+        slope_diff = abs(slope_next - slope_prev)
+
+        # Check if the current position is a local extreme
+        is_local_extreme = (
+                (current_pos[1] >= prev_pos[1] and current_pos[1] > next_pos[1]) or
+                (current_pos[1] > prev_pos[1] and current_pos[1] >= next_pos[1]) or
+                (current_pos[1] <= prev_pos[1] and current_pos[1] < next_pos[1]) or
+                (current_pos[1] < prev_pos[1] and current_pos[1] <= next_pos[1])
+        )
+
+        # Calculate time difference in milliseconds
+        time_diff_ms = (current_pos[0] - filtered_positions[-1][0]) * 1000 / fps
+
+        # Add to filtered list based on conditions
+        if (is_local_extreme or slope_diff > slope_threshold) and time_diff_ms > min_interval_ms:
+            filtered_positions.append(current_pos)
+
+    # Ensure the last point meets the interval requirement
+    if len(filtered_positions) > 1 and (
+            positions[-1][0] - filtered_positions[-1][0]) * 1000 / fps >= min_interval_ms:
+        filtered_positions.append(positions[-1])
+
+    return filtered_positions
+
diff --git a/script_generator/funscript/util/util.py b/script_generator/funscript/util/util.py
new file mode 100644
index 00000000..9412b8d9
--- /dev/null
+++ b/script_generator/funscript/util/util.py
@@ -0,0 +1,80 @@
+import json
+import os
+
+from script_generator.constants import FUNSCRIPT_AUTHOR, FUNSCRIPT_VERSION
+from script_generator.debug.logger import log
+
+
+def load_funscript_json(funscript_path):
+    try:
+        with open(funscript_path, "r") as f:
+            funscript_data = json.load(f)
+        actions = funscript_data.get("actions", [])
+        funscript_times = [action["at"] for action in actions]
+        funscript_positions = [action["pos"] for action in actions]
+        return funscript_times, funscript_positions
+    except FileNotFoundError:
+        log.error(f"Funscript file not found at {funscript_path}")
+        raise
+
+def load_funscript(funscript_path):
+    if not os.path.exists(funscript_path):
+        log.error(f"Funscript not found at {funscript_path}")
+        return None, None, None, None
+
+    with open(funscript_path, 'r') as f:
+        try:
+            log.info(f"Loading funscript from {funscript_path}")
+            data = json.load(f)
+        except json.JSONDecodeError as e:
+            log.error(f"JSONDecodeError: {e}")
+            log.error(f"Error occurred at line {e.lineno}, column {e.colno}")
+            log.error("Dumping the problematic JSON data:")
+            f.seek(0)  # Reset file pointer to the beginning
+            log.error(f.read())
+            return None, None, None, None
+
+    times = []
+    positions = []
+
+    for action in data['actions']:
+        times.append(action['at'])
+        positions.append(action['pos'])
+    log.info(f"Loaded funscript with {len(times)} actions")
+
+    # Access the chapters
+    chapters = data.get("metadata", {}).get("chapters", [])
+
+    relevant_chapters_export = []
+    irrelevant_chapters_export = []
+    # Print the chapters
+    for chapter in chapters:
+        if len(chapter['startTime']) > 8:
+            chapter['startTime'] = chapter['startTime'][:8]
+        if len(chapter['endTime']) > 8:
+            chapter['endTime'] = chapter['endTime'][:8]
+        log.info(f"Chapter: {chapter['name']}, Start: {chapter['startTime']}, End: {chapter['endTime']}")
+        # convert 00:00:00 to milliseconds
+        start_time_ms = int(chapter['startTime'].split(':')[0]) * 60 * 60 * 1000 + int(
+            chapter['startTime'].split(':')[1]) * 60 * 1000 + int(chapter['startTime'].split(':')[2]) * 1000
+        end_time_ms = int(chapter['endTime'].split(':')[0]) * 60 * 60 * 1000 + int(
+            chapter['endTime'].split(':')[1]) * 60 * 1000 + int(chapter['endTime'].split(':')[2]) * 1000
+        if chapter['name'] in ['POV Kissing', 'Close Up', 'Asslicking', 'Creampie']:
+            irrelevant_chapters_export.append([chapter['name'], start_time_ms, end_time_ms])
+        else:  # if chapter['name'] == 'Blow Job':
+            relevant_chapters_export.append([chapter['name'], start_time_ms, end_time_ms])
+
+    return times, positions, relevant_chapters_export, irrelevant_chapters_export
+
+# TODO replace with proper JSON serialization
+def write_funscript(distances, output_path, fps):
+    with open(output_path, 'w') as f:
+        f.write(f'{{"version":"{FUNSCRIPT_VERSION}","inverted":false,"range":100,"author":"{FUNSCRIPT_AUTHOR}","actions":[{{"at":0,"pos":100}},')
+        i = 0
+        for frame, position in distances:
+            time_ms = int(frame * 1000 / fps)
+            if i > 0:
+                f.write(",")
+            f.write(f'{{"at":{time_ms},"pos":{int(position)}}}')
+            i += 1
+        f.write("]}")
\ No newline at end of file
diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index 318a26f6..0c3035b9 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -16,13 +16,13 @@
 
 
 class App(tk.Tk):
-    def __init__(self):
+    def __init__(self, state: AppState= None):
         super().__init__()
         if hasattr(ctypes, "windll"):
             ctypes.windll.shcore.SetProcessDpiAwareness(2)  # For Windows DPI scaling
         # self.tk.call('tk', 'scaling', 1.0)
         self.title(f"VR & 2D POV Funscript AI Generator - v" + VERSION)
-        self.geometry('800x900' if is_mac() else '722x790')
+        self.geometry('800x900' if is_mac() else '708x790')
         self.resizable(False, False)
 
         self.iconphoto(False, tk.PhotoImage(file=LOGO))
@@ -45,7 +45,7 @@ def __init__(self):
         menu_bar.add_cascade(label="View", menu=view_menu)
         self.config(menu=menu_bar)
 
-        self.state = AppState()
+        self.state = state if state else AppState()
         self.state.set_is_cli(False)
 
         # Dictionary to store pages
@@ -95,7 +95,7 @@ class PageNames:
     SETTINGS = "Settings"
 
 
-def start_app():
-    app = App()
+def start_app(state: AppState= None):
+    app = App(state)
     app.show_frame(PageNames.FUNSCRIPT_GENERATOR)
     app.mainloop()
diff --git a/script_generator/gui/controller/debug_video.py b/script_generator/gui/controller/debug_video.py
index c8c155af..6e8d924b 100644
--- a/script_generator/gui/controller/debug_video.py
+++ b/script_generator/gui/controller/debug_video.py
@@ -1,12 +1,10 @@
-import os
 import threading
 from tkinter import messagebox
 
+from script_generator.debug.debug_data import get_metrics_file_info
+from script_generator.debug.logger import log
 from script_generator.debug.video_player.play import play_debug_video
-from script_generator.object_detection.util.utils import get_metrics_file_info
 from script_generator.state.app_state import AppState
-from script_generator.utils.file import get_output_file_path
-from script_generator.debug.logger import log
 from script_generator.utils.helpers import is_mac
 
 
@@ -30,4 +28,4 @@ def run():
         run()
     else:
         processing_thread = threading.Thread(target=run)
-        processing_thread.start()
\ No newline at end of file
+        processing_thread.start()
diff --git a/script_generator/gui/controller/process_video.py b/script_generator/gui/controller/generate_funscript.py
similarity index 94%
rename from script_generator/gui/controller/process_video.py
rename to script_generator/gui/controller/generate_funscript.py
index 62a97f18..efef233b 100644
--- a/script_generator/gui/controller/process_video.py
+++ b/script_generator/gui/controller/generate_funscript.py
@@ -3,14 +3,14 @@
 
 from script_generator.scripts.tracking_analysis import tracking_analysis
 from script_generator.gui.messages.messages import ProgressMessage, UpdateGUIState
-from script_generator.object_detection.utils import check_skip_object_detection
+from script_generator.object_detection.util.object_detection import check_skip_object_detection
 from script_generator.scripts.analyze_video import analyze_video
 from script_generator.state.app_state import AppState, log_state_settings
 from script_generator.utils.helpers import to_int_or_none
 from script_generator.debug.logger import log
 
 
-def video_analysis(state: AppState, root):
+def generate_funscript(state: AppState, root):
     configured, msg = state.is_configured()
     if not configured:
         log.warn(msg)
diff --git a/script_generator/gui/controller/regenerate_funscript.py b/script_generator/gui/controller/regenerate_funscript.py
index 70145528..36d2c877 100644
--- a/script_generator/gui/controller/regenerate_funscript.py
+++ b/script_generator/gui/controller/regenerate_funscript.py
@@ -1,7 +1,7 @@
 from tkinter import messagebox
 
 from script_generator.debug.logger import log
-from utils.lib_FunscriptHandler import FunscriptGenerator
+from script_generator.funscript.create_funscript import create_funscript
 
 
 def regenerate_funscript(state):
@@ -11,30 +11,5 @@ def regenerate_funscript(state):
 
     log.info("Regenerating Funscript with tweaked settings...")
 
-    state.set_video_info()
-
-    # Apply tweaks to funscript_data
-    if state.boost_enabled:
-        log.info(f"Applying Boost: Up {state.boost_up_percent}%, Down {state.boost_down_percent}%")
-        # Add boost logic here
-
-    if state.threshold_enabled:
-        log.info(f"Applying Threshold: Low {state.threshold_low}, High {state.threshold_high}")
-        # Add threshold logic here
-
-    if state.vw_simplification_enabled:
-        log.info(f"Applying VW Simplification with Factor: {state.vw_factor} then rounding to {state.rounding}")
-        # Add VW simplification logic here
-
-    # Save and regenerate funscript
-    funscript_handler = FunscriptGenerator()
-
-    # Simplifying the funscript data and generating the file
-    funscript_handler.generate(state)
-
-    log.info("Funscript re-generation complete.")
-
-    # Optional, compare generated funscript with reference funscript if specified, or a simple generic report
-    funscript_handler.create_report_funscripts(state)
-
-    log.info("Report generation complete.")
\ No newline at end of file
+    create_funscript(state)
+    log.info("Funscript re-generation complete.")
\ No newline at end of file
diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index dfca09a1..5ef0a42e 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -98,12 +98,12 @@ def on_value_change(*args):
         return container, entry, value
 
     @staticmethod
-    def button(parent, button_text, on_click, row=0, column=0, tooltip_text=None, style_name="Custom.TButton"):
+    def button(parent, button_text, on_click, row=0, column=0, tooltip_text=None, style_name="Custom.TButton", sticky="w"):
         style = ttk.Style()
         style.configure(style_name, padding=(10, 3))
 
         button = ttk.Button(parent, text=button_text, command=on_click, style=style_name)
-        button.grid(row=row, column=column, sticky="w", padx=PADDING_X, pady=PADDING_Y)
+        button.grid(row=row, column=column, sticky=sticky, padx=PADDING_X, pady=PADDING_Y)
 
         if tooltip_text:
             Tooltip(button, tooltip_text)
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 251a1bef..78a2b780 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -1,17 +1,19 @@
 import tkinter as tk
 from tkinter import ttk, messagebox
 
+from script_generator.debug.debug_data import get_metrics_file_info
 from script_generator.debug.logger import log
+from script_generator.funscript.debug.heatmap import generate_heatmap
+from script_generator.funscript.debug.report import create_funscript_report
 from script_generator.gui.controller.debug_video import debug_video
-from script_generator.gui.controller.process_video import video_analysis
+from script_generator.gui.controller.generate_funscript import generate_funscript
 from script_generator.gui.controller.regenerate_funscript import regenerate_funscript
 from script_generator.gui.controller.stop_processing import stop_processing
 from script_generator.gui.messages.messages import UIMessage, ProgressMessage, UpdateGUIState
 from script_generator.gui.utils.utils import enable_widgets, disable_widgets, set_progressbars_done, reset_progressbars
 from script_generator.gui.utils.widgets import Widgets
 from script_generator.gui.views.popups.create_debug_video import render_debug_video_popup
-from script_generator.object_detection.util.utils import get_metrics_file_info
-from script_generator.object_detection.utils import get_raw_yolo_file_info
+from script_generator.object_detection.util.data import get_raw_yolo_file_info
 from script_generator.state.app_state import AppState
 
 
@@ -30,7 +32,7 @@ def __init__(self, parent, controller):
         # endregion
 
         # region VIDEO SELECTION
-        video_selection = Widgets.frame(wrapper, title="Video Selection", main_section=True)
+        video_selection = Widgets.frame(wrapper, title="Video", main_section=True)
 
         def update_video_path():
             state.set_video_info()
@@ -80,7 +82,7 @@ def handle_process_btn():
                 state.has_raw_yolo = False
                 state.has_tracking_data = False
                 reset_progressbars([(yolo_p, yolo_p_perc), (track_p, track_p_perc)])
-                video_analysis(state, controller)
+                generate_funscript(state, controller)
                 update_ui_for_state()
             else:
                 # TODO add proper stop for analysis
@@ -228,14 +230,30 @@ def handle_process_btn():
             state=state,
             row=0
         )
-        debug_video_section = Widgets.frame(debugging, title="Share debug video", row=2)
+        debug_video_section = Widgets.frame(debugging, title="Share debug files", row=2)
         gen_video_btn = Widgets.button(
             debug_video_section,
             "Generate sharable debug video",
             lambda: Widgets.create_popup(title="Generate debug video", master=controller, width=650, height=205, content_builder=lambda window, user_action: render_debug_video_popup(window, state)),
-            row=1,
             column=0,
-            tooltip_text="Render a debug video once funscript processing is complete that can be\neasily shared on Discord for showcasing issues or areas of improvement."
+            tooltip_text="Render a debug video once funscript processing is complete that can be\neasily shared on Discord for showcasing issues or areas of improvement.",
+            sticky="ew"
+        )
+        gen_report_btn = Widgets.button(
+            debug_video_section,
+            "Generate report",
+            lambda: create_funscript_report(state),
+            column=1,
+            tooltip_text="Generates a heatmap of your funscript",
+            sticky="ew"
+        )
+        gen_heatmap_btn = Widgets.button(
+            debug_video_section,
+            "Generate heatmap",
+            lambda: generate_heatmap(state),
+            column=2,
+            tooltip_text="Generates a heatmap of your funscript",
+            sticky="ew"
         )
         # endregion
 
@@ -248,16 +266,16 @@ def update_ui_for_state():
                 processing_btn.config(text="Start processing")
 
             if state.has_raw_yolo and state.has_tracking_data:
-                enable_widgets([play_btn, regenerate_btn, gen_video_btn, max_fps_e])
+                enable_widgets([play_btn, regenerate_btn, gen_video_btn, max_fps_e, gen_report_btn, gen_heatmap_btn])
                 set_progressbars_done([(yolo_p, yolo_p_perc), (track_p, track_p_perc)])
                 processing_btn.config(text="Re-run object detection and or tracking")
             elif state.has_raw_yolo and not state.has_tracking_data:
-                disable_widgets([play_btn, regenerate_btn, gen_video_btn, max_fps_e])
+                disable_widgets([play_btn, regenerate_btn, gen_video_btn, max_fps_e, gen_report_btn, gen_heatmap_btn])
                 set_progressbars_done([(yolo_p, yolo_p_perc)])
                 reset_progressbars([(track_p, track_p_perc)])
                 processing_btn.config(text="Re-run object detection and or start tracking")
             else:
-                disable_widgets([play_btn, regenerate_btn, gen_video_btn, max_fps_e])
+                disable_widgets([play_btn, regenerate_btn, gen_video_btn, max_fps_e, gen_report_btn, gen_heatmap_btn])
                 reset_progressbars([(yolo_p, yolo_p_perc), (track_p, track_p_perc)])
                 processing_btn.config(text="Start processing")
 
diff --git a/script_generator/object_detection/box_record.py b/script_generator/object_detection/data_classes/box_record.py
similarity index 100%
rename from script_generator/object_detection/box_record.py
rename to script_generator/object_detection/data_classes/box_record.py
diff --git a/script_generator/object_detection/object_detection_result.py b/script_generator/object_detection/data_classes/object_detection_result.py
similarity index 100%
rename from script_generator/object_detection/object_detection_result.py
rename to script_generator/object_detection/data_classes/object_detection_result.py
diff --git a/script_generator/object_detection/util/utils.py b/script_generator/object_detection/util/data.py
similarity index 56%
rename from script_generator/object_detection/util/utils.py
rename to script_generator/object_detection/util/data.py
index 5c10d1f8..cadbbf81 100644
--- a/script_generator/object_detection/util/utils.py
+++ b/script_generator/object_detection/util/data.py
@@ -3,23 +3,15 @@
 import torch
 from ultralytics import YOLO
 
-from script_generator.constants import MODELS_PATH, MODEL_FILENAMES
-from script_generator.debug.logger import log
+from script_generator.constants import MODELS_PATH, MODEL_FILENAMES, OBJECT_DETECTION_VERSION
+from script_generator.debug.logger import log, log_od
+from script_generator.utils.file import get_output_file_path
 from script_generator.utils.helpers import is_mac
 from script_generator.utils.json_utils import get_data_file_info
+from script_generator.utils.msgpack_utils import save_msgpack_json, load_msgpack_json
+from script_generator.utils.version import version_is_less_than
 
 
-def get_metrics_file_info(state):
-    result_msgpack = get_data_file_info(state.video_path, "_debug_logs.msgpack")
-    if result_msgpack[0]:
-        return result_msgpack
-
-    result_json = get_data_file_info(state.video_path, "_debug_logs.json")
-    if result_json[0]:
-        return result_json
-
-    return False, None, None
-
 def get_yolo_model_path():
     yolo_models = [os.path.join(MODELS_PATH, filename) for filename in MODEL_FILENAMES]
     # Check if the device is an Apple device
@@ -47,4 +39,33 @@ def load_yolo_model(yolo_model_path):
         log.warn("The YOLO model is missing. Please download and place the appropriate YOLO model in the models directory.")
         return None
 
-    return YOLO(yolo_model_path, task="detect")
\ No newline at end of file
+    return YOLO(yolo_model_path, task="detect")
+
+
+def get_raw_yolo_file_info(state):
+    result_msgpack = get_data_file_info(state.video_path, ".msgpack", "rawyolo")
+    if result_msgpack[0]:
+        return result_msgpack
+
+    return False, None, None
+
+
+def save_yolo_data(state, data):
+    path, _ = get_output_file_path(state.video_path, ".msgpack", "rawyolo")
+    json_data = {"version": OBJECT_DETECTION_VERSION, "data": data}
+    save_msgpack_json(path, json_data)
+
+
+def load_yolo_data(state):
+    exists, path, filename = get_raw_yolo_file_info(state)
+    if not exists:
+        return False, None, path, filename
+
+    json = load_msgpack_json(path)
+
+    if not isinstance(json, dict) or not json.get("version") or version_is_less_than(json["version"], OBJECT_DETECTION_VERSION) or not json.get("data"):
+        if version_is_less_than(json["version"], OBJECT_DETECTION_VERSION):
+            log_od.warn(f"A raw yolo was found but was skipped due to an outdated version: {path}")
+        return False, None, path, filename
+
+    return True, json["data"], path, filename
diff --git a/script_generator/object_detection/utils.py b/script_generator/object_detection/util/object_detection.py
similarity index 81%
rename from script_generator/object_detection/utils.py
rename to script_generator/object_detection/util/object_detection.py
index 901f3cb1..cb3f31f1 100644
--- a/script_generator/object_detection/utils.py
+++ b/script_generator/object_detection/util/object_detection.py
@@ -3,9 +3,9 @@
 from script_generator.constants import CLASS_REVERSE_MATCH
 from script_generator.debug.logger import log
 from script_generator.gui.utils.widgets import Widgets
-from script_generator.object_detection.box_record import BoxRecord
-from script_generator.object_detection.object_detection_result import ObjectDetectionResult
-from script_generator.utils.json_utils import get_data_file_info
+from script_generator.object_detection.data_classes.box_record import BoxRecord
+from script_generator.object_detection.data_classes.object_detection_result import ObjectDetectionResult
+from script_generator.object_detection.util.data import get_raw_yolo_file_info
 
 
 def check_skip_object_detection(state, root):
@@ -31,17 +31,6 @@ def check_skip_object_detection(state, root):
     return "generate"
 
 
-def get_raw_yolo_file_info(state):
-    result_msgpack = get_data_file_info(state.video_path, "_rawyolo.msgpack")
-    if result_msgpack[0]:
-        return result_msgpack
-
-    result_json = get_data_file_info(state.video_path, "_rawyolo.json")
-    if result_json[0]:
-        return result_json
-
-    return False, None, None
-
 def make_data_boxes(records):
     """
     Convert YOLO records into BoxRecord objects.
@@ -57,6 +46,7 @@ def make_data_boxes(records):
         result.add_record(frame_idx, box_record)
     return result
 
+
 def parse_yolo_data_looking_for_penis(data, start_frame):
     """
     Parse YOLO data to find the first instance of a penis.
diff --git a/script_generator/object_detection/post_process_worker.py b/script_generator/object_detection/workers/post_process_worker.py
similarity index 94%
rename from script_generator/object_detection/post_process_worker.py
rename to script_generator/object_detection/workers/post_process_worker.py
index 7149f9eb..fd81b39d 100644
--- a/script_generator/object_detection/post_process_worker.py
+++ b/script_generator/object_detection/workers/post_process_worker.py
@@ -6,11 +6,12 @@
 from script_generator.constants import CLASS_REVERSE_MATCH, CLASS_COLORS
 from script_generator.debug.logger import log
 from script_generator.gui.messages.messages import UpdateGUIState
-from script_generator.object_detection.object_detection_result import ObjectDetectionResult
-from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
+from script_generator.object_detection.data_classes.object_detection_result import ObjectDetectionResult
+from script_generator.object_detection.util.data import save_yolo_data
+from script_generator.tasks.workers.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 from script_generator.utils.file import get_output_file_path
 from script_generator.utils.msgpack_utils import save_msgpack_json
-from script_generator.video.info.video_info import get_cropped_dimensions
+from script_generator.video.data_classes.video_info import get_cropped_dimensions
 
 
 class PostProcessWorker(AbstractTaskProcessor):
@@ -158,9 +159,7 @@ def on_last_item(self):
 
         self.state.analyze_task.end_time = time.time()
 
-        # Write the detection records to a JSON file
-        raw_yolo_path, _ = get_output_file_path(self.state.video_path, "_rawyolo.msgpack")
-        save_msgpack_json(raw_yolo_path, self.records)
+        save_yolo_data(self.state, self.records)
 
 def handle_user_input(window_name):
     key = cv2.waitKey(1) & 0xFF
diff --git a/script_generator/object_detection/yolo_worker.py b/script_generator/object_detection/workers/yolo_worker.py
similarity index 86%
rename from script_generator/object_detection/yolo_worker.py
rename to script_generator/object_detection/workers/yolo_worker.py
index c19816a2..615a8ad7 100644
--- a/script_generator/object_detection/yolo_worker.py
+++ b/script_generator/object_detection/workers/yolo_worker.py
@@ -1,7 +1,8 @@
 import time
 
 from script_generator.constants import YOLO_CONF, YOLO_BATCH_SIZE, YOLO_PERSIST
-from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
+from script_generator.debug.logger import log_od
+from script_generator.tasks.workers.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 
 
 class YoloWorker(AbstractTaskProcessor):
@@ -25,6 +26,8 @@ def task_logic(self):
                     self.process_batch(batch, tasks)
                     batch = []
                     tasks = []
+            else:
+                log_od.warn(f"Rendered frame missing on Yolo task")
 
         # Process any remaining tasks in the batch
         if batch:
diff --git a/script_generator/scripts/analyze_video.py b/script_generator/scripts/analyze_video.py
index 339e8a2e..313c6a3a 100644
--- a/script_generator/scripts/analyze_video.py
+++ b/script_generator/scripts/analyze_video.py
@@ -8,13 +8,15 @@
 from script_generator.debug.logger import log
 from script_generator.gui.messages.messages import ProgressMessage
 from script_generator.state.app_state import AppState
-from script_generator.tasks.abstract_task_processor import TaskProcessorTypes
-from script_generator.tasks.analyze_video_task import AnalyzeVideoTask
+from script_generator.tasks.workers.abstract_task_processor import TaskProcessorTypes
+from script_generator.tasks.data_classes.analyze_video_task import AnalyzeVideoTask
+from script_generator.utils.data_classes.meta_data import MetaData
 from script_generator.utils.file import check_create_output_folder
 
 if TYPE_CHECKING:
     from script_generator.video.analyse_frame_task import AnalyzeFrameTask
 
+
 def analyze_video(state: AppState) -> List["AnalyzeFrameTask"]:
     log.info(f"[OBJECT DETECTION] Starting up pipeline{' in sequential mode' if SEQUENTIAL_MODE else ''}...")
 
@@ -25,6 +27,9 @@ def analyze_video(state: AppState) -> List["AnalyzeFrameTask"]:
         # make sure the output folder exists for this video
         check_create_output_folder(state.video_path)
 
+        # Get meta file
+        meta = MetaData.get_create_meta(state)
+
         # Initialize batch task
         state.set_video_info()
         if state.video_reader == "FFmpeg + OpenGL (Windows)":
@@ -92,6 +97,8 @@ def run_thread(thread, thread_name, out_queue):
                 eta="Done"
             ))
 
+        meta.finish_analyze_video(state)
+
         return a.result_q.queue
 
     except Exception as e:
diff --git a/script_generator/scripts/generate_debug_video.py b/script_generator/scripts/generate_debug_video.py
index 4edae236..25aef0df 100644
--- a/script_generator/scripts/generate_debug_video.py
+++ b/script_generator/scripts/generate_debug_video.py
@@ -17,7 +17,7 @@ def run():
 
         log.info(f"Generating debug video raw video generated now converting...")
 
-        debug_video_path, _ = get_output_file_path(state.video_path, "_debug.mp4", True)
+        debug_video_path, _ = get_output_file_path(state.video_path, "_debug.mp4", None, True)
         ffmpeg_command = [
             state.ffmpeg_path, "-y",
             *get_hwaccel_read_args(state),
diff --git a/script_generator/scripts/tracking_analysis.py b/script_generator/scripts/tracking_analysis.py
index 61e7acd5..913782cf 100644
--- a/script_generator/scripts/tracking_analysis.py
+++ b/script_generator/scripts/tracking_analysis.py
@@ -1,56 +1,33 @@
-import os
-
-from script_generator.object_detection.analyze_tracking_results import analyze_tracking_results
-from script_generator.object_detection.utils import make_data_boxes, parse_yolo_data_looking_for_penis, get_raw_yolo_file_info
-from script_generator.utils.file import get_output_file_path
-from script_generator.utils.json_utils import load_json_from_file
-from script_generator.debug.logger import log
-from script_generator.utils.msgpack_utils import load_msgpack_json
-from script_generator.video.info.video_info import get_cropped_dimensions
-from utils.lib_FunscriptHandler import FunscriptGenerator
+from script_generator.analysis.workers.analyze_tracking_results import analyze_tracking_results
+from script_generator.debug.logger import log_tr
+from script_generator.funscript.create_funscript import create_funscript
+from script_generator.object_detection.util.data import get_raw_yolo_file_info
+from script_generator.object_detection.util.object_detection import parse_yolo_data_looking_for_penis
+from script_generator.utils.data_classes.meta_data import MetaData
 
 
 def tracking_analysis(state):
-    # Load YOLO detection results from file
     exists, raw_yolo_path, _ = get_raw_yolo_file_info(state)
     if not exists:
-        log.info(f"Raw yolo json not found in {raw_yolo_path}")
-        return
-
-    yolo_data = load_msgpack_json(raw_yolo_path)
-
-    state.set_video_info()
-    video_info = state.video_info
-
-    results = make_data_boxes(yolo_data)
-
-    # Looking for the first instance of penis within the YOLO results
-    first_penis_frame = parse_yolo_data_looking_for_penis(yolo_data, 0)
-
-    if first_penis_frame is None:
-        log.error(f"No penis instance found in video. Further tracking is not possible.")
+        log_tr.info(f"Raw yolo json not found in {raw_yolo_path}")
         return
 
-    # Deciding whether we start from there or from a user-specified later frame
-    # state.frame_start = max(max(first_penis_frame - int(video_info.fps), state.frame_start - int(video_info.fps)), 0)
-    state.frame_start_track = max(max(first_penis_frame - int(video_info.fps), state.frame_start - int(video_info.fps)), 0)
-
-    state.frame_end = state.video_info.total_frames if not state.frame_end else state.frame_end
-
-    # logger.info(f"Frame Start adjusted to: {state.frame_start}")
-    log.info(f"Frame Start adjusted to: {state.frame_start_track}")
+    # Get meta file
+    meta = MetaData.get_create_meta(state)
 
     # Performing the tracking part and generation of the raw funscript data
-    state.funscript_data = analyze_tracking_results(state, results)
+    state.funscript_data = analyze_tracking_results(state)
 
+    # Save debug file
     state.debug_data.save_debug_file()
 
-    funscript_handler = FunscriptGenerator()
+    if not state.funscript_data:
+        return
 
-    # Simplifying the funscript data and generating the file
-    funscript_handler.generate(state)
+    # Generate funscript
+    create_funscript(state)
 
-    # Optionally, compare generated funscript with reference funscript if specified, or a simple generic report
-    funscript_handler.create_report_funscripts(state)
+    # Update meta file
+    meta.finish_tracking_analysis(state)
 
-    log.info(f"Finished processing video: {state.video_path}")
+    log_tr.info(f"Finished processing video: {state.video_path}")
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index baa412db..069797dd 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -5,14 +5,13 @@
 from typing import Literal, Optional, TYPE_CHECKING
 
 from script_generator.config.config_manager import ConfigManager
-from script_generator.debug.debug_data import DebugData
+from script_generator.debug.debug_data import DebugData, get_metrics_file_info
 from script_generator.debug.logger import log
-from script_generator.object_detection.util.utils import get_metrics_file_info, load_yolo_model
-from script_generator.object_detection.utils import get_raw_yolo_file_info
-from script_generator.video.info.video_info import VideoInfo, get_video_info
+from script_generator.object_detection.util.data import load_yolo_model, get_raw_yolo_file_info
+from script_generator.video.data_classes.video_info import VideoInfo, get_video_info
 
 if TYPE_CHECKING:
-    from script_generator.tasks.analyze_video_task import AnalyzeVideoTask
+    from script_generator.tasks.data_classes.analyze_video_task import AnalyzeVideoTask
 
 class AppState:
     _instance: Optional["AppState"] = None
@@ -137,13 +136,7 @@ def reload_video_info(self):
 
 def log_state_settings(state: AppState):
     settings = [
-        ("Processing video", state.video_path),
-        ("Video Reader", state.video_reader),
-        ("Debug Mode", state.save_debug_file),
-        ("Live Preview Mode", state.live_preview_mode),
-        ("Frame Start", state.frame_start),
-        ("Frame End", state.frame_end),
-        ("Reference Script", state.reference_script),
+        ("Processing video", state.video_path)
     ]
 
     for setting_name, setting_value in settings:
diff --git a/script_generator/tasks/abstract_task.py b/script_generator/tasks/data_classes/abstract_task.py
similarity index 100%
rename from script_generator/tasks/abstract_task.py
rename to script_generator/tasks/data_classes/abstract_task.py
diff --git a/script_generator/tasks/analyze_video_task.py b/script_generator/tasks/data_classes/analyze_video_task.py
similarity index 90%
rename from script_generator/tasks/analyze_video_task.py
rename to script_generator/tasks/data_classes/analyze_video_task.py
index ba21f770..4b60674b 100644
--- a/script_generator/tasks/analyze_video_task.py
+++ b/script_generator/tasks/data_classes/analyze_video_task.py
@@ -5,10 +5,10 @@
 from typing import List, TYPE_CHECKING
 
 from script_generator.constants import QUEUE_MAXSIZE
-from script_generator.tasks.abstract_task import Task
+from script_generator.tasks.data_classes.abstract_task import Task
 
-from script_generator.object_detection.post_process_worker import PostProcessWorker
-from script_generator.object_detection.yolo_worker import YoloWorker
+from script_generator.object_detection.workers.post_process_worker import PostProcessWorker
+from script_generator.object_detection.workers.yolo_worker import YoloWorker
 from script_generator.video.workers.ffmpeg_worker import VideoWorker
 from script_generator.video.workers.vr_to_2d_worker import VrTo2DWorker
 
diff --git a/script_generator/tasks/abstract_task_processor.py b/script_generator/tasks/workers/abstract_task_processor.py
similarity index 100%
rename from script_generator/tasks/abstract_task_processor.py
rename to script_generator/tasks/workers/abstract_task_processor.py
diff --git a/script_generator/utils/data_classes/meta_data.py b/script_generator/utils/data_classes/meta_data.py
new file mode 100644
index 00000000..43a4c247
--- /dev/null
+++ b/script_generator/utils/data_classes/meta_data.py
@@ -0,0 +1,128 @@
+import datetime
+import json
+import os
+from dataclasses import dataclass, asdict
+from typing import TYPE_CHECKING
+
+from script_generator.constants import OBJECT_DETECTION_VERSION, TRACKING_VERSION, FUNSCRIPT_VERSION
+from script_generator.utils.file import get_output_file_path
+from script_generator.utils.json_utils import get_data_file_info
+from script_generator.video.data_classes.video_info import VideoInfo
+
+if TYPE_CHECKING:
+    from script_generator.state.app_state import AppState
+
+
+@dataclass
+class MetaData:
+    video_info: VideoInfo
+    created_date: str
+    updated_date: str
+    yolo_model: str | None
+    raw_yolo_version: str | None
+    raw_yolo_date: str | None
+    metrics_version: str | None
+    metrics_date: str | None
+    funscript_date: str | None
+    tracking_version: str | None
+    funscript_version: str | None
+
+    def to_json(self) -> str:
+        data = asdict(self)
+        return json.dumps(data, indent=4)
+
+    def finish_analyze_video(self, state: "AppState"):
+        now_str = datetime.datetime.now().isoformat()
+        self.yolo_model = os.path.basename(state.yolo_model_path)
+        self.raw_yolo_date = self.updated_date = now_str
+        self.raw_yolo_version = OBJECT_DETECTION_VERSION
+        MetaData._write_meta(state, self)
+
+    def finish_tracking_analysis(self, state: "AppState"):
+        now_str = datetime.datetime.now().isoformat()
+        self.metrics_date = self.funscript_date = self.updated_date = now_str
+        self.metrics_version = TRACKING_VERSION
+        self.funscript_version = FUNSCRIPT_VERSION
+        MetaData._write_meta(state, self)
+
+    @classmethod
+    def from_json(cls, json_str: str) -> "MetaData":
+        data = json.loads(json_str)
+
+        # Re-created video info class
+        video_info_data = data.pop("video_info")
+        projection = video_info_data.pop("projection", None)
+        fov = video_info_data.pop("fov", None)
+        is_fisheye = video_info_data.pop("is_fisheye", None)
+        video_info_obj = VideoInfo(**video_info_data)
+        video_info_obj.projection = projection
+        video_info_obj.fov = fov
+        video_info_obj.is_fisheye = is_fisheye
+
+        return cls(video_info=video_info_obj, **data)
+
+    @staticmethod
+    def get_create_meta(state: "AppState") -> "MetaData":
+        exists, file_path, _ = get_data_file_info(state.video_path, ".json", "metadata")
+        if exists:
+            return MetaData.load_meta(file_path)
+        else:
+            now_str = datetime.datetime.now().isoformat()
+            meta = MetaData(
+                video_info=state.video_info,
+                created_date=now_str,
+                updated_date=now_str,
+                raw_yolo_date=None,
+                metrics_version=None,
+                metrics_date=None,
+                yolo_model=None,
+                raw_yolo_version=None,
+                tracking_version=None,
+                funscript_version=None,
+                funscript_date=None
+            )
+            MetaData._write_meta(state, meta)
+
+            bat_path, _ = get_output_file_path(state.video_path, ".bat", "Restore GUI")
+            MetaData._create_bat_file(bat_path)
+
+            return meta
+
+    @staticmethod
+    def load_meta(file_path: str) -> "MetaData":
+        with open(file_path, 'r', encoding='utf-8') as f:
+            json_str = f.read()
+        return MetaData.from_json(json_str)
+
+    @staticmethod
+    def _write_meta(state: "AppState", meta: "MetaData"):
+        file_path, _ = get_output_file_path(state.video_path, ".json", "metadata")
+        with open(file_path, 'w', encoding='utf-8') as f:
+            f.write(meta.to_json())
+
+    @staticmethod
+    def _create_bat_file(path):
+        os.makedirs(os.path.dirname(path), exist_ok=True)
+
+        bat_content = """@echo off
+cd /d "%~dp0"
+set "SCRIPT_DIR=%CD%"
+set "CONDA_PATH=C:\\Users\\%USERNAME%\\miniconda3\\Scripts\\activate.bat"
+
+if not exist "%CONDA_PATH%" (
+    echo Conda not found at %CONDA_PATH%. Please check your installation.
+    pause
+    exit /b 1
+)
+
+call "%CONDA_PATH%" VRFunAIGen
+
+cd ..
+cd ..
+python -m script_generator.cli.open_gui_from_meta "%SCRIPT_DIR%\\metadata.json"
+
+pause
+"""
+
+        with open(path, "w", encoding="utf-8") as bat_file:
+            bat_file.write(bat_content)
\ No newline at end of file
diff --git a/script_generator/utils/file.py b/script_generator/utils/file.py
index ab6e7553..9ec1d4b6 100644
--- a/script_generator/utils/file.py
+++ b/script_generator/utils/file.py
@@ -1,15 +1,16 @@
 import os
 
-from script_generator.constants import OUTPUT_PATH
+from script_generator.constants import OUTPUT_PATH, VIDEO_EXTENSIONS
 
-def get_output_file_path(video_path, suffix, add_spoiler_prefix=False):
+
+def get_output_file_path(video_path, suffix, filename=None, add_spoiler_prefix=False):
     """"
     Get the OUTPUT_PATH filename for a specific suffix (e.g. _raw_yolo.json)
     """
     filename_base = os.path.basename(video_path)[:-4]
-    filename = f"{'SPOILER_' if add_spoiler_prefix else ''}{filename_base}{suffix}"
-    path = os.path.join(OUTPUT_PATH, filename_base, filename)
-    return path, filename
+    filename_output = f"{'SPOILER_' if add_spoiler_prefix else ''}{filename if filename else filename_base}{suffix}"
+    path = os.path.join(OUTPUT_PATH, filename_base, filename_output)
+    return path, filename_output
 
 def check_create_output_folder(video_path):
     output_dir, _ = get_output_file_path(video_path, "")
@@ -19,4 +20,16 @@ def check_create_output_folder(video_path):
 
 def ensure_path_exists(path):
     if not os.path.exists(path):
-        os.makedirs(path)
\ No newline at end of file
+        os.makedirs(path)
+
+
+def get_video_files(folder):
+    """Recursively find all video files in the given folder."""
+    video_files = []
+
+    for root, _, files in os.walk(folder):
+        for file in files:
+            if any(file.lower().endswith(ext) for ext in VIDEO_EXTENSIONS):
+                video_files.append(os.path.join(root, file))
+
+    return video_files
\ No newline at end of file
diff --git a/script_generator/utils/json_utils.py b/script_generator/utils/json_utils.py
index 2b9fb112..ac867313 100644
--- a/script_generator/utils/json_utils.py
+++ b/script_generator/utils/json_utils.py
@@ -38,12 +38,11 @@ def load_json_from_file(file_path):
 
     with open(file_path, 'r') as f:
         data = json.load(f)
-        log.info(f"Loaded data from {file_path}, length: {len(data)}")
     return data
 
 
-def get_data_file_info(video_path, suffix):
-    raw_yolo_path, raw_yolo_filename = get_output_file_path(video_path, suffix)
+def get_data_file_info(video_path, suffix, filename=None):
+    raw_yolo_path, raw_yolo_filename = get_output_file_path(video_path, suffix, filename)
     if os.path.exists(raw_yolo_path):
         file_size = os.path.getsize(raw_yolo_path)
         if file_size <= 5: # prevent jsons with []
diff --git a/script_generator/utils/msgpack_utils.py b/script_generator/utils/msgpack_utils.py
index f4f0dd9f..070e8a49 100644
--- a/script_generator/utils/msgpack_utils.py
+++ b/script_generator/utils/msgpack_utils.py
@@ -20,22 +20,6 @@ def load_msgpack_json(output_path):
             log.error(f"Failed to load from msgpack: {e}")
             raise
 
-    if os.path.exists(json_path):
-        try:
-            with open(json_path, "r") as f:
-                data = json.load(f)
-
-            with open(msgpack_path, "wb") as f:
-                f.write(msgpack.packb(data, use_bin_type=True))
-
-            os.remove(json_path)
-            log.info(f"Converted JSON to msgpack and removed the old file: {json_path}")
-
-            return data
-        except Exception as e:
-            log.error(f"Failed to load or convert JSON: {e}")
-            raise
-
     raise FileNotFoundError(f"Neither msgpack nor JSON file exists at {output_path}")
 
 def save_msgpack_json(path, data):
diff --git a/script_generator/utils/terminal.py b/script_generator/utils/terminal.py
new file mode 100644
index 00000000..223f4652
--- /dev/null
+++ b/script_generator/utils/terminal.py
@@ -0,0 +1,53 @@
+import subprocess
+import platform
+import os
+
+
+def open_new_terminal(command, conda_env="VRFunAIGen", relative_path_up=2):
+    """
+    Opens a new terminal window, navigates up a specified number of directory levels,
+    activates the given Conda environment, runs the command, and then (on Windows)
+    automatically closes when finished.
+
+    :param command: The command to run (string).
+    :param conda_env: Name of the Conda environment to activate.
+    :param relative_path_up: Number of levels to move up from this script's directory.
+    :return: Popen handle (or None on failure)
+    """
+    system = platform.system()
+    base_path = os.path.abspath(os.path.join(os.path.dirname(__file__), *[".."] * relative_path_up))
+
+    if isinstance(command, list):
+        command = " ".join(command)
+
+    if system == "Windows":
+        conda_activate = f'C:\\Users\\{os.getenv("USERNAME")}\\miniconda3\\Scripts\\activate.bat'
+        if not os.path.exists(conda_activate):
+            print(f"Conda not found at {conda_activate}. Please check your installation.")
+            return None
+        # Use /WAIT so that the call blocks until the spawned terminal (and its command) completes.
+        cmd = f'start /WAIT cmd /C "cd /d {base_path} && call \"{conda_activate}\" {conda_env} && {command}"'
+        return subprocess.Popen(cmd, shell=True)
+
+    elif system == "Darwin":  # macOS
+        # On macOS, using osascript; 'exit' makes the terminal close when done.
+        cmd = (f'osascript -e \'tell application "Terminal" to do script '
+               f"\"cd {base_path}; source ~/.bash_profile; conda activate {conda_env}; {command}; exit\"\'")
+        return subprocess.Popen(cmd, shell=True)
+
+    elif system == "Linux":
+        # Try common terminal emulators; note: behavior may vary.
+        term_cmds = [
+            f'gnome-terminal -- bash -c "cd {base_path}; source ~/.bashrc; conda activate {conda_env}; {command}; exit"',
+            f'konsole -e bash -c "cd {base_path}; source ~/.bashrc; conda activate {conda_env}; {command}; exit"',
+            f'x-terminal-emulator -e bash -c "cd {base_path}; source ~/.bashrc; conda activate {conda_env}; {command}; exit"'
+        ]
+        for term_cmd in term_cmds:
+            try:
+                return subprocess.Popen(term_cmd, shell=True)
+            except FileNotFoundError:
+                continue
+        return None
+    else:
+        print(f"Unsupported OS: {system}")
+        return None
\ No newline at end of file
diff --git a/script_generator/utils/version.py b/script_generator/utils/version.py
new file mode 100644
index 00000000..22530d78
--- /dev/null
+++ b/script_generator/utils/version.py
@@ -0,0 +1,9 @@
+def version_is_less_than(version_a: str, version_b: str) -> bool:
+    """
+    Compare two semantic version strings (e.g., "0.2.1" and "0.10.0")
+    and return True if version_a < version_b.
+    """
+    parts_a = [int(x) for x in version_a.split('.')]
+    parts_b = [int(x) for x in version_b.split('.')]
+
+    return parts_a < parts_b
\ No newline at end of file
diff --git a/script_generator/video/analyse_frame_task.py b/script_generator/video/analyse_frame_task.py
index d11ec0b9..d07fe86b 100644
--- a/script_generator/video/analyse_frame_task.py
+++ b/script_generator/video/analyse_frame_task.py
@@ -3,7 +3,7 @@
 
 import numpy as np
 
-from script_generator.tasks.abstract_task import Task
+from script_generator.tasks.data_classes.abstract_task import Task
 
 
 @dataclass
diff --git a/script_generator/video/info/video_info.py b/script_generator/video/data_classes/video_info.py
similarity index 92%
rename from script_generator/video/info/video_info.py
rename to script_generator/video/data_classes/video_info.py
index a33d33e0..61299e08 100644
--- a/script_generator/video/info/video_info.py
+++ b/script_generator/video/data_classes/video_info.py
@@ -1,7 +1,8 @@
 import json
+import os
 import re
 import subprocess
-from dataclasses import dataclass, field
+from dataclasses import dataclass, field, asdict, fields
 
 from script_generator.constants import RENDER_RESOLUTION
 from script_generator.debug.errors import FFProbeError
@@ -22,6 +23,7 @@ class VideoInfo:
     projection: str = field(init=False)
     fov: int = field(init=False)
     is_fisheye = False
+    size_bytes: int = -1
 
     def __post_init__(self):
         info = get_projection_and_fov_from_filename(self.path)
@@ -29,6 +31,14 @@ def __post_init__(self):
         self.fov = info["fov"]
         self.is_fisheye = info["is_fisheye"]
 
+    def to_json(self) -> str:
+        return json.dumps(asdict(self), indent=4)
+
+    @classmethod
+    def from_json(cls, json_str: str) -> "VideoInfo":
+        data = json.loads(json_str)
+        return cls(**{field.name: data.get(field.name) for field in fields(cls)})
+
 
 def get_projection_and_fov_from_filename(filename):
     filename = filename.replace("_FB360", "")
@@ -123,6 +133,7 @@ def get_video_info(video_path):
 
         # Check if the video is VR (2:1 aspect ratio)
         is_vr = height == width // 2
+        size_bytes = os.path.getsize(video_path)
 
         log.info(f"Video Info: {codec_name}, {width}x{height}, {fps:.2f} fps, {nb_frames} frames, {duration:.2f} sec, {bit_depth}-bit, is vr: {is_vr}")
 
@@ -131,7 +142,7 @@ def get_video_info(video_path):
         else:
             log.info("Video Format: 2D - Based on its ratio")
 
-        return VideoInfo(video_path, codec_name, width, height, duration, int(nb_frames), fps, bit_depth, is_vr)
+        return VideoInfo(video_path, codec_name, width, height, duration, int(nb_frames), fps, bit_depth, is_vr, size_bytes)
 
     except subprocess.CalledProcessError as e:
         log.error(f"FFProbe command failed: {e.output.decode('utf-8')}")
diff --git a/script_generator/video/ffmpeg/commands.py b/script_generator/video/ffmpeg/commands.py
index 07ef4e92..688d3da6 100644
--- a/script_generator/video/ffmpeg/commands.py
+++ b/script_generator/video/ffmpeg/commands.py
@@ -1,7 +1,7 @@
 from script_generator.state.app_state import AppState
 from script_generator.video.ffmpeg.filters import get_video_filters
 from script_generator.video.ffmpeg.hwaccel import get_hwaccel_read_args, supports_cuda_scale
-from script_generator.video.info.video_info import get_cropped_dimensions, VideoInfo
+from script_generator.video.data_classes.video_info import get_cropped_dimensions, VideoInfo
 
 def get_ffmpeg_read_cmd(state: AppState, frame_start: int | None, output="-", disable_opengl=False):
     video = state.video_info
diff --git a/script_generator/video/workers/ffmpeg_worker.py b/script_generator/video/workers/ffmpeg_worker.py
index 72757940..0c03cad2 100644
--- a/script_generator/video/workers/ffmpeg_worker.py
+++ b/script_generator/video/workers/ffmpeg_worker.py
@@ -3,8 +3,8 @@
 import numpy as np
 
 from script_generator.debug.errors import FFMpegError
-from script_generator.debug.logger import log, log_vid
-from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
+from script_generator.debug.logger import log_vid
+from script_generator.tasks.workers.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 from script_generator.video.analyse_frame_task import AnalyzeFrameTask
 from script_generator.video.ffmpeg.commands import get_ffmpeg_read_cmd
 
diff --git a/script_generator/video/workers/vr_to_2d_worker.py b/script_generator/video/workers/vr_to_2d_worker.py
index 6abb9826..31ccfc3a 100644
--- a/script_generator/video/workers/vr_to_2d_worker.py
+++ b/script_generator/video/workers/vr_to_2d_worker.py
@@ -4,7 +4,7 @@
 from OpenGL.GLU import *
 
 from script_generator.constants import RENDER_RESOLUTION, VR_TO_2D_PITCH
-from script_generator.tasks.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
+from script_generator.tasks.workers.abstract_task_processor import AbstractTaskProcessor, TaskProcessorTypes
 from script_generator.video.opengl.helpers import create_180_dome, render_dome
 
 
diff --git a/tests/test_detect_compare_unwarped.py b/tests/test_detect_compare_unwarped.py
deleted file mode 100644
index e4be47b3..00000000
--- a/tests/test_detect_compare_unwarped.py
+++ /dev/null
@@ -1,60 +0,0 @@
-from ultralytics import YOLO
-import cv2
-import numpy as np
-
-from script_generator.video.ffmpeg.video_reader import VideoReaderFFmpeg
-
-# Paths to the model and input image
-model_file = "../models/k00gar-11n-200ep-best.mlpackage"
-
-#video_path = video = "/Users/k00gar/Downloads/EBVR-093-C.mp4"
-video_path = video = "/Users/k00gar/Downloads/SLR_SLR Originals_Vote for me_1920p_51071_FISHEYE190_alpha.mp4"
-#video_path = video = "/Users/k00gar/Downloads/730-czechvr-3d-7680x3840-60fps-oculusrift_uhq_h265.mp4"
-
-frame_id = 79080  # 21*60*60
-
-output_path_1 = video_path[:-4] + "_" + str(frame_id) + "_ffmpeg_unwarped.jpg"  # Save location for the annotated frame
-output_path_2 = video_path[:-4] + "_" + str(frame_id) + "_original.jpg"
-output_path_3 = video_path[:-4] + "_" + str(frame_id) + "_results.jpg"
-
-cap1 = VideoReaderFFmpeg(video_path, is_vr=True)
-cap2 = VideoReaderFFmpeg(video_path, is_vr=True, unwarp=False)
-
-cap1.set(cv2.CAP_PROP_POS_FRAMES, frame_id)
-cap2.set(cv2.CAP_PROP_POS_FRAMES, frame_id)
-
-ret1, frame1 = cap1.read()
-ret2, frame2 = cap2.read()
-
-# we will only keep left half of frame2
-# frame2 = frame2[:, :frame2.shape[1] // 2, :]
-
-cap1.release()
-cap2.release()
-
-# Load the YOLO model
-model = YOLO(model_file, task="detect")
-
-# Perform inference
-results = model.predict(frame1)
-
-# Annotate the frame
-annotated_frame_1 = results[0].plot()
-cv2.imwrite(output_path_1, annotated_frame_1)
-
-# Perform inference
-results = model.predict(frame2)
-
-# Annotate the frame
-annotated_frame_2 = results[0].plot()
-
-# paste both image side by side
-annotated_frame_3 = np.concatenate((annotated_frame_2, annotated_frame_1), axis=1)
-
-# Display the annotated frame
-cv2.imshow("Concatenated result", annotated_frame_3)
-cv2.imwrite(output_path_3, annotated_frame_3)
-
-# Wait for a key press to close the display window
-cv2.waitKey(0)
-cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/tests/test_generate_funscript.py b/tests/test_generate_funscript.py
index a7cc4a88..9b8bc5ac 100644
--- a/tests/test_generate_funscript.py
+++ b/tests/test_generate_funscript.py
@@ -1,22 +1,12 @@
 import os
 
-from script_generator.scripts.generate_funscript import generate_funscript
+from script_generator.cli.shared.generate_funscript import generate_funscript
 from script_generator.state.app_state import AppState
 
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
 app_state = AppState()
-# app_state.video_path = "C:/cvr/funscript-generator/test_short.mp4"
-# app_state.video_path = "C:/cvr/funscript-generator/test_extra_short.mp4"
-# app_state.video_path = "C:/cvr/funscript-generator/test_medium.mp4"
 app_state.video_path = "C:/cvr/funscript-generator/test_medium.mp4"
-app_state.video_reader = "FFmpeg + OpenGL (Windows)"
+app_state.video_reader = "FFmpeg"
 
-# Warmup run
 generate_funscript(app_state)
-
-# app_state.video_reader = "FFmpeg + OpenGL (Windows)"
-# generate_funscript(app_state)
-
-# app_state.video_reader = "FFmpeg"
-# generate_funscript(app_state)
\ No newline at end of file
diff --git a/utils/lib_FunscriptHandler.py b/utils/lib_FunscriptHandler.py
deleted file mode 100644
index 8f1a5e39..00000000
--- a/utils/lib_FunscriptHandler.py
+++ /dev/null
@@ -1,870 +0,0 @@
-import datetime
-import json
-import logging
-import os
-import shutil
-
-import cv2
-import matplotlib
-import numpy as np
-from scipy.signal import savgol_filter
-from simplification.cutil import simplify_coords
-
-from script_generator.constants import HEATMAP_COLORS, FUNSCRIPT_AUTHOR
-from script_generator.constants import STEP_SIZE, VERSION
-from script_generator.debug.logger import log
-from script_generator.state.app_state import AppState
-from script_generator.utils.file import get_output_file_path
-from script_generator.utils.json_utils import load_json_from_file
-
-matplotlib.use('Agg')  # Use a non-interactive backend
-import matplotlib.pyplot as plt
-import matplotlib.gridspec as gridspec
-import matplotlib.colors as mcolors
-from matplotlib.cm import ScalarMappable
-from matplotlib.colors import LinearSegmentedColormap, Normalize
-
-
-class FunscriptGenerator:
-    def generate(self, state):
-        output_path, _ = get_output_file_path(state.video_path, ".funscript")
-
-        raw_funscript_path, _ = get_output_file_path(state.video_path, "_rawfunscript.json")
-        if os.path.exists(raw_funscript_path) and (state.funscript_data is None or len(state.funscript_data) == 0):
-            log.info("len funscript data is 0, trying to load file")
-            # Read the funscript data from the JSON file
-            with open(raw_funscript_path, 'r') as f:
-                log.info(f"Loading funscript from {raw_funscript_path}")
-                try:
-                    data = json.load(f)
-                except Exception as e:
-                    log.error(f"Error loading funscript from {raw_funscript_path}: {e}")
-                    return
-        else:
-            data = state.funscript_data
-
-        try:
-            log.info(f"Generating funscript based on {len(data)} points...")
-
-            # Extract timestamps and positions
-            ats = [p[0] for p in data]
-            positions = [p[1] for p in data]
-
-            log.info(f"Positions adjustment - step 1 (noise removal)")
-            # Run the Savitzky-Golay filter
-            positions = savgol_filter(positions, int(state.video_info.fps // 4), 3)
-
-            # zip adjusted positions
-            zip_positions = list(zip(ats, positions))
-
-            # Apply VW simplification if enabled
-            if state.vw_simplification_enabled:
-                log.info("Positions adjustment - step 2 (VW algorithm simplification)")
-                zip_positions = simplify_coords(zip_positions, state.vw_factor)
-                log.info(f"Length of VW filtered positions: {len(zip_positions)}")
-            else:
-                log.info("Skipping positions adjustment - step 2 (VW algorithm simplification)")
-
-            # Extract timestamps and positions
-            ats = [p[0] for p in zip_positions]
-            positions = [p[1] for p in zip_positions]
-
-            # Remap positions to 0-100 range
-            log.info("Positions adjustment - step 3 (remapping)")
-            adjusted_positions = np.interp(positions, (min(positions), max(positions)), (0, 100))
-
-            # Apply thresholding
-            if state.threshold_enabled:
-                log.info(f"Positions adjustment - step 4 (thresholding)")
-                adjusted_positions = adjusted_positions.tolist()  # Convert to list
-                adjusted_positions = [
-                    0 if p < state.threshold_low else 100 if p > state.threshold_high else p for p in
-                    adjusted_positions]
-            else:
-                log.info("Skipping positions adjustment - step 4 (thresholding)")
-
-            # Apply amplitude boosting
-            if state.boost_enabled:
-                log.info("Positions adjustment - step 5 (amplitude boosting)")
-                # self.boost_amplitude(adjusted_positions, boost_factor=1.2, min_value=0, max_value=100)
-                adjusted_positions = self.adjust_peaks_and_lows(
-                    adjusted_positions,
-                    peak_boost=state.boost_up_percent,
-                    low_reduction=state.boost_down_percent
-                )
-            else:
-                log.info("Skipping positions adjustment - step 5 (amplitude boosting)")
-
-            # Round position values to the closest multiple of 5, still between 0 and 100
-            if state.vw_simplification_enabled:
-                log.info(
-                    f"Positions adjustment - step 6 (rounding to the closest multiple of {state.rounding})")
-                adjusted_positions = [round(p / state.rounding) * state.rounding for p in
-                                      adjusted_positions]
-
-            else:
-                log.info(
-                    f"Skipping positions adjustment - step 6 (rounding to the closest multiple of {state.rounding})")
-
-            # Recombine timestamps and adjusted positions
-            log.info("Re-assembling ats and positions")
-            zip_adjusted_positions = list(zip(ats, adjusted_positions))
-
-            # Write the final funscript
-            self.write_funscript(zip_adjusted_positions, output_path, state.video_info.fps)
-
-            # copy funscript if specified
-            destinations = {}
-            video_folder = os.path.dirname(state.video_path)
-            filename_base = os.path.splitext(os.path.basename(state.video_path))[0]
-
-            if state.copy_funscript_to_movie_dir:
-                movie_dest = os.path.join(video_folder, f"{filename_base}.funscript")
-                destinations["movie"] = movie_dest
-
-            if state.funscript_output_dir:
-                output_dest = os.path.join(state.funscript_output_dir, f"{filename_base}.funscript")
-                destinations["output"] = output_dest
-
-            for dest_name, dest_path in destinations.items():
-                do_copy = True
-
-                if os.path.exists(dest_path):
-                    json_data = load_json_from_file(dest_path)
-
-                    if json_data.get("author") == FUNSCRIPT_AUTHOR:
-                        if state.make_funscript_backup:
-                            timestamp = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
-                            # Use the destination's directory for the backup path
-                            backup_path = os.path.join(os.path.dirname(dest_path), f"{filename_base}_{timestamp}.funscript.bak")
-                            log.info(f"Funscript made by this app already exists in {dest_path}, backing up as {backup_path}...")
-                            shutil.move(dest_path, backup_path)
-                    else:
-                        log.warn(f"Skipping copying funscript to {dest_path} because the existing funscript in this destination was not created by this app.")
-                        do_copy = False
-
-                if do_copy:
-                    shutil.copy(output_path, dest_path)
-                    log.info(f"Copied funscript to {dest_path}.")
-
-            log.info(f"Funscript generation complete")
-
-            # Generate a heatmap
-            self.generate_heatmap(output_path, output_path[:-10] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png")
-        except Exception as e:
-            log.error(f"Error generating funscript: {e}")
-            raise
-
-    # TODO replace with proper JSON serialization
-    def write_funscript(self, distances, output_path, fps):
-        with open(output_path, 'w') as f:
-            f.write(f'{{"version":"{VERSION}","inverted":false,"range":100,"author":"{FUNSCRIPT_AUTHOR}","actions":[{{"at":0,"pos":100}},')
-            i = 0
-            for frame, position in distances:
-                time_ms = int(frame * 1000 / fps)
-                if i > 0:
-                    f.write(",")
-                f.write(f'{{"at":{time_ms},"pos":{int(position)}}}')
-                i += 1
-            f.write("]}")
-
-    def generate_heatmap(self, funscript_path, output_image_path):
-        try:
-            # Load funscript data
-            times, positions, _, _ = self.load_funscript(funscript_path)
-            if not times or not positions:
-                log.info("Failed to load funscript data.")
-                return
-
-            # add a timing: 0, position: 100 at the beginning if no value for 0
-            if times[0] != 0:
-                times.insert(0, 0)
-                positions.insert(0, 100)
-
-            # Print loaded data for debugging
-            # logger.debug(f"Times: {times}")
-            # logger.debug(f"Positions: {positions}")
-            log.info(f"Total Actions: {len(times)}")
-            log.info(f"Time Range: {times[0]} to {datetime.timedelta(seconds=int(times[-1] / 1000))}")
-
-            # Calculate speed (position change per time interval)
-            times = np.array(times)
-            positions = np.array(positions)
-            valid_indices = ~np.isnan(times) & ~np.isnan(positions)
-            filtered_positions = positions[valid_indices]
-            filtered_times = times[valid_indices]
-
-            # Calculate speed (position change per time interval)
-            # We add 1e-10 to prevent dividing by zero
-            speeds = np.abs(np.diff(filtered_positions) / (np.diff(filtered_times) + 1e-10)) * 1000  # Positions per second
-
-            log.debug(f"Speeds: {speeds}")
-
-            def get_color(intensity):
-                if intensity <= 0:
-                    return HEATMAP_COLORS[0]
-                if intensity > 5 * STEP_SIZE:
-                    return HEATMAP_COLORS[6]
-                intensity += STEP_SIZE / 2.0
-                index = int(intensity // STEP_SIZE)
-                t = (intensity - index * STEP_SIZE) / STEP_SIZE
-                return [
-                    HEATMAP_COLORS[index][0] + (HEATMAP_COLORS[index + 1][0] - HEATMAP_COLORS[index][0]) * t,
-                    HEATMAP_COLORS[index][1] + (HEATMAP_COLORS[index + 1][1] - HEATMAP_COLORS[index][1]) * t,
-                    HEATMAP_COLORS[index][2] + (HEATMAP_COLORS[index + 1][2] - HEATMAP_COLORS[index][2]) * t
-                ]
-
-            # Create figure and plot
-            plt.figure(figsize=(30, 2))
-            ax = plt.gca()
-
-            # Draw lines between points with colors based on speed
-            for i in range(len(times) - 1):
-                x_start = times[i] / 1000  # Convert ms to seconds
-                x_end = times[i + 1] / 1000
-                y_start = positions[i]
-                y_end = positions[i + 1]
-                speed = speeds[i]
-
-                # Get color based on speed
-                color = get_color(speed)
-                line_color = (color[0] / 255, color[1] / 255, color[2] / 255)  # Normalize to [0, 1]
-
-                # Plot the line
-                ax.plot([x_start, x_end], [y_start, y_end], color=line_color, linewidth=2)
-
-            # Customize plot
-            ax.set_title(
-                f'Funscript Heatmap\nDuration: {datetime.timedelta(seconds=int(times[-1] / 1000))} - Avg. Speed {int(np.mean(speeds))} - Actions: {len(times)}')
-            ax.set_xlabel('Time (s)')
-            ax.set_yticks(np.arange(0, 101, 10))
-            ax.set_xlim(times[0] / 1000, times[-1] / 1000)
-            ax.set_ylim(0, 100)
-
-            # Remove borders (spines)
-            for spine in ax.spines.values():
-                spine.set_visible(False)
-
-            # Add colorbar
-            cmap = mcolors.LinearSegmentedColormap.from_list("custom_heatmap", [
-                (HEATMAP_COLORS[i][0] / 255, HEATMAP_COLORS[i][1] / 255, HEATMAP_COLORS[i][2] / 255) for i in
-                range(len(HEATMAP_COLORS))
-            ])
-            norm = mcolors.Normalize(vmin=0, vmax=5 * STEP_SIZE)
-            sm = ScalarMappable(cmap=cmap, norm=norm)
-            sm.set_array([])
-            # cbar = plt.colorbar(sm, ax=ax, orientation='horizontal', pad=0.2,
-            #                    ticks=np.arange(0, 5 * step_size + 1, step_size))
-            # cbar.set_label('Speed (positions/s)')
-
-            # Save the figure
-            plt.savefig(output_image_path, bbox_inches='tight', dpi=200)  # Increase resolution
-            plt.close()
-            log.info(f"Funscript heatmap saved to {output_image_path}")
-        except Exception as e:
-            log.error(f"Error generating heatmap for funscript: {e}")
-
-    def boost_amplitude(self, signal, boost_factor, min_value=0, max_value=100):
-        """
-        Boosts the amplitude of a signal and ensures it stays within a specified range.
-
-        Parameters:
-            signal (list or np.ndarray): The input signal to boost.
-            boost_factor (float): The factor by which to boost the signal's amplitude.
-            min_value (int, optional): Minimum value of the range. Default is 0.
-            max_value (int, optional): Maximum value of the range. Default is 100.
-
-        Returns:
-            np.ndarray: The boosted signal clamped to the specified range.
-        """
-        # Convert to numpy array for easier manipulation
-        signal = np.array(signal)
-
-        # Calculate the midpoint for rescaling
-        midpoint = (min_value + max_value) / 2
-
-        # Boost the signal
-        boosted_signal = midpoint + (signal - midpoint) * boost_factor
-
-        # Clamp the signal to the specified range
-        boosted_signal = np.clip(boosted_signal, min_value, max_value)
-
-        return boosted_signal
-
-    def filter_positions(self, positions, fps):
-        """
-        Filters positions to remove unnecessary points while preserving key features.
-
-        Args:
-            positions (list or np.array): List of [timestamp, value] pairs.
-            fps (int): Frames per second of the video.
-
-        Returns:
-            list: Filtered list of [timestamp, value] pairs.
-        """
-        if not positions:
-            return []
-
-        # Ensure positions is a list of [timestamp, value] pairs
-        positions = np.array(positions, dtype=float)
-        if positions.ndim != 2 or positions.shape[1] != 2:
-            raise ValueError("positions must be a list of [timestamp, value] pairs")
-
-        filtered_positions = [positions[0]]  # Start with the first position
-
-        min_interval_ms = 50  # Minimum interval between points in milliseconds
-        slope_threshold = 0.2  # Adjusted slope threshold for gradual changes
-
-        for i in range(1, len(positions) - 1):
-            current_pos = positions[i]
-            prev_pos = positions[i - 1]
-            next_pos = positions[i + 1]
-
-            # Skip consecutive duplicate positions
-            if current_pos[1] == filtered_positions[-1][1] and current_pos[1] == next_pos[1]:
-                continue
-
-            # Calculate slopes
-            time_diff_prev = current_pos[0] - prev_pos[0]
-            time_diff_next = next_pos[0] - current_pos[0]
-
-            slope_prev = (current_pos[1] - prev_pos[1]) / time_diff_prev if time_diff_prev != 0 else 0
-            slope_next = (next_pos[1] - current_pos[1]) / time_diff_next if time_diff_next != 0 else 0
-            slope_diff = abs(slope_next - slope_prev)
-
-            # Check if the current position is a local extreme
-            is_local_extreme = (
-                    (current_pos[1] >= prev_pos[1] and current_pos[1] > next_pos[1]) or
-                    (current_pos[1] > prev_pos[1] and current_pos[1] >= next_pos[1]) or
-                    (current_pos[1] <= prev_pos[1] and current_pos[1] < next_pos[1]) or
-                    (current_pos[1] < prev_pos[1] and current_pos[1] <= next_pos[1])
-            )
-
-            # Calculate time difference in milliseconds
-            time_diff_ms = (current_pos[0] - filtered_positions[-1][0]) * 1000 / fps
-
-            # Add to filtered list based on conditions
-            if (is_local_extreme or slope_diff > slope_threshold) and time_diff_ms > min_interval_ms:
-                filtered_positions.append(current_pos)
-
-        # Ensure the last point meets the interval requirement
-        if len(filtered_positions) > 1 and (
-                positions[-1][0] - filtered_positions[-1][0]) * 1000 / fps >= min_interval_ms:
-            filtered_positions.append(positions[-1])
-
-        return filtered_positions
-
-    def load_funscript(self, funscript_path):
-        # if the funscript path exists
-        if not os.path.exists(funscript_path):
-            log.error(f"Funscript not found at {funscript_path}")
-            return None, None, None, None
-
-        with open(funscript_path, 'r') as f:
-            try:
-                log.info(f"Loading funscript from {funscript_path}")
-                data = json.load(f)
-            except json.JSONDecodeError as e:
-                log.error(f"JSONDecodeError: {e}")
-                log.error(f"Error occurred at line {e.lineno}, column {e.colno}")
-                log.error("Dumping the problematic JSON data:")
-                f.seek(0)  # Reset file pointer to the beginning
-                log.error(f.read())
-                return None, None, None, None
-
-        times = []
-        positions = []
-
-        for action in data['actions']:
-            times.append(action['at'])
-            positions.append(action['pos'])
-        log.info(f"Loaded funscript with {len(times)} actions")
-
-        # Access the chapters
-        chapters = data.get("metadata", {}).get("chapters", [])
-
-        relevant_chapters_export = []
-        irrelevant_chapters_export = []
-        # Print the chapters
-        for chapter in chapters:
-            if len(chapter['startTime']) > 8:
-                chapter['startTime'] = chapter['startTime'][:8]
-            if len(chapter['endTime']) > 8:
-                chapter['endTime'] = chapter['endTime'][:8]
-            log.info(f"Chapter: {chapter['name']}, Start: {chapter['startTime']}, End: {chapter['endTime']}")
-            # convert 00:00:00 to milliseconds
-            startTime_ms = int(chapter['startTime'].split(':')[0]) * 60 * 60 * 1000 + int(
-                chapter['startTime'].split(':')[1]) * 60 * 1000 + int(chapter['startTime'].split(':')[2]) * 1000
-            endTime_ms = int(chapter['endTime'].split(':')[0]) * 60 * 60 * 1000 + int(
-                chapter['endTime'].split(':')[1]) * 60 * 1000 + int(chapter['endTime'].split(':')[2]) * 1000
-            if chapter['name'] in ['POV Kissing', 'Close Up', 'Asslicking', 'Creampie']:
-                irrelevant_chapters_export.append([chapter['name'], startTime_ms, endTime_ms])
-            else:  # if chapter['name'] == 'Blow Job':
-                relevant_chapters_export.append([chapter['name'], startTime_ms, endTime_ms])
-
-        return times, positions, relevant_chapters_export, irrelevant_chapters_export
-
-    def create_report_funscripts(self, state):
-        generated_paths = []
-        output_path, _ = get_output_file_path(state.video_path, ".funscript")
-        generated_paths.append(output_path)
-
-        if state.reference_script:
-            # Load reference funscript
-            ref_times, ref_positions, _, _ = self.load_funscript(state.reference_script)
-
-            # if no 0 at the beginning, add it
-            if ref_times and ref_times[0] != 0:
-                ref_times.insert(0, 0)
-                ref_positions.insert(0, 100)
-
-            # Determine total duration in seconds
-            total_duration = ref_times[-1] / 1000 if ref_times else 0
-        else:
-            ref_times, ref_positions = [], []
-            gen_times, gen_positions, _, _ = self.load_funscript(generated_paths[0])
-            total_duration = gen_times[-1] / 1000 if gen_times else 0
-
-        # Select 6 random non-overlapping 20-second sections
-        sections = self.select_random_sections(total_duration, section_duration=10, num_sections=6)
-
-        screenshots_done = False
-        screenshots = []
-
-        # Load generated funscripts
-        for generated_path in generated_paths:
-            gen_times, gen_positions, _, _ = self.load_funscript(generated_path)
-            # Extract data for each section
-            ref_sections = []
-            gen_sections = []
-            for start, end in sections:
-                if state.reference_script:
-                    ref_sec = self.extract_section(ref_times, ref_positions, start, end)
-                    ref_sections.append(ref_sec)
-                gen_sec = self.extract_section(gen_times, gen_positions, start, end)
-                gen_sections.append(gen_sec)
-
-            if not screenshots_done:
-                # Capture screenshots, but only once
-                screenshots = self.capture_screenshots(state.video_path, state.video_info.is_vr, sections)
-                screenshots_done = True
-
-            # Plot and combine
-            report_path, _ = get_output_file_path(state.video_path, "_report.png")
-            self.create_combined_plot(
-                state, ref_sections, gen_sections, screenshots, sections, report_path,
-                ref_times, ref_positions, gen_times, gen_positions
-            )
-
-    def select_random_sections(self, total_duration, section_duration=10, num_sections=6):
-        sections = []
-        segment_duration = total_duration / num_sections  # Duration of each segment
-
-        for i in range(num_sections):
-            # Define the start and end of the current segment
-            segment_start = i * segment_duration
-            segment_end = (i + 1) * segment_duration
-
-            # Ensure the 10-second section stays within the segment
-            max_start = segment_end - section_duration
-            if max_start < segment_start:
-                # raise ValueError(f"Segment {i} is too short to fit a {section_duration}-second section.")
-                continue
-
-            # Randomly select a start time within the segment
-            start = np.random.uniform(segment_start, max_start)
-            end = start + section_duration
-
-            # Add the section to the list
-            sections.append((start, end))
-
-        return sections
-
-    def extract_section(self, times, positions, start, end):
-        if times is None or not isinstance(times, (list, tuple)):
-            logging.warning(f"No times for current section {start} - {end}")
-            return [], []
-        start_ms = start * 1000
-        end_ms = end * 1000
-        indices = [i for i, t in enumerate(times) if start_ms <= t <= end_ms]
-        return [times[i] for i in indices], [positions[i] for i in indices]
-
-    def capture_screenshots(self, video_path, is_vr, sections):
-        cap = cv2.VideoCapture(video_path)
-        screenshots = []
-        for start, _ in sections:
-            cap.set(cv2.CAP_PROP_POS_MSEC, start * 1000)
-            screenshots.append(np.zeros((100, 160, 3), dtype=np.uint8))
-        cap.release()
-        return screenshots
-
-    def create_combined_plot(self, state: AppState, ref_sections, gen_sections, screenshots, sections, output_image_path, ref_times, ref_positions, gen_times, gen_positions):
-        """
-        Creates a combined plot with heatmaps as a header, comparative information, and section comparisons below.
-
-        Args:
-            state: AppState
-            ref_sections (list): List of reference sections (times, positions).
-            gen_sections (list): List of generated sections (times, positions).
-            screenshots (list): List of screenshots for each section.
-            sections (list): List of tuples (start, end) for each section.
-            output_image_path (str): Path to save the combined plot.
-            ref_times (list): Times from the reference funscript.
-            ref_positions (list): Positions from the reference funscript.
-            gen_times (list): Times from the generated funscript.
-            gen_positions (list): Positions from the generated funscript.
-        """
-
-        # TODO why is this empty sometimes? and result in errors
-        if not gen_times or not gen_positions:
-            log.error("Could not created combined plot")
-            return
-
-        # Create a flexible grid layout
-        fig = plt.figure(figsize=(28, 24))
-        gs = gridspec.GridSpec(5, 4, height_ratios=[1, .5, 2, 2, 2], width_ratios=[1, 2, 1, 2])
-
-        # Heatmaps (First row: 2 columns spanning the entire width)
-        if ref_sections:
-            ax_ref_heatmap = fig.add_subplot(gs[0, :2])
-            self.generate_heatmap_inline(ax_ref_heatmap, ref_times, ref_positions, state)
-            ax_ref_heatmap.set_title('Reference Funscript Heatmap', fontsize=14)
-
-        ax_gen_heatmap = fig.add_subplot(gs[0, 2:])
-        self.generate_heatmap_inline(ax_gen_heatmap, gen_times, gen_positions, state)
-        ax_gen_heatmap.set_title('Generated Funscript Heatmap', fontsize=14)
-
-        if ref_sections:
-            # Comparative information (Second row: 2 columns)
-            ax_comparative_left = fig.add_subplot(gs[1, :2])
-            ref_metrics = self._calculate_metrics(ref_times, ref_positions)
-            ref_comparative_text = (
-                f"Reference:\n"
-                f"Number of Strokes: {ref_metrics['num_strokes']}\n"
-                f"Avg Stroke Duration: {ref_metrics['avg_stroke_duration']:.2f}s\n"
-                f"Avg Speed: {int(ref_metrics['avg_speed'])} positions/s\n"
-                f"Avg Depth of Stroke: {int(ref_metrics['avg_depth'])}\n"
-                f"Avg Max: {int(ref_metrics['avg_max'])}\n"
-                f"Avg Min: {int(ref_metrics['avg_min'])}"
-            )
-            ax_comparative_left.text(0.5, 0.5, ref_comparative_text, fontsize=12, va='center', ha='center')
-            ax_comparative_left.axis('off')
-
-        ax_comparative_right = fig.add_subplot(gs[1, 2:])
-        gen_metrics = self._calculate_metrics(gen_times, gen_positions)
-
-        gen_comparative_text = (
-            f"Generated:\n"
-            f"Number of Strokes: {gen_metrics['num_strokes']}\n"
-            f"Avg Stroke Duration: {gen_metrics['avg_stroke_duration']:.2f}s\n"
-            f"Avg Speed: {int(gen_metrics['avg_speed'])} positions/s\n"
-            f"Avg Depth of Stroke: {int(gen_metrics['avg_depth'])}\n"
-            f"Avg Max: {int(gen_metrics['avg_max'])}\n"
-            f"Avg Min: {int(gen_metrics['avg_min'])}"
-        )
-        ax_comparative_right.text(0.5, 0.5, gen_comparative_text, fontsize=12, va='center', ha='center')
-        ax_comparative_right.axis('off')
-
-        # Sections (Rows 3-8: Each row has 2 subplots for screenshot and data plot)
-        for i in range(3, 6):  # Section rows
-            for j in range(2):  # Two columns per row
-                idx = (i - 3) * 2 + j  # Section index
-                if idx >= len(sections):
-                    break
-
-                # Screenshot (first column)
-                ax_screenshot = fig.add_subplot(gs[i - 1, j * 2])
-                ax_screenshot.imshow(screenshots[idx])
-                ax_screenshot.axis('off')
-
-                # Funscript comparison (second column)
-                ax_plot = fig.add_subplot(gs[i - 1, j * 2 + 1])
-                # Scale the y axis 0 to 100
-                ax_plot.set_ylim(0, 100)
-                gen_times_sec = [t / 1000 for t in gen_sections[idx][0]]
-                ax_plot.plot(gen_times_sec, gen_sections[idx][1], label='Generated', color='blue')
-
-                if ref_sections:
-                    ref_times_sec = [t / 1000 for t in ref_sections[idx][0]]
-                    ax_plot.plot(ref_times_sec, ref_sections[idx][1], label='Reference', color='red')
-
-                start_time = datetime.timedelta(seconds=int(sections[idx][0]))  # datetime.datetime.fromtimestamp(sections[idx][0]).strftime('%H:%M:%S')
-                end_time = datetime.timedelta(seconds=int(sections[idx][1]))  # datetime.datetime.fromtimestamp(sections[idx][1]).strftime('%H:%M:%S')
-                ax_plot.set_title(f'Section {idx + 1}: {start_time} - {end_time}', fontsize=10)
-                ax_plot.set_xlabel('Time (s)')
-                ax_plot.set_ylabel('Position')
-                ax_plot.legend()
-
-        # Adjust layout
-        plt.tight_layout()
-
-        # Save the figure
-        # Add "SPOILER_" to the filename
-        directory, filename = os.path.split(output_image_path)
-        new_filename = f"SPOILER_{filename}"
-        output_image_path = os.path.join(directory, new_filename)
-        plt.savefig(output_image_path[:-4] + f"_{datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.png", dpi=100)
-
-    def generate_heatmap_inline(self, ax, times, positions, state):
-        """
-        Generates a heatmap on the given axes using the existing `generate_heatmap` logic.
-
-        Args:
-            ax (matplotlib.axes.Axes): The axes to plot the heatmap on.
-            times (list): List of times from the funscript.
-            positions (list): List of positions from the funscript.
-        """
-        if not times or not positions:
-            return
-
-        # Bug fix, happened on some reference scripts with 2 identical times values : keep only the first one
-        for i in range(1, len(times)):
-            if times[i] == times[i - 1]:
-                times.pop(i)
-                positions.pop(i)
-                log.info(f"Removed duplicate time value {times[i]}")
-                break
-
-        # Calculate speed (position change per time interval)
-        # prevent division by zero by adding 1e-10
-        speeds = np.abs(np.diff(positions) / (np.diff(times) + 1e-10)) * 1000  # Positions per second
-
-        def get_color(intensity):
-            if np.isnan(intensity):
-                return [128, 128, 128]
-            if intensity <= 0:
-                return HEATMAP_COLORS[0]
-            if intensity > 5 * STEP_SIZE:
-                return HEATMAP_COLORS[-1]
-            intensity += STEP_SIZE / 2.0
-            index = int(intensity // STEP_SIZE)
-            t = (intensity - index * STEP_SIZE) / STEP_SIZE
-            return [
-                HEATMAP_COLORS[index][0] + (HEATMAP_COLORS[index + 1][0] - HEATMAP_COLORS[index][0]) * t,
-                HEATMAP_COLORS[index][1] + (HEATMAP_COLORS[index + 1][1] - HEATMAP_COLORS[index][1]) * t,
-                HEATMAP_COLORS[index][2] + (HEATMAP_COLORS[index + 1][2] - HEATMAP_COLORS[index][2]) * t
-            ]
-
-        # Draw lines between points with colors based on speed
-        for i in range(len(times) - 1):
-            x_start = times[i] / 1000  # Convert ms to seconds
-            x_end = times[i + 1] / 1000
-            y_start = positions[i]
-            y_end = positions[i + 1]
-            speed = speeds[i]
-
-            # Get color based on speed
-            color = get_color(speed)
-            line_color = (color[0] / 255, color[1] / 255, color[2] / 255)  # Normalize to [0, 1]
-
-            # Plot the line
-            ax.plot([x_start, x_end], [y_start, y_end], color=line_color, linewidth=2)
-
-        # Customize plot
-        avg_speed = int(np.nanmean(speeds))
-
-        # Set title with safe average speed
-        ax.set_title(
-            f'Funscript Heatmap\nDuration: {datetime.timedelta(seconds=int(times[-1] / 1000))} - Avg. Speed {avg_speed} - Actions: {len(times)}'
-        )
-        ax.set_xlabel('Time (s)')
-        ax.set_yticks(np.arange(0, 101, 10))
-        ax.set_xlim(times[0] / 1000, times[-1] / 1000)
-        ax.set_ylim(0, 100)
-
-        # Remove borders (spines)
-        for spine in ax.spines.values():
-            spine.set_visible(False)
-
-        # Add colorbar
-        cmap = LinearSegmentedColormap.from_list("custom_heatmap", [
-            (HEATMAP_COLORS[i][0] / 255, HEATMAP_COLORS[i][1] / 255, HEATMAP_COLORS[i][2] / 255) for i in
-            range(len(HEATMAP_COLORS))
-        ])
-        norm = Normalize(vmin=0, vmax=5 * STEP_SIZE)
-        sm = ScalarMappable(cmap=cmap, norm=norm)
-        sm.set_array([])
-
-    def _calculate_metrics(self, times, positions):
-        """
-        Calculates metrics for a funscript.
-
-        Args:
-            times (list): List of times from the funscript.
-            positions (list): List of positions from the funscript.
-
-        Returns:
-            dict: Dictionary containing the calculated metrics.
-        """
-        if not times or not positions:
-            return {}
-
-        times = np.array(times)
-        positions = np.array(positions)
-
-        # Calculate number of strokes
-        num_strokes = len(times) - 1
-
-        # Calculate average stroke duration
-        stroke_durations = np.diff(times) / 1000  # Convert to seconds
-        avg_stroke_duration = np.mean(stroke_durations)
-
-        # Calculate average speed
-        # prevent division by zero by adding 1e-10
-        speeds = np.abs(np.diff(positions) / (np.diff(times) + 1e-10)) * 1000  # Positions per second
-        avg_speed = np.mean(speeds)
-
-        # Calculate average depth of stroke
-        depths = np.abs(np.diff(positions))
-        avg_depth = np.mean(depths)
-
-        # Calculate average max and min
-        avg_max = np.mean([max(positions[i], positions[i + 1]) for i in range(len(positions) - 1)])
-        avg_min = np.mean([min(positions[i], positions[i + 1]) for i in range(len(positions) - 1)])
-
-        return {
-            'num_strokes': num_strokes,
-            'avg_stroke_duration': avg_stroke_duration,
-            'avg_speed': avg_speed,
-            'avg_depth': avg_depth,
-            'avg_max': avg_max,
-            'avg_min': avg_min
-        }
-
-    def adjust_peaks_and_lows(self, positions, peak_boost=10, low_reduction=10, max_flat_length=5):
-        """
-        Adjusts the peaks and lows of a funscript while avoiding long flat sections at 0 or 100.
-
-        Args:
-            positions (list or np.array): List or array of positions (0-100) from the funscript.
-            peak_boost (int): Amount to increase peaks by.
-            low_reduction (int): Amount to decrease lows by.
-            max_flat_length (int): Maximum allowed length of flat sections at 0 or 100.
-
-        Returns:
-            list: Adjusted positions.
-        """
-
-        # Ensure positions is a list of [timestamp, value] pairs
-        if isinstance(positions, np.ndarray):
-            positions = positions.tolist()
-        elif not isinstance(positions, list):
-            raise ValueError("positions must be a list or numpy array of [timestamp, value] pairs")
-
-        if not positions or len(positions) < 3:
-            return positions
-
-        # Convert positions to a numpy array for easier manipulation
-        positions = np.array(positions, dtype=float)
-
-        # Identify plateaus before boosting
-        original_plateaus = self._find_plateaus(positions)
-        # Identify peaks and lows
-        peaks = self._find_local_maxima(positions)
-        lows = self._find_local_minima(positions)
-        # Adjust peaks and lows
-        positions[peaks] = np.clip(positions[peaks] + peak_boost, 0, 100)
-        positions[lows] = np.clip(positions[lows] - low_reduction, 0, 100)
-        # Identify plateaus after boosting
-        adjusted_plateaus = self._find_plateaus(positions)
-        # Compare plateaus and adjust artificially created flats
-        positions = self._compare_and_adjust_plateaus(positions, original_plateaus, adjusted_plateaus, max_flat_length)
-        return positions.tolist()
-
-    def _find_local_maxima(self, positions):
-        """
-        Identifies local maxima (peaks) in the positions.
-
-        Args:
-            positions (np.array): Array of positions.
-
-        Returns:
-            np.array: Boolean array where True indicates a peak.
-        """
-        peaks = np.zeros_like(positions, dtype=bool)
-        for i in range(1, len(positions) - 1):
-            if positions[i] > positions[i - 1] and positions[i] > positions[i + 1]:
-                peaks[i] = True
-        return peaks
-
-    def _find_local_minima(self, positions):
-        """
-        Identifies local minima (lows) in the positions.
-
-        Args:
-            positions (np.array): Array of positions.
-
-        Returns:
-            np.array: Boolean array where True indicates a low.
-        """
-        lows = np.zeros_like(positions, dtype=bool)
-        for i in range(1, len(positions) - 1):
-            if positions[i] < positions[i - 1] and positions[i] < positions[i + 1]:
-                lows[i] = True
-        return lows
-
-    def _find_plateaus(self, positions):
-        """
-        Identifies flat sections (plateaus) in the positions.
-
-        Args:
-            positions (np.array): Array of positions.
-
-        Returns:
-            list: List of tuples (start, end) representing the indices of plateaus.
-        """
-        plateaus = []
-        start = 0
-        for i in range(1, len(positions)):
-            if positions[i] != positions[i - 1]:
-                if i - start > 1:  # Plateau must have at least 2 points
-                    plateaus.append((start, i - 1))
-                start = i
-        if len(positions) - start > 1:  # Check the last plateau
-            plateaus.append((start, len(positions) - 1))
-        return plateaus
-
-    def _compare_and_adjust_plateaus(self, positions, original_plateaus, adjusted_plateaus, max_flat_length):
-        """
-        Compares plateaus before and after adjustments and breaks artificially created flats.
-
-        Args:
-            positions (np.array): Array of positions.
-            original_plateaus (list): List of plateaus before adjustments.
-            adjusted_plateaus (list): List of plateaus after adjustments.
-            max_flat_length (int): Maximum allowed length of flat sections at 0 or 100.
-
-        Returns:
-            np.array: Adjusted positions.
-        """
-        positions = positions.copy()  # Work on a copy to avoid modifying the original array
-        for plateau in adjusted_plateaus:
-            start, end = plateau
-            value = positions[start]
-
-            # Check if the plateau is at 0 or 100 and was not present in the original data
-            if (value == 0 or value == 100) and not self._is_plateau_in_original(plateau, original_plateaus):
-                # Check if the plateau exceeds the maximum allowed length
-                if end - start + 1 > max_flat_length:
-                    # Break the plateau by adjusting the values
-                    for i in range(start, end + 1):
-                        positions[i] = positions[i] + 1 if value == 100 else positions[i] - 1
-
-        return positions
-
-    def _is_plateau_in_original(self, plateau, original_plateaus):
-        """
-        Checks if a plateau was present in the original data.
-
-        Args:
-            plateau (tuple): Tuple (start, end) representing the indices of the plateau.
-            original_plateaus (list): List of plateaus in the original data.
-
-        Returns:
-            bool: True if the plateau was present in the original data, False otherwise.
-        """
-        start, end = plateau
-        for original_start, original_end in original_plateaus:
-            if start >= original_start and end <= original_end:
-                return True
-        return False
diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index 691b654c..8513409c 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -3,7 +3,7 @@
 import numpy as np
 
 from script_generator.constants import CLASS_NAMES
-from script_generator.debug.logger import log
+from script_generator.debug.logger import log_tr
 
 
 class LockedPenisBox:
@@ -257,7 +257,7 @@ def calculate_distance(self, penis_box, other_box):
         if other_box is None:
             return None
         if penis_box is None:
-            log.warning(f"Penis box is None for frame {self.current_frame_id}, cannot compute distance")
+            log_tr.warning(f"Penis box is None for frame {self.current_frame_id}, cannot compute distance")
             return None
         ox1, oy1, ox2, oy2 = other_box
         y_pos = (oy1 + 2 * oy2) // 3
@@ -277,7 +277,7 @@ def detect_sex_position_change(self, sex_position, reason):
         position_counts = {position: self.sex_position_history.count(position) for position in self.sex_position_history}
         most_frequent_position = max(position_counts, key=position_counts.get, default="Not relevant")
         if most_frequent_position != self.sex_position:
-            log.debug(f"@{self.current_frame_id} - Sex position switched to: {most_frequent_position}")
+            log_tr.debug(f"@{self.current_frame_id} - Sex position switched to: {most_frequent_position}")
             self.sex_position = most_frequent_position
             self.sex_position_reason = reason
 
@@ -336,7 +336,7 @@ def tracking_logic(self, state, sorted_boxes):
                 and not self.penetration:  # equivalent to 3 seconds
             if self.locked_penis_box.active:  # is_active():
                 self.locked_penis_box.deactivate()
-                log.info(f"@{self.current_frame_id} - Deactivated locked_penis_box")
+                log_tr.debug(f"@{self.current_frame_id} - Deactivated locked_penis_box")
 
         if self.locked_penis_box.is_active() and self.locked_penis_box.visible < 100 and not self.glans_detected:
             self.penetration = True
@@ -367,7 +367,7 @@ def tracking_logic(self, state, sorted_boxes):
                             close_up_detected = True
                             self.detect_sex_position_change('Close up', 'Butt box size beyond threshold')
                             if not self.close_up:
-                                log.info(
+                                log_tr.debug(
                                     f"@{self.current_frame_id} - Close up detected - butt size beyond threshold: {int((butt_box_area / self.image_area) * 100)}%"
                                 )
                                 self.close_up = True
@@ -381,7 +381,7 @@ def tracking_logic(self, state, sorted_boxes):
                             close_up_detected = True
                             self.detect_sex_position_change('Close up', 'No penis detected')
                             if not self.close_up:
-                                log.info(f"@{self.current_frame_id} - Close up detected - no penis detected")
+                                log_tr.info(f"@{self.current_frame_id} - Close up detected - no penis detected")
                                 self.close_up = True
                             self.penetration = False
                             distance = 100
@@ -391,7 +391,7 @@ def tracking_logic(self, state, sorted_boxes):
                     close_up_detected = True
                     self.detect_sex_position_change('Close up', 'No penis detected')
                     if not self.close_up:
-                        log.info(f"@{self.current_frame_id} - Close up detected - no penis detected")
+                        log_tr.info(f"@{self.current_frame_id} - Close up detected - no penis detected")
                         self.close_up = True
                     self.penetration = False
                     distance = 100
@@ -559,7 +559,7 @@ def handle_class_first(self, class_name, box, conf):
         """
         if class_name == 'penis':
             if box is not None and self.penis_box is None:
-                log.debug(f"@{self.current_frame_id} - Penis detected with confidence {conf}")
+                log_tr.debug(f"@{self.current_frame_id} - Penis detected with confidence {conf}")
             self.penis_box = box
 
             if self.penis_box:

From ad51b718b008d5346e56cbc2b655a4d9d4938d3a Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 2 Feb 2025 16:44:53 +0100
Subject: [PATCH 213/277] Removed venv option as all examples are for conda

---
 README.md | 23 -----------------------
 1 file changed, 23 deletions(-)

diff --git a/README.md b/README.md
index 1d425458..9971dcc7 100644
--- a/README.md
+++ b/README.md
@@ -84,29 +84,6 @@ Before using this project, ensure you have the following installed:
    cd VR-Funscript-AI-Generator
    ```
 ### Install dependencies
-
-#### Option 1: venv
-**If your GPU supports CUDA (NVIDIA)**
-```bash
-python -m venv VRFunAIGen
-VRFunAIGen\Scripts\activate # windows
-source VRFunAIGen/bin/activate # mac
-pip install -r requirements.txt
-pip uninstall torch torchvision torchaudio
-pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
-python FSGenerator.py
-```
-
-**If your GPU doesn't support cuda**
-```bash
-python -m venv VRFunAIGen
-VRFunAIGen\Scripts\activate # windows
-source VRFunAIGen/bin/activate # mac
-pip install -r requirements.txt
-python FSGenerator.py
-```
-
-#### Option 2: Conda
 * Install miniconda (https://docs.anaconda.com/miniconda/install/)
 * Start a miniconda command prompt
    

From c389613e794e3f39c5cfb038febd984eb08b6383 Mon Sep 17 00:00:00 2001
From: k00gar <arnaud.chevreux@gmail.com>
Date: Sun, 2 Feb 2025 16:54:25 +0100
Subject: [PATCH 214/277] Tweaked window geometry for mac

---
 script_generator/gui/app.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index 0c3035b9..e9e3a779 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -22,7 +22,7 @@ def __init__(self, state: AppState= None):
             ctypes.windll.shcore.SetProcessDpiAwareness(2)  # For Windows DPI scaling
         # self.tk.call('tk', 'scaling', 1.0)
         self.title(f"VR & 2D POV Funscript AI Generator - v" + VERSION)
-        self.geometry('800x900' if is_mac() else '708x790')
+        self.geometry('780x800' if is_mac() else '708x790')
         self.resizable(False, False)
 
         self.iconphoto(False, tk.PhotoImage(file=LOGO))

From 4b47674938edb025a4681cc452335e4415aee80d Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 2 Feb 2025 17:00:44 +0100
Subject: [PATCH 215/277] Updated readme for cli and performance

---
 README.md | 24 +++++++++++++++++-------
 1 file changed, 17 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index 9971dcc7..31e19ae2 100644
--- a/README.md
+++ b/README.md
@@ -84,7 +84,7 @@ Before using this project, ensure you have the following installed:
    cd VR-Funscript-AI-Generator
    ```
 ### Install dependencies
-* Install miniconda (https://docs.anaconda.com/miniconda/install/)
+* Install miniconda (https://docs.anaconda.com/miniconda/install/) (We removed venv as all the bat files use conda)
 * Start a miniconda command prompt
    
 **If your GPU supports CUDA (NVIDIA)**
@@ -121,13 +121,20 @@ You can use Start windows.bat to launch the gui on windows if you installed with
 
 ## Command Line Usage
 
-To generate a script with cmd or terminal, run the following command
+To generate a single script with cmd or terminal, run the following command
 
 ```bash
 python -m script_generator.cli.generate_funscript_single /path/to/video.mp4
 ```
+See examples/windows/Process single video.bat for an example
 
-### Command-Line Arguments
+To generate scripts for all files in a folder use
+```bash
+python -m script_generator.cli.generate_funscript_folder /path/to/folder
+```
+See examples/windows/Process folder.bat for an example
+
+### Command-Line Arguments (Shared)
 #### Required Arguments
 - **`video_path`** Path to the input video file.  
 
@@ -147,14 +154,17 @@ python -m script_generator.cli.generate_funscript_single /path/to/video.mp4
 - **`--vw-factor`** Determines the degree of simplification. Higher values lead to fewer points.
 - **`--rounding`** Set the rounding factor for script values to adjust precision.
 
+
+### Command-Line Arguments (Folder mode)
+- **`--replace-outdated`** Will regenerate outdated funscripts.
+- **`--replace-up-to-date`** Will regenerate funscripts that are up to date and made by this app too.
+- **`--num-workers`** Number of subprocesses to run in parallel. If you have beefy hardware 4 seems to be the sweet spot but technically your VRAM is the limit.
 ---
 
 ## Performance & Parallel Processing
-Our pipeline's current bottleneck lies in the Python code within YOLO.track (the object detection library we use), which is challenging to parallelize effectively. We have plans to parallelize this in the future but for now it remains a limitation.
-
-Currently, we got about 35-40fps for 8192x4096 VR videos on an i5-13600K with a 4090 and 32GB DDR4 ram. Utilization is about 20% CPU, 20% video decoding and 7% GPU usage. We suspect this means performance should be similar for lower end NVIDIA cards.
+Our pipeline's current bottleneck lies in the Python code within YOLO.track (the object detection library we use), which is challenging to parallelize effectively.
 
-However, when you have high-performance hardware this also means effectively run multiple processes simultaneously via the command line or launching multiple instances of the GUI. For reference, the above test setup was able to process at a combined 100fps while still being CPU limited.
+However, when you have high-performance hardware you can use the command line (see above) to processes multiple videos simultaneously. Alternatively you can launch multiple instances of the GUI.
 
 **Important considerations:**
 - Each instance requires the YOLO model to load, so monitor your VRAM usage to determine how many instances your hardware can support. For example, an NVIDIA RTX 4090 can handle up to six instances simultaneously.

From 9d86c8b76f0d9c3d8799c2afb126f1f40caf9bcc Mon Sep 17 00:00:00 2001
From: k00gar <arnaud.chevreux@gmail.com>
Date: Sun, 2 Feb 2025 17:27:08 +0100
Subject: [PATCH 216/277] Tweaked debug video encoding quality setting

---
 script_generator/scripts/generate_debug_video.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script_generator/scripts/generate_debug_video.py b/script_generator/scripts/generate_debug_video.py
index 25aef0df..302a058c 100644
--- a/script_generator/scripts/generate_debug_video.py
+++ b/script_generator/scripts/generate_debug_video.py
@@ -25,7 +25,7 @@ def run():
             "-c:v", "hevc_nvenc" if state.ffmpeg_hwaccel == "cuda" else "libx265",
             "-preset", "p4" if state.ffmpeg_hwaccel == "cuda" else "fast",
             *(["-b:v", "5000k"] if state.ffmpeg_hwaccel == "cuda" else []),
-            *(["-crf", "26"] if state.ffmpeg_hwaccel != "cuda" else []),
+            *(["-crf", "20"] if state.ffmpeg_hwaccel != "cuda" else []),
             "-movflags", "+faststart",
             debug_video_path
         ]

From 7215ea01cea668d7cff41a0663554e539cef2aa8 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 2 Feb 2025 17:34:33 +0100
Subject: [PATCH 217/277] Refixed report screenshots Clear prev state on video
 change

---
 examples/windows/Process folder.bat        |  4 ++--
 examples/windows/Processs single video.bat |  4 ++--
 script_generator/funscript/debug/report.py | 11 ++++++++++-
 script_generator/state/app_state.py        | 10 ++++++++++
 4 files changed, 24 insertions(+), 5 deletions(-)

diff --git a/examples/windows/Process folder.bat b/examples/windows/Process folder.bat
index 5f5fdf8e..ae744b4a 100644
--- a/examples/windows/Process folder.bat	
+++ b/examples/windows/Process folder.bat	
@@ -3,7 +3,7 @@ REM %~dp0 means current dir, we use \..\.. to move up two directories to the roo
 REM you can replace this to the hardcoded path to your directory to make the bat file work from anywhere
 cd /d "%~dp0\..\.."
 
-REM Set the path to your conda activation script and activate the environment.
+REM Sets the path to your conda activation script and activate the environment.
 set "CONDA_PATH=C:\Users\%USERNAME%\miniconda3\Scripts\activate.bat"
 if not exist "%CONDA_PATH%" (
     echo Conda not found at %CONDA_PATH%. Please check your installation.
@@ -13,7 +13,7 @@ if not exist "%CONDA_PATH%" (
 REM Activate the conda environment
 call "%CONDA_PATH%" VRFunAIGen
 
-REM Run the CLI command. Your path would look something like this on windows "C:\my-video-folder"
+REM Run the CLI command. Your path should look something like this: "C:\my-video-folder"
 python -m script_generator.cli.generate_funscript_folder /path/to/your/folder --num-workers=2 --replace-outdated
 
 pause
\ No newline at end of file
diff --git a/examples/windows/Processs single video.bat b/examples/windows/Processs single video.bat
index 640c0d31..1946d09c 100644
--- a/examples/windows/Processs single video.bat	
+++ b/examples/windows/Processs single video.bat	
@@ -3,7 +3,7 @@ REM %~dp0 means current dir, we use \..\.. to move up two directories to the roo
 REM you can replace this to the hardcoded path to your directory to make the bat file work from anywhere
 cd /d "%~dp0\..\.."
 
-REM Set the path to your conda activation script and activate the environment.
+REM Sets the path to your conda activation script and activate the environment.
 set "CONDA_PATH=C:\Users\%USERNAME%\miniconda3\Scripts\activate.bat"
 if not exist "%CONDA_PATH%" (
     echo Conda not found at %CONDA_PATH%. Please check your installation.
@@ -13,7 +13,7 @@ if not exist "%CONDA_PATH%" (
 REM Activate the conda environment
 call "%CONDA_PATH%" VRFunAIGen
 
-REM Run the CLI command. Your path would look something like this on windows "C:\my-movie.mp4"
+REM Run the CLI command. Your path should look something like this: "C:\my-movie.mp4"
 python -m script_generator.cli.generate_funscript_single /path/to/your/video.mp4
 
 pause
\ No newline at end of file
diff --git a/script_generator/funscript/debug/report.py b/script_generator/funscript/debug/report.py
index 6193732d..2acca676 100644
--- a/script_generator/funscript/debug/report.py
+++ b/script_generator/funscript/debug/report.py
@@ -95,8 +95,17 @@ def extract_section(times, positions, start, end):
 def capture_screenshots(video_path, is_vr, sections):
     cap = cv2.VideoCapture(video_path)
     screenshots = []
+
     for start, _ in sections:
         cap.set(cv2.CAP_PROP_POS_MSEC, start * 1000)
-        screenshots.append(np.zeros((100, 160, 3), dtype=np.uint8))
+        ret, frame = cap.read()
+
+        if ret and frame is not None:
+            if is_vr:
+                frame = frame[:, :frame.shape[1] // 2]
+            screenshots.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+        else:
+            screenshots.append(np.zeros((100, 160, 3), dtype=np.uint8))
+
     cap.release()
     return screenshots
\ No newline at end of file
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index 069797dd..0ad56aec 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -120,6 +120,16 @@ def set_video_info(self):
                         self.has_raw_yolo, _, _ = get_raw_yolo_file_info(self)
                         self.has_tracking_data, _, _ = get_metrics_file_info(self)
                         self.max_preview_fps = math.ceil(self.video_info.fps)
+
+                        # TODO move this to a task an remove this here
+                        self.funscript_data = []
+                        self.funscript_frames = []
+                        self.funscript_distances = []
+                        self.offset_x: int = 0
+                        self.frame_start_track = 0
+                        self.current_frame_id = 0
+                        self.frame_area = 0
+                        self.debug_data = DebugData(self)
                     except Exception as e:
                         if not self.is_cli:
                             messagebox.showerror("Error", e)

From eb13d99fdb121e1770a303b8b32dcc044a7a6d88 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 2 Feb 2025 18:28:14 +0100
Subject: [PATCH 218/277] Properly unload video Messagebox always from main
 thread

---
 script_generator/gui/app.py                   |  1 +
 .../gui/controller/generate_funscript.py      |  5 +++-
 .../workers/post_process_worker.py            |  2 ++
 script_generator/state/app_state.py           | 27 +++++++++++--------
 .../video/workers/ffmpeg_worker.py            |  3 +++
 5 files changed, 26 insertions(+), 12 deletions(-)

diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index e9e3a779..1232d3ce 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -47,6 +47,7 @@ def __init__(self, state: AppState= None):
 
         self.state = state if state else AppState()
         self.state.set_is_cli(False)
+        self.state.set_root(self)
 
         # Dictionary to store pages
         self.frames = {}
diff --git a/script_generator/gui/controller/generate_funscript.py b/script_generator/gui/controller/generate_funscript.py
index efef233b..dcfc68cf 100644
--- a/script_generator/gui/controller/generate_funscript.py
+++ b/script_generator/gui/controller/generate_funscript.py
@@ -69,9 +69,12 @@ def run():
             state.analyze_task = None
             state.update_ui(UpdateGUIState(attr="is_processing", value=False))
             log.error(f"Error during video analysis: {e}")
-            messagebox.showerror("Error", f"Could not process video:\n{e}")
             import traceback
             traceback.print_exc()
 
+            # if GUI is enabled make sure to use the main thread
+            if state.root:
+                state.root.after(0, messagebox.showerror, "Error", f"Could not process video:\n{e}")
+
     processing_thread = threading.Thread(target=run)
     processing_thread.start()
diff --git a/script_generator/object_detection/workers/post_process_worker.py b/script_generator/object_detection/workers/post_process_worker.py
index fd81b39d..385c9b5b 100644
--- a/script_generator/object_detection/workers/post_process_worker.py
+++ b/script_generator/object_detection/workers/post_process_worker.py
@@ -20,6 +20,8 @@ class PostProcessWorker(AbstractTaskProcessor):
     test_result = ObjectDetectionResult()  # Test result object for debugging
 
     def task_logic(self):
+        self.records = []
+        self.test_result = ObjectDetectionResult()
         state = self.state
         width, height = get_cropped_dimensions(state.video_info)
 
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index 0ad56aec..4b7199de 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -26,6 +26,7 @@ def __init__(self):
         if self._initialized:
             return
         self._initialized = True
+        self.root = None
 
         self.is_cli = True
         self.config_manager = ConfigManager(self)
@@ -90,6 +91,9 @@ def __init__(self):
     def set_is_cli(self, cli):
         self.is_cli = cli
 
+    def set_root(self, root):
+        self.root = root
+
     def is_configured(self):
         message_prefix = "Cannot process the video."
         checks = [
@@ -108,6 +112,17 @@ def is_configured(self):
     def set_video_info(self):
         # If movie changed
         if self.video_info is None or self.video_info.path != self.video_path:
+
+            # TODO move this to a task an remove this here
+            self.funscript_data = []
+            self.funscript_frames = []
+            self.funscript_distances = []
+            self.offset_x: int = 0
+            self.frame_start_track = 0
+            self.current_frame_id = 0
+            self.frame_area = 0
+            self.debug_data = DebugData(self)
+
             if not self.video_path:
                 self.video_info = None
                 self.has_raw_yolo = False
@@ -120,19 +135,9 @@ def set_video_info(self):
                         self.has_raw_yolo, _, _ = get_raw_yolo_file_info(self)
                         self.has_tracking_data, _, _ = get_metrics_file_info(self)
                         self.max_preview_fps = math.ceil(self.video_info.fps)
-
-                        # TODO move this to a task an remove this here
-                        self.funscript_data = []
-                        self.funscript_frames = []
-                        self.funscript_distances = []
-                        self.offset_x: int = 0
-                        self.frame_start_track = 0
-                        self.current_frame_id = 0
-                        self.frame_area = 0
-                        self.debug_data = DebugData(self)
                     except Exception as e:
                         if not self.is_cli:
-                            messagebox.showerror("Error", e)
+                            messagebox.showerror("Error", str(e))
                     finally:
                         return
 
diff --git a/script_generator/video/workers/ffmpeg_worker.py b/script_generator/video/workers/ffmpeg_worker.py
index 0c03cad2..e4869c6f 100644
--- a/script_generator/video/workers/ffmpeg_worker.py
+++ b/script_generator/video/workers/ffmpeg_worker.py
@@ -15,6 +15,9 @@ class VideoWorker(AbstractTaskProcessor):
     read_frames = True
 
     def task_logic(self):
+        self.process = None
+        self.read_frames = True
+
         cmd, frame_size, width, height = get_ffmpeg_read_cmd(
             self.state,
             self.state.frame_start

From cc545e3028cbdc1ce6aebe61ffd10240d9920400 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 3 Feb 2025 20:16:18 +0100
Subject: [PATCH 219/277] Tweaks

---
 README.md                                     | 10 ++-
 .../cli/generate_funscript_folder.py          | 63 +++++++++++++++----
 .../cli/generate_funscript_txt_file.py        |  2 +-
 script_generator/debug/video_player/play.py   |  1 -
 .../funscript/debug/combined_plot.py          |  1 -
 .../util/check_existing_funscript.py          |  8 ++-
 .../object_detection/util/object_detection.py |  4 +-
 script_generator/scripts/analyze_video.py     | 19 +++---
 .../video/data_classes/video_info.py          | 14 ++---
 9 files changed, 86 insertions(+), 36 deletions(-)

diff --git a/README.md b/README.md
index 31e19ae2..42f00844 100644
--- a/README.md
+++ b/README.md
@@ -162,12 +162,18 @@ See examples/windows/Process folder.bat for an example
 ---
 
 ## Performance & Parallel Processing
-Our pipeline's current bottleneck lies in the Python code within YOLO.track (the object detection library we use), which is challenging to parallelize effectively.
+Our pipeline's current bottleneck lies in the Python code within YOLO.track (the object detection library we use), which is challenging to parallelize effectively in a single process.
 
 However, when you have high-performance hardware you can use the command line (see above) to processes multiple videos simultaneously. Alternatively you can launch multiple instances of the GUI.
 
+We tested speeds of about 60 to 110 fps for 8k 8bit vr videos when running a single process. Which translates to faster then realtime processing already. However, running in parallel mode we tested 
+speeds of about 160 to 190 frames per second (for object detection). Meaning processing times of about 20 to 30 minutes for 8bit 8k VR videos for the complete process. More then twice the speed of realtime!
+
+Keep in mind your results may vary as this is very dependent on your hardware. Cuda capable cards will have an advantage here. However, since the pipeline is largely CPU and video decode bottlenecked 
+a top of the line card like the 4090 is not required to get similar results. Having enough VRAM to run 3-6 processes, paired with a good CPU, will speed things up considerably though. 
+
 **Important considerations:**
-- Each instance requires the YOLO model to load, so monitor your VRAM usage to determine how many instances your hardware can support. For example, an NVIDIA RTX 4090 can handle up to six instances simultaneously.
+- Each instance requires the YOLO model to load which means you'll need to keep checks on your VRAM to see how many you can load.
 - The optimal number of instances depends on a combination of factors, including your CPU, GPU, RAM, and system configuration. So experiment with different setups to find the ideal configuration for your hardware! 😊
 
 ---
diff --git a/script_generator/cli/generate_funscript_folder.py b/script_generator/cli/generate_funscript_folder.py
index b0e49977..9e99dc4c 100644
--- a/script_generator/cli/generate_funscript_folder.py
+++ b/script_generator/cli/generate_funscript_folder.py
@@ -4,6 +4,7 @@
 import sys
 import time
 from collections import deque
+from datetime import timedelta
 
 from script_generator.cli.shared.common_args import (
     add_shared_generate_funscript_args,
@@ -135,27 +136,56 @@ def log_files_by_category(category, file_list):
 
         log.info(f"Starting batch generation with up to {args.num_workers} parallel subprocesses.")
 
+        # Dictionary to keep track of the submitted tasks
+        future_to_video = {}
+
+        global_start_time = time.time()  # overall start time for the batch
+
         # Use a ThreadPoolExecutor to limit concurrency.
         with concurrent.futures.ThreadPoolExecutor(max_workers=args.num_workers) as executor:
-            futures = []
+            running = []
             # Submit all tasks.
             while tasks:
                 video_path = tasks.popleft()
-                log.info(f"[Submit] {video_path}")
-                future = executor.submit(run_task, video_path)
-                futures.append((video_path, future))
+                log.debug(f"[Submit] {video_path}")
+                running.append(video_path)
+                future = executor.submit(run_task_timed, video_path)
+                future_to_video[future] = video_path
                 time.sleep(0.1)  # slight delay to stagger submissions
 
-            # Wait for all submitted tasks to complete.
-            for video_path, future in futures:
+            total_count = len(future_to_video)
+            completed_count = 0
+            processing_times = []  # times for completed tasks
+
+            # Process futures as they complete.
+            for future in concurrent.futures.as_completed(future_to_video):
+                video_path = future_to_video[future]
                 try:
-                    ret = future.result()  # blocks until the task completes
-                    if ret == 0:
-                        log.info(f"[Done] {video_path}")
-                    else:
-                        log.warning(f"[Failed] {video_path} (exit code {ret})")
+                    ret, elapsed = future.result()
                 except Exception as e:
                     log.error(f"Error processing {video_path}: {e}", exc_info=True)
+                    ret, elapsed = -1, 0
+                completed_count += 1
+                processing_times.append(elapsed)
+
+                # Gather names of movies still in progress.
+                currently_processing = [
+                    v for fut, v in future_to_video.items() if not fut.done()
+                ]
+                total_run_time = time.time() - global_start_time
+                average_time = sum(processing_times) / len(processing_times) if processing_times else 0
+
+
+                queued_paths = "\n".join(f" - {movie}" for movie in running) if running else "None"
+                log.info(
+                    f"Progress: {completed_count}/{total_count} completed. Total run time: {str(timedelta(seconds=int(total_run_time)))} Average time per completed movie: {str(timedelta(seconds=int(average_time)))} seconds (not taking into account progress on running processes)."
+                    f"Queued Movies:\n{queued_paths}"
+                )
+
+                if ret == 0:
+                    log.info(f"[Done] {video_path}")
+                else:
+                    log.warning(f"[Failed] {video_path} (exit code {ret})")
 
         log.info("All funscript generation tasks completed.")
 
@@ -176,5 +206,16 @@ def run_task(video_path):
     proc.wait()
     return proc.returncode
 
+
+def run_task_timed(video_path):
+    """
+    Wraps run_task to record how long it takes.
+    Returns a tuple: (exit code, elapsed_time_in_seconds).
+    """
+    start = time.time()
+    ret = run_task(video_path)
+    elapsed = time.time() - start
+    return ret, elapsed
+
 if __name__ == "__main__":
     main()
diff --git a/script_generator/cli/generate_funscript_txt_file.py b/script_generator/cli/generate_funscript_txt_file.py
index f87f5c14..2618bc0a 100644
--- a/script_generator/cli/generate_funscript_txt_file.py
+++ b/script_generator/cli/generate_funscript_txt_file.py
@@ -1 +1 @@
-# TODO
\ No newline at end of file
+# TODO implement movies.txt cli command
\ No newline at end of file
diff --git a/script_generator/debug/video_player/play.py b/script_generator/debug/video_player/play.py
index 7cbd509a..eb4d9f05 100644
--- a/script_generator/debug/video_player/play.py
+++ b/script_generator/debug/video_player/play.py
@@ -75,7 +75,6 @@ def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=1
 
     last_frame = video_player.current_frame
 
-    # TODO cleanup
     def get_ceiled_fps(value):
         try:
             return math.ceil(float(value))
diff --git a/script_generator/funscript/debug/combined_plot.py b/script_generator/funscript/debug/combined_plot.py
index 4f419a0b..b004f8f3 100644
--- a/script_generator/funscript/debug/combined_plot.py
+++ b/script_generator/funscript/debug/combined_plot.py
@@ -26,7 +26,6 @@ def create_combined_plot(state: AppState, ref_sections, gen_sections, screenshot
         gen_positions (list): Positions from the generated funscript.
     """
 
-    # TODO why is this empty sometimes? and result in errors
     if not gen_times or not gen_positions:
         log_fun.error("Could not created combined plot")
         return
diff --git a/script_generator/funscript/util/check_existing_funscript.py b/script_generator/funscript/util/check_existing_funscript.py
index c169e255..1b53d25d 100644
--- a/script_generator/funscript/util/check_existing_funscript.py
+++ b/script_generator/funscript/util/check_existing_funscript.py
@@ -2,6 +2,7 @@
 import datetime
 
 from script_generator.constants import FUNSCRIPT_AUTHOR, FUNSCRIPT_VERSION
+from script_generator.debug.logger import log_fun
 from script_generator.utils.json_utils import load_json_from_file
 from script_generator.utils.version import version_is_less_than
 
@@ -10,7 +11,12 @@ def check_existing_funscript(dest_path: str, filename_base: str, make_funscript_
     if not os.path.exists(dest_path):
         return False, False, None, False
 
-    json_data = load_json_from_file(dest_path)
+    try:
+        json_data = load_json_from_file(dest_path)
+    except Exception as e:
+        log_fun.info(f"Could not load funscript data for file: {dest_path}: {str(e)}")
+        return True, False, None, False
+
     is_ours = json_data.get("author") == FUNSCRIPT_AUTHOR
 
     backup_path = None
diff --git a/script_generator/object_detection/util/object_detection.py b/script_generator/object_detection/util/object_detection.py
index cb3f31f1..97839068 100644
--- a/script_generator/object_detection/util/object_detection.py
+++ b/script_generator/object_detection/util/object_detection.py
@@ -1,7 +1,7 @@
 import os
 
 from script_generator.constants import CLASS_REVERSE_MATCH
-from script_generator.debug.logger import log
+from script_generator.debug.logger import log, log_tr
 from script_generator.gui.utils.widgets import Widgets
 from script_generator.object_detection.data_classes.box_record import BoxRecord
 from script_generator.object_detection.data_classes.object_detection_result import ObjectDetectionResult
@@ -71,5 +71,5 @@ def parse_yolo_data_looking_for_penis(data, start_frame):
             prev_frame = frame_idx
 
             if cons_frames > threshold:
-                log.info(f"First instance of Glans/Penis found in frame {frame_idx - threshold}")
+                log_tr.info(f"First instance of Glans/Penis found in frame {frame_idx - threshold}")
                 return penis_frame - threshold
diff --git a/script_generator/scripts/analyze_video.py b/script_generator/scripts/analyze_video.py
index 313c6a3a..adcd6bb4 100644
--- a/script_generator/scripts/analyze_video.py
+++ b/script_generator/scripts/analyze_video.py
@@ -5,11 +5,11 @@
 from tqdm import tqdm
 
 from script_generator.constants import SEQUENTIAL_MODE, UPDATE_PROGRESS_INTERVAL
-from script_generator.debug.logger import log
+from script_generator.debug.logger import log_od
 from script_generator.gui.messages.messages import ProgressMessage
 from script_generator.state.app_state import AppState
-from script_generator.tasks.workers.abstract_task_processor import TaskProcessorTypes
 from script_generator.tasks.data_classes.analyze_video_task import AnalyzeVideoTask
+from script_generator.tasks.workers.abstract_task_processor import TaskProcessorTypes
 from script_generator.utils.data_classes.meta_data import MetaData
 from script_generator.utils.file import check_create_output_folder
 
@@ -18,7 +18,7 @@
 
 
 def analyze_video(state: AppState) -> List["AnalyzeFrameTask"]:
-    log.info(f"[OBJECT DETECTION] Starting up pipeline{' in sequential mode' if SEQUENTIAL_MODE else ''}...")
+    log_od.info(f"[OBJECT DETECTION] Starting up pipeline{' in sequential mode' if SEQUENTIAL_MODE else ''}...")
 
     log_thread_stop_event = threading.Event()
     threads = []
@@ -34,11 +34,11 @@ def analyze_video(state: AppState) -> List["AnalyzeFrameTask"]:
         state.set_video_info()
         if state.video_reader == "FFmpeg + OpenGL (Windows)":
             if not state.video_info.is_vr:
-                log.warn("Disabled OpenGL in the pipeline as it's not needed for 2D videos")
+                log_od.warn("Disabled OpenGL in the pipeline as it's not needed for 2D videos")
                 state.video_reader = "FFmpeg"
 
             if state.video_info.is_fisheye:
-                log.warn("Disabled OpenGL as fisheye is not yet supported with the opengl feature")
+                log_od.warn("Disabled OpenGL as fisheye is not yet supported with the opengl feature")
                 state.video_reader = "FFmpeg"
 
         use_open_gl = state.video_reader == "FFmpeg + OpenGL (Windows)"
@@ -61,7 +61,7 @@ def run_thread(thread, thread_name, out_queue):
                 thread.start()
                 thread.join()
                 out_queue.put(None)
-                log.info(f"[OBJECT DETECTION] {thread_name} thread done in {time.time() - start_time} s")
+                log_od.info(f"[OBJECT DETECTION] {thread_name} thread done in {time.time() - start_time} s")
 
             run_thread(a.decode_thread, TaskProcessorTypes.VIDEO, a.opengl_q)
             if use_open_gl:
@@ -102,7 +102,7 @@ def run_thread(thread, thread_name, out_queue):
         return a.result_q.queue
 
     except Exception as e:
-        log.error(f"An error occurred during video analysis: {e}")
+        log_od.error(f"An error occurred during video analysis: {e}")
         # Signal all threads to stop and perform cleanup
         log_thread_stop_event.set()
         for thread in threads:
@@ -158,13 +158,12 @@ def log_progress(state, analyze_task, stop_event):
                         eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
                     ))
                 except Exception as e:
-                    log.error(f"Error in state.update_ui: {e}")
+                    log_od.error(f"Error in state.update_ui: {e}")
 
             time.sleep(UPDATE_PROGRESS_INTERVAL)
 
 def log_performance(state, results_queue):
     analyze_task = state.analyze_task
-    # TODO filter out sentinals in task processor
     tasks = [task for task in results_queue.queue if hasattr(task, 'profile')]
     total_frames = len(tasks)
 
@@ -223,4 +222,4 @@ def log_performance(state, results_queue):
     log_message += f"{'-' * 60}\n"
 
     for line in log_message.splitlines():
-        log.info(line)
\ No newline at end of file
+        log_od.info(line)
\ No newline at end of file
diff --git a/script_generator/video/data_classes/video_info.py b/script_generator/video/data_classes/video_info.py
index 61299e08..b7ba999d 100644
--- a/script_generator/video/data_classes/video_info.py
+++ b/script_generator/video/data_classes/video_info.py
@@ -6,7 +6,7 @@
 
 from script_generator.constants import RENDER_RESOLUTION
 from script_generator.debug.errors import FFProbeError
-from script_generator.debug.logger import log, log_vid
+from script_generator.debug.logger import log_vid
 
 
 @dataclass
@@ -72,7 +72,7 @@ def get_projection_and_fov_from_filename(filename):
             fov = pattern["fov"]
             is_fisheye = pattern["is_fisheye"]
             break
-    log.info(f"Video Format: Projection={projection}, FOV={fov}, is_fisheye={is_fisheye}")
+    log_vid.info(f"Video Format: Projection={projection}, FOV={fov}, is_fisheye={is_fisheye}")
 
     return {"projection": projection, "fov": fov, "is_fisheye": is_fisheye}
 
@@ -135,18 +135,18 @@ def get_video_info(video_path):
         is_vr = height == width // 2
         size_bytes = os.path.getsize(video_path)
 
-        log.info(f"Video Info: {codec_name}, {width}x{height}, {fps:.2f} fps, {nb_frames} frames, {duration:.2f} sec, {bit_depth}-bit, is vr: {is_vr}")
+        log_vid.info(f"Video Info: {codec_name}, {width}x{height}, {fps:.2f} fps, {nb_frames} frames, {duration:.2f} sec, {bit_depth}-bit, is vr: {is_vr}")
 
         if is_vr:
-            log.info("Video Format: VR SBS - Based on its 2:1 ratio")
+            log_vid.info("Video Format: VR SBS - Based on its 2:1 ratio")
         else:
-            log.info("Video Format: 2D - Based on its ratio")
+            log_vid.info("Video Format: 2D - Based on its ratio")
 
         return VideoInfo(video_path, codec_name, width, height, duration, int(nb_frames), fps, bit_depth, is_vr, size_bytes)
 
     except subprocess.CalledProcessError as e:
-        log.error(f"FFProbe command failed: {e.output.decode('utf-8')}")
+        log_vid.error(f"FFProbe command failed: {e.output.decode('utf-8')}")
         raise FFProbeError("FFProbe command execution failed.")
     except (ValueError, KeyError, IndexError) as e:
-        log.error(f"Error parsing FFProbe output: {e}")
+        log_vid.error(f"Error parsing FFProbe output: {e}")
         raise FFProbeError("Failed to parse FFProbe output.")
\ No newline at end of file

From fe732e3a87475dade7159cc46bc1d526f1a48bb2 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Thu, 6 Feb 2025 13:36:19 +0100
Subject: [PATCH 220/277] fixed the vw factor being a float, not an integer

---
 script_generator/gui/utils/widgets.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index 5ef0a42e..a218204d 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -227,7 +227,7 @@ def range_selector(parent, label_text, row, state, attr, values, column=0, toolt
         dropdown = ttk.Combobox(parent, textvariable=selected_value, values=values, width=5, state="readonly")
         dropdown.grid(row=row, column=column + 1, sticky="w", padx=PADDING_X, pady=PADDING_Y)
 
-        dropdown.bind("<<ComboboxSelected>>", lambda _: setattr(state, attr, int(selected_value.get())))
+        dropdown.bind("<<ComboboxSelected>>", lambda _: setattr(state, attr, float(selected_value.get())))
 
         if tooltip_text:
             Tooltip(dropdown, tooltip_text)

From 29524247abce30cb971d5d65e76a8f3be5c89aa5 Mon Sep 17 00:00:00 2001
From: jwvanderbeck <jwvanderbeck@gmail.com>
Date: Sat, 8 Feb 2025 10:21:32 -0500
Subject: [PATCH 221/277] Update readme for new setup

---
 README.md                                 | 69 ++++++++++++++---------
 requirements.txt => core.requirements.txt |  2 -
 cpu.requirements.txt                      |  2 +
 cuda.requirements.txt                     |  3 +
 4 files changed, 47 insertions(+), 29 deletions(-)
 rename requirements.txt => core.requirements.txt (86%)
 create mode 100644 cpu.requirements.txt
 create mode 100644 cuda.requirements.txt

diff --git a/README.md b/README.md
index 42f00844..d1acd506 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,7 @@ This project is a Python-based tool for generating Funscript files from VR video
 
 If you find this project useful, consider supporting me on:
 
-- **Ko-fi**: [![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/H2H818EIJV) 
+- **Ko-fi**: [![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/H2H818EIJV)
 - **Patreon**: [https://www.patreon.com/c/k00gar](https://www.patreon.com/c/k00gar)
 
 Your support helps me continue developing and improving this project!
@@ -41,7 +41,7 @@ This project started as a dream to automate Funscript generation for VR videos.
 - **Transition to YOLO**: To improve accuracy and speed, the project shifted to using YOLO object detection. A custom YOLO model was trained on a dataset of VR video frames, significantly improving detection quality. The new approach runs at 90 FPS on a Mac mini M4 pro, making it much more efficient.
 
 - **Original Post**: For more details and discussions, check out the original post on EroScripts:  
-[VR Funscript Generation Helper (Python + CV/AI)](https://discuss.eroscripts.com/t/vr-funscript-generation-helper-python-now-cv-ai/202554)
+  [VR Funscript Generation Helper (Python + CV/AI)](https://discuss.eroscripts.com/t/vr-funscript-generation-helper-python-now-cv-ai/202554)
 
 ---
 
@@ -69,52 +69,58 @@ The pipeline for generating Funscript files is as follows:
 ---
 
 ## Prerequisites
- 
+
 Before using this project, ensure you have the following installed:
 
 - **Python 3.8 or higher (tested on 3.11 https://www.python.org/downloads/release/python-3118/)**
-- **FFmpeg** added to your PATH or specified under the settings menu (https://www.ffmpeg.org/download.html)
---
+- ## **FFmpeg** added to your PATH or specified under the settings menu (https://www.ffmpeg.org/download.html)
 
 ## Installation
 
 ### Clone the repository
-   ```bash
-   git clone https://github.com/ack00gar/VR-Funscript-AI-Generator.git
-   cd VR-Funscript-AI-Generator
-   ```
+
+```bash
+git clone https://github.com/ack00gar/VR-Funscript-AI-Generator.git
+cd VR-Funscript-AI-Generator
+```
+
 ### Install dependencies
-* Install miniconda (https://docs.anaconda.com/miniconda/install/) (We removed venv as all the bat files use conda)
-* Start a miniconda command prompt
-   
-**If your GPU supports CUDA (NVIDIA)**
+
+- Install miniconda (https://docs.anaconda.com/miniconda/install/) (We removed venv as all the bat files use conda)
+- Start a miniconda command prompt
+
+#### If your GPU supports CUDA (NVIDIA)
+
 ```bash
 conda create -n VRFunAIGen python=3.11
 conda activate VRFunAIGen
-pip install -r requirements.txt
-pip uninstall torch torchvision torchaudio
-pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
+pip install -r core.requirements.txt
+pip install -r cuda.requirements.txt
 python FSGenerator.py
 ```
+
 While executing, you’ll need to say “yes” a few times. The lines “pip uninstall / pip3 install” is to replace the “CPU” version of torch with a “cuda enabled / GPU” version (you might need to install nvidia CUDA stuff for it to works, I’m not sure).
 
-**If your GPU doesn't support cuda**
+#### If your GPU doesn't support cuda
+
 ```bash
 conda create -n VRFunAIGen python=3.11
 conda activate VRFunAIGen
-pip install -r requirements.txt
+pip install -r core.requirements.txt
+pip install -r cpu.requirements.txt
 python FSGenerator.py
 ```
 
 ### Download the YOLO model
+
 - Place your YOLO model file (e.g., `k00gar-11n-RGB-200ep-best.mlpackage`) in the `models/` sub-directory.
 - Alternatively, you can specify a custom path to the model using the `--yolo_model` argument.
 
-
 **(Optional) Settings**
 Find the settings menu in the app to configure optional option.
 
 ### Start script
+
 You can use Start windows.bat to launch the gui on windows if you installed with conda
 
 ---
@@ -126,24 +132,31 @@ To generate a single script with cmd or terminal, run the following command
 ```bash
 python -m script_generator.cli.generate_funscript_single /path/to/video.mp4
 ```
+
 See examples/windows/Process single video.bat for an example
 
 To generate scripts for all files in a folder use
+
 ```bash
 python -m script_generator.cli.generate_funscript_folder /path/to/folder
 ```
+
 See examples/windows/Process folder.bat for an example
 
 ### Command-Line Arguments (Shared)
+
 #### Required Arguments
-- **`video_path`** Path to the input video file.  
+
+- **`video_path`** Path to the input video file.
 
 #### Optional Arguments
+
 - **`--reuse-yolo`** Re-use an existing raw YOLO output file instead of generating a new one when available.
 - **`--copy-funscript`** Copies the final funscript to the movie directory.
 - **`--save-debug-file`** Saves a debug file to disk with all collected metrics. Also allows you to re-use tracking data.
 
 #### Optional Funscript Tweaking Settings
+
 - **`--boost-enabled`** Enable boosting to adjust the motion range dynamically.
 - **`--boost-up-percent`** Increase the peaks by a specified percentage to enhance upper motion limits.
 - **`--boost-down-percent`** Reduce the lower peaks by a specified percentage to limit downward motion.
@@ -154,25 +167,28 @@ See examples/windows/Process folder.bat for an example
 - **`--vw-factor`** Determines the degree of simplification. Higher values lead to fewer points.
 - **`--rounding`** Set the rounding factor for script values to adjust precision.
 
-
 ### Command-Line Arguments (Folder mode)
+
 - **`--replace-outdated`** Will regenerate outdated funscripts.
 - **`--replace-up-to-date`** Will regenerate funscripts that are up to date and made by this app too.
 - **`--num-workers`** Number of subprocesses to run in parallel. If you have beefy hardware 4 seems to be the sweet spot but technically your VRAM is the limit.
+
 ---
 
 ## Performance & Parallel Processing
+
 Our pipeline's current bottleneck lies in the Python code within YOLO.track (the object detection library we use), which is challenging to parallelize effectively in a single process.
 
 However, when you have high-performance hardware you can use the command line (see above) to processes multiple videos simultaneously. Alternatively you can launch multiple instances of the GUI.
 
-We tested speeds of about 60 to 110 fps for 8k 8bit vr videos when running a single process. Which translates to faster then realtime processing already. However, running in parallel mode we tested 
+We tested speeds of about 60 to 110 fps for 8k 8bit vr videos when running a single process. Which translates to faster then realtime processing already. However, running in parallel mode we tested
 speeds of about 160 to 190 frames per second (for object detection). Meaning processing times of about 20 to 30 minutes for 8bit 8k VR videos for the complete process. More then twice the speed of realtime!
 
-Keep in mind your results may vary as this is very dependent on your hardware. Cuda capable cards will have an advantage here. However, since the pipeline is largely CPU and video decode bottlenecked 
-a top of the line card like the 4090 is not required to get similar results. Having enough VRAM to run 3-6 processes, paired with a good CPU, will speed things up considerably though. 
+Keep in mind your results may vary as this is very dependent on your hardware. Cuda capable cards will have an advantage here. However, since the pipeline is largely CPU and video decode bottlenecked
+a top of the line card like the 4090 is not required to get similar results. Having enough VRAM to run 3-6 processes, paired with a good CPU, will speed things up considerably though.
 
 **Important considerations:**
+
 - Each instance requires the YOLO model to load which means you'll need to keep checks on your VRAM to see how many you can load.
 - The optimal number of instances depends on a combination of factors, including your CPU, GPU, RAM, and system configuration. So experiment with different setups to find the ideal configuration for your hardware! 😊
 
@@ -182,20 +198,19 @@ a top of the line card like the 4090 is not required to get similar results. Hav
 
 - For VR only **Fisheye** and **Equirectangular** 180° videos are supported
 - 2D POV videos have limited support
-- 2D / VR is automatically detected for fisheye/equirectangular detection make sure you keep the file format information in the filename (_FISHEYE190, _MKX200, _LR_180, etc.) 
+- 2D / VR is automatically detected for fisheye/equirectangular detection make sure you keep the file format information in the filename (\_FISHEYE190, \_MKX200, \_LR_180, etc.)
 
 ### Configuration / User settings
 
 See config.py for customizations and user settings (will be replaced with a yaml soon).
 
-
 ---
 
 ## Output Files
 
 The script generates the following files in the output directory of you project folder:
 
-1. `_rawyolo.msgpack`: Raw YOLO detection data. Can be re-used when re-generating scripts 
+1. `_rawyolo.msgpack`: Raw YOLO detection data. Can be re-used when re-generating scripts
 2. `_cuts.msgpack`: Detected scene changes.
 3. `_rawfunscript.json`: Raw Funscript data. Can be re-used when re-generating script with different settings.
 4. `.funscript`: Final Funscript file.
diff --git a/requirements.txt b/core.requirements.txt
similarity index 86%
rename from requirements.txt
rename to core.requirements.txt
index a311d2e5..f3e7e5d0 100644
--- a/requirements.txt
+++ b/core.requirements.txt
@@ -1,5 +1,3 @@
-torch~=2.5.1
-torchvision~=0.20.1
 numpy
 ultralytics~=8.3.59
 glfw~=2.8.0
diff --git a/cpu.requirements.txt b/cpu.requirements.txt
new file mode 100644
index 00000000..f3845bba
--- /dev/null
+++ b/cpu.requirements.txt
@@ -0,0 +1,2 @@
+torch~=2.5.1
+torchvision~=0.20.1
diff --git a/cuda.requirements.txt b/cuda.requirements.txt
new file mode 100644
index 00000000..346ab7a5
--- /dev/null
+++ b/cuda.requirements.txt
@@ -0,0 +1,3 @@
+-i https://download.pytorch.org/whl/cu121
+torch~=2.5.1
+torchvision~=0.20.1

From 8a6a09d1c6c47bb53f8e55e1350247f7affdf215 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Mon, 10 Feb 2025 12:31:28 +0100
Subject: [PATCH 222/277] Added generation of raw and unfiltered .funscript
 file for advanced scripting / users

---
 script_generator/funscript/create_funscript.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/script_generator/funscript/create_funscript.py b/script_generator/funscript/create_funscript.py
index 7ef76e17..170dd873 100644
--- a/script_generator/funscript/create_funscript.py
+++ b/script_generator/funscript/create_funscript.py
@@ -36,6 +36,11 @@ def create_funscript(state):
         ats = [p[0] for p in data]
         positions = [p[1] for p in data]
 
+        # saving raw, unfiltered funscript for advanced scripters
+        zip_raw_positions = zip(ats, positions)
+        raw_unfiltered_funscript_path, _ = get_output_file_path(state.video_path, ".funscript", "rawfunscript")
+        write_funscript(zip_raw_positions, raw_unfiltered_funscript_path, state.video_info.fps)
+
         log_fun.info(f"Positions adjustment - step 1 (noise removal)")
         # Run the Savitzky-Golay filter
         positions = savgol_filter(positions, int(state.video_info.fps // 4), 3)

From a1130fd470edc0aca5ddb5a4adf89c93ec3474e9 Mon Sep 17 00:00:00 2001
From: ack00gar <arnaud.chevreux@gmail.com>
Date: Tue, 11 Feb 2025 23:04:56 +0100
Subject: [PATCH 223/277] Fixed state.frame_end ghosting in the GUI, leading to
 early cut of processing of videos after a shorter one was already processed
 in the same GUI instance.

---
 .../analysis/workers/analyze_tracking_results.py     | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/script_generator/analysis/workers/analyze_tracking_results.py b/script_generator/analysis/workers/analyze_tracking_results.py
index 44d6b481..d4cca83a 100644
--- a/script_generator/analysis/workers/analyze_tracking_results.py
+++ b/script_generator/analysis/workers/analyze_tracking_results.py
@@ -33,7 +33,8 @@ def analyze_tracking_results(state: AppState):
 
     # Deciding whether we start from there or from a user-specified later frame
     state.frame_start_track = max(max(first_penis_frame - int(state.video_info.fps), state.frame_start - int(state.video_info.fps)), 0)
-    state.frame_end = state.video_info.total_frames if not state.frame_end else state.frame_end
+    # state.frame_end = state.video_info.total_frames if not state.frame_end else state.frame_end
+    frame_end = state.video_info.total_frames if not state.frame_end else state.frame_end
 
     # logger.info(f"Frame Start adjusted to: {state.frame_start}")
     log_tr.info(f"Frame Start adjusted to: {state.frame_start_track}")
@@ -89,7 +90,8 @@ def analyze_tracking_results(state: AppState):
 
     for frame_pos in tqdm(
             # range(state.frame_start, state.frame_end), unit="f", desc="Analyzing tracking data", position=0,
-            range(state.frame_start_track, state.frame_end),
+            # range(state.frame_start_track, state.frame_end),
+            range(state.frame_start_track, frame_end),
             unit="f",
             desc="Analyzing tracking data", position=0,
             unit_scale=False,
@@ -235,13 +237,15 @@ def analyze_tracking_results(state: AppState):
                 last_ui_update_time = current_time
                 elapsed_time = current_time - start_time
                 frames_processed = frame_pos - state.frame_start + 1
-                frames_remaining = state.frame_end - frame_pos - 1
+                # frames_remaining = state.frame_end - frame_pos - 1
+                frames_remaining = frame_end - frame_pos - 1
                 eta = (elapsed_time / frames_processed) * frames_remaining if frames_processed > 0 else 0
 
                 state.update_ui(ProgressMessage(
                     process="TRACKING_ANALYSIS",
                     frames_processed=frames_processed,
-                    total_frames=state.frame_end,
+                    # total_frames=state.frame_end,
+                    total_frames=frame_end,
                     eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
                 ))
 

From da22d0d7112d617da51f8c4799b4a96c2c868111 Mon Sep 17 00:00:00 2001
From: jwvanderbeck <jwvanderbeck@gmail.com>
Date: Tue, 11 Feb 2025 18:42:21 -0500
Subject: [PATCH 224/277] Add default version, sanitize json version strings

---
 .../util/check_existing_funscript.py          |  2 +-
 script_generator/utils/version.py             | 23 +++++++++++++++++--
 2 files changed, 22 insertions(+), 3 deletions(-)

diff --git a/script_generator/funscript/util/check_existing_funscript.py b/script_generator/funscript/util/check_existing_funscript.py
index 1b53d25d..b6b8f573 100644
--- a/script_generator/funscript/util/check_existing_funscript.py
+++ b/script_generator/funscript/util/check_existing_funscript.py
@@ -27,6 +27,6 @@ def check_existing_funscript(dest_path: str, filename_base: str, make_funscript_
 
     out_of_date = False
     if is_ours:
-        out_of_date = version_is_less_than(json_data.get("version"), FUNSCRIPT_VERSION)
+        out_of_date = version_is_less_than(json_data.get("version", "0.0.1"), FUNSCRIPT_VERSION)
 
     return True, is_ours, backup_path, out_of_date
\ No newline at end of file
diff --git a/script_generator/utils/version.py b/script_generator/utils/version.py
index 22530d78..8e58f346 100644
--- a/script_generator/utils/version.py
+++ b/script_generator/utils/version.py
@@ -1,9 +1,28 @@
+import re
+from script_generator.debug.logger import log_fun
+
+def sanitize_version(version: str) -> str:
+    """
+    Extracts the leading semantic version (digits and dots) from a version string.
+    For example, "0.0.1_25-01-16" becomes "0.0.1".
+    """
+
+    match = re.match(r"(\d+(?:\.\d+)*)", version)
+    return match.group(1) if match else version
+
 def version_is_less_than(version_a: str, version_b: str) -> bool:
     """
     Compare two semantic version strings (e.g., "0.2.1" and "0.10.0")
     and return True if version_a < version_b.
     """
-    parts_a = [int(x) for x in version_a.split('.')]
-    parts_b = [int(x) for x in version_b.split('.')]
+
+    # Sanitize the version strings by extracting the semantic version part.
+    sanitized_a = sanitize_version(version_a)
+    sanitized_b = sanitize_version(version_b)
+
+    log_fun.debug(f"Comparing versions: {sanitized_a} < {sanitized_b}")
+    
+    parts_a = [int(x) for x in sanitized_a.split('.')]
+    parts_b = [int(x) for x in sanitized_b.split('.')]
 
     return parts_a < parts_b
\ No newline at end of file

From 35bfec4c0dc0695b248808ea2d7c6f897a886e3d Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Tue, 18 Feb 2025 19:08:11 +0100
Subject: [PATCH 225/277] Merged fixes from dev branch

---
 script_generator/constants.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/script_generator/constants.py b/script_generator/constants.py
index 12d44414..52a63c56 100644
--- a/script_generator/constants.py
+++ b/script_generator/constants.py
@@ -1,8 +1,10 @@
 import os
 import platform
 
+import torch
+
 VERSION = "0.2.0"
-OBJECT_DETECTION_VERSION = "0.1.0"
+OBJECT_DETECTION_VERSION = "1.0.0"
 TRACKING_VERSION = "0.1.0"
 FUNSCRIPT_VERSION = "0.1.0"
 CONFIG_VERSION = 1
@@ -13,7 +15,7 @@
 
 RENDER_RESOLUTION = 640
 TEXTURE_RESOLUTION = RENDER_RESOLUTION * 1.3  # Texture size that is used to texture the opengl sphere
-YOLO_BATCH_SIZE = 1 if platform.system() == "Darwin" else 30  # Mac doesn't support batching
+YOLO_BATCH_SIZE = 1 if platform.system() == "Darwin" or not torch.cuda.is_available() else 30  # Mac doesn't support batching. Note TensorRT (.engine) is compiled for a batch size of 30
 YOLO_PERSIST = True  # Big impact on performance but also improves tracking
 
 ##################################################################################################
@@ -49,7 +51,8 @@
     "copy_funscript_to_movie_dir": True,
     "funscript_output_dir": None,
     "make_funscript_backup": True,
-    "log_level": "INFO"
+    "log_level": "INFO",
+    "tracking_logic_version": 1
 }
 
 ##################################################################################################
@@ -90,10 +93,11 @@
     'hips center': 10
 }
 CLASS_COLORS = {
+    "locked_penis": (0, 255, 255), # yellow
     "penis": (255, 0, 0),  # red
     "glans": (0, 128, 0),  # green
     "pussy": (0, 0, 255),  # blue
-    "butt": (255, 255, 0),  # yellow
+    "butt": (0, 180, 255),  # deep yellow
     "anus": (128, 0, 128),  # purple
     "breast": (255, 165, 0),  # orange
     "navel": (0, 255, 255),  # cyan

From 6800ccece95fc57654849c9a94873b7d4afd09de Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sat, 22 Feb 2025 15:37:39 +0100
Subject: [PATCH 226/277] Added support for 2D portrait videos

---
 script_generator/video/ffmpeg/filters.py | 23 +++++++++++++++++------
 1 file changed, 17 insertions(+), 6 deletions(-)

diff --git a/script_generator/video/ffmpeg/filters.py b/script_generator/video/ffmpeg/filters.py
index ffb8a1fe..4b7d897f 100644
--- a/script_generator/video/ffmpeg/filters.py
+++ b/script_generator/video/ffmpeg/filters.py
@@ -47,10 +47,21 @@ def get_2d_video_filters(video, width, height):
     state = AppState()
     cuda = state.ffmpeg_hwaccel == "cuda"
 
-    if video.height > RENDER_RESOLUTION:
-        scale_width = int(video.width * (height / video.height))
-        crop = f",crop={width}:{height}:(iw-{width})/2:0"
-        # hardware accelerated output is not supported with > 8 bit
-        return f"[0:v]scale_cuda={scale_width}:{height},hwdownload,format=nv12{crop}" if supports_cuda_scale(state) else f"[0:v]scale={scale_width}:{height}{crop}"
+    # in portrait, we squash the video because we don't really know where the penis is
+    if video.height > video.width:
+        if supports_cuda_scale(state):
+            return f"[0:v]scale_cuda={width}:{height},hwdownload,format=nv12"
+        else:
+            return f"[0:v]scale={width}:{height}"
+
+    # in landscape, we crop to the center
     else:
-        return "[0:v]hwdownload,format=nv12" if cuda else ""
\ No newline at end of file
+        if video.height > RENDER_RESOLUTION:
+            scale_width = int(video.width * (height / video.height))
+            crop = f",crop={width}:{height}:(iw-{width})/2:0"
+            if supports_cuda_scale(state):
+                return f"[0:v]scale_cuda={scale_width}:{height},hwdownload,format=nv12{crop}"
+            else:
+                return f"[0:v]scale={scale_width}:{height}{crop}"
+        else:
+            return "[0:v]hwdownload,format=nv12" if cuda else ""

From 884851c4b42025ae0ef75d25441cf8d4eaa3ee39 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 23 Feb 2025 09:50:33 +0100
Subject: [PATCH 227/277] - Added TensorRT support - Added support for portrait
 videos - Renamed app - Edit video settings popup - Added help page - Fixed
 generate debug video - Fixed passing along arguments in folder cli and added
 logging - Disabled YOLO analytics - Auto load reference script - Added new
 model recognition - Improved process folder command - Expanded debug overlays
 - Some small bugfixes

---
 README.md                                     |  27 +-
 core.requirements.txt                         |   3 +-
 cuda.requirements.txt                         |   1 +
 ...ults.py => analyze_tracking_results_v1.py} |  23 +-
 .../workers/analyze_tracking_results_v2.py    | 282 ++++++++++++++++++
 .../cli/generate_funscript_folder.py          | 118 ++++++--
 .../cli/generate_funscript_single.py          |  20 +-
 script_generator/cli/shared/common_args.py    |   2 +
 .../cli/shared/generate_funscript.py          |  12 +-
 script_generator/config/config_manager.py     |   1 +
 script_generator/constants.py                 |  45 ++-
 script_generator/debug/logger.py              |   3 +-
 .../debug/video_player/controls.py            |  27 +-
 .../debug/video_player/debug_overlay.py       |  79 +++--
 .../debug/video_player/interaction.py         | 113 ++++++-
 script_generator/debug/video_player/play.py   |  55 ++--
 .../debug/video_player/static_overlay.py      |  53 ++++
 .../{state.py => video_player.py}             |  21 +-
 .../funscript/create_funscript.py             |   4 +
 script_generator/gui/app.py                   |  59 +++-
 script_generator/gui/utils/widgets.py         | 107 +++++--
 .../gui/views/funscript_generator.py          |  54 ++--
 script_generator/gui/views/help.py            |  25 ++
 .../gui/views/help_debug_video.py             |  59 ++++
 .../gui/views/popups/edit_video_info.py       |  35 +++
 script_generator/gui/views/settings.py        |  33 +-
 .../object_detection/util/data.py             |  10 +-
 .../object_detection/workers/yolo_worker.py   |  15 +-
 script_generator/scripts/analyze_video.py     |   9 +-
 .../scripts/generate_debug_video.py           |   2 +-
 script_generator/scripts/tracking_analysis.py |   7 +-
 script_generator/state/app_state.py           |  30 +-
 .../tasks/workers/abstract_task_processor.py  |   4 +-
 script_generator/utils/helpers.py             |  11 +-
 script_generator/utils/json_utils.py          |   3 +-
 script_generator/utils/version.py             |   4 +-
 .../video/data_classes/video_info.py          |  16 +-
 script_generator/video/ffmpeg/commands.py     |   7 +-
 script_generator/video/ffmpeg/filters.py      |   8 +-
 script_generator/video/ffmpeg/hwaccel.py      |  48 ++-
 script_generator/video/ffmpeg/video_reader.py |  21 +-
 41 files changed, 1187 insertions(+), 269 deletions(-)
 rename script_generator/analysis/workers/{analyze_tracking_results.py => analyze_tracking_results_v1.py} (94%)
 create mode 100644 script_generator/analysis/workers/analyze_tracking_results_v2.py
 create mode 100644 script_generator/debug/video_player/static_overlay.py
 rename script_generator/debug/video_player/{state.py => video_player.py} (61%)
 create mode 100644 script_generator/gui/views/help.py
 create mode 100644 script_generator/gui/views/help_debug_video.py
 create mode 100644 script_generator/gui/views/popups/edit_video_info.py

diff --git a/README.md b/README.md
index d1acd506..c334c217 100644
--- a/README.md
+++ b/README.md
@@ -2,13 +2,6 @@
 
 This project is a Python-based tool for generating Funscript files from VR videos using Computer Vision (CV) and AI techniques. It leverages YOLO (You Only Look Once) object detection and custom tracking algorithms to automate the process of creating Funscript files for interactive devices.
 
-If you find this project useful, consider supporting me on:
-
-- **Ko-fi**: [![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/H2H818EIJV)
-- **Patreon**: [https://www.patreon.com/c/k00gar](https://www.patreon.com/c/k00gar)
-
-Your support helps me continue developing and improving this project!
-
 Join the **Discord community** for discussions and support: [Discord Community](https://discord.gg/WYkjMbtCZA)
 
 The necessary YOLO models will also be available via the Discord.
@@ -112,9 +105,25 @@ python FSGenerator.py
 ```
 
 ### Download the YOLO model
+- Place the YOLO model file(s) in the `models/` sub-directory. If you aren't sure you can add all the models and let the app decide the best option for you.
+
+We support multiple model formats across Windows, macOS, and Linux.
+#### Recommendations
+- NVIDIA Cards: we recommend the .engine model
+- AMD Cards: we recommend .pt (requires ROCm see below)
+- Mac: we recommend .mlmodel
+
+#### Models
+- **.pt (PyTorch)**: Requires CUDA (for NVIDIA GPUs) or ROCm (for AMD GPUs) for acceleration.
+- **.onnx (ONNX Runtime)**: Best for CPU users as it offers broad compatibility and efficiency.
+- **.engine (TensorRT)**: For NVIDIA GPUs: Provides very significant efficiency improvements.
+- **.mlmodel (Core ML)**: Optimized for macOS users. Runs efficiently on Apple devices with Core ML.
+
+In most cases, the app will automatically detect the best model from your models directory at launch, but if the right model wasn't present at this time or the right dependencies where not installed, you might need to override it under settings. The same applies when we release a new version of the model.
+
 
-- Place your YOLO model file (e.g., `k00gar-11n-RGB-200ep-best.mlpackage`) in the `models/` sub-directory.
-- Alternatively, you can specify a custom path to the model using the `--yolo_model` argument.
+### AMD GPU acceleration
+Coming soon
 
 **(Optional) Settings**
 Find the settings menu in the app to configure optional option.
diff --git a/core.requirements.txt b/core.requirements.txt
index f3e7e5d0..edc9e9a9 100644
--- a/core.requirements.txt
+++ b/core.requirements.txt
@@ -10,4 +10,5 @@ scipy~=1.15.1
 matplotlib~=3.10.0
 simplification~=0.7.13
 msgpack~=1.1.0
-pillow~=11.1.0
\ No newline at end of file
+pillow~=11.1.0
+orjson=3.10.15
\ No newline at end of file
diff --git a/cuda.requirements.txt b/cuda.requirements.txt
index 346ab7a5..6c8016d1 100644
--- a/cuda.requirements.txt
+++ b/cuda.requirements.txt
@@ -1,3 +1,4 @@
 -i https://download.pytorch.org/whl/cu121
 torch~=2.5.1
 torchvision~=0.20.1
+tensorrt=10.8.0.43
\ No newline at end of file
diff --git a/script_generator/analysis/workers/analyze_tracking_results.py b/script_generator/analysis/workers/analyze_tracking_results_v1.py
similarity index 94%
rename from script_generator/analysis/workers/analyze_tracking_results.py
rename to script_generator/analysis/workers/analyze_tracking_results_v1.py
index d4cca83a..8bcd6f89 100644
--- a/script_generator/analysis/workers/analyze_tracking_results.py
+++ b/script_generator/analysis/workers/analyze_tracking_results_v1.py
@@ -19,7 +19,7 @@
 from utils.lib_ObjectTracker import ObjectTracker
 
 
-def analyze_tracking_results(state: AppState):
+def analyze_tracking_results_v1(state: AppState):
     exists, yolo_data, raw_yolo_path, _ = load_yolo_data(state)
     results = make_data_boxes(yolo_data)
     width, height = get_cropped_dimensions(state.video_info)
@@ -27,18 +27,17 @@ def analyze_tracking_results(state: AppState):
 
     # Looking for the first instance of penis within the YOLO results
     first_penis_frame = parse_yolo_data_looking_for_penis(yolo_data, 0)
+    no_penis_frame = False
     if first_penis_frame is None:
         log_tr.error(f"No penis instance found in video. Further tracking is not possible.")
-        return
+        first_penis_frame = 0
+        no_penis_frame = True
 
     # Deciding whether we start from there or from a user-specified later frame
     state.frame_start_track = max(max(first_penis_frame - int(state.video_info.fps), state.frame_start - int(state.video_info.fps)), 0)
     # state.frame_end = state.video_info.total_frames if not state.frame_end else state.frame_end
     frame_end = state.video_info.total_frames if not state.frame_end else state.frame_end
-
-    # logger.info(f"Frame Start adjusted to: {state.frame_start}")
-    log_tr.info(f"Frame Start adjusted to: {state.frame_start_track}")
-    
+    log_tr.info(f"Frame Start adjusted to first frame with a penis or penis tip: {state.frame_start_track}")
 
     video_info = state.video_info
     fps = video_info.fps
@@ -108,11 +107,15 @@ def analyze_tracking_results(state: AppState):
             tracker.previous_distances = previous_distances
 
         if frame_pos in list_of_frames:
-            # Get sorted boxes for the current frame
+            # Get sorted boxes based on class priority for the current frame
             sorted_boxes = results.get_boxes(frame_pos)
-            tracker.tracking_logic(state, sorted_boxes)  # Apply tracking logic
 
-            if tracker.distance:
+            # Perform tracking logic
+            #if state.frame_start_track >= frame_pos and not no_penis_frame:
+            if state.frame_start_track <= frame_pos and not no_penis_frame:
+                tracker.tracking_logic(state, sorted_boxes)  # Apply tracking logic
+
+            if tracker and tracker.distance:
                 # Append Funscript data if distance is available
                 state.funscript_frames.append(frame_pos)
                 state.funscript_distances.append(int(tracker.distance))
@@ -182,7 +185,7 @@ def analyze_tracking_results(state: AppState):
         if state.live_preview_mode:
             if not reader:
                 reader = VideoReaderFFmpeg(state, frame_pos)
-                reader.set(cv2.CAP_PROP_POS_FRAMES, frame_pos)
+                reader.set_frame(frame_pos)
 
             ret, frame = reader.read()
             frame = frame.copy()
diff --git a/script_generator/analysis/workers/analyze_tracking_results_v2.py b/script_generator/analysis/workers/analyze_tracking_results_v2.py
new file mode 100644
index 00000000..784092e1
--- /dev/null
+++ b/script_generator/analysis/workers/analyze_tracking_results_v2.py
@@ -0,0 +1,282 @@
+import json
+import time
+from datetime import timedelta
+
+import cv2
+from tqdm import tqdm
+
+from script_generator.constants import UPDATE_PROGRESS_INTERVAL
+from script_generator.constants import CLASS_COLORS
+from script_generator.debug.video_player.overlay_widgets import OverlayWidgets
+from script_generator.gui.messages.messages import ProgressMessage, UpdateGUIState
+from script_generator.object_detection.util.data import load_yolo_data
+from script_generator.object_detection.util.object_detection import make_data_boxes, parse_yolo_data_looking_for_penis
+from script_generator.state.app_state import AppState
+from script_generator.utils.file import get_output_file_path
+from script_generator.debug.logger import log, log_tr
+from script_generator.video.ffmpeg.video_reader import VideoReaderFFmpeg
+from script_generator.video.data_classes.video_info import get_cropped_dimensions
+from utils.lib_ObjectTracker import ObjectTracker
+
+
+def analyze_tracking_results_v2(state: AppState):
+    exists, yolo_data, raw_yolo_path, _ = load_yolo_data(state)
+    results = make_data_boxes(yolo_data)
+    width, height = get_cropped_dimensions(state.video_info)
+    list_of_frames = results.get_all_frame_ids()  # Get all frame IDs with detections
+
+    # Looking for the first instance of penis within the YOLO results
+    first_penis_frame = parse_yolo_data_looking_for_penis(yolo_data, 0)
+    no_penis_frame = False
+    if first_penis_frame is None:
+        log_tr.error(f"No penis instance found in video. Further tracking is not possible.")
+        first_penis_frame = 0
+        no_penis_frame = True
+
+    # Deciding whether we start from there or from a user-specified later frame
+    state.frame_start_track = max(max(first_penis_frame - int(state.video_info.fps), state.frame_start - int(state.video_info.fps)), 0)
+    state.frame_end = state.video_info.total_frames if not state.frame_end else state.frame_end
+    log_tr.info(f"Frame Start adjusted to first frame with a penis or penis tip: {state.frame_start_track}")
+
+    video_info = state.video_info
+    fps = video_info.fps
+    reader = None
+
+    state.frame_area = width * height
+    debug_window_open = False
+    cuts = []
+
+    """ discarding the scene detection for now
+    # Load scene cuts if the file exists
+    cuts_path, _ = get_output_file_path(state.video_path, "_cuts.json")
+    if os.path.exists(cuts_path):
+        logger.info(f"Loading cuts from {cuts_path}")
+        with open(cuts_path, 'r') as f:
+            cuts = json.load(f)
+        logger.info(f"Loaded {len(cuts)} cuts : {cuts}")
+
+        if state.update_ui:
+            state.update_ui(ProgressMessage(
+                process="SCENE_DETECTION",
+                frames_processed=state.video_info.total_frames,
+                total_frames=state.frame_end,
+                eta="Done"
+            ))
+    else:
+        # Detect scene changes if the cuts file does not exist
+        scene_list = detect_scene_changes(state, video_info.is_vr, 0.9, state.frame_start, state.frame_end)
+        logger.info(f"Analyzing frames {state.frame_start} to {state.frame_end}")
+        cuts = [scene[1] for scene in scene_list]
+        cuts = cuts[:-1]  # Remove the last entry
+        # Save the cuts to a file
+        with open(cuts_path, 'w') as f:
+            json.dump(cuts, f)
+    """
+
+    state.funscript_frames = []  # List to store Funscript frames
+    state.funscript_distances = []
+    state.funscript_data = []
+
+    # tracker = ObjectTracker(fps, state.frame_start, image_area, video_info.is_vr)  # Initialize the object tracker
+    tracker = ObjectTracker(state)
+
+    # Start time for ETA calculation
+    start_time = time.time()
+
+    last_ui_update_time = time.time()
+
+    for frame_pos in tqdm(
+            # range(state.frame_start, state.frame_end), unit="f", desc="Analyzing tracking data", position=0,
+            range(state.frame_start, state.frame_end),
+            unit="f",
+            desc="Analyzing tracking data", position=0,
+            unit_scale=False,
+            unit_divisor=1
+    ):
+        state.current_frame_id = frame_pos
+        if frame_pos in cuts:
+            # Reinitialize the tracker at scene cuts
+            log_tr.info(f"Reaching cut at frame {frame_pos}")
+            previous_distances = tracker.previous_distances
+            log_tr.info(f"Reinitializing tracker with previous distances: {previous_distances}")
+            # tracker = ObjectTracker(fps, frame_pos, image_area, video_info.is_vr)
+            tracker = ObjectTracker(state)
+            tracker.previous_distances = previous_distances
+
+        if frame_pos in list_of_frames:
+            # Get sorted boxes based on class priority for the current frame
+            sorted_boxes = results.get_boxes(frame_pos)
+
+            # Perform tracking logic
+            #if state.frame_start_track >= frame_pos and not no_penis_frame:
+            if state.frame_start_track <= frame_pos and not no_penis_frame:
+                tracker.tracking_logic(state, sorted_boxes)  # Apply tracking logic
+
+            if tracker and tracker.distance:
+                # Append Funscript data if distance is available
+                state.funscript_frames.append(frame_pos)
+                state.funscript_distances.append(int(tracker.distance))
+
+            if state.save_debug_file:
+                # Log debugging information
+                bounding_boxes = []
+                for box in sorted_boxes:
+                    if box[4] in tracker.normalized_absolute_tracked_positions:
+                        if box[4] == 0:  # generic track_id for 'hips center'
+                            str_dist_penis = 'None'
+                        else:
+                            if box[4] in tracker.normalized_distance_to_penis:
+                                str_dist_penis = str(int(tracker.normalized_distance_to_penis[box[4]][-1]))
+                            else:
+                                str_dist_penis = 'None'
+                        str_abs_pos = str(int(tracker.normalized_absolute_tracked_positions[box[4]][-1]))
+                        position = 'p: ' + str_dist_penis + ' | ' + 'a: ' + str_abs_pos
+                        if box[4] in tracker.pct_weights and len(tracker.pct_weights[box[4]]) > 0:
+                            weight = tracker.pct_weights[box[4]][-1]
+                            position += ' | w: ' + str(weight)
+                    else:
+                        position = None
+                    bounding_boxes.append({
+                        'box': box[0],
+                        'conf': box[1],
+                        'class_name': box[3],
+                        'track_id': box[4],
+                        'position': position,
+                    })
+
+                # Add debug information to the debugger class so it can be saved later
+                state.debug_data.add_frame(
+                    frame_pos,
+                    bounding_boxes=bounding_boxes,
+                    variables={
+                        'frame': frame_pos,
+                        # time of the frame hh:mm:ss
+                        'time': str(timedelta(seconds=int(frame_pos / fps))),
+                        'distance': tracker.distance,
+                        'Penetration': tracker.penetration,
+                        'sex_position': tracker.sex_position,
+                        'sex_position_reason': tracker.sex_position_reason,
+                        'tracked_body_part': tracker.tracked_body_part,
+                        'locked_penis_box': tracker.locked_penis_box.to_dict(),
+                        'glans_detected': tracker.glans_detected,
+                        'cons._glans_detections': tracker.consecutive_detections['glans'],
+                        'cons._glans_non_detections': tracker.consecutive_non_detections['glans'],
+                        'cons._penis_detections': tracker.consecutive_detections['penis'],
+                        'cons._penis_non_detections': tracker.consecutive_non_detections['penis'],
+                        'breast_tracking': tracker.breast_tracking,
+                    }
+                )
+
+        # Display object detection tracking results in a live preview window
+        window_name = "Tracking analysis preview"
+
+        # Close window and release reader. Also, we don't want to call cv2.getWindowProperty every iteration
+        if debug_window_open and not state.live_preview_mode:
+            if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) >= 1:
+                cv2.destroyWindow(window_name)
+                debug_window_open = False
+            if reader:
+                reader.release()
+                reader = None
+
+        if state.live_preview_mode:
+            if not reader:
+                reader = VideoReaderFFmpeg(state, frame_pos)
+                reader.set_frame(frame_pos)
+
+            ret, frame = reader.read()
+            frame = frame.copy()
+
+            for box in tracker.tracked_boxes:
+                frame = OverlayWidgets.draw_bounding_box(
+                    frame,
+                    box[0],
+                    str(box[2]) + ": " + box[1],
+                    CLASS_COLORS[str(box[1])],
+                    state.offset_x
+                )
+
+            if tracker.locked_penis_box is not None and tracker.locked_penis_box.is_active():
+                frame = OverlayWidgets.draw_bounding_box(
+                    frame,
+                    tracker.locked_penis_box.box,
+                    "Locked_Penis",
+                    CLASS_COLORS['penis'],
+                    state.offset_x
+                )
+            else:
+                log_tr.debug("No active locked penis box to draw.")
+
+            if tracker.glans_detected:
+                frame = OverlayWidgets.draw_bounding_box(
+                    frame,
+                    tracker.boxes['glans'],
+                    "Glans",
+                    CLASS_COLORS['glans'],
+                    state.offset_x
+                )
+            if state.funscript_distances:
+                frame = OverlayWidgets.draw_gauge(frame, state.funscript_distances[-1])
+
+            # Reinitialize the window if needed
+            if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) < 1 and state.live_preview_mode:
+                cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
+                cv2.resizeWindow(window_name, int(width * 2), int(height * 2))
+                debug_window_open = True
+            cv2.imshow(window_name, frame)
+
+            if not handle_user_input(window_name) or not state.live_preview_mode:
+                if state.update_ui and state.live_preview_mode:
+                    state.update_ui(UpdateGUIState(attr="live_preview_mode", value=False))
+                state.live_preview_mode = False
+
+        # Update progress periodically
+        if state.update_ui:
+            current_time = time.time()
+            if current_time - last_ui_update_time >= UPDATE_PROGRESS_INTERVAL:
+                last_ui_update_time = current_time
+                elapsed_time = current_time - start_time
+                frames_processed = frame_pos - state.frame_start + 1
+                frames_remaining = state.frame_end - frame_pos - 1
+                eta = (elapsed_time / frames_processed) * frames_remaining if frames_processed > 0 else 0
+
+                state.update_ui(ProgressMessage(
+                    process="TRACKING_ANALYSIS",
+                    frames_processed=frames_processed,
+                    total_frames=state.frame_end,
+                    eta=time.strftime("%H:%M:%S", time.gmtime(eta)) if eta != float('inf') else "Calculating..."
+                ))
+
+    # stop processing when the task is force closed
+    if state.analyze_task and state.analyze_task.is_stopped:
+        return
+
+    # Final UI update: we're done
+    if state.update_ui:
+        state.update_ui(ProgressMessage(
+            process="TRACKING_ANALYSIS",
+            frames_processed=state.video_info.total_frames,
+            total_frames=state.video_info.total_frames,
+            eta="Done"
+        ))
+
+    # Prepare Funscript data
+    state.funscript_data = list(zip(state.funscript_frames, state.funscript_distances))
+
+    # Save the raw funscript data to JSON
+    raw_funscript_path, _ = get_output_file_path(state.video_path, ".json", "rawfunscript")
+    with open(raw_funscript_path, 'w') as f:
+        json.dump(state.funscript_data, f)
+
+    return state.funscript_data
+
+def handle_user_input(window_name):
+    key = cv2.waitKey(1) & 0xFF
+
+    # Check if the window has been closed
+    if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) < 1:
+        return False
+
+    if key == ord("q"):
+        return False
+    return True
\ No newline at end of file
diff --git a/script_generator/cli/generate_funscript_folder.py b/script_generator/cli/generate_funscript_folder.py
index 9e99dc4c..6d9fa085 100644
--- a/script_generator/cli/generate_funscript_folder.py
+++ b/script_generator/cli/generate_funscript_folder.py
@@ -1,6 +1,7 @@
 import argparse
 import concurrent.futures
 import os
+import platform
 import sys
 import time
 from collections import deque
@@ -11,6 +12,7 @@
     validate_and_adjust_args,
     build_app_state_from_args,
 )
+from script_generator.constants import OUTPUT_PATH
 from script_generator.debug.logger import log
 from script_generator.funscript.util.check_existing_funscript import check_existing_funscript
 from script_generator.utils.file import get_video_files
@@ -34,6 +36,12 @@ def main():
         default=False,
         help="Will regenerate funscripts that are up to date and made by this app too."
     )
+    parser.add_argument(
+        "--process-external-script",
+        action="store_true",
+        default=False,
+        help="Will generate a new script even when the movie already has a script not made by this app. Existing funscript will not be replaced."
+    )
     parser.add_argument(
         "--num-workers",
         type=int,
@@ -70,15 +78,15 @@ def main():
         files_by_us = []
         files_by_us_out_of_date = []
         files_not_by_us = []
+        files_not_by_us_regenerate = []
 
         for video_file in paths:
             filename_base = os.path.splitext(os.path.basename(video_file))[0]
             video_folder = os.path.dirname(video_file)
             funscript_path = os.path.join(video_folder, f"{filename_base}.funscript")
+            has_raw_yolo = False
 
-            file_exists, is_ours, _, out_of_date = check_existing_funscript(
-                funscript_path, filename_base, False
-            )
+            file_exists, is_ours, _, out_of_date = check_existing_funscript(funscript_path, filename_base, False)
 
             # If there's a separate output directory, check there too
             if state.funscript_output_dir and os.path.exists(state.funscript_output_dir):
@@ -93,6 +101,10 @@ def main():
                 if not out_of_date and out_of_date_d:
                     out_of_date = out_of_date_d
 
+            if os.path.exists(OUTPUT_PATH) and os.path.getsize(OUTPUT_PATH) > 100:
+                raw_yolo_dest = os.path.join(OUTPUT_PATH, filename_base, "rawyolo.msgpack")
+                has_raw_yolo = os.path.exists(raw_yolo_dest)
+
             if not file_exists:
                 files_none.append(video_file)
             elif is_ours:
@@ -100,26 +112,37 @@ def main():
                     files_by_us_out_of_date.append(video_file)
                 else:
                     files_by_us.append(video_file)
-            else:
-                files_not_by_us.append(video_file)
+            elif file_exists:
+                if has_raw_yolo or not args.process_external_script:
+                    files_not_by_us.append(video_file)
+                else:
+                    files_not_by_us_regenerate.append(video_file)
 
         def log_files_by_category(category, file_list):
-            log.info(f"{category} ({len(file_list)}):")
+            log.info(f"{'-' * 100}")
+            log.info(f" {category} ({len(file_list)}):")
+            log.info(f"{'-' * 100}")
             for f in file_list:
                 log.info(f" - {f}")
 
         log_files_by_category("Files with no funscript", files_none)
-        log_files_by_category(
-            f"Files with existing funscript by this app (>= current version, will "
-            f"{'' if args.replace_up_to_date else 'NOT '}be processed)",
-            files_by_us
-        )
-        log_files_by_category(
-            f"Files with existing funscript by this app (< current version, will "
-            f"{'' if args.replace_outdated else 'NOT '}be re-generated)",
-            files_by_us_out_of_date
-        )
-        log_files_by_category("Files with existing funscript not by this app (ignored)", files_not_by_us)
+        if len(files_not_by_us_regenerate) > 0:
+            log_files_by_category("Files with funscript not by this app to generate", files_not_by_us_regenerate)
+        if len(files_by_us) > 0:
+            log_files_by_category(
+                f"Files with existing funscript by this app (>= current version, will "
+                f"{'' if args.replace_up_to_date else 'NOT '}be processed)",
+                files_by_us
+            )
+        if len(files_by_us_out_of_date) > 0:
+            log_files_by_category(
+                f"Files with existing funscript by this app (< current version, will "
+                f"{'' if args.replace_outdated else 'NOT '}be re-generated)",
+                files_by_us_out_of_date
+            )
+        if len(files_not_by_us) > 0:
+            log_files_by_category("Files with existing funscript not by this app (ignored)", files_not_by_us)
+        log.info(f"{'-' * 100}")
 
         # Decide which files to process
         to_process = files_none.copy()
@@ -127,6 +150,8 @@ def log_files_by_category(category, file_list):
             to_process.extend(files_by_us_out_of_date)
         if args.replace_up_to_date:
             to_process.extend(files_by_us)
+        if files_not_by_us_regenerate:
+            to_process.extend(files_not_by_us_regenerate)
 
         if not to_process:
             log.info("No files need new funscript generation.")
@@ -149,7 +174,7 @@ def log_files_by_category(category, file_list):
                 video_path = tasks.popleft()
                 log.debug(f"[Submit] {video_path}")
                 running.append(video_path)
-                future = executor.submit(run_task_timed, video_path)
+                future = executor.submit(run_task_timed, video_path, args)
                 future_to_video[future] = video_path
                 time.sleep(0.1)  # slight delay to stagger submissions
 
@@ -172,14 +197,24 @@ def log_files_by_category(category, file_list):
                 currently_processing = [
                     v for fut, v in future_to_video.items() if not fut.done()
                 ]
+
+                # Calculate total runtime and average completion time
                 total_run_time = time.time() - global_start_time
-                average_time = sum(processing_times) / len(processing_times) if processing_times else 0
+                average_time = total_run_time / completed_count if completed_count > 0 else 0
 
+                # Display first 5 movies, add '...' if there are more
+                queue_count = len(currently_processing)
+                if queue_count > 5:
+                    queued_paths = "\n".join(f" - {movie}" for movie in currently_processing[:5]) + "\n..."
+                else:
+                    queued_paths = "\n".join(f" - {movie}" for movie in currently_processing) if currently_processing else "None"
 
-                queued_paths = "\n".join(f" - {movie}" for movie in running) if running else "None"
                 log.info(
-                    f"Progress: {completed_count}/{total_count} completed. Total run time: {str(timedelta(seconds=int(total_run_time)))} Average time per completed movie: {str(timedelta(seconds=int(average_time)))} seconds (not taking into account progress on running processes)."
-                    f"Queued Movies:\n{queued_paths}"
+                    f"Progress: {completed_count}/{total_count} completed | Queue: {queue_count} remaining | "
+                    f"Total run time: {str(timedelta(seconds=int(total_run_time)))} | "
+                    f"Average completion time: {str(timedelta(seconds=int(average_time)))}s "
+                    "(calculated from completed movies, not ongoing processes)."
+                    f"\nQueued Movies:\n{queued_paths}"
                 )
 
                 if ret == 0:
@@ -192,30 +227,61 @@ def log_files_by_category(category, file_list):
     except Exception as e:
         log.error(f"An error occurred: {e}", exc_info=True)
 
-def run_task(video_path):
+
+def run_task(video_path, args):
     """
     Runs the funscript generation for one video in a new terminal window.
+    Passes only relevant arguments to the process_file command.
     Returns the exit code (0 if successful).
     """
+    # Escape ampersand only for Windows
+    if platform.system() == "Windows":
+        video_path = video_path.replace("&", "^&")
+
+    # Define the arguments relevant to process_file
+    process_file_args = {
+        "reuse_yolo", "copy_funscript", "frame_start", "frame_end",
+        "video_reader", "save_debug_file", "boost_enabled",
+        "boost_up_percent", "boost_down_percent", "threshold_enabled",
+        "threshold_low", "threshold_high", "vw_simplification_enabled",
+        "vw_factor", "rounding"
+    }
+
+    # Build the command dynamically
     cmd = f'python -m script_generator.cli.generate_funscript_single "{video_path}"'
+
+    for arg, value in vars(args).items():
+        if arg in process_file_args and value is not None:
+            cli_arg = f"--{arg.replace('_', '-')}"
+
+            if isinstance(value, bool):
+                if value:
+                    cmd += f" {cli_arg}"
+            else:
+                cmd += f" {cli_arg}={value}"
+
+    # Launch the process in a new terminal
     proc = open_new_terminal(cmd, relative_path_up=2)
+
     if proc is None:
         print(f"Failed to launch terminal for: {video_path}")
         return -1
-    # Wait for the process to finish. Because we used /WAIT on Windows
+
+    # Wait for the process to finish
     proc.wait()
     return proc.returncode
 
 
-def run_task_timed(video_path):
+def run_task_timed(video_path, args):
     """
     Wraps run_task to record how long it takes.
     Returns a tuple: (exit code, elapsed_time_in_seconds).
     """
     start = time.time()
-    ret = run_task(video_path)
+    ret = run_task(video_path, args)
     elapsed = time.time() - start
     return ret, elapsed
 
+
 if __name__ == "__main__":
     main()
diff --git a/script_generator/cli/generate_funscript_single.py b/script_generator/cli/generate_funscript_single.py
index e69fb649..f29ba5f6 100644
--- a/script_generator/cli/generate_funscript_single.py
+++ b/script_generator/cli/generate_funscript_single.py
@@ -6,7 +6,7 @@
     validate_and_adjust_args,
     build_app_state_from_args,
 )
-from script_generator.cli.shared.generate_funscript import generate_funscript
+from script_generator.cli.shared.generate_funscript import generate_funscript_cli
 from script_generator.debug.logger import log
 
 
@@ -24,20 +24,22 @@ def main():
     args = parser.parse_args()
     validate_and_adjust_args(args)
 
-    provided_args = set()
-    for token in sys.argv[1:]:
-        if token.startswith("--"):
-            flag_name = token.lstrip("-").replace("-", "_")
-            provided_args.add(flag_name)
-        else:
-            provided_args.add("video_path")
+    provided_args = {
+        arg.lstrip("-").replace("-", "_")
+        for arg in sys.argv[1:]
+        if arg.startswith("--")
+    }
+    provided_args.add("video_path")
+
+    explicit_args = {arg: getattr(args, arg) for arg in vars(args) if arg in provided_args}
+    log.info(f"Provided command-line arguments: {explicit_args}")
 
     try:
         log.info(f"Processing video: {args.video_path}")
 
         state = build_app_state_from_args(args, provided_args)
         state.set_video_info()
-        generate_funscript(state)
+        generate_funscript_cli(state)
         log.info("Funscript generation complete.")
 
     except Exception as e:
diff --git a/script_generator/cli/shared/common_args.py b/script_generator/cli/shared/common_args.py
index 9e255cff..3ce699dc 100644
--- a/script_generator/cli/shared/common_args.py
+++ b/script_generator/cli/shared/common_args.py
@@ -4,9 +4,11 @@
 from script_generator.constants import VALID_VIDEO_READERS
 from script_generator.debug.logger import log
 from script_generator.state.app_state import AppState
+from ultralytics import settings
 
 # TODO this is a workaround and needs to be fixed properly
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+settings.update({"sync": False}) # Disable analytics and crash reporting
 
 def add_shared_generate_funscript_args(parser: argparse.ArgumentParser) -> None:
     """
diff --git a/script_generator/cli/shared/generate_funscript.py b/script_generator/cli/shared/generate_funscript.py
index 2a01fc77..5ddd266b 100644
--- a/script_generator/cli/shared/generate_funscript.py
+++ b/script_generator/cli/shared/generate_funscript.py
@@ -1,11 +1,11 @@
-from script_generator.object_detection.util.data import get_raw_yolo_file_info, load_yolo_data
+from script_generator.debug.logger import log
+from script_generator.object_detection.util.data import load_yolo_data
 from script_generator.scripts.analyze_video import analyze_video
-from script_generator.scripts.tracking_analysis import tracking_analysis
 from script_generator.state.app_state import AppState, log_state_settings
-from script_generator.debug.logger import log
 from script_generator.utils.helpers import to_int_or_none
 
-def generate_funscript(state: AppState):
+
+def generate_funscript_cli(state: AppState):
     try:
         configured, msg = state.is_configured()
         if not configured:
@@ -23,8 +23,10 @@ def generate_funscript(state: AppState):
         # analyze video if required
         if not state.use_existing_raw_yolo or not exists:
             analyze_video(state)
+        else:
+            log.info("Skipping yolo analysis as it was already generated")
 
-        tracking_analysis(state)
+        generate_funscript(state)
     except Exception as e:
         log.error(f"Error during video analysis: {e}")
         import traceback
diff --git a/script_generator/config/config_manager.py b/script_generator/config/config_manager.py
index c0c5fcc0..06bf8cf3 100644
--- a/script_generator/config/config_manager.py
+++ b/script_generator/config/config_manager.py
@@ -7,6 +7,7 @@
 from script_generator.video.ffmpeg.hwaccel import get_preferred_hwaccel
 from script_generator.video.util.ffmpeg import get_ffmpeg_paths
 
+
 class ConfigManager:
     def __init__(self, app_state):
         self.app_state = app_state
diff --git a/script_generator/constants.py b/script_generator/constants.py
index 52a63c56..b5cf7f48 100644
--- a/script_generator/constants.py
+++ b/script_generator/constants.py
@@ -3,7 +3,7 @@
 
 import torch
 
-VERSION = "0.2.0"
+VERSION = "0.2.1"
 OBJECT_DETECTION_VERSION = "1.0.0"
 TRACKING_VERSION = "0.1.0"
 FUNSCRIPT_VERSION = "0.1.0"
@@ -128,20 +128,49 @@
 OUTPUT_PATH = os.path.join(PROJECT_PATH, "output")
 MODELS_PATH = os.path.join(PROJECT_PATH, "models")
 MODEL_FILENAMES = [
-    #    "k00gar-11n-200ep-best.mlpackage",
-    #    "k00gar-11n-200ep-best.pt",
-    #    "k00gar-11n-200ep-best.onnx",
+    "FunGen-12s-pov-1.0.0.engine",
+    "FunGen-12s-pov-1.0.0.pt",
+    "FunGen-12s-pov-1.0.0.mlpackage",
+    "FunGen-12s-pov-1.0.0.onnx",
+    "FunGen-12s-mix-1.0.0.engine",
+    "FunGen-12s-mix-1.0.0.mlpackage",
+    "FunGen-12s-mix-1.0.0.pt",
+    "FunGen-12s-mix-1.0.0.onnx",
+    "FunGen-11n-mix-1.0.0.engine",
+    "FunGen-11n-mix-1.0.0.mlpackage",
+    "FunGen-11n-mix-1.0.0.onnx",
+    "FunGen-11n-mix-1.0.0.pt",
+    "FunGen-11s-mix-1.0.0.engine",
+    "FunGen-11s-mix-1.0.0.mlpackage",
+    "FunGen-11s-mix-1.0.0.onnx",
+    "FunGen-11s-mix-1.0.0.pt",
     "k00gar-11n-RGB-200ep-best.mlpackage",
     "k00gar-11n-RGB-200ep-best.pt",
-    "k00gar-11n-RGB-200ep-best.onnx",
-    "yolo11n-pose.mlpackage",
-    "yolo11n-pose.pt",
-    "yolo11n-pose.onnx"
+    "k00gar-11n-RGB-200ep-best.onnx"
 ]
 LOGO = os.path.join(PROJECT_PATH, "resources", "logo.png")
 ICON = os.path.join(PROJECT_PATH, "resources", "icon.ico")
 CONFIG_FILE_PATH = os.path.join(PROJECT_PATH, "config.json")
 
+##################################################################################################
+# DEBUG VIDEO
+##################################################################################################
+
+FUNSCRIPT_BUFFER_SIZE = 500
+
+##################################################################################################
+# KEY CODES
+##################################################################################################
+
+LEFT = 2424832
+RIGHT = 2555904
+SPACE = 32
+Q = 113
+COMMA = 44
+PERIOD = 46
+LEFT_BRACKET = 91
+RIGHT_BRACKET = 93
+
 ##################################################################################################
 # DIV
 ##################################################################################################
diff --git a/script_generator/debug/logger.py b/script_generator/debug/logger.py
index f53a9bd9..0b4034df 100644
--- a/script_generator/debug/logger.py
+++ b/script_generator/debug/logger.py
@@ -36,7 +36,8 @@ def emit(self, record):
 )
 
 log = logging.getLogger("Main")
-log_od = logging.getLogger("ObjectDetection")
+log_od = logging.getLogger("Detection")
+log_sc = logging.getLogger("Scene")
 log_tr = logging.getLogger("Tracking")
 log_vid = logging.getLogger("Video")
 log_fun = logging.getLogger("Funscript")
diff --git a/script_generator/debug/video_player/controls.py b/script_generator/debug/video_player/controls.py
index c7fa8327..198215b3 100644
--- a/script_generator/debug/video_player/controls.py
+++ b/script_generator/debug/video_player/controls.py
@@ -1,6 +1,7 @@
 import cv2
+import numpy as np
 
-from script_generator.debug.video_player.state import VideoPlayer
+from script_generator.debug.video_player.video_player import VideoPlayer
 
 
 def draw_media_controls(frame, video_player: VideoPlayer):
@@ -16,4 +17,26 @@ def draw_media_controls(frame, video_player: VideoPlayer):
 
     # Progress indicator
     progress_x = int((video_player.current_frame / video_player.total_frames) * width)
-    cv2.rectangle(frame, (0, bar_y_start), (progress_x, height), (0, 255, 0), -1)
\ No newline at end of file
+    cv2.rectangle(frame, (0, bar_y_start), (progress_x, height), (0, 255, 0), -1)
+
+def draw_media_controls_static_overlay(metrics, first_frame):
+    height, width, _ = first_frame.shape
+    overlay_height = 14
+    y_start = height - overlay_height
+    frames = len(metrics)
+    px_frame = width / frames
+
+    overlay = np.zeros((height, width, 3), dtype=np.uint8)
+
+    prev_sex_position = None
+    for frame_pos, key in enumerate(metrics.keys()):
+        metric = metrics[key]
+        variables =  metric["variables"]
+        sex_position = variables["sex_position"]
+        if prev_sex_position != sex_position:
+            prev_sex_position = sex_position
+
+            x_pos = int(min(640, px_frame * frame_pos))
+            cv2.line(overlay, (x_pos, y_start), (x_pos, height), (0, 0, 255), 1)
+
+    return overlay
\ No newline at end of file
diff --git a/script_generator/debug/video_player/debug_overlay.py b/script_generator/debug/video_player/debug_overlay.py
index 0c1a645c..60313c92 100644
--- a/script_generator/debug/video_player/debug_overlay.py
+++ b/script_generator/debug/video_player/debug_overlay.py
@@ -3,6 +3,7 @@
 
 from script_generator.constants import CLASS_COLORS
 from script_generator.debug.video_player.overlay_widgets import OverlayWidgets
+from script_generator.state.app_state import AppState
 
 
 def get_funscript_value(interpolator, frame_id, fps):
@@ -15,6 +16,7 @@ def get_funscript_value(interpolator, frame_id, fps):
 FONT_SIZE = 0.3
 
 def draw_overlay(
+    state: "AppState",
     frame,
     frame_id,
     logs,
@@ -44,23 +46,6 @@ def draw_overlay(
         variables = logs[frame_id].get("variables", {})
         bounding_boxes = logs[frame_id].get("bounding_boxes", [])
 
-    # Draw bounding boxes
-    for box in bounding_boxes:
-        x1, y1, x2, y2 = box["box"]
-        class_name = box["class_name"]
-        position = box["position"]
-        color = CLASS_COLORS.get(class_name, (0, 255, 0))
-        cv2.rectangle(frame, (x1, y1), (x2, y2), color, 1)
-        cv2.putText(
-            frame,
-            f"{class_name} {position}",
-            (x1, y1 - 5),
-            cv2.FONT_HERSHEY_SIMPLEX,
-            FONT_SIZE,
-            color,
-            1
-        )
-
     # Display variables
     y_offset_stats = 20
     for key, value in variables.items():
@@ -75,15 +60,22 @@ def draw_overlay(
         )
         y_offset_stats += 12
 
-    # Draw locked penis box if present
-    locked_penis_box = variables.get("locked_penis_box")
-    if locked_penis_box and locked_penis_box.get("active", False):
-        x1, y1, x2, y2 = locked_penis_box["box"]
-        color = CLASS_COLORS.get("penis", (0, 255, 0))
-        cv2.rectangle(frame, (x1, y1), (x2, y2), color, 1)
+    # Draw bounding boxes
+    for box in bounding_boxes:
+        x1, y1, x2, y2 = box["box"]
+        class_name = box["class_name"]
+        position = box["position"]
+        box_excluded = position is None and state.debug_mode == "funscript"
+        color = CLASS_COLORS.get(class_name, (0, 255, 0)) if not box_excluded else (70, 70, 70)
+        alpha = 0.5 if box_excluded else 0.8
+
+        overlay = frame.copy()
+        cv2.rectangle(overlay, (x1, y1), (x2, y2), color, 1)
+
+        text = f"{class_name} {position}" if state.debug_mode == "funscript" else f"{class_name} c:{box['conf']} id:{box['track_id']}"
         cv2.putText(
-            frame,
-            "Locked Penis",
+            overlay,
+            text,
             (x1, y1 - 5),
             cv2.FONT_HERSHEY_SIMPLEX,
             FONT_SIZE,
@@ -91,6 +83,28 @@ def draw_overlay(
             1
         )
 
+        cv2.addWeighted(overlay, alpha, frame, 1 - alpha, 0, frame)
+
+    if state.debug_mode == "funscript":
+        # Draw locked penis box if present
+        locked_penis_box = variables.get("locked_penis_box")
+        if locked_penis_box and locked_penis_box.get("active", False):
+            overlay = frame.copy()
+            x1, y1, x2, y2 = locked_penis_box["box"]
+            alpha = 0.4
+            color = CLASS_COLORS.get("locked_penis", (255, 255, 0))
+            cv2.rectangle(frame, (x1, y1), (x2, y2), color, 1)
+            cv2.putText(
+                frame,
+                "Locked Penis",
+                (x1, y1 - 5),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                FONT_SIZE,
+                color,
+                1
+            )
+            cv2.addWeighted(overlay, alpha, frame, 1 - alpha, 0, frame)
+
     # Compute distance and funscript values for rolling buffer
     distance = variables.get("distance", 0)
     funscript_value, funscript_value_ref = 0, 0
@@ -99,9 +113,6 @@ def draw_overlay(
     if funscript_interpolator_ref:
         funscript_value_ref = get_funscript_value(funscript_interpolator_ref, frame_id, fps)
 
-    # Draw gauge (example usage)
-    OverlayWidgets.draw_gauge(frame, funscript_value)
-
     # Shift rolling window buffers left and insert new values
     distance_buffer = np.roll(distance_buffer, -1)
     distance_buffer[-1] = distance
@@ -114,9 +125,13 @@ def draw_overlay(
 
     # Draw rolling window curves
     graph_height = 100
-    graph_y_start = frame.shape[0] - graph_height - 15 # B, G, R | R, G, B
-    draw_rolling_window_curve(frame, distance_buffer, (0, 255, 0), 0.5, graph_height, graph_y_start)
-    draw_rolling_window_curve(frame, funscript_buffer, (255, 0, 0), 0.5, graph_height, graph_y_start)
+    graph_y_start = frame.shape[0] - graph_height - 15  # B, G, R | R, G, B
+    if state.debug_mode == "funscript":
+        # Draw gauge (example usage)
+        OverlayWidgets.draw_gauge(frame, funscript_value)
+
+        draw_rolling_window_curve(frame, distance_buffer, (0, 255, 0), 0.5, graph_height, graph_y_start)
+        draw_rolling_window_curve(frame, funscript_buffer, (255, 0, 0), 0.5, graph_height, graph_y_start)
     if funscript_interpolator_ref:
         draw_rolling_window_curve(frame, funscript_buffer_ref, (0, 0, 255), 0.5, graph_height, graph_y_start)
 
@@ -140,5 +155,3 @@ def draw_rolling_window_curve(frame, buffer, color, alpha, graph_height, graph_y
         cv2.line(overlay, (x1, y1), (x2, y2), color, 1)
 
     cv2.addWeighted(overlay, alpha, frame, 1 - alpha, 0, frame)
-
-
diff --git a/script_generator/debug/video_player/interaction.py b/script_generator/debug/video_player/interaction.py
index 7d2beae6..0fad39f6 100644
--- a/script_generator/debug/video_player/interaction.py
+++ b/script_generator/debug/video_player/interaction.py
@@ -1,22 +1,121 @@
+from typing import TYPE_CHECKING, Tuple
+
 import cv2
 
-from script_generator.debug.video_player.constants import BAR_HEIGHT
+from script_generator.constants import LEFT, RIGHT, SPACE, Q, COMMA, PERIOD, LEFT_BRACKET, RIGHT_BRACKET
 from script_generator.debug.logger import log
+from script_generator.gui.messages.messages import UpdateGUIState
+from script_generator.video.data_classes.video_info import get_cropped_dimensions
+
+if TYPE_CHECKING:
+    from script_generator.state.app_state import AppState
+    from script_generator.debug.video_player.video_player import VideoPlayer
 
 
-def mouse_callback(event, x, y, flags, param):
+def mouse_callback(event, x, y, flags, params: Tuple["AppState", "VideoPlayer"]):
     """
     Called when the user clicks on the OpenCV window.
     We pass in the video player via param for seeking.
     """
+    state, video_player = params
+    video_info = state.video_info
+
     if event == cv2.EVENT_LBUTTONDOWN:
-        video_player = param
-        height = int(video_player.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-        bar_y_start = height - BAR_HEIGHT
+        width, height = get_cropped_dimensions(video_info)
+        bar_y_start = height - 10
 
         # If clicked in the progress bar area, adjust the seek
         if y >= bar_y_start:
-            width = int(video_player.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+
+            # allow seeking when a static debug frame is set but pause the video
+            if state.static_debug_frame:
+                state.static_debug_frame = None
+                if state.update_ui:
+                    state.update_ui(UpdateGUIState(attr="static_debug_frame", value=None))
+                video_player.paused = True
+
+            video_player.funscript_graph.reset()
+
             target_frame = int((x / width) * video_player.total_frames)
             video_player.set_frame(target_frame)
-            log.info(f"Seek to frame: {target_frame}")
\ No newline at end of file
+            log.info(f"Seek to frame: {target_frame}")
+
+
+def handle_user_input(window_name, state: "AppState", video_player: "VideoPlayer", metrics):
+    key = cv2.waitKeyEx(1)
+
+    if key == -1:
+        return True
+
+    log.info(f"Key pressed: {key}")
+
+    # Check if the window has been closed
+    if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) < 1:
+        return False
+
+    if key == Q:
+        return False
+
+    elif key == SPACE:  # Toggle pause on space bar
+        video_player.paused = not video_player.paused
+
+        if not video_player.paused and state.static_debug_frame:
+            state.static_debug_frame = None
+            if state.update_ui:
+                state.update_ui(UpdateGUIState(attr="static_debug_frame", value=None))
+
+    elif key == COMMA:
+        video_player.set_frame(video_player.current_frame - int(video_player.video_info.fps * 2))
+
+    elif key == PERIOD:
+        video_player.set_frame(video_player.current_frame + int(video_player.video_info.fps * 2))
+
+    elif key == RIGHT:
+        seek_to_position(metrics, video_player, True)
+
+    elif key == LEFT:
+        seek_to_position(metrics, video_player, False)
+
+    elif key == LEFT_BRACKET:
+        video_player.set_frame(max(video_player.current_frame - 1, 0))
+
+    elif key == RIGHT_BRACKET:
+        video_player.set_frame(min(video_player.current_frame + 1, video_player.total_frames - 1))
+
+    return True
+
+
+def seek_to_position(metrics, video_player: "VideoPlayer", is_next: bool = True):
+    positions = metrics.get("positions", [])
+    tolerance = int(video_player.video_info.fps) if not video_player.paused else 2
+    seek_frame = None
+
+    if is_next:
+        curr_position = next((p for p in positions if int(p.get("start", 0)) <= video_player.current_frame < int(p.get("end", 0))), None)
+    else:
+        curr_position = next((p for p in positions if int(p.get("start", 0)) + tolerance * 2 <= video_player.current_frame + (tolerance if not video_player.paused else -1) < int(p.get("end", 0))), None)
+
+
+    # Currently inside a position?
+    if curr_position:
+        seek_frame = curr_position['end'] if is_next else curr_position['start']
+
+    # Not inside position?
+    else:
+        # Find the next or previous position
+        sorted_positions = sorted(positions, key=lambda x: int(x['start']))
+        if is_next:
+            next_position = next((p for p in sorted_positions if int(p['start']) > video_player.current_frame), None)
+            if next_position:
+                seek_frame = next_position['start']
+        else:
+            prev_position = next((p for p in reversed(sorted_positions) if int(p['end']) < video_player.current_frame), None)
+            if prev_position:
+                tol = tolerance if not video_player.paused else -1
+                if prev_position['end'] + tol >= video_player.current_frame:
+                    seek_frame = prev_position['start']
+                else:
+                    seek_frame = prev_position['end']
+
+    if seek_frame is not None:
+        video_player.set_frame(seek_frame)
diff --git a/script_generator/debug/video_player/play.py b/script_generator/debug/video_player/play.py
index eb4d9f05..7ce6563a 100644
--- a/script_generator/debug/video_player/play.py
+++ b/script_generator/debug/video_player/play.py
@@ -9,18 +9,21 @@
 
 from script_generator.debug.debug_data import load_debug_metrics
 from script_generator.debug.logger import log
-from script_generator.debug.video_player.controls import draw_media_controls
+from script_generator.debug.video_player.controls import draw_media_controls, draw_media_controls_static_overlay
 from script_generator.debug.video_player.debug_overlay import draw_overlay
 from script_generator.debug.video_player.interaction import mouse_callback
-from script_generator.debug.video_player.state import VideoPlayer
+from script_generator.debug.video_player.video_player import VideoPlayer
 from script_generator.funscript.util.util import load_funscript_json
 from script_generator.utils.file import get_output_file_path
 from script_generator.video.data_classes.video_info import get_cropped_dimensions
+from script_generator.debug.video_player.interaction import mouse_callback, handle_user_input
 
 
 def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=100, save_video_mode=False):
     video_info = state.video_info
-    _, metrics, _, _ = load_debug_metrics(state)
+    metrics_exist, metrics, metrics_path, _ = load_debug_metrics(state)
+    if not metrics_exist:
+        log.warn(f"Could not play debug video as metrics file was not found in: {metrics_path}")
     width, height = get_cropped_dimensions(video_info)
 
     video_player = VideoPlayer(
@@ -31,13 +34,14 @@ def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=1
 
     # Prepare funscript interpolation if available
     funscript_path, _ = get_output_file_path(state.video_path, ".funscript")
-    funscript_times, funscript_positions = load_funscript_json(funscript_path)
+    funscript_found = os.path.exists(funscript_path)
+    funscript_times, funscript_positions = load_funscript_json(funscript_path) if funscript_found else ([], [])
     funscript_interpolator = interp1d(
         funscript_times,
         funscript_positions,
         kind="linear",
         fill_value="extrapolate"
-    )
+    ) if funscript_found and len(funscript_times) > 0 and len(funscript_positions) > 0 else None
     funscript_interpolator_ref = None
     if state.reference_script and os.path.exists(state.reference_script):
         funscript_times_ref, funscript_positions_ref = load_funscript_json(state.reference_script)
@@ -69,15 +73,17 @@ def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=1
         cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
         # zoom in the image is only 640x640 now
         cv2.resizeWindow(window_name, int(width * 2), int(height * 2))
+        media_controls_overlay = np.array([])
 
         # Attach mouse callback for seeking
-        cv2.setMouseCallback(window_name, mouse_callback, param=video_player)
+        cv2.setMouseCallback(window_name, mouse_callback, param=(state, video_player))
 
     last_frame = video_player.current_frame
 
     def get_ceiled_fps(value):
         try:
-            return math.ceil(float(value))
+            fps = math.ceil(float(value))
+            return 60 if fps == 0 else fps
         except (ValueError, TypeError):
             return 60
 
@@ -86,12 +92,23 @@ def get_ceiled_fps(value):
         loop_start_time = time.time()
 
         # Handle user input
-        if not save_video_mode and not handle_user_input(window_name, video_player):
+        if not save_video_mode and not handle_user_input(window_name, state, video_player, metrics):
             break
 
         if video_player.current_frame >= video_player.end_frame:
             break
 
+        if state.static_debug_frame:
+            static_frame = to_int_or_fallback(state.static_debug_frame, None)
+            if static_frame:
+                new_frame = static_frame
+                if new_frame == last_frame:
+                    time.sleep(0.01)
+                    continue
+                else:
+                    video_player.current_frame = new_frame
+                    video_player.set_frame(video_player.current_frame)
+
         # Check if paused and the frame hasn't changed (allows seeking while being paused)
         if video_player.paused and video_player.current_frame == last_frame:
             # Avoid a tight loop hogging CPU if paused
@@ -111,6 +128,7 @@ def get_ceiled_fps(value):
 
         # Call your overlay function
         distance_buffer, funscript_buffer, funscript_buffer_ref = draw_overlay(
+            state=state,
             frame=frame,
             frame_id=video_player.current_frame,
             logs=metrics,
@@ -128,6 +146,12 @@ def get_ceiled_fps(value):
         else:
             # Display the frame
             draw_media_controls(frame, video_player)
+
+            if not media_controls_overlay.any():
+                media_controls_overlay = draw_media_controls_static_overlay(metrics, frame)
+
+            frame = cv2.addWeighted(frame, 1.0, media_controls_overlay, 1.0, 0)
+
             cv2.imshow("Debug Video", frame)
 
             # Throttle the loop to maintain DESIRED_FPS if processing is faster
@@ -141,18 +165,3 @@ def get_ceiled_fps(value):
         return temp_video_path
     else:
         cv2.destroyAllWindows()
-
-
-def handle_user_input(window_name, video_player):
-    key = cv2.waitKey(1) & 0xFF
-
-    # Check if the window has been closed
-    if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) < 1:
-        return False
-
-    if key == ord("q"):
-        return False
-    elif key == ord(" "):  # Toggle pause on space bar
-        video_player.paused = not video_player.paused
-
-    return True
\ No newline at end of file
diff --git a/script_generator/debug/video_player/static_overlay.py b/script_generator/debug/video_player/static_overlay.py
new file mode 100644
index 00000000..b959d20d
--- /dev/null
+++ b/script_generator/debug/video_player/static_overlay.py
@@ -0,0 +1,53 @@
+import cv2
+
+from script_generator.constants import CLASS_COLORS
+from script_generator.debug.video_player.controls import PROGRESS_BAR_HEIGHT
+from script_generator.state.app_state import AppState
+from script_generator.video.data_classes.video_info import get_cropped_dimensions
+
+
+def draw_static_overlay(state: "AppState", metrics, overlay):
+    width, height = get_cropped_dimensions(state.video_info)
+    frames = state.video_info.total_frames
+    px_frame = width / frames  # pixels per frame
+    # overlay = np.zeros((height, width, 4), dtype=np.uint8)
+    progress_bar_top = height - PROGRESS_BAR_HEIGHT
+
+    if state.tracking_logic_version == 1:
+        return overlay
+
+    # Draw positions
+    positions = metrics["positions"]
+    for position in positions:
+        start, end = position["start"], position["end"]
+        px_start = int(px_frame * start)
+        px_end = int(px_frame * end)
+        color = (240, 240, 0)
+        cv2.rectangle(
+            overlay,
+            (px_start, progress_bar_top - 1),
+            (px_end, progress_bar_top + 1),
+            color,
+            -1
+        )
+        cv2.line(overlay, (px_start, height - PROGRESS_BAR_HEIGHT - 5), (px_start, height), color, 1)
+        cv2.line(overlay, (px_end, height - PROGRESS_BAR_HEIGHT - 5), (px_end, height), color, 1)
+
+        for sex_act in position["sex_acts"]:
+            px_start = int(px_frame * sex_act["start"])
+            px_end = int(px_frame * sex_act["end"])
+
+            cv2.rectangle(
+                overlay,
+                (px_start, progress_bar_top - 4),
+                (px_end, progress_bar_top - 2),
+                CLASS_COLORS[sex_act["cls"]],
+                -1
+            )
+
+    cuts = metrics.get("cuts", [])
+    for cut in cuts:
+        px_cut = int(px_frame * cut)
+        cv2.line(overlay, (px_cut, height - PROGRESS_BAR_HEIGHT + 2), (px_cut, height), (0, 0, 255), 1)
+
+    return overlay
diff --git a/script_generator/debug/video_player/state.py b/script_generator/debug/video_player/video_player.py
similarity index 61%
rename from script_generator/debug/video_player/state.py
rename to script_generator/debug/video_player/video_player.py
index 8636a850..e843031a 100644
--- a/script_generator/debug/video_player/state.py
+++ b/script_generator/debug/video_player/video_player.py
@@ -1,5 +1,7 @@
 import cv2
+import numpy as np
 
+from script_generator.constants import FUNSCRIPT_BUFFER_SIZE
 from script_generator.state.app_state import AppState
 from script_generator.video.ffmpeg.video_reader import VideoReaderFFmpeg
 
@@ -13,12 +15,21 @@ def __init__(self, state: AppState, start_frame, end_frame):
         self.end_frame = state.video_info.total_frames if not end_frame else end_frame
         self.current_frame = 0
 
-        self.cap = VideoReaderFFmpeg(state, start_frame)
+        # for rolling funscripts
+        # self.funscript_graph = FunscriptGraph(state)
+
+        # a buffer to show outliers longer then 1 frame and slowly fade them out
+        self.outlier_buffer = []
+
+        self.reader = VideoReaderFFmpeg(state, start_frame)
         self.paused = False
 
+        if start_frame != 0:
+            self.set_frame(start_frame)
+
     def release(self):
-        if self.cap:
-            self.cap.release()
+        if self.reader:
+            self.reader.release()
 
     def set_frame(self, frame_id):
         if frame_id < 0:
@@ -27,10 +38,10 @@ def set_frame(self, frame_id):
             frame_id = self.total_frames - 1
 
         self.current_frame = frame_id
-        self.cap.set(cv2.CAP_PROP_POS_FRAMES, self.current_frame)
+        self.reader.set_frame(self.current_frame)
 
     def read_frame(self):
-        ret, frame = self.cap.read()
+        ret, frame = self.reader.read()
         if ret and not self.paused:
             self.current_frame += 1
         return ret, frame
\ No newline at end of file
diff --git a/script_generator/funscript/create_funscript.py b/script_generator/funscript/create_funscript.py
index 170dd873..4bcc396c 100644
--- a/script_generator/funscript/create_funscript.py
+++ b/script_generator/funscript/create_funscript.py
@@ -16,6 +16,7 @@ def create_funscript(state):
     output_path, _ = get_output_file_path(state.video_path, ".funscript")
 
     raw_funscript_path, _ = get_output_file_path(state.video_path, ".json", "rawfunscript")
+
     if os.path.exists(raw_funscript_path) and (state.funscript_data is None or len(state.funscript_data) == 0):
         log_fun.info("len funscript data is 0, trying to load file")
         # Read the funscript data from the JSON file
@@ -29,6 +30,9 @@ def create_funscript(state):
     else:
         data = state.funscript_data
 
+    if not data:
+        log_fun.warn(f"No funscript data found")
+
     try:
         log_fun.info(f"Generating funscript based on {len(data)} points...")
 
diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index 1232d3ce..d680c544 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -1,10 +1,13 @@
 import ctypes
 import os
 import tkinter as tk
+from ultralytics import settings
 
 from script_generator.constants import LOGO, ICON
 from script_generator.gui.controller.stop_processing import stop_processing
 from script_generator.gui.views.funscript_generator import FunscriptGeneratorPage
+from script_generator.gui.views.help import HelpPage
+from script_generator.gui.views.help_debug_video import HelpDebugVideoPage
 from script_generator.gui.views.settings import SettingsPage
 from script_generator.state.app_state import AppState
 from script_generator.utils.helpers import is_mac
@@ -13,7 +16,7 @@
 
 # TODO this is a workaround and needs to be fixed properly
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
-
+settings.update({"sync": False}) # Disable analytics and crash reporting
 
 class App(tk.Tk):
     def __init__(self, state: AppState= None):
@@ -21,9 +24,9 @@ def __init__(self, state: AppState= None):
         if hasattr(ctypes, "windll"):
             ctypes.windll.shcore.SetProcessDpiAwareness(2)  # For Windows DPI scaling
         # self.tk.call('tk', 'scaling', 1.0)
-        self.title(f"VR & 2D POV Funscript AI Generator - v" + VERSION)
-        self.geometry('780x800' if is_mac() else '708x790')
-        self.resizable(False, False)
+        self.title(f"FunGen - AI-Powered FunScript Generation v{VERSION}")
+        # Base size to prevent major resizing
+        self.geometry('780x1000' if is_mac() else '708x975')
 
         self.iconphoto(False, tk.PhotoImage(file=LOGO))
         self.iconbitmap(ICON)
@@ -43,6 +46,27 @@ def __init__(self, state: AppState= None):
         view_menu.add_command(label="Funscript generator", command=lambda: self.show_frame(PageNames.FUNSCRIPT_GENERATOR))
         view_menu.add_command(label="Settings", command=lambda: self.show_frame(PageNames.SETTINGS))
         menu_bar.add_cascade(label="View", menu=view_menu)
+        help_menu = tk.Menu(menu_bar, tearoff=0)
+        # TODO add options menu?
+        # options_menu = tk.Menu(menu_bar, tearoff=0)
+        # self.show_funscript_settings = tk.BooleanVar(value=False)
+        # self.show_debug_options = tk.BooleanVar(value=False)
+        # options_menu.add_checkbutton(
+        #     label="Show Funscript options",
+        #     variable=self.show_funscript_settings,
+        #     onvalue=True,
+        #     offvalue=False
+        # )
+        # options_menu.add_checkbutton(
+        #     label="Show debug options",
+        #     variable=self.show_debug_options,
+        #     onvalue=True,
+        #     offvalue=False
+        # )
+        # menu_bar.add_cascade(label="Options", menu=options_menu)
+        help_menu.add_command(label="General", command=lambda: self.show_frame(PageNames.HELP))
+        # help_menu.add_command(label="Debug video", command=lambda: self.show_frame(PageNames.HELP_DEBUG_VIDEO))
+        menu_bar.add_cascade(label="Help", menu=help_menu)
         self.config(menu=menu_bar)
 
         self.state = state if state else AppState()
@@ -58,10 +82,22 @@ def __init__(self, state: AppState= None):
 
         self.protocol("WM_DELETE_WINDOW", self.on_close)
 
+        # Initialize with the default page and scale to contents
+        self.show_frame(PageNames.FUNSCRIPT_GENERATOR)
+        self.scale_to_contents()
+
     def reset_topmost(self):
         self.focus_force()
         self.attributes("-topmost", False)
 
+    def scale_to_contents(self):
+        """Force the window to update and scale to its contents."""
+        self.update_idletasks()
+        width = self.winfo_reqwidth()
+        height = self.winfo_reqheight()
+        self.geometry(f"{width}x{height}")
+        self.resizable(False, False)
+
     def show_frame(self, page_name):
         """Show a frame, creating it if necessary."""
         if page_name not in self.frames:
@@ -79,11 +115,14 @@ def show_frame(self, page_name):
         frame.tkraise()
 
     def create_page(self, page_name):
-        if page_name == PageNames.FUNSCRIPT_GENERATOR:
-            return FunscriptGeneratorPage(parent=self.container, controller=self)
-        elif page_name == PageNames.SETTINGS:
-            return SettingsPage(parent=self.container, controller=self)
-        return None
+        """Factory method to create pages dynamically."""
+        pages = {
+            PageNames.FUNSCRIPT_GENERATOR: FunscriptGeneratorPage,
+            PageNames.SETTINGS: SettingsPage,
+            PageNames.HELP: HelpPage,
+            PageNames.HELP_DEBUG_VIDEO: HelpDebugVideoPage,
+        }
+        return pages.get(page_name, lambda *args, **kwargs: None)(parent=self.container, controller=self)
 
     def on_close(self):
         log.info("Application is closing...")
@@ -94,6 +133,8 @@ def on_close(self):
 class PageNames:
     FUNSCRIPT_GENERATOR = "Funscript generator"
     SETTINGS = "Settings"
+    HELP = "Help"
+    HELP_DEBUG_VIDEO = "Help: Debug video"
 
 
 def start_app(state: AppState= None):
diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index a218204d..2bf294c7 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -13,17 +13,17 @@
 
 class Widgets:
     @staticmethod
-    def frame(parent, title=None, main_section=False, **grid_kwargs):
+    def frame(parent, title=None, main_section=False, min_height=0, inner_padding=(0, 0), **grid_kwargs):
         if title:
             frame_cls = ttk.LabelFrame
             if main_section:
                 style = ttk.Style(parent)
                 style.configure("Bold.TLabelframe.Label", font=("TkDefaultFont", 10, "bold"))
-                frame = frame_cls(parent, text=title, style="Bold.TLabelframe")
+                frame = frame_cls(parent, text=title, style="Bold.TLabelframe", padding=inner_padding)
             else:
-                frame = frame_cls(parent, text=title)
+                frame = frame_cls(parent, text=title, padding=inner_padding)
         else:
-            frame = ttk.Frame(parent)
+            frame = ttk.Frame(parent, padding=inner_padding)
 
         grid_kwargs.setdefault("sticky", "nsew")  # Default to stretch in all directions
         grid_kwargs.setdefault("padx", 5)
@@ -39,14 +39,19 @@ def configure_grid(event):
             for j in range(frame.grid_size()[1]):  # Rows
                 if frame.grid_rowconfigure(j)["weight"] == 0:  # Avoid overwriting custom weights
                     frame.grid_rowconfigure(j, weight=1)
+                    frame.grid_rowconfigure(j, minsize=min_height)
 
         frame.bind("<Configure>", configure_grid)
 
         return frame
 
     @staticmethod
-    def label(frame, text, tooltip_text=None, **grid_kwargs):
-        label = ttk.Label(frame, text=text)
+    def label(frame, text, tooltip_text=None, align="center", **grid_kwargs):
+        align_options = {"left": "w", "center": "center", "right": "e"}
+        anchor_value = align_options.get(align, "center")
+
+        label = ttk.Label(frame, text=text, anchor=anchor_value, justify=align)
+
         label.grid(**grid_kwargs)
 
         if tooltip_text:
@@ -54,6 +59,37 @@ def label(frame, text, tooltip_text=None, **grid_kwargs):
 
         return label
 
+    @staticmethod
+    def link(frame, text, command, tooltip_text=None, visible=True, **grid_kwargs):
+        """Creates a clickable hyperlink-style label with a dynamic show method"""
+        label = tk.Label(
+            frame,
+            text=text,
+            fg="blue",
+            cursor="hand2",
+            font=("Arial", 10, "underline")
+        )
+
+        label.bind("<Button-1>", lambda e: command())
+
+        if tooltip_text:
+            Tooltip(label, tooltip_text)
+
+        if visible:
+            label.grid(**grid_kwargs)
+        else:
+            label.grid_remove()
+
+        def show(is_visible: bool):
+            if is_visible:
+                label.grid(**grid_kwargs)
+            else:
+                label.grid_remove()
+
+        label.show = show
+
+        return label
+
     @staticmethod
     def entry(frame, default_value="", tooltip_text=None, **grid_kwargs):
         entry = ttk.Entry(frame, textvariable=tk.StringVar(value=default_value))
@@ -65,7 +101,7 @@ def entry(frame, default_value="", tooltip_text=None, **grid_kwargs):
         return entry
 
     @staticmethod
-    def input(parent, label_text, state, attr, row=0, column=0, label_width_px=LABEL_WIDTH, entry_width_px=200, command=None, tooltip_text=None):
+    def input(parent, label_text, state, attr, row=0, column=0, label_width_px=LABEL_WIDTH, width=None, command=None, tooltip_text=None):
         container = ttk.Frame(parent)
         container.grid(row=row, column=column, sticky="ew", padx=5, pady=5)
         container.columnconfigure(1, weight=1)
@@ -86,10 +122,17 @@ def on_value_change(*args):
         label = tk.Label(container, text=label_text, anchor="w", width=label_width_px // 7)
         label.grid(row=0, column=0, sticky="w", padx=(5, 2))
 
-        entry_container = ttk.Frame(container, width=entry_width_px)
+        entry_container = ttk.Frame(container)
         entry_container.grid(row=0, column=1, sticky="ew", padx=(2, 5))
         entry_container.grid_propagate(False)  # Prevent resizing
-        entry = ttk.Entry(entry_container, textvariable=value, width=entry_width_px)
+
+        entry_kwargs = {"textvariable": value}
+        if width:
+            char_width = width // 7
+            entry_kwargs["width"] = char_width
+            entry_container.configure(width=width)
+
+        entry = ttk.Entry(entry_container, **entry_kwargs)
         entry.pack(fill="both", expand=True)
 
         if tooltip_text:
@@ -98,22 +141,44 @@ def on_value_change(*args):
         return container, entry, value
 
     @staticmethod
-    def button(parent, button_text, on_click, row=0, column=0, tooltip_text=None, style_name="Custom.TButton", sticky="w"):
-        style = ttk.Style()
-        style.configure(style_name, padding=(10, 3))
+    def button(parent, button_text, command, row=0, column=0, tooltip_text=None, visible=True, default_style=False, **grid_kwargs):
+        grid_kwargs.setdefault("pady", PADDING_Y)
+        grid_kwargs.setdefault("padx", PADDING_X)
+        grid_kwargs.setdefault("sticky", "w")
 
-        button = ttk.Button(parent, text=button_text, command=on_click, style=style_name)
-        button.grid(row=row, column=column, sticky=sticky, padx=PADDING_X, pady=PADDING_Y)
+        if default_style:
+            button = ttk.Button(parent, text=button_text, command=command)
+        else:
+            style = ttk.Style()
+            style.configure("Custom.TButton", padding=(10, 3))
+            button = ttk.Button(parent, text=button_text, command=command, style="Custom.TButton")
 
         if tooltip_text:
             Tooltip(button, tooltip_text)
 
+        if visible:
+            button.grid(row=row, column=column, **grid_kwargs)
+        else:
+            button.grid_remove()
+
+        def show(is_visible: bool):
+            if is_visible:
+                button.grid(row=row, column=column, **grid_kwargs)
+            else:
+                button.grid_remove()
+
+        button.show = show
+
         return button
 
     @staticmethod
-    def file_selection(parent, label_text, button_text, file_selector_title, file_types, state, attr, command=None, row=0, label_width_px=150, button_width_px=100, tooltip_text=None, select_folder=False):
+    def file_selection(parent, label_text, button_text, file_selector_title, file_types, state, attr, command=None, row=0, label_width_px=150, button_width_px=100, tooltip_text=None, select_folder=False, **grid_kwargs):
+        grid_kwargs.setdefault("pady", 5)
+        grid_kwargs.setdefault("padx", 5)
+        grid_kwargs.setdefault("sticky","nsew")
+
         container = tk.Frame(parent)
-        container.grid(row=row, column=0, sticky="nsew", padx=5, pady=5)
+        container.grid(row=row, column=0,  **grid_kwargs)
 
         def on_change(val):
             setattr(state, attr, val)
@@ -186,7 +251,7 @@ def labeled_progress(parent, label_text, row=0, column=0, progress_length=300, l
         return container, progress_bar, progress_label, percentage_label
 
     @staticmethod
-    def dropdown(parent, label_text, options, default_value, state, attr, row=0, column=0, command=None, label_width_px=LABEL_WIDTH, tooltip_text=None, **grid_kwargs):
+    def dropdown(parent, label_text, options, default_value, state, attr, row=0, column=0, command=None, label_width_px=LABEL_WIDTH, dropdown_width_px=None, tooltip_text=None, **grid_kwargs):
         selected_value = tk.StringVar(value=default_value)
 
         # Create a container for the dropdown
@@ -199,8 +264,10 @@ def dropdown(parent, label_text, options, default_value, state, attr, row=0, col
         label = tk.Label(container, text=label_text, anchor="w", width=label_width_px // 7)
         label.grid(row=0, column=0, sticky="w", padx=(5, 2))
 
-        # Dropdown (Combobox) widget
-        dropdown = ttk.Combobox(container, textvariable=selected_value, values=options, state="readonly")
+        dropdown_kwargs = {"textvariable": selected_value, "values": options, "state": "readonly"}
+        if dropdown_width_px is not None:
+            dropdown_kwargs["width"] = dropdown_width_px // 7
+        dropdown = ttk.Combobox(container, **dropdown_kwargs)
         dropdown.grid(row=0, column=1, sticky="ew", padx=(2, 5))
 
         def on_change(val):
@@ -369,7 +436,7 @@ def disclaimer(parent, tooltip_text=None):
             text="Individual and personal use only.\nNot for commercial use.\nhttps://github.com/ack00gar",
             font=("Arial", 10, "italic", "bold"), justify="center"
         )
-        footer_label.grid(row=8, column=0, columnspan=100, padx=5, pady=5, sticky="s")
+        footer_label.grid(row=8, column=0, columnspan=100, padx=5, pady=(10, 20), sticky="s")
 
         if tooltip_text:
             Tooltip(footer_label, tooltip_text)
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 78a2b780..3c992151 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -11,8 +11,9 @@
 from script_generator.gui.controller.stop_processing import stop_processing
 from script_generator.gui.messages.messages import UIMessage, ProgressMessage, UpdateGUIState
 from script_generator.gui.utils.utils import enable_widgets, disable_widgets, set_progressbars_done, reset_progressbars
-from script_generator.gui.utils.widgets import Widgets
+from script_generator.gui.utils.widgets import Widgets, LABEL_WIDTH
 from script_generator.gui.views.popups.create_debug_video import render_debug_video_popup
+from script_generator.gui.views.popups.edit_video_info import render_video_edit_popup
 from script_generator.object_detection.util.data import get_raw_yolo_file_info
 from script_generator.state.app_state import AppState
 
@@ -34,9 +35,15 @@ def __init__(self, parent, controller):
         # region VIDEO SELECTION
         video_selection = Widgets.frame(wrapper, title="Video", main_section=True)
 
+
         def update_video_path():
             state.set_video_info()
             update_ui_for_state()
+            fov = f", FOV {state.video_info.fov}" if state.video_info and state.video_info.is_vr else ""
+            new_label = f"VR: {state.video_info.is_vr}, Fisheye: {state.video_info.is_fisheye}{fov}" if state.video_info else ""
+            video_label.config(text=new_label)
+            video_info_btn.show(state.video_info)
+            ref_path.set(state.reference_script if state.reference_script else "")
 
         _, fs_entry, fs_button, _ = Widgets.file_selection(
             attr="video_path",
@@ -47,19 +54,15 @@ def update_video_path():
             file_types=[("Text Files", "*.mp4 *.avi *.mov *.mkv"), ("All Files", "*.*")],
             state=state,
             tooltip_text="The video to generate a funscript for. For proper detection of fisheye keep the suffix like _FISHEYE190, _MKX200, etc. in the filename\n\nIn the future we'll add the option to override this in the UI.",
-            command=lambda val: update_video_path()
+            command=lambda val: update_video_path(),
+            pady=(0, 0)
         )
 
-        # _, _, reader_dropdown, _ = Widgets.dropdown(
-        #     attr="video_reader",
-        #     parent=video_selection,
-        #     label_text="Video Reader",
-        #     options=["FFmpeg", *([] if is_mac() else ["FFmpeg + OpenGL (Windows)"])],
-        #     default_value=state.video_reader,
-        #     tooltip_text=("On Mac only FFmpeg is supported" if is_mac() else "FFmpeg + OpenGL is usually about 30% faster on a good GPU."),
-        #     state=state,
-        #     row=1
-        # )
+        video_info_container = Widgets.frame(video_selection, row=1, padx=(0, 0), pady=(0, 5), min_height=35)
+        video_label = Widgets.label(video_info_container, "", None, align="left", padx=(LABEL_WIDTH + 17, 0), sticky="w", row=0)
+        video_info_btn = Widgets.button(video_info_container, "Edit", command=lambda: Widgets.create_popup(
+            title="Edit video settings", master=controller, width=350, height=120, content_builder=lambda window, user_action: render_video_edit_popup(window, state, update_video_path)),
+                                        tooltip_text="Override the default detected values", visible=False, default_style=True, padx=(0, 10), column=90, row=0)
         # endregion
 
         # # region OPTIONAL SETTINGS
@@ -205,21 +208,34 @@ def handle_process_btn():
             state=state,
             attr="max_preview_fps",
             row=1,
-            column=29,
+            column=28,
             label_width_px=45,
-            entry_width_px=10,
+            width=55,
             tooltip_text="The maximum FPS for the debug video"
         )
+        _, _, debug_mode_dropdown, _ = Widgets.dropdown(
+            attr="debug_mode",
+            parent=general,
+            label_text="Mode",
+            options=["funscript", "detection"],
+            default_value=state.debug_mode,
+            tooltip_text="Change the debug metrics\nfunscript: Script overlay and funscript metrics\ndetection: Shows object detection boxes, confidence score and tracking id",
+            state=state,
+            label_width_px=33,
+            dropdown_width_px=80,
+            row=1,
+            column=29
+        )
         play_btn = Widgets.button(
             general,
             "Play debug video",
             lambda: debug_video(state),
             row=1,
             column=30,
-            tooltip_text="Opens a debug video player overlaid with debugging information (Press space to pause).\n\nThis overlay shows object detection boxes and a live funscript overlay,\namong other useful debugging information.\nCan only be triggered after the funscript generation process has completed.\nNeeds the 'Save debug information' option activated during processing."
+            tooltip_text="Opens a debug video player overlaid with debugging metrics (Press space to pause).\n\nThis overlay shows object detection boxes and a live funscript overlay,\namong other useful debugging information.\nCan only be triggered after the funscript generation process has completed.\nNeeds the 'Save debug metrics' option activated during processing."
         )
         script_compare = Widgets.frame(debugging, title="Script compare", row=1)
-        _, ref_entry, ref_button, _ = Widgets.file_selection(
+        _, ref_entry, ref_button, ref_path = Widgets.file_selection(
             attr="reference_script",
             parent=script_compare,
             label_text="Reference Script",
@@ -266,16 +282,16 @@ def update_ui_for_state():
                 processing_btn.config(text="Start processing")
 
             if state.has_raw_yolo and state.has_tracking_data:
-                enable_widgets([play_btn, regenerate_btn, gen_video_btn, max_fps_e, gen_report_btn, gen_heatmap_btn])
+                enable_widgets([play_btn, regenerate_btn, gen_video_btn, max_fps_e, debug_mode_dropdown, gen_report_btn, gen_heatmap_btn])
                 set_progressbars_done([(yolo_p, yolo_p_perc), (track_p, track_p_perc)])
                 processing_btn.config(text="Re-run object detection and or tracking")
             elif state.has_raw_yolo and not state.has_tracking_data:
-                disable_widgets([play_btn, regenerate_btn, gen_video_btn, max_fps_e, gen_report_btn, gen_heatmap_btn])
+                disable_widgets([play_btn, regenerate_btn, gen_video_btn, max_fps_e, debug_mode_dropdown, gen_report_btn, gen_heatmap_btn])
                 set_progressbars_done([(yolo_p, yolo_p_perc)])
                 reset_progressbars([(track_p, track_p_perc)])
                 processing_btn.config(text="Re-run object detection and or start tracking")
             else:
-                disable_widgets([play_btn, regenerate_btn, gen_video_btn, max_fps_e, gen_report_btn, gen_heatmap_btn])
+                disable_widgets([play_btn, regenerate_btn, gen_video_btn, max_fps_e, debug_mode_dropdown, gen_report_btn, gen_heatmap_btn])
                 reset_progressbars([(yolo_p, yolo_p_perc), (track_p, track_p_perc)])
                 processing_btn.config(text="Start processing")
 
diff --git a/script_generator/gui/views/help.py b/script_generator/gui/views/help.py
new file mode 100644
index 00000000..7667a48a
--- /dev/null
+++ b/script_generator/gui/views/help.py
@@ -0,0 +1,25 @@
+import tkinter as tk
+from tkinter import ttk
+
+from script_generator.debug.logger import set_log_level
+from script_generator.gui.utils.widgets import Widgets
+from script_generator.state.app_state import AppState
+
+
+class HelpPage(tk.Frame):
+    def __init__(self, parent, controller):
+        super().__init__(parent)
+        self.controller = controller
+
+        self.grid_columnconfigure(0, weight=1)
+
+        models_section = Widgets.frame(self, title="Model Compatibility", main_section=True, row=1, inner_padding=(10, 0))
+        models_text = (
+            "We support multiple model formats across Windows, macOS, and Linux.\n\n"
+            "- **.pt (PyTorch)**: Requires CUDA (for NVIDIA GPUs) or ROCm (for AMD GPUs) for acceleration.\n"
+            "- **.onnx (ONNX Runtime)**: Best for CPU users as it offers broad compatibility and efficiency.\n"
+            "- **.engine (TensorRT)**: For NVIDIA GPUs: Provides significant efficiency improvements.\n"
+            "- **.mlmodel (Core ML)**: Optimized for macOS users. Runs efficiently on Apple devices with Core ML.\n\n"
+            "In most cases, the app will automatically detect the best model available at launch, but if the right model wasn't present\nor the right dependencies weren't installed, you might need to override it."
+        )
+        Widgets.label(models_section, models_text, align="left", sticky="w")
\ No newline at end of file
diff --git a/script_generator/gui/views/help_debug_video.py b/script_generator/gui/views/help_debug_video.py
new file mode 100644
index 00000000..f35425c5
--- /dev/null
+++ b/script_generator/gui/views/help_debug_video.py
@@ -0,0 +1,59 @@
+import tkinter as tk
+from tkinter import ttk
+
+from script_generator.debug.logger import set_log_level
+from script_generator.gui.utils.widgets import Widgets
+from script_generator.state.app_state import AppState
+
+
+class HelpDebugVideoPage(tk.Frame):
+    def __init__(self, parent, controller):
+        super().__init__(parent)
+        self.controller = controller
+
+        self.grid_columnconfigure(0, weight=1)
+
+        general_debug_section = Widgets.frame(self, title="Play Debug Video", main_section=True, row=0, inner_padding=(10, 0))
+        general_debug_text = (
+            "When pressing the play debug video button, a video player popup will appear, allowing you to review the funscript generation\nprocess.\n\n"
+
+            "Debugging Options:\n"
+            "  - Position Debugging, Tracking Debugging, and Region Debugging can be toggled on or off to show more or less overlay\ninformation.\n\n"
+
+            "Frame: Allows skipping to a specific frame in the video. The video will pause until you press the seekbar of clear the input.\n\n"
+
+            "Max FPS: Limits the maximum FPS for the preview, which helps with slow-motion playback to better analyze movements.\n\n"
+
+            "Mode: Selects what type of overlay information is displayed.\n"
+            "  - Funscript: Shows details related to funscript generation.\n"
+            "  - Detection: Displays bounding boxes with their confidence scores.\n\n"
+
+            "Box Labels: Controls which metrics appear above bounding boxes.\n\n"
+
+            "These settings also apply when exporting a debug video. See 'Generate Sharable Debug Video' in 'Share Debug Files' for more\ndetails."
+        )
+
+        Widgets.label(general_debug_section, general_debug_text, align="left", sticky="w")
+
+        general_settings = Widgets.frame(self, title="Seekbar overlay", main_section=True, row=1, inner_padding=(10, 0))
+        controls_text = (
+            "The seekbar has various overlays to indicate segments\n"
+            "Light blue markers: This indicates a segment where the penis is in a specific location\n"
+            "Red line: This indicates a hard cut in the video\n"
+            "Colors above light blue markers: These represent the sex act that is happening at that time. and are color coded to the class\n"
+            "colors belonging to the major box involved. For instance a pussy class is red so a vaginal sex segment will also be marked red.\n"
+        )
+        Widgets.label(general_settings, controls_text, align="left", sticky="w")
+
+        general_settings = Widgets.frame(self, title="Controls", main_section=True, row=2, inner_padding=(10, 0))
+        controls_text = (
+            "q = Quit video\n"
+            "space = Pause video\n"
+            "-> = Skip to next position\n"
+            "<- = Skip to previous position\n"
+            ", = Seek backward 2 seconds\n"
+            ". = Seek forward 2 seconds\n"
+            "[ = Seek backward 1 frame\n"
+            "] = Seek forward 1 frame\n"
+        )
+        Widgets.label(general_settings, controls_text, align="left", sticky="w")
diff --git a/script_generator/gui/views/popups/edit_video_info.py b/script_generator/gui/views/popups/edit_video_info.py
new file mode 100644
index 00000000..13ace2b7
--- /dev/null
+++ b/script_generator/gui/views/popups/edit_video_info.py
@@ -0,0 +1,35 @@
+from script_generator.gui.utils.widgets import Widgets
+from script_generator.state.app_state import AppState
+from script_generator.video.data_classes.video_info import VideoInfo
+
+
+class EditVideoState:
+    def __init__(self, video_info: VideoInfo):
+        self.is_vr = video_info.is_vr
+        self.is_fisheye = video_info.is_fisheye
+        self.fov = video_info.fov
+
+def render_video_edit_popup(window, state: AppState, on_update):
+    state.set_video_info()
+    edit_video_state = EditVideoState(state.video_info)
+    def update():
+        video_info = state.video_info
+        video_info.is_vr = edit_video_state.is_vr
+        video_info.is_fisheye = edit_video_state.is_fisheye
+        video_info.fov = edit_video_state.fov
+        on_update()
+
+    Widgets.checkbox(window, "Is 3D", edit_video_state, "is_vr", row=2, command=lambda val: update())
+
+    Widgets.checkbox(window, "Fish eye", edit_video_state, "is_fisheye", row=3, command=lambda val: update())
+    Widgets.input(
+        window,
+        "FOV",
+        state=edit_video_state,
+        attr="fov",
+        row=4,
+        tooltip_text="The FOV of the VR video. Usually 180 but various formats can have\na different FOV. For instance MKX200 has 200 degrees.",
+        width=200,
+        command=lambda val: update()
+    )
+
diff --git a/script_generator/gui/views/settings.py b/script_generator/gui/views/settings.py
index 7e3a56bb..e3030299 100644
--- a/script_generator/gui/views/settings.py
+++ b/script_generator/gui/views/settings.py
@@ -59,9 +59,9 @@ def __init__(self, parent, controller):
             label_text="YOLO model",
             button_text="Browse",
             file_selector_title="Select YOLO model",
-            file_types=[("YOLO Model Files", "*.onnx"), ("YOLO Model Files", "*.pt"), ("CoreML Model Files", "*.mlmodel"), ("All Files", "*.*")],
+            file_types=[("YOLO Model Files", "*.onnx"), ("YOLO Model Files", "*.pt"), ("TensorRT", "*.engine"), ("CoreML Model Files", "*.mlmodel"), ("All Files", "*.*")],
             state=self.state,
-            tooltip_text="Path to the FFmpeg executable.",
+            tooltip_text="Path to the YOLO model file.",
             command=lambda val: c.save(),
             row=0
         )
@@ -119,4 +119,31 @@ def handle_log_level(level):
             tooltip_text="Set the log level",
             state=self.state,
             command=handle_log_level
-        )
\ No newline at end of file
+        )
+
+        # def handle_tracking_version(level):
+        #     self.state.tracking_logic_version = 1 if level == "Version 1" else 2
+        #     c.save()
+        #
+        # Widgets.dropdown(
+        #     attr="tracking_logic_version",
+        #     parent=dev_settings,
+        #     label_text="Tracking version",
+        #     options=["Version 1", "Version 2 (DEV ONLY!)"],
+        #     default_value=("Version 1" if self.state.tracking_logic_version == 1 else "Version 2 (DEV ONLY!)"),
+        #     tooltip_text="Set tracking version. This is for development purposes only!\nTracking logic will be slowly added and implemented this will surely give you bad\nresults if you use it before it's ready. You have been warned :)",
+        #     state=self.state,
+        #     command=handle_tracking_version,
+        #     row=1
+        # )
+
+        # _, _, reader_dropdown, _ = Widgets.dropdown(
+        #     attr="video_reader",
+        #     parent=window,
+        #     label_text="Video Reader",
+        #     options=["FFmpeg", *([] if is_mac() else ["FFmpeg + OpenGL (Windows)"])],
+        #     default_value=state.video_reader,
+        #     tooltip_text=("On Mac only FFmpeg is supported" if is_mac() else "FFmpeg + OpenGL is usually about 30% faster on a good GPU."),
+        #     state=state,
+        #     row=1
+        # )
diff --git a/script_generator/object_detection/util/data.py b/script_generator/object_detection/util/data.py
index cadbbf81..a1ffefdd 100644
--- a/script_generator/object_detection/util/data.py
+++ b/script_generator/object_detection/util/data.py
@@ -63,9 +63,11 @@ def load_yolo_data(state):
 
     json = load_msgpack_json(path)
 
-    if not isinstance(json, dict) or not json.get("version") or version_is_less_than(json["version"], OBJECT_DETECTION_VERSION) or not json.get("data"):
-        if version_is_less_than(json["version"], OBJECT_DETECTION_VERSION):
-            log_od.warn(f"A raw yolo was found but was skipped due to an outdated version: {path}")
-        return False, None, path, filename
+    # TODO re-enable
+    # if not isinstance(json, dict) or not json.get("version") or version_is_less_than(json["version"], OBJECT_DETECTION_VERSION) or not json.get("data"):
+    #     if version_is_less_than(json["version"], OBJECT_DETECTION_VERSION):
+    #         # TODO add message box and reprocess if out of date
+    #         log_od.warn(f"A raw yolo was found but was skipped due to an outdated version: {path}")
+    #     return False, None, path, filename
 
     return True, json["data"], path, filename
diff --git a/script_generator/object_detection/workers/yolo_worker.py b/script_generator/object_detection/workers/yolo_worker.py
index 615a8ad7..827761b0 100644
--- a/script_generator/object_detection/workers/yolo_worker.py
+++ b/script_generator/object_detection/workers/yolo_worker.py
@@ -29,19 +29,24 @@ def task_logic(self):
             else:
                 log_od.warn(f"Rendered frame missing on Yolo task")
 
-        # Process any remaining tasks in the batch
+        # Process any remaining tasks in the batch with padding
         if batch:
+            # Pad the batch with the last frame to reach YOLO_BATCH_SIZE
+            last_frame = batch[-1]
+            while len(batch) < YOLO_BATCH_SIZE:
+                batch.append(last_frame.copy())
+
             self.process_batch(batch, tasks)
 
     def process_batch(self, frames, tasks):
         start_time = time.time()
         # Yolo expects bgr images when using numpy frames
         # yolo_results = self.state.yolo_model(frames, conf=YOLO_CONF, verbose=False) # replace with this line for pipeline speed testing
-        yolo_results = self.state.yolo_model.track(frames, persist=YOLO_PERSIST, conf=YOLO_CONF, verbose=False)
-        avg_time = (time.time() - start_time) / len(tasks)
+        yolo_results = self.state.yolo_model.track(frames, persist=YOLO_PERSIST, conf=YOLO_CONF, verbose=False) # , tracker="bytetrack.yaml") # , iou=0.5, show=True
+        avg_time = (time.time() - start_time) / len(tasks)  # Use original tasks length, not padded
 
-        for t, result in zip(tasks, yolo_results):
+        # Only process the actual tasks, ignore padded results
+        for t, result in zip(tasks, yolo_results[:len(tasks)]):
             t.yolo_results = result
-            batch_time = time.time() - start_time
             t.duration(str(self.process_type), avg_time)
             self.finish_task(t)
diff --git a/script_generator/scripts/analyze_video.py b/script_generator/scripts/analyze_video.py
index adcd6bb4..51d314a9 100644
--- a/script_generator/scripts/analyze_video.py
+++ b/script_generator/scripts/analyze_video.py
@@ -14,11 +14,11 @@
 from script_generator.utils.file import check_create_output_folder
 
 if TYPE_CHECKING:
-    from script_generator.video.analyse_frame_task import AnalyzeFrameTask
+    pass
 
 
-def analyze_video(state: AppState) -> List["AnalyzeFrameTask"]:
-    log_od.info(f"[OBJECT DETECTION] Starting up pipeline{' in sequential mode' if SEQUENTIAL_MODE else ''}...")
+def analyze_video(state: AppState):
+    log_od.info(f"OBJECT DETECTION Starting up pipeline...")
 
     log_thread_stop_event = threading.Event()
     threads = []
@@ -80,7 +80,8 @@ def run_thread(thread, thread_name, out_queue):
                 if thread is not None:
                     thread.check_exception()
 
-        state.analyze_task.end_time = time.time()
+        if state.analyze_task:
+            state.analyze_task.end_time = time.time()
 
         log_thread_stop_event.set()
 
diff --git a/script_generator/scripts/generate_debug_video.py b/script_generator/scripts/generate_debug_video.py
index 302a058c..05b9efed 100644
--- a/script_generator/scripts/generate_debug_video.py
+++ b/script_generator/scripts/generate_debug_video.py
@@ -34,7 +34,7 @@ def run():
 
         subprocess.run(ffmpeg_command)
 
-        os.remove(temp_video_path)
+        # os.remove(temp_video_path)
         log.info(f"Debug video generated and stored in: {debug_video_path}")
 
     thread = threading.Thread(target=run)
diff --git a/script_generator/scripts/tracking_analysis.py b/script_generator/scripts/tracking_analysis.py
index 913782cf..32cb90eb 100644
--- a/script_generator/scripts/tracking_analysis.py
+++ b/script_generator/scripts/tracking_analysis.py
@@ -1,8 +1,8 @@
-from script_generator.analysis.workers.analyze_tracking_results import analyze_tracking_results
+from script_generator.analysis.workers.analyze_tracking_results_v1 import analyze_tracking_results_v1
+from script_generator.analysis.workers.analyze_tracking_results_v2 import analyze_tracking_results_v2
 from script_generator.debug.logger import log_tr
 from script_generator.funscript.create_funscript import create_funscript
 from script_generator.object_detection.util.data import get_raw_yolo_file_info
-from script_generator.object_detection.util.object_detection import parse_yolo_data_looking_for_penis
 from script_generator.utils.data_classes.meta_data import MetaData
 
 
@@ -16,7 +16,8 @@ def tracking_analysis(state):
     meta = MetaData.get_create_meta(state)
 
     # Performing the tracking part and generation of the raw funscript data
-    state.funscript_data = analyze_tracking_results(state)
+    state.funscript_data = analyze_tracking_results_v1(state) if state.tracking_logic_version == 1 else analyze_tracking_results_v2(state)
+
 
     # Save debug file
     state.debug_data.save_debug_file()
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index 4b7199de..0883c24a 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -7,6 +7,7 @@
 from script_generator.config.config_manager import ConfigManager
 from script_generator.debug.debug_data import DebugData, get_metrics_file_info
 from script_generator.debug.logger import log
+from script_generator.funscript.util.check_existing_funscript import check_existing_funscript
 from script_generator.object_detection.util.data import load_yolo_model, get_raw_yolo_file_info
 from script_generator.video.data_classes.video_info import VideoInfo, get_video_info
 
@@ -47,10 +48,17 @@ def __init__(self):
 
         # Gui/settings debug
         self.log_level = c.get("log_level")
+        self.tracking_logic_version = c.get("tracking_logic_version")
         self.save_debug_file: bool = True
         self.live_preview_mode: bool = False
         self.reference_script: string = None
         self.max_preview_fps = 60
+        self.static_debug_frame = None
+        self.debug_mode: Literal["funscript", "detection"] = "funscript"
+        self.debug_positions: bool = True
+        self.debug_tracking: bool = True
+        self.debug_regions: bool = True
+        self.debug_outliers: bool = True
 
         # Gui/settings Funscript Tweaking Variables
         self.boost_enabled: bool = True
@@ -63,7 +71,7 @@ def __init__(self):
         self.vw_factor: float = 8.0
         self.rounding: int = 5
 
-        # TODO move this to a analyse results class
+        # TODO remove when swtiched to tracking logic v2
         self.funscript_data = []
         self.funscript_frames = []
         self.funscript_distances = []
@@ -128,6 +136,7 @@ def set_video_info(self):
                 self.has_raw_yolo = False
                 self.has_tracking_data = False
                 self.max_preview_fps = 60
+                self.reference_script = None
             else:
                 if os.path.exists(self.video_path) and not os.path.isdir(self.video_path):
                     try:
@@ -135,6 +144,15 @@ def set_video_info(self):
                         self.has_raw_yolo, _, _ = get_raw_yolo_file_info(self)
                         self.has_tracking_data, _, _ = get_metrics_file_info(self)
                         self.max_preview_fps = math.ceil(self.video_info.fps)
+
+                        # Auto load reference script when available
+                        # TODO make a generic function
+                        video_folder = os.path.dirname(self.video_path)
+                        filename_base = os.path.splitext(os.path.basename(self.video_path))[0]
+                        ref_funscript = os.path.join(video_folder, f"{filename_base}.funscript")
+                        file_exists, is_ours, _, out_of_date = check_existing_funscript(ref_funscript, filename_base, False)
+                        if file_exists and not is_ours:
+                            self.reference_script = ref_funscript
                     except Exception as e:
                         if not self.is_cli:
                             messagebox.showerror("Error", str(e))
@@ -150,10 +168,8 @@ def reload_video_info(self):
 
 
 def log_state_settings(state: AppState):
-    settings = [
-        ("Processing video", state.video_path)
-    ]
-
-    for setting_name, setting_value in settings:
-        log.info(f"{setting_name}: {setting_value}")
+    log.info(f"Processing video: {state.video_path}")
+    state.video_info.log_stats()
 
+    if state.video_info.bit_depth > 8:
+        log.info(f"Note: This video has a bit depth of {state.video_info.bit_depth}, which limits hardware acceleration and may result in processing times up to 8x slower.")
diff --git a/script_generator/tasks/workers/abstract_task_processor.py b/script_generator/tasks/workers/abstract_task_processor.py
index da5aa4d8..338657c2 100644
--- a/script_generator/tasks/workers/abstract_task_processor.py
+++ b/script_generator/tasks/workers/abstract_task_processor.py
@@ -84,8 +84,8 @@ def run(self):
             # Propagate sentinel to the output queue
             self.output_queue.put(None)
             log.error(f"An error occurred during task execution on thread {self.process_type}: {e}")
-            # import traceback
-            # traceback.print_exc()
+            import traceback
+            traceback.print_exc()
         finally:
             self._stop_event.set()
 
diff --git a/script_generator/utils/helpers.py b/script_generator/utils/helpers.py
index eb7edf55..4b1d720f 100644
--- a/script_generator/utils/helpers.py
+++ b/script_generator/utils/helpers.py
@@ -1,4 +1,5 @@
 import platform
+from functools import reduce
 
 
 def is_mac():
@@ -14,4 +15,12 @@ def to_int_or_fallback(value, fallback):
     try:
         return int(value)
     except (ValueError, TypeError):
-        return fallback
\ No newline at end of file
+        return fallback
+
+def optional_chain(obj, *attrs):
+    """Use similarly to Javascript optional chain ?."""
+    return reduce(lambda acc, attr: getattr(acc, attr, None) if acc else None, attrs, obj)
+
+def optional_chain_fallback(obj, fallback, *attrs):
+    """Use similarly to Javascript optional chain ?."""
+    return reduce(lambda acc, attr: getattr(acc, attr, fallback) if acc else fallback, attrs, obj)
\ No newline at end of file
diff --git a/script_generator/utils/json_utils.py b/script_generator/utils/json_utils.py
index ac867313..9aabf582 100644
--- a/script_generator/utils/json_utils.py
+++ b/script_generator/utils/json_utils.py
@@ -1,4 +1,5 @@
 import json
+import orjson
 import os
 import time
 
@@ -22,7 +23,7 @@ def write_json_to_file(file_path, data):
 
         # Write the data to the file (overwrites if it exists)
     with open(file_path, 'w') as f:
-        json.dump(data, f)
+        orjson.dump(data, f)
     export_end = time.time()
     log.info(f"Done in {export_end - export_start} seconds.")
 
diff --git a/script_generator/utils/version.py b/script_generator/utils/version.py
index 8e58f346..2fcfebd7 100644
--- a/script_generator/utils/version.py
+++ b/script_generator/utils/version.py
@@ -1,6 +1,7 @@
 import re
 from script_generator.debug.logger import log_fun
 
+
 def sanitize_version(version: str) -> str:
     """
     Extracts the leading semantic version (digits and dots) from a version string.
@@ -10,6 +11,7 @@ def sanitize_version(version: str) -> str:
     match = re.match(r"(\d+(?:\.\d+)*)", version)
     return match.group(1) if match else version
 
+
 def version_is_less_than(version_a: str, version_b: str) -> bool:
     """
     Compare two semantic version strings (e.g., "0.2.1" and "0.10.0")
@@ -21,7 +23,7 @@ def version_is_less_than(version_a: str, version_b: str) -> bool:
     sanitized_b = sanitize_version(version_b)
 
     log_fun.debug(f"Comparing versions: {sanitized_a} < {sanitized_b}")
-    
+
     parts_a = [int(x) for x in sanitized_a.split('.')]
     parts_b = [int(x) for x in sanitized_b.split('.')]
 
diff --git a/script_generator/video/data_classes/video_info.py b/script_generator/video/data_classes/video_info.py
index b7ba999d..2c848262 100644
--- a/script_generator/video/data_classes/video_info.py
+++ b/script_generator/video/data_classes/video_info.py
@@ -3,6 +3,7 @@
 import re
 import subprocess
 from dataclasses import dataclass, field, asdict, fields
+from datetime import timedelta
 
 from script_generator.constants import RENDER_RESOLUTION
 from script_generator.debug.errors import FFProbeError
@@ -34,6 +35,10 @@ def __post_init__(self):
     def to_json(self) -> str:
         return json.dumps(asdict(self), indent=4)
 
+    def log_stats(self):
+        log_vid.info(
+            f"Video Info: codec: {self.codec_name}, dimensions: {self.width}x{self.height}, fps: {self.fps:.2f}, num frames: {self.total_frames}, duration: {str(timedelta(seconds=int(self.duration)))}, {self.bit_depth}-bit, is vr: {self.is_vr}, projection: {self.projection}, fov: {self.fov}, is_fisheye: {self.is_fisheye}")
+
     @classmethod
     def from_json(cls, json_str: str) -> "VideoInfo":
         data = json.loads(json_str)
@@ -41,7 +46,7 @@ def from_json(cls, json_str: str) -> "VideoInfo":
 
 
 def get_projection_and_fov_from_filename(filename):
-    filename = filename.replace("_FB360", "")
+    filename = filename.replace("_FB360", "") # 3D audio format
     projection = "LR_180"
     is_fisheye = False
     fov = 180
@@ -71,8 +76,8 @@ def get_projection_and_fov_from_filename(filename):
             projection = pattern["projection"]
             fov = pattern["fov"]
             is_fisheye = pattern["is_fisheye"]
+            is_vr = True
             break
-    log_vid.info(f"Video Format: Projection={projection}, FOV={fov}, is_fisheye={is_fisheye}")
 
     return {"projection": projection, "fov": fov, "is_fisheye": is_fisheye}
 
@@ -135,13 +140,6 @@ def get_video_info(video_path):
         is_vr = height == width // 2
         size_bytes = os.path.getsize(video_path)
 
-        log_vid.info(f"Video Info: {codec_name}, {width}x{height}, {fps:.2f} fps, {nb_frames} frames, {duration:.2f} sec, {bit_depth}-bit, is vr: {is_vr}")
-
-        if is_vr:
-            log_vid.info("Video Format: VR SBS - Based on its 2:1 ratio")
-        else:
-            log_vid.info("Video Format: 2D - Based on its ratio")
-
         return VideoInfo(video_path, codec_name, width, height, duration, int(nb_frames), fps, bit_depth, is_vr, size_bytes)
 
     except subprocess.CalledProcessError as e:
diff --git a/script_generator/video/ffmpeg/commands.py b/script_generator/video/ffmpeg/commands.py
index 688d3da6..b92dc0d9 100644
--- a/script_generator/video/ffmpeg/commands.py
+++ b/script_generator/video/ffmpeg/commands.py
@@ -1,7 +1,8 @@
 from script_generator.state.app_state import AppState
+from script_generator.video.data_classes.video_info import get_cropped_dimensions
 from script_generator.video.ffmpeg.filters import get_video_filters
-from script_generator.video.ffmpeg.hwaccel import get_hwaccel_read_args, supports_cuda_scale
-from script_generator.video.data_classes.video_info import get_cropped_dimensions, VideoInfo
+from script_generator.video.ffmpeg.hwaccel import get_hwaccel_read_args, supports_scale_cuda
+
 
 def get_ffmpeg_read_cmd(state: AppState, frame_start: int | None, output="-", disable_opengl=False):
     video = state.video_info
@@ -17,7 +18,7 @@ def get_ffmpeg_read_cmd(state: AppState, frame_start: int | None, output="-", di
         # VAAPI requires specific pixel formats and filters
         video_filter = ["-vf", f"{vf},format=nv12,hwupload"] if vf else ["-vf", "format=nv12,hwupload"]
 
-    if supports_cuda_scale(state):
+    if supports_scale_cuda(state):
         video_filter = ["-noautoscale"] + video_filter  # explicitly tell ffmpeg that scaling is done by cuda
 
     frame_size = width * height * 3  # Size of one frame in bytes
diff --git a/script_generator/video/ffmpeg/filters.py b/script_generator/video/ffmpeg/filters.py
index 4b7d897f..90143080 100644
--- a/script_generator/video/ffmpeg/filters.py
+++ b/script_generator/video/ffmpeg/filters.py
@@ -1,6 +1,6 @@
 from script_generator.constants import RENDER_RESOLUTION, VR_TO_2D_PITCH
 from script_generator.state.app_state import AppState
-from script_generator.video.ffmpeg.hwaccel import supports_cuda_scale
+from script_generator.video.ffmpeg.hwaccel import supports_scale_cuda
 
 
 def get_video_filters(video, video_reader, hwaccel, width, height, disable_opengl=False):
@@ -20,7 +20,7 @@ def get_vr_video_filters(video, disable_opengl=False):
     cuda = state.ffmpeg_hwaccel == "cuda"
 
     # hardware accelerated output is not supported with > 8 bit
-    scale = f"[0:v]scale_cuda={RENDER_RESOLUTION * 2}:-2,hwdownload" if supports_cuda_scale(state) else f"[0:v]scale={RENDER_RESOLUTION * 2}:-2"
+    scale = f"[0:v]scale_cuda={RENDER_RESOLUTION * 2}:-2,hwdownload" if supports_scale_cuda(state) else f"[0:v]scale={RENDER_RESOLUTION * 2}:-2"
     crop = f"crop={RENDER_RESOLUTION}:{RENDER_RESOLUTION}:0:0"
     out_format = f"format=nv12," if cuda else ""
 
@@ -49,7 +49,7 @@ def get_2d_video_filters(video, width, height):
 
     # in portrait, we squash the video because we don't really know where the penis is
     if video.height > video.width:
-        if supports_cuda_scale(state):
+        if supports_scale_cuda(state):
             return f"[0:v]scale_cuda={width}:{height},hwdownload,format=nv12"
         else:
             return f"[0:v]scale={width}:{height}"
@@ -59,7 +59,7 @@ def get_2d_video_filters(video, width, height):
         if video.height > RENDER_RESOLUTION:
             scale_width = int(video.width * (height / video.height))
             crop = f",crop={width}:{height}:(iw-{width})/2:0"
-            if supports_cuda_scale(state):
+            if supports_scale_cuda(state):
                 return f"[0:v]scale_cuda={scale_width}:{height},hwdownload,format=nv12{crop}"
             else:
                 return f"[0:v]scale={scale_width}:{height}{crop}"
diff --git a/script_generator/video/ffmpeg/hwaccel.py b/script_generator/video/ffmpeg/hwaccel.py
index 0544b35a..b1c013e1 100644
--- a/script_generator/video/ffmpeg/hwaccel.py
+++ b/script_generator/video/ffmpeg/hwaccel.py
@@ -83,7 +83,7 @@ def get_preferred_hwaccel(ffmpeg_path):
     supported = _list_ffmpeg_hwaccels(ffmpeg_path)
     for hw in ["cuda", "vaapi", "amf", "videotoolbox", "qsv", "d3d11va"]:
         if hw in supported and _test_hwaccel(ffmpeg_path, hw):
-            log_vid.info(f"Setting preferred FFmpeg hardware acceleration too: {hw}")
+            log_vid.info(f"Setting preferred FFmpeg hardware acceleration to: {hw}")
             return hw
     log_vid.info("No working hwaccel found.")
     return None
@@ -92,7 +92,7 @@ def get_preferred_hwaccel(ffmpeg_path):
 def get_hwaccel_read_args(state):
     hwaccel = state.ffmpeg_hwaccel
     if hwaccel == "cuda":
-        if supports_cuda_scale(state):
+        if supports_scale_cuda(state):
             return ["-hwaccel", "cuda", "-hwaccel_output_format", "cuda"]
         return ["-hwaccel", "cuda"]
     if hwaccel == "vaapi":
@@ -108,13 +108,18 @@ def get_hwaccel_read_args(state):
     return []
 
 
+_filters_checked = False
 scale_cuda = None
-def _has_scale_cuda(ffmpeg_path):
-    global scale_cuda
-    if scale_cuda is not None:
-        return scale_cuda
+scale_npp = None
+
+def _get_ffmpeg_filters(ffmpeg_path):
+    """
+    Runs FFmpeg once to check support for both scale_cuda and scale_npp filters.
+    """
+    global _filters_checked, scale_cuda, scale_npp
+    if _filters_checked:
+        return
 
-    # Check if FFmpeg supports the scale_cuda filter.
     try:
         r = subprocess.run(
             [ffmpeg_path, "-hide_banner", "-filters"],
@@ -125,18 +130,29 @@ def _has_scale_cuda(ffmpeg_path):
         )
         filters = r.stdout.lower()
         scale_cuda = "scale_cuda" in filters
-        log_vid.info(f"FFmpeg {'supports' if scale_cuda else 'does not support'} scale_cuda")
-        return scale_cuda
+        scale_npp = "scale_npp" in filters
+        log_vid.info(f"FFmpeg supports scale_cuda: {scale_cuda}, scale_npp: {scale_npp}")
     except Exception as e:
-        log_vid.error(f"Failed to check scale_cuda support: {e}")
+        log_vid.error(f"Failed to retrieve FFmpeg filters: {e}")
         scale_cuda = False
-        return False
+        scale_npp = False
+    _filters_checked = True
+
 
-def supports_cuda_scale(state: "AppState"):
+def _supports_scale_acceleration(state):
     video = state.video_info
     return (
-        state.ffmpeg_hwaccel == "cuda"
-        and video.bit_depth == 8
-        and (video.codec_name != "h264" or (video.width <= 4096 and video.height <= 4096))
-        and _has_scale_cuda(state.ffmpeg_path)
+        state.ffmpeg_hwaccel == "cuda" and
+        video.bit_depth == 8 and
+        (video.codec_name != "h264" or (video.width <= 4096 and video.height <= 4096))
     )
+
+def supports_scale_cuda(state: "AppState"):
+    if not _filters_checked:
+        _get_ffmpeg_filters(state.ffmpeg_path)
+
+    return _supports_scale_acceleration(state) and scale_cuda
+
+def supports_scale_npp(state: "AppState"):
+    return _supports_scale_acceleration(state) and scale_npp
+
diff --git a/script_generator/video/ffmpeg/video_reader.py b/script_generator/video/ffmpeg/video_reader.py
index b4bc97b4..9dac543e 100644
--- a/script_generator/video/ffmpeg/video_reader.py
+++ b/script_generator/video/ffmpeg/video_reader.py
@@ -59,24 +59,9 @@ def read(self):
             log.error(f"Error reading frame: {e}")
             return False, None
 
-    def set(self, prop_id, value):
-        """Set properties like frame position (mimics OpenCV's cap.set())."""
-        if prop_id == cv2.CAP_PROP_POS_FRAMES:
-            self.start_frame = int(value)
-            self._start_process(start_frame=self.start_frame)
-        else:
-            log.error(f"Unsupported property ID: {prop_id}")
-
-    def get(self, prop_id):
-        """Get properties like FPS, width, height (mimics OpenCV's cap.get())."""
-        props = {
-            cv2.CAP_PROP_FPS: self.state.video_info.fps,
-            cv2.CAP_PROP_FRAME_WIDTH: self.width,
-            cv2.CAP_PROP_FRAME_HEIGHT: self.height,
-            cv2.CAP_PROP_FRAME_COUNT: self.state.video_info.total_frames,
-            cv2.CAP_PROP_POS_FRAMES: self.current_frame_number,
-        }
-        return props.get(prop_id, log.error(f"Unsupported property ID: {prop_id}"))
+    def set_frame(self, frame_id):
+        self.start_frame = int(frame_id)
+        self._start_process(start_frame=frame_id)
 
     def release(self):
         """Release resources and terminate the FFmpeg process."""

From a3212a587d0188108e4a522e82093d85ffb3bd1c Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 23 Feb 2025 09:59:58 +0100
Subject: [PATCH 228/277] - Improved yolo model selection dropdown

---
 script_generator/gui/views/settings.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script_generator/gui/views/settings.py b/script_generator/gui/views/settings.py
index e3030299..506adf2b 100644
--- a/script_generator/gui/views/settings.py
+++ b/script_generator/gui/views/settings.py
@@ -59,7 +59,7 @@ def __init__(self, parent, controller):
             label_text="YOLO model",
             button_text="Browse",
             file_selector_title="Select YOLO model",
-            file_types=[("YOLO Model Files", "*.onnx"), ("YOLO Model Files", "*.pt"), ("TensorRT", "*.engine"), ("CoreML Model Files", "*.mlmodel"), ("All Files", "*.*")],
+            file_types=[("YOLO Model Files", "*.onnx *.pt *.engine *.mlmodel"), ("All Files", "*.*")],
             state=self.state,
             tooltip_text="Path to the YOLO model file.",
             command=lambda val: c.save(),

From c1bb8f0541326e6546cf0df681ff306de7aa5fd6 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 23 Feb 2025 10:14:01 +0100
Subject: [PATCH 229/277] Improved auto matching for yolo models

---
 script_generator/config/config_manager.py     |  2 +-
 .../object_detection/util/data.py             | 49 ++++++++++++-------
 script_generator/state/app_state.py           |  2 +-
 3 files changed, 34 insertions(+), 19 deletions(-)

diff --git a/script_generator/config/config_manager.py b/script_generator/config/config_manager.py
index 06bf8cf3..4d240ca2 100644
--- a/script_generator/config/config_manager.py
+++ b/script_generator/config/config_manager.py
@@ -58,7 +58,7 @@ def _ensure_defaults(self, config):
         checks = [
             ("ffmpeg_path", lambda: get_ffmpeg_paths()[0], self._is_valid_path),
             ("ffprobe_path", lambda: get_ffmpeg_paths()[1], self._is_valid_path),
-            ("yolo_model_path", get_yolo_model_path, self._is_valid_path),
+            ("yolo_model_path", get_yolo_model_path if not config["yolo_model_path"] else config["yolo_model_path"], self._is_valid_path),
             ("ffmpeg_hwaccel", lambda: get_preferred_hwaccel(config["ffmpeg_path"]) if config["ffmpeg_path"] else None, lambda val: val is not None)
         ]
 
diff --git a/script_generator/object_detection/util/data.py b/script_generator/object_detection/util/data.py
index a1ffefdd..b9384635 100644
--- a/script_generator/object_detection/util/data.py
+++ b/script_generator/object_detection/util/data.py
@@ -12,27 +12,42 @@
 from script_generator.utils.version import version_is_less_than
 
 
+def find_model_by_extension(extension):
+    """Finds the first model that matches the given file extension."""
+    for filename in MODEL_FILENAMES:
+        if filename.endswith(extension):
+            return os.path.join(MODELS_PATH, filename)
+    return None
+
 def get_yolo_model_path():
-    yolo_models = [os.path.join(MODELS_PATH, filename) for filename in MODEL_FILENAMES]
-    # Check if the device is an Apple device
+    """Selects the appropriate YOLO model based on platform and hardware capabilities."""
     if is_mac():
-        log.info(f"Apple device detected, loading {yolo_models[0]} for MPS inference.")
-        return yolo_models[0]
-
-    # Check if CUDA is available (for GPU support)
-    elif torch.cuda.is_available():
-        log.info(f"CUDA is available, loading {yolo_models[1]} for GPU inference.")
-        return yolo_models[1]
-
-    # Fallback to ONNX model for other platforms without CUDA
-    else:
-        log.info("CUDA not available, if this is unexpected, please install CUDA and check your version of torch.")
-        log.info("You might need to install a dependency with the following command (example):")
-        log.info("pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118")
-        log.info(f"Falling back to CPU inference, loading {yolo_models[2]}.")
+        model_path = find_model_by_extension(".mlpackage")
+        if model_path:
+            log.info(f"Apple device detected, loading {model_path} for MPS inference.")
+            return model_path
+
+    if torch.cuda.is_available():
+        model_path = find_model_by_extension(".engine")
+        if model_path:
+            log.info(f"CUDA is available, loading {model_path} for GPU inference.")
+            return model_path
+
+        model_path = find_model_by_extension(".pt")
+        if model_path:
+            log.info(f"CUDA is available, loading {model_path} for GPU inference.")
+            return model_path
+
+    # Default to ONNX for CPU-based inference
+    model_path = find_model_by_extension(".onnx")
+    if model_path:
+        log.info("CUDA not available, falling back to ONNX model for CPU inference.")
         log.info("WARNING: CPU inference may be slow on some devices.")
+        return model_path
 
-        return yolo_models[2]
+    # Fallback in case no model is found
+    log.error("No suitable model found. Check your MODEL_FILENAMES list.")
+    return None
 
 def load_yolo_model(yolo_model_path):
     if not yolo_model_path or not os.path.exists(str(yolo_model_path)):
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index 0883c24a..6a5d6dff 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -108,7 +108,7 @@ def is_configured(self):
             (self.video_path, f"{message_prefix} Please select a valid video file."),
             (self.ffprobe_path, f"{message_prefix} FFprobe is missing. Please provide the correct path."),
             (self.ffmpeg_path, f"{message_prefix} FFMPEG is missing. Please provide the correct path."),
-            (self.yolo_model, f"{message_prefix} YOLO model is not loaded. Please make sure to download the YOLO model to the models directory."),
+            (self.yolo_model_path, f"{message_prefix} YOLO model is not loaded. Please make sure to download the YOLO model to the models directory."),
         ]
 
         for path, error_message in checks:

From 87bc61a0d960f5ad85a6407b16d6370fa9954d9c Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 23 Feb 2025 10:18:43 +0100
Subject: [PATCH 230/277] Small fix for debug overlay due to merge

---
 script_generator/debug/video_player/interaction.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script_generator/debug/video_player/interaction.py b/script_generator/debug/video_player/interaction.py
index 0fad39f6..92d6fa5e 100644
--- a/script_generator/debug/video_player/interaction.py
+++ b/script_generator/debug/video_player/interaction.py
@@ -34,7 +34,7 @@ def mouse_callback(event, x, y, flags, params: Tuple["AppState", "VideoPlayer"])
                     state.update_ui(UpdateGUIState(attr="static_debug_frame", value=None))
                 video_player.paused = True
 
-            video_player.funscript_graph.reset()
+            # video_player.funscript_graph.reset()
 
             target_frame = int((x / width) * video_player.total_frames)
             video_player.set_frame(target_frame)

From db60ca12ebb36f8bfd3a8afbd391e167d90ddbe5 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 23 Feb 2025 10:21:25 +0100
Subject: [PATCH 231/277] Changed funscript author

---
 script_generator/constants.py                               | 3 ++-
 script_generator/funscript/util/check_existing_funscript.py | 4 ++--
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/script_generator/constants.py b/script_generator/constants.py
index b5cf7f48..bca4323a 100644
--- a/script_generator/constants.py
+++ b/script_generator/constants.py
@@ -175,4 +175,5 @@
 # DIV
 ##################################################################################################
 
-FUNSCRIPT_AUTHOR = "FunGen_k00gar_AI"
+FUNSCRIPT_AUTHOR = "FunGen"
+OLD_FUNSCRIPT_AUTHOR = "FunGen_k00gar_AI"
\ No newline at end of file
diff --git a/script_generator/funscript/util/check_existing_funscript.py b/script_generator/funscript/util/check_existing_funscript.py
index b6b8f573..a998cfa6 100644
--- a/script_generator/funscript/util/check_existing_funscript.py
+++ b/script_generator/funscript/util/check_existing_funscript.py
@@ -1,7 +1,7 @@
 import os
 import datetime
 
-from script_generator.constants import FUNSCRIPT_AUTHOR, FUNSCRIPT_VERSION
+from script_generator.constants import FUNSCRIPT_AUTHOR, FUNSCRIPT_VERSION, OLD_FUNSCRIPT_AUTHOR
 from script_generator.debug.logger import log_fun
 from script_generator.utils.json_utils import load_json_from_file
 from script_generator.utils.version import version_is_less_than
@@ -17,7 +17,7 @@ def check_existing_funscript(dest_path: str, filename_base: str, make_funscript_
         log_fun.info(f"Could not load funscript data for file: {dest_path}: {str(e)}")
         return True, False, None, False
 
-    is_ours = json_data.get("author") == FUNSCRIPT_AUTHOR
+    is_ours = json_data.get("author") == FUNSCRIPT_AUTHOR or json_data.get("author") == OLD_FUNSCRIPT_AUTHOR
 
     backup_path = None
     if is_ours and make_funscript_backup:

From 8dd8b4f2d00bd894e316a6c6a91ae3e9f632fe3e Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 23 Feb 2025 10:57:28 +0100
Subject: [PATCH 232/277] Upped version added yolo model logging

---
 script_generator/constants.py                  | 2 +-
 script_generator/object_detection/util/data.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/script_generator/constants.py b/script_generator/constants.py
index bca4323a..c4d3e4ed 100644
--- a/script_generator/constants.py
+++ b/script_generator/constants.py
@@ -3,7 +3,7 @@
 
 import torch
 
-VERSION = "0.2.1"
+VERSION = "0.3.0"
 OBJECT_DETECTION_VERSION = "1.0.0"
 TRACKING_VERSION = "0.1.0"
 FUNSCRIPT_VERSION = "0.1.0"
diff --git a/script_generator/object_detection/util/data.py b/script_generator/object_detection/util/data.py
index b9384635..5642fd49 100644
--- a/script_generator/object_detection/util/data.py
+++ b/script_generator/object_detection/util/data.py
@@ -53,7 +53,7 @@ def load_yolo_model(yolo_model_path):
     if not yolo_model_path or not os.path.exists(str(yolo_model_path)):
         log.warn("The YOLO model is missing. Please download and place the appropriate YOLO model in the models directory.")
         return None
-
+    log.info(f"Loading YOLO model: {yolo_model_path}")
     return YOLO(yolo_model_path, task="detect")
 
 

From f1f06b056ed8c954b837c108b641f6bc0dfd715f Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 23 Feb 2025 11:12:48 +0100
Subject: [PATCH 233/277] Correctly updated versions on model files

---
 script_generator/constants.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/script_generator/constants.py b/script_generator/constants.py
index c4d3e4ed..781216fd 100644
--- a/script_generator/constants.py
+++ b/script_generator/constants.py
@@ -128,14 +128,14 @@
 OUTPUT_PATH = os.path.join(PROJECT_PATH, "output")
 MODELS_PATH = os.path.join(PROJECT_PATH, "models")
 MODEL_FILENAMES = [
-    "FunGen-12s-pov-1.0.0.engine",
-    "FunGen-12s-pov-1.0.0.pt",
-    "FunGen-12s-pov-1.0.0.mlpackage",
-    "FunGen-12s-pov-1.0.0.onnx",
-    "FunGen-12s-mix-1.0.0.engine",
-    "FunGen-12s-mix-1.0.0.mlpackage",
-    "FunGen-12s-mix-1.0.0.pt",
-    "FunGen-12s-mix-1.0.0.onnx",
+    "FunGen-12s-pov-1.1 .0.engine",
+    "FunGen-12s-pov-1.1.0.pt",
+    "FunGen-12s-pov-1.1.0.mlpackage",
+    "FunGen-12s-pov-1.1.0.onnx",
+    "FunGen-12s-mix-1.1.0.engine",
+    "FunGen-12s-mix-1.1.0.mlpackage",
+    "FunGen-12s-mix-1.1.0.pt",
+    "FunGen-12s-mix-1.1.0.onnx",
     "FunGen-11n-mix-1.0.0.engine",
     "FunGen-11n-mix-1.0.0.mlpackage",
     "FunGen-11n-mix-1.0.0.onnx",

From 744ff97f7247ae8a5a7f0e6983b275fd74cc5198 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 23 Feb 2025 11:19:29 +0100
Subject: [PATCH 234/277] Readme updates

---
 README.md | 25 ++++++++++---------------
 1 file changed, 10 insertions(+), 15 deletions(-)

diff --git a/README.md b/README.md
index c334c217..c7f719f5 100644
--- a/README.md
+++ b/README.md
@@ -57,7 +57,6 @@ The pipeline for generating Funscript files is as follows:
 2. **Tracking Algorithm**: A custom tracking algorithm processes the YOLO detection results to track the positions of objects over time. The algorithm calculates distances and interactions between objects to determine the Funscript position.
 3. **Funscript Generation**: The tracked data is used to generate a raw Funscript file.
 4. **Simplifier**: The raw Funscript data is simplified to remove noise and smooth out the motion. The final `.funscript` file is saved.
-5. **Heatmap Generation**: A heatmap is generated to visualize the Funscript data.
 
 ---
 
@@ -122,10 +121,10 @@ We support multiple model formats across Windows, macOS, and Linux.
 In most cases, the app will automatically detect the best model from your models directory at launch, but if the right model wasn't present at this time or the right dependencies where not installed, you might need to override it under settings. The same applies when we release a new version of the model.
 
 
-### AMD GPU acceleration
+#### AMD GPU acceleration
 Coming soon
 
-**(Optional) Settings**
+### (Optional) Settings
 Find the settings menu in the app to configure optional option.
 
 ### Start script
@@ -207,11 +206,8 @@ a top of the line card like the 4090 is not required to get similar results. Hav
 
 - For VR only **Fisheye** and **Equirectangular** 180° videos are supported
 - 2D POV videos have limited support
-- 2D / VR is automatically detected for fisheye/equirectangular detection make sure you keep the file format information in the filename (\_FISHEYE190, \_MKX200, \_LR_180, etc.)
-
-### Configuration / User settings
-
-See config.py for customizations and user settings (will be replaced with a yaml soon).
+- 2D / VR is automatically detected for fisheye/equirectangular detection (make sure you keep the file format information in the filename _FISHEYE190, _MKX200, _LR_180, etc.)
+- 2D / VR settings can also be overwritten in the UI if the app doesn't detect it properly
 
 ---
 
@@ -220,13 +216,12 @@ See config.py for customizations and user settings (will be replaced with a yaml
 The script generates the following files in the output directory of you project folder:
 
 1. `_rawyolo.msgpack`: Raw YOLO detection data. Can be re-used when re-generating scripts
-2. `_cuts.msgpack`: Detected scene changes.
-3. `_rawfunscript.json`: Raw Funscript data. Can be re-used when re-generating script with different settings.
-4. `.funscript`: Final Funscript file.
-5. `_heatmap.png`: Heatmap visualization of the Funscript data.
-6. `_comparefunscripts.png`: Comparison visualization between the generated Funscript and the reference Funscript (if provided).
-7. `_adjusted.funscript`: Funscript file with adjusted amplitude.
-8. `_metrics.msgpack`: Contains all the raw metrics collected and can be used to debug your video when processing is completed.
+2. `_rawfunscript.json`: Raw Funscript data. Can be re-used when re-generating script with different settings.
+3. `.funscript`: Final Funscript file.
+4. `_heatmap.png`: Heatmap visualization of the Funscript data.
+5. `_comparefunscripts.png`: Comparison visualization between the generated Funscript and the reference Funscript (if provided).
+6. `_adjusted.funscript`: Funscript file with adjusted amplitude.
+7. `_metrics.msgpack`: Contains all the raw metrics collected and can be used to debug your video when processing is completed.
 
 ---
 

From 6bcb69bd08f6a342f4654cd269961aa4245d4688 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 23 Feb 2025 11:31:41 +0100
Subject: [PATCH 235/277] Readme updates

---
 README.md | 79 +++++++++++++++++++------------------------------------
 1 file changed, 27 insertions(+), 52 deletions(-)

diff --git a/README.md b/README.md
index c7f719f5..b1eadfed 100644
--- a/README.md
+++ b/README.md
@@ -1,62 +1,16 @@
-# VR-Funscript-AI-Generator
+# FunGen
 
-This project is a Python-based tool for generating Funscript files from VR videos using Computer Vision (CV) and AI techniques. It leverages YOLO (You Only Look Once) object detection and custom tracking algorithms to automate the process of creating Funscript files for interactive devices.
+FunGen is a Python-based tool that uses AI to generate Funscript files from VR and 2D POV videos. It enables fully automated funscript creation for individual scenes or entire folders of videos.
 
 Join the **Discord community** for discussions and support: [Discord Community](https://discord.gg/WYkjMbtCZA)
 
-The necessary YOLO models will also be available via the Discord.
+Note: The necessary YOLO models will also be available via the Discord.
 
 ---
 
-## DISCLAIMER
+### DISCLAIMER
 
-This project is at its very early stages of development, still faulty and broken, and is for research and educational purposes only. It is not intended for commercial use.
-Please, do not use this project for any commercial purposes without prior consent from the author. It is for individual use only.
-
----
-
-## Features
-
-- **YOLO Object Detection**: Uses a pre-trained YOLO model to detect and track objects in video frames.
-- **Funscript Generation**: Generates Funscript data based on the tracked objects' movements.
-- **Scene Change Detection**: Automatically detects scene changes in the video to improve tracking accuracy.
-- **Visualization**: Provides real-time visualization of object tracking and Funscript data (in test mode).
-- **VR Support**: Optimized for VR videos, with options to process specific regions of the frame.
-
----
-
-## Project Genesis and Evolution
-
-This project started as a dream to automate Funscript generation for VR videos. Here’s a brief history of its development:
-
-- **Initial Approach (OpenCV Trackers)**: The first version relied on OpenCV trackers to detect and track objects in the video. While functional, the approach was slow (8–20 FPS) and struggled with occlusions and complex scenes.
-
-- **Transition to YOLO**: To improve accuracy and speed, the project shifted to using YOLO object detection. A custom YOLO model was trained on a dataset of VR video frames, significantly improving detection quality. The new approach runs at 90 FPS on a Mac mini M4 pro, making it much more efficient.
-
-- **Original Post**: For more details and discussions, check out the original post on EroScripts:  
-  [VR Funscript Generation Helper (Python + CV/AI)](https://discuss.eroscripts.com/t/vr-funscript-generation-helper-python-now-cv-ai/202554)
-
----
-
-## YOLO Model
-
-The YOLO model used in this project is based on YOLOv11n, which was fine-tuned with 10 new classes and 4,500+ frames randomly extracted from a VR video library. Here’s how the model was developed:
-
-- **Initial Training**: A few hundred frames were manually tagged and boxed to create an initial dataset. The model was trained on this dataset to generate preliminary detection results.
-- **Iterative Improvement**: The trained model was used to suggest bounding boxes in additional frames. The suggested boxes were manually adjusted, and the dataset was expanded. This process was repeated iteratively to improve the model’s accuracy.
-- **Final Training**: After gathering 4,500+ images and 30,149 annotations, the model was trained for 200 epochs. YOLOv11s and YOLOv11m were also tested, but YOLOv11n was chosen for its balance of accuracy and inference speed.
-- **Hardware**: The model runs on a Mac using MPS (Metal Performance Shaders) for accelerated inference on ARM chips. Other versions of the model (ONNX and PT) are also available for use on other platforms.
-
----
-
-## Pipeline Overview
-
-The pipeline for generating Funscript files is as follows:
-
-1. **YOLO Object Detection**: A YOLO model detects relevant objects (e.g., penis, hands, mouth, etc.) in each frame of the video. The detection results are saved to a `.json` file.
-2. **Tracking Algorithm**: A custom tracking algorithm processes the YOLO detection results to track the positions of objects over time. The algorithm calculates distances and interactions between objects to determine the Funscript position.
-3. **Funscript Generation**: The tracked data is used to generate a raw Funscript file.
-4. **Simplifier**: The raw Funscript data is simplified to remove noise and smooth out the motion. The final `.funscript` file is saved.
+This project is still at the early stages of development. It is not intended for commercial use. Please, do not use this project for any commercial purposes without prior consent from the author. It is for individual use only.
 
 ---
 
@@ -65,7 +19,7 @@ The pipeline for generating Funscript files is as follows:
 Before using this project, ensure you have the following installed:
 
 - **Python 3.8 or higher (tested on 3.11 https://www.python.org/downloads/release/python-3118/)**
-- ## **FFmpeg** added to your PATH or specified under the settings menu (https://www.ffmpeg.org/download.html)
+- **FFmpeg** added to your PATH or specified under the settings menu (https://www.ffmpeg.org/download.html)
 
 ## Installation
 
@@ -225,6 +179,27 @@ The script generates the following files in the output directory of you project
 
 ---
 
+## About the project
+### Project Genesis and Evolution
+
+This project started as a dream to automate Funscript generation for VR videos. Here’s a brief history of its development:
+
+- **Initial Approach (OpenCV Trackers)**: The first version relied on OpenCV trackers to detect and track objects in the video. While functional, the approach was slow (8–20 FPS) and struggled with occlusions and complex scenes.
+- **Transition to YOLO**: To improve accuracy and speed, the project shifted to using YOLO object detection. A custom YOLO model was trained on a dataset of 1000nds annotated VR video frames, significantly improving detection quality.
+- **Original Post**: For more details and discussions, check out the original post on EroScripts:  
+  [VR Funscript Generation Helper (Python + CV/AI)](https://discuss.eroscripts.com/t/vr-funscript-generation-helper-python-now-cv-ai/202554)
+
+### Pipeline Overview
+
+The pipeline for generating Funscript files is as follows:
+
+1. **YOLO Object Detection**: A YOLO model detects relevant objects (e.g., penis, hands, mouth, etc.) in each frame of the video. 
+2. **Tracking Algorithm**: A custom tracking algorithm processes the YOLO detection results to track the positions of objects over time. The algorithm calculates distances and interactions between objects to determine the Funscript positions.
+3. **Funscript Generation**: The tracked data is used to generate a raw Funscript file.
+4. **Simplifier**: The raw Funscript data is simplified to remove noise and smooth out the motion resulting in a final `.funscript` file.
+
+----
+
 ## Contributing
 
 Contributions are welcome! If you'd like to contribute, please follow these steps:

From 34091f4b9653358d2769fa24facf5c3f23e7c96e Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 23 Feb 2025 11:37:53 +0100
Subject: [PATCH 236/277] Readme updates

---
 README.md | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index b1eadfed..579a69c5 100644
--- a/README.md
+++ b/README.md
@@ -20,6 +20,7 @@ Before using this project, ensure you have the following installed:
 
 - **Python 3.8 or higher (tested on 3.11 https://www.python.org/downloads/release/python-3118/)**
 - **FFmpeg** added to your PATH or specified under the settings menu (https://www.ffmpeg.org/download.html)
+- **Miniconda** (https://docs.anaconda.com/miniconda/install/)
 
 ## Installation
 
@@ -30,12 +31,9 @@ git clone https://github.com/ack00gar/VR-Funscript-AI-Generator.git
 cd VR-Funscript-AI-Generator
 ```
 
-### Install dependencies
+### Start a miniconda command prompt
 
-- Install miniconda (https://docs.anaconda.com/miniconda/install/) (We removed venv as all the bat files use conda)
-- Start a miniconda command prompt
-
-#### If your GPU supports CUDA (NVIDIA)
+### If your GPU supports CUDA (NVIDIA)
 
 ```bash
 conda create -n VRFunAIGen python=3.11
@@ -47,7 +45,7 @@ python FSGenerator.py
 
 While executing, you’ll need to say “yes” a few times. The lines “pip uninstall / pip3 install” is to replace the “CPU” version of torch with a “cuda enabled / GPU” version (you might need to install nvidia CUDA stuff for it to works, I’m not sure).
 
-#### If your GPU doesn't support cuda
+### If your GPU doesn't support cuda
 
 ```bash
 conda create -n VRFunAIGen python=3.11

From aedbb934780825c342b9b50178b5a33a4caaf9a0 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 23 Feb 2025 11:40:30 +0100
Subject: [PATCH 237/277] Fixed requirements txt's

---
 core.requirements.txt | 2 +-
 cuda.requirements.txt | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/core.requirements.txt b/core.requirements.txt
index edc9e9a9..294ab807 100644
--- a/core.requirements.txt
+++ b/core.requirements.txt
@@ -11,4 +11,4 @@ matplotlib~=3.10.0
 simplification~=0.7.13
 msgpack~=1.1.0
 pillow~=11.1.0
-orjson=3.10.15
\ No newline at end of file
+orjson~=3.10.15
\ No newline at end of file
diff --git a/cuda.requirements.txt b/cuda.requirements.txt
index 6c8016d1..b0567383 100644
--- a/cuda.requirements.txt
+++ b/cuda.requirements.txt
@@ -1,4 +1,4 @@
 -i https://download.pytorch.org/whl/cu121
 torch~=2.5.1
 torchvision~=0.20.1
-tensorrt=10.8.0.43
\ No newline at end of file
+tensorrt~=10.8.0.43
\ No newline at end of file

From bd74455bc98bd1d61affc2e3388e24305cf01634 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 23 Feb 2025 11:59:41 +0100
Subject: [PATCH 238/277] Moved tensorrt to a separate install command for now

---
 README.md             | 1 +
 cuda.requirements.txt | 3 +--
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 579a69c5..be21013c 100644
--- a/README.md
+++ b/README.md
@@ -40,6 +40,7 @@ conda create -n VRFunAIGen python=3.11
 conda activate VRFunAIGen
 pip install -r core.requirements.txt
 pip install -r cuda.requirements.txt
+pip install tensorrt
 python FSGenerator.py
 ```
 
diff --git a/cuda.requirements.txt b/cuda.requirements.txt
index b0567383..cdef5236 100644
--- a/cuda.requirements.txt
+++ b/cuda.requirements.txt
@@ -1,4 +1,3 @@
 -i https://download.pytorch.org/whl/cu121
 torch~=2.5.1
-torchvision~=0.20.1
-tensorrt~=10.8.0.43
\ No newline at end of file
+torchvision~=0.20.1
\ No newline at end of file

From fd83f0cdc64825a40061e8c9aa986349ee8ca8a0 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 23 Feb 2025 13:47:09 +0100
Subject: [PATCH 239/277] Auto loading of YOLO model and better detection

---
 .../cli/shared/generate_funscript.py          |  1 +
 script_generator/config/config_manager.py     |  2 +-
 script_generator/constants.py                 |  2 +-
 .../gui/controller/generate_funscript.py      |  1 +
 .../object_detection/util/data.py             | 54 +++++++++----------
 script_generator/state/app_state.py           |  7 ++-
 6 files changed, 35 insertions(+), 32 deletions(-)

diff --git a/script_generator/cli/shared/generate_funscript.py b/script_generator/cli/shared/generate_funscript.py
index 5ddd266b..bb8a0e72 100644
--- a/script_generator/cli/shared/generate_funscript.py
+++ b/script_generator/cli/shared/generate_funscript.py
@@ -7,6 +7,7 @@
 
 def generate_funscript_cli(state: AppState):
     try:
+        state.load_yolo()
         configured, msg = state.is_configured()
         if not configured:
             log.warn(msg)
diff --git a/script_generator/config/config_manager.py b/script_generator/config/config_manager.py
index 4d240ca2..3907795a 100644
--- a/script_generator/config/config_manager.py
+++ b/script_generator/config/config_manager.py
@@ -58,7 +58,7 @@ def _ensure_defaults(self, config):
         checks = [
             ("ffmpeg_path", lambda: get_ffmpeg_paths()[0], self._is_valid_path),
             ("ffprobe_path", lambda: get_ffmpeg_paths()[1], self._is_valid_path),
-            ("yolo_model_path", get_yolo_model_path if not config["yolo_model_path"] else config["yolo_model_path"], self._is_valid_path),
+            ("yolo_model_path", lambda: get_yolo_model_path() if not config["yolo_model_path"] else config["yolo_model_path"], self._is_valid_path),
             ("ffmpeg_hwaccel", lambda: get_preferred_hwaccel(config["ffmpeg_path"]) if config["ffmpeg_path"] else None, lambda val: val is not None)
         ]
 
diff --git a/script_generator/constants.py b/script_generator/constants.py
index 781216fd..4a4ad685 100644
--- a/script_generator/constants.py
+++ b/script_generator/constants.py
@@ -128,7 +128,7 @@
 OUTPUT_PATH = os.path.join(PROJECT_PATH, "output")
 MODELS_PATH = os.path.join(PROJECT_PATH, "models")
 MODEL_FILENAMES = [
-    "FunGen-12s-pov-1.1 .0.engine",
+    "FunGen-12s-pov-1.1.0.engine",
     "FunGen-12s-pov-1.1.0.pt",
     "FunGen-12s-pov-1.1.0.mlpackage",
     "FunGen-12s-pov-1.1.0.onnx",
diff --git a/script_generator/gui/controller/generate_funscript.py b/script_generator/gui/controller/generate_funscript.py
index dcfc68cf..472a86cc 100644
--- a/script_generator/gui/controller/generate_funscript.py
+++ b/script_generator/gui/controller/generate_funscript.py
@@ -11,6 +11,7 @@
 
 
 def generate_funscript(state: AppState, root):
+    state.load_yolo()
     configured, msg = state.is_configured()
     if not configured:
         log.warn(msg)
diff --git a/script_generator/object_detection/util/data.py b/script_generator/object_detection/util/data.py
index 5642fd49..bcdf31d9 100644
--- a/script_generator/object_detection/util/data.py
+++ b/script_generator/object_detection/util/data.py
@@ -4,55 +4,51 @@
 from ultralytics import YOLO
 
 from script_generator.constants import MODELS_PATH, MODEL_FILENAMES, OBJECT_DETECTION_VERSION
-from script_generator.debug.logger import log, log_od
+from script_generator.debug.logger import log
 from script_generator.utils.file import get_output_file_path
 from script_generator.utils.helpers import is_mac
 from script_generator.utils.json_utils import get_data_file_info
 from script_generator.utils.msgpack_utils import save_msgpack_json, load_msgpack_json
-from script_generator.utils.version import version_is_less_than
 
 
-def find_model_by_extension(extension):
-    """Finds the first model that matches the given file extension."""
+def find_model(extension):
+    """Finds and returns the first model that matches the given file extension."""
     for filename in MODEL_FILENAMES:
         if filename.endswith(extension):
-            return os.path.join(MODELS_PATH, filename)
+            model_path = os.path.join(MODELS_PATH, filename)
+            if os.path.exists(model_path):
+                return model_path
     return None
 
+
 def get_yolo_model_path():
     """Selects the appropriate YOLO model based on platform and hardware capabilities."""
-    if is_mac():
-        model_path = find_model_by_extension(".mlpackage")
-        if model_path:
-            log.info(f"Apple device detected, loading {model_path} for MPS inference.")
-            return model_path
-
-    if torch.cuda.is_available():
-        model_path = find_model_by_extension(".engine")
-        if model_path:
-            log.info(f"CUDA is available, loading {model_path} for GPU inference.")
-            return model_path
 
-        model_path = find_model_by_extension(".pt")
-        if model_path:
-            log.info(f"CUDA is available, loading {model_path} for GPU inference.")
+    model_checks = [
+        (".mlpackage", is_mac(), "Apple device detected, using MPS inference."),
+        (".engine", torch.cuda.is_available(), "CUDA available, using GPU inference using TensorRT."),
+        (".pt", torch.cuda.is_available(), "CUDA available, using GPU inference (better TensorRT model not found but recommended)."),
+        (".onnx", True, "CUDA not available, using ONNX model for CPU inference.")
+    ]
+
+    for ext, condition, message in model_checks:
+        if condition and (model_path := find_model(ext)):
+            log.info(f"{message} Loading {model_path}.")
+            if ext == ".onnx":
+                log.info("WARNING: CPU inference may be slow on some devices.")
             return model_path
 
-    # Default to ONNX for CPU-based inference
-    model_path = find_model_by_extension(".onnx")
-    if model_path:
-        log.info("CUDA not available, falling back to ONNX model for CPU inference.")
-        log.info("WARNING: CPU inference may be slow on some devices.")
-        return model_path
-
-    # Fallback in case no model is found
     log.error("No suitable model found. Check your MODEL_FILENAMES list.")
     return None
 
 def load_yolo_model(yolo_model_path):
-    if not yolo_model_path or not os.path.exists(str(yolo_model_path)):
-        log.warn("The YOLO model is missing. Please download and place the appropriate YOLO model in the models directory.")
+    if not yolo_model_path or not os.path.exists(yolo_model_path):
+        if yolo_model_path:
+            log.warn(f"No file found at specified YOLO model path: {yolo_model_path}")
+        else:
+            log.warn("The YOLO model is missing. Please download and place the appropriate YOLO model in the models directory.")
         return None
+
     log.info(f"Loading YOLO model: {yolo_model_path}")
     return YOLO(yolo_model_path, task="detect")
 
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index 6a5d6dff..02ee6200 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -102,13 +102,18 @@ def set_is_cli(self, cli):
     def set_root(self, root):
         self.root = root
 
+    def load_yolo(self):
+        if self.yolo_model_path and not self.yolo_model:
+            self.yolo_model = load_yolo_model(self.yolo_model_path)
+
     def is_configured(self):
         message_prefix = "Cannot process the video."
         checks = [
             (self.video_path, f"{message_prefix} Please select a valid video file."),
             (self.ffprobe_path, f"{message_prefix} FFprobe is missing. Please provide the correct path."),
             (self.ffmpeg_path, f"{message_prefix} FFMPEG is missing. Please provide the correct path."),
-            (self.yolo_model_path, f"{message_prefix} YOLO model is not loaded. Please make sure to download the YOLO model to the models directory."),
+            (self.yolo_model_path, f"{message_prefix} YOLO model path not set. Please make sure to download the YOLO model to the models directory and that the path under settings is correct."),
+            (self.yolo_model, f"{message_prefix} YOLO model is not loaded. Please make sure to download the YOLO model to the models directory."),
         ]
 
         for path, error_message in checks:

From 53faacee202467c1b0a4f4ace8695a92ee476f90 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 23 Feb 2025 14:40:01 +0100
Subject: [PATCH 240/277] Added updated ultralytics to requirements so .pt
 works properly Added a check to see if the same Cuda Compute is installed

---
 core.requirements.txt                          |  2 +-
 script_generator/object_detection/util/data.py | 18 +++++++++++++++---
 2 files changed, 16 insertions(+), 4 deletions(-)

diff --git a/core.requirements.txt b/core.requirements.txt
index 294ab807..93a40e4a 100644
--- a/core.requirements.txt
+++ b/core.requirements.txt
@@ -1,5 +1,5 @@
 numpy
-ultralytics~=8.3.59
+ultralytics~=8.3.78
 glfw~=2.8.0
 pyopengl~=3.1.7
 imageio~=2.36.1
diff --git a/script_generator/object_detection/util/data.py b/script_generator/object_detection/util/data.py
index bcdf31d9..d73ec4e1 100644
--- a/script_generator/object_detection/util/data.py
+++ b/script_generator/object_detection/util/data.py
@@ -21,13 +21,25 @@ def find_model(extension):
     return None
 
 
+def is_compute_compatible(required_major=8, required_minor=9):
+    """Checks if the current GPU has the required compute capability."""
+    if torch.cuda.is_available():
+        gpu_major, gpu_minor = torch.cuda.get_device_capability()
+        if gpu_major > required_major or (gpu_major == required_major and gpu_minor >= required_minor):
+            return True
+        else:
+            log.info(f"❌ Incompatible NVIDIA Cuda Compute Capability: Expected {required_major}.{required_minor}, but found {gpu_major}.{gpu_minor}. Falling back to .pt model for compatibility.")
+        return False
+    return False
+
+
 def get_yolo_model_path():
     """Selects the appropriate YOLO model based on platform and hardware capabilities."""
 
     model_checks = [
         (".mlpackage", is_mac(), "Apple device detected, using MPS inference."),
-        (".engine", torch.cuda.is_available(), "CUDA available, using GPU inference using TensorRT."),
-        (".pt", torch.cuda.is_available(), "CUDA available, using GPU inference (better TensorRT model not found but recommended)."),
+        (".engine", torch.cuda.is_available() and is_compute_compatible(), "CUDA available and compatible, using GPU inference with TensorRT."),
+        (".pt", torch.cuda.is_available(), "CUDA available, using GPU inference (TensorRT model not found or not compatible with Compute capability)."),
         (".onnx", True, "CUDA not available, using ONNX model for CPU inference.")
     ]
 
@@ -38,7 +50,7 @@ def get_yolo_model_path():
                 log.info("WARNING: CPU inference may be slow on some devices.")
             return model_path
 
-    log.error("No suitable model found. Check your MODEL_FILENAMES list.")
+    log.error("No suitable model found. Please make sure to download one of our models and place it in the models directory.")
     return None
 
 def load_yolo_model(yolo_model_path):

From a47fb1cfdd498d399f28160cad215d9adb883c91 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 23 Feb 2025 15:43:25 +0100
Subject: [PATCH 241/277] Added generate .engine TensorRT bat file

---
 Generate TensorRT.bat                         | 19 +++++++++++++++++++
 README.md                                     |  2 +-
 core.requirements.txt                         |  2 +-
 generate_tensorrt.py                          | 16 ++++++++++++++++
 .../object_detection/util/data.py             | 14 +-------------
 5 files changed, 38 insertions(+), 15 deletions(-)
 create mode 100644 Generate TensorRT.bat
 create mode 100644 generate_tensorrt.py

diff --git a/Generate TensorRT.bat b/Generate TensorRT.bat
new file mode 100644
index 00000000..6d9e0d17
--- /dev/null
+++ b/Generate TensorRT.bat	
@@ -0,0 +1,19 @@
+@echo off
+cd /d "%~dp0"
+set "CONDA_PATH=C:\Users\%USERNAME%\miniconda3\Scripts\activate.bat"
+
+if not exist "%CONDA_PATH%" (
+    echo Conda not found at %CONDA_PATH%. Please check your installation.
+    pause
+    exit /b 1
+)
+call "%CONDA_PATH%" VRFunAIGen
+
+:: Debug: Print Python path and package versions
+python -c "import sys; print('Python Path:', sys.executable)"
+python -c "import torch; print('Torch Version:', torch.__version__)"
+python -c "import ultralytics; print('Ultralytics Version:', ultralytics.__version__)"
+python -c "import sys; print('Active Conda Env:', sys.prefix)"
+
+python generate_tensorrt.py
+pause
\ No newline at end of file
diff --git a/README.md b/README.md
index be21013c..b3e0bc82 100644
--- a/README.md
+++ b/README.md
@@ -68,7 +68,7 @@ We support multiple model formats across Windows, macOS, and Linux.
 #### Models
 - **.pt (PyTorch)**: Requires CUDA (for NVIDIA GPUs) or ROCm (for AMD GPUs) for acceleration.
 - **.onnx (ONNX Runtime)**: Best for CPU users as it offers broad compatibility and efficiency.
-- **.engine (TensorRT)**: For NVIDIA GPUs: Provides very significant efficiency improvements.
+- **.engine (TensorRT)**: For NVIDIA GPUs: Provides very significant efficiency improvements (this file needs to be build by running "Generate TensorRT.bat" after adding the base ".pt" model to the models directory)
 - **.mlmodel (Core ML)**: Optimized for macOS users. Runs efficiently on Apple devices with Core ML.
 
 In most cases, the app will automatically detect the best model from your models directory at launch, but if the right model wasn't present at this time or the right dependencies where not installed, you might need to override it under settings. The same applies when we release a new version of the model.
diff --git a/core.requirements.txt b/core.requirements.txt
index 93a40e4a..4f8324b1 100644
--- a/core.requirements.txt
+++ b/core.requirements.txt
@@ -1,5 +1,5 @@
 numpy
-ultralytics~=8.3.78
+ultralytics==8.3.78
 glfw~=2.8.0
 pyopengl~=3.1.7
 imageio~=2.36.1
diff --git a/generate_tensorrt.py b/generate_tensorrt.py
new file mode 100644
index 00000000..0737ef8a
--- /dev/null
+++ b/generate_tensorrt.py
@@ -0,0 +1,16 @@
+import os
+
+from ultralytics import YOLO
+
+# SETTINGS (CHANGE THIS IF NEEDED):
+model_to_convert = "FunGen-12s-pov-1.1.0.pt"
+
+# Conversion Code
+model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "models", model_to_convert)
+if not os.path.exists(model_path):
+    print(f"Model not found in path: {model_path}")
+    print(f"Did you download the latest model and put it in the models directory?")
+
+model = YOLO(model_path)
+model.export(format="engine", half=True, batch=30, simplify=True)
+print("Successfully generated .engine TensorRT model in models directory")
\ No newline at end of file
diff --git a/script_generator/object_detection/util/data.py b/script_generator/object_detection/util/data.py
index d73ec4e1..1fbcf257 100644
--- a/script_generator/object_detection/util/data.py
+++ b/script_generator/object_detection/util/data.py
@@ -21,24 +21,12 @@ def find_model(extension):
     return None
 
 
-def is_compute_compatible(required_major=8, required_minor=9):
-    """Checks if the current GPU has the required compute capability."""
-    if torch.cuda.is_available():
-        gpu_major, gpu_minor = torch.cuda.get_device_capability()
-        if gpu_major > required_major or (gpu_major == required_major and gpu_minor >= required_minor):
-            return True
-        else:
-            log.info(f"❌ Incompatible NVIDIA Cuda Compute Capability: Expected {required_major}.{required_minor}, but found {gpu_major}.{gpu_minor}. Falling back to .pt model for compatibility.")
-        return False
-    return False
-
-
 def get_yolo_model_path():
     """Selects the appropriate YOLO model based on platform and hardware capabilities."""
 
     model_checks = [
         (".mlpackage", is_mac(), "Apple device detected, using MPS inference."),
-        (".engine", torch.cuda.is_available() and is_compute_compatible(), "CUDA available and compatible, using GPU inference with TensorRT."),
+        (".engine", torch.cuda.is_available(), "CUDA available and compatible, using GPU inference with TensorRT."),
         (".pt", torch.cuda.is_available(), "CUDA available, using GPU inference (TensorRT model not found or not compatible with Compute capability)."),
         (".onnx", True, "CUDA not available, using ONNX model for CPU inference.")
     ]

From 33caf332f9cd8edc3a1d4a6ef1246a78cdd45c43 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaas@gmail.com>
Date: Mon, 24 Feb 2025 08:35:37 +0100
Subject: [PATCH 242/277] Update README.md

Yeah removed outdated cuda install comment
---
 README.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/README.md b/README.md
index b3e0bc82..62c0282c 100644
--- a/README.md
+++ b/README.md
@@ -44,8 +44,6 @@ pip install tensorrt
 python FSGenerator.py
 ```
 
-While executing, you’ll need to say “yes” a few times. The lines “pip uninstall / pip3 install” is to replace the “CPU” version of torch with a “cuda enabled / GPU” version (you might need to install nvidia CUDA stuff for it to works, I’m not sure).
-
 ### If your GPU doesn't support cuda
 
 ```bash

From 72ea539a9d13b8d9d64e14dfb6b07c01016a8a16 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaas@gmail.com>
Date: Mon, 24 Feb 2025 08:37:25 +0100
Subject: [PATCH 243/277] Update README.md

Clearer Yolo model download instructions
---
 README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 62c0282c..902337f2 100644
--- a/README.md
+++ b/README.md
@@ -55,9 +55,10 @@ python FSGenerator.py
 ```
 
 ### Download the YOLO model
-- Place the YOLO model file(s) in the `models/` sub-directory. If you aren't sure you can add all the models and let the app decide the best option for you.
+Go to our discord to download the latest YOLO model for free. When downloaded place the YOLO model file(s) in the `models/` sub-directory. If you aren't sure you can add all the models and let the app decide the best option for you.
 
 We support multiple model formats across Windows, macOS, and Linux.
+
 #### Recommendations
 - NVIDIA Cards: we recommend the .engine model
 - AMD Cards: we recommend .pt (requires ROCm see below)

From a9cb64e6dfd52a5e3dcf45ae519dc1d1d5b059e9 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 24 Feb 2025 08:52:39 +0100
Subject: [PATCH 244/277] Readme improvements

---
 README.md | 95 ++++++++++++++++++++++++++++++-------------------------
 1 file changed, 52 insertions(+), 43 deletions(-)

diff --git a/README.md b/README.md
index 902337f2..1fd06b99 100644
--- a/README.md
+++ b/README.md
@@ -22,7 +22,7 @@ Before using this project, ensure you have the following installed:
 - **FFmpeg** added to your PATH or specified under the settings menu (https://www.ffmpeg.org/download.html)
 - **Miniconda** (https://docs.anaconda.com/miniconda/install/)
 
-## Installation
+# Installation
 
 ### Clone the repository
 
@@ -54,17 +54,17 @@ pip install -r cpu.requirements.txt
 python FSGenerator.py
 ```
 
-### Download the YOLO model
+## Download the YOLO model
 Go to our discord to download the latest YOLO model for free. When downloaded place the YOLO model file(s) in the `models/` sub-directory. If you aren't sure you can add all the models and let the app decide the best option for you.
 
 We support multiple model formats across Windows, macOS, and Linux.
 
-#### Recommendations
+### Recommendations
 - NVIDIA Cards: we recommend the .engine model
 - AMD Cards: we recommend .pt (requires ROCm see below)
 - Mac: we recommend .mlmodel
 
-#### Models
+### Models
 - **.pt (PyTorch)**: Requires CUDA (for NVIDIA GPUs) or ROCm (for AMD GPUs) for acceleration.
 - **.onnx (ONNX Runtime)**: Best for CPU users as it offers broad compatibility and efficiency.
 - **.engine (TensorRT)**: For NVIDIA GPUs: Provides very significant efficiency improvements (this file needs to be build by running "Generate TensorRT.bat" after adding the base ".pt" model to the models directory)
@@ -73,19 +73,19 @@ We support multiple model formats across Windows, macOS, and Linux.
 In most cases, the app will automatically detect the best model from your models directory at launch, but if the right model wasn't present at this time or the right dependencies where not installed, you might need to override it under settings. The same applies when we release a new version of the model.
 
 
-#### AMD GPU acceleration
+### AMD GPU acceleration
 Coming soon
 
-### (Optional) Settings
+## GUI Settings
 Find the settings menu in the app to configure optional option.
 
-### Start script
+## Start script
 
 You can use Start windows.bat to launch the gui on windows if you installed with conda
 
 ---
 
-## Command Line Usage
+# Command Line Usage
 
 To generate a single script with cmd or terminal, run the following command
 
@@ -103,20 +103,29 @@ python -m script_generator.cli.generate_funscript_folder /path/to/folder
 
 See examples/windows/Process folder.bat for an example
 
-### Command-Line Arguments (Shared)
+## Command-Line Arguments
+
+### Command-Line Arguments (Single file)
+**Required**
+- **`video_path`** Path to the input video file
 
-#### Required Arguments
+### Command-Line Arguments (Folder mode)
+**Required**
+- **`folder_path`** Path to the video folder (required).
 
-- **`video_path`** Path to the input video file.
+**Optional**
+- **`--replace-outdated`** Will regenerate outdated funscripts.
+- **`--replace-up-to-date`** Will regenerate funscripts that are up to date and made by this app too.
+- **`--num-workers`** Number of subprocesses to run in parallel. If you have beefy hardware 4 seems to be the sweet spot but technically your VRAM is the limit.
 
-#### Optional Arguments
 
+### Command-Line Arguments (Shared)
+These arguments are used by both single file and folder mode
 - **`--reuse-yolo`** Re-use an existing raw YOLO output file instead of generating a new one when available.
 - **`--copy-funscript`** Copies the final funscript to the movie directory.
 - **`--save-debug-file`** Saves a debug file to disk with all collected metrics. Also allows you to re-use tracking data.
 
-#### Optional Funscript Tweaking Settings
-
+**Funscript Tweaking Settings**
 - **`--boost-enabled`** Enable boosting to adjust the motion range dynamically.
 - **`--boost-up-percent`** Increase the peaks by a specified percentage to enhance upper motion limits.
 - **`--boost-down-percent`** Reduce the lower peaks by a specified percentage to limit downward motion.
@@ -127,15 +136,10 @@ See examples/windows/Process folder.bat for an example
 - **`--vw-factor`** Determines the degree of simplification. Higher values lead to fewer points.
 - **`--rounding`** Set the rounding factor for script values to adjust precision.
 
-### Command-Line Arguments (Folder mode)
-
-- **`--replace-outdated`** Will regenerate outdated funscripts.
-- **`--replace-up-to-date`** Will regenerate funscripts that are up to date and made by this app too.
-- **`--num-workers`** Number of subprocesses to run in parallel. If you have beefy hardware 4 seems to be the sweet spot but technically your VRAM is the limit.
 
 ---
 
-## Performance & Parallel Processing
+# Performance & Parallel Processing
 
 Our pipeline's current bottleneck lies in the Python code within YOLO.track (the object detection library we use), which is challenging to parallelize effectively in a single process.
 
@@ -154,40 +158,35 @@ a top of the line card like the 4090 is not required to get similar results. Hav
 
 ---
 
-## Miscellaneous
+# Miscellaneous
 
-- For VR only **Fisheye** and **Equirectangular** 180° videos are supported
-- 2D POV videos have limited support
-- 2D / VR is automatically detected for fisheye/equirectangular detection (make sure you keep the file format information in the filename _FISHEYE190, _MKX200, _LR_180, etc.)
-- 2D / VR settings can also be overwritten in the UI if the app doesn't detect it properly
+- For VR only sbs (side by side) **Fisheye** and **Equirectangular** 180° videos are supported at the moment
+- 2D POV videos are supported but work best when they are centered properly
+- 2D / VR is automatically detected as is fisheye / equirectangular and FOV (make sure you keep the file format information in the filename _FISHEYE190, _MKX200, _LR_180, etc.)
+- Detection settings can also be overwritten in the UI if the app doesn't detect it properly
 
 ---
 
-## Output Files
+# Output Files
 
 The script generates the following files in the output directory of you project folder:
 
 1. `_rawyolo.msgpack`: Raw YOLO detection data. Can be re-used when re-generating scripts
 2. `_rawfunscript.json`: Raw Funscript data. Can be re-used when re-generating script with different settings.
 3. `.funscript`: Final Funscript file.
-4. `_heatmap.png`: Heatmap visualization of the Funscript data.
-5. `_comparefunscripts.png`: Comparison visualization between the generated Funscript and the reference Funscript (if provided).
-6. `_adjusted.funscript`: Funscript file with adjusted amplitude.
-7. `_metrics.msgpack`: Contains all the raw metrics collected and can be used to debug your video when processing is completed.
+4. `_metrics.msgpack`: Contains all the raw metrics collected and can be used to debug your video when processing is completed.
 
----
+Optional files
 
-## About the project
-### Project Genesis and Evolution
+1. `_heatmap.png`: Heatmap visualization of the Funscript data.
+2. `_comparefunscripts.png`: Comparison visualization between the generated Funscript and the reference Funscript (if provided).
+3. `_adjusted.funscript`: Funscript file with adjusted amplitude.
 
-This project started as a dream to automate Funscript generation for VR videos. Here’s a brief history of its development:
+---
 
-- **Initial Approach (OpenCV Trackers)**: The first version relied on OpenCV trackers to detect and track objects in the video. While functional, the approach was slow (8–20 FPS) and struggled with occlusions and complex scenes.
-- **Transition to YOLO**: To improve accuracy and speed, the project shifted to using YOLO object detection. A custom YOLO model was trained on a dataset of 1000nds annotated VR video frames, significantly improving detection quality.
-- **Original Post**: For more details and discussions, check out the original post on EroScripts:  
-  [VR Funscript Generation Helper (Python + CV/AI)](https://discuss.eroscripts.com/t/vr-funscript-generation-helper-python-now-cv-ai/202554)
+# About the project
 
-### Pipeline Overview
+## Pipeline Overview
 
 The pipeline for generating Funscript files is as follows:
 
@@ -196,9 +195,19 @@ The pipeline for generating Funscript files is as follows:
 3. **Funscript Generation**: The tracked data is used to generate a raw Funscript file.
 4. **Simplifier**: The raw Funscript data is simplified to remove noise and smooth out the motion resulting in a final `.funscript` file.
 
+
+## Project Genesis and Evolution
+
+This project started as a dream to automate Funscript generation for VR videos. Here’s a brief history of its development:
+
+- **Initial Approach (OpenCV Trackers)**: The first version relied on OpenCV trackers to detect and track objects in the video. While functional, the approach was slow (8–20 FPS) and struggled with occlusions and complex scenes.
+- **Transition to YOLO**: To improve accuracy and speed, the project shifted to using YOLO object detection. A custom YOLO model was trained on a dataset of 1000nds annotated VR video frames, significantly improving detection quality.
+- **Original Post**: For more details and discussions, check out the original post on EroScripts:  
+  [VR Funscript Generation Helper (Python + CV/AI)](https://discuss.eroscripts.com/t/vr-funscript-generation-helper-python-now-cv-ai/202554)
+
 ----
 
-## Contributing
+# Contributing
 
 Contributions are welcome! If you'd like to contribute, please follow these steps:
 
@@ -209,7 +218,7 @@ Contributions are welcome! If you'd like to contribute, please follow these step
 
 ---
 
-## License
+# License
 
 This project is licensed under the **Non-Commercial License**. You are free to use the software for personal, non-commercial purposes only. Commercial use, redistribution, or modification for commercial purposes is strictly prohibited without explicit permission from the copyright holder.
 
@@ -221,7 +230,7 @@ See the [LICENSE](LICENSE) file for full details.
 
 ---
 
-## Acknowledgments
+# Acknowledgments
 
 - **YOLO**: Thanks to the Ultralytics team for the YOLO implementation.
 - **FFmpeg**: For video processing capabilities.
@@ -229,7 +238,7 @@ See the [LICENSE](LICENSE) file for full details.
 
 ---
 
-## Support
+# Support
 
 If you encounter any issues or have questions, please open an issue on GitHub.
 

From b6ee6ba93f7ea000919597e31650ac87b1014b8a Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 24 Feb 2025 09:01:04 +0100
Subject: [PATCH 245/277] Updated git clone command and renamed the folder to
 prevent path length issues

---
 README.md | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 1fd06b99..e857d988 100644
--- a/README.md
+++ b/README.md
@@ -27,11 +27,16 @@ Before using this project, ensure you have the following installed:
 ### Clone the repository
 
 ```bash
-git clone https://github.com/ack00gar/VR-Funscript-AI-Generator.git
-cd VR-Funscript-AI-Generator
+git clone https://github.com/ack00gar/FunGen-AI-Powered-Funscript-Generator.git FunGen
+cd FunGen
 ```
 
 ### Start a miniconda command prompt
+After installing Miniconda look for a program called "Anaconda prompt (miniconda3)"
+Then navigate to the root folder of the project for example on windows:
+```
+cd /d D:\path\to\fungenfolder
+```
 
 ### If your GPU supports CUDA (NVIDIA)
 

From 4b9beadb2d9bf05bd088500558d88d377040e2f6 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 24 Feb 2025 09:02:33 +0100
Subject: [PATCH 246/277] Updated git clone command and renamed the folder to
 prevent path length issues

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index e857d988..5705666e 100644
--- a/README.md
+++ b/README.md
@@ -33,9 +33,9 @@ cd FunGen
 
 ### Start a miniconda command prompt
 After installing Miniconda look for a program called "Anaconda prompt (miniconda3)"
-Then navigate to the root folder of the project for example on windows:
+Then navigate to the root folder of the project for example on windows (correct the path):
 ```
-cd /d D:\path\to\fungenfolder
+cd /d C:\path\to\fungenfolder
 ```
 
 ### If your GPU supports CUDA (NVIDIA)

From 548d21b2e33047bd5f31db1fe50a9995618f38be Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 24 Feb 2025 09:05:38 +0100
Subject: [PATCH 247/277] Small readme tweak

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 5705666e..8b0a5335 100644
--- a/README.md
+++ b/README.md
@@ -86,7 +86,7 @@ Find the settings menu in the app to configure optional option.
 
 ## Start script
 
-You can use Start windows.bat to launch the gui on windows if you installed with conda
+You can use Start windows.bat to launch the gui on windows.
 
 ---
 

From 55e77499bc525315a9c2a020ea993226f163a050 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 24 Feb 2025 09:07:34 +0100
Subject: [PATCH 248/277] Small readme tweak

---
 README.md | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 8b0a5335..993e46e4 100644
--- a/README.md
+++ b/README.md
@@ -92,7 +92,10 @@ You can use Start windows.bat to launch the gui on windows.
 
 # Command Line Usage
 
-To generate a single script with cmd or terminal, run the following command
+FunGen supports a GUI for all it's features but we also provide command line usage for those who prefer this. 
+Below are some examples on how to generate script with the command line:
+
+**To generate a single script with cmd or terminal, run the following command**
 
 ```bash
 python -m script_generator.cli.generate_funscript_single /path/to/video.mp4
@@ -100,7 +103,7 @@ python -m script_generator.cli.generate_funscript_single /path/to/video.mp4
 
 See examples/windows/Process single video.bat for an example
 
-To generate scripts for all files in a folder use
+**To generate scripts for all files in a folder use**
 
 ```bash
 python -m script_generator.cli.generate_funscript_folder /path/to/folder

From a1583010ee6c0ce466857f9bffd61fab259ee034 Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 24 Feb 2025 11:46:17 +0100
Subject: [PATCH 249/277] Added batch size to logging

---
 script_generator/object_detection/util/data.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/script_generator/object_detection/util/data.py b/script_generator/object_detection/util/data.py
index 1fbcf257..97217a38 100644
--- a/script_generator/object_detection/util/data.py
+++ b/script_generator/object_detection/util/data.py
@@ -3,7 +3,7 @@
 import torch
 from ultralytics import YOLO
 
-from script_generator.constants import MODELS_PATH, MODEL_FILENAMES, OBJECT_DETECTION_VERSION
+from script_generator.constants import MODELS_PATH, MODEL_FILENAMES, OBJECT_DETECTION_VERSION, YOLO_BATCH_SIZE
 from script_generator.debug.logger import log
 from script_generator.utils.file import get_output_file_path
 from script_generator.utils.helpers import is_mac
@@ -49,7 +49,7 @@ def load_yolo_model(yolo_model_path):
             log.warn("The YOLO model is missing. Please download and place the appropriate YOLO model in the models directory.")
         return None
 
-    log.info(f"Loading YOLO model: {yolo_model_path}")
+    log.info(f"Loading YOLO model, batch size: {YOLO_BATCH_SIZE}, model path {yolo_model_path}")
     return YOLO(yolo_model_path, task="detect")
 
 

From 1b3803be60163659e112bb87878fda4b13f772ee Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Mon, 24 Feb 2025 12:09:45 +0100
Subject: [PATCH 250/277] ONNX will use a batch size of 30 now too

---
 script_generator/constants.py                  | 2 +-
 script_generator/object_detection/util/data.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/script_generator/constants.py b/script_generator/constants.py
index 4a4ad685..1a077ccc 100644
--- a/script_generator/constants.py
+++ b/script_generator/constants.py
@@ -15,7 +15,7 @@
 
 RENDER_RESOLUTION = 640
 TEXTURE_RESOLUTION = RENDER_RESOLUTION * 1.3  # Texture size that is used to texture the opengl sphere
-YOLO_BATCH_SIZE = 1 if platform.system() == "Darwin" or not torch.cuda.is_available() else 30  # Mac doesn't support batching. Note TensorRT (.engine) is compiled for a batch size of 30
+YOLO_BATCH_SIZE = 1 if platform.system() == "Darwin" else 30  # Mac doesn't support batching. Note TensorRT (.engine) and .onnx is compiled for a batch size of 30
 YOLO_PERSIST = True  # Big impact on performance but also improves tracking
 
 ##################################################################################################
diff --git a/script_generator/object_detection/util/data.py b/script_generator/object_detection/util/data.py
index 97217a38..6eff5524 100644
--- a/script_generator/object_detection/util/data.py
+++ b/script_generator/object_detection/util/data.py
@@ -49,7 +49,7 @@ def load_yolo_model(yolo_model_path):
             log.warn("The YOLO model is missing. Please download and place the appropriate YOLO model in the models directory.")
         return None
 
-    log.info(f"Loading YOLO model, batch size: {YOLO_BATCH_SIZE}, model path {yolo_model_path}")
+    log.info(f"Loading YOLO model: {yolo_model_path}")
     return YOLO(yolo_model_path, task="detect")
 
 

From 0f3fa774ed67516d489dc6d99c009bc2a0d2224a Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Tue, 25 Feb 2025 07:18:24 +0100
Subject: [PATCH 251/277] Fixed cli

---
 script_generator/cli/shared/generate_funscript.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/script_generator/cli/shared/generate_funscript.py b/script_generator/cli/shared/generate_funscript.py
index bb8a0e72..9b69b707 100644
--- a/script_generator/cli/shared/generate_funscript.py
+++ b/script_generator/cli/shared/generate_funscript.py
@@ -1,6 +1,7 @@
 from script_generator.debug.logger import log
 from script_generator.object_detection.util.data import load_yolo_data
 from script_generator.scripts.analyze_video import analyze_video
+from script_generator.scripts.tracking_analysis import tracking_analysis
 from script_generator.state.app_state import AppState, log_state_settings
 from script_generator.utils.helpers import to_int_or_none
 
@@ -27,7 +28,7 @@ def generate_funscript_cli(state: AppState):
         else:
             log.info("Skipping yolo analysis as it was already generated")
 
-        generate_funscript(state)
+        tracking_analysis(state)
     except Exception as e:
         log.error(f"Error during video analysis: {e}")
         import traceback

From 46812c7a8faf48b9cd74a6579bacddefb3dfcd0d Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Fri, 28 Feb 2025 19:25:57 +0100
Subject: [PATCH 252/277] Updated readme for cli

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 993e46e4..bfe5af5c 100644
--- a/README.md
+++ b/README.md
@@ -112,6 +112,7 @@ python -m script_generator.cli.generate_funscript_folder /path/to/folder
 See examples/windows/Process folder.bat for an example
 
 ## Command-Line Arguments
+Note that these commands will never replace funscripts not generated by this app. Also, for settings that are not overwritten by flags the values from the GUI will be used. 
 
 ### Command-Line Arguments (Single file)
 **Required**

From 1acbcf2400f4faeed593b1a15561f43f4636347b Mon Sep 17 00:00:00 2001
From: k00gar <arnaud.chevreux@gmail.com>
Date: Sat, 1 Mar 2025 10:44:37 +0100
Subject: [PATCH 253/277] Updated miniconda install link in the README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index bfe5af5c..0352ae5d 100644
--- a/README.md
+++ b/README.md
@@ -20,7 +20,7 @@ Before using this project, ensure you have the following installed:
 
 - **Python 3.8 or higher (tested on 3.11 https://www.python.org/downloads/release/python-3118/)**
 - **FFmpeg** added to your PATH or specified under the settings menu (https://www.ffmpeg.org/download.html)
-- **Miniconda** (https://docs.anaconda.com/miniconda/install/)
+- **Miniconda** (https://www.anaconda.com/docs/getting-started/miniconda/install)
 
 # Installation
 

From d83dd90d5ef9d00bb5560fb3bbffd7d960b8decb Mon Sep 17 00:00:00 2001
From: Volcano Fishing <volcanofishing@goodlittlediver.com>
Date: Sat, 1 Mar 2025 13:27:11 -0500
Subject: [PATCH 254/277] Added ROCm requirements file for AMD Linux. Add
 platform check so unsupported icon doesn't crash gui on Linux.

---
 README.md                   | 20 +++++++++++++++-----
 rocm.requirements.txt       |  3 +++
 script_generator/gui/app.py |  4 +++-
 3 files changed, 21 insertions(+), 6 deletions(-)
 create mode 100644 rocm.requirements.txt

diff --git a/README.md b/README.md
index 0352ae5d..1b49e156 100644
--- a/README.md
+++ b/README.md
@@ -59,6 +59,16 @@ pip install -r cpu.requirements.txt
 python FSGenerator.py
 ```
 
+### If your GPU supports ROCm (AMD Linux Only)
+
+```bash
+conda create -n VRFunAIGen python=3.11
+conda activate VRFunAIGen
+pip install -r core.requirements.txt
+pip install -r rocm.requirements.txt
+python FSGenerator.py
+```
+
 ## Download the YOLO model
 Go to our discord to download the latest YOLO model for free. When downloaded place the YOLO model file(s) in the `models/` sub-directory. If you aren't sure you can add all the models and let the app decide the best option for you.
 
@@ -92,7 +102,7 @@ You can use Start windows.bat to launch the gui on windows.
 
 # Command Line Usage
 
-FunGen supports a GUI for all it's features but we also provide command line usage for those who prefer this. 
+FunGen supports a GUI for all it's features but we also provide command line usage for those who prefer this.
 Below are some examples on how to generate script with the command line:
 
 **To generate a single script with cmd or terminal, run the following command**
@@ -112,7 +122,7 @@ python -m script_generator.cli.generate_funscript_folder /path/to/folder
 See examples/windows/Process folder.bat for an example
 
 ## Command-Line Arguments
-Note that these commands will never replace funscripts not generated by this app. Also, for settings that are not overwritten by flags the values from the GUI will be used. 
+Note that these commands will never replace funscripts not generated by this app. Also, for settings that are not overwritten by flags the values from the GUI will be used.
 
 ### Command-Line Arguments (Single file)
 **Required**
@@ -199,7 +209,7 @@ Optional files
 
 The pipeline for generating Funscript files is as follows:
 
-1. **YOLO Object Detection**: A YOLO model detects relevant objects (e.g., penis, hands, mouth, etc.) in each frame of the video. 
+1. **YOLO Object Detection**: A YOLO model detects relevant objects (e.g., penis, hands, mouth, etc.) in each frame of the video.
 2. **Tracking Algorithm**: A custom tracking algorithm processes the YOLO detection results to track the positions of objects over time. The algorithm calculates distances and interactions between objects to determine the Funscript positions.
 3. **Funscript Generation**: The tracked data is used to generate a raw Funscript file.
 4. **Simplifier**: The raw Funscript data is simplified to remove noise and smooth out the motion resulting in a final `.funscript` file.
@@ -211,7 +221,7 @@ This project started as a dream to automate Funscript generation for VR videos.
 
 - **Initial Approach (OpenCV Trackers)**: The first version relied on OpenCV trackers to detect and track objects in the video. While functional, the approach was slow (8–20 FPS) and struggled with occlusions and complex scenes.
 - **Transition to YOLO**: To improve accuracy and speed, the project shifted to using YOLO object detection. A custom YOLO model was trained on a dataset of 1000nds annotated VR video frames, significantly improving detection quality.
-- **Original Post**: For more details and discussions, check out the original post on EroScripts:  
+- **Original Post**: For more details and discussions, check out the original post on EroScripts:
   [VR Funscript Generation Helper (Python + CV/AI)](https://discuss.eroscripts.com/t/vr-funscript-generation-helper-python-now-cv-ai/202554)
 
 ----
@@ -251,7 +261,7 @@ See the [LICENSE](LICENSE) file for full details.
 
 If you encounter any issues or have questions, please open an issue on GitHub.
 
-Join the **Discord community** for discussions and support:  
+Join the **Discord community** for discussions and support:
 [Discord Community](https://discord.gg/WYkjMbtCZA)
 
 ---
diff --git a/rocm.requirements.txt b/rocm.requirements.txt
new file mode 100644
index 00000000..e7790883
--- /dev/null
+++ b/rocm.requirements.txt
@@ -0,0 +1,3 @@
+-i https://download.pytorch.org/whl/rocm6.2
+torch~=2.5.1
+torchvision~=0.20.1
diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index d680c544..ab818d68 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -1,5 +1,6 @@
 import ctypes
 import os
+import platform
 import tkinter as tk
 from ultralytics import settings
 
@@ -29,7 +30,8 @@ def __init__(self, state: AppState= None):
         self.geometry('780x1000' if is_mac() else '708x975')
 
         self.iconphoto(False, tk.PhotoImage(file=LOGO))
-        self.iconbitmap(ICON)
+        if platform.system() == "Windows":
+            self.iconbitmap(ICON) # Ensure icon works for Windows and is skipped on Linux
 
         self.grid_rowconfigure(0, weight=1)
         self.grid_columnconfigure(0, weight=1)

From 23bdd8ce30b219969293aa3b3e0d6dc7a8263f5a Mon Sep 17 00:00:00 2001
From: spatialflux <frankservaar@gmail.com>
Date: Sun, 2 Mar 2025 12:28:38 +0100
Subject: [PATCH 255/277] Also added icon to mac

---
 script_generator/gui/app.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/script_generator/gui/app.py b/script_generator/gui/app.py
index ab818d68..7e6465be 100644
--- a/script_generator/gui/app.py
+++ b/script_generator/gui/app.py
@@ -11,7 +11,7 @@
 from script_generator.gui.views.help_debug_video import HelpDebugVideoPage
 from script_generator.gui.views.settings import SettingsPage
 from script_generator.state.app_state import AppState
-from script_generator.utils.helpers import is_mac
+from script_generator.utils.helpers import is_mac, is_windows
 from script_generator.debug.logger import log
 from script_generator.constants import VERSION
 
@@ -30,8 +30,8 @@ def __init__(self, state: AppState= None):
         self.geometry('780x1000' if is_mac() else '708x975')
 
         self.iconphoto(False, tk.PhotoImage(file=LOGO))
-        if platform.system() == "Windows":
-            self.iconbitmap(ICON) # Ensure icon works for Windows and is skipped on Linux
+        if is_windows() or is_mac():
+            self.iconbitmap(ICON) # Ensure icon works for Windows and mac but is skipped on Linux
 
         self.grid_rowconfigure(0, weight=1)
         self.grid_columnconfigure(0, weight=1)

From 63fced142fb364dfefec81f62810bf90b31f64b1 Mon Sep 17 00:00:00 2001
From: k00gar <arnaud.chevreux@gmail.com>
Date: Sun, 2 Mar 2025 19:45:04 +0100
Subject: [PATCH 256/277] Allowing rounding value of 1 to skip pos rounding
 step.

---
 script_generator/funscript/create_funscript.py    | 4 ++--
 script_generator/gui/views/funscript_generator.py | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/script_generator/funscript/create_funscript.py b/script_generator/funscript/create_funscript.py
index 4bcc396c..d97c0937 100644
--- a/script_generator/funscript/create_funscript.py
+++ b/script_generator/funscript/create_funscript.py
@@ -91,7 +91,7 @@ def create_funscript(state):
             log_fun.info("Skipping positions adjustment - step 5 (amplitude boosting)")
 
         # Round position values to the closest multiple of 5, still between 0 and 100
-        if state.vw_simplification_enabled:
+        if state.vw_simplification_enabled and state.rounding != 1:
             log_fun.info(
                 f"Positions adjustment - step 6 (rounding to the closest multiple of {state.rounding})")
             adjusted_positions = [round(p / state.rounding) * state.rounding for p in
@@ -99,7 +99,7 @@ def create_funscript(state):
 
         else:
             log_fun.info(
-                f"Skipping positions adjustment - step 6 (rounding to the closest multiple of {state.rounding})")
+                f"Skipping positions adjustment - step 6 (rounding)")
 
         # Recombine timestamps and adjusted positions
         log_fun.info("Re-assembling ats and positions")
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 3c992151..343ef127 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -175,7 +175,7 @@ def handle_process_btn():
             label_text="Rounding",
             state=self.state,
             attr='rounding',
-            values=[5, 10],
+            values=[1, 5, 10],
             row=2,
             tooltip_text="Rounding factor for values"
         )

From 1b6f8c065d9d7d0bcae3c982740abd07a25b6b61 Mon Sep 17 00:00:00 2001
From: k00gar <arnaud.chevreux@gmail.com>
Date: Sun, 2 Mar 2025 19:47:29 +0100
Subject: [PATCH 257/277] Fixed missing is_windows() in helpers.py

---
 script_generator/utils/helpers.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/script_generator/utils/helpers.py b/script_generator/utils/helpers.py
index 4b1d720f..522698a1 100644
--- a/script_generator/utils/helpers.py
+++ b/script_generator/utils/helpers.py
@@ -5,6 +5,9 @@
 def is_mac():
     return platform.system() == "Darwin"
 
+def is_windows():
+    return platform.system() == "Windows"
+
 def to_int_or_none(value):
     try:
         return int(value)

From 11674f7bdfaf9bcc30336ed227d0df356d4a21ca Mon Sep 17 00:00:00 2001
From: k00gar <arnaud.chevreux@gmail.com>
Date: Sun, 2 Mar 2025 23:36:51 +0100
Subject: [PATCH 258/277] Moved from 2D landscape cropping to resizing and
 padding.

---
 script_generator/video/ffmpeg/filters.py | 23 +++++++++++++----------
 1 file changed, 13 insertions(+), 10 deletions(-)

diff --git a/script_generator/video/ffmpeg/filters.py b/script_generator/video/ffmpeg/filters.py
index 90143080..0b6d690e 100644
--- a/script_generator/video/ffmpeg/filters.py
+++ b/script_generator/video/ffmpeg/filters.py
@@ -54,14 +54,17 @@ def get_2d_video_filters(video, width, height):
         else:
             return f"[0:v]scale={width}:{height}"
 
-    # in landscape, we crop to the center
-    else:
-        if video.height > RENDER_RESOLUTION:
-            scale_width = int(video.width * (height / video.height))
-            crop = f",crop={width}:{height}:(iw-{width})/2:0"
-            if supports_scale_cuda(state):
-                return f"[0:v]scale_cuda={scale_width}:{height},hwdownload,format=nv12{crop}"
-            else:
-                return f"[0:v]scale={scale_width}:{height}{crop}"
+    if video.width > video.height:
+        new_width = 640
+        new_height = int((video.height / video.width) * new_width)
+
+        if supports_scale_cuda(state):
+            scale_filter = f"scale_cuda={new_width}:{new_height},hwdownload,format=nv12"
+        else:
+            scale_filter = f"scale={new_width}:{new_height}"
+
+        if new_height < 640:
+            pad_y = (640 - new_height) // 2
+            return f"[0:v]{scale_filter},pad=640:640:0:{pad_y}:black"
         else:
-            return "[0:v]hwdownload,format=nv12" if cuda else ""
+            return f"[0:v]{scale_filter}"

From 1faa4ac2afb16ed452c4c5e6370141a02eb062d7 Mon Sep 17 00:00:00 2001
From: k00gar <arnaud.chevreux@gmail.com>
Date: Mon, 17 Mar 2025 13:00:52 +0100
Subject: [PATCH 259/277] Added moveable gauge in debug mode

---
 .../debug/video_player/debug_overlay.py          |  4 +++-
 .../debug/video_player/interaction.py            |  7 +++++++
 .../debug/video_player/overlay_widgets.py        | 16 +++++++++++++++-
 script_generator/debug/video_player/play.py      |  1 +
 .../debug/video_player/video_player.py           |  7 ++++++-
 script_generator/gui/utils/widgets.py            |  5 +++--
 script_generator/state/app_state.py              |  4 ++--
 7 files changed, 37 insertions(+), 7 deletions(-)

diff --git a/script_generator/debug/video_player/debug_overlay.py b/script_generator/debug/video_player/debug_overlay.py
index 60313c92..499b978d 100644
--- a/script_generator/debug/video_player/debug_overlay.py
+++ b/script_generator/debug/video_player/debug_overlay.py
@@ -3,6 +3,7 @@
 
 from script_generator.constants import CLASS_COLORS
 from script_generator.debug.video_player.overlay_widgets import OverlayWidgets
+from script_generator.debug.video_player.video_player import VideoPlayer
 from script_generator.state.app_state import AppState
 
 
@@ -17,6 +18,7 @@ def get_funscript_value(interpolator, frame_id, fps):
 
 def draw_overlay(
     state: "AppState",
+    video_player: VideoPlayer,
     frame,
     frame_id,
     logs,
@@ -128,7 +130,7 @@ def draw_overlay(
     graph_y_start = frame.shape[0] - graph_height - 15  # B, G, R | R, G, B
     if state.debug_mode == "funscript":
         # Draw gauge (example usage)
-        OverlayWidgets.draw_gauge(frame, funscript_value)
+        OverlayWidgets.draw_gauge(frame, funscript_value, *video_player.gauge_position)
 
         draw_rolling_window_curve(frame, distance_buffer, (0, 255, 0), 0.5, graph_height, graph_y_start)
         draw_rolling_window_curve(frame, funscript_buffer, (255, 0, 0), 0.5, graph_height, graph_y_start)
diff --git a/script_generator/debug/video_player/interaction.py b/script_generator/debug/video_player/interaction.py
index 92d6fa5e..1a5a19f6 100644
--- a/script_generator/debug/video_player/interaction.py
+++ b/script_generator/debug/video_player/interaction.py
@@ -6,6 +6,7 @@
 from script_generator.debug.logger import log
 from script_generator.gui.messages.messages import UpdateGUIState
 from script_generator.video.data_classes.video_info import get_cropped_dimensions
+from script_generator.constants import GAUGE_HEIGHT, GAUGE_WIDTH
 
 if TYPE_CHECKING:
     from script_generator.state.app_state import AppState
@@ -40,6 +41,12 @@ def mouse_callback(event, x, y, flags, params: Tuple["AppState", "VideoPlayer"])
             video_player.set_frame(target_frame)
             log.info(f"Seek to frame: {target_frame}")
 
+    elif event == cv2.EVENT_RBUTTONDOWN:
+        width, height = get_cropped_dimensions(video_info)
+        new_x = max(0, min(x, width - GAUGE_WIDTH))
+        new_y = max(0, min(y, height) - GAUGE_HEIGHT)
+        video_player.gauge_position = new_x, new_y
+
 
 def handle_user_input(window_name, state: "AppState", video_player: "VideoPlayer", metrics):
     key = cv2.waitKeyEx(1)
diff --git a/script_generator/debug/video_player/overlay_widgets.py b/script_generator/debug/video_player/overlay_widgets.py
index b92b810e..59f52c0b 100644
--- a/script_generator/debug/video_player/overlay_widgets.py
+++ b/script_generator/debug/video_player/overlay_widgets.py
@@ -14,7 +14,7 @@ def draw_bounding_box(image, box, label, color, offset_x=0):
         return image
 
     @staticmethod
-    def draw_gauge(image, distance, x=15, y=20):
+    def old_draw_gauge(image, distance, x=15, y=20):
         gauge_width = 12
         gauge_height = 100
         gauge_x = image.shape[1] - x
@@ -24,3 +24,17 @@ def draw_gauge(image, distance, x=15, y=20):
         cv2.rectangle(image, (gauge_x, gauge_y + gauge_height - fill_height), (gauge_x + gauge_width, gauge_y + gauge_height), (0, 255, 0), -1)
         cv2.putText(image, str(int(distance)), (gauge_x, gauge_y + gauge_height - fill_height - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.25, (0, 255, 0), 1)
         return image
+
+    @staticmethod
+    def draw_gauge(image, distance, x, y):
+        distance = max(0, min(100, distance))
+        gauge_width = 12
+        gauge_height = 100
+        gauge_x = min(max(0, x), image.shape[1] - gauge_width)
+        gauge_y = min(max(0, y), image.shape[0] - gauge_height)
+        cv2.rectangle(image, (gauge_x, gauge_y), (gauge_x + gauge_width, gauge_y + gauge_height), (0, 0, 0), -1)
+        fill_height = int((distance / 100) * gauge_height)
+        cv2.rectangle(image, (gauge_x, gauge_y + gauge_height - fill_height), (gauge_x + gauge_width, gauge_y + gauge_height), (211, 149, 46), -1)
+        cv2.putText(image, str(int(distance)), (gauge_x, gauge_y + gauge_height - fill_height - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.25, (211, 149, 46), 1)
+
+        return image
\ No newline at end of file
diff --git a/script_generator/debug/video_player/play.py b/script_generator/debug/video_player/play.py
index 7ce6563a..8e4007cc 100644
--- a/script_generator/debug/video_player/play.py
+++ b/script_generator/debug/video_player/play.py
@@ -129,6 +129,7 @@ def get_ceiled_fps(value):
         # Call your overlay function
         distance_buffer, funscript_buffer, funscript_buffer_ref = draw_overlay(
             state=state,
+            video_player=video_player,
             frame=frame,
             frame_id=video_player.current_frame,
             logs=metrics,
diff --git a/script_generator/debug/video_player/video_player.py b/script_generator/debug/video_player/video_player.py
index e843031a..31cc6821 100644
--- a/script_generator/debug/video_player/video_player.py
+++ b/script_generator/debug/video_player/video_player.py
@@ -1,8 +1,9 @@
 import cv2
 import numpy as np
 
-from script_generator.constants import FUNSCRIPT_BUFFER_SIZE
+from script_generator.constants import FUNSCRIPT_BUFFER_SIZE, GAUGE_WIDTH, GAUGE_HEIGHT
 from script_generator.state.app_state import AppState
+from script_generator.video.data_classes.video_info import get_cropped_dimensions
 from script_generator.video.ffmpeg.video_reader import VideoReaderFFmpeg
 
 
@@ -18,6 +19,10 @@ def __init__(self, state: AppState, start_frame, end_frame):
         # for rolling funscripts
         # self.funscript_graph = FunscriptGraph(state)
 
+        # gauge position
+        width, height = get_cropped_dimensions(self.video_info)
+        self.gauge_position = (width - GAUGE_WIDTH), (height - GAUGE_HEIGHT)
+
         # a buffer to show outliers longer then 1 frame and slowly fade them out
         self.outlier_buffer = []
 
diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index 2bf294c7..3e1ebfed 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -433,8 +433,9 @@ def custom_popup(title, content_builder, master=None, width=400, height=300):
     def disclaimer(parent, tooltip_text=None):
         footer_label = ttk.Label(
             parent,
-            text="Individual and personal use only.\nNot for commercial use.\nhttps://github.com/ack00gar",
-            font=("Arial", 10, "italic", "bold"), justify="center"
+            # text="Individual and personal use only.\nNot for commercial use.\nhttps://github.com/ack00gar",
+            text="Individual and personal use only. Not for commercial use.",
+            font=("Arial", 10), justify="center"
         )
         footer_label.grid(row=8, column=0, columnspan=100, padx=5, pady=(10, 20), sticky="s")
 
diff --git a/script_generator/state/app_state.py b/script_generator/state/app_state.py
index 02ee6200..964ff4b7 100644
--- a/script_generator/state/app_state.py
+++ b/script_generator/state/app_state.py
@@ -62,13 +62,13 @@ def __init__(self):
 
         # Gui/settings Funscript Tweaking Variables
         self.boost_enabled: bool = True
-        self.boost_up_percent: int = 10
+        self.boost_up_percent: int = 15
         self.boost_down_percent: int = 15
         self.threshold_enabled: bool = True
         self.threshold_low: int = 10
         self.threshold_high: int = 90
         self.vw_simplification_enabled: bool = True
-        self.vw_factor: float = 8.0
+        self.vw_factor: float = 4.0
         self.rounding: int = 5
 
         # TODO remove when swtiched to tracking logic v2

From 664891eceafb7746592a59753401e0d5c5ac4b2b Mon Sep 17 00:00:00 2001
From: k00gar <arnaud.chevreux@gmail.com>
Date: Mon, 17 Mar 2025 13:01:41 +0100
Subject: [PATCH 260/277] Added moveable gauge in debug mode

---
 script_generator/constants.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/script_generator/constants.py b/script_generator/constants.py
index 1a077ccc..3552102d 100644
--- a/script_generator/constants.py
+++ b/script_generator/constants.py
@@ -81,7 +81,7 @@
 }
 CLASS_NAMES = {
     'face': 0,
-    'hand': 1, 'left hand': 1, 'right hand': 1,
+    'hand': 1,  # 'left hand': 1, 'right hand': 1,
     'penis': 2,
     'glans': 3,
     'pussy': 4,
@@ -158,6 +158,8 @@
 
 FUNSCRIPT_BUFFER_SIZE = 500
 
+GAUGE_WIDTH, GAUGE_HEIGHT = 12, 100
+
 ##################################################################################################
 # KEY CODES
 ##################################################################################################

From 8ae57f60f861762f8f469f27ad21005a9344280d Mon Sep 17 00:00:00 2001
From: Volcano Fishing <volcanofishing@goodlittlediver.com>
Date: Tue, 18 Mar 2025 11:09:10 -0400
Subject: [PATCH 261/277] Added vulkan as an option for ffmpeg hwccel. Updated
 the Hardware acceleration option in the gui to a dropdown for discoverability
 as there are only finite options anyway.

---
 script_generator/gui/views/settings.py   |  6 ++++--
 script_generator/video/ffmpeg/hwaccel.py | 10 ++++++++--
 2 files changed, 12 insertions(+), 4 deletions(-)

diff --git a/script_generator/gui/views/settings.py b/script_generator/gui/views/settings.py
index 506adf2b..f13d2db7 100644
--- a/script_generator/gui/views/settings.py
+++ b/script_generator/gui/views/settings.py
@@ -94,12 +94,14 @@ def __init__(self, parent, controller):
             row=1
         )
 
-        Widgets.input(
+        Widgets.dropdown(
             attr="ffmpeg_hwaccel",
             parent=ffmpeg_settings,
             label_text="Hardware acceleration",
-            state=self.state,
+            options=["None", "cuda", "vaapi", "amf", "qsv", "d3d11va", "videotoolbox", "vulkan"],
+            default_value=self.state.ffmpeg_hwaccel,
             tooltip_text="Used hardware acceleration. Can be overwritten here or set to None to disable it when your\nexperiencing issues. However if you do, please let us know so we can better\nsupport you in the future.",
+            state=self.state,
             command=lambda val: c.save(),
             row=2
         )
diff --git a/script_generator/video/ffmpeg/hwaccel.py b/script_generator/video/ffmpeg/hwaccel.py
index b1c013e1..21b02d97 100644
--- a/script_generator/video/ffmpeg/hwaccel.py
+++ b/script_generator/video/ffmpeg/hwaccel.py
@@ -40,6 +40,11 @@
         "-pix_fmt", "yuv420p",
         "-c:v", "h264_videotoolbox",
         "-f", "null", "-"
+    ],
+    "vulkan": [
+        "-init_hw_device", "vulkan",
+        "-f", "lavfi", "-i", "testsrc=size=256x256:duration=0.1:rate=1",
+        "-f", "null", "-"
     ]
 }
 
@@ -81,7 +86,7 @@ def _test_hwaccel(ffmpeg_path, hw):
 
 def get_preferred_hwaccel(ffmpeg_path):
     supported = _list_ffmpeg_hwaccels(ffmpeg_path)
-    for hw in ["cuda", "vaapi", "amf", "videotoolbox", "qsv", "d3d11va"]:
+    for hw in ["cuda", "vaapi", "amf", "videotoolbox", "qsv", "d3d11va", "vulkan"]:
         if hw in supported and _test_hwaccel(ffmpeg_path, hw):
             log_vid.info(f"Setting preferred FFmpeg hardware acceleration to: {hw}")
             return hw
@@ -105,6 +110,8 @@ def get_hwaccel_read_args(state):
         return ["-hwaccel", "qsv"]
     if hwaccel == "d3d11va":
         return ["-hwaccel", "d3d11va"]
+    if hwaccel == "vulkan":
+        return ["-hwaccel", "vulkan"]
     return []
 
 
@@ -155,4 +162,3 @@ def supports_scale_cuda(state: "AppState"):
 
 def supports_scale_npp(state: "AppState"):
     return _supports_scale_acceleration(state) and scale_npp
-

From 8a1c1cd212b984a0384f151b5cf8a926ab8bbd69 Mon Sep 17 00:00:00 2001
From: k00gar <arnaud.chevreux@gmail.com>
Date: Tue, 18 Mar 2025 20:36:08 +0100
Subject: [PATCH 262/277] Temp fix for gauge display in live preview

---
 .../debug/video_player/overlay_widgets.py          | 14 +-------------
 1 file changed, 1 insertion(+), 13 deletions(-)

diff --git a/script_generator/debug/video_player/overlay_widgets.py b/script_generator/debug/video_player/overlay_widgets.py
index 59f52c0b..b872a070 100644
--- a/script_generator/debug/video_player/overlay_widgets.py
+++ b/script_generator/debug/video_player/overlay_widgets.py
@@ -14,19 +14,7 @@ def draw_bounding_box(image, box, label, color, offset_x=0):
         return image
 
     @staticmethod
-    def old_draw_gauge(image, distance, x=15, y=20):
-        gauge_width = 12
-        gauge_height = 100
-        gauge_x = image.shape[1] - x
-        gauge_y = image.shape[0] - 100 - y
-        cv2.rectangle(image, (gauge_x, gauge_y), (gauge_x + gauge_width, gauge_y + gauge_height), (0, 0, 0), -1)
-        fill_height = int((distance / 100) * gauge_height)
-        cv2.rectangle(image, (gauge_x, gauge_y + gauge_height - fill_height), (gauge_x + gauge_width, gauge_y + gauge_height), (0, 255, 0), -1)
-        cv2.putText(image, str(int(distance)), (gauge_x, gauge_y + gauge_height - fill_height - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.25, (0, 255, 0), 1)
-        return image
-
-    @staticmethod
-    def draw_gauge(image, distance, x, y):
+    def draw_gauge(image, distance, x=15, y=20):
         distance = max(0, min(100, distance))
         gauge_width = 12
         gauge_height = 100

From c3749460a2d50c3dfe2b832177b9459630108069 Mon Sep 17 00:00:00 2001
From: Volcano Fishing <volcanofishing@goodlittlediver.com>
Date: Wed, 19 Mar 2025 11:36:54 -0400
Subject: [PATCH 263/277] Updated the file browse function to allow an inital
 directory and use the current selected one when browse is clicked. Updated
 all browse buttons to use the new initial directory logic.

---
 script_generator/gui/utils/widgets.py         | 25 ++++++++++++++++---
 .../gui/views/funscript_generator.py          |  7 ++++--
 script_generator/gui/views/settings.py        | 14 +++++++----
 3 files changed, 36 insertions(+), 10 deletions(-)

diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index 3e1ebfed..cfe82fe4 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -172,7 +172,7 @@ def show(is_visible: bool):
         return button
 
     @staticmethod
-    def file_selection(parent, label_text, button_text, file_selector_title, file_types, state, attr, command=None, row=0, label_width_px=150, button_width_px=100, tooltip_text=None, select_folder=False, **grid_kwargs):
+    def file_selection(parent, label_text, button_text, file_selector_title, file_types, state, attr, command=None, row=0, label_width_px=150, button_width_px=100, tooltip_text=None, select_folder=False, initial_dir=None, **grid_kwargs):
         grid_kwargs.setdefault("pady", 5)
         grid_kwargs.setdefault("padx", 5)
         grid_kwargs.setdefault("sticky","nsew")
@@ -208,10 +208,29 @@ def on_change(val):
         button_container.grid(row=0, column=2, sticky="e", padx=(2, 5))
         button_container.grid_propagate(False)  # Prevent resizing
         def browse():
+            current_path = getattr(state, attr, "")
+            initial_directory = initial_dir
+
+            if initial_directory is None and current_path and os.path.exists(current_path):
+                initial_directory = os.path.dirname(current_path)
+
+            if initial_directory is None or not os.path.exists(initial_directory):
+                initial_directory = os.path.abspath(os.path.dirname(__file__))
+                # I don't know a better way to do this, I've set it to go back to the project root if know previous path exists.
+                for _ in range(3):
+                    initial_directory = os.path.dirname(initial_directory)
+
             if select_folder:
-                selected_path = filedialog.askdirectory(title=file_selector_title)
+                selected_path = filedialog.askdirectory(
+                    title=file_selector_title,
+                    initialdir=initial_directory
+                )
             else:
-                selected_path = filedialog.askopenfilename(title=file_selector_title, filetypes=file_types)
+                selected_path = filedialog.askopenfilename(
+                    title=file_selector_title,
+                    filetypes=file_types,
+                    initialdir=initial_directory
+                )
             if selected_path:
                 file_path.set(selected_path)
                 setattr(state, attr, selected_path)
diff --git a/script_generator/gui/views/funscript_generator.py b/script_generator/gui/views/funscript_generator.py
index 343ef127..d0756d38 100644
--- a/script_generator/gui/views/funscript_generator.py
+++ b/script_generator/gui/views/funscript_generator.py
@@ -16,6 +16,7 @@
 from script_generator.gui.views.popups.edit_video_info import render_video_edit_popup
 from script_generator.object_detection.util.data import get_raw_yolo_file_info
 from script_generator.state.app_state import AppState
+import os
 
 
 class FunscriptGeneratorPage(tk.Frame):
@@ -55,7 +56,8 @@ def update_video_path():
             state=state,
             tooltip_text="The video to generate a funscript for. For proper detection of fisheye keep the suffix like _FISHEYE190, _MKX200, etc. in the filename\n\nIn the future we'll add the option to override this in the UI.",
             command=lambda val: update_video_path(),
-            pady=(0, 0)
+            pady=(0, 0),
+            initial_dir=os.path.dirname(str(state.video_path)) if state.video_path else None
         )
 
         video_info_container = Widgets.frame(video_selection, row=1, padx=(0, 0), pady=(0, 5), min_height=35)
@@ -244,7 +246,8 @@ def handle_process_btn():
             file_types=[("Funscript Files", "*.funscript"), ("All Files", "*.*")],
             tooltip_text="If provided the reference script will be compared in the\nfunscript report that is generated on completion and be shown\nin the live display funscript overlay when enabled.",
             state=state,
-            row=0
+            row=0,
+            initial_dir=os.path.dirname(str(state.reference_script)) if state.reference_script else None
         )
         debug_video_section = Widgets.frame(debugging, title="Share debug files", row=2)
         gen_video_btn = Widgets.button(
diff --git a/script_generator/gui/views/settings.py b/script_generator/gui/views/settings.py
index f13d2db7..3a4be4a8 100644
--- a/script_generator/gui/views/settings.py
+++ b/script_generator/gui/views/settings.py
@@ -3,6 +3,7 @@
 from script_generator.debug.logger import set_log_level
 from script_generator.gui.utils.widgets import Widgets
 from script_generator.state.app_state import AppState
+import os
 
 
 class SettingsPage(tk.Frame):
@@ -25,7 +26,6 @@ def __init__(self, parent, controller):
             command=lambda val: c.save(),
             row=0
         )
-
         Widgets.file_selection(
             attr="funscript_output_dir",
             parent=general_settings,
@@ -37,7 +37,8 @@ def __init__(self, parent, controller):
             tooltip_text="Set an optional output folder where the final funscript will be copied to.",
             command=lambda val: c.save(),
             select_folder=True,
-            row=1
+            row=1,
+            initial_dir=self.state.funscript_output_dir if self.state.funscript_output_dir and os.path.exists(self.state.funscript_output_dir) else None
         )
 
         Widgets.checkbox(
@@ -63,7 +64,8 @@ def __init__(self, parent, controller):
             state=self.state,
             tooltip_text="Path to the YOLO model file.",
             command=lambda val: c.save(),
-            row=0
+            row=0,
+            initial_dir=os.path.dirname(self.state.yolo_model_path) if self.state.yolo_model_path else None
         )
 
         ffmpeg_settings = Widgets.frame(self, title="FFmpeg", main_section=True, row=2)
@@ -78,7 +80,8 @@ def __init__(self, parent, controller):
             state=self.state,
             tooltip_text="Path to the FFmpeg executable.",
             command=lambda val: c.save(),
-            row=0
+            row=0,
+            initial_dir=os.path.dirname(self.state.ffmpeg_path) if self.state.ffmpeg_path else None
         )
 
         Widgets.file_selection(
@@ -91,7 +94,8 @@ def __init__(self, parent, controller):
             state=self.state,
             tooltip_text="Path to the FFprobe executable.",
             command=lambda val: c.save(),
-            row=1
+            row=1,
+            initial_dir=os.path.dirname(self.state.ffprobe_path) if self.state.ffprobe_path else None
         )
 
         Widgets.dropdown(

From 6fde597b90f3c03ab1ef9ae2e44ee38f9a968475 Mon Sep 17 00:00:00 2001
From: gavonmothma <40217343+gavonmothma@users.noreply.github.com>
Date: Thu, 27 Mar 2025 21:36:27 -0400
Subject: [PATCH 264/277] Update README.md

---
 README.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/README.md b/README.md
index 1b49e156..e2e80a17 100644
--- a/README.md
+++ b/README.md
@@ -22,6 +22,9 @@ Before using this project, ensure you have the following installed:
 - **FFmpeg** added to your PATH or specified under the settings menu (https://www.ffmpeg.org/download.html)
 - **Miniconda** (https://www.anaconda.com/docs/getting-started/miniconda/install)
 
+Easy install of Miniconda for Windows users:
+Click Start, type "cmd", right click on Command Prompt, and select "Run as administrator." Enter "winget install -e --id Anaconda.Miniconda3" and press enter. Miniconda should then download and install.
+
 # Installation
 
 ### Clone the repository

From bded673b42fae9317da0a3991c8b550fa8fa2ed1 Mon Sep 17 00:00:00 2001
From: k00gar <arnaud.chevreux@gmail.com>
Date: Wed, 2 Apr 2025 19:13:29 +0200
Subject: [PATCH 265/277] Initiating the crash-dummies branch

---
 script_generator/constants.py                 |   3 +-
 script_generator/debug/debug_data.py          |  17 +++
 .../debug/video_player/debug_overlay.py       |   2 +-
 .../debug/video_player/interaction.py         |   4 +-
 script_generator/debug/video_player/play.py   |   2 +-
 script_generator/funscript/util/util.py       |  42 +++++-
 script_generator/gui/utils/widgets.py         |   3 +-
 .../data_classes/object_detection_result.py   |  22 +++-
 script_generator/scripts/tracking_analysis.py |   2 +-
 utils/lib_KalmanFilter.py                     |  88 ++++++-------
 utils/lib_ObjectTracker.py                    | 120 ++++++++++++------
 11 files changed, 203 insertions(+), 102 deletions(-)

diff --git a/script_generator/constants.py b/script_generator/constants.py
index 3552102d..674edaf5 100644
--- a/script_generator/constants.py
+++ b/script_generator/constants.py
@@ -6,7 +6,7 @@
 VERSION = "0.3.0"
 OBJECT_DETECTION_VERSION = "1.0.0"
 TRACKING_VERSION = "0.1.0"
-FUNSCRIPT_VERSION = "0.1.0"
+FUNSCRIPT_VERSION = "0.1.1"
 CONFIG_VERSION = 1
 
 ##################################################################################################
@@ -168,6 +168,7 @@
 RIGHT = 2555904
 SPACE = 32
 Q = 113
+ESC = 27
 COMMA = 44
 PERIOD = 46
 LEFT_BRACKET = 91
diff --git a/script_generator/debug/debug_data.py b/script_generator/debug/debug_data.py
index 2a53b184..3d27fed6 100644
--- a/script_generator/debug/debug_data.py
+++ b/script_generator/debug/debug_data.py
@@ -21,6 +21,23 @@ def add_frame(self, frame_id, variables=None, bounding_boxes=None):
         if bounding_boxes:
             self.metrics[frame_id]["bounding_boxes"].extend(bounding_boxes)
 
+    def get_metrics_at_frame(self, frame_id):
+        return self.metrics.get(frame_id)
+
+    def get_variables_at_frame(self, frame_id):
+        return self.metrics.get(frame_id, {}).get("variables")
+
+    def get_all_frames(self):
+        return self.metrics
+
+    def edit_frame(self, frame_id, variables=None, bounding_boxes=None):
+        if frame_id in self.metrics:
+            if variables:
+                self.metrics[frame_id]["variables"].update(variables)
+
+            if bounding_boxes:
+                self.metrics[frame_id]["bounding_boxes"].extend(bounding_boxes)
+
     def save_debug_file(self):
         save_debug_metrics(self.app_state, self.metrics)
 
diff --git a/script_generator/debug/video_player/debug_overlay.py b/script_generator/debug/video_player/debug_overlay.py
index 499b978d..e5ba06b1 100644
--- a/script_generator/debug/video_player/debug_overlay.py
+++ b/script_generator/debug/video_player/debug_overlay.py
@@ -98,7 +98,7 @@ def draw_overlay(
             cv2.rectangle(frame, (x1, y1), (x2, y2), color, 1)
             cv2.putText(
                 frame,
-                "Locked Penis",
+                "Locked P.",
                 (x1, y1 - 5),
                 cv2.FONT_HERSHEY_SIMPLEX,
                 FONT_SIZE,
diff --git a/script_generator/debug/video_player/interaction.py b/script_generator/debug/video_player/interaction.py
index 1a5a19f6..0fd75497 100644
--- a/script_generator/debug/video_player/interaction.py
+++ b/script_generator/debug/video_player/interaction.py
@@ -2,7 +2,7 @@
 
 import cv2
 
-from script_generator.constants import LEFT, RIGHT, SPACE, Q, COMMA, PERIOD, LEFT_BRACKET, RIGHT_BRACKET
+from script_generator.constants import LEFT, RIGHT, SPACE, Q, ESC, COMMA, PERIOD, LEFT_BRACKET, RIGHT_BRACKET
 from script_generator.debug.logger import log
 from script_generator.gui.messages.messages import UpdateGUIState
 from script_generator.video.data_classes.video_info import get_cropped_dimensions
@@ -60,7 +60,7 @@ def handle_user_input(window_name, state: "AppState", video_player: "VideoPlayer
     if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) < 1:
         return False
 
-    if key == Q:
+    if key in [Q, ESC]:
         return False
 
     elif key == SPACE:  # Toggle pause on space bar
diff --git a/script_generator/debug/video_player/play.py b/script_generator/debug/video_player/play.py
index 8e4007cc..cb6065d6 100644
--- a/script_generator/debug/video_player/play.py
+++ b/script_generator/debug/video_player/play.py
@@ -19,7 +19,7 @@
 from script_generator.debug.video_player.interaction import mouse_callback, handle_user_input
 
 
-def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=100, save_video_mode=False):
+def play_debug_video(state, start_frame=0, end_frame=None, rolling_window_size=300, save_video_mode=False):
     video_info = state.video_info
     metrics_exist, metrics, metrics_path, _ = load_debug_metrics(state)
     if not metrics_exist:
diff --git a/script_generator/funscript/util/util.py b/script_generator/funscript/util/util.py
index 9412b8d9..648b5ca8 100644
--- a/script_generator/funscript/util/util.py
+++ b/script_generator/funscript/util/util.py
@@ -67,14 +67,42 @@ def load_funscript(funscript_path):
     return times, positions, relevant_chapters_export, irrelevant_chapters_export
 
 # TODO replace with proper JSON serialization
-def write_funscript(distances, output_path, fps):
-    with open(output_path, 'w') as f:
-        f.write(f'{{"version":"{FUNSCRIPT_VERSION}","inverted":false,"range":100,"author":"{FUNSCRIPT_AUTHOR}","actions":[{{"at":0,"pos":100}},')
+def write_funscript(distances, output_path, fps, timestamps = None):
+    chapters = ""
+    # Iterate through the chapters to form this format : "chapters":[{"endTime":"00:08:26.200","name":"BJ1","startTime":"00:06:04.466"},{"endTime":"00:15:14.699","name":"BJ2","startTime":"00:09:13.733"}]
+    if timestamps:
         i = 0
-        for frame, position in distances:
+        len_t = len(timestamps)
+        chapters = ',"chapters":['
+        for timestamp in timestamps:
+            chapters += f'{{"startTime":"{timestamp[2]}","endTime":"{timestamp[3]}","name":"{timestamp[4]}"}}'
+            chapters += "," if i < len_t - 1 else ""
+            i += 1
+        chapters += "]"
+
+    output = f'{{"version":"{FUNSCRIPT_VERSION}","inverted":false,"range":100,"author":"{FUNSCRIPT_AUTHOR}"{chapters},"actions":[{{"at":0,"pos":100}},'
+    i = 0
+    for frame, position in distances:
+        if position :
             time_ms = int(frame * 1000 / fps)
             if i > 0:
-                f.write(",")
-            f.write(f'{{"at":{time_ms},"pos":{int(position)}}}')
+                output += ","
+            output += f'{{"at":{time_ms},"pos":{int(position)}}}'
             i += 1
-        f.write("]}")
\ No newline at end of file
+    output += "]}"
+
+    with open(output_path, 'w') as f:
+        f.write(output)
+
+
+    # with open(output_path, 'w') as f:
+    #     f.write(f'{{"version":"{FUNSCRIPT_VERSION}","inverted":false,"range":100,"author":"{FUNSCRIPT_AUTHOR}","actions":[{{"at":0,"pos":100}},')
+    #     i = 0
+    #     for frame, position in distances:
+    #         if position :
+    #             time_ms = int(frame * 1000 / fps)
+    #             if i > 0:
+    #                 f.write(",")
+    #             f.write(f'{{"at":{time_ms},"pos":{int(position)}}}')
+    #             i += 1
+    #     f.write("]}")
\ No newline at end of file
diff --git a/script_generator/gui/utils/widgets.py b/script_generator/gui/utils/widgets.py
index cfe82fe4..d9fc615e 100644
--- a/script_generator/gui/utils/widgets.py
+++ b/script_generator/gui/utils/widgets.py
@@ -452,8 +452,7 @@ def custom_popup(title, content_builder, master=None, width=400, height=300):
     def disclaimer(parent, tooltip_text=None):
         footer_label = ttk.Label(
             parent,
-            # text="Individual and personal use only.\nNot for commercial use.\nhttps://github.com/ack00gar",
-            text="Individual and personal use only. Not for commercial use.",
+            text="Non-commercial personal use only.",
             font=("Arial", 10), justify="center"
         )
         footer_label.grid(row=8, column=0, columnspan=100, padx=5, pady=(10, 20), sticky="s")
diff --git a/script_generator/object_detection/data_classes/object_detection_result.py b/script_generator/object_detection/data_classes/object_detection_result.py
index 41a1e45d..5f3722ea 100644
--- a/script_generator/object_detection/data_classes/object_detection_result.py
+++ b/script_generator/object_detection/data_classes/object_detection_result.py
@@ -1,5 +1,5 @@
 from script_generator.constants import CLASS_PRIORITY_ORDER
-
+from typing import Dict, List, Tuple
 
 class ObjectDetectionResult:
     def __init__(self):
@@ -45,3 +45,23 @@ def get_all_frame_ids(self):
         :return: A list of frame IDs.
         """
         return list(self.frame_data.keys())
+
+    def get_all_boxes(self, total_frames: int) -> Dict[int, List[Tuple]]:
+        """
+        Retrieve all bounding boxes for all frames, including empty records for frames with no detections.
+
+        Args:
+            total_frames: Total number of frames in the video.
+
+        Returns:
+            A dictionary where:
+            - Keys are frame IDs.
+            - Values are lists of sorted bounding boxes for that frame (empty list if no boxes).
+        """
+        all_boxes = {}
+        for frame_id in range(total_frames):
+            if frame_id in self.frame_data:
+                all_boxes[frame_id] = self.get_boxes(frame_id)
+            else:
+                all_boxes[frame_id] = []  # Empty record for frames with no detections
+        return all_boxes
\ No newline at end of file
diff --git a/script_generator/scripts/tracking_analysis.py b/script_generator/scripts/tracking_analysis.py
index 32cb90eb..a64a0d11 100644
--- a/script_generator/scripts/tracking_analysis.py
+++ b/script_generator/scripts/tracking_analysis.py
@@ -1,4 +1,4 @@
-from script_generator.analysis.workers.analyze_tracking_results_v1 import analyze_tracking_results_v1
+from script_generator.analysis.workers.analyze_tracking_results_v1_test3 import analyze_tracking_results_v1
 from script_generator.analysis.workers.analyze_tracking_results_v2 import analyze_tracking_results_v2
 from script_generator.debug.logger import log_tr
 from script_generator.funscript.create_funscript import create_funscript
diff --git a/utils/lib_KalmanFilter.py b/utils/lib_KalmanFilter.py
index 548ceeaa..a01dcd4a 100644
--- a/utils/lib_KalmanFilter.py
+++ b/utils/lib_KalmanFilter.py
@@ -37,47 +37,47 @@
 #         self.prediction_count += 1
 #         return prediction[0]
 #
-# class KalmanFilter_distance:
-#     def __init__(self):
-#         # Initialize a 1D Kalman filter (state: [distance, velocity])
-#         self.kf = cv2.KalmanFilter(2, 1)
-#
-#         # Measurement matrix (we only observe distance)
-#         self.kf.measurementMatrix = np.array([[1, 0]], np.float32)
-#
-#         # Transition matrix (assume constant velocity model)
-#         self.kf.transitionMatrix = np.array([[1, 1],
-#                                              [0, 1]], np.float32)
-#
-#         # Process noise covariance (tune these values)
-#         self.kf.processNoiseCov = np.eye(2, dtype=np.float32) * 0.01
-#
-#         # Measurement noise covariance (tune this value)
-#         self.kf.measurementNoiseCov = np.array([[1]], np.float32) * 0.1
-#
-#         # Initial state covariance (tune these values)
-#         self.kf.errorCovPost = np.eye(2, dtype=np.float32) * 1
-#
-#         # Initialize state (distance = 100, velocity = 0)
-#         self.kf.statePost = np.array([[100], [0]], np.float32)
-#
-#         self.distance = 100  # Current filtered distance
-#         self.detected = False  # Whether a measurement was detected
-#
-#     def update(self, measurement):
-#         """
-#         Update the Kalman filter with a new distance measurement.
-#         """
-#         measured = np.array([[np.float32(measurement)]])
-#         self.kf.correct(measured)
-#         self.distance = self.kf.statePost[0, 0]
-#         self.detected = True
-#
-#     def predict(self):
-#         """
-#         Predict the next distance using the Kalman filter.
-#         """
-#         prediction = self.kf.predict()
-#         self.distance = prediction[0, 0]
-#         self.detected = False
-#         return self.distance
+class KalmanFilter_distance:
+    def __init__(self):
+        # Initialize a 1D Kalman filter (state: [distance, velocity])
+        self.kf = cv2.KalmanFilter(2, 1)
+
+        # Measurement matrix (we only observe distance)
+        self.kf.measurementMatrix = np.array([[1, 0]], np.float32)
+
+        # Transition matrix (assume constant velocity model)
+        self.kf.transitionMatrix = np.array([[1, 1],
+                                             [0, 1]], np.float32)
+
+        # Process noise covariance (tune these values)
+        self.kf.processNoiseCov = np.eye(2, dtype=np.float32) * 0.01
+
+        # Measurement noise covariance (tune this value)
+        self.kf.measurementNoiseCov = np.array([[1]], np.float32) * 0.1
+
+        # Initial state covariance (tune these values)
+        self.kf.errorCovPost = np.eye(2, dtype=np.float32) * 1
+
+        # Initialize state (distance = 100, velocity = 0)
+        self.kf.statePost = np.array([[100], [0]], np.float32)
+
+        self.distance = 100  # Current filtered distance
+        self.detected = False  # Whether a measurement was detected
+
+    def update(self, measurement):
+        """
+        Update the Kalman filter with a new distance measurement.
+        """
+        measured = np.array([[np.float32(measurement)]])
+        self.kf.correct(measured)
+        self.distance = self.kf.statePost[0, 0]
+        self.detected = True
+
+    def predict(self):
+        """
+        Predict the next distance using the Kalman filter.
+        """
+        prediction = self.kf.predict()
+        self.distance = prediction[0, 0]
+        self.detected = False
+        return self.distance
diff --git a/utils/lib_ObjectTracker.py b/utils/lib_ObjectTracker.py
index 8513409c..79c5e443 100644
--- a/utils/lib_ObjectTracker.py
+++ b/utils/lib_ObjectTracker.py
@@ -1,9 +1,10 @@
 import math
-from collections import deque
+from collections import deque, Counter
 import numpy as np
 
 from script_generator.constants import CLASS_NAMES
 from script_generator.debug.logger import log_tr
+from utils.lib_KalmanFilter import KalmanFilter_distance
 
 
 class LockedPenisBox:
@@ -80,8 +81,9 @@ def __init__(self, state):
             global_state
         """
         self.state = state
+        self.frame_end = state.video_info.total_frames
         self.class_names = CLASS_NAMES  # List of class names to track
-        # self.distance_kf = KF.KalmanFilter()  # Kalman filter for distance smoothing
+        self.distance_kf = KalmanFilter_distance()  # Kalman filter for distance smoothing
 
         self.frame = None  # Current video frame
         self.current_frame_id = state.current_frame_id  # frame_pos  # Current frame ID
@@ -107,8 +109,11 @@ def __init__(self, state):
         # Sex position tracking
         self.sex_position = "Not relevant"  # Current sex position
         self.sex_position_reason = ""  # Reason for the current sex position
-        max_history = int(self.fps) * 10  # Maximum history for sex position tracking
+        max_history = int(self.fps) * 4  # Maximum history for sex position tracking
         self.sex_position_history = deque(maxlen=max_history)  # History of sex positions
+        self.last_sex_position_change_index = 0
+        self.sex_position_segments = [[0, 0, "Not relevant", "Initial segment"]]  # List of position segments
+        self.segment_cache = {}  # Cache for segment-specific calculations
 
         # Position and distance tracking
         self.tracked_positions = {}  # Tracked positions for each track_id
@@ -141,28 +146,14 @@ def update_distance(self, raw_distance):
             int: Filtered distance.
         """
         if raw_distance is None:
-            # Predict distance using exponential moving average (EMA)
-            filtered_distance = (
-                0.1 * self.previous_distances[-3]
-                + 0.2 * self.previous_distances[-2]
-                + 0.7 * self.previous_distances[-1]
-            )
+            # Predict distance using Kalman filter
+            filtered_distance = self.distance_kf.predict()
         else:
-            # Apply EMA to raw distance
-            ema_distance = (
-                0.1 * self.previous_distances[-2]
-                + 0.2 * self.previous_distances[-1]
-                + 0.7 * raw_distance
-            )
-
-            # Limit speed of distance change
-            if abs(self.distance - ema_distance) > self.max_speed:
-                ema_distance = self.distance + np.sign(ema_distance - self.distance) * self.max_speed
-
-            filtered_distance = ema_distance
+            self.distance_kf.update(raw_distance)
+            filtered_distance = raw_distance
 
         # Ensure distance is within bounds (0-100)
-        filtered_distance = int(max(0, min(100, filtered_distance)))
+        # filtered_distance = int(max(0, min(100, filtered_distance)))
         self.distance = filtered_distance
 
         # Update previous distances
@@ -263,24 +254,83 @@ def calculate_distance(self, penis_box, other_box):
         y_pos = (oy1 + 2 * oy2) // 3
         x_pos = (ox1 + ox2) // 2
         px1, py1, px2, py2 = penis_box
-        return math.sqrt((x_pos - px2) ** 2 + (y_pos - py2) ** 2)
+        return math.sqrt((x_pos - ((px1 + px2) // 2)) ** 2 + (y_pos - py2) ** 2)
 
     def detect_sex_position_change(self, sex_position, reason):
         """
-        Detect and log changes in the sex position.
+        Detect and update the current sex position based on history.
 
         Args:
-            sex_position (str): New sex position.
-            reason (str): Reason for the change.
+            sex_position (str): The new sex position.
+            reason (str): The reason for the change.
         """
         self.sex_position_history.append(sex_position)
-        position_counts = {position: self.sex_position_history.count(position) for position in self.sex_position_history}
-        most_frequent_position = max(position_counts, key=position_counts.get, default="Not relevant")
+
+        if sex_position == self.sex_position:
+            return  # No change, exit early
+
+        # Use Counter to efficiently count positions
+        position_counts = Counter(self.sex_position_history)
+        most_frequent_position = position_counts.most_common(1)[0][0]
+
         if most_frequent_position != self.sex_position:
             log_tr.debug(f"@{self.current_frame_id} - Sex position switched to: {most_frequent_position}")
             self.sex_position = most_frequent_position
             self.sex_position_reason = reason
 
+            # Find the last change
+            self.last_sex_position_change_index = next(
+                (i for i, pos in enumerate(reversed(self.sex_position_history)) if pos != most_frequent_position),
+                len(self.sex_position_history))
+            if len(self.sex_position_segments) > 0:
+                self.sex_position_segments[-1][1] = self.current_frame_id - self.last_sex_position_change_index
+            self.sex_position_segments.append(
+                [self.current_frame_id - self.last_sex_position_change_index + 1, self.frame_end, self.sex_position, self.sex_position_reason])
+
+    def clean_and_merge_segments(self, min_duration=15):
+        """
+        Remove short segments and merge adjacent segments of the same position.
+
+        Args:
+            min_duration (int): Minimum duration (in frames) for a segment to be kept.
+        """
+        if len(self.sex_position_segments) < 2:
+            return  # Not enough segments to process
+
+        merged_segments = []
+        i = 0
+        min_duration = min_duration * self.fps
+
+        while i < len(self.sex_position_segments):
+            start, end, position, reason = self.sex_position_segments[i]
+            duration = end - start
+
+            # If this is a short segment and surrounded by the same position
+            if duration < min_duration and 0 < i < len(self.sex_position_segments) - 1:
+                prev_start, prev_end, prev_position, _ = self.sex_position_segments[i - 1]
+                next_start, next_end, next_position, next_reason = self.sex_position_segments[i + 1]
+
+                if prev_position == next_position:
+                    log_tr.debug(f"Merging short segment @{start}-{end} into surrounding {prev_position}.")
+                    # Extend previous segment to include the short one and next segment
+                    merged_segments[-1][1] = next_end  # Merge with next segment
+                    i += 1  # Skip next segment as it's merged
+                else:
+                    log_tr.debug(f"Merging short segment @{start}-{end} into previous {prev_position}.")
+                    merged_segments[-1][1] = end  # Merge with previous segment
+
+            else:
+                # Merge with the last segment if positions match
+                if merged_segments and merged_segments[-1][2] == position:
+                    log_tr.debug(f"Merging consecutive segment @{start}-{end} into previous {position}.")
+                    merged_segments[-1][1] = end  # Extend previous segment
+                else:
+                    merged_segments.append([start, end, position, reason])
+
+            i += 1
+
+        self.sex_position_segments = merged_segments
+
     def tracking_logic(self, state, sorted_boxes):
         """
         Main tracking logic to process detected boxes and update tracking state.
@@ -570,20 +620,6 @@ def handle_class_first(self, class_name, box, conf):
                         if current_height > self.locked_penis_box.get_height():
                             self.locked_penis_box.update(self.penis_box, current_height)
 
-                        # Move locked penis box towards current penis box
-                        if self.isVR:
-                            max_move = 180 // int(self.fps)
-                        else:
-                            # in 2D POV, camera is not still, actuation needs to be faster
-                            max_move = 360 // int(self.fps)
-
-                        if abs(self.penis_box[0] - self.locked_penis_box.get_box()[0]) > max_move:
-                            px1 = self.penis_box[0] + np.sign(self.penis_box[0] - self.locked_penis_box.get_box()[0]) * max_move
-                        if abs(self.penis_box[2] - self.locked_penis_box.get_box()[2]) > max_move:
-                            px2 = self.penis_box[2] + np.sign(self.penis_box[2] - self.locked_penis_box.get_box()[2]) * max_move
-                        if abs(self.penis_box[3] - self.locked_penis_box.get_box()[3]) > max_move:
-                            py2 = self.locked_penis_box.get_box()[3] + np.sign(self.penis_box[3] - self.locked_penis_box.get_box()[3]) * max_move
-
                         self.locked_penis_box.update((px1, py2 - self.locked_penis_box.get_height(), px2, py2), self.locked_penis_box.get_height())
 
                         penis_box_area = (self.penis_box[2] - self.penis_box[0]) * (self.penis_box[3] - self.penis_box[1])

From 7abb1dd5978b38a38692f60195e991631dafd675 Mon Sep 17 00:00:00 2001
From: k00gar <arnaud.chevreux@gmail.com>
Date: Wed, 2 Apr 2025 19:14:33 +0200
Subject: [PATCH 266/277] Initiating the crash-dummies branch

---
 .../analyze_tracking_results_v1_test3.py      | 1486 +++++++++++++++++
 1 file changed, 1486 insertions(+)
 create mode 100644 script_generator/analysis/workers/analyze_tracking_results_v1_test3.py

diff --git a/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py b/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py
new file mode 100644
index 00000000..073ff915
--- /dev/null
+++ b/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py
@@ -0,0 +1,1486 @@
+import numpy as np
+import cv2
+from tqdm import tqdm
+from typing import Tuple, List, Dict
+from datetime import timedelta
+from scipy.signal import savgol_filter, find_peaks
+from simplification.cutil import simplify_coords_vw  #, simplify_coords
+from copy import deepcopy
+
+from script_generator.state.app_state import AppState
+from script_generator.object_detection.util.data import load_yolo_data
+from script_generator.object_detection.util.object_detection import make_data_boxes
+from script_generator.video.data_classes.video_info import get_cropped_dimensions
+from script_generator.debug.logger import log, log_tr
+# from utils.lib_ObjectTracker import ObjectTracker
+from script_generator.utils.file import get_output_file_path
+from script_generator.funscript.util.util import write_funscript
+
+
+def _assign_frame_position(contacts: List[Dict]) -> str:
+    """
+    Assign a major position to a frame based on the detected body parts in contact.
+
+    Args:
+        contacts: List of contacts for the frame, each containing a "class_name".
+
+    Returns:
+        The most likely major position for the frame.
+    """
+    if not contacts:
+        return "Not relevant"
+
+    # Count the occurrences of each detected body part
+    detected_parts = [contact["class_name"] for contact in contacts]
+
+    most_likely_position = 'Not relevant'
+
+    if 'pussy' in detected_parts:
+        most_likely_position = 'Cowgirl / Missionary'
+    elif 'butt' in detected_parts:
+        most_likely_position = 'Rev. Cowgirl / Doggy'
+    elif 'face' in detected_parts and 'hand' in detected_parts:
+        #most_likely_position = 'Blowjob'
+        most_likely_position = 'Handjob / Blowjob'
+    elif 'face' in detected_parts:
+        #most_likely_position = 'Blowjob'
+        most_likely_position = 'Handjob / Blowjob'
+    elif 'hand' in detected_parts and 'breast' in detected_parts:
+        most_likely_position = 'Boobjob'
+    elif 'hand' in detected_parts:
+        #most_likely_position = 'Handjob'
+        most_likely_position = 'Handjob / Blowjob'
+    elif 'foot' in detected_parts:
+        most_likely_position = 'Footjob'
+    elif 'breast' in detected_parts:
+        most_likely_position = 'Boobjob'
+
+    return most_likely_position
+
+def _aggregate_segments(frame_positions: List[str], fps: float, min_segment_duration: float = 10.0) -> List[Dict]:
+    """
+    Aggregate frames into segments based on consistent positions. A new segment is created when the position
+    changes consistently. Each segment must be at least `min_segment_duration` seconds long.
+
+    Args:
+        frame_positions: List of positions for each frame.
+        fps: Frames per second of the video.
+        min_segment_duration: Minimum duration of a segment in seconds.
+
+    Returns:
+        A list of segments, each with:
+        - start_frame: Start frame of the segment.
+        - end_frame: End frame of the segment.
+        - start_time: Start time of the segment.
+        - end_time: End time of the segment.
+        - major_position: Major position of the segment.
+    """
+    segments = []
+    current_segment = None
+    min_frames_per_segment = int(min_segment_duration * fps)
+
+    for frame_pos, position in enumerate(frame_positions):
+        if current_segment is None:
+            # Start a new segment
+            current_segment = {
+                "start_frame": frame_pos,
+                "end_frame": frame_pos,
+                "positions": [position]
+            }
+        else:
+            # Check if the current frame's position matches the segment's major position
+            segment_major_position = max(set(current_segment["positions"]),
+                                         key=current_segment["positions"].count)
+            if position == segment_major_position:
+                # Extend the current segment
+                current_segment["end_frame"] = frame_pos
+                current_segment["positions"].append(position)
+            else:
+                # Check if the position change is consistent over the next few frames
+                # (e.g., at least 1 second of consistent change)
+                consistent_change_frames = int(fps)  # 1 second of consistent change
+                consistent_change = True
+
+                # Check the next `consistent_change_frames` frames
+                for i in range(1, consistent_change_frames + 1):
+                    if frame_pos + i >= len(frame_positions) or frame_positions[frame_pos + i] != position:
+                        consistent_change = False
+                        break
+
+                if consistent_change:
+                    # Finalize the current segment if it meets the minimum duration
+                    if current_segment["end_frame"] - current_segment["start_frame"] + 1 >= min_frames_per_segment:
+                        segments.append({
+                            "start_frame": current_segment["start_frame"],
+                            "end_frame": current_segment["end_frame"],
+                            "start_time": current_segment["start_frame"] / fps,
+                            "end_time": current_segment["end_frame"] / fps,
+                            "major_position": segment_major_position
+                        })
+                    # Start a new segment
+                    current_segment = {
+                        "start_frame": frame_pos,
+                        "end_frame": frame_pos,
+                        "positions": [position]
+                    }
+                else:
+                    # Continue the current segment
+                    current_segment["end_frame"] = frame_pos
+                    current_segment["positions"].append(position)
+
+    # Finalize the last segment if it meets the minimum duration
+    if current_segment is not None and current_segment["end_frame"] - current_segment[
+        "start_frame"] + 1 >= min_frames_per_segment:
+        segment_major_position = max(set(current_segment["positions"]), key=current_segment["positions"].count)
+        segments.append({
+            "start_frame": current_segment["start_frame"],
+            "end_frame": current_segment["end_frame"],
+            "start_time": current_segment["start_frame"] / fps,
+            "end_time": current_segment["end_frame"] / fps,
+            "major_position": segment_major_position
+        })
+
+    # Merge consecutive segments of the same position
+    merged_segments = []
+    for segment in segments:
+        if merged_segments and segment["major_position"] == merged_segments[-1]["major_position"]:
+            # Merge with the previous segment
+            merged_segments[-1]["end_frame"] = segment["end_frame"]
+            merged_segments[-1]["end_time"] = segment["end_time"]
+        else:
+            # Add as a new segment
+            merged_segments.append(segment)
+
+    # Remove segments shorter than min_segment_duration
+    final_segments = []
+    for segment in merged_segments:
+        segment_duration = segment["end_time"] - segment["start_time"]
+        if segment_duration >= min_segment_duration:
+            final_segments.append(segment)
+        elif final_segments:
+            # Merge short segment with the previous segment
+            final_segments[-1]["end_frame"] = segment["end_frame"]
+            final_segments[-1]["end_time"] = segment["end_time"]
+
+    return final_segments
+
+def _calculate_speed(distances):
+    """Calculate speed based on absolute deltas between consecutive distances."""
+    if len(distances) < 2:
+        return 0
+
+    total_delta = 0
+    for i in range(1, len(distances)):
+        total_delta += abs(distances[i] - distances[i - 1])
+
+    # Average delta as a simple speed measure
+    return round(total_delta / (len(distances) - 1), 2)
+
+def _frame_to_timecode(frame_number, fps):
+    """Convert frame number to timecode format."""
+    total_seconds = frame_number / fps
+    hours = int(total_seconds // 3600)
+    minutes = int((total_seconds % 3600) // 60)
+    seconds = int(total_seconds % 60)
+    milliseconds = int((total_seconds % 1) * 1000)
+    return f"{hours:02}:{minutes:02}:{seconds:02}.{milliseconds:03}"
+
+def _initialize_kalman_filter() -> cv2.KalmanFilter:
+    """
+    Initialize a Kalman filter for tracking the height of the penis box.
+    """
+    kalman_filter = cv2.KalmanFilter(2, 1)  # 2 state variables (height, d_height), 1 measurement variable (height)
+    kalman_filter.measurementMatrix = np.array([[1, 0]], np.float32)
+    kalman_filter.transitionMatrix = np.array([[1, 1], [0, 1]], np.float32)
+    kalman_filter.processNoiseCov = np.eye(2, dtype=np.float32) * 0.01
+    kalman_filter.measurementNoiseCov = np.eye(1, dtype=np.float32) * 0.1
+    kalman_filter.errorCovPost = np.eye(2, dtype=np.float32)
+    return kalman_filter
+
+
+def _get_central_third(frame_width: int, frame_height: int) -> Tuple[int, int, int, int]:
+    """
+    Get the coordinates of the central third of the frame.
+    """
+    x1 = frame_width // 3
+    x2 = 2 * frame_width // 3
+    y1 = frame_height // 3
+    y2 = 2 * frame_height // 3
+    return x1, y1, x2, y2
+
+def _calculate_iou(box1: Tuple[int, int, int, int], box2: Tuple[int, int, int, int]) -> float:
+    """
+    Calculate Intersection over Union (IOU) between two bounding boxes.
+    """
+    x1_1, y1_1, x2_1, y2_1 = box1
+    x1_2, y1_2, x2_2, y2_2 = box2
+
+    # Calculate intersection area
+    x1_inter = max(x1_1, x1_2)
+    y1_inter = max(y1_1, y1_2)
+    x2_inter = min(x2_1, x2_2)
+    y2_inter = min(y2_1, y2_2)
+
+    inter_area = max(0, x2_inter - x1_inter) * max(0, y2_inter - y1_inter)
+
+    # Calculate union area
+    box1_area = (x2_1 - x1_1) * (y2_1 - y1_1)
+    box2_area = (x2_2 - x1_2) * (y2_2 - y1_2)
+    union_area = box1_area + box2_area - inter_area
+
+    return inter_area / union_area if union_area > 0 else 0
+
+def _calculate_normalized_distance(box1: Tuple[int, int, int, int], box2: Tuple[int, int, int, int], frame_width: int, frame_height: int) -> float:
+    """
+    Calculate the normalized Euclidean distance between the centers of two bounding boxes.
+    """
+    # Calculate box centers
+    center1 = ((box1[0] + box1[2]) / 2, (box1[1] + box1[3]) / 2)
+    center2 = ((box2[0] + box2[2]) / 2, (box2[1] + box2[3]) / 2)
+
+    # Calculate Euclidean distance
+    distance = np.sqrt((center1[0] - center2[0]) ** 2 + (center1[1] - center2[1]) ** 2)
+
+    # Normalize by the frame diagonal
+    frame_diagonal = np.sqrt(frame_width ** 2 + frame_height ** 2)
+    normalized_distance = distance / frame_diagonal
+
+    return normalized_distance
+
+def _calculate_normalized_distance_to_base(locked_penis_box: Tuple[int, int, int, int],
+                                           class_name: str,
+                                           class_box: Tuple[int, int, int, int],
+                                           max_distance: int) -> float:
+    """
+    Calculate the normalized distance (0 to 100) between the base of the locked penis and a relevant class box.
+
+    Args:
+        locked_penis_box: The locked penis box (x1, y1, x2, y2).
+        class_box: The bounding box of the relevant class (x1, y1, x2, y2).
+
+    Returns:
+        Normalized distance (0 to 100), where 0 is at the base of the penis and 100 is farthest away.
+    """
+    # Base of the locked penis is at y2 (bottom of the box)
+    penis_base_y = locked_penis_box[3]
+
+    # if class_name in ['hand', 'foot']:
+    #     # top y
+    #     box_y = class_box[1]
+    # else:
+    #     # bottom y
+    #     box_y = class_box[3]
+
+    # Determine which y-coordinate to use based on class
+    # box_y = (class_box[3] + class_box[1]) // 2 if class_name in ('hand', 'foot') else class_box[3] if class_name not in ['butt', 'pussy'] else (9 * class_box[3] + class_box[1]) // 10
+
+    if class_name in ['face']:
+        box_y = class_box[3]
+    elif class_name in ['hand']:
+        box_y = class_box[1]
+    elif class_name in ['butt']:
+        box_y = (9 * class_box[3] + class_box[1]) // 10
+    else:
+        box_y = (class_box[3] + class_box[1]) // 2
+    #box_y = (class_box[3] + class_box[1]) // 2 if class_name != 'butt' else (9 * class_box[3] + class_box[1]) // 10
+
+    # Distance from the base of the penis to the class bottom
+    # distance = abs(box_y - penis_base_y)
+    distance = penis_base_y - box_y
+    # distance = max(penis_base_y - box_y, 0)
+
+    normalized_distance = int((distance / max_distance) * 100) if max_distance > 0 else 100 # min((distance / max_distance) * 100 if max_distance > 0 else 100, 100)
+
+    return normalized_distance
+    #return int((distance / max_distance) * 100 if max_distance > 0 else 100)
+
+def _normalize_funscript_sparse(funscript_data, segments):
+    result = deepcopy(funscript_data)
+
+    for seg_counter, segment in enumerate(segments):
+        position_type = segment['major_position']
+        start_frame = segment['start_frame']
+        end_frame = segment['end_frame']
+
+        # Extract only frames within this segment
+        segment_indices = [i for i, (f, _) in enumerate(result) if start_frame <= f <= end_frame]
+        if not segment_indices:
+            continue  # Skip empty segments
+
+        segment_data = [result[i] for i in segment_indices]
+        values = [x[1] for x in segment_data]
+
+        p01 = np.percentile(values, 1)
+        p99 = np.percentile(values, 99)
+
+        scale_range = p99 - p01
+
+        # Adjust the values properly
+        for i in segment_indices:
+            frame, val = result[i]
+
+            if position_type in ['Not relevant', 'Close up']:
+                val = 100  # Directly set to 100
+            else:
+                if val <= p01:
+                    val = 0  # Ensure lowest values are mapped to 0
+                elif val >= p99:
+                    val = 100  # Ensure highest values are mapped to 100
+                else:
+                    if scale_range < 1e-6:
+                        val = 50  # If no variation, set to mid-range
+                    else:
+                        # Rescale values within p01 - p99 range
+                        val = (val - p01) / scale_range * 100
+
+                        # Apply specific scaling for certain positions
+                        # if position_type in ['Handjob / Blowjob', 'Footjob', 'Boobjob']:
+                        #     val = val * 0.7 + 30  # Keeps within ~30-95 range
+
+            val = int(max(0, min(100, round(val))))  # Ensure final value stays in bounds
+            result[i] = (frame, val)
+
+    return result
+
+def interpolate_box(box1: Tuple[int, int, int, int], box2: Tuple[int, int, int, int], t: float) -> Tuple[int, int, int, int]:
+    """
+    Interpolate between two bounding boxes.
+    """
+    x1 = int(box1[0] + t * (box2[0] - box1[0]))
+    y1 = int(box1[1] + t * (box2[1] - box1[1]))
+    x2 = int(box1[2] + t * (box2[2] - box1[2]))
+    y2 = int(box1[3] + t * (box2[3] - box1[3]))
+    return (x1, y1, x2, y2)
+
+def analyze_tracking_results_v1(state: AppState):
+    """
+    Analyze tracking results, focusing on the penis box and other detected classes.
+    """
+    video_info = state.video_info
+    fps = video_info.fps
+    int_fps = int(fps)
+    exists, yolo_data, raw_yolo_path, _ = load_yolo_data(state)
+    results = make_data_boxes(yolo_data)
+    width, height = get_cropped_dimensions(state.video_info)
+    frame_area = width * height
+    list_of_frames = results.get_all_frame_ids()  # Get all frame IDs with detections
+    total_frames = state.video_info.total_frames
+
+    # Get central third of the frame
+    x1, y1, x2, y2 = _get_central_third(width, height)
+
+
+    # region PASS 1 - Interpolate the position of boxes when there are gaps (flickering) for the same track_id
+    # PASS 1: Interpolate the position of boxes when there are gaps (flickering) for the same track_id.
+
+    # Dictionary to store the last seen frame and box for each track_id
+    track_history = {}  # Format: {track_id: {"last_frame": int, "last_box": Tuple[int, int, int, int]}}
+
+    # Get sorted boxes for all frames
+
+    sorted_boxes = results.get_all_boxes(total_frames)
+
+    # List to store the final output after interpolation
+    interpolated_frames = [[] for _ in range(total_frames)]
+    total_interpolated = 0
+
+    MAX_GAP = 10  # Limit interpolation to 10-frame gaps
+
+    for frame_pos in tqdm(range(total_frames), desc="Pass 1 - Interpolating boxes (linear)", unit="f"):
+        frame_boxes = sorted_boxes[frame_pos] if frame_pos < len(sorted_boxes) else []
+
+        for box in frame_boxes:
+            box_coords, confidence, class_id, class_name, track_id = box
+
+            if track_id in track_history:
+                last_frame = track_history[track_id]["last_frame"]
+                last_box = track_history[track_id]["last_box"]
+
+                # Ensure the gap is reasonable
+                if 1 < (frame_pos - last_frame) <= MAX_GAP:
+                    for i in range(last_frame + 1, frame_pos):
+                        total_interpolated += 1
+                        t = (i - last_frame) / (frame_pos - last_frame)
+
+                        interpolated_box = interpolate_box(last_box, box_coords, t)
+
+                        # Ensure the interpolated box is not too far from expected motion
+                        if np.linalg.norm(np.array(interpolated_box[:2]) - np.array(last_box[:2])) < 50:
+                            interpolated_frames[i].append(
+                                (interpolated_box, confidence, class_id, class_name, track_id)
+                            )
+
+            # Update track history
+            track_history[track_id] = {"last_frame": frame_pos, "last_box": box_coords}
+            interpolated_frames[frame_pos].append(box)
+
+    print(f"Interpolated {total_interpolated} detections")
+    #endregion
+
+
+    # region PASS 2 : Tracking
+
+    # PASS 2 : Kalman every detected boxes for fps duration, and if they match a newly detected box of same class, merge their track_ids
+    # Dictionary to store last known locations and track_ids of disappeared objects
+    disappeared_objects = {}  # Format: {class_name: {track_id: {"last_box": Tuple[int, int, int, int], "last_frame": int}}}
+
+    # Dictionary to store active track_ids and their last known frame
+    active_tracks = {}  # Format: {class_name: {track_id: last_frame}}
+
+    # Dictionary to store mappings between matched track_ids
+    track_id_mapping = {}  # Format: {new_track_id: original_track_id}
+
+    # Set to store track_ids that have been checked and did not match any disappeared track_id
+    unmatched_track_ids = set()
+
+    # List to store the final output after merging
+    merged_frames = []
+
+    total_merged = 0
+
+    # Weights for IOU and distance
+    w1 = 0.7  # Weight for IOU
+    w2 = 0.3  # Weight for distance
+
+    for frame_pos, frame_boxes in tqdm(enumerate(interpolated_frames), desc="Pass 2 - Merging track_ids", unit="f"):
+        merged_frame_boxes = []
+
+        # Step 1: Suppress overlapping boxes of the same class
+        suppressed_boxes = []
+        suppressed_track_ids = set()  # Track IDs of suppressed boxes
+
+        for i, box1 in enumerate(frame_boxes):
+            suppress = False
+            for j, box2 in enumerate(frame_boxes):
+                if i != j and box1[3] == box2[3]:  # Same class
+                    iou = _calculate_iou(box1[0], box2[0])
+                    if iou > 0.3:  # High overlap
+                        # Suppress the box with lower confidence
+                        if box1[1] < box2[1]:
+                            suppress = True
+                            suppressed_track_ids.add(box1[4])  # Add suppressed track_id
+                            break
+            if not suppress:
+                suppressed_boxes.append(box1)
+
+        # Step 2: Add suppressed track_ids to disappeared_objects
+        for track_id in suppressed_track_ids:
+            class_name = None
+            last_box = None
+
+            # Find the class and last box of the suppressed track_id
+            for box in frame_boxes:
+                if box[4] == track_id:
+                    class_name = box[3]
+                    last_box = box[0]
+                    break
+
+            if class_name and last_box:
+                if class_name not in disappeared_objects:
+                    disappeared_objects[class_name] = {}
+                disappeared_objects[class_name][track_id] = {
+                    "last_box": last_box,
+                    "last_frame": frame_pos
+                }
+
+        # Step 3: Process each box in the current frame
+        for box in suppressed_boxes:
+            box_coords = box[0]
+            confidence = box[1]
+            class_id = box[2]
+            class_name = box[3]
+            track_id = box[4]
+
+            # Check if this track_id has been mapped to another track_id
+            if track_id in track_id_mapping:
+                # Reassign the track_id to the mapped value
+                track_id = track_id_mapping[track_id]
+
+            # Check if this is the first appearance of the track_id and it hasn't been matched before
+            if track_id not in unmatched_track_ids and track_id not in track_id_mapping:
+                # Check if this class has disappeared objects
+                if class_name in disappeared_objects:
+                    # Find the best match based on combined IOU and distance
+                    best_match = None
+                    best_score = -1
+
+                    for disappeared_track_id, disappeared_data in disappeared_objects[class_name].items():
+                        # Skip if the disappeared track_id has already been mapped
+                        if disappeared_track_id in track_id_mapping.values():
+                            continue
+
+                        # Calculate IOU and normalized distance
+                        iou = _calculate_iou(box_coords, disappeared_data["last_box"])
+                        normalized_distance = _calculate_normalized_distance(box_coords, disappeared_data["last_box"], width, height)
+
+                        # Calculate combined score
+                        score = (w1 * iou) + (w2 * (1 - normalized_distance))
+
+                        if score > best_score:
+                            best_score = score
+                            best_match = disappeared_track_id
+
+                    if best_score > 0.1:  # Minimum score threshold for matching
+                        # Reallocate the track_id of the disappeared object
+                        track_id_mapping[track_id] = best_match  # Map the new track_id to the original one
+                        track_id = best_match
+                        total_merged += 1
+                        del disappeared_objects[class_name][best_match]  # Remove from disappeared objects
+                    else:
+                        # No match found, add to unmatched_track_ids
+                        unmatched_track_ids.add(track_id)
+
+            # Add the box to the merged frame
+            merged_frame_boxes.append((box_coords, confidence, class_id, class_name, track_id))
+
+            # Update active tracks
+            if class_name not in active_tracks:
+                active_tracks[class_name] = {}
+            active_tracks[class_name][track_id] = frame_pos
+
+        # Step 4: Update disappeared objects
+        for class_name in list(active_tracks.keys()):
+            for track_id in list(active_tracks[class_name].keys()):
+                if frame_pos - active_tracks[class_name][track_id] > 3 * fps:  # Increased threshold (3xFPS)
+                    # Add to disappeared_objects
+                    if class_name not in disappeared_objects:
+                        disappeared_objects[class_name] = {}
+                    disappeared_objects[class_name][track_id] = {
+                        "last_box": box_coords,  # Last known box coordinates
+                        "last_frame": active_tracks[class_name][track_id]  # Last frame where the box was seen
+                    }
+                    del active_tracks[class_name][track_id]  # Remove from active tracks
+
+        # Step 5: Remove disappeared objects that have been missing for more than 3xFPS frames
+        for class_name in list(disappeared_objects.keys()):
+            for track_id in list(disappeared_objects[class_name].keys()):
+                if frame_pos - disappeared_objects[class_name][track_id]["last_frame"] > 3 * fps:
+                    del disappeared_objects[class_name][track_id]
+
+        # Add the merged frame to the output
+        merged_frames.append(merged_frame_boxes)
+
+    print(f"Merged {total_merged} boxes' track_ids")
+    # endregion
+
+
+    # region PASS 3 - Building the locked_penis box
+
+    # Initialize tracking data
+    tracking_data = []  # List to store tracking data for each frame
+    penetration = False
+
+    # Initialize locked_penis variables
+    locked_penis = {
+        "active": False,  # Whether the lock is active
+        "last_box": None,  # Last known full box (x1, y1, x2, y2)
+        "max_height": 0,  # Maximum height of the penis box
+        'max_penetration_height': 0, # Maximum penetration height of the penis box
+        'area': 0,  # Area of the penis box
+        "visible_part": 100,
+        "consecutive_detections": 0,  # Number of consecutive frames with penis detection
+        "consecutive_non_detections": 0,  # Number of consecutive frames without penis detection
+        "glans_detected": False,  # Whether the glans is detected
+    }
+
+    # Initialize Kalman filter for height prediction
+    kalman_filter = _initialize_kalman_filter()
+
+    # TODO  : add if is_Vr ?
+
+    # Precompute central boxes for all frames
+    central_boxes_all_frames = []
+    for frame_pos in range(state.video_info.total_frames):
+        if frame_pos < len(merged_frames):
+            central_boxes = [
+                box for box in merged_frames[frame_pos]
+                if x1 <= (box[0][0] + box[0][2]) // 2 <= x2  # and y1 <= (box[0][1] + box[0][3]) // 2 <= y2
+            ]
+        else:
+            central_boxes = []
+        central_boxes_all_frames.append(central_boxes)
+
+    last_penis_box = None
+    last_locked_penis_box = None
+    last_active = False
+    last_max_height = 0
+    last_area = 0
+    last_max_penetration_height = 0
+    last_visible_part = 100
+    last_glans_detected = False
+
+    # Process each frame
+    for frame_pos in tqdm(
+            range(state.video_info.total_frames),
+            unit="f",
+            desc="Pass 3 - Building the locked penis box",
+            position=0,
+            unit_scale=False,
+            unit_divisor=1
+    ):
+
+        frame_data = {
+            "frame_id": frame_pos,
+            "penis_box": None,
+            "locked_penis": {
+                'box': [0, 0, 0, 0] if not last_locked_penis_box else last_locked_penis_box,  # Default box
+                'active': last_active,  # Default state
+                'max_height': last_max_height,
+                'max_penetration_height': last_max_penetration_height,
+                'area' : last_area,
+                'consecutive_detections': 0,
+                'consecutive_non_detections': 0,
+                'visible_part': last_visible_part,
+                'glans_detected': last_glans_detected
+            },
+            "detected_boxes": [],
+            "distances_to_penis": []
+        }
+
+        central_boxes = central_boxes_all_frames[frame_pos]
+
+        for box in central_boxes:
+            if box[3] != "penis":
+                frame_data["detected_boxes"].append(box)
+
+        # Locate the penis box (if multiple, select the one with the max y2)
+        penis_boxes = [box for box in central_boxes if box[3] == "penis"]
+        if penis_boxes:
+            selected_penis_box = max(penis_boxes, key=lambda box: box[0][3])  # Select box with max y2
+            frame_data["penis_box"] = selected_penis_box[0]  # Store the box coordinates (x1, y1, x2, y2)
+
+            frame_data["detected_boxes"].append(selected_penis_box)
+
+            # Update locked_penis variables
+            locked_penis["consecutive_detections"] += 1
+            locked_penis["consecutive_non_detections"] = 0
+
+            # Update last known box
+            last_penis_box = selected_penis_box[0]
+
+            # Update max height (only increase, never decrease unless glans is detected)
+            current_height = selected_penis_box[0][3] - selected_penis_box[0][1]
+            if current_height > locked_penis["max_height"]:
+                locked_penis["max_height"] = current_height
+                locked_penis["max_penetration_height"] = int(0.65 * current_height)
+
+
+            locked_penis["glans_detected"] = False
+            # Check for glans within the penis box
+            glans_boxes = [box for box in central_boxes if box[3] == "glans"]
+            for glans_box in glans_boxes:
+                if _calculate_iou(selected_penis_box[0], glans_box[0]) > 0:
+                    locked_penis["glans_detected"] = True
+                    # Adjust height if glans is detected within the penis box
+                    glans_bottom_height = selected_penis_box[0][3] - glans_box[0][3]
+                    glans_top_height = selected_penis_box[0][3] - glans_box[0][1]
+                    locked_penis["max_penetration_height"] = max(0, min(locked_penis["max_height"], glans_bottom_height))
+                    locked_penis["max_height"] = max(0, min(locked_penis["max_height"], current_height), glans_top_height)
+            #if not locked_penis["glans_detected"] and current_height > locked_penis["max_penetration_height"]:
+            #    locked_penis["max_penetration_height"] = current_height
+
+            # Check if navel box is within the penis box, then this is a "giant penis" type error, we need to limit the size
+            navel_boxes = [box for box in central_boxes if box[3] == "navel"]
+            if frame_data["locked_penis"]["active"]:
+                for navel_box in navel_boxes:
+                    if _calculate_iou(frame_data["locked_penis"]["box"], navel_box[0]) > 0:
+                        navel_height = selected_penis_box[0][3] - navel_box[0][3]
+                        locked_penis["max_height"] = max(0, min(locked_penis["max_height"], navel_height))
+
+
+            # Activate locked_penis after fps / 5 frames with consistent detection
+            if not locked_penis["active"] and locked_penis["consecutive_detections"] >= fps / 5: #10:
+                locked_penis["active"] = True
+
+            # Update Kalman filter with the observed height
+            kalman_filter.correct(np.array([[current_height]], dtype=np.float32))
+        else:
+            # No penis detection in this frame
+            locked_penis["consecutive_detections"] = 0
+            locked_penis["consecutive_non_detections"] += 1
+
+            # Deactivate locked_penis after 3 * fps frames without detection
+            if locked_penis["active"] and not penetration and locked_penis["consecutive_non_detections"] >= 3 * fps:
+                locked_penis["active"] = False
+
+        # If locked_penis is active, use the locked box and predicted height
+        if locked_penis["active"]:
+            # Predict height using the Kalman filter
+            predicted_height = int(kalman_filter.predict()[0][0])
+
+            # Constrain the predicted height within the bounds of the locked penis box
+            predicted_height = min(max(predicted_height, 0), locked_penis["max_height"])
+
+            frame_data["penis_box"] = (
+                last_penis_box[0],  # x1
+                last_penis_box[3] - predicted_height,  # y1
+                last_penis_box[2],  # x2
+                last_penis_box[3]  # y2
+            )
+
+            # Create the locked penis box
+            locked_penis["last_box"] = (
+                last_penis_box[0],  # x1
+                last_penis_box[3] - locked_penis["max_height"],  # y1
+                last_penis_box[2],  # x2
+                last_penis_box[3]  # y2
+            )
+
+            if locked_penis["max_height"] == 0:
+                locked_penis["visible_part"] = 0
+                locked_penis["area"] = 0
+            else:
+                locked_penis["visible_part"] = int((predicted_height / locked_penis["max_height"]) * 100)
+                locked_penis["area"] = int((locked_penis['last_box'][2] - locked_penis['last_box'][0]) * (locked_penis['last_box'][3] - locked_penis['last_box'][1]))
+
+        last_locked_penis_box = locked_penis["last_box"]
+        last_active = locked_penis["active"]
+        last_max_height = locked_penis["max_height"]
+        last_max_penetration_height = locked_penis["max_penetration_height"]
+        last_visible_part = locked_penis["visible_part"]
+        last_glans_detected = locked_penis["glans_detected"]
+        last_area = locked_penis["area"]
+
+        if last_penis_box:
+            frame_data["locked_penis"] = {
+                'box' : (
+                    last_penis_box[0],  # x1
+                    last_penis_box[3] - locked_penis["max_height"],  # y1
+                    last_penis_box[2],  # x2
+                    last_penis_box[3]  # y2
+                    ),
+                'active': locked_penis["active"],
+                'consecutive_detections': locked_penis["consecutive_detections"],
+                'consecutive_non_detections': locked_penis["consecutive_non_detections"],
+                'max_height': locked_penis["max_height"],
+                'max_penetration_height': locked_penis["max_penetration_height"],
+                'area': locked_penis["area"],
+                'visible_part': locked_penis["visible_part"],
+                'glans_detected': locked_penis["glans_detected"]
+            }
+
+        # Append frame data to tracking data
+        tracking_data.append(frame_data)
+        #locked_penis_history.append(locked_penis)
+
+    # endregion
+
+
+    # region PASS 4 - Identify all boxes, classes, track_id in contact with the locked penis box
+    # PASS 4 - Identify all boxes, classes, track_id in contact with the locked penis box
+    contact_data = []  # List to store contact data for each frame
+
+    # Step 1: Assign a position to each frame
+    frame_positions = []
+    for frame_data in tqdm(tracking_data, desc="Pass 4 - Assigning positions", unit="f"):
+        position = "Not relevant"
+        if frame_data["locked_penis"]["active"]:
+            # Identify contacts and assign a position
+            contacts = []
+            for box in frame_data["detected_boxes"]:
+                box_coords = box[0]
+                class_name = box[3]
+                iou = _calculate_iou(frame_data["locked_penis"]["box"], box_coords)
+                if iou > 0.05:
+                    box_area = (box[0][2] - box[0][0]) * (box[0][3] - box[0][1])
+                    if class_name not in ["hand", "foot"] or (
+                            class_name in ["hand", "foot"] and box_area > 0.7 * locked_penis["area"] and box_area < 2 * locked_penis["area"]):
+                        contacts.append({"class_name": class_name})
+            position = _assign_frame_position(contacts)
+        elif not frame_data["locked_penis"]["active"]:
+            for box in frame_data["detected_boxes"]:
+                box_coords = box[0]
+                class_name = box[3]
+                # if pussy present and pussy box area > 10% of the frame area then, closeup
+                if class_name in ["pussy", "butt"]:
+                    box_area = (box_coords[2] - box_coords[0]) * (box_coords[3] - box_coords[1])
+                    if box_area > 0.07 * frame_area:
+                        position = "Close up"
+                        break
+
+
+            if position != "Close up":
+                position = "Not relevant"
+        else:
+            position = "Not relevant"
+        frame_positions.append(position)
+
+    # Step 2: Aggregate frames into segments
+    segments = _aggregate_segments(frame_positions, fps, min_segment_duration=1.0)
+
+    keep_going = True
+    # Step 3: Clean and merge segments
+    while keep_going:
+        keep_going = False
+        for i in range(len(segments) - 1):
+            if i < len(segments) - 2:
+                if segments[i]["major_position"] == segments[i + 1]:
+                    segments[i]["end_frame"] = segments[i + 1]["end_frame"]
+                    segments.pop(i + 1)
+                    keep_going = True
+                    break
+                if segments[i]["major_position"] == segments[i + 2]["major_position"] != "Not relevant" and (segments[i + 1]["end_frame"] - segments[i+1]["start_frame"] < 10 * fps):
+                    segments[i]["end_frame"] = segments[i + 2]["end_frame"]
+                    segments.pop(i + 2)
+                    segments.pop(i + 1)
+                    keep_going = True
+                    break
+                if  i>0 and segments[i]["major_position"] != segments[i + 1]["major_position"] and (segments[i]["end_frame"] - segments[i]["start_frame"] < 5 * fps):
+                    segments[i-1]["end_frame"] = segments[i]["end_frame"]
+                    segments.pop(i)
+                    keep_going = True
+                    break
+
+
+    # # Print segment one by one
+    # log_tr.info("Technical segments:")
+    # for segment in segments:
+    #     start_frame = segment["start_frame"]
+    #     end_frame = segment["end_frame"]
+    #     position = segment["major_position"]
+    #     start_time = _frame_to_timecode(start_frame, fps)
+    #     end_time = _frame_to_timecode(end_frame, fps)
+    #     log_tr.info(f"From {start_frame} to {end_frame} - {start_time} to {end_time} - duration: {int((end_frame - start_frame) / fps)} s -  {position}")
+
+    timestamps = []
+    keep_going = True
+    iteration = 1
+
+    i = 0
+
+    # Initial pass to create timestamps
+    for segment in segments:
+        start_frame = segment["start_frame"]
+        end_frame = segment["end_frame"]
+        start_time = _frame_to_timecode(start_frame, fps)
+        end_time = _frame_to_timecode(end_frame, fps)
+        position = segment["major_position"]
+        timestamps.append([start_frame, end_frame, start_time, end_time, position])
+
+
+    while keep_going:
+        keep_going = False
+        for i in range(len(timestamps)-1):
+            if i < len(timestamps)-2 and timestamps[i][4] == timestamps[i + 2][4] and timestamps[i + 1][4] == 'Not relevant' and (timestamps[i+1][1] - timestamps[i+1][0] < 10 * fps):
+                # Merge with the previous timestamp
+                timestamps[i][1] = timestamps[i + 2][1]  # Update end_frame
+                timestamps[i][3] = timestamps[i + 2][3]
+                # pop line i+2 and i+1
+                timestamps.pop(i + 2)
+                timestamps.pop(i + 1)
+                keep_going = True
+                break
+            if i < len(timestamps)-1 and timestamps[i][4] == timestamps[i + 1][4]:
+                # Merge with the next timestamp
+                timestamps[i][1] = timestamps[i + 1][1]  # Update end_frame
+                timestamps[i][3] = timestamps[i + 1][3]
+                # pop line i+1
+                timestamps.pop(i + 1)
+                keep_going = True
+                break
+            if i < len(timestamps)-1 and timestamps[i][4] != timestamps[i + 1][4] and timestamps[i + 1][4] == 'Not relevant' and (timestamps[i+1][1] - timestamps[i+1][0] < 15 * fps):
+                # Merge with the next timestamp
+                timestamps[i][1] = timestamps[i + 1][1]  # Update end_frame
+                timestamps[i][3] = timestamps[i + 1][3]
+                # pop line i+1
+                timestamps.pop(i + 1)
+                keep_going = True
+                break
+            if i < len(timestamps)-1 and timestamps[i][4] in ['Handjob', 'Blowjob', 'Boobjob'] and timestamps[i + 1][4] in ['Handjob', 'Blowjob', 'Boobjob']  and ((timestamps[i][1] - timestamps[i][0] < 15 * fps) or (timestamps[i+1][1] - timestamps[i+1][0] < 15 * fps)):
+                # Merge with the next timestamp
+                timestamps[i][4] = 'Blowjob'
+                timestamps[i][1] = timestamps[i + 1][1]  # Update end_frame
+                timestamps[i][3] = timestamps[i + 1][3]
+                # pop line i+1
+                timestamps.pop(i + 1)
+                keep_going = True
+                break
+            if i >= 1 and (timestamps[i][1] - timestamps[i][0] < 15 * fps):
+                timestamps[i][4] = 'Not relevant'
+                keep_going = True
+                break
+
+
+
+    # Print the final timestamps
+    log_tr.info("Final timestamps:")
+    for timestamp in timestamps:
+        start_frame, end_frame, start_time, end_time, position = timestamp
+        log_tr.info(f"From {start_frame} to {end_frame} - {start_time} to {end_time} - duration : {(end_frame - start_frame) // fps}s - {position}")
+
+    # endregion
+
+
+    # region PASS 5 - Determine the final distance for each frame, depending on the classes relevant to the position of the segment the frame is in
+    # PASS 5 - Determine the final distance for each frame, depending on the classes relevant to the position of the segment the frame is in
+
+    distance = []
+    relevant_classes = []
+    prev_nb_touching = 0
+    prev_fastest_track_id = 0
+
+    transition_active = False
+    transition_frames = 2 * int(fps)
+    remaining_transition_frames = transition_frames
+
+    # Dictionary to maintain track history {track_id: [last_10_distances]}
+    track_history = {}
+
+    # Dictionary to maintain previous frame's distance for each track {track_id: previous_distance}
+    previous_distances = {}
+
+    # kalman filter for distance estimation
+    # kf_distance = _initialize_kalman_filter()
+
+    # # Update Kalman filter with the observed distance
+    # kalman_filter.correct(np.array([[comp_distance]], dtype=np.float32))
+    #
+    # predicted_distance = int(kalman_filter.predict()[0][0])
+    # # Constrain the predicted distance between 0 and 100
+    # predicted_distance = min(max(predicted_distance, 0), 100)
+
+    for frame_pos in tqdm(range(total_frames), desc="Pass 5 - Determining distance", unit="f"):
+        # Determine the position the frame belongs to in the segments
+
+        comp_distance = 0
+        nb_touching = 0
+        track_speeds = {}
+        fastest_track_id = 0
+
+        position = "Not relevant"
+        bounding_boxes = []
+        counter = 0
+
+        if not tracking_data[frame_pos]["locked_penis"]:
+            comp_distance = 100
+            counter += 1
+            print(f"Frame {frame_pos} has no locked penis - skipped ({counter}/{total_frames})")
+            penis_box = [0,0,0,0]
+            penis_active = 'Undetected'
+            penis_consecutive_detections = 0
+            penis_consecutive_non_detections = 0
+            penis_max_height = 0
+            penis_max_penetration_height = 0
+            penis_visible_part = 0
+            penis_glans_detected = False
+            penis_box_area = 0
+        else:
+            penis = tracking_data[frame_pos]["locked_penis"]
+            penis_box = tuple(penis['box'])
+            penis_active = penis['active']
+            penis_consecutive_detections = penis['consecutive_detections']
+            penis_consecutive_non_detections = penis['consecutive_non_detections']
+            penis_max_height = penis['max_height']
+            if frame_pos == 20930:
+                log_tr.info(f"Penis max height: {penis_max_height}")
+            penis_max_penetration_height = penis['max_penetration_height']
+            penis_visible_part = penis['visible_part']
+            penis_glans_detected = penis['glans_detected']
+            penis_box_area = penis['area']
+
+            for segment in segments:
+                if segment["start_frame"] <= frame_pos <= segment["end_frame"]:
+                    position = segment["major_position"]
+                    break
+
+            relevant_classes = []
+            penetration = False
+
+            if position == "Not relevant":
+                relevant_classes = []
+                comp_distance = 100
+            elif position == "Cowgirl / Missionary":
+                relevant_classes = ["pussy"]
+                if not penis_glans_detected:
+                    penetration = True
+            elif position == "Rev. Cowgirl / Doggy":
+                relevant_classes = ["butt"]
+                if not penis_glans_detected:
+                    penetration = True
+            elif position == "Handjob / Blowjob":
+                relevant_classes = ["face", "hand"]
+            elif position == "Boobjob":
+                relevant_classes = ["breast"]
+                # relevant_classes = ["breast", "hand"]
+            #elif position == "Handjob":
+            #    relevant_classes = ["hand"]
+            elif position == "Footjob":
+                relevant_classes = ["foot"]
+            else:
+                relevant_classes = []
+                comp_distance = 100
+
+            # Check in the detections the relevant classes boxes that are touching the locked penis
+            bounding_boxes = []
+            #if locked_penis_box and frame_pos < len(tracking_data):
+            for box in merged_frames[frame_pos]:  #
+            # for box in tracking_data[frame_pos]["detected_boxes"]:
+                box_coords = box[0]
+                class_name = box[3]
+                track_id = box[4]
+                box_area = (box[0][2] - box[0][0]) * (box[0][3] - box[0][1])
+                relative_p_area = round(box_area / penis_box_area, 1) if penis_box_area != 0 else 0
+                relative_f_area = round(box_area / frame_area, 2) if frame_area != 0 else 0
+                iou = _calculate_iou(penis_box, box_coords)
+
+                # Update track history
+                if track_id not in track_history:
+                    track_history[track_id] = []
+                pos = (box_coords[1] + box_coords[3]) // 2
+                track_history[track_id].append(pos)
+                # Keep only last "fps" distances
+                if len(track_history[track_id]) > int_fps:
+                    track_history[track_id] = track_history[track_id][-int_fps:]
+                # Calculate speed
+                speed = _calculate_speed(track_history[track_id])
+
+                if class_name in relevant_classes:
+                    # iou = _calculate_iou(penis_box, box_coords)
+                    if iou > 0.01:
+                        if class_name not in ["hand", "foot"] or (class_name in ["hand", "foot"] and box_area > 0.5 * penis_box_area and box_area < 3 * penis_box_area):
+                            if penetration:
+                                # max_height = penis_max_penetration_height
+                                max_height = penis_max_height
+                            else:
+                                max_height = penis_max_height
+
+                            dist = _calculate_normalized_distance_to_base(penis_box, class_name, box_coords, max_height)
+
+                            if frame_pos == 20930:
+                                log_tr.info(f"Distance: {dist} for box {box_coords} of class {class_name} where penis max height is {max_height} and penis box is {str(penis_box)}")
+
+                            comp_distance += dist
+                            nb_touching += 1
+                            pos_info = 'p: ' + str(int(dist))
+                            int_position = int(dist)
+                        else:
+                            pos_info = 'Area excluded'
+                            int_position = 0
+                    else:
+                        pos_info = 'IOU excluded'
+                        int_position = 0
+                else:
+                    pos_info = 'Non relevant'
+                    int_position = 0
+
+                bounding_boxes.append({
+                    'box': box[0],
+                    'conf': box[1],
+                    'class_name': box[3],
+                    'track_id': box[4],
+                     'position': '' if box[3] in ['penis', 'glans'] else pos_info + ' | s: ' + str(speed) + ' | i: ' + str(round(iou, 2)) + ' | fa: ' + str(
+                         relative_f_area) + ' | pa: ' + str(relative_p_area),
+                    'int_position': int_position,
+                    'speed': speed,
+                    'iou': iou,
+                })
+
+            # Clean up track history - remove tracks not present in current frame
+            current_track_ids = {box[4] for box in tracking_data[frame_pos]["detected_boxes"]}
+            for track_id in list(track_history.keys()):
+                if track_id not in current_track_ids:
+                    del track_history[track_id]
+
+            if track_history:
+                # Calculate speed for each track and find the maximum
+                fastest_track_id = max(track_history.keys(),
+                                       key=lambda x: _calculate_speed(track_history[x]))
+            else:
+                fastest_track_id = None  # or 0 if you prefer
+
+
+
+
+            if penis_box and position in ["Cowgirl / Missionary", "Rev. Cowgirl / Doggy"]:  #and nb_touching == 0:
+                    comp_distance += penis_visible_part
+                    nb_touching += 1
+
+            if nb_touching == 0:
+                comp_distance = 100
+            elif position == "Handjob / Blowjob":
+
+                if nb_touching != prev_nb_touching or fastest_track_id != prev_fastest_track_id:
+                    transition_active = True
+                    remaining_transition_frames = transition_frames
+
+                prev_nb_touching = nb_touching
+                prev_fastest_track_id = fastest_track_id
+
+                total_speeds = sum(box['speed'] for box in bounding_boxes if box['class_name'] in relevant_classes)
+                weighted_distance = (
+                    sum(box['int_position'] * box['speed'] for box in bounding_boxes if box['class_name'] in relevant_classes)
+                    / total_speeds if total_speeds != 0 else 0
+                )
+                if total_speeds != 0:
+                    comp_distance =  int(weighted_distance)  #int(max(0, min(100, weighted_distance)))
+                else:
+                    comp_distance = distance[-1]
+
+            else:
+                comp_distance = int(comp_distance / nb_touching)  # int(max(0, min(100, comp_distance / nb_touching)))
+
+
+
+
+        if transition_active:
+            if remaining_transition_frames > 0:
+                # previous distance + 1 or -1 depending on the sign of the previous distance - comp_distance
+                transition_distance = distance[-1] + (1 if (comp_distance - distance[-1] > 0) else -1)
+                # comp_distance = transition_distance
+                remaining_transition_frames -= 1
+                distance.append(transition_distance)
+            else:
+                transition_active = False
+                remaining_transition_frames = transition_frames
+                distance.append(comp_distance)
+        else:
+            distance.append(comp_distance)
+
+        # Add frame data to debug_data
+        state.debug_data.add_frame(
+            frame_pos,
+            bounding_boxes=bounding_boxes,
+            variables={
+                'frame': frame_pos,
+                'time': str(timedelta(seconds=int(frame_pos / fps))),
+                'distance': distance[-1], #comp_distance,
+                'transition': str(transition_active) + (" | " + str(remaining_transition_frames) + " frames left" if transition_active else ""),
+                'raw_distance': comp_distance,
+                'penetration': penetration,
+                'sex_position': position,
+                'relevant_classes': relevant_classes,
+                #'sex_position_reason': 'NA',
+                #'tracked_body_part': 'NA',
+                'locked_penis_box': {
+                    'box': penis_box,
+                    'active': penis_active,
+                    'visible': penis_visible_part,
+                    'height': penis_max_height,
+                    'penetration_height': penis_max_penetration_height,
+                },
+                'glans_detected': False,
+                #'cons._glans_detections': 0,
+                #'cons._glans_non_detections': 0,
+                'cons.penis_detections': penis_consecutive_detections,
+                'cons.penis_non_detections': penis_consecutive_non_detections,
+                #'breast_tracking': False,
+            }
+        )
+    # endregion
+
+
+    # region PASS 6 - Re-parse multi-contact sections : 'Handjob / Blowjob',  'Boobjob', 'Footjob'
+    # PASS 6 - Re-parse multi-contact sections : 'Handjob / Blowjob',  'Boobjob', 'Footjob'
+
+    # for i, segment in tqdm(enumerate(segments), desc="Pass 6 - Re-parse multi-contact sections", unit="section"):
+    #     if segment["major_position"] in ["Handjob / Blowjob"]:
+    #         current_leader = None
+    #         leader_history = defaultdict(lambda: {
+    #             'total_speed': 0, 'count': 0, 'last_positions': [],
+    #             'consistency': 0, 'position_history': [], 'frames_present': 0
+    #         })
+    #
+    #         transition_frames = int(fps)  # 1 second transition
+    #         remaining_transition = 0
+    #         transition_start_value = 0
+    #         transition_target_value = 0
+    #         leader_stability_threshold = fps * 0.5  # Leader must be stable for at least 0.5 seconds
+    #         leader_stability_counter = 0
+    #
+    #         for s in range(segment["start_frame"], segment["end_frame"] + 1):
+    #             frame_data = state.debug_data.get_metrics_at_frame(s)
+    #             variables = frame_data['variables']
+    #             relevant_classes = variables.get('relevant_classes', [])
+    #
+    #             # Organize bounding boxes for quick lookup
+    #             relevant_boxes_by_track_id = {
+    #                 box['track_id']: box for box in frame_data['bounding_boxes']
+    #                 if box['class_name'] in relevant_classes and box['int_position'] > 0
+    #             }
+    #
+    #             if not relevant_boxes_by_track_id:
+    #                 # No relevant contacts, maintain last known distance
+    #                 distance[s] = distance[s - 1] if s > 0 else 0
+    #                 continue
+    #
+    #             # Compute leader scores
+    #             potential_leaders = {}
+    #             for track_id, box in relevant_boxes_by_track_id.items():
+    #                 speed, position = box['speed'], box['int_position']
+    #                 history = leader_history[track_id]
+    #
+    #                 # Update statistics
+    #                 history['total_speed'] += speed
+    #                 history['count'] += 1
+    #                 history['frames_present'] += 1
+    #                 history['position_history'].append(position)
+    #
+    #                 # Maintain fixed-size position history
+    #                 if len(history['position_history']) > 5:
+    #                     history['position_history'].pop(0)
+    #
+    #                 # Compute movement consistency
+    #                 if len(history['position_history']) >= 2:
+    #                     movement = position - history['position_history'][-2]
+    #                     last_movement = history['position_history'][-2] - history['last_positions'][-1] if history[
+    #                         'last_positions'] else 0
+    #
+    #                     if movement * last_movement >= 0:  # Same direction
+    #                         history['consistency'] = min(history['consistency'] + 1, 10)
+    #                     else:  # Direction change
+    #                         history['consistency'] = max(history['consistency'] - 2, 0)
+    #
+    #                 # Maintain short-term movement history
+    #                 history['last_positions'].append(position)
+    #                 if len(history['last_positions']) > 3:
+    #                     history['last_positions'].pop(0)
+    #
+    #                 # Compute stability score
+    #                 avg_speed = history['total_speed'] / max(1, history['count'])
+    #                 consistency = history['consistency']
+    #                 presence_ratio = history['frames_present'] / (s - segment["start_frame"] + 1)
+    #
+    #                 # Penalize tracks that appear sporadically
+    #                 if presence_ratio < 0.7:
+    #                     consistency *= 0.5
+    #
+    #                 # Score potential leaders
+    #                 potential_leaders[track_id] = avg_speed * (1 + consistency * 0.1) * (0.5 + presence_ratio * 0.5)
+    #
+    #             # Determine new leader
+    #             current_score = potential_leaders.get(current_leader, 0)
+    #             potential_new_leaders = {
+    #                 k: v for k, v in potential_leaders.items() if v > current_score * 1.3
+    #             }
+    #
+    #             if potential_new_leaders:
+    #                 new_leader_candidate = max(potential_new_leaders, key=potential_new_leaders.get)
+    #                 leader_stability_counter += 1
+    #
+    #                 # Confirm switch only after stability threshold
+    #                 if leader_stability_counter >= leader_stability_threshold:
+    #                     new_leader = new_leader_candidate
+    #                     leader_stability_counter = 0
+    #                 else:
+    #                     new_leader = current_leader
+    #             else:
+    #                 new_leader = current_leader
+    #                 leader_stability_counter = max(0, leader_stability_counter - 1)
+    #
+    #             # Initialize leader if none exists
+    #             if current_leader is None and potential_leaders:
+    #                 new_leader = max(potential_leaders, key=potential_leaders.get)
+    #                 leader_stability_counter = 0
+    #
+    #             # Handle leader transitions smoothly
+    #             if new_leader != current_leader and new_leader is not None:
+    #                 if current_leader is not None and leader_stability_counter >= leader_stability_threshold:
+    #                     remaining_transition = transition_frames
+    #                     transition_start_value = distance[s - 1] if s > 0 else 0
+    #                     transition_target_value = relevant_boxes_by_track_id[new_leader]['int_position']
+    #                 else:
+    #                     remaining_transition = 0
+    #
+    #                 current_leader = new_leader
+    #
+    #             # Compute distance using transitions
+    #             if remaining_transition > 0:
+    #                 progress = 1 - (remaining_transition / transition_frames)
+    #                 distance[s] = int(
+    #                     transition_start_value + (transition_target_value - transition_start_value) * progress)
+    #                 remaining_transition -= 1
+    #             elif current_leader:
+    #                 distance[s] = relevant_boxes_by_track_id.get(current_leader, {}).get('int_position',
+    #                                                                                      distance[s - 1])
+    #             elif s > 0:
+    #                 distance[s] = distance[s - 1]
+    #
+    #             # Reduce jitter from low-speed movements
+    #             avg_speed = sum(box['speed'] for box in relevant_boxes_by_track_id.values()) / len(
+    #                 relevant_boxes_by_track_id)
+    #             if avg_speed < 2 and s > 0:
+    #                 distance[s] = int((distance[s - 1] + distance[s]) / 2)
+    #
+    #             # Update debug data efficiently
+    #             frame_data['variables'].update({
+    #                 'distance': distance[s],
+    #                 'leader_track_id': current_leader,
+    #                 'transition_active': remaining_transition > 0
+    #             })
+    #
+    #             if s % 10 == 0:  # Reduce redundant debug data updates
+    #                 debug_info = state.debug_data.get_variables_at_frame(s)
+    #                 debug_info.update({
+    #                     "distance": int(distance[s]),
+    #                     "raw_distance": int(distance[s]),
+    #                     "tracked_body_part": relevant_boxes_by_track_id.get(current_leader, {}).get('class_name', None)
+    #                 })
+    # # endregion
+
+
+
+    # for i, segment in tqdm(enumerate(segments), desc="Pass 6 - Remapping sections 0 - 100", unit="section") :
+    #     if segment["major_position"] in ["Not relevant", "Close up"]:
+    #         continue
+    #
+    #     # retrieve min and max of the distance list in that portion of the video segment["start_frame"] to segment["end_frame"] and remap it 0 - 100
+    #     min_distance = min(distance[segment["start_frame"]:segment["end_frame"]])
+    #     max_distance = max(distance[segment["start_frame"]:segment["end_frame"]])
+    #
+    #     if min_distance != max_distance:
+    #         for i in range(segment["start_frame"], segment["end_frame"]):
+    #             distance[i] = int((distance[i] - min_distance) / (max_distance - min_distance) * 100)
+    #         # save actuated remapped min and max
+    #
+    #     new_min = min(distance[segment["start_frame"]:segment["end_frame"]])
+    #     new_max = max(distance[segment["start_frame"]:segment["end_frame"]])
+    #
+    #     log_tr.info(f"Remapped segment {i} - {segment['major_position']} from {min_distance} to {max_distance} to {new_min} to {new_max}")
+
+    # endregion
+
+
+    # region PASS 6 - Processing signal - Denoising
+    # PASS 6 - Processing signal - Denoising
+
+    log_tr.info("Pass 6 - Processing signal - Denoising with Savitzky-Golay algorithm")
+    # Run savitzky golay algorithm
+    distance = savgol_filter(distance, 11, 2)
+
+    for frame_pos in tqdm(range(len(distance)), desc="Pass 7 - Processing signal - Applying edits", unit="frame"):
+
+        debug_info = state.debug_data.get_variables_at_frame(frame_pos)
+        debug_info["distance"] = int(distance[frame_pos])
+
+    # endregion
+
+
+    # region PASS 7 - Simplifying signal
+    # PASS 7 - Simplifying signal
+
+    log_tr.info("Pass 8 - Simplifying signal - Identifying peaks and valleys")
+
+    # zip frame_pos and distance
+    funscript_data = list(zip(range(len(distance)), distance))
+
+    # test_funscript_data = _normalize_funscript(funscript_data, segments)  #, int_fps)
+    test_funscript_data = funscript_data
+
+    # SUB PASS 1
+
+    frames, positions = zip(*test_funscript_data)  # Unzip the frame and position
+    positions = np.array(positions)  # Convert positions to a numpy array
+
+    # Find peaks (local maxima)
+    peaks, _ = find_peaks(positions, prominence=0.1)
+
+    # Find valleys (local minima) by inverting the signal
+    valleys, _ = find_peaks(-positions, prominence=0.1)
+
+    # Combine the indices of peaks and valleys
+    keep_indices = set(peaks).union(valleys)
+    log_tr.info(f"Pass 8 - Simplifying signal - Found {len(keep_indices)} peaks and valleys")
+
+    # SUB PASS 2
+
+    data_len = len(test_funscript_data)
+    removed = 0
+    to_remove = set()
+
+    for i in keep_indices:
+        if i <= 0 or i >= data_len - 1:
+            continue  # Skip first and last frame to avoid out-of-range
+
+        current = test_funscript_data[i][1]
+        prev = test_funscript_data[i - 1][1]
+        next_ = test_funscript_data[i + 1][1]
+
+        if prev < current < next_ or prev > current > next_:
+            to_remove.add(i)
+            removed += 1
+
+    # Apply safe filtering
+    keep_indices.difference_update(to_remove)
+
+    log_tr.info(f"Pass 8 - Simplifying signal - Removed {removed} intermediate points, kept {len(keep_indices)} peaks and valleys")
+
+    temp_funscript_data = [test_funscript_data[i] for i in keep_indices]
+
+    vw_funscript_data = simplify_coords_vw(temp_funscript_data, 2.0)
+
+    log_tr.info(f"Number of positions before simplifying: {len(funscript_data)}")
+    log_tr.info(f"Number of positions after 1st pass of simplifying: {len(temp_funscript_data)}")
+    log_tr.info(f"Number of positions after 2nd pass of simplifying: {len(vw_funscript_data)}")
+
+    # endregion
+
+    # region PASS 9 - Amplifying signal
+    # PASS 9 - Amplifying signal
+    log_tr.info("Pass 9 - Amplifying signal + Simplifying pass 2")
+
+    amplification = False
+
+    if amplification:
+        amplified_funscript_data = _normalize_funscript_sparse(vw_funscript_data, segments)
+        log_tr.inf("Pass 9 - Amplification activated")
+    else:
+        log_tr.inf("Pass 9 - Amplification deactivated")
+        amplified_funscript_data = vw_funscript_data
+
+    cleaned_data = amplified_funscript_data[:]  # Copy data
+    pass_counter = 0
+    max_passes = 10
+    variation_threshold = 10
+
+    while pass_counter < max_passes:
+        deleted_counter = 0
+        new_data = [cleaned_data[0]]  # Always keep the first point
+
+        for i in range(1, len(cleaned_data) - 1):
+            prev, current, next_ = cleaned_data[i - 1][1], cleaned_data[i][1], cleaned_data[i + 1][1]
+
+            # Compute local variation
+            diff_prev = abs(current - prev)
+            diff_next = abs(current - next_)
+
+            # Keep only meaningful variations
+            if diff_prev >= variation_threshold or diff_next >= variation_threshold:
+                new_data.append(cleaned_data[i])
+            else:
+                deleted_counter += 1
+
+        new_data.append(cleaned_data[-1])  # Always keep the last point
+
+        # Stop if no changes
+        if deleted_counter == 0:
+            break
+
+        cleaned_data = new_data  # Update dataset
+        pass_counter += 1
+
+    vw_funscript_data = cleaned_data
+
+    log_tr.info(f"Pass 9 - Amplification and Simplification complete - Kept {len(vw_funscript_data)} peaks and valleys")
+
+    # amplified_funscript_data = vw_funscript_data
+    # endregion
+
+    # region PASS 10 - Generating funscript
+    # PASS 10 - Generating funscript
+
+    log_tr.info("Pass 10 - Generating funscript")
+
+    output_path, _ = get_output_file_path(state.video_path, ".funscript")
+
+    write_funscript(amplified_funscript_data, output_path, state.video_info.fps, timestamps)
+
+    # write_funscript(custom_normalized, output_path, state.video_info.fps, timestamps)
+
+
+
+    # endregion
+
+
+
+
+

From 5f9b3ac1b7ba8438e5170df312956c696ed9b733 Mon Sep 17 00:00:00 2001
From: k00gar <arnaud.chevreux@gmail.com>
Date: Wed, 2 Apr 2025 20:14:46 +0200
Subject: [PATCH 267/277] Typo fix

---
 video_funscript_segment_extractor.py | 232 +++++++++++++++++++++++++++
 1 file changed, 232 insertions(+)
 create mode 100644 video_funscript_segment_extractor.py

diff --git a/video_funscript_segment_extractor.py b/video_funscript_segment_extractor.py
new file mode 100644
index 00000000..2afdc77f
--- /dev/null
+++ b/video_funscript_segment_extractor.py
@@ -0,0 +1,232 @@
+import tkinter as tk
+from tkinter import ttk, messagebox, filedialog
+import cv2
+from PIL import Image, ImageTk
+import subprocess
+import json
+import os
+
+
+class VideoSegmentExtractor:
+    def __init__(self, root):
+        self.root = root
+        self.video_path = None
+        self.funscript_path = None
+        self.cap = None
+        self.total_frames = 0
+        self.fps = 0
+        self.start_time = 0  # Start time in seconds
+        self.duration = 30  # Default duration in seconds
+        self.category = "HJ-BJ"  # Default category
+
+        # GUI Elements
+        self.canvas = tk.Canvas(root, width=640, height=360)
+        self.canvas.pack(pady=10)
+
+        # Video and Funscript name labels
+        self.video_name_label = tk.Label(root, text="Selected Video: None", anchor="w")
+        self.video_name_label.pack(fill="x", padx=10, pady=5)
+
+        self.funscript_name_label = tk.Label(root, text="Selected Funscript: None", anchor="w")
+        self.funscript_name_label.pack(fill="x", padx=10, pady=5)
+
+        # Start time input (hours, minutes, seconds)
+        self.time_frame = tk.Frame(root)
+        self.time_frame.pack(pady=10)
+
+        self.hour_label = tk.Label(self.time_frame, text="Hours:")
+        self.hour_label.grid(row=0, column=0, padx=5)
+        self.hour_var = tk.IntVar(value=0)
+        self.hour_entry = tk.Entry(self.time_frame, textvariable=self.hour_var, width=5)
+        self.hour_entry.grid(row=0, column=1, padx=5)
+
+        self.minute_label = tk.Label(self.time_frame, text="Minutes:")
+        self.minute_label.grid(row=0, column=2, padx=5)
+        self.minute_var = tk.IntVar(value=0)
+        self.minute_entry = tk.Entry(self.time_frame, textvariable=self.minute_var, width=5)
+        self.minute_entry.grid(row=0, column=3, padx=5)
+
+        self.second_label = tk.Label(self.time_frame, text="Seconds:")
+        self.second_label.grid(row=0, column=4, padx=5)
+        self.second_var = tk.IntVar(value=0)
+        self.second_entry = tk.Entry(self.time_frame, textvariable=self.second_var, width=5)
+        self.second_entry.grid(row=0, column=5, padx=5)
+
+        self.update_time_button = tk.Button(self.time_frame, text="Update Start Time", command=self.update_start_time)
+        self.update_time_button.grid(row=0, column=6, padx=10)
+
+        # Duration dropdown with label
+        self.duration_frame = tk.Frame(root)
+        self.duration_frame.pack(pady=10)
+
+        self.duration_label = tk.Label(self.duration_frame, text="Select Duration (seconds):")
+        self.duration_label.grid(row=0, column=0, padx=5)
+
+        self.duration_var = tk.IntVar(value=self.duration)
+        self.duration_dropdown = ttk.Combobox(self.duration_frame, textvariable=self.duration_var, values=[10, 30, 60, 120, 240, 300], width=10)
+        self.duration_dropdown.grid(row=0, column=1, padx=5)
+        self.duration_dropdown.bind("<<ComboboxSelected>>", self.update_duration)
+
+        # Category dropdown with label
+        self.category_frame = tk.Frame(root)
+        self.category_frame.pack(pady=10)
+
+        self.category_label = tk.Label(self.category_frame, text="Select Category:")
+        self.category_label.grid(row=0, column=0, padx=5)
+
+        self.category_var = tk.StringVar(value=self.category)
+        self.category_dropdown = ttk.Combobox(self.category_frame, textvariable=self.category_var, 
+                                              values=["HJ-BJ", "Doggy", "Cowgirl", "RevCowgirl", "Pronebone"], width=15)
+        self.category_dropdown.grid(row=0, column=1, padx=5)
+        self.category_dropdown.bind("<<ComboboxSelected>>", self.update_category)
+
+        # Progress label
+        self.progress_label = tk.Label(root, text="Progress: 0%")
+        self.progress_label.pack(pady=10)
+
+        # Buttons frame (Select Video, Select Funscript, Extract Segment)
+        self.buttons_frame = tk.Frame(root)
+        self.buttons_frame.pack(pady=10)
+
+        self.select_video_button = ttk.Button(self.buttons_frame, text="Select Video", command=self.select_video)
+        self.select_video_button.grid(row=0, column=0, padx=5)
+
+        self.select_funscript_button = ttk.Button(self.buttons_frame, text="Select Funscript", command=self.select_funscript)
+        self.select_funscript_button.grid(row=0, column=1, padx=5)
+
+        self.extract_button = ttk.Button(self.buttons_frame, text="Extract Segment", command=self.extract_segment, state=tk.DISABLED)
+        self.extract_button.grid(row=0, column=2, padx=5)
+
+    def select_video(self):
+        self.video_path = filedialog.askopenfilename(
+            filetypes=[("Video Files", "*.mp4 *.avi *.mkv"), ("All Files", "*.*")])
+        if self.video_path:
+            self.video_name_label.config(text=f"Selected Video: {os.path.basename(self.video_path)}")
+            self.load_video()
+
+    def select_funscript(self):
+        self.funscript_path = filedialog.askopenfilename(
+            filetypes=[("Funscript Files", "*.funscript"), ("All Files", "*.*")])
+        if self.funscript_path:
+            self.funscript_name_label.config(text=f"Selected Funscript: {os.path.basename(self.funscript_path)}")
+            if self.video_path:
+                self.extract_button.config(state=tk.NORMAL)
+
+    def load_video(self):
+        self.cap = cv2.VideoCapture(self.video_path)
+        if not self.cap.isOpened():
+            messagebox.showerror("Error", "Could not open video file.")
+            return
+
+        self.total_frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        self.fps = self.cap.get(cv2.CAP_PROP_FPS)
+
+        if self.fps <= 0:
+            messagebox.showerror("Error", "Invalid video FPS.")
+            return
+
+        self.update_frame(0)
+
+    def update_frame(self, frame_number):
+        self.cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
+        ret, frame = self.cap.read()
+        if ret:
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frame = cv2.resize(frame, (640, 360))
+            self.photo = ImageTk.PhotoImage(image=Image.fromarray(frame))
+            self.canvas.create_image(0, 0, anchor=tk.NW, image=self.photo)
+
+    def update_start_time(self):
+        try:
+            hours = self.hour_var.get()
+            minutes = self.minute_var.get()
+            seconds = self.second_var.get()
+            self.start_time = hours * 3600 + minutes * 60 + seconds
+            self.update_frame(int(self.start_time * self.fps))
+            self.progress_label.config(text=f"Selected Start Time: {self.start_time:.2f}s")
+        except Exception as e:
+            messagebox.showerror("Error", f"Invalid time input: {str(e)}")
+
+    def update_duration(self, event):
+        self.duration = self.duration_var.get()
+
+    def update_category(self, event):
+        self.category = self.category_var.get()
+
+    def extract_segment(self):
+        start_time = self.start_time
+        duration = self.duration
+        category = self.category
+
+        # Create Extracts folder and category subfolder
+        extracts_folder = os.path.join(os.path.expanduser("~"), "Bench_extracts")
+        category_folder = os.path.join(extracts_folder, category)
+        os.makedirs(category_folder, exist_ok=True)
+
+        base_name = os.path.splitext(os.path.basename(self.video_path))[0]
+        output_video = f"{base_name}_segment_{int(start_time)}.mp4"
+        output_funscript = f"{base_name}_segment_{int(start_time)}-ref.funscript"
+
+        output_video = os.path.join(category_folder, output_video)
+        output_funscript = os.path.join(category_folder, output_funscript)
+
+        # Extract video segment
+        self.extract_video_segment(self.video_path, output_video, start_time, duration)
+
+        # Extract funscript segment if funscript is selected
+        if self.funscript_path:
+            self.extract_funscript_segment(self.funscript_path, output_funscript, start_time, duration)
+
+    def extract_video_segment(self, input_video, output_video, start_time, duration):
+        try:
+            self.progress_label.config(text="Extracting video...")
+            command = ['ffmpeg', '-i', input_video, '-ss', str(start_time), '-t', str(duration),
+                       '-c', 'copy', '-reset_timestamps', '1', output_video]
+            print(f"Running command: {' '.join(command)}")
+            process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+            stdout, stderr = process.communicate()
+            print(stdout.decode('utf-8'))
+            print(stderr.decode('utf-8'))
+            if process.returncode != 0:
+                raise Exception(stderr.decode('utf-8'))
+
+            if os.path.exists(output_video):
+                messagebox.showinfo("Success", f"Video segment saved to {output_video}")
+            else:
+                messagebox.showerror("Error", "Video extraction failed.")
+        except Exception as e:
+            messagebox.showerror("Error", f"Video extraction failed: {str(e)}")
+        finally:
+            self.progress_label.config(text="Progress: 100%")
+
+    def extract_funscript_segment(self, input_funscript, output_funscript, start_time, duration):
+        try:
+            with open(input_funscript, 'r') as f:
+                funscript_data = json.load(f)
+
+            # Convert times to milliseconds
+            start_time_ms = int(start_time * 1000)
+            end_time_ms = start_time_ms + int(duration * 1000)
+
+            # Filter actions within the time range
+            segment_actions = [action for action in funscript_data['actions']
+                               if start_time_ms <= action['at'] <= end_time_ms]
+
+            # Adjust timestamps relative to new start time
+            for action in segment_actions:
+                action['at'] -= start_time_ms
+
+            funscript_data['actions'] = segment_actions
+            with open(output_funscript, 'w') as f:
+                json.dump(funscript_data, f, indent=4)
+
+            messagebox.showinfo("Success", f"Funscript segment saved to {output_funscript}")
+        except Exception as e:
+            messagebox.showerror("Error", f"Funscript extraction failed: {str(e)}")
+
+
+if __name__ == "__main__":
+    root = tk.Tk()
+    root.title("Video Segment Extractor")
+    app = VideoSegmentExtractor(root)
+    root.mainloop()
\ No newline at end of file

From 87397850b9782dcc4f3f3e58977b3056ba177e44 Mon Sep 17 00:00:00 2001
From: k00gar <arnaud.chevreux@gmail.com>
Date: Wed, 2 Apr 2025 22:24:27 +0200
Subject: [PATCH 268/277] Typo fix

---
 .../analysis/workers/analyze_tracking_results_v1_test3.py     | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py b/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py
index 073ff915..265c3b93 100644
--- a/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py
+++ b/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py
@@ -1422,9 +1422,9 @@ def analyze_tracking_results_v1(state: AppState):
 
     if amplification:
         amplified_funscript_data = _normalize_funscript_sparse(vw_funscript_data, segments)
-        log_tr.inf("Pass 9 - Amplification activated")
+        log_tr.info("Pass 9 - Amplification activated")
     else:
-        log_tr.inf("Pass 9 - Amplification deactivated")
+        log_tr.info("Pass 9 - Amplification deactivated")
         amplified_funscript_data = vw_funscript_data
 
     cleaned_data = amplified_funscript_data[:]  # Copy data

From 87c47466503415449ac4d4f5c00c9d36840b10a3 Mon Sep 17 00:00:00 2001
From: k00gar <arnaud.chevreux@gmail.com>
Date: Thu, 3 Apr 2025 10:01:40 +0200
Subject: [PATCH 269/277] Crash-dummies update

---
 script_generator/constants.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script_generator/constants.py b/script_generator/constants.py
index 674edaf5..310f7410 100644
--- a/script_generator/constants.py
+++ b/script_generator/constants.py
@@ -3,7 +3,7 @@
 
 import torch
 
-VERSION = "0.3.0"
+VERSION = "0.3.1 - Crash-Dummies"
 OBJECT_DETECTION_VERSION = "1.0.0"
 TRACKING_VERSION = "0.1.0"
 FUNSCRIPT_VERSION = "0.1.1"

From 8ed4d4bdc5051e6e7a38a9d1c11709e41f13b8ee Mon Sep 17 00:00:00 2001
From: k00gar <arnaud.chevreux@gmail.com>
Date: Thu, 3 Apr 2025 10:03:09 +0200
Subject: [PATCH 270/277] Crash-dummies update

---
 script_generator/funscript/util/util.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/script_generator/funscript/util/util.py b/script_generator/funscript/util/util.py
index 648b5ca8..ea4f55c2 100644
--- a/script_generator/funscript/util/util.py
+++ b/script_generator/funscript/util/util.py
@@ -1,7 +1,7 @@
 import json
 import os
 
-from script_generator.constants import FUNSCRIPT_AUTHOR, FUNSCRIPT_VERSION
+from script_generator.constants import FUNSCRIPT_AUTHOR, FUNSCRIPT_VERSION, VERSION
 from script_generator.debug.logger import log
 
 
@@ -80,7 +80,7 @@ def write_funscript(distances, output_path, fps, timestamps = None):
             i += 1
         chapters += "]"
 
-    output = f'{{"version":"{FUNSCRIPT_VERSION}","inverted":false,"range":100,"author":"{FUNSCRIPT_AUTHOR}"{chapters},"actions":[{{"at":0,"pos":100}},'
+    output = f'{{"app_version":"{VERSION}","version":"{FUNSCRIPT_VERSION}","inverted":false,"range":100,"author":"{FUNSCRIPT_AUTHOR}"{chapters},"actions":[{{"at":0,"pos":100}},'
     i = 0
     for frame, position in distances:
         if position :

From c64ce31fcb7104f5f06dd009d461af44635fafaa Mon Sep 17 00:00:00 2001
From: k00gar <arnaud.chevreux@gmail.com>
Date: Thu, 3 Apr 2025 10:03:28 +0200
Subject: [PATCH 271/277] Crash-dummies update

---
 script_generator/constants.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script_generator/constants.py b/script_generator/constants.py
index 310f7410..af81d3e2 100644
--- a/script_generator/constants.py
+++ b/script_generator/constants.py
@@ -5,7 +5,7 @@
 
 VERSION = "0.3.1 - Crash-Dummies"
 OBJECT_DETECTION_VERSION = "1.0.0"
-TRACKING_VERSION = "0.1.0"
+TRACKING_VERSION = "0.1.1"
 FUNSCRIPT_VERSION = "0.1.1"
 CONFIG_VERSION = 1
 

From 9edc56943fbcecfc05699a4f81694cedceae734c Mon Sep 17 00:00:00 2001
From: k00gar <arnaud.chevreux@gmail.com>
Date: Thu, 3 Apr 2025 17:36:14 +0200
Subject: [PATCH 272/277] Fixed the amplification process

---
 .../analyze_tracking_results_v1_test3.py      | 80 +++++++++++++++++--
 1 file changed, 75 insertions(+), 5 deletions(-)

diff --git a/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py b/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py
index 265c3b93..12823246 100644
--- a/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py
+++ b/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py
@@ -294,7 +294,7 @@ def _calculate_normalized_distance_to_base(locked_penis_box: Tuple[int, int, int
     return normalized_distance
     #return int((distance / max_distance) * 100 if max_distance > 0 else 100)
 
-def _normalize_funscript_sparse(funscript_data, segments):
+def prev_normalize_funscript_sparse(funscript_data, segments):
     result = deepcopy(funscript_data)
 
     for seg_counter, segment in enumerate(segments):
@@ -310,8 +310,8 @@ def _normalize_funscript_sparse(funscript_data, segments):
         segment_data = [result[i] for i in segment_indices]
         values = [x[1] for x in segment_data]
 
-        p01 = np.percentile(values, 1)
-        p99 = np.percentile(values, 99)
+        p01 = np.percentile(values, 5)
+        p99 = np.percentile(values, 95)
 
         scale_range = p99 - p01
 
@@ -331,7 +331,7 @@ def _normalize_funscript_sparse(funscript_data, segments):
                         val = 50  # If no variation, set to mid-range
                     else:
                         # Rescale values within p01 - p99 range
-                        val = (val - p01) / scale_range * 100
+                        val = ((val - p01) / scale_range) * 100
 
                         # Apply specific scaling for certain positions
                         # if position_type in ['Handjob / Blowjob', 'Footjob', 'Boobjob']:
@@ -342,6 +342,76 @@ def _normalize_funscript_sparse(funscript_data, segments):
 
     return result
 
+
+def _normalize_funscript_sparse(funscript_data, segments):
+    result = deepcopy(funscript_data)
+    for seg_counter, segment in enumerate(segments):
+        position_type = segment['major_position']
+        start_frame = segment['start_frame']
+        end_frame = segment['end_frame']
+        # Extract only frames within this segment
+        segment_indices = [i for i, (f, _) in enumerate(result) if start_frame <= f <= end_frame]
+        if not segment_indices:
+            continue  # Skip empty segments
+
+        segment_data = [result[i] for i in segment_indices]
+        values = [x[1] for x in segment_data]
+
+        # Still use percentiles for outlier detection
+        p01 = np.percentile(values, 5)
+        p99 = np.percentile(values, 95)
+
+        # But also track the min and max of non-outlier values
+        filtered_values = [v for v in values if p01 <= v <= p99]
+        if not filtered_values:
+            # If all values are outliers, use the full range
+            filtered_min = min(values)
+            filtered_max = max(values)
+        else:
+            filtered_min = min(filtered_values)
+            filtered_max = max(filtered_values)
+
+        scale_range = filtered_max - filtered_min
+
+        # Adjust the values properly
+        for i in segment_indices:
+            frame, val = result[i]
+            if position_type in ['Not relevant', 'Close up']:
+                val = 100  # Directly set to 100
+            else:
+                if val <= p01:
+                    # For values below p01, preserve relative differences
+                    # Map them to the range 0-5 based on their position relative to min
+                    min_val = min(values)
+                    if val == min_val and min_val == p01:
+                        val = 0
+                    elif min_val == p01:
+                        val = 0  # Edge case
+                    else:
+                        val = ((val - min_val) / (p01 - min_val)) * 5
+
+                elif val >= p99:
+                    # For values above p99, preserve relative differences
+                    # Map them to the range 95-100 based on their position relative to max
+                    max_val = max(values)
+                    if val == max_val and max_val == p99:
+                        val = 100
+                    elif max_val == p99:
+                        val = 100  # Edge case
+                    else:
+                        val = 95 + ((val - p99) / (max_val - p99)) * 5
+
+                else:
+                    # For non-outlier values, scale them to range 5-95
+                    if scale_range < 1e-6:
+                        val = 50  # If no variation, set to mid-range
+                    else:
+                        val = 5 + ((val - filtered_min) / scale_range) * 90
+
+            val = int(max(0, min(100, round(val))))  # Ensure final value stays in bounds
+            result[i] = (frame, val)
+    return result
+
 def interpolate_box(box1: Tuple[int, int, int, int], box2: Tuple[int, int, int, int], t: float) -> Tuple[int, int, int, int]:
     """
     Interpolate between two bounding boxes.
@@ -1418,7 +1488,7 @@ def analyze_tracking_results_v1(state: AppState):
     # PASS 9 - Amplifying signal
     log_tr.info("Pass 9 - Amplifying signal + Simplifying pass 2")
 
-    amplification = False
+    amplification = True
 
     if amplification:
         amplified_funscript_data = _normalize_funscript_sparse(vw_funscript_data, segments)

From 1f44cb490428fb8c91b9fede3942fa06de46e8d3 Mon Sep 17 00:00:00 2001
From: k00gar <arnaud.chevreux@gmail.com>
Date: Thu, 3 Apr 2025 17:36:23 +0200
Subject: [PATCH 273/277] Fixed the funscript metadata

---
 script_generator/funscript/util/util.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/script_generator/funscript/util/util.py b/script_generator/funscript/util/util.py
index ea4f55c2..504ba2cd 100644
--- a/script_generator/funscript/util/util.py
+++ b/script_generator/funscript/util/util.py
@@ -73,14 +73,18 @@ def write_funscript(distances, output_path, fps, timestamps = None):
     if timestamps:
         i = 0
         len_t = len(timestamps)
-        chapters = ',"chapters":['
+        chapters = '"chapters":['
         for timestamp in timestamps:
             chapters += f'{{"startTime":"{timestamp[2]}","endTime":"{timestamp[3]}","name":"{timestamp[4]}"}}'
             chapters += "," if i < len_t - 1 else ""
             i += 1
         chapters += "]"
 
-    output = f'{{"app_version":"{VERSION}","version":"{FUNSCRIPT_VERSION}","inverted":false,"range":100,"author":"{FUNSCRIPT_AUTHOR}"{chapters},"actions":[{{"at":0,"pos":100}},'
+    output = f'{{"version":"1.0","inverted":false,"range":100,"author":"{FUNSCRIPT_AUTHOR}","actions":[{{"at":0,"pos":100}},'
+
+    # order distances by frame asc
+    distances = sorted(distances, key=lambda x: x[0])
+
     i = 0
     for frame, position in distances:
         if position :
@@ -89,7 +93,7 @@ def write_funscript(distances, output_path, fps, timestamps = None):
                 output += ","
             output += f'{{"at":{time_ms},"pos":{int(position)}}}'
             i += 1
-    output += "]}"
+    output += f'], "metadata":{{"app_version":"{VERSION}", "version":"{FUNSCRIPT_VERSION}", {chapters}}}}}'
 
     with open(output_path, 'w') as f:
         f.write(output)

From 35d9290985086f4438f3dc1146f2346589a12570 Mon Sep 17 00:00:00 2001
From: k00gar <arnaud.chevreux@gmail.com>
Date: Thu, 3 Apr 2025 18:47:17 +0200
Subject: [PATCH 274/277] Fixed the funscript metadata and amplification

---
 script_generator/constants.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script_generator/constants.py b/script_generator/constants.py
index af81d3e2..7d510bfb 100644
--- a/script_generator/constants.py
+++ b/script_generator/constants.py
@@ -3,7 +3,7 @@
 
 import torch
 
-VERSION = "0.3.1 - Crash-Dummies"
+VERSION = "0.3.2 - Crash-Dummies"
 OBJECT_DETECTION_VERSION = "1.0.0"
 TRACKING_VERSION = "0.1.1"
 FUNSCRIPT_VERSION = "0.1.1"

From a62184474f0ef967bfdc3e6155a4ef79746a448e Mon Sep 17 00:00:00 2001
From: k00gar <arnaud.chevreux@gmail.com>
Date: Fri, 4 Apr 2025 18:54:50 +0200
Subject: [PATCH 275/277] Transition tweaking

---
 .../analyze_tracking_results_v1_test3.py      | 28 +++++++------------
 1 file changed, 10 insertions(+), 18 deletions(-)

diff --git a/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py b/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py
index 12823246..c569fe76 100644
--- a/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py
+++ b/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py
@@ -1041,8 +1041,6 @@ def analyze_tracking_results_v1(state: AppState):
             penis_consecutive_detections = penis['consecutive_detections']
             penis_consecutive_non_detections = penis['consecutive_non_detections']
             penis_max_height = penis['max_height']
-            if frame_pos == 20930:
-                log_tr.info(f"Penis max height: {penis_max_height}")
             penis_max_penetration_height = penis['max_penetration_height']
             penis_visible_part = penis['visible_part']
             penis_glans_detected = penis['glans_detected']
@@ -1116,9 +1114,6 @@ def analyze_tracking_results_v1(state: AppState):
 
                             dist = _calculate_normalized_distance_to_base(penis_box, class_name, box_coords, max_height)
 
-                            if frame_pos == 20930:
-                                log_tr.info(f"Distance: {dist} for box {box_coords} of class {class_name} where penis max height is {max_height} and penis box is {str(penis_box)}")
-
                             comp_distance += dist
                             nb_touching += 1
                             pos_info = 'p: ' + str(int(dist))
@@ -1194,10 +1189,16 @@ def analyze_tracking_results_v1(state: AppState):
 
         if transition_active:
             if remaining_transition_frames > 0:
-                # previous distance + 1 or -1 depending on the sign of the previous distance - comp_distance
-                transition_distance = distance[-1] + (1 if (comp_distance - distance[-1] > 0) else -1)
-                # comp_distance = transition_distance
-                remaining_transition_frames -= 1
+                if comp_distance - distance[-1] == 0:
+                    transition_distance = distance[-1]
+                    log_tr.info(f"Used {transition_frames - remaining_transition_frames} transition frames before meeting at {comp_distance}")
+                    transition_active = False
+                    remaining_transition_frames = transition_frames
+                else:
+                    # previous distance + 1 or -1 depending on the sign of the previous distance - comp_distance
+                    transition_distance = distance[-1] + (1 if (comp_distance - distance[-1] > 0) else -1)
+                    # comp_distance = transition_distance
+                    remaining_transition_frames -= 1
                 distance.append(transition_distance)
             else:
                 transition_active = False
@@ -1544,13 +1545,4 @@ def analyze_tracking_results_v1(state: AppState):
 
     write_funscript(amplified_funscript_data, output_path, state.video_info.fps, timestamps)
 
-    # write_funscript(custom_normalized, output_path, state.video_info.fps, timestamps)
-
-
-
     # endregion
-
-
-
-
-

From 9191cb3630a382e7cdc1bb698810c2014c3d895f Mon Sep 17 00:00:00 2001
From: k00gar <arnaud.chevreux@gmail.com>
Date: Sat, 12 Apr 2025 15:27:16 +0200
Subject: [PATCH 276/277] Interpolation tweaking

---
 .../analysis/workers/analyze_tracking_results_v1_test3.py       | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py b/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py
index c569fe76..d73fc90b 100644
--- a/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py
+++ b/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py
@@ -454,7 +454,7 @@ def analyze_tracking_results_v1(state: AppState):
     interpolated_frames = [[] for _ in range(total_frames)]
     total_interpolated = 0
 
-    MAX_GAP = 10  # Limit interpolation to 10-frame gaps
+    MAX_GAP = int_fps  # 10  # Limit interpolation to MAX_GAP frames gaps
 
     for frame_pos in tqdm(range(total_frames), desc="Pass 1 - Interpolating boxes (linear)", unit="f"):
         frame_boxes = sorted_boxes[frame_pos] if frame_pos < len(sorted_boxes) else []

From 8b4f5823ccb8a6bf75e823b73d91006d411ef277 Mon Sep 17 00:00:00 2001
From: walton <bobpage1337@gmail.com>
Date: Sun, 20 Apr 2025 21:10:46 +0300
Subject: [PATCH 277/277] fix

---
 .../workers/analyze_tracking_results_v1_test3.py      |  7 ++++++-
 script_generator/funscript/util/util.py               | 11 +++++------
 2 files changed, 11 insertions(+), 7 deletions(-)

diff --git a/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py b/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py
index d73fc90b..934aa08b 100644
--- a/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py
+++ b/script_generator/analysis/workers/analyze_tracking_results_v1_test3.py
@@ -15,6 +15,7 @@
 # from utils.lib_ObjectTracker import ObjectTracker
 from script_generator.utils.file import get_output_file_path
 from script_generator.funscript.util.util import write_funscript
+from script_generator.funscript.util.export_funscript import export_funscript
 
 
 def _assign_frame_position(contacts: List[Dict]) -> str:
@@ -1491,6 +1492,8 @@ def analyze_tracking_results_v1(state: AppState):
 
     amplification = True
 
+    vw_funscript_data = sorted(vw_funscript_data, key=lambda x: x[0])
+
     if amplification:
         amplified_funscript_data = _normalize_funscript_sparse(vw_funscript_data, segments)
         log_tr.info("Pass 9 - Amplification activated")
@@ -1543,6 +1546,8 @@ def analyze_tracking_results_v1(state: AppState):
 
     output_path, _ = get_output_file_path(state.video_path, ".funscript")
 
-    write_funscript(amplified_funscript_data, output_path, state.video_info.fps, timestamps)
+    write_funscript(cleaned_data, output_path, state.video_info.fps, timestamps)
+
+    export_funscript(state)
 
     # endregion
diff --git a/script_generator/funscript/util/util.py b/script_generator/funscript/util/util.py
index 504ba2cd..37b4366b 100644
--- a/script_generator/funscript/util/util.py
+++ b/script_generator/funscript/util/util.py
@@ -87,12 +87,11 @@ def write_funscript(distances, output_path, fps, timestamps = None):
 
     i = 0
     for frame, position in distances:
-        if position :
-            time_ms = int(frame * 1000 / fps)
-            if i > 0:
-                output += ","
-            output += f'{{"at":{time_ms},"pos":{int(position)}}}'
-            i += 1
+        time_ms = int(frame * 1000 / fps)
+        if i > 0:
+            output += ","
+        output += f'{{"at":{time_ms},"pos":{int(position)}}}'
+        i += 1
     output += f'], "metadata":{{"app_version":"{VERSION}", "version":"{FUNSCRIPT_VERSION}", {chapters}}}}}'
 
     with open(output_path, 'w') as f: