Yay, agents register now :)

Broker/API/broker.py

@@ -80,17 +80,16 @@ def agent():
     public = response["public"]
 
     public = base64.b64encode(public).decode("utf-8")
-
     # make env file
     with open("./package/.env", "w") as f:
         f.write(f"AID={aid}\n")
-        f.write(f"PUBLIC_TEST={public}")
-        f.write(f"HOST_IP={HOST_IP}\n")
+        f.write(f"PUBLIC_TEST={public}\n")
+        f.write(f"HOST_IP=206.189.188.197")
 
     subprocess.run(["makensis", "./package/setup.nsi"])
 
     filePath = "./package/MyFastAPIAppSetup.exe"
-    return FileResponse(filePath, filename="MyFastAPIAppSetup.exe")
+    return FileResponse(filePath, filename="AgentSetup.exe")
 
 @app.get("/windows")
 def windows():

Broker/API/package/setup.nsi

@@ -39,18 +39,20 @@ Var RadioBoth
 Var TextBox
 Var HOST_IP
 
-StrCpy $HOST_IP "206.189.188.197"
-
 ; Pages
 !insertmacro MUI_PAGE_WELCOME
+; !insertmacro MUI_PAGE_LICENSE "${NSISDIR}\Docs\Modern UI\License.txt"  ; Comment out or remove this line
+!insertmacro MUI_PAGE_COMPONENTS
+!insertmacro MUI_PAGE_DIRECTORY
 Page custom AgentTypePage AgentTypeLeave
 Page custom CorporationPage CorporationLeave
-!insertmacro MUI_PAGE_DIRECTORY
 !insertmacro MUI_PAGE_INSTFILES
 !insertmacro MUI_PAGE_FINISH
 
+!insertmacro MUI_UNPAGE_WELCOME
 !insertmacro MUI_UNPAGE_CONFIRM
 !insertmacro MUI_UNPAGE_INSTFILES
+!insertmacro MUI_UNPAGE_FINISH
 
 ; Language
 !insertmacro MUI_LANGUAGE "English"
@@ -145,7 +147,6 @@ Section "MainSection" SEC01
   FileWrite $0 "$\r$\n"
   FileWrite $0 "AGENT_TYPE=$AgentType$\r$\n"
   FileWrite $0 "CORPORATION_NAME=$CorporationName$\r$\n"
-  FileWrite $0 "HOST_IP=$HOST_IP$\r$\n"
   FileClose $0
 
   ; Install Python dependencies

Process/pipe4/temp/outputUnit.py

@@ -0,0 +1,93 @@
+from units import Unit
+import numpy as np
+import cv2
+from dataToken import DataToken
+
+
+class outputUnit(Unit):
+    def __init__(self):
+        super().__init__(id="outputUnit", input_type=DataToken, output_type=np.ndarray)
+        self.all = False
+        self.lidar = False
+        self.taggr = False
+        self.bb = False
+        self.la = False
+
+
+    def set_flags(self, flags):
+        if 'all' in flags:
+            self.all = True
+        if 'lidar' in flags:
+            self.lidar = True
+        if 'taggr' in flags:
+            self.taggr = True
+        if 'bb' in flags:
+            self.bb = True
+        if 'la' in flags:
+            self.la = True
+
+
+    def process(self, data_token):
+        image = data_token.get_sensor_data('camera')
+        annotated_frame = image.copy()
+        min_distances = [] 
+
+        if (self.lidar or self.all) and data_token.get_flag('has_lidar_data'):
+            lidar_data = data_token.get_processing_result('infusrUnit')
+            pixel_x = lidar_data['pixel_x']
+            pixel_y = lidar_data['pixel_y']
+            colors = lidar_data['colors']
+            for i in range(len(pixel_x)):
+                color = (int(colors[i][2]), int(colors[i][1]), int(colors[i][0]))
+                cv2.circle(annotated_frame, (pixel_x[i], pixel_y[i]), radius=2, color=color, thickness=-1)
+
+        if (self.taggr or self.all) and data_token.get_flag('has_tagger_data'):
+            tagger_data = data_token.get_processing_result('taggrUnit')
+            pixel_data = tagger_data['pixel_data']
+
+            min_distances = [None] * len(pixel_data)
+
+            for data in pixel_data:
+                pixel_x = data['pixel_x']
+                pixel_y = data['pixel_y']
+                min_distance = data['min_distance']
+                bbox_index = data['bbox_index'] 
+
+                cv2.circle(annotated_frame, (pixel_x, pixel_y), radius=2, color=(0, 255, 0), thickness=-1)
+
+                if min_distances[bbox_index] is None or min_distance < min_distances[bbox_index]:
+                    min_distances[bbox_index] = min_distance
+
+        if (self.bb or self.all):
+            bounding_boxes = data_token.get_processing_result('yoloUnit')
+            for i, bbox in enumerate(bounding_boxes):
+                x_min, y_min, x_max, y_max, score, class_id = bbox
+                x_min, y_min, x_max, y_max = int(x_min), int(y_min), int(x_max), int(y_max)
+                cv2.rectangle(annotated_frame, (x_min, y_min), (x_max, y_max), (0, 255, 0), 1)
+
+                if min_distances[i] is not None:
+                    text = f"{bbox[-1]}  Dist: {min_distances[i]:.2f}m"
+                    cv2.putText(annotated_frame, text, (x_min, y_min - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+                else:
+                    text = f"{bbox[-1]}"
+                    cv2.putText(annotated_frame, text, (x_min, y_min - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+
+        if (self.la or self.all) and data_token.get_flag('has_lane_data'):
+            output = data_token.get_processing_result('laneUnit')
+            mask = output['mask']
+
+            if len(mask.shape) == 3 and mask.shape[2] == 3:
+                mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
+
+            if mask.shape != annotated_frame.shape[:2]:
+                mask = cv2.resize(mask, (annotated_frame.shape[1], annotated_frame.shape[0]), interpolation=cv2.INTER_NEAREST)
+
+            colored_mask = np.zeros_like(annotated_frame)
+            colored_mask[mask > 0] = [0, 255, 0] 
+
+            alpha = 0.5
+            annotated_frame = cv2.addWeighted(annotated_frame, 1 - alpha, colored_mask, alpha, 0)
+
+
+        print(f"{self.id}: Outputting final result with shape: {annotated_frame.shape}")
+        return annotated_frame