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make object processor resilient to plasma failures
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blakeblackshear committed Mar 13, 2020
1 parent b6fcb88 commit 8507bbb
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Showing 3 changed files with 120 additions and 104 deletions.
6 changes: 0 additions & 6 deletions Dockerfile
Original file line number Diff line number Diff line change
Expand Up @@ -51,12 +51,6 @@ RUN wget -q https://storage.googleapis.com/download.tensorflow.org/models/tflite
mv /detect.tflite /cpu_model.tflite && \
rm /cpu_model.zip

RUN apt -qq update && apt -qq install --no-install-recommends -y \
gdb \
python3.7-dbg \
&& rm -rf /var/lib/apt/lists/* \
&& (apt-get autoremove -y; apt-get autoclean -y)

WORKDIR /opt/frigate/
ADD frigate frigate/
COPY detect_objects.py .
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5 changes: 2 additions & 3 deletions detect_objects.py
Original file line number Diff line number Diff line change
Expand Up @@ -71,13 +71,12 @@ def start_plasma_store():
return plasma_process

class CameraWatchdog(threading.Thread):
def __init__(self, camera_processes, config, tflite_process, tracked_objects_queue, object_processor, plasma_process):
def __init__(self, camera_processes, config, tflite_process, tracked_objects_queue, plasma_process):
threading.Thread.__init__(self)
self.camera_processes = camera_processes
self.config = config
self.tflite_process = tflite_process
self.tracked_objects_queue = tracked_objects_queue
self.object_processor = object_processor
self.plasma_process = plasma_process

def run(self):
Expand Down Expand Up @@ -202,7 +201,7 @@ def on_connect(client, userdata, flags, rc):
object_processor = TrackedObjectProcessor(CONFIG['cameras'], client, MQTT_TOPIC_PREFIX, tracked_objects_queue)
object_processor.start()

camera_watchdog = CameraWatchdog(camera_processes, CONFIG['cameras'], tflite_process, tracked_objects_queue, object_processor, plasma_process)
camera_watchdog = CameraWatchdog(camera_processes, CONFIG['cameras'], tflite_process, tracked_objects_queue, plasma_process)
camera_watchdog.start()

# create a flask app that encodes frames a mjpeg on demand
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213 changes: 118 additions & 95 deletions frigate/object_processing.py
Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
import json
import hashlib
import datetime
import time
import copy
import cv2
import threading
Expand Down Expand Up @@ -44,109 +45,131 @@ def get_best(self, camera, label):

def get_current_frame(self, camera):
return self.camera_data[camera]['current_frame']

def run(self):
def connect_plasma_client(self):
while True:
try:
self.plasma_client = plasma.connect("/tmp/plasma")
while True:
camera, frame_time, tracked_objects = self.tracked_objects_queue.get()
return
except:
print(f"TrackedObjectProcessor: unable to connect plasma client")
time.sleep(10)

def get_from_plasma(self, object_id):
while True:
try:
return self.plasma_client.get(object_id, timeout_ms=0)
except:
self.connect_plasma_client()
time.sleep(1)

def delete_from_plasma(self, object_ids):
while True:
try:
self.plasma_client.delete(object_ids)
return
except:
self.connect_plasma_client()
time.sleep(1)

config = self.config[camera]
best_objects = self.camera_data[camera]['best_objects']
current_object_status = self.camera_data[camera]['object_status']
self.camera_data[camera]['tracked_objects'] = tracked_objects
def run(self):
self.connect_plasma_client()
while True:
camera, frame_time, tracked_objects = self.tracked_objects_queue.get()

###
# Draw tracked objects on the frame
###
object_id_hash = hashlib.sha1(str.encode(f"{camera}{frame_time}"))
object_id_bytes = object_id_hash.digest()
object_id = plasma.ObjectID(object_id_bytes)
current_frame = self.plasma_client.get(object_id, timeout_ms=0)
config = self.config[camera]
best_objects = self.camera_data[camera]['best_objects']
current_object_status = self.camera_data[camera]['object_status']
self.camera_data[camera]['tracked_objects'] = tracked_objects

if not current_frame is plasma.ObjectNotAvailable:
# draw the bounding boxes on the frame
for obj in tracked_objects.values():
thickness = 2
color = COLOR_MAP[obj['label']]

if obj['frame_time'] != frame_time:
thickness = 1
color = (255,0,0)
###
# Draw tracked objects on the frame
###
object_id_hash = hashlib.sha1(str.encode(f"{camera}{frame_time}"))
object_id_bytes = object_id_hash.digest()
object_id = plasma.ObjectID(object_id_bytes)
current_frame = self.get_from_plasma(object_id)

# draw the bounding boxes on the frame
box = obj['box']
draw_box_with_label(current_frame, box[0], box[1], box[2], box[3], obj['label'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
# draw the regions on the frame
region = obj['region']
cv2.rectangle(current_frame, (region[0], region[1]), (region[2], region[3]), (0,255,0), 1)

if config['snapshots']['show_timestamp']:
time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")
cv2.putText(current_frame, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)
if not current_frame is plasma.ObjectNotAvailable:
# draw the bounding boxes on the frame
for obj in tracked_objects.values():
thickness = 2
color = COLOR_MAP[obj['label']]

if obj['frame_time'] != frame_time:
thickness = 1
color = (255,0,0)

###
# Set the current frame as ready
###
self.camera_data[camera]['current_frame'] = current_frame
# draw the bounding boxes on the frame
box = obj['box']
draw_box_with_label(current_frame, box[0], box[1], box[2], box[3], obj['label'], f"{int(obj['score']*100)}% {int(obj['area'])}", thickness=thickness, color=color)
# draw the regions on the frame
region = obj['region']
cv2.rectangle(current_frame, (region[0], region[1]), (region[2], region[3]), (0,255,0), 1)

if config['snapshots']['show_timestamp']:
time_to_show = datetime.datetime.fromtimestamp(frame_time).strftime("%m/%d/%Y %H:%M:%S")
cv2.putText(current_frame, time_to_show, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, fontScale=.8, color=(255, 255, 255), thickness=2)

# store the object id, so you can delete it at the next loop
previous_object_id = self.camera_data[camera]['object_id']
if not previous_object_id is None:
self.plasma_client.delete([previous_object_id])
self.camera_data[camera]['object_id'] = object_id

###
# Maintain the highest scoring recent object and frame for each label
###
for obj in tracked_objects.values():
# if the object wasn't seen on the current frame, skip it
if obj['frame_time'] != frame_time:
continue
if obj['label'] in best_objects:
now = datetime.datetime.now().timestamp()
# if the object is a higher score than the current best score
# or the current object is more than 1 minute old, use the new object
if obj['score'] > best_objects[obj['label']]['score'] or (now - best_objects[obj['label']]['frame_time']) > 60:
obj['frame'] = np.copy(self.camera_data[camera]['current_frame'])
best_objects[obj['label']] = obj
else:
obj['frame'] = np.copy(self.camera_data[camera]['current_frame'])
best_objects[obj['label']] = obj
###
# Set the current frame as ready
###
self.camera_data[camera]['current_frame'] = current_frame

###
# Report over MQTT
###
# count objects with more than 2 entries in history by type
obj_counter = Counter()
for obj in tracked_objects.values():
if len(obj['history']) > 1:
obj_counter[obj['label']] += 1

# report on detected objects
for obj_name, count in obj_counter.items():
new_status = 'ON' if count > 0 else 'OFF'
if new_status != current_object_status[obj_name]:
current_object_status[obj_name] = new_status
self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}", new_status, retain=False)
# send the best snapshot over mqtt
best_frame = cv2.cvtColor(best_objects[obj_name]['frame'], cv2.COLOR_RGB2BGR)
ret, jpg = cv2.imencode('.jpg', best_frame)
if ret:
jpg_bytes = jpg.tobytes()
self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}/snapshot", jpg_bytes, retain=True)
# store the object id, so you can delete it at the next loop
previous_object_id = self.camera_data[camera]['object_id']
if not previous_object_id is None:
self.delete_from_plasma([previous_object_id])
self.camera_data[camera]['object_id'] = object_id

###
# Maintain the highest scoring recent object and frame for each label
###
for obj in tracked_objects.values():
# if the object wasn't seen on the current frame, skip it
if obj['frame_time'] != frame_time:
continue
if obj['label'] in best_objects:
now = datetime.datetime.now().timestamp()
# if the object is a higher score than the current best score
# or the current object is more than 1 minute old, use the new object
if obj['score'] > best_objects[obj['label']]['score'] or (now - best_objects[obj['label']]['frame_time']) > 60:
obj['frame'] = np.copy(self.camera_data[camera]['current_frame'])
best_objects[obj['label']] = obj
else:
obj['frame'] = np.copy(self.camera_data[camera]['current_frame'])
best_objects[obj['label']] = obj

# expire any objects that are ON and no longer detected
expired_objects = [obj_name for obj_name, status in current_object_status.items() if status == 'ON' and not obj_name in obj_counter]
for obj_name in expired_objects:
current_object_status[obj_name] = 'OFF'
self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}", 'OFF', retain=False)
# send updated snapshot over mqtt
best_frame = cv2.cvtColor(best_objects[obj_name]['frame'], cv2.COLOR_RGB2BGR)
ret, jpg = cv2.imencode('.jpg', best_frame)
if ret:
jpg_bytes = jpg.tobytes()
self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}/snapshot", jpg_bytes, retain=True)
except:
pass
###
# Report over MQTT
###
# count objects with more than 2 entries in history by type
obj_counter = Counter()
for obj in tracked_objects.values():
if len(obj['history']) > 1:
obj_counter[obj['label']] += 1

# report on detected objects
for obj_name, count in obj_counter.items():
new_status = 'ON' if count > 0 else 'OFF'
if new_status != current_object_status[obj_name]:
current_object_status[obj_name] = new_status
self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}", new_status, retain=False)
# send the best snapshot over mqtt
best_frame = cv2.cvtColor(best_objects[obj_name]['frame'], cv2.COLOR_RGB2BGR)
ret, jpg = cv2.imencode('.jpg', best_frame)
if ret:
jpg_bytes = jpg.tobytes()
self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}/snapshot", jpg_bytes, retain=True)

# expire any objects that are ON and no longer detected
expired_objects = [obj_name for obj_name, status in current_object_status.items() if status == 'ON' and not obj_name in obj_counter]
for obj_name in expired_objects:
current_object_status[obj_name] = 'OFF'
self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}", 'OFF', retain=False)
# send updated snapshot over mqtt
best_frame = cv2.cvtColor(best_objects[obj_name]['frame'], cv2.COLOR_RGB2BGR)
ret, jpg = cv2.imencode('.jpg', best_frame)
if ret:
jpg_bytes = jpg.tobytes()
self.client.publish(f"{self.topic_prefix}/{camera}/{obj_name}/snapshot", jpg_bytes, retain=True)

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