make object processor resilient to plasma failures

Dockerfile

@@ -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 .

detect_objects.py

@@ -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):
@@ -202,7 +201,7 @@ def main():
     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

frigate/object_processing.py

@@ -1,6 +1,7 @@
 import json
 import hashlib
 import datetime
+import time
 import copy
 import cv2
 import threading
@@ -45,108 +46,130 @@ class TrackedObjectProcessor(threading.Thread):
     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:
+                return
-                    camera, frame_time, tracked_objects = self.tracked_objects_queue.get()
-
-                    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
-
-                    ###
-                    # 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)
-
-                    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 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)
-
-                        ###
-                        # Set the current frame as ready
-                        ###
-                        self.camera_data[camera]['current_frame'] = current_frame
-
-                        # 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
-
-                    ###
-                    # 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)
             except:
-                pass
+                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)
+
+    def run(self):
+        self.connect_plasma_client()
+        while True:
+            camera, frame_time, tracked_objects = self.tracked_objects_queue.get()
+
+            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
+
+            ###
+            # 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)
+
+            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 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)
+
+                ###
+                # Set the current frame as ready
+                ###
+                self.camera_data[camera]['current_frame'] = current_frame
+
+                # 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
+
+            ###
+            # 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)