add a few more metrics to debug

detect_objects.py

@@ -66,11 +66,12 @@ class CameraWatchdog(threading.Thread):
         time.sleep(10)
         while True:
             # wait a bit before checking
-            time.sleep(10)
+            time.sleep(30)
 
             for name, camera_process in self.camera_processes.items():
                 process = camera_process['process']
-                if (datetime.datetime.now().timestamp() - self.object_processor.get_current_frame_time(name)) > 30:
+                if (not self.object_processor.get_current_frame_time(name) is None and 
+                    (datetime.datetime.now().timestamp() - self.object_processor.get_current_frame_time(name)) > 30):
                     print(f"Last frame for {name} is more than 30 seconds old...")
                     if process.is_alive():
                         process.terminate()
@@ -84,6 +85,8 @@ class CameraWatchdog(threading.Thread):
                     print(f"Process for {name} is not alive. Starting again...")
                     camera_process['fps'].value = float(self.config[name]['fps'])
                     camera_process['skipped_fps'].value = 0.0
+                    camera_process['detection_fps'].value = 0.0
+                    self.object_processor.camera_data[name]['current_frame_time'] = None
                     process = mp.Process(target=track_camera, args=(name, self.config[name], FFMPEG_DEFAULT_CONFIG, GLOBAL_OBJECT_CONFIG, 
                         self.tflite_process.detect_lock, self.tflite_process.detect_ready, self.tflite_process.frame_ready, self.tracked_objects_queue, 
                         camera_process['fps'], camera_process['skipped_fps'], camera_process['detection_fps']))
@@ -117,7 +120,7 @@ def main():
 
     # start plasma store
     plasma_cmd = ['plasma_store', '-m', '400000000', '-s', '/tmp/plasma']
-    plasma_process = sp.Popen(plasma_cmd, stdout=sp.DEVNULL, stderr=sp.DEVNULL)
+    plasma_process = sp.Popen(plasma_cmd, stdout=sp.DEVNULL)
     time.sleep(1)
     rc = plasma_process.poll()
     if rc is not None:
@@ -144,8 +147,7 @@ def main():
         camera_processes[name] = {
             'fps': mp.Value('d', float(config['fps'])),
             'skipped_fps': mp.Value('d', 0.0),
-            'detection_fps': mp.Value('d', 0.0),
-            'last_frame': datetime.datetime.now().timestamp()
+            'detection_fps': mp.Value('d', 0.0)
         }
         camera_process = mp.Process(target=track_camera, args=(name, config, FFMPEG_DEFAULT_CONFIG, GLOBAL_OBJECT_CONFIG, 
             tflite_process.detect_lock, tflite_process.detect_ready, tflite_process.frame_ready, tracked_objects_queue, 
@@ -192,6 +194,11 @@ def main():
             'inference_speed': round(tflite_process.avg_inference_speed.value*1000, 2)
         }
 
+        rc = plasma_process.poll()
+        stats['plasma_store_rc'] = rc
+
+        stats['tracked_objects_queue'] = tracked_objects_queue.qsize()
+
         return jsonify(stats)
 
     @app.route('/<camera_name>/<label>/best.jpg')

frigate/object_processing.py

@@ -34,7 +34,9 @@ class TrackedObjectProcessor(threading.Thread):
             'best_objects': {},
             'object_status': defaultdict(lambda: defaultdict(lambda: 'OFF')),
             'tracked_objects': {},
-            'current_frame_time': datetime.datetime.now().timestamp()
+            'current_frame_time': None,
+            'current_frame': np.zeros((720,1280,3), np.uint8),
+            'object_id': None
         })
         
     def get_best(self, camera, label):
@@ -64,33 +66,40 @@ class TrackedObjectProcessor(threading.Thread):
             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)
-
-            # 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)
+            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
-                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)
+                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
-            self.camera_data[camera]['current_frame_time'] = frame_time
+                    # 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
+                self.camera_data[camera]['current_frame_time'] = frame_time
+
+                # 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
@@ -104,10 +113,10 @@ class TrackedObjectProcessor(threading.Thread):
                     # 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(current_frame)
+                        obj['frame'] = np.copy(self.camera_data[camera]['current_frame'])
                         best_objects[obj['label']] = obj
                 else:
-                    obj['frame'] = np.copy(current_frame)
+                    obj['frame'] = np.copy(self.camera_data[camera]['current_frame'])
                     best_objects[obj['label']] = obj
 
             ###

frigate/objects.py

@@ -49,14 +49,6 @@ class ObjectTracker():
             obj['history'] = [entry]
 
     def match_and_update(self, frame_time, new_objects):
-        if len(new_objects) == 0:
-            for id in list(self.tracked_objects.keys()):
-                if self.disappeared[id] >= self.max_disappeared:
-                    self.deregister(id)
-                else:
-                    self.disappeared[id] += 1
-            return
-            
         # group by name
         new_object_groups = defaultdict(lambda: [])
         for obj in new_objects:
@@ -69,6 +61,18 @@ class ObjectTracker():
                 'frame_time': frame_time
             })
         
+        # update any tracked objects with labels that are not
+        # seen in the current objects and deregister if needed
+        for obj in list(self.tracked_objects.values()):
+            if not obj['label'] in new_object_groups:
+                if self.disappeared[obj['id']] >= self.max_disappeared:
+                    self.deregister(obj['id'])
+                else:
+                    self.disappeared[obj['id']] += 1
+        
+        if len(new_objects) == 0:
+            return
+        
         # track objects for each label type
         for label, group in new_object_groups.items():
             current_objects = [o for o in self.tracked_objects.values() if o['label'] == label]

frigate/video.py

@@ -20,7 +20,6 @@ from frigate.objects import ObjectTracker
 from frigate.edgetpu import RemoteObjectDetector
 from frigate.motion import MotionDetector
 
-# TODO: add back opencv fallback
 def get_frame_shape(source):
     ffprobe_cmd = " ".join([
         'ffprobe',
@@ -125,7 +124,11 @@ def track_camera(name, config, ffmpeg_global_config, global_objects_config, dete
     expected_fps = config['fps']
     take_frame = config.get('take_frame', 1)
 
-    frame_shape = get_frame_shape(ffmpeg_input)
+    if 'width' in config and 'height' in config:
+        frame_shape = (config['height'], config['width'], 3)
+    else:
+        frame_shape = get_frame_shape(ffmpeg_input)
+
     frame_size = frame_shape[0] * frame_shape[1] * frame_shape[2]
 
     try: