add a min_fps option

detect_objects.py

@@ -106,11 +106,11 @@ def main():
     for name, config in CONFIG['cameras'].items():
         camera_stats_values[name] = {
             'fps': mp.Value('d', 10.0),
-            'avg_wait': mp.Value('d', 0.0)
+            'skipped_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, 
-            camera_stats_values[name]['fps'], camera_stats_values[name]['avg_wait']))
+            camera_stats_values[name]['fps'], camera_stats_values[name]['skipped_fps']))
         camera_process.daemon = True
         camera_processes.append(camera_process)
 
@@ -141,7 +141,7 @@ def main():
         for name, camera_stats in camera_stats_values.items():
             stats[name] = {
                 'fps': camera_stats['fps'].value,
-                'avg_wait': camera_stats['avg_wait'].value
+                'skipped_fps': camera_stats['skipped_fps'].value
             }
 
         return jsonify(stats)

frigate/video.py

@@ -670,7 +670,7 @@ def create_tensor_input(frame, region):
     # Expand dimensions since the model expects images to have shape: [1, 300, 300, 3]
     return np.expand_dims(cropped_frame, axis=0)
 
-def track_camera(name, config, ffmpeg_global_config, global_objects_config, detect_lock, detect_ready, frame_ready, detected_objects_queue, fps, avg_wait):
+def track_camera(name, config, ffmpeg_global_config, global_objects_config, detect_lock, detect_ready, frame_ready, detected_objects_queue, fps, skipped_fps):
     print(f"Starting process for {name}: {os.getpid()}")
 
     # Merge the ffmpeg config with the global config
@@ -693,8 +693,10 @@ def track_camera(name, config, ffmpeg_global_config, global_objects_config, dete
     for obj in objects_with_config:
         object_filters[obj] = {**global_object_filters.get(obj, {}), **camera_object_filters.get(obj, {})}
 
+    min_fps = config.get('min_fps', 0)
     take_frame = config.get('take_frame', 1)
 
+    # TODO: some kind of watchdog replacement...
     # watchdog_timeout = config.get('watchdog_timeout', 300)
 
     frame_shape = get_frame_shape(ffmpeg_input)
@@ -737,14 +739,11 @@ def track_camera(name, config, ffmpeg_global_config, global_objects_config, dete
     plasma_client = plasma.connect("/tmp/plasma")
     frame_num = 0
     fps_tracker = EventsPerSecond()
+    skipped_fps_tracker = EventsPerSecond()
     fps_tracker.start()
+    skipped_fps_tracker.start()
     while True:
-        # TODO: implement something to determine if it had to wait for a frame at all
-        # to determine if it might be behind and the buffer is filling up
-        start = datetime.datetime.now().timestamp()
         frame_bytes = ffmpeg_process.stdout.read(frame_size)
-        duration = datetime.datetime.now().timestamp()-start
-        avg_wait.value = (avg_wait.value*9 + duration)/10
 
         if not frame_bytes:
             # TODO: restart the ffmpeg process and track number of restarts
@@ -768,6 +767,14 @@ def track_camera(name, config, ffmpeg_global_config, global_objects_config, dete
         # look for motion
         motion_boxes = motion_detector.detect(frame)
 
+        # skip object detection if we are below the min_fps
+        if frame_num > 50 and fps.value < min_fps:
+            skipped_fps_tracker.update()
+            skipped_fps.value = skipped_fps_tracker.eps()
+            continue
+        
+        skipped_fps.value = skipped_fps_tracker.eps()
+
         tracked_objects = object_tracker.tracked_objects.values()
 
         # merge areas of motion that intersect with a known tracked object into a single area to look at