use yuv420p pixel format for motion

config/config.example.yml

@@ -66,7 +66,7 @@ save_clips:
 # - -f
 # - rawvideo
 # - -pix_fmt
-# - rgb24
+# - yuv420p
 
 ####################
 # Global object configuration. Applies to all cameras

detect_objects.py

@@ -55,7 +55,7 @@
  '-use_wallclock_as_timestamps', '1']),
  'output_args': FFMPEG_CONFIG.get('output_args',
  ['-f', 'rawvideo',
- '-pix_fmt', 'rgb24'])
+ '-pix_fmt', 'yuv420p'])
 }
 
 GLOBAL_OBJECT_CONFIG = CONFIG.get('objects', {})

frigate/motion.py

@@ -4,6 +4,7 @@
 
 class MotionDetector():
  def __init__(self, frame_shape, mask, resize_factor=4):
+ self.frame_shape = frame_shape
  self.resize_factor = resize_factor
  self.motion_frame_size = (int(frame_shape[0]/resize_factor), int(frame_shape[1]/resize_factor))
  self.avg_frame = np.zeros(self.motion_frame_size, np.float)
@@ -16,22 +17,24 @@ def __init__(self, frame_shape, mask, resize_factor=4):
  def detect(self, frame):
  motion_boxes = []
 
+ gray = frame[0:self.frame_shape[0], 0:self.frame_shape[1]]
+
  # resize frame
- resized_frame = cv2.resize(frame, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
+ resized_frame = cv2.resize(gray, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
 
  # convert to grayscale
- gray = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2GRAY)
+ # resized_frame = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2GRAY)
 
  # mask frame
- gray[self.mask] = [255]
+ resized_frame[self.mask] = [255]
 
  # it takes ~30 frames to establish a baseline
  # dont bother looking for motion
  if self.frame_counter < 30:
  self.frame_counter += 1
  else:
  # compare to average
- frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(self.avg_frame))
+ frameDelta = cv2.absdiff(resized_frame, cv2.convertScaleAbs(self.avg_frame))
 
  # compute the average delta over the past few frames
  # the alpha value can be modified to configure how sensitive the motion detection is.
@@ -70,10 +73,10 @@ def detect(self, frame):
  # TODO: this really depends on FPS
  if self.motion_frame_count >= 10:
  # only average in the current frame if the difference persists for at least 3 frames
- cv2.accumulateWeighted(gray, self.avg_frame, 0.2)
+ cv2.accumulateWeighted(resized_frame, self.avg_frame, 0.2)
  else:
  # when no motion, just keep averaging the frames together
- cv2.accumulateWeighted(gray, self.avg_frame, 0.2)
+ cv2.accumulateWeighted(resized_frame, self.avg_frame, 0.2)
  self.motion_frame_count = 0
 
  return motion_boxes

frigate/object_processing.py

@@ -93,7 +93,7 @@ def update(self, frame_time, tracked_objects):
  # get the new frame and delete the old frame
  frame_id = f"{self.name}{frame_time}"
  with self.current_frame_lock:
- self._current_frame = self.frame_manager.get(frame_id, self.config['frame_shape'])
+ self._current_frame = self.frame_manager.get(frame_id, (self.config['frame_shape'][0]*3//2, self.config['frame_shape'][1]))
  if not self.previous_frame_id is None:
  self.frame_manager.delete(self.previous_frame_id)
  self.previous_frame_id = frame_id

frigate/video.py

@@ -120,7 +120,7 @@ def capture_frames(ffmpeg_process, camera_name, frame_shape, frame_manager: Fram
  stop_event: mp.Event, current_frame: mp.Value):
 
  frame_num = 0
- frame_size = frame_shape[0] * frame_shape[1] * frame_shape[2]
+ frame_size = frame_shape[0] * frame_shape[1] * 3 // 2
  skipped_fps.start()
  while True:
  if stop_event.is_set():
@@ -276,7 +276,7 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape,
 
  current_frame_time.value = frame_time
 
- frame = frame_manager.get(f"{camera_name}{frame_time}", frame_shape)
+ frame = frame_manager.get(f"{camera_name}{frame_time}", (frame_shape[0]*3//2, frame_shape[1]))
 
  if frame is None:
  print(f"{camera_name}: frame {frame_time} is not in memory store.")
@@ -304,10 +304,13 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape,
  regions = [calculate_region(frame_shape, a[0], a[1], a[2], a[3], 1.0)
  for a in combined_regions]
 
+ if len(regions) > 0:
+ rgb_frame = cv2.cvtColor(frame, cv2.COLOR_YUV2RGB_I420)
+
  # resize regions and detect
  detections = []
  for region in regions:
- detections.extend(detect(object_detector, frame, region, objects_to_track, object_filters, mask))
+ detections.extend(detect(object_detector, rgb_frame, region, objects_to_track, object_filters, mask))
 
  #########
  # merge objects, check for clipped objects and look again up to 4 times
@@ -340,7 +343,7 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape,
  box[0], box[1],
  box[2], box[3])
 
- selected_objects.extend(detect(object_detector, frame, region, objects_to_track, object_filters, mask))
+ selected_objects.extend(detect(object_detector, rgb_frame, region, objects_to_track, object_filters, mask))
 
  refining = True
  else: