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use yuv420p pixel format for motion

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This commit is contained in:
Blake Blackshear 2020-10-10 10:07:14 -05:00
parent f946813ccb
commit a611cbb942
5 changed files with 19 additions and 13 deletions
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2
config/config.example.yml
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@ -66,7 +66,7 @@ save_clips:
# - -f
# - rawvideo
# - -pix_fmt
# - rgb24
# - yuv420p
####################
# Global object configuration. Applies to all cameras

2
detect_objects.py
View File

@ -55,7 +55,7 @@ FFMPEG_DEFAULT_CONFIG = {
'-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', {})

15
frigate/motion.py
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@ -4,6 +4,7 @@ import numpy as np
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)
@ -16,14 +17,16 @@ class MotionDetector():
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
@ -31,7 +34,7 @@ class MotionDetector():
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 @@ class MotionDetector():
# 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

2
frigate/object_processing.py
View File

@ -93,7 +93,7 @@ class CameraState():
# 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

11
frigate/video.py
View File

@ -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: