mirror of
https://github.com/blakeblackshear/frigate.git
synced 2025-10-27 10:52:11 +01:00
allow motion based retention when detect is disabled
This commit is contained in:
Blake Blackshear
parent
5792cf042e
commit
583912db9c
409
frigate/video.py
409
frigate/video.py
@ -491,212 +491,219 @@ def process_frames(
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logger.info(f"{camera_name}: frame {frame_time} is not in memory store.")
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continue
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if not detection_enabled.value:
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fps.value = fps_tracker.eps()
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object_tracker.match_and_update(frame_time, [])
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detected_objects_queue.put(
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(camera_name, frame_time, object_tracker.tracked_objects, [], [])
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)
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detection_fps.value = object_detector.fps.eps()
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frame_manager.close(f"{camera_name}{frame_time}")
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continue
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# look for motion
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motion_boxes = motion_detector.detect(frame)
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# get stationary object ids
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# check every Nth frame for stationary objects
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# disappeared objects are not stationary
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# also check for overlapping motion boxes
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stationary_object_ids = [
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obj["id"]
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for obj in object_tracker.tracked_objects.values()
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# if there hasn't been motion for 10 frames
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if obj["motionless_count"] >= 10
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# and it isn't due for a periodic check
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and (
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detect_config.stationary_interval == 0
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or obj["motionless_count"] % detect_config.stationary_interval != 0
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)
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# and it hasn't disappeared
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and object_tracker.disappeared[obj["id"]] == 0
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# and it doesn't overlap with any current motion boxes
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and not intersects_any(obj["box"], motion_boxes)
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]
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regions = []
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# get tracked object boxes that aren't stationary
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tracked_object_boxes = [
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obj["box"]
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for obj in object_tracker.tracked_objects.values()
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if not obj["id"] in stationary_object_ids
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]
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# combine motion boxes with known locations of existing objects
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combined_boxes = reduce_boxes(motion_boxes + tracked_object_boxes)
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region_min_size = max(model_shape[0], model_shape[1])
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# compute regions
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regions = [
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calculate_region(
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frame_shape,
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a[0],
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a[1],
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a[2],
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a[3],
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region_min_size,
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multiplier=random.uniform(1.2, 1.5),
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)
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for a in combined_boxes
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]
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# consolidate regions with heavy overlap
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regions = [
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calculate_region(
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frame_shape, a[0], a[1], a[2], a[3], region_min_size, multiplier=1.0
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)
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for a in reduce_boxes(regions, 0.4)
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]
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# if starting up, get the next startup scan region
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if startup_scan_counter < 9:
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ymin = int(frame_shape[0] / 3 * startup_scan_counter / 3)
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ymax = int(frame_shape[0] / 3 + ymin)
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xmin = int(frame_shape[1] / 3 * startup_scan_counter / 3)
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xmax = int(frame_shape[1] / 3 + xmin)
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regions.append(
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calculate_region(
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frame_shape, xmin, ymin, xmax, ymax, region_min_size, multiplier=1.2
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)
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)
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startup_scan_counter += 1
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# resize regions and detect
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# seed with stationary objects
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detections = [
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(
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obj["label"],
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obj["score"],
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obj["box"],
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obj["area"],
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obj["region"],
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)
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for obj in object_tracker.tracked_objects.values()
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if obj["id"] in stationary_object_ids
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]
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for region in regions:
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detections.extend(
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detect(
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object_detector,
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frame,
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model_shape,
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region,
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objects_to_track,
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object_filters,
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)
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)
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#########
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# merge objects, check for clipped objects and look again up to 4 times
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#########
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refining = len(regions) > 0
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refine_count = 0
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while refining and refine_count < 4:
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refining = False
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# group by name
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detected_object_groups = defaultdict(lambda: [])
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for detection in detections:
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detected_object_groups[detection[0]].append(detection)
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selected_objects = []
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for group in detected_object_groups.values():
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# apply non-maxima suppression to suppress weak, overlapping bounding boxes
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boxes = [
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(o[2][0], o[2][1], o[2][2] - o[2][0], o[2][3] - o[2][1])
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for o in group
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]
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confidences = [o[1] for o in group]
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idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
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for index in idxs:
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obj = group[index[0]]
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if clipped(obj, frame_shape):
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box = obj[2]
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# calculate a new region that will hopefully get the entire object
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region = calculate_region(
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frame_shape, box[0], box[1], box[2], box[3], region_min_size
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)
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regions.append(region)
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selected_objects.extend(
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detect(
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object_detector,
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frame,
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model_shape,
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region,
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objects_to_track,
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object_filters,
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)
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)
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refining = True
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else:
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selected_objects.append(obj)
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# set the detections list to only include top, complete objects
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# and new detections
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detections = selected_objects
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if refining:
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refine_count += 1
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## drop detections that overlap too much
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consolidated_detections = []
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# if detection was run on this frame, consolidate
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if len(regions) > 0:
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# group by name
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detected_object_groups = defaultdict(lambda: [])
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for detection in detections:
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detected_object_groups[detection[0]].append(detection)
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# loop over detections grouped by label
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for group in detected_object_groups.values():
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# if the group only has 1 item, skip
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if len(group) == 1:
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consolidated_detections.append(group[0])
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continue
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# sort smallest to largest by area
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sorted_by_area = sorted(group, key=lambda g: g[3])
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for current_detection_idx in range(0, len(sorted_by_area)):
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current_detection = sorted_by_area[current_detection_idx][2]
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overlap = 0
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for to_check_idx in range(
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min(current_detection_idx + 1, len(sorted_by_area)),
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len(sorted_by_area),
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):
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to_check = sorted_by_area[to_check_idx][2]
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# if 90% of smaller detection is inside of another detection, consolidate
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if (
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area(intersection(current_detection, to_check))
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/ area(current_detection)
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> 0.9
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):
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overlap = 1
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break
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if overlap == 0:
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consolidated_detections.append(
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sorted_by_area[current_detection_idx]
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)
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# now that we have refined our detections, we need to track objects
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object_tracker.match_and_update(frame_time, consolidated_detections)
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# else, just update the frame times for the stationary objects
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# if detection is disabled
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if not detection_enabled.value:
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object_tracker.match_and_update(frame_time, [])
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else:
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object_tracker.update_frame_times(frame_time)
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# get stationary object ids
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# check every Nth frame for stationary objects
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# disappeared objects are not stationary
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# also check for overlapping motion boxes
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stationary_object_ids = [
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obj["id"]
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for obj in object_tracker.tracked_objects.values()
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# if there hasn't been motion for 10 frames
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if obj["motionless_count"] >= 10
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# and it isn't due for a periodic check
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and (
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detect_config.stationary_interval == 0
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or obj["motionless_count"] % detect_config.stationary_interval != 0
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)
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# and it hasn't disappeared
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and object_tracker.disappeared[obj["id"]] == 0
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# and it doesn't overlap with any current motion boxes
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and not intersects_any(obj["box"], motion_boxes)
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]
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# get tracked object boxes that aren't stationary
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tracked_object_boxes = [
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obj["box"]
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for obj in object_tracker.tracked_objects.values()
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if not obj["id"] in stationary_object_ids
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]
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# combine motion boxes with known locations of existing objects
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combined_boxes = reduce_boxes(motion_boxes + tracked_object_boxes)
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region_min_size = max(model_shape[0], model_shape[1])
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# compute regions
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regions = [
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calculate_region(
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frame_shape,
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a[0],
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a[1],
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a[2],
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a[3],
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region_min_size,
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multiplier=random.uniform(1.2, 1.5),
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)
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for a in combined_boxes
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]
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# consolidate regions with heavy overlap
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regions = [
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calculate_region(
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frame_shape, a[0], a[1], a[2], a[3], region_min_size, multiplier=1.0
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)
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for a in reduce_boxes(regions, 0.4)
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]
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# if starting up, get the next startup scan region
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if startup_scan_counter < 9:
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ymin = int(frame_shape[0] / 3 * startup_scan_counter / 3)
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ymax = int(frame_shape[0] / 3 + ymin)
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xmin = int(frame_shape[1] / 3 * startup_scan_counter / 3)
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xmax = int(frame_shape[1] / 3 + xmin)
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regions.append(
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calculate_region(
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frame_shape,
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xmin,
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ymin,
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xmax,
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ymax,
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region_min_size,
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multiplier=1.2,
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)
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)
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startup_scan_counter += 1
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# resize regions and detect
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# seed with stationary objects
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detections = [
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(
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obj["label"],
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obj["score"],
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obj["box"],
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obj["area"],
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obj["region"],
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)
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for obj in object_tracker.tracked_objects.values()
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if obj["id"] in stationary_object_ids
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]
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for region in regions:
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detections.extend(
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detect(
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object_detector,
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frame,
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model_shape,
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region,
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objects_to_track,
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object_filters,
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)
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)
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#########
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# merge objects, check for clipped objects and look again up to 4 times
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#########
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refining = len(regions) > 0
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refine_count = 0
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while refining and refine_count < 4:
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refining = False
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# group by name
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detected_object_groups = defaultdict(lambda: [])
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for detection in detections:
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detected_object_groups[detection[0]].append(detection)
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selected_objects = []
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for group in detected_object_groups.values():
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# apply non-maxima suppression to suppress weak, overlapping bounding boxes
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boxes = [
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(o[2][0], o[2][1], o[2][2] - o[2][0], o[2][3] - o[2][1])
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for o in group
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]
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confidences = [o[1] for o in group]
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idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
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for index in idxs:
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obj = group[index[0]]
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if clipped(obj, frame_shape):
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box = obj[2]
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# calculate a new region that will hopefully get the entire object
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region = calculate_region(
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frame_shape,
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box[0],
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box[1],
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box[2],
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box[3],
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region_min_size,
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)
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regions.append(region)
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selected_objects.extend(
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detect(
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object_detector,
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frame,
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model_shape,
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region,
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objects_to_track,
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object_filters,
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)
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)
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refining = True
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else:
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selected_objects.append(obj)
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# set the detections list to only include top, complete objects
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# and new detections
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detections = selected_objects
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if refining:
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refine_count += 1
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## drop detections that overlap too much
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consolidated_detections = []
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# if detection was run on this frame, consolidate
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if len(regions) > 0:
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# group by name
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detected_object_groups = defaultdict(lambda: [])
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for detection in detections:
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detected_object_groups[detection[0]].append(detection)
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# loop over detections grouped by label
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for group in detected_object_groups.values():
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# if the group only has 1 item, skip
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if len(group) == 1:
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consolidated_detections.append(group[0])
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continue
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# sort smallest to largest by area
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sorted_by_area = sorted(group, key=lambda g: g[3])
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for current_detection_idx in range(0, len(sorted_by_area)):
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current_detection = sorted_by_area[current_detection_idx][2]
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overlap = 0
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for to_check_idx in range(
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min(current_detection_idx + 1, len(sorted_by_area)),
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len(sorted_by_area),
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):
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to_check = sorted_by_area[to_check_idx][2]
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# if 90% of smaller detection is inside of another detection, consolidate
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if (
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area(intersection(current_detection, to_check))
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/ area(current_detection)
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> 0.9
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):
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overlap = 1
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break
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if overlap == 0:
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consolidated_detections.append(
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sorted_by_area[current_detection_idx]
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)
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# now that we have refined our detections, we need to track objects
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object_tracker.match_and_update(frame_time, consolidated_detections)
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# else, just update the frame times for the stationary objects
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else:
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object_tracker.update_frame_times(frame_time)
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# add to the queue if not full
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if detected_objects_queue.full():
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Block a user