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allow motion based retention when detect is disabled

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Blake Blackshear 2022-02-06 14:49:54 -06:00
parent 5792cf042e
commit 583912db9c
1 changed files with 208 additions and 201 deletions
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409
frigate/video.py
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@ -491,212 +491,219 @@ def process_frames(
logger.info(f"{camera_name}: frame {frame_time} is not in memory store.") logger.info(f"{camera_name}: frame {frame_time} is not in memory store.")
continue continue
if not detection_enabled.value:
fps.value = fps_tracker.eps()
object_tracker.match_and_update(frame_time, [])
detected_objects_queue.put(
(camera_name, frame_time, object_tracker.tracked_objects, [], [])
)
detection_fps.value = object_detector.fps.eps()
frame_manager.close(f"{camera_name}{frame_time}")
continue
# look for motion # look for motion
motion_boxes = motion_detector.detect(frame) motion_boxes = motion_detector.detect(frame)
# get stationary object ids regions = []
# check every Nth frame for stationary objects
# disappeared objects are not stationary
# also check for overlapping motion boxes
stationary_object_ids = [
obj["id"]
for obj in object_tracker.tracked_objects.values()
# if there hasn't been motion for 10 frames
if obj["motionless_count"] >= 10
# and it isn't due for a periodic check
and (
detect_config.stationary_interval == 0
or obj["motionless_count"] % detect_config.stationary_interval != 0
)
# and it hasn't disappeared
and object_tracker.disappeared[obj["id"]] == 0
# and it doesn't overlap with any current motion boxes
and not intersects_any(obj["box"], motion_boxes)
]
# get tracked object boxes that aren't stationary # if detection is disabled
tracked_object_boxes = [ if not detection_enabled.value:
obj["box"] object_tracker.match_and_update(frame_time, [])
for obj in object_tracker.tracked_objects.values()
if not obj["id"] in stationary_object_ids
]
# combine motion boxes with known locations of existing objects
combined_boxes = reduce_boxes(motion_boxes + tracked_object_boxes)
region_min_size = max(model_shape[0], model_shape[1])
# compute regions
regions = [
calculate_region(
frame_shape,
a[0],
a[1],
a[2],
a[3],
region_min_size,
multiplier=random.uniform(1.2, 1.5),
)
for a in combined_boxes
]
# consolidate regions with heavy overlap
regions = [
calculate_region(
frame_shape, a[0], a[1], a[2], a[3], region_min_size, multiplier=1.0
)
for a in reduce_boxes(regions, 0.4)
]
# if starting up, get the next startup scan region
if startup_scan_counter < 9:
ymin = int(frame_shape[0] / 3 * startup_scan_counter / 3)
ymax = int(frame_shape[0] / 3 + ymin)
xmin = int(frame_shape[1] / 3 * startup_scan_counter / 3)
xmax = int(frame_shape[1] / 3 + xmin)
regions.append(
calculate_region(
frame_shape, xmin, ymin, xmax, ymax, region_min_size, multiplier=1.2
)
)
startup_scan_counter += 1
# resize regions and detect
# seed with stationary objects
detections = [
(
obj["label"],
obj["score"],
obj["box"],
obj["area"],
obj["region"],
)
for obj in object_tracker.tracked_objects.values()
if obj["id"] in stationary_object_ids
]
for region in regions:
detections.extend(
detect(
object_detector,
frame,
model_shape,
region,
objects_to_track,
object_filters,
)
)
#########
# merge objects, check for clipped objects and look again up to 4 times
#########
refining = len(regions) > 0
refine_count = 0
while refining and refine_count < 4:
refining = False
# group by name
detected_object_groups = defaultdict(lambda: [])
for detection in detections:
detected_object_groups[detection[0]].append(detection)
selected_objects = []
for group in detected_object_groups.values():
# apply non-maxima suppression to suppress weak, overlapping bounding boxes
boxes = [
(o[2][0], o[2][1], o[2][2] - o[2][0], o[2][3] - o[2][1])
for o in group
]
confidences = [o[1] for o in group]
idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
for index in idxs:
obj = group[index[0]]
if clipped(obj, frame_shape):
box = obj[2]
# calculate a new region that will hopefully get the entire object
region = calculate_region(
frame_shape, box[0], box[1], box[2], box[3], region_min_size
)
regions.append(region)
selected_objects.extend(
detect(
object_detector,
frame,
model_shape,
region,
objects_to_track,
object_filters,
)
)
refining = True
else:
selected_objects.append(obj)
# set the detections list to only include top, complete objects
# and new detections
detections = selected_objects
if refining:
refine_count += 1
## drop detections that overlap too much
consolidated_detections = []
# if detection was run on this frame, consolidate
if len(regions) > 0:
# group by name
detected_object_groups = defaultdict(lambda: [])
for detection in detections:
detected_object_groups[detection[0]].append(detection)
# loop over detections grouped by label
for group in detected_object_groups.values():
# if the group only has 1 item, skip
if len(group) == 1:
consolidated_detections.append(group[0])
continue
# sort smallest to largest by area
sorted_by_area = sorted(group, key=lambda g: g[3])
for current_detection_idx in range(0, len(sorted_by_area)):
current_detection = sorted_by_area[current_detection_idx][2]
overlap = 0
for to_check_idx in range(
min(current_detection_idx + 1, len(sorted_by_area)),
len(sorted_by_area),
):
to_check = sorted_by_area[to_check_idx][2]
# if 90% of smaller detection is inside of another detection, consolidate
if (
area(intersection(current_detection, to_check))
/ area(current_detection)
> 0.9
):
overlap = 1
break
if overlap == 0:
consolidated_detections.append(
sorted_by_area[current_detection_idx]
)
# now that we have refined our detections, we need to track objects
object_tracker.match_and_update(frame_time, consolidated_detections)
# else, just update the frame times for the stationary objects
else: else:
object_tracker.update_frame_times(frame_time) # get stationary object ids
# check every Nth frame for stationary objects
# disappeared objects are not stationary
# also check for overlapping motion boxes
stationary_object_ids = [
obj["id"]
for obj in object_tracker.tracked_objects.values()
# if there hasn't been motion for 10 frames
if obj["motionless_count"] >= 10
# and it isn't due for a periodic check
and (
detect_config.stationary_interval == 0
or obj["motionless_count"] % detect_config.stationary_interval != 0
)
# and it hasn't disappeared
and object_tracker.disappeared[obj["id"]] == 0
# and it doesn't overlap with any current motion boxes
and not intersects_any(obj["box"], motion_boxes)
]
# get tracked object boxes that aren't stationary
tracked_object_boxes = [
obj["box"]
for obj in object_tracker.tracked_objects.values()
if not obj["id"] in stationary_object_ids
]
# combine motion boxes with known locations of existing objects
combined_boxes = reduce_boxes(motion_boxes + tracked_object_boxes)
region_min_size = max(model_shape[0], model_shape[1])
# compute regions
regions = [
calculate_region(
frame_shape,
a[0],
a[1],
a[2],
a[3],
region_min_size,
multiplier=random.uniform(1.2, 1.5),
)
for a in combined_boxes
]
# consolidate regions with heavy overlap
regions = [
calculate_region(
frame_shape, a[0], a[1], a[2], a[3], region_min_size, multiplier=1.0
)
for a in reduce_boxes(regions, 0.4)
]
# if starting up, get the next startup scan region
if startup_scan_counter < 9:
ymin = int(frame_shape[0] / 3 * startup_scan_counter / 3)
ymax = int(frame_shape[0] / 3 + ymin)
xmin = int(frame_shape[1] / 3 * startup_scan_counter / 3)
xmax = int(frame_shape[1] / 3 + xmin)
regions.append(
calculate_region(
frame_shape,
xmin,
ymin,
xmax,
ymax,
region_min_size,
multiplier=1.2,
)
)
startup_scan_counter += 1
# resize regions and detect
# seed with stationary objects
detections = [
(
obj["label"],
obj["score"],
obj["box"],
obj["area"],
obj["region"],
)
for obj in object_tracker.tracked_objects.values()
if obj["id"] in stationary_object_ids
]
for region in regions:
detections.extend(
detect(
object_detector,
frame,
model_shape,
region,
objects_to_track,
object_filters,
)
)
#########
# merge objects, check for clipped objects and look again up to 4 times
#########
refining = len(regions) > 0
refine_count = 0
while refining and refine_count < 4:
refining = False
# group by name
detected_object_groups = defaultdict(lambda: [])
for detection in detections:
detected_object_groups[detection[0]].append(detection)
selected_objects = []
for group in detected_object_groups.values():
# apply non-maxima suppression to suppress weak, overlapping bounding boxes
boxes = [
(o[2][0], o[2][1], o[2][2] - o[2][0], o[2][3] - o[2][1])
for o in group
]
confidences = [o[1] for o in group]
idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
for index in idxs:
obj = group[index[0]]
if clipped(obj, frame_shape):
box = obj[2]
# calculate a new region that will hopefully get the entire object
region = calculate_region(
frame_shape,
box[0],
box[1],
box[2],
box[3],
region_min_size,
)
regions.append(region)
selected_objects.extend(
detect(
object_detector,
frame,
model_shape,
region,
objects_to_track,
object_filters,
)
)
refining = True
else:
selected_objects.append(obj)
# set the detections list to only include top, complete objects
# and new detections
detections = selected_objects
if refining:
refine_count += 1
## drop detections that overlap too much
consolidated_detections = []
# if detection was run on this frame, consolidate
if len(regions) > 0:
# group by name
detected_object_groups = defaultdict(lambda: [])
for detection in detections:
detected_object_groups[detection[0]].append(detection)
# loop over detections grouped by label
for group in detected_object_groups.values():
# if the group only has 1 item, skip
if len(group) == 1:
consolidated_detections.append(group[0])
continue
# sort smallest to largest by area
sorted_by_area = sorted(group, key=lambda g: g[3])
for current_detection_idx in range(0, len(sorted_by_area)):
current_detection = sorted_by_area[current_detection_idx][2]
overlap = 0
for to_check_idx in range(
min(current_detection_idx + 1, len(sorted_by_area)),
len(sorted_by_area),
):
to_check = sorted_by_area[to_check_idx][2]
# if 90% of smaller detection is inside of another detection, consolidate
if (
area(intersection(current_detection, to_check))
/ area(current_detection)
> 0.9
):
overlap = 1
break
if overlap == 0:
consolidated_detections.append(
sorted_by_area[current_detection_idx]
)
# now that we have refined our detections, we need to track objects
object_tracker.match_and_update(frame_time, consolidated_detections)
# else, just update the frame times for the stationary objects
else:
object_tracker.update_frame_times(frame_time)
# add to the queue if not full # add to the queue if not full
if detected_objects_queue.full(): if detected_objects_queue.full():