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Fix bug in intersection logic (#6780)

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* Fix bug in intersection logic

* Fix isort

* Remove unrelated test

* Formatting

* Fix type in test
This commit is contained in:
Nicolas Mowen
2023-06-12 16:38:22 -06:00
committed by GitHub
parent ff90db30e6
commit ec4d79eafc
3 changed files with 65 additions and 35 deletions
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70
frigate/video.py
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@@ -669,6 +669,40 @@ def get_cluster_region(frame_shape, min_region, cluster, boxes):
)
def get_consolidated_object_detections(detected_object_groups):
"""Drop detections that overlap too much"""
consolidated_detections = []
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]
intersect_box = intersection(current_detection, to_check)
# if 90% of smaller detection is inside of another detection, consolidate
if (
intersect_box is not None
and area(intersect_box) / area(current_detection) > 0.9
):
overlap = 1
break
if overlap == 0:
consolidated_detections.append(sorted_by_area[current_detection_idx])
return consolidated_detections
def process_frames(
camera_name: str,
frame_queue: mp.Queue,
@@ -849,9 +883,6 @@ def process_frames(
# set the detections list to only include top objects
detections = selected_objects
## drop detections that overlap too much
consolidated_detections = []
# if detection was run on this frame, consolidate
if len(regions) > 0:
# group by name
@@ -859,36 +890,9 @@ def process_frames(
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]
)
consolidated_detections = get_consolidated_object_detections(
detected_object_groups
)
# 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