Mirrors/blakeblackshear.frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2024-11-21 19:07:46 +01:00
Code Issues Projects Releases Wiki Activity

Fix bug in intersection logic (#6780)

Browse Source 
* 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
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 65 additions and 35 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

16
frigate/test/test_video.py
View File

@ -5,6 +5,7 @@ import numpy as np
from norfair.drawing.color import Palette
from norfair.drawing.drawer import Drawer
from frigate.util import intersection
from frigate.video import (
get_cluster_boundary,
get_cluster_candidates,
@ -63,7 +64,7 @@ def save_cluster_boundary_image(name, boxes, bounding_boxes):
)
class TestConfig(unittest.TestCase):
class TestRegion(unittest.TestCase):
def setUp(self):
self.frame_shape = (1000, 2000)
self.min_region_size = 160
@ -176,3 +177,16 @@ class TestConfig(unittest.TestCase):
# save_clusters_image("dont_combine", boxes, cluster_candidates, regions)
assert len(regions) == 2
class TestObjectBoundingBoxes(unittest.TestCase):
def setUp(self) -> None:
pass
def test_box_intersection(self):
box_a = [2012, 191, 2031, 205]
box_b = [887, 92, 985, 151]
box_c = [899, 128, 1080, 175]
assert intersection(box_a, box_b) == None
assert intersection(box_b, box_c) == (899, 128, 985, 151)

14
frigate/util.py
View File

@ -572,7 +572,16 @@ def yuv_region_2_bgr(frame, region):
raise
def intersection(box_a, box_b):
def intersection(box_a, box_b) -> Optional[list[int]]:
"""Return intersection box or None if boxes do not intersect."""
if (
box_a[2] < box_b[0]
or box_a[0] > box_b[2]
or box_a[1] > box_b[3]
or box_a[3] < box_b[1]
):
return None
return (
max(box_a[0], box_b[0]),
max(box_a[1], box_b[1]),
@ -589,6 +598,9 @@ def intersection_over_union(box_a, box_b):
# determine the (x, y)-coordinates of the intersection rectangle
intersect = intersection(box_a, box_b)
if intersect is None:
return 0.0
# compute the area of intersection rectangle
inter_area = max(0, intersect[2] - intersect[0] + 1) * max(
0, intersect[3] - intersect[1] + 1

70
frigate/video.py
View File

@ -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