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simplify object consolidation

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This commit is contained in:
Blake Blackshear 2023-06-10 14:24:09 -05:00
parent c061ff14ee
commit 4e93e8c427
1 changed files with 28 additions and 66 deletions
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94
frigate/video.py
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@ -4,7 +4,6 @@ import math
import multiprocessing as mp
import os
import queue
import random
import signal
import subprocess as sp
import threading
@ -29,7 +28,6 @@ from frigate.util import (
SharedMemoryFrameManager,
area,
calculate_region,
clipped,
intersection,
intersection_over_union,
listen,
@ -825,74 +823,38 @@ def process_frames(
)
#########
# merge objects, check for clipped objects and look again up to 4 times
# merge objects
#########
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)
# 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
# o[2] is the box of the object: xmin, ymin, xmax, ymax
# apply max/min to ensure values do not exceed the known frame size
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)
selected_objects = []
for group in detected_object_groups.values():
# apply non-maxima suppression to suppress weak, overlapping bounding boxes
# o[2] is the box of the object: xmin, ymin, xmax, ymax
# apply max/min to ensure values do not exceed the known frame size
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)
# add objects
for index in idxs:
index = index if isinstance(index, np.int32) else index[0]
obj = group[index]
selected_objects.append(obj)
for index in idxs:
index = index if isinstance(index, np.int32) else index[0]
obj = group[index]
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(
detect_config,
object_detector,
frame,
model_config,
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
# set the detections list to only include top objects
detections = selected_objects
## drop detections that overlap too much
consolidated_detections = []