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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 multiprocessing as mp
import os import os
import queue import queue
import random
import signal import signal
import subprocess as sp import subprocess as sp
import threading import threading
@ -29,7 +28,6 @@ from frigate.util import (
SharedMemoryFrameManager, SharedMemoryFrameManager,
area, area,
calculate_region, calculate_region,
clipped,
intersection, intersection,
intersection_over_union, intersection_over_union,
listen, 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 # group by name
refine_count = 0 detected_object_groups = defaultdict(lambda: [])
while refining and refine_count < 4: for detection in detections:
refining = False detected_object_groups[detection[0]].append(detection)
# group by name selected_objects = []
detected_object_groups = defaultdict(lambda: []) for group in detected_object_groups.values():
for detection in detections: # apply non-maxima suppression to suppress weak, overlapping bounding boxes
detected_object_groups[detection[0]].append(detection) # 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 = [] # add objects
for group in detected_object_groups.values(): for index in idxs:
# apply non-maxima suppression to suppress weak, overlapping bounding boxes index = index if isinstance(index, np.int32) else index[0]
# o[2] is the box of the object: xmin, ymin, xmax, ymax obj = group[index]
# apply max/min to ensure values do not exceed the known frame size selected_objects.append(obj)
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: # set the detections list to only include top objects
index = index if isinstance(index, np.int32) else index[0] detections = selected_objects
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
## drop detections that overlap too much ## drop detections that overlap too much
consolidated_detections = [] consolidated_detections = []