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Revamp object consolidation logic (#8289)

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* Separate object reduction to own function and reduce confidence of boxes on edge of region

* Add tests for different scenarios

* Formatting
This commit is contained in:
Nicolas Mowen
2023-10-23 18:20:21 -06:00
committed by GitHub
parent e9376ca285
commit 2fb7200fb7
3 changed files with 190 additions and 81 deletions
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46
frigate/video.py
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@@ -7,10 +7,8 @@ import signal
import subprocess as sp
import threading
import time
from collections import defaultdict
import cv2
import numpy as np
from setproctitle import setproctitle
from frigate.config import CameraConfig, DetectConfig, ModelConfig
@@ -39,12 +37,12 @@ from frigate.util.object import (
get_cluster_candidates,
get_cluster_region,
get_cluster_region_from_grid,
get_consolidated_object_detections,
get_min_region_size,
get_startup_regions,
inside_any,
intersects_any,
is_object_filtered,
reduce_detections,
)
from frigate.util.services import listen
@@ -688,50 +686,10 @@ def process_frames(
)
)
#########
# merge objects
#########
# 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)
# add objects
for index in idxs:
index = index if isinstance(index, np.int32) else index[0]
obj = group[index]
selected_objects.append(obj)
# set the detections list to only include top objects
detections = selected_objects
consolidated_detections = reduce_detections(frame_shape, 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)
consolidated_detections = get_consolidated_object_detections(
detected_object_groups
)
tracked_detections = [
d
for d in consolidated_detections