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group by label before suppressing boxes

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Blake Blackshear 2020-01-07 20:44:00 -06:00
parent 1d4fbbdba3
commit 634b87307f
1 changed files with 5 additions and 4 deletions
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frigate/objects.py
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@ -5,6 +5,7 @@ import cv2
import prctl import prctl
import itertools import itertools
import numpy as np import numpy as np
from collections import defaultdict
from scipy.spatial import distance as dist from scipy.spatial import distance as dist
from frigate.util import draw_box_with_label, LABELS, compute_intersection_rectangle, compute_intersection_over_union, calculate_region from frigate.util import draw_box_with_label, LABELS, compute_intersection_rectangle, compute_intersection_over_union, calculate_region
@ -113,19 +114,19 @@ class RegionRefiner(threading.Thread):
detected_objects = self.camera.detected_objects[frame_time].copy() detected_objects = self.camera.detected_objects[frame_time].copy()
# print(f"{frame_time} finished") # print(f"{frame_time} finished")
detected_object_groups = defaultdict(lambda: [])) detected_object_groups = defaultdict(lambda: [])
# group by name # group by name
for obj in detected_objects: for obj in detected_objects:
detected_object_groups[obj['name']].append(obj) detected_object_groups[obj['name']].append(obj)
look_again = False look_again = False
selected_objects = [] selected_objects = []
for name, group in detected_object_groups.items(): for group in detected_object_groups.values():
# apply non-maxima suppression to suppress weak, overlapping bounding boxes # apply non-maxima suppression to suppress weak, overlapping bounding boxes
boxes = [(o['box']['xmin'], o['box']['ymin'], o['box']['xmax']-o['box']['xmin'], o['box']['ymax']-o['box']['ymin']) boxes = [(o['box']['xmin'], o['box']['ymin'], o['box']['xmax']-o['box']['xmin'], o['box']['ymax']-o['box']['ymin'])
for o in detected_objects] for o in group]
confidences = [o['score'] for o in detected_objects] confidences = [o['score'] for o in group]
idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4) idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
for index in idxs: for index in idxs: