while (max_score > 0.5):
query_img = bridge.crop_bbox(
int(video_dets[max_score_index][0]),
video_dets[max_score_index][1:5])
# gallery_imgs = []
query_feat = reid.extract_feats([query_img])
gallery_feats = reid.extract_feats(gallery_imgs)
min_dist, min_dist_index = get_similar_box(
query_feat, gallery_feats, used_mask)
position = [
int(video_dets[min_dist_index][1]),
int(video_dets[min_dist_index][2])
]
old_score = float(video_dets[min_dist_index][-1])
video_dets[min_dist_index][-1] = bridge.update_score(
video_dets[max_score_index][-1],
video_dets[min_dist_index][-1], min_dist)
print("max score id:{}, min dist id:{}".format(
video_dets[max_score_index][0],
video_dets[min_dist_index][0]))
print("update image:id-{}-bbox-{} score from {} to {}".format(
int(video_dets[min_dist_index][0]), position, old_score,
video_dets[min_dist_index][-1]))
max_score, max_score_index = select_max_det(
video_dets, used_mask)
used_mask[
max_score_index] = True # 标记已经被选择的框，排除后迭代，直到max score小于阈值0.2
# 保存update后的结果
for det in video_dets:
