overall_box[overall_box < 0] = 0
overall_score = np.concatenate((dscores, tscores), axis=0)
# Get key frame RE-ID from tracklet
template_features = tracklet.get_features()
# print(template_features.shape)
# print(frame.shape, overall_box)
after_reid_score, reid_features = reid_rescore(reid_module, frame, template_features, overall_box, overall_score)
# print(after_reid_score)
best_idx = np.argmax(after_reid_score)
best_bbox = overall_box[best_idx]
best_score = after_reid_score[best_idx]
running_stats.push(best_score)
# Score better than one sigma, treat as key frame
if best_score >= running_stats.mean() + running_stats.standard_deviation():
tracklet.push_frame(frame[best_bbox[1]:best_bbox[3], best_bbox[0]:best_bbox[2], :], np.squeeze(reid_features[best_idx]))
# print(count, best_score)
# print(overall_box)
# if confidence is low than LOST_THRESHOLD, flag_lost = True
cv2.circle(frame, (int(tracker.tracker.center_pos[0]), int(tracker.tracker.center_pos[1])), 4, (0, 0, 255), -1)
if best_score < LOST_THRESHOLD:
flag_lost = True
lost_frame_count += 1
else:
flag_lost = False
lost_frame_cout = 0
# Get key frame RE-ID from tracklet
template_features = tracklet.get_features()
# print(template_features.shape)
# print(frame.shape, overall_box)
after_reid_score, reid_features = reid_rescore(
reid_module, frame, template_features, overall_box,
overall_score)
# print(after_reid_score)
best_idx = np.argmax(after_reid_score)
best_bbox = overall_box[best_idx]
best_score = after_reid_score[best_idx]
running_stats.push(best_score)
# Score better than one sigma, treat as key frame
if best_score >= running_stats.mean(
) + running_stats.standard_deviation():
tracklet.push_frame(
frame[best_bbox[1]:best_bbox[3],
best_bbox[0]:best_bbox[2], :],
np.squeeze(reid_features[best_idx]))
# print(count, best_score)
# print(overall_box)
# if confidence is low than LOST_THRESHOLD, flag_lost = True
cv2.circle(frame, (int(tracker.tracker.center_pos[0]),
int(tracker.tracker.center_pos[1])), 4,
(0, 0, 255), -1)
if best_score < LOST_THRESHOLD:
flag_lost = True
lost_frame_count += 1
overall_box[overall_box < 0] = 0
overall_score = np.concatenate((dscores, tscores), axis=0)
# Get key frame RE-ID from tracklet
template_features = tracklet.get_features()
# print(template_features.shape)
# print(frame.shape, overall_box)
after_reid_score, reid_features = reid_rescore(reid_module, frame, template_features, overall_box, overall_score)
# print(after_reid_score)
best_idx = np.argmax(after_reid_score)
best_bbox = overall_box[best_idx]
best_score = after_reid_score[best_idx]
running_stats.push(best_score)
# Score better than one sigma, treat as key frame
if best_score >= running_stats.mean() + running_stats.standard_deviation():
tracklet.push_frame(frame[best_bbox[1]:best_bbox[3], best_bbox[0]:best_bbox[2], :], np.squeeze(reid_features[best_idx]))
# print(count, best_score)
# print(overall_box)
# if confidence is low than LOST_THRESHOLD, flag_lost = True
cv2.circle(frame, (int(tracker.tracker.center_pos[0]), int(tracker.tracker.center_pos[1])), 4, (0, 0, 255), -1)
if count > 30 and best_score < running_stats.mean() - running_stats.standard_deviation():
flag_lost = True
lost_frame_count += 1
else:
flag_lost = False
lost_frame_cout = 0
