def accumulate_mot_metrics(accs, gt_tracks, history):
log.info('Accumulating MOT metrics...')
last_frame = find_max_frame_num(gt_tracks)
for time in tqdm(range(last_frame), 'Processing...'):
active_detections = get_detections_from_tracks(history, time)
gt_detections = get_detections_from_tracks(gt_tracks, time)
for i, camera_gt_detections in enumerate(gt_detections):
gt_boxes = []
gt_labels = []
for obj in camera_gt_detections:
gt_boxes.append([
obj.rect[0], obj.rect[1], obj.rect[2] - obj.rect[0],
obj.rect[3] - obj.rect[1]
])
gt_labels.append(obj.label)
ht_boxes = []
ht_labels = []
for obj in active_detections[i]:
ht_boxes.append([
obj.rect[0], obj.rect[1], obj.rect[2] - obj.rect[0],
obj.rect[3] - obj.rect[1]
])
ht_labels.append(obj.label)
distances = mm.distances.iou_matrix(np.array(gt_boxes),
np.array(ht_boxes),
max_iou=0.5)
accs[i].update(gt_labels, ht_labels, distances)
return accs
def main():
"""Visualize the results of the multi camera multi person tracker demo"""
parser = argparse.ArgumentParser(description='Multi camera multi person \
tracking visualization demo script')
parser.add_argument('-i', type=str, nargs='+',
help='Input videos')
parser.add_argument('--history_file', type=str, default='', required=True,
help='File with tracker history')
parser.add_argument('--output_video', type=str, default='', required=False,
help='Output video file')
parser.add_argument('--gt_files', type=str, nargs='+', required=False,
help='Files with ground truth annotation')
parser.add_argument('--timeline', type=str, default='',
help='Plot and save timeline')
parser.add_argument('--match_gt_ids', default=False, action='store_true',
help='Match GT ids to ids from history')
parser.add_argument('--merge_outputs', default=False, action='store_true',
help='Merge GT and history tracks into one frame')
args = parser.parse_args()
capture = MulticamCapture(args.i)
with open(args.history_file) as hist_f:
history = json.load(hist_f)
assert len(history) == capture.get_num_sources()
# Configure output video files
output_video = None
output_video_gt = None
frame_size, fps_source = capture.get_source_parameters()
if len(args.output_video):
video_output_size, fps = calc_output_video_params(frame_size, fps_source, args.gt_files, args.merge_outputs)
fourcc = cv.VideoWriter_fourcc(*'XVID')
output_video = cv.VideoWriter(args.output_video, fourcc, fps, video_output_size)
if args.gt_files and not args.merge_outputs:
ext = args.output_video.split('.')[-1]
output_path = args.output_video[:len(args.output_video) - len(ext) - 1] + '_gt.' + ext
output_video_gt = cv.VideoWriter(output_path, fourcc, fps, video_output_size)
# Read GT tracks if necessary
if args.gt_files:
assert len(args.gt_files) == capture.get_num_sources()
gt_tracks, _ = read_gt_tracks(args.gt_files)
accs = [mm.MOTAccumulator(auto_id=True) for _ in args.gt_files]
else:
gt_tracks = None
# If we need for matching GT IDs, accumulate metrics
if gt_tracks and args.match_gt_ids:
accumulate_mot_metrics(accs, gt_tracks, history)
match_gt_indices(gt_tracks, history, accs)
metrics_accumulated = True
else:
metrics_accumulated = False
# Process frames
win_name = 'Multi camera tracking history visualizer'
time = 0
key = -1
while True:
print('\rVisualizing frame: {}'.format(time), end="")
key = check_pressed_keys(key)
if key == 27:
break
has_frames, frames = capture.get_frames()
if not has_frames:
break
if gt_tracks:
gt_detections = get_detections_from_tracks(gt_tracks, time)
vis_gt = visualize_multicam_detections(copy.deepcopy(frames), gt_detections, fps='Ground truth')
else:
vis_gt = None
active_detections = get_detections_from_tracks(history, time)
vis = visualize_multicam_detections(frames, active_detections)
if vis_gt is not None:
if args.merge_outputs:
vis = np.hstack([vis, vis_gt])
cv.imshow(win_name, vis)
else:
cv.imshow('GT', vis_gt)
cv.imshow(win_name, vis)
else:
cv.imshow(win_name, vis)
time += 1
if output_video:
output_video.write(cv.resize(vis, video_output_size))
if vis_gt is not None and output_video_gt is not None:
output_video_gt.write(cv.resize(vis_gt, video_output_size))
if len(args.timeline):
for i in range(len(history)):
log.info('Source_{}: drawing timeline...'.format(i))
plot_timeline(i, time, history[i], save_path=args.timeline, name='SCT')
if gt_tracks:
for i in range(len(gt_tracks)):
log.info('GT_{}: drawing timeline...'.format(i))
plot_timeline(i, time, gt_tracks[i], save_path=args.timeline, name='GT')
if gt_tracks:
if not metrics_accumulated:
accumulate_mot_metrics(accs, gt_tracks, history)
mh = mm.metrics.create()
summary = mh.compute_many(accs,
metrics=mm.metrics.motchallenge_metrics,
generate_overall=True,
names=['video ' + str(i) for i in range(len(accs))])
strsummary = mm.io.render_summary(summary,
formatters=mh.formatters,
namemap=mm.io.motchallenge_metric_names)
print(strsummary)