def main(data_root='', seqs=('', ), args=""):
logger.setLevel(logging.INFO)
data_type = 'mot'
result_root = os.path.join(Path(data_root), "mot_results")
mkdir_if_missing(result_root)
cfg = get_config()
cfg.merge_from_file(args.config_detection)
cfg.merge_from_file(args.config_deepsort)
# run tracking
accs = []
for seq in seqs:
logger.info('start seq: {}'.format(seq))
result_filename = os.path.join(result_root, '{}.txt'.format(seq))
video_path = data_root + "/" + seq + "/video/video.mp4"
with VideoTracker(cfg, args, video_path, result_filename) as vdo_trk:
vdo_trk.run()
# eval
logger.info('Evaluate seq: {}'.format(seq))
evaluator = Evaluator(data_root, seq, data_type)
accs.append(evaluator.eval_file(result_filename))
# get summary
metrics = mm.metrics.motchallenge_metrics
mh = mm.metrics.create()
summary = Evaluator.get_summary(accs, seqs, metrics)
strsummary = mm.io.render_summary(summary,
formatters=mh.formatters,
namemap=mm.io.motchallenge_metric_names)
print(strsummary)
Evaluator.save_summary(summary,
os.path.join(result_root, 'summary_global.xlsx'))
def main(seqs=('2', ), det_types=('', )):
# run tracking
accs = []
data_root = args.data_root + '/MOT{}/train'.format(args.mot_version)
choice = (0, 0, 4, 0, 3, 3)
TrackerConfig.set_configure(choice)
choice_str = TrackerConfig.get_configure_str(choice)
seq_names = []
for seq in seqs:
for det_type in det_types:
result_filename = track_seq(seq, det_type, choice_str)
seq_name = 'MOT{}-{}{}'.format(args.mot_version, seq.zfill(2),
det_type)
seq_names.append(seq_name)
print('Evaluate seq:{}'.format(seq_name))
evaluator = Evaluator(data_root, seq_name, 'mot')
accs.append(evaluator.eval_file(result_filename))
# get summary
# metrics = ['mota', 'num_switches', 'idp', 'idr', 'idf1', 'precision', 'recall']
metrics = mm.metrics.motchallenge_metrics
# metrics = None
mh = mm.metrics.create()
summary = Evaluator.get_summary(accs, seq_names, metrics)
strsummary = mm.io.render_summary(summary,
formatters=mh.formatters,
namemap=mm.io.motchallenge_metric_names)
print(strsummary)
Evaluator.save_summary(
summary,
os.path.join(os.path.join(args.log_folder, choice_str),
'summary_{}.xlsx'.format(args.exp_name)))
def main(opt, data_root='/data/MOT16/train', det_root=None, seqs=('MOT16-05',), exp_name='demo',
save_images=False, save_videos=False, show_image=True):
logger.setLevel(logging.INFO)
result_root = os.path.join(data_root, '..', 'results', exp_name)
mkdir_if_missing(result_root)
data_type = 'mot'
# Read config
cfg_dict = parse_model_cfg(opt.cfg)
opt.img_size = [int(cfg_dict[0]['width']), int(cfg_dict[0]['height'])]
# run tracking
accs = []
n_frame = 0
timer_avgs, timer_calls = [], []
for seq in seqs:
output_dir = os.path.join(data_root, '..','outputs', exp_name, seq) if save_images or save_videos else None
logger.info('start seq: {}'.format(seq))
dataloader = datasets.LoadImages(osp.join(data_root, seq, 'img1'), opt.img_size)
result_filename = os.path.join(result_root, '{}.txt'.format(seq))
meta_info = open(os.path.join(data_root, seq, 'seqinfo.ini')).read()
frame_rate = int(meta_info[meta_info.find('frameRate')+10:meta_info.find('\nseqLength')])
nf, ta, tc = eval_seq(opt, dataloader, data_type, result_filename,
save_dir=output_dir, show_image=show_image, frame_rate=frame_rate)
n_frame += nf
timer_avgs.append(ta)
timer_calls.append(tc)
# eval
logger.info('Evaluate seq: {}'.format(seq))
evaluator = Evaluator(data_root, seq, data_type)
accs.append(evaluator.eval_file(result_filename))
if save_videos:
output_video_path = osp.join(output_dir, '{}.mp4'.format(seq))
cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -c:v copy {}'.format(output_dir, output_video_path)
os.system(cmd_str)
timer_avgs = np.asarray(timer_avgs)
timer_calls = np.asarray(timer_calls)
all_time = np.dot(timer_avgs, timer_calls)
avg_time = all_time / np.sum(timer_calls)
logger.info('Time elapsed: {:.2f} seconds, FPS: {:.2f}'.format(all_time, 1.0 / avg_time))
# get summary
metrics = mm.metrics.motchallenge_metrics
mh = mm.metrics.create()
summary = Evaluator.get_summary(accs, seqs, metrics)
strsummary = mm.io.render_summary(
summary,
formatters=mh.formatters,
namemap=mm.io.motchallenge_metric_names
)
print(strsummary)
sys.stdout.flush()
Evaluator.save_summary(summary, os.path.join(result_root, 'summary_{}.xlsx'.format(exp_name)))
def main(data_root='', seqs=('', ), args=""):
logger = get_logger()
logger.setLevel(logging.INFO)
model_name = args.MODEL_NAME
data_type = 'mot'
analyse_every_frames = args.frame_interval
dataset_name = data_root.split(sep='/')[-1]
tracker_type = get_tracker_type(args)
result_root = os.path.join("mot_results", model_name, dataset_name)
mkdir_if_missing(result_root)
cfg = get_config()
cfg.merge_from_file(args.config_detection)
cfg.merge_from_file(args.config_tracker)
args.save_path = result_root
# run tracking
accs = []
for seq in seqs:
logger.info('start seq: {}'.format(seq))
result_filename = os.path.join(result_root, '{}.txt'.format(seq))
seq_root = os.path.join(data_root, seq)
video_root = os.path.join(seq_root, "video")
video_path = os.path.join(video_root, os.listdir(video_root)[0])
logger.info(f"Result filename: {result_filename}")
logger.info(f'Frame interval: {analyse_every_frames}')
if not os.path.exists(result_filename):
with VideoTracker(cfg, args, video_path,
result_filename) as vdo_trk:
vdo_trk.run()
else:
print(
f"Result file {result_filename} already exists. Skipping processing"
)
# eval
logger.info('Evaluate seq: {}'.format(seq))
evaluator = Evaluator(data_root, seq, data_type)
accs.append(evaluator.eval_file(result_filename))
# get summary
metrics = mm.metrics.motchallenge_metrics
mh = mm.metrics.create()
summary = Evaluator.get_summary(accs, seqs, metrics)
strsummary = mm.io.render_summary(summary,
formatters=mh.formatters,
namemap=mm.io.motchallenge_metric_names)
print(strsummary)
Evaluator.save_summary(summary,
os.path.join(result_root, 'summary_global.xlsx'))
def main(data_root=os.path.expanduser('~/Data/MOT16/train'),
det_root=None,
seqs=('MOT16-05', ),
exp_name='demo',
save_image=False,
show_image=True,
args=None):
logger.setLevel(logging.INFO)
result_root = os.path.join(data_root, '..', 'results', exp_name)
mkdirs(result_root)
data_type = 'mot'
# run tracking
accs = []
for seq in seqs:
output_dir = os.path.join(data_root, 'outputs',
seq) if save_image else None
logger.info('start seq: {}'.format(seq))
loader = get_loader(data_root, det_root, seq)
result_filename = os.path.join(result_root, '{}.txt'.format(seq))
eval_seq(loader,
data_type,
result_filename,
save_dir=output_dir,
show_image=show_image,
args=args)
# eval
logger.info('Evaluate seq: {}'.format(seq))
evaluator = Evaluator(data_root, seq, data_type)
accs.append(evaluator.eval_file(result_filename))
# get summary
# metrics = ['mota', 'num_switches', 'idp', 'idr', 'idf1', 'precision', 'recall']
metrics = mm.metrics.motchallenge_metrics
# metrics = None
mh = mm.metrics.create()
summary = Evaluator.get_summary(accs, seqs, metrics)
strsummary = mm.io.render_summary(summary,
formatters=mh.formatters,
namemap=mm.io.motchallenge_metric_names)
print(strsummary)
Evaluator.save_summary(
summary, os.path.join(result_root, f'summary_{exp_name}.xlsx'))
def main(opt,
data_root='/data/MOT16/train',
det_root=None,
seqs=('MOT16-05', ),
exp_name='demo',
save_images=False,
save_videos=False,
show_image=True):
logger.setLevel(logging.INFO)
result_root = os.path.join(data_root, '..', 'results', exp_name)
mkdir_if_missing(result_root)
data_type = 'mot'
# run tracking
timer = Timer()
accs = []
n_frame = 0
timer.tic()
for seq in seqs:
output_dir = os.path.join(data_root, '..', 'outputs', exp_name,
seq) if save_images or save_videos else None
logger.info('start seq: {}'.format(seq))
dataloader = datasets.LoadImages(osp.join(data_root, seq, 'img1'),
opt.img_size)
result_filename = os.path.join(result_root, '{}.txt'.format(seq))
meta_info = open(os.path.join(data_root, seq, 'seqinfo.ini')).read()
frame_rate = int(meta_info[meta_info.find('frameRate') +
10:meta_info.find('\nseqLength')])
n_frame += eval_seq(opt,
dataloader,
data_type,
result_filename,
save_dir=output_dir,
show_image=show_image,
frame_rate=frame_rate)
# eval
logger.info('Evaluate seq: {}'.format(seq))
evaluator = Evaluator(data_root, seq, data_type)
accs.append(evaluator.eval_file(result_filename))
if save_videos:
output_video_path = osp.join(output_dir, '{}.mp4'.format(seq))
cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -c:v copy {}'.format(
output_dir, output_video_path)
os.system(cmd_str)
timer.toc()
logger.info('Time elapsed: {}, FPS {}'.format(timer.average_time, n_frame /
timer.average_time))
# get summary
# metrics = ['mota', 'num_switches', 'idp', 'idr', 'idf1', 'precision', 'recall']
metrics = mm.metrics.motchallenge_metrics
mh = mm.metrics.create()
summary = Evaluator.get_summary(accs, seqs, metrics)
strsummary = mm.io.render_summary(summary,
formatters=mh.formatters,
namemap=mm.io.motchallenge_metric_names)
print(strsummary)
Evaluator.save_summary(
summary, os.path.join(result_root, 'summary_{}.xlsx'.format(exp_name)))
timer_calls = np.asarray(timer_calls)
all_time = np.dot(timer_avgs, timer_calls)
avg_time = all_time / np.sum(timer_calls)
logger.info('Time elapsed: {:.2f} seconds, FPS: {:.2f}'.format(all_time, 1.0 / avg_time))
# get summary
metrics = mm.metrics.motchallenge_metrics
mh = mm.metrics.create()
summary = Evaluator.get_summary(accs, seqs, metrics)
strsummary = mm.io.render_summary(
summary,
formatters=mh.formatters,
namemap=mm.io.motchallenge_metric_names
)
print(strsummary)
Evaluator.save_summary(summary, os.path.join(result_root, 'summary_{}.xlsx'.format(exp_name)))
if __name__ == '__main__':
parser = argparse.ArgumentParser(prog='track.py')
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
parser.add_argument('--weights', type=str, default='weights/latest.pt', help='path to weights file')
parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold')
parser.add_argument('--nms-thres', type=float, default=0.4, help='iou threshold for non-maximum suppression')
parser.add_argument('--min-box-area', type=float, default=200, help='filter out tiny boxes')
parser.add_argument('--track-buffer', type=int, default=30, help='tracking buffer')
parser.add_argument('--test-mot16', action='store_true', help='tracking buffer')
parser.add_argument('--save-images', action='store_true', help='save tracking results (image)')
parser.add_argument('--save-videos', action='store_true', help='save tracking results (video)')