def main(data_root='../results', seqs=('MOT16-02', ), exp_name='demo'):
logger.setLevel(logging.INFO)
data_type = 'mot'
# run tracking
accs = []
n_frame = 0
timer_avgs, timer_calls = [], []
for seq in seqs:
result_root = os.path.join(data_root, seq)
result_filename = os.path.join(result_root,
'{}.txt'.format('kalman_iou'))
print(result_filename)
# 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_{}.xlsx'.format(exp_name)))
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
import torch
my_visible_devs = '1' # '0, 3' # 设置可运行GPU编号
os.environ['CUDA_VISIBLE_DEVICES'] = my_visible_devs
device = torch.device('cuda: 0' if torch.cuda.is_available() else 'cpu')
import os.path as osp
from lib.opts import opts # import opts
from lib.tracking_utils.utils import mkdir_if_missing
from lib.tracking_utils.log import logger
import lib.datasets.dataset.jde as datasets
from track import eval_seq, eval_seq_and_output_dets
logger.setLevel(logging.INFO)
def run_demo(opt):
"""
:param opt:
:return:
"""
result_root = opt.output_root if opt.output_root != '' else '.'
mkdir_if_missing(result_root)
# clear existing frame results
frame_res_dir = result_root + '/frames'
if os.path.isdir(frame_res_dir):
shutil.rmtree(frame_res_dir)
os.makedirs(frame_res_dir)
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
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)
Evaluator.save_summary(
summary, os.path.join(result_root, 'summary_{}.xlsx'.format(exp_name)))