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ada_track_half.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import os.path as osp
import cv2
import logging
import motmetrics as mm
import numpy as np
import torch
import src.lib.datasets.dataset.jde as datasets
from src.keep_pred_by_gt import load_tracks, do_filter, save_tracks
from src.lib.ada_opts import ada_opts
from src.lib.opts import opts
from src.lib.tracker.ada_multitracker import AdaTracker
from src.lib.tracking_utils import visualization as vis
from src.lib.tracking_utils.evaluation import Evaluator
from src.lib.tracking_utils.log import logger
from src.lib.tracking_utils.timer import Timer
from src.lib.tracking_utils.utils import mkdir_if_missing
import random
def write_results(filename, results, data_type):
if data_type == 'mot':
save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\n'
elif data_type == 'kitti':
save_format = '{frame} {id} pedestrian 0 0 -10 {x1} {y1} {x2} {y2} -10 -10 -10 -1000 -1000 -1000 -10\n'
else:
raise ValueError(data_type)
with open(filename, 'w') as f:
for frame_id, tlwhs, track_ids in results:
if data_type == 'kitti':
frame_id -= 1
for tlwh, track_id in zip(tlwhs, track_ids):
if track_id < 0:
continue
x1, y1, w, h = tlwh
x2, y2 = x1 + w, y1 + h
line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h)
f.write(line)
logger.info('save results to {}'.format(filename))
def eval_seq(opt, dataloader, data_type, result_filename, save_dir=None, show_image=True, frame_rate=30, seq=-1):
if save_dir:
mkdir_if_missing(save_dir)
# tracker = JDETracker(opt, frame_rate=frame_rate)
tracker = AdaTracker(opt, frame_rate=frame_rate)
timer = Timer()
results = []
len_all = len(dataloader)
start_frame = int(len_all / 2)
frame_id = int(len_all / 2)
for i, (path, img, img0) in enumerate(dataloader):
if i < start_frame:
continue
if frame_id % 20 == 0:
logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1. / max(1e-5, timer.average_time)))
# run tracking
timer.tic()
blob = torch.from_numpy(img).cuda().unsqueeze(0)
online_targets = tracker.update(blob, img0)
online_tlwhs = []
online_ids = []
# online_scores = []
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id
vertical = tlwh[2] / tlwh[3] > 1.6
if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical:
online_tlwhs.append(tlwh)
online_ids.append(tid)
# online_scores.append(t.score)
timer.toc()
# save results
results.append((frame_id + 1, online_tlwhs, online_ids))
# results.append((frame_id + 1, online_tlwhs, online_ids, online_scores))
if show_image or save_dir is not None:
online_im = vis.plot_tracking(img0, online_tlwhs, online_ids, frame_id=frame_id,
fps=1. / timer.average_time)
if show_image:
cv2.imshow('online_im', online_im)
cv2.waitKey(1)
if save_dir is not None:
cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), online_im)
frame_id += 1
# save results
write_results(result_filename, results, data_type)
# write_results_score(result_filename, results, data_type)
return frame_id, timer.average_time, timer.calls
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, seq=seq)
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)))
if __name__ == '__main__':
# os.environ['CUDA_VISIBLE_DEVICES'] = '1'
# opt = opts().init()
opt = ada_opts().init()
if not opt.val_mot16:
seqs_str = '''KITTI-13
KITTI-17
ADL-Rundle-6
PETS09-S2L1
TUD-Campus
TUD-Stadtmitte'''
data_root = os.path.join(opt.data_dir, 'MOT15/images/train')
else:
seqs_str = '''MOT16-02
MOT16-04
MOT16-05
MOT16-09
MOT16-10
MOT16-11
MOT16-13'''
data_root = os.path.join(opt.data_dir, 'MOT16/train')
if opt.test_mot16:
seqs_str = '''MOT16-01
MOT16-03
MOT16-06
MOT16-07
MOT16-08
MOT16-12
MOT16-14'''
seqs_str = '''MOT16-06 MOT16-07 MOT16-08'''
data_root = os.path.join(opt.data_dir, 'MOT16/test')
if opt.test_mot15:
seqs_str = '''ADL-Rundle-1
ADL-Rundle-3
AVG-TownCentre
ETH-Crossing
ETH-Jelmoli
ETH-Linthescher
KITTI-16
KITTI-19
PETS09-S2L2
TUD-Crossing
Venice-1'''
data_root = os.path.join(opt.data_dir, 'MOT15/images/test')
if opt.test_mot17:
seqs_str = '''MOT17-01-SDP
MOT17-03-SDP
MOT17-06-SDP
MOT17-07-SDP
MOT17-08-SDP
MOT17-12-SDP
MOT17-14-SDP'''
data_root = os.path.join(opt.data_dir, 'MOT17/images/test')
if opt.val_mot17:
seqs_str = '''MOT17-02-SDP
MOT17-04-SDP
MOT17-05-SDP
MOT17-09-SDP
MOT17-10-SDP
MOT17-11-SDP
MOT17-13-SDP
'''
data_root = os.path.join(opt.data_dir, 'MOT17/images/train')
if opt.val_mot15:
seqs_str = '''Venice-2
KITTI-13
KITTI-17
ETH-Bahnhof
ETH-Sunnyday
PETS09-S2L1
TUD-Campus
TUD-Stadtmitte
ADL-Rundle-6
ADL-Rundle-8
ETH-Pedcross2
TUD-Stadtmitte'''
data_root = os.path.join(opt.data_dir, 'MOT15/images/train')
if opt.val_mot20:
seqs_str = '''MOT20-01
MOT20-02
MOT20-03
MOT20-05
'''
data_root = os.path.join(opt.data_dir, 'MOT20/images/train')
if opt.test_mot20:
seqs_str = '''MOT20-04
MOT20-06
MOT20-07
MOT20-08
'''
data_root = os.path.join(opt.data_dir, 'MOT20/images/test')
seqs = [seq.strip() for seq in seqs_str.split()]
main(opt,
data_root=data_root,
seqs=seqs,
exp_name='MOT_17_val',
show_image=False,
save_images=False,
save_videos=False)