def track_tune(tracker, net, video, config):
arch = config['arch']
benchmark_name = config['benchmark']
resume = config['resume']
hp = config[
'hp'] # penalty_k, scale_lr, window_influence, adaptive size (for vot2017 or later)
tracker_path = join('test',
(benchmark_name + resume.split('/')[-1].split('.')[0] +
'_small_size_{:.4f}'.format(hp['small_sz']) +
'_big_size_{:.4f}'.format(hp['big_sz']) +
'_penalty_k_{:.4f}'.format(hp['penalty_k']) +
'_w_influence_{:.4f}'.format(hp['window_influence']) +
'_scale_lr_{:.4f}'.format(hp['lr'])).replace(
'.', '_')) # no .
if not os.path.exists(tracker_path):
os.makedirs(tracker_path)
if 'VOT' in benchmark_name:
baseline_path = join(tracker_path, 'baseline')
video_path = join(baseline_path, video['name'])
if not os.path.exists(video_path):
os.makedirs(video_path)
result_path = join(video_path, video['name'] + '_001.txt')
else:
raise ValueError('Only VOT is supported')
# occ for parallel running
if not os.path.exists(result_path):
fin = open(result_path, 'w')
fin.close()
else:
if benchmark_name.startswith('VOT'):
return 0
else:
raise ValueError('Only VOT is supported')
start_frame, lost_times, toc = 0, 0, 0
regions = [] # result and states[1 init / 2 lost / 0 skip]
image_files, gt = video['image_files'], video['gt']
for f, image_file in enumerate(image_files):
im = cv2.imread(image_file)
if len(im.shape) == 2:
im = cv2.cvtColor(im, cv2.COLOR_GRAY2BGR)
if f == start_frame: # init
cx, cy, w, h = get_axis_aligned_bbox(gt[f])
target_pos = np.array([cx, cy])
target_sz = np.array([w, h])
state = tracker.init(im, target_pos, target_sz, net,
hp=hp) # init tracker
regions.append([float(1)] if 'VOT' in benchmark_name else gt[f])
elif f > start_frame: # tracking
state = tracker.track(state, im) # track
location = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
b_overlap = poly_iou(gt[f],
location) if 'VOT' in benchmark_name else 1
if b_overlap > 0:
regions.append(location)
else:
regions.append([float(2)])
lost_times += 1
start_frame = f + 5 # skip 5 frames
else: # skip
regions.append([float(0)])
# save results for OTB
if benchmark_name.startswith('VOT'):
return regions
else:
raise ValueError('Only VOT is supported')
def track(tracker, net, video, args):
start_frame, lost_times, toc = 0, 0, 0
# save result to evaluate
if args.epoch_test:
suffix = args.resume.split('/')[-1]
suffix = suffix.split('.')[0]
tracker_path = os.path.join('result', args.dataset, args.arch + suffix)
else:
tracker_path = os.path.join('result', args.dataset, args.arch)
if not os.path.exists(tracker_path):
os.makedirs(tracker_path)
if 'VOT' in args.dataset:
baseline_path = join(tracker_path, 'baseline')
video_path = join(baseline_path, video['name'])
if not os.path.exists(video_path):
os.makedirs(video_path)
result_path = join(video_path, video['name'] + '_001.txt')
else:
result_path = join(tracker_path, '{:s}.txt'.format(video['name']))
if os.path.exists(result_path):
return 0 # for mult-gputesting
regions = [] # result and states[1 init / 2 lost / 0 skip]
image_files, gt = video['image_files'], video['gt']
for f, image_file in enumerate(image_files):
im = cv2.imread(image_file)
if len(im.shape) == 2:
im = cv2.cvtColor(im, cv2.COLOR_GRAY2BGR)
tic = cv2.getTickCount()
if f == start_frame: # init
cx, cy, w, h = get_axis_aligned_bbox(gt[f])
target_pos = np.array([cx, cy])
target_sz = np.array([w, h])
state = tracker.init(im, target_pos, target_sz,
net) # init tracker
regions.append(1 if 'VOT' in args.dataset else gt[f])
elif f > start_frame: # tracking
state = tracker.track(state, im) # track
location = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
b_overlap = poly_iou(gt[f],
location) if 'VOT' in args.dataset else 1
if b_overlap > 0:
regions.append(location)
else:
regions.append(2)
lost_times += 1
start_frame = f + 5 # skip 5 frames
else: # skip
regions.append(0)
toc += cv2.getTickCount() - tic
toc /= cv2.getTickFrequency()
with open(result_path, "w") as fin:
if 'VOT' in args.dataset:
for x in regions:
if isinstance(x, int):
fin.write("{:d}\n".format(x))
else:
p_bbox = x.copy()
fin.write(','.join([str(i) for i in p_bbox]) + '\n')
else:
for x in regions:
p_bbox = x.copy()
fin.write(','.join([
str(i + 1) if idx == 0 or idx == 1 else str(i)
for idx, i in enumerate(p_bbox)
]) + '\n')
print('Video: {:12s} Time: {:2.1f}s Speed: {:3.1f}fps Lost: {:d}'.format(
video['name'], toc, f / toc, lost_times))
return lost_times
def track_tune(tracker, net, video, config):
arch = config['arch']
benchmark_name = config['benchmark']
resume = config['resume']
hp = config['hp'] # scale_step, scale_penalty, scale_lr, window_influence
tracker_path = join('test',
(benchmark_name + resume.split('/')[-1].split('.')[0] +
'_step_{:.4f}'.format(hp['scale_step']) +
'_penalty_s_{:.4f}'.format(hp['scale_penalty']) +
'_w_influence_{:.4f}'.format(hp['w_influence']) +
'_scale_lr_{:.4f}'.format(hp['scale_lr'])).replace(
'.', '_')) # no .
if not os.path.exists(tracker_path):
os.makedirs(tracker_path)
result_path = join(tracker_path, '{:s}.txt'.format(video['name']))
# occ for parallel running
if not os.path.exists(result_path):
fin = open(result_path, 'w')
fin.close()
else:
return tracker_path
start_frame, lost_times, toc = 0, 0, 0
regions = [] # result and states[1 init / 2 lost / 0 skip]
image_files, gt = video['image_files'], video['gt']
for f, image_file in enumerate(image_files):
im = cv2.imread(image_file)
if len(im.shape) == 2:
im = cv2.cvtColor(im, cv2.COLOR_GRAY2BGR)
if f == start_frame: # init
cx, cy, w, h = get_axis_aligned_bbox(gt[f])
target_pos = np.array([cx, cy])
target_sz = np.array([w, h])
state = tracker.init(im, target_pos, target_sz, net,
hp=hp) # init tracker
location = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
regions.append([float(1)] if 'VOT' in benchmark_name else gt[f])
elif f > start_frame: # tracking
state = tracker.track(state, im) # track
location = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
b_overlap = poly_iou(gt[f],
location) if 'VOT' in benchmark_name else 1
if b_overlap > 0:
regions.append(location)
else:
regions.append([float(2)])
lost_times += 1
start_frame = f + 5 # skip 5 frames
else: # skip
regions.append([float(0)])
if benchmark_name.startswith('VOT'):
return regions
elif benchmark_name.startswith('OTB'):
with open(result_path, "w") as fin:
for x in regions:
p_bbox = x.copy()
fin.write(','.join([
str(i + 1) if idx == 0 or idx == 1 else str(i)
for idx, i in enumerate(p_bbox)
]) + '\n')
return tracker_path
else:
raise ValueError('not supported')
def track(siam_tracker, online_tracker, siam_net, video, args):
start_frame, toc = 0, 0
# save result to evaluate
if args.epoch_test:
suffix = args.resume.split('/')[-1]
suffix = suffix.split('.')[0]
tracker_path = os.path.join('result', args.dataset, args.arch + suffix)
else:
tracker_path = os.path.join('result', args.dataset, args.arch)
if not os.path.exists(tracker_path):
os.makedirs(tracker_path)
if 'VOT' in args.dataset:
baseline_path = os.path.join(tracker_path, 'baseline')
video_path = os.path.join(baseline_path, video['name'])
if not os.path.exists(video_path):
os.makedirs(video_path)
result_path = os.path.join(video_path, video['name'] + '_001.txt')
else:
result_path = os.path.join(tracker_path,
'{:s}.txt'.format(video['name']))
if os.path.exists(result_path):
return # for mult-gputesting
regions = []
lost = 0
image_files, gt = video['image_files'], video['gt']
for f, image_file in enumerate(image_files):
im = cv2.imread(image_file)
rgb_im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
if len(im.shape) == 2:
im = cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) # align with training
tic = cv2.getTickCount()
if f == start_frame: # init
cx, cy, w, h = get_axis_aligned_bbox(gt[f])
target_pos = np.array([cx, cy])
target_sz = np.array([w, h])
state = siam_tracker.init(im, target_pos, target_sz,
siam_net) # init tracker
if args.online:
online_tracker.init(im,
rgb_im,
siam_net,
target_pos,
target_sz,
True,
dataname=args.dataset,
resume=args.resume)
# location = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
regions.append(1 if 'VOT' in args.dataset else gt[f])
elif f > start_frame: # tracking
if args.online:
state = online_tracker.track(im, rgb_im, siam_tracker, state)
else:
state = siam_tracker.track(state, im)
location = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
b_overlap = poly_iou(gt[f],
location) if 'VOT' in args.dataset else 1
if b_overlap > 0:
regions.append(location)
else:
regions.append(2)
start_frame = f + 5
lost += 1
else:
regions.append(0)
toc += cv2.getTickCount() - tic
with open(result_path, "w") as fin:
if 'VOT' in args.dataset:
for x in regions:
if isinstance(x, int):
fin.write("{:d}\n".format(x))
else:
p_bbox = x.copy()
fin.write(','.join([str(i) for i in p_bbox]) + '\n')
elif 'OTB' in args.dataset or 'LASOT' in args.dataset:
for x in regions:
p_bbox = x.copy()
fin.write(','.join([
str(i + 1) if idx == 0 or idx == 1 else str(i)
for idx, i in enumerate(p_bbox)
]) + '\n')
elif 'VISDRONE' in args.dataset or 'GOT10K' in args.dataset or 'TN' in args.dataset:
for x in regions:
p_bbox = x.copy()
fin.write(','.join([str(i)
for idx, i in enumerate(p_bbox)]) + '\n')
toc /= cv2.getTickFrequency()
print('Video: {:12s} Time: {:2.1f}s Speed: {:3.1f}fps Lost {}'.format(
video['name'], toc, f / toc, lost))
def track(video, args):
start_frame, toc = 0, 0
# save result to evaluate
tracker_path = os.path.join('result', args.dataset)
if not os.path.exists(tracker_path):
os.makedirs(tracker_path)
if 'VOT' in args.dataset:
baseline_path = os.path.join(tracker_path, 'baseline')
video_path = os.path.join(baseline_path, video['name'])
if not os.path.exists(video_path):
os.makedirs(video_path)
result_path = os.path.join(video_path, video['name'] + '_001.txt')
else:
result_path = os.path.join(tracker_path,
'{:s}.txt'.format(video['name']))
if os.path.exists(result_path):
return # for mult-gputesting
regions = []
lost = 0
# for rgbt splited test
in_image_files, rgb_image_files, gt = video['infrared_imgs'], video[
'visiable_imgs'], video['gt']
for f, in_f in enumerate(in_image_files):
in_im = cv2.imread(in_f)
in_im = cv2.cvtColor(in_im, cv2.COLOR_BGR2RGB) # align with training
rgb_im = cv2.imread(rgb_image_files[f])
rgb_im = cv2.cvtColor(rgb_im, cv2.COLOR_BGR2RGB) # align with training
tic = cv2.getTickCount()
if f == start_frame: # init
print('===============> init tracker')
tracker = AASTracker(rgb_im, in_im, gt[f])
regions.append(1)
elif f > start_frame: # tracking
state = tracker.track(rgb_im, in_im)
pos = np.array([state[0], state[1]])
sz = np.array([state[2], state[3]])
location = cxy_wh_2_rect(pos, sz)
b_overlap = poly_iou(gt[f],
location) if 'VOT' in args.dataset else 1
if b_overlap > 0:
regions.append(location)
else:
regions.append(2)
start_frame = f + 5
lost += 1
else:
regions.append(0)
toc += cv2.getTickCount() - tic
with open(result_path, "w") as fin:
if 'VOT' in args.dataset:
for x in regions:
if isinstance(x, int):
fin.write("{:d}\n".format(x))
else:
p_bbox = x.copy()
fin.write(','.join([str(i) for i in p_bbox]) + '\n')
toc /= cv2.getTickFrequency()
print('Video: {:12s} Time: {:2.1f}s Speed: {:3.1f}fps Lost {}'.format(
video['name'], toc, f / toc, lost))
def track_tune(tracker, net, video, config):
arch = config['arch']
benchmark_name = config['benchmark']
resume = config['resume']
hp = config['hp'] # scale_step, scale_penalty, scale_lr, window_influence
tracker_path = join('test', (benchmark_name + resume.split('/')[-1].split('.')[0] +
'_small_size_{:.4f}'.format(hp['small_sz']) +
'_big_size_{:.4f}'.format(hp['big_sz']) +
'_lambda_u_{:.4f}'.format(hp['choose_thr']) +
'_lambda_s_{:.4f}'.format(hp['choose_thr']) +
'_cyclic_thr_{:.4f}'.format(hp['choose_thr']) +
'_choose_thr_{:.4f}'.format(hp['choose_thr']) +
'_penalty_k_{:.4f}'.format(hp['penalty_k']) +
'_w_influence_{:.4f}'.format(hp['window_influence']) +
'_scale_lr_{:.4f}'.format(hp['lr'])).replace('.', '_')) # no .
if not os.path.exists(tracker_path):
os.makedirs(tracker_path)
if 'VOT' in benchmark_name:
baseline_path = join(tracker_path, 'baseline')
video_path = join(baseline_path, video['name'])
if not os.path.exists(video_path):
os.makedirs(video_path)
result_path = join(video_path, video['name'] + '_001.txt')
elif 'GOT10K' in benchmark_name:
re_video_path = os.path.join(tracker_path, video['name'])
if not exists(re_video_path): os.makedirs(re_video_path)
result_path = os.path.join(re_video_path, '{:s}.txt'.format(video['name']))
else:
result_path = join(tracker_path, '{:s}.txt'.format(video['name']))
# occ for parallel running
if not os.path.exists(result_path):
fin = open(result_path, 'w')
fin.close()
else:
if benchmark_name.startswith('OTB'):
return tracker_path
elif benchmark_name.startswith('VOT') or benchmark_name.startswith('GOT10K'):
return 0
else:
print('benchmark not supported now')
return
start_frame, lost_times, toc = 0, 0, 0
regions = [] # result and states[1 init / 2 lost / 0 skip]
# for rgbt splited test
image_files, gt = video['image_files'], video['gt']
for f, image_file in enumerate(image_files):
im = cv2.imread(image_file)
if len(im.shape) == 2:
im = cv2.cvtColor(im, cv2.COLOR_GRAY2BGR)
if f == start_frame: # init
cx, cy, w, h = get_axis_aligned_bbox(gt[f])
target_pos = np.array([cx, cy])
target_sz = np.array([w, h])
mask_gt = None
state = tracker.init(im, target_pos, target_sz, net, online=False, mask=mask_gt, debug=False, hp=hp) # init tracker
location = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
regions.append([float(1)] if 'VOT' in benchmark_name else gt[f])
elif f > start_frame: # tracking
state = tracker.track(state, im) # track
location = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
b_overlap = poly_iou(gt[f], location) if 'VOT' in benchmark_name else 1
polygon = state['polygon']
if not polygon is None:
polygon = [polygon[0][0], polygon[0][1], polygon[1][0], polygon[1][1], polygon[2][0], polygon[2][1],
polygon[3][0], polygon[3][1]]
polygon = np.array(polygon)
else:
x1, y1, w, h = location
x2, y2 = x1 + w, y1 + h
polygon = np.array([x1, y1, x2, y1, x2, y2, x1, y2])
if poly_iou(np.array(location), np.array(polygon)) > state['choose_thr']:
record = polygon
else:
x1, y1, w, h = location
x2, y2 = x1 + w, y1 + h
polygon = np.array([x1, y1, x2, y1, x2, y2, x1, y2])
record = polygon
if not 'VOT' in benchmark_name: # change polygon to [x, y, w, h]
x1, y1, x2, y2 = record[0], record[1], record[4], record[5]
record = np.array([x1, y1, x2 - x1 + 1, y2 - y1 + 1])
if b_overlap > 0:
regions.append(record)
else:
regions.append([float(2)])
lost_times += 1
start_frame = f + 5 # skip 5 frames
else: # skip
regions.append([float(0)])
# save results for OTB
if 'OTB' in benchmark_name or 'LASOT' in benchmark_name:
with open(result_path, "w") as fin:
for x in regions:
p_bbox = x.copy()
fin.write(
','.join([str(i + 1) if idx == 0 or idx == 1 else str(i) for idx, i in enumerate(p_bbox)]) + '\n')
elif 'VISDRONE' in benchmark_name or 'GOT10K' in benchmark_name:
with open(result_path, "w") as fin:
for x in regions:
p_bbox = x.copy()
fin.write(','.join([str(i) for idx, i in enumerate(p_bbox)]) + '\n')
elif 'VOT' in benchmark_name:
with open(result_path, "w") as fin:
for x in regions:
if isinstance(x, int):
fin.write("{:d}\n".format(x))
else:
p_bbox = x.copy()
fin.write(','.join([str(i) for i in p_bbox]) + '\n')
if 'OTB' in benchmark_name or 'VIS' in benchmark_name or 'VOT' in benchmark_name or 'GOT10K' in benchmark_name:
return tracker_path
else:
print('benchmark not supported now')
def track_box(siam_tracker, online_tracker, siam_net, video, args):
"""
track a benchmark with only box annoated
attention: not for benchmark evaluation, just a demo
"""
tracker_path = os.path.join('result', args.dataset, args.arch)
if 'VOT' in args.dataset:
baseline_path = os.path.join(tracker_path, 'baseline')
video_path = os.path.join(baseline_path, video['name'])
if not os.path.exists(video_path):
os.makedirs(video_path)
result_path = os.path.join(video_path, video['name'] + '_001.txt')
else:
result_path = os.path.join(tracker_path, '{:s}.txt'.format(video['name']))
if os.path.exists(result_path):
return # for mult-gputesting
regions = []
b_overlaps, b_overlaps2, b_overlaps3 = [], [], []
lost = 0
start_frame, toc = 0, 0
image_files, gt = video['image_files'], video['gt']
for f, image_file in enumerate(image_files):
im = cv2.imread(image_file)
if args.online:
rgb_im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
if len(im.shape) == 2: im = cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) # align with training
tic = cv2.getTickCount()
if f == start_frame: # init
cx, cy, w, h = get_axis_aligned_bbox(gt[f])
target_pos = np.array([cx, cy])
target_sz = np.array([w, h])
mask_gt = None
state = siam_tracker.init(im, target_pos, target_sz, siam_net, online=args.online, mask=mask_gt, debug=args.debug) # init siamese tracker
if args.online:
online_tracker.init(im, rgb_im, siam_net, target_pos, target_sz, True, dataname=args.dataset, resume=args.resume)
elif f > start_frame: # tracking
if args.online:
state = online_tracker.track(im, rgb_im, siam_tracker, state)
else:
state = siam_tracker.track(state, im, name=image_file)
mask = state['mask']
location = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
b_overlap = poly_iou(gt[f], location) if 'VOT' in args.dataset else 1
polygon = state['polygon']
if not polygon is None:
polygon = [polygon[0][0], polygon[0][1], polygon[1][0], polygon[1][1], polygon[2][0], polygon[2][1], polygon[3][0], polygon[3][1]]
polygon = np.array(polygon)
# b_overlap2 = poly_iou(gt[f], polygon)
else:
x1, y1, w, h = location
x2, y2 = x1 + w, y1 + h
polygon = np.array([x1, y1, x2, y1, x2, y2, x1, y2])
if poly_iou(np.array(location), np.array(polygon)) > state['choose_thr']:
record = polygon
# b_overlaps3.append(b_overlap2)
else:
x1, y1, w, h = location
x2, y2 = x1 + w, y1 + h
polygon = np.array([x1, y1, x2, y1, x2, y2, x1, y2])
record = polygon
# b_overlaps3.append(b_overlap)
# print('b_overlap: {}, b_overlap2: {}'.format(b_overlap, b_overlap2))
# b_overlaps.append(b_overlap)
# b_overlaps2.append(b_overlap2)
if not 'VOT' in benchmark_name: # change polygon to [x, y, w, h]
x1, y1, x2, y2 = record[0], record[1], record[4], record[5]
record = np.array([x1, y1, x2 - x1 + 1, y2 - y1 + 1])
if b_overlap > 0:
regions.append(record)
else:
regions.append(2)
start_frame = f + 5
lost += 1
if args.vis:
COLORS = np.random.randint(128, 255, size=(1, 3), dtype="uint8")
COLORS = np.vstack([[0, 0, 0], COLORS]).astype("uint8")
mask = COLORS[mask]
output = ((0.4 * im) + (0.6 * mask)).astype("uint8")
cv2.imshow("mask", output)
cv2.waitKey(1)
toc += cv2.getTickCount() - tic
# print('b_overlap: {}, b_overlap2: {}, b_overlap3: {}'.format(np.array(b_overlaps).mean(), np.array(b_overlaps2).mean(), np.array(b_overlaps3).mean()))
with open(result_path, "w") as fin:
if 'VOT' in args.dataset:
for x in regions:
if isinstance(x, int):
fin.write("{:d}\n".format(x))
else:
p_bbox = x.copy()
fin.write(','.join([str(i) for i in p_bbox]) + '\n')
elif 'OTB' in args.dataset or 'LASOT' in args.dataset:
for x in regions:
p_bbox = x.copy()
fin.write(
','.join([str(i + 1) if idx == 0 or idx == 1 else str(i) for idx, i in enumerate(p_bbox)]) + '\n')
elif 'VISDRONE' in args.dataset or 'GOT10K' in args.dataset:
for x in regions:
p_bbox = x.copy()
fin.write(','.join([str(i) for idx, i in enumerate(p_bbox)]) + '\n')
toc /= cv2.getTickFrequency()
print('Video: {:12s} Time: {:2.1f}s Speed: {:3.1f}fps'.format(video['name'], toc, f / toc))
