def __init__(self, rf_model_code, enable_rf=True):
model_name = 'RTMDNet' + '{}-{}'.format(RF_type.format(rf_model_code),
selector_path)
if not enable_rf:
model_name = model_name.replace(RF_type.format(rf_model_code), '')
super(RTMDNet_RF, self).__init__(name=model_name)
self.enable_rf = enable_rf
self.tracker = RT_MDNet()
if self.enable_rf:
self.RF_module = RefineModule(refine_path.format(rf_model_code),
selector_path,
search_factor=sr,
input_sz=input_sz)
def __init__(self, threshold=0.65):
self.THRES = threshold
'''create tracker'''
self.base_tracker = tracker = RT_MDNet(
) # a tracker is a object consisting of not only a NN and some post-processing
'''Alpha-Refine'''
self.alpha = RefineModule(refine_path, sr, input_sz=input_sz)
class RTMDNet_RF(GOT10kTracker):
def __init__(self, rf_model_code, enable_rf=True):
model_name = 'RTMDNet' + '{}-{}'.format(RF_type.format(rf_model_code),
selector_path)
if not enable_rf:
model_name = model_name.replace(RF_type.format(rf_model_code), '')
super(RTMDNet_RF, self).__init__(name=model_name)
self.enable_rf = enable_rf
self.tracker = RT_MDNet()
if self.enable_rf:
self.RF_module = RefineModule(refine_path.format(rf_model_code),
selector_path,
search_factor=sr,
input_sz=input_sz)
def init(self, image, box):
image = np.array(image)
self.im_H, self.im_W, _ = image.shape
cx, cy, w, h = get_axis_aligned_bbox(np.array(box))
gt_bbox_ = [cx - (w - 1) / 2, cy - (h - 1) / 2, w, h]
# initialize tracker
self.tracker.initialize_seq(image, np.array(gt_bbox_))
# initilize refine module
if self.enable_rf:
self.RF_module.initialize(image, np.array(gt_bbox_))
self.box = box
def update(self, image):
image = np.array(image)
pred_bbox = self.tracker.track(image)
if self.enable_rf:
# refine tracking results
pred_bbox = self.RF_module.refine(image, np.array(pred_bbox))
pred_bbox = bbox_clip(
pred_bbox, (self.im_H, self.im_W)) # boundary and size limit
'''update state'''
self.tracker.target_bbox = pred_bbox.copy()
return pred_bbox
def main(model_code):
# create dataset
dataset = DatasetFactory.create_dataset(name=args.dataset, dataset_root=dataset_root_, load_img=False)
'''##### build a Refinement module #####'''
RF_module = RefineModule(refine_path.format(model_code), selector_path, search_factor=sr,
input_sz=input_sz)
model_name = 'RT_MDNet' + '_{}-{}'.format(RF_type.format(model_code), selector_path) + '_%d' % (args.run_id)
if args.dataset in ['VOT2016', 'VOT2018', 'VOT2019']:
# restart tracking
for v_idx, video in enumerate(dataset):
if args.video != '':
# test one special video
if video.name != args.video:
continue
tracker = RT_MDNet()
frame_counter = 0
lost_number = 0
toc = 0
pred_bboxes = []
for idx, (img, gt_bbox) in enumerate(video):
img_RGB = cv2.cvtColor(img,cv2.COLOR_BGR2RGB) # RGB format
if len(gt_bbox) == 4:
gt_bbox = [gt_bbox[0], gt_bbox[1],
gt_bbox[0], gt_bbox[1]+gt_bbox[3]-1,
gt_bbox[0]+gt_bbox[2]-1, gt_bbox[1]+gt_bbox[3]-1,
gt_bbox[0]+gt_bbox[2]-1, gt_bbox[1]]
tic = cv2.getTickCount()
if idx == frame_counter:
H,W,_ = img.shape
cx, cy, w, h = get_axis_aligned_bbox(np.array(gt_bbox))
gt_bbox_ = [cx-(w-1)/2, cy-(h-1)/2, w, h]
'''initialize tracker'''
tracker.initialize_seq(img_RGB, np.array(gt_bbox_))
'''initilize refine module for specific video'''
RF_module.initialize(img_RGB, np.array(gt_bbox_))
pred_bbox = gt_bbox_
pred_bboxes.append(1)
elif idx > frame_counter:
'''track'''
ori_bbox = tracker.track(img_RGB)
'''refine tracking result'''
pred_bbox = RF_module.refine(img_RGB, np.array(ori_bbox))
pred_bbox = bbox_clip(pred_bbox, (H, W))
tracker.target_bbox = pred_bbox.copy()
overlap = vot_overlap(pred_bbox, gt_bbox, (img.shape[1], img.shape[0]))
if overlap > 0:
# not lost
pred_bboxes.append(pred_bbox)
else:
# lost object
pred_bboxes.append(2)
frame_counter = idx + 5 # skip 5 frames
lost_number += 1
else:
pred_bboxes.append(0)
toc += cv2.getTickCount() - tic
if idx == 0:
cv2.destroyAllWindows()
if args.vis and idx > frame_counter:
cv2.polylines(img, [np.array(gt_bbox, np.int).reshape((-1, 1, 2))],
True, (0, 255, 0), 3)
bbox = list(map(int, pred_bbox))
cv2.rectangle(img, (bbox[0], bbox[1]),
(bbox[0]+bbox[2], bbox[1]+bbox[3]), (0, 255, 255), 3)
cv2.putText(img, str(idx), (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2)
cv2.putText(img, str(lost_number), (40, 80), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
cv2.imshow(video.name, img)
cv2.waitKey(1)
toc /= cv2.getTickFrequency()
# save results
video_path = os.path.join(save_dir, args.dataset, model_name,
'baseline', video.name)
if not os.path.isdir(video_path):
os.makedirs(video_path)
result_path = os.path.join(video_path, '{}_001.txt'.format(video.name))
with open(result_path, 'w') as f:
for x in pred_bboxes:
if isinstance(x, int):
f.write("{:d}\n".format(x))
else:
f.write(','.join([vot_float2str("%.4f", i) for i in x])+'\n')
print('({:3d}) Video: {:12s} Time: {:4.1f}s Speed: {:3.1f}fps Lost: {:d}'.format(
v_idx+1, video.name, toc, idx / toc, lost_number))
def main():
# create dataset
dataset = DatasetFactory.create_dataset(name=args.dataset,
dataset_root=dataset_root_,
load_img=False)
model_name = 'RTMDNet-oracle'
# OPE tracking
for v_idx, video in enumerate(dataset):
if os.path.exists(
os.path.join(save_dir, args.dataset, model_name,
'{}.txt'.format(video.name))):
continue
if args.video != '':
# test one special video
if video.name != args.video:
continue
'''build tracker'''
tracker = RT_MDNet()
toc = 0
pred_bboxes = []
scores = []
track_times = []
for idx, (img, gt_bbox) in enumerate(video):
img_RGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB format
tic = cv2.getTickCount()
if idx == 0:
H, W, _ = img.shape
cx, cy, w, h = get_axis_aligned_bbox(np.array(gt_bbox))
gt_bbox_ = [cx - (w - 1) / 2, cy - (h - 1) / 2, w, h]
'''initialize tracker'''
tracker.initialize_seq(img_RGB, np.array(gt_bbox_))
pred_bbox = gt_bbox_
scores.append(None)
pred_bboxes.append(pred_bbox)
else:
ori_bbox = tracker.track(img_RGB)
pred_bbox = bbox_clip(ori_bbox, (H, W))
oracle_box = pred_bbox.copy()
cx, cy, _, _ = get_axis_aligned_bbox(np.array(gt_bbox))
oracle_box[:2] = np.array([cx, cy]) - oracle_box[2:] / 2
tracker.target_bbox = oracle_box
pred_bboxes.append(pred_bbox)
toc += cv2.getTickCount() - tic
track_times.append(
(cv2.getTickCount() - tic) / cv2.getTickFrequency())
if idx == 0:
cv2.destroyAllWindows()
if args.vis and idx > 0:
gt_bbox = list(map(int, gt_bbox))
ori_bbox = list(map(int, ori_bbox))
pred_bbox = list(map(int, pred_bbox))
cv2.rectangle(
img, (gt_bbox[0], gt_bbox[1]),
(gt_bbox[0] + gt_bbox[2], gt_bbox[1] + gt_bbox[3]),
(0, 0, 255), 3)
cv2.rectangle(img, (oracle_box[0], oracle_box[1]),
(oracle_box[0] + oracle_box[2],
oracle_box[1] + oracle_box[3]), (255, 0, 0), 3)
cv2.rectangle(
img, (pred_bbox[0], pred_bbox[1]),
(pred_bbox[0] + pred_bbox[2], pred_bbox[1] + pred_bbox[3]),
(0, 255, 0), 3)
cv2.putText(img, str(idx), (40, 40), cv2.FONT_HERSHEY_SIMPLEX,
1, (0, 255, 255), 2)
cv2.imshow(video.name, img)
cv2.waitKey(1)
toc /= cv2.getTickFrequency()
# save results
model_path = os.path.join(save_dir, args.dataset, model_name)
if not os.path.isdir(model_path):
os.makedirs(model_path)
result_path = os.path.join(model_path, '{}.txt'.format(video.name))
with open(result_path, 'w') as f:
for x in pred_bboxes:
f.write(','.join([str(i) for i in x]) + '\n')
print('({:3d}) Video: {:12s} Time: {:5.1f}s Speed: {:3.1f}fps'.format(
v_idx + 1, video.name, toc, idx / toc))
def main():
# create dataset
dataset = DatasetFactory.create_dataset(name=args.dataset,
dataset_root=dataset_root_,
load_img=False)
'''##### build a Refinement module #####'''
RF_module = RefineModule(refine_path,
selector_path,
search_factor=sr,
input_sz=input_sz)
model_name = 'RTMDNet' + '{}-{}'.format(RF_type, selector_path)
# OPE tracking
for v_idx, video in enumerate(dataset):
if os.path.exists(
os.path.join(save_dir, args.dataset, model_name,
'{}.txt'.format(video.name))):
continue
if args.video != '':
# test one special video
if video.name != args.video:
continue
'''build tracker'''
tracker = RT_MDNet()
toc = 0
pred_bboxes = []
scores = []
track_times = []
for idx, (img, gt_bbox) in enumerate(video):
img_RGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB format
tic = cv2.getTickCount()
if idx == 0:
H, W, _ = img.shape
cx, cy, w, h = get_axis_aligned_bbox(np.array(gt_bbox))
gt_bbox_ = [cx - (w - 1) / 2, cy - (h - 1) / 2, w, h]
'''initialize tracker'''
tracker.initialize_seq(img_RGB, np.array(gt_bbox_))
'''initilize refine module for specific video'''
RF_module.initialize(img_RGB, np.array(gt_bbox_))
pred_bbox = gt_bbox_
scores.append(None)
if 'VOT2018-LT' == args.dataset:
pred_bboxes.append([1])
else:
pred_bboxes.append(pred_bbox)
else:
ori_bbox = tracker.track(img_RGB)
'''##### refine tracking results #####'''
pred_bbox = RF_module.refine(img_RGB, np.array(ori_bbox))
'''boundary and size limit'''
pred_bbox = bbox_clip(pred_bbox, (H, W))
'''update state'''
tracker.target_bbox = pred_bbox.copy()
pred_bboxes.append(pred_bbox)
# scores.append(outputs['best_score'])
toc += cv2.getTickCount() - tic
track_times.append(
(cv2.getTickCount() - tic) / cv2.getTickFrequency())
if idx == 0:
cv2.destroyAllWindows()
if args.vis and idx > 0:
gt_bbox = list(map(int, gt_bbox))
ori_bbox = list(map(int, ori_bbox))
pred_bbox = list(map(int, pred_bbox))
cv2.rectangle(
img, (gt_bbox[0], gt_bbox[1]),
(gt_bbox[0] + gt_bbox[2], gt_bbox[1] + gt_bbox[3]),
(0, 0, 255), 3)
cv2.rectangle(
img, (ori_bbox[0], ori_bbox[1]),
(ori_bbox[0] + ori_bbox[2], ori_bbox[1] + ori_bbox[3]),
(255, 0, 0), 3)
cv2.rectangle(
img, (pred_bbox[0], pred_bbox[1]),
(pred_bbox[0] + pred_bbox[2], pred_bbox[1] + pred_bbox[3]),
(0, 255, 0), 3)
cv2.putText(img, str(idx), (40, 40), cv2.FONT_HERSHEY_SIMPLEX,
1, (0, 255, 255), 2)
cv2.imshow(video.name, img)
cv2.waitKey(1)
toc /= cv2.getTickFrequency()
# save results
model_path = os.path.join(save_dir, args.dataset, model_name)
if not os.path.isdir(model_path):
os.makedirs(model_path)
result_path = os.path.join(model_path, '{}.txt'.format(video.name))
with open(result_path, 'w') as f:
for x in pred_bboxes:
f.write(','.join([str(i) for i in x]) + '\n')
print('({:3d}) Video: {:12s} Time: {:5.1f}s Speed: {:3.1f}fps'.format(
v_idx + 1, video.name, toc, idx / toc))
def __init__(self, threshold=0.65):
self.THRES = threshold
'''create tracker'''
self.base_tracker = tracker = RT_MDNet(
) # a tracker is a object consisting of not only a NN and some post-processing
def main():
# create dataset
dataset_root = dataset_root_
frames_dir = os.path.join(dataset_root, 'frames')
seq_list = sorted(os.listdir(frames_dir))
model_name = 'RTMDNet'
# OPE tracking
for v_idx, seq_name in enumerate(seq_list):
if args.video != '':
# test one special video
if seq_name != args.video:
continue
'''build tracker'''
toc = 0
pred_bboxes = []
scores = []
track_times = []
tracker = RT_MDNet()
seq_frame_dir = os.path.join(frames_dir, seq_name)
num_frames = len(os.listdir(seq_frame_dir))
gt_file = os.path.join(dataset_root, 'anno', '%s.txt' % seq_name)
gt_bbox = np.loadtxt(gt_file, dtype=np.float32,
delimiter=',').squeeze()
for idx in range(num_frames):
frame_path = os.path.join(seq_frame_dir, '%d.jpg' % idx)
img = cv2.imread(frame_path)
'''get RGB format image'''
img_RGB = img[:, :, ::-1].copy() # BGR --> RGB
tic = cv2.getTickCount()
if idx == 0:
H, W, _ = img.shape
cx, cy, w, h = get_axis_aligned_bbox(np.array(gt_bbox))
gt_bbox_ = [cx - (w - 1) / 2, cy - (h - 1) / 2, w, h]
'''initialize tracker'''
tracker.initialize_seq(img_RGB, np.array(gt_bbox_))
pred_bbox = gt_bbox_
scores.append(None)
pred_bboxes.append(pred_bbox)
else:
pred_bbox = tracker.track(img_RGB)
pred_bboxes.append(pred_bbox)
toc += cv2.getTickCount() - tic
track_times.append(
(cv2.getTickCount() - tic) / cv2.getTickFrequency())
if idx == 0:
cv2.destroyAllWindows()
if args.vis and idx > 0:
pred_bbox = list(map(int, pred_bbox))
cv2.rectangle(
img, (pred_bbox[0], pred_bbox[1]),
(pred_bbox[0] + pred_bbox[2], pred_bbox[1] + pred_bbox[3]),
(0, 255, 255), 3)
cv2.putText(img, str(idx), (40, 40), cv2.FONT_HERSHEY_SIMPLEX,
1, (0, 255, 255), 2)
cv2.imshow(seq_name, img)
cv2.waitKey(1)
toc /= cv2.getTickFrequency()
# save results
model_path = os.path.join(save_dir, 'trackingnet', model_name)
if not os.path.isdir(model_path):
os.makedirs(model_path)
result_path = os.path.join(model_path, '{}.txt'.format(seq_name))
with open(result_path, 'w') as f:
for x in pred_bboxes:
f.write(','.join([str(i) for i in x]) + '\n')
print('({:3d}) Video: {:12s} Time: {:5.1f}s Speed: {:3.1f}fps'.format(
v_idx + 1, seq_name, toc, idx / toc))