def main():
RF_module = RefineModule(refine_path,
selector_path,
search_factor=sr,
input_sz=input_sz)
# refine_method = args.refine_method
model_name = 'siamrpn_' + RF_type + '_m2b_{}'.format(args.thres)
snapshot_path = os.path.join(
project_path_, 'experiments/%s/model.pth' % args.tracker_name)
config_path = os.path.join(
project_path_, 'experiments/%s/config.yaml' % args.tracker_name)
cfg.merge_from_file(config_path)
# create model
model = ModelBuilder() # a sub-class of `torch.nn.Module`
model = load_pretrain(model, snapshot_path).cuda().eval()
# build tracker
tracker = build_tracker(
model) # tracker is a object consisting of NN and some post-processing
# create dataset
dataset = DatasetFactory.create_dataset(name=args.dataset,
dataset_root=dataset_root_,
load_img=False)
# 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
toc = 0
pred_bboxes = []
scores = []
track_times = []
for idx, (img, gt_bbox) in enumerate(video):
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]
tracker.init(img, gt_bbox_)
'''##### initilize refinement module for specific video'''
RF_module.initialize(cv2.cvtColor(img, cv2.COLOR_BGR2RGB),
np.array(gt_bbox_))
pred_bbox = gt_bbox_
scores.append(None)
pred_bboxes.append(pred_bbox)
else:
outputs = tracker.track(img)
pred_bbox = outputs['bbox']
'''##### refine tracking results #####'''
mask_pred = RF_module.get_mask(
cv2.cvtColor(img, cv2.COLOR_BGR2RGB), np.array(pred_bbox))
pred_bbox = mask2bbox(mask_pred,
pred_bbox,
MASK_THRESHOLD=args.thres)
x1, y1, w, h = pred_bbox.tolist()
'''add boundary and min size limit'''
x1, y1, x2, y2 = bbox_clip(x1, y1, x1 + w, y1 + h, (H, W))
w = x2 - x1
h = y2 - y1
pred_bbox = np.array([x1, y1, w, h])
tracker.center_pos = np.array([x1 + w / 2, y1 + h / 2])
tracker.size = np.array([w, h])
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))
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, 255, 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, 255), 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 + '_' + str(selector_path))
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 tracker
tracker_info = Tracker(args.tracker_name, args.tracker_param, None)
params = tracker_info.get_parameters()
params.visualization = args.vis
params.debug = args.debug
params.visdom_info = {
'use_visdom': False,
'server': '127.0.0.1',
'port': 8097
}
tracker = tracker_info.tracker_class(params)
'''Refinement module'''
RF_module = RefineModule(refine_path,
selector_path,
search_factor=sr,
input_sz=input_sz)
model_name = args.tracker_name + '_' + args.tracker_param + '{}-{}'.format(
RF_type, selector_path) + '_%d' % (args.run_id)
# create dataset
dataset = DatasetFactory.create_dataset(name=args.dataset,
dataset_root=dataset_root_,
load_img=False)
# OPE tracking
for v_idx, video in enumerate(dataset):
color = np.array(COLORS[random.randint(0,
len(COLORS) - 1)])[None,
None, ::-1]
vis_result = os.path.join(
'/home/zxy/Desktop/AlphaRefine/CVPR21/material/quality_analysis/mask_vis',
'{}'.format(video.name))
if args.video != '':
# test one special video
if video.name != args.video:
continue
else:
print()
if not os.path.exists(vis_result):
os.makedirs(vis_result)
toc = 0
pred_bboxes = []
scores = []
track_times = []
for idx, (img, gt_bbox) in enumerate(video):
'''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'''
gt_bbox_np = np.array(gt_bbox_)
gt_bbox_torch = torch.from_numpy(gt_bbox_np.astype(np.float32))
init_info = {}
init_info['init_bbox'] = gt_bbox_torch
_ = tracker.initialize(img_RGB, init_info)
'''##### initilize refinement module for specific video'''
RF_module.initialize(img_RGB, np.array(gt_bbox_))
pred_bbox = gt_bbox_
scores.append(None)
pred_bboxes.append(pred_bbox)
else:
'''Track'''
outputs = tracker.track(img_RGB)
pred_bbox = outputs['target_bbox']
'''##### refine tracking results #####'''
pred_bbox = RF_module.refine(
cv2.cvtColor(img, cv2.COLOR_BGR2RGB), np.array(pred_bbox))
x1, y1, w, h = pred_bbox.tolist()
'''add boundary and min size limit'''
x1, y1, x2, y2 = bbox_clip(x1, y1, x1 + w, y1 + h, (H, W))
w = x2 - x1
h = y2 - y1
new_pos = torch.from_numpy(
np.array([y1 + h / 2, x1 + w / 2]).astype(np.float32))
new_target_sz = torch.from_numpy(
np.array([h, w]).astype(np.float32))
new_scale = torch.sqrt(new_target_sz.prod() /
tracker.base_target_sz.prod())
##### update
tracker.pos = new_pos.clone()
tracker.target_sz = new_target_sz
tracker.target_scale = new_scale
mask_pred = RF_module.get_mask(
cv2.cvtColor(img, cv2.COLOR_BGR2RGB), np.array(pred_bbox))
from external.pysot.toolkit.visualization import draw_mask
draw_mask(img,
mask_pred,
idx=idx,
show=True,
save_dir='dimpsuper_armask_crocodile-3')
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:
im4show = img
mask_pred = np.uint8(mask_pred > 0.5)[:, :, None]
contours, _ = cv2.findContours(mask_pred.squeeze(),
cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
im4show = im4show * (1 - mask_pred) + np.uint8(
im4show * mask_pred /
2) + mask_pred * np.uint8(color) * 128
pred_bbox = list(map(int, pred_bbox))
# gt_bbox = list(map(int, gt_bbox))
# cv2.rectangle(im4show, (gt_bbox[0], gt_bbox[1]),
# (gt_bbox[0]+gt_bbox[2], gt_bbox[1]+gt_bbox[3]), (0, 255, 0), 3)
# cv2.rectangle(im4show, (pred_bbox[0], pred_bbox[1]),
# (pred_bbox[0]+pred_bbox[2], pred_bbox[1]+pred_bbox[3]), color[::-1].squeeze().tolist(), 3)
cv2.drawContours(im4show, contours, -1, color[::-1].squeeze(),
2)
cv2.putText(im4show, str(idx), (40, 40),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2)
# cv2.imshow(video.name, im4show)
cv2.imwrite(os.path.join(vis_result, '{:06}.jpg'.format(idx)),
im4show)
cv2.waitKey(1)
toc /= cv2.getTickFrequency()