def __init__(self, args):
super(PatchTrainer, self).__init__()
# Setup device
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
torch.backends.cudnn.benchmark = True
# Setup tracker cfg
cfg = load_config(args)
p = TrackerConfig()
p.renew()
self.p = p
# Setup tracker
siammask = Tracker(p=p, anchors=cfg['anchors'])
if args.resume:
assert isfile(args.resume), 'Please download {} first.'.format(args.resume)
siammask = load_pretrain(siammask, args.resume)
siammask.eval().to(self.device)
self.model = siammask
def main():
# args.base_path = base_path
args.resume = "../SiamMask/experiments/siammask_sharp/SiamMask_DAVIS.pth"
args.config = "../SiamMask/experiments/siammask_sharp/config_davis.json"
print(join(args.base_path, 'groundtruth_rect.txt'))
# Setup device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
torch.backends.cudnn.benchmark = True
# Setup Model
cfg = load_config(args)
p = TrackerConfig()
p.renew()
siammask = Tracker(p=p, anchors=cfg['anchors'])
if args.resume:
assert isfile(args.resume), 'Please download {} first.'.format(args.resume)
siammask = load_pretrain(siammask, args.resume)
siammask.eval().to(device)
# Parse Image file
img_files = sorted(glob.glob(join(join(args.base_path, 'imgs'), '*.jp*')))
ims = [cv2.imread(imf) for imf in img_files]
# Select ROI
cv2.namedWindow("SiamMask", cv2.WND_PROP_FULLSCREEN)
try:
init_rect = cv2.selectROI('SiamMask', ims[0], False, False)
gts = None
x, y, w, h = init_rect
except:
exit()
file1 = open(join(args.base_path, 'groundtruth_rect.txt'), 'w')
file1.write('{0:d},{1:d},{2:d},{3:d}\n'.format(x, y, w, h))
toc = 0
for f, im in enumerate(ims):
tic = cv2.getTickCount()
if f == 0: # init
target_pos = np.array([x + w / 2, y + h / 2])
target_sz = np.array([w, h])
state = tracker_init(im, target_pos, target_sz, siammask, device=device) # init tracker
state['gts'] = gts
state['device'] = device
elif f > 0: # tracking
state = tracker_track(state, im, siammask, device=device) # track
target_pos, target_sz =state['target_pos'], state['target_sz']
x, y = (target_pos - target_sz/2).astype(int)
x2, y2 = (target_pos + target_sz/2).astype(int)
cv2.rectangle(im, (x, y), (x2, y2), (0, 255, 0), 4)
cv2.imshow('SiamMask', im)
key = cv2.waitKey(1)
if key == ord('q'):
break
file1.write('{0:d},{1:d},{2:d},{3:d}\n'.format(x, y, x2-x, y2-y))
toc += cv2.getTickCount() - tic
file1.close()
toc /= cv2.getTickFrequency()
fps = f / toc
print('SiamMask Time: {:02.1f}s Speed: {:3.1f}fps (with visulization!)'.format(toc, fps))
cv2.namedWindow("SiamMask", cv2.WND_PROP_FULLSCREEN)
# Setup device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
torch.backends.cudnn.benchmark = True
# Setup Model
cfg = load_config(args)
p = TrackerConfig()
p.renew()
siammask = Tracker(p=p, anchors=cfg['anchors'])
if args.resume:
assert isfile(args.resume), 'Please download {} first.'.format(
args.resume)
siammask = load_pretrain(siammask, args.resume)
siammask.eval().to(device)
model = siammask
# Setup Dataset
dataloader = DataLoader(AttackDataset(root_dir='data/Phone1',
step=1,
test=True),
batch_size=100)
# Load Patch
pert_sz_ratio = (0.5, 0.5)
patch = cv2.imread('patch_sm.png')
patch = kornia.image_to_tensor(patch).to(torch.float) # (3, H, W)
patch = patch.clone().detach().requires_grad_(False) # (3, H, W)
# For save tracking result as img file