def __init__(self, im, init_rect, config=TrackerConfig(), gpu=True):
self.gpu = gpu
self.config = config
self.net = DCFNet(config)
self.net.load_param(config.feature_path)
self.net.eval()
if gpu:
self.net.cuda()
# confine results
target_pos, target_sz = rect1_2_cxy_wh(init_rect)
self.min_sz = np.maximum(config.min_scale_factor * target_sz, 4)
self.max_sz = np.minimum(im.shape[:2],
config.max_scale_factor * target_sz)
# crop template
window_sz = target_sz * (1 + config.padding)
bbox = cxy_wh_2_bbox(target_pos, window_sz)
patch = crop_chw(im, bbox, self.config.crop_sz)
target = patch - config.net_average_image
self.net.update(torch.Tensor(np.expand_dims(target, axis=0)).cuda())
self.target_pos, self.target_sz = target_pos, target_sz
self.patch_crop = np.zeros(
(config.num_scale, patch.shape[0], patch.shape[1], patch.shape[2]),
np.float32) # buff
net.eval().cuda()
speed = []
# loop videos
for video_id, video in enumerate(videos): # run without resetting
video_path_name = annos[video]['name']
init_rect = np.array(annos[video]['init_rect']).astype(np.float)
image_files = [
join(base_path, video_path_name, 'img', im_f)
for im_f in annos[video]['image_files']
]
n_images = len(image_files)
tic = time.time() # time start
target_pos, target_sz = rect1_2_cxy_wh(
init_rect) # OTB label is 1-indexed
im = cv2.imread(image_files[0]) # HxWxC
# confine results
min_sz = np.maximum(config.min_scale_factor * target_sz, 4)
max_sz = np.minimum(im.shape[:2], config.max_scale_factor * target_sz)
# crop template
window_sz = target_sz * (1 + config.padding)
bbox = cxy_wh_2_bbox(target_pos, window_sz)
patch = crop_chw(im, bbox, config.crop_sz)
target = patch - config.net_average_image
net.update(torch.Tensor(np.expand_dims(target, axis=0)).cuda())
