def track(self, im): for i in range(self.config.num_scale): # crop multi-scale search region window_sz = self.target_sz * (self.config.scale_factor[i] * (1 + self.config.padding)) bbox = cxy_wh_2_bbox(self.target_pos, window_sz) self.patch_crop[i, :] = crop_chw(im, bbox, self.config.crop_sz) search = self.patch_crop - self.config.net_average_image if self.gpu: response = self.net(torch.Tensor(search).cuda()) else: response = self.net(torch.Tensor(search)) peak, idx = torch.max(response.view(self.config.num_scale, -1), 1) peak = peak.data.cpu().numpy() * self.config.scale_penalties best_scale = np.argmax(peak) r_max, c_max = np.unravel_index(idx[best_scale], self.config.net_input_size) if r_max > self.config.net_input_size[0] / 2: r_max = r_max - self.config.net_input_size[0] if c_max > self.config.net_input_size[1] / 2: c_max = c_max - self.config.net_input_size[1] window_sz = self.target_sz * (self.config.scale_factor[best_scale] * (1 + self.config.padding)) self.target_pos = self.target_pos + np.array([c_max, r_max]) * window_sz / self.config.net_input_size self.target_sz = np.minimum(np.maximum(window_sz / (1 + self.config.padding), self.min_sz), self.max_sz) # model update window_sz = self.target_sz * (1 + self.config.padding) bbox = cxy_wh_2_bbox(self.target_pos, window_sz) patch = crop_chw(im, bbox, self.config.crop_sz) target = patch - self.config.net_average_image self.net.update(torch.Tensor(np.expand_dims(target, axis=0)).cuda(), lr=self.config.interp_factor) return cxy_wh_2_rect1(self.target_pos, self.target_sz) # 1-index
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()) res = [cxy_wh_2_rect1(target_pos, target_sz)] # save in .txt patch_crop = np.zeros( (config.num_scale, patch.shape[0], patch.shape[1], patch.shape[2]), np.float32) for f in range(1, n_images): # track im = cv2.imread(image_files[f]) for i in range(config.num_scale): # crop multi-scale search region window_sz = target_sz * (config.scale_factor[i] * (1 + config.padding)) bbox = cxy_wh_2_bbox(target_pos, window_sz) patch_crop[i, :] = crop_chw(im, bbox, config.crop_sz) search = patch_crop - config.net_average_image response = net(torch.Tensor(search).cuda()) peak, idx = torch.max(response.view(config.num_scale, -1), 1)
im = video.frame_at(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()) track = [cxy_wh_2_rect1(target_pos, target_sz)] patch_crop = np.zeros( (config.num_scale, patch.shape[0], patch.shape[1], patch.shape[2]), np.float32) for f in range(1, n_images): # track im = video.frame_at(f) for i in range(config.num_scale): # crop multi-scale search region window_sz = target_sz * (config.scale_factor[i] * (1 + config.padding)) bbox = cxy_wh_2_bbox(target_pos, window_sz) patch_crop[i, :] = crop_chw(im, bbox, config.crop_sz) search = patch_crop - config.net_average_image search = torch.Tensor(search).cuda()