(img, bbox, data_split)
| 171 | return scale, rot, color_scale, do_flip, crop_hw, sample_ratio, sample_prob |
| 172 | |
| 173 | def augmentation_keep_size(img, bbox, data_split): |
| 174 | ori_shape = img.shape[:2][::-1] |
| 175 | if getattr(cfg, 'no_aug', False) and data_split == 'train': |
| 176 | scale, rot, color_scale, do_flip,size,crop = 1.0, 0.0, np.array([1, 1, 1]), False, ori_shape, np.array([1,1]) |
| 177 | |
| 178 | size = random.choice(cfg.train_sizes) |
| 179 | max_size = cfg.train_max_size |
| 180 | elif data_split == 'train': |
| 181 | scale, rot, color_scale, do_flip, crop = get_aug_config() |
| 182 | rot=0 |
| 183 | # scale, rot, do_flip, crop = 1.0, 0.0, False, np.array([1,1]) |
| 184 | size = random.choice(cfg.train_sizes) |
| 185 | max_size = cfg.train_max_size |
| 186 | else: |
| 187 | scale, rot, color_scale, do_flip, crop = 1.0, 0.0, np.array([1, 1, 1]), False, np.array([1,1]) |
| 188 | size = random.choice(cfg.test_sizes) |
| 189 | max_size = cfg.test_max_size |
| 190 | |
| 191 | crop_bbox_wh = (bbox[2:]*crop).astype(np.uint32) |
| 192 | xy_range = img.shape[:2][::-1]-crop_bbox_wh |
| 193 | crop_bbox_xywh = np.array([np.random.randint(0,xy_range[0]+1),np.random.randint(0,xy_range[1]+1),crop_bbox_wh[0],crop_bbox_wh[1]]) |
| 194 | reshape_size = resize(crop_bbox_xywh[2:], size, max_size) |
| 195 | |
| 196 | img, trans, inv_trans = generate_patch_image(img, crop_bbox_xywh, 1, rot, do_flip, reshape_size[::-1]) |
| 197 | img = np.clip(img * color_scale[None, None, :], 0, 255) |
| 198 | return img, trans, inv_trans, rot, do_flip |
| 199 | |
| 200 | def augmentation_instance_sample(img, bbox, data_split,data,dataname): |
| 201 | ori_shape = img.shape[:2][::-1] |
no test coverage detected