(self, img, crop_size=None)
| 292 | return score |
| 293 | |
| 294 | def process_image(self, img, crop_size=None): |
| 295 | p_img = img |
| 296 | |
| 297 | if img.shape[2] < 3: |
| 298 | im_b = p_img |
| 299 | im_g = p_img |
| 300 | im_r = p_img |
| 301 | p_img = np.concatenate((im_b, im_g, im_r), axis=2) |
| 302 | |
| 303 | p_img = normalize(p_img, self.norm_mean, self.norm_std) |
| 304 | |
| 305 | if crop_size is not None: |
| 306 | p_img, margin = pad_image_to_shape(p_img, crop_size, cv2.BORDER_CONSTANT, value=0) |
| 307 | p_img = p_img.transpose(2, 0, 1) |
| 308 | |
| 309 | return p_img, margin |
| 310 | |
| 311 | p_img = p_img.transpose(2, 0, 1) |
| 312 | |
| 313 | return p_img |
| 314 | |
| 315 | # add new funtion for rgb and modal X segmentation |
| 316 | def sliding_eval_rgbX(self, img, modal_x, crop_size, stride_rate, device=None): |
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