(model_path, image_path, output)
| 268 | |
| 269 | |
| 270 | def run(model_path, image_path, output): |
| 271 | pred_config = PredictConfig( |
| 272 | model=Model(), |
| 273 | session_init=SmartInit(model_path), |
| 274 | input_names=['image'], |
| 275 | output_names=['output' + str(k) for k in range(1, 7)]) |
| 276 | predictor = OfflinePredictor(pred_config) |
| 277 | im = cv2.imread(image_path) |
| 278 | assert im is not None |
| 279 | im = cv2.resize( |
| 280 | im, (im.shape[1] // 16 * 16, im.shape[0] // 16 * 16) |
| 281 | )[None, :, :, :].astype('float32') |
| 282 | outputs = predictor(im) |
| 283 | if output is None: |
| 284 | for k in range(6): |
| 285 | pred = outputs[k][0] |
| 286 | cv2.imwrite("out{}.png".format( |
| 287 | '-fused' if k == 5 else str(k + 1)), pred * 255) |
| 288 | logger.info("Results saved to out*.png") |
| 289 | else: |
| 290 | pred = outputs[5][0] |
| 291 | cv2.imwrite(output, pred * 255) |
| 292 | |
| 293 | |
| 294 | if __name__ == '__main__': |
no test coverage detected