()
| 341 | |
| 342 | |
| 343 | def test_adjust_gamma(): |
| 344 | # test assertion if gamma <= 0 |
| 345 | with pytest.raises(AssertionError): |
| 346 | transform = dict(type='AdjustGamma', gamma=0) |
| 347 | build_from_cfg(transform, PIPELINES) |
| 348 | |
| 349 | # test assertion if gamma is list |
| 350 | with pytest.raises(AssertionError): |
| 351 | transform = dict(type='AdjustGamma', gamma=[1.2]) |
| 352 | build_from_cfg(transform, PIPELINES) |
| 353 | |
| 354 | # test with gamma = 1.2 |
| 355 | transform = dict(type='AdjustGamma', gamma=1.2) |
| 356 | transform = build_from_cfg(transform, PIPELINES) |
| 357 | results = dict() |
| 358 | img = mmcv.imread( |
| 359 | osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color') |
| 360 | original_img = copy.deepcopy(img) |
| 361 | results['img'] = img |
| 362 | results['img_shape'] = img.shape |
| 363 | results['ori_shape'] = img.shape |
| 364 | # Set initial values for default meta_keys |
| 365 | results['pad_shape'] = img.shape |
| 366 | results['scale_factor'] = 1.0 |
| 367 | |
| 368 | results = transform(results) |
| 369 | |
| 370 | inv_gamma = 1.0 / 1.2 |
| 371 | table = np.array([((i / 255.0)**inv_gamma) * 255 |
| 372 | for i in np.arange(0, 256)]).astype('uint8') |
| 373 | converted_img = mmcv.lut_transform( |
| 374 | np.array(original_img, dtype=np.uint8), table) |
| 375 | assert np.allclose(results['img'], converted_img) |
| 376 | assert str(transform) == f'AdjustGamma(gamma={1.2})' |
| 377 | |
| 378 | |
| 379 | def test_rerange(): |
nothing calls this directly
no outgoing calls
no test coverage detected