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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import numpy as np | |
| import torch | |
| def test_contour_expand(): | |
| from mmcv.ops import contour_expand | |
| np_internal_kernel_label = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], | |
| [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], | |
| [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], | |
| [0, 0, 1, 1, 0, 0, 0, 0, 2, 0], | |
| [0, 0, 1, 1, 0, 0, 0, 0, 2, 0], | |
| [0, 0, 1, 1, 0, 0, 0, 0, 2, 0], | |
| [0, 0, 1, 1, 0, 0, 0, 0, 2, 0], | |
| [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], | |
| [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], | |
| [0, 0, 0, 0, 0, 0, 0, 0, 0, | |
| 0]]).astype(np.int32) | |
| np_kernel_mask1 = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], | |
| [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], | |
| [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], | |
| [0, 0, 1, 1, 1, 1, 1, 1, 1, 0], | |
| [0, 0, 1, 1, 1, 1, 1, 1, 1, 0], | |
| [0, 0, 1, 1, 1, 1, 1, 1, 1, 0], | |
| [0, 0, 1, 1, 1, 1, 1, 1, 1, 0], | |
| [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], | |
| [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], | |
| [0, 0, 0, 0, 0, 0, 0, 0, 0, | |
| 0]]).astype(np.uint8) | |
| np_kernel_mask2 = (np_internal_kernel_label > 0).astype(np.uint8) | |
| np_kernel_mask = np.stack([np_kernel_mask1, np_kernel_mask2]) | |
| min_area = 1 | |
| kernel_region_num = 3 | |
| result = contour_expand(np_kernel_mask, np_internal_kernel_label, min_area, | |
| kernel_region_num) | |
| gt = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], | |
| [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 2, 2, 2, 0], | |
| [0, 0, 1, 1, 1, 1, 2, 2, 2, 0], [0, 0, 1, 1, 1, 1, 2, 2, 2, 0], | |
| [0, 0, 1, 1, 1, 1, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], | |
| [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] | |
| assert np.allclose(result, gt) | |
| np_kernel_mask_t = torch.from_numpy(np_kernel_mask) | |
| np_internal_kernel_label_t = torch.from_numpy(np_internal_kernel_label) | |
| result = contour_expand(np_kernel_mask_t, np_internal_kernel_label_t, | |
| min_area, kernel_region_num) | |
| assert np.allclose(result, gt) | |