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|
| | import unittest |
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|
| | import numpy as np |
| | from parameterized import parameterized |
| |
|
| | from monai.transforms import CropForeground |
| |
|
| | TEST_CASE_1 = [ |
| | {"select_fn": lambda x: x > 0, "channel_indices": None, "margin": 0}, |
| | np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 1, 2, 1, 0], [0, 0, 0, 0, 0]]]), |
| | np.array([[[1, 2, 1], [2, 3, 2], [1, 2, 1]]]), |
| | ] |
| |
|
| | TEST_CASE_2 = [ |
| | {"select_fn": lambda x: x > 1, "channel_indices": None, "margin": 0}, |
| | np.array([[[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 3, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]]]), |
| | np.array([[[3]]]), |
| | ] |
| |
|
| | TEST_CASE_3 = [ |
| | {"select_fn": lambda x: x > 0, "channel_indices": 0, "margin": 0}, |
| | np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 1, 2, 1, 0], [0, 0, 0, 0, 0]]]), |
| | np.array([[[1, 2, 1], [2, 3, 2], [1, 2, 1]]]), |
| | ] |
| |
|
| | TEST_CASE_4 = [ |
| | {"select_fn": lambda x: x > 0, "channel_indices": None, "margin": 1}, |
| | np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]), |
| | np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 0, 0, 0, 0]]]), |
| | ] |
| |
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|
| | class TestCropForeground(unittest.TestCase): |
| | @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4]) |
| | def test_value(self, argments, image, expected_data): |
| | result = CropForeground(**argments)(image) |
| | np.testing.assert_allclose(result, expected_data) |
| |
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|
| | if __name__ == "__main__": |
| | unittest.main() |
| |
|