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| import unittest |
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| import numpy as np |
| from parameterized import parameterized |
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| from monai.transforms import CropForegroundd |
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| TEST_CASE_1 = [ |
| { |
| "keys": ["img", "label"], |
| "source_key": "label", |
| "select_fn": lambda x: x > 0, |
| "channel_indices": None, |
| "margin": 0, |
| }, |
| { |
| "img": np.array([[[1, 0, 2, 0, 1], [0, 1, 2, 1, 0], [2, 2, 3, 2, 2], [0, 1, 2, 1, 0], [1, 0, 2, 0, 1]]]), |
| "label": np.array([[[0, 0, 0, 0, 0], [0, 1, 0, 1, 0], [0, 0, 1, 0, 0], [0, 1, 0, 1, 0], [0, 0, 0, 0, 0]]]), |
| }, |
| np.array([[[1, 2, 1], [2, 3, 2], [1, 2, 1]]]), |
| ] |
|
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| TEST_CASE_2 = [ |
| {"keys": ["img"], "source_key": "img", "select_fn": lambda x: x > 1, "channel_indices": None, "margin": 0}, |
| {"img": 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]]]), |
| ] |
|
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| TEST_CASE_3 = [ |
| {"keys": ["img"], "source_key": "img", "select_fn": lambda x: x > 0, "channel_indices": 0, "margin": 0}, |
| {"img": 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]]]), |
| ] |
|
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| TEST_CASE_4 = [ |
| {"keys": ["img"], "source_key": "img", "select_fn": lambda x: x > 0, "channel_indices": None, "margin": 1}, |
| {"img": 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 TestCropForegroundd(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 = CropForegroundd(**argments)(image) |
| np.testing.assert_allclose(result["img"], expected_data) |
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|
|
| if __name__ == "__main__": |
| unittest.main() |
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|