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from numpy.testing import assert_array_equal |
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2
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from torchio import DATA, AFFINE |
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from torchio.transforms import Resample |
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from torchio.utils import nib_to_sitk |
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from ...utils import TorchioTestCase |
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class TestResample(TorchioTestCase): |
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"""Tests for `Resample`.""" |
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def test_spacing(self): |
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# Should this raise an error if sizes are different? |
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spacing = 2 |
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transform = Resample(spacing) |
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transformed = transform(self.sample) |
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for image_dict in transformed.values(): |
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image = nib_to_sitk(image_dict[DATA], image_dict[AFFINE]) |
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self.assertEqual(image.GetSpacing(), 3 * (spacing,)) |
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def test_reference(self): |
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sample = self.get_inconsistent_sample() |
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reference_name = 't1' |
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transform = Resample(reference_name) |
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transformed = transform(sample) |
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ref_image_dict = sample[reference_name] |
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for image_dict in transformed.values(): |
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self.assertEqual( |
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ref_image_dict[DATA].shape, image_dict[DATA].shape) |
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assert_array_equal(ref_image_dict[AFFINE], image_dict[AFFINE]) |
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def test_wrong_spacing_length(self): |
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with self.assertRaises(ValueError): |
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Resample((1, 2)) |
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def test_wrong_spacing_value(self): |
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with self.assertRaises(ValueError): |
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Resample(0) |
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def test_wrong_target_type(self): |
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with self.assertRaises(ValueError): |
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Resample(None) |
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def test_missing_reference(self): |
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transform = Resample('missing') |
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with self.assertRaises(ValueError): |
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transform(self.sample) |
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fepegar /
torchio
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