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1
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import numpy as np |
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2
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from numpy.testing import assert_array_equal |
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3
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from torchio import DATA, AFFINE |
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4
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from torchio.transforms import Resample |
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5
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from torchio.utils import nib_to_sitk |
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6
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from ...utils import TorchioTestCase |
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7
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8
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9
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class TestResample(TorchioTestCase): |
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"""Tests for `Resample`.""" |
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11
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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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13
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spacing = 2 |
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14
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transform = Resample(spacing) |
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15
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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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18
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self.assertEqual(image.GetSpacing(), 3 * (spacing,)) |
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19
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20
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def test_reference(self): |
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21
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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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30
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31
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def test_affine(self): |
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32
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spacing = 1 |
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33
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affine_name = 'pre_affine' |
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transform = Resample(spacing, pre_affine_name=affine_name) |
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35
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transformed = transform(self.sample) |
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36
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for image_dict in transformed.values(): |
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37
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if affine_name in image_dict.keys(): |
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new_affine = np.eye(4) |
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39
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new_affine[0, 3] = 10 |
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40
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assert_array_equal(image_dict[AFFINE], new_affine) |
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else: |
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assert_array_equal(image_dict[AFFINE], np.eye(4)) |
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43
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44
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def test_missing_affine(self): |
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transform = Resample(1, pre_affine_name='missing') |
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with self.assertRaises(ValueError): |
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transform(self.sample) |
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48
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49
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def test_wrong_spacing_length(self): |
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50
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with self.assertRaises(ValueError): |
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51
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Resample((1, 2)) |
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52
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53
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def test_wrong_spacing_value(self): |
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54
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with self.assertRaises(ValueError): |
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55
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Resample(0) |
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56
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57
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def test_wrong_target_type(self): |
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58
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with self.assertRaises(ValueError): |
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59
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Resample(None) |
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60
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61
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def test_missing_reference(self): |
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62
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transform = Resample('missing') |
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63
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with self.assertRaises(ValueError): |
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transform(self.sample) |
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65
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fepegar /
torchio
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