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import torch |
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2
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import numpy as np |
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3
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import torchio as tio |
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4
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from ...utils import TorchioTestCase |
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5
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6
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7
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class TestResample(TorchioTestCase): |
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"""Tests for `Resample`.""" |
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9
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def test_spacing(self): |
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10
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# Should this raise an error if sizes are different? |
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11
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spacing = 2 |
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12
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transform = tio.Resample(spacing) |
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13
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transformed = transform(self.sample_subject) |
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14
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for image in transformed.get_images(intensity_only=False): |
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15
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self.assertEqual(image.spacing, 3 * (spacing,)) |
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16
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17
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def test_reference_name(self): |
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18
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subject = self.get_inconsistent_shape_subject() |
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19
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reference_name = 't1' |
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20
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transform = tio.Resample(reference_name) |
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21
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transformed = transform(subject) |
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reference_image = subject[reference_name] |
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23
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for image in transformed.get_images(intensity_only=False): |
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24
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self.assertEqual(reference_image.shape, image.shape) |
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25
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self.assertTensorAlmostEqual(reference_image.affine, image.affine) |
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26
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27
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def test_affine(self): |
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spacing = 1 |
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29
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affine_name = 'pre_affine' |
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30
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transform = tio.Resample(spacing, pre_affine_name=affine_name) |
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31
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transformed = transform(self.sample_subject) |
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32
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for image in transformed.values(): |
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33
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if affine_name in image: |
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34
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target_affine = np.eye(4) |
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35
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target_affine[:3, 3] = 10, 0, -0.1 |
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self.assertTensorAlmostEqual(image.affine, target_affine) |
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else: |
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38
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self.assertTensorEqual(image.affine, np.eye(4)) |
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39
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40
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def test_missing_affine(self): |
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41
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transform = tio.Resample(1, pre_affine_name='missing') |
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42
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with self.assertRaises(ValueError): |
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transform(self.sample_subject) |
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45
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def test_reference_path(self): |
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reference_image, reference_path = self.get_reference_image_and_path() |
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47
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transform = tio.Resample(reference_path) |
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48
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transformed = transform(self.sample_subject) |
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49
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for image in transformed.values(): |
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self.assertEqual(reference_image.shape, image.shape) |
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51
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self.assertTensorAlmostEqual(reference_image.affine, image.affine) |
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52
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53
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def test_wrong_spacing_length(self): |
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54
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with self.assertRaises(RuntimeError): |
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tio.Resample((1, 2))(self.sample_subject) |
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56
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57
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def test_wrong_spacing_value(self): |
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58
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with self.assertRaises(ValueError): |
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59
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tio.Resample(0)(self.sample_subject) |
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60
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61
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def test_wrong_target_type(self): |
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62
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with self.assertRaises(RuntimeError): |
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63
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tio.Resample(None)(self.sample_subject) |
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64
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65
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def test_missing_reference(self): |
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66
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transform = tio.Resample('missing') |
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with self.assertRaises(ValueError): |
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transform(self.sample_subject) |
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69
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70
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def test_2d(self): |
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71
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image = tio.ScalarImage(tensor=torch.rand(1, 2, 3, 1)) |
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72
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transform = tio.Resample(0.5) |
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73
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shape = transform(image).shape |
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74
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self.assertEqual(shape, (1, 4, 6, 1)) |
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75
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76
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def test_input_list(self): |
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77
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tio.Resample([1, 2, 3]) |
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78
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79
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def test_image_target(self): |
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80
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tio.Resample(self.sample_subject.t1)(self.sample_subject) |
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81
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82
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def test_bad_affine(self): |
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83
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shape = 1, 2, 3 |
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84
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affine = np.eye(3) |
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85
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target = shape, affine |
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86
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transform = tio.Resample(target) |
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87
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with self.assertRaises(RuntimeError): |
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88
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transform(self.sample_subject) |
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89
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fepegar /
torchio
