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import numpy as np |
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import pytest |
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import torch |
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import torchio as tio |
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from ...utils import TorchioTestCase |
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9
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10
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class TestResample(TorchioTestCase): |
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"""Tests for `Resample`.""" |
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13
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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 = tio.Resample(spacing) |
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transformed = transform(self.sample_subject) |
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for image in transformed.get_images(intensity_only=False): |
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assert image.spacing == 3 * (spacing,) |
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def test_reference_name(self): |
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subject = self.get_inconsistent_shape_subject() |
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reference_name = 't1' |
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transform = tio.Resample(reference_name) |
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transformed = transform(subject) |
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reference_image = subject[reference_name] |
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for image in transformed.get_images(intensity_only=False): |
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assert reference_image.shape == image.shape |
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self.assert_tensor_almost_equal( |
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reference_image.affine, |
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image.affine, |
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) |
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34
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def test_affine(self): |
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spacing = 1 |
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affine_name = 'pre_affine' |
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transform = tio.Resample(spacing, pre_affine_name=affine_name) |
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transformed = transform(self.sample_subject) |
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for image in transformed.values(): |
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if affine_name in image: |
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target_affine = np.eye(4) |
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target_affine[:3, 3] = 10, 0, -0.1 |
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self.assert_tensor_almost_equal(image.affine, target_affine) |
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else: |
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self.assert_tensor_equal(image.affine, np.eye(4)) |
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def test_missing_affine(self): |
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transform = tio.Resample(1, pre_affine_name='missing') |
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with pytest.raises(ValueError): |
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transform(self.sample_subject) |
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52
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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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transform = tio.Resample(reference_path) |
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transformed = transform(self.sample_subject) |
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for image in transformed.values(): |
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assert reference_image.shape == image.shape |
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self.assert_tensor_almost_equal( |
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reference_image.affine, |
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image.affine, |
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) |
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63
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def test_wrong_spacing_length(self): |
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with pytest.raises(RuntimeError): |
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tio.Resample((1, 2))(self.sample_subject) |
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67
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def test_wrong_spacing_value(self): |
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with pytest.raises(ValueError): |
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tio.Resample(0)(self.sample_subject) |
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71
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def test_wrong_target_type(self): |
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with pytest.raises(RuntimeError): |
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tio.Resample(None)(self.sample_subject) |
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75
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def test_missing_reference(self): |
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transform = tio.Resample('missing') |
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with pytest.raises(ValueError): |
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transform(self.sample_subject) |
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80
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def test_2d(self): |
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"""Check that image is still 2D after resampling.""" |
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image = tio.ScalarImage(tensor=torch.rand(1, 2, 3, 1)) |
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transform = tio.Resample(0.5) |
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shape = transform(image).shape |
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assert shape == (1, 4, 6, 1) |
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87
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def test_input_list(self): |
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tio.Resample([1, 2, 3])(self.sample_subject) |
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90
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def test_input_array(self): |
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resample = tio.Resample(np.asarray([1, 2, 3])) |
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resample(self.sample_subject) |
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94
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def test_image_target(self): |
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tio.Resample(self.sample_subject.t1)(self.sample_subject) |
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97
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def test_bad_affine(self): |
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shape = 1, 2, 3 |
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affine = np.eye(3) |
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target = shape, affine |
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transform = tio.Resample(target) |
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with pytest.raises(RuntimeError): |
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transform(self.sample_subject) |
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104
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105
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def test_resample_flip(self): |
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image = torch.rand(1, 10, 10, 10) |
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resample = tio.Resample(1.35) |
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flip = tio.Flip(0) |
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flipped_and_resampled = resample(flip(image)) |
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resampled_and_flipped = flip(resample(image)) |
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self.assert_tensor_almost_equal( |
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flipped_and_resampled.data, |
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resampled_and_flipped.data, |
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) |
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115
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TorchIO-project /
torchio
