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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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5
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6
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class TestMask(TorchioTestCase): |
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"""Tests for :class:`tio.Mask` class.""" |
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def test_single_mask(self): |
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negated_mask = self.sample_subject.label.data.logical_not() |
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masked_voxel_indices = negated_mask.nonzero(as_tuple=True) |
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transform = tio.Mask(masking_method='label') |
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transformed = transform(self.sample_subject) |
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assert (transformed.t1.data[masked_voxel_indices] == 0).all() |
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def test_single_mask_nonzero_background(self): |
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background_value = 314159 |
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negated_mask = self.sample_subject.label.data.logical_not() |
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masked_voxel_indices = negated_mask.nonzero(as_tuple=True) |
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transform = tio.Mask(masking_method='label', |
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outside_value=background_value) |
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transformed = transform(self.sample_subject) |
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assert (transformed.t1.data[masked_voxel_indices] |
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== background_value).all() |
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def test_mask_specified_label(self): |
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mask_label = [1] |
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negated_mask = self.sample_subject.label.data.logical_not() |
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masked_voxel_indices = negated_mask.nonzero(as_tuple=True) |
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transform = tio.Mask(masking_method='label', labels=mask_label) |
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transformed = transform(self.sample_subject) |
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assert (transformed.t1.data[masked_voxel_indices] == 0).all() |
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def test_mask_specified_label_small(self): |
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40
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def to_image(*numbers): |
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return torch.as_tensor(numbers).reshape(1, 1, 1, len(numbers)) |
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image_tensor = to_image(1, 6, 7, 3, 0) |
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label_tensor = to_image(0, 1, 2, 3, 4) |
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mask_labels = [1, 2] |
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subject = tio.Subject( |
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image=tio.ScalarImage(tensor=image_tensor), |
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label=tio.LabelMap(tensor=label_tensor), |
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) |
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transform = tio.Mask(masking_method='label', labels=mask_labels) |
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transformed = transform(subject) |
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masked_list = transformed.image.data.flatten().tolist() |
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assert masked_list == [0, 6, 7, 0, 0] |
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55
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def test_mask_example(self): |
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subject = tio.datasets.Colin27() |
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negated_mask = subject.brain.data.logical_not() |
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masked_voxel_indices = negated_mask.nonzero(as_tuple=True) |
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60
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transform = tio.Mask(masking_method='brain') |
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transformed = transform(subject) |
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assert (transformed.t1.data[masked_voxel_indices] == 0).all() |
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fepegar /
torchio
