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#!/usr/bin/env python |
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"""Tests for `utils` package.""" |
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import unittest |
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import torch |
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import numpy as np |
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import SimpleITK as sitk |
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from torchio import RandomFlip |
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from torchio.utils import ( |
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to_tuple, |
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get_stem, |
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guess_type, |
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nib_to_sitk, |
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sitk_to_nib, |
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apply_transform_to_file, |
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) |
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from .utils import TorchioTestCase |
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class TestUtils(TorchioTestCase): |
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"""Tests for `utils` module.""" |
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def test_to_tuple(self): |
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assert to_tuple(1) == (1,) |
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assert to_tuple((1,)) == (1,) |
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assert to_tuple(1, length=3) == (1, 1, 1) |
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assert to_tuple((1, 2)) == (1, 2) |
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assert to_tuple((1, 2), length=3) == (1, 2) |
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assert to_tuple([1, 2], length=3) == (1, 2) |
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def test_get_stem(self): |
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assert get_stem('/home/image.nii.gz') == 'image' |
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assert get_stem('/home/image.nii') == 'image' |
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assert get_stem('/home/image.nrrd') == 'image' |
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def test_guess_type(self): |
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assert guess_type('None') is None |
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assert isinstance(guess_type('1'), int) |
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assert isinstance(guess_type('1.5'), float) |
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assert isinstance(guess_type('(1, 3, 5)'), tuple) |
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assert isinstance(guess_type('(1,3,5)'), tuple) |
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assert isinstance(guess_type('[1,3,5]'), list) |
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assert isinstance(guess_type('test'), str) |
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def test_apply_transform_to_file(self): |
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transform = RandomFlip() |
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apply_transform_to_file( |
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self.get_image_path('input'), |
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transform, |
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self.get_image_path('output'), |
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verbose=True, |
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) |
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def test_sitk_to_nib(self): |
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data = np.random.rand(10, 12) |
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image = sitk.GetImageFromArray(data) |
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tensor, affine = sitk_to_nib(image) |
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self.assertAlmostEqual(data.sum(), tensor.sum()) |
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class TestNibabelToSimpleITK(TorchioTestCase): |
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def setUp(self): |
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super().setUp() |
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self.affine = np.eye(4) |
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def test_wrong_num_dims(self): |
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with self.assertRaises(ValueError): |
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nib_to_sitk(np.random.rand(10, 10), self.affine) |
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def test_2d_single(self): |
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data = np.random.rand(1, 10, 12, 1) |
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image = nib_to_sitk(data, self.affine) |
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assert image.GetDimension() == 2 |
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assert image.GetSize() == (10, 12) |
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assert image.GetNumberOfComponentsPerPixel() == 1 |
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def test_2d_multi(self): |
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data = np.random.rand(5, 10, 12, 1) |
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image = nib_to_sitk(data, self.affine) |
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assert image.GetDimension() == 2 |
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assert image.GetSize() == (10, 12) |
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assert image.GetNumberOfComponentsPerPixel() == 5 |
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def test_2d_3d_single(self): |
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data = np.random.rand(1, 10, 12, 1) |
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image = nib_to_sitk(data, self.affine, force_3d=True) |
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assert image.GetDimension() == 3 |
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assert image.GetSize() == (10, 12, 1) |
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assert image.GetNumberOfComponentsPerPixel() == 1 |
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def test_2d_3d_multi(self): |
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data = np.random.rand(5, 10, 12, 1) |
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image = nib_to_sitk(data, self.affine, force_3d=True) |
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assert image.GetDimension() == 3 |
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assert image.GetSize() == (10, 12, 1) |
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assert image.GetNumberOfComponentsPerPixel() == 5 |
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def test_3d_single(self): |
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data = np.random.rand(1, 8, 10, 12) |
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image = nib_to_sitk(data, self.affine) |
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assert image.GetDimension() == 3 |
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assert image.GetSize() == (8, 10, 12) |
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assert image.GetNumberOfComponentsPerPixel() == 1 |
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def test_3d_multi(self): |
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data = np.random.rand(5, 8, 10, 12) |
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image = nib_to_sitk(data, self.affine) |
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assert image.GetDimension() == 3 |
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assert image.GetSize() == (8, 10, 12) |
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assert image.GetNumberOfComponentsPerPixel() == 5 |
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fepegar /
torchio
