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import tempfile |
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import unittest |
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from pathlib import Path |
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import torch |
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import pytest |
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import numpy as np |
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from numpy.testing import assert_array_equal |
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import nibabel as nib |
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import SimpleITK as sitk |
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from ..utils import TorchioTestCase |
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from torchio.data import io, ScalarImage |
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class TestIO(TorchioTestCase): |
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"""Tests for `io` module.""" |
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def setUp(self): |
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super().setUp() |
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self.write_dicom() |
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string = ( |
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'1.5 0.18088 -0.124887 0.65072 ' |
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'-0.20025 0.965639 -0.165653 -11.6452 ' |
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'0.0906326 0.18661 0.978245 11.4002 ' |
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'0 0 0 1 ' |
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) |
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tensor = torch.from_numpy(np.fromstring(string, sep=' ').reshape(4, 4)) |
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self.matrix = tensor |
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def write_dicom(self): |
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self.dicom_dir = self.dir / 'dicom' |
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self.dicom_dir.mkdir(exist_ok=True) |
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self.dicom_path = self.dicom_dir / 'dicom.dcm' |
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self.nii_path = self.get_image_path('read_image') |
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writer = sitk.ImageFileWriter() |
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writer.SetFileName(str(self.dicom_path)) |
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image = sitk.ReadImage(str(self.nii_path)) |
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image = sitk.Cast(image, sitk.sitkUInt16) |
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image = image[0] # dicom reader supports 2D only |
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writer.Execute(image) |
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40
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def test_read_image(self): |
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# I need to find something readable by nib but not sitk |
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io.read_image(self.nii_path) |
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def test_save_rgb(self): |
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im = ScalarImage(tensor=torch.rand(1, 4, 5, 1)) |
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with self.assertWarns(UserWarning): |
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im.save(self.dir / 'test.jpg') |
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def test_read_dicom_file(self): |
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io.read_image(self.dicom_path) |
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def test_read_dicom_dir(self): |
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io.read_image(self.dicom_dir) |
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def test_dicom_dir_missing(self): |
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with self.assertRaises(FileNotFoundError): |
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io._read_dicom('missing') |
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59
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def test_dicom_dir_no_files(self): |
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empty = self.dir / 'empty' |
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empty.mkdir() |
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with self.assertRaises(FileNotFoundError): |
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io._read_dicom(empty) |
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65
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def write_read_matrix(self, suffix): |
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out_path = self.dir / f'matrix{suffix}' |
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io.write_matrix(self.matrix, out_path) |
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matrix = io.read_matrix(out_path) |
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assert torch.allclose(matrix, self.matrix) |
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70
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71
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def test_matrix_itk(self): |
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self.write_read_matrix('.tfm') |
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self.write_read_matrix('.h5') |
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75
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def test_matrix_txt(self): |
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self.write_read_matrix('.txt') |
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# This doesn't work as a method of the class |
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libs = 'sitk', 'nibabel' |
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parameters = [] |
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for save_lib in libs: |
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for load_lib in libs: |
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for dims in 2, 3, 4: |
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parameters.append((save_lib, load_lib, dims)) |
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86
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87
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88
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@pytest.mark.parametrize(('save_lib', 'load_lib', 'dims'), parameters) |
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89
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def test_write_nd_with_a_read_it_with_b(save_lib, load_lib, dims): |
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shape = [1, 4, 5, 6] |
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if dims == 2: |
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shape[-1] = 1 |
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elif dims == 4: |
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shape[0] = 2 |
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tensor = torch.randn(*shape) |
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affine = np.eye(4) |
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tempdir = Path(tempfile.gettempdir()) / '.torchio_tests' |
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tempdir.mkdir(exist_ok=True) |
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path = tempdir / 'test_io.nii' |
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save_function = getattr(io, f'_write_{save_lib}') |
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load_function = getattr(io, f'_read_{save_lib}') |
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save_function(tensor, affine, path) |
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loaded_tensor, loaded_affine = load_function(path) |
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assert_array_equal( |
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tensor.squeeze(), loaded_tensor.squeeze(), |
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f'Save lib: {save_lib}; load lib: {load_lib}; dims: {dims}' |
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) |
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assert_array_equal(affine, loaded_affine) |
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fepegar /
torchio
