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import copy |
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import shutil |
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3
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import random |
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4
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import tempfile |
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5
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import unittest |
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6
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from pathlib import Path |
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7
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8
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import torch |
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9
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import numpy as np |
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import nibabel as nib |
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from torchio.datasets import IXITiny |
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from torchio import INTENSITY, LABEL, DATA, Image, ImagesDataset, Subject |
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class TorchioTestCase(unittest.TestCase): |
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17
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def setUp(self): |
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"""Set up test fixtures, if any.""" |
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self.dir = Path(tempfile.gettempdir()) / '.torchio_tests' |
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self.dir.mkdir(exist_ok=True) |
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random.seed(42) |
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np.random.seed(42) |
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24
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registration_matrix = np.array([ |
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25
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[1, 0, 0, 10], |
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[0, 1, 0, 0], |
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[0, 0, 1.2, 0], |
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[0, 0, 0, 1] |
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]) |
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31
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subject_a = Subject( |
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t1=Image(self.get_image_path('t1_a'), INTENSITY), |
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) |
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subject_b = Subject( |
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t1=Image(self.get_image_path('t1_b'), INTENSITY), |
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label=Image(self.get_image_path('label_b', binary=True), LABEL), |
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) |
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subject_c = Subject( |
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label=Image(self.get_image_path('label_c', binary=True), LABEL), |
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40
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) |
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subject_d = Subject( |
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42
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t1=Image( |
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self.get_image_path('t1_d'), |
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INTENSITY, |
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pre_affine=registration_matrix, |
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), |
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t2=Image(self.get_image_path('t2_d'), INTENSITY), |
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label=Image(self.get_image_path('label_d', binary=True), LABEL), |
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) |
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self.subjects_list = [ |
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subject_a, |
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subject_b, |
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subject_c, |
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subject_d, |
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] |
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self.dataset = ImagesDataset(self.subjects_list) |
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self.sample = self.dataset[-1] |
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59
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def make_2d(self, sample): |
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sample = copy.deepcopy(sample) |
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for image in sample.get_images(intensity_only=False): |
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image[DATA] = image[DATA][:, 0:1, ...] |
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63
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return sample |
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64
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65
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def make_4d(self, sample): |
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sample = copy.deepcopy(sample) |
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for image in sample.get_images(intensity_only=False): |
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image[DATA] = torch.cat(4 * (image[DATA],)) |
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69
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return sample |
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71
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def get_inconsistent_sample(self): |
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"""Return a sample containing images of different shape.""" |
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subject = Subject( |
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t1=Image(self.get_image_path('t1_inc'), INTENSITY), |
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t2=Image( |
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self.get_image_path('t2_inc', shape=(10, 20, 31)), INTENSITY), |
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label=Image( |
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self.get_image_path( |
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'label_inc', |
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shape=(8, 17, 25), |
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binary=True, |
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), |
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LABEL, |
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), |
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label2=Image( |
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self.get_image_path( |
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'label2_inc', |
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shape=(18, 17, 25), |
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binary=True, |
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), |
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LABEL, |
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92
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), |
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) |
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subjects_list = [subject] |
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dataset = ImagesDataset(subjects_list) |
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return dataset[0] |
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98
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def get_reference_image_and_path(self): |
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"""Return a reference image and its path""" |
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path = self.get_image_path('ref', shape=(10, 20, 31), spacing=(1, 1, 2)) |
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image = Image(path, INTENSITY) |
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return image, path |
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103
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104
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def tearDown(self): |
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105
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"""Tear down test fixtures, if any.""" |
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print('Deleting', self.dir) |
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shutil.rmtree(self.dir) |
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108
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109
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def get_ixi_tiny(self): |
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root_dir = Path(tempfile.gettempdir()) / 'torchio' / 'ixi_tiny' |
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return IXITiny(root_dir, download=True) |
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113
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def get_image_path( |
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self, |
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stem, |
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binary=False, |
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shape=(10, 20, 30), |
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118
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spacing=(1, 1, 1), |
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): |
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120
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data = np.random.rand(*shape) |
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121
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if binary: |
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data = (data > 0.5).astype(np.uint8) |
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123
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affine = np.diag((*spacing, 1)) |
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124
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suffix = random.choice(('.nii.gz', '.nii', '.nrrd', '.img')) |
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path = self.dir / f'{stem}{suffix}' |
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if np.random.rand() > 0.5: |
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127
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path = str(path) |
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128
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image = Image(tensor=data, affine=affine) |
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image.save(path) |
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return path |
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131
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132
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def assertTensorNotEqual(self, a, b): |
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133
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if a.shape != b.shape: |
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return |
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135
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assert not torch.all(torch.eq(a, b)) |
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136
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fepegar /
torchio
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