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import copy |
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import os |
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import random |
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import shutil |
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import tempfile |
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import unittest |
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from collections.abc import Sequence |
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from pathlib import Path |
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from random import shuffle |
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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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class TorchioTestCase(unittest.TestCase): |
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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()) / os.urandom(24).hex() |
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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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registration_matrix = np.array( |
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[ |
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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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) |
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subject_a = tio.Subject( |
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t1=tio.ScalarImage(self.get_image_path('t1_a')), |
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) |
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subject_b = tio.Subject( |
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t1=tio.ScalarImage(self.get_image_path('t1_b')), |
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label=tio.LabelMap(self.get_image_path('label_b', binary=True)), |
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) |
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subject_c = tio.Subject( |
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label=tio.LabelMap(self.get_image_path('label_c', binary=True)), |
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) |
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subject_d = tio.Subject( |
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t1=tio.ScalarImage( |
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self.get_image_path('t1_d'), |
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pre_affine=registration_matrix, |
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), |
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t2=tio.ScalarImage(self.get_image_path('t2_d')), |
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label=tio.LabelMap(self.get_image_path('label_d', binary=True)), |
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) |
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subject_a4 = tio.Subject( |
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t1=tio.ScalarImage(self.get_image_path('t1_a'), components=4), |
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) |
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self.subjects_list = [ |
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subject_a, |
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subject_a4, |
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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 = tio.SubjectsDataset(self.subjects_list) |
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self.sample_subject = self.dataset[-1] # subject_d |
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self.subject_4d = self.dataset[1] |
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def make_2d(self, subject): |
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subject = copy.deepcopy(subject) |
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for image in subject.get_images(intensity_only=False): |
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image.set_data(image.data[..., :1]) |
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return subject |
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def make_multichannel(self, subject): |
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subject = copy.deepcopy(subject) |
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for image in subject.get_images(intensity_only=False): |
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image.set_data(torch.cat(4 * (image.data,))) |
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return subject |
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def flip_affine_x(self, subject): |
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subject = copy.deepcopy(subject) |
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for image in subject.get_images(intensity_only=False): |
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image.affine = np.diag((-1, 1, 1, 1)) @ image.affine |
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return subject |
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def get_inconsistent_shape_subject(self): |
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"""Return a subject containing images of different shape.""" |
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subject = tio.Subject( |
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t1=tio.ScalarImage(self.get_image_path('t1_inc')), |
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t2=tio.ScalarImage( |
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self.get_image_path('t2_inc', shape=(10, 20, 31)), |
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), |
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label=tio.LabelMap( |
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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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), |
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label2=tio.LabelMap( |
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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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), |
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) |
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return subject |
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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( |
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'ref', |
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shape=(10, 20, 31), |
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spacing=(1, 1, 2), |
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) |
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image = tio.ScalarImage(path) |
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return image, path |
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def get_subject_with_partial_volume_label_map(self, components=1): |
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"""Return a subject with a partial-volume label map.""" |
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return tio.Subject( |
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t1=tio.ScalarImage( |
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self.get_image_path('t1_d'), |
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), |
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label=tio.LabelMap( |
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self.get_image_path( |
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'label_d2', |
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binary=False, |
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components=components, |
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), |
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), |
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) |
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def get_subject_with_labels(self, labels): |
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return tio.Subject( |
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label=tio.LabelMap( |
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self.get_image_path( |
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'label_multi', |
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labels=labels, |
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), |
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), |
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) |
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@staticmethod |
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def get_unique_labels(data: torch.Tensor) -> set[int]: |
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labels = data.unique().tolist() |
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return set(labels) |
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@staticmethod |
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def get_tensor_with_labels(labels: Sequence) -> torch.Tensor: |
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tensor = torch.as_tensor(list(labels)) |
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return tensor.repeat_interleave(2).reshape(1, 1, 1, -1) |
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def tearDown(self): |
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"""Tear down test fixtures, if any.""" |
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shutil.rmtree(self.dir) |
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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 tio.datasets.IXITiny(root_dir, download=True) |
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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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labels=None, |
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shape=(10, 20, 30), |
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spacing=(1, 1, 1), |
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components=1, |
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add_nans=False, |
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suffix=None, |
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force_binary_foreground=True, |
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): |
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shape = (*shape, 1) if len(shape) == 2 else shape |
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data = np.random.rand(components, *shape) |
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if binary: |
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data = (data > 0.5).astype(np.uint8) |
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if not data.sum() and force_binary_foreground: |
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data[..., 0] = 1 |
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elif labels is not None: |
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data = (data * (len(labels) + 1)).astype(np.uint8) |
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new_data = np.zeros_like(data) |
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for i, label in enumerate(labels): |
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new_data[data == (i + 1)] = label |
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if not (new_data == label).sum(): |
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new_data[..., i] = label |
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data = new_data |
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elif self.flip_coin(): # cast some images |
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data *= 100 |
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dtype = np.uint8 if self.flip_coin() else np.uint16 |
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data = data.astype(dtype) |
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if add_nans: |
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data[:] = np.nan |
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affine = np.diag((*spacing, 1)) |
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if suffix is None: |
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extensions = '.nii.gz', '.nii', '.nrrd', '.img', '.mnc' |
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suffix = random.choice(extensions) |
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path = self.dir / f'{stem}{suffix}' |
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if self.flip_coin(): |
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path = str(path) |
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image = tio.ScalarImage( |
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tensor=data, |
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affine=affine, |
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check_nans=not add_nans, |
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) |
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image.save(path) |
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return path |
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def flip_coin(self): |
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return np.random.rand() > 0.5 |
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def get_tests_data_dir(self): |
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return Path(__file__).parent / 'image_data' |
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def assert_tensor_not_equal(self, *args, **kwargs): # noqa: N802 |
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with pytest.raises(AssertionError): |
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self.assert_tensor_equal(*args, **kwargs) |
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@staticmethod |
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def assert_tensor_equal(*args, **kwargs): # noqa: N802 |
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torch.testing.assert_close( |
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*args, |
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rtol=0, |
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atol=0, |
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check_dtype=False, |
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**kwargs, |
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) |
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@staticmethod |
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def assert_tensor_almost_equal(*args, **kwargs): # noqa: N802 |
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torch.testing.assert_close( |
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*args, |
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**kwargs, |
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check_dtype=False, |
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) |
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@staticmethod |
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def assert_tensor_all_zeros(tensor): # noqa: N802 |
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assert torch.all(tensor == 0) |
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241
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def get_large_composed_transform(self): |
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all_classes = get_all_random_transforms() |
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shuffle(all_classes) |
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transforms = [t() for t in all_classes] |
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# Hack as default patch size for RandomSwap is 15 and sample_subject |
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# is (10, 20, 30) |
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for tr in transforms: |
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if tr.name == 'RandomSwap': |
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tr.patch_size = np.array((10, 10, 10)) |
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return tio.Compose(transforms) |
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252
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253
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def get_all_random_transforms(): |
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transforms_names = [ |
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name for name in dir(tio.transforms) if name.startswith('Random') |
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] |
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classes = [getattr(tio.transforms, name) for name in transforms_names] |
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return classes |
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TorchIO-project /
torchio
