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#!/usr/bin/env python |
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"""Tests for Image.""" |
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import copy |
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import tempfile |
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import torch |
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import numpy as np |
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import nibabel as nib |
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import torchio as tio |
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from ..utils import TorchioTestCase |
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class TestImage(TorchioTestCase): |
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"""Tests for `Image`.""" |
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def test_image_not_found(self): |
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with self.assertRaises(FileNotFoundError): |
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tio.ScalarImage('nopath') |
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def test_wrong_path_value(self): |
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with self.assertRaises(RuntimeError): |
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tio.ScalarImage('~&./@#"!?X7=+') |
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def test_wrong_path_type(self): |
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with self.assertRaises(TypeError): |
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tio.ScalarImage(5) |
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def test_wrong_affine(self): |
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with self.assertRaises(TypeError): |
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tio.ScalarImage(5, affine=1) |
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def test_tensor_flip(self): |
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sample_input = torch.ones((4, 30, 30, 30)) |
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tio.RandomFlip()(sample_input) |
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def test_tensor_affine(self): |
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sample_input = torch.ones((4, 10, 10, 10)) |
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tio.RandomAffine()(sample_input) |
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def test_wrong_scalar_image_type(self): |
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data = torch.ones((1, 10, 10, 10)) |
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with self.assertRaises(ValueError): |
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tio.ScalarImage(tensor=data, type=tio.LABEL) |
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def test_wrong_label_map_type(self): |
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data = torch.ones((1, 10, 10, 10)) |
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with self.assertRaises(ValueError): |
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tio.LabelMap(tensor=data, type=tio.INTENSITY) |
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def test_no_input(self): |
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with self.assertRaises(ValueError): |
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tio.ScalarImage() |
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def test_bad_key(self): |
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with self.assertRaises(ValueError): |
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tio.ScalarImage(path='', data=5) |
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def test_repr(self): |
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subject = tio.Subject( |
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t1=tio.ScalarImage(self.get_image_path('repr_test')), |
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) |
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assert 'memory' not in repr(subject['t1']) |
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subject.load() |
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assert 'memory' in repr(subject['t1']) |
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def test_data_tensor(self): |
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subject = copy.deepcopy(self.sample_subject) |
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subject.load() |
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self.assertIs(subject.t1.data, subject.t1.tensor) |
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def test_bad_affine(self): |
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with self.assertRaises(ValueError): |
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tio.ScalarImage(tensor=torch.rand(1, 2, 3, 4), affine=np.eye(3)) |
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def test_nans_tensor(self): |
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tensor = np.random.rand(1, 2, 3, 4) |
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tensor[0, 0, 0, 0] = np.nan |
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with self.assertWarns(RuntimeWarning): |
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image = tio.ScalarImage(tensor=tensor, check_nans=True) |
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image.set_check_nans(False) |
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def test_get_center(self): |
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tensor = torch.rand(1, 3, 3, 3) |
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image = tio.ScalarImage(tensor=tensor) |
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ras = image.get_center() |
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lps = image.get_center(lps=True) |
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self.assertEqual(ras, (1, 1, 1)) |
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self.assertEqual(lps, (-1, -1, 1)) |
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def test_with_list_of_missing_files(self): |
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with self.assertRaises(FileNotFoundError): |
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tio.ScalarImage(path=['nopath', 'error']) |
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def test_with_a_list_of_paths(self): |
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shape = (5, 5, 5) |
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path1 = self.get_image_path('path1', shape=shape) |
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path2 = self.get_image_path('path2', shape=shape) |
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image = tio.ScalarImage(path=[path1, path2]) |
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self.assertEqual(image.shape, (2, 5, 5, 5)) |
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self.assertEqual(image[tio.STEM], ['path1', 'path2']) |
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def test_with_a_list_of_images_with_different_shapes(self): |
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path1 = self.get_image_path('path1', shape=(5, 5, 5)) |
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path2 = self.get_image_path('path2', shape=(7, 5, 5)) |
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image = tio.ScalarImage(path=[path1, path2]) |
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with self.assertRaises(RuntimeError): |
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image.load() |
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def test_with_a_list_of_images_with_different_affines(self): |
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path1 = self.get_image_path('path1', spacing=(1, 1, 1)) |
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path2 = self.get_image_path('path2', spacing=(1, 2, 1)) |
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image = tio.ScalarImage(path=[path1, path2]) |
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with self.assertWarns(RuntimeWarning): |
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image.load() |
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def test_with_a_list_of_2d_paths(self): |
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shape = (5, 6) |
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path1 = self.get_image_path('path1', shape=shape, suffix='.nii') |
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path2 = self.get_image_path('path2', shape=shape, suffix='.img') |
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path3 = self.get_image_path('path3', shape=shape, suffix='.hdr') |
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image = tio.ScalarImage(path=[path1, path2, path3]) |
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self.assertEqual(image.shape, (3, 5, 6, 1)) |
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self.assertEqual(image[tio.STEM], ['path1', 'path2', 'path3']) |
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def test_axis_name_2d(self): |
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path = self.get_image_path('im2d', shape=(5, 6)) |
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image = tio.ScalarImage(path) |
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height_idx = image.axis_name_to_index('t') |
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width_idx = image.axis_name_to_index('l') |
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self.assertEqual(image.height, image.shape[height_idx]) |
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self.assertEqual(image.width, image.shape[width_idx]) |
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def test_plot(self): |
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image = self.sample_subject.t1 |
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image.plot(show=False, output_path=self.dir / 'image.png') |
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def test_data_type_uint16_array(self): |
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tensor = np.random.rand(1, 3, 3, 3).astype(np.uint16) |
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image = tio.ScalarImage(tensor=tensor) |
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self.assertEqual(image.data.dtype, torch.int32) |
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def test_data_type_uint32_array(self): |
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tensor = np.random.rand(1, 3, 3, 3).astype(np.uint32) |
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image = tio.ScalarImage(tensor=tensor) |
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self.assertEqual(image.data.dtype, torch.int64) |
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def test_save_image_with_data_type_boolean(self): |
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tensor = np.random.rand(1, 3, 3, 3).astype(bool) |
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image = tio.ScalarImage(tensor=tensor) |
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image.save(self.dir / 'image.nii') |
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def test_load_uint(self): |
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affine = np.eye(4) |
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for dtype in np.uint16, np.uint32: |
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data = np.ones((3, 3, 3), dtype=dtype) |
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img = nib.Nifti1Image(data, affine) |
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with tempfile.NamedTemporaryFile(suffix='.nii') as f: |
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nib.save(img, f.name) |
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tio.ScalarImage(f.name).load() |
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def test_pil_3d(self): |
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with self.assertRaises(RuntimeError): |
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tio.ScalarImage(tensor=torch.rand(1, 2, 3, 4)).as_pil() |
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def test_pil_1(self): |
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tio.ScalarImage(tensor=torch.rand(1, 2, 3, 1)).as_pil() |
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def test_pil_2(self): |
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with self.assertRaises(RuntimeError): |
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tio.ScalarImage(tensor=torch.rand(2, 2, 3, 1)).as_pil() |
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def test_pil_3(self): |
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tio.ScalarImage(tensor=torch.rand(3, 2, 3, 1)).as_pil() |
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def test_set_data(self): |
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with self.assertWarns(DeprecationWarning): |
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im = self.sample_subject.t1 |
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im.data = im.data |
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def test_no_type(self): |
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with self.assertWarns(UserWarning): |
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tio.Image(tensor=torch.rand(1, 2, 3, 4)) |
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def test_custom_reader(self): |
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path = self.dir / 'im.npy' |
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def numpy_reader(path): |
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return np.load(path), np.eye(4) |
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def assert_shape(shape_in, shape_out): |
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np.save(path, np.random.rand(*shape_in)) |
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image = tio.ScalarImage(path, reader=numpy_reader) |
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assert image.shape == shape_out |
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assert_shape((5, 5), (1, 5, 5, 1)) |
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assert_shape((5, 5, 3), (3, 5, 5, 1)) |
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assert_shape((3, 5, 5), (3, 5, 5, 1)) |
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assert_shape((5, 5, 5), (1, 5, 5, 5)) |
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assert_shape((1, 5, 5, 5), (1, 5, 5, 5)) |
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assert_shape((4, 5, 5, 5), (4, 5, 5, 5)) |
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def test_fast_gif(self): |
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with self.assertWarns(UserWarning): |
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with tempfile.NamedTemporaryFile(suffix='.gif') as f: |
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self.sample_subject.t1.to_gif(0, 0.0001, f.name) |
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def test_gif_rgb(self): |
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with tempfile.NamedTemporaryFile(suffix='.gif') as f: |
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tio.ScalarImage(tensor=torch.rand(3, 4, 5, 6)).to_gif(0, 1, f.name) |
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fepegar /
torchio
