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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 torch |
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import numpy as np |
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from torchio import INTENSITY, LABEL, Image, ScalarImage, LabelMap, Subject |
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from ..utils import TorchioTestCase |
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from torchio import RandomFlip, RandomAffine |
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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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Image('nopath', type=INTENSITY) |
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def test_wrong_path_type(self): |
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with self.assertRaises(TypeError): |
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Image(5, type=INTENSITY) |
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def test_wrong_affine(self): |
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with self.assertRaises(TypeError): |
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Image(5, type=INTENSITY, 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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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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RandomAffine()(sample_input) |
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def test_crop_attributes(self): |
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cropped = self.sample.crop((1, 1, 1), (5, 5, 5)) |
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self.assertIs(self.sample.t1['pre_affine'], cropped.t1['pre_affine']) |
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def test_crop_does_not_create_wrong_path(self): |
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data = torch.ones((10, 10, 10)) |
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image = Image(tensor=data) |
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cropped = image.crop((1, 1, 1), (5, 5, 5)) |
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self.assertIs(cropped.path, None) |
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def test_scalar_image_type(self): |
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data = torch.ones((10, 10, 10)) |
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image = ScalarImage(tensor=data) |
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self.assertIs(image.type, INTENSITY) |
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def test_label_map_type(self): |
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data = torch.ones((10, 10, 10)) |
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label = LabelMap(tensor=data) |
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self.assertIs(label.type, LABEL) |
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def test_wrong_scalar_image_type(self): |
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data = torch.ones((10, 10, 10)) |
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with self.assertRaises(ValueError): |
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ScalarImage(tensor=data, type=LABEL) |
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def test_wrong_label_map_type(self): |
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data = torch.ones((10, 10, 10)) |
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with self.assertRaises(ValueError): |
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LabelMap(tensor=data, type=INTENSITY) |
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def test_crop_scalar_image_type(self): |
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data = torch.ones((10, 10, 10)) |
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image = ScalarImage(tensor=data) |
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cropped = image.crop((1, 1, 1), (5, 5, 5)) |
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self.assertIs(cropped.type, INTENSITY) |
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def test_crop_label_map_type(self): |
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data = torch.ones((10, 10, 10)) |
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label = LabelMap(tensor=data) |
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cropped = label.crop((1, 1, 1), (5, 5, 5)) |
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self.assertIs(cropped.type, LABEL) |
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def test_no_input(self): |
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with self.assertRaises(ValueError): |
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image = Image() |
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def test_bad_key(self): |
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with self.assertRaises(ValueError): |
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image = Image(path='', data=5) |
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def test_repr(self): |
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sample = Subject(t1=ScalarImage(self.get_image_path('repr_test'))) |
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assert 'shape' not in repr(sample['t1']) |
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sample.load() |
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assert 'shape' in repr(sample['t1']) |
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def test_data_tensor(self): |
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sample = copy.deepcopy(self.sample) |
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sample.load() |
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self.assertIs(sample.t1.data, sample.t1.tensor) |
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def test_bad_affine(self): |
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with self.assertRaises(ValueError): |
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Image(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(UserWarning): |
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image = Image(tensor=tensor) |
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image.set_check_nans(False) |
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def test_nans_file(self): |
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image = Image(self.get_image_path('repr_test', add_nans=True)) |
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with self.assertWarns(UserWarning): |
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image._load() |
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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 = Image(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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fepegar /
torchio
