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import torch |
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import torchio as tio |
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3
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import numpy as np |
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4
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from ...utils import TorchioTestCase |
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5
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6
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7
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class TestRescaleIntensity(TorchioTestCase): |
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8
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"""Tests for :class:`tio.RescaleIntensity` class.""" |
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9
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10
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def test_rescale_to_same_intentisy(self): |
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11
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min_t1 = float(self.sample_subject.t1.data.min()) |
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12
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max_t1 = float(self.sample_subject.t1.data.max()) |
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13
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transform = tio.RescaleIntensity(out_min_max=(min_t1, max_t1)) |
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transformed = transform(self.sample_subject) |
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assert np.allclose( |
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transformed.t1.data, |
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self.sample_subject.t1.data, |
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rtol=0, |
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atol=1e-05, |
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20
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) |
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21
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22
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def test_min_max(self): |
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transform = tio.RescaleIntensity(out_min_max=(0, 1)) |
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transformed = transform(self.sample_subject) |
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self.assertEqual(transformed.t1.data.min(), 0) |
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self.assertEqual(transformed.t1.data.max(), 1) |
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27
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28
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def test_percentiles(self): |
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low_quantile = np.percentile(self.sample_subject.t1.data, 5) |
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high_quantile = np.percentile(self.sample_subject.t1.data, 95) |
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low_indices = (self.sample_subject.t1.data < low_quantile).nonzero( |
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32
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as_tuple=True) |
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high_indices = (self.sample_subject.t1.data > high_quantile).nonzero( |
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as_tuple=True) |
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rescale = tio.RescaleIntensity(out_min_max=(0, 1), percentiles=(5, 95)) |
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transformed = rescale(self.sample_subject) |
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assert (transformed.t1.data[low_indices] == 0).all() |
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assert (transformed.t1.data[high_indices] == 1).all() |
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39
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40
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def test_masking_using_label(self): |
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transform = tio.RescaleIntensity( |
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out_min_max=(0, 1), percentiles=(5, 95), masking_method='label') |
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transformed = transform(self.sample_subject) |
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mask = self.sample_subject.label.data > 0 |
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low_quantile = np.percentile(self.sample_subject.t1.data[mask], 5) |
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high_quantile = np.percentile(self.sample_subject.t1.data[mask], 95) |
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low_indices = (self.sample_subject.t1.data < low_quantile).nonzero( |
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as_tuple=True) |
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high_indices = (self.sample_subject.t1.data > high_quantile).nonzero( |
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as_tuple=True) |
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self.assertEqual(transformed.t1.data.min(), 0) |
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self.assertEqual(transformed.t1.data.max(), 1) |
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assert (transformed.t1.data[low_indices] == 0).all() |
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assert (transformed.t1.data[high_indices] == 1).all() |
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56
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def test_ct(self): |
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ct_max = 1500 |
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ct_min = -2000 |
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ct_range = ct_max - ct_min |
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tensor = torch.rand(1, 30, 30, 30) * ct_range + ct_min |
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ct = tio.ScalarImage(tensor=tensor) |
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ct_air = -1000 |
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ct_bone = 1000 |
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rescale = tio.RescaleIntensity( |
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out_min_max=(-1, 1), |
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in_min_max=(ct_air, ct_bone), |
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) |
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rescaled = rescale(ct) |
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assert rescaled.data.min() < -1 |
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assert rescaled.data.max() > 1 |
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72
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def test_out_min_higher_than_out_max(self): |
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with self.assertRaises(ValueError): |
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tio.RescaleIntensity(out_min_max=(1, 0)) |
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76
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def test_too_many_values_for_out_min_max(self): |
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with self.assertRaises(ValueError): |
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tio.RescaleIntensity(out_min_max=(1, 2, 3)) |
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80
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def test_wrong_out_min_max_type(self): |
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with self.assertRaises(ValueError): |
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tio.RescaleIntensity(out_min_max='wrong') |
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84
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def test_min_percentile_higher_than_max_percentile(self): |
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with self.assertRaises(ValueError): |
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86
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tio.RescaleIntensity(out_min_max=(0, 1), percentiles=(1, 0)) |
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87
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88
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def test_too_many_values_for_percentiles(self): |
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89
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with self.assertRaises(ValueError): |
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90
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tio.RescaleIntensity(out_min_max=(0, 1), percentiles=(1, 2, 3)) |
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91
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92
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def test_wrong_percentiles_type(self): |
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93
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with self.assertRaises(ValueError): |
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tio.RescaleIntensity(out_min_max=(0, 1), percentiles='wrong') |
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95
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fepegar /
torchio
