torchio.transforms.augmentation.spatial.random_affine.Affine.__init__() - Code Metrics - Inspection of "Add some deterministic transforms" - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#353)

by Fernando

created 2020-11-21 18:05 UTC

Affine.init() B

↳ Parent: torchio.transforms.augmentation.spatial.random_affine

Complexity

Conditions

Size

Total Lines	48
Code Lines	42

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	2
eloc	42
nop	8
dl	0
loc	48
rs	8.872
c	0
b	0
f	0

How to fix Many Parameters

import copy
from numbers import Number
from typing import Tuple, Optional, Sequence, Union

import torch
import numpy as np
import SimpleITK as sitk

from ....data.subject import Subject
from ....utils import nib_to_sitk, get_major_sitk_version, to_tuple
from ....torchio import (
    INTENSITY,
    DATA,
    AFFINE,
    TYPE,
    TypeRangeFloat,
    TypeSextetFloat,
    TypeTripletFloat,
)
from ... import SpatialTransform
from .. import RandomTransform


TypeOneToSixFloat = Union[TypeRangeFloat, TypeTripletFloat, TypeSextetFloat]


class RandomAffine(RandomTransform, SpatialTransform):
    r"""Random affine transformation.

    Args:
        scales: Tuple :math:`(a_1, b_1, a_2, b_2, a_3, b_3)` defining the
            scaling ranges.
            The scaling values along each dimension are :math:`(s_1, s_2, s_3)`,
            where :math:`s_i \sim \mathcal{U}(a_i, b_i)`.
            If two values :math:`(a, b)` are provided,
            then :math:`s_i \sim \mathcal{U}(a, b)`.
            If only one value :math:`x` is provided,
            then :math:`s_i \sim \mathcal{U}(1 - x, 1 + x)`.
            If three values :math:`(x_1, x_2, x_3)` are provided,
            then :math:`s_i \sim \mathcal{U}(1 - x_i, 1 + x_i)`.
            For example, using ``scales=(0.5, 0.5)`` will zoom out the image,
            making the objects inside look twice as small while preserving
            the physical size and position of the image bounds.
        degrees: Tuple :math:`(a_1, b_1, a_2, b_2, a_3, b_3)` defining the
            rotation ranges in degrees.
            Rotation angles around each axis are
            :math:`(\theta_1, \theta_2, \theta_3)`,
            where :math:`\theta_i \sim \mathcal{U}(a_i, b_i)`.
            If two values :math:`(a, b)` are provided,
            then :math:`\theta_i \sim \mathcal{U}(a, b)`.
            If only one value :math:`x` is provided,
            then :math:`\theta_i \sim \mathcal{U}(-x, x)`.
            If three values :math:`(x_1, x_2, x_3)` are provided,
            then :math:`\theta_i \sim \mathcal{U}(-x_i, x_i)`.
        translation: Tuple :math:`(a_1, b_1, a_2, b_2, a_3, b_3)` defining the
            translation ranges in mm.
            Translation along each axis is :math:`(t_1, t_2, t_3)`,
            where :math:`t_i \sim \mathcal{U}(a_i, b_i)`.
            If two values :math:`(a, b)` are provided,
            then :math:`t_i \sim \mathcal{U}(a, b)`.
            If only one value :math:`x` is provided,
            then :math:`t_i \sim \mathcal{U}(-x, x)`.
            If three values :math:`(x_1, x_2, x_3)` are provided,
            then :math:`t_i \sim \mathcal{U}(-x_i, x_i)`.
        isotropic: If ``True``, the scaling factor along all dimensions is the
            same, i.e. :math:`s_1 = s_2 = s_3`.
        center: If ``'image'``, rotations and scaling will be performed around
            the image center. If ``'origin'``, rotations and scaling will be
            performed around the origin in world coordinates.
        default_pad_value: As the image is rotated, some values near the
            borders will be undefined.
            If ``'minimum'``, the fill value will be the image minimum.
            If ``'mean'``, the fill value is the mean of the border values.
            If ``'otsu'``, the fill value is the mean of the values at the
            border that lie under an
            `Otsu threshold <https://ieeexplore.ieee.org/document/4310076>`_.
            If it is a number, that value will be used.
        image_interpolation: See :ref:`Interpolation`.
        p: Probability that this transform will be applied.
        keys: See :py:class:`~torchio.transforms.Transform`.

    Example:
        >>> import torchio as tio
        >>> subject = tio.datasets.Colin27()
        >>> transform = tio.RandomAffine(
        ...     scales=(0.9, 1.2),
        ...     degrees=10,
        ...     isotropic=True,
        ...     image_interpolation='nearest',
        ... )
        >>> transformed = transform(subject)

    From the command line::

        $ torchio-transform t1.nii.gz RandomAffine --kwargs "scales=(0.9, 1.2) degrees=10 isotropic=True image_interpolation=nearest" --seed 42 affine_min.nii.gz

    """
    def __init__(
            self,
            scales: TypeOneToSixFloat = 0.1,
            degrees: TypeOneToSixFloat = 10,
            translation: TypeOneToSixFloat = 0,
            isotropic: bool = False,
            center: str = 'image',
            default_pad_value: Union[str, float] = 'minimum',
            image_interpolation: str = 'linear',
            p: float = 1,
            keys: Optional[Sequence[str]] = None,
            ):
        super().__init__(p=p, keys=keys)
        self.isotropic = isotropic
        _parse_scales_isotropic(scales, isotropic)
        self.scales = self.parse_params(scales, 1, 'scales', min_constraint=0)
        self.degrees = self.parse_params(degrees, 0, 'degrees')
        self.translation = self.parse_params(translation, 0, 'translation')
        if center not in ('image', 'origin'):
            message = (
                'Center argument must be "image" or "origin",'
                f' not "{center}"'
            )
            raise ValueError(message)
        self.center = center
        self.default_pad_value = _parse_default_value(default_pad_value)
        self.image_interpolation = self.parse_interpolation(image_interpolation)

    def get_params(
            self,
            scales: TypeSextetFloat,
            degrees: TypeSextetFloat,
            translation: TypeSextetFloat,
            isotropic: bool,
            ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        scaling_params = self.sample_uniform_sextet(scales)
        if isotropic:
            scaling_params.fill_(scaling_params[0])
        rotation_params = self.sample_uniform_sextet(degrees)
        translation_params = self.sample_uniform_sextet(translation)
        return scaling_params, rotation_params, translation_params

    def apply_transform(self, subject: Subject) -> Subject:
        subject.check_consistent_spatial_shape()
        scaling_params, rotation_params, translation_params = self.get_params(
            self.scales,
            self.degrees,
            self.translation,
            self.isotropic,
        )
        arguments = dict(
            scales=scaling_params.tolist(),
            degrees=rotation_params.tolist(),
            translation=translation_params.tolist(),
            center=self.center,
            default_pad_value=self.default_pad_value,
            image_interpolation=self.image_interpolation,
        )
        transform = Affine(**arguments)
        transformed = transform(subject)
        return transformed


class Affine(SpatialTransform):
    r"""Apply affine transformation.

    Args:
        scales: Tuple :math:`(s_1, s_2, s_3)` defining the
            scaling values along each dimension.
        degrees: Tuple :math:`(\theta_1, \theta_2, \theta_3)` defining the
            rotation around each axis.
        translation: Tuple :math:`(t_1, t_2, t_3)` defining the
            translation in mm along each axis.
        center: If ``'image'``, rotations and scaling will be performed around
            the image center. If ``'origin'``, rotations and scaling will be
            performed around the origin in world coordinates.
        default_pad_value: As the image is rotated, some values near the
            borders will be undefined.
            If ``'minimum'``, the fill value will be the image minimum.
            If ``'mean'``, the fill value is the mean of the border values.
            If ``'otsu'``, the fill value is the mean of the values at the
            border that lie under an
            `Otsu threshold <https://ieeexplore.ieee.org/document/4310076>`_.
            If it is a number, that value will be used.
        image_interpolation: See :ref:`Interpolation`.
        keys: See :py:class:`~torchio.transforms.Transform`.
    """
    def __init__(
            self,
            scales: TypeTripletFloat,
            degrees: TypeTripletFloat,
            translation: TypeTripletFloat,
            center: str = 'image',
            default_pad_value: Union[str, float] = 'minimum',
            image_interpolation: str = 'linear',
            keys: Optional[Sequence[str]] = None,
            ):
        super().__init__(keys=keys)
        self.scales = self.parse_params(
            scales,
            None,
            'scales',
            make_ranges=False,
            min_constraint=0,
        )
        self.degrees = self.parse_params(
            degrees,
            None,
            'degrees',
            make_ranges=False,
        )
        self.translation = self.parse_params(
            translation,
            None,
            'translation',
            make_ranges=False,
        )
        if center not in ('image', 'origin'):
            message = (
                'Center argument must be "image" or "origin",'
                f' not "{center}"'
            )
            raise ValueError(message)
        self.center = center
        self.use_image_center = center == 'image'
        self.default_pad_value = _parse_default_value(default_pad_value)
        self.image_interpolation = self.parse_interpolation(image_interpolation)
        self.invert_transform = False
        self.args_names = (
            'scales',
            'degrees',
            'translation',
            'center',
            'default_pad_value',
            'image_interpolation',
        )

    @staticmethod
    def get_scaling_transform(
            scaling_params: Sequence[float],
            center_lps: Optional[TypeTripletFloat] = None,
            ) -> sitk.ScaleTransform:
        # scaling_params are inverted so that they are more intuitive
        # For example, 1.5 means the objects look 1.5 times larger
        transform = sitk.ScaleTransform(3)
        scaling_params = 1 / np.array(scaling_params)
        transform.SetScale(scaling_params)
        if center_lps is not None:
            transform.SetCenter(center_lps)
        return transform

    @staticmethod
    def get_rotation_transform(
            degrees: Sequence[float],
            translation: Sequence[float],
            center_lps: Optional[TypeTripletFloat] = None,
            ) -> sitk.Euler3DTransform:
        transform = sitk.Euler3DTransform()
        radians = np.radians(degrees)
        transform.SetRotation(*radians)
        transform.SetTranslation(translation)
        if center_lps is not None:
            transform.SetCenter(center_lps)
        return transform

    def apply_transform(self, subject: Subject) -> Subject:
        scaling_params = np.array(self.scales).copy()
        rotation_params = np.array(self.degrees).copy()
        translation_params = np.array(self.translation).copy()
        subject.check_consistent_spatial_shape()
        for image in self.get_images(subject):
            if image[TYPE] != INTENSITY:
                interpolation = 'nearest'
            else:
                interpolation = self.image_interpolation

            if image.is_2d():
                scaling_params[2] = 1
                rotation_params[:-1] = 0

            if self.use_image_center:
                center = image.get_center(lps=True)
            else:
                center = None

            transformed_tensors = []
            for tensor in image[DATA]:

                transformed_tensor = self.apply_affine_transform(
                    tensor,
                    image[AFFINE],
                    scaling_params.tolist(),
                    rotation_params.tolist(),
                    translation_params.tolist(),
                    interpolation,
                    center_lps=center,
                )
                transformed_tensors.append(transformed_tensor)
            image[DATA] = torch.stack(transformed_tensors)
        return subject

    def apply_affine_transform(
            self,
            tensor: torch.Tensor,
            affine: np.ndarray,
            scaling_params: Sequence[float],
            rotation_params: Sequence[float],
            translation_params: Sequence[float],
            interpolation: str,
            center_lps: Optional[TypeTripletFloat] = None,
            ) -> torch.Tensor:
        assert tensor.ndim == 3

        image = nib_to_sitk(tensor[np.newaxis], affine, force_3d=True)
        floating = reference = image

        scaling_transform = self.get_scaling_transform(
            scaling_params,
            center_lps=center_lps,
        )
        rotation_transform = self.get_rotation_transform(
            rotation_params,
            translation_params,
            center_lps=center_lps,
        )

        sitk_major_version = get_major_sitk_version()
        if sitk_major_version == 1:
            transform = sitk.Transform(3, sitk.sitkComposite)
            transform.AddTransform(scaling_transform)
            transform.AddTransform(rotation_transform)
        elif sitk_major_version == 2:
            transforms = [scaling_transform, rotation_transform]
            transform = sitk.CompositeTransform(transforms)

        if self.invert_transform:
            transform = transform.GetInverse()


        if self.default_pad_value == 'minimum':
            default_value = tensor.min().item()
        elif self.default_pad_value == 'mean':
            default_value = get_borders_mean(image, filter_otsu=False)
        elif self.default_pad_value == 'otsu':
            default_value = get_borders_mean(image, filter_otsu=True)
        else:
            default_value = self.default_pad_value

        resampler = sitk.ResampleImageFilter()
        resampler.SetInterpolator(self.get_sitk_interpolator(interpolation))
        resampler.SetReferenceImage(reference)
        resampler.SetDefaultPixelValue(float(default_value))
        resampler.SetOutputPixelType(sitk.sitkFloat32)
        resampler.SetTransform(transform)
        resampled = resampler.Execute(floating)

        np_array = sitk.GetArrayFromImage(resampled)
        np_array = np_array.transpose()  # ITK to NumPy
        tensor = torch.from_numpy(np_array)
        return tensor


# flake8: noqa: E201, E203, E243
def get_borders_mean(image, filter_otsu=True):
    # pylint: disable=bad-whitespace
    array = sitk.GetArrayViewFromImage(image)
    borders_tuple = (
        array[ 0,  :,  :],
        array[-1,  :,  :],
        array[ :,  0,  :],
        array[ :, -1,  :],
        array[ :,  :,  0],
        array[ :,  :, -1],
    )
    borders_flat = np.hstack([border.ravel() for border in borders_tuple])
    if not filter_otsu:
        return borders_flat.mean()
    borders_reshaped = borders_flat.reshape(1, 1, -1)
    borders_image = sitk.GetImageFromArray(borders_reshaped)
    otsu = sitk.OtsuThresholdImageFilter()
    otsu.Execute(borders_image)
    threshold = otsu.GetThreshold()
    values = borders_flat[borders_flat < threshold]
    if values.any():
        default_value = values.mean()
    else:
        default_value = borders_flat.mean()
    return default_value

def _parse_scales_isotropic(scales, isotropic):
    params = to_tuple(scales)
    if isotropic and len(scales) in (3, 6):
        message = (
            'If "isotropic" is True, the value for "scales" must have'
            f' length 1 or 2, but "{scales}" was passed'
        )
        raise ValueError(message)

def _parse_default_value(value: Union[str, float]) -> Union[str, float]:
    if isinstance(value, Number) or value in ('minimum', 'otsu', 'mean'):
        return value
    message = (
        'Value for default_pad_value must be "minimum", "otsu", "mean"'
        ' or a number'
    )
    raise ValueError(message)


1			import copy
2			from numbers import Number
3			from typing import Tuple, Optional, Sequence, Union
4
5			import torch
6			import numpy as np
7			import SimpleITK as sitk
8
9			from ....data.subject import Subject
10			from ....utils import nib_to_sitk, get_major_sitk_version, to_tuple
11			from ....torchio import (
12			INTENSITY,
13			DATA,
14			AFFINE,
15			TYPE,
16			TypeRangeFloat,
17			TypeSextetFloat,
18			TypeTripletFloat,
19			)
20			from ... import SpatialTransform
21			from .. import RandomTransform
22
23
24			TypeOneToSixFloat = Union[TypeRangeFloat, TypeTripletFloat, TypeSextetFloat]
25
26
27			class RandomAffine(RandomTransform, SpatialTransform):
28			r"""Random affine transformation.
29
30			Args:
31			scales: Tuple :math:`(a_1, b_1, a_2, b_2, a_3, b_3)` defining the
32			scaling ranges.
33			The scaling values along each dimension are :math:`(s_1, s_2, s_3)`,
34			where :math:`s_i \sim \mathcal{U}(a_i, b_i)`.
35			If two values :math:`(a, b)` are provided,
36			then :math:`s_i \sim \mathcal{U}(a, b)`.
37			If only one value :math:`x` is provided,
38			then :math:`s_i \sim \mathcal{U}(1 - x, 1 + x)`.
39			If three values :math:`(x_1, x_2, x_3)` are provided,
40			then :math:`s_i \sim \mathcal{U}(1 - x_i, 1 + x_i)`.
41			For example, using ``scales=(0.5, 0.5)`` will zoom out the image,
42			making the objects inside look twice as small while preserving
43			the physical size and position of the image bounds.
44			degrees: Tuple :math:`(a_1, b_1, a_2, b_2, a_3, b_3)` defining the
45			rotation ranges in degrees.
46			Rotation angles around each axis are
47			:math:`(\theta_1, \theta_2, \theta_3)`,
48			where :math:`\theta_i \sim \mathcal{U}(a_i, b_i)`.
49			If two values :math:`(a, b)` are provided,
50			then :math:`\theta_i \sim \mathcal{U}(a, b)`.
51			If only one value :math:`x` is provided,
52			then :math:`\theta_i \sim \mathcal{U}(-x, x)`.
53			If three values :math:`(x_1, x_2, x_3)` are provided,
54			then :math:`\theta_i \sim \mathcal{U}(-x_i, x_i)`.
55			translation: Tuple :math:`(a_1, b_1, a_2, b_2, a_3, b_3)` defining the
56			translation ranges in mm.
57			Translation along each axis is :math:`(t_1, t_2, t_3)`,
58			where :math:`t_i \sim \mathcal{U}(a_i, b_i)`.
59			If two values :math:`(a, b)` are provided,
60			then :math:`t_i \sim \mathcal{U}(a, b)`.
61			If only one value :math:`x` is provided,
62			then :math:`t_i \sim \mathcal{U}(-x, x)`.
63			If three values :math:`(x_1, x_2, x_3)` are provided,
64			then :math:`t_i \sim \mathcal{U}(-x_i, x_i)`.
65			isotropic: If ``True``, the scaling factor along all dimensions is the
66			same, i.e. :math:`s_1 = s_2 = s_3`.
67			center: If ``'image'``, rotations and scaling will be performed around
68			the image center. If ``'origin'``, rotations and scaling will be
69			performed around the origin in world coordinates.
70			default_pad_value: As the image is rotated, some values near the
71			borders will be undefined.
72			If ``'minimum'``, the fill value will be the image minimum.
73			If ``'mean'``, the fill value is the mean of the border values.
74			If ``'otsu'``, the fill value is the mean of the values at the
75			border that lie under an
76			`Otsu threshold <https://ieeexplore.ieee.org/document/4310076>`_.
77			If it is a number, that value will be used.
78			image_interpolation: See :ref:`Interpolation`.
79			p: Probability that this transform will be applied.
80			keys: See :py:class:`~torchio.transforms.Transform`.
81
82			Example:
83			>>> import torchio as tio
84			>>> subject = tio.datasets.Colin27()
85			>>> transform = tio.RandomAffine(
86			... scales=(0.9, 1.2),
87			... degrees=10,
88			... isotropic=True,
89			... image_interpolation='nearest',
90			... )
91			>>> transformed = transform(subject)
92
93			From the command line::
94
95			$ torchio-transform t1.nii.gz RandomAffine --kwargs "scales=(0.9, 1.2) degrees=10 isotropic=True image_interpolation=nearest" --seed 42 affine_min.nii.gz
96
97			"""
98			def __init__(
99			self,
100			scales: TypeOneToSixFloat = 0.1,
101			degrees: TypeOneToSixFloat = 10,
102			translation: TypeOneToSixFloat = 0,
103			isotropic: bool = False,
104			center: str = 'image',
105			default_pad_value: Union[str, float] = 'minimum',
106			image_interpolation: str = 'linear',
107			p: float = 1,
108			keys: Optional[Sequence[str]] = None,
109			):
110			super().__init__(p=p, keys=keys)
111			self.isotropic = isotropic
112			_parse_scales_isotropic(scales, isotropic)
113			self.scales = self.parse_params(scales, 1, 'scales', min_constraint=0)
114			self.degrees = self.parse_params(degrees, 0, 'degrees')
115			self.translation = self.parse_params(translation, 0, 'translation')
116			if center not in ('image', 'origin'):
117			message = (
118			'Center argument must be "image" or "origin",'
119			f' not "{center}"'
120			)
121			raise ValueError(message)
122			self.center = center
123			self.default_pad_value = _parse_default_value(default_pad_value)
124			self.image_interpolation = self.parse_interpolation(image_interpolation)
125
126			def get_params(
127			self,
128			scales: TypeSextetFloat,
129			degrees: TypeSextetFloat,
130			translation: TypeSextetFloat,
131			isotropic: bool,
132			) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
133			scaling_params = self.sample_uniform_sextet(scales)
134			if isotropic:
135			scaling_params.fill_(scaling_params[0])
136			rotation_params = self.sample_uniform_sextet(degrees)
137			translation_params = self.sample_uniform_sextet(translation)
138			return scaling_params, rotation_params, translation_params
139
140			def apply_transform(self, subject: Subject) -> Subject:
141			subject.check_consistent_spatial_shape()
142			scaling_params, rotation_params, translation_params = self.get_params(
143			self.scales,
144			self.degrees,
145			self.translation,
146			self.isotropic,
147			)
148			arguments = dict(
149			scales=scaling_params.tolist(),
150			degrees=rotation_params.tolist(),
151			translation=translation_params.tolist(),
152			center=self.center,
153			default_pad_value=self.default_pad_value,
154			image_interpolation=self.image_interpolation,
155			)
156			transform = Affine(**arguments)
157			transformed = transform(subject)
158			return transformed
159
160
161			class Affine(SpatialTransform):
162			r"""Apply affine transformation.
163
164			Args:
165			scales: Tuple :math:`(s_1, s_2, s_3)` defining the
166			scaling values along each dimension.
167			degrees: Tuple :math:`(\theta_1, \theta_2, \theta_3)` defining the
168			rotation around each axis.
169			translation: Tuple :math:`(t_1, t_2, t_3)` defining the
170			translation in mm along each axis.
171			center: If ``'image'``, rotations and scaling will be performed around
172			the image center. If ``'origin'``, rotations and scaling will be
173			performed around the origin in world coordinates.
174			default_pad_value: As the image is rotated, some values near the
175			borders will be undefined.
176			If ``'minimum'``, the fill value will be the image minimum.
177			If ``'mean'``, the fill value is the mean of the border values.
178			If ``'otsu'``, the fill value is the mean of the values at the
179			border that lie under an
180			`Otsu threshold <https://ieeexplore.ieee.org/document/4310076>`_.
181			If it is a number, that value will be used.
182			image_interpolation: See :ref:`Interpolation`.
183			keys: See :py:class:`~torchio.transforms.Transform`.
184			"""
185			def __init__(
186			self,
187			scales: TypeTripletFloat,
188			degrees: TypeTripletFloat,
189			translation: TypeTripletFloat,
190			center: str = 'image',
191			default_pad_value: Union[str, float] = 'minimum',
192			image_interpolation: str = 'linear',
193			keys: Optional[Sequence[str]] = None,
194			):
195			super().__init__(keys=keys)
196			self.scales = self.parse_params(
197			scales,
198			None,
199			'scales',
200			make_ranges=False,
201			min_constraint=0,
202			)
203			self.degrees = self.parse_params(
204			degrees,
205			None,
206			'degrees',
207			make_ranges=False,
208			)
209			self.translation = self.parse_params(
210			translation,
211			None,
212			'translation',
213			make_ranges=False,
214			)
215			if center not in ('image', 'origin'):
216			message = (
217			'Center argument must be "image" or "origin",'
218			f' not "{center}"'
219			)
220			raise ValueError(message)
221			self.center = center
222			self.use_image_center = center == 'image'
223			self.default_pad_value = _parse_default_value(default_pad_value)
224			self.image_interpolation = self.parse_interpolation(image_interpolation)
225			self.invert_transform = False
226			self.args_names = (
227			'scales',
228			'degrees',
229			'translation',
230			'center',
231			'default_pad_value',
232			'image_interpolation',
233			)
234
235			@staticmethod
236			def get_scaling_transform(
237			scaling_params: Sequence[float],
238			center_lps: Optional[TypeTripletFloat] = None,
239			) -> sitk.ScaleTransform:
240			# scaling_params are inverted so that they are more intuitive
241			# For example, 1.5 means the objects look 1.5 times larger
242			transform = sitk.ScaleTransform(3)
243			scaling_params = 1 / np.array(scaling_params)
244			transform.SetScale(scaling_params)
245			if center_lps is not None:
246			transform.SetCenter(center_lps)
247			return transform
248
249			@staticmethod
250			def get_rotation_transform(
251			degrees: Sequence[float],
252			translation: Sequence[float],
253			center_lps: Optional[TypeTripletFloat] = None,
254			) -> sitk.Euler3DTransform:
255			transform = sitk.Euler3DTransform()
256			radians = np.radians(degrees)
257			transform.SetRotation(*radians)
258			transform.SetTranslation(translation)
259			if center_lps is not None:
260			transform.SetCenter(center_lps)
261			return transform
262
263			def apply_transform(self, subject: Subject) -> Subject:
264			scaling_params = np.array(self.scales).copy()
265			rotation_params = np.array(self.degrees).copy()
266			translation_params = np.array(self.translation).copy()
267			subject.check_consistent_spatial_shape()
268			for image in self.get_images(subject):
269			if image[TYPE] != INTENSITY:
270			interpolation = 'nearest'
271			else:
272			interpolation = self.image_interpolation
273
274			if image.is_2d():
275			scaling_params[2] = 1
276			rotation_params[:-1] = 0
277
278			if self.use_image_center:
279			center = image.get_center(lps=True)
280			else:
281			center = None
282
283			transformed_tensors = []
284			for tensor in image[DATA]:
			0 ignored issues – show Comprehensibility Best Practice introduced 2020-08-02 14:22 UTC by Report Bug Copy Issue Report The variable `DATA` does not seem to be defined. Loading history...
285			transformed_tensor = self.apply_affine_transform(
286			tensor,
287			image[AFFINE],
288			scaling_params.tolist(),
289			rotation_params.tolist(),
290			translation_params.tolist(),
291			interpolation,
292			center_lps=center,
293			)
294			transformed_tensors.append(transformed_tensor)
295			image[DATA] = torch.stack(transformed_tensors)
296			return subject
297
298			def apply_affine_transform(
299			self,
300			tensor: torch.Tensor,
301			affine: np.ndarray,
302			scaling_params: Sequence[float],
303			rotation_params: Sequence[float],
304			translation_params: Sequence[float],
305			interpolation: str,
306			center_lps: Optional[TypeTripletFloat] = None,
307			) -> torch.Tensor:
308			assert tensor.ndim == 3
309
310			image = nib_to_sitk(tensor[np.newaxis], affine, force_3d=True)
311			floating = reference = image
312
313			scaling_transform = self.get_scaling_transform(
314			scaling_params,
315			center_lps=center_lps,
316			)
317			rotation_transform = self.get_rotation_transform(
318			rotation_params,
319			translation_params,
320			center_lps=center_lps,
321			)
322
323			sitk_major_version = get_major_sitk_version()
324			if sitk_major_version == 1:
325			transform = sitk.Transform(3, sitk.sitkComposite)
326			transform.AddTransform(scaling_transform)
327			transform.AddTransform(rotation_transform)
328			elif sitk_major_version == 2:
329			transforms = [scaling_transform, rotation_transform]
330			transform = sitk.CompositeTransform(transforms)
331
332			if self.invert_transform:
333			transform = transform.GetInverse()
			0 ignored issues – show introduced 2020-11-16 23:09 UTC by Report Bug Copy Issue Report The variable `transform` does not seem to be defined for all execution paths. Loading history...
334
335			if self.default_pad_value == 'minimum':
336			default_value = tensor.min().item()
337			elif self.default_pad_value == 'mean':
338			default_value = get_borders_mean(image, filter_otsu=False)
339			elif self.default_pad_value == 'otsu':
340			default_value = get_borders_mean(image, filter_otsu=True)
341			else:
342			default_value = self.default_pad_value
343
344			resampler = sitk.ResampleImageFilter()
345			resampler.SetInterpolator(self.get_sitk_interpolator(interpolation))
346			resampler.SetReferenceImage(reference)
347			resampler.SetDefaultPixelValue(float(default_value))
348			resampler.SetOutputPixelType(sitk.sitkFloat32)
349			resampler.SetTransform(transform)
350			resampled = resampler.Execute(floating)
351
352			np_array = sitk.GetArrayFromImage(resampled)
353			np_array = np_array.transpose() # ITK to NumPy
354			tensor = torch.from_numpy(np_array)
355			return tensor
356
357
358			# flake8: noqa: E201, E203, E243
359			def get_borders_mean(image, filter_otsu=True):
360			# pylint: disable=bad-whitespace
361			array = sitk.GetArrayViewFromImage(image)
362			borders_tuple = (
363			array[ 0, :, :],
364			array[-1, :, :],
365			array[ :, 0, :],
366			array[ :, -1, :],
367			array[ :, :, 0],
368			array[ :, :, -1],
369			)
370			borders_flat = np.hstack([border.ravel() for border in borders_tuple])
371			if not filter_otsu:
372			return borders_flat.mean()
373			borders_reshaped = borders_flat.reshape(1, 1, -1)
374			borders_image = sitk.GetImageFromArray(borders_reshaped)
375			otsu = sitk.OtsuThresholdImageFilter()
376			otsu.Execute(borders_image)
377			threshold = otsu.GetThreshold()
378			values = borders_flat[borders_flat < threshold]
379			if values.any():
380			default_value = values.mean()
381			else:
382			default_value = borders_flat.mean()
383			return default_value
384
385			def _parse_scales_isotropic(scales, isotropic):
386			params = to_tuple(scales)
387			if isotropic and len(scales) in (3, 6):
388			message = (
389			'If "isotropic" is True, the value for "scales" must have'
390			f' length 1 or 2, but "{scales}" was passed'
391			)
392			raise ValueError(message)
393
394			def _parse_default_value(value: Union[str, float]) -> Union[str, float]:
395			if isinstance(value, Number) or value in ('minimum', 'otsu', 'mean'):
396			return value
397			message = (
398			'Value for default_pad_value must be "minimum", "otsu", "mean"'
399			' or a number'
400			)
401			raise ValueError(message)
402

fepegar / torchio

Pull Request — master (#353)

Affine.__init__() B

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Affine.init() B