torchio.transforms.augmentation.spatial.random_affine.RandomAffine.__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 2020-11-18 20:09 UTC

RandomAffine.init() A

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

Complexity

Conditions

Size

Total Lines	27
Code Lines	24

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	2
eloc	24
nop	10
dl	0
loc	27
rs	9.304
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 Interpolation, get_sitk_interpolator
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: Interpolation,
            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(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 Interpolation, get_sitk_interpolator
22			from .. import RandomTransform
23
24
25			TypeOneToSixFloat = Union[TypeRangeFloat, TypeTripletFloat, TypeSextetFloat]
26
27
28			class RandomAffine(RandomTransform, SpatialTransform):
29			r"""Random affine transformation.
30
31			Args:
32			scales: Tuple :math:`(a_1, b_1, a_2, b_2, a_3, b_3)` defining the
33			scaling ranges.
34			The scaling values along each dimension are :math:`(s_1, s_2, s_3)`,
35			where :math:`s_i \sim \mathcal{U}(a_i, b_i)`.
36			If two values :math:`(a, b)` are provided,
37			then :math:`s_i \sim \mathcal{U}(a, b)`.
38			If only one value :math:`x` is provided,
39			then :math:`s_i \sim \mathcal{U}(1 - x, 1 + x)`.
40			If three values :math:`(x_1, x_2, x_3)` are provided,
41			then :math:`s_i \sim \mathcal{U}(1 - x_i, 1 + x_i)`.
42			For example, using ``scales=(0.5, 0.5)`` will zoom out the image,
43			making the objects inside look twice as small while preserving
44			the physical size and position of the image bounds.
45			degrees: Tuple :math:`(a_1, b_1, a_2, b_2, a_3, b_3)` defining the
46			rotation ranges in degrees.
47			Rotation angles around each axis are
48			:math:`(\theta_1, \theta_2, \theta_3)`,
49			where :math:`\theta_i \sim \mathcal{U}(a_i, b_i)`.
50			If two values :math:`(a, b)` are provided,
51			then :math:`\theta_i \sim \mathcal{U}(a, b)`.
52			If only one value :math:`x` is provided,
53			then :math:`\theta_i \sim \mathcal{U}(-x, x)`.
54			If three values :math:`(x_1, x_2, x_3)` are provided,
55			then :math:`\theta_i \sim \mathcal{U}(-x_i, x_i)`.
56			translation: Tuple :math:`(a_1, b_1, a_2, b_2, a_3, b_3)` defining the
57			translation ranges in mm.
58			Translation along each axis is :math:`(t_1, t_2, t_3)`,
59			where :math:`t_i \sim \mathcal{U}(a_i, b_i)`.
60			If two values :math:`(a, b)` are provided,
61			then :math:`t_i \sim \mathcal{U}(a, b)`.
62			If only one value :math:`x` is provided,
63			then :math:`t_i \sim \mathcal{U}(-x, x)`.
64			If three values :math:`(x_1, x_2, x_3)` are provided,
65			then :math:`t_i \sim \mathcal{U}(-x_i, x_i)`.
66			isotropic: If ``True``, the scaling factor along all dimensions is the
67			same, i.e. :math:`s_1 = s_2 = s_3`.
68			center: If ``'image'``, rotations and scaling will be performed around
69			the image center. If ``'origin'``, rotations and scaling will be
70			performed around the origin in world coordinates.
71			default_pad_value: As the image is rotated, some values near the
72			borders will be undefined.
73			If ``'minimum'``, the fill value will be the image minimum.
74			If ``'mean'``, the fill value is the mean of the border values.
75			If ``'otsu'``, the fill value is the mean of the values at the
76			border that lie under an
77			`Otsu threshold <https://ieeexplore.ieee.org/document/4310076>`_.
78			If it is a number, that value will be used.
79			image_interpolation: See :ref:`Interpolation`.
80			p: Probability that this transform will be applied.
81			keys: See :py:class:`~torchio.transforms.Transform`.
82
83			Example:
84			>>> import torchio as tio
85			>>> subject = tio.datasets.Colin27()
86			>>> transform = tio.RandomAffine(
87			... scales=(0.9, 1.2),
88			... degrees=10,
89			... isotropic=True,
90			... image_interpolation='nearest',
91			... )
92			>>> transformed = transform(subject)
93
94			From the command line::
95
96			$ 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
97
98			"""
99			def __init__(
100			self,
101			scales: TypeOneToSixFloat = 0.1,
102			degrees: TypeOneToSixFloat = 10,
103			translation: TypeOneToSixFloat = 0,
104			isotropic: bool = False,
105			center: str = 'image',
106			default_pad_value: Union[str, float] = 'minimum',
107			image_interpolation: str = 'linear',
108			p: float = 1,
109			keys: Optional[Sequence[str]] = None,
110			):
111			super().__init__(p=p, keys=keys)
112			self.isotropic = isotropic
113			_parse_scales_isotropic(scales, isotropic)
114			self.scales = self.parse_params(scales, 1, 'scales', min_constraint=0)
115			self.degrees = self.parse_params(degrees, 0, 'degrees')
116			self.translation = self.parse_params(translation, 0, 'translation')
117			if center not in ('image', 'origin'):
118			message = (
119			'Center argument must be "image" or "origin",'
120			f' not "{center}"'
121			)
122			raise ValueError(message)
123			self.center = center
124			self.default_pad_value = _parse_default_value(default_pad_value)
125			self.image_interpolation = self.parse_interpolation(image_interpolation)
126
127			def get_params(
128			self,
129			scales: TypeSextetFloat,
130			degrees: TypeSextetFloat,
131			translation: TypeSextetFloat,
132			isotropic: bool,
133			) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
134			scaling_params = self.sample_uniform_sextet(scales)
135			if isotropic:
136			scaling_params.fill_(scaling_params[0])
137			rotation_params = self.sample_uniform_sextet(degrees)
138			translation_params = self.sample_uniform_sextet(translation)
139			return scaling_params, rotation_params, translation_params
140
141			def apply_transform(self, subject: Subject) -> Subject:
142			subject.check_consistent_spatial_shape()
143			scaling_params, rotation_params, translation_params = self.get_params(
144			self.scales,
145			self.degrees,
146			self.translation,
147			self.isotropic,
148			)
149			arguments = dict(
150			scales=scaling_params.tolist(),
151			degrees=rotation_params.tolist(),
152			translation=translation_params.tolist(),
153			center=self.center,
154			default_pad_value=self.default_pad_value,
155			image_interpolation=self.image_interpolation,
156			)
157			transform = Affine(**arguments)
158			transformed = transform(subject)
159			return transformed
160
161
162			class Affine(SpatialTransform):
163			r"""Apply affine transformation.
164
165			Args:
166			scales: Tuple :math:`(s_1, s_2, s_3)` defining the
167			scaling values along each dimension.
168			degrees: Tuple :math:`(\theta_1, \theta_2, \theta_3)` defining the
169			rotation around each axis.
170			translation: Tuple :math:`(t_1, t_2, t_3)` defining the
171			translation in mm along each axis.
172			center: If ``'image'``, rotations and scaling will be performed around
173			the image center. If ``'origin'``, rotations and scaling will be
174			performed around the origin in world coordinates.
175			default_pad_value: As the image is rotated, some values near the
176			borders will be undefined.
177			If ``'minimum'``, the fill value will be the image minimum.
178			If ``'mean'``, the fill value is the mean of the border values.
179			If ``'otsu'``, the fill value is the mean of the values at the
180			border that lie under an
181			`Otsu threshold <https://ieeexplore.ieee.org/document/4310076>`_.
182			If it is a number, that value will be used.
183			image_interpolation: See :ref:`Interpolation`.
184			keys: See :py:class:`~torchio.transforms.Transform`.
185			"""
186			def __init__(
187			self,
188			scales: TypeTripletFloat,
189			degrees: TypeTripletFloat,
190			translation: TypeTripletFloat,
191			center: str = 'image',
192			default_pad_value: Union[str, float] = 'minimum',
193			image_interpolation: str = 'linear',
194			keys: Optional[Sequence[str]] = None,
195			):
196			super().__init__(keys=keys)
197			self.scales = self.parse_params(
198			scales,
199			None,
200			'scales',
201			make_ranges=False,
202			min_constraint=0,
203			)
204			self.degrees = self.parse_params(
205			degrees,
206			None,
207			'degrees',
208			make_ranges=False,
209			)
210			self.translation = self.parse_params(
211			translation,
212			None,
213			'translation',
214			make_ranges=False,
215			)
216			if center not in ('image', 'origin'):
217			message = (
218			'Center argument must be "image" or "origin",'
219			f' not "{center}"'
220			)
221			raise ValueError(message)
222			self.center = center
223			self.use_image_center = center == 'image'
224			self.default_pad_value = _parse_default_value(default_pad_value)
225			self.image_interpolation = self.parse_interpolation(image_interpolation)
226			self.invert_transform = False
227			self.args_names = (
228			'scales',
229			'degrees',
230			'translation',
231			'center',
232			'default_pad_value',
233			'image_interpolation',
234			)
235
236			@staticmethod
237			def get_scaling_transform(
238			scaling_params: Sequence[float],
239			center_lps: Optional[TypeTripletFloat] = None,
240			) -> sitk.ScaleTransform:
241			# scaling_params are inverted so that they are more intuitive
242			# For example, 1.5 means the objects look 1.5 times larger
243			transform = sitk.ScaleTransform(3)
244			scaling_params = 1 / np.array(scaling_params)
245			transform.SetScale(scaling_params)
246			if center_lps is not None:
247			transform.SetCenter(center_lps)
248			return transform
249
250			@staticmethod
251			def get_rotation_transform(
252			degrees: Sequence[float],
253			translation: Sequence[float],
254			center_lps: Optional[TypeTripletFloat] = None,
255			) -> sitk.Euler3DTransform:
256			transform = sitk.Euler3DTransform()
257			radians = np.radians(degrees)
258			transform.SetRotation(*radians)
259			transform.SetTranslation(translation)
260			if center_lps is not None:
261			transform.SetCenter(center_lps)
262			return transform
263
264			def apply_transform(self, subject: Subject) -> Subject:
265			scaling_params = np.array(self.scales).copy()
266			rotation_params = np.array(self.degrees).copy()
267			translation_params = np.array(self.translation).copy()
268			subject.check_consistent_spatial_shape()
269			for image in self.get_images(subject):
270			if image[TYPE] != INTENSITY:
271			interpolation = 'nearest'
272			else:
273			interpolation = self.image_interpolation
274
275			if image.is_2d():
276			scaling_params[2] = 1
277			rotation_params[:-1] = 0
278
279			if self.use_image_center:
280			center = image.get_center(lps=True)
281			else:
282			center = None
283
284			transformed_tensors = []
285			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...
286			transformed_tensor = self.apply_affine_transform(
287			tensor,
288			image[AFFINE],
289			scaling_params.tolist(),
290			rotation_params.tolist(),
291			translation_params.tolist(),
292			interpolation,
293			center_lps=center,
294			)
295			transformed_tensors.append(transformed_tensor)
296			image[DATA] = torch.stack(transformed_tensors)
297			return subject
298
299			def apply_affine_transform(
300			self,
301			tensor: torch.Tensor,
302			affine: np.ndarray,
303			scaling_params: Sequence[float],
304			rotation_params: Sequence[float],
305			translation_params: Sequence[float],
306			interpolation: Interpolation,
307			center_lps: Optional[TypeTripletFloat] = None,
308			) -> torch.Tensor:
309			assert tensor.ndim == 3
310
311			image = nib_to_sitk(tensor[np.newaxis], affine, force_3d=True)
312			floating = reference = image
313
314			scaling_transform = self.get_scaling_transform(
315			scaling_params,
316			center_lps=center_lps,
317			)
318			rotation_transform = self.get_rotation_transform(
319			rotation_params,
320			translation_params,
321			center_lps=center_lps,
322			)
323
324			sitk_major_version = get_major_sitk_version()
325			if sitk_major_version == 1:
326			transform = sitk.Transform(3, sitk.sitkComposite)
327			transform.AddTransform(scaling_transform)
328			transform.AddTransform(rotation_transform)
329			elif sitk_major_version == 2:
330			transforms = [scaling_transform, rotation_transform]
331			transform = sitk.CompositeTransform(transforms)
332
333			if self.invert_transform:
334			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...
335
336			if self.default_pad_value == 'minimum':
337			default_value = tensor.min().item()
338			elif self.default_pad_value == 'mean':
339			default_value = get_borders_mean(image, filter_otsu=False)
340			elif self.default_pad_value == 'otsu':
341			default_value = get_borders_mean(image, filter_otsu=True)
342			else:
343			default_value = self.default_pad_value
344
345			resampler = sitk.ResampleImageFilter()
346			resampler.SetInterpolator(get_sitk_interpolator(interpolation))
347			resampler.SetReferenceImage(reference)
348			resampler.SetDefaultPixelValue(float(default_value))
349			resampler.SetOutputPixelType(sitk.sitkFloat32)
350			resampler.SetTransform(transform)
351			resampled = resampler.Execute(floating)
352
353			np_array = sitk.GetArrayFromImage(resampled)
354			np_array = np_array.transpose() # ITK to NumPy
355			tensor = torch.from_numpy(np_array)
356			return tensor
357
358
359			# flake8: noqa: E201, E203, E243
360			def get_borders_mean(image, filter_otsu=True):
361			# pylint: disable=bad-whitespace
362			array = sitk.GetArrayViewFromImage(image)
363			borders_tuple = (
364			array[ 0, :, :],
365			array[-1, :, :],
366			array[ :, 0, :],
367			array[ :, -1, :],
368			array[ :, :, 0],
369			array[ :, :, -1],
370			)
371			borders_flat = np.hstack([border.ravel() for border in borders_tuple])
372			if not filter_otsu:
373			return borders_flat.mean()
374			borders_reshaped = borders_flat.reshape(1, 1, -1)
375			borders_image = sitk.GetImageFromArray(borders_reshaped)
376			otsu = sitk.OtsuThresholdImageFilter()
377			otsu.Execute(borders_image)
378			threshold = otsu.GetThreshold()
379			values = borders_flat[borders_flat < threshold]
380			if values.any():
381			default_value = values.mean()
382			else:
383			default_value = borders_flat.mean()
384			return default_value
385
386			def _parse_scales_isotropic(scales, isotropic):
387			params = to_tuple(scales)
388			if isotropic and len(scales) in (3, 6):
389			message = (
390			'If "isotropic" is True, the value for "scales" must have'
391			f' length 1 or 2, but "{scales}" was passed'
392			)
393			raise ValueError(message)
394
395			def _parse_default_value(value: Union[str, float]) -> Union[str, float]:
396			if isinstance(value, Number) or value in ('minimum', 'otsu', 'mean'):
397			return value
398			message = (
399			'Value for default_pad_value must be "minimum", "otsu", "mean"'
400			' or a number'
401			)
402			raise ValueError(message)
403

fepegar / torchio

Pull Request — master (#353)

RandomAffine.__init__() A

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RandomAffine.init() A