torchio.data.image.ScalarImage.__init__() - Code Metrics - Inspection of "Add ScalarImage and LabelMap classes" - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 42a45f...85e78d )

by Fernando

created 2020-07-14 09:16 UTC

torchio.data.image.ScalarImage.init() A

↳ Parent: torchio.data.image

Complexity

Conditions

Size

Total Lines	4
Code Lines	4

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	2
eloc	4
nop	3
dl	0
loc	4
rs	10
c	0
b	0
f	0

import warnings
from pathlib import Path
from typing import Any, Dict, Tuple, Optional

import torch
import humanize
import numpy as np
import nibabel as nib
import SimpleITK as sitk

from ..utils import nib_to_sitk, get_rotation_and_spacing_from_affine, get_stem
from ..torchio import (
    TypeData,
    TypePath,
    TypeTripletInt,
    TypeTripletFloat,
    DATA,
    TYPE,
    AFFINE,
    PATH,
    STEM,
    INTENSITY,
    LABEL,
)
from .io import read_image, write_image


class Image(dict):
    r"""TorchIO image.

    TorchIO images are `lazy loaders`_, i.e. the data is only loaded from disk
    when needed.

    Example:
        >>> import torchio
        >>> image = torchio.Image('t1.nii.gz', type=torchio.INTENSITY)
        >>> image  # not loaded yet
        Image(path: t1.nii.gz; type: intensity)
        >>> times_two = 2 * image.data  # data is loaded and cached here
        >>> image
        Image(shape: (1, 256, 256, 176); spacing: (1.00, 1.00, 1.00); orientation: PIR+; memory: 44.0 MiB; type: intensity)
        >>> image.save('doubled_image.nii.gz')

    For information about medical image orientation, check out `NiBabel docs`_,
    the `3D Slicer wiki`_, `Graham Wideman's website`_ or `FSL docs`_.

    Args:
        path: Path to a file that can be read by
            :mod:`SimpleITK` or :mod:`nibabel` or to a directory containing
            DICOM files.
        type: Type of image, such as :attr:`torchio.INTENSITY` or
            :attr:`torchio.LABEL`. This will be used by the transforms to
            decide whether to apply an operation, or which interpolation to use
            when resampling. For example, `preprocessing`_ and `augmentation`_
            intensity transforms will only be applied to images with type
            :attr:`torchio.INTENSITY`. Spatial transforms will be applied to
            all types, and nearest neighbor interpolation is always used to
            resample images with type :attr:`torchio.LABEL`.
            The type :attr:`torchio.SAMPLING_MAP` may be used with instances of
            :py:class:`~torchio.data.sampler.weighted.WeightedSampler`.
        tensor: If :attr:`path` is not given, :attr:`tensor` must be a 3D
            :py:class:`torch.Tensor` or NumPy array with dimensions
            :math:`(D, H, W)`.
        affine: If :attr:`path` is not given, :attr:`affine` must be a
            :math:`4 \times 4` NumPy array. If ``None``, :attr:`affine` is an
            identity matrix.
        check_nans: If ``True``, issues a warning if NaNs are found
            in the image.
        **kwargs: Items that will be added to image dictionary within the
            subject sample.

    Example:
        >>> import torch
        >>> import torchio
        >>> # Loading from a file
        >>> t1_image = torchio.Image('t1.nii.gz', type=torchio.INTENSITY)
        >>> # Also:
        >>> image = torchio.ScalarImage('t1.nii.gz')
        >>> label_image = torchio.Image('t1_seg.nii.gz', type=torchio.LABEL)
        >>> # Also:
        >>> label_image = torchio.LabelMap('t1_seg.nii.gz')
        >>> image = torchio.Image(tensor=torch.rand(3, 4, 5))
        >>> image = torchio.Image('safe_image.nrrd', check_nans=False)
        >>> data, affine = image.data, image.affine
        >>> affine.shape
        (4, 4)
        >>> image.data is image[torchio.DATA]
        True
        >>> image.data is image.tensor
        True
        >>> type(image.data)
        torch.Tensor

    .. _lazy loaders: https://en.wikipedia.org/wiki/Lazy_loading
    .. _preprocessing: https://torchio.readthedocs.io/transforms/preprocessing.html#intensity
    .. _augmentation: https://torchio.readthedocs.io/transforms/augmentation.html#intensity
    .. _NiBabel docs: https://nipy.org/nibabel/image_orientation.html
    .. _3D Slicer wiki: https://www.slicer.org/wiki/Coordinate_systems
    .. _FSL docs: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Orientation%20Explained
    .. _Graham Wideman's website: http://www.grahamwideman.com/gw/brain/orientation/orientterms.htm

    """
    def __init__(
            self,
            path: Optional[TypePath] = None,
            type: str = INTENSITY,
            tensor: Optional[TypeData] = None,
            affine: Optional[TypeData] = None,
            check_nans: bool = True,
            **kwargs: Dict[str, Any],
            ):
        if path is None and tensor is None:
            raise ValueError('A value for path or tensor must be given')
        if path is not None:
            if tensor is not None or affine is not None:
                message = 'If a path is given, tensor and affine must be None'
                raise ValueError(message)
        self._loaded = False
        tensor = self.parse_tensor(tensor)
        affine = self.parse_affine(affine)
        if tensor is not None:
            if affine is None:
                affine = np.eye(4)
            self[DATA] = tensor
            self[AFFINE] = affine
            self._loaded = True
        for key in (DATA, AFFINE, TYPE, PATH, STEM):

            if key in kwargs:
                message = f'Key "{key}" is reserved. Use a different one'
                raise ValueError(message)

        super().__init__(**kwargs)
        self.path = self._parse_path(path)
        self[PATH] = '' if self.path is None else str(self.path)
        self[STEM] = '' if self.path is None else get_stem(self.path)
        self[TYPE] = type
        self.check_nans = check_nans

    def __repr__(self):
        properties = []
        if self._loaded:
            properties.extend([
                f'shape: {self.shape}',
                f'spacing: {self.get_spacing_string()}',
                f'orientation: {"".join(self.orientation)}+',
                f'memory: {humanize.naturalsize(self.memory, binary=True)}',
            ])
        else:
            properties.append(f'path: "{self.path}"')
        properties.append(f'type: {self.type}')
        properties = '; '.join(properties)
        string = f'{self.__class__.__name__}({properties})'
        return string

    def __getitem__(self, item):
        if item in (DATA, AFFINE):
            if item not in self:
                self.load()
        return super().__getitem__(item)

    @property
    def data(self):
        return self[DATA]

    @property
    def tensor(self):
        return self.data

    @property
    def affine(self):
        return self[AFFINE]

    @property
    def type(self):
        return self[TYPE]

    @property
    def shape(self) -> Tuple[int, int, int, int]:
        return tuple(self.data.shape)

    @property
    def spatial_shape(self) -> TypeTripletInt:
        return self.shape[1:]

    @property
    def orientation(self):
        return nib.aff2axcodes(self.affine)

    @property
    def spacing(self):
        _, spacing = get_rotation_and_spacing_from_affine(self.affine)
        return tuple(spacing)

    @property
    def memory(self):
        return np.prod(self.shape) * 4  # float32, i.e. 4 bytes per voxel

    def get_spacing_string(self):
        strings = [f'{n:.2f}' for n in self.spacing]
        string = f'({", ".join(strings)})'
        return string

    @staticmethod
    def _parse_path(path: TypePath) -> Path:
        if path is None:
            return None
        try:
            path = Path(path).expanduser()
        except TypeError:
            message = f'Conversion to path not possible for variable: {path}'
            raise TypeError(message)
        if not (path.is_file() or path.is_dir()):  # might be a dir with DICOM
            raise FileNotFoundError(f'File not found: {path}')
        return path

    @staticmethod
    def parse_tensor(tensor: TypeData) -> torch.Tensor:
        if tensor is None:
            return None
        if isinstance(tensor, np.ndarray):
            tensor = torch.from_numpy(tensor)
        num_dimensions = tensor.dim()
        if num_dimensions != 3:
            message = (
                'The input tensor must have 3 dimensions (D, H, W),'
                f' but has {num_dimensions}: {tensor.shape}'
            )
            raise RuntimeError(message)
        tensor = tensor.unsqueeze(0)  # add channels dimension
        tensor = tensor.float()
        return tensor

    @staticmethod
    def parse_affine(affine: np.ndarray) -> np.ndarray:
        if affine is None:
            return np.eye(4)
        if not isinstance(affine, np.ndarray):
            raise TypeError(f'Affine must be a NumPy array, not {type(affine)}')
        if affine.shape != (4, 4):
            raise ValueError(f'Affine shape must be (4, 4), not {affine.shape}')
        return affine

    def load(self) -> Tuple[torch.Tensor, np.ndarray]:
        r"""Load the image from disk.

        The file is expected to be monomodal/grayscale and 2D or 3D.
        A channels dimension is added to the tensor.

        Returns:
            Tuple containing a 4D data tensor of size
            :math:`(1, D_{in}, H_{in}, W_{in})`
            and a 2D 4x4 affine matrix
        """
        if self._loaded:
            return
        if self.path is None:
            return
        tensor, affine = read_image(self.path)
        # https://github.com/pytorch/pytorch/issues/9410#issuecomment-404968513
        tensor = tensor[(None,) * (3 - tensor.ndim)]  # force to be 3D
        # Remove next line and uncomment the two following ones once/if this issue
        # gets fixed:
        # https://github.com/pytorch/pytorch/issues/29010
        # See also https://discuss.pytorch.org/t/collating-named-tensors/78650/4
        tensor = tensor.unsqueeze(0)  # add channels dimension
        # name_dimensions(tensor, affine)
        # tensor = tensor.align_to('channels', ...)
        if self.check_nans and torch.isnan(tensor).any():
            warnings.warn(f'NaNs found in file "{self.path}"')
        self[DATA] = tensor
        self[AFFINE] = affine
        self._loaded = True

    def save(self, path):
        """Save image to disk.

        Args:
            path: String or instance of :py:class:`pathlib.Path`.
        """
        tensor = self[DATA].squeeze()  # assume 2D if (1, 1, H, W)
        affine = self[AFFINE]
        write_image(tensor, affine, path)

    def is_2d(self) -> bool:
        return self.shape[-3] == 1

    def numpy(self) -> np.ndarray:
        """Get a NumPy array containing the image data."""
        return self[DATA].numpy()

    def as_sitk(self) -> sitk.Image:
        """Get the image as an instance of :py:class:`sitk.Image`."""
        return nib_to_sitk(self[DATA][0], self[AFFINE])

    def get_center(self, lps: bool = False) -> TypeTripletFloat:
        """Get image center in RAS+ or LPS+ coordinates.

        Args:
            lps: If ``True``, the coordinates will be in LPS+ orientation, i.e.
                the first dimension grows towards the left, etc. Otherwise, the
                coordinates will be in RAS+ orientation.
        """
        image = self.as_sitk()
        size = np.array(image.GetSize())
        center_index = (size - 1) / 2
        l, p, s = image.TransformContinuousIndexToPhysicalPoint(center_index)
        if lps:
            return (l, p, s)
        else:
            return (-l, -p, s)

    def set_check_nans(self, check_nans):
        self.check_nans = check_nans

    def crop(self, index_ini, index_fin):
        # TODO: add the rest of kwargs
        new_origin = nib.affines.apply_affine(self.affine, index_ini)
        new_affine = self.affine.copy()
        new_affine[:3, 3] = new_origin
        i0, j0, k0 = index_ini
        i1, j1, k1 = index_fin
        patch = self.data[0, i0:i1, j0:j1, k0:k1].clone()
        return self.__class__(tensor=patch, affine=new_affine, type=self.type)


class ScalarImage(Image):
    """Alias for :py:class:`~torchio.Image` of type :py:attr:`torchio.INTENSITY`.

    See :py:class:`~torchio.Image` for more information.

    Raises:
        ValueError: A :py:attr:`type` is used for instantiation.
    """
    def __init__(self, *args, **kwargs):
        if 'type' in kwargs:
            raise ValueError('Type of ScalarImage is always torchio.INTENSITY')
        super().__init__(*args, **kwargs, type=INTENSITY)


class LabelMap(Image):
    """Alias for :py:class:`~torchio.Image` of type :py:attr:`torchio.LABEL`.

    See :py:class:`~torchio.Image` for more information.

    Raises:
        ValueError: A :py:attr:`type` is used for instantiation.
    """
    def __init__(self, *args, **kwargs):
        if 'type' in kwargs:
            raise ValueError('Type of LabelMap is always torchio.LABEL')
        super().__init__(*args, **kwargs, type=LABEL)


1			import warnings
2			from pathlib import Path
3			from typing import Any, Dict, Tuple, Optional
4
5			import torch
6			import humanize
7			import numpy as np
8			import nibabel as nib
9			import SimpleITK as sitk
10
11			from ..utils import nib_to_sitk, get_rotation_and_spacing_from_affine, get_stem
12			from ..torchio import (
13			TypeData,
14			TypePath,
15			TypeTripletInt,
16			TypeTripletFloat,
17			DATA,
18			TYPE,
19			AFFINE,
20			PATH,
21			STEM,
22			INTENSITY,
23			LABEL,
24			)
25			from .io import read_image, write_image
26
27
28			class Image(dict):
29			r"""TorchIO image.
30
31			TorchIO images are `lazy loaders`_, i.e. the data is only loaded from disk
32			when needed.
33
34			Example:
35			>>> import torchio
36			>>> image = torchio.Image('t1.nii.gz', type=torchio.INTENSITY)
37			>>> image # not loaded yet
38			Image(path: t1.nii.gz; type: intensity)
39			>>> times_two = 2 * image.data # data is loaded and cached here
40			>>> image
41			Image(shape: (1, 256, 256, 176); spacing: (1.00, 1.00, 1.00); orientation: PIR+; memory: 44.0 MiB; type: intensity)
42			>>> image.save('doubled_image.nii.gz')
43
44			For information about medical image orientation, check out `NiBabel docs`_,
45			the `3D Slicer wiki`_, `Graham Wideman's website`_ or `FSL docs`_.
46
47			Args:
48			path: Path to a file that can be read by
49			:mod:`SimpleITK` or :mod:`nibabel` or to a directory containing
50			DICOM files.
51			type: Type of image, such as :attr:`torchio.INTENSITY` or
52			:attr:`torchio.LABEL`. This will be used by the transforms to
53			decide whether to apply an operation, or which interpolation to use
54			when resampling. For example, `preprocessing`_ and `augmentation`_
55			intensity transforms will only be applied to images with type
56			:attr:`torchio.INTENSITY`. Spatial transforms will be applied to
57			all types, and nearest neighbor interpolation is always used to
58			resample images with type :attr:`torchio.LABEL`.
59			The type :attr:`torchio.SAMPLING_MAP` may be used with instances of
60			:py:class:`~torchio.data.sampler.weighted.WeightedSampler`.
61			tensor: If :attr:`path` is not given, :attr:`tensor` must be a 3D
62			:py:class:`torch.Tensor` or NumPy array with dimensions
63			:math:`(D, H, W)`.
64			affine: If :attr:`path` is not given, :attr:`affine` must be a
65			:math:`4 \times 4` NumPy array. If ``None``, :attr:`affine` is an
66			identity matrix.
67			check_nans: If ``True``, issues a warning if NaNs are found
68			in the image.
69			**kwargs: Items that will be added to image dictionary within the
70			subject sample.
71
72			Example:
73			>>> import torch
74			>>> import torchio
75			>>> # Loading from a file
76			>>> t1_image = torchio.Image('t1.nii.gz', type=torchio.INTENSITY)
77			>>> # Also:
78			>>> image = torchio.ScalarImage('t1.nii.gz')
79			>>> label_image = torchio.Image('t1_seg.nii.gz', type=torchio.LABEL)
80			>>> # Also:
81			>>> label_image = torchio.LabelMap('t1_seg.nii.gz')
82			>>> image = torchio.Image(tensor=torch.rand(3, 4, 5))
83			>>> image = torchio.Image('safe_image.nrrd', check_nans=False)
84			>>> data, affine = image.data, image.affine
85			>>> affine.shape
86			(4, 4)
87			>>> image.data is image[torchio.DATA]
88			True
89			>>> image.data is image.tensor
90			True
91			>>> type(image.data)
92			torch.Tensor
93
94			.. _lazy loaders: https://en.wikipedia.org/wiki/Lazy_loading
95			.. _preprocessing: https://torchio.readthedocs.io/transforms/preprocessing.html#intensity
96			.. _augmentation: https://torchio.readthedocs.io/transforms/augmentation.html#intensity
97			.. _NiBabel docs: https://nipy.org/nibabel/image_orientation.html
98			.. _3D Slicer wiki: https://www.slicer.org/wiki/Coordinate_systems
99			.. _FSL docs: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Orientation%20Explained
100			.. _Graham Wideman's website: http://www.grahamwideman.com/gw/brain/orientation/orientterms.htm
101
102			"""
103			def __init__(
104			self,
105			path: Optional[TypePath] = None,
106			type: str = INTENSITY,
107			tensor: Optional[TypeData] = None,
108			affine: Optional[TypeData] = None,
109			check_nans: bool = True,
110			**kwargs: Dict[str, Any],
111			):
112			if path is None and tensor is None:
113			raise ValueError('A value for path or tensor must be given')
114			if path is not None:
115			if tensor is not None or affine is not None:
116			message = 'If a path is given, tensor and affine must be None'
117			raise ValueError(message)
118			self._loaded = False
119			tensor = self.parse_tensor(tensor)
120			affine = self.parse_affine(affine)
121			if tensor is not None:
122			if affine is None:
123			affine = np.eye(4)
124			self[DATA] = tensor
125			self[AFFINE] = affine
126			self._loaded = True
127			for key in (DATA, AFFINE, TYPE, PATH, STEM):
			0 ignored issues – show Comprehensibility Best Practice introduced 2020-06-21 16:53 UTC by Report Bug Copy Issue Report The variable `AFFINE` does not seem to be defined. Loading history... Comprehensibility Best Practice introduced 2020-06-21 16:53 UTC by Report Bug Copy Issue Report The variable `DATA` does not seem to be defined. Loading history... Comprehensibility Best Practice introduced 2020-06-21 16:53 UTC by Report Bug Copy Issue Report The variable `TYPE` does not seem to be defined. Loading history... Comprehensibility Best Practice introduced 2020-06-21 16:53 UTC by Report Bug Copy Issue Report The variable `PATH` does not seem to be defined. Loading history... Comprehensibility Best Practice introduced 2020-06-21 16:53 UTC by Report Bug Copy Issue Report The variable `STEM` does not seem to be defined. Loading history...
128			if key in kwargs:
129			message = f'Key "{key}" is reserved. Use a different one'
130			raise ValueError(message)
131
132			super().__init__(**kwargs)
133			self.path = self._parse_path(path)
134			self[PATH] = '' if self.path is None else str(self.path)
135			self[STEM] = '' if self.path is None else get_stem(self.path)
136			self[TYPE] = type
137			self.check_nans = check_nans
138
139			def __repr__(self):
140			properties = []
141			if self._loaded:
142			properties.extend([
143			f'shape: {self.shape}',
144			f'spacing: {self.get_spacing_string()}',
145			f'orientation: {"".join(self.orientation)}+',
146			f'memory: {humanize.naturalsize(self.memory, binary=True)}',
147			])
148			else:
149			properties.append(f'path: "{self.path}"')
150			properties.append(f'type: {self.type}')
151			properties = '; '.join(properties)
152			string = f'{self.__class__.__name__}({properties})'
153			return string
154
155			def __getitem__(self, item):
156			if item in (DATA, AFFINE):
157			if item not in self:
158			self.load()
159			return super().__getitem__(item)
160
161			@property
162			def data(self):
163			return self[DATA]
164
165			@property
166			def tensor(self):
167			return self.data
168
169			@property
170			def affine(self):
171			return self[AFFINE]
172
173			@property
174			def type(self):
175			return self[TYPE]
176
177			@property
178			def shape(self) -> Tuple[int, int, int, int]:
179			return tuple(self.data.shape)
180
181			@property
182			def spatial_shape(self) -> TypeTripletInt:
183			return self.shape[1:]
184
185			@property
186			def orientation(self):
187			return nib.aff2axcodes(self.affine)
188
189			@property
190			def spacing(self):
191			_, spacing = get_rotation_and_spacing_from_affine(self.affine)
192			return tuple(spacing)
193
194			@property
195			def memory(self):
196			return np.prod(self.shape) * 4 # float32, i.e. 4 bytes per voxel
197
198			def get_spacing_string(self):
199			strings = [f'{n:.2f}' for n in self.spacing]
200			string = f'({", ".join(strings)})'
201			return string
202
203			@staticmethod
204			def _parse_path(path: TypePath) -> Path:
205			if path is None:
206			return None
207			try:
208			path = Path(path).expanduser()
209			except TypeError:
210			message = f'Conversion to path not possible for variable: {path}'
211			raise TypeError(message)
212			if not (path.is_file() or path.is_dir()): # might be a dir with DICOM
213			raise FileNotFoundError(f'File not found: {path}')
214			return path
215
216			@staticmethod
217			def parse_tensor(tensor: TypeData) -> torch.Tensor:
218			if tensor is None:
219			return None
220			if isinstance(tensor, np.ndarray):
221			tensor = torch.from_numpy(tensor)
222			num_dimensions = tensor.dim()
223			if num_dimensions != 3:
224			message = (
225			'The input tensor must have 3 dimensions (D, H, W),'
226			f' but has {num_dimensions}: {tensor.shape}'
227			)
228			raise RuntimeError(message)
229			tensor = tensor.unsqueeze(0) # add channels dimension
230			tensor = tensor.float()
231			return tensor
232
233			@staticmethod
234			def parse_affine(affine: np.ndarray) -> np.ndarray:
235			if affine is None:
236			return np.eye(4)
237			if not isinstance(affine, np.ndarray):
238			raise TypeError(f'Affine must be a NumPy array, not {type(affine)}')
239			if affine.shape != (4, 4):
240			raise ValueError(f'Affine shape must be (4, 4), not {affine.shape}')
241			return affine
242
243			def load(self) -> Tuple[torch.Tensor, np.ndarray]:
244			r"""Load the image from disk.
245
246			The file is expected to be monomodal/grayscale and 2D or 3D.
247			A channels dimension is added to the tensor.
248
249			Returns:
250			Tuple containing a 4D data tensor of size
251			:math:`(1, D_{in}, H_{in}, W_{in})`
252			and a 2D 4x4 affine matrix
253			"""
254			if self._loaded:
255			return
256			if self.path is None:
257			return
258			tensor, affine = read_image(self.path)
259			# https://github.com/pytorch/pytorch/issues/9410#issuecomment-404968513
260			tensor = tensor[(None,) * (3 - tensor.ndim)] # force to be 3D
261			# Remove next line and uncomment the two following ones once/if this issue
262			# gets fixed:
263			# https://github.com/pytorch/pytorch/issues/29010
264			# See also https://discuss.pytorch.org/t/collating-named-tensors/78650/4
265			tensor = tensor.unsqueeze(0) # add channels dimension
266			# name_dimensions(tensor, affine)
267			# tensor = tensor.align_to('channels', ...)
268			if self.check_nans and torch.isnan(tensor).any():
269			warnings.warn(f'NaNs found in file "{self.path}"')
270			self[DATA] = tensor
271			self[AFFINE] = affine
272			self._loaded = True
273
274			def save(self, path):
275			"""Save image to disk.
276
277			Args:
278			path: String or instance of :py:class:`pathlib.Path`.
279			"""
280			tensor = self[DATA].squeeze() # assume 2D if (1, 1, H, W)
281			affine = self[AFFINE]
282			write_image(tensor, affine, path)
283
284			def is_2d(self) -> bool:
285			return self.shape[-3] == 1
286
287			def numpy(self) -> np.ndarray:
288			"""Get a NumPy array containing the image data."""
289			return self[DATA].numpy()
290
291			def as_sitk(self) -> sitk.Image:
292			"""Get the image as an instance of :py:class:`sitk.Image`."""
293			return nib_to_sitk(self[DATA][0], self[AFFINE])
294
295			def get_center(self, lps: bool = False) -> TypeTripletFloat:
296			"""Get image center in RAS+ or LPS+ coordinates.
297
298			Args:
299			lps: If ``True``, the coordinates will be in LPS+ orientation, i.e.
300			the first dimension grows towards the left, etc. Otherwise, the
301			coordinates will be in RAS+ orientation.
302			"""
303			image = self.as_sitk()
304			size = np.array(image.GetSize())
305			center_index = (size - 1) / 2
306			l, p, s = image.TransformContinuousIndexToPhysicalPoint(center_index)
307			if lps:
308			return (l, p, s)
309			else:
310			return (-l, -p, s)
311
312			def set_check_nans(self, check_nans):
313			self.check_nans = check_nans
314
315			def crop(self, index_ini, index_fin):
316			# TODO: add the rest of kwargs
317			new_origin = nib.affines.apply_affine(self.affine, index_ini)
318			new_affine = self.affine.copy()
319			new_affine[:3, 3] = new_origin
320			i0, j0, k0 = index_ini
321			i1, j1, k1 = index_fin
322			patch = self.data[0, i0:i1, j0:j1, k0:k1].clone()
323			return self.__class__(tensor=patch, affine=new_affine, type=self.type)
324
325
326			class ScalarImage(Image):
327			"""Alias for :py:class:`~torchio.Image` of type :py:attr:`torchio.INTENSITY`.
328
329			See :py:class:`~torchio.Image` for more information.
330
331			Raises:
332			ValueError: A :py:attr:`type` is used for instantiation.
333			"""
334			def __init__(self, args, *kwargs):
335			if 'type' in kwargs:
336			raise ValueError('Type of ScalarImage is always torchio.INTENSITY')
337			super().__init__(args, *kwargs, type=INTENSITY)
338
339
340			class LabelMap(Image):
341			"""Alias for :py:class:`~torchio.Image` of type :py:attr:`torchio.LABEL`.
342
343			See :py:class:`~torchio.Image` for more information.
344
345			Raises:
346			ValueError: A :py:attr:`type` is used for instantiation.
347			"""
348			def __init__(self, args, *kwargs):
349			if 'type' in kwargs:
350			raise ValueError('Type of LabelMap is always torchio.LABEL')
351			super().__init__(args, *kwargs, type=LABEL)
352

fepegar / torchio

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