torchio.data.image.Image.__init__() - Code Metrics - Inspection of "Add support to pass multiple paths to Image (#261)" - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( d76542...7bf0dc )

by Fernando

created 2020-08-11 23:28 UTC

torchio.data.image.Image.init() C

↳ Parent: torchio.data.image

Complexity

Conditions

Size

Total Lines	48
Code Lines	37

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	10
eloc	37
nop	9
dl	0
loc	48
rs	5.9999
c	0
b	0
f	0

How to fix Complexity Many Parameters

import warnings
from pathlib import Path
from typing import Any, Dict, Tuple, Optional, Union, Sequence, List

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,
    ensure_4d,
)
from ..torchio import (
    TypeData,
    TypePath,
    TypeTripletInt,
    TypeTripletFloat,
    DATA,
    TYPE,
    AFFINE,
    PATH,
    STEM,
    INTENSITY,
    LABEL,
)
from .io import read_image, write_image


PROTECTED_KEYS = DATA, AFFINE, TYPE, PATH, STEM


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

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

    Args:
        path: Path to a file or sequence of paths to files that can be read by
            :mod:`SimpleITK` or :mod:`nibabel`, or to a directory containing
            DICOM files. If :py:attr:`tensor` is given, the data in
            :py:attr:`path` will not be read. The data is expected to be 2D or
            3D, and may have multiple channels (see :attr:`num_spatial_dims` and
            :attr:`channels_last`). If a sequence of paths is given, data
            will be concatenated on the channel dimension so spatial
            dimensions must match.
        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 :py:attr:`path` is not given, :attr:`tensor` must be a 4D
            :py:class:`torch.Tensor` or NumPy array with dimensions
            :math:`(C, D, H, W)`. If it is not 4D, TorchIO will try to guess
            the dimensions meanings. If 2D, the shape will be interpreted as
            :math:`(H, W)`. If 3D, the number of spatial dimensions should be
            determined in :attr:`num_spatial_dims`. If :attr:`num_spatial_dims`
            is not given and the shape is 3 along the first or last dimensions,
            it will be interpreted as a multichannel 2D image. Otherwise, it
            be interpreted as a 3D image with a single channel.
        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. If ``False``, images will not be checked for the
            presence of NaNs.
        num_spatial_dims: If ``2`` and the input tensor has 3 dimensions, it
            will be interpreted as a multichannel 2D image. If ``3`` and the
            input has 3 dimensions, it will be interpreted as a
            single-channel 3D volume.
        channels_last: If ``True``, the last dimension of the input will be
            interpreted as the channels. Defaults to ``True`` if :attr:`path` is
            given and ``False`` otherwise.
        **kwargs: Items that will be added to the image dictionary, e.g.
            acquisition parameters.

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

    Example:
        >>> import torchio
        >>> image = torchio.ScalarImage('t1.nii.gz')  # subclass of Image
        >>> image  # not loaded yet
        ScalarImage(path: t1.nii.gz; type: intensity)
        >>> times_two = 2 * image.data  # data is loaded and cached here
        >>> image
        ScalarImage(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')

    .. _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
    .. _SimpleITK docs: https://simpleitk.readthedocs.io/en/master/fundamentalConcepts.html
    .. _Graham Wideman's website: http://www.grahamwideman.com/gw/brain/orientation/orientterms.htm
    """
    def __init__(
            self,
            path: Union[TypePath, Sequence[TypePath], None] = None,
            type: str = None,
            tensor: Optional[TypeData] = None,
            affine: Optional[TypeData] = None,
            check_nans: bool = True,
            num_spatial_dims: Optional[int] = None,
            channels_last: Optional[bool] = None,
            **kwargs: Dict[str, Any],
            ):
        self.check_nans = check_nans
        self.num_spatial_dims = num_spatial_dims

        if type is None:
            warnings.warn(
                'Not specifying the image type is deprecated and will be'
                ' mandatory in the future. You can probably use ScalarImage or'
                ' LabelMap instead'
            )
            type = INTENSITY

        if path is None and tensor is None:
            raise ValueError('A value for path or tensor must be given')
        self._loaded = False

        # Number of channels are typically stored in the last dimensions in disk
        # But if a tensor is given, the channels should be in the first dim
        if channels_last is None:
            channels_last = path is not None
        self.channels_last = channels_last

        tensor = self.parse_tensor(tensor)
        affine = self.parse_affine(affine)
        if tensor is not None:
            self[DATA] = tensor
            self[AFFINE] = affine
            self._loaded = True
        for key in PROTECTED_KEYS:
            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

    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)

    def __array__(self):
        return self[DATA].numpy()

    def __copy__(self):
        kwargs = dict(
            tensor=self.data,
            affine=self.affine,
            type=self.type,
            path=self.path,
            channels_last=False,
        )
        for key, value in self.items():
            if key in PROTECTED_KEYS: continue
            kwargs[key] = value  # should I copy? deepcopy?
        return self.__class__(**kwargs)

    @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 axis_name_to_index(self, axis: str):
        """Convert an axis name to an axis index.

        Args:
            axis: Possible inputs are ``'Left'``, ``'Right'``, ``'Anterior'``,
            ``'Posterior'``, ``'Inferior'``, ``'Superior'``. Lower-case versions
            and first letters are also valid, as only the first letter will be
            used.

        .. note:: If you are working with animals, you should probably use
            ``'Superior'``, ``'Inferior'``, ``'Anterior'`` and ``'Posterior'``
            for ``'Dorsal'``, ``'Ventral'``, ``'Rostral'`` and ``'Caudal'``,
            respectively.
        """
        if not isinstance(axis, str):
            raise ValueError('Axis must be a string')
        axis = axis[0].upper()

        # Generally, TorchIO tensors are (C, D, H, W)
        if axis == 'H':
            return -2
        elif axis == 'W':
            return -1
        else:
            try:
                index = self.orientation.index(axis)
            except ValueError:
                index = self.orientation.index(self.flip_axis(axis))
            # Return negative indices so that it does not matter whether we
            # refer to spatial dimensions or not
            index = -4 + index
            return index

    @staticmethod
    def flip_axis(axis):
        if axis == 'R': return 'L'
        elif axis == 'L': return 'R'
        elif axis == 'A': return 'P'
        elif axis == 'P': return 'A'
        elif axis == 'I': return 'S'
        elif axis == 'S': return 'I'
        else:
            message = (
                f'Axis not understood. Please use a value in {tuple("LRAPIS")}'
            )
            raise ValueError(message)

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

    def get_bounds(self):
        """Get image bounds in mm."""
        first_index = 3 * (-0.5,)
        last_index = np.array(self.spatial_shape) - 0.5
        first_point = nib.affines.apply_affine(self.affine, first_index)
        last_point = nib.affines.apply_affine(self.affine, last_index)
        array = np.array((first_point, last_point))
        bounds_x, bounds_y, bounds_z = array.T.tolist()
        return bounds_x, bounds_y, bounds_z

    @staticmethod
    def _parse_single_path(
            path: TypePath
            ) -> Path:
        try:
            path = Path(path).expanduser()
        except TypeError:
            message = (
                f'Expected type str or Path but found {path} with '
                f'{type(path)} instead'
            )
            raise TypeError(message)
        except RuntimeError:
            message = (
                f'Conversion to path not possible for variable: {path}'
            )
            raise RuntimeError(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

    def _parse_path(
            self,
            path: Union[TypePath, Sequence[TypePath]]
            ) -> Union[Path, List[Path]]:
        if path is None:
            return None
        if isinstance(path, (str, Path)):
            return self._parse_single_path(path)
        else:
            return [self._parse_single_path(p) for p in path]

    def parse_tensor(self, tensor: TypeData) -> torch.Tensor:
        if tensor is None:
            return None
        if isinstance(tensor, np.ndarray):
            tensor = torch.from_numpy(tensor.astype(np.float32))
        elif isinstance(tensor, torch.Tensor):
            tensor = tensor.float()
        tensor = self.parse_tensor_shape(tensor)
        if self.check_nans and torch.isnan(tensor).any():
            warnings.warn(f'NaNs found in tensor')
        return tensor

    def parse_tensor_shape(self, tensor: torch.Tensor) -> torch.Tensor:
        return ensure_4d(tensor, self.channels_last, self.num_spatial_dims)

    @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) -> None:
        r"""Load the image from disk.

        Returns:
            Tuple containing a 4D tensor of size :math:`(C, D, H, W)` and a 2D
            :math:`4 \times 4` affine matrix to convert voxel indices to world
            coordinates.
        """
        if self._loaded:
            return

        paths = self.path if isinstance(self.path, list) else [self.path]
        tensor, affine = read_image(paths[0])
        tensor = self.parse_tensor_shape(tensor)

        if self.check_nans and torch.isnan(tensor).any():
            warnings.warn(f'NaNs found in file "{paths[0]}"')

        tensors = [tensor]
        for path in paths[1:]:
            new_tensor, new_affine = read_image(path)
            new_tensor = self.parse_tensor_shape(new_tensor)

            if self.check_nans and torch.isnan(tensor).any():
                warnings.warn(f'NaNs found in file "{path}"')

            if not np.array_equal(affine, new_affine):
                message = 'Files have different affine matrices'
                warnings.warn(message, RuntimeWarning)

            if not tensor.shape[1:] == new_tensor.shape[1:]:
                message = (
                    f'Files shape do not match, found {tensor.shape}'
                    f'and {new_tensor.shape}'
                )
                RuntimeError(message)

            tensors.append(new_tensor)

        tensor = torch.cat(tensors)

        self[DATA] = tensor
        self[AFFINE] = affine
        self._loaded = True

    def save(self, path, squeeze=True, channels_last=True):
        """Save image to disk.

        Args:
            path: String or instance of :py:class:`pathlib.Path`.
            squeeze: If ``True``, the singleton dimensions will be removed
                before saving.
            channels_last: If ``True``, the channels will be saved in the last
                dimension.
        """
        write_image(
            self[DATA],
            self[AFFINE],
            path,
            squeeze=squeeze,
            channels_last=channels_last,
        )

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

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

    def as_sitk(self) -> sitk.Image:
        """Get the image as an instance of :py:class:`sitk.Image`."""
        return nib_to_sitk(self[DATA], 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.
        """
        size = np.array(self.spatial_shape)
        center_index = (size - 1) / 2
        r, a, s = nib.affines.apply_affine(self.affine, center_index)
        if lps:
            return (-r, -a, s)
        else:
            return (r, a, s)

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

    def crop(self, index_ini: TypeTripletInt, index_fin: TypeTripletInt):
        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[:, i0:i1, j0:j1, k0:k1].clone()
        kwargs = dict(
            tensor=patch,
            affine=new_affine,
            type=self.type,
            path=self.path,
            channels_last=False,
        )
        for key, value in self.items():
            if key in PROTECTED_KEYS: continue
            kwargs[key] = value  # should I copy? deepcopy?
        return self.__class__(**kwargs)


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

    Example:
        >>> import torch
        >>> import torchio
        >>> # Loading from a file
        >>> t1_image = torchio.ScalarImage('t1.nii.gz')
        >>> dmri = torchio.ScalarImage(tensor=torch.rand(32, 128, 128, 88))
        >>> image = torchio.ScalarImage('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

    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 and kwargs['type'] != INTENSITY:
            raise ValueError('Type of ScalarImage is always torchio.INTENSITY')
        kwargs.update({'type': INTENSITY})
        super().__init__(*args, **kwargs)


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

    Example:
        >>> import torch
        >>> import torchio
        >>> labels = torchio.LabelMap(tensor=torch.rand(128, 128, 68) > 0.5)
        >>> labels = torchio.LabelMap('t1_seg.nii.gz')  # loading from a file
        >>> tpm = torchio.LabelMap(                     # loading from files
        ...     'gray_matter.nii.gz',
        ...     'white_matter.nii.gz',
        ...     'csf.nii.gz',
        ... )

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

    Raises:
        ValueError: If a value for :py:attr:`type` is given.
    """
    def __init__(self, *args, **kwargs):
        if 'type' in kwargs and kwargs['type'] != LABEL:
            raise ValueError('Type of LabelMap is always torchio.LABEL')
        kwargs.update({'type': LABEL})
        super().__init__(*args, **kwargs)


1			import warnings
2			from pathlib import Path
3			from typing import Any, Dict, Tuple, Optional, Union, Sequence, List
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 (
12			nib_to_sitk,
13			get_rotation_and_spacing_from_affine,
14			get_stem,
15			ensure_4d,
16			)
17			from ..torchio import (
18			TypeData,
19			TypePath,
20			TypeTripletInt,
21			TypeTripletFloat,
22			DATA,
23			TYPE,
24			AFFINE,
25			PATH,
26			STEM,
27			INTENSITY,
28			LABEL,
29			)
30			from .io import read_image, write_image
31
32
33			PROTECTED_KEYS = DATA, AFFINE, TYPE, PATH, STEM
34
35
36			class Image(dict):
37			r"""TorchIO image.
38
39			For information about medical image orientation, check out `NiBabel docs`_,
40			the `3D Slicer wiki`_, `Graham Wideman's website`_, `FSL docs`_ or
41			`SimpleITK docs`_.
42
43			Args:
44			path: Path to a file or sequence of paths to files that can be read by
45			:mod:`SimpleITK` or :mod:`nibabel`, or to a directory containing
46			DICOM files. If :py:attr:`tensor` is given, the data in
47			:py:attr:`path` will not be read. The data is expected to be 2D or
48			3D, and may have multiple channels (see :attr:`num_spatial_dims` and
49			:attr:`channels_last`). If a sequence of paths is given, data
50			will be concatenated on the channel dimension so spatial
51			dimensions must match.
52			type: Type of image, such as :attr:`torchio.INTENSITY` or
53			:attr:`torchio.LABEL`. This will be used by the transforms to
54			decide whether to apply an operation, or which interpolation to use
55			when resampling. For example, `preprocessing`_ and `augmentation`_
56			intensity transforms will only be applied to images with type
57			:attr:`torchio.INTENSITY`. Spatial transforms will be applied to
58			all types, and nearest neighbor interpolation is always used to
59			resample images with type :attr:`torchio.LABEL`.
60			The type :attr:`torchio.SAMPLING_MAP` may be used with instances of
61			:py:class:`~torchio.data.sampler.weighted.WeightedSampler`.
62			tensor: If :py:attr:`path` is not given, :attr:`tensor` must be a 4D
63			:py:class:`torch.Tensor` or NumPy array with dimensions
64			:math:`(C, D, H, W)`. If it is not 4D, TorchIO will try to guess
65			the dimensions meanings. If 2D, the shape will be interpreted as
66			:math:`(H, W)`. If 3D, the number of spatial dimensions should be
67			determined in :attr:`num_spatial_dims`. If :attr:`num_spatial_dims`
68			is not given and the shape is 3 along the first or last dimensions,
69			it will be interpreted as a multichannel 2D image. Otherwise, it
70			be interpreted as a 3D image with a single channel.
71			affine: If :attr:`path` is not given, :attr:`affine` must be a
72			:math:`4 \times 4` NumPy array. If ``None``, :attr:`affine` is an
73			identity matrix.
74			check_nans: If ``True``, issues a warning if NaNs are found
75			in the image. If ``False``, images will not be checked for the
76			presence of NaNs.
77			num_spatial_dims: If ``2`` and the input tensor has 3 dimensions, it
78			will be interpreted as a multichannel 2D image. If ``3`` and the
79			input has 3 dimensions, it will be interpreted as a
80			single-channel 3D volume.
81			channels_last: If ``True``, the last dimension of the input will be
82			interpreted as the channels. Defaults to ``True`` if :attr:`path` is
83			given and ``False`` otherwise.
84			**kwargs: Items that will be added to the image dictionary, e.g.
85			acquisition parameters.
86
87			TorchIO images are `lazy loaders`_, i.e. the data is only loaded from disk
88			when needed.
89
90			Example:
91			>>> import torchio
92			>>> image = torchio.ScalarImage('t1.nii.gz') # subclass of Image
93			>>> image # not loaded yet
94			ScalarImage(path: t1.nii.gz; type: intensity)
95			>>> times_two = 2 * image.data # data is loaded and cached here
96			>>> image
97			ScalarImage(shape: (1, 256, 256, 176); spacing: (1.00, 1.00, 1.00); orientation: PIR+; memory: 44.0 MiB; type: intensity)
98			>>> image.save('doubled_image.nii.gz')
99
100			.. _lazy loaders: https://en.wikipedia.org/wiki/Lazy_loading
101			.. _preprocessing: https://torchio.readthedocs.io/transforms/preprocessing.html#intensity
102			.. _augmentation: https://torchio.readthedocs.io/transforms/augmentation.html#intensity
103			.. _NiBabel docs: https://nipy.org/nibabel/image_orientation.html
104			.. _3D Slicer wiki: https://www.slicer.org/wiki/Coordinate_systems
105			.. _FSL docs: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Orientation%20Explained
106			.. _SimpleITK docs: https://simpleitk.readthedocs.io/en/master/fundamentalConcepts.html
107			.. _Graham Wideman's website: http://www.grahamwideman.com/gw/brain/orientation/orientterms.htm
108			"""
109			def __init__(
110			self,
111			path: Union[TypePath, Sequence[TypePath], None] = None,
112			type: str = None,
113			tensor: Optional[TypeData] = None,
114			affine: Optional[TypeData] = None,
115			check_nans: bool = True,
116			num_spatial_dims: Optional[int] = None,
117			channels_last: Optional[bool] = None,
118			**kwargs: Dict[str, Any],
119			):
120			self.check_nans = check_nans
121			self.num_spatial_dims = num_spatial_dims
122
123			if type is None:
124			warnings.warn(
125			'Not specifying the image type is deprecated and will be'
126			' mandatory in the future. You can probably use ScalarImage or'
127			' LabelMap instead'
128			)
129			type = INTENSITY
130
131			if path is None and tensor is None:
132			raise ValueError('A value for path or tensor must be given')
133			self._loaded = False
134
135			# Number of channels are typically stored in the last dimensions in disk
136			# But if a tensor is given, the channels should be in the first dim
137			if channels_last is None:
138			channels_last = path is not None
139			self.channels_last = channels_last
140
141			tensor = self.parse_tensor(tensor)
142			affine = self.parse_affine(affine)
143			if tensor is not None:
144			self[DATA] = tensor
145			self[AFFINE] = affine
146			self._loaded = True
147			for key in PROTECTED_KEYS:
148			if key in kwargs:
149			message = f'Key "{key}" is reserved. Use a different one'
150			raise ValueError(message)
151
152			super().__init__(**kwargs)
153			self.path = self._parse_path(path)
154			self[PATH] = '' if self.path is None else str(self.path)
155			self[STEM] = '' if self.path is None else get_stem(self.path)
156			self[TYPE] = type
157
158			def __repr__(self):
159			properties = []
160			if self._loaded:
161			properties.extend([
162			f'shape: {self.shape}',
163			f'spacing: {self.get_spacing_string()}',
164			f'orientation: {"".join(self.orientation)}+',
165			f'memory: {humanize.naturalsize(self.memory, binary=True)}',
166			])
167			else:
168			properties.append(f'path: "{self.path}"')
169			properties.append(f'type: {self.type}')
170			properties = '; '.join(properties)
171			string = f'{self.__class__.__name__}({properties})'
172			return string
173
174			def __getitem__(self, item):
175			if item in (DATA, AFFINE):
176			if item not in self:
177			self._load()
178			return super().__getitem__(item)
179
180			def __array__(self):
181			return self[DATA].numpy()
182
183			def __copy__(self):
184			kwargs = dict(
185			tensor=self.data,
186			affine=self.affine,
187			type=self.type,
188			path=self.path,
189			channels_last=False,
190			)
191			for key, value in self.items():
192			if key in PROTECTED_KEYS: continue
193			kwargs[key] = value # should I copy? deepcopy?
194			return self.__class__(**kwargs)
195
196			@property
197			def data(self):
198			return self[DATA]
199
200			@property
201			def tensor(self):
202			return self.data
203
204			@property
205			def affine(self):
206			return self[AFFINE]
207
208			@property
209			def type(self):
210			return self[TYPE]
211
212			@property
213			def shape(self) -> Tuple[int, int, int, int]:
214			return tuple(self.data.shape)
215
216			@property
217			def spatial_shape(self) -> TypeTripletInt:
218			return self.shape[1:]
219
220			@property
221			def orientation(self):
222			return nib.aff2axcodes(self.affine)
223
224			@property
225			def spacing(self):
226			_, spacing = get_rotation_and_spacing_from_affine(self.affine)
227			return tuple(spacing)
228
229			@property
230			def memory(self):
231			return np.prod(self.shape) * 4 # float32, i.e. 4 bytes per voxel
232
233			def axis_name_to_index(self, axis: str):
234			"""Convert an axis name to an axis index.
235
236			Args:
237			axis: Possible inputs are ``'Left'``, ``'Right'``, ``'Anterior'``,
238			``'Posterior'``, ``'Inferior'``, ``'Superior'``. Lower-case versions
239			and first letters are also valid, as only the first letter will be
240			used.
241
242			.. note:: If you are working with animals, you should probably use
243			``'Superior'``, ``'Inferior'``, ``'Anterior'`` and ``'Posterior'``
244			for ``'Dorsal'``, ``'Ventral'``, ``'Rostral'`` and ``'Caudal'``,
245			respectively.
246			"""
247			if not isinstance(axis, str):
248			raise ValueError('Axis must be a string')
249			axis = axis[0].upper()
250
251			# Generally, TorchIO tensors are (C, D, H, W)
252			if axis == 'H':
253			return -2
254			elif axis == 'W':
255			return -1
256			else:
257			try:
258			index = self.orientation.index(axis)
259			except ValueError:
260			index = self.orientation.index(self.flip_axis(axis))
261			# Return negative indices so that it does not matter whether we
262			# refer to spatial dimensions or not
263			index = -4 + index
264			return index
265
266			@staticmethod
267			def flip_axis(axis):
268			if axis == 'R': return 'L'
269			elif axis == 'L': return 'R'
270			elif axis == 'A': return 'P'
271			elif axis == 'P': return 'A'
272			elif axis == 'I': return 'S'
273			elif axis == 'S': return 'I'
274			else:
275			message = (
276			f'Axis not understood. Please use a value in {tuple("LRAPIS")}'
277			)
278			raise ValueError(message)
279
280			def get_spacing_string(self):
281			strings = [f'{n:.2f}' for n in self.spacing]
282			string = f'({", ".join(strings)})'
283			return string
284
285			def get_bounds(self):
286			"""Get image bounds in mm."""
287			first_index = 3 * (-0.5,)
288			last_index = np.array(self.spatial_shape) - 0.5
289			first_point = nib.affines.apply_affine(self.affine, first_index)
290			last_point = nib.affines.apply_affine(self.affine, last_index)
291			array = np.array((first_point, last_point))
292			bounds_x, bounds_y, bounds_z = array.T.tolist()
293			return bounds_x, bounds_y, bounds_z
294
295			@staticmethod
296			def _parse_single_path(
297			path: TypePath
298			) -> Path:
299			try:
300			path = Path(path).expanduser()
301			except TypeError:
302			message = (
303			f'Expected type str or Path but found {path} with '
304			f'{type(path)} instead'
305			)
306			raise TypeError(message)
307			except RuntimeError:
308			message = (
309			f'Conversion to path not possible for variable: {path}'
310			)
311			raise RuntimeError(message)
312
313			if not (path.is_file() or path.is_dir()): # might be a dir with DICOM
314			raise FileNotFoundError(f'File not found: {path}')
315			return path
316
317			def _parse_path(
318			self,
319			path: Union[TypePath, Sequence[TypePath]]
320			) -> Union[Path, List[Path]]:
321			if path is None:
322			return None
323			if isinstance(path, (str, Path)):
324			return self._parse_single_path(path)
325			else:
326			return [self._parse_single_path(p) for p in path]
327
328			def parse_tensor(self, tensor: TypeData) -> torch.Tensor:
329			if tensor is None:
330			return None
331			if isinstance(tensor, np.ndarray):
332			tensor = torch.from_numpy(tensor.astype(np.float32))
333			elif isinstance(tensor, torch.Tensor):
334			tensor = tensor.float()
335			tensor = self.parse_tensor_shape(tensor)
336			if self.check_nans and torch.isnan(tensor).any():
337			warnings.warn(f'NaNs found in tensor')
338			return tensor
339
340			def parse_tensor_shape(self, tensor: torch.Tensor) -> torch.Tensor:
341			return ensure_4d(tensor, self.channels_last, self.num_spatial_dims)
342
343			@staticmethod
344			def parse_affine(affine: np.ndarray) -> np.ndarray:
345			if affine is None:
346			return np.eye(4)
347			if not isinstance(affine, np.ndarray):
348			raise TypeError(f'Affine must be a NumPy array, not {type(affine)}')
349			if affine.shape != (4, 4):
350			raise ValueError(f'Affine shape must be (4, 4), not {affine.shape}')
351			return affine
352
353			def _load(self) -> None:
354			r"""Load the image from disk.
355
356			Returns:
357			Tuple containing a 4D tensor of size :math:`(C, D, H, W)` and a 2D
358			:math:`4 \times 4` affine matrix to convert voxel indices to world
359			coordinates.
360			"""
361			if self._loaded:
362			return
363
364			paths = self.path if isinstance(self.path, list) else [self.path]
365			tensor, affine = read_image(paths[0])
366			tensor = self.parse_tensor_shape(tensor)
367
368			if self.check_nans and torch.isnan(tensor).any():
369			warnings.warn(f'NaNs found in file "{paths[0]}"')
370
371			tensors = [tensor]
372			for path in paths[1:]:
373			new_tensor, new_affine = read_image(path)
374			new_tensor = self.parse_tensor_shape(new_tensor)
375
376			if self.check_nans and torch.isnan(tensor).any():
377			warnings.warn(f'NaNs found in file "{path}"')
378
379			if not np.array_equal(affine, new_affine):
380			message = 'Files have different affine matrices'
381			warnings.warn(message, RuntimeWarning)
382
383			if not tensor.shape[1:] == new_tensor.shape[1:]:
384			message = (
385			f'Files shape do not match, found {tensor.shape}'
386			f'and {new_tensor.shape}'
387			)
388			RuntimeError(message)
389
390			tensors.append(new_tensor)
391
392			tensor = torch.cat(tensors)
393
394			self[DATA] = tensor
395			self[AFFINE] = affine
396			self._loaded = True
397
398			def save(self, path, squeeze=True, channels_last=True):
399			"""Save image to disk.
400
401			Args:
402			path: String or instance of :py:class:`pathlib.Path`.
403			squeeze: If ``True``, the singleton dimensions will be removed
404			before saving.
405			channels_last: If ``True``, the channels will be saved in the last
406			dimension.
407			"""
408			write_image(
409			self[DATA],
410			self[AFFINE],
411			path,
412			squeeze=squeeze,
413			channels_last=channels_last,
414			)
415
416			def is_2d(self) -> bool:
417			return self.shape[-3] == 1
418
419			def numpy(self) -> np.ndarray:
420			"""Get a NumPy array containing the image data."""
421			return np.asarray(self)
422
423			def as_sitk(self) -> sitk.Image:
424			"""Get the image as an instance of :py:class:`sitk.Image`."""
425			return nib_to_sitk(self[DATA], self[AFFINE])
426
427			def get_center(self, lps: bool = False) -> TypeTripletFloat:
428			"""Get image center in RAS+ or LPS+ coordinates.
429
430			Args:
431			lps: If ``True``, the coordinates will be in LPS+ orientation, i.e.
432			the first dimension grows towards the left, etc. Otherwise, the
433			coordinates will be in RAS+ orientation.
434			"""
435			size = np.array(self.spatial_shape)
436			center_index = (size - 1) / 2
437			r, a, s = nib.affines.apply_affine(self.affine, center_index)
438			if lps:
439			return (-r, -a, s)
440			else:
441			return (r, a, s)
442
443			def set_check_nans(self, check_nans: bool):
444			self.check_nans = check_nans
445
446			def crop(self, index_ini: TypeTripletInt, index_fin: TypeTripletInt):
447			new_origin = nib.affines.apply_affine(self.affine, index_ini)
448			new_affine = self.affine.copy()
449			new_affine[:3, 3] = new_origin
450			i0, j0, k0 = index_ini
451			i1, j1, k1 = index_fin
452			patch = self.data[:, i0:i1, j0:j1, k0:k1].clone()
453			kwargs = dict(
454			tensor=patch,
455			affine=new_affine,
456			type=self.type,
457			path=self.path,
458			channels_last=False,
459			)
460			for key, value in self.items():
461			if key in PROTECTED_KEYS: continue
462			kwargs[key] = value # should I copy? deepcopy?
463			return self.__class__(**kwargs)
464
465
466			class ScalarImage(Image):
467			"""Alias for :py:class:`~torchio.Image` of type :py:attr:`torchio.INTENSITY`.
468
469			Example:
470			>>> import torch
471			>>> import torchio
472			>>> # Loading from a file
473			>>> t1_image = torchio.ScalarImage('t1.nii.gz')
474			>>> dmri = torchio.ScalarImage(tensor=torch.rand(32, 128, 128, 88))
475			>>> image = torchio.ScalarImage('safe_image.nrrd', check_nans=False)
476			>>> data, affine = image.data, image.affine
477			>>> affine.shape
478			(4, 4)
479			>>> image.data is image[torchio.DATA]
480			True
481			>>> image.data is image.tensor
482			True
483			>>> type(image.data)
484			torch.Tensor
485
486			See :py:class:`~torchio.Image` for more information.
487
488			Raises:
489			ValueError: A :py:attr:`type` is used for instantiation.
490			"""
491			def __init__(self, args, *kwargs):
492			if 'type' in kwargs and kwargs['type'] != INTENSITY:
493			raise ValueError('Type of ScalarImage is always torchio.INTENSITY')
494			kwargs.update({'type': INTENSITY})
495			super().__init__(args, *kwargs)
496
497
498			class LabelMap(Image):
499			"""Alias for :py:class:`~torchio.Image` of type :py:attr:`torchio.LABEL`.
500
501			Example:
502			>>> import torch
503			>>> import torchio
504			>>> labels = torchio.LabelMap(tensor=torch.rand(128, 128, 68) > 0.5)
505			>>> labels = torchio.LabelMap('t1_seg.nii.gz') # loading from a file
506			>>> tpm = torchio.LabelMap( # loading from files
507			... 'gray_matter.nii.gz',
508			... 'white_matter.nii.gz',
509			... 'csf.nii.gz',
510			... )
511
512			See :py:class:`~torchio.data.image.Image` for more information.
513
514			Raises:
515			ValueError: If a value for :py:attr:`type` is given.
516			"""
517			def __init__(self, args, *kwargs):
518			if 'type' in kwargs and kwargs['type'] != LABEL:
519			raise ValueError('Type of LabelMap is always torchio.LABEL')
520			kwargs.update({'type': LABEL})
521			super().__init__(args, *kwargs)
522

fepegar / torchio

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torchio.data.image.Image.__init__() C

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