torchio.data.image.Image.flip_axis() - Code Metrics - Inspection of "Use a single return statement" - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 2c67ba...a6348a )

by Fernando

created 2020-12-19 14:12 UTC

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

↳ Parent: torchio.data.image

Complexity

Conditions

Size

Total Lines	15
Code Lines	14

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	9
eloc	14
nop	1
dl	0
loc	15
rs	6.6666
c	0
b	0
f	0

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

import torch
import humanize
import numpy as np
from PIL import Image as ImagePIL
import nibabel as nib
import SimpleITK as sitk

from ..utils import get_stem
from ..typing import TypeData, TypePath, TypeTripletInt, TypeTripletFloat
from ..constants import DATA, TYPE, AFFINE, PATH, STEM, INTENSITY, LABEL
from .io import (
    ensure_4d,
    read_image,
    write_image,
    nib_to_sitk,
    check_uint_to_int,
    get_rotation_and_spacing_from_affine,
)


PROTECTED_KEYS = DATA, AFFINE, TYPE, PATH, STEM
TypeBound = Tuple[float, float]
TypeBounds = Tuple[TypeBound, TypeBound, TypeBound]


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 :attr:`tensor` is given, the data in
            :attr:`path` will not be read.
            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
            :class:`~torchio.data.sampler.weighted.WeightedSampler`.
        tensor: If :attr:`path` is not given, :attr:`tensor` must be a 4D
            :class:`torch.Tensor` or NumPy array with dimensions
            :math:`(C, W, H, D)`.
        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.
        **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 as tio
        >>> image = tio.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 = False,  # removed by ITK by default
            channels_last: bool = False,
            **kwargs: Dict[str, Any],
            ):
        self.check_nans = check_nans
        self.channels_last = channels_last

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

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

        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}"')
        if self._loaded:
            properties.append(f'dtype: {self.data.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,
        )
        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) -> torch.Tensor:
        """Tensor data. Same as :class:`Image.tensor`."""
        return self[DATA]

    @data.setter
    def data(self, tensor: TypeData):
        self[DATA] = self._parse_tensor(tensor, none_ok=False)

    @property
    def tensor(self) -> torch.Tensor:
        """Tensor data. Same as :class:`Image.data`."""
        return self.data

    @property
    def affine(self) -> np.ndarray:
        """Affine matrix to transform voxel indices into world coordinates."""
        return self[AFFINE]

    @affine.setter
    def affine(self, matrix):
        self[AFFINE] = self._parse_affine(matrix)

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

    @property
    def shape(self) -> Tuple[int, int, int, int]:
        """Tensor shape as :math:`(C, W, H, D)`."""
        return tuple(self.data.shape)

    @property
    def spatial_shape(self) -> TypeTripletInt:
        """Tensor spatial shape as :math:`(W, H, D)`."""
        return self.shape[1:]

    def check_is_2d(self) -> None:
        if not self.is_2d():
            message = f'Image is not 2D. Spatial shape: {self.spatial_shape}'
            raise RuntimeError(message)

    @property
    def height(self) -> int:
        """Image height, if 2D."""
        self.check_is_2d()
        return self.spatial_shape[1]

    @property
    def width(self) -> int:
        """Image width, if 2D."""
        self.check_is_2d()
        return self.spatial_shape[0]

    @property
    def orientation(self) -> Tuple[str, str, str]:
        """Orientation codes."""
        return nib.aff2axcodes(self.affine)

    @property
    def spacing(self) -> Tuple[float, float, float]:
        """Voxel spacing in mm."""
        _, spacing = get_rotation_and_spacing_from_affine(self.affine)
        return tuple(spacing)

    @property
    def memory(self) -> float:
        """Number of Bytes that the tensor takes in the RAM."""
        return np.prod(self.shape) * 4  # float32, i.e. 4 bytes per voxel

    @property
    def bounds(self) -> np.ndarray:
        """Position of centers of voxels in smallest and largest coordinates."""
        ini = 0, 0, 0
        fin = np.array(self.spatial_shape) - 1
        point_ini = nib.affines.apply_affine(self.affine, ini)
        point_fin = nib.affines.apply_affine(self.affine, fin)
        return np.array((point_ini, point_fin))

    def axis_name_to_index(self, axis: str) -> int:
        """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.

        .. note:: If your images are 2D, you can use ``'Top'``, ``'Bottom'``,
            ``'Left'`` and ``'Right'``.
        """
        # Top and bottom are used for the vertical 2D axis as the use of
        # Height vs Horizontal might be ambiguous

        if not isinstance(axis, str):
            raise ValueError('Axis must be a string')
        axis = axis[0].upper()

        # Generally, TorchIO tensors are (C, W, H, D)
        if axis in 'TB':  # Top, Bottom
            return -2
        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 = -3 + index
            return index

    # flake8: noqa: E701
    @staticmethod
    def flip_axis(axis: str) -> str:
        if axis == 'R': flipped_axis = 'L'
        elif axis == 'L': flipped_axis = 'R'
        elif axis == 'A': flipped_axis = 'P'
        elif axis == 'P': flipped_axis = 'A'
        elif axis == 'I': flipped_axis = 'S'
        elif axis == 'S': flipped_axis = 'I'
        elif axis == 'T': flipped_axis = 'B'
        elif axis == 'B': flipped_axis = 'T'
        else:
            values = ', '.join('LRPAISTB')
            message = f'Axis not understood. Please use one of: {values}'
            raise ValueError(message)
        return flipped_axis

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

    def get_bounds(self) -> TypeBounds:
        """Get minimum and maximum world coordinates occupied by the image."""
        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]]
            ) -> Optional[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,
            none_ok: bool = True,
            ) -> torch.Tensor:
        if tensor is None:
            if none_ok:
                return None
            else:
                raise RuntimeError('Input tensor cannot be None')
        if isinstance(tensor, np.ndarray):
            tensor = check_uint_to_int(tensor)
            tensor = torch.from_numpy(tensor)
        elif not isinstance(tensor, torch.Tensor):
            message = 'Input tensor must be a PyTorch tensor or NumPy array'
            raise TypeError(message)
        if tensor.ndim != 4:
            raise ValueError('Input tensor must be 4D')
        if tensor.dtype == torch.bool:
            tensor = tensor.to(torch.uint8)
        if self.check_nans and torch.isnan(tensor).any():
            warnings.warn(f'NaNs found in tensor', RuntimeWarning)
        return tensor

    def parse_tensor_shape(self, tensor: torch.Tensor) -> torch.Tensor:
        return ensure_4d(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) -> None:
        r"""Load the image from disk.

        Returns:
            Tuple containing a 4D tensor of size :math:`(C, W, H, D)` 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 = self.read_and_check(paths[0])
        tensors = [tensor]
        for path in paths[1:]:
            new_tensor, new_affine = self.read_and_check(path)
            if not np.array_equal(affine, new_affine):
                message = (
                    'Files have different affine matrices.'
                    f'\nMatrix of {paths[0]}:'
                    f'\n{affine}'
                    f'\nMatrix of {path}:'
                    f'\n{new_affine}'
                )
                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 read_and_check(self, path: TypePath) -> Tuple[torch.Tensor, np.ndarray]:
        tensor, affine = read_image(path)
        tensor = self.parse_tensor_shape(tensor)
        if self.channels_last:
            tensor = tensor.permute(3, 0, 1, 2)
        if self.check_nans and torch.isnan(tensor).any():
            warnings.warn(f'NaNs found in file "{path}"', RuntimeWarning)
        return tensor, affine

    def save(self, path: TypePath, squeeze: bool = True) -> None:
        """Save image to disk.

        Args:
            path: String or instance of :class:`pathlib.Path`.
            squeeze: If ``True``, singleton dimensions will be removed
                before saving.
        """
        write_image(
            self.data,
            self.affine,
            path,
            squeeze=squeeze,
        )

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

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

    def as_sitk(self, **kwargs) -> sitk.Image:
        """Get the image as an instance of :class:`sitk.Image`."""
        return nib_to_sitk(self.data, self.affine, **kwargs)

    def as_pil(self) -> ImagePIL:
        """Get the image as an instance of :class:`PIL.Image`.

        .. note:: Values will be clamped to 0-255 and cast to uint8.
        """
        self.check_is_2d()
        tensor = self.data
        if len(tensor) == 1:
            tensor = torch.cat(3 * [tensor])
        if len(tensor) != 3:
            raise RuntimeError('The image must have 1 or 3 channels')
        tensor = tensor.permute(3, 1, 2, 0)[0]
        array = tensor.clamp(0, 255).numpy()
        return ImagePIL.fromarray(array.astype(np.uint8))

    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) -> None:
        self.check_nans = check_nans

    def plot(self, **kwargs) -> None:
        """Plot image."""
        if self.is_2d():
            self.as_pil().show()
        else:
            from ..visualization import plot_volume  # avoid circular import
            plot_volume(self, **kwargs)


class ScalarImage(Image):
    """Image whose pixel values represent scalars.

    Example:
        >>> import torch
        >>> import torchio as tio
        >>> # Loading from a file
        >>> t1_image = tio.ScalarImage('t1.nii.gz')
        >>> dmri = tio.ScalarImage(tensor=torch.rand(32, 128, 128, 88))
        >>> image = tio.ScalarImage('safe_image.nrrd', check_nans=False)
        >>> data, affine = image.data, image.affine
        >>> affine.shape
        (4, 4)
        >>> image.data is image[tio.DATA]
        True
        >>> image.data is image.tensor
        True
        >>> type(image.data)
        torch.Tensor

    See :class:`~torchio.Image` for more information.
    """
    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):
    """Image whose pixel values represent categorical labels.

    Intensity transforms are not applied to these images.

    Example:
        >>> import torch
        >>> import torchio as tio
        >>> labels = tio.LabelMap(tensor=torch.rand(1, 128, 128, 68) > 0.5)
        >>> labels = tio.LabelMap('t1_seg.nii.gz')  # loading from a file
        >>> tpm = tio.LabelMap(                     # loading from files
        ...     'gray_matter.nii.gz',
        ...     'white_matter.nii.gz',
        ...     'csf.nii.gz',
        ... )

    See :class:`~torchio.Image` for more information.
    """
    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			from PIL import Image as ImagePIL
9			import nibabel as nib
10			import SimpleITK as sitk
11
12			from ..utils import get_stem
13			from ..typing import TypeData, TypePath, TypeTripletInt, TypeTripletFloat
14			from ..constants import DATA, TYPE, AFFINE, PATH, STEM, INTENSITY, LABEL
15			from .io import (
16			ensure_4d,
17			read_image,
18			write_image,
19			nib_to_sitk,
20			check_uint_to_int,
21			get_rotation_and_spacing_from_affine,
22			)
23
24
25			PROTECTED_KEYS = DATA, AFFINE, TYPE, PATH, STEM
26			TypeBound = Tuple[float, float]
27			TypeBounds = Tuple[TypeBound, TypeBound, TypeBound]
28
29
30			class Image(dict):
31			r"""TorchIO image.
32
33			For information about medical image orientation, check out `NiBabel docs`_,
34			the `3D Slicer wiki`_, `Graham Wideman's website`_, `FSL docs`_ or
35			`SimpleITK docs`_.
36
37			Args:
38			path: Path to a file or sequence of paths to files that can be read by
39			:mod:`SimpleITK` or :mod:`nibabel`, or to a directory containing
40			DICOM files. If :attr:`tensor` is given, the data in
41			:attr:`path` will not be read.
42			If a sequence of paths is given, data
43			will be concatenated on the channel dimension so spatial
44			dimensions must match.
45			type: Type of image, such as :attr:`torchio.INTENSITY` or
46			:attr:`torchio.LABEL`. This will be used by the transforms to
47			decide whether to apply an operation, or which interpolation to use
48			when resampling. For example, `preprocessing`_ and `augmentation`_
49			intensity transforms will only be applied to images with type
50			:attr:`torchio.INTENSITY`. Spatial transforms will be applied to
51			all types, and nearest neighbor interpolation is always used to
52			resample images with type :attr:`torchio.LABEL`.
53			The type :attr:`torchio.SAMPLING_MAP` may be used with instances of
54			:class:`~torchio.data.sampler.weighted.WeightedSampler`.
55			tensor: If :attr:`path` is not given, :attr:`tensor` must be a 4D
56			:class:`torch.Tensor` or NumPy array with dimensions
57			:math:`(C, W, H, D)`.
58			affine: If :attr:`path` is not given, :attr:`affine` must be a
59			:math:`4 \times 4` NumPy array. If ``None``, :attr:`affine` is an
60			identity matrix.
61			check_nans: If ``True``, issues a warning if NaNs are found
62			in the image. If ``False``, images will not be checked for the
63			presence of NaNs.
64			**kwargs: Items that will be added to the image dictionary, e.g.
65			acquisition parameters.
66
67			TorchIO images are `lazy loaders`_, i.e. the data is only loaded from disk
68			when needed.
69
70			Example:
71			>>> import torchio as tio
72			>>> image = tio.ScalarImage('t1.nii.gz') # subclass of Image
73			>>> image # not loaded yet
74			ScalarImage(path: t1.nii.gz; type: intensity)
75			>>> times_two = 2 * image.data # data is loaded and cached here
76			>>> image
77			ScalarImage(shape: (1, 256, 256, 176); spacing: (1.00, 1.00, 1.00); orientation: PIR+; memory: 44.0 MiB; type: intensity)
78			>>> image.save('doubled_image.nii.gz')
79
80			.. _lazy loaders: https://en.wikipedia.org/wiki/Lazy_loading
81			.. _preprocessing: https://torchio.readthedocs.io/transforms/preprocessing.html#intensity
82			.. _augmentation: https://torchio.readthedocs.io/transforms/augmentation.html#intensity
83			.. _NiBabel docs: https://nipy.org/nibabel/image_orientation.html
84			.. _3D Slicer wiki: https://www.slicer.org/wiki/Coordinate_systems
85			.. _FSL docs: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Orientation%20Explained
86			.. _SimpleITK docs: https://simpleitk.readthedocs.io/en/master/fundamentalConcepts.html
87			.. _Graham Wideman's website: http://www.grahamwideman.com/gw/brain/orientation/orientterms.htm
88			"""
89			def __init__(
90			self,
91			path: Union[TypePath, Sequence[TypePath], None] = None,
92			type: str = None,
93			tensor: Optional[TypeData] = None,
94			affine: Optional[TypeData] = None,
95			check_nans: bool = False, # removed by ITK by default
96			channels_last: bool = False,
97			**kwargs: Dict[str, Any],
98			):
99			self.check_nans = check_nans
100			self.channels_last = channels_last
101
102			if type is None:
103			warnings.warn(
104			'Not specifying the image type is deprecated and will be'
105			' mandatory in the future. You can probably use tio.ScalarImage'
106			'or LabelMap instead', DeprecationWarning,
107			)
108			type = INTENSITY
109
110			if path is None and tensor is None:
111			raise ValueError('A value for path or tensor must be given')
112			self._loaded = False
113
114			tensor = self._parse_tensor(tensor)
115			affine = self._parse_affine(affine)
116			if tensor is not None:
117			self.data = tensor
118			self.affine = affine
119			self._loaded = True
120			for key in PROTECTED_KEYS:
121			if key in kwargs:
122			message = f'Key "{key}" is reserved. Use a different one'
123			raise ValueError(message)
124
125			super().__init__(**kwargs)
126			self.path = self._parse_path(path)
127
128			self[PATH] = '' if self.path is None else str(self.path)
129			self[STEM] = '' if self.path is None else get_stem(self.path)
130			self[TYPE] = type
131
132			def __repr__(self):
133			properties = []
134			if self._loaded:
135			properties.extend([
136			f'shape: {self.shape}',
137			f'spacing: {self.get_spacing_string()}',
138			f'orientation: {"".join(self.orientation)}+',
139			f'memory: {humanize.naturalsize(self.memory, binary=True)}',
140			])
141			else:
142			properties.append(f'path: "{self.path}"')
143			if self._loaded:
144			properties.append(f'dtype: {self.data.type()}')
145			properties = '; '.join(properties)
146			string = f'{self.__class__.__name__}({properties})'
147			return string
148
149			def __getitem__(self, item):
150			if item in (DATA, AFFINE):
151			if item not in self:
152			self.load()
153			return super().__getitem__(item)
154
155			def __array__(self):
156			return self.data.numpy()
157
158			def __copy__(self):
159			kwargs = dict(
160			tensor=self.data,
161			affine=self.affine,
162			type=self.type,
163			path=self.path,
164			)
165			for key, value in self.items():
166			if key in PROTECTED_KEYS: continue
167			kwargs[key] = value # should I copy? deepcopy?
168			return self.__class__(**kwargs)
169
170			@property
171			def data(self) -> torch.Tensor:
172			"""Tensor data. Same as :class:`Image.tensor`."""
173			return self[DATA]
174
175			@data.setter
176			def data(self, tensor: TypeData):
177			self[DATA] = self._parse_tensor(tensor, none_ok=False)
178
179			@property
180			def tensor(self) -> torch.Tensor:
181			"""Tensor data. Same as :class:`Image.data`."""
182			return self.data
183
184			@property
185			def affine(self) -> np.ndarray:
186			"""Affine matrix to transform voxel indices into world coordinates."""
187			return self[AFFINE]
188
189			@affine.setter
190			def affine(self, matrix):
191			self[AFFINE] = self._parse_affine(matrix)
192
193			@property
194			def type(self) -> str:
195			return self[TYPE]
196
197			@property
198			def shape(self) -> Tuple[int, int, int, int]:
199			"""Tensor shape as :math:`(C, W, H, D)`."""
200			return tuple(self.data.shape)
201
202			@property
203			def spatial_shape(self) -> TypeTripletInt:
204			"""Tensor spatial shape as :math:`(W, H, D)`."""
205			return self.shape[1:]
206
207			def check_is_2d(self) -> None:
208			if not self.is_2d():
209			message = f'Image is not 2D. Spatial shape: {self.spatial_shape}'
210			raise RuntimeError(message)
211
212			@property
213			def height(self) -> int:
214			"""Image height, if 2D."""
215			self.check_is_2d()
216			return self.spatial_shape[1]
217
218			@property
219			def width(self) -> int:
220			"""Image width, if 2D."""
221			self.check_is_2d()
222			return self.spatial_shape[0]
223
224			@property
225			def orientation(self) -> Tuple[str, str, str]:
226			"""Orientation codes."""
227			return nib.aff2axcodes(self.affine)
228
229			@property
230			def spacing(self) -> Tuple[float, float, float]:
231			"""Voxel spacing in mm."""
232			_, spacing = get_rotation_and_spacing_from_affine(self.affine)
233			return tuple(spacing)
234
235			@property
236			def memory(self) -> float:
237			"""Number of Bytes that the tensor takes in the RAM."""
238			return np.prod(self.shape) * 4 # float32, i.e. 4 bytes per voxel
239
240			@property
241			def bounds(self) -> np.ndarray:
242			"""Position of centers of voxels in smallest and largest coordinates."""
243			ini = 0, 0, 0
244			fin = np.array(self.spatial_shape) - 1
245			point_ini = nib.affines.apply_affine(self.affine, ini)
246			point_fin = nib.affines.apply_affine(self.affine, fin)
247			return np.array((point_ini, point_fin))
248
249			def axis_name_to_index(self, axis: str) -> int:
250			"""Convert an axis name to an axis index.
251
252			Args:
253			axis: Possible inputs are ``'Left'``, ``'Right'``, ``'Anterior'``,
254			``'Posterior'``, ``'Inferior'``, ``'Superior'``. Lower-case
255			versions and first letters are also valid, as only the first
256			letter will be used.
257
258			.. note:: If you are working with animals, you should probably use
259			``'Superior'``, ``'Inferior'``, ``'Anterior'`` and ``'Posterior'``
260			for ``'Dorsal'``, ``'Ventral'``, ``'Rostral'`` and ``'Caudal'``,
261			respectively.
262
263			.. note:: If your images are 2D, you can use ``'Top'``, ``'Bottom'``,
264			``'Left'`` and ``'Right'``.
265			"""
266			# Top and bottom are used for the vertical 2D axis as the use of
267			# Height vs Horizontal might be ambiguous
268
269			if not isinstance(axis, str):
270			raise ValueError('Axis must be a string')
271			axis = axis[0].upper()
272
273			# Generally, TorchIO tensors are (C, W, H, D)
274			if axis in 'TB': # Top, Bottom
275			return -2
276			else:
277			try:
278			index = self.orientation.index(axis)
279			except ValueError:
280			index = self.orientation.index(self.flip_axis(axis))
281			# Return negative indices so that it does not matter whether we
282			# refer to spatial dimensions or not
283			index = -3 + index
284			return index
285
286			# flake8: noqa: E701
287			@staticmethod
288			def flip_axis(axis: str) -> str:
289			if axis == 'R': flipped_axis = 'L'
290			elif axis == 'L': flipped_axis = 'R'
291			elif axis == 'A': flipped_axis = 'P'
292			elif axis == 'P': flipped_axis = 'A'
293			elif axis == 'I': flipped_axis = 'S'
294			elif axis == 'S': flipped_axis = 'I'
295			elif axis == 'T': flipped_axis = 'B'
296			elif axis == 'B': flipped_axis = 'T'
297			else:
298			values = ', '.join('LRPAISTB')
299			message = f'Axis not understood. Please use one of: {values}'
300			raise ValueError(message)
301			return flipped_axis
302
303			def get_spacing_string(self) -> str:
304			strings = [f'{n:.2f}' for n in self.spacing]
305			string = f'({", ".join(strings)})'
306			return string
307
308			def get_bounds(self) -> TypeBounds:
309			"""Get minimum and maximum world coordinates occupied by the image."""
310			first_index = 3 * (-0.5,)
311			last_index = np.array(self.spatial_shape) - 0.5
312			first_point = nib.affines.apply_affine(self.affine, first_index)
313			last_point = nib.affines.apply_affine(self.affine, last_index)
314			array = np.array((first_point, last_point))
315			bounds_x, bounds_y, bounds_z = array.T.tolist()
316			return bounds_x, bounds_y, bounds_z
317
318			@staticmethod
319			def _parse_single_path(
320			path: TypePath
321			) -> Path:
322			try:
323			path = Path(path).expanduser()
324			except TypeError:
325			message = (
326			f'Expected type str or Path but found {path} with '
327			f'{type(path)} instead'
328			)
329			raise TypeError(message)
330			except RuntimeError:
331			message = (
332			f'Conversion to path not possible for variable: {path}'
333			)
334			raise RuntimeError(message)
335
336			if not (path.is_file() or path.is_dir()): # might be a dir with DICOM
337			raise FileNotFoundError(f'File not found: "{path}"')
338			return path
339
340			def _parse_path(
341			self,
342			path: Union[TypePath, Sequence[TypePath]]
343			) -> Optional[Union[Path, List[Path]]]:
344			if path is None:
345			return None
346			if isinstance(path, (str, Path)):
347			return self._parse_single_path(path)
348			else:
349			return [self._parse_single_path(p) for p in path]
350
351			def _parse_tensor(
352			self,
353			tensor: TypeData,
354			none_ok: bool = True,
355			) -> torch.Tensor:
356			if tensor is None:
357			if none_ok:
358			return None
359			else:
360			raise RuntimeError('Input tensor cannot be None')
361			if isinstance(tensor, np.ndarray):
362			tensor = check_uint_to_int(tensor)
363			tensor = torch.from_numpy(tensor)
364			elif not isinstance(tensor, torch.Tensor):
365			message = 'Input tensor must be a PyTorch tensor or NumPy array'
366			raise TypeError(message)
367			if tensor.ndim != 4:
368			raise ValueError('Input tensor must be 4D')
369			if tensor.dtype == torch.bool:
370			tensor = tensor.to(torch.uint8)
371			if self.check_nans and torch.isnan(tensor).any():
372			warnings.warn(f'NaNs found in tensor', RuntimeWarning)
373			return tensor
374
375			def parse_tensor_shape(self, tensor: torch.Tensor) -> torch.Tensor:
376			return ensure_4d(tensor)
377
378			@staticmethod
379			def _parse_affine(affine: np.ndarray) -> np.ndarray:
380			if affine is None:
381			return np.eye(4)
382			if not isinstance(affine, np.ndarray):
383			raise TypeError(f'Affine must be a NumPy array, not {type(affine)}')
384			if affine.shape != (4, 4):
385			raise ValueError(f'Affine shape must be (4, 4), not {affine.shape}')
386			return affine
387
388			def load(self) -> None:
389			r"""Load the image from disk.
390
391			Returns:
392			Tuple containing a 4D tensor of size :math:`(C, W, H, D)` and a 2D
393			:math:`4 \times 4` affine matrix to convert voxel indices to world
394			coordinates.
395			"""
396			if self._loaded:
397			return
398			paths = self.path if isinstance(self.path, list) else [self.path]
399			tensor, affine = self.read_and_check(paths[0])
400			tensors = [tensor]
401			for path in paths[1:]:
402			new_tensor, new_affine = self.read_and_check(path)
403			if not np.array_equal(affine, new_affine):
404			message = (
405			'Files have different affine matrices.'
406			f'\nMatrix of {paths[0]}:'
407			f'\n{affine}'
408			f'\nMatrix of {path}:'
409			f'\n{new_affine}'
410			)
411			warnings.warn(message, RuntimeWarning)
412			if not tensor.shape[1:] == new_tensor.shape[1:]:
413			message = (
414			f'Files shape do not match, found {tensor.shape}'
415			f'and {new_tensor.shape}'
416			)
417			RuntimeError(message)
418			tensors.append(new_tensor)
419			tensor = torch.cat(tensors)
420			self.data = tensor
421			self.affine = affine
422			self._loaded = True
423
424			def read_and_check(self, path: TypePath) -> Tuple[torch.Tensor, np.ndarray]:
425			tensor, affine = read_image(path)
426			tensor = self.parse_tensor_shape(tensor)
427			if self.channels_last:
428			tensor = tensor.permute(3, 0, 1, 2)
429			if self.check_nans and torch.isnan(tensor).any():
430			warnings.warn(f'NaNs found in file "{path}"', RuntimeWarning)
431			return tensor, affine
432
433			def save(self, path: TypePath, squeeze: bool = True) -> None:
434			"""Save image to disk.
435
436			Args:
437			path: String or instance of :class:`pathlib.Path`.
438			squeeze: If ``True``, singleton dimensions will be removed
439			before saving.
440			"""
441			write_image(
442			self.data,
443			self.affine,
444			path,
445			squeeze=squeeze,
446			)
447
448			def is_2d(self) -> bool:
449			return self.shape[-1] == 1
450
451			def numpy(self) -> np.ndarray:
452			"""Get a NumPy array containing the image data."""
453			return np.asarray(self)
454
455			def as_sitk(self, **kwargs) -> sitk.Image:
456			"""Get the image as an instance of :class:`sitk.Image`."""
457			return nib_to_sitk(self.data, self.affine, **kwargs)
458
459			def as_pil(self) -> ImagePIL:
460			"""Get the image as an instance of :class:`PIL.Image`.
461
462			.. note:: Values will be clamped to 0-255 and cast to uint8.
463			"""
464			self.check_is_2d()
465			tensor = self.data
466			if len(tensor) == 1:
467			tensor = torch.cat(3 * [tensor])
468			if len(tensor) != 3:
469			raise RuntimeError('The image must have 1 or 3 channels')
470			tensor = tensor.permute(3, 1, 2, 0)[0]
471			array = tensor.clamp(0, 255).numpy()
472			return ImagePIL.fromarray(array.astype(np.uint8))
473
474			def get_center(self, lps: bool = False) -> TypeTripletFloat:
475			"""Get image center in RAS+ or LPS+ coordinates.
476
477			Args:
478			lps: If ``True``, the coordinates will be in LPS+ orientation, i.e.
479			the first dimension grows towards the left, etc. Otherwise, the
480			coordinates will be in RAS+ orientation.
481			"""
482			size = np.array(self.spatial_shape)
483			center_index = (size - 1) / 2
484			r, a, s = nib.affines.apply_affine(self.affine, center_index)
485			if lps:
486			return (-r, -a, s)
487			else:
488			return (r, a, s)
489
490			def set_check_nans(self, check_nans: bool) -> None:
491			self.check_nans = check_nans
492
493			def plot(self, **kwargs) -> None:
494			"""Plot image."""
495			if self.is_2d():
496			self.as_pil().show()
497			else:
498			from ..visualization import plot_volume # avoid circular import
499			plot_volume(self, **kwargs)
500
501
502			class ScalarImage(Image):
503			"""Image whose pixel values represent scalars.
504
505			Example:
506			>>> import torch
507			>>> import torchio as tio
508			>>> # Loading from a file
509			>>> t1_image = tio.ScalarImage('t1.nii.gz')
510			>>> dmri = tio.ScalarImage(tensor=torch.rand(32, 128, 128, 88))
511			>>> image = tio.ScalarImage('safe_image.nrrd', check_nans=False)
512			>>> data, affine = image.data, image.affine
513			>>> affine.shape
514			(4, 4)
515			>>> image.data is image[tio.DATA]
516			True
517			>>> image.data is image.tensor
518			True
519			>>> type(image.data)
520			torch.Tensor
521
522			See :class:`~torchio.Image` for more information.
523			"""
524			def __init__(self, args, *kwargs):
525			if 'type' in kwargs and kwargs['type'] != INTENSITY:
526			raise ValueError('Type of ScalarImage is always torchio.INTENSITY')
527			kwargs.update({'type': INTENSITY})
528			super().__init__(args, *kwargs)
529
530
531			class LabelMap(Image):
532			"""Image whose pixel values represent categorical labels.
533
534			Intensity transforms are not applied to these images.
535
536			Example:
537			>>> import torch
538			>>> import torchio as tio
539			>>> labels = tio.LabelMap(tensor=torch.rand(1, 128, 128, 68) > 0.5)
540			>>> labels = tio.LabelMap('t1_seg.nii.gz') # loading from a file
541			>>> tpm = tio.LabelMap( # loading from files
542			... 'gray_matter.nii.gz',
543			... 'white_matter.nii.gz',
544			... 'csf.nii.gz',
545			... )
546
547			See :class:`~torchio.Image` for more information.
548			"""
549			def __init__(self, args, *kwargs):
550			if 'type' in kwargs and kwargs['type'] != LABEL:
551			raise ValueError('Type of LabelMap is always torchio.LABEL')
552			kwargs.update({'type': LABEL})
553			super().__init__(args, *kwargs)
554

fepegar / torchio

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

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