torchio.data.image.Image.__init__() - Code Metrics - Inspection of "Fix computation of memory usage" - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( e8c1dc...d14fe8 )

by Fernando

created 2021-02-07 16:31 UTC

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

↳ Parent: torchio.data.image

Complexity

Conditions

Size

Total Lines	42
Code Lines	34

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	9
eloc	34
nop	8
dl	0
loc	42
rs	6.6666
c	0
b	0
f	0

How to fix 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 deprecated import deprecated

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]

deprecation_message = (
    'Setting the image data with the property setter is deprecated. Use the'
    ' set_data() method instead'
)


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 tio.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.set_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
    @deprecated(version='0.18.16', reason=deprecation_message)
    def data(self, tensor: TypeData):
        self.set_data(tensor)

    def set_data(self, tensor: TypeData):
        """Store a 4D tensor in the :attr:`data` key and attribute.

        Args:
            tensor: 4D tensor with dimensions :math:`(C, W, H, D)`.
        """
        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 itemsize(self):
        """Element size of the data type."""
        return self.data.element_size()

    @property
    def memory(self) -> float:
        """Number of Bytes that the tensor takes in the RAM."""
        return np.prod(self.shape) * self.itemsize

    @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 type'
                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: TypeData) -> np.ndarray:
        if affine is None:
            return np.eye(4)
        if isinstance(affine, torch.Tensor):
            affine = affine.numpy()
        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.set_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):
        """Get the image as an instance of :class:`PIL.Image`.

        .. note:: Values will be clamped to 0-255 and cast to uint8.
        .. note:: To use this method, `Pillow` needs to be installed:
            `pip install Pillow`.
        """
        try:
            from PIL import Image as ImagePIL
        except ModuleNotFoundError as e:
            message = (
                'Please install Pillow to use Image.as_pil():'
                ' pip install Pillow'
            )
            raise RuntimeError(message) from e

        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			import nibabel as nib
9			import SimpleITK as sitk
10			from deprecated import deprecated
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			deprecation_message = (
30			'Setting the image data with the property setter is deprecated. Use the'
31			' set_data() method instead'
32			)
33
34
35			class Image(dict):
36			r"""TorchIO image.
37
38			For information about medical image orientation, check out `NiBabel docs`_,
39			the `3D Slicer wiki`_, `Graham Wideman's website`_, `FSL docs`_ or
40			`SimpleITK docs`_.
41
42			Args:
43			path: Path to a file or sequence of paths to files that can be read by
44			:mod:`SimpleITK` or :mod:`nibabel`, or to a directory containing
45			DICOM files. If :attr:`tensor` is given, the data in
46			:attr:`path` will not be read.
47			If a sequence of paths is given, data
48			will be concatenated on the channel dimension so spatial
49			dimensions must match.
50			type: Type of image, such as :attr:`torchio.INTENSITY` or
51			:attr:`torchio.LABEL`. This will be used by the transforms to
52			decide whether to apply an operation, or which interpolation to use
53			when resampling. For example, `preprocessing`_ and `augmentation`_
54			intensity transforms will only be applied to images with type
55			:attr:`torchio.INTENSITY`. Spatial transforms will be applied to
56			all types, and nearest neighbor interpolation is always used to
57			resample images with type :attr:`torchio.LABEL`.
58			The type :attr:`torchio.SAMPLING_MAP` may be used with instances of
59			:class:`~torchio.data.sampler.weighted.WeightedSampler`.
60			tensor: If :attr:`path` is not given, :attr:`tensor` must be a 4D
61			:class:`torch.Tensor` or NumPy array with dimensions
62			:math:`(C, W, H, D)`.
63			affine: If :attr:`path` is not given, :attr:`affine` must be a
64			:math:`4 \times 4` NumPy array. If ``None``, :attr:`affine` is an
65			identity matrix.
66			check_nans: If ``True``, issues a warning if NaNs are found
67			in the image. If ``False``, images will not be checked for the
68			presence of NaNs.
69			**kwargs: Items that will be added to the image dictionary, e.g.
70			acquisition parameters.
71
72			TorchIO images are `lazy loaders`_, i.e. the data is only loaded from disk
73			when needed.
74
75			Example:
76			>>> import torchio as tio
77			>>> image = tio.ScalarImage('t1.nii.gz') # subclass of Image
78			>>> image # not loaded yet
79			ScalarImage(path: t1.nii.gz; type: intensity)
80			>>> times_two = 2 * image.data # data is loaded and cached here
81			>>> image
82			ScalarImage(shape: (1, 256, 256, 176); spacing: (1.00, 1.00, 1.00); orientation: PIR+; memory: 44.0 MiB; type: intensity)
83			>>> image.save('doubled_image.nii.gz')
84
85			.. _lazy loaders: https://en.wikipedia.org/wiki/Lazy_loading
86			.. _preprocessing: https://torchio.readthedocs.io/transforms/preprocessing.html#intensity
87			.. _augmentation: https://torchio.readthedocs.io/transforms/augmentation.html#intensity
88			.. _NiBabel docs: https://nipy.org/nibabel/image_orientation.html
89			.. _3D Slicer wiki: https://www.slicer.org/wiki/Coordinate_systems
90			.. _FSL docs: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Orientation%20Explained
91			.. _SimpleITK docs: https://simpleitk.readthedocs.io/en/master/fundamentalConcepts.html
92			.. _Graham Wideman's website: http://www.grahamwideman.com/gw/brain/orientation/orientterms.htm
93			"""
94			def __init__(
95			self,
96			path: Union[TypePath, Sequence[TypePath], None] = None,
97			type: str = None,
98			tensor: Optional[TypeData] = None,
99			affine: Optional[TypeData] = None,
100			check_nans: bool = False, # removed by ITK by default
101			channels_last: bool = False,
102			**kwargs: Dict[str, Any],
103			):
104			self.check_nans = check_nans
105			self.channels_last = channels_last
106
107			if type is None:
108			warnings.warn(
109			'Not specifying the image type is deprecated and will be'
110			' mandatory in the future. You can probably use tio.ScalarImage'
111			' or tio.LabelMap instead', DeprecationWarning,
112			)
113			type = INTENSITY
114
115			if path is None and tensor is None:
116			raise ValueError('A value for path or tensor must be given')
117			self._loaded = False
118
119			tensor = self._parse_tensor(tensor)
120			affine = self._parse_affine(affine)
121			if tensor is not None:
122			self.set_data(tensor)
123			self.affine = affine
124			self._loaded = True
125			for key in PROTECTED_KEYS:
126			if key in kwargs:
127			message = f'Key "{key}" is reserved. Use a different one'
128			raise ValueError(message)
129
130			super().__init__(**kwargs)
131			self.path = self._parse_path(path)
132
133			self[PATH] = '' if self.path is None else str(self.path)
134			self[STEM] = '' if self.path is None else get_stem(self.path)
135			self[TYPE] = type
136
137			def __repr__(self):
138			properties = []
139			if self._loaded:
140			properties.extend([
141			f'shape: {self.shape}',
142			f'spacing: {self.get_spacing_string()}',
143			f'orientation: {"".join(self.orientation)}+',
144			f'memory: {humanize.naturalsize(self.memory, binary=True)}',
145			])
146			else:
147			properties.append(f'path: "{self.path}"')
148			if self._loaded:
149			properties.append(f'dtype: {self.data.type()}')
150			properties = '; '.join(properties)
151			string = f'{self.__class__.__name__}({properties})'
152			return string
153
154			def __getitem__(self, item):
155			if item in (DATA, AFFINE):
156			if item not in self:
157			self.load()
158			return super().__getitem__(item)
159
160			def __array__(self):
161			return self.data.numpy()
162
163			def __copy__(self):
164			kwargs = dict(
165			tensor=self.data,
166			affine=self.affine,
167			type=self.type,
168			path=self.path,
169			)
170			for key, value in self.items():
171			if key in PROTECTED_KEYS: continue
172			kwargs[key] = value # should I copy? deepcopy?
173			return self.__class__(**kwargs)
174
175			@property
176			def data(self) -> torch.Tensor:
177			"""Tensor data. Same as :class:`Image.tensor`."""
178			return self[DATA]
179
180			@data.setter
181			@deprecated(version='0.18.16', reason=deprecation_message)
182			def data(self, tensor: TypeData):
183			self.set_data(tensor)
184
185			def set_data(self, tensor: TypeData):
186			"""Store a 4D tensor in the :attr:`data` key and attribute.
187
188			Args:
189			tensor: 4D tensor with dimensions :math:`(C, W, H, D)`.
190			"""
191			self[DATA] = self._parse_tensor(tensor, none_ok=False)
192
193			@property
194			def tensor(self) -> torch.Tensor:
195			"""Tensor data. Same as :class:`Image.data`."""
196			return self.data
197
198			@property
199			def affine(self) -> np.ndarray:
200			"""Affine matrix to transform voxel indices into world coordinates."""
201			return self[AFFINE]
202
203			@affine.setter
204			def affine(self, matrix):
205			self[AFFINE] = self._parse_affine(matrix)
206
207			@property
208			def type(self) -> str:
209			return self[TYPE]
210
211			@property
212			def shape(self) -> Tuple[int, int, int, int]:
213			"""Tensor shape as :math:`(C, W, H, D)`."""
214			return tuple(self.data.shape)
215
216			@property
217			def spatial_shape(self) -> TypeTripletInt:
218			"""Tensor spatial shape as :math:`(W, H, D)`."""
219			return self.shape[1:]
220
221			def check_is_2d(self) -> None:
222			if not self.is_2d():
223			message = f'Image is not 2D. Spatial shape: {self.spatial_shape}'
224			raise RuntimeError(message)
225
226			@property
227			def height(self) -> int:
228			"""Image height, if 2D."""
229			self.check_is_2d()
230			return self.spatial_shape[1]
231
232			@property
233			def width(self) -> int:
234			"""Image width, if 2D."""
235			self.check_is_2d()
236			return self.spatial_shape[0]
237
238			@property
239			def orientation(self) -> Tuple[str, str, str]:
240			"""Orientation codes."""
241			return nib.aff2axcodes(self.affine)
242
243			@property
244			def spacing(self) -> Tuple[float, float, float]:
245			"""Voxel spacing in mm."""
246			_, spacing = get_rotation_and_spacing_from_affine(self.affine)
247			return tuple(spacing)
248
249			@property
250			def itemsize(self):
251			"""Element size of the data type."""
252			return self.data.element_size()
253
254			@property
255			def memory(self) -> float:
256			"""Number of Bytes that the tensor takes in the RAM."""
257			return np.prod(self.shape) * self.itemsize
258
259			@property
260			def bounds(self) -> np.ndarray:
261			"""Position of centers of voxels in smallest and largest coordinates."""
262			ini = 0, 0, 0
263			fin = np.array(self.spatial_shape) - 1
264			point_ini = nib.affines.apply_affine(self.affine, ini)
265			point_fin = nib.affines.apply_affine(self.affine, fin)
266			return np.array((point_ini, point_fin))
267
268			def axis_name_to_index(self, axis: str) -> int:
269			"""Convert an axis name to an axis index.
270
271			Args:
272			axis: Possible inputs are ``'Left'``, ``'Right'``, ``'Anterior'``,
273			``'Posterior'``, ``'Inferior'``, ``'Superior'``. Lower-case
274			versions and first letters are also valid, as only the first
275			letter will be used.
276
277			.. note:: If you are working with animals, you should probably use
278			``'Superior'``, ``'Inferior'``, ``'Anterior'`` and ``'Posterior'``
279			for ``'Dorsal'``, ``'Ventral'``, ``'Rostral'`` and ``'Caudal'``,
280			respectively.
281
282			.. note:: If your images are 2D, you can use ``'Top'``, ``'Bottom'``,
283			``'Left'`` and ``'Right'``.
284			"""
285			# Top and bottom are used for the vertical 2D axis as the use of
286			# Height vs Horizontal might be ambiguous
287
288			if not isinstance(axis, str):
289			raise ValueError('Axis must be a string')
290			axis = axis[0].upper()
291
292			# Generally, TorchIO tensors are (C, W, H, D)
293			if axis in 'TB': # Top, Bottom
294			return -2
295			else:
296			try:
297			index = self.orientation.index(axis)
298			except ValueError:
299			index = self.orientation.index(self.flip_axis(axis))
300			# Return negative indices so that it does not matter whether we
301			# refer to spatial dimensions or not
302			index = -3 + index
303			return index
304
305			# flake8: noqa: E701
306			@staticmethod
307			def flip_axis(axis: str) -> str:
308			if axis == 'R': flipped_axis = 'L'
309			elif axis == 'L': flipped_axis = 'R'
310			elif axis == 'A': flipped_axis = 'P'
311			elif axis == 'P': flipped_axis = 'A'
312			elif axis == 'I': flipped_axis = 'S'
313			elif axis == 'S': flipped_axis = 'I'
314			elif axis == 'T': flipped_axis = 'B'
315			elif axis == 'B': flipped_axis = 'T'
316			else:
317			values = ', '.join('LRPAISTB')
318			message = f'Axis not understood. Please use one of: {values}'
319			raise ValueError(message)
320			return flipped_axis
321
322			def get_spacing_string(self) -> str:
323			strings = [f'{n:.2f}' for n in self.spacing]
324			string = f'({", ".join(strings)})'
325			return string
326
327			def get_bounds(self) -> TypeBounds:
328			"""Get minimum and maximum world coordinates occupied by the image."""
329			first_index = 3 * (-0.5,)
330			last_index = np.array(self.spatial_shape) - 0.5
331			first_point = nib.affines.apply_affine(self.affine, first_index)
332			last_point = nib.affines.apply_affine(self.affine, last_index)
333			array = np.array((first_point, last_point))
334			bounds_x, bounds_y, bounds_z = array.T.tolist()
335			return bounds_x, bounds_y, bounds_z
336
337			@staticmethod
338			def _parse_single_path(
339			path: TypePath
340			) -> Path:
341			try:
342			path = Path(path).expanduser()
343			except TypeError:
344			message = (
345			f'Expected type str or Path but found {path} with type'
346			f' {type(path)} instead'
347			)
348			raise TypeError(message)
349			except RuntimeError:
350			message = (
351			f'Conversion to path not possible for variable: {path}'
352			)
353			raise RuntimeError(message)
354
355			if not (path.is_file() or path.is_dir()): # might be a dir with DICOM
356			raise FileNotFoundError(f'File not found: "{path}"')
357			return path
358
359			def _parse_path(
360			self,
361			path: Union[TypePath, Sequence[TypePath]]
362			) -> Optional[Union[Path, List[Path]]]:
363			if path is None:
364			return None
365			if isinstance(path, (str, Path)):
366			return self._parse_single_path(path)
367			else:
368			return [self._parse_single_path(p) for p in path]
369
370			def _parse_tensor(
371			self,
372			tensor: TypeData,
373			none_ok: bool = True,
374			) -> torch.Tensor:
375			if tensor is None:
376			if none_ok:
377			return None
378			else:
379			raise RuntimeError('Input tensor cannot be None')
380			if isinstance(tensor, np.ndarray):
381			tensor = check_uint_to_int(tensor)
382			tensor = torch.from_numpy(tensor)
383			elif not isinstance(tensor, torch.Tensor):
384			message = 'Input tensor must be a PyTorch tensor or NumPy array'
385			raise TypeError(message)
386			if tensor.ndim != 4:
387			raise ValueError('Input tensor must be 4D')
388			if tensor.dtype == torch.bool:
389			tensor = tensor.to(torch.uint8)
390			if self.check_nans and torch.isnan(tensor).any():
391			warnings.warn(f'NaNs found in tensor', RuntimeWarning)
392			return tensor
393
394			def parse_tensor_shape(self, tensor: torch.Tensor) -> torch.Tensor:
395			return ensure_4d(tensor)
396
397			@staticmethod
398			def _parse_affine(affine: TypeData) -> np.ndarray:
399			if affine is None:
400			return np.eye(4)
401			if isinstance(affine, torch.Tensor):
402			affine = affine.numpy()
403			if not isinstance(affine, np.ndarray):
404			raise TypeError(f'Affine must be a NumPy array, not {type(affine)}')
405			if affine.shape != (4, 4):
406			raise ValueError(f'Affine shape must be (4, 4), not {affine.shape}')
407			return affine
408
409			def load(self) -> None:
410			r"""Load the image from disk.
411
412			Returns:
413			Tuple containing a 4D tensor of size :math:`(C, W, H, D)` and a 2D
414			:math:`4 \times 4` affine matrix to convert voxel indices to world
415			coordinates.
416			"""
417			if self._loaded:
418			return
419			paths = self.path if isinstance(self.path, list) else [self.path]
420			tensor, affine = self.read_and_check(paths[0])
421			tensors = [tensor]
422			for path in paths[1:]:
423			new_tensor, new_affine = self.read_and_check(path)
424			if not np.array_equal(affine, new_affine):
425			message = (
426			'Files have different affine matrices.'
427			f'\nMatrix of {paths[0]}:'
428			f'\n{affine}'
429			f'\nMatrix of {path}:'
430			f'\n{new_affine}'
431			)
432			warnings.warn(message, RuntimeWarning)
433			if not tensor.shape[1:] == new_tensor.shape[1:]:
434			message = (
435			f'Files shape do not match, found {tensor.shape}'
436			f'and {new_tensor.shape}'
437			)
438			RuntimeError(message)
439			tensors.append(new_tensor)
440			tensor = torch.cat(tensors)
441			self.set_data(tensor)
442			self.affine = affine
443			self._loaded = True
444
445			def read_and_check(self, path: TypePath) -> Tuple[torch.Tensor, np.ndarray]:
446			tensor, affine = read_image(path)
447			tensor = self.parse_tensor_shape(tensor)
448			if self.channels_last:
449			tensor = tensor.permute(3, 0, 1, 2)
450			if self.check_nans and torch.isnan(tensor).any():
451			warnings.warn(f'NaNs found in file "{path}"', RuntimeWarning)
452			return tensor, affine
453
454			def save(self, path: TypePath, squeeze: bool = True) -> None:
455			"""Save image to disk.
456
457			Args:
458			path: String or instance of :class:`pathlib.Path`.
459			squeeze: If ``True``, singleton dimensions will be removed
460			before saving.
461			"""
462			write_image(
463			self.data,
464			self.affine,
465			path,
466			squeeze=squeeze,
467			)
468
469			def is_2d(self) -> bool:
470			return self.shape[-1] == 1
471
472			def numpy(self) -> np.ndarray:
473			"""Get a NumPy array containing the image data."""
474			return np.asarray(self)
475
476			def as_sitk(self, **kwargs) -> sitk.Image:
477			"""Get the image as an instance of :class:`sitk.Image`."""
478			return nib_to_sitk(self.data, self.affine, **kwargs)
479
480			def as_pil(self):
481			"""Get the image as an instance of :class:`PIL.Image`.
482
483			.. note:: Values will be clamped to 0-255 and cast to uint8.
484			.. note:: To use this method, `Pillow` needs to be installed:
485			`pip install Pillow`.
486			"""
487			try:
488			from PIL import Image as ImagePIL
489			except ModuleNotFoundError as e:
490			message = (
491			'Please install Pillow to use Image.as_pil():'
492			' pip install Pillow'
493			)
494			raise RuntimeError(message) from e
495
496			self.check_is_2d()
497			tensor = self.data
498			if len(tensor) == 1:
499			tensor = torch.cat(3 * [tensor])
500			if len(tensor) != 3:
501			raise RuntimeError('The image must have 1 or 3 channels')
502			tensor = tensor.permute(3, 1, 2, 0)[0]
503			array = tensor.clamp(0, 255).numpy()
504			return ImagePIL.fromarray(array.astype(np.uint8))
505
506			def get_center(self, lps: bool = False) -> TypeTripletFloat:
507			"""Get image center in RAS+ or LPS+ coordinates.
508
509			Args:
510			lps: If ``True``, the coordinates will be in LPS+ orientation, i.e.
511			the first dimension grows towards the left, etc. Otherwise, the
512			coordinates will be in RAS+ orientation.
513			"""
514			size = np.array(self.spatial_shape)
515			center_index = (size - 1) / 2
516			r, a, s = nib.affines.apply_affine(self.affine, center_index)
517			if lps:
518			return (-r, -a, s)
519			else:
520			return (r, a, s)
521
522			def set_check_nans(self, check_nans: bool) -> None:
523			self.check_nans = check_nans
524
525			def plot(self, **kwargs) -> None:
526			"""Plot image."""
527			if self.is_2d():
528			self.as_pil().show()
529			else:
530			from ..visualization import plot_volume # avoid circular import
531			plot_volume(self, **kwargs)
532
533
534			class ScalarImage(Image):
535			"""Image whose pixel values represent scalars.
536
537			Example:
538			>>> import torch
539			>>> import torchio as tio
540			>>> # Loading from a file
541			>>> t1_image = tio.ScalarImage('t1.nii.gz')
542			>>> dmri = tio.ScalarImage(tensor=torch.rand(32, 128, 128, 88))
543			>>> image = tio.ScalarImage('safe_image.nrrd', check_nans=False)
544			>>> data, affine = image.data, image.affine
545			>>> affine.shape
546			(4, 4)
547			>>> image.data is image[tio.DATA]
548			True
549			>>> image.data is image.tensor
550			True
551			>>> type(image.data)
552			torch.Tensor
553
554			See :class:`~torchio.Image` for more information.
555			"""
556			def __init__(self, args, *kwargs):
557			if 'type' in kwargs and kwargs['type'] != INTENSITY:
558			raise ValueError('Type of ScalarImage is always torchio.INTENSITY')
559			kwargs.update({'type': INTENSITY})
560			super().__init__(args, *kwargs)
561
562
563			class LabelMap(Image):
564			"""Image whose pixel values represent categorical labels.
565
566			Intensity transforms are not applied to these images.
567
568			Example:
569			>>> import torch
570			>>> import torchio as tio
571			>>> labels = tio.LabelMap(tensor=torch.rand(1, 128, 128, 68) > 0.5)
572			>>> labels = tio.LabelMap('t1_seg.nii.gz') # loading from a file
573			>>> tpm = tio.LabelMap( # loading from files
574			... 'gray_matter.nii.gz',
575			... 'white_matter.nii.gz',
576			... 'csf.nii.gz',
577			... )
578
579			See :class:`~torchio.Image` for more information.
580			"""
581			def __init__(self, args, *kwargs):
582			if 'type' in kwargs and kwargs['type'] != LABEL:
583			raise ValueError('Type of LabelMap is always torchio.LABEL')
584			kwargs.update({'type': LABEL})
585			super().__init__(args, *kwargs)
586

fepegar / torchio

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

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