torchio.data.image.Image.__init__() - Code Metrics - Inspection of "Faster transforms" - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Pull Request — master (#334)

by Fernando

created 2020-10-15 19:29 UTC

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

↳ Parent: torchio.data.image

Complexity

Conditions

Size

Total Lines	42
Code Lines	33

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	9
eloc	33
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
from PIL import Image as ImagePIL
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.
            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, 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
        >>> 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 = 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 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

        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,
        )
        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 :py:class:`Image.tensor`."""
        return self[DATA]

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

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

    @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):
        """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):
        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:
            values = ', '.join('LRPAISTB')
            message = f'Axis not understood. Please use one of: {values}'
            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()
        if tensor.ndim != 4:
            raise ValueError('Input tensor must be 4D')
        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)

    @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):
        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}"')
        return tensor, affine

    def save(self, path: TypePath, squeeze: bool = 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.
        """
        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 :py:class:`sitk.Image`."""
        return nib_to_sitk(self[DATA], self[AFFINE], **kwargs)

    def as_pil(self):
        """Get the image as an instance of :py:class:`PIL.Image`."""
        self.check_is_2d()
        return ImagePIL.open(self.path)

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

fepegar / torchio

Pull Request — master (#334)

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

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