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

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Pull Request — master (#332)

by Fernando

created 2020-10-15 13:07 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
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 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 central slices of the image."""
        from ..visualization import plot_volume  # avoid circular import
        plot_volume(self, **kwargs)

    def crop(self, index_ini: TypeTripletInt, index_fin: TypeTripletInt):
        new_origin = nib.affines.apply_affine(self.affine, index_ini)
        new_affine = self.affine.copy()
        new_affine[:3, 3] = new_origin
        i0, j0, k0 = index_ini
        i1, j1, k1 = index_fin
        patch = self.data[:, i0:i1, j0:j1, k0:k1].clone()
        kwargs = dict(
            tensor=patch,
            affine=new_affine,
            type=self.type,
            path=self.path,
        )
        for key, value in self.items():
            if key in PROTECTED_KEYS: continue
            kwargs[key] = value  # should I copy? deepcopy?
        return self.__class__(**kwargs)


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

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

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

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


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

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

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

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


1			import warnings
2			from pathlib import Path
3			from typing import Any, Dict, Tuple, Optional, Union, Sequence, List
4
5			import torch
6			import humanize
7			import numpy as np
8			import nibabel as nib
9			import SimpleITK as sitk
10
11			from ..utils import (
12			nib_to_sitk,
13			get_rotation_and_spacing_from_affine,
14			get_stem,
15			ensure_4d,
16			)
17			from ..torchio import (
18			TypeData,
19			TypePath,
20			TypeTripletInt,
21			TypeTripletFloat,
22			DATA,
23			TYPE,
24			AFFINE,
25			PATH,
26			STEM,
27			INTENSITY,
28			LABEL,
29			)
30			from .io import read_image, write_image
31
32
33			PROTECTED_KEYS = DATA, AFFINE, TYPE, PATH, STEM
34
35
36			class Image(dict):
37			r"""TorchIO image.
38
39			For information about medical image orientation, check out `NiBabel docs`_,
40			the `3D Slicer wiki`_, `Graham Wideman's website`_, `FSL docs`_ or
41			`SimpleITK docs`_.
42
43			Args:
44			path: Path to a file or sequence of paths to files that can be read by
45			:mod:`SimpleITK` or :mod:`nibabel`, or to a directory containing
46			DICOM files. If :py:attr:`tensor` is given, the data in
47			:py:attr:`path` will not be read.
48			If a sequence of paths is given, data
49			will be concatenated on the channel dimension so spatial
50			dimensions must match.
51			type: Type of image, such as :attr:`torchio.INTENSITY` or
52			:attr:`torchio.LABEL`. This will be used by the transforms to
53			decide whether to apply an operation, or which interpolation to use
54			when resampling. For example, `preprocessing`_ and `augmentation`_
55			intensity transforms will only be applied to images with type
56			:attr:`torchio.INTENSITY`. Spatial transforms will be applied to
57			all types, and nearest neighbor interpolation is always used to
58			resample images with type :attr:`torchio.LABEL`.
59			The type :attr:`torchio.SAMPLING_MAP` may be used with instances of
60			:py:class:`~torchio.data.sampler.weighted.WeightedSampler`.
61			tensor: If :py:attr:`path` is not given, :attr:`tensor` must be a 4D
62			:py:class:`torch.Tensor` or NumPy array with dimensions
63			:math:`(C, W, H, D)`.
64			affine: If :attr:`path` is not given, :attr:`affine` must be a
65			:math:`4 \times 4` NumPy array. If ``None``, :attr:`affine` is an
66			identity matrix.
67			check_nans: If ``True``, issues a warning if NaNs are found
68			in the image. If ``False``, images will not be checked for the
69			presence of NaNs.
70			**kwargs: Items that will be added to the image dictionary, e.g.
71			acquisition parameters.
72
73			TorchIO images are `lazy loaders`_, i.e. the data is only loaded from disk
74			when needed.
75
76			Example:
77			>>> import torchio
78			>>> image = torchio.ScalarImage('t1.nii.gz') # subclass of Image
79			>>> image # not loaded yet
80			ScalarImage(path: t1.nii.gz; type: intensity)
81			>>> times_two = 2 * image.data # data is loaded and cached here
82			>>> image
83			ScalarImage(shape: (1, 256, 256, 176); spacing: (1.00, 1.00, 1.00); orientation: PIR+; memory: 44.0 MiB; type: intensity)
84			>>> image.save('doubled_image.nii.gz')
85
86			.. _lazy loaders: https://en.wikipedia.org/wiki/Lazy_loading
87			.. _preprocessing: https://torchio.readthedocs.io/transforms/preprocessing.html#intensity
88			.. _augmentation: https://torchio.readthedocs.io/transforms/augmentation.html#intensity
89			.. _NiBabel docs: https://nipy.org/nibabel/image_orientation.html
90			.. _3D Slicer wiki: https://www.slicer.org/wiki/Coordinate_systems
91			.. _FSL docs: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Orientation%20Explained
92			.. _SimpleITK docs: https://simpleitk.readthedocs.io/en/master/fundamentalConcepts.html
93			.. _Graham Wideman's website: http://www.grahamwideman.com/gw/brain/orientation/orientterms.htm
94			"""
95			def __init__(
96			self,
97			path: Union[TypePath, Sequence[TypePath], None] = None,
98			type: str = None,
99			tensor: Optional[TypeData] = None,
100			affine: Optional[TypeData] = None,
101			check_nans: bool = False, # removed by ITK by default
102			channels_last: bool = False,
103			**kwargs: Dict[str, Any],
104			):
105			self.check_nans = check_nans
106			self.channels_last = channels_last
107
108			if type is None:
109			warnings.warn(
110			'Not specifying the image type is deprecated and will be'
111			' mandatory in the future. You can probably use ScalarImage or'
112			' LabelMap instead'
113			)
114			type = INTENSITY
115
116			if path is None and tensor is None:
117			raise ValueError('A value for path or tensor must be given')
118			self._loaded = False
119
120			tensor = self._parse_tensor(tensor)
121			affine = self._parse_affine(affine)
122			if tensor is not None:
123			self[DATA] = tensor
124			self[AFFINE] = affine
125			self._loaded = True
126			for key in PROTECTED_KEYS:
127			if key in kwargs:
128			message = f'Key "{key}" is reserved. Use a different one'
129			raise ValueError(message)
130
131			super().__init__(**kwargs)
132			self.path = self._parse_path(path)
133
134			self[PATH] = '' if self.path is None else str(self.path)
135			self[STEM] = '' if self.path is None else get_stem(self.path)
136			self[TYPE] = type
137
138			def __repr__(self):
139			properties = []
140			if self._loaded:
141			properties.extend([
142			f'shape: {self.shape}',
143			f'spacing: {self.get_spacing_string()}',
144			f'orientation: {"".join(self.orientation)}+',
145			f'memory: {humanize.naturalsize(self.memory, binary=True)}',
146			])
147			else:
148			properties.append(f'path: "{self.path}"')
149			properties.append(f'type: {self.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 :py:class:`Image.tensor`."""
178			return self[DATA]
179
180			@property
181			def tensor(self) -> torch.Tensor:
182			"""Tensor data. Same as :py:class:`Image.data`."""
183			return self.data
184
185			@property
186			def affine(self) -> np.ndarray:
187			"""Affine matrix to transform voxel indices into world coordinates."""
188			return self[AFFINE]
189
190			@property
191			def type(self) -> str:
192			return self[TYPE]
193
194			@property
195			def shape(self) -> Tuple[int, int, int, int]:
196			"""Tensor shape as :math:`(C, W, H, D)`."""
197			return tuple(self.data.shape)
198
199			@property
200			def spatial_shape(self) -> TypeTripletInt:
201			"""Tensor spatial shape as :math:`(W, H, D)`."""
202			return self.shape[1:]
203
204			def check_is_2d(self) -> None:
205			if not self.is_2d():
206			message = f'Image is not 2D. Spatial shape: {self.spatial_shape}'
207			raise RuntimeError(message)
208
209			@property
210			def height(self) -> int:
211			"""Image height, if 2D."""
212			self.check_is_2d()
213			return self.spatial_shape[1]
214
215			@property
216			def width(self) -> int:
217			"""Image width, if 2D."""
218			self.check_is_2d()
219			return self.spatial_shape[0]
220
221			@property
222			def orientation(self) -> Tuple[str, str, str]:
223			"""Orientation codes."""
224			return nib.aff2axcodes(self.affine)
225
226			@property
227			def spacing(self) -> Tuple[float, float, float]:
228			"""Voxel spacing in mm."""
229			_, spacing = get_rotation_and_spacing_from_affine(self.affine)
230			return tuple(spacing)
231
232			@property
233			def memory(self) -> float:
234			"""Number of Bytes that the tensor takes in the RAM."""
235			return np.prod(self.shape) * 4 # float32, i.e. 4 bytes per voxel
236
237			@property
238			def bounds(self) -> np.ndarray:
239			"""Position of centers of voxels in smallest and largest coordinates."""
240			ini = 0, 0, 0
241			fin = np.array(self.spatial_shape) - 1
242			point_ini = nib.affines.apply_affine(self.affine, ini)
243			point_fin = nib.affines.apply_affine(self.affine, fin)
244			return np.array((point_ini, point_fin))
245
246			def axis_name_to_index(self, axis: str):
247			"""Convert an axis name to an axis index.
248
249			Args:
250			axis: Possible inputs are ``'Left'``, ``'Right'``, ``'Anterior'``,
251			``'Posterior'``, ``'Inferior'``, ``'Superior'``. Lower-case
252			versions and first letters are also valid, as only the first
253			letter will be used.
254
255			.. note:: If you are working with animals, you should probably use
256			``'Superior'``, ``'Inferior'``, ``'Anterior'`` and ``'Posterior'``
257			for ``'Dorsal'``, ``'Ventral'``, ``'Rostral'`` and ``'Caudal'``,
258			respectively.
259
260			.. note:: If your images are 2D, you can use ``'Top'``, ``'Bottom'``,
261			``'Left'`` and ``'Right'``.
262			"""
263			# Top and bottom are used for the vertical 2D axis as the use of
264			# Height vs Horizontal might be ambiguous
265
266			if not isinstance(axis, str):
267			raise ValueError('Axis must be a string')
268			axis = axis[0].upper()
269
270			# Generally, TorchIO tensors are (C, W, H, D)
271			if axis in 'TB': # Top, Bottom
272			return -2
273			else:
274			try:
275			index = self.orientation.index(axis)
276			except ValueError:
277			index = self.orientation.index(self.flip_axis(axis))
278			# Return negative indices so that it does not matter whether we
279			# refer to spatial dimensions or not
280			index = -3 + index
281			return index
282
283			# flake8: noqa: E701
284			@staticmethod
285			def flip_axis(axis):
286			if axis == 'R': return 'L'
287			elif axis == 'L': return 'R'
288			elif axis == 'A': return 'P'
289			elif axis == 'P': return 'A'
290			elif axis == 'I': return 'S'
291			elif axis == 'S': return 'I'
292			else:
293			values = ', '.join('LRPAISTB')
294			message = f'Axis not understood. Please use one of: {values}'
295			raise ValueError(message)
296
297			def get_spacing_string(self):
298			strings = [f'{n:.2f}' for n in self.spacing]
299			string = f'({", ".join(strings)})'
300			return string
301
302			def get_bounds(self):
303			"""Get image bounds in mm."""
304			first_index = 3 * (-0.5,)
305			last_index = np.array(self.spatial_shape) - 0.5
306			first_point = nib.affines.apply_affine(self.affine, first_index)
307			last_point = nib.affines.apply_affine(self.affine, last_index)
308			array = np.array((first_point, last_point))
309			bounds_x, bounds_y, bounds_z = array.T.tolist()
310			return bounds_x, bounds_y, bounds_z
311
312			@staticmethod
313			def _parse_single_path(
314			path: TypePath
315			) -> Path:
316			try:
317			path = Path(path).expanduser()
318			except TypeError:
319			message = (
320			f'Expected type str or Path but found {path} with '
321			f'{type(path)} instead'
322			)
323			raise TypeError(message)
324			except RuntimeError:
325			message = (
326			f'Conversion to path not possible for variable: {path}'
327			)
328			raise RuntimeError(message)
329
330			if not (path.is_file() or path.is_dir()): # might be a dir with DICOM
331			raise FileNotFoundError(f'File not found: "{path}"')
332			return path
333
334			def _parse_path(
335			self,
336			path: Union[TypePath, Sequence[TypePath]]
337			) -> Union[Path, List[Path]]:
338			if path is None:
339			return None
340			if isinstance(path, (str, Path)):
341			return self._parse_single_path(path)
342			else:
343			return [self._parse_single_path(p) for p in path]
344
345			def _parse_tensor(self, tensor: TypeData) -> torch.Tensor:
346			if tensor is None:
347			return None
348			if isinstance(tensor, np.ndarray):
349			tensor = torch.from_numpy(tensor.astype(np.float32))
350			elif isinstance(tensor, torch.Tensor):
351			tensor = tensor.float()
352			if tensor.ndim != 4:
353			raise ValueError('Input tensor must be 4D')
354			if self.check_nans and torch.isnan(tensor).any():
355			warnings.warn(f'NaNs found in tensor')
356			return tensor
357
358			def parse_tensor_shape(self, tensor: torch.Tensor) -> torch.Tensor:
359			return ensure_4d(tensor)
360
361			@staticmethod
362			def _parse_affine(affine: np.ndarray) -> np.ndarray:
363			if affine is None:
364			return np.eye(4)
365			if not isinstance(affine, np.ndarray):
366			raise TypeError(f'Affine must be a NumPy array, not {type(affine)}')
367			if affine.shape != (4, 4):
368			raise ValueError(f'Affine shape must be (4, 4), not {affine.shape}')
369			return affine
370
371			def load(self) -> None:
372			r"""Load the image from disk.
373
374			Returns:
375			Tuple containing a 4D tensor of size :math:`(C, W, H, D)` and a 2D
376			:math:`4 \times 4` affine matrix to convert voxel indices to world
377			coordinates.
378			"""
379			if self._loaded:
380			return
381			paths = self.path if isinstance(self.path, list) else [self.path]
382			tensor, affine = self.read_and_check(paths[0])
383			tensors = [tensor]
384			for path in paths[1:]:
385			new_tensor, new_affine = self.read_and_check(path)
386			if not np.array_equal(affine, new_affine):
387			message = (
388			'Files have different affine matrices.'
389			f'\nMatrix of {paths[0]}:'
390			f'\n{affine}'
391			f'\nMatrix of {path}:'
392			f'\n{new_affine}'
393			)
394			warnings.warn(message, RuntimeWarning)
395			if not tensor.shape[1:] == new_tensor.shape[1:]:
396			message = (
397			f'Files shape do not match, found {tensor.shape}'
398			f'and {new_tensor.shape}'
399			)
400			RuntimeError(message)
401			tensors.append(new_tensor)
402			tensor = torch.cat(tensors)
403			self[DATA] = tensor
404			self[AFFINE] = affine
405			self._loaded = True
406
407			def read_and_check(self, path):
408			tensor, affine = read_image(path)
409			tensor = self.parse_tensor_shape(tensor)
410			if self.channels_last:
411			tensor = tensor.permute(3, 0, 1, 2)
412			if self.check_nans and torch.isnan(tensor).any():
413			warnings.warn(f'NaNs found in file "{path}"')
414			return tensor, affine
415
416			def save(self, path: TypePath, squeeze: bool = True):
417			"""Save image to disk.
418
419			Args:
420			path: String or instance of :py:class:`pathlib.Path`.
421			squeeze: If ``True``, the singleton dimensions will be removed
422			before saving.
423			"""
424			write_image(
425			self[DATA],
426			self[AFFINE],
427			path,
428			squeeze=squeeze,
429			)
430
431			def is_2d(self) -> bool:
432			return self.shape[-1] == 1
433
434			def numpy(self) -> np.ndarray:
435			"""Get a NumPy array containing the image data."""
436			return np.asarray(self)
437
438			def as_sitk(self, **kwargs) -> sitk.Image:
439			"""Get the image as an instance of :py:class:`sitk.Image`."""
440			return nib_to_sitk(self[DATA], self[AFFINE], **kwargs)
441
442			def get_center(self, lps: bool = False) -> TypeTripletFloat:
443			"""Get image center in RAS+ or LPS+ coordinates.
444
445			Args:
446			lps: If ``True``, the coordinates will be in LPS+ orientation, i.e.
447			the first dimension grows towards the left, etc. Otherwise, the
448			coordinates will be in RAS+ orientation.
449			"""
450			size = np.array(self.spatial_shape)
451			center_index = (size - 1) / 2
452			r, a, s = nib.affines.apply_affine(self.affine, center_index)
453			if lps:
454			return (-r, -a, s)
455			else:
456			return (r, a, s)
457
458			def set_check_nans(self, check_nans: bool):
459			self.check_nans = check_nans
460
461			def plot(self, **kwargs) -> None:
462			"""Plot central slices of the image."""
463			from ..visualization import plot_volume # avoid circular import
464			plot_volume(self, **kwargs)
465
466			def crop(self, index_ini: TypeTripletInt, index_fin: TypeTripletInt):
467			new_origin = nib.affines.apply_affine(self.affine, index_ini)
468			new_affine = self.affine.copy()
469			new_affine[:3, 3] = new_origin
470			i0, j0, k0 = index_ini
471			i1, j1, k1 = index_fin
472			patch = self.data[:, i0:i1, j0:j1, k0:k1].clone()
473			kwargs = dict(
474			tensor=patch,
475			affine=new_affine,
476			type=self.type,
477			path=self.path,
478			)
479			for key, value in self.items():
480			if key in PROTECTED_KEYS: continue
481			kwargs[key] = value # should I copy? deepcopy?
482			return self.__class__(**kwargs)
483
484
485			class ScalarImage(Image):
486			"""Alias for :py:class:`~torchio.Image` of type :py:attr:`torchio.INTENSITY`.
487
488			Example:
489			>>> import torch
490			>>> import torchio
491			>>> # Loading from a file
492			>>> t1_image = torchio.ScalarImage('t1.nii.gz')
493			>>> dmri = torchio.ScalarImage(tensor=torch.rand(32, 128, 128, 88))
494			>>> image = torchio.ScalarImage('safe_image.nrrd', check_nans=False)
495			>>> data, affine = image.data, image.affine
496			>>> affine.shape
497			(4, 4)
498			>>> image.data is image[torchio.DATA]
499			True
500			>>> image.data is image.tensor
501			True
502			>>> type(image.data)
503			torch.Tensor
504
505			See :py:class:`~torchio.Image` for more information.
506
507			Raises:
508			ValueError: A :py:attr:`type` is used for instantiation.
509			"""
510			def __init__(self, args, *kwargs):
511			if 'type' in kwargs and kwargs['type'] != INTENSITY:
512			raise ValueError('Type of ScalarImage is always torchio.INTENSITY')
513			kwargs.update({'type': INTENSITY})
514			super().__init__(args, *kwargs)
515
516
517			class LabelMap(Image):
518			"""Alias for :py:class:`~torchio.Image` of type :py:attr:`torchio.LABEL`.
519
520			Example:
521			>>> import torch
522			>>> import torchio
523			>>> labels = torchio.LabelMap(tensor=torch.rand(128, 128, 68) > 0.5)
524			>>> labels = torchio.LabelMap('t1_seg.nii.gz') # loading from a file
525			>>> tpm = torchio.LabelMap( # loading from files
526			... 'gray_matter.nii.gz',
527			... 'white_matter.nii.gz',
528			... 'csf.nii.gz',
529			... )
530
531			See :py:class:`~torchio.data.image.Image` for more information.
532
533			Raises:
534			ValueError: If a value for :py:attr:`type` is given.
535			"""
536			def __init__(self, args, *kwargs):
537			if 'type' in kwargs and kwargs['type'] != LABEL:
538			raise ValueError('Type of LabelMap is always torchio.LABEL')
539			kwargs.update({'type': LABEL})
540			super().__init__(args, *kwargs)
541

fepegar / torchio

Pull Request — master (#332)

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

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