torchio.data.image.Image.__init__() - Code Metrics - Inspection of "Remove checks and casts to float32 (#380)" - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Push — master ( 0bdb47...b0bac6 )

by Fernando

created 2020-12-13 21:42 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
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,
    check_uint_to_int,
)
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
TypeBound = Tuple[float, float]
TypeBounds = Tuple[TypeBound, TypeBound, TypeBound]


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

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

    Args:
        path: Path to a file or sequence of paths to files that can be read by
            :mod:`SimpleITK` or :mod:`nibabel`, or to a directory containing
            DICOM files. If :attr:`tensor` is given, the data in
            :attr:`path` will not be read.
            If a sequence of paths is given, data
            will be concatenated on the channel dimension so spatial
            dimensions must match.
        type: Type of image, such as :attr:`torchio.INTENSITY` or
            :attr:`torchio.LABEL`. This will be used by the transforms to
            decide whether to apply an operation, or which interpolation to use
            when resampling. For example, `preprocessing`_ and `augmentation`_
            intensity transforms will only be applied to images with type
            :attr:`torchio.INTENSITY`. Spatial transforms will be applied to
            all types, and nearest neighbor interpolation is always used to
            resample images with type :attr:`torchio.LABEL`.
            The type :attr:`torchio.SAMPLING_MAP` may be used with instances of
            :class:`~torchio.data.sampler.weighted.WeightedSampler`.
        tensor: If :attr:`path` is not given, :attr:`tensor` must be a 4D
            :class:`torch.Tensor` or NumPy array with dimensions
            :math:`(C, W, H, D)`.
        affine: If :attr:`path` is not given, :attr:`affine` must be a
            :math:`4 \times 4` NumPy array. If ``None``, :attr:`affine` is an
            identity matrix.
        check_nans: If ``True``, issues a warning if NaNs are found
            in the image. If ``False``, images will not be checked for the
            presence of NaNs.
        **kwargs: Items that will be added to the image dictionary, e.g.
            acquisition parameters.

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

    Example:
        >>> import torchio as tio
        >>> image = tio.ScalarImage('t1.nii.gz')  # subclass of Image
        >>> image  # not loaded yet
        ScalarImage(path: t1.nii.gz; type: intensity)
        >>> times_two = 2 * image.data  # data is loaded and cached here
        >>> image
        ScalarImage(shape: (1, 256, 256, 176); spacing: (1.00, 1.00, 1.00); orientation: PIR+; memory: 44.0 MiB; type: intensity)
        >>> image.save('doubled_image.nii.gz')

    .. _lazy loaders: https://en.wikipedia.org/wiki/Lazy_loading
    .. _preprocessing: https://torchio.readthedocs.io/transforms/preprocessing.html#intensity
    .. _augmentation: https://torchio.readthedocs.io/transforms/augmentation.html#intensity
    .. _NiBabel docs: https://nipy.org/nibabel/image_orientation.html
    .. _3D Slicer wiki: https://www.slicer.org/wiki/Coordinate_systems
    .. _FSL docs: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Orientation%20Explained
    .. _SimpleITK docs: https://simpleitk.readthedocs.io/en/master/fundamentalConcepts.html
    .. _Graham Wideman's website: http://www.grahamwideman.com/gw/brain/orientation/orientterms.htm
    """
    def __init__(
            self,
            path: Union[TypePath, Sequence[TypePath], None] = None,
            type: str = None,
            tensor: Optional[TypeData] = None,
            affine: Optional[TypeData] = None,
            check_nans: bool = False,  # removed by ITK by default
            channels_last: bool = False,
            **kwargs: Dict[str, Any],
            ):
        self.check_nans = check_nans
        self.channels_last = channels_last

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

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

        tensor = self._parse_tensor(tensor)
        affine = self._parse_affine(affine)
        if tensor is not None:
            self.data = tensor
            self.affine = affine
            self._loaded = True
        for key in PROTECTED_KEYS:
            if key in kwargs:
                message = f'Key "{key}" is reserved. Use a different one'
                raise ValueError(message)

        super().__init__(**kwargs)
        self.path = self._parse_path(path)

        self[PATH] = '' if self.path is None else str(self.path)
        self[STEM] = '' if self.path is None else get_stem(self.path)
        self[TYPE] = type

    def __repr__(self):
        properties = []
        if self._loaded:
            properties.extend([
                f'shape: {self.shape}',
                f'spacing: {self.get_spacing_string()}',
                f'orientation: {"".join(self.orientation)}+',
                f'memory: {humanize.naturalsize(self.memory, binary=True)}',
            ])
        else:
            properties.append(f'path: "{self.path}"')
        properties.append(f'dtype: {self.data.type()}')
        properties = '; '.join(properties)
        string = f'{self.__class__.__name__}({properties})'
        return string

    def __getitem__(self, item):
        if item in (DATA, AFFINE):
            if item not in self:
                self.load()
        return super().__getitem__(item)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    def axis_name_to_index(self, axis: str) -> int:
        """Convert an axis name to an axis index.

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

        .. note:: If you are working with animals, you should probably use
            ``'Superior'``, ``'Inferior'``, ``'Anterior'`` and ``'Posterior'``
            for ``'Dorsal'``, ``'Ventral'``, ``'Rostral'`` and ``'Caudal'``,
            respectively.

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

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

        # Generally, TorchIO tensors are (C, W, H, D)
        if axis in 'TB':  # Top, Bottom
            return -2
        else:
            try:
                index = self.orientation.index(axis)
            except ValueError:
                index = self.orientation.index(self.flip_axis(axis))
            # Return negative indices so that it does not matter whether we
            # refer to spatial dimensions or not
            index = -3 + index
            return index

    # flake8: noqa: E701
    @staticmethod
    def flip_axis(axis: str) -> str:
        if axis == 'R': 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) -> str:
        strings = [f'{n:.2f}' for n in self.spacing]
        string = f'({", ".join(strings)})'
        return string

    def get_bounds(self) -> TypeBounds:
        """Get minimum and maximum world coordinates occupied by the image."""
        first_index = 3 * (-0.5,)
        last_index = np.array(self.spatial_shape) - 0.5
        first_point = nib.affines.apply_affine(self.affine, first_index)
        last_point = nib.affines.apply_affine(self.affine, last_index)
        array = np.array((first_point, last_point))
        bounds_x, bounds_y, bounds_z = array.T.tolist()
        return bounds_x, bounds_y, bounds_z

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

        if not (path.is_file() or path.is_dir()):   # might be a dir with DICOM
            raise FileNotFoundError(f'File not found: "{path}"')
        return path

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

    def _parse_tensor(
            self,
            tensor: TypeData,
            none_ok: bool = True,
            ) -> torch.Tensor:
        if tensor is None:
            if none_ok:
                return None
            else:
                raise RuntimeError('Input tensor cannot be None')
        if isinstance(tensor, np.ndarray):
            tensor = check_uint_to_int(tensor)
            tensor = torch.from_numpy(tensor)
        elif not isinstance(tensor, torch.Tensor):
            message = 'Input tensor must be a PyTorch tensor or NumPy array'
            raise TypeError(message)
        if tensor.ndim != 4:
            raise ValueError('Input tensor must be 4D')
        if tensor.dtype == torch.bool:
            tensor = tensor.to(torch.uint8)
        if self.check_nans and torch.isnan(tensor).any():
            warnings.warn(f'NaNs found in tensor', RuntimeWarning)
        return tensor

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

    @staticmethod
    def _parse_affine(affine: np.ndarray) -> np.ndarray:
        if affine is None:
            return np.eye(4)
        if not isinstance(affine, np.ndarray):
            raise TypeError(f'Affine must be a NumPy array, not {type(affine)}')
        if affine.shape != (4, 4):
            raise ValueError(f'Affine shape must be (4, 4), not {affine.shape}')
        return affine

    def load(self) -> None:
        r"""Load the image from disk.

        Returns:
            Tuple containing a 4D tensor of size :math:`(C, W, H, D)` and a 2D
            :math:`4 \times 4` affine matrix to convert voxel indices to world
            coordinates.
        """
        if self._loaded:
            return
        paths = self.path if isinstance(self.path, list) else [self.path]
        tensor, affine = self.read_and_check(paths[0])
        tensors = [tensor]
        for path in paths[1:]:
            new_tensor, new_affine = self.read_and_check(path)
            if not np.array_equal(affine, new_affine):
                message = (
                    'Files have different affine matrices.'
                    f'\nMatrix of {paths[0]}:'
                    f'\n{affine}'
                    f'\nMatrix of {path}:'
                    f'\n{new_affine}'
                )
                warnings.warn(message, RuntimeWarning)
            if not tensor.shape[1:] == new_tensor.shape[1:]:
                message = (
                    f'Files shape do not match, found {tensor.shape}'
                    f'and {new_tensor.shape}'
                )
                RuntimeError(message)
            tensors.append(new_tensor)
        tensor = torch.cat(tensors)
        self.data = tensor
        self.affine = affine
        self._loaded = True

    def read_and_check(self, path: TypePath) -> Tuple[torch.Tensor, np.ndarray]:
        tensor, affine = read_image(path)
        tensor = self.parse_tensor_shape(tensor)
        if self.channels_last:
            tensor = tensor.permute(3, 0, 1, 2)
        if self.check_nans and torch.isnan(tensor).any():
            warnings.warn(f'NaNs found in file "{path}"', RuntimeWarning)
        return tensor, affine

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

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

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

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

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

    def as_pil(self) -> ImagePIL:
        """Get the image as an instance of :class:`PIL.Image`."""
        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) -> None:
        self.check_nans = check_nans

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


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

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

    See :class:`~torchio.Image` for more information.
    """
    def __init__(self, *args, **kwargs):
        if 'type' in kwargs and kwargs['type'] != INTENSITY:
            raise ValueError('Type of ScalarImage is always torchio.INTENSITY')
        kwargs.update({'type': INTENSITY})
        super().__init__(*args, **kwargs)


class LabelMap(Image):
    """Image whose pixel values represent categorical labels.

    Intensity transforms are not applied to these images.

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

    See :class:`~torchio.Image` for more information.
    """
    def __init__(self, *args, **kwargs):
        if 'type' in kwargs and kwargs['type'] != LABEL:
            raise ValueError('Type of LabelMap is always torchio.LABEL')
        kwargs.update({'type': LABEL})
        super().__init__(*args, **kwargs)


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

fepegar / torchio

Push — master ( 0bdb47...b0bac6 )

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

Complexity

Size

Duplication

Importance

How to fix Many Parameters

Many Parameters

Duplication Side-by-Side

Filter issues like

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