torchio.data.image.Image.get_center() - Code Metrics - Inspection of "Add support to take more types as transform input..." - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Push — master ( ddc71b...32d696 )

by Fernando

created 2020-08-10 14:21 UTC

torchio.data.image.Image.get_center() A

↳ Parent: torchio.data.image

Complexity

Conditions

Size

Total Lines	15
Code Lines	7

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	2
eloc	7
nop	2
dl	0
loc	15
rs	10
c	0
b	0
f	0

import warnings
from pathlib import Path
from typing import Any, Dict, Tuple, Optional

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 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. The data is expected to be 2D or
            3D, and may have multiple channels (see :attr:`num_spatial_dims` and
            :attr:`channels_last`).
        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, D, H, W)`. If it is not 4D, TorchIO will try to guess
            the dimensions meanings. If 2D, the shape will be interpreted as
            :math:`(H, W)`. If 3D, the number of spatial dimensions should be
            determined in :attr:`num_spatial_dims`. If :attr:`num_spatial_dims`
            is not given and the shape is 3 along the first or last dimensions,
            it will be interpreted as a multichannel 2D image. Otherwise, it
            be interpreted as a 3D image with a single channel.
        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.
        num_spatial_dims: If ``2`` and the input tensor has 3 dimensions, it
            will be interpreted as a multichannel 2D image. If ``3`` and the
            input has 3 dimensions, it will be interpreted as a
            single-channel 3D volume.
        channels_last: If ``True``, the last dimension of the input will be
            interpreted as the channels. Defaults to ``True`` if :attr:`path` is
            given and ``False`` otherwise.
        **kwargs: Items that will be added to the image dictionary, e.g.
            acquisition parameters.

    Example:
        >>> import torch
        >>> import torchio
        >>> # Loading from a file
        >>> t1_image = torchio.Image('t1.nii.gz', type=torchio.INTENSITY)
        >>> label_image = torchio.Image('t1_seg.nii.gz', type=torchio.LABEL)
        >>> image = torchio.Image(tensor=torch.rand(3, 4, 5))
        >>> image = torchio.Image('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

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

    Example:
        >>> import torchio
        >>> image = torchio.Image('t1.nii.gz')
        >>> image  # not loaded yet
        Image(path: t1.nii.gz; type: intensity)
        >>> times_two = 2 * image.data  # data is loaded and cached here
        >>> image
        Image(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: Optional[TypePath] = None,
            type: str = None,
            tensor: Optional[TypeData] = None,
            affine: Optional[TypeData] = None,
            check_nans: bool = True,
            num_spatial_dims: Optional[int] = None,
            channels_last: Optional[bool] = None,
            **kwargs: Dict[str, Any],
            ):
        self.check_nans = check_nans
        self.num_spatial_dims = num_spatial_dims

        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

        # Number of channels are typically stored in the last dimensions in disk
        # But if a tensor is given, the channels should be in the first dim
        if channels_last is None:
            channels_last = path is not None
        self.channels_last = channels_last

        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,
            channels_last=False,
        )
        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):
        return self[DATA]

    @property
    def tensor(self):
        return self.data

    @property
    def affine(self):
        return self[AFFINE]

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

    @property
    def shape(self) -> Tuple[int, int, int, int]:
        return tuple(self.data.shape)

    @property
    def spatial_shape(self) -> TypeTripletInt:
        return self.shape[1:]

    @property
    def orientation(self):
        return nib.aff2axcodes(self.affine)

    @property
    def spacing(self):
        _, spacing = get_rotation_and_spacing_from_affine(self.affine)
        return tuple(spacing)

    @property
    def memory(self):
        return np.prod(self.shape) * 4  # float32, i.e. 4 bytes per voxel

    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_path(path: TypePath) -> Path:
        if path is None:
            return None
        try:
            path = Path(path).expanduser()
        except TypeError:
            message = f'Conversion to path not possible for variable: {path}'
            raise TypeError(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_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()
        tensor = self.parse_tensor_shape(tensor)
        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, self.channels_last, self.num_spatial_dims)

    @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) -> Tuple[torch.Tensor, np.ndarray]:
        r"""Load the image from disk.

        Returns:
            Tuple containing a 4D tensor of size :math:`(C, D, H, W)` and a 2D
            :math:`4 \times 4` affine matrix to convert voxel indices to world
            coordinates.
        """
        if self._loaded:
            return
        tensor, affine = read_image(self.path)
        tensor = self.parse_tensor_shape(tensor)

        if self.check_nans and torch.isnan(tensor).any():
            warnings.warn(f'NaNs found in file "{self.path}"')
        self[DATA] = tensor
        self[AFFINE] = affine
        self._loaded = True

    def save(self, path, squeeze=True, channels_last=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.
            channels_last: If ``True``, the channels will be saved in the last
                dimension.
        """
        write_image(
            self[DATA],
            self[AFFINE],
            path,
            squeeze=squeeze,
            channels_last=channels_last,
        )

    def is_2d(self) -> bool:
        return self.shape[-3] == 1

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

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

    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 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,
            channels_last=False,
        )
        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
        >>> image = torchio.ScalarImage('t1.nii.gz')  # loading from a file
        >>> image = torchio.ScalarImage(tensor=torch.rand(128, 128, 68))  # from tensor
        >>> 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

    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
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 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. The data is expected to be 2D or
48			3D, and may have multiple channels (see :attr:`num_spatial_dims` and
49			:attr:`channels_last`).
50			type: Type of image, such as :attr:`torchio.INTENSITY` or
51			:attr:`torchio.LABEL`. This will be used by the transforms to
52			decide whether to apply an operation, or which interpolation to use
53			when resampling. For example, `preprocessing`_ and `augmentation`_
54			intensity transforms will only be applied to images with type
55			:attr:`torchio.INTENSITY`. Spatial transforms will be applied to
56			all types, and nearest neighbor interpolation is always used to
57			resample images with type :attr:`torchio.LABEL`.
58			The type :attr:`torchio.SAMPLING_MAP` may be used with instances of
59			:py:class:`~torchio.data.sampler.weighted.WeightedSampler`.
60			tensor: If :py:attr:`path` is not given, :attr:`tensor` must be a 4D
61			:py:class:`torch.Tensor` or NumPy array with dimensions
62			:math:`(C, D, H, W)`. If it is not 4D, TorchIO will try to guess
63			the dimensions meanings. If 2D, the shape will be interpreted as
64			:math:`(H, W)`. If 3D, the number of spatial dimensions should be
65			determined in :attr:`num_spatial_dims`. If :attr:`num_spatial_dims`
66			is not given and the shape is 3 along the first or last dimensions,
67			it will be interpreted as a multichannel 2D image. Otherwise, it
68			be interpreted as a 3D image with a single channel.
69			affine: If :attr:`path` is not given, :attr:`affine` must be a
70			:math:`4 \times 4` NumPy array. If ``None``, :attr:`affine` is an
71			identity matrix.
72			check_nans: If ``True``, issues a warning if NaNs are found
73			in the image. If ``False``, images will not be checked for the
74			presence of NaNs.
75			num_spatial_dims: If ``2`` and the input tensor has 3 dimensions, it
76			will be interpreted as a multichannel 2D image. If ``3`` and the
77			input has 3 dimensions, it will be interpreted as a
78			single-channel 3D volume.
79			channels_last: If ``True``, the last dimension of the input will be
80			interpreted as the channels. Defaults to ``True`` if :attr:`path` is
81			given and ``False`` otherwise.
82			**kwargs: Items that will be added to the image dictionary, e.g.
83			acquisition parameters.
84
85			Example:
86			>>> import torch
87			>>> import torchio
88			>>> # Loading from a file
89			>>> t1_image = torchio.Image('t1.nii.gz', type=torchio.INTENSITY)
90			>>> label_image = torchio.Image('t1_seg.nii.gz', type=torchio.LABEL)
91			>>> image = torchio.Image(tensor=torch.rand(3, 4, 5))
92			>>> image = torchio.Image('safe_image.nrrd', check_nans=False)
93			>>> data, affine = image.data, image.affine
94			>>> affine.shape
95			(4, 4)
96			>>> image.data is image[torchio.DATA]
97			True
98			>>> image.data is image.tensor
99			True
100			>>> type(image.data)
101			torch.Tensor
102
103			TorchIO images are `lazy loaders`_, i.e. the data is only loaded from disk
104			when needed.
105
106			Example:
107			>>> import torchio
108			>>> image = torchio.Image('t1.nii.gz')
109			>>> image # not loaded yet
110			Image(path: t1.nii.gz; type: intensity)
111			>>> times_two = 2 * image.data # data is loaded and cached here
112			>>> image
113			Image(shape: (1, 256, 256, 176); spacing: (1.00, 1.00, 1.00); orientation: PIR+; memory: 44.0 MiB; type: intensity)
114			>>> image.save('doubled_image.nii.gz')
115
116			.. _lazy loaders: https://en.wikipedia.org/wiki/Lazy_loading
117			.. _preprocessing: https://torchio.readthedocs.io/transforms/preprocessing.html#intensity
118			.. _augmentation: https://torchio.readthedocs.io/transforms/augmentation.html#intensity
119			.. _NiBabel docs: https://nipy.org/nibabel/image_orientation.html
120			.. _3D Slicer wiki: https://www.slicer.org/wiki/Coordinate_systems
121			.. _FSL docs: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Orientation%20Explained
122			.. _SimpleITK docs: https://simpleitk.readthedocs.io/en/master/fundamentalConcepts.html
123			.. _Graham Wideman's website: http://www.grahamwideman.com/gw/brain/orientation/orientterms.htm
124			"""
125			def __init__(
126			self,
127			path: Optional[TypePath] = None,
128			type: str = None,
129			tensor: Optional[TypeData] = None,
130			affine: Optional[TypeData] = None,
131			check_nans: bool = True,
132			num_spatial_dims: Optional[int] = None,
133			channels_last: Optional[bool] = None,
134			**kwargs: Dict[str, Any],
135			):
136			self.check_nans = check_nans
137			self.num_spatial_dims = num_spatial_dims
138
139			if type is None:
140			warnings.warn(
141			'Not specifying the image type is deprecated and will be'
142			' mandatory in the future. You can probably use ScalarImage or'
143			' LabelMap instead'
144			)
145			type = INTENSITY
146
147			if path is None and tensor is None:
148			raise ValueError('A value for path or tensor must be given')
149			self._loaded = False
150
151			# Number of channels are typically stored in the last dimensions in disk
152			# But if a tensor is given, the channels should be in the first dim
153			if channels_last is None:
154			channels_last = path is not None
155			self.channels_last = channels_last
156
157			tensor = self.parse_tensor(tensor)
158			affine = self.parse_affine(affine)
159			if tensor is not None:
160			self[DATA] = tensor
161			self[AFFINE] = affine
162			self._loaded = True
163			for key in PROTECTED_KEYS:
164			if key in kwargs:
165			message = f'Key "{key}" is reserved. Use a different one'
166			raise ValueError(message)
167
168			super().__init__(**kwargs)
169			self.path = self._parse_path(path)
170			self[PATH] = '' if self.path is None else str(self.path)
171			self[STEM] = '' if self.path is None else get_stem(self.path)
172			self[TYPE] = type
173
174			def __repr__(self):
175			properties = []
176			if self._loaded:
177			properties.extend([
178			f'shape: {self.shape}',
179			f'spacing: {self.get_spacing_string()}',
180			f'orientation: {"".join(self.orientation)}+',
181			f'memory: {humanize.naturalsize(self.memory, binary=True)}',
182			])
183			else:
184			properties.append(f'path: "{self.path}"')
185			properties.append(f'type: {self.type}')
186			properties = '; '.join(properties)
187			string = f'{self.__class__.__name__}({properties})'
188			return string
189
190			def __getitem__(self, item):
191			if item in (DATA, AFFINE):
192			if item not in self:
193			self._load()
194			return super().__getitem__(item)
195
196			def __array__(self):
197			return self[DATA].numpy()
198
199			def __copy__(self):
200			kwargs = dict(
201			tensor=self.data,
202			affine=self.affine,
203			type=self.type,
204			path=self.path,
205			channels_last=False,
206			)
207			for key, value in self.items():
208			if key in PROTECTED_KEYS: continue
209			kwargs[key] = value # should I copy? deepcopy?
210			return self.__class__(**kwargs)
211
212			@property
213			def data(self):
214			return self[DATA]
215
216			@property
217			def tensor(self):
218			return self.data
219
220			@property
221			def affine(self):
222			return self[AFFINE]
223
224			@property
225			def type(self):
226			return self[TYPE]
227
228			@property
229			def shape(self) -> Tuple[int, int, int, int]:
230			return tuple(self.data.shape)
231
232			@property
233			def spatial_shape(self) -> TypeTripletInt:
234			return self.shape[1:]
235
236			@property
237			def orientation(self):
238			return nib.aff2axcodes(self.affine)
239
240			@property
241			def spacing(self):
242			_, spacing = get_rotation_and_spacing_from_affine(self.affine)
243			return tuple(spacing)
244
245			@property
246			def memory(self):
247			return np.prod(self.shape) * 4 # float32, i.e. 4 bytes per voxel
248
249			def get_spacing_string(self):
250			strings = [f'{n:.2f}' for n in self.spacing]
251			string = f'({", ".join(strings)})'
252			return string
253
254			def get_bounds(self):
255			"""Get image bounds in mm."""
256			first_index = 3 * (-0.5,)
257			last_index = np.array(self.spatial_shape) - 0.5
258			first_point = nib.affines.apply_affine(self.affine, first_index)
259			last_point = nib.affines.apply_affine(self.affine, last_index)
260			array = np.array((first_point, last_point))
261			bounds_x, bounds_y, bounds_z = array.T.tolist()
262			return bounds_x, bounds_y, bounds_z
263
264			@staticmethod
265			def _parse_path(path: TypePath) -> Path:
266			if path is None:
267			return None
268			try:
269			path = Path(path).expanduser()
270			except TypeError:
271			message = f'Conversion to path not possible for variable: {path}'
272			raise TypeError(message)
273			if not (path.is_file() or path.is_dir()): # might be a dir with DICOM
274			raise FileNotFoundError(f'File not found: {path}')
275			return path
276
277			def parse_tensor(self, tensor: TypeData) -> torch.Tensor:
278			if tensor is None:
279			return None
280			if isinstance(tensor, np.ndarray):
281			tensor = torch.from_numpy(tensor.astype(np.float32))
282			elif isinstance(tensor, torch.Tensor):
283			tensor = tensor.float()
284			tensor = self.parse_tensor_shape(tensor)
285			if self.check_nans and torch.isnan(tensor).any():
286			warnings.warn(f'NaNs found in tensor')
287			return tensor
288
289			def parse_tensor_shape(self, tensor: torch.Tensor) -> torch.Tensor:
290			return ensure_4d(tensor, self.channels_last, self.num_spatial_dims)
291
292			@staticmethod
293			def parse_affine(affine: np.ndarray) -> np.ndarray:
294			if affine is None:
295			return np.eye(4)
296			if not isinstance(affine, np.ndarray):
297			raise TypeError(f'Affine must be a NumPy array, not {type(affine)}')
298			if affine.shape != (4, 4):
299			raise ValueError(f'Affine shape must be (4, 4), not {affine.shape}')
300			return affine
301
302			def _load(self) -> Tuple[torch.Tensor, np.ndarray]:
303			r"""Load the image from disk.
304
305			Returns:
306			Tuple containing a 4D tensor of size :math:`(C, D, H, W)` and a 2D
307			:math:`4 \times 4` affine matrix to convert voxel indices to world
308			coordinates.
309			"""
310			if self._loaded:
311			return
312			tensor, affine = read_image(self.path)
313			tensor = self.parse_tensor_shape(tensor)
314
315			if self.check_nans and torch.isnan(tensor).any():
316			warnings.warn(f'NaNs found in file "{self.path}"')
317			self[DATA] = tensor
318			self[AFFINE] = affine
319			self._loaded = True
320
321			def save(self, path, squeeze=True, channels_last=True):
322			"""Save image to disk.
323
324			Args:
325			path: String or instance of :py:class:`pathlib.Path`.
326			squeeze: If ``True``, the singleton dimensions will be removed
327			before saving.
328			channels_last: If ``True``, the channels will be saved in the last
329			dimension.
330			"""
331			write_image(
332			self[DATA],
333			self[AFFINE],
334			path,
335			squeeze=squeeze,
336			channels_last=channels_last,
337			)
338
339			def is_2d(self) -> bool:
340			return self.shape[-3] == 1
341
342			def numpy(self) -> np.ndarray:
343			"""Get a NumPy array containing the image data."""
344			return np.asarray(self)
345
346			def as_sitk(self) -> sitk.Image:
347			"""Get the image as an instance of :py:class:`sitk.Image`."""
348			return nib_to_sitk(self[DATA], self[AFFINE])
349
350			def get_center(self, lps: bool = False) -> TypeTripletFloat:
351			"""Get image center in RAS+ or LPS+ coordinates.
352
353			Args:
354			lps: If ``True``, the coordinates will be in LPS+ orientation, i.e.
355			the first dimension grows towards the left, etc. Otherwise, the
356			coordinates will be in RAS+ orientation.
357			"""
358			size = np.array(self.spatial_shape)
359			center_index = (size - 1) / 2
360			r, a, s = nib.affines.apply_affine(self.affine, center_index)
361			if lps:
362			return (-r, -a, s)
363			else:
364			return (r, a, s)
365
366			def set_check_nans(self, check_nans: bool):
367			self.check_nans = check_nans
368
369			def crop(self, index_ini: TypeTripletInt, index_fin: TypeTripletInt):
370			new_origin = nib.affines.apply_affine(self.affine, index_ini)
371			new_affine = self.affine.copy()
372			new_affine[:3, 3] = new_origin
373			i0, j0, k0 = index_ini
374			i1, j1, k1 = index_fin
375			patch = self.data[:, i0:i1, j0:j1, k0:k1].clone()
376			kwargs = dict(
377			tensor=patch,
378			affine=new_affine,
379			type=self.type,
380			path=self.path,
381			channels_last=False,
382			)
383			for key, value in self.items():
384			if key in PROTECTED_KEYS: continue
385			kwargs[key] = value # should I copy? deepcopy?
386			return self.__class__(**kwargs)
387
388
389			class ScalarImage(Image):
390			"""Alias for :py:class:`~torchio.Image` of type :py:attr:`torchio.INTENSITY`.
391
392			Example:
393			>>> import torch
394			>>> import torchio
395			>>> image = torchio.ScalarImage('t1.nii.gz') # loading from a file
396			>>> image = torchio.ScalarImage(tensor=torch.rand(128, 128, 68)) # from tensor
397			>>> data, affine = image.data, image.affine
398			>>> affine.shape
399			(4, 4)
400			>>> image.data is image[torchio.DATA]
401			True
402			>>> image.data is image.tensor
403			True
404			>>> type(image.data)
405			torch.Tensor
406
407			See :py:class:`~torchio.Image` for more information.
408
409			Raises:
410			ValueError: A :py:attr:`type` is used for instantiation.
411			"""
412			def __init__(self, args, *kwargs):
413			if 'type' in kwargs and kwargs['type'] != INTENSITY:
414			raise ValueError('Type of ScalarImage is always torchio.INTENSITY')
415			kwargs.update({'type': INTENSITY})
416			super().__init__(args, *kwargs)
417
418
419			class LabelMap(Image):
420			"""Alias for :py:class:`~torchio.Image` of type :py:attr:`torchio.LABEL`.
421
422			Example:
423			>>> import torch
424			>>> import torchio
425			>>> labels = torchio.LabelMap(tensor=torch.rand(128, 128, 68) > 0.5)
426			>>> labels = torchio.LabelMap('t1_seg.nii.gz') # loading from a file
427
428			See :py:class:`~torchio.data.image.Image` for more information.
429
430			Raises:
431			ValueError: If a value for :py:attr:`type` is given.
432			"""
433			def __init__(self, args, *kwargs):
434			if 'type' in kwargs and kwargs['type'] != LABEL:
435			raise ValueError('Type of LabelMap is always torchio.LABEL')
436			kwargs.update({'type': LABEL})
437			super().__init__(args, *kwargs)
438

fepegar / torchio

Push — master ( ddc71b...32d696 )

torchio.data.image.Image.get_center() A

Complexity

Size

Duplication

Importance

Duplication Side-by-Side

Filter issues like