Source code for

import warnings
from pathlib import Path
from import Iterable
from typing import Any, Dict, Tuple, Optional, Union, Sequence, List, Callable

import torch
import humanize
import numpy as np
import nibabel as nib
import SimpleITK as sitk
from deprecated import deprecated

from ..utils import get_stem
from ..typing import (
from ..constants import DATA, TYPE, AFFINE, PATH, STEM, INTENSITY, LABEL
from .io import (

TypeBound = Tuple[float, float]
TypeBounds = Tuple[TypeBound, TypeBound, TypeBound]

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

[docs]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:``. 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: :math:`4 \times 4` matrix to convert voxel coordinates to world coordinates. If ``None``, an identity matrix will be used. See the `NiBabel docs on coordinates`_ for more information. 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. channels_last: If ``True``, the read tensor will be permuted so the last dimension becomes the first. This is useful, e.g., when NIfTI images have been saved with the channels dimension being the fourth instead of the fifth. reader: Callable object that takes a path and returns a 4D tensor and a 2D, :math:`4 \times 4` affine matrix. This can be used if your data is saved in a custom format, such as ``.npy`` (see example below). If the affine matrix is ``None``, an identity matrix will be used. **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 >>> import numpy as np >>> image = tio.ScalarImage('t1.nii.gz') # subclass of Image >>> image # not loaded yet ScalarImage(path: t1.nii.gz; type: intensity) >>> times_two = 2 * # 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) >>>'doubled_image.nii.gz') >>> numpy_reader = lambda path: np.load(path), np.eye(4) >>> image = tio.ScalarImage('t1.npy', reader=numpy_reader) .. _lazy loaders: .. _preprocessing: .. _augmentation: .. _NiBabel docs: .. _NiBabel docs on coordinates: .. _3D Slicer wiki: .. _FSL docs: .. _SimpleITK docs: .. _Graham Wideman's website: """ 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, reader: Callable = read_image, **kwargs: Dict[str, Any], ): self.check_nans = check_nans self.channels_last = channels_last self.reader = reader if type is None: warnings.warn( 'Not specifying the image type is deprecated and will be' ' mandatory in the future. You can probably use tio.ScalarImage' ' or tio.LabelMap instead', ) type = INTENSITY if path is None and tensor is None: raise ValueError('A value for path or tensor must be given') self._loaded = False tensor = self._parse_tensor(tensor) affine = self._parse_affine(affine) if tensor is not None: self.set_data(tensor) self.affine = affine self._loaded = True for key in PROTECTED_KEYS: if key in kwargs: message = f'Key "{key}" is reserved. Use a different one' raise ValueError(message) super().__init__(**kwargs) self.path = self._parse_path(path) self[PATH] = '' if self.path is None else str(self.path) self[STEM] = '' if self.path is None else get_stem(self.path) self[TYPE] = type def __repr__(self): properties = [] properties.extend([ f'shape: {self.shape}', f'spacing: {self.get_spacing_string()}', f'orientation: {"".join(self.orientation)}+', ]) if self._loaded: properties.append(f'dtype: {}') properties.append(f'memory: {humanize.naturalsize(self.memory, binary=True)}') else: properties.append(f'path: "{self.path}"') 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 def __copy__(self): kwargs = dict(, 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 # type: ignore @deprecated(version='0.18.16', reason=deprecation_message) def data(self, tensor: TypeData): self.set_data(tensor)
[docs] def set_data(self, tensor: TypeData): """Store a 4D tensor in the :attr:`data` key and attribute. Args: tensor: 4D tensor with dimensions :math:`(C, W, H, D)`. """ self[DATA] = self._parse_tensor(tensor, none_ok=False)
@property def tensor(self) -> torch.Tensor: """Tensor data. Same as :class:``.""" return @property def affine(self) -> np.ndarray: """Affine matrix to transform voxel indices into world coordinates.""" # If path is a dir (probably DICOM), just load the data # Same if it's a list of paths (used to create a 4D image) if self._loaded or (isinstance(self.path, Path) and self.path.is_dir()): affine = self[AFFINE] else: affine = read_affine(self.path) return 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)`.""" custom_reader = self.reader is not read_image multipath = not isinstance(self.path, (str, Path)) if self._loaded or custom_reader or multipath or self.path.is_dir(): shape = tuple( else: shape = read_shape(self.path) return 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 direction(self) -> TypeDirection3D: _, _, direction = get_sitk_metadata_from_ras_affine( self.affine, lps=False) return direction @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 origin(self) -> Tuple[float, float, float]: """Center of first voxel in array, in mm.""" return tuple(self.affine[:3, 3]) @property def itemsize(self): """Element size of the data type.""" return @property def memory(self) -> float: """Number of Bytes that the tensor takes in the RAM.""" return * self.itemsize @property def bounds(self) -> np.ndarray: """Position of centers of voxels in smallest and largest coordinates.""" ini = 0, 0, 0 fin = np.array(self.spatial_shape) - 1 point_ini = nib.affines.apply_affine(self.affine, ini) point_fin = nib.affines.apply_affine(self.affine, fin) return np.array((point_ini, point_fin)) @property def num_channels(self) -> int: """Get the number of channels in the associated 4D tensor.""" return len(
[docs] def axis_name_to_index(self, axis: str) -> int: """Convert an axis name to an axis index. Args: axis: Possible inputs are ``'Left'``, ``'Right'``, ``'Anterior'``, ``'Posterior'``, ``'Inferior'``, ``'Superior'``. Lower-case versions and first letters are also valid, as only the first letter will be used. .. note:: If you are working with animals, you should probably use ``'Superior'``, ``'Inferior'``, ``'Anterior'`` and ``'Posterior'`` for ``'Dorsal'``, ``'Ventral'``, ``'Rostral'`` and ``'Caudal'``, respectively. .. note:: If your images are 2D, you can use ``'Top'``, ``'Bottom'``, ``'Left'`` and ``'Right'``. """ # Top and bottom are used for the vertical 2D axis as the use of # Height vs Horizontal might be ambiguous if not isinstance(axis, str): raise ValueError('Axis must be a string') axis = axis[0].upper() # Generally, TorchIO tensors are (C, W, H, D) if axis in 'TB': # Top, Bottom return -2 else: try: index = self.orientation.index(axis) except ValueError: index = self.orientation.index(self.flip_axis(axis)) # Return negative indices so that it does not matter whether we # refer to spatial dimensions or not index = -3 + index return index
# flake8: noqa: E701 @staticmethod def flip_axis(axis: str) -> str: if axis == 'R': flipped_axis = 'L' elif axis == 'L': flipped_axis = 'R' elif axis == 'A': flipped_axis = 'P' elif axis == 'P': flipped_axis = 'A' elif axis == 'I': flipped_axis = 'S' elif axis == 'S': flipped_axis = 'I' elif axis == 'T': flipped_axis = 'B' # top / bottom elif axis == 'B': flipped_axis = 'T' else: values = ', '.join('LRPAISTB') message = f'Axis not understood. Please use one of: {values}' raise ValueError(message) return flipped_axis def get_spacing_string(self) -> str: strings = [f'{n:.2f}' for n in self.spacing] string = f'({", ".join(strings)})' return string
[docs] def get_bounds(self) -> TypeBounds: """Get minimum and maximum world coordinates occupied by the image.""" first_index = 3 * (-0.5,) last_index = np.array(self.spatial_shape) - 0.5 first_point = nib.affines.apply_affine(self.affine, first_index) last_point = nib.affines.apply_affine(self.affine, last_index) array = np.array((first_point, last_point)) bounds_x, bounds_y, bounds_z = array.T.tolist() return bounds_x, bounds_y, bounds_z
@staticmethod def _parse_single_path( path: TypePath ) -> Path: try: path = Path(path).expanduser() except TypeError: message = ( f'Expected type str or Path but found {path} with type' f' {type(path)} instead' ) raise TypeError(message) except RuntimeError: message = ( f'Conversion to path not possible for variable: {path}' ) raise RuntimeError(message) if not (path.is_file() or path.is_dir()): # might be a dir with DICOM raise FileNotFoundError(f'File not found: "{path}"') return path def _parse_path( self, path: Union[TypePath, Sequence[TypePath], None] ) -> Optional[Union[Path, List[Path]]]: if path is None: return None if isinstance(path, Iterable) and not isinstance(path, str): return [self._parse_single_path(p) for p in path] else: return self._parse_single_path(path) def _parse_tensor( self, tensor: Optional[TypeData], none_ok: bool = True, ) -> Optional[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.as_tensor(tensor) elif not isinstance(tensor, torch.Tensor): message = ( 'Input tensor must be a PyTorch tensor or NumPy array,' f' but type "{type(tensor)}" was found' ) raise TypeError(message) ndim = tensor.ndim if ndim != 4: raise ValueError(f'Input tensor must be 4D, but it is {ndim}D') if tensor.dtype == torch.bool: tensor = if self.check_nans and torch.isnan(tensor).any(): warnings.warn(f'NaNs found in tensor', RuntimeWarning) return tensor @staticmethod def _parse_tensor_shape(tensor: torch.Tensor) -> TypeData: return ensure_4d(tensor) @staticmethod def _parse_affine(affine: Optional[TypeData]) -> np.ndarray: if affine is None: return np.eye(4) if isinstance(affine, torch.Tensor): affine = affine.numpy() if not isinstance(affine, np.ndarray): raise TypeError(f'Affine must be a NumPy array, not {type(affine)}') if affine.shape != (4, 4): raise ValueError(f'Affine shape must be (4, 4), not {affine.shape}') return affine.astype(np.float64)
[docs] 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 = self.set_data(tensor) self.affine = affine self._loaded = True
def read_and_check(self, path: TypePath) -> Tuple[torch.Tensor, np.ndarray]: tensor, affine = self.reader(path) tensor = self._parse_tensor_shape(tensor) tensor = self._parse_tensor(tensor) affine = self._parse_affine(affine) 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
[docs] def save(self, path: TypePath, squeeze: Optional[bool] = None) -> None: """Save image to disk. Args: path: String or instance of :class:`pathlib.Path`. squeeze: Whether to remove singleton dimensions before saving. If ``None``, the array will be squeezed if the output format is JP(E)G, PNG, BMP or TIF(F). """ write_image(, self.affine, path, squeeze=squeeze, )
def is_2d(self) -> bool: return self.shape[-1] == 1
[docs] def numpy(self) -> np.ndarray: """Get a NumPy array containing the image data.""" return np.asarray(self)
[docs] def as_sitk(self, **kwargs) -> sitk.Image: """Get the image as an instance of :class:`sitk.Image`.""" return nib_to_sitk(, self.affine, **kwargs)
[docs] @classmethod def from_sitk(cls, sitk_image): """Instantiate a new TorchIO image from a :class:`sitk.Image`. Example: >>> import torchio as tio >>> import SimpleITK as sitk >>> sitk_image = sitk.Image(20, 30, 40, sitk.sitkUInt16) >>> tio.LabelMap.from_sitk(sitk_image) LabelMap(shape: (1, 20, 30, 40); spacing: (1.00, 1.00, 1.00); orientation: LPS+; memory: 93.8 KiB; dtype: torch.IntTensor) >>> sitk_image = sitk.Image((224, 224), sitk.sitkVectorFloat32, 3) >>> tio.ScalarImage.from_sitk(sitk_image) ScalarImage(shape: (3, 224, 224, 1); spacing: (1.00, 1.00, 1.00); orientation: LPS+; memory: 588.0 KiB; dtype: torch.FloatTensor) """ tensor, affine = sitk_to_nib(sitk_image) return cls(tensor=tensor, affine=affine)
[docs] def as_pil(self, transpose=True): """Get the image as an instance of :class:`PIL.Image`. .. note:: Values will be clamped to 0-255 and cast to uint8. .. note:: To use this method, `Pillow` needs to be installed: `pip install Pillow`. """ try: from PIL import Image as ImagePIL except ModuleNotFoundError as e: message = ( 'Please install Pillow to use Image.as_pil():' ' pip install Pillow' ) raise RuntimeError(message) from e self.check_is_2d() tensor = if len(tensor) == 1: tensor = * [tensor]) if len(tensor) != 3: raise RuntimeError('The image must have 1 or 3 channels') if transpose: tensor = tensor.permute(3, 2, 1, 0) else: tensor = tensor.permute(3, 1, 2, 0) array = tensor.clamp(0, 255).numpy()[0] return ImagePIL.fromarray(array.astype(np.uint8))
[docs] 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
[docs] 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)
[docs]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.affine >>> affine.shape (4, 4) >>> is image[tio.DATA] True >>> is image.tensor True >>> type( 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)
[docs]class LabelMap(Image): """Image whose pixel values represent categorical labels. 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', ... ) Intensity transforms are not applied to these images. Nearest neighbor interpolation is always used to resample label maps, independently of the specified interpolation type in the transform instantiation. 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)