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import warnings |
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from pathlib import Path |
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from typing import Any, Dict, Tuple, Optional |
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import torch |
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import numpy as np |
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import SimpleITK as sitk |
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from ..utils import nib_to_sitk |
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from ..torchio import ( |
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TypePath, |
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TypeTripletInt, |
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TypeTripletFloat, |
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DATA, |
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TYPE, |
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AFFINE, |
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PATH, |
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STEM, |
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INTENSITY, |
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) |
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from .io import read_image |
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class Image(dict): |
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r"""Class to store information about an image. |
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Args: |
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path: Path to a file that can be read by |
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:mod:`SimpleITK` or :mod:`nibabel` or to a directory containing |
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DICOM files. |
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type: Type of image, such as :attr:`torchio.INTENSITY` or |
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:attr:`torchio.LABEL`. This will be used by the transforms to |
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decide whether to apply an operation, or which interpolation to use |
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when resampling. |
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tensor: If :attr:`path` is not given, :attr:`tensor` must be a 4D |
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:py:class:`torch.Tensor` with dimensions :math:`(C, D, H, W)`, |
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where :math:`C` is the number of channels and :math:`D, H, W` |
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are the spatial dimensions. |
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affine: If :attr:`path` is not given, :attr:`affine` must be a |
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:math:`4 \times 4` NumPy array. If ``None``, :attr:`affine` is an |
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identity matrix. |
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**kwargs: Items that will be added to image dictionary within the |
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subject sample. |
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""" |
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def __init__( |
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self, |
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path: Optional[TypePath] = None, |
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type: str = INTENSITY, |
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tensor: Optional[torch.Tensor] = None, |
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affine: Optional[torch.Tensor] = None, |
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**kwargs: Dict[str, Any], |
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): |
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if path is None and tensor is None: |
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raise ValueError('A value for path or tensor must be given') |
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if path is not None: |
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if tensor is not None or affine is not None: |
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message = 'If a path is given, tensor and affine must be None' |
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raise ValueError(message) |
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self.tensor = self.parse_tensor(tensor) |
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self.affine = self.parse_affine(affine) |
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if self.affine is None: |
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self.affine = np.eye(4) |
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for key in (DATA, AFFINE, TYPE, PATH, STEM): |
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if key in kwargs: |
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raise ValueError(f'Key {key} is reserved. Use a different one') |
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super().__init__(**kwargs) |
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self.path = self._parse_path(path) |
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self.type = type |
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self.is_sample = False # set to True by ImagesDataset |
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@property |
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def shape(self) -> Tuple[int, int, int, int]: |
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return self[DATA].shape |
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@property |
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def spatial_shape(self) -> TypeTripletInt: |
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return self.shape[1:] |
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@staticmethod |
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def _parse_path(path: TypePath) -> Path: |
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if path is None: |
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return None |
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try: |
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path = Path(path).expanduser() |
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except TypeError: |
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message = f'Conversion to path not possible for variable: {path}' |
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raise TypeError(message) |
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if not (path.is_file() or path.is_dir()): # might be a dir with DICOM |
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raise FileNotFoundError(f'File not found: {path}') |
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return path |
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@staticmethod |
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def parse_tensor(tensor: torch.Tensor) -> torch.Tensor: |
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if tensor is None: |
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return None |
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num_dimensions = tensor.dim() |
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if num_dimensions != 3: |
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message = ( |
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'The input tensor must have 3 dimensions (D, H, W),' |
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f' but has {num_dimensions}: {tensor.shape}' |
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) |
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raise RuntimeError(message) |
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tensor = tensor.unsqueeze(0) # add channels dimension |
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return tensor |
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@staticmethod |
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def parse_affine(affine: np.ndarray) -> np.ndarray: |
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if affine is None: |
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return np.eye(4) |
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if not isinstance(affine, np.ndarray): |
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raise TypeError(f'Affine must be a NumPy array, not {type(affine)}') |
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if affine.shape != (4, 4): |
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raise ValueError(f'Affine shape must be (4, 4), not {affine.shape}') |
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return affine |
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def load(self, check_nans: bool = True) -> Tuple[torch.Tensor, np.ndarray]: |
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r"""Load the image from disk. |
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The file is expected to be monomodal/grayscale and 2D or 3D. |
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A channels dimension is added to the tensor. |
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Args: |
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check_nans: If ``True``, issues a warning if NaNs are found |
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in the image |
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Returns: |
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Tuple containing a 4D data tensor of size |
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:math:`(1, D_{in}, H_{in}, W_{in})` |
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and a 2D 4x4 affine matrix |
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""" |
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if self.path is None: |
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return self.tensor, self.affine |
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tensor, affine = read_image(self.path) |
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# https://github.com/pytorch/pytorch/issues/9410#issuecomment-404968513 |
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tensor = tensor[(None,) * (3 - tensor.ndim)] # force to be 3D |
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tensor = tensor.unsqueeze(0) # add channels dimension |
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if check_nans and torch.isnan(tensor).any(): |
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warnings.warn(f'NaNs found in file "{self.path}"') |
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return tensor, affine |
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def is_2d(self) -> bool: |
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return self.shape[-3] == 1 |
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def numpy(self) -> np.ndarray: |
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return self[DATA].numpy() |
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def as_sitk(self) -> sitk.Image: |
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return nib_to_sitk(self.data, self.affine) |
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def get_center(self, lps: bool = False) -> TypeTripletFloat: |
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"""Get image center in RAS (default) or LPS coordinates.""" |
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image = self.as_sitk() |
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size = np.array(image.GetSize()) |
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center_index = (size - 1) / 2 |
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l, p, s = image.TransformContinuousIndexToPhysicalPoint(center_index) |
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if lps: |
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return (l, p, s) |
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else: |
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return (-l, -p, s) |
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fepegar /
torchio
