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from typing import Optional, List, Sequence, Union |
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import torch |
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import numpy as np |
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import nibabel as nib |
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import SimpleITK as sitk |
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from ..typing import TypeData |
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from ..data.subject import Subject |
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from ..data.image import Image, ScalarImage |
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from ..data.io import nib_to_sitk, sitk_to_nib |
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TypeTransformInput = Union[ |
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Subject, |
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Image, |
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torch.Tensor, |
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np.ndarray, |
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sitk.Image, |
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dict, |
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nib.Nifti1Image, |
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] |
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class DataParser: |
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def __init__( |
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self, |
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data: TypeTransformInput, |
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keys: Optional[Sequence[str]] = None, |
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): |
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self.data = data |
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self.keys = keys |
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self.default_image_name = 'default_image_name' |
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self.is_tensor = False |
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self.is_array = False |
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self.is_dict = False |
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self.is_image = False |
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self.is_sitk = False |
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self.is_nib = False |
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def get_subject(self): |
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if isinstance(self.data, nib.Nifti1Image): |
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tensor = self.data.get_fdata(dtype=np.float32) |
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data = ScalarImage(tensor=tensor, affine=self.data.affine) |
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subject = self._get_subject_from_image(data) |
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self.is_nib = True |
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elif isinstance(self.data, (np.ndarray, torch.Tensor)): |
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subject = self._parse_tensor(self.data) |
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self.is_array = isinstance(self.data, np.ndarray) |
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self.is_tensor = True |
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elif isinstance(self.data, Image): |
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subject = self._get_subject_from_image(self.data) |
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self.is_image = True |
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elif isinstance(self.data, Subject): |
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subject = self.data |
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elif isinstance(self.data, sitk.Image): |
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subject = self._get_subject_from_sitk_image(self.data) |
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self.is_sitk = True |
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elif isinstance(self.data, dict): # e.g. Eisen or MONAI dicts |
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if self.keys is None: |
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message = ( |
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'If the input is a dictionary, a value for "include" must' |
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' be specified when instantiating the transform. See the' |
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' docs for Transform:' |
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' https://torchio.readthedocs.io/transforms/transforms.html#torchio.transforms.Transform' # noqa: E501 |
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) |
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raise RuntimeError(message) |
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subject = self._get_subject_from_dict(self.data, self.keys) |
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self.is_dict = True |
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else: |
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raise ValueError(f'Input type not recognized: {type(self.data)}') |
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self._parse_subject(subject) |
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return subject |
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def get_output(self, transformed): |
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if self.is_tensor or self.is_sitk: |
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image = transformed[self.default_image_name] |
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transformed = image.data |
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if self.is_array: |
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transformed = transformed.numpy() |
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elif self.is_sitk: |
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transformed = nib_to_sitk(image.data, image.affine) |
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elif self.is_image: |
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transformed = transformed[self.default_image_name] |
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elif self.is_dict: |
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transformed = dict(transformed) |
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for key, value in transformed.items(): |
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if isinstance(value, Image): |
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transformed[key] = value.data |
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elif self.is_nib: |
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image = transformed[self.default_image_name] |
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data = image.data |
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if len(data) > 1: |
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message = ( |
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'Multichannel images not supported for input of type' |
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' nibabel.nifti.Nifti1Image' |
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) |
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raise RuntimeError(message) |
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transformed = nib.Nifti1Image(data[0].numpy(), image.affine) |
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return transformed |
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@staticmethod |
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def _parse_subject(subject: Subject) -> None: |
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if not isinstance(subject, Subject): |
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message = ( |
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'Input to a transform must be a tensor or an instance' |
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f' of torchio.Subject, not "{type(subject)}"' |
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) |
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raise RuntimeError(message) |
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def _parse_tensor(self, data: TypeData) -> Subject: |
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if data.ndim != 4: |
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message = ( |
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'The input must be a 4D tensor with dimensions' |
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f' (channels, x, y, z) but it has shape {tuple(data.shape)}.' |
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' Tips: if it is a volume, please add the channels dimension;' |
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' if it is 2D, also add a dimension of size 1 for the z axis' |
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) |
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raise ValueError(message) |
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return self._get_subject_from_tensor(data) |
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def _get_subject_from_tensor(self, tensor: torch.Tensor) -> Subject: |
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image = ScalarImage(tensor=tensor) |
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return self._get_subject_from_image(image) |
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def _get_subject_from_image(self, image: Image) -> Subject: |
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subject = Subject({self.default_image_name: image}) |
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return subject |
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@staticmethod |
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def _get_subject_from_dict( |
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data: dict, |
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image_keys: List[str], |
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) -> Subject: |
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subject_dict = {} |
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for key, value in data.items(): |
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if key in image_keys: |
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value = ScalarImage(tensor=value) |
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subject_dict[key] = value |
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return Subject(subject_dict) |
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def _get_subject_from_sitk_image(self, image): |
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tensor, affine = sitk_to_nib(image) |
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image = ScalarImage(tensor=tensor, affine=affine) |
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return self._get_subject_from_image(image) |
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fepegar /
torchio
