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import copy |
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import collections |
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from typing import Dict, Sequence, Optional, Callable |
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from deprecated import deprecated |
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from torch.utils.data import Dataset |
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from ..utils import get_stem |
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from ..torchio import DATA, AFFINE, TYPE, PATH, STEM, TypePath |
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from .image import Image |
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from .io import write_image |
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from .subject import Subject |
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class ImagesDataset(Dataset): |
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"""Base TorchIO dataset. |
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:py:class:`~torchio.data.dataset.ImagesDataset` |
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is a reader of 3D medical images that directly |
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inherits from :class:`torch.utils.data.Dataset`. |
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It can be used with a :class:`torch.utils.data.DataLoader` |
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for efficient loading and augmentation. |
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It receives a list of subjects, where each subject is an instance of |
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:py:class:`torchio.data.subject.Subject` containing instances of |
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:py:class:`torchio.data.image.Image`. |
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The file format must be compatible with `NiBabel`_ or `SimpleITK`_ readers. |
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It can also be a directory containing |
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`DICOM`_ files. |
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Indexing an :py:class:`~torchio.data.dataset.ImagesDataset` returns an |
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instance of :py:class:`~torchio.data.subject.Subject`. Check out the |
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documentation for both classes for usage examples. |
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Example: |
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>>> sample = images_dataset[0] |
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>>> sample |
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Subject(Keys: ('image', 'label'); images: 2) |
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>>> image = sample['image'] # or sample.image |
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>>> image.shape |
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torch.Size([1, 176, 256, 256]) |
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>>> image.affine |
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array([[ 0.03, 1.13, -0.08, -88.54], |
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[ 0.06, 0.08, 0.95, -129.66], |
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[ 1.18, -0.06, -0.11, -67.15], |
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[ 0. , 0. , 0. , 1. ]]) |
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Args: |
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subjects: Sequence of instances of |
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:class:`~torchio.data.subject.Subject`. |
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transform: An instance of :py:class:`torchio.transforms.Transform` |
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that will be applied to each sample. |
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Example: |
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>>> import torchio |
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>>> from torchio import ImagesDataset, Image, Subject |
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>>> from torchio.transforms import RescaleIntensity, RandomAffine, Compose |
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>>> subject_a = Subject([ |
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... t1=Image('~/Dropbox/MRI/t1.nrrd', type=torchio.INTENSITY), |
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... t2=Image('~/Dropbox/MRI/t2.mha', type=torchio.INTENSITY), |
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... label=Image('~/Dropbox/MRI/t1_seg.nii.gz', type=torchio.LABEL), |
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... age=31, |
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... name='Fernando Perez', |
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>>> ]) |
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>>> subject_b = Subject( |
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... t1=Image('/tmp/colin27_t1_tal_lin.minc', type=torchio.INTENSITY), |
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... t2=Image('/tmp/colin27_t2_tal_lin_dicom', type=torchio.INTENSITY), |
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... label=Image('/tmp/colin27_seg1.nii.gz', type=torchio.LABEL), |
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... age=56, |
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... name='Colin Holmes', |
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... ) |
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>>> subjects_list = [subject_a, subject_b] |
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>>> transforms = [ |
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... RescaleIntensity((0, 1)), |
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... RandomAffine(), |
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... ] |
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>>> transform = Compose(transforms) |
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>>> subjects_dataset = ImagesDataset(subjects_list, transform=transform) |
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>>> subject_sample = subjects_dataset[0] |
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.. _NiBabel: https://nipy.org/nibabel/#nibabel |
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.. _SimpleITK: https://itk.org/Wiki/ITK/FAQ#What_3D_file_formats_can_ITK_import_and_export.3F |
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.. _DICOM: https://www.dicomstandard.org/ |
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.. _affine matrix: https://nipy.org/nibabel/coordinate_systems.html |
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""" |
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def __init__( |
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self, |
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subjects: Sequence[Subject], |
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transform: Optional[Callable] = None, |
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): |
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self._parse_subjects_list(subjects) |
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self.subjects = subjects |
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self._transform: Optional[Callable] |
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self.set_transform(transform) |
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def __len__(self): |
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return len(self.subjects) |
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def __getitem__(self, index: int) -> dict: |
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if not isinstance(index, int): |
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raise ValueError(f'Index "{index}" must be int, not {type(index)}') |
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subject = self.subjects[index] |
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sample = copy.deepcopy(subject) |
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# Apply transform (this is usually the bottleneck) |
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if self._transform is not None: |
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sample = self._transform(sample) |
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return sample |
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def set_transform(self, transform: Optional[Callable]) -> None: |
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"""Set the :attr:`transform` attribute. |
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Args: |
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transform: An instance of :py:class:`torchio.transforms.Transform`. |
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""" |
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if transform is not None and not callable(transform): |
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raise ValueError( |
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f'The transform must be a callable object, not {transform}') |
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self._transform = transform |
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@staticmethod |
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def _parse_subjects_list(subjects_list: Sequence[Subject]) -> None: |
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# Check that it's list or tuple |
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if not isinstance(subjects_list, collections.abc.Sequence): |
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raise TypeError( |
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f'Subject list must be a sequence, not {type(subjects_list)}') |
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# Check that it's not empty |
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if not subjects_list: |
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raise ValueError('Subjects list is empty') |
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# Check each element |
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for subject in subjects_list: |
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if not isinstance(subject, Subject): |
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message = ( |
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'Subjects list must contain instances of torchio.Subject,' |
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f' not "{type(subject)}"' |
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) |
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raise TypeError(message) |
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@classmethod |
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@deprecated( |
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'ImagesDataset.save_sample is deprecated. Use Image.save instead' |
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) |
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def save_sample( |
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cls, |
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sample: Subject, |
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output_paths_dict: Dict[str, TypePath], |
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) -> None: |
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for key, output_path in output_paths_dict.items(): |
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tensor = sample[key][DATA].squeeze() # assume 2D if (1, 1, H, W) |
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affine = sample[key][AFFINE] |
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write_image(tensor, affine, output_path) |
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fepegar /
torchio
