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|
| | from typing import Any, Callable, Optional, Sequence |
| |
|
| | import numpy as np |
| | from torch.utils.data import Dataset |
| |
|
| | from monai.transforms import LoadNifti, Randomizable, apply_transform |
| | from monai.utils import get_seed |
| |
|
| |
|
| | class NiftiDataset(Dataset, Randomizable): |
| | """ |
| | Loads image/segmentation pairs of Nifti files from the given filename lists. Transformations can be specified |
| | for the image and segmentation arrays separately. |
| | """ |
| |
|
| | def __init__( |
| | self, |
| | image_files: Sequence[str], |
| | seg_files: Optional[Sequence[str]] = None, |
| | labels: Optional[Sequence[float]] = None, |
| | as_closest_canonical: bool = False, |
| | transform: Optional[Callable] = None, |
| | seg_transform: Optional[Callable] = None, |
| | image_only: bool = True, |
| | dtype: Optional[np.dtype] = np.float32, |
| | ) -> None: |
| | """ |
| | Initializes the dataset with the image and segmentation filename lists. The transform `transform` is applied |
| | to the images and `seg_transform` to the segmentations. |
| | |
| | Args: |
| | image_files: list of image filenames |
| | seg_files: if in segmentation task, list of segmentation filenames |
| | labels: if in classification task, list of classification labels |
| | as_closest_canonical: if True, load the image as closest to canonical orientation |
| | transform: transform to apply to image arrays |
| | seg_transform: transform to apply to segmentation arrays |
| | image_only: if True return only the image volume, other return image volume and header dict |
| | dtype: if not None convert the loaded image to this data type |
| | |
| | Raises: |
| | ValueError: When ``seg_files`` length differs from ``image_files``. |
| | |
| | """ |
| |
|
| | if seg_files is not None and len(image_files) != len(seg_files): |
| | raise ValueError( |
| | "Must have same the number of segmentation as image files: " |
| | f"images={len(image_files)}, segmentations={len(seg_files)}." |
| | ) |
| |
|
| | self.image_files = image_files |
| | self.seg_files = seg_files |
| | self.labels = labels |
| | self.as_closest_canonical = as_closest_canonical |
| | self.transform = transform |
| | self.seg_transform = seg_transform |
| | self.image_only = image_only |
| | self.dtype = dtype |
| | self.set_random_state(seed=get_seed()) |
| |
|
| | self._seed = 0 |
| |
|
| | def __len__(self) -> int: |
| | return len(self.image_files) |
| |
|
| | def randomize(self, data: Optional[Any] = None) -> None: |
| | self._seed = self.R.randint(np.iinfo(np.int32).max) |
| |
|
| | def __getitem__(self, index: int): |
| | self.randomize() |
| | meta_data = None |
| | img_loader = LoadNifti( |
| | as_closest_canonical=self.as_closest_canonical, image_only=self.image_only, dtype=self.dtype |
| | ) |
| | if self.image_only: |
| | img = img_loader(self.image_files[index]) |
| | else: |
| | img, meta_data = img_loader(self.image_files[index]) |
| | seg = None |
| | if self.seg_files is not None: |
| | seg_loader = LoadNifti(image_only=True) |
| | seg = seg_loader(self.seg_files[index]) |
| | label = None |
| | if self.labels is not None: |
| | label = self.labels[index] |
| |
|
| | if self.transform is not None: |
| | if isinstance(self.transform, Randomizable): |
| | self.transform.set_random_state(seed=self._seed) |
| | img = apply_transform(self.transform, img) |
| |
|
| | data = [img] |
| |
|
| | if self.seg_transform is not None: |
| | if isinstance(self.seg_transform, Randomizable): |
| | self.seg_transform.set_random_state(seed=self._seed) |
| | seg = apply_transform(self.seg_transform, seg) |
| |
|
| | if seg is not None: |
| | data.append(seg) |
| | if label is not None: |
| | data.append(label) |
| | if not self.image_only and meta_data is not None: |
| | data.append(meta_data) |
| | if len(data) == 1: |
| | return data[0] |
| | |
| | return tuple(data) |
| |
|
