Source code for mmseg.datasets.decathlon

# Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
from typing import List

from mmengine.fileio import load

from mmseg.registry import DATASETS
from .basesegdataset import BaseSegDataset

[docs]@DATASETS.register_module() class DecathlonDataset(BaseSegDataset): """Dataset for Dacathlon dataset. The dataset.json format is shown as follows .. code-block:: none { "name": "BRATS", "tensorImageSize": "4D", "modality": { "0": "FLAIR", "1": "T1w", "2": "t1gd", "3": "T2w" }, "labels": { "0": "background", "1": "edema", "2": "non-enhancing tumor", "3": "enhancing tumour" }, "numTraining": 484, "numTest": 266, "training": [ { "image": "./imagesTr/BRATS_306.nii.gz" "label": "./labelsTr/BRATS_306.nii.gz" ... } ] "test": [ "./imagesTs/BRATS_557.nii.gz" ... ] } """
[docs] def load_data_list(self) -> List[dict]: """Load annotation from directory or annotation file. Returns: list[dict]: All data info of dataset. """ # `self.ann_file` denotes the absolute annotation file path if # `self.root=None` or relative path if `self.root=/path/to/data/`. annotations = load(self.ann_file) if not isinstance(annotations, dict): raise TypeError(f'The annotations loaded from annotation file ' f'should be a dict, but got {type(annotations)}!') raw_data_list = annotations[ 'training'] if not self.test_mode else annotations['test'] data_list = [] for raw_data_info in raw_data_list: # `2:` works for removing './' in file path, which will break # loading from cloud storage. if isinstance(raw_data_info, dict): data_info = dict( img_path=osp.join(self.data_root, raw_data_info['image'] [2:])) data_info['seg_map_path'] = osp.join( self.data_root, raw_data_info['label'][2:]) else: data_info = dict( img_path=osp.join(self.data_root, raw_data_info)[2:]) data_info['label_map'] = self.label_map data_info['reduce_zero_label'] = self.reduce_zero_label data_info['seg_fields'] = [] data_list.append(data_info) annotations.pop('training') annotations.pop('test') metainfo = copy.deepcopy(annotations) metainfo['classes'] = [*metainfo['labels'].values()] # Meta information load from annotation file will not influence the # existed meta information load from `BaseDataset.METAINFO` and # `metainfo` arguments defined in constructor. for k, v in metainfo.items(): self._metainfo.setdefault(k, v) return data_list
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