Spaces:
Sleeping
Sleeping
| import os | |
| from batchgenerators.utilities.file_and_folder_operations import * | |
| from nnunetv2.paths import nnUNet_raw | |
| from nnunetv2.utilities.utils import get_filenames_of_train_images_and_targets | |
| if __name__ == '__main__': | |
| # creates a dummy dataset where there are no files in imagestr and labelstr | |
| source_dataset = 'Dataset004_Hippocampus' | |
| target_dataset = 'Dataset987_dummyDataset4' | |
| target_dataset_dir = join(nnUNet_raw, target_dataset) | |
| maybe_mkdir_p(target_dataset_dir) | |
| dataset = get_filenames_of_train_images_and_targets(join(nnUNet_raw, source_dataset)) | |
| # the returned dataset will have absolute paths. We should use relative paths so that you can freely copy | |
| # datasets around between systems. As long as the source dataset is there it will continue working even if | |
| # nnUNet_raw is in different locations | |
| # paths must be relative to target_dataset_dir!!! | |
| for k in dataset.keys(): | |
| dataset[k]['label'] = os.path.relpath(dataset[k]['label'], target_dataset_dir) | |
| dataset[k]['images'] = [os.path.relpath(i, target_dataset_dir) for i in dataset[k]['images']] | |
| # load old dataset.json | |
| dataset_json = load_json(join(nnUNet_raw, source_dataset, 'dataset.json')) | |
| dataset_json['dataset'] = dataset | |
| # save | |
| save_json(dataset_json, join(target_dataset_dir, 'dataset.json'), sort_keys=False) | |