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# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Tuple
import numpy as np
from batchgenerators.utilities.file_and_folder_operations import *
def get_identifiers_from_splitted_files(folder: str):
uniques = np.unique([i[:-12] for i in subfiles(folder, suffix='.nii.gz', join=False)])
return uniques
def generate_dataset_json(output_file: str, imagesTr_dir: str, imagesTs_dir: str, modalities: Tuple,
labels: dict, dataset_name: str, license: str = "hands off!", dataset_description: str = "",
dataset_reference="", dataset_release='0.0'):
"""
:param output_file: This needs to be the full path to the dataset.json you intend to write, so
output_file='DATASET_PATH/dataset.json' where the folder DATASET_PATH points to is the one with the
imagesTr and labelsTr subfolders
:param imagesTr_dir: path to the imagesTr folder of that dataset
:param imagesTs_dir: path to the imagesTs folder of that dataset. Can be None
:param modalities: tuple of strings with modality names. must be in the same order as the images (first entry
corresponds to _0000.nii.gz, etc). Example: ('T1', 'T2', 'FLAIR').
:param labels: dict with int->str (key->value) mapping the label IDs to label names. Note that 0 is always
supposed to be background! Example: {0: 'background', 1: 'edema', 2: 'enhancing tumor'}
:param dataset_name: The name of the dataset. Can be anything you want
:param license:
:param dataset_description:
:param dataset_reference: website of the dataset, if available
:param dataset_release:
:return:
"""
train_identifiers = get_identifiers_from_splitted_files(imagesTr_dir)
if imagesTs_dir is not None:
test_identifiers = get_identifiers_from_splitted_files(imagesTs_dir)
else:
test_identifiers = []
json_dict = {}
json_dict['name'] = dataset_name
json_dict['description'] = dataset_description
json_dict['tensorImageSize'] = "4D"
json_dict['reference'] = dataset_reference
json_dict['licence'] = license
json_dict['release'] = dataset_release
json_dict['modality'] = {str(i): modalities[i] for i in range(len(modalities))}
json_dict['labels'] = {str(i): labels[i] for i in labels.keys()}
json_dict['numTraining'] = len(train_identifiers)
json_dict['numTest'] = len(test_identifiers)
json_dict['training'] = [
{'image': "./imagesTr/%s.nii.gz" % i, "label": "./labelsTr/%s.nii.gz" % i} for i
in
train_identifiers]
json_dict['test'] = ["./imagesTs/%s.nii.gz" % i for i in test_identifiers]
if not output_file.endswith("dataset.json"):
print("WARNING: output file name is not dataset.json! This may be intentional or not. You decide. "
"Proceeding anyways...")
save_json(json_dict, os.path.join(output_file)) |