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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 collections import OrderedDict
from nnunet.paths import nnUNet_raw_data
from batchgenerators.utilities.file_and_folder_operations import *
import shutil
import SimpleITK as sitk
def convert_for_submission(source_dir, target_dir):
"""
I believe they want .nii, not .nii.gz
:param source_dir:
:param target_dir:
:return:
"""
files = subfiles(source_dir, suffix=".nii.gz", join=False)
maybe_mkdir_p(target_dir)
for f in files:
img = sitk.ReadImage(join(source_dir, f))
out_file = join(target_dir, f[:-7] + ".nii")
sitk.WriteImage(img, out_file)
if __name__ == "__main__":
base = "/media/fabian/DeepLearningData/SegTHOR"
task_id = 55
task_name = "SegTHOR"
foldername = "Task%03.0d_%s" % (task_id, task_name)
out_base = join(nnUNet_raw_data, foldername)
imagestr = join(out_base, "imagesTr")
imagests = join(out_base, "imagesTs")
labelstr = join(out_base, "labelsTr")
maybe_mkdir_p(imagestr)
maybe_mkdir_p(imagests)
maybe_mkdir_p(labelstr)
train_patient_names = []
test_patient_names = []
train_patients = subfolders(join(base, "train"), join=False)
for p in train_patients:
curr = join(base, "train", p)
label_file = join(curr, "GT.nii.gz")
image_file = join(curr, p + ".nii.gz")
shutil.copy(image_file, join(imagestr, p + "_0000.nii.gz"))
shutil.copy(label_file, join(labelstr, p + ".nii.gz"))
train_patient_names.append(p)
test_patients = subfiles(join(base, "test"), join=False, suffix=".nii.gz")
for p in test_patients:
p = p[:-7]
curr = join(base, "test")
image_file = join(curr, p + ".nii.gz")
shutil.copy(image_file, join(imagests, p + "_0000.nii.gz"))
test_patient_names.append(p)
json_dict = OrderedDict()
json_dict['name'] = "SegTHOR"
json_dict['description'] = "SegTHOR"
json_dict['tensorImageSize'] = "4D"
json_dict['reference'] = "see challenge website"
json_dict['licence'] = "see challenge website"
json_dict['release'] = "0.0"
json_dict['modality'] = {
"0": "CT",
}
json_dict['labels'] = {
"0": "background",
"1": "esophagus",
"2": "heart",
"3": "trachea",
"4": "aorta",
}
json_dict['numTraining'] = len(train_patient_names)
json_dict['numTest'] = len(test_patient_names)
json_dict['training'] = [{'image': "./imagesTr/%s.nii.gz" % i.split("/")[-1], "label": "./labelsTr/%s.nii.gz" % i.split("/")[-1]} for i in
train_patient_names]
json_dict['test'] = ["./imagesTs/%s.nii.gz" % i.split("/")[-1] for i in test_patient_names]
save_json(json_dict, os.path.join(out_base, "dataset.json"))
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