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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
from multiprocessing import Pool
import nibabel
def reorient(filename):
img = nibabel.load(filename)
img = nibabel.as_closest_canonical(img)
nibabel.save(img, filename)
if __name__ == "__main__":
base = "/media/fabian/DeepLearningData/Pancreas-CT"
# reorient
p = Pool(8)
results = []
for f in subfiles(join(base, "data"), suffix=".nii.gz"):
results.append(p.map_async(reorient, (f, )))
_ = [i.get() for i in results]
for f in subfiles(join(base, "TCIA_pancreas_labels-02-05-2017"), suffix=".nii.gz"):
results.append(p.map_async(reorient, (f, )))
_ = [i.get() for i in results]
task_id = 62
task_name = "NIHPancreas"
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 = []
cases = list(range(1, 83))
folder_data = join(base, "data")
folder_labels = join(base, "TCIA_pancreas_labels-02-05-2017")
for c in cases:
casename = "pancreas_%04.0d" % c
shutil.copy(join(folder_data, "PANCREAS_%04.0d.nii.gz" % c), join(imagestr, casename + "_0000.nii.gz"))
shutil.copy(join(folder_labels, "label%04.0d.nii.gz" % c), join(labelstr, casename + ".nii.gz"))
train_patient_names.append(casename)
json_dict = OrderedDict()
json_dict['name'] = task_name
json_dict['description'] = task_name
json_dict['tensorImageSize'] = "4D"
json_dict['reference'] = "see website"
json_dict['licence'] = "see website"
json_dict['release'] = "0.0"
json_dict['modality'] = {
"0": "CT",
}
json_dict['labels'] = {
"0": "background",
"1": "Pancreas",
}
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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