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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 multiprocessing import Pool
import SimpleITK as sitk
import numpy as np
from batchgenerators.utilities.file_and_folder_operations import *
from nnunet.paths import nnUNet_raw_data
from nnunet.paths import preprocessing_output_dir
from skimage.io import imread
def load_tiff_convert_to_nifti(img_file, lab_file, img_out_base, anno_out, spacing):
img = imread(img_file)
img_itk = sitk.GetImageFromArray(img.astype(np.float32))
img_itk.SetSpacing(np.array(spacing)[::-1])
sitk.WriteImage(img_itk, join(img_out_base + "_0000.nii.gz"))
if lab_file is not None:
l = imread(lab_file)
l[l > 0] = 1
l_itk = sitk.GetImageFromArray(l.astype(np.uint8))
l_itk.SetSpacing(np.array(spacing)[::-1])
sitk.WriteImage(l_itk, anno_out)
def prepare_task(base, task_id, task_name, spacing):
p = Pool(16)
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 = []
res = []
for train_sequence in [i for i in subfolders(base + "_train", join=False) if not i.endswith("_GT")]:
train_cases = subfiles(join(base + '_train', train_sequence), suffix=".tif", join=False)
for t in train_cases:
casename = train_sequence + "_" + t[:-4]
img_file = join(base + '_train', train_sequence, t)
lab_file = join(base + '_train', train_sequence + "_GT", "SEG", "man_seg" + t[1:])
if not isfile(lab_file):
continue
img_out_base = join(imagestr, casename)
anno_out = join(labelstr, casename + ".nii.gz")
res.append(
p.starmap_async(load_tiff_convert_to_nifti, ((img_file, lab_file, img_out_base, anno_out, spacing),)))
train_patient_names.append(casename)
for test_sequence in [i for i in subfolders(base + "_test", join=False) if not i.endswith("_GT")]:
test_cases = subfiles(join(base + '_test', test_sequence), suffix=".tif", join=False)
for t in test_cases:
casename = test_sequence + "_" + t[:-4]
img_file = join(base + '_test', test_sequence, t)
lab_file = None
img_out_base = join(imagests, casename)
anno_out = None
res.append(
p.starmap_async(load_tiff_convert_to_nifti, ((img_file, lab_file, img_out_base, anno_out, spacing),)))
test_patient_names.append(casename)
_ = [i.get() for i in res]
json_dict = {}
json_dict['name'] = task_name
json_dict['description'] = ""
json_dict['tensorImageSize'] = "4D"
json_dict['reference'] = ""
json_dict['licence'] = ""
json_dict['release'] = "0.0"
json_dict['modality'] = {
"0": "BF",
}
json_dict['labels'] = {
"0": "background",
"1": "cell",
}
json_dict['numTraining'] = len(train_patient_names)
json_dict['numTest'] = len(test_patient_names)
json_dict['training'] = [{'image': "./imagesTr/%s.nii.gz" % i, "label": "./labelsTr/%s.nii.gz" % i} for i in
train_patient_names]
json_dict['test'] = ["./imagesTs/%s.nii.gz" % i for i in test_patient_names]
save_json(json_dict, os.path.join(out_base, "dataset.json"))
p.close()
p.join()
if __name__ == "__main__":
base = "/media/fabian/My Book/datasets/CellTrackingChallenge/Fluo-C3DH-A549_ManAndSim"
task_id = 75
task_name = 'Fluo_C3DH_A549_ManAndSim'
spacing = (1, 0.126, 0.126)
prepare_task(base, task_id, task_name, spacing)
task_name = "Task075_Fluo_C3DH_A549_ManAndSim"
labelsTr = join(nnUNet_raw_data, task_name, "labelsTr")
cases = subfiles(labelsTr, suffix='.nii.gz', join=False)
splits = []
splits.append(
{'train': [i[:-7] for i in cases if i.startswith('01_') or i.startswith('02_SIM')],
'val': [i[:-7] for i in cases if i.startswith('02_') and not i.startswith('02_SIM')]}
)
splits.append(
{'train': [i[:-7] for i in cases if i.startswith('02_') or i.startswith('01_SIM')],
'val': [i[:-7] for i in cases if i.startswith('01_') and not i.startswith('01_SIM')]}
)
splits.append(
{'train': [i[:-7] for i in cases if i.startswith('01_') or i.startswith('02_') and not i.startswith('02_SIM')],
'val': [i[:-7] for i in cases if i.startswith('02_SIM')]}
)
splits.append(
{'train': [i[:-7] for i in cases if i.startswith('02_') or i.startswith('01_') and not i.startswith('01_SIM')],
'val': [i[:-7] for i in cases if i.startswith('01_SIM')]}
)
save_pickle(splits, join(preprocessing_output_dir, task_name, "splits_final.pkl"))
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