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import shutil
from batchgenerators.utilities.file_and_folder_operations import *
import SimpleITK as sitk
from nnunet.paths import nnUNet_raw_data
if __name__ == '__main__':
#data is available at http://medicalsegmentation.com/covid19/
download_dir = '/home/fabian/Downloads'
task_id = 69
task_name = "CovidSeg"
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 = []
# the niftis are 3d, but they are just stacks of 2d slices from different patients. So no 3d U-Net, please
# the training stack has 100 slices, so we split it into 5 equally sized parts (20 slices each) for cross-validation
training_data = sitk.GetArrayFromImage(sitk.ReadImage(join(download_dir, 'tr_im.nii.gz')))
training_labels = sitk.GetArrayFromImage(sitk.ReadImage(join(download_dir, 'tr_mask.nii.gz')))
for f in range(5):
this_name = 'part_%d' % f
data = training_data[f::5]
labels = training_labels[f::5]
sitk.WriteImage(sitk.GetImageFromArray(data), join(imagestr, this_name + '_0000.nii.gz'))
sitk.WriteImage(sitk.GetImageFromArray(labels), join(labelstr, this_name + '.nii.gz'))
train_patient_names.append(this_name)
shutil.copy(join(download_dir, 'val_im.nii.gz'), join(imagests, 'val_im.nii.gz'))
test_patient_names.append('val_im')
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": "nonct",
}
json_dict['labels'] = {
"0": "background",
"1": "stuff1",
"2": "stuff2",
"3": "stuff3",
}
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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