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| 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) |
|
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|
|
|
| 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")) |
|
|
