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