128 lines
4.6 KiB
Python
128 lines
4.6 KiB
Python
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import json
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import os
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import shutil
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import glob
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import re
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import pandas as pd
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# 創建 dataset.json 內容
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dataset_json = {
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"channel_names": {
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"0": "CT+CTC",
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"1": "T1C"
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},
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"labels": {
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"background": "0",
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"Brainstem": "1",
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"Right_Eye": "2",
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"Left_Eye": "3",
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"Optic_Chiasm": "4",
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"Right_Optic_Nerve": "5",
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"Left_Optic_Nerve": "6"
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},
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"numTraining": 0,
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"file_ending": ".nii.gz"
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}
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# 定義目錄
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source_image1_dir = "/home/onlylian/resampled_CT+CTC_image"
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source_image2_dir = "/home/onlylian/resampled_T1C_image"
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source_label_dir = "/home/onlylian/resampled_label"
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target_base_dir = "/home/onlylian/nnUNet/nnUNet_raw/Dataset021_OAR"
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imagesTr_dir = os.path.join(target_base_dir, "imagesTr")
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labelsTr_dir = os.path.join(target_base_dir, "labelsTr")
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# 創建目錄
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os.makedirs(imagesTr_dir, exist_ok=True)
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os.makedirs(labelsTr_dir, exist_ok=True)
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def extract_patient_id(filename):
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match = re.search(r'(CT\+CTC|T1C|label)_([^.]+)\.nii\.gz', os.path.basename(filename))
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return match.group(2) if match else None
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# 獲取並排序原始圖像文件
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source_image1_files = sorted(glob.glob(os.path.join(source_image1_dir, "*.nii.gz")))
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source_image2_files = sorted(glob.glob(os.path.join(source_image2_dir, "*.nii.gz")))
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processed_files = 0
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patient_mapping = []
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# 創建病歷號到文件名的映射
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label_files = glob.glob(os.path.join(source_label_dir, "*.nii.gz"))
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label_map = {extract_patient_id(f): f for f in label_files}
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print(f"找到 {len(label_map)} 個標籤文件")
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print(f"找到 {len(source_image1_files)} 個 CT+CTC 圖像文件")
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print(f"找到 {len(source_image2_files)} 個 T1C 圖像文件")
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# 處理所有資料
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for index, (source_image1_file, source_image2_file) in enumerate(zip(source_image1_files, source_image2_files), start=1):
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if index % 10 == 0: # 每處理10個文件顯示一次進度
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print(f"\n正在處理檔案 {index}/{len(source_image1_files)}:")
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patient_id1 = extract_patient_id(source_image1_file)
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patient_id2 = extract_patient_id(source_image2_file)
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if patient_id1 != patient_id2 or patient_id1 is None or patient_id2 is None:
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print(f"警告: 病歷號不匹配或無法提取，跳過這對文件: {source_image1_file}, {source_image2_file}")
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continue
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# 使用新的文件名格式
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new_filename = f"OAR_{index:03d}"
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# 複製並重命名圖像文件
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target_image1_file = os.path.join(imagesTr_dir, f"{new_filename}_0000.nii.gz")
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target_image2_file = os.path.join(imagesTr_dir, f"{new_filename}_0001.nii.gz")
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shutil.copyfile(source_image1_file, target_image1_file)
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shutil.copyfile(source_image2_file, target_image2_file)
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# 處理標籤文件
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if patient_id1 in label_map:
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source_label_file = label_map[patient_id1]
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target_label_file = os.path.join(labelsTr_dir, f"{new_filename}.nii.gz")
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shutil.copyfile(source_label_file, target_label_file)
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processed_files += 1
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# 添加到映射列表
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patient_mapping.append({
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"編號": f"{index:03d}",
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"病歷號": patient_id1
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})
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# 輸出處理細節
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print(f"CT+CTC: {source_image1_file}")
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print(f"T1C: {source_image2_file}")
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print(f"複製圖像檔案: {source_image1_file} 到 {target_image1_file}")
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print(f"複製圖像檔案: {source_image2_file} 到 {target_image2_file}")
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print(f"複製標籤檔案: {source_label_file} 到 {target_label_file}")
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print() # 空行分隔每筆記錄
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else:
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print(f"警告: 未找到 {patient_id1} 的標籤檔案。")
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print("\n所有資料處理完畢。")
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# 更新 dataset.json
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dataset_json["numTraining"] = processed_files
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# 寫入 dataset.json 檔案
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dataset_json_path = os.path.join(target_base_dir, "dataset.json")
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with open(dataset_json_path, 'w') as f:
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json.dump(dataset_json, f, indent=4)
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print(f"dataset.json 已寫入到 {dataset_json_path}")
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print(f"總共處理了 {processed_files} 個完整的資料集（圖像對加標籤）")
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# 創建 Excel 文件
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df = pd.DataFrame(patient_mapping)
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excel_path = os.path.join(target_base_dir, "patient_mapping.xlsx")
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df.to_excel(excel_path, index=False)
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print(f"病人映射表已保存到 {excel_path}")
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# 輸出處理摘要
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print("\n處理摘要:")
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print(f"總共找到的 CT+CTC 圖像: {len(source_image1_files)}")
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print(f"總共找到的 T1C 圖像: {len(source_image2_files)}")
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print(f"總共找到的標籤文件: {len(label_map)}")
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print(f"成功處理的完整資料集: {processed_files}")
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print(f"跳過的文件對數量: {len(source_image1_files) - processed_files}")
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