import os import json import shutil from pathlib import Path def convert_to_nnunet_format( input_images_path, input_labels_path, output_path, dataset_name="Dataset001_pelvic", train_ratio=0.8, test_ratio=0.2 ): """ 将CT文件和标签转换为nnU-Net格式 Args: input_images_path: CT文件路径 (路径A) input_labels_path: 标签文件路径 (路径B) output_path: 输出路径 dataset_name: 数据集名称 train_ratio: 训练集比例 test_ratio: 测试集比例 """ # 创建输出目录结构 dataset_path = Path(output_path) / dataset_name imagesTr_path = dataset_path / "imagesTr" labelsTr_path = dataset_path / "labelsTr" imagesTs_path = dataset_path / "imagesTs" # 创建目录 imagesTr_path.mkdir(parents=True, exist_ok=True) labelsTr_path.mkdir(parents=True, exist_ok=True) imagesTs_path.mkdir(parents=True, exist_ok=True) # 获取所有.mha文件 input_images = Path(input_images_path) input_labels = Path(input_labels_path) image_files = sorted(list(input_images.glob("*.mha"))) print(f"找到 {len(image_files)} 个图像文件") if len(image_files) == 0: raise ValueError(f"在路径 {input_images_path} 中没有找到.mha文件") # 验证对应的标签文件是否存在 valid_pairs = [] for img_file in image_files: # 假设标签文件与图像文件同名 label_file = input_labels / img_file.name if label_file.exists(): valid_pairs.append((img_file, label_file)) else: print(f"警告: 图像文件 {img_file.name} 没有对应的标签文件") print(f"找到 {len(valid_pairs)} 对有效的图像-标签文件") if len(valid_pairs) == 0: raise ValueError("没有找到有效的图像-标签文件对") # 计算训练集和测试集数量 total_files = len(valid_pairs) num_train = int(total_files * train_ratio) num_test = total_files - num_train print(f"训练集: {num_train} 个文件") print(f"测试集: {num_test} 个文件") # 复制文件并重命名 train_cases = [] test_cases = [] for i, (img_file, label_file) in enumerate(valid_pairs): # nnU-Net命名格式: case_identifier_0000.nii.gz case_id = f"{dataset_name.split('_')[1]}_{i:03d}" if i < num_train: # 训练集 # 复制图像文件 dst_img = imagesTr_path / f"{case_id}_0000.mha" shutil.copy2(img_file, dst_img) # 复制标签文件 dst_label = labelsTr_path / f"{case_id}.mha" shutil.copy2(label_file, dst_label) train_cases.append(case_id) else: # 测试集 dst_img = imagesTs_path / f"{case_id}_0000.mha" shutil.copy2(img_file, dst_img) test_cases.append(case_id) print(f"文件复制完成") print(f"训练集文件: {len(train_cases)} 个") print(f"测试集文件: {len(test_cases)} 个") # 创建dataset.json dataset_json = { "channel_names": { "0": "CT" }, # "labels": { # "background": 0, # "middle": 1, # "right": 2, # "left": 3 # }, "labels": { "background": 0, "1": 1, "2": 2, "3": 3, "4": 4, "5": 5, "6": 6, "7": 7, "8": 8, "9": 9, "10": 10, "11": 11 }, "numTraining": len(train_cases), "file_ending": ".mha", "dataset_name": dataset_name, "reference": "Pelvic CT Dataset", "description": "Pelvic CT segmentation dataset", "tensorImageSize": "4D", "modality": { "0": "CT" }, "dim": 3 } # 如果你有多个标签类别，请修改labels字段，例如： # "labels": { # "background": 0, # "bone": 1, # "muscle": 2, # "organ": 3 # } # 保存dataset.json json_path = dataset_path / "dataset.json" with open(json_path, 'w', encoding='utf-8') as f: json.dump(dataset_json, f, indent=4, ensure_ascii=False) print(f"dataset.json 已创建: {json_path}") # 打印最终目录结构 print("\n创建的目录结构:") print(f"{dataset_path}/") print(f"├── dataset.json") print(f"├── imagesTr/ ({len(train_cases)} files)") print(f"├── imagesTs/ ({len(test_cases)} files)") print(f"└── labelsTr/ ({len(train_cases)} files)") return dataset_path def main(): # 直接定义路径参数 # images_path = "/research/phd_y3/pelvic_project/Data/images" # labels_path = "/research/phd_y3/pelvic_project/Data/3_part_labels" # output_path = "/research/phd_y3/pelvic_project/Code/for_nnUNet/nnUNet_raw_data" # dataset_name = "Dataset001_pelvic_three_parts" # images_path = "/research/phd_y3/pelvic_project/Data/mid_sacrum_bbox_images" # labels_path = "/research/phd_y3/pelvic_project/Data/mid_sacrum_bbox_labels" # output_path = "/research/phd_y3/pelvic_project/Code/for_nnUNet/nnUNet_raw_data" # dataset_name = "Dataset002_mid_sacrum" # images_path = "/research/phd_y3/pelvic_project/Data/right_hip_bbox_images" # labels_path = "/research/phd_y3/pelvic_project/Data/right_hip_bbox_labels" # output_path = "/research/phd_y3/pelvic_project/Code/for_nnUNet/nnUNet_raw_data" # dataset_name = "Dataset003_right_hip" # images_path = "/research/phd_y3/pelvic_project/Data/left_hip_bbox_images" # labels_path = "/research/phd_y3/pelvic_project/Data/left_hip_bbox_labels" # output_path = "/research/phd_y3/pelvic_project/Code/for_nnUNet/nnUNet_raw_data" # dataset_name = "Dataset004_left_hip" images_path = "/research/phd_y3/pelvic_project/Data/images" labels_path = "/research/phd_y3/pelvic_project/Data/organ+3_part_label_fixed" output_path = "/research/phd_y3/pelvic_project/Code/for_nnUNet/nnUNet_raw_data" dataset_name = "Dataset005_organ_pelvic" train_ratio = 0.7 test_ratio = 0.3 print(f"开始处理数据集转换:") print(f"图像路径: {images_path}") print(f"标签路径: {labels_path}") print(f"输出路径: {output_path}") print(f"数据集名称: {dataset_name}") print(f"训练集比例: {train_ratio}") print(f"测试集比例: {test_ratio}") print("-" * 50) # 验证输入路径 if not os.path.exists(images_path): print(f"❌ 图像路径不存在: {images_path}") return 1 if not os.path.exists(labels_path): print(f"❌ 标签路径不存在: {labels_path}") return 1 # 转换数据 try: result_path = convert_to_nnunet_format( images_path, labels_path, output_path, dataset_name, train_ratio, test_ratio ) print(f"\n✅ 转换完成! 数据集保存在: {result_path}") print(f"\n下一步:") print(f"1. 设置环境变量: export nnUNet_raw_data_base='{Path(output_path).absolute()}'") print(f"2. 运行nnU-Net预处理: nnUNet_plan_and_preprocess -t {dataset_name.split('_')[0].replace('Dataset', '')}") except Exception as e: print(f"❌ 转换失败: {str(e)}") return 1 return 0 if __name__ == "__main__": exit(main())