import json import os def process_dataset(input_file, output_file, base_path="."): """ 处理数据集,只保留image和conversations字段,添加id字段,转换图像路径为绝对路径 Args: input_file: 输入JSON文件路径 output_file: 输出JSON文件路径 base_path: 图像文件的基础路径 """ # 读取原始数据 with open(input_file, 'r', encoding='utf-8') as f: input_data = json.load(f) processed_data = [] for item in input_data: # 获取所有图像路径并转换为绝对路径 absolute_image_paths = [] for image_path in item["image"]: absolute_path = os.path.abspath(os.path.join(base_path, image_path)) absolute_image_paths.append(absolute_path) # 提取第一张图像名作为id(不包含扩展名) first_image_name = os.path.basename(item["image"][0]) image_id = os.path.splitext(first_image_name)[0] img_len = len(item["image"]) item["conversations"][0]['value'] = "\n"*img_len + item["conversations"][0]['value'] # 构建新的数据项 new_item = { "id": image_id, "image": absolute_image_paths, # 保存所有图像的绝对路径 "conversations": item["conversations"] } processed_data.append(new_item) # 保存处理后的数据 with open(output_file, 'w', encoding='utf-8') as f: json.dump(processed_data, f, indent=2, ensure_ascii=False) print(f"处理完成!共处理 {len(processed_data)} 条数据") print(f"输出文件:{output_file}") def process_data_directly(input_data, base_path="."): """ 直接处理数据列表(如果数据已经在内存中) Args: input_data: 原始数据列表 base_path: 图像文件的基础路径 Returns: 处理后的数据列表 """ processed_data = [] for item in input_data: # 获取图像路径 image_path = item["image"][0] # 提取图像名作为id image_name = os.path.basename(image_path) image_id = os.path.splitext(image_name)[0] # 转换为绝对路径 absolute_image_path = os.path.abspath(os.path.join(base_path, image_path)) # 构建新的数据项 new_item = { "id": image_id, "image": [absolute_image_path], "conversations": item["conversations"] } processed_data.append(new_item) return processed_data # 示例使用 if __name__ == "__main__": # 方法1: 从文件读取并处理 process_dataset("train_data_processed.json", "train_data_convs.json", base_path="/mnt/dolphinfs/ssd_pool/docker/user/hadoop-mlm-hl/hadoop-mlm/common/spatial_data/spatial_relation/SAT")