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import csv
import json
from pathlib import Path
def csv_to_json(csv_path, json_path, file_stem):
# 初始化JSON数据结构
json_data = []
with open(csv_path, 'r', encoding='utf-8') as csv_file:
csv_reader = csv.DictReader(csv_file)
for index, row in enumerate(csv_reader):
# 构建media_path
folder_name = row['folder_name']
media_path = "./" + (Path("data") / file_stem / folder_name).as_posix()
# 收集五个caption作为答案
answer = [
row['caption_1'],
row['caption_2'],
row['caption_3'],
row['caption_4'],
row['caption_5']
]
# 构建JSON条目
entry = {
"index": index,
"media_type": "Video",
"media_paths": media_path,
"description": "",
"task_type": "Vision-Question-Answer",
"question": ["Please generate descriptive captions for this multi-view video."],
"question_type": "free-form",
"annotations": {},
"options": [],
"answer": answer,
"source": "4D-Bench",
"domain": "Embodied_ai"
}
json_data.append(entry)
# 写入JSON文件
with open(json_path, 'w', encoding='utf-8') as json_file:
json.dump(json_data, json_file, indent=2)
# 使用示例
if __name__ == "__main__":
# 用户需要修改以下参数
INPUT_CSV = "/mnt/data/users/zys/proj/vlm_reasoning/unprocessed_data/emb_ai/4d/4D_Object_Captioning/data/human_annotations.csv" # 输入CSV文件路径
OUTPUT_JSON = "/mnt/data/users/zys/proj/vlm_reasoning/dataset/4D_Object_Captioning.json" # 输出JSON文件路径
FILE_STEM = "4D_Object_Captioning" # media_path中的file_stem部分
csv_to_json(INPUT_CSV, OUTPUT_JSON, FILE_STEM) |