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import json |
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import os |
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import logging |
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import argparse |
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from PIL import Image |
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from datasets import Dataset |
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import io |
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logging.basicConfig(level=logging.INFO) |
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logger = logging.getLogger(__name__) |
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def load_questions_from_meta_qa(meta_qa_file): |
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with open(meta_qa_file, "r") as f: |
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questions = [line.strip() for line in f if line.strip()] |
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return questions |
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def process_parquet_files(data_dir, output_jsonl, meta_qa_file=None, output_imgs=None, process_qa=False): |
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""" |
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Process Parquet files to generate a JSONL file with optional image export and QA list creation. |
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Args: |
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data_dir (str): Directory containing Parquet files. |
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output_jsonl (str): Output JSONL file path. |
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meta_qa_file (str, optional): Path to the meta_qa_en.txt file for QA list creation. |
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output_imgs (str, optional): Directory path to save images. If None, images are not saved. |
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process_qa (bool): Whether to process and include QA pairs in the output. |
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Returns: |
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None |
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""" |
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if output_imgs and not os.path.exists(output_imgs): |
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os.makedirs(output_imgs) |
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questions = None |
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if process_qa and meta_qa_file: |
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questions = load_questions_from_meta_qa(meta_qa_file) |
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jsonl_data = [] |
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parquet_files = [os.path.join(data_dir, f) for f in os.listdir(data_dir) if f.endswith(".parquet")] |
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for parquet_file in parquet_files: |
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dataset = Dataset.from_parquet(parquet_file) |
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for row in dataset: |
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json_item = { |
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"internal_id": row["internal_id"], |
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"url": row["url"], |
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"annotation": row["annotation"], |
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"meta_result": row["meta_result"], |
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"meta_mask": row["meta_mask"], |
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} |
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if output_imgs: |
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img_data = row["image"] |
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img_path = os.path.join(output_imgs, f"{row['internal_id']}.jpg") |
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try: |
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with open(img_path, "wb") as img_file: |
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img_file.write(img_data) |
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json_item["image_path"] = img_path |
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except Exception as e: |
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logger.error(f"Error saving image for internal_id {row['internal_id']}: {e}") |
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if process_qa and questions: |
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qa_list = [] |
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meta_result = row["meta_result"] |
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meta_mask = row["meta_mask"] |
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for idx, mask in enumerate(meta_mask): |
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if mask == 1: |
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question = questions[idx] |
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answer = 'yes' if meta_result[idx] == 1 else 'no' |
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qa_list.append({"question": question, "answer": answer}) |
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json_item["qa_list"] = qa_list |
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jsonl_data.append(json_item) |
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with open(output_jsonl, "w") as outfile: |
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for json_item in jsonl_data: |
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outfile.write(json.dumps(json_item) + "\n") |
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logger.info(f"Finished writing JSONL file with {len(jsonl_data)} items.") |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser(description="Convert VisionReward Parquet dataset files to JSONL format with optional image extraction and QA list generation.") |
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parser.add_argument("--data_dir", type=str, default='data', help="Directory containing Parquet files.") |
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parser.add_argument("--output_jsonl", type=str, default='annotation.jsonl', help="Path to the output JSONL file.") |
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parser.add_argument("--meta_qa_file", type=str, default="meta_qa_en.txt", help="Optional: Path to the meta_qa_en.txt file for QA list generation.") |
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parser.add_argument("--save_imgs", action="store_true", help="Optional: Whether to save images.") |
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parser.add_argument("--process_qa", action="store_true", help="Optional: Process and include QA pairs in the output.") |
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args = parser.parse_args() |
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output_imgs = 'imgs' if args.save_imgs else None |
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process_parquet_files( |
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data_dir=args.data_dir, |
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output_jsonl=args.output_jsonl, |
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meta_qa_file=args.meta_qa_file, |
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output_imgs=output_imgs, |
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process_qa=args.process_qa |
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) |
