wehub-resource-sync
61 lines
2.0 KiB
Python
61 lines
2.0 KiB
Python
import base64
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import io
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import random
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import os
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# import pandas as pd
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from PIL import Image
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from tqdm import tqdm
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import json
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import string
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import pdb
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# Example:
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# json_ann['questions'][0]
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# {
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# 'answer': 'A',
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# 'choice_a': 'One',
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# 'choice_b': 'Two',
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# 'choice_c': 'Three',
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# 'choice_d': 'Four',
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# 'data_id': '1454426_2591111986',
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# 'data_type': 'image',
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# 'question': 'How many towels are in the image?',
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# 'question_id': '101669',
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# 'question_type_id': 5
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# }
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def convert_json_to_txt(json_path, image_path, txt_path, answer_path):
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json_ann = json.load(open(json_path, 'rb'))
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# pdb.set_trace()
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with open(txt_path, 'w', encoding='utf-8') as f, open(answer_path, 'w', encoding='utf-8') as fa:
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for index, item in tqdm(enumerate(json_ann['questions'])):
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if item['data_type'] != 'image':
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continue
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question = item['question'].replace('\n', ' ')
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image_file_path = os.path.join(image_path, item['data_id'])
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options = {
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'A': item['choice_a'],
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'B': item['choice_b'],
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'C': item['choice_c'],
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'D': item['choice_d'],
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}
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for key, choice in options.items():
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choice = choice.replace('\n', ' ').strip()
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sentence = f'[image]{image_file_path}<tab>Question: {question} Answer: {choice}'
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f.write(sentence + '\n')
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fa.write("{}\t{}\t{}\t{}\t{}\t{}\n".format(index, question, choice, item['answer'], item['question_id'], item['question_type_id']))
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if __name__ == '__main__':
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save_dir = '/path/to/data'
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convert_json_to_txt(
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f'{save_dir}/SEED-Bench/SEED-Bench.json',
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f'{save_dir}/SEED-Bench/SEED-Bench-image',
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f'{save_dir}/SEED-Bench/seed_bench_pplformat.txt',
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f'{save_dir}/SEED-Bench/seed_bench_pplformat.answer'
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)
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