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import os
import json
from PIL import Image
from datasets import Dataset, DatasetDict, Features, Sequence, Value
from datasets import Image as ImageData
from io import BytesIO # 新增导入
from datasets import disable_caching
disable_caching() # 完全禁用缓存
def generate_examples(json_path):
with open(json_path, 'r', encoding='utf-8') as f:
# i = 0
for line in f:
# i += 2 # renew cache
# print(i) # verify cache renewed
try:
data = json.loads(line)
# 转换图片路径并加载图像
raw_path = data['images'][0]
# ========== 新增图像处理部分开始 ==========
with Image.open(raw_path, "r") as img:
# 统一转换为RGB模式
if img.mode != 'RGB':
img = img.convert('RGB')
resized_img = img
# ========== 新增图像处理部分结束 ==========
# 提取图片ID（文件名数字部分）
filename = os.path.basename(raw_path) # 141126815887.jpg
img_id = os.path.splitext(filename)[0] # 141126815887
yield {
"images": [resized_img],
"problem": data["query"],
"answer": data["response"],
"id": img_id
}
# print(data["query"])
# print(data["response"])
# exit()
except Exception as e:
data = json.loads(line)
print(data)
print(f"Error processing {raw_path}: {str(e)}")
continue
def main():
train_json_path="./magic_mirror_data_train_r1.jsonl"
test_json_path="./magic_mirror_data_test_r1.jsonl"
base_dir = "./"
train_parquet_path =base_dir + "magic_mirror_data_train_r1.parquet"
test_parquet_path =base_dir + "magic_mirror_data_test_r1.parquet"
train_json_path = "./magic_mirror_data_train_resample_r1.jsonl"
train_parquet_path = "./magic_mirror_data_train_resample_r1.parquet"
# 创建训练集和测试集
test_ds = Dataset.from_generator(generate_examples, gen_kwargs={"json_path": test_json_path})
test_ds.cast_column("images", Sequence(ImageData())).to_parquet(test_parquet_path)
train_ds = Dataset.from_generator(generate_examples, gen_kwargs={"json_path": train_json_path})
train_ds.cast_column("images", Sequence(ImageData())).to_parquet(train_parquet_path)
if __name__ == "__main__":
main()
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