136 lines
4.9 KiB
Python
136 lines
4.9 KiB
Python
import logging
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from Util.ObsUtil import ObsUploader
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prompt = """
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### 几何图形识别专家指令（输入：纯几何图形照片）
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**任务目标**
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精确提取图形中的几何元素及其空间关系，为GeoGebra重建建立数学模型
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## 一、坐标系建立规则（必须遵守）
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1. 原点设定：
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- 若存在明显顶点，选最左下角的点为原点(0,0)
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- 若图形对称，选对称中心为原点
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- 示例：原点O = 三角形ABC的顶点A
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2. 坐标轴定向：
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- 优先顺序：水平线段 > 垂直线段 > 最长线段
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- 具体规则：
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if 存在水平线段: 以该线段为x轴正方向
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elif 存在垂直结构: 以最左侧垂直线为y轴
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else: 以最长线段为基准轴
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## 二、元素列举
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1. 按点，线，三角形，四边形，梯形，平行四边形，矩形，正方形，圆等由简单到复杂的顺序列举所有图形
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2. 详细描述元素之间的关系，比如点D在线段AB上
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3. 详细描述元素之间的位置关系，比如D 在A点正上方，B在CD边的上方中间位置
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"""
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from openai import OpenAI
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from Config.Config import ALY_LLM_BASE_URL, ALY_LLM_API_KEY, OBS_AK, OBS_SERVER, OBS_SK, OBS_PREFIX, OBS_BUCKET
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logger = logging.getLogger(__name__)
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# 批量处理图片
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def batch_qvq(output_dir, img_list):
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img_url_list = []
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for file_path in img_list:
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# 创建上传器实例
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uploader = ObsUploader(OBS_AK, OBS_SK, "https://" + OBS_SERVER)
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# 上传参数
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object_key = OBS_PREFIX + "/" + file_path
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# 执行上传
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success, result = uploader.upload_file(OBS_BUCKET, object_key, file_path)
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# 处理结果
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if success:
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logger.info(f'{file_path}上传成功!')
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# 获取上传文件的 URL
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file_url = f"https://{OBS_BUCKET}.{OBS_SERVER}/{object_key}"
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img_url_list.append(file_url)
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else:
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logger.error(f'{file_path}上传失败!')
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if 'errorCode' in result:
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logger.info(f'错误代码: {result["errorCode"]}')
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logger.info(f'错误信息: {result["errorMessage"]}')
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else:
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logger.error(f'错误信息: {result["error"]}')
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# 多张图片开始解析
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answer_content = ""
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for img_url in img_url_list:
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answer_content = answer_content + qvq_single(img_url)
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# 保存结果到JSON文件
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qvq_result = f"{output_dir}/QvqResult.json"
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with open(qvq_result, "w", encoding='utf-8') as f:
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f.write(answer_content)
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return qvq_result
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def qvq_single(image_url):
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# 初始化OpenAI客户端
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client = OpenAI(
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api_key=ALY_LLM_API_KEY,
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base_url=ALY_LLM_BASE_URL
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)
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reasoning_content = "" # 定义完整思考过程
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answer_content = "" # 定义完整回复
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is_answering = False # 判断是否结束思考过程并开始回复
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# 创建聊天完成请求
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completion = client.chat.completions.create(
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model="qvq-max", # 此处以 qvq-max 为例，可按需更换模型名称
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messages=[
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{
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"role": "user",
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"content": [
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{
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"type": "image_url",
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"image_url": {
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"url": image_url
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},
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},
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{"type": "text",
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"text": prompt},
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],
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},
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],
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stream=True,
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)
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print("\n" + "=" * 20 + "思考过程" + "=" * 20 + "\n")
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for chunk in completion:
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# 如果chunk.choices为空，则打印usage
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if not chunk.choices:
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print("\nUsage:")
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print(chunk.usage)
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else:
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delta = chunk.choices[0].delta
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# 打印思考过程
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if hasattr(delta, 'reasoning_content') and delta.reasoning_content != None:
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print(delta.reasoning_content, end='', flush=True)
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reasoning_content += delta.reasoning_content
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else:
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# 开始回复
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if delta.content != "" and is_answering is False:
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print("\n" + "=" * 20 + "完整回复" + "=" * 20 + "\n")
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is_answering = True
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# 打印回复过程
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print(delta.content, end='', flush=True)
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answer_content += delta.content
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# print("=" * 20 + "完整思考过程" + "=" * 20 + "\n")
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# print(reasoning_content)
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# print("=" * 20 + "完整回复" + "=" * 20 + "\n")
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answer_content = answer_content.replace("```json", "")
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answer_content = answer_content.replace("```", "")
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return answer_content
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if __name__ == '__main__':
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img_url = "https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/extracted/26c367a3c3cf20e62e1d6c7904f6abf6/image_1.png"
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ans = qvq_single(img_url)
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print(ans)
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