207 lines
6.9 KiB
Python
207 lines
6.9 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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DeepSeek-OCR 多种调用方式测试
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基于官方GitHub文档的提示词模板
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"""
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import base64
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from pathlib import Path
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from openai import OpenAI
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# API配置
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CLARIFAI_PAT = "941fba50c8c04be590a9b2d21b7d8347"
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# 初始化OpenAI客户端
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client = OpenAI(
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base_url="https://api.clarifai.com/v2/ext/openai/v1",
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api_key=CLARIFAI_PAT
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)
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def process_image_with_prompt(image_path, prompt, mode_name, temperature=0.0, max_tokens=2048):
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"""使用指定提示词处理图片"""
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print(f"\n🎯 {mode_name}")
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print("-" * 50)
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print(f"📸 处理图片: {image_path}")
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print(f"📝 提示词: {prompt}")
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if not Path(image_path).exists():
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print(f"❌ 文件不存在: {image_path}")
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return None
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try:
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# 读取图片
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print("🔄 读取图片...")
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image_base64 = base64.b64encode(Path(image_path).read_bytes()).decode()
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print(f"📊 图片编码完成，大小: {len(image_base64)} 字符")
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# 调用API
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print(f"🤖 调用DeepSeek-OCR...")
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response = client.chat.completions.create(
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model="https://clarifai.com/deepseek-ai/deepseek-ocr/models/DeepSeek-OCR",
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messages=[{
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"role": "user",
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"content": [
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{"type": "text", "text": prompt},
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{
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"type": "image_url",
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"image_url": {"url": f"data:image/jpeg;base64,{image_base64}"}
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}
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]
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}],
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temperature=temperature,
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max_tokens=max_tokens
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)
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result = response.choices[0].message.content
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print(f"✅ 处理完成！")
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return result
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except Exception as e:
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print(f"❌ 处理失败: {e}")
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return None
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def test_all_modes(image_path):
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"""测试所有DeepSeek-OCR模式"""
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print("🚀 DeepSeek-OCR 多种调用方式测试")
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print("=" * 60)
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# 定义所有模式 - 基于官方GitHub文档
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modes = {
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"基础OCR模式": {
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"prompt": "OCR this image.",
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"description": "基础文字识别，不考虑布局"
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},
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"自由OCR模式": {
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"prompt": "Free OCR.",
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"description": "自由格式OCR，不考虑布局"
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},
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"Markdown转换模式": {
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"prompt": "Convert the document to markdown.",
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"description": "将文档转换为Markdown格式"
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},
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"图表解析模式": {
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"prompt": "Parse the figure.",
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"description": "解析图表和图形数据"
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},
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"详细描述模式": {
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"prompt": "Describe this image in detail.",
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"description": "详细描述图片内容"
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},
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"目标定位模式": {
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"prompt": "Locate all text elements in the image.",
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"description": "定位图片中的所有文字元素"
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},
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"结构化提取模式": {
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"prompt": "Extract structured data from this document, including tables, lists, and formatted text.",
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"description": "提取结构化数据"
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},
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"中文优化模式": {
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"prompt": "请识别图片中的所有中文文字，并保持原有的格式和布局",
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"description": "专门针对中文文字识别优化"
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},
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"英文优化模式": {
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"prompt": "Please extract all English text from this image while preserving the original layout and formatting",
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"description": "专门针对英文文字识别优化"
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},
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"表格识别模式": {
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"prompt": "Identify and extract all tables from this image, preserving the table structure and data",
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"description": "专门识别和提取表格"
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}
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}
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results = {}
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# 测试每种模式
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for mode_name, mode_config in modes.items():
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print(f"\n{mode_name}")
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print(f"描述: {mode_config['description']}")
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result = process_image_with_prompt(
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image_path,
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mode_config['prompt'],
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mode_name
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)
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if result:
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results[mode_name] = {
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'prompt': mode_config['prompt'],
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'result': result,
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'description': mode_config['description']
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}
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# 显示结果预览
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print(f"📋 结果预览:")
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preview = result[:200] + "..." if len(result) > 200 else result
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print(f"{preview}")
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print(f"📊 结果长度: {len(result)} 字符")
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else:
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results[mode_name] = {
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'prompt': mode_config['prompt'],
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'result': "处理失败",
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'description': mode_config['description']
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}
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# 短暂延迟，避免API限制
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import time
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time.sleep(1)
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return results
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def save_results(results, output_file="ocr_modes_results.json"):
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"""保存所有结果"""
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try:
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import json
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with open(output_file, 'w', encoding='utf-8') as f:
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# 转换为可JSON序列化的格式
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json_results = {}
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for mode, data in results.items():
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json_results[mode] = {
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'prompt': data['prompt'],
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'description': data['description'],
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'result': data['result']
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}
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json.dump(json_results, f, ensure_ascii=False, indent=2)
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print(f"\n💾 所有结果已保存到: {output_file}")
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except Exception as e:
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print(f"\n❌ 保存结果失败: {e}")
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def main():
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print("🚀 DeepSeek-OCR 多种调用方式测试")
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print("=" * 60)
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print("📚 基于官方GitHub文档的提示词模板")
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print("🔗 GitHub: https://github.com/deepseek-ai/DeepSeek-OCR")
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print("=" * 60)
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# 使用之前测试过的截图图片
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image_path = "/opt/agent/agents/users/jojo/project/user_upload/截屏2025-10-24 12.34.04.png"
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# 测试所有模式
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results = test_all_modes(image_path)
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# 保存结果
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save_results(results)
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# 生成总结报告
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print("\n📊 测试总结报告")
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print("=" * 60)
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successful_modes = [mode for mode, data in results.items() if data['result'] != "处理失败"]
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failed_modes = [mode for mode, data in results.items() if data['result'] == "处理失败"]
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print(f"✅ 成功模式: {len(successful_modes)} 个")
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for mode in successful_modes:
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result_length = len(results[mode]['result'])
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print(f" - {mode}: {result_length} 字符")
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if failed_modes:
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print(f"\n❌ 失败模式: {len(failed_modes)} 个")
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for mode in failed_modes:
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print(f" - {mode}")
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print(f"\n🎯 成功率: {len(successful_modes)/len(results)*100:.1f}%")
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print("\n✅ 测试完成！")
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if __name__ == "__main__":
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main()
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