feat: 视觉记录系统 v1.0.0 - 摄像头定时拍照+智能分析+Web界面

2026-04-16 09:42:53 +08:00
commit 49785191c3
14 changed files with 1997 additions and 0 deletions
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+"""
+图片分析模块 - 调用大模型 Vision API
+"""
+import base64
+import requests
+import json
+import re
+from pathlib import Path
+from config import LLM_API_URL, LLM_API_KEY, LLM_MODEL, ANALYSIS_PROMPT
+
+
+class ImageAnalyzer:
+    """图片分析器"""
+    
+    def __init__(self):
+        self.api_url = LLM_API_URL
+        self.api_key = LLM_API_KEY
+        self.model = LLM_MODEL
+    
+    def encode_image(self, image_path):
+        """将图片转为 base64"""
+        with open(image_path, 'rb') as f:
+            return base64.b64encode(f.read()).decode('utf-8')
+    
+    def analyze(self, image_path):
+        """分析图片
+        
+        Returns:
+            dict: {'success': bool, 'events': list, 'error': str}
+        """
+        try:
+            # 编码图片
+            image_base64 = self.encode_image(image_path)
+            
+            # 构建请求（OpenAI Vision 格式）
+            headers = {
+                "Authorization": f"Bearer {self.api_key}",
+                "Content-Type": "application/json"
+            }
+            
+            payload = {
+                "model": self.model,
+                "messages": [
+                    {
+                        "role": "user",
+                        "content": [
+                            {
+                                "type": "text",
+                                "text": ANALYSIS_PROMPT
+                            },
+                            {
+                                "type": "image_url",
+                                "image_url": {
+                                    "url": f"data:image/jpeg;base64,{image_base64}"
+                                }
+                            }
+                        ]
+                    }
+                ],
+                "max_tokens": 500
+            }
+            
+            # 发送请求
+            response = requests.post(
+                f"{self.api_url}/chat/completions",
+                headers=headers,
+                json=payload,
+                timeout=30
+            )
+            
+            if response.status_code != 200:
+                return {
+                    'success': False,
+                    'error': f"API 错误: {response.status_code} - {response.text}"
+                }
+            
+            # 解析结果
+            result = response.json()
+            content = result['choices'][0]['message']['content']
+            
+            # 提取事件信息
+            events = self.parse_events(content)
+            
+            return {
+                'success': True,
+                'raw_response': content,
+                'events': events
+            }
+            
+        except requests.Timeout:
+            return {'success': False, 'error': 'API 请求超时'}
+        except Exception as e:
+            return {'success': False, 'error': str(e)}
+    
+    def parse_events(self, content):
+        """解析事件信息"""
+        events = []
+        
+        # 如果无明显事件
+        if "无明显事件" in content or "没有明显" in content:
+            return [{
+                'event_type': '无事件',
+                'description': '无明显事件',
+                'confidence': '高'
+            }]
+        
+        # 尝试解析结构化格式
+        # 事件类型: xxx
+        # 描述: xxx
+        # 置信度: xxx
+        
+        pattern = r"事件类型[:：]\s*(.+?)\s*描述[:：]\s*(.+?)\s*置信度[:：]\s*(.+)"
+        matches = re.findall(pattern, content, re.DOTALL)
+        
+        if matches:
+            for match in matches:
+                events.append({
+                    'event_type': match[0].strip(),
+                    'description': match[1].strip(),
+                    'confidence': match[2].strip()
+                })
+        else:
+            # 无法解析结构，将整个内容作为描述
+            events.append({
+                'event_type': '其他',
+                'description': content[:200],
+                'confidence': '中'
+            })
+        
+        return events
+
+
+def analyze_image(image_path):
+    """便捷函数"""
+    analyzer = ImageAnalyzer()
+    return analyzer.analyze(image_path)
+
+
+if __name__ == "__main__":
+    # 测试分析（需要先有一张测试图片）
+    test_image = Path(__file__).parent / "data" / "images" / "test.jpg"
+    if test_image.exists():
+        print(f"分析图片: {test_image}")
+        result = analyze_image(test_image)
+        print(f"结果: {json.dumps(result, ensure_ascii=False, indent=2)}")
+    else:
+        print("没有测试图片，请先拍照")