good version for 算法注册

services/image-recognition/ai_algorithm.py

@@ -0,0 +1,71 @@
+import logging
+import base64
+from io import BytesIO
+from typing import List, Dict, Any
+
+logger = logging.getLogger(__name__)
+
+
+class ImageRecognizer:
+    """图像识别器"""
+    
+    def __init__(self):
+        """初始化图像识别器"""
+        logger.info("初始化图像识别器")
+        # 这里可以加载预训练模型
+        # 示例中使用简单的规则识别
+    
+    def recognize(self, images: List[str], params: Dict[str, Any] = None) -> List[Dict[str, Any]]:
+        """识别图像
+        
+        Args:
+            images: 图像列表，每个图像为base64编码字符串
+            params: 识别参数
+            
+        Returns:
+            识别结果列表
+        """
+        if params is None:
+            params = {}
+        
+        threshold = params.get("threshold", 0.5)
+        
+        results = []
+        for image_base64 in images:
+            # 简单的规则识别示例
+            recognition = self._simple_recognize(image_base64)
+            results.append({
+                "image": image_base64[:100] + "..." if len(image_base64) > 100 else image_base64,
+                "label": recognition["label"],
+                "confidence": recognition["confidence"]
+            })
+        
+        return results
+    
+    def _simple_recognize(self, image_base64: str) -> Dict[str, Any]:
+        """简单的图像识别实现
+        
+        Args:
+            image_base64: base64编码的图像
+            
+        Returns:
+            识别结果
+        """
+        # 简单的规则识别（基于图像大小和内容特征）
+        try:
+            # 解码base64
+            image_data = base64.b64decode(image_base64)
+            
+            # 计算图像大小特征
+            image_size = len(image_data)
+            
+            # 基于大小的简单分类
+            if image_size < 10240:  # 小于10KB
+                return {"label": "小图像", "confidence": 0.8}
+            elif image_size < 102400:  # 小于100KB
+                return {"label": "中等图像", "confidence": 0.85}
+            else:  # 大于100KB
+                return {"label": "大图像", "confidence": 0.9}
+        except Exception as e:
+            logger.error(f"Image recognition error: {str(e)}")
+            return {"label": "未知", "confidence": 0.5}