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core/face.py

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+import os
+import numpy as np
+import cv2
+from PIL import Image  # 确保正确导入Image类
+from insightface.app import FaceAnalysis
+# 导入获取人脸信息的服务
+from service.face_service import get_all_face_name_with_eigenvalue
+
+# 全局变量
+_face_app = None
+_known_faces_embeddings = {}  # 存储姓名到特征值的映射
+_known_faces_names = []  # 存储所有已知姓名
+
+
+def load_model():
+    """加载人脸识别模型及已知人脸特征库"""
+    global _face_app, _known_faces_embeddings, _known_faces_names
+
+    # 初始化InsightFace模型
+    try:
+        _face_app = FaceAnalysis(name='buffalo_l', root=os.path.expanduser('~/.insightface'))
+        _face_app.prepare(ctx_id=0, det_size=(640, 640))
+    except Exception as e:
+        print(f"Face model load failed: {e}")
+        return False
+
+    # 从服务获取所有人脸姓名和特征值
+    try:
+        face_data = get_all_face_name_with_eigenvalue()
+
+        # 处理获取到的人脸数据
+        for person_name, eigenvalue_data in face_data.items():
+            # 处理特征值数据 - 兼容数组和字符串两种格式
+            if isinstance(eigenvalue_data, np.ndarray):
+                # 如果已经是numpy数组，直接使用
+                eigenvalue = eigenvalue_data.astype(np.float32)
+            elif isinstance(eigenvalue_data, str):
+                # 清理字符串：移除方括号、换行符和多余空格
+                cleaned = eigenvalue_data.replace('[', '').replace(']', '').replace('\n', '').strip()
+                # 按空格或逗号分割（处理可能的不同分隔符）
+                values = [v for v in cleaned.split() if v]
+                # 转换为数组
+                eigenvalue = np.array(list(map(float, values)), dtype=np.float32)
+            else:
+                # 不支持的类型
+                print(f"Unsupported eigenvalue type for {person_name}")
+                continue
+
+            # 归一化处理
+            norm = np.linalg.norm(eigenvalue)
+            if norm != 0:
+                eigenvalue = eigenvalue / norm
+
+            _known_faces_embeddings[person_name] = eigenvalue
+            _known_faces_names.append(person_name)
+
+    except Exception as e:
+        print(f"Error loading face data from service: {e}")
+
+    return True if _face_app else False
+
+
+def detect(frame, threshold=0.4):
+    """检测并识别人脸，返回结果元组(是否匹配到已知人脸, 结果字符串)"""
+    global _face_app, _known_faces_embeddings, _known_faces_names
+
+    if not _face_app or not _known_faces_names or frame is None:
+        return (False, "未初始化或无效帧")
+
+    try:
+        faces = _face_app.get(frame)
+    except Exception as e:
+        print(f"Face detect error: {e}")
+        return (False, f"检测错误: {str(e)}")
+
+    result_parts = []
+    has_matched = False  # 新增标记：是否有匹配到的已知人脸
+
+    for face in faces:
+        # 特征归一化
+        embedding = face.embedding.astype(np.float32)
+        norm = np.linalg.norm(embedding)
+        if norm == 0:
+            continue
+        embedding = embedding / norm
+
+        # 对比已知人脸
+        max_sim, best_name = -1.0, "Unknown"
+        for name in _known_faces_names:
+            known_emb = _known_faces_embeddings[name]
+            sim = np.dot(embedding, known_emb)
+            if sim > max_sim:
+                max_sim = sim
+                best_name = name
+
+        # 判断匹配结果
+        is_match = max_sim >= threshold
+        if is_match:
+            has_matched = True  # 只要有一个匹配成功，就标记为True
+
+        bbox = face.bbox
+        result_parts.append(
+            f"{'匹配' if is_match else '不匹配'}: {best_name} (相似度: {max_sim:.2f}, 边界框: {bbox})"
+        )
+
+    # 构建结果字符串
+    if not result_parts:
+        result_str = "未检测到人脸"
+    else:
+        result_str = "; ".join(result_parts)
+
+    # 第一个返回值改为：是否匹配到已知人脸
+    return (has_matched, result_str)