打开摄像头，录入并保存人脸及名字，放入数据集

src/AI_Drone_Face_Recognition_Human_Tracking/utils.py.py

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+import os
+import cv2
+from deepface import DeepFace
+import numpy as np
+
+
+class FaceDB:
+    """人脸数据库管理类"""
+
+    def __init__(self, face_dir="faces"):
+        self.face_dir = face_dir
+        self.face_data = {}  # 存储{人名: 人脸特征}
+        self._load_face_database()
+
+    def _load_face_database(self):
+        """加载人脸库中的所有人脸特征"""
+        if not os.path.exists(self.face_dir):
+            os.makedirs(self.face_dir)
+            print(f"创建人脸库目录: {self.face_dir}")
+
+        self.face_data.clear()  # 清空旧数据
+        for img_name in os.listdir(self.face_dir):
+            if img_name.endswith((".jpg", ".png")):
+                person_name = os.path.splitext(img_name)[0]
+                img_path = os.path.join(self.face_dir, img_name)
+                try:
+                    # 提取人脸特征（Facenet模型）
+                    embedding = DeepFace.represent(
+                        img_path,
+                        model_name="Facenet",
+                        enforce_detection=True
+                    )[0]["embedding"]
+                    self.face_data[person_name] = embedding
+                    print(f"✅ 成功加载人脸: {person_name}")
+                except Exception as e:
+                    print(f"❌ 加载{img_name}失败: {str(e)[:50]}...")
+
+    def recognize_face(self, frame, threshold=0.6):
+        """识别人脸，返回匹配的人名或None"""
+        try:
+            # 提取当前帧人脸特征
+            frame_embedding = DeepFace.represent(
+                frame,
+                model_name="Facenet",
+                enforce_detection=True
+            )[0]["embedding"]
+
+            # 对比人脸库（计算欧式距离）
+            min_distance = float("inf")
+            matched_name = None
+            for name, embedding in self.face_data.items():
+                distance = np.linalg.norm(np.array(frame_embedding) - np.array(embedding))
+                if distance < min_distance and distance < threshold:
+                    min_distance = distance
+                    matched_name = name
+            return matched_name, min_distance  # 新增返回距离，方便调试
+        except Exception as e:
+            print(f"❌ 人脸识别失败: {str(e)[:50]}...")
+            return None, float("inf")
+
+    def add_face(self, frame, person_name):
+        """添加人脸到数据库"""
+        # 先检测人脸，确保有效
+        try:
+            # 验证帧中有人脸
+            DeepFace.extract_faces(frame, enforce_detection=True)
+            # 保存人脸图片
+            img_path = os.path.join(self.face_dir, f"{person_name}.jpg")
+            cv2.imwrite(img_path, frame)
+            # 重新加载数据库
+            self._load_face_database()
+            print(f"✅ 已添加[{person_name}]到人脸库，路径: {img_path}")
+            return True
+        except Exception as e:
+            print(f"❌ 添加人脸失败: {str(e)[:50]}...")
+            return False
+
+    def list_faces(self):
+        """列出人脸库中所有已注册的人名"""
+        return list(self.face_data.keys())
+
+
+def draw_detection_box(frame, bbox, label, color=(0, 255, 0)):
+    """绘制检测框和标签"""
+    x1, y1, x2, y2 = map(int, bbox)
+    cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
+    cv2.putText(
+        frame, label, (x1, y1 - 10),
+        cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2
+    )
+    return frame
+
+
+# ===================== 独立运行测试逻辑 =====================
+if __name__ == "__main__":
+    # 初始化人脸库
+    face_db = FaceDB(face_dir="faces")
+    print(f"\n当前人脸库已注册: {face_db.list_faces()}\n")
+
+    # 打开摄像头
+    cap = cv2.VideoCapture(0)
+    if not cap.isOpened():
+        print("❌ 无法打开摄像头，请检查设备！")
+        exit(1)
+
+    # 设置摄像头分辨率
+    cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
+    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
+
+    print("=" * 50)
+    print("🎯 FaceDB 测试工具（独立运行模式）")
+    print("按键说明：")
+    print("  a → 添加当前帧人脸到数据库")
+    print("  r → 识别当前帧人脸")
+    print("  l → 列出已注册人脸")
+    print("  q → 退出程序")
+    print("=" * 50)
+
+    while True:
+        # 读取摄像头帧
+        ret, frame = cap.read()
+        if not ret:
+            print("❌ 无法读取摄像头画面！")
+            break
+
+        # 显示实时画面
+        cv2.imshow("FaceDB Test (utils.py)", frame)
+
+        # 按键处理
+        key = cv2.waitKey(1) & 0xFF
+        if key == ord('q'):
+            print("🔚 退出测试程序...")
+            break
+
+        elif key == ord('a'):
+            # 添加人脸
+            person_name = input("\n请输入要添加的人名: ").strip()
+            if not person_name:
+                print("❌ 人名不能为空！")
+                continue
+            face_db.add_face(frame, person_name)
+
+        elif key == ord('r'):
+            # 识别人脸
+            print("\n🔍 正在识别人脸...")
+            name, distance = face_db.recognize_face(frame)
+            if name:
+                print(f"✅ 识别成功！匹配到: {name} (距离: {distance:.4f})")
+            else:
+                print(f"❌ 未识别到匹配人脸（最小距离: {distance:.4f}）")
+
+        elif key == ord('l'):
+            # 列出已注册人脸
+            faces = face_db.list_faces()
+            print(f"\n📋 人脸库已注册名单: {faces if faces else '空'}")
+
+    # 释放资源
+    cap.release()
+    cv2.destroyAllWindows()
+    print("\n✅ 资源已释放，程序结束")