feat(face): 实现基于InsightFace的人脸识别系统

- 集成InsightFace模型用于人脸检测和特征提取
- 实现人脸特征向量的余弦相似度计算- 构建SQLite数据库存储和管理人脸特征数据- 支持单个人脸注册、批量注册和人脸识别功能
- 添加人脸数据的增删查功能- 实现从文件夹自动批量注册人脸
- 支持设置识别阈值以提高识别准确性
- 添加详细的日志输出和错误处理机制

10.检测屏幕内容.py

@@ -8,7 +8,7 @@ import numpy as np
 # 加载训练好的模型
 # best.pt: 最佳模型，适用于生产
 # last.pt: 最后一轮训练的模型，适用于继续训练
-yolo = YOLO('runs/detect/train7/weights/best.pt')
+yolo = YOLO('models/face/best.pt')
 
 # 指定屏幕范围
 # x,y,width,height  全屏None

16.人脸比对.py

@@ -0,0 +1,42 @@
+import insightface
+from insightface.app import FaceAnalysis
+import cv2
+
+# 1. 创建人脸分析应用
+# 这会自动下载预训练模型（第一次运行时会下载，约 200MB）
+app = FaceAnalysis(name='buffalo_l') # 'buffalo_l' 是当前最准的模型集合
+app.prepare(ctx_id=0, det_size=(640, 640)) # ctx_id=0 表示使用CPU， ctx_id=1/gpu_id 表示使用GPU
+
+# 2. 加载图片
+img1 = cv2.imread('./resources/face/01.png')
+# img2 = cv2.imread('./resources/face/02.png')
+img2 = cv2.imread('./resources/face/16.jpg')
+
+# 3. 进行人脸检测和特征提取
+faces1 = app.get(img1)
+faces2 = app.get(img2)
+
+# 检查是否检测到人脸
+if len(faces1) == 0 or len(faces2) == 0:
+    print("未在图片中检测到人脸！")
+    exit()
+
+# 取每张图片中检测到的第一个人脸
+face1 = faces1[0]
+face2 = faces2[0]
+
+# 4. 获取人脸特征向量（嵌入）
+embedding1 = face1.embedding
+embedding2 = face2.embedding
+
+# 5. 计算特征向量之间的相似度（使用余弦相似度）
+from numpy.linalg import norm
+cos_sim = embedding1 @ embedding2.T / (norm(embedding1) * norm(embedding2))
+print(f"人脸相似度（余弦）： {cos_sim:.4f}")
+
+# 6. 根据阈值判断是否为同一个人
+threshold = 0.6  # 常用阈值，可根据场景调整（范围一般在0.2-0.8之间，值越大判断越严格）
+if cos_sim > threshold:
+    print("判断为同一个人！")
+else:
+    print("判断为不同人。")

17.人脸识别.py

@@ -0,0 +1,59 @@
+import insightface
+from insightface.app import FaceAnalysis
+import cv2
+import numpy as np
+from numpy.linalg import norm
+
+# 初始化模型
+app = FaceAnalysis(name='buffalo_l')
+app.prepare(ctx_id=0, det_size=(640, 640))
+
+# 创建一个数据库来存储已知人脸的特征和姓名
+face_database = {}
+
+# --- 注册阶段：将已知人脸加入数据库 ---
+def register_face(image_path, person_name):
+    img = cv2.imread(image_path)
+    faces = app.get(img)
+    if len(faces) != 1:
+        print(f"警告：在 {image_path} 中未检测到或检测到多张人脸，跳过。")
+        return
+    embedding = faces[0].embedding
+    face_database[person_name] = embedding
+    print(f"成功注册：{person_name}")
+
+# 注册多个人
+img1 = cv2.imread('./resources/face/01.png')
+
+register_face('./resources/face/01.png', '01')
+register_face('./resources/face/02.png', '02')
+
+# --- 识别阶段：识别未知图片中的人 ---
+def recognize_face(image_path):
+    img = cv2.imread(image_path)
+    faces = app.get(img)
+    if len(faces) == 0:
+        print("未检测到人脸。")
+        return
+
+    for i, face in enumerate(faces):
+        unknown_embedding = face.embedding
+        max_sim = -1
+        identity = "未知"
+
+        # 与数据库中的每个人脸进行比对
+        for name, known_embedding in face_database.items():
+            cos_sim = unknown_embedding @ known_embedding.T / (norm(unknown_embedding) * norm(known_embedding))
+            if cos_sim > max_sim:
+                max_sim = cos_sim
+                identity = name
+
+        # 根据阈值决定最终身份
+        threshold = 0.6
+        if max_sim < threshold:
+            identity = "未知"
+
+        print(f"人脸 {i+1}: 识别为 【{identity}】, 相似度：{max_sim:.4f}")
+
+# 识别一张新图片
+recognize_face('./resources/face/16.jpg')

18.人脸识别-保存数据库.py

@@ -0,0 +1,265 @@
+import insightface
+from insightface.app import FaceAnalysis
+import cv2
+import numpy as np
+import sqlite3
+import os
+from numpy.linalg import norm
+
+# 初始化InsightFace模型
+app = FaceAnalysis(name='buffalo_l')
+app.prepare(ctx_id=0, det_size=(640, 640))
+
+
+class FaceDatabase:
+    def __init__(self, db_path='face_database.db'):
+        self.db_path = db_path
+        self.init_database()
+
+    def init_database(self):
+        """初始化数据库"""
+        conn = sqlite3.connect(self.db_path)
+        cursor = conn.cursor()
+        cursor.execute('''
+            CREATE TABLE IF NOT EXISTS faces (
+                id INTEGER PRIMARY KEY AUTOINCREMENT,
+                name TEXT UNIQUE NOT NULL,
+                embedding BLOB NOT NULL,
+                created_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+            )
+        ''')
+        conn.commit()
+        conn.close()
+        print(f"数据库初始化完成: {self.db_path}")
+
+    def add_face(self, name, embedding):
+        """添加人脸到数据库"""
+        conn = sqlite3.connect(self.db_path)
+        cursor = conn.cursor()
+
+        # 将numpy数组转换为bytes
+        embedding_blob = embedding.tobytes()
+
+        try:
+            cursor.execute(
+                'INSERT INTO faces (name, embedding) VALUES (?, ?)',
+                (name, embedding_blob)
+            )
+            conn.commit()
+            print(f"✅ 成功注册: {name}")
+            return True
+        except sqlite3.IntegrityError:
+            print(f"⚠️ 姓名已存在: {name}")
+            return False
+        finally:
+            conn.close()
+
+    def get_all_faces(self):
+        """获取所有人脸数据"""
+        conn = sqlite3.connect(self.db_path)
+        cursor = conn.cursor()
+
+        cursor.execute('SELECT name, embedding FROM faces')
+        results = cursor.fetchall()
+        conn.close()
+
+        face_database = {}
+        for name, embedding_blob in results:
+            # 将bytes转换回numpy数组
+            embedding = np.frombuffer(embedding_blob, dtype=np.float32)
+            face_database[name] = embedding
+
+        print(f"📊 从数据库加载了 {len(face_database)} 个人脸特征")
+        return face_database
+
+    def delete_face(self, name):
+        """删除人脸"""
+        conn = sqlite3.connect(self.db_path)
+        cursor = conn.cursor()
+
+        cursor.execute('DELETE FROM faces WHERE name = ?', (name,))
+        affected_rows = cursor.rowcount
+        conn.commit()
+        conn.close()
+
+        if affected_rows > 0:
+            print(f"🗑️ 已删除: {name}")
+            return True
+        else:
+            print(f"❌ 未找到: {name}")
+            return False
+
+    def face_exists(self, name):
+        """检查人脸是否存在"""
+        conn = sqlite3.connect(self.db_path)
+        cursor = conn.cursor()
+
+        cursor.execute('SELECT 1 FROM faces WHERE name = ?', (name,))
+        exists = cursor.fetchone() is not None
+        conn.close()
+
+        return exists
+
+
+# 创建全局数据库实例
+face_db = FaceDatabase()
+
+
+def register_face(image_path, person_name):
+    """注册人脸"""
+    # 检查图片是否存在
+    if not os.path.exists(image_path):
+        print(f"❌ 图片不存在: {image_path}")
+        return False
+
+    # 读取图片
+    img = cv2.imread(image_path)
+    if img is None:
+        print(f"❌ 无法读取图片: {image_path}")
+        return False
+
+    # 使用InsightFace检测人脸
+    faces = app.get(img)
+
+    if len(faces) == 0:
+        print(f"❌ 在 {image_path} 中未检测到人脸")
+        return False
+    elif len(faces) > 1:
+        print(f"⚠️ 在 {image_path} 中检测到 {len(faces)} 张人脸，使用第一张")
+
+    # 提取人脸特征
+    embedding = faces[0].embedding
+    print(f"📐 提取到特征向量，维度: {embedding.shape}")
+
+    # 保存到数据库
+    return face_db.add_face(person_name, embedding)
+
+
+def register_faces_from_folder(folder_path):
+    """从文件夹批量注册人脸"""
+    if not os.path.exists(folder_path):
+        print(f"❌ 文件夹不存在: {folder_path}")
+        return
+
+    supported_formats = ('.jpg', '.jpeg', '.png', '.bmp', '.tiff')
+    registered_count = 0
+
+    for filename in os.listdir(folder_path):
+        if filename.lower().endswith(supported_formats):
+            # 使用文件名（不含扩展名）作为人名
+            person_name = os.path.splitext(filename)[0]
+            image_path = os.path.join(folder_path, filename)
+
+            if register_face(image_path, person_name):
+                registered_count += 1
+
+    print(f"🎉 批量注册完成，共注册 {registered_count} 个人脸")
+
+
+def recognize_face(image_path, threshold=0.6):
+    """识别人脸"""
+    # 检查图片是否存在
+    if not os.path.exists(image_path):
+        print(f"❌ 图片不存在: {image_path}")
+        return
+
+    # 读取图片
+    img = cv2.imread(image_path)
+    if img is None:
+        print(f"❌ 无法读取图片: {image_path}")
+        return
+
+    # 从数据库加载所有人脸特征
+    face_database = face_db.get_all_faces()
+
+    if not face_database:
+        print("❌ 数据库中无人脸数据，请先注册人脸")
+        return
+
+    # 使用InsightFace检测人脸
+    faces = app.get(img)
+
+    if len(faces) == 0:
+        print("❌ 未检测到人脸")
+        return
+
+    print(f"🔍 检测到 {len(faces)} 张人脸，开始识别...")
+
+    for i, face in enumerate(faces):
+        unknown_embedding = face.embedding
+        max_sim = -1
+        identity = "未知"
+        best_match_name = None
+
+        # 与数据库中的每个人脸进行比对
+        for name, known_embedding in face_database.items():
+            # 计算余弦相似度
+            cos_sim = unknown_embedding @ known_embedding.T / (norm(unknown_embedding) * norm(known_embedding))
+
+            if cos_sim > max_sim:
+                max_sim = cos_sim
+                identity = name
+                best_match_name = name
+
+        # 根据阈值决定最终身份
+        if max_sim < threshold:
+            identity = "未知"
+
+        # 输出结果
+        status = "✅" if identity != "未知" else "❓"
+        print(f"{status} 人脸 {i + 1}: 识别为 【{identity}】, 相似度: {max_sim:.4f}")
+
+        # 显示匹配详情
+        if identity != "未知":
+            matched_embedding = face_database[best_match_name]
+            print(f"   匹配特征: {best_match_name}, 范数: {norm(matched_embedding):.4f}")
+
+
+def list_registered_faces():
+    """列出所有已注册的人脸"""
+    face_database = face_db.get_all_faces()
+
+    if not face_database:
+        print("📭 数据库为空")
+        return
+
+    print(f"\n📋 已注册的人脸 ({len(face_database)} 个):")
+    for i, name in enumerate(face_database.keys(), 1):
+        print(f"  {i}. {name}")
+
+
+def delete_face(person_name):
+    """删除指定人脸"""
+    return face_db.delete_face(person_name)
+
+
+# 使用示例
+if __name__ == "__main__":
+    print("=" * 50)
+    print("🎭 InsightFace 人脸识别系统 (SQLite版本)")
+    print("=" * 50)
+
+    # 1. 批量注册人脸
+    print("\n1. 📁 批量注册人脸")
+    register_faces_from_folder('./resources/face')
+
+    # 2. 单个注册
+    print("\n2. 👤 单个注册")
+    register_face('./resources/face/01.png', '张三')
+    register_face('./resources/face/02.png', '李四')
+
+    # 3. 查看已注册的人脸
+    print("\n3. 📊 已注册人脸列表")
+    list_registered_faces()
+
+    # 4. 识别人脸
+    print("\n4. 🔍 人脸识别测试")
+    recognize_face('./resources/face/16.jpg', threshold=0.6)
+
+    # 5. 删除测试
+    # print("\n5. 🗑️ 删除人脸测试")
+    # delete_face('测试删除')
+
+    print("\n" + "=" * 50)
+    print("✨ 程序执行完成")
+    print("=" * 50)

requirements.txt

@@ -4,4 +4,5 @@ numpy~=2.0.2
 pyautogui~=0.9.54
 moviepy~=2.2.1
 torch~=2.8.0
-PyYAML~=6.0.2
+PyYAML~=6.0.2
+insightface~=0.7.3