备份一些py文件

backup/face_recognition_system.py

@@ -0,0 +1,249 @@
+import os
+import cv2
+import numpy as np
+from insightface.app import FaceAnalysis
+from insightface.data import get_image as ins_get_image
+import pickle
+from typing import List, Tuple, Dict
+import json
+
+
+class FaceRecognitionSystem:
+    def __init__(self, model_name: str = 'buffalo_l'):
+        """
+        初始化人脸识别系统
+        Args:
+            model_name: 模型名称，可选 'buffalo_l', 'buffalo_s' 等
+        """
+        self.app = FaceAnalysis(name=model_name, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+        self.app.prepare(ctx_id=0, det_size=(640, 640))
+        self.face_database = {}  # 存储人脸特征向量
+        self.database_file = "face_database.pkl"
+
+        # 加载已有数据库
+        self.load_database()
+
+    def extract_face_features(self, image_path: str) -> List[Dict]:
+        """
+        从图像中提取人脸特征
+        Args:
+            image_path: 图像路径
+        Returns:
+            List[Dict]: 包含人脸信息的列表
+        """
+        img = cv2.imread(image_path)
+        if img is None:
+            raise ValueError(f"无法读取图像: {image_path}")
+
+        faces = self.app.get(img)
+        results = []
+
+        for i, face in enumerate(faces):
+            face_info = {
+                'bbox': face.bbox.astype(int).tolist(),  # 人脸框
+                'kps': face.kps.astype(int).tolist(),  # 关键点
+                'embedding': face.embedding.tolist(),  # 特征向量
+                'gender': face.gender,  # 性别
+                'age': face.age  # 年龄
+            }
+            results.append(face_info)
+
+        return results
+
+    def register_face(self, image_path: str, person_id: str):
+        """
+        注册人脸到数据库
+        Args:
+            image_path: 图像路径
+            person_id: 人员ID
+        """
+        faces = self.extract_face_features(image_path)
+
+        if not faces:
+            print(f"在图像 {image_path} 中未检测到人脸")
+            return False
+
+        if len(faces) > 1:
+            print(f"在图像 {image_path} 中检测到多个人脸，将使用第一个人脸")
+
+        # 存储第一个人脸的特征
+        self.face_database[person_id] = {
+            'embedding': np.array(faces[0]['embedding']),
+            'image_path': image_path
+        }
+
+        self.save_database()
+        print(f"成功注册人脸: {person_id}")
+        return True
+
+    def compare_faces(self, embedding1: np.ndarray, embedding2: np.ndarray) -> float:
+        """
+        计算两个人脸特征的相似度
+        Args:
+            embedding1: 特征向量1
+            embedding2: 特征向量2
+        Returns:
+            float: 相似度得分 (0-1之间，越大越相似)
+        """
+        # 计算余弦相似度
+        similarity = np.dot(embedding1, embedding2) / (
+                np.linalg.norm(embedding1) * np.linalg.norm(embedding2)
+        )
+        return float(similarity)
+
+    def one_vs_one(self, image_path1: str, image_path2: str) -> Tuple[float, bool]:
+        """
+        1v1人脸比对
+        Args:
+            image_path1: 图像1路径
+            image_path2: 图像2路径
+        Returns:
+            Tuple[float, bool]: (相似度得分, 是否同一人)
+        """
+        faces1 = self.extract_face_features(image_path1)
+        faces2 = self.extract_face_features(image_path2)
+
+        if not faces1 or not faces2:
+            return 0.0, False
+
+        embedding1 = np.array(faces1[0]['embedding'])
+        embedding2 = np.array(faces2[0]['embedding'])
+
+        similarity = self.compare_faces(embedding1, embedding2)
+        is_same = similarity > 0.6  # 阈值可根据实际情况调整
+
+        return similarity, is_same
+
+    def one_vs_many(self, image_path: str, threshold: float = 0.6) -> List[Tuple[str, float]]:
+        """
+        1vn人脸检索
+        Args:
+            image_path: 查询图像路径
+            threshold: 相似度阈值
+        Returns:
+            List[Tuple[str, float]]: 匹配结果 (人员ID, 相似度)
+        """
+        faces = self.extract_face_features(image_path)
+        if not faces:
+            return []
+
+        query_embedding = np.array(faces[0]['embedding'])
+        results = []
+
+        for person_id, data in self.face_database.items():
+            similarity = self.compare_faces(query_embedding, data['embedding'])
+            if similarity > threshold:
+                results.append((person_id, similarity))
+
+        # 按相似度降序排序
+        results.sort(key=lambda x: x[1], reverse=True)
+        return results
+
+    def save_database(self):
+        """保存人脸数据库到文件"""
+        # 将numpy数组转换为列表以便序列化
+        save_data = {}
+        for person_id, data in self.face_database.items():
+            save_data[person_id] = {
+                'embedding': data['embedding'].tolist(),
+                'image_path': data['image_path']
+            }
+
+        with open(self.database_file, 'wb') as f:
+            pickle.dump(save_data, f)
+
+    def load_database(self):
+        """从文件加载人脸数据库"""
+        if os.path.exists(self.database_file):
+            with open(self.database_file, 'rb') as f:
+                save_data = pickle.load(f)
+
+            # 将列表转换回numpy数组
+            for person_id, data in save_data.items():
+                self.face_database[person_id] = {
+                    'embedding': np.array(data['embedding']),
+                    'image_path': data['image_path']
+                }
+            print(f"已加载数据库，包含 {len(self.face_database)} 个人脸")
+
+    def visualize_detection(self, image_path: str, save_path: str = None):
+        """
+        可视化人脸检测结果
+        Args:
+            image_path: 图像路径
+            save_path: 保存路径
+        """
+        img = cv2.imread(image_path)
+        faces = self.app.get(img)
+
+        for face in faces:
+            # 绘制人脸框
+            bbox = face.bbox.astype(int)
+            cv2.rectangle(img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2)
+
+            # 绘制关键点
+            for kp in face.kps.astype(int):
+                cv2.circle(img, (kp[0], kp[1]), 2, (0, 0, 255), -1)
+
+            # 显示性别和年龄
+            info = f"{'M' if face.gender == 1 else 'F'}/{face.age}"
+            cv2.putText(img, info, (bbox[0], bbox[1] - 10),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
+
+        if save_path:
+            cv2.imwrite(save_path, img)
+
+        return img
+
+
+# 使用示例
+def main():
+    # 初始化系统
+    face_system = FaceRecognitionSystem()
+
+    # # 创建测试图像目录
+    # os.makedirs("test_images", exist_ok=True)
+    #
+    # # 示例1: 注册人脸
+    # print("=== 注册人脸 ===")
+    # # 假设你有一些人脸图像放在 test_images 目录下
+    # test_images = {
+    #     "person_001": "test_images/person1.png",
+    #     "person_002": "test_images/person2.jpg",
+    #     # "person_003": "test_images/person3.jpg"
+    # }
+    #
+    # for person_id, img_path in test_images.items():
+    #     if os.path.exists(img_path):
+    #         face_system.register_face(img_path, person_id)
+
+    # 示例2: 1v1比对
+    print("\n=== 1v1人脸比对 ===")
+    img1 = "test_data/register/person1.png"
+    # img1 = "test_data/query/file___media_Photo_103_IMG_1737872809_085_IMG_20250126_142509.jpg"
+    img2 = "test_data/query/file___media_Photo_152_IMG_1737876072_134_IMG_20250126_151932.jpg"
+
+
+    if os.path.exists(img1) and os.path.exists(img2):
+        similarity, is_same = face_system.one_vs_one(img1, img2)
+        print(f"相似度: {similarity:.4f}, 是否同一人: {is_same}")
+
+    # # 示例3: 1vn检索
+    # print("\n=== 1vn人脸检索 ===")
+    # query_img = "test_images/query.jpg"
+    # if os.path.exists(query_img):
+    #     results = face_system.one_vs_many(query_img)
+    #     print("检索结果:")
+    #     for person_id, score in results:
+    #         print(f"  {person_id}: {score:.4f}")
+    #
+    # # 示例4: 可视化检测结果
+    # print("\n=== 人脸检测可视化 ===")
+    # test_img = "test_images/test.jpg"
+    # if os.path.exists(test_img):
+    #     output_img = face_system.visualize_detection(test_img, "output/detection_result.jpg")
+    #     print("检测结果已保存到 output/detection_result.jpg")
+
+
+if __name__ == "__main__":
+    main()