要实现一个基于树莓派4B的智能人脸识别安防系统，可以分为以下几个步骤： 1. **硬件准备**： - 树莓派4B - FFC接口的CSI摄像头（如树莓派官方摄像头） - SD卡（已安装树莓派操作系统，如Raspberry Pi OS） 2. **软件准备**： - 安装OpenCV库：`pip install opencv-python` - 安装face_recognition库：`pip install face_recognition` - 安装numpy库：`pip install numpy` 3. **功能实现步骤**： - 采集人脸数据 - 训练人脸识别模型 - 实现人脸识别 以下是完整的代码实现： ### 1. 采集人脸数据 ```python import cv2 import os # 创建保存人脸数据的文件夹 dataset_dir = "dataset" if not os.path.exists(dataset_dir): os.makedirs(dataset_dir) # 输入用户ID user_id = input("请输入用户ID: ") user_dir = os.path.join(dataset_dir, user_id) if not os.path.exists(user_dir): os.makedirs(user_dir) # 打开摄像头 cap = cv2.VideoCapture(0) face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') count = 0 max_samples = 30 # 采集30张人脸图像 while True: ret, frame = cap.read() gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(100, 100)) for (x, y, w, h) in faces: count += 1 face = frame[y:y+h, x:x+w] cv2.imwrite(os.path.join(user_dir, f"{count}.jpg"), face) cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2) cv2.imshow("Collecting Faces", frame) if cv2.waitKey(1) & 0xFF == ord('q') or count >= max_samples: break cap.release() cv2.destroyAllWindows() ``` ### 2. 训练人脸识别模型 ```python import face_recognition import os import pickle dataset_dir = "dataset" encodings = [] names = [] for person_dir in os.listdir(dataset_dir): person_path = os.path.join(dataset_dir, person_dir) if not os.path.isdir(person_path): continue for image_file in os.listdir(person_path): image_path = os.path.join(person_path, image_file) image = face_recognition.load_image_file(image_path) encoding = face_recognition.face_encodings(image) if encoding: encodings.append(encoding[0]) names.append(person_dir) # 保存训练好的模型 with open("face_encodings.pickle", "wb") as f: pickle.dump({"encodings": encodings, "names": names}, f) print("训练完成！") ``` ### 3. 实现人脸识别 ```python import face_recognition import cv2 import pickle # 加载训练好的模型 with open("face_encodings.pickle", "rb") as f: data = pickle.load(f) known_encodings = data["encodings"] known_names = data["names"] # 打开摄像头 cap = cv2.VideoCapture(0) while True: ret, frame = cap.read() rgb_frame = frame[:, :, ::-1] # OpenCV图像默认是BGR格式，转换为RGB face_locations = face_recognition.face_locations(rgb_frame) face_encodings = face_recognition.face_encodings(rgb_frame, face_locations) for face_encoding, face_location in zip(face_encodings, face_locations): matches = face_recognition.compare_faces(known_encodings, face_encoding) name = "Unknown" face_distances = face_recognition.face_distance(known_encodings, face_encoding) best_match_index = face_distances.argmin() if matches[best_match_index]: name = known_names[best_match_index] top, right, bottom, left = face_location cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2) cv2.putText(frame, name, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2) cv2.imshow("Face Recognition", frame) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows() ``` ### 使用说明 1. **采集人脸数据**：运行第一个脚本，输入用户ID，采集人脸数据。 2. **训练模型**：运行第二个脚本，训练人脸识别模型。 3. **人脸识别**：运行第三个脚本，实时进行人脸识别。 ### 注意事项 - 确保摄像头正常工作。 - 采集人脸时尽量保持光线一致，避免遮挡。 - 可以扩展系统以支持更多用户和更复杂的场景。 ---