如何使用MongoDB开发一个简单的人脸识别系统

import cv2

def face_detection(image_path):
    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
    img = cv2.imread(image_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    
    if len(faces) == 0:
        return None
    
    (x, y, w, h) = faces[0]
    face_img = img[y:y+h, x:x+w]
    return face_img

def feature_extraction(face_img):
    face_recognizer = cv2.face.LBPHFaceRecognizer_create()
    face_recognizer.read('face_recognizer.xml')
    
    gray = cv2.cvtColor(face_img, cv2.COLOR_BGR2GRAY)
    face_vector = face_recognizer.predict(gray)
    
    return face_vector

image_path = 'example.jpg'
face_img = face_detection(image_path)
if face_img is not None:
    face_vector = feature_extraction(face_img)
    # 将人脸图片和特征向量存储到MongoDB中
    # ...

import cv2

def face_recognition(face_img):
    # 从MongoDB中加载人脸数据集合
    # ...

    face_recognizer = cv2.face.LBPHFaceRecognizer_create()
    face_recognizer.train(faces, labels)

    gray = cv2.cvtColor(face_img, cv2.COLOR_BGR2GRAY)
    face_vector = feature_extraction(face_img)
    
    label, confidence = face_recognizer.predict(face_vector)
    
    if confidence < 70:
        return label
    else:
        return None

face_img = cv2.imread('test.jpg')
label = face_recognition(face_img)
if label is not None:
    # 从MongoDB中获取该标签对应的人脸信息
    # ...