python基于opencv的人脸识别代码

以下是基于OpenCV的人脸识别的Python代码示例：

import cv2

# 加载Haar级联分类器
faceCascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")

# 加载训练数据集
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read("trainer.yml")

# 加载标签
labels = {"person_name": 1}
with open("labels.pickle", 'rb') as f:
    labels = pickle.load(f)
    labels = {v:k for k,v in labels.items()}

# 打开摄像头
cap = cv2.VideoCapture(0)

while True:
    # 读取视频帧
    ret, frame = cap.read()

    # 转换为灰度图像
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # 检测人脸
    faces = faceCascade.detectMultiScale(gray, scaleFactor=1.5, minNeighbors=5)

    # 对每个检测到的人脸进行识别
    for (x, y, w, h) in faces:
        roi_gray = gray[y:y+h, x:x+w]
        id_, confidence = recognizer.predict(roi_gray)
        if confidence >= 45 and confidence <= 85:
            font = cv2.FONT_HERSHEY_SIMPLEX
            name = labels[id_]
            cv2.putText(frame, name, (x,y), font, 1, (255,255,255), 2, cv2.LINE_AA)

        # 画出人脸检测框
        color = (255, 0, 0)
        stroke = 2
        end_cord_x = x + w
        end_cord_y = y + h
        cv2.rectangle(frame, (x, y), (end_cord_x, end_cord_y), color, stroke)

    # 显示视频帧
    cv2.imshow('frame',frame)
    if cv2.waitKey(20) & 0xFF == ord('q'):
        break

# 释放摄像头资源
cap.release()
cv2.destroyAllWindows()

这个代码示例中，我们首先加载了Haar级联分类器和训练数据集，然后打开摄像头，并对每个检测到的人脸进行识别。如果置信度（confidence）在45到85之间，就将人脸识别结果绘制在视频帧上。最后，我们通过cv2.imshow()函数显示视频帧，并使用cv2.waitKey()函数等待用户按下“q”键退出循环。