opencv-python人脸检测简单实现

opencv-python人脸检测简单实现
Haar Cascade的大概描述根据这个博文：
https://blog.csdn.net/wutao1530663/article/details/78294349
GITHUB上XML分类器的下载：
https://github.com/opencv/opencv/tree/master/data/haarcascades
附上代码：

import cv2
import numpy as np

face_cascade = cv2.CascadeClassifier('F:\\Users\\74430\\Anaconda3\\Lib\\site-packages\\opencv_python-4.0.0.21.dist-info\\haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('F:\\Users\\74430\\Anaconda3\\Lib\\site-packages\\opencv_python-4.0.0.21.dist-info\\haarcascade_eye.xml')
cap = cv2.VideoCapture(0)

while True:
    ret, img = cap.read()
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.1, 5)
    if len(faces) > 0:
        for faceRect in faces:
            x, y, w, h = faceRect
            cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
            roi_gray = gray[y:y + h // 2, x:x + w]
            roi_color = img[y:y + h // 2, x:x + w]
            eyes = eye_cascade.detectMultiScale(roi_gray, 1.1, 1, cv2.CASCADE_SCALE_IMAGE, (2, 2))
            for (ex, ey, ew, eh) in eyes:
                cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2)
    cv2.imshow("img", img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

 

代码中的两个xml要去下载，输入绝对路径然后就可以实现了。这段代码的正确率并不高，效率也很低。