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在
opencv根目录\sources\data\haarcascades中提供了很多训练好的分类器，我们使用haarcascade_frontalface_alt.xml分类器。
Haar cascade是Paul Viola和 Michael Jone在2001年，论文”Rapid Object Detection using a
Boosted Cascade of Simple Features”提出的一种Object Detection方法。
import cv2 # 读入图像 img = cv2.imread("test.jpg") # 加载人脸特征，该文件在
python安装目录\Lib\site-packages\cv2\data 下 face_cascade =
cv2.CascadeClassifier(r'haarcascade_frontalface_default.xml') #
将读取的图像转为COLOR_BGR2GRAY，减少计算强度 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #
检测出的人脸个数 faces = face_cascade.detectMultiScale(gray, scaleFactor = 1.15,
minNeighbors = 5, minSize = (5, 5)) print("Face : {0}".format(len(faces))) #
用矩形圈出人脸的位置 for(x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x + w, y + h),
(0, 255, 0), 2) cv2.namedWindow("Faces") cv2.imshow("Faces", img)
cv2.waitKey(0) cv2.destroyAllWindows()
