图片人脸检测

#coding=utf-8import cv2import dlibpath = "img/meinv.png"img = cv2.imread(path)gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)#人脸分类器detector = dlib.get_frontal_face_detector()# 获取人脸检测器predictor = dlib.shape_predictor( "C:\\Python36\\Lib\\site-packages\\dlib-data\\shape_predictor_68_face_landmarks.dat")dets = detector(gray, 1)for face in dets: shape = predictor(img, face) # 寻找人脸的68个标定点 # 遍历所有点，打印出其坐标，并圈出来 for pt in shape.parts(): pt_pos = (pt.x, pt.y) cv2.circle(img, pt_pos, 2, (0, 255, 0), 1) cv2.imshow("image", img)cv2.waitKey(0)cv2.destroyAllWindows()

视频人脸检测

# coding=utf-8import cv2import dlibdetector = dlib.get_frontal_face_detector() #使用默认的人类识别器模型def discern(img): gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) dets = detector(gray, 1) for face in dets: left = face.left() top = face.top() right = face.right() bottom = face.bottom() cv2.rectangle(img, (left, top), (right, bottom), (0, 255, 0), 2) cv2.imshow("image", img)cap = cv2.VideoCapture(0)while (1): ret, img = cap.read() discern(img) if cv2.waitKey(1) & 0xFF == ord('q'): breakcap.release()cv2.destroyAllWindows()

那么，OpenCV和Dlib的视频识别对比，有两个地方是不同的：

1.Dlib模型识别的准确率和效果要好于OpenCV；

2.Dlib识别的性能要比OpenCV差，使用视频测试的时候Dlib有明显的卡顿，但是OpenCV就好很多，基本看不出来；

以上就是python实现图片，视频人脸识别(dlib版)的详细内容，更多关于python 人脸识别的资料请关注其它相关文章！