Posted in Python onNovember 18, 2020
图片人脸检测
#coding=utf-8 import cv2 import dlib path = "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-8 import cv2 import dlib detector = 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'): break cap.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三水点靠木其它相关文章!
python实现图片,视频人脸识别(dlib版)
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https://github.com/LeonPython/faceai/blob/master/doc/videoOpenCV.md声明:登载此文出于传递更多信息之目的,并不意味着赞同其观点或证实其描述。
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