人脸识别opencv2.4.9
2017-12-01 18:08
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先从你的opencv安装目录里面data/hahaarcascade的文件夹下的haarcascade_frontalface_alt.xml
复制到你的工程目录下。
然后直接调用就好了。
复制到你的工程目录下。
然后直接调用就好了。
#include "opencv2/calib3d/calib3d.hpp" #include "opencv2/imgproc/imgproc.hpp" #include "opencv2/highgui/highgui.hpp" #include "opencv2/features2d/features2d.hpp" #include "opencv2/nonfree/nonfree.hpp" #include "opencv2/legacy/legacy.hpp" #include <opencv2/objdetect/objdetect.hpp> #include <vector> #include <iostream> #include <stdio.h> using namespace cv; using namespace std; void detectAndDraw( Mat& img, CascadeClassifier& cascade, double scale, bool tryflip ); int main() { Mat src1 = imread("1.jpg"); CascadeClassifier cascade, nestedCascade; bool stop = false; //训练好的文件名称,放置在可执行文件同目录下 cascade.load("haarcascade_frontalface_alt.xml"); //nestedCascade.load("D:\\opencv\\sources\\data\\haarcascades\\haarcascade_eye.xml"); detectAndDraw( src1, cascade,2,0 ); // Mat src2 = imread("right02.jpg"); if(src1.empty())// || src2.empty() { cout << "image is empty!" << endl; return 0; } //cvtColor(src1,gray,CV_BGR2GRAY); int minHessian = 1000; SurfFeatureDetector detector(minHessian); vector<KeyPoint> keypoints1; detector.detect(src1,keypoints1); // detector.detect(src2,keypoints2); Mat img_keypoints; drawKeypoints(src1, keypoints1, img_keypoints, Scalar::all(-1),DrawMatchesFlags::DEFAULT); imshow("src1", img_keypoints); waitKey(0); return 0; } void detectAndDraw( Mat& img, CascadeClassifier& cascade, double scale, bool tryflip ) { int i = 0; double t = 0; //建立用于存放人脸的向量容器 vector<Rect> faces, faces2; //定义一些颜色,用来标示不同的人脸 const static Scalar colors[] = { CV_RGB(0,0,255), CV_RGB(0,128,255), CV_RGB(0,255,255), CV_RGB(0,255,0), CV_RGB(255,128,0), CV_RGB(255,255,0), CV_RGB(255,0,0), CV_RGB(255,0,255)} ; //建立缩小的图片,加快检测速度 //nt cvRound (double value) 对一个double型的数进行四舍五入,并返回一个整型数! Mat gray, smallImg( cvRound (img.rows/scale), cvRound(img.cols/scale), CV_8UC1 ); //转成灰度图像,Harr特征基于灰度图 cvtColor( img, gray, CV_BGR2GRAY ); imshow("灰度",gray); //改变图像大小,使用双线性差值 resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR ); imshow("缩小尺寸",smallImg); //变换后的图像进行直方图均值化处理 equalizeHist( smallImg, smallImg ); imshow("直方图均值处理",smallImg); //程序开始和结束插入此函数获取时间,经过计算求得算法执行时间 t = (double)cvGetTickCount(); //检测人脸 //detectMultiScale函数中smallImg表示的是要检测的输入图像为smallImg,faces表示检测到的人脸目标序列,1.1表示 //每次图像尺寸减小的比例为1.1,2表示每一个目标至少要被检测到3次才算是真的目标(因为周围的像素和不同的窗口大 //小都可以检测到人脸),CV_HAAR_SCALE_IMAGE表示不是缩放分类器来检测,而是缩放图像,Size(30, 30)为目标的 //最小最大尺寸 cascade.detectMultiScale( smallImg, faces, 1.1, 2, 0 //|CV_HAAR_FIND_BIGGEST_OBJECT //|CV_HAAR_DO_ROUGH_SEARCH |CV_HAAR_SCALE_IMAGE ,Size(30, 30)); //如果使能,翻转图像继续检测 cout << "faces = " << faces.size() << endl; if( tryflip ) { flip(smallImg, smallImg, 1); imshow("反转图像",smallImg); cascade.detectMultiScale( smallImg, faces2, 1.1, 2, 0 //|CV_HAAR_FIND_BIGGEST_OBJECT //|CV_HAAR_DO_ROUGH_SEARCH |CV_HAAR_SCALE_IMAGE ,Size(30, 30) ); for( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); r++ ) { faces.push_back(Rect(smallImg.cols - r->x - r->width, r->y, r->width, r->height)); } } cout << "face = " << faces.size() << endl; t = (double)cvGetTickCount() - t; cout << "t = " << (t/((double)cvGetTickFrequ 4000 ency()*1000.)) << endl; // qDebug( "detection time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) ); for( vector<Rect>::const_iterator r = faces.begin(); r != faces.end(); r++, i++ ) { Mat smallImgROI; Point center; Scalar color = colors[i%8]; int radius; double aspect_ratio = (double)r->width/r->height; if( 0.75 < aspect_ratio && aspect_ratio < 1.3 ) { //标示人脸时在缩小之前的图像上标示,所以这里根据缩放比例换算回去 center.x = cvRound((r->x + r->width*0.5)*scale); center.y = cvRound((r->y + r->height*0.5)*scale); radius = cvRound((r->width + r->height)*0.25*scale); circle( img, center, radius, color, 3, 8, 0 ); } else rectangle( img, cvPoint(cvRound(r->x*scale), cvRound(r->y*scale)), cvPoint(cvRound((r->x + r->width-1)*scale), cvRound((r->y + r->height-1)*scale)), color, 3, 8, 0); smallImgROI = smallImg(*r); } imshow( "识别结果", img ); }
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