人脸识别opencv2.4.9

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。

#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)cvGetTickFrequency()*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 );}