C++怎么利用opencv实现人脸检测功能

本篇内容主要讲解"C++怎么利用opencv实现人脸检测功能"，感兴趣的朋友不妨来看看。本文介绍的方法操作简单快捷，实用性强。下面就让小编来带大家学习"C++怎么利用opencv实现人脸检测功能"吧!

Linux系统下安装opencv我就再啰嗦一次，防止有些人没有安装没调试出来喷小编的程序是个坑，
sudo apt-get install libcv-dev
sudo apt-get install libopencv-dev
看看你的usr/share/opencv/haarcascades目录下有没有出现几个训练集.XML文件，接下来我拿人脸和眼睛检测作为实例玩一下，程序如下：

好多人不会编译opencv，我再多写几句解决一下好多菜鸟的困难吧

copy完代码之后，保存为xiaorun.cpp哦，记得编译试用个g++ -o xiaorun ./xiaorun.cpp -lopencv_highgui -lopenc_imgproc -lopencv_core -lopencv_objdetect

即可实现

#include #include #include #include #include using namespace cv;using namespace std;void detectAndDraw( Mat& img, CascadeClassifier& cascade,          CascadeClassifier& nestedCascade,          double scale, bool tryflip );int main(){  CascadeClassifier cascade, nestedCascade;  bool stop = false;  cascade.load("/usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml");  nestedCascade.load("/usr/share/opencv/haarcascades/haarcascade_eye.xml");  // frame = imread("renlian.jpg");  VideoCapture cap(0);  //打开默认摄像头  if(!cap.isOpened())  {    return -1;  }  Mat frame;  Mat edges;while(!stop){cap>>frame; detectAndDraw( frame, cascade, nestedCascade,2,0 ); if(waitKey(30) >=0) stop = true; imshow("cam",frame);}  //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("/usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml");//  nestedCascade.load("/usr/share/opencv/haarcascades/aarcascade_eye.xml");//  frame = imread("renlian.jpg");//  detectAndDraw( frame, cascade, nestedCascade,2,0 );  // waitKey();  //while(!stop)  //{  //  cap>>frame;  //  detectAndDraw( frame, cascade, nestedCascade,2,0 );    if(waitKey(30) >=0)   stop = true;  //}  return 0;}void detectAndDraw( Mat& img, CascadeClassifier& cascade,          CascadeClassifier& nestedCascade,          double scale, bool tryflip ){  int i = 0;  double t = 0;  //建立用于存放人脸的向量容器  vector 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  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::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));    }  }  t = (double)cvGetTickCount() - t;  //  qDebug( "detection time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) );  for( vector::const_iterator r = faces.begin(); r != faces.end(); r++, i++ )  {    Mat smallImgROI;    vector nestedObjects;    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);    if( nestedCascade.empty() )      continue;    smallImgROI = smallImg(*r);    //同样方法检测人眼    nestedCascade.detectMultiScale( smallImgROI, nestedObjects,      1.1, 2, 0      //|CV_HAAR_FIND_BIGGEST_OBJECT      //|CV_HAAR_DO_ROUGH_SEARCH      //|CV_HAAR_DO_CANNY_PRUNING      |CV_HAAR_SCALE_IMAGE      ,Size(30, 30) );    for( vector::const_iterator nr = nestedObjects.begin(); nr != nestedObjects.end(); nr++ )    {      center.x = cvRound((r->x + nr->x + nr->width*0.5)*scale);      center.y = cvRound((r->y + nr->y + nr->height*0.5)*scale);      radius = cvRound((nr->width + nr->height)*0.25*scale);      circle( img, center, radius, color, 3, 8, 0 );    }  }  // imshow( "识别结果", img );}

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