小編所有的帖子都是基於unbuntu系統的，當然稍作修改同樣試用於windows的，經過小編的絞盡腦汁，把剛剛發的那篇python 實現人臉和眼睛的檢測的程式用C++ 實現了，當然，也參考了不少大神的部落格，下面我們就一起來看看：

Linux系統下安裝opencv我就再囉嗦一次，防止有些人沒有安裝沒調試出來噴小編的程式是個坑，
sudo apt-get install libcv-dev
sudo apt-get install libopencv-dev
看看你的usr/share/opencv/haarcascades目錄下有沒有出現幾個訓練集.XML檔案，接下來我拿人臉和眼睛檢測作為例項玩一下，程式如下：
、
好多人不會編譯opencv，我再多寫幾句解決一下好多菜鳥的困難吧
copy完程式碼之後，儲存為xiaorun.cpp哦，記得編譯試用
個g++ -o xiaorun ./xiaorun.cpp -lopencv_highgui -lopenc_imgproc -lopencv_core -lopencv_objdetect
即可實現

#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/objdetect/objdetect.hpp>
#include <iostream>
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
 
);    //開啟預設攝像頭
    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;
    //訓練好的檔名稱，放置在可執行檔案同目錄下
   // 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;
    //建立用於存放人臉的向量容器
    vector<Rect> faces, faces2;
    //定義一些顏色，用來標示不同的人臉
    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)} ;
    //建立縮小的圖片，加快檢測速度
    //nt cvRound (double value) 對一個double型的數進行四捨五入，並返回一個整型數！
    Mat gray, smallImg( cvRound (img.rows/scale), cvRound(img.cols/scale), CV_8UC1 );
    //轉成灰度影象，Harr特徵基於灰度圖
    cvtColor( img, gray, CV_BGR2GRAY );
   // imshow("灰度",gray);
    //改變影象大小，使用雙線性差值
    resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR );
  //  imshow("縮小尺寸",smallImg);
    //變換後的影象進行直方圖均值化處理
    equalizeHist( smallImg, smallImg );
    //imshow("直方圖均值處理",smallImg);
    //程式開始和結束插入此函式獲取時間，經過計算求得演算法執行時間
    t = (double)cvGetTickCount();
    //檢測人臉
    //detectMultiScale函式中smallImg表示的是要檢測的輸入影象為smallImg，faces表示檢測到的人臉目標序列，1.1表示
    //每次影象尺寸減小的比例為1.1，2表示每一個目標至少要被檢測到3次才算是真的目標(因為周圍的畫素和不同的視窗大
    //小都可以檢測到人臉),CV_HAAR_SCALE_IMAGE表示不是縮放分類器來檢測，而是縮放影象，Size(30, 30)為目標的
    //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，翻轉影象繼續檢測
    if( tryflip )
    {
        flip(smallImg, smallImg, 1);
    //    imshow("反轉影象",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));
        }
    }
    t = (double)cvGetTickCount() - t;
    //   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;
        vector<Rect> 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 )
        {
            //標示人臉時在縮小之前的影象上標示，所以這裡根據縮放比例換算回去
            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);
        //同樣方法檢測人眼
        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<Rect>::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( "識別結果", img );
}
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