opencv读取视频数据输出人脸识别结果

opencv读取视频数据输出人脸识别结果

输入源可以使用视频，也可以使用图片输入，

具体在程序中有标注

#include 
#include 
#include 
#include 
#include "stdio.h"


using namespace cv;
using namespace std;

void detectAndDraw( Mat& img, CascadeClassifier& cascade,
                   CascadeClassifier& nestedCascade,
                   double scale, bool tryflip,int _iPicNum  );

//读取文件，进行人脸识别
int Read_Fream()
{
//VideoCapture cap(0);    //打开默认摄像头
//if(!cap.isOpened())
//{
//    return -1;
//}


Mat frame;
Mat edges;


CascadeClassifier cascade, nestedCascade;
bool stop = false;


//把opencv自带的文件，放置在可执行文件同目录下

cascade.load("haarcascade_frontalface_alt.xml");

nestedCascade.load("haarcascade_eye.xml");


VideoCapture capture;

//文件采用多种格式，只要是opencv能是别的即可
capture.open("2518.mp4");


double rate = capture.get(CV_CAP_PROP_FPS);


printf("fream num %lf \n",rate);

int i = 0;

//namedWindow("Extracted frame");  

//循环读取录像文件
while(capture.read(frame))
{

// imshow("Extracted frame",frame);  
 
detectAndDraw( frame, cascade, nestedCascade,4,0 ,i + 1);
//保存照片文件时的下标
i++; 
}


return 0;
}

//读取照片，进行人脸识别
int Read_Pic()
{

Mat frame;
Mat edges;


CascadeClassifier cascade, nestedCascade;
bool stop = false;


//把opencv自带的文件，放置在可执行文件同目录下

cascade.load("haarcascade_frontalface_alt.xml");

nestedCascade.load("haarcascade_eye.xml");

int i = 0;
for(i = 0;i < 4800;i+=20)
{
char cbuf[128] = {0};
printf("read picture %d \n",i + 1);
//图片按照自己命名规则读取
sprintf(cbuf,"../zhaopian/tupian/%d.jpg",i + 1);

frame = imread(cbuf);

detectAndDraw( frame, cascade, nestedCascade,4,0 ,i + 1);

}
waitKey();
}

int main()
{
//示例为读取文件进行人脸识别，还可以调用读取照片进行人脸识别
Read_Fream();

return 0;
}


void detectAndDraw( Mat& img, CascadeClassifier& cascade,
                   CascadeClassifier& nestedCascade,
                   double scale, bool tryflip,int _iPicNum )
{
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 );

// imshow("yuanshiimg",img);

//转成灰度图像，Harr特征基于灰度图
cvtColor( img, gray, CV_BGR2GRAY );

//灰度
// imshow("huidu",gray);

//改变图像大小，使用双线性差值
resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR );

// 缩小尺寸
// imshow("suoxiaochicun",smallImg);

//变换后的图像进行直方图均值化处理
equalizeHist( smallImg, smallImg );


// equalizeHist( gray, smallImg );


//直方图均值处理
// imshow("zhifangtuyunzhichuli",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(10, 10));

   //如果使能，翻转图像继续检测
   if( tryflip )
   {
       flip(smallImg, smallImg, 1);

//反转图像
//        imshow("fanzhuangtuxiagn",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;

printf("`````````````nead times  %lf \n",t);



    //   qDebug( "detection time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) );


int iEnable = 0;

    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;


printf("width  %d height %d \n", r->width,r->height);

        if( 0.75 < aspect_ratio && aspect_ratio < 1.3 )
        {
        
iEnable = 1;


            //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);

printf("renlian weizhi x %d y %d radius %d  \n",  center.x,  center.y,radius);
Rect rect1(center.x -radius,  center.y - radius, radius *2 , radius*2);

Mat roi1;

img(rect1).copyTo(roi1); // copy the region rect1 from the image to roi1

char cbuf[12] = {0};

sprintf(cbuf,"test %d ",i);

char cbuf1[12] = {0};

sprintf(cbuf1,"jieguo/%d_%d.jpg",_iPicNum,i);

imwrite(cbuf1,roi1);


//imshow(cbuf, roi1);

circle( img, center, radius, color, 3, 8, 0 );


//img为源图像指针
//center为画圆的圆心坐标
//radius为圆的半径
//color为设定圆的颜色，规则根据B（蓝）G（绿）R（红）
//thickness 如果是正数，表示组成圆的线条的粗细程度。否则，表示圆是否被填充
//line_type 线条的类型。默认是8
//shift 圆心坐标点和半径值的小数点位数







        }
        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(iEnable)
{
char cbuf[18] = {0};

sprintf(cbuf,"jieguo/%d.jpg",_iPicNum);

imwrite(cbuf,img);
}

// imshow( "shibiejieguo1111", img );

        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 );
        }
#endif

    }

//识别结果，需要支持显示才能显示
   // imshow( "shibiejieguo", img );

}