C++如何利用opencv实现人脸检测详情

小编所有的帖子都是基于unbuntu系统的，当然稍作修改同样试用于windows的，经过小编的绞尽脑汁，把刚刚发的那篇python 实现人脸和眼睛的检测的程序用C++ 实现了，当然，也参考了不少大神的博客，下面我们就一起来看看：

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 );

}