基于QT和opencv的瞳孔定位及跟踪程序

本篇博客是基于上一篇博客《基于opencv和QT的瞳孔精确检测程序》的进一步开发和完善，实现了瞳孔坐标和位置的输出功能，本文将给出完整的瞳孔定位及跟踪程序，本文是作者的独立开发成果，转载请注明出处http://blog.csdn.net/zyx1990412/article/details/51254127。实现原理：通过上一篇博客的介绍，我们可以实现对每一帧图像进行瞳孔检测，并得到瞳孔的精确坐标，即实现了瞳孔定位的功能。为了实现瞳孔跟踪的功能，我们可以通过手动按钮记录下瞳孔当前帧的坐标，作为跟踪的参考标准，然后将参考坐标与后续定位的坐标作比较，得到后续帧的相对坐标。本文的瞳孔跟踪功能是简单地计算当前帧相对于参考帧的相对位置，以确定瞳孔的当前相对位置。其原理图可以用图一表示。图一2.首先利用QT搭建一个界面，如图二。图二2.实现摄像头视频的读取和显示功能，详细的方法在《基于QT和opencv的摄像头（本地图片）读取并输出程序》这篇博客中有介绍。3.建立一个图像处理类，对每一帧图像进行处理。主要功能是检测瞳孔中心，显示瞳孔中心定位结果图，并将瞳孔中心的绝对坐标保存在Lcircles和Rcircles中，方便外部函数读取。这个类的实现在后文给出。

class ImgProcess
{
private:
Mat inimg;//输入图像
Mat outimg;//输出结果
Mat Leye;
Mat Reye;
Mat Leye_G;
Mat Reye_G;
CvRect drawing_box;
public:
vector<Vec3f> Lcircles;
vector<Vec3f> Rcircles;
ImgProcess(Mat image):inimg(image),drawing_box(cvRect(0, 0, 50, 20)){}
void EyeDetect();//人眼检测
Mat Outputimg();//输出结果
void DivideEye();//分左右眼
Mat OutLeye();//输出结果
Mat OutReye();
Mat EdgeDetect(Mat &edgeimg);//边缘检测
void EyeEdge();//分别检测左右眼
vector<Vec3f> Hough(Mat &midImage);//hough变换
void FindCenter();//定位中心
Mat PlotC(vector<Vec3f> circles,Mat &midImage);//画HOUGH变换的检测结果
};

4．在mainwindow中编写槽函数，触发定位和跟踪事件。

namespace Ui {
class MainWindow;
}

class MainWindow : public QMainWindow
{
Q_OBJECT

public:
explicit MainWindow(QWidget *parent = 0);
~MainWindow();

private slots:
void on_pushButton_clicked();//打开摄像头，进行瞳孔精确检测及定位
void on_pushButton_2_clicked();//打开本地图片
void on_pushButton_3_clicked();//瞳孔定位，建立标准坐标
void on_pushButton_4_clicked();//是够启动瞳孔跟踪
void on_pushButton_5_clicked();//关闭摄像头

private:
Ui::MainWindow *ui;
CvRect lpc_box=cvRect( 0,0,0,0);//保存当前帧的瞳孔坐标
CvRect rpc_box=cvRect( 0,0,0,0);
CvRect lp_bz=cvRect( 0,0,0,0);//保存标准瞳孔坐标
CvRect rp_bz=cvRect( 0,0,0,0);
Mat pic1;//坐标图
Mat pic2;
bool Lstop=false;
bool Tack=false;
void eyeTack();//瞳孔跟踪函数
//cv::Mat image;
};

(1)打开摄像头后，图像处理类对每一帧图像进行处理，并将图像处理类得到的瞳孔绝对坐标保存到lpc_box和rpc_box中，详细介绍参考《基于opencv和QT的瞳孔精确检测程序》。(2)触发瞳孔定位函数后，将图像处理类中得到的瞳孔绝对坐标保存到参考坐标lp_bz和rp_bz中。并将结果在lcdNumber上输出(3)触发瞳孔跟踪函数后，通过当前帧绝对坐标和参考坐标的计算，得到相对坐标。并将结果在lcdNumber上输出。(4)关闭摄像头，循环终止。图三最终的效果如图三，由于参数的设置和函数的精确等问题，其定位的效果还不能达到最佳效果。佩戴眼镜时的检测效果不好。图中的坐标图是直接读取已经画好的坐标图，如图四，图中的蓝点是根据相对坐标在坐标图上用opencv的画圆函数画出的。图四PS:本文所编写的瞳孔跟踪函数实际上核心是瞳孔的实时检测和定位，并没有用到传统意义上的目标跟踪函数。作者在毕业设计中的研究重点是基于MeanShift算法的目标跟踪算法，针对瞳孔跟踪，进行了加入帧差法和LBP的改进，作者将在以后的博客中进行详细介绍，并利用vs2013+opencv进行编程验证。源代码mainwindow.h

#ifndef MAINWINDOW_H
#define MAINWINDOW_H
#include <QMainWindow>
#include "ui_mainwindow.h"
#include "detectanddisplay.h"
#include "imgprocess.h"
#include <QLabel>
#include"Mat2QImage.h"

namespace Ui {
class MainWindow;
}

class MainWindow : public QMainWindow
{
Q_OBJECT

public:
explicit MainWindow(QWidget *parent = 0);
~MainWindow();

private slots:
void on_pushButton_clicked();//打开摄像头，进行瞳孔精确检测及定位
void on_pushButton_2_clicked();//打开本地图片
void on_pushButton_3_clicked();//瞳孔定位，建立标准坐标
void on_pushButton_4_clicked();//是够启动瞳孔跟踪
void on_pushButton_5_clicked();//关闭摄像头

private:
Ui::MainWindow *ui;
CvRect lpc_box=cvRect( 0,0,0,0);//保存当前帧的瞳孔坐标
CvRect rpc_box=cvRect( 0,0,0,0);
CvRect lp_bz=cvRect( 0,0,0,0);//保存标准瞳孔坐标
CvRect rp_bz=cvRect( 0,0,0,0);
Mat pic1;//坐标图
Ma
4000
t pic2;
bool Lstop=false;
bool Tack=false;
void eyeTack();//瞳孔跟踪函数
//cv::Mat image;
};

#endif // MAINWINDOW_H

mainwindow.cpp

#include "mainwindow.h"

MainWindow::MainWindow(QWidget *parent) :
QMainWindow(parent),
ui(new Ui::MainWindow)
{
ui->setupUi(this);
}

MainWindow::~MainWindow()
{
delete ui;
}

void MainWindow::on_pushButton_clicked()//打开摄像头，进行瞳孔精确检测及定位
{
VideoCapture cap(0);    //打开默认摄像头
if(!cap.isOpened())
{
cout<<"no cap"<<endl;
}
Mat frame; //保存当前帧的图像
Lstop=false;//控制是否循环检测

while(!Lstop)
{
cap>>frame;//将摄像头的当前帧输出到frame

ImgProcess pro(frame);//建立视频处理类
pro.EyeDetect();//人眼检测
Mat image=pro.Outputimg();//输出检测图像
imshow( "camera", image );
QImage img=Mat2QImage(image);//将mat格式转换为Qimage格式
ui->label->setPixmap(QPixmap::fromImage(img));//将结果在label上显示
//ui->label->setScaledContents(true);//使图像尺寸与label大小匹配
pro.DivideEye();//分成左右眼
pro.EyeEdge();//瞳孔边缘检测
pro.FindCenter();//hough变换求圆心
Mat mleye=pro.OutLeye();//输出瞳孔定位结果
QImage qleye=Mat2QImage(mleye);
ui->label_2->setPixmap(QPixmap::fromImage(qleye));
//ui->label_2->setScaledContents(true);
Mat mreye=pro.OutReye();
QImage qreye=Mat2QImage(mreye);
ui->label_3->setPixmap(QPixmap::fromImage(qreye));
//ui->label_3->setScaledContents(true);
if (pro.Lcircles.size()>0)//将检测结果输出
{

lpc_box.x=pro.Lcircles[0][0];
lpc_box.y=pro.Lcircles[0][1];
lpc_box.height=pro.Lcircles[0][2];
lpc_box.width=pro.Lcircles[0][2];
}
ui->lcdNumber_6->display(lpc_box.x);//显示坐标
ui->lcdNumber_5->display(lpc_box.y);
ui->lcdNumber_9->display(lpc_box.height);

if (pro.Rcircles.size()>0)
{

rpc_box.x=pro.Rcircles[0][0];
rpc_box.y=pro.Rcircles[0][1];
rpc_box.height=pro.Rcircles[0][2];
rpc_box.width=pro.Rcircles[0][2];
}
ui->lcdNumber_8->display(rpc_box.x);
ui->lcdNumber_7->display(rpc_box.y);
ui->lcdNumber_10->display(rpc_box.height);

if( Tack==true)//是够启动跟踪功能
{
eyeTack();
}

int c = waitKey(30);//每一帧间隔30ms
if (c >= 0)//任意键暂停
{
waitKey(0);
}

}
cvDestroyWindow("camera");
}

void MainWindow::on_pushButton_2_clicked()//打开本地图片
{
Mat image0;
image0=cv::imread("IF.jpg");//图片的路径
ImgProcess pro(image0);
pro.EyeDetect();
Mat image=pro.Outputimg();
QImage img=Mat2QImage(image);
ui->label->setPixmap(QPixmap::fromImage(img));
// ui->label->setScaledContents(true);

pro.DivideEye();
pro.EyeEdge();
pro.FindCenter();
Mat mleye=pro.OutLeye();
QImage qleye=Mat2QImage(mleye);
ui->label_2->setPixmap(QPixmap::fromImage(qleye));
//ui->label_2->setScaledContents(true);
Mat mreye=pro.OutReye();
QImage qreye=Mat2QImage(mreye);
ui->label_3->setPixmap(QPixmap::fromImage(qreye));
//ui->label_3->setScaledContents(true);
waitKey(1000);

}

void MainWindow::on_pushButton_3_clicked()//瞳孔定位，建立标准坐标
{
lp_bz=lpc_box;
rp_bz=rpc_box;
ui->lcdNumber->display(lpc_box.x);
ui->lcdNumber_2->display(lpc_box.y);
ui->lcdNumber_4->display(rpc_box.x);
ui->lcdNumber_3->display(rpc_box.y);
pic1=cv::imread("zb.jpg");//读坐标图
cv::resize(pic1,pic1,Size(100,100));
QImage zbimg1=Mat2QImage(pic1);
ui->label_4->setPixmap(QPixmap::fromImage(zbimg1));
ui->label_4->setScaledContents(true);
pic2=cv::imread("zb1.jpg");
cv::resize(pic2,pic2,cvSize(100,100));
QImage zbimg2=Mat2QImage(pic2);
ui->label_5->setPixmap(QPixmap::fromImage(zbimg2));
ui->label_5->setScaledContents(true);
}

void MainWindow::on_pushButton_4_clicked()//是够启动瞳孔跟踪
{
Tack=true;
}

void MainWindow::eyeTack()//瞳孔跟踪函数
{
float ldx,ldy,rdx,rdy,plx,ply,prx,pry;//左右眼瞳孔坐标，d相对坐标差
ldx=(lpc_box.x-lp_bz.x)/lp_bz.width;
ldy=(lpc_box.y-lp_bz.y)/lp_bz.width;
rdx=(rpc_box.x-rp_bz.x)/rp_bz.width;
rdy=(rpc_box.y-rp_bz.y)/rp_bz.width;

plx=ldx*10+50;
ply=ldy*10+50;
Point Lcenter(plx, ply);//瞳孔中心在坐标图中的位置
pic1=cv::imread("zb.jpg");
cv::resize(pic1,pic1,Size(100,100));
circle( pic1, Lcenter, 3, Scalar(255,0,0), -1,8);//画瞳孔中心
QImage zbimg1=Mat2QImage(pic1);
ui->label_4->setPixmap(QPixmap::fromImage(zbimg1));
ui->label_4->setScaledContents(true);

prx=rdx*10+50;//右眼
pry=rdy*10+50;
Point Rcenter(prx, pry);
pic2=cv::imread("zb1.jpg");
cv::resize(pic2,pic2,Size(100,100));
circle( pic2, Rcenter, 3, Scalar(255,0,0), -1,8);
QImage zbimg2=Mat2QImage(pic2);
ui->label_5->setPixmap(QPixmap::fromImage(zbimg2));
ui->label_5->setScaledContents(true);

ui->lcdNumber->display(ldx);
ui->lcdNumber_2->display(ldy);
ui->lcdNumber_4->display(rdx);
ui->lcdNumber_3->display(rdy);
}

void MainWindow::on_pushButton_5_clicked()//关闭摄像头
{
Lstop=true;
}

imgprocess.h

#ifndef IMGPROCESS_H
#define IMGPROCESS_H
#include <opencv2/opencv.hpp>
#include <iostream>
#include <stdio.h>
#include "detectanddisplay.h"

//视频处理类
class ImgProcess
{
private:
Mat inimg;//输入图像
Mat outimg;//输出结果
Mat Leye;
Mat Reye;
Mat Leye_G;
Mat Reye_G;
CvRect drawing_box;

public:
vector<Vec3f> Lcircles;
vector<Vec3f> Rcircles;
ImgProcess(Mat image):inimg(image),drawing_box(cvRect(0, 0, 50, 20)){}
void EyeDetect();//人眼检测
Mat Outputimg();//输出结果
void DivideEye();//分左右眼
Mat OutLeye();//输出结果
Mat OutReye();
Mat EdgeDetect(Mat &edgeimg);//边缘检测
void EyeEdge();//分别检测左右眼
vector<Vec3f> Hough(Mat &midImage);//hough变换
void FindCenter();//定位中心
Mat PlotC(vector<Vec3f> circles,Mat &midImage);//画HOUGH变换的检测结果
};

#endif // IMGPROCESS_H

imgprocess.cpp

#include "imgprocess.h"void ImgProcess::EyeDetect(){detectAndDisplay( inimg,drawing_box );outimg=inimg;}Mat ImgProcess::Outputimg(){return outimg;}void ImgProcess::DivideEye(){if (drawing_box.width>0){CvRect leye_box;leye_box.x=drawing_box.x+1;leye_box.y=drawing_box.y+1;leye_box.height=drawing_box.height-1;leye_box.width=floor(drawing_box.width/2)-1;CvRect reye_box;reye_box.x=leye_box.x+leye_box.width;reye_box.y=drawing_box.y+1;reye_box.height=drawing_box.height-1;reye_box.width=leye_box.width-1;Leye=inimg(leye_box);Reye=inimg(reye_box);//    imshow("L",Leye);//    imshow("R",Reye);}}Mat ImgProcess::OutLeye(){return Leye;}Mat ImgProcess::OutReye(){return Reye;}Mat ImgProcess::EdgeDetect(Mat &edgeimg){Mat edgeout;cvtColor(edgeimg,edgeimg,CV_BGR2GRAY);GaussianBlur( edgeimg,edgeimg, Size(9, 9), 2, 2 );equalizeHist( edgeimg, edgeimg );Canny(edgeimg,edgeout,100,200,3);//输入图像,输出图像,低阈值,高阈值，opencv建议是低阈值的3倍,内部sobel滤波器大小return edgeout;}void ImgProcess::EyeEdge(){Leye_G=EdgeDetect(Leye);Reye_G=EdgeDetect(Reye);//imshow("L",Leye_G);//imshow("R",Reye_G);}vector<Vec3f> ImgProcess::Hough(Mat &midImage){vector<Vec3f> circles;HoughCircles( midImage, circles, CV_HOUGH_GRADIENT,1.5, 5, 100, 20, drawing_box.height/4, drawing_box.height/3 );return circles;}Mat ImgProcess::PlotC(vector<Vec3f> circles,Mat &midImage){for( size_t i = 0; i < circles.size(); i++ ){Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));int radius = cvRound(circles[i][2]);//cout<<i<<":"<<circles[i][0]<<","<<circles[i][1]<<","<<circles[i][2]<<endl;//绘制圆心cvRound进行四舍五入circle( midImage, center, 1, Scalar(255,0,0), -1,8);//绘制圆轮廓circle( midImage, center, radius, Scalar(255,0,0), 1,8 );}return midImage;}void ImgProcess::FindCenter(){Lcircles=Hough(Leye_G);Rcircles=Hough(Reye_G);Leye=PlotC(Lcircles,Leye);Reye=PlotC(Rcircles,Reye);}

detectanddisplay.h

#ifndef DETECTANDDISPLAY_H#define DETECTANDDISPLAY_H#include <opencv2/opencv.hpp>#include <iostream>#include <stdio.h>using namespace std;using namespace cv;//检测人眼区域void  detectAndDisplay( Mat &frame,CvRect &box );#endif // DETECTANDDISPLAY_H

detectanddisplay.cpp

#include "detectanddisplay.h"void  detectAndDisplay( Mat &frame,CvRect &box ){string face_cascade_name = "haarcascade_mcs_eyepair_big.xml";//导入已经训练完成的样本CascadeClassifier face_cascade;//建立分类器string window_name = "camera";if( !face_cascade.load( face_cascade_name ) ){printf("[error] no cascade\n");}std::vector<Rect> faces;//用于保存检测结果的向量Mat frame_gray;cvtColor( frame, frame_gray, CV_BGR2GRAY );//转换成灰度图equalizeHist( frame_gray, frame_gray );//直方图均值化face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) );//用于检测人眼的函数//画方框for( int i = 0; i < faces.size(); i++ ){Point centera( faces[i].x, faces[i].y);Point centerb( faces[i].x + faces[i].width, faces[i].y + faces[i].height );rectangle(frame,centera,centerb,Scalar(255,0,0));box=faces[0];}//imshow( window_name, frame );}

Mat2QImage.h

#ifndef MAT2QIMAGE_H#define MAT2QIMAGE_H#include <QtGui>#include <QDebug>#include <iostream>#include <opencv/cv.h>#include <opencv/highgui.h>using namespace cv;using namespace std;QImage Mat2QImage(const Mat&);//mat格式转换为Qimage格式#endif

Mat2QImage.cpp

#include "Mat2QImage.h"QImage Mat2QImage(const Mat& mat){// 8-bits unsigned, NO. OF CHANNELS=1if(mat.type()==CV_8UC1){// cout<<"1"<<endl;// Set the color table (used to translate colour indexes to qRgb values)QVector<QRgb> colorTable;for (int i=0; i<256; i++)colorTable.push_back(qRgb(i,i,i));// Copy input Matconst uchar *qImageBuffer = (const uchar*)mat.data;// Create QImage with same dimensions as input MatQImage img(qImageBuffer, mat.cols, mat.rows, mat.step, QImage::Format_Indexed8);img.setColorTable(colorTable);return img;}// 8-bits unsigned, NO. OF CHANNELS=3if(mat.type()==CV_8UC3){// cout<<"3"<<endl;// Copy input Matconst uchar *qImageBuffer = (const uchar*)mat.data;// Create QImage with same dimensions as input MatQImage img(qImageBuffer, mat.cols, mat.rows, mat.step, QImage::Format_RGB888);return img.rgbSwapped();}else{qDebug() << "ERROR: Mat could not be converted to QImage.";return QImage();}}

main.cpp

#include "mainwindow.h"#include <QApplication>int main(int argc, char *argv[]){QApplication a(argc, argv);MainWindow w;w.show();return a.exec();}

test0.pro

#-------------------------------------------------## Project created by QtCreator 2016-03-16T10:24:54##-------------------------------------------------QT       += core guigreaterThan(QT_MAJOR_VERSION, 4): QT += widgetsTARGET = test0TEMPLATE = appSOURCES += main.cpp\mainwindow.cpp \imgprocess.cpp \detectanddisplay.cpp \others.cpp \Mat2QImage.cppHEADERS  += mainwindow.h \detectanddisplay.h \imgprocess.h \others.h \Mat2QImage.hFORMS    += mainwindow.uiINCLUDEPATH += d:\opencv\build\include\INCLUDEPATH += d:\opencv\build\include\opencv\INCLUDEPATH += d:\opencv\build\include\opencv2\CONFIG(debug,debug|release) {LIBS += -Ld:\opencv\build\x64\vc12\lib \-lopencv_core2410d \-lopencv_highgui2410d \-lopencv_imgproc2410d \-lopencv_features2d2410d \-lopencv_calib3d2410d \-lopencv_video2410d \-lopencv_objdetect2410d} else {LIBS += -Ld:\opencv\build\x64\vc12\lib \-lopencv_core2410 \-lopencv_highgui2410 \-lopencv_imgproc2410 \-lopencv_features2d2410 \-lopencv_calib3d2410 \-lopencv_video2410 \-lopencv_objdetect2410}