opencv双目视觉定位_opencv矩形轮廓检测

(1) 2024-05-31 08:23

Hi,大家好,我是编程小6,很荣幸遇见你,我把这些年在开发过程中遇到的问题或想法写出来,今天说一说opencv双目视觉定位_opencv矩形轮廓检测,希望能够帮助你!!!。

1、引言
在一年之前小编写了一篇双目测距的博文,引入了大量的童鞋阅读,其博文介绍了详细的相机标定与双目测距过程和代码

https://blog.csdn.net/xiao__run/article/details/78900652

摄像头如前面文章所示,大家可自行购买,小编就是在这家购买

https://shop224405513.taobao.com/search.htm?spm=a1z10.1-c-s.0.0.751b3e49u0Kz6o&search=y

文章评论特别多,由此可见很多读者遇到了很多的问题,有标定不准的,测距距离不准,误差特别大,视差图很差的等各种问题。今天小编再写一篇博文,可能对您有所帮助。

双目测距流程如图

opencv双目视觉定位_opencv矩形轮廓检测_https://bianchenghao6.com/blog__第1张

2 标定
首先准备一张标定板,标定板要比较大,不能反光等特性,小博的标定板如图
opencv双目视觉定位_opencv矩形轮廓检测_https://bianchenghao6.com/blog__第2张接下来拍照,大约二十张,就可以了,我重新修改了下拍照程序

#include <iostream>
#include <opencv2/opencv.hpp>
 
using namespace std;
using namespace cv;
 
 
int main()
{ 
   

/* //双目摄像头,两个usb cv::VideoCapture capl(0); cv::VideoCapture capr(1); int i = 0; cv::Mat cam_left; cv::Mat cam_right; char filename_l[15]; char filename_r[15]; while(capl.read(cam_left) && capr.read(cam_right)) { cv::imshow("cam_left", cam_left); cv::imshow("cam_right", cam_right); char c = cv::waitKey(1); char s[40]; if(c==' ') //按空格采集图像 { sprintf(filename_l, "/home/lqx/ClionProjects/Calibration/left_img/left%d.jpg",i); imwrite(filename_l, cam_left); sprintf(filename_r, "/home/lqx/ClionProjects/Calibration/right_img/right%d.jpg",i++); imwrite(filename_r, cam_right); //printf(s, "%s%d%s\n", "the ",i++,"th image"); cout << "save the "<< i <<"th image\n"<< endl; } if(c=='q' || c=='Q') // 按q退出 { break; } } return 0; */

    //双目摄像头,一个usb
    //图像大小为640*240,左右各为320*240
    cv::VideoCapture cap(0);
    int i = 0;
    cap.set(CV_CAP_PROP_FRAME_WIDTH,640);//可以

    cap.set(CV_CAP_PROP_FRAME_HEIGHT,240);
    cv::Mat cam, cam_left,cam_right;
    char filename_l[15];
    char filename_r[15];
    while(cap.read(cam) )
    { 
   

        cam_right=cam(Rect(0,0,320,240));
        cam_left=cam(Rect(320,0,320,240));

        cv::imshow("cam_left", cam_left);
        cv::imshow("cam_right", cam_right);
        char c = cv::waitKey(1);
        char s[40];

        if(c==' ') //按空格采集图像
        { 
   
            sprintf(filename_l, "left_img/left%d.jpg",i);
            imwrite(filename_l, cam_left);
            sprintf(filename_r, "right_img/right%d.jpg",i++);
            imwrite(filename_r, cam_right);
            //printf(s, "%s%d%s\n", "the ",i++,"th image");
            cout << "save the "<< i <<"th image\n"<< endl;
        }
        if(c=='q' || c=='Q') // 按q退出
        { 
   
            break;
        }
    }

    return 0;
}

调用上述代码即可得到左右二十多张图像。

接下来我们使用matlab工具箱的标定工具进行标定,也可以使用我上面的博文进行标定。我建议使用matlab标定,结果会更准确点。
标定得到内参和外参如下:

//T 矩阵参数
    Mat T = Mat::zeros(3,1,CV_64F);
    T.at<double>(0,0)= 119.5815;
    T.at<double>(1,0)=-0.5328;
    T.at<double>(2,0)=-1.7296;

//R矩阵
   // Rodrigues(rec,R);
   // cout<<R<<endl;
    Mat R=Mat::eye(3,3,CV_64F);
    R.at<double>(0,1)= 0.000055498;
    R.at<double>(0,2)= -0.0184;
    R.at<double>(1,0)= -0.0001629;
    R.at<double>(1,2)= 0.0118;
    R.at<double>(2,0)= 0.0184;
    R.at<double>(2,1)= -0.0118;


    //*******************左相机参数**********************************
    //*******************左相机参数**********************************
    double fx=202.0386,fy=202.6377;
    double cx=156.0528,cy=116.9724;

    Size size(320,240);

    Rect left(0,0,320,240);
    Rect right(320,0,320,240);
    Mat cameraMatrixL=Mat ::eye(3,3,CV_64F);
    Mat cameraMatrixR=Mat ::eye(3,3,CV_64F);
   //左边相机内参 
    cameraMatrixL.at<double>(0,0)=fx;
    cameraMatrixL.at<double>(0,2)=cx;
    cameraMatrixL.at<double>(1,1)=fy;
    cameraMatrixL.at<double>(1,2)=cy;
   //左边相机畸变
 Mat distCoffsL=Mat::zeros(5,1,CV_64F);
    distCoffsL.at<double>(0,0)=-0.0446;
    distCoffsL.at<double>(1,0)=0.0597;
    distCoffsL.at<double>(2,0)=0;
    distCoffsL.at<double>(3,0)=0.;
    distCoffsL.at<double>(4,0)=0;

    //*******************右相机参数**********************************
    cameraMatrixR.at<double>(0,0)=204.1203;
    cameraMatrixR.at<double>(0,2)=164.9597;
    cameraMatrixR.at<double>(1,1)=204.5797;
    cameraMatrixR.at<double>(1,2)=117.9534;
    Mat distCoffsR=Mat::zeros(5,1,CV_64F);

    distCoffsR.at<double>(0,0)=-0.0352;
    distCoffsR.at<double>(1,0)=0.0345;
    distCoffsR.at<double>(2,0)=0.;
    distCoffsR.at<double>(3,0)=0;
    distCoffsR.at<double>(4,0)=0;

3、 BM与SGBM匹配算法
SGBM是一种立体匹配算法,准确度和速度适中,工程中比较常用

oid  stereo_SGBM_match(int, void*)
{ 
   
	int mindisparity = 0;
	int ndisparities = 64;  
	int SADWindowSize = 11; 
	cv::Ptr<cv::StereoSGBM> sgbm = cv::StereoSGBM::create(mindisparity, ndisparities, SADWindowSize);

	int P1 = 8 * left_camera_calibration.channels() * SADWindowSize* SADWindowSize;
	int P2 = 32 * left_camera_calibration.channels() * SADWindowSize* SADWindowSize;
	sgbm->setP1(P1);
	sgbm->setP2(P2);
	sgbm->setPreFilterCap(15);
	sgbm->setUniquenessRatio(6);
	sgbm->setSpeckleRange(2);
	sgbm->setSpeckleWindowSize(100);
	sgbm->setDisp12MaxDiff(1);
        //sgbm->setNumDisparities(1);
	sgbm->setMode(cv::StereoSGBM::MODE_HH);
        Mat disp,disp8U;
	sgbm->compute(left_camera_calibration, right_camrera_calibration, disp);
	disp.convertTo(disp, CV_32F, 1.0 / 16);                //除以16得到真实视差值
	disp8U = Mat(disp.rows, disp.cols, CV_8UC1);       //显示
	//normalize(disp, disp8U, 0, 255, NORM_MINMAX, CV_8UC1);
	disp.convertTo(disp8U,CV_8U,255/(numDisparities*16.));
	reprojectImageTo3D(disp,xyz,Q);
        xyz=xyz*16;
	imshow("disparity",disp8U);

}

BM算法之前博文已经提过

oid  stereo_match(int, void*)
{ 
   
    bm->setBlockSize(2*blockSize+5);
    bm->setROI1(validROIL);
    bm->setROI2(validROIR);
    bm->setPreFilterCap(31);
    bm->setMinDisparity(0);  
//最小视差,默认值为0, 可以是负值,int型
    bm->setNumDisparities(numDisparities * 16 + 16);
//视差窗口,即最大视差值与最小视差值之差,窗口大小必须是16的整数倍,int型
    bm->setTextureThreshold(10);
    bm->setUniquenessRatio(uniquenessRation);
    bm->setSpeckleWindowSize(100);
    bm->setSpeckleRange(32);
    bm->setDisp12MaxDiff(-1);
    Mat disp,disp8;
    bm->compute(left_camera_calibration,right_camrera_calibration,disp);
    disp.convertTo(disp8,CV_8U,255/((numDisparities*16+16)*16.));
    reprojectImageTo3D(disp,xyz,Q);
    xyz=xyz*16;
    imshow("disparity",disp8);

}

4、视差图测距
我们先看下效果图,效果蛮不错,误差1m以内大约1cm以内;
opencv双目视觉定位_opencv矩形轮廓检测_https://bianchenghao6.com/blog__第3张opencv双目视觉定位_opencv矩形轮廓检测_https://bianchenghao6.com/blog__第4张opencv双目视觉定位_opencv矩形轮廓检测_https://bianchenghao6.com/blog__第5张可以看到本文的测距方法还是挺准,视差图效果也不错。

最后给出部分工程代码吧,此代码未经我优化,仅仅作为学习使用,其实经优化,在树莓派等设备上也能实时生成视差图测距。

#include <iostream>
#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;


int framewidth=320;
int frameheight=240;
Size imageSize=Size(framewidth,frameheight);
Mat left_camera,right_camera,left_camera_calibration,right_camrera_calibration,xyz;
Point origin;
Rect selection;
bool selectObject= false;
int blockSize=0,uniquenessRation=0,numDisparities=0;
Rect validROIL,validROIR;

Mat view,rview,mapL1,map_L2,mapR1,mapR2,RL,PL,RR,PR,Q;
Ptr<StereoBM>bm=StereoBM::create(16,9);
int mindisparity = 0;
int ndisparities = 64;  
int SADWindowSize = 11; 
cv::Ptr<cv::StereoSGBM> sgbm = cv::StereoSGBM::create(mindisparity, ndisparities, SADWindowSize);
//*******************bm**********************************
//*******************bm**********************************

/*
void  stereo_match(int, void*)
{ 
   
    bm->setBlockSize(2*blockSize+5);
    bm->setROI1(validROIL);
    bm->setROI2(validROIR);
    bm->setPreFilterCap(31);
    bm->setMinDisparity(0);  
//最小视差,默认值为0, 可以是负值,int型
    bm->setNumDisparities(numDisparities * 16 + 16);
//视差窗口,即最大视差值与最小视差值之差,窗口大小必须是16的整数倍,int型
    bm->setTextureThreshold(10);
    bm->setUniquenessRatio(uniquenessRation);
    bm->setSpeckleWindowSize(100);
    bm->setSpeckleRange(32);
    bm->setDisp12MaxDiff(-1);
    Mat disp,disp8;
    bm->compute(left_camera_calibration,right_camrera_calibration,disp);
    disp.convertTo(disp8,CV_8U,255/((numDisparities*16+16)*16.));
    reprojectImageTo3D(disp,xyz,Q);
    xyz=xyz*16;
    imshow("disparity",disp8);

}

*/
//*******************bm**********************************
//*******************bm**********************************

void  stereo_SGBM_match(int, void*)
{ 
   

	int P1 = 8 * left_camera_calibration.channels() * SADWindowSize* SADWindowSize;
	int P2 = 32 * left_camera_calibration.channels() * SADWindowSize* SADWindowSize;
	sgbm->setP1(P1);
	sgbm->setP2(P2);
	sgbm->setPreFilterCap(15);
	sgbm->setUniquenessRatio(6);
	sgbm->setSpeckleRange(2);
	sgbm->setSpeckleWindowSize(100);
	sgbm->setDisp12MaxDiff(1);
        //sgbm->setNumDisparities(1);
	sgbm->setMode(cv::StereoSGBM::MODE_HH);
        Mat disp,disp8U;
	sgbm->compute(left_camera_calibration, right_camrera_calibration, disp);
	disp.convertTo(disp, CV_32F, 1.0 / 16);                //除以16得到真实视差值
	disp8U = Mat(disp.rows, disp.cols, CV_8UC1);       //显示
	//normalize(disp, disp8U, 0, 255, NORM_MINMAX, CV_8UC1);
	disp.convertTo(disp8U,CV_8U,255/(numDisparities*16.));
	reprojectImageTo3D(disp,xyz,Q);
        xyz=xyz*16;
	imshow("disparity",disp8U);

}



static void onMouse(int envent,int x,int y,int, void*)
{ 
   
    if (selectObject)
    { 
   
        selection.x=MIN(x,origin.x);
        selection.y=MIN(y,origin.y);
        selection.width=abs(x-origin.x);
        selection.height=abs(y-origin.y);

    }
    switch (envent)
    { 
   
        case EVENT_LBUTTONDOWN:
            origin=Point(x,y);
            selection=Rect(x,y,0,0);
            selectObject= true;
            cout<<origin<<"in the world coordinate is: "<<xyz.at<Vec3f>(origin)<<endl;
            break;
        case EVENT_LBUTTONUP:
            selectObject= false;
            if(selection.width>0 && selection.height>0)
            break;

    }
}



int main()
{ 
   
//T 矩阵参数
    Mat T = Mat::zeros(3,1,CV_64F);
    T.at<double>(0,0)= 119.5815;
    T.at<double>(1,0)=-0.5328;
    T.at<double>(2,0)=-1.7296;


    //Mat rec = Mat::zeros(3,1,CV_64F);
    //rec.at<double>(0,0)= 0.0191;
   // rec.at<double>(1,0)=0.03125;
    //rec.at<double>(2,0)=-0.00960;


    //Mat R;

//R矩阵
   // Rodrigues(rec,R);
   // cout<<R<<endl;
    Mat R=Mat::eye(3,3,CV_64F);
    R.at<double>(0,1)= 0.000055498;
    R.at<double>(0,2)= -0.0184;
    R.at<double>(1,0)= -0.0001629;
    R.at<double>(1,2)= 0.0118;
    R.at<double>(2,0)= 0.0184;
    R.at<double>(2,1)= -0.0118;


    //*******************左相机参数**********************************
    //*******************左相机参数**********************************
    double fx=202.0386,fy=202.6377;
    double cx=156.0528,cy=116.9724;

    Size size(320,240);

    Rect left(0,0,320,240);
    Rect right(320,0,320,240);
    Mat cameraMatrixL=Mat ::eye(3,3,CV_64F);
    Mat cameraMatrixR=Mat ::eye(3,3,CV_64F);
   //左边相机内参 
    cameraMatrixL.at<double>(0,0)=fx;
    cameraMatrixL.at<double>(0,2)=cx;
    cameraMatrixL.at<double>(1,1)=fy;
    cameraMatrixL.at<double>(1,2)=cy;
   //左边相机畸变
 Mat distCoffsL=Mat::zeros(5,1,CV_64F);
    distCoffsL.at<double>(0,0)=-0.0446;
    distCoffsL.at<double>(1,0)=0.0597;
    distCoffsL.at<double>(2,0)=0;
    distCoffsL.at<double>(3,0)=0.;
    distCoffsL.at<double>(4,0)=0;
    //*******************做相机参数**********************************
    //*******************右相机参数**********************************
    cameraMatrixR.at<double>(0,0)=204.1203;
    cameraMatrixR.at<double>(0,2)=164.9597;
    cameraMatrixR.at<double>(1,1)=204.5797;
    cameraMatrixR.at<double>(1,2)=117.9534;
    Mat distCoffsR=Mat::zeros(5,1,CV_64F);

    distCoffsR.at<double>(0,0)=-0.0352;
    distCoffsR.at<double>(1,0)=0.0345;
    distCoffsR.at<double>(2,0)=0.;
    distCoffsR.at<double>(3,0)=0;
    distCoffsR.at<double>(4,0)=0;
    //*******************有相机参数**********************************
    //*******************有相机参数**********************************


    cout<<"calibration ......."<<endl;
    //Mat new_cameraMatrix_L=getOptimalNewCameraMatrix(cameraMatrix1, distCoeffs1, image_size, 1, image_size, 0);

    stereoRectify(cameraMatrixL,distCoffsL,cameraMatrixR,distCoffsR,size,R,T,RL,RR,PL,PR,Q,CALIB_ZERO_DISPARITY,0,size,&validROIL,&validROIR);

    initUndistortRectifyMap(cameraMatrixL,distCoffsL,RL,PL ,size,CV_16SC2,mapL1,map_L2);

    initUndistortRectifyMap(cameraMatrixR,distCoffsR,RR,PR ,size,CV_16SC2,mapR1,mapR2);
    VideoCapture cap (0);
    Mat rectifyL,rectifyR;
    cap.set(CV_CAP_PROP_FRAME_WIDTH, 640);
    cap.set(CV_CAP_PROP_FRAME_HEIGHT, 320);

    while (cap.isOpened())
    { 
   
        Mat frame,grayL,grayR;
        cap>>frame;
        left_camera=frame(left);
        right_camera=frame(right);
        imshow("left_org",left_camera);
        imshow("right_org",right_camera);
        imshow("org",frame);

        cvtColor(left_camera,grayL,CV_RGB2GRAY);
        cvtColor(right_camera,grayR,CV_RGB2GRAY);

        remap(grayL,left_camera_calibration,mapL1,map_L2,INTER_LINEAR);
        remap(grayR,right_camrera_calibration,mapR1,mapR2,INTER_LINEAR);
/*
        Mat canvas;
        double sf;
        int w ,h;
        sf=240. /320;
        w=cvRound(240*sf);
        h=cvRound(320*sf);
        canvas.create(h,w*2,CV_8UC3);

        Mat canvasPart=canvas(Rect(0,0,w,h));
        resize(left_camera_calibration,canvasPart,canvasPart.size(),0,0,INTER_AREA);
        Rect vroiL(cvRound(validROIL.x*sf),cvRound(validROIL.y*sf),cvRound(validROIL.width*sf),cvRound(validROIL.height*sf));

        canvasPart=canvas(Rect(w,0,w,h));
        resize(right_camrera_calibration,canvasPart,canvasPart.size(),0,0,INTER_AREA);
        Rect vroiR(cvRound(validROIR.x*sf),cvRound(validROIR.y*sf),cvRound(validROIR.width*sf),cvRound(validROIR.height*sf));

        for(int i=0;i<canvas.rows;i+=16)
            line(canvas,Point(0,i),Point(canvas.cols,i),Scalar(0,255,0),1,8);
        imshow("rectify",canvas);
*/
        namedWindow("disparity",CV_WINDOW_AUTOSIZE);

// createTrackbar("blocksize:\n","disparity",&blockSize,16,stereo_match);

// createTrackbar("UniquenessRatio:\n","disparity",&uniquenessRation,50,stereo_match);

        createTrackbar("NumDisparities:\n","disparity",&numDisparities,16,stereo_SGBM_match);

        setMouseCallback("disparity",onMouse,0);

// stereo_match(0,0);
	
	stereo_SGBM_match(0, 0);
	// Mat output;
	//sgbm->compute(left_camera_calibration,right_camrera_calibration,output);
	//imshow("SGBM",output);
        //cvtColor(left_camera_calibration,left_camera_calibration,CV_GRAY2BGR);

       // cvtColor(right_camrera_calibration,right_camrera_calibration,CV_GRAY2BGR);

        imshow("cali_right",right_camrera_calibration);

        imshow("cali_left",left_camera_calibration);

        int key=waitKey(1);
        if (key==27)
        { 
   
            break;
        }




    }


    return 0;
}

若测得距离为负数,可将T向量的三个值变换个符号即可,OK ,先讲这么多,觉得不错的点个赞哦。

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