opencv双目测距(BM 与SGBM匹配）

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

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
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usbcv::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*240cv::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；
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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