前言
首先进行双目摄像头定标，获取双目摄像头内部的参数后，进行测距；本文的双目视觉测距是基于BM算法。注意：双目定标的效果会影响测距的精准度，建议大家在做双目定标时，做好一些（尽量让误差小）。
一、双目测距--输入图片
效果1：
效果2：
本人通过测试，误差是1cm.
其中参数：BlockSize、UniquenessRatio、NumDisparities 根据实际情况来调整；
选择C++运行效率高，BM算法可以自定义修改，比较灵活；尝试过Python版的BM算法双目测距，效果没C++好。
源代码：
/* 双目测距 */
#include <opencv2/opencv.hpp>
#include <iostream>
#include <math.h>
using namespace std;
using namespace cv;
const int imageWidth = 640; //摄像头的分辨率
const int imageHeight = 360;
Vec3f point3;
float d;
Size imageSize = Size(imageWidth, imageHeight);
Mat rgbImageL, grayImageL;
Mat rgbImageR, grayImageR;
Mat rectifyImageL, rectifyImageR;
Rect validROIL;//图像校正之后，会对图像进行裁剪，这里的validROI就是指裁剪之后的区域
Rect validROIR;
Mat mapLx, mapLy, mapRx, mapRy; //映射表
Mat Rl, Rr, Pl, Pr, Q; //校正旋转矩阵R，投影矩阵P 重投影矩阵Q
Mat xyz; //三维坐标
Point origin; //鼠标按下的起始点
Rect selection; //定义矩形选框
bool selectObject = false; //是否选择对象
int blockSize = 0, uniquenessRatio = 0, numDisparities = 0;
Ptr<StereoBM> bm = StereoBM::create(16, 9);
/*事先标定好的左相机的内参矩阵
fx 0 cx
0 fy cy
0 0 1
*/
Mat cameraMatrixL = (Mat_<double>(3, 3) << 418.523322187048, -1.26842201390676, 343.908870120890,
0, 421.222568242056, 235.466208987968,
0, 0, 1);
//获得的畸变参数
/*418.523322187048 0 0
-1.26842201390676 421.222568242056 0
344.758267538961 243.318992284899 1 */ //2
Mat distCoeffL = (Mat_<double>(5, 1) << 0.006636837611004, 0.050240447649195, 0.006681263320267, 0.003130367429418, 0);
//[0.006636837611004,0.050240447649195] [0.006681263320267,0.003130367429418]
/*事先标定好的右相机的内参矩阵
fx 0 cx
0 fy cy
0 0 1
*/
Mat cameraMatrixR = (Mat_<double>(3, 3) << 417.417985082506, 0.498638151824367, 309.903372309072,
0, 419.795432389420, 230.6,
0, 0, 1);
/*
417.417985082506 0 0
0.498638151824367 419.795432389420 0
309.903372309072 236.256106972796 1
*/ //2
Mat distCoeffR = (Mat_<double>(5, 1) << -0.038407383078874, 0.236392800301615, 0.004121779274885, 0.002296129959664, 0);
//[-0.038407383078874,0.236392800301615] [0.004121779274885,0.002296129959664]
Mat T = (Mat_<double>(3, 1) << -1.210187345641146e+02, 0.519235426836325, -0.425535566316217);//T平移向量
//[-1.210187345641146e+02,0.519235426836325,-0.425535566316217]
//对应Matlab所得T参数
//Mat rec = (Mat_<double>(3, 1) << -0.00306, -0.03207, 0.00206);//rec旋转向量，对应matlab om参数 我
Mat rec = (Mat_<double>(3, 3) << 0.999341122700880, -0.00206388651740061, 0.0362361815232777,
0.000660748031451783, 0.999250989651683, 0.0386913826603732,
-0.0362888948713456, -0.0386419468010579, 0.998593969567432); //rec旋转向量，对应matlab om参数 我
/* 0.999341122700880 0.000660748031451783 -0.0362888948713456
-0.00206388651740061 0.999250989651683 -0.0386419468010579
0.0362361815232777 0.0386913826603732 0.998593969567432 */
//Mat T = (Mat_<double>(3, 1) << -48.4, 0.241, -0.0344);//T平移向量
//[-1.210187345641146e+02,0.519235426836325,-0.425535566316217]
//对应Matlab所得T参数
//Mat rec = (Mat_<double>(3, 1) << -0.039, -0.04658, 0.00106);//rec旋转向量，对应matlab om参数 倬华
Mat R;//R 旋转矩阵
/*****立体匹配*****/
void stereo_match(int, void*)
{
bm->setBlockSize(2 * blockSize + 5); //SAD窗口大小，5~21之间为宜
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(uniquenessRatio);//uniquenessRatio主要可以防止误匹配
bm->setSpeckleWindowSize(100);
bm->setSpeckleRange(32);
bm->setDisp12MaxDiff(-1);
Mat disp, disp8;
bm->compute(rectifyImageL, rectifyImageR, disp);//输入图像必须为灰度图
disp.convertTo(disp8, CV_8U, 255 / ((numDisparities * 16 + 16)*16.));//计算出的视差是CV_16S格式
reprojectImageTo3D(disp, xyz, Q, true); //在实际求距离时，ReprojectTo3D出来的X / W, Y / W, Z / W都要乘以16(也就是W除以16)，才能得到正确的三维坐标信息。
xyz = xyz * 16;
imshow("disparity", disp8);
}
/*****描述：鼠标操作回调*****/
static void onMouse(int event, int x, int y, int, void*)
{
if (selectObject)
{
selection.x = MIN(x, origin.x);
selection.y = MIN(y, origin.y);
selection.width = std::abs(x - origin.x);
selection.height = std::abs(y - origin.y);
}
switch (event)
{
case EVENT_LBUTTONDOWN: //鼠标左按钮按下的事件
origin = Point(x, y);
selection = Rect(x, y, 0, 0);
selectObject = true;
//cout << origin << "in world coordinate is: " << xyz.at<Vec3f>(origin) << endl;
point3 = xyz.at<Vec3f>(origin);
point3[0];
//cout << "point3[0]:" << point3[0] << "point3[1]:" << point3[1] << "point3[2]:" << point3[2]<<endl;
cout << "世界坐标：" << endl;
cout << "x: " << point3[0] << " y: " << point3[1] << " z: " << point3[2] << endl;
d = point3[0] * point3[0]+ point3[1] * point3[1]+ point3[2] * point3[2];
d = sqrt(d); //mm
// cout << "距离是:" << d << "mm" << endl;
d = d / 10.0; //cm
cout << "距离是:" << d << "cm" << endl;
// d = d/1000.0; //m
// cout << "距离是:" << d << "m" << endl;
break;
case EVENT_LBUTTONUP: //鼠标左按钮释放的事件
selectObject = false;
if (selection.width > 0 && selection.height > 0)
break;
}
}
/*****主函数*****/
int main()
{
/*
立体校正
*/
Rodrigues(rec, R); //Rodrigues变换
stereoRectify(cameraMatrixL, distCoeffL, cameraMatrixR, distCoeffR, imageSize, R, T, Rl, Rr, Pl, Pr, Q, CALIB_ZERO_DISPARITY,
0, imageSize, &validROIL, &validROIR);
initUndistortRectifyMap(cameraMatrixL, distCoeffL, Rl, Pr, imageSize, CV_32FC1, mapLx, mapLy);
initUndistortRectifyMap(cameraMatrixR, distCoeffR, Rr, Pr, imageSize, CV_32FC1, mapRx, mapRy);
/*
读取图片
*/
rgbImageL = imread("image_left_1.jpg", CV_LOAD_IMAGE_COLOR);
cvtColor(rgbImageL, grayImageL, CV_BGR2GRAY);
rgbImageR = imread("image_right_1.jpg", CV_LOAD_IMAGE_COLOR);
cvtColor(rgbImageR, grayImageR, CV_BGR2GRAY);
imshow("ImageL Before Rectify", grayImageL);
imshow("ImageR Before Rectify", grayImageR);
/*
经过remap之后，左右相机的图像已经共面并且行对准了
*/
remap(grayImageL, rectifyImageL, mapLx, mapLy, INTER_LINEAR);
remap(grayImageR, rectifyImageR, mapRx, mapRy, INTER_LINEAR);
/*
把校正结果显示出来
*/
Mat rgbRectifyImageL, rgbRectifyImageR;
cvtColor(rectifyImageL, rgbRectifyImageL, CV_GRAY2BGR); //伪彩色图
cvtColor(rectifyImageR, rgbRectifyImageR, CV_GRAY2BGR);
//单独显示
//rectangle(rgbRectifyImageL, validROIL, Scalar(0, 0, 255), 3, 8);
//rectangle(rgbRectifyImageR, validROIR, Scalar(0, 0, 255), 3, 8);
imshow("ImageL After Rectify", rgbRectifyImageL);
imshow("ImageR After Rectify", rgbRectifyImageR);
//显示在同一张图上
Mat canvas;
double sf;
int w, h;
sf = 600. / MAX(imageSize.width, imageSize.height);
w = cvRound(imageSize.width * sf);
h = cvRound(imageSize.height * sf);
canvas.create(h, w * 2, CV_8UC3); //注意通道
//左图像画到画布上
Mat canvasPart = canvas(Rect(w * 0, 0, w, h)); //得到画布的一部分
resize(rgbRectifyImageL, canvasPart, canvasPart.size(), 0, 0, INTER_AREA); //把图像缩放到跟canvasPart一样大小
Rect vroiL(cvRound(validROIL.x*sf), cvRound(validROIL.y*sf), //获得被截取的区域
cvRound(validROIL.width*sf), cvRound(validROIL.height*sf));
//rectangle(canvasPart, vroiL, Scalar(0, 0, 255), 3, 8); //画上一个矩形
cout << "Painted ImageL" << endl;
//右图像画到画布上
canvasPart = canvas(Rect(w, 0, w, h)); //获得画布的另一部分
resize(rgbRectifyImageR, canvasPart, canvasPart.size(), 0, 0, INTER_LINEAR);
Rect vroiR(cvRound(validROIR.x * sf), cvRound(validROIR.y*sf),
cvRound(validROIR.width * sf), cvRound(validROIR.height * sf));
//rectangle(canvasPart, vroiR, Scalar(0, 0, 255), 3, 8);
cout << "Painted ImageR" << endl;
//画上对应的线条
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("rectified", canvas);
/*
立体匹配
*/
namedWindow("disparity", CV_WINDOW_AUTOSIZE);
// 创建SAD窗口 Trackbar
createTrackbar("BlockSize:\n", "disparity", &blockSize, 8, stereo_match);
// 创建视差唯一性百分比窗口 Trackbar
createTrackbar("UniquenessRatio:\n", "disparity", &uniquenessRatio, 50, stereo_match);
// 创建视差窗口 Trackbar
createTrackbar("NumDisparities:\n", "disparity", &numDisparities, 16, stereo_match);
//鼠标响应函数setMouseCallback(窗口名称, 鼠标回调函数, 传给回调函数的参数，一般取0)
setMouseCallback("disparity", onMouse, 0);
stereo_match(0, 0);
waitKey(0);
return 0;
}
流程说明：
先采集左右摄像头的图片，然后，修改一下指定的图片，可以进行测距。
里面有双目摄像头的参数，具体需要自己定标和矫正后，然后，填入。
双目定标可以参考我这篇博客：https://guo-pu.blog.csdn.net/article/details/86602452
双目数据转化可以参考我这篇博客：https://guo-pu.blog.csdn.net/article/details/86710737
详细讲解摄像头参数：
1）Mat cameraMatrixL 左相机的内参矩阵
2）Mat distCoeffL = (Mat_(5, 1) ....... 左相机 畸变参数 即K1，K2，P1，P2，K3。
3） Mat cameraMatrixR 右相机的内参矩阵
4）Mat distCoeffR = (Mat_(5, 1) ....... 右相机畸变参数 即K1，K2，P1，P2，K3。
5) Mat T = (Mat_(3, 1) << -1.210187345641146e+02, 0.519235426836325, -0.425535566316217);// 相机的 平移向量
6) Mat rec = (Mat_(3, 3) << 0.99934112270088................... 相机的旋转向量
一共6个相机参数，1、2是 左相机的参数； 3、4是 右相机的参数； 5、6是相机（相对）整体的参数。
二、实时采集摄像头数据，进行双目测距
效果如下图：
源代码：
/******************************/
/* 立体匹配和测距 */
/******************************/
#include <opencv2/opencv.hpp>
#include <iostream>
#include <math.h>
using namespace std;
using namespace cv;
const int imageWidth = 640; //摄像头的分辨率
const int imageHeight = 360;
Vec3f point3;
float d;
Size imageSize = Size(imageWidth, imageHeight);
Mat rgbImageL, grayImageL;
Mat rgbImageR, grayImageR;
Mat rectifyImageL, rectifyImageR;
Rect validROIL;//图像校正之后，会对图像进行裁剪，这里的validROI就是指裁剪之后的区域
Rect validROIR;
Mat mapLx, mapLy, mapRx, mapRy; //映射表
Mat Rl, Rr, Pl, Pr, Q; //校正旋转矩阵R，投影矩阵P 重投影矩阵Q
Mat xyz; //三维坐标
Point origin; //鼠标按下的起始点
Rect selection; //定义矩形选框
bool selectObject = false; //是否选择对象
int blockSize = 0, uniquenessRatio = 0, numDisparities = 0;
Ptr<StereoBM> bm = StereoBM::create(16, 9);
/*事先标定好的左相机的内参矩阵
fx 0 cx
0 fy cy
0 0 1
*/
Mat cameraMatrixL = (Mat_<double>(3, 3) << 418.523322187048, -1.26842201390676, 343.908870120890,
0, 421.222568242056, 235.466208987968,
0, 0, 1);
//获得的畸变参数
/*418.523322187048 0 0
-1.26842201390676 421.222568242056 0
344.758267538961 243.318992284899 1 */ //2
Mat distCoeffL = (Mat_<double>(5, 1) << 0.006636837611004, 0.050240447649195, 0.006681263320267, 0.003130367429418, 0);
//[0.006636837611004,0.050240447649195] [0.006681263320267,0.003130367429418]
/*事先标定好的右相机的内参矩阵
fx 0 cx
0 fy cy
0 0 1
*/
Mat cameraMatrixR = (Mat_<double>(3, 3) << 417.417985082506, 0.498638151824367, 309.903372309072,
0, 419.795432389420, 230.6,
0, 0, 1);
/*
417.417985082506 0 0
0.498638151824367 419.795432389420 0
309.903372309072 236.256106972796 1
*/ //2
Mat distCoeffR = (Mat_<double>(5, 1) << -0.038407383078874, 0.236392800301615, 0.004121779274885, 0.002296129959664, 0);
//[-0.038407383078874,0.236392800301615] [0.004121779274885,0.002296129959664]
Mat T = (Mat_<double>(3, 1) << -1.210187345641146e+02, 0.519235426836325, -0.425535566316217);//T平移向量
//[-1.210187345641146e+02,0.519235426836325,-0.425535566316217]
//对应Matlab所得T参数
//Mat rec = (Mat_<double>(3, 1) << -0.00306, -0.03207, 0.00206);//rec旋转向量，对应matlab om参数 我
Mat rec = (Mat_<double>(3, 3) << 0.999341122700880, -0.00206388651740061, 0.0362361815232777,
0.000660748031451783, 0.999250989651683, 0.0386913826603732,
-0.0362888948713456, -0.0386419468010579, 0.998593969567432); //rec旋转向量，对应matlab om参数 我
/* 0.999341122700880 0.000660748031451783 -0.0362888948713456
-0.00206388651740061 0.999250989651683 -0.0386419468010579
0.0362361815232777 0.0386913826603732 0.998593969567432 */
//Mat T = (Mat_<double>(3, 1) << -48.4, 0.241, -0.0344);//T平移向量 //[-1.210187345641146e+02,0.519235426836325,-0.425535566316217]
//对应Matlab所得T参数
//Mat rec = (Mat_<double>(3, 1) << -0.039, -0.04658, 0.00106);//rec旋转向量，对应matlab om参数 倬华
Mat R;//R 旋转矩阵
/*****立体匹配*****/
void stereo_match(int, void*)
{
bm->setBlockSize(2 * blockSize + 5); //SAD窗口大小，5~21之间为宜
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(uniquenessRatio);//uniquenessRatio主要可以防止误匹配
bm->setSpeckleWindowSize(100);
bm->setSpeckleRange(32);
bm->setDisp12MaxDiff(-1);
Mat disp, disp8;
bm->compute(rectifyImageL, rectifyImageR, disp);//输入图像必须为灰度图
disp.convertTo(disp8, CV_8U, 255 / ((numDisparities * 16 + 16)*16.));//计算出的视差是CV_16S格式
reprojectImageTo3D(disp, xyz, Q, true); //在实际求距离时，ReprojectTo3D出来的X / W, Y / W, Z / W都要乘以16(也就是W除以16)，才能得到正确的三维坐标信息。
xyz = xyz * 16;
imshow("disparity", disp8);
}
/*****描述：鼠标操作回调*****/
static void onMouse(int event, int x, int y, int, void*)
{
if (selectObject)
{
selection.x = MIN(x, origin.x);
selection.y = MIN(y, origin.y);
selection.width = std::abs(x - origin.x);
selection.height = std::abs(y - origin.y);
}
switch (event)
{
case EVENT_LBUTTONDOWN: //鼠标左按钮按下的事件
origin = Point(x, y);
selection = Rect(x, y, 0, 0);
selectObject = true;
//cout << origin << "in world coordinate is: " << xyz.at<Vec3f>(origin) << endl;
point3 = xyz.at<Vec3f>(origin);
point3[0];
//cout << "point3[0]:" << point3[0] << "point3[1]:" << point3[1] << "point3[2]:" << point3[2]<<endl;
cout << "世界坐标：" << endl;
cout << "x: " << point3[0] << " y: " << point3[1] << " z: " << point3[2] << endl;
d = point3[0] * point3[0]+ point3[1] * point3[1]+ point3[2] * point3[2];
d = sqrt(d); //mm
// cout << "距离是:" << d << "mm" << endl;
d = d / 10.0; //cm
cout << "距离是:" << d << "cm" << endl;
// d = d/1000.0; //m
// cout << "距离是:" << d << "m" << endl;
break;
case EVENT_LBUTTONUP: //鼠标左按钮释放的事件
selectObject = false;
if (selection.width > 0 && selection.height > 0)
break;
}
}
/*****主函数*****/
int main()
{
/*
立体校正
*/
Rodrigues(rec, R); //Rodrigues变换
stereoRectify(cameraMatrixL, distCoeffL, cameraMatrixR, distCoeffR, imageSize, R, T, Rl, Rr, Pl, Pr, Q, CALIB_ZERO_DISPARITY,
0, imageSize, &validROIL, &validROIR);
initUndistortRectifyMap(cameraMatrixL, distCoeffL, Rl, Pl, imageSize, CV_32FC1, mapLx, mapLy);
initUndistortRectifyMap(cameraMatrixR, distCoeffR, Rr, Pr, imageSize, CV_32FC1, mapRx, mapRy);
/*
打开摄像头
*/
VideoCapture cap;
cap.open(1); //打开相机，电脑自带摄像头一般编号为0，外接摄像头编号为1，主要是在设备管理器中查看自己摄像头的编号。
cap.set(CV_CAP_PROP_FRAME_WIDTH, 2560); //设置捕获视频的宽度
cap.set(CV_CAP_PROP_FRAME_HEIGHT, 720); //设置捕获视频的高度
if (!cap.isOpened()) //判断是否成功打开相机
{
cout << "摄像头打开失败!" << endl;
return -1;
}
Mat frame, frame_L, frame_R;
cap >> frame; //从相机捕获一帧图像
cout << "Painted ImageL" << endl;
cout << "Painted ImageR" << endl;
while (1) {
double fScale = 0.5; //定义缩放系数，对2560*720图像进行缩放显示（2560*720图像过大，液晶屏分辨率较小时，需要缩放才可完整显示在屏幕）
Size dsize = Size(frame.cols*fScale, frame.rows*fScale);
Mat imagedst = Mat(dsize, CV_32S);
resize(frame, imagedst, dsize);
char image_left[200];
char image_right[200];
frame_L = imagedst(Rect(0, 0, 640, 360)); //获取缩放后左Camera的图像
// namedWindow("Video_L", 1);
// imshow("Video_L", frame_L);
frame_R = imagedst(Rect(640, 0, 640, 360)); //获取缩放后右Camera的图像
// namedWindow("Video_R", 2);
// imshow("Video_R", frame_R);
cap >> frame;
/*
读取图片
*/
//rgbImageL = imread("image_left_1.jpg", CV_LOAD_IMAGE_COLOR);
cvtColor(frame_L, grayImageL, CV_BGR2GRAY);
//rgbImageR = imread("image_right_1.jpg", CV_LOAD_IMAGE_COLOR);
cvtColor(frame_R, grayImageR, CV_BGR2GRAY);
// imshow("ImageL Before Rectify", grayImageL);
// imshow("ImageR Before Rectify", grayImageR);
/*
经过remap之后，左右相机的图像已经共面并且行对准了
*/
remap(grayImageL, rectifyImageL, mapLx, mapLy, INTER_LINEAR);
remap(grayImageR, rectifyImageR, mapRx, mapRy, INTER_LINEAR);
/*
把校正结果显示出来
*/
Mat rgbRectifyImageL, rgbRectifyImageR;
cvtColor(rectifyImageL, rgbRectifyImageL, CV_GRAY2BGR); //伪彩色图
cvtColor(rectifyImageR, rgbRectifyImageR, CV_GRAY2BGR);
//单独显示
//rectangle(rgbRectifyImageL, validROIL, Scalar(0, 0, 255), 3, 8);
//rectangle(rgbRectifyImageR, validROIR, Scalar(0, 0, 255), 3, 8);
// imshow("ImageL After Rectify", rgbRectifyImageL);
// imshow("ImageR After Rectify", rgbRectifyImageR);
//显示在同一张图上
Mat canvas;
double sf;
int w, h;
sf = 600. / MAX(imageSize.width, imageSize.height);
w = cvRound(imageSize.width * sf);
h = cvRound(imageSize.height * sf);
canvas.create(h, w * 2, CV_8UC3); //注意通道
//左图像画到画布上
Mat canvasPart = canvas(Rect(w * 0, 0, w, h)); //得到画布的一部分
resize(rgbRectifyImageL, canvasPart, canvasPart.size(), 0, 0, INTER_AREA); //把图像缩放到跟canvasPart一样大小
Rect vroiL(cvRound(validROIL.x*sf), cvRound(validROIL.y*sf), //获得被截取的区域
cvRound(validROIL.width*sf), cvRound(validROIL.height*sf));
//rectangle(canvasPart, vroiL, Scalar(0, 0, 255), 3, 8); //画上一个矩形
// cout << "Painted ImageL" << endl;
//右图像画到画布上
canvasPart = canvas(Rect(w, 0, w, h)); //获得画布的另一部分
resize(rgbRectifyImageR, canvasPart, canvasPart.size(), 0, 0, INTER_LINEAR);
Rect vroiR(cvRound(validROIR.x * sf), cvRound(validROIR.y*sf),
cvRound(validROIR.width * sf), cvRound(validROIR.height * sf));
//rectangle(canvasPart, vroiR, Scalar(0, 0, 255), 3, 8);
// cout << "Painted ImageR" << endl;
//画上对应的线条
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("rectified", canvas);
/*
立体匹配
*/
namedWindow("disparity", CV_WINDOW_AUTOSIZE);
// 创建SAD窗口 Trackbar
createTrackbar("BlockSize:\n", "disparity", &blockSize, 8, stereo_match);
// 创建视差唯一性百分比窗口 Trackbar
createTrackbar("UniquenessRatio:\n", "disparity", &uniquenessRatio, 50, stereo_match);
// 创建视差窗口 Trackbar
createTrackbar("NumDisparities:\n", "disparity", &numDisparities, 16, stereo_match);
//鼠标响应函数setMouseCallback(窗口名称, 鼠标回调函数, 传给回调函数的参数，一般取0)
setMouseCallback("disparity", onMouse, 0);
stereo_match(0, 0);
waitKey(10);
} //wheil
return 0;
}希望对你有帮助。
如果发现有待优化的地方，欢迎交流。
补充说明：
1.关于如何求出世界坐标？
1）x,y,z 是由
Vec3f point3;
point3 = xyz.at(origin); 来转化的。
cout << "x: " << point3[0] << " y: " << point3[1] << " z: " << point3[2] << endl;
2）x,y,z求平方和后开根号，是两点的距离公式，即点（0，0，0）------双目摄像头的中心点，和点（x,y,z）进行两点求距离。