最近一直在研究双目视觉测距，资料真的特别多网上，有matlab 的，python的，C++的，但个人感觉都不详细，对于小白，特别不容易上手，在这里我提供一个傻瓜式教程吧，利用matlab来进行标注，图形界面，无须任何代码，然后利用C++实现测距与深度图，原理太多我就不提了，小白直接照做就OK
1、准备工作
*硬件准备*
https://item.taobao.com/item.htm?spm=a1z10.1-c-s.w4004-17093912817.2.6af681c0jaZTur&id=562773790704
摄像头一个（如图），淘宝连接
*软件准备*
VS+opencv3.1
Matlab+toolbox标定工具箱
C++代码
Vs+opencv配置各位见这篇博客 https://www.cnblogs.com/linshuhe/p/5764394.html，讲解的够详细了，我们需要用VS+opencv3.1实现实时测距
2**matlab标定**
matlab用于单目摄像头，双目摄像头的标定，这个我也已经做过了，各位直接参考这篇博客 http://blog.csdn.net/hyacinthkiss/article/details/41317087
各位准备好上面那些工具以后我们才能正式开始
我们通过matlab标定可以获得以下数据
3 *C++与opencv实现测距 *
我们把matlab数据填写到下列代码中，各位切记不要填写错误，以免导致出现离奇的数据，我已经注释的非常清楚了；
/*
事先标定好的相机的参数
fx 0 cx
0 fy cy
0 0 1
*/
Mat cameraMatrixL = (Mat_<double>(3, 3) << 682.55880, 0, 384.13666,
0, 682.24569, 311.19558,
0, 0, 1);
//对应matlab里的左相机标定矩阵
Mat distCoeffL = (Mat_<double>(5, 1) << -0.51614, 0.36098, 0.00523, -0.00225, 0.00000);
//对应Matlab所得左i相机畸变参数
Mat cameraMatrixR = (Mat_<double>(3, 3) << 685.03817, 0, 397.39092,
0, 682.54282, 272.04875,
0, 0, 1);
//对应matlab里的右相机标定矩阵
Mat distCoeffR = (Mat_<double>(5, 1) << -0.46640, 0.22148, 0.00947, -0.00242, 0.00000);
//对应Matlab所得右相机畸变参数
Mat T = (Mat_<double>(3, 1) << -61.34485, 2.89570, -4.76870);//T平移向量
//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
Mat R;//R 旋转矩阵
4、完整代码 （我这里以上述摄像头拍摄的两张图来作为测距，同样可修改作为视频的实时测距，一定要将图片拷贝到你的工程目录下）
/******************************/
/* 立体匹配和测距 */
/******************************/
#include <opencv2/opencv.hpp>
#include <iostream>
using namespace std;
using namespace cv;
const int imageWidth = 800; //摄像头的分辨率
const int imageHeight = 600;
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);
/*
事先标定好的相机的参数
fx 0 cx
0 fy cy
0 0 1
*/
Mat cameraMatrixL = (Mat_<double>(3, 3) << 682.55880, 0, 384.13666,
0, 682.24569, 311.19558,
0, 0, 1);
//对应matlab里的左相机标定矩阵
Mat distCoeffL = (Mat_<double>(5, 1) << -0.51614, 0.36098, 0.00523, -0.00225, 0.00000);
//对应Matlab所得左i相机畸变参数
Mat cameraMatrixR = (Mat_<double>(3, 3) << 685.03817, 0, 397.39092,
0, 682.54282, 272.04875,
0, 0, 1);
//对应matlab里的右相机标定矩阵
Mat distCoeffR = (Mat_<double>(5, 1) << -0.46640, 0.22148, 0.00947, -0.00242, 0.00000);
//对应Matlab所得右相机畸变参数
Mat T = (Mat_<double>(3, 1) << -61.34485, 2.89570, -4.76870);//T平移向量
//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
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;
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("left.bmp", CV_LOAD_IMAGE_COLOR);
cvtColor(rgbImageL, grayImageL, CV_BGR2GRAY);
rgbImageR = imread("right.bmp", 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;
}如果对此代码和标定过程，或者遇到什么困难，大家可发邮件到[email protected],或者加入到 193369905进行讨论，谢谢大家