1.参考自别的文章：
/******************************/
/* 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);
Mat distCoeffL = (Mat_<double>(5, 1) << -0.51614, 0.36098, 0.00523, -0.00225, 0.00000);
Mat cameraMatrixR = (Mat_<double>(3, 3) << 685.03817, 0, 397.39092,
0, 682.54282, 272.04875,
0, 0, 1);
Mat distCoeffR = (Mat_<double>(5, 1) << -0.46640, 0.22148, 0.00947, -0.00242, 0.00000);
Mat T = (Mat_<double>(3, 1) << -61.34485, 2.89570, -4.76870);//T平移向量
Mat rec = (Mat_<double>(3, 1) << -0.00306, -0.03207, 0.00206);//rec旋转向量
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("left1.jpg", CV_LOAD_IMAGE_COLOR);
cvtColor(rgbImageL, grayImageL, CV_BGR2GRAY);
rgbImageR = imread("right1.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;
}
2.opencv自己进行标定：
// stereoCalibration.cpp : 定义控制台应用程序的入口点。
//
//在进行双目摄像头的标定之前，最好事先分别对两个摄像头进行单目视觉的标定
//分别确定两个摄像头的内参矩阵，然后再开始进行双目摄像头的标定
//在此例程中是先对两个摄像头进行单独标定(见上一篇单目标定文章)，然后在进行立体标定
#include "stdafx.h"
#include <opencv2/opencv.hpp>
#include <highgui.hpp>
#include "cv.h"
#include <cv.hpp>
#include <iostream>
using namespace std;
using namespace cv;
const int imageWidth = 640; //摄像头的分辨率
const int imageHeight = 480;
const int boardWidth = 9; //横向的角点数目
const int boardHeight = 6; //纵向的角点数据
const int boardCorner = boardWidth * boardHeight; //总的角点数据
const int frameNumber = 13; //相机标定时需要采用的图像帧数
const int squareSize = 20; //标定板黑白格子的大小 单位mm
const Size boardSize = Size(boardWidth, boardHeight); //
Size imageSize = Size(imageWidth, imageHeight);
Mat R, T, E, F; //R 旋转矢量 T平移矢量 E本征矩阵 F基础矩阵
vector<Mat> rvecs; //旋转向量
vector<Mat> tvecs; //平移向量
vector<vector<Point2f>> imagePointL; //左边摄像机所有照片角点的坐标集合
vector<vector<Point2f>> imagePointR; //右边摄像机所有照片角点的坐标集合
vector<vector<Point3f>> objRealPoint; //各副图像的角点的实际物理坐标集合
vector<Point2f> cornerL; //左边摄像机某一照片角点坐标集合
vector<Point2f> cornerR; //右边摄像机某一照片角点坐标集合
Mat rgbImageL, grayImageL;
Mat rgbImageR, grayImageR;
Mat Rl, Rr, Pl, Pr, Q; //校正旋转矩阵R，投影矩阵P 重投影矩阵Q (下面有具体的含义解释）
Mat mapLx, mapLy, mapRx, mapRy; //映射表
Rect validROIL, validROIR; //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
/*
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) << 532.782, 0, 532.904,
0, 342.505, 233.876,
0, 0, 1);
Mat distCoeffL = (Mat_<double>(5, 1) << -0.28095, 0.0255745, 0.00122226, -0.000137736, 0.162946);
/*
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) << 532.782, 0, 532.904,
0, 342.505, 233.876,
0, 0, 1);
Mat distCoeffR = (Mat_<double>(5, 1) << -0.28095, 0.0255745, 0.00122226, -0.000137736, 0.162946);
/*计算标定板上模块的实际物理坐标*/
void calRealPoint(vector<vector<Point3f>>& obj, int boardwidth, int boardheight, int imgNumber, int squaresize)
{
// Mat imgpoint(boardheight, boardwidth, CV_32FC3,Scalar(0,0,0));
vector<Point3f> imgpoint;
for (int rowIndex = 0; rowIndex < boardheight; rowIndex++)
{
for (int colIndex = 0; colIndex < boardwidth; colIndex++)
{
// imgpoint.at<Vec3f>(rowIndex, colIndex) = Vec3f(rowIndex * squaresize, colIndex*squaresize, 0);
imgpoint.push_back(Point3f(rowIndex * squaresize, colIndex * squaresize, 0));
}
}
for (int imgIndex = 0; imgIndex < imgNumber; imgIndex++)
{
obj.push_back(imgpoint);
}
}
void outputCameraParam(void)
{
/*保存数据*/
/*输出数据*/
FileStorage fs("intrinsics.yml", FileStorage::WRITE);
if (fs.isOpened())
{
fs << "cameraMatrixL" << cameraMatrixL << "cameraDistcoeffL" << distCoeffL <<"cameraMatrixR" << cameraMatrixR << "cameraDistcoeffR" << distCoeffR;
fs.release();
cout << "cameraMatrixL=:" << cameraMatrixL <<endl<< "cameraDistcoeffL=:" << distCoeffL <<endl<<"cameraMatrixR=:" << cameraMatrixR <<endl<< "cameraDistcoeffR=:" << distCoeffR<<endl;
}
else
{
cout << "Error: can not save the intrinsics!!!!!" << endl;
}
fs.open("extrinsics.yml", FileStorage::WRITE);
if (fs.isOpened())
{
fs << "R" << R << "T" << T << "Rl" << Rl << "Rr" << Rr << "Pl" << Pl << "Pr" << Pr << "Q" << Q;
cout << "R=" << R << endl << "T=" << T << endl << "Rl=" << Rl << endl << "Rr=" << Rr << endl << "Pl=" << Pl << endl << "Pr=" << Pr << endl << "Q=" << Q << endl;
fs.release();
}
else
cout << "Error: can not save the extrinsic parameters\n";
}
int _tmain(int argc, _TCHAR* argv[])
{
Mat img;
int goodFrameCount = 0;
namedWindow("ImageL");
namedWindow("ImageR");
cout << "按Q退出 ..." << endl;
while (goodFrameCount < frameNumber)
{
char filename[100];
/*读取左边的图像*/
sprintf_s(filename, "image\\left%02d.jpg", goodFrameCount + 1);
rgbImageL = imread(filename, CV_LOAD_IMAGE_COLOR);
cvtColor(rgbImageL, grayImageL, CV_BGR2GRAY);
/*读取右边的图像*/
sprintf_s(filename, "image\\right%02d.jpg", goodFrameCount + 1);
rgbImageR = imread(filename, CV_LOAD_IMAGE_COLOR);
cvtColor(rgbImageR, grayImageR, CV_BGR2GRAY);
bool isFindL, isFindR;
isFindL = findChessboardCorners(rgbImageL, boardSize, cornerL);
isFindR = findChessboardCorners(rgbImageR, boardSize, cornerR);
if (isFindL == true && isFindR == true) //如果两幅图像都找到了所有的角点 则说明这两幅图像是可行的
{
/*
Size(5,5) 搜索窗口的一半大小
Size(-1,-1) 死区的一半尺寸
TermCriteria(CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 20, 0.1)迭代终止条件
*/
cornerSubPix(grayImageL, cornerL, Size(5, 5), Size(-1, -1), TermCriteria(CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 20, 0.1));
drawChessboardCorners(rgbImageL, boardSize, cornerL, isFindL);
imshow("chessboardL", rgbImageL);
imagePointL.push_back(cornerL);
cornerSubPix(grayImageR, cornerR, Size(5, 5), Size(-1, -1), TermCriteria(CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 20, 0.1));
drawChessboardCorners(rgbImageR, boardSize, cornerR, isFindR);
imshow("chessboardR", rgbImageR);
imagePointR.push_back(cornerR);
/*
本来应该判断这两幅图像是不是好的，如果可以匹配的话才可以用来标定
但是在这个例程当中，用的图像是系统自带的图像，都是可以匹配成功的。
所以这里就没有判断
*/
//string filename = "res\\image\\calibration";
//filename += goodFrameCount + ".jpg";
//cvSaveImage(filename.c_str(), &IplImage(rgbImage)); //把合格的图片保存起来
goodFrameCount++;
cout << "The image is good" << endl;
}
else
{
cout << "The image is bad please try again" << endl;
}
if (waitKey(10) == 'q')
{
break;
}
}
/*
计算实际的校正点的三维坐标
根据实际标定格子的大小来设置
*/
calRealPoint(objRealPoint, boardWidth, boardHeight, frameNumber, squareSize);
cout << "cal real successful" << endl;
/*
标定摄像头
由于左右摄像机分别都经过了单目标定
所以在此处选择flag = CALIB_USE_INTRINSIC_GUESS
*/
double rms = stereoCalibrate(objRealPoint, imagePointL, imagePointR,
cameraMatrixL, distCoeffL,
cameraMatrixR, distCoeffR,
Size(imageWidth, imageHeight), R, T, E, F,
CALIB_USE_INTRINSIC_GUESS,
TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 100, 1e-5));
cout << "Stereo Calibration done with RMS error = " << rms << endl;
/*
立体校正的时候需要两幅图像共面并且行对准 以使得立体匹配更加的可靠
使得两幅图像共面的方法就是把两个摄像头的图像投影到一个公共成像面上，这样每幅图像从本图像平面投影到公共图像平面都需要一个旋转矩阵R
stereoRectify 这个函数计算的就是从图像平面投影都公共成像平面的旋转矩阵Rl,Rr。 Rl,Rr即为左右相机平面行对准的校正旋转矩阵。
左相机经过Rl旋转，右相机经过Rr旋转之后，两幅图像就已经共面并且行对准了。
其中Pl,Pr为两个相机的投影矩阵，其作用是将3D点的坐标转换到图像的2D点的坐标:P*[X Y Z 1]' =[x y w]
Q矩阵为重投影矩阵，即矩阵Q可以把2维平面(图像平面)上的点投影到3维空间的点:Q*[x y d 1] = [X Y Z W]。其中d为左右两幅图像的时差
*/
stereoRectify(cameraMatrixL, distCoeffL, cameraMatrixR, distCoeffR, imageSize, R, T, Rl, Rr, Pl, Pr, Q,
CALIB_ZERO_DISPARITY,-1,imageSize,&validROIL,&validROIR);
/*
根据stereoRectify 计算出来的R 和 P 来计算图像的映射表 mapx,mapy
mapx,mapy这两个映射表接下来可以给remap()函数调用，来校正图像，使得两幅图像共面并且行对准
ininUndistortRectifyMap()的参数newCameraMatrix就是校正后的摄像机矩阵。在openCV里面，校正后的计算机矩阵Mrect是跟投影矩阵P一起返回的。
所以我们在这里传入投影矩阵P，此函数可以从投影矩阵P中读出校正后的摄像机矩阵
*/
initUndistortRectifyMap(cameraMatrixL, distCoeffL, Rl, Pr, imageSize, CV_32FC1, mapLx, mapLy);
initUndistortRectifyMap(cameraMatrixR, distCoeffR, Rr, Pr, imageSize, CV_32FC1, mapRx, mapRy);
Mat rectifyImageL, rectifyImageR;
cvtColor(grayImageL, rectifyImageL, CV_GRAY2BGR);
cvtColor(grayImageR, rectifyImageR, CV_GRAY2BGR);
imshow("Rectify Before", rectifyImageL);
/*
经过remap之后，左右相机的图像已经共面并且行对准了
*/
remap(rectifyImageL, rectifyImageL, mapLx, mapLy, INTER_LINEAR);
remap(rectifyImageR, rectifyImageR, mapRx, mapRy, INTER_LINEAR);
imshow("ImageL", rectifyImageL);
imshow("ImageR", rectifyImageR);
/*保存并输出数据*/
outputCameraParam();
/*
把校正结果显示出来
把左右两幅图像显示到同一个画面上
这里只显示了最后一副图像的校正结果。并没有把所有的图像都显示出来
*/
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);
/*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(rectifyImageL, 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(rectifyImageR, 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, 255, 0), 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);
cout << "wait key" << endl;
waitKey(0);
system("pause");
return 0;
}
