概述：
opencv内部也有双目标定的函数，但是结果有时候会飘，所以想先用MATLAB标定工具箱试试看。使用MATLAB先队两个相机进行单目标定，然后进行双目标定，将结果保存在xml文件中。xml文件时opencv支持的文件格式，更详细的解释可以去搜索相关资料。
1.工具准备：
MATLAB标定工具箱。
这一步我认为应该单独强调一下。我的MATLAB版本是a，应用程序里面自带一个标定工具箱但是只支持单目。后来在http://www.vision.caltech.edu/bouguetj/calib_doc/#examples了解到应该再下载一个双目标定的工具箱。下载链接
将其解压到Matlab的工作目录下，然后将其包含到matlab的工作路径下。运行calib_gui就可以打开matlab标定工具箱主界面：
2.标定过程
1.点击Image names会出现当前MATLAB路径下的所有文件，如上图，输入L表示对L开头的图片进行标定，然后输入后缀b表示图片格式是bmp格式的。
2.点击Extract grid corners，检测角点，这个过程比较麻烦，一路回车下来，会依次弹出各张图片，需要用鼠标顺时针或逆时针选定最外层的角点。选择完第一张的时候会要求输入棋盘格的宽度，我的棋盘格长宽都是15mm。
之后就是对所有L开头的图片选取四个最外层角点，这一步完成后，点击Calibration进行标定，点击Save保存结果Calib_Result.mat，将Calib_Results.mat修改为Calib_Results_left.mat方便进行双目标定。
右相机同理。
3.双目标定
在MATLAB控制台输入stereo_gui，打开双目标定工具箱，点击Load left and right calibration files，再点击stereo calibration进行标定。得到如下结果：
3.保存为xml文件
Distortion_Camera_Left.xml
<?xml version="1.0"?> <opencv_storage> <Distortion type_id="opencv-matrix"> <rows>5</rows> <cols>1</cols> <dt>d</dt> <data> -0.15761 4.24565 -0.00236 0.02423 0.00000 </data> </Distortion> </opencv_storage>
Distortion_Camera_Right.xml
<?xml version="1.0"?><opencv_storage><Distortion type_id="opencv-matrix"> <rows>5</rows> <cols>1</cols> <dt>d</dt> <data> -0.09281 1.60263 0.00408 -0.00825 0.00000</data></Distortion></opencv_storage>
Intrinsics_Camera_Left.xml
<?xml version="1.0"?><opencv_storage><Intrinsics_Camera_Left type_id="opencv-matrix"> <rows>3</rows> <cols>3</cols> <dt>d</dt> <data> 2752.23366 0.732.902630.2786.18468 529.17431 0.0. 1.</data></Intrinsics_Camera_Left></opencv_storage>
Intrinsics_Camera_Right.xml
<?xml version="1.0"?><opencv_storage><Intrinsics_Camera_Right type_id="opencv-matrix"> <rows>3</rows> <cols>3</cols> <dt>d</dt> <data> 2667.95661 0.584.91164 0.2699.92379 539.37941 0.0.1. </data></Intrinsics_Camera_Right></opencv_storage>
RotRodrigues.xml
<?xml version="1.0"?> <opencv_storage> <RotRodrigues type_id="opencv-matrix"> <rows>3</rows> <cols>1</cols> <dt>d</dt> <data> 0.02812 0.54890 0.00618 </data> </RotRodrigues> </opencv_storage>
Translation.xml
<?xml version="1.0"?><opencv_storage><Translation type_id="opencv-matrix"> <rows>3</rows> <cols>1</cols> <dt>d</dt> <data> -160.89829 -1.36460 25.91248</data></Translation></opencv_storage>
4.opencv读取xml文件，并进行双目校正
//读取内部参数 CvMat *Intrinsics_Camera_Left = (CvMat *)cvLoad("Intrinsics_Camera_Left.xml");CvMat *Intrinsics_Camera_Right = (CvMat *)cvLoad("Intrinsics_Camera_Right.xml");CvMat *Distortion_Camera_Left = (CvMat *)cvLoad("Distortion_Camera_Left.xml");CvMat *Distortion_Camera_Right = (CvMat *)cvLoad("Distortion_Camera_Right.xml");CvMat *Translation_matlab = (CvMat *)cvLoad("Translation.xml");CvMat *RotRodrigues_matlab = (CvMat *)cvLoad("RotRodrigues.xml");CvMat *R_opencv = cvCreateMat(3, 3, CV_64F);cvRodrigues2(RotRodrigues_matlab, R_opencv);//创建映射矩阵IplImage *Left_Mapx = cvCreateImage(cvSize(1280, 960), IPL_DEPTH_32F, 1);IplImage *Left_Mapy = cvCreateImage(cvSize(1280, 960), IPL_DEPTH_32F, 1);IplImage *Right_Mapx = cvCreateImage(cvSize(1280, 960), IPL_DEPTH_32F, 1);IplImage *Right_Mapy = cvCreateImage(cvSize(1280, 960), IPL_DEPTH_32F, 1);CvMat *Rl = cvCreateMat(3, 3, CV_64F);CvMat *Rr = cvCreateMat(3, 3, CV_64F);CvMat *Pl = cvCreateMat(3, 4, CV_64F);CvMat *Pr = cvCreateMat(3, 4, CV_64F);//双目校正cvStereoRectify(Intrinsics_Camera_Left, Intrinsics_Camera_Right,Distortion_Camera_Left, Distortion_Camera_Right,cvSize(1280, 960), R_opencv, Translation_matlab,Rl, Rr, Pl, Pr, 0, 2048, 0);//增加图像缩放，去除死区 //畸变矫正cvInitUndistortRectifyMap(Intrinsics_Camera_Left, Distortion_Camera_Left, Rl, Pl,Left_Mapx, Left_Mapy);cvInitUndistortRectifyMap(Intrinsics_Camera_Right, Distortion_Camera_Right, Rr, Pr,Right_Mapx, Right_Mapy);
cvRemap(IplimgLeft, img_left_Change, Left_Mapx, Left_Mapy);cvRemap(IplimgRight, img_right_Change, Right_Mapx, Right_Mapy); cvLine(img_left_Change, cvPoint(0, 96 * 1), cvPoint(1280 - 1, 96), cvScalar(255, 0, 0)); cvLine(img_left_Change, cvPoint(0, 96 * 2), cvPoint(1280 - 1, 96 * 2), cvScalar(255, 0, 0));cvLine(img_left_Change, cvPoint(0, 96 * 3), cvPoint(1280 - 1, 96 * 3), cvScalar(255, 0, 0));cvLine(img_left_Change, cvPoint(0, 96 * 4), cvPoint(1280 - 1, 96 * 4), cvScalar(255, 0, 0));cvLine(img_left_Change, cvPoint(0, 96 * 5), cvPoint(1280 - 1, 96 * 5), cvScalar(255, 0, 0));cvLine(img_left_Change, cvPoint(0, 96 * 6), cvPoint(1280 - 1, 96 * 6), cvScalar(255, 0, 0));cvLine(img_left_Change, cvPoint(0, 96 * 7), cvPoint(1280 - 1, 96 * 7), cvScalar(255, 0, 0));cvLine(img_left_Change, cvPoint(0, 96 * 8), cvPoint(1280 - 1, 96 * 8), cvScalar(255, 0, 0));cvLine(img_left_Change, cvPoint(0, 96 * 9), cvPoint(1280 - 1, 96 * 9), cvScalar(255, 0, 0));cvLine(img_right_Change, cvPoint(0, 96 * 1), cvPoint(1280 - 1, 96), cvScalar(255, 0, 0));cvLine(img_right_Change, cvPoint(0, 96 * 2), cvPoint(1280 - 1, 96 * 2), cvScalar(255, 0, 0));cvLine(img_right_Change, cvPoint(0, 96 * 3), cvPoint(1280 - 1, 96 * 3), cvScalar(255, 0, 0));cvLine(img_right_Change, cvPoint(0, 96 * 4), cvPoint(1280 - 1, 96 * 4), cvScalar(255, 0, 0));cvLine(img_right_Change, cvPoint(0, 96 * 5), cvPoint(1280 - 1, 96 * 5), cvScalar(255, 0, 0));cvLine(img_right_Change, cvPoint(0, 96 * 6), cvPoint(1280 - 1, 96 * 6), cvScalar(255, 0, 0));cvLine(img_right_Change, cvPoint(0, 96 * 7), cvPoint(1280 - 1, 96 * 7), cvScalar(255, 0, 0));cvLine(img_right_Change, cvPoint(0, 96 * 8), cvPoint(1280 - 1, 96 * 8), cvScalar(255, 0, 0));cvLine(img_right_Change, cvPoint(0, 96 * 9), cvPoint(1280 - 1, 96 * 9), cvScalar(255, 0, 0));
