基于matlab标定数据，使用opencv实现双目立体摄像头的标定（源代码）
我参考了这几位大牛的博客，受益匪浅：
http://blog.csdn.net/sunanger_wang/article/details/7744025
http://www.opencv.org.cn/forum.php?mod=viewthread&tid=12963
http://blog.csdn.net/scyscyao/article/details/5562024
http://blog.csdn.net/chenyusiyuan/article/details/5967291
最后那位有连续三篇的内容进行了详细的介绍，非常棒。
由于看到那些博客下面的一些问题都没人回答，那些算是细节，但大家知道有时候就是这么一点小时会卡很久。
再好的解释也没源代码和示例图来的快，小弟就把这几天摸索的内容放上来。
我的平台：2个罗技C170摄像头
嘿嘿。。比较简陋
matlab标定：
相信仔细看过原理以及上面的博客就能用matlab计算出双目的结果，这儿有一点要注意，就是要计算双目参数，左右两个摄像头必须同时拍摄棋盘格，一共20张！我一开始是单独拍摄，后来发现单目的数据可以标定出来，双目的就会报错，，想想原理也是，双目标定必须是同时拍摄的才行呀。这儿贴一段自己写的同时采集图像的代码，按下c，就能保存图像。
#include
#include
using namespace std;
int pic_num = 13;
char* name_l = (char*)malloc(sizeof(char)*200);
char* name_r = (char*)malloc(sizeof(char)*200);
int main()
{
cout << "测试两个摄像头同时读取数据" << endl;
CvCapture* cap_left;
CvCapture* cap_right;
IplImage *img0 , *img1;
IplImage *img_left, *img_right;
IplImage *img_left_Change, *img_right_Change;
cvNamedWindow("camera_left");
cvNamedWindow("camera_right");
cap_left = cvCreateCameraCapture(1);
assert(cap_left != NULL);
cap_right = cvCreateCameraCapture(0);
assert(cap_right != NULL);
while(1)
{
cvGrabFrame(cap_left);
cvGrabFrame(cap_right);
img0 = cvRetrieveFrame(cap_left);//img0 1 只是指针，这个可以在单独的采图线程里刷新
img1 = cvRetrieveFrame(cap_right);
if( !img0 || !img1)
{
cout << "camera0 error" << endl;
break;
}
img_left = cvCloneImage(img0);
img_right = cvCloneImage(img1);
cvShowImage("camera_left", img_left);
cvShowImage("camera_right", img_right);
char c = cvWaitKey(33);
if(c == 27)
break;
if(c == 'c')
{
sprintf(name_l, "leftPic%d.jpg", pic_num);
sprintf(name_r, "rightPic%d.jpg", pic_num);
pic_num++;
cvSaveImage(name_l, img_left);
cvSaveImage(name_r, img_right);
}
cvReleaseImage(&img_left);
cvReleaseImage(&img_right);
}
cvReleaseCapture(&cap_left);
cvReleaseCapture(&cap_right);
cvDestroyWindow("camera_left");
cvDestroyWindow("camera_right");
return 0;
} opencv上场：
因为大多数人都推荐使用matlab标定箱，那下一步就是将标定的数据存到xml文件里。xml不了解的请自行百度。
由于有些人一直在问怎么写文件，我这儿就把所有要自己写的文件都贴出来：
Intrinsics_Camera_Left.xml：
3
3
d
690.09102 0. 325.38988
0. 686.25294 286.9196
0. 0. 1.
Intrinsics_Camera_Right.xml：
3
3
d
691.94410 0. 342.74569
0. 685.94161 231.16984
0. 0. 1.
Distortion_Camera_Left.xml：
5
1
d
-0.02240 -0.05900 0.00894 -0.00590 0.00000
Distortion_Camera_Right.xml：
5
1
d
0.05543 -0.29862 -0.00669 0.01307 0.00000
Translation.xml
3
1
d
-194.10083 6.39147 -11.45062
RotRodrigues.xml
3
1
d
-0.06416 -0.11879 -0.07601
将这些分别保存下来，里面的内容是根据matlab的结果写进去的。
下一步就是立体图像矫正了。具体原理请看之前的博客,非常详细！
小弟这儿就贴一下代码，让各位能更好地理解：
#include
#include
using namespace std;
int main()
{
cout << "测试两个摄像头同时读取数据" << endl;
//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);
//创建映射阵
IplImage *Left_Mapx = cvCreateImage(cvSize(640,480), IPL_DEPTH_32F, 1);
IplImage *Left_Mapy = cvCreateImage(cvSize(640,480), IPL_DEPTH_32F, 1);
IplImage *Right_Mapx = cvCreateImage(cvSize(640,480), IPL_DEPTH_32F, 1);
IplImage *Right_Mapy = cvCreateImage(cvSize(640,480), 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);
//cvStereoRectify(Intrinsics_Camera_Left, Intrinsics_Camera_Right, \
Distortion_Camera_Left, Distortion_Camera_Right, \
cvSize(640, 480), R_opencv, Translation_matlab, \
Rl, Rr, Pl, Pr);
cvStereoRectify(Intrinsics_Camera_Left, Intrinsics_Camera_Right, \
Distortion_Camera_Left, Distortion_Camera_Right, \
cvSize(640, 480), R_opencv, Translation_matlab, \
Rl, Rr, Pl, Pr, 0, 1024, 0);//增加图像缩放，去除死区
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);
CvCapture* cap_left;
CvCapture* cap_right;
IplImage *img0 , *img1;
IplImage *img_left, *img_right;
IplImage *img_left_Change, *img_right_Change;
cvNamedWindow("camera_left");
cvNamedWindow("camera_right");
cap_left = cvCreateCameraCapture(1);
assert(cap_left != NULL);
cap_right = cvCreateCameraCapture(0);
assert(cap_right != NULL);
while(1)
{
cvGrabFrame(cap_left);
cvGrabFrame(cap_right);
img0 = cvRetrieveFrame(cap_left);
img1 = cvRetrieveFrame(cap_right);
if( !img0 || !img1)
{
cout << "camera0 error" << endl;
break;
}
img_left = cvCloneImage(img0);
img_right = cvCloneImage(img1);
img_left_Change = cvCloneImage(img0);
img_right_Change = cvCloneImage(img1);
cvRemap(img_left, img_left_Change, Left_Mapx, Left_Mapy);
cvRemap(img_right, img_right_Change, Right_Mapx, Right_Mapy);
cvLine(img_left_Change, cvPoint(0,48), cvPoint(640-1, 48), cvScalar(255, 0, 0));
cvLine(img_left_Change, cvPoint(0,48*2), cvPoint(640-1, 48*2), cvScalar(255, 0, 0));
cvLine(img_left_Change, cvPoint(0,48*3), cvPoint(640-1, 48*3), cvScalar(255, 0, 0));
cvLine(img_left_Change, cvPoint(0,48*4), cvPoint(640-1, 48*4), cvScalar(255, 0, 0));
cvLine(img_left_Change, cvPoint(0,48*5), cvPoint(640-1, 48*5), cvScalar(255, 0, 0));
cvLine(img_left_Change, cvPoint(0,48*6), cvPoint(640-1, 48*6), cvScalar(255, 0, 0));
cvLine(img_left_Change, cvPoint(0,48*7), cvPoint(640-1, 48*7), cvScalar(255, 0, 0));
cvLine(img_left_Change, cvPoint(0,48*8), cvPoint(640-1, 48*8), cvScalar(255, 0, 0));
cvLine(img_left_Change, cvPoint(0,48*9), cvPoint(640-1, 48*9), cvScalar(255, 0, 0));
cvLine(img_right_Change, cvPoint(0,48), cvPoint(640-1, 48), cvScalar(255, 0, 0));
cvLine(img_right_Change, cvPoint(0,48*2), cvPoint(640-1, 48*2), cvScalar(255, 0, 0));
cvLine(img_right_Change, cvPoint(0,48*3), cvPoint(640-1, 48*3), cvScalar(255, 0, 0));
cvLine(img_right_Change, cvPoint(0,48*4), cvPoint(640-1, 48*4), cvScalar(255, 0, 0));
cvLine(img_right_Change, cvPoint(0,48*5), cvPoint(640-1, 48*5), cvScalar(255, 0, 0));
cvLine(img_right_Change, cvPoint(0,48*6), cvPoint(640-1, 48*6), cvScalar(255, 0, 0));
cvLine(img_right_Change, cvPoint(0,48*7), cvPoint(640-1, 48*7), cvScalar(255, 0, 0));
cvLine(img_right_Change, cvPoint(0,48*8), cvPoint(640-1, 48*8), cvScalar(255, 0, 0));
cvLine(img_right_Change, cvPoint(0,48*9), cvPoint(640-1, 48*9), cvScalar(255, 0, 0));
cvShowImage("camera_left", img_left);
cvShowImage("camera_right", img_right);
cvShowImage("camera_left_Change", img_left_Change);
cvShowImage("camera_right_Change", img_right_Change);
char c = cvWaitKey(33);
if(c == 27)
break;
cvReleaseImage(&img_left);
cvReleaseImage(&img_right);
cvReleaseImage(&img_left_Change);
cvReleaseImage(&img_right_Change);
}
cvReleaseCapture(&cap_left);
cvReleaseCapture(&cap_right);
cvDestroyWindow("camera_left");
cvDestroyWindow("camera_right");
return 0;
} cvStereoRectify可以在最后加上参数，去除黑色的死区。
有些博客里提到位移向量为什么是负的，并且说要修改为正数，比如我这儿是
-194.10083 6.39147 -11.45062
第一项要改为194.10083 ，我不是很理解，程序里没有修改，但是结果还是正确的。
最后再次感谢上面提到的各位博主！