打算直接使用matlab來標定，畢竟opencv自帶的標定不太準確，數學工具還是要利用起來的。
1、單目標定參考
已完成單目標定。具體的內外畸變引數部落格寫的很詳細了。
程式執行示例：
#include "opencv2/opencv.hpp"
#include
using namespace cv;
using namespace std;
int main()
mat frame;
mat framecalibration;
inputvideo >> frame;
mat cameramatrix = mat::eye(3, 3, cv_64f);
cameramatrix.at(0, 0) = 8.020452698431196e+02;
cameramatrix.at(0, 1) = 0;
cameramatrix.at(0, 2) = 3.477773258387357e+02;
cameramatrix.at(1, 1) = 7.986372500898794e+02;
cameramatrix.at(1, 2) = 2.607218924683198e+02;
mat distcoeffs = mat::zeros(5, 1, cv_64f);
distcoeffs.at(0, 0) = -0.174672717467200;
distcoeffs.at(1, 0) = 0.214495208306129;
distcoeffs.at(2, 0) = 0;
distcoeffs.at(3, 0) = 0;
distcoeffs.at(4, 0) = 0;
mat view, rview, map1, map2;
size imagesize;
imagesize = frame.size();
initundistortrectifymap(cameramatrix, distcoeffs, mat(),
getoptimalnewcameramatrix(cameramatrix, distcoeffs, imagesize, 1, imagesize, 0),
imagesize, cv_16sc2, map1, map2);
while (1) //show the image captured in the window and repeat
return 0;
}
2、雙目標定參考
要使用兩個攝像頭一起開啟來照相，注意兩個攝像頭之間的距離，然後使用matlab標定完成之後，得到了我們所需要的引數
stereo calibration parameters after loading the individual calibration files:
intrinsic parameters of left camera:
focal length: fc_left = [ 780.17175 777.71912 ] � [ 18.51103 17.77195 ]
principal point: cc_left = [ 341.09426 259.67745 ] � [ 18.38629 13.32784 ]
skew: alpha_c_left = [ 0.00000 ] � [ 0.00000 ] => angle of pixel axes = 90.00000 � 0.00000 degrees
distortion: kc_left = [ -0.12758 -0.14219 0.00102 -0.00229 0.00000 ] � [ 0.06322 0.40645 0.00392 0.00556 0.00000 ]
intrinsic parameters of right camera:
focal length: fc_right = [ 804.25246 798.53096 ] � [ 26.86921 25.78932 ]
principal point: cc_right = [ 313.31520 266.26038 ] � [ 21.46775 18.09631 ]
skew: alpha_c_right = [ 0.00000 ] � [ 0.00000 ] => angle of pixel axes = 90.00000 � 0.00000 degrees
distortion: kc_right = [ -0.06840 -1.01339 0.00618 -0.00825 0.00000 ] � [ 0.10903 0.96165 0.00489 0.00631 0.00000 ]
extrinsic parameters (position of right camera wrt left camera):
rotation vector: om = [ 0.00866 0.02203 -0.03950 ]
translation vector: t = [ -337.30574 5.34897 65.73400 ]
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