#include "Stafx.h"
; //棋盘上有13个格子，那么角点的数目12
;
; //图片的总张数
int main(int argc, char** argv)
{
;
int board_n=board_h*board_w; //一张图像上，角点的数目
CvSize board_sz=cvSize(board_w,board_h);
CvMat* object_points=cvCreateMat(image_count*board_n,,CV_32FC1); //实际坐标系（以棋盘左上角第一个角点为坐标原点），角点的坐标，单位是方块
CvMat* image_points=cvCreateMat(image_count*board_n,,CV_32FC1); //在图像上找到角点的坐标,坐标原点图像左上角，单位像素
CvMat* point_counts=cvCreateMat(board_n,,CV_32SC1); //每个图像上角点个数
; //累计图像上所有角点被找到图像的张数
while (count++<image_count)
{
std::string filename="E:\\软件学习\\LearningOpenCV_Code\\calibration\\";
];
itoa(count,str,);//转换为字符串
std::string str1;
int length=strlen(str);
*(str+length)='.';
*(str+length+)='j';
*(str+length+)='p';
*(str+length+)='g';
*(str+length+)='\0';
str1=str;
filename+=str1;
IplImage* src=cvLoadImage(filename.c_str(),CV_LOAD_IMAGE_UNCHANGED);
IplImage* gray=cvCreateImage(cvGetSize(src),IPL_DEPTH_8U,);
cvCvtColor(src,gray,CV_RGB2GRAY);
CvPoint2D32f* corners=new CvPoint2D32f[board_n]; //一张图像上角点的坐标
int corner_count; //一张图像上角点的数目
int found=cvFindChessboardCorners(src,board_sz,corners,&corner_count,
CV_CALIB_CB_ADAPTIVE_THRESH|CV_CALIB_CB_FILTER_QUADS);//找到棋盘的角点,如果所有角点找到返回1，否则返回0，这里指的是所有的角点
cvFindCornerSubPix(gray, corners, corner_count, cvSize(,),cvSize(-,-),
cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,, 0.1)); //寻找亚素点,gray一定要是8位单通道的图像
cvDrawChessboardCorners(src,board_sz,corners,corner_count,found); //将角点画出，src一定要是彩色图像
//cvShowImage("1",src);
//cvWaitKey();
if (corner_count==board_n) //一张图像上所有角点被找到
{
int step=a*board_n; //第k幅角点全部找到图像上角点的存储的起始地址
;
for (int i=step;j<corner_count;i++,j++)
{
CV_MAT_ELEM(*object_points,)=j/board_w; //角点的横坐标，单位不是像素，而是以棋盘上每个方块为一个单位
CV_MAT_ELEM(*object_points,)=j%board_w; //角点的纵坐标，每个方块为一个单位
CV_MAT_ELEM(*object_points,)=; //齐次坐标,表示点
CV_MAT_ELEM(*image_points,)=corners[j].x; //图像坐标系的坐标
CV_MAT_ELEM(*image_points,)=corners[j].y;
};
CV_MAT_ELEM(*point_counts,)=board_n;
a++;
}
}
//由于图像中存在所有角点未找到的情况，所以上面object_points的空间未存满，需要重新定义
CvMat* object_points2=cvCreateMat(a*board_n,,CV_32FC1);
CvMat* image_points2=cvCreateMat(a*board_n,,CV_32FC1);
CvMat* point_counts2=cvCreateMat(a,,CV_32SC1);
CvMat* intrinsic_matrix=cvCreateMat(,,CV_32FC1); //相机内参数矩阵
CvMat* distortion_coeffs=cvCreateMat(,,CV_32FC1); //畸变系数矩阵
CvMat* rotation_vector=cvCreateMat(a,,CV_32FC1); //旋转矩阵
CvMat* translation_vector=cvCreateMat(a,,CV_32FC1); //平移矩阵
;i<a*board_n;i++)
{
CV_MAT_ELEM(*object_points2,)=CV_MAT_ELEM(*object_points,);
CV_MAT_ELEM(*object_points2,)=CV_MAT_ELEM(*object_points,);
CV_MAT_ELEM(*object_points2,)=;
CV_MAT_ELEM(*image_points2,)=CV_MAT_ELEM(*image_points,);
CV_MAT_ELEM(*image_points2,)=CV_MAT_ELEM(*image_points,);
}
;i<a;i++)
CV_MAT_ELEM(*point_counts2,)=CV_MAT_ELEM(*point_counts,);
cvReleaseMat(&object_points);
cvReleaseMat(&image_points);
cvReleaseMat(&point_counts);
//内置参数矩阵设置,初始化
CV_MAT_ELEM(*intrinsic_matrix,,)=1.0;
CV_MAT_ELEM(*intrinsic_matrix,,)=1.0;
//校正相机参数,cvSize(1600,1200)为输入图像的真实长度和宽度，单位为像素
cvCalibrateCamera2(object_points2,image_points2,point_counts2,cvSize(,),
intrinsic_matrix,distortion_coeffs,rotation_vector,translation_vector,CV_CALIB_FIX_ASPECT_RATIO);
//图像校正
IplImage* mapx=cvCreateImage(cvSize(,),IPL_DEPTH_32F,);
IplImage* mapy=cvCreateImage(cvSize(,),IPL_DEPTH_32F,);
cvInitUndistortMap(intrinsic_matrix,distortion_coeffs,mapx,mapy);
IplImage* test_image=cvLoadImage("E:\\软件学习\\LearningOpenCV_Code\\calibration\\22.jpg",CV_LOAD_IMAGE_UNCHANGED);
if (!test_image)
{
std::cout<<"error"<<std::endl;
}
cvShowImage("原图像",test_image);
IplImage* t=cvCloneImage(test_image);
cvRemap(t,test_image,mapx,mapy);
cvShowImage("校正后图像",test_image);
cvWaitKey();
;
}测试图片：opencv课后习题答案中LearningOpencv_Code中calibration文件中的图片