OpenCV: 摄像机标定原理
#include "stdafx.h"
#include "cv.h"
#include "highgui.h"
#include <string>
#include <iostream>
using namespace std;
int main()
{
int cube_length=7;
CvCapture* capture;
capture=cvCreateCameraCapture(0);
if(capture==0){
printf("无法捕获摄像头设备！\n\n");
return 0;
}else{
printf("捕获摄像头设备成功！！\n\n");
}
IplImage* frame;
cvNamedWindow("摄像机帧截取窗口",1); //cvNamedWindow()函数用于在屏幕上创建一个窗口，将被显示的图像包含于该窗口中。函数的第一个参数指定了该窗口的窗口标题,如果要使用HighGUI库所提供的其他函数与该窗口进行交互时，我们将通过该参数值引用这个窗口。
printf("按“C”键截取当前帧并保存为标定图片...\n按“Q”键退出截取帧过程...\n\n");
int number_image=1;
char *str1;
str1=".jpg";
char filename[20]="";
while(true)
{
frame=cvQueryFrame(capture);// 从摄像头或者文件中抓取并返回一帧
if(!frame)
break;
cvShowImage("摄像机帧截取窗口",frame); //图像显示
if(cvWaitKey(10)=='c'){
sprintf_s (filename,"%d.jpg",number_image); // int sprintf_s( char *buffer, size_t sizeOfBuffer, const char *format [, argument] ... );
这个函数的主要作用是将若干个argument按照format格式存到buffer中
cvSaveImage(filename,frame);//保存
cout<<"成功获取当前帧，并以文件名"<<filename<<"保存...\n\n";
printf("按“C”键截取当前帧并保存为标定图片...\n按“Q”键退出截取帧过程...\n\n");
number_image++;
}else if(cvWaitKey(10)=='q'){
printf("截取图像帧过程完成...\n\n");
cout<<"共成功截取"<<--number_image<<"帧图像！！\n\n";
break;
}
}
cvReleaseImage(&frame); //释放图像
cvDestroyWindow("摄像机帧截取窗口");
IplImage * show;
cvNamedWindow("RePlay",1);
int a=1;
int number_image_copy=number_image;
CvSize board_size=cvSize(7,7); // Cvsizes:OpenCV的基本数据类型之一。表示矩阵框大小，以像素为精度。与CvPoint结构类似，但数据成员是integer类型的width和height。
//cvSize是
int board_width=board_size.width;
int board_height=board_size.height;
int total_per_image=board_width*board_height;
CvPoint2D32f * image_points_buf = new CvPoint2D32f[total_per_image];
CvMat * image_points=cvCreateMat(number_image*total_per_image,2,CV_32FC1);//图像坐标系
CvMat * object_points=cvCreateMat(number_image*total_per_image,3,CV_32FC1);//世界坐标系
CvMat * point_counts=cvCreateMat(number_image,1,CV_32SC1);//
CvMat * intrinsic_matrix=cvCreateMat(3,3,CV_32FC1);//
CvMat * distortion_coeffs=cvCreateMat(5,1,CV_32FC1);
int count;
int found;
int step;
int successes=0;
while(a<=number_image_copy){
sprintf_s (filename,"%d.jpg",a);
show=cvLoadImage(filename,-1);
found=cvFindChessboardCorners(show,board_size,image_points_buf,&count,
CV_CALIB_CB_ADAPTIVE_THRESH|CV_CALIB_CB_FILTER_QUADS);
if(found==0){
cout<<"第"<<a<<"帧图片无法找到棋盘格所有角点!\n\n";
cvNamedWindow("RePlay",1);
cvShowImage("RePlay",show);
cvWaitKey(0);
}else{
cout<<"第"<<a<<"帧图像成功获得"<<count<<"个角点...\n";
cvNamedWindow("RePlay",1);
IplImage * gray_image= cvCreateImage(cvGetSize(show),8,1); //创建头并分配数据IplImage* cvCreateImage( CvSize size, int depth, int channels ); depth 图像元素的位深度
cvCvtColor(show,gray_image,CV_BGR2GRAY); // cvCvtColor(...),是Opencv里的颜色空间转换函数，可以实现rgb颜色向HSV,HSI等颜色空间的转换，也可以转换为灰度图像。
cout<<"获取源图像灰度图过程完成...\n";
cvFindCornerSubPix(gray_image,image_points_buf,count,cvSize(11,11),cvSize(-1,-1),由于非常接近P的像素产生了很小的特征值，所以这个自相关矩阵并不总是可逆的。为了解决这个问题，一般可以简单地剔除离P点非常近的像素。输入参数:ero_zone定义了一个禁区(与win相似，但通常比win小)，这个区域在方程组以及自相关矩阵中不被考虑。如果不需要这样一个禁区，则zero_zone应设置为cvSize(-1，-1)0
cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,0.1));
cout<<"灰度图亚像素化过程完成...\n";
cvDrawChessboardCorners(show,board_size,image_points_buf,count,found);
cout<<"在源图像上绘制角点过程完成...\n\n";
cvShowImage("RePlay",show);
cvWaitKey(0);
}
if(total_per_image==count){
step=successes*total_per_image;
for(int i=step,j=0;j<total_per_image;++i,++j){
CV_MAT_ELEM(*image_points,float,i,0)=image_points_buf[j].x; // opencv中用来访问矩阵每个元素的宏，这个宏只对单通道矩阵有效，多通道CV_MAT_ELEM( matrix, elemtype, row, col )参数 matrix：要访问的矩阵 elemtype：矩阵元素的类型 row：所要访问元素的行数 col：所要访问元素的列数
CV_MAT_ELEM(*image_points,float,i,1)=image_points_buf[j].y;// 求完每个角点横纵坐标值都存在image_point_buf里
CV_MAT_ELEM(*object_points,float,i,0)=(float)(j/cube_length);
CV_MAT_ELEM(*object_points,float,i,1)=(float)(j%cube_length);
CV_MAT_ELEM(*object_points,float,i,2)=0.0f;
}
CV_MAT_ELEM(*point_counts,int,successes,0)=total_per_image;
successes++;
}
a++;
}
cvReleaseImage(&show);
cvDestroyWindow("RePlay");
cout<<"*********************************************\n";
cout<<number_image<<"帧图片中，标定成功的图片为"<<successes<<"帧...\n";
cout<<number_image<<"帧图片中，标定失败的图片为"<<number_image-successes<<"帧...\n\n";
cout<<"*********************************************\n\n";
cout<<"按任意键开始计算摄像机内参数...\n\n";
CvCapture* capture1;
capture1=cvCreateCameraCapture(0);
IplImage * show_colie;
show_colie=cvQueryFrame(capture1);
CvMat * object_points2=cvCreateMat(successes*total_per_image,3,CV_32FC1); // OpenCV 中重要的矩阵变换函数，使用方法为cvMat* cvCreateMat ( int rows, int cols, int type ); 这里type可以是任何预定义类型，预定义类型的结构如下：CV_<bit_depth> (S|U|F)C<number_of_channels>。
CvMat * image_points2=cvCreateMat(successes*total_per_image,2,CV_32FC1);
CvMat * point_counts2=cvCreateMat(successes,1,CV_32SC1);
for(int i=0;i<successes*total_per_image;++i){
CV_MAT_ELEM(*image_points2,float,i,0)=CV_MAT_ELEM(*image_points,float,i,0);//用来存储角点提取成功的图像的角点
CV_MAT_ELEM(*image_points2,float,i,1)=CV_MAT_ELEM(*image_points,float,i,1);
CV_MAT_ELEM(*object_points2,float,i,0)=CV_MAT_ELEM(*object_points,float,i,0);
CV_MAT_ELEM(*object_points2,float,i,1)=CV_MAT_ELEM(*object_points,float,i,1);
CV_MAT_ELEM(*object_points2,float,i,2)=CV_MAT_ELEM(*object_points,float,i,2);
}
for(int i=0;i<successes;++i){
CV_MAT_ELEM(*point_counts2,int,i,0)=CV_MAT_ELEM(*point_counts,int,i,0);
}
cvReleaseMat(&object_points);
cvReleaseMat(&image_points);
cvReleaseMat(&point_counts);
CV_MAT_ELEM(*intrinsic_matrix,float,0,0)=1.0f;
CV_MAT_ELEM(*intrinsic_matrix,float,1,1)=1.0f;
cvCalibrateCamera2(object_points2,image_points2,point_counts2,cvGetSize(show_colie),
intrinsic_matrix,distortion_coeffs,NULL,NULL,0);
cout<<"摄像机内参数矩阵为：\n";
cout<<CV_MAT_ELEM(*intrinsic_matrix,float,0,0)<<" "<<CV_MAT_ELEM(*intrinsic_matrix,float,0,1)
<<" "<<CV_MAT_ELEM(*intrinsic_matrix,float,0,2)
<<"\n\n";
cout<<CV_MAT_ELEM(*intrinsic_matrix,float,1,0)<<" "<<CV_MAT_ELEM(*intrinsic_matrix,float,1,1)
<<" "<<CV_MAT_ELEM(*intrinsic_matrix,float,1,2)
<<"\n\n";
cout<<CV_MAT_ELEM(*intrinsic_matrix,float,2,0)<<" "<<CV_MAT_ELEM(*intrinsic_matrix,float,2,1)
<<" "<<CV_MAT_ELEM(*intrinsic_matrix,float,2,2)
<<"\n\n";
cout<<"畸变系数矩阵为：\n";
cout<<CV_MAT_ELEM(*distortion_coeffs,float,0,0)<<" "<<CV_MAT_ELEM(*distortion_coeffs,float,1,0)
<<" "<<CV_MAT_ELEM(*distortion_coeffs,float,2,0)
<<" "<<CV_MAT_ELEM(*distortion_coeffs,float,3,0)
<<" "<<CV_MAT_ELEM(*distortion_coeffs,float,4,0)
<<"\n\n";
cvSave("Intrinsics.xml",intrinsic_matrix);
cvSave("Distortion.xml",distortion_coeffs);
cout<<"摄像机矩阵、畸变系数向量已经分别存储在名为Intrinsics.xml、Distortion.xml文档中\n\n";
CvMat * intrinsic=(CvMat *)cvLoad("Intrinsics.xml");
CvMat * distortion=(CvMat *)cvLoad("Distortion.xml");
IplImage * mapx=cvCreateImage(cvGetSize(show_colie),IPL_DEPTH_32F,1);
IplImage * mapy=cvCreateImage(cvGetSize(show_colie),IPL_DEPTH_32F,1);
cvInitUndistortMap(intrinsic,distortion,mapx,mapy);
cvNamedWindow("原始图像",1);
cvNamedWindow("非畸变图像",1);
cout<<"按‘E’键退出显示...\n\n";
while(show_colie){
IplImage * clone=cvCloneImage(show_colie);
cvShowImage("原始图像",show_colie);
cvRemap(clone,show_colie,mapx,mapy);
cvReleaseImage(&clone);
cvShowImage("非畸变图像",show_colie);
if(cvWaitKey(10)=='e'){
break;
}
show_colie=cvQueryFrame(capture1);
}
return 0;
}
各标定步骤实现方法
1 计算标靶平面与图像平面之间的映射矩阵