【OpenCV】8th-摄像头标定

一.基本知识与标定程序

摄像头标定

具体的函数使用：Cv照相机定标和三维重建#照相机定标

opencv-相机标定步骤、评估标定误差以及标定之后图像坐标到世界坐标的转换

单目摄像机标定程序

摄像机标定–矫正畸变

#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);

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);
if(!frame)
break;
cvShowImage("摄像机帧截取窗口",frame);

if(cvWaitKey(10)=='c'){
sprintf_s (filename,"%d.jpg",number_image);
cvSaveImage(filename,frame);
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);
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);
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);
CvMat * object_points=cvCreateMat(number_image*total_per_image,3,CV_32FC1);
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);
cvCvtColor(show,gray_image,CV_BGR2GRAY);
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),
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
4000
("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;
CV_MAT_ELEM(*image_points,float,i,1)=image_points_buf[j].y;
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);
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);
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;

}

个人理解OpenCV对标定的处理是这样的：

1.打印一幅棋盘图贴到一个平板上，转动该模板，用摄像机拍摄20张(一般多于6张即可，多了结果可以更精确)图片

2.对于每一张图片都用cvFindChessboardCorners提取角点坐标，这个函数提取的仅是一个粗略坐标，然后调用cvFindCornerSubPix()来获取精确角点坐标。提出来后可以用cvDrawChessboardCorners画到图片上。

有几张图片，这个过程就重复多少遍。最终提取的20张图片角点坐标全存到N*2维矩阵指针image_points中。

3.初始化定标点的三维坐标，也是20张图片上的角点坐标全赋值。可以认为每张图上对应点的坐标是一样的(Z坐标为0)。角点的三维坐标都存到N*3维矩阵指针object_points中。

4.调用cvCalibrateCamera2求摄像机的内外参数矩阵。对于外参数，该函数实际得到的是N*3维的旋转矢量和N*3维的平移矢量，而不是矩阵。N行的矢量，第i行就对应着第i张图片的外参数。如果想得到3*3的矩阵形式，需要先把第i行的值取出来，再调用函数cvRodrigues2进行转换。

虽然每张图片的物体坐标我们假设是一样的，但是投影坐标坐标不同，所以获得的外参数是不一样的。个人理解，几张图片的参考点是不一样的。都是以模板物理位置为参考点，得到的外参数是摄像机相对于该位置的旋转和平移。

二.利用sample中例程

opencv 单目相机标定 自带例子程序的使用

OpenCV sample目录下自带两个与相机标定的cpp文件即：calibration.cpp和calibration_artificial.cpp

在cmd命令下写代码。