OpenCV 立体视觉详细解析（三）---立体标定和校正源码分析
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#pragma warning( disable: 4996 )
/* *************** License:**************************
Oct. 3, 2008
Right to use this code in any way you want without warrenty, support or any guarentee of it working.
BOOK: It would be nice if you cited it:
Learning OpenCV: Computer Vision with the OpenCV Library
by Gary Bradski and Adrian Kaehler
Published by O'Reilly Media, October 3, 2008
AVAILABLE AT:
http://www.amazon.com/Learning-OpenCV-Computer-Vision-Library/dp/0596516134
Or: http://oreilly.com/catalog/9780596516130/
ISBN-10: 0596516134 or: ISBN-13: 978-0596516130
OTHER OPENCV SITES:
* The source code is on sourceforge at:
http://sourceforge.net/projects/opencvlibrary/
* The OpenCV wiki page (As of Oct 1, 2008 this is down for changing over servers, but should come back):
http://opencvlibrary.sourceforge.net/
* An active user group is at:
http://tech.groups.yahoo.com/group/OpenCV/
* The minutes of weekly OpenCV development meetings are at:
http://pr.willowgarage.com/wiki/OpenCV
************************************************** */
#include "opencv2/opencv.hpp"
#include <iostream>
#include <stdio.h>
#include <stdarg.h>
#include <opencv/cv.h>
#include <opencv/cvaux.h>
#include <opencv/cxcore.h>
#include <opencv/highgui.h>
using namespace std;
//
// Given a list of chessboard images, the number of corners (nx, ny)
// on the chessboards, and a flag: useCalibrated for calibrated (0) or
// uncalibrated (1: use cvStereoCalibrate(), 2: compute fundamental
// matrix separately) stereo. Calibrate the cameras and display the
// rectified results along with the computed disparity images.
//
static void
StereoCalib(const char* imageList, int nx, int ny, int useUncalibrated)
{
int displayCorners = 0;
int showUndistorted = 1;
bool isVerticalStereo = false;//OpenCV can handle left-right
//or up-down camera arrangements
const int maxScale = 1;
const float squareSize = 1.f; //Set this to your actual square size
FILE* f = fopen(imageList, "rt");
int i, j, lr, nframes, n = nx*ny, N = 0;
vector<string> imageNames[2];
vector<CvPoint3D32f> objectPoints;
vector<CvPoint2D32f> points[2];
vector<int> npoints;
vector<uchar> active[2];
vector<CvPoint2D32f> temp(n);
CvSize imageSize = {0,0};
// ARRAY AND VECTOR STORAGE:
double M1[3][3], M2[3][3], D1[5], D2[5];
double R[3][3], T[3], E[3][3], F[3][3];
CvMat _M1 = cvMat(3, 3, CV_64F, M1 );
CvMat _M2 = cvMat(3, 3, CV_64F, M2 );
CvMat _D1 = cvMat(1, 5, CV_64F, D1 );
CvMat _D2 = cvMat(1, 5, CV_64F, D2 );
CvMat _R = cvMat(3, 3, CV_64F, R );
CvMat _T = cvMat(3, 1, CV_64F, T );
CvMat _E = cvMat(3, 3, CV_64F, E );
CvMat _F = cvMat(3, 3, CV_64F, F );
if( displayCorners )
cvNamedWindow( "corners", 1 );
// READ IN THE LIST OF CHESSBOARDS:
if( !f )
{
fprintf(stderr, "can not open file %s\n", imageList );
return;
}
for(i=0;;i++)
{
char buf[1024];
int count = 0, result=0;
lr = i % 2;
vector<CvPoint2D32f>& pts = points[lr];
if( !fgets( buf, sizeof(buf)-3, f ))
break;
size_t len = strlen(buf);
while( len > 0 && isspace(buf[len-1]))
buf[--len] = '\0';
if( buf[0] == '#')
continue;
IplImage* img = cvLoadImage( buf, 0 );
if( !img )
break;
imageSize = cvGetSize(img);
imageNames[lr].push_back(buf);
//FIND CHESSBOARDS AND CORNERS THEREIN:
for( int s = 1; s <= maxScale; s++ )
{
IplImage* timg = img;
if( s > 1 )
{
timg = cvCreateImage(cvSize(img->width*s,img->height*s),
img->depth, img->nChannels );
cvResize( img, timg, CV_INTER_CUBIC );
}
//寻找角点
result = cvFindChessboardCorners( timg, cvSize(nx, ny),
&temp[0], &count,
CV_CALIB_CB_ADAPTIVE_THRESH |
CV_CALIB_CB_NORMALIZE_IMAGE);
if( timg != img )
cvReleaseImage( &timg );
if( result || s == maxScale )
for( j = 0; j < count; j++ )
{
temp[j].x /= s;
temp[j].y /= s;
}
if( result )
break;
}
if( displayCorners )
{
printf("%s\n", buf);
IplImage* cimg = cvCreateImage( imageSize, 8, 3 );
cvCvtColor( img, cimg, CV_GRAY2BGR );
cvDrawChessboardCorners( cimg, cvSize(nx, ny), &temp[0],
count, result );
cvShowImage( "corners", cimg );
cvReleaseImage( &cimg );
if( cvWaitKey(0) == 27 ) //Allow ESC to quit
exit(-1);
}
else
putchar('.');
N = pts.size();
pts.resize(N + n, cvPoint2D32f(0,0));
active[lr].push_back((uchar)result);
//assert( result != 0 );
if( result )
{
//Calibration will suffer without subpixel interpolation
cvFindCornerSubPix( img, &temp[0], count,
cvSize(11, 11), cvSize(-1,-1),
cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,
30, 0.01) );
copy( temp.begin(), temp.end(), pts.begin() + N );
}
cvReleaseImage( &img );
}
fclose(f);
printf("\n");
// HARVEST CHESSBOARD 3D OBJECT POINT LIST:
nframes = active[0].size();//Number of good chessboads found
objectPoints.resize(nframes*n);
for( i = 0; i < ny; i++ )
for( j = 0; j < nx; j++ )
objectPoints[i*nx + j] =
cvPoint3D32f(i*squareSize, j*squareSize, 0);
for( i = 1; i < nframes; i++ )
copy( objectPoints.begin(), objectPoints.begin() + n,
objectPoints.begin() + i*n );
npoints.resize(nframes,n);
N = nframes*n;
CvMat _objectPoints = cvMat(1, N, CV_32FC3, &objectPoints[0] );
CvMat _imagePoints1 = cvMat(1, N, CV_32FC2, &points[0][0] );
CvMat _imagePoints2 = cvMat(1, N, CV_32FC2, &points[1][0] );
CvMat _npoints = cvMat(1, npoints.size(), CV_32S, &npoints[0] );
//
cvSetIdentity(&_M1);
cvSetIdentity(&_M2);
cvZero(&_D1);
cvZero(&_D2);
// CALIBRATE THE STEREO CAMERAS
printf("Running stereo calibration ...");
fflush(stdout);
//立体标定，返回，内参数，外参数，立体参数
//参数1：N*3的矩阵，它包含三维物体在M幅图像中的每一幅图像上的K个点的物理坐标，N=K×M
//参数2,3：都是N×2的矩阵，分别存储由objectPoints提供的所有物体的参考点在左右坐标系的位置，
//当使用棋盘来完成两个摄像机的标定，那么这两个参数正好分别是调用cvFindCheessboardConers（）求取左右视图的返回值
//参数4：每幅图像上的点数目，为M×1的矩阵
//参数5，7：3*3的内参数摄像机矩阵，输出参数。
//参数6,8：摄像机1和2的畸变矩阵。
//参数9：是以像素为单位的图像大小。它仅用于细化或计算内参数
//参数10：输出联系左右相机的旋转矩阵
//参数11：联系左右相机的平移向量
//参数12：E，输出参数，本征矩阵
//参数13：F，输出参数，基础矩阵
//参数14：设置了内部最优化准则，典型的调用是cvTermCriteria(CV_TERMCRIT_ITER+ CV_TERMCRIT_EPS, 100, 1e-5)
//参数15：
//一旦有了该函数输出的旋转或者平移值（R，T）或者基础矩阵F，我们就可以利用这些结果来校正两幅立体图像，使得极线沿着图像行对准
//并且传古两幅图像的扫面线也是相同的。
cvStereoCalibrate( &_objectPoints, &_imagePoints1,
&_imagePoints2, &_npoints,
&_M1, &_D1, &_M2, &_D2,
imageSize, &_R, &_T, &_E, &_F,
cvTermCriteria(CV_TERMCRIT_ITER+
CV_TERMCRIT_EPS, 100, 1e-5),
CV_CALIB_FIX_ASPECT_RATIO +
CV_CALIB_ZERO_TANGENT_DIST +
CV_CALIB_SAME_FOCAL_LENGTH );
printf(" done\n");
// CALIBRATION QUALITY CHECK
// because the output fundamental matrix implicitly
// includes all the output information,
// we can check the quality of calibration using the
// epipolar geometry constraint: m2^t*F*m1=0
vector<CvPoint3D32f> lines[2];
points[0].resize(N);
points[1].resize(N);
_imagePoints1 = cvMat(1, N, CV_32FC2, &points[0][0] );
_imagePoints2 = cvMat(1, N, CV_32FC2, &points[1][0] );
lines[0].resize(N);
lines[1].resize(N);
//极线：N×3的浮点数矩阵，极线的表达式：ax+by+c=0
CvMat _L1 = cvMat(1, N, CV_32FC3, &lines[0][0]);
CvMat _L2 = cvMat(1, N, CV_32FC3, &lines[1][0]);
//Always work in undistorted space
//对原始点做去畸变处理
//输入参数为一系列的2D点。
//
cvUndistortPoints( &_imagePoints1, &_imagePoints1,
&_M1, &_D1, 0, &_M1 );
cvUndistortPoints( &_imagePoints2, &_imagePoints2,
&_M2, &_D2, 0, &_M2 );
//计算极线
//根据一幅图像中的点列，计算其在另一副图像中对应的极线，每一个点都对应一个极线
cvComputeCorrespondEpilines( &_imagePoints1, 1, &_F, &_L1 );
cvComputeCorrespondEpilines( &_imagePoints2, 2, &_F, &_L2 );
double avgErr = 0;
for( i = 0; i < N; i++ )
{
double err = fabs(points[0][i].x*lines[1][i].x +
points[0][i].y*lines[1][i].y + lines[1][i].z)
+ fabs(points[1][i].x*lines[0][i].x +
points[1][i].y*lines[0][i].y + lines[0][i].z);
avgErr += err;
}
printf( "avg err = %g\n", avgErr/(nframes*n) );
//COMPUTE AND DISPLAY RECTIFICATION
// int showUndistorted = 1; 显示去畸变后的图像
if( showUndistorted )
{
CvMat* mx1 = cvCreateMat( imageSize.height,
imageSize.width, CV_32F );
CvMat* my1 = cvCreateMat( imageSize.height,
imageSize.width, CV_32F );
CvMat* mx2 = cvCreateMat( imageSize.height,
imageSize.width, CV_32F );
CvMat* my2 = cvCreateMat( imageSize.height,
imageSize.width, CV_32F );
CvMat* img1r = cvCreateMat( imageSize.height,
imageSize.width, CV_8U );
CvMat* img2r = cvCreateMat( imageSize.height,
imageSize.width, CV_8U );
CvMat* disp = cvCreateMat( imageSize.height,
imageSize.width, CV_16S );
CvMat* vdisp = cvCreateMat( imageSize.height,
imageSize.width, CV_8U );
CvMat* pair;
double R1[3][3], R2[3][3], P1[3][4], P2[3][4];
//3*3的矩阵：由cvSteroCalibrate返回的左右摄像机平面间的行对准的校正旋转矩阵
CvMat _R1 = cvMat(3, 3, CV_64F, R1);
CvMat _R2 = cvMat(3, 3, CV_64F, R2);
//如果使用标定立体校正：Bouguet算法 StereoCalib("ch12_list.txt", 9, 6, 1); 最后一个参数
// IF BY CALIBRATED (BOUGUET'S METHOD)
if( useUncalibrated == 0 )
{
CvMat _P1 = cvMat(3, 4, CV_64F, P1);
CvMat _P2 = cvMat(3, 4, CV_64F, P2);
//立体校正
//
cvStereoRectify( &_M1, &_M2, &_D1, &_D2, imageSize,
&_R, &_T,
&_R1, &_R2, &_P1, &_P2, 0,
0/*CV_CALIB_ZERO_DISPARITY*/ );
isVerticalStereo = fabs(P2[1][3]) > fabs(P2[0][3]);
//Precompute maps for cvRemap()
//计算左右视图的校正查找映射表
//
cvInitUndistortRectifyMap(&_M1,&_D1,&_R1,&_P1,mx1,my1);
cvInitUndistortRectifyMap(&_M2,&_D2,&_R2,&_P2,mx2,my2);
}
//OR ELSE HARTLEY'S METHOD
//如果使用非标定立体校正算法：Hartley算法
else if( useUncalibrated == 1 || useUncalibrated == 2 )
// use intrinsic parameters of each camera, but
// compute the rectification transformation directly
// from the fundamental matrix
{
double H1[3][3], H2[3][3], iM[3][3];
CvMat _H1 = cvMat(3, 3, CV_64F, H1);
CvMat _H2 = cvMat(3, 3, CV_64F, H2);
CvMat _iM = cvMat(3, 3, CV_64F, iM);
//Just to show you could have independently used F
if( useUncalibrated == 2 )
//计算基础矩阵
cvFindFundamentalMat( &_imagePoints1,
&_imagePoints2, &_F);
//非标定立体校正
cvStereoRectifyUncalibrated( &_imagePoints1,
&_imagePoints2, &_F,
imageSize,
&_H1, &_H2, 3);
cvInvert(&_M1, &_iM);
cvMatMul(&_H1, &_M1, &_R1);
cvMatMul(&_iM, &_R1, &_R1);
cvInvert(&_M2, &_iM);
cvMatMul(&_H2, &_M2, &_R2);
cvMatMul(&_iM, &_R2, &_R2);
//Precompute map for cvRemap()
//计算左右视图的校正查找映射表
cvInitUndistortRectifyMap(&_M1,&_D1,&_R1,&_M1,mx1,my1);
cvInitUndistortRectifyMap(&_M2,&_D1,&_R2,&_M2,mx2,my2);
}
else
assert(0);
cvNamedWindow( "rectified", 1 );
// RECTIFY THE IMAGES AND FIND DISPARITY MAPS
if( !isVerticalStereo )
pair = cvCreateMat( imageSize.height, imageSize.width*2,
CV_8UC3 );
else
pair = cvCreateMat( imageSize.height*2, imageSize.width,
CV_8UC3 );
//Setup for finding stereo corrrespondences
//初始化块状匹配状态（内部分配和参数）
CvStereoBMState *BMState = cvCreateStereoBMState();
assert(BMState != 0);
BMState->preFilterSize=41;
BMState->preFilterCap=31;
BMState->SADWindowSize=41;
BMState->minDisparity=-64;
BMState->numberOfDisparities=128;
BMState->textureThreshold=10;
BMState->uniquenessRatio=15;
for( i = 0; i < nframes; i++ )
{
IplImage* img1=cvLoadImage(imageNames[0][i].c_str(),0);
IplImage* img2=cvLoadImage(imageNames[1][i].c_str(),0);
if( img1 && img2 )
{
CvMat part;
cvRemap( img1, img1r, mx1, my1 );
cvRemap( img2, img2r, mx2, my2 );
if( !isVerticalStereo || useUncalibrated != 0 )
{
// When the stereo camera is oriented vertically,
// useUncalibrated==0 does not transpose the
// image, so the epipolar lines in the rectified
// images are vertical. Stereo correspondence
// function does not support such a case.
//计算视差
//参数1：校正后的左边图像
//参数2：校正后的右边图像
//参数3：输出参数，视差映射，和输入图像等大小的单通道图像。
//参数4：CvStereoBMState *BMState = cvCreateStereoBMState();
//
cvFindStereoCorrespondenceBM( img1r, img2r, disp,
BMState);
cvNormalize( disp, vdisp, 0, 256, CV_MINMAX );
cvNamedWindow( "disparity" );
cvShowImage( "disparity", vdisp );
}
if( !isVerticalStereo )
{
cvGetCols( pair, &part, 0, imageSize.width );
cvCvtColor( img1r, &part, CV_GRAY2BGR );
cvGetCols( pair, &part, imageSize.width,
imageSize.width*2 );
cvCvtColor( img2r, &part, CV_GRAY2BGR );
for( j = 0; j < imageSize.height; j += 16 )
cvLine( pair, cvPoint(0,j),
cvPoint(imageSize.width*2,j),
CV_RGB(0,255,0));
}
else
{
cvGetRows( pair, &part, 0, imageSize.height );
cvCvtColor( img1r, &part, CV_GRAY2BGR );
cvGetRows( pair, &part, imageSize.height,
imageSize.height*2 );
cvCvtColor( img2r, &part, CV_GRAY2BGR );
for( j = 0; j < imageSize.width; j += 16 )
cvLine( pair, cvPoint(j,0),
cvPoint(j,imageSize.height*2),
CV_RGB(0,255,0));
}
cvShowImage( "rectified", pair );
if( cvWaitKey() == 27 )
break;
}
cvReleaseImage( &img1 );
cvReleaseImage( &img2 );
}
cvReleaseStereoBMState(&BMState);
cvReleaseMat( &mx1 );
cvReleaseMat( &my1 );
cvReleaseMat( &mx2 );
cvReleaseMat( &my2 );
cvReleaseMat( &img1r );
cvReleaseMat( &img2r );
cvReleaseMat( &disp );
}
}
int main(void)
{
StereoCalib("ch12_list.txt", 9, 6, 1);
return 0;
}
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