利用opencv自带的example进行双目标定
利用opencv自带的example进行双目标定
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include
#include
#include
#include
#include
#include
#include
#include
using namespace cv;
using namespace std;
static int print_help()
{
cout <<
" Given a list of chessboard images, the number of corners (nx, ny)\n"
" on the chessboards, and a flag: useCalibrated for \n"
" calibrated (0) or\n"
" uncalibrated \n"
" (1: use cvStereoCalibrate(), 2: compute fundamental\n"
" matrix separately) stereo. \n"
" Calibrate the cameras and display the\n"
" rectified results along with the computed disparity images. \n" << endl;
cout << "Usage:\n ./stereo_calib -w board_width -h board_height [-nr /*dot not view results*/] \n" << endl;
return 0;
}
static void
StereoCalib(const vector& imagelist, Size boardSize, bool useCalibrated=true, bool showRectified=true)
{
if( imagelist.size() % 2 != 0 )//成对的图像
{
cout << "Error: the image list contains odd (non-even) number of elements\n";
return;
}
bool displayCorners = true;//true;
const int maxScale = 2;
//实际的正方格尺寸
//const float squareSize = 1.f; // Set this to your actual square size
const float squareSize = 36.f; // danwei mm
// ARRAY AND VECTOR STORAGE:
//创建图像坐标和世界坐标系坐标矩阵
vector > imagePoints[2];//图像点(存储角点)
vector > objectPoints; //物体三维坐标点
Size imageSize;
int i, j, k, nimages = (int)imagelist.size()/2; //左右两个相机图片
//确定左右视图矩阵的数量，比如10副图，左右矩阵分别为5个
imagePoints[0].resize(nimages);
imagePoints[1].resize(nimages);
vector goodImageList;
cout << "nimages: " << nimages <& corners = imagePoints[k][j];
for( int scale = 1; scale <= maxScale; scale++ )
{
Mat timg;
//图像是8bit的灰度或彩色图像
if( scale == 1 )
timg = img;
else
resize(img, timg, Size(), scale, scale);//转换成 8bit的灰度或彩色图像
// //参数需为 8bit的灰度或彩色图像
found = findChessboardCorners(timg, boardSize, corners,//得到棋盘内角点坐标 存入 imagePoin
CALIB_CB_ADAPTIVE_THRESH | CALIB_CB_NORMALIZE_IMAGE);
if(found)
{
//如果为多通道图像
if( scale > 1 )
{
Mat cornersMat(corners);
cornersMat *= 1./scale;
}
break;
}
}
//显示角点
if( displayCorners )
{
cout << filename << endl;
Mat cimg, cimg1;
cvtColor(img, cimg, COLOR_GRAY2BGR);//转换成灰度图
drawChessboardCorners(cimg, boardSize, corners, found);//显示
double sf = 640./MAX(img.rows, img.cols);//尺度因子
resize(cimg, cimg1, Size(), sf, sf);//变换到合适大小
imshow("corners", cimg);
char c = (char)waitKey(50);//等待500ms
if( c == 27 || c == 'q' || c == 'Q' ) //Allow ESC to quit
exit(-1);
}
else
putchar('.');
if( !found )
{
cout << "ChessboardCorners not found" <
/*
/外参数
// R--右相机相对左相机的旋转矩阵
// T--右相机相对左相机的平移矩阵
// R1,R2--左右相机校准变换（旋转）矩阵 3×3
// P1,P2--左右相机在校准后坐标系中的投影矩阵 3×4
// Q--视差-深度映射矩阵，我利用它来计算单个目标点的三维坐标
*/
Mat R1, R2, P1, P2, Q;//由stereoRectify()求得
Rect validRoi[2];//图像校正之后，会对图像进行裁剪，这里的validROI就是指裁剪之后的区域
// 校准双目图像 摆正两幅图像
stereoRectify(cameraMatrix[0], distCoeffs[0],
cameraMatrix[1], distCoeffs[1],
imageSize, R, T, R1, R2, P1, P2, Q,
CALIB_ZERO_DISPARITY, 1, imageSize, &validRoi[0], &validRoi[1]);
/*
R T 左相机到右相机 的 旋转 平移矩阵 R 3*3 T 3*1 T中第一个Tx为 基线长度
立体校正的时候需要两幅图像共面并且行对准 以使得立体匹配更加的可靠
使得两幅图像共面的方法就是把两个摄像头的图像投影到一个公共成像面上，这样每幅图像从本图像平面投影到公共图像平面都需要一个旋转矩阵R
stereoRectify 这个函数计算的就是从图像平面投影都公共成像平面的旋转矩阵Rl,Rr。 Rl,Rr即为左右相机平面行对准的校正旋转矩阵。
左相机经过Rl旋转，右相机经过Rr旋转之后，两幅图像就已经共面并且行对准了。
其中Pl,Pr为两个相机的投影矩阵，其作用是将3D点的坐标转换到图像的2D点的坐标:P*[X Y Z 1]' =[x y w]
Q矩阵为重投影矩阵，即矩阵Q可以把2维平面(图像平面)上的点投影到3维空间的点:Q*[x y d 1] = [X Y Z W]。其中d为左右两幅图像的视差
*/
fs.open("extrinsics.yml", FileStorage::WRITE);
if( fs.isOpened() )
{
fs << "R" << R << "T" << T << "R1" << R1 << "R2" << R2 << "P1" << P1 << "P2" << P2 << "Q" << Q;
fs.release();
}
else
cout << "Error: can not save the extrinsic parameters\n";
cout << "R1=" << R1<& l )
{
l.resize(0);
FileStorage fs(filename, FileStorage::READ);
if( !fs.isOpened() )
return false;
FileNode n = fs.getFirstTopLevelNode();
if( n.type() != FileNode::SEQ )
return false;
FileNodeIterator it = n.begin(), it_end = n.end();
for( ; it != it_end; ++it )
l.push_back((string)*it);
return true;
}
int main(int argc, char** argv)
{
Size boardSize;
string imagelistfn;
bool showRectified = true;
for( int i = 1; i < argc; i++ )
{
if( string(argv[i]) == "-w" )
{
if( sscanf(argv[++i], "%d", &boardSize.width) != 1 || boardSize.width <= 0 )
{
cout << "invalid board width" << endl;
return print_help();
}
}
else if( string(argv[i]) == "-h" )
{
if( sscanf(argv[++i], "%d", &boardSize.height) != 1 || boardSize.height <= 0 )
{
cout << "invalid board height" << endl;
return print_help();
}
}
else if( string(argv[i]) == "-nr" )
showRectified = false;
else if( string(argv[i]) == "--help" )
return print_help();
else if( argv[i][0] == '-' )
{
cout << "invalid option " << argv[i] << endl;
return 0;
}
else
imagelistfn = argv[i];
}
if( imagelistfn == "" )
{
imagelistfn = "stereo_calib.xml";
boardSize = Size(9, 6);
}
else if( boardSize.width <= 0 || boardSize.height <= 0 )
{
cout << "if you specified XML file with chessboards, you should also specify the board width and height (-w and -h options)" << endl;
return 0;
}
vector imagelist;
bool ok = readStringList(imagelistfn, imagelist);
if(!ok || imagelist.empty())
{
cout << "can not open " << imagelistfn << " or the string list is empty" << endl;
return print_help();
}
StereoCalib(imagelist, boardSize, true, showRectified);
return 0;
}
得到内参和外参
%YAML:1.0
M1: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [ 4.9118398778494270e+02, 0., 3.0200043006172234e+02, 0.,
4.5254392629023482e+02, 2.4429761654279827e+02, 0., 0., 1. ]
D1: !!opencv-matrix
rows: 1
cols: 12
dt: d
data: [ 6.5502968305794426e-02, -4.3702707841165300e-01, 0., 0., 0.,
0., 0., -7.3822045850612006e-01, 0., 0., 0., 0. ]
M2: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [ 4.9118398778494270e+02, 0., 3.0251380762012855e+02, 0.,
4.5254392629023482e+02, 2.4325509154105887e+02, 0., 0., 1. ]
D2: !!opencv-matrix
rows: 1
cols: 12
dt: d
data: [ 1.1948494081208850e-03, -8.6262227111310374e-02, 0., 0., 0.,
0., 0., -1.6192883952372866e-01, 0., 0., 0., 0. ]
M1,M2–内参矩阵
D1,D2–畸变向量
摄像机内参矩阵：
Fx s x0
K= 0 Fy y0
0 0 1
fx fy 为焦距 x0 y0 主点坐标（相对成像平面）s为坐标倾斜参数（理想情况下为0）
1
D= { K1,K2,P1, P2{K3{K4,K5,K6}, { S1,S2,S3,S4} } }
k1,k2,k3,k4,k5,k6为径向畸变，p1,p2为切向畸变
%YAML:1.0
R: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [ 9.9884316762675907e-01, -4.8077480586275118e-02,
-9.3933264525424703e-04, 4.8079103838389509e-02,
9.9884192810661887e-01, 1.7895335563569564e-03,
8.5220856570500326e-04, -1.8326256377959298e-03,
9.9999795760983046e-01 ]
T: !!opencv-matrix
rows: 3
cols: 1
dt: d
data: [ -7.5570887376059744e+01, -1.0940390462549969e+00,
9.5613262991292036e-01 ]
R1: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [ 9.9934373466630544e-01, -3.3587861035826658e-02,
-1.3563022312117781e-02, 3.3602237038357116e-02,
9.9943493791852078e-01, 8.3338695733287842e-04,
1.3527366677186979e-02, -1.2885879250515359e-03,
9.9990767068361885e-01 ]
R2: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [ 9.9981522140855861e-01, 1.4474315827705250e-02,
-1.2649791345115189e-02, -1.4487731769855497e-02,
9.9989457722534925e-01, -9.6956872107580310e-04,
1.2634423925127316e-02, 1.1526563494858851e-03,
9.9991951811904345e-01 ]
P1: !!opencv-matrix
rows: 3
cols: 4
dt: d
data: [ 4.4205452306772054e+02, 0., 3.1627860260009766e+02, 0., 0.,
4.4205452306772054e+02, 2.4280571746826172e+02, 0., 0., 0., 1.,
0. ]
P2: !!opencv-matrix
rows: 3
cols: 4
dt: d
data: [ 4.4205452306772054e+02, 0., 3.1627860260009766e+02,
-3.3412626514892305e+04, 0., 4.4205452306772054e+02,
2.4280571746826172e+02, 0., 0., 0., 1., 0. ]
Q: !!opencv-matrix
rows: 4
cols: 4
dt: d
data: [ 1., 0., 0., -3.1627860260009766e+02, 0., 1., 0.,
-2.4280571746826172e+02, 0., 0., 0., 4.4205452306772054e+02, 0.,
0., 1.3230163838532507e-02, 0. ]
extrinsic params外参
R–右相机相对左相机的旋转矩阵
T–右相机相对左相机的平移矩阵
R1,R2–左右相机校准变换（旋转）矩阵
P1,P2–左右相机在校准后坐标系中的投影矩阵
Q–视差-深度映射矩阵，我利用它来计算单个目标点的三维坐标
---------------------
作者：Lary_Rock
来源：CSDN
原文：https://blog.csdn.net/ruidongren/article/details/79900259
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