opencv双目标定
opencv-3.0.0-alpha\samples\cpp中编译stereo_calib.cpp
执行 cpp-example-stereo_calib -w 9 -h 6 stereo_calib.xml
stereo_calib.xml对应的是图片
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include \n" << endl;
return 0;
}
static void
StereoCalib(const vector<string>& 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<vector// 文件列表 需要交替 "left01.jpg" "right01.jpg"...
for( i = j = 0; i < nimages; i++ ) //5对图像
{
for( k = 0; k < 2; k++ )//左右两个相机图片 k=0,1
{
const string& filename = imagelist[i*2+k]; //逐个读取图片
Mat img = imread(filename, 0); //图像数据
if(img.empty())
break;
if( imageSize == Size() )
imageSize = img.size();
else if( img.size() != imageSize )// 图片需要保持一样的大小
{
cout << "The image " << filename << " has the size different from the first image size. Skipping the pair\n";
break;
}
bool found = false;
vectorbreak;
}
// 亚像素级优化
cornerSubPix(img, corners, Size(11,11), Size(-1,-1),
TermCriteria(TermCriteria::COUNT+TermCriteria::EPS,
30, 0.01));
}
if( k == 2 )//上面的for循环结束后 k=2
{
goodImageList.push_back(imagelist[i*2]);
goodImageList.push_back(imagelist[i*2+1]);
j++;
}
}
cout << j << " pairs have been successfully detected.\n";
nimages = j;
if( nimages < 2 )
{
cout << "Error: too little pairs to run the calibration\n";
return;
}
imagePoints[0].resize(nimages);//左相机 角点位置
imagePoints[1].resize(nimages);//右相机 角点位置
// 角点实际 位置 按照 squareSize 得出
objectPoints.resize(nimages);
for( i = 0; i < nimages; i++ )
{
for( j = 0; j < boardSize.height; j++ ) //每一行
for( k = 0; k < boardSize.width; k++ ) //每一列
objectPoints[i].push_back(Point3f(j*squareSize, k*squareSize, 0)); //直接转为float类型，坐标为行、列
}
cout << "Running stereo calibration ...\n";
//创建内参矩阵
Mat cameraMatrix[2], distCoeffs[2];//左右两个相机 的内参数矩阵和 畸变参数
//cameraMatrix[0] = Mat::eye(3, 3, CV_64F);//初始化内参数矩阵
//cameraMatrix[1] = Mat::eye(3, 3, CV_64F);
cameraMatrix[0] = initCameraMatrix2D(objectPoints,imagePoints[0],imageSize,0);
cameraMatrix[1] = initCameraMatrix2D(objectPoints,imagePoints[1],imageSize,0);
Mat R, T, E, F;//R 旋转矢量 T平移矢量 E本征矩阵 F基础矩阵
double rms = stereoCalibrate(objectPoints, imagePoints[0], imagePoints[1],//真实点坐标 左右两个相机点坐标
cameraMatrix[0], distCoeffs[0],
cameraMatrix[1], distCoeffs[1],
imageSize, R, T, E, F,
CALIB_FIX_ASPECT_RATIO +
CALIB_ZERO_TANGENT_DIST +
CALIB_SAME_FOCAL_LENGTH +
CALIB_RATIONAL_MODEL +
CALIB_FIX_K3 + CALIB_FIX_K4 + CALIB_FIX_K5,
TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, 1e-5) );
cout << "done with RMS error=" << rms << endl;
cout << "cameraMatrix[0]=" << cameraMatrix[0]<cout << "cameraMatrix[1]=" << cameraMatrix[1]<// 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
//计算标定误差
double err = 0;
int npoints = 0;
vectorcout << "R2=" << R2<cout << "P1=" << P1<cout << "P2=" << P2<// OpenCV can handle left-right
// or up-down camera arrangements
// 辨认 左右结构的相机 或者 上下结构的相机
bool isVerticalStereo = fabs(P2.at<double>(1, 3)) > fabs(P2.at<double>(0, 3));
// COMPUTE AND DISPLAY RECTIFICATION
if( !showRectified )
return;
Mat rmap[2][2];//映射表
// IF BY CALIBRATED (BOUGUET'S METHOD)
if( useCalibrated )
{
// we already computed everything
}
// OR ELSE HARTLEY'S METHOD
else
// use intrinsic parameters of each camera, but
// compute the rectification transformation directly
// from the fundamental matrix
{
vector" H1:"<"*"<0].inv()*H1*cameraMatrix[0];
R2 = cameraMatrix[1].inv()*H2*cameraMatrix[1];
P1 = cameraMatrix[0];
P2 = cameraMatrix[1];
cout << "R1 = " << R1<cout << "R2 = " << R2<cout << "P1 = " << P1<cout << "P2 = " << P2</*
根据stereoRectify 计算出来的R 和 P 来计算图像的映射表 mapx,mapy
mapx,mapy这两个映射表接下来可以给remap()函数调用，来校正图像，使得两幅图像共面并且行对准
ininUndistortRectifyMap()的参数newCameraMatrix就是校正后的摄像机矩阵。在openCV里面，校正后的计算机矩阵Mrect是跟投影矩阵P一起返回的。
所以我们在这里传入投影矩阵P，此函数可以从投影矩阵P中读出校正后的摄像机矩阵
*/
//Precompute maps for cv::remap()
initUndistortRectifyMap(cameraMatrix[0], distCoeffs[0], R1, P1, imageSize, CV_16SC2, rmap[0][0], rmap[0][1]);
initUndistortRectifyMap(cameraMatrix[1], distCoeffs[1], R2, P2, imageSize, CV_16SC2, rmap[1][0], rmap[1][1]);
//cout <<"cameraMatrix[0]"<
//cout <<"cameraMatrix[1]"<
/*
把校正结果显示出来
把左右两幅图像显示到同一个画面上
这里只显示了最后一副图像的校正结果。并没有把所有的图像都显示出来
*/
Mat canvas;
double sf;
int w, h;
if( !isVerticalStereo )
{
sf =600./MAX(imageSize.width, imageSize.height);
w = cvRound(imageSize.width*sf);
h = cvRound(imageSize.height*sf);
canvas.create(h, w*2, CV_8UC3);
}
else
{
sf = 300./MAX(imageSize.width, imageSize.height);
w = cvRound(imageSize.width*sf);
h = cvRound(imageSize.height*sf);
canvas.create(h*2, w, CV_8UC3);
}
for( i = 0; i < nimages; i++ )
{
for( k = 0; k < 2; k++ )
{
Mat img = imread(goodImageList[i*2+k], 0), rimg, cimg;
//经过remap之后，左右相机的图像已经共面并且行对准了
remap(img, rimg, rmap[k][0], rmap[k][1], INTER_LINEAR);
cvtColor(rimg, cimg, COLOR_GRAY2BGR);
imshow("cimg", cimg);
cout << "isVerticalStereo=" << isVerticalStereo <<" sf="<//得到画布的一部分
Mat canvasPart = !isVerticalStereo ? canvas(Rect(w*k, 0, w, h)) : canvas(Rect(0, h*k, w, h));
imshow("rectified-000", canvas);
resize(cimg, canvasPart, canvasPart.size(), 0, 0, INTER_AREA);
if( useCalibrated )
{
//获得被截取的区域
Rect vroi(cvRound(validRoi[k].x*sf), cvRound(validRoi[k].y*sf),
cvRound(validRoi[k].width*sf), cvRound(validRoi[k].height*sf));
rectangle(canvasPart, vroi, Scalar(0,0,255), 3, 8);画上一个矩形
}
}
//画上对应的线条
if( !isVerticalStereo )
for( j = 0; j < canvas.rows; j += 16 )
//画平行线
line(canvas, Point(0, j), Point(canvas.cols, j), Scalar(0, 255, 0), 1, 8);
else
for( j = 0; j < canvas.cols; j += 16 )
line(canvas, Point(j, 0), Point(j, canvas.rows), Scalar(0, 255, 0), 1, 8);
imshow("rectified", canvas);
char c = (char)waitKey();
if( c == 27 || c == 'q' || c == 'Q' )
break;
}
}
static bool readStringList( const string& filename, vector<string>& 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<string> 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）
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