相机标定——单目、双目
双目标定
作者：雪天枫
https://www.cnblogs.com/polly333/p/5013505.html
#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 void StereoCalib(const vector& imagelist, Size boardSize, bool useCalibrated=true, bool showRectified=true)
{
if( imagelist.size() % 2 != 0 )
{
cout << "Error: the image list contains odd (non-even) number of elements\n";
return;
}
bool displayCorners = false;//true;
const int maxScale = 2;
const float squareSize = 1.f; // Set this to your actual square size
// ARRAY AND VECTOR STORAGE:
//创建图像坐标和世界坐标系坐标矩阵
vector<vector > imagePoints[2];
vector<vector > objectPoints;
Size imageSize;
int i, j, k, nimages = (int)imagelist.size()/2;
//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;
for( i = j = 0; i < nimages; i++ )
{
for( k = 0; k < 2; k++ )
{
//逐个读取图片
const string& filename = imagelist[i*2+k];
Mat img = imread(filename, 0);
if(img.empty())
break;
if( imageSize == Size() )
imageSize = img.size();
else if( img.size() != imageSize )
{
cout << "The image " << filename << " has the size different from the first image size. Skipping the pair\n";
break;
}
bool found = false;
//设置图像矩阵的引用，此时指向左右视图的矩阵首地址
vector& corners = imagePoints[k][j];
for( int scale = 1; scale 1 )
{
Mat cornersMat(corners);
cornersMat *= 1./scale;
}
break;
}
}
if( displayCorners )
{
cout << filename << endl;
Mat cimg, cimg1;
cvtColor(img, cimg, COLOR_GRAY2BGR);
drawChessboardCorners(cimg, boardSize, corners, found);
double sf = 640./MAX(img.rows, img.cols);
resize(cimg, cimg1, Size(), sf, sf);
imshow("corners", cimg1);
char c = (char)waitKey(500);
if( c == 27 || c == 'q' || c == 'Q' ) //Allow ESC to quit
exit(-1);
}
else
putchar('.');
if( !found )
break;
cornerSubPix(img, corners, Size(11,11), Size(-1,-1),
TermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,
30, 0.01));
}
if( 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);
imagePoints[1].resize(nimages);
// 图像点的世界坐标系
objectPoints.resize(nimages);
for( i = 0; i < nimages; i++ )
{
for( j = 0; j < boardSize.height; j++ )
for( k = 0; k < boardSize.width; k++ )
//直接转为float类型，坐标为行、列
objectPoints[i].push_back(Point3f(j*squareSize, k*squareSize, 0));
}
cout << "Running stereo calibration ...\n";
//创建内参矩阵
Mat cameraMatrix[2], distCoeffs[2];
cameraMatrix[0] = Mat::eye(3, 3, CV_64F);
cameraMatrix[1] = Mat::eye(3, 3, CV_64F);
Mat R, T, E, F;
//求解双目标定的参数
double rms = stereoCalibrate(objectPoints, imagePoints[0], imagePoints[1],
cameraMatrix[0], distCoeffs[0],
cameraMatrix[1], distCoeffs[1],
imageSize, R, T, E, F,
TermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, 1e-5),
CV_CALIB_FIX_ASPECT_RATIO +
CV_CALIB_ZERO_TANGENT_DIST +
CV_CALIB_SAME_FOCAL_LENGTH +
CV_CALIB_RATIONAL_MODEL +
CV_CALIB_FIX_K3 + CV_CALIB_FIX_K4 + CV_CALIB_FIX_K5);
cout << "done with RMS error=" << rms << endl;
// 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;
vector lines[2];
for( i = 0; i < nimages; i++ )
{
int npt = (int)imagePoints[0][i].size();
Mat imgpt[2];
for( k = 0; k < 2; k++ )
{
imgpt[k] = Mat(imagePoints[k][i]);
//校正图像点坐标
undistortPoints(imgpt[k], imgpt[k], cameraMatrix[k], distCoeffs[k], Mat(), cameraMatrix[k]);
//求解对极线
computeCorrespondEpilines(imgpt[k], k+1, F, lines[k]);
}
//计算求解点与实际点的误差
for( j = 0; j < npt; j++ )
{
double errij = fabs(imagePoints[0][i][j].x*lines[1][j][0] +
imagePoints[0][i][j].y*lines[1][j][1] + lines[1][j][2]) +
fabs(imagePoints[1][i][j].x*lines[0][j][0] +
imagePoints[1][i][j].y*lines[0][j][1] + lines[0][j][2]);
err += errij;
}
npoints += npt;
}
cout << "average reprojection err = " << err/npoints << endl;
// save intrinsic parameters
FileStorage fs("intrinsics.yml", CV_STORAGE_WRITE);
if( fs.isOpened() )
{
fs << "M1" << cameraMatrix[0] << "D1" << distCoeffs[0] <<
"M2" << cameraMatrix[1] << "D2" << distCoeffs[1];
fs.release();
}
else
cout << "Error: can not save the intrinsic parameters\n";
Mat R1, R2, P1, P2, Q;
Rect validRoi[2];
//双目视觉校正，根据内参矩阵，两摄像机之间平移矩阵以及投射矩阵
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]);
fs.open("extrinsics.yml", CV_STORAGE_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 intrinsic parameters\n";
// OpenCV can handle left-right
// or up-down camera arrangements
bool isVerticalStereo = fabs(P2.at(1, 3)) > fabs(P2.at(0, 3));
// COMPUTE AND DISPLAY RECTIFICATION
if( !showRectified )
return;
Mat rmap[2][2];
// IF BY CALIBRATED (CALIBRATE'S METHOD)
//用标定的话，就不许计算左右相机的透射矩阵
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 allimgpt[2];
for( k = 0; k < 2; k++ )
{
for( i = 0; i < nimages; i++ )
std::copy(imagePoints[k][i].begin(), imagePoints[k][i].end(), back_inserter(allimgpt[k]));
}
F = findFundamentalMat(Mat(allimgpt[0]), Mat(allimgpt[1]), FM_8POINT, 0, 0);
Mat H1, H2;
stereoRectifyUncalibrated(Mat(allimgpt[0]), Mat(allimgpt[1]), F, imageSize, H1, H2, 3);
R1 = cameraMatrix[0].inv()*H1*cameraMatrix[0];
R2 = cameraMatrix[1].inv()*H2*cameraMatrix[1];
P1 = cameraMatrix[0];
P2 = cameraMatrix[1];
}
//Precompute maps for cv::remap()
//根据左右相机的投射矩阵，校正图像
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]);
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;
remap(img, rimg, rmap[k][0], rmap[k][1], CV_INTER_LINEAR);
cvtColor(rimg, cimg, COLOR_GRAY2BGR);
Mat canvasPart = !isVerticalStereo ? canvas(Rect(w*k, 0, w, h)) : canvas(Rect(0, h*k, w, h));
resize(cimg, canvasPart, canvasPart.size(), 0, 0, CV_INTER_AREA);
if( useCalibrated )
{
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);
}
}
if( !isVerticalStereo )
for( j = 0; j < canvas.rows; j += 16 )
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& 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;
}
单目标定
https://www.cnblogs.com/zyly/p/9366080.html
/*************************************************************************************
*
* Description：相机标定，张氏标定法 单目标定
* Author ：JNU
* Data ：2018.7.22
*
************************************************************************************/
#include
#include
#include
#include
#include
#include
#include
using namespace cv;
using namespace std;
void main(char *args)
{
//保存文件名称
std::vector filenames;
//需要更改的参数
//左相机标定，指定左相机图片路径，以及标定结果保存文件
string infilename = "sample/left/filename.txt"; //如果是右相机把left改为right
string outfilename = "sample/left/caliberation_result.txt";
//标定所用图片文件的路径,每一行保存一个标定图片的路径 ifstream 是从硬盘读到内存
ifstream fin(infilename);
//保存标定的结果 ofstream 是从内存写到硬盘
ofstream fout(outfilename);
/*
1.读取毎一幅图像，从中提取出角点，然后对角点进行亚像素精确化、获取每个角点在像素坐标系中的坐标
像素坐标系的原点位于图像的左上角
*/
std::cout << "开始提取角点......" << std::endl;;
//图像数量
int imageCount = 0;
//图像尺寸
cv::Size imageSize;
//标定板上每行每列的角点数
cv::Size boardSize = cv::Size(9, 6);
//缓存每幅图像上检测到的角点
std::vector imagePointsBuf;
//保存检测到的所有角点
std::vector<std::vector> imagePointsSeq;
char filename[100];
if (fin.is_open())
{
//读取完毕？
while (!fin.eof())
{
//一次读取一行
fin.getline(filename, sizeof(filename) / sizeof(char));
//保存文件名
filenames.push_back(filename);
//读取图片
Mat imageInput = cv::imread(filename);
//读入第一张图片时获取图宽高信息
if (imageCount == 0)
{
imageSize.width = imageInput.cols;
imageSize.height = imageInput.rows;
std::cout << "imageSize.width = " << imageSize.width << std::endl;
std::cout << "imageSize.height = " << imageSize.height << std::endl;
}
std::cout << "imageCount = " << imageCount << std::endl;
imageCount++;
//提取每一张图片的角点
if (cv::findChessboardCorners(imageInput, boardSize, imagePointsBuf) == 0)
{
//找不到角点
std::cout << "Can not find chessboard corners!" << std::endl;
exit(1);
}
else
{
Mat viewGray;
//转换为灰度图片
cv::cvtColor(imageInput, viewGray, cv::COLOR_BGR2GRAY);
//亚像素精确化 对粗提取的角点进行精确化
cv::find4QuadCornerSubpix(viewGray, imagePointsBuf, cv::Size(5, 5));
//保存亚像素点
imagePointsSeq.push_back(imagePointsBuf);
//在图像上显示角点位置
cv::drawChessboardCorners(viewGray, boardSize, imagePointsBuf, true);
//显示图片
//cv::imshow("Camera Calibration", viewGray);
cv::imwrite("test.jpg", viewGray);
//等待0.5s
//waitKey(500);
}
}
//计算每张图片上的角点数 54
int cornerNum = boardSize.width * boardSize.height;
//角点总数
int total = imagePointsSeq.size()*cornerNum;
std::cout << "total = " << total << std::endl;
for (int i = 0; i < total; i++)
{
int num = i / cornerNum;
int p = i%cornerNum;
//cornerNum是每幅图片的角点个数，此判断语句是为了输出，便于调试
if (p == 0)
{
std::cout << "\n第 " << num+1 <: " << std::endl;
}
//输出所有的角点
std::cout<<p+1<<":("<< imagePointsSeq[num][p].x;
std::cout << imagePointsSeq[num][p].y<<")\t";
if ((p+1) % 3 == 0)
{
std::cout << std::endl;
}
}
std::cout << "角点提取完成!" << std::endl;
/*
2.摄像机标定 世界坐标系原点位于标定板左上角(第一个方格的左上角)
*/
std::cout << "开始标定" << std::endl;
//棋盘三维信息，设置棋盘在世界坐标系的坐标
//实际测量得到标定板上每个棋盘格的大小
cv::Size squareSize = cv::Size(26, 26);
//毎幅图片角点数量
std::vector pointCounts;
//保存标定板上角点的三维坐标
std::vector<std::vector> objectPoints;
//摄像机内参数矩阵 M=[fx γ u0,0 fy v0,0 0 1]
cv::Mat cameraMatrix = cv::Mat(3, 3, CV_64F, Scalar::all(0));
//摄像机的5个畸变系数k1,k2,p1,p2,k3
cv::Mat distCoeffs = cv::Mat(1, 5, CV_64F, Scalar::all(0));
//每幅图片的旋转向量
std::vector tvecsMat;
//每幅图片的平移向量
std::vector rvecsMat;
//初始化标定板上角点的三维坐标
int i, j, t;
for (t = 0; t < imageCount; t++)
{
std::vector tempPointSet;
//行数
for (i = 0; i < boardSize.height; i++)
{
//列数
for (j = 0; j < boardSize.width; j++)
{
cv::Point3f realPoint;
//假设标定板放在世界坐标系中z=0的平面上。
realPoint.x = i*squareSize.width;
realPoint.y = j*squareSize.height;
realPoint.z = 0;
tempPointSet.push_back(realPoint);
}
}
objectPoints.push_back(tempPointSet);
}
//初始化每幅图像中的角点数量，假定每幅图像中都可以看到完整的标定板
for (i = 0; i < imageCount; i++)
{
pointCounts.push_back(boardSize.width*boardSize.height);
}
//开始标定
cv::calibrateCamera(objectPoints, imagePointsSeq, imageSize, cameraMatrix, distCoeffs, rvecsMat, tvecsMat);
std::cout << "标定完成" << std::endl;
//对标定结果进行评价
std::cout << "开始评价标定结果......" << std::endl;
//所有图像的平均误差的总和
double totalErr = 0.0;
//每幅图像的平均误差
double err = 0.0;
//保存重新计算得到的投影点
std::vector imagePoints2;
std::cout << "每幅图像的标定误差:" << std::endl;
fout << "每幅图像的标定误差:" << std::endl;
for (i = 0; i < imageCount; i++)
{
std::vector tempPointSet = objectPoints[i];
//通过得到的摄像机内外参数，对空间的三维点进行重新投影计算，得到新的投影点imagePoints2（在像素坐标系下的点坐标）
cv::projectPoints(tempPointSet, rvecsMat[i], tvecsMat[i], cameraMatrix, distCoeffs, imagePoints2);
//计算新的投影点和旧的投影点之间的误差
std::vector tempImagePoint = imagePointsSeq[i];
cv::Mat tempImagePointMat = cv::Mat(1, tempImagePoint.size(), CV_32FC2);
cv::Mat imagePoints2Mat = cv::Mat(1, imagePoints2.size(), CV_32FC2);
for (int j = 0; j < tempImagePoint.size(); j++)
{
imagePoints2Mat.at(0, j) = cv::Vec2f(imagePoints2[j].x, imagePoints2[j].y);
tempImagePointMat.at(0, j) = cv::Vec2f(tempImagePoint[j].x, tempImagePoint[j].y);
}
//Calculates an absolute difference norm or a relative difference norm.
err = cv::norm(imagePoints2Mat, tempImagePointMat, NORM_L2);
totalErr += err /= pointCounts[i];
std::cout << " 第" << i + 1 << "幅图像的平均误差：" << err << "像素" << endl;
fout<< "第" << i + 1 << "幅图像的平均误差：" << err << "像素" << endl;
}
//每张图像的平均总误差
std::cout << " 总体平均误差:" << totalErr / imageCount << "像素" << std::endl;
fout << "总体平均误差:" << totalErr / imageCount << "像素" << std::endl;
std::cout << "评价完成!" << std::endl;
//保存标定结果
std::cout << "开始保存标定结果....." << std::endl;
//保存每张图像的旋转矩阵
cv::Mat rotationMatrix = cv::Mat(3, 3, CV_32FC1, Scalar::all(0));
fout << "相机内参数矩阵:" << std::endl;
fout << cameraMatrix << std::endl << std::endl;
fout << "畸变系数:" << std::endl;
fout << distCoeffs << std::endl << std::endl;
for (int i = 0; i < imageCount; i++)
{
fout << "第" << i + 1 << "幅图像的旋转向量:" << std::endl;
fout << tvecsMat[i] << std::endl;
//将旋转向量转换为相对应的旋转矩阵
cv::Rodrigues(tvecsMat[i], rotationMatrix);
fout << "第" << i + 1 << "幅图像的旋转矩阵:" << std::endl;
fout << rotationMatrix << std::endl;
fout << "第" << i + 1 << "幅图像的平移向量:" << std::endl;
fout << rvecsMat[i] << std::endl;
}
std::cout << "保存完成" << std::endl;
/************************************************************************
显示定标结果
*************************************************************************/
cv::Mat mapx = cv::Mat(imageSize, CV_32FC1);
cv::Mat mapy = cv::Mat(imageSize, CV_32FC1);
cv::Mat R = cv::Mat::eye(3, 3, CV_32F);
std::cout << "显示矫正图像" << endl;
for (int i = 0; i != imageCount; i++)
{
std::cout << "Frame #" << i + 1 << "..." << endl;
//计算图片畸变矫正的映射矩阵mapx、mapy(不进行立体校正、立体校正需要使用双摄)
initUndistortRectifyMap(cameraMatrix, distCoeffs, R, cameraMatrix, imageSize, CV_32FC1, mapx, mapy);
//读取一张图片
Mat imageSource = imread(filenames[i]);
Mat newimage = imageSource.clone();
//另一种不需要转换矩阵的方式
//undistort(imageSource,newimage,cameraMatrix,distCoeffs);
//进行校正
remap(imageSource, newimage, mapx, mapy, INTER_LINEAR);
imshow("原始图像", imageSource);
imshow("矫正后图像", newimage);
waitKey();
}
//释放资源
fin.close();
fout.close();
system("pause");
}
}
作者：雪天枫
