相机标定采用的是opencv自带的标定文件。主要有下面几个文件：
（1）camera_calibration.cpp，这个文件就是生成标定参数的文件，前期准备好后，只需要运行这个文件就可以了。
(2) default.xml PS:有的opencv版本里不叫这个名字，叫in_VID5.xml，但内容是一样的，具体的需要查看camera_calibration.cpp文件中这个位置：
这个文件中需要修改的是棋盘格内角点的数目，就是棋盘格的数量减1
square_size这个参数，实测无影响
（3）VID5.xml ，这个文件中保存了需要标定的棋盘格照片位置。
在标定前，应该对采集的棋盘格照片做一下增强，比如直方图均衡、对比度增强增强等，但是实测增强后的效果反而不好，不知道是什么原因，如有知道的大佬还请告知。
事后分析，对比了增强前后找到的角点位置，有一定差异，怀疑增强后的角点位置没找准，增强后连噪声一并增强了，应该再做一下去噪处理，但这个没有验证。
下面贴一下，对camera_calibration.cpp中相关函数的参数介绍和用法：
#include <iostream>
#include <sstream>
#include <string>
#include <ctime>
#include <cstdio>
#include <opencv2/core.hpp>
#include <opencv2/core/utility.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/calib3d.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/videoio.hpp>
#include <opencv2/highgui.hpp>
using namespace cv;
using namespace std;
class Settings
{
public:
Settings() : goodInput(false) {}
enum Pattern { NOT_EXISTING, CHESSBOARD, CIRCLES_GRID, ASYMMETRIC_CIRCLES_GRID };
enum InputType { INVALID, CAMERA, VIDEO_FILE, IMAGE_LIST };
void write(FileStorage& fs) const //Write serialization for this class
{
fs << "{"
<< "BoardSize_Width" << boardSize.width
<< "BoardSize_Height" << boardSize.height
<< "Square_Size" << squareSize
<< "Calibrate_Pattern" << patternToUse
<< "Calibrate_NrOfFrameToUse" << nrFrames
<< "Calibrate_FixAspectRatio" << aspectRatio
<< "Calibrate_AssumeZeroTangentialDistortion" << calibZeroTangentDist
<< "Calibrate_FixPrincipalPointAtTheCenter" << calibFixPrincipalPoint
<< "Write_DetectedFeaturePoints" << writePoints
<< "Write_extrinsicParameters" << writeExtrinsics
<< "Write_gridPoints" << writeGrid
<< "Write_outputFileName" << outputFileName
<< "Show_UndistortedImage" << showUndistorted
<< "Input_FlipAroundHorizontalAxis" << flipVertical
<< "Input_Delay" << delay
<< "Input" << input
<< "}";
}
void read(const FileNode& node) //Read serialization for this class
{
node["BoardSize_Width" ] >> boardSize.width;
node["BoardSize_Height"] >> boardSize.height;
node["Calibrate_Pattern"] >> patternToUse;
node["Square_Size"] >> squareSize;
node["Calibrate_NrOfFrameToUse"] >> nrFrames;
node["Calibrate_FixAspectRatio"] >> aspectRatio;
node["Write_DetectedFeaturePoints"] >> writePoints;
node["Write_extrinsicParameters"] >> writeExtrinsics;
node["Write_gridPoints"] >> writeGrid;
node["Write_outputFileName"] >> outputFileName;
node["Calibrate_AssumeZeroTangentialDistortion"] >> calibZeroTangentDist;
node["Calibrate_FixPrincipalPointAtTheCenter"] >> calibFixPrincipalPoint;
node["Calibrate_UseFisheyeModel"] >> useFisheye;
node["Input_FlipAroundHorizontalAxis"] >> flipVertical;
node["Show_UndistortedImage"] >> showUndistorted;
node["Input"] >> input;
node["Input_Delay"] >> delay;
node["Fix_K1"] >> fixK1;
node["Fix_K2"] >> fixK2;
node["Fix_K3"] >> fixK3;
node["Fix_K4"] >> fixK4;
node["Fix_K5"] >> fixK5;
validate();
}
void validate()
{
goodInput = true;
if (boardSize.width <= 0 || boardSize.height <= 0)
{
cerr << "Invalid Board size: " << boardSize.width << " " << boardSize.height << endl;
goodInput = false;
}
if (squareSize <= 10e-6)
{
cerr << "Invalid square size " << squareSize << endl;
goodInput = false;
}
if (nrFrames <= 0)
{
cerr << "Invalid number of frames " << nrFrames << endl;
goodInput = false;
}
if (input.empty()) // Check for valid input
inputType = INVALID;
else
{
if (input[0] >= '0' && input[0] <= '9')
{
stringstream ss(input);
ss >> cameraID;
inputType = CAMERA;
}
else
{
if (isListOfImages(input) && readStringList(input, imageList))
{
inputType = IMAGE_LIST;
nrFrames = (nrFrames < (int)imageList.size()) ? nrFrames : (int)imageList.size();
}
else
inputType = VIDEO_FILE;
}
if (inputType == CAMERA)
inputCapture.open(cameraID);
if (inputType == VIDEO_FILE)
inputCapture.open(input);
if (inputType != IMAGE_LIST && !inputCapture.isOpened())
inputType = INVALID;
}
if (inputType == INVALID)
{
cerr << " Input does not exist: " << input;
goodInput = false;
}
flag = 0;
if(calibFixPrincipalPoint) flag |= CALIB_FIX_PRINCIPAL_POINT;
if(calibZeroTangentDist) flag |= CALIB_ZERO_TANGENT_DIST;
if(aspectRatio) flag |= CALIB_FIX_ASPECT_RATIO;
if(fixK1) flag |= CALIB_FIX_K1;
if(fixK2) flag |= CALIB_FIX_K2;
if(fixK3) flag |= CALIB_FIX_K3;
if(fixK4) flag |= CALIB_FIX_K4;
if(fixK5) flag |= CALIB_FIX_K5;
if (useFisheye) {
// the fisheye model has its own enum, so overwrite the flags
flag = fisheye::CALIB_FIX_SKEW | fisheye::CALIB_RECOMPUTE_EXTRINSIC;
if(fixK1) flag |= fisheye::CALIB_FIX_K1;
if(fixK2) flag |= fisheye::CALIB_FIX_K2;
if(fixK3) flag |= fisheye::CALIB_FIX_K3;
if(fixK4) flag |= fisheye::CALIB_FIX_K4;
if (calibFixPrincipalPoint) flag |= fisheye::CALIB_FIX_PRINCIPAL_POINT;
}
calibrationPattern = NOT_EXISTING;
if (!patternToUse.compare("CHESSBOARD")) calibrationPattern = CHESSBOARD;
if (!patternToUse.compare("CIRCLES_GRID")) calibrationPattern = CIRCLES_GRID;
if (!patternToUse.compare("ASYMMETRIC_CIRCLES_GRID")) calibrationPattern = ASYMMETRIC_CIRCLES_GRID;
if (calibrationPattern == NOT_EXISTING)
{
cerr << " Camera calibration mode does not exist: " << patternToUse << endl;
goodInput = false;
}
atImageList = 0;
}
Mat nextImage()
{
Mat result;
if( inputCapture.isOpened() )
{
Mat view0;
inputCapture >> view0;
view0.copyTo(result);
}
else if( atImageList < imageList.size() )
result = imread(imageList[atImageList++], IMREAD_COLOR);
return result;
}
static bool readStringList( const string& filename, vector<string>& l )
{
l.clear();
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;
}
static bool isListOfImages( const string& filename)
{
string s(filename);
// Look for file extension
if( s.find(".xml") == string::npos && s.find(".yaml") == string::npos && s.find(".yml") == string::npos )
return false;
else
return true;
}
public:
Size boardSize; // The size of the board -> Number of items by width and height
Pattern calibrationPattern; // One of the Chessboard, circles, or asymmetric circle pattern
float squareSize; // The size of a square in your defined unit (point, millimeter,etc).
int nrFrames; // The number of frames to use from the input for calibration
float aspectRatio; // The aspect ratio
int delay; // In case of a video input
bool writePoints; // Write detected feature points
bool writeExtrinsics; // Write extrinsic parameters
bool writeGrid; // Write refined 3D target grid points
bool calibZeroTangentDist; // Assume zero tangential distortion
bool calibFixPrincipalPoint; // Fix the principal point at the center
bool flipVertical; // Flip the captured images around the horizontal axis
string outputFileName; // The name of the file where to write
bool showUndistorted; // Show undistorted images after calibration
string input; // The input ->
bool useFisheye; // use fisheye camera model for calibration
bool fixK1; // fix K1 distortion coefficient
bool fixK2; // fix K2 distortion coefficient
bool fixK3; // fix K3 distortion coefficient
bool fixK4; // fix K4 distortion coefficient
bool fixK5; // fix K5 distortion coefficient
int cameraID;
vector<string> imageList;
size_t atImageList;
VideoCapture inputCapture;
InputType inputType;
bool goodInput;
int flag;
private:
string patternToUse;
};
static inline void read(const FileNode& node, Settings& x, const Settings& default_value = Settings())
{
if(node.empty())
x = default_value;
else
x.read(node);
}
enum { DETECTION = 0, CAPTURING = 1, CALIBRATED = 2 };
bool runCalibrationAndSave(Settings& s, Size imageSize, Mat& cameraMatrix, Mat& distCoeffs,
vector<vector<Point2f> > imagePoints, float grid_width, bool release_object);
int main(int argc, char* argv[])
{
///OpenCV中CommandLineParse类主要是命令行解析类。主要目：方便用户在命令行使用过程中减少工作量。具体使用方式如下：
const String keys
= "{help h usage ? | | print this message }"
"{@settings |default.xml| input setting file }"
"{d | | actual distance between top-left and top-right corners of "
"the calibration grid }"
"{winSize | 11 | Half of search window for cornerSubPix }";
//定义CommandLineParser类对象parser，并调用其构造函数对其进行初始化
CommandLineParser parser(argc, argv, keys);
//在此类中获取某个变量的方法如下：string pos_path = parser.get<string>("pos_path");
parser.about("This is a camera calibration sample.\n"
"Usage: camera_calibration [configuration_file -- default ./default.xml]\n"
"Near the sample file you'll find the configuration file, which has detailed help of "
"how to edit it. It may be any OpenCV supported file format XML/YAML.");
if (!parser.check()) {
parser.printErrors();
return 0;
}
if (parser.has("help")) {
parser.printMessage();
return 0;
}
//! [file_read]
Settings s;
//inputSettingsFile：default.xml
const string inputSettingsFile = parser.get<string>(0);
//OpenCV中可以使用FileStorage 对xml，yml等文本文件的读写。
//可扩展标记语言，标准通用标记语言的子集，简称XML。是一种用于标记电子文件使其具有结构性的标记语言。YAML是一个可读性高，用来表达数据序列化的格式。
FileStorage fs(inputSettingsFile, FileStorage::READ); // Read the settings
if (!fs.isOpened())//FileStorage::isOpened() ：判断文件是否打开
{
cout << "Could not open the configuration file: \"" << inputSettingsFile << "\"" << endl;
parser.printMessage();
return -1;
}
fs["Settings"] >> s;
fs.release(); // close Settings file
//! [file_read]
//FileStorage fout("settings.yml", FileStorage::WRITE); // write config as YAML
//fout << "Settings" << s;
if (!s.goodInput)
{
cout << "Invalid input detected. Application stopping. " << endl;
return -1;
}
int winSize = parser.get<int>("winSize");
float grid_width = s.squareSize * (s.boardSize.width - 1);//grid_width=240
bool release_object = false;
if (parser.has("d")) {
grid_width = parser.get<float>("d");
release_object = true;
}
//明一个名为imagePoints的容器，其元素为vector的容器 Point2f(x,y)中的x代表在图像中的列，y代表图像中的行。
//Point2f a=Point2f(2,1.5)； 或者 Point2f a(2, 1.5)；或者Point2f a;a.x = 2;a.y = 1.5; 错误用法：Point2f a=(2,1.5);
vector<vector<Point2f> > imagePoints;
Mat cameraMatrix, distCoeffs;
Size imageSize;
//三元运算符 Exp1 ? Exp2 : Exp3; 如果 Exp1 为真，则计算 Exp2 的值，否则计算 Exp3 的值
int mode = s.inputType == Settings::IMAGE_LIST ? CAPTURING : DETECTION;//=1
clock_t prevTimestamp = 0;
const Scalar RED(0,0,255), GREEN(0,255,0);
const char ESC_KEY = 27;
//! [get_input]
int num = 0;
for(;;)//C++ 程序员偏向于使用 for(;;) 结构来表示一个无限循环
{
Mat view;
bool blinkOutput = false;
view = s.nextImage();
//----- 如果没有更多的图像，或得到足够的，则停止校准并显示结果 -------------
if( mode == CAPTURING && imagePoints.size() >= (size_t)s.nrFrames )
{
if(runCalibrationAndSave(s, imageSize, cameraMatrix, distCoeffs, imagePoints, grid_width,
release_object))
mode = CALIBRATED;
else
mode = DETECTION;
}
if(view.empty()) // If there are no more images stop the loop
{
// if calibration threshold was not reached yet, calibrate now
if( mode != CALIBRATED && !imagePoints.empty() )
runCalibrationAndSave(s, imageSize, cameraMatrix, distCoeffs, imagePoints, grid_width,
release_object);
break;
}
//! [get_input]
imageSize = view.size(); // Format input image.
if( s.flipVertical ) flip( view, view, 0 );
//! [find_pattern]
vector<Point2f> pointBuf;
bool found;
// | 是位运算符，规则：有1为1 CALIB_CB_ADAPTIVE_THRESH=1（01） CALIB_CB_NORMALIZE_IMAGE=2（10） 01|10=11 =3
int chessBoardFlags = CALIB_CB_ADAPTIVE_THRESH | CALIB_CB_NORMALIZE_IMAGE;
if(!s.useFisheye) {//CALIB_CB_FAST_CHECK=8
// fast check erroneously fails with high distortions like fisheye
chessBoardFlags |= CALIB_CB_FAST_CHECK;
}
/// chessBoardFlags=11
switch( s.calibrationPattern ) // Find feature points on the input format
{
case Settings::CHESSBOARD://查找棋盘内角的位置。（输入图像，棋盘行和列的内角数，检测到拐角的输出数组，操作标志）11=1+2+8 即1，2，8标志位功能全部使用
//1：自适应阈值将图像转换为黑白 2：应用固定或自适应阈值之前，使用equalizeHist对图像伽玛进行归一化 8：对图像进行快速检查，查找棋盘角，如果没有找到，则快捷调用。这可以大大加快退化条件下的调用，当没有棋盘被观察到
found = findChessboardCorners( view, s.boardSize, pointBuf, chessBoardFlags);
break;
case Settings::CIRCLES_GRID://该函数试图确定输入图像是否包含圆形网格。
found = findCirclesGrid( view, s.boardSize, pointBuf );
break;
case Settings::ASYMMETRIC_CIRCLES_GRID:// CALIB_CB_ASYMMETRIC_GRID使用不对称的圆形图案。
found = findCirclesGrid( view, s.boardSize, pointBuf, CALIB_CB_ASYMMETRIC_GRID );
break;
default:
found = false;
break;
}
//! [find_pattern]
//! [pattern_found]
if ( found) // If done with success,
{
// improve the found corners' coordinate accuracy for chessboard
if( s.calibrationPattern == Settings::CHESSBOARD)
{
Mat viewGray;
cvtColor(view, viewGray, COLOR_BGR2GRAY);
//该函数迭代寻找角点或径向鞍点的亚像素精确位置（输入图像，输入角的初始坐标和为输出提供的细化坐标，搜索窗口边长的一半（（11*2+1）*（11*2+1）=46）
//（搜索区域中间的死区面积的一半，下面的公式中没有进行求和。它有时用于避免自相关矩阵可能出现的奇异性。(-1，-1)表示没有这样的大小。），角点细化迭代过程的终止准则）
cornerSubPix( viewGray, pointBuf, Size(winSize,winSize),//（TermCriteria：（类型，迭代次数活元素的最大数量，期望的精度或参数变化））
Size(-1,-1), TermCriteria( TermCriteria::EPS+TermCriteria::COUNT, 30, 0.0001 ));
}
if( mode == CAPTURING && //对于相机，延迟时间后才能重新采样
(!s.inputCapture.isOpened() || clock() - prevTimestamp > s.delay*1e-3*CLOCKS_PER_SEC) )
{
imagePoints.push_back(pointBuf);
prevTimestamp = clock();
blinkOutput = s.inputCapture.isOpened();
}
// Draw the corners.该函数绘制检测到的单个棋盘角，如果没有找到棋盘，则绘制为红色圆圈，如果找到棋盘，则绘制为与线相连的彩色角。
//（输入图像，每个棋盘行和列的内角数（7，5），检测到的角的数组，findChessboardCorners的输出，是否找到完整单板的参数-findChessboardCorners的返回值）
std::cout << "-----------corners:" << endl << pointBuf<<endl;
drawChessboardCorners( view, s.boardSize, Mat(pointBuf), found );
}
//! [pattern_found]
//----------------------------- Output Text ------------------------------------------------
//! [output_text]
string msg = (mode == CAPTURING) ? "100/100" :
mode == CALIBRATED ? "Calibrated" : "Press 'g' to start";
int baseLine = 0;
//函数cv::getTextSize计算并返回包含指定文本框的大小。
Size textSize = getTextSize(msg, 1, 1, 1, &baseLine);//textSize=(w=71,h=10)
Point textOrigin(view.cols - 2*textSize.width - 10, view.rows - 2*baseLine - 10);//textOrigin=(168,218)
if( mode == CAPTURING )
{
if(s.showUndistorted)
msg = cv::format( "%d/%d Undist", (int)imagePoints.size(), s.nrFrames );
else
msg = cv::format( "%d/%d", (int)imagePoints.size(), s.nrFrames );
}
putText( view, msg, textOrigin, 1, 1, mode == CALIBRATED ? GREEN : RED);
if( blinkOutput )
bitwise_not(view, view);
//! [output_text]
//------------------------- Video capture output undistorted ------------------------------
//! [output_undistorted]
if( mode == CALIBRATED && s.showUndistorted )
{
Mat temp = view.clone();
if (s.useFisheye)
{
Mat newCamMat;
fisheye::estimateNewCameraMatrixForUndistortRectify(cameraMatrix, distCoeffs, imageSize,
Matx33d::eye(), newCamMat, 1);
cv::fisheye::undistortImage(temp, view, cameraMatrix, distCoeffs, newCamMat);
}
else
undistort(temp, view, cameraMatrix, distCoeffs);
}
//! [output_undistorted]
//------------------------------ Show image and check for input commands -------------------
//! [await_input]
num=num+1;
std::string path = "E:/VS2022/AWORK/distortion/Corner_detection/un_enhance/"+std::to_string(num)+".png";
cv::imwrite(path,view);
cv::imshow("Image View", view);
cv::waitKey(0);
char key = (char)cv::waitKey(s.inputCapture.isOpened() ? 50 : s.delay);//key=-1
if( key == ESC_KEY )
break;
if( key == 'u' && mode == CALIBRATED )
s.showUndistorted = !s.showUndistorted;
if( s.inputCapture.isOpened() && key == 'g' )
{
mode = CAPTURING;
imagePoints.clear();
}
//! [await_input]
}
// -----------------------Show the undistorted image for the image list ------------------------
//! [show_results]
if( s.inputType == Settings::IMAGE_LIST && s.showUndistorted && !cameraMatrix.empty())
{
Mat view, rview, map1, map2;
if (s.useFisheye)
{
Mat newCamMat;
//估计新的相机固有矩阵的不失真或整流。
fisheye::estimateNewCameraMatrixForUndistortRectify(cameraMatrix, distCoeffs, imageSize,
Matx33d::eye(), newCamMat, 1);
fisheye::initUndistortRectifyMap(cameraMatrix, distCoeffs, Matx33d::eye(), newCamMat, imageSize,
CV_16SC2, map1, map2);
}
else
{ //计算不失真和校正映射的图像转换通过重映射。如果第二个参数为空则使用零失真，如果第三或第四为空则使用单位矩阵。
initUndistortRectifyMap(
cameraMatrix, distCoeffs, Mat(), //getOptimalNewCameraMatrix：根据自由缩放参数返回新的相机固有矩阵。
//第四个参数：0：当未失真图像中的所有像素有效时 1：当所有源图像像素保留在未失真图像中时 false=0,true=1;roi=0即不裁剪
//当alpha=1时，所有像素均保留，但存在黑色边框。当alpha=0时，损失最多的像素，没有黑色边框,但会放大视场。
getOptimalNewCameraMatrix(cameraMatrix, distCoeffs, imageSize, 1, imageSize, 0), imageSize,
CV_16SC2, map1, map2);
}
for(size_t i = 0; i < s.imageList.size(); i++ )
{
view = imread(s.imageList[i], IMREAD_COLOR);
if(view.empty())
continue;
cv::remap(view, rview, map1, map2, INTER_LINEAR);
std::string path = "E:/VS2022/AWORK/distortion/correction_ori/un_enhance/" + std::to_string(i) + ".png";
cv::imwrite(path, rview);
cv::imshow("Image View", rview);
char c = (char)cv::waitKey();
if( c == ESC_KEY || c == 'q' || c == 'Q' )
break;
}
}
//! [show_results]
return 0;
}
//! [compute_errors]
static double computeReprojectionErrors( const vector<vector<Point3f> >& objectPoints,
const vector<vector<Point2f> >& imagePoints,
const vector<Mat>& rvecs, const vector<Mat>& tvecs,
const Mat& cameraMatrix , const Mat& distCoeffs,
vector<float>& perViewErrors, bool fisheye)
{
vector<Point2f> imagePoints2;
size_t totalPoints = 0;
double totalErr = 0, err;
perViewErrors.resize(objectPoints.size());
for(size_t i = 0; i < objectPoints.size(); ++i )
{
if (fisheye)
{
fisheye::projectPoints(objectPoints[i], imagePoints2, rvecs[i], tvecs[i], cameraMatrix,
distCoeffs);
}
else
{
projectPoints(objectPoints[i], rvecs[i], tvecs[i], cameraMatrix, distCoeffs, imagePoints2);
}
err = norm(imagePoints[i], imagePoints2, NORM_L2);
size_t n = objectPoints[i].size();
perViewErrors[i] = (float) std::sqrt(err*err/n);
totalErr += err*err;
totalPoints += n;
}
return std::sqrt(totalErr/totalPoints);
}
//! [compute_errors]
//! [board_corners]
static void calcBoardCornerPositions(Size boardSize, float squareSize, vector<Point3f>& corners,
Settings::Pattern patternType /*= Settings::CHESSBOARD*/)
{
corners.clear();
switch(patternType)
{
case Settings::CHESSBOARD:
case Settings::CIRCLES_GRID:
for( int i = 0; i < boardSize.height; ++i )
for( int j = 0; j < boardSize.width; ++j )
corners.push_back(Point3f(j*squareSize, i*squareSize, 0));
break;
case Settings::ASYMMETRIC_CIRCLES_GRID:
for( int i = 0; i < boardSize.height; i++ )
for( int j = 0; j < boardSize.width; j++ )
corners.push_back(Point3f((2*j + i % 2)*squareSize, i*squareSize, 0));
break;
default:
break;
}
}
//! [board_corners]
static bool runCalibration( Settings& s, Size& imageSize, Mat& cameraMatrix, Mat& distCoeffs,
vector<vector<Point2f> > imagePoints, vector<Mat>& rvecs, vector<Mat>& tvecs,
vector<float>& reprojErrs, double& totalAvgErr, vector<Point3f>& newObjPoints,
float grid_width, bool release_object)
{
//! [fixed_aspect]
cameraMatrix = Mat::eye(3, 3, CV_64F);
if( !s.useFisheye && s.flag & CALIB_FIX_ASPECT_RATIO )
cameraMatrix.at<double>(0,0) = s.aspectRatio;
//! [fixed_aspect]
if (s.useFisheye) {
distCoeffs = Mat::zeros(4, 1, CV_64F);
} else {
distCoeffs = Mat::zeros(8, 1, CV_64F);
}
vector<vector<Point3f> > objectPoints(1);
calcBoardCornerPositions(s.boardSize, s.squareSize, objectPoints[0], s.calibrationPattern);
objectPoints[0][s.boardSize.width - 1].x = objectPoints[0][0].x + grid_width;
newObjPoints = objectPoints[0];
objectPoints.resize(imagePoints.size(),objectPoints[0]);
//Find intrinsic and extrinsic camera parameters
double rms;
if (s.useFisheye) {
Mat _rvecs, _tvecs;
rms = fisheye::calibrate(objectPoints, imagePoints, imageSize, cameraMatrix, distCoeffs, _rvecs,
_tvecs, s.flag);
rvecs.reserve(_rvecs.rows);
tvecs.reserve(_tvecs.rows);
for(int i = 0; i < int(objectPoints.size()); i++){
rvecs.push_back(_rvecs.row(i));
tvecs.push_back(_tvecs.row(i));
}
} else {
int iFixedPoint = -1;
if (release_object)
iFixedPoint = s.boardSize.width - 1;
rms = calibrateCameraRO(objectPoints, imagePoints, imageSize, iFixedPoint,
cameraMatrix, distCoeffs, rvecs, tvecs, newObjPoints,
s.flag | CALIB_USE_LU);
}
if (release_object) {
cout << "New board corners: " << endl;
cout << newObjPoints[0] << endl;
cout << newObjPoints[s.boardSize.width - 1] << endl;
cout << newObjPoints[s.boardSize.width * (s.boardSize.height - 1)] << endl;
cout << newObjPoints.back() << endl;
}
cout << "Re-projection error reported by calibrateCamera: "<< rms << endl;
bool ok = checkRange(cameraMatrix) && checkRange(distCoeffs);
objectPoints.clear();
objectPoints.resize(imagePoints.size(), newObjPoints);
totalAvgErr = computeReprojectionErrors(objectPoints, imagePoints, rvecs, tvecs, cameraMatrix,
distCoeffs, reprojErrs, s.useFisheye);
for (int i = 0; i < int(objectPoints.size()); i++) {
cout << "--------------------------------objectPoints["<<i<<"]= " << objectPoints[i] << endl;
}
cout << "--------------------------------totalAvgErr: " << totalAvgErr << endl;
return ok;
}
// Print camera parameters to the output file
static void saveCameraParams( Settings& s, Size& imageSize, Mat& cameraMatrix, Mat& distCoeffs,
const vector<Mat>& rvecs, const vector<Mat>& tvecs,
const vector<float>& reprojErrs, const vector<vector<Point2f> >& imagePoints,
double totalAvgErr, const vector<Point3f>& newObjPoints )
{
FileStorage fs( s.outputFileName, FileStorage::WRITE );
time_t tm;
time( &tm );
struct tm *t2 = localtime( &tm );
char buf[1024];
strftime( buf, sizeof(buf), "%c", t2 );
fs << "calibration_time" << buf;
if( !rvecs.empty() || !reprojErrs.empty() )
fs << "nr_of_frames" << (int)std::max(rvecs.size(), reprojErrs.size());
fs << "image_width" << imageSize.width;
fs << "image_height" << imageSize.height;
fs << "board_width" << s.boardSize.width;
fs << "board_height" << s.boardSize.height;
fs << "square_size" << s.squareSize;
if( !s.useFisheye && s.flag & CALIB_FIX_ASPECT_RATIO )
fs << "fix_aspect_ratio" << s.aspectRatio;
if (s.flag)
{
std::stringstream flagsStringStream;
if (s.useFisheye)
{
flagsStringStream << "flags:"
<< (s.flag & fisheye::CALIB_FIX_SKEW ? " +fix_skew" : "")
<< (s.flag & fisheye::CALIB_FIX_K1 ? " +fix_k1" : "")
<< (s.flag & fisheye::CALIB_FIX_K2 ? " +fix_k2" : "")
<< (s.flag & fisheye::CALIB_FIX_K3 ? " +fix_k3" : "")
<< (s.flag & fisheye::CALIB_FIX_K4 ? " +fix_k4" : "")
<< (s.flag & fisheye::CALIB_RECOMPUTE_EXTRINSIC ? " +recompute_extrinsic" : "");
}
else
{
flagsStringStream << "flags:"
<< (s.flag & CALIB_USE_INTRINSIC_GUESS ? " +use_intrinsic_guess" : "")
<< (s.flag & CALIB_FIX_ASPECT_RATIO ? " +fix_aspectRatio" : "")
<< (s.flag & CALIB_FIX_PRINCIPAL_POINT ? " +fix_principal_point" : "")
<< (s.flag & CALIB_ZERO_TANGENT_DIST ? " +zero_tangent_dist" : "")
<< (s.flag & CALIB_FIX_K1 ? " +fix_k1" : "")
<< (s.flag & CALIB_FIX_K2 ? " +fix_k2" : "")
<< (s.flag & CALIB_FIX_K3 ? " +fix_k3" : "")
<< (s.flag & CALIB_FIX_K4 ? " +fix_k4" : "")
<< (s.flag & CALIB_FIX_K5 ? " +fix_k5" : "");
}
fs.writeComment(flagsStringStream.str());
}
fs << "flags" << s.flag;
fs << "fisheye_model" << s.useFisheye;
fs << "camera_matrix" << cameraMatrix;
fs << "distortion_coefficients" << distCoeffs;
fs << "avg_reprojection_error" << totalAvgErr;
if (s.writeExtrinsics && !reprojErrs.empty())
fs << "per_view_reprojection_errors" << Mat(reprojErrs);
if(s.writeExtrinsics && !rvecs.empty() && !tvecs.empty() )
{
CV_Assert(rvecs[0].type() == tvecs[0].type());
Mat bigmat((int)rvecs.size(), 6, CV_MAKETYPE(rvecs[0].type(), 1));
bool needReshapeR = rvecs[0].depth() != 1 ? true : false;
bool needReshapeT = tvecs[0].depth() != 1 ? true : false;
for( size_t i = 0; i < rvecs.size(); i++ )
{
Mat r = bigmat(Range(int(i), int(i+1)), Range(0,3));
Mat t = bigmat(Range(int(i), int(i+1)), Range(3,6));
if(needReshapeR)
rvecs[i].reshape(1, 1).copyTo(r);
else
{
//*.t() is MatExpr (not Mat) so we can use assignment operator
CV_Assert(rvecs[i].rows == 3 && rvecs[i].cols == 1);
r = rvecs[i].t();
}
if(needReshapeT)
tvecs[i].reshape(1, 1).copyTo(t);
else
{
CV_Assert(tvecs[i].rows == 3 && tvecs[i].cols == 1);
t = tvecs[i].t();
}
}
fs.writeComment("a set of 6-tuples (rotation vector + translation vector) for each view");
fs << "extrinsic_parameters" << bigmat;
}
if(s.writePoints && !imagePoints.empty() )
{
Mat imagePtMat((int)imagePoints.size(), (int)imagePoints[0].size(), CV_32FC2);
for( size_t i = 0; i < imagePoints.size(); i++ )
{
Mat r = imagePtMat.row(int(i)).reshape(2, imagePtMat.cols);
Mat imgpti(imagePoints[i]);
imgpti.copyTo(r);
}
fs << "image_points" << imagePtMat;
}
if( s.writeGrid && !newObjPoints.empty() )
{
fs << "grid_points" << newObjPoints;
}
}
//! [run_and_save]
bool runCalibrationAndSave(Settings& s, Size imageSize, Mat& cameraMatrix, Mat& distCoeffs,
vector<vector<Point2f> > imagePoints, float grid_width, bool release_object)
{
vector<Mat> rvecs, tvecs;
vector<float> reprojErrs;
double totalAvgErr = 0;
vector<Point3f> newObjPoints;
bool ok = runCalibration(s, imageSize, cameraMatrix, distCoeffs, imagePoints, rvecs, tvecs, reprojErrs,
totalAvgErr, newObjPoints, grid_width, release_object);
cout << (ok ? "Calibration succeeded" : "Calibration failed")
<< ". avg re projection error = " << totalAvgErr << endl;
if (ok)
saveCameraParams(s, imageSize, cameraMatrix, distCoeffs, rvecs, tvecs, reprojErrs, imagePoints,
totalAvgErr, newObjPoints);
return ok;
}
//! [run_and_save]