本文主要是介绍标定系列——基于OpenCV实现普通相机、鱼眼相机不同标定板下的标定（五），希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
标定系列——基于OpenCV实现相机标定（五）
- 说明
- 代码解析
- VID5.xml
- in_VID5.xml
- camera_calibration.cpp
说明
该程序可以实现多种标定板的相机标定工作
代码解析
VID5.xml
<?xml version="1.0"?><!-- 相机拍摄的标定板图像路径名 -->
<opencv_storage>
<images>
images/CameraCalibration/VID5/xx1.jpg
images/CameraCalibration/VID5/xx2.jpg
images/CameraCalibration/VID5/xx3.jpg
images/CameraCalibration/VID5/xx4.jpg
images/CameraCalibration/VID5/xx5.jpg
images/CameraCalibration/VID5/xx6.jpg
images/CameraCalibration/VID5/xx7.jpg
images/CameraCalibration/VID5/xx8.jpg
</images>
</opencv_storage>
in_VID5.xml
<?xml version="1.0"?>
<opencv_storage>
<Settings><!-- 标定板尺寸. (可以是正方形、圆形) --><BoardSize_Width>9</BoardSize_Width><BoardSize_Height>6</BoardSize_Height><!-- 用户定义的方格的尺寸 (像素,毫米)--><Square_Size>50</Square_Size><Marker_Size>25</Marker_Size><!-- 相机标定所使用的标定板类型. 可以是CHESSBOARD CHARUCOBOARD CIRCLES_GRID ASYMMETRIC_CIRCLES_GRID --><Calibrate_Pattern>"CHESSBOARD"</Calibrate_Pattern><ArUco_Dict_Name>DICT_4X4_50</ArUco_Dict_Name><ArUco_Dict_File_Name></ArUco_Dict_File_Name><!-- 用于标定的输入来源。使用输入摄像头 -> 提供摄像头的ID，例如 "1"使用输入视频 -> 提供输入视频的路径，例如 "/tmp/x.avi"使用图像列表 -> 提供含有图像列表的XML或YAML文件的路径，例如 "/tmp/circles_list.xml"--><Input>"images/CameraCalibration/VID5/VID5.xml"</Input><!-- 如果为真（非零），则沿水平轴翻转输入图像 --><Input_FlipAroundHorizontalAxis>0</Input_FlipAroundHorizontalAxis><!-- 摄像头的帧之间的时间延迟 --><Input_Delay>100</Input_Delay> <!-- 用于标定的帧数量 --><Calibrate_NrOfFrameToUse>25</Calibrate_NrOfFrameToUse><!-- 只考虑fy作为自由参数，比率fx/fy与输入cameraMatrix中的相同 --><Calibrate_FixAspectRatio> 1 </Calibrate_FixAspectRatio><!-- 如果为真（非零），切向畸变系数将被设置为零并保持为零 --><Calibrate_AssumeZeroTangentialDistortion>1</Calibrate_AssumeZeroTangentialDistortion><!-- 如果为真（非零），在全局优化过程中主点不会改变 --><Calibrate_FixPrincipalPointAtTheCenter> 1 </Calibrate_FixPrincipalPointAtTheCenter><!-- 输出日志文件名 --><Write_outputFileName>"out_camera_data.xml"</Write_outputFileName><!-- 如果为真（非零），将检测到的特征点写入输出文件 --><Write_DetectedFeaturePoints>1</Write_DetectedFeaturePoints><!-- 如果为真（非零），我们将外部相机参数写入输出文件 --><Write_extrinsicParameters>1</Write_extrinsicParameters><!-- 如果为真（非零），我们将优化后的3D目标网格点写入输出文件 --><Write_gridPoints>1</Write_gridPoints><!-- 如果为真（非零），校准后我们显示无畸变的图像 --><Show_UndistortedImage>1</Show_UndistortedImage><!-- 如果为真（非零），将使用鱼眼相机模型进行标定 --><Calibrate_UseFisheyeModel>0</Calibrate_UseFisheyeModel><!-- 如果为真（非零），畸变系数k1将等于零 --><Fix_K1>0</Fix_K1><!-- 如果为真（非零），畸变系数k2将等于零 --><Fix_K2>0</Fix_K2><!-- 如果为真（非零），畸变系数k3将等于零 --><Fix_K3>0</Fix_K3><!-- 如果为真（非零），畸变系数k4将等于零 --><Fix_K4>1</Fix_K4><!-- 如果为真（非零），畸变系数k5将等于零 --><Fix_K5>1</Fix_K5>
</Settings>
</opencv_storage>
camera_calibration.cpp
核心代码就是camera_calibration.cpp，主要通过多张标定板图像进行相机的内参和畸变参数的计算，大体看了一下，里面的逻辑很清晰，就不做过多注解了
#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>
#include "opencv2/objdetect/charuco_detector.hpp"using namespace cv;
using namespace std;class Settings
{
public:Settings() : goodInput(false) {}enum Pattern { NOT_EXISTING, CHESSBOARD, CHARUCOBOARD, CIRCLES_GRID, ASYMMETRIC_CIRCLES_GRID };enum InputType { INVALID, CAMERA, VIDEO_FILE, IMAGE_LIST };void write(FileStorage& fs) const //将数据写入文件{fs << "{"<< "BoardSize_Width" << boardSize.width<< "BoardSize_Height" << boardSize.height<< "Square_Size" << squareSize<< "Marker_Size" << markerSize<< "Calibrate_Pattern" << patternToUse<< "ArUco_Dict_Name" << arucoDictName<< "ArUco_Dict_File_Name" << arucoDictFileName<< "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) //从文件中读{node["BoardSize_Width"] >> boardSize.width;node["BoardSize_Height"] >> boardSize.height;node["Calibrate_Pattern"] >> patternToUse;node["ArUco_Dict_Name"] >> arucoDictName;node["ArUco_Dict_File_Name"] >> arucoDictFileName;node["Square_Size"] >> squareSize;node["Marker_Size"] >> markerSize;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();}// 输入值验证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 inputinputType = 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();}elseinputType = 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 flagsflag = 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("CHARUCOBOARD")) calibrationPattern = CHARUCOBOARD;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;}// 获取图像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;}//读取图像名，保存在vectorstatic 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 extensionif( s.find(".xml") == string::npos && s.find(".yaml") == string::npos && s.find(".yml") == string::npos )return false;elsereturn true;}public:Size boardSize; // The size of the board -> Number of items by width and heightPattern calibrationPattern; // One of the Chessboard, ChArUco board, circles, or asymmetric circle patternfloat squareSize; // The size of a square in your defined unit (point, millimeter,etc).float markerSize; // The size of a marker in your defined unit (point, millimeter,etc).string arucoDictName; // The Name of ArUco dictionary which you use in ChArUco patternstring arucoDictFileName; // The Name of file which contains ArUco dictionary for ChArUco patternint nrFrames; // The number of frames to use from the input for calibrationfloat aspectRatio; // The aspect ratioint delay; // In case of a video inputbool writePoints; // Write detected feature pointsbool writeExtrinsics; // Write extrinsic parametersbool writeGrid; // Write refined 3D target grid pointsbool calibZeroTangentDist; // Assume zero tangential distortionbool calibFixPrincipalPoint; // Fix the principal point at the centerbool flipVertical; // Flip the captured images around the horizontal axisstring outputFileName; // The name of the file where to writebool showUndistorted; // Show undistorted images after calibrationstring input; // The input ->bool useFisheye; // use fisheye camera model for calibrationbool fixK1; // fix K1 distortion coefficientbool fixK2; // fix K2 distortion coefficientbool fixK3; // fix K3 distortion coefficientbool fixK4; // fix K4 distortion coefficientbool fixK5; // fix K5 distortion coefficientint 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;elsex.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[])
{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 }";CommandLineParser parser(argc, argv, keys);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;const string inputSettingsFile = parser.get<string>(0);FileStorage fs(inputSettingsFile, FileStorage::READ); // Read the settingsif (!fs.isOpened()){cout << "Could not open the configuration file: \"" << inputSettingsFile << "\"" << endl;parser.printMessage();return -1;}fs["Settings"] >> s;fs.release(); // close Settings file//! [file_read]if (!s.goodInput){cout << "Invalid input detected. Application stopping. " << endl;return -1;}int winSize = parser.get<int>("winSize"); // 获取角点搜索窗口大小的一半float grid_width = s.squareSize * (s.boardSize.width - 1);if (s.calibrationPattern == Settings::Pattern::CHARUCOBOARD) {grid_width = s.squareSize * (s.boardSize.width - 2);}bool release_object = false;if (parser.has("d")) {grid_width = parser.get<float>("d");release_object = true;}// 创建CharucoBoard棋盘对象cv::aruco::Dictionary dictionary;// 如果标定模式为CHARUCOBOARD，创建相应的字典if (s.calibrationPattern == Settings::CHARUCOBOARD) {if (s.arucoDictFileName == "") {cv::aruco::PredefinedDictionaryType arucoDict;if (s.arucoDictName == "DICT_4X4_50") { arucoDict = cv::aruco::DICT_4X4_50; }else if (s.arucoDictName == "DICT_4X4_100") { arucoDict = cv::aruco::DICT_4X4_100; }else if (s.arucoDictName == "DICT_4X4_250") { arucoDict = cv::aruco::DICT_4X4_250; }else if (s.arucoDictName == "DICT_4X4_1000") { arucoDict = cv::aruco::DICT_4X4_1000; }else if (s.arucoDictName == "DICT_5X5_50") { arucoDict = cv::aruco::DICT_5X5_50; }else if (s.arucoDictName == "DICT_5X5_100") { arucoDict = cv::aruco::DICT_5X5_100; }else if (s.arucoDictName == "DICT_5X5_250") { arucoDict = cv::aruco::DICT_5X5_250; }else if (s.arucoDictName == "DICT_5X5_1000") { arucoDict = cv::aruco::DICT_5X5_1000; }else if (s.arucoDictName == "DICT_6X6_50") { arucoDict = cv::aruco::DICT_6X6_50; }else if (s.arucoDictName == "DICT_6X6_100") { arucoDict = cv::aruco::DICT_6X6_100; }else if (s.arucoDictName == "DICT_6X6_250") { arucoDict = cv::aruco::DICT_6X6_250; }else if (s.arucoDictName == "DICT_6X6_1000") { arucoDict = cv::aruco::DICT_6X6_1000; }else if (s.arucoDictName == "DICT_7X7_50") { arucoDict = cv::aruco::DICT_7X7_50; }else if (s.arucoDictName == "DICT_7X7_100") { arucoDict = cv::aruco::DICT_7X7_100; }else if (s.arucoDictName == "DICT_7X7_250") { arucoDict = cv::aruco::DICT_7X7_250; }else if (s.arucoDictName == "DICT_7X7_1000") { arucoDict = cv::aruco::DICT_7X7_1000; }else if (s.arucoDictName == "DICT_ARUCO_ORIGINAL") { arucoDict = cv::aruco::DICT_ARUCO_ORIGINAL; }else if (s.arucoDictName == "DICT_APRILTAG_16h5") { arucoDict = cv::aruco::DICT_APRILTAG_16h5; }else if (s.arucoDictName == "DICT_APRILTAG_25h9") { arucoDict = cv::aruco::DICT_APRILTAG_25h9; }else if (s.arucoDictName == "DICT_APRILTAG_36h10") { arucoDict = cv::aruco::DICT_APRILTAG_36h10; }else if (s.arucoDictName == "DICT_APRILTAG_36h11") { arucoDict = cv::aruco::DICT_APRILTAG_36h11; }else {cout << "incorrect name of aruco dictionary \n";return 1;}dictionary = cv::aruco::getPredefinedDictionary(arucoDict);}else {cv::FileStorage dict_file(s.arucoDictFileName, cv::FileStorage::Mode::READ);cv::FileNode fn(dict_file.root());dictionary.readDictionary(fn);}}else {// default dictionarydictionary = cv::aruco::getPredefinedDictionary(0);}// 创建CharucoBoard对象和检测器cv::aruco::CharucoBoard ch_board({s.boardSize.width, s.boardSize.height}, s.squareSize, s.markerSize, dictionary);cv::aruco::CharucoDetector ch_detector(ch_board);std::vector<int> markerIds;vector<vector<Point2f> > imagePoints;Mat cameraMatrix, distCoeffs;Size imageSize;int mode = s.inputType == Settings::IMAGE_LIST ? CAPTURING : DETECTION;clock_t prevTimestamp = 0;const Scalar RED(0,0,255), GREEN(0,255,0);const char ESC_KEY = 27;//! [get_input]// 循环处理图像for(;;){Mat view;bool blinkOutput = false;view = s.nextImage();//----- If no more image, or got enough, then stop calibration and show result -------------if( mode == CAPTURING && imagePoints.size() >= (size_t)s.nrFrames ){// 调用标定函数，成功则切换到CALIBRATED模式，否则回到DETECTION模式if(runCalibrationAndSave(s, imageSize, cameraMatrix, distCoeffs, imagePoints, grid_width,release_object))mode = CALIBRATED;elsemode = DETECTION;}if(view.empty()) // If there are no more images stop the loop{// if calibration threshold was not reached yet, calibrate nowif( 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;int chessBoardFlags = CALIB_CB_ADAPTIVE_THRESH | CALIB_CB_NORMALIZE_IMAGE;if(!s.useFisheye) {// fast check erroneously fails with high distortions like fisheyechessBoardFlags |= CALIB_CB_FAST_CHECK;}switch( s.calibrationPattern ) // Find feature points on the input format{case Settings::CHESSBOARD:found = findChessboardCorners( view, s.boardSize, pointBuf, chessBoardFlags);break;case Settings::CHARUCOBOARD:ch_detector.detectBoard( view, pointBuf, markerIds);found = pointBuf.size() == (size_t)((s.boardSize.height - 1)*(s.boardSize.width - 1));break;case Settings::CIRCLES_GRID:found = findCirclesGrid( view, s.boardSize, pointBuf );break;case Settings::ASYMMETRIC_CIRCLES_GRID: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 chessboardif( s.calibrationPattern == Settings::CHESSBOARD){Mat viewGray;cvtColor(view, viewGray, COLOR_BGR2GRAY);cornerSubPix( viewGray, pointBuf, Size(winSize,winSize),Size(-1,-1), TermCriteria( TermCriteria::EPS+TermCriteria::COUNT, 30, 0.0001 ));}if( mode == CAPTURING && // For camera only take new samples after delay time(!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.if(s.calibrationPattern == Settings::CHARUCOBOARD)drawChessboardCorners( view, cv::Size(s.boardSize.width-1, s.boardSize.height-1), Mat(pointBuf), found );elsedrawChessboardCorners( 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;Size textSize = getTextSize(msg, 1, 1, 1, &baseLine);Point textOrigin(view.cols - 2*textSize.width - 10, view.rows - 2*baseLine - 10);if( mode == CAPTURING ){if(s.showUndistorted)msg = cv::format( "%d/%d Undist", (int)imagePoints.size(), s.nrFrames );elsemsg = 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);}elseundistort(temp, view, cameraMatrix, distCoeffs);}//! [output_undistorted]//------------------------------ Show image and check for input commands -------------------//! [await_input]imshow("Image View", view);char key = (char)waitKey(s.inputCapture.isOpened() ? 50 : s.delay);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) // 如果使用鱼眼镜头模型进行畸变矫正{Mat newCamMat; // 定义新的相机矩阵// 估计畸变校正和矩形映射所需的新相机矩阵fisheye::estimateNewCameraMatrixForUndistortRectify(cameraMatrix, distCoeffs, imageSize,Matx33d::eye(), newCamMat, 1);// 初始化畸变矫正和矩形映射fisheye::initUndistortRectifyMap(cameraMatrix, distCoeffs, Matx33d::eye(), newCamMat, imageSize,CV_16SC2, map1, map2);}else{initUndistortRectifyMap(cameraMatrix, distCoeffs, Mat(),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;remap(view, rview, map1, map2, INTER_LINEAR);imshow("Image View", rview);char c = (char)waitKey();if( c == ESC_KEY || c == 'q' || c == 'Q' )break;}}//! [show_results]return 0;
}//! [compute_errors] 计算重映射误差的函数
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) // 如果是鱼眼镜头模型，使用fisheye命名空间的函数来投影点{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]计算棋盘格角点位置
static void calcBoardCornerPositions(Size boardSize, float squareSize, vector<Point3f>& corners,Settings::Pattern patternType /*= Settings::CHESSBOARD*/)
{corners.clear();switch(patternType){case Settings::CHESSBOARD: // 标准棋盘格case Settings::CIRCLES_GRID: // 圆形网格棋盘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::CHARUCOBOARD: // CHARUCO棋盘for (int i = 0; i < boardSize.height - 1; ++i) {for (int j = 0; j < boardSize.width - 1; ++j) {corners.push_back(Point3f(j*squareSize, i*squareSize, 0));}}break;case Settings::ASYMMETRIC_CIRCLES_GRID: // 非对称圆形网格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);if (s.calibrationPattern == Settings::Pattern::CHARUCOBOARD) {objectPoints[0][s.boardSize.width - 2].x = objectPoints[0][0].x + grid_width;}else {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 parametersdouble 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);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;fs << "marker_size" << s.markerSize;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 operatorCV_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]
注：该标定例程为OpenCV自带，可自行查找，也可从我的博客下载https://download.csdn.net/download/jppdss/89046059
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