基于OpenCV实现的相机标定C++程序

/*------------------------------------------------------------------------------------------*\ This file contains material supporting chapter 9 of the cookbook: Computer Vision Programming using the OpenCV Library. by Robert Laganiere, Packt Publishing, 2011. This program is free software; permission is hereby granted to use, copy, modify, and distribute this source code, or portions thereof, for any purpose, without fee, subject to the restriction that the copyright notice may not be removed or altered from any source or altered source distribution. The software is released on an as-is basis and without any warranties of any kind. In particular, the software is not guaranteed to be fault-tolerant or free from failure. The author disclaims all warranties with regard to this software, any use, and any consequent failure, is purely the responsibility of the user. Copyright (C) 2010-2011 Robert Laganiere, www.laganiere.name \*------------------------------------------------------------------------------------------*/ #include "CameraCalibrator.h" // Open chessboard images and extract corner points int CameraCalibrator::addChessboardPoints( const std::vector<std::string>& filelist, cv::Size & boardSize) { // the points on the chessboard std::vector<cv::Point2f> imageCorners; std::vector<cv::Point3f> objectCorners; // 3D Scene Points: // Initialize the chessboard corners // in the chessboard reference frame // The corners are at 3D location (X,Y,Z)= (i,j,0) for (int i=0; i<boardSize.height; i++) { for (int j=0; j<boardSize.width; j++) { objectCorners.push_back(cv::Point3f(i, j, 0.0f)); } } // 2D Image points: cv::Mat image; // to contain chessboard image int successes = 0; // for all viewpoints for (int i=0; i<filelist.size(); i++) { // Open the image image = cv::imread(filelist[i],0); // Get the chessboard corners bool found = cv::findChessboardCorners( image, boardSize, imageCorners); // Get subpixel accuracy on the corners cv::cornerSubPix(image, imageCorners, cv::Size(5,5), cv::Size(-1,-1), cv::TermCriteria(cv::TermCriteria::MAX_ITER + cv::TermCriteria::EPS, 30, // max number of iterations 0.1)); // min accuracy // If we have a good board, add it to our data if (imageCorners.size() == boardSize.area()) { // Add image and scene points from one view addPoints(imageCorners, objectCorners); successes++; } //Draw the corners cv::drawChessboardCorners(image, boardSize, imageCorners, found); cv::imshow("Corners on Chessboard", image); cv::waitKey(100); } return successes; } // Add scene points and corresponding image points void CameraCalibrator::addPoints(const std::vector<cv::Point2f>& imageCorners, const std::vector<cv::Point3f>& objectCorners) { // 2D image points from one view imagePoints.push_back(imageCorners); // corresponding 3D scene points objectPoints.push_back(objectCorners); } // Calibrate the camera // returns the re-projection error double CameraCalibrator::calibrate(cv::Size &imageSize) { // undistorter must be reinitialized mustInitUndistort= true; //Output rotations and translations std::vector<cv::Mat> rvecs, tvecs; // start calibration return calibrateCamera(objectPoints, // the 3D points imagePoints, // the image points imageSize, // image size cameraMatrix, // output camera matrix distCoeffs, // output distortion matrix rvecs, tvecs, // Rs, Ts flag); // set options // ,CV_CALIB_USE_INTRINSIC_GUESS); } // remove distortion in an image (after calibration) cv::Mat CameraCalibrator::remap(const cv::Mat &image) { cv::Mat undistorted; if (mustInitUndistort) { // called once per calibration cv::initUndistortRectifyMap( cameraMatrix, // computed camera matrix distCoeffs, // computed distortion matrix cv::Mat(), // optional rectification (none) cv::Mat(), // camera matrix to generate undistorted cv::Size(640,480), // image.size(), // size of undistorted CV_32FC1, // type of output map map1, map2); // the x and y mapping functions mustInitUndistort= false; } // Apply mapping functions cv::remap(image, undistorted, map1, map2, cv::INTER_LINEAR); // interpolation type return undistorted; } // Set the calibration options // 8radialCoeffEnabled should be true if 8 radial coefficients are required (5 is default) // tangentialParamEnabled should be true if tangeantial distortion is present void CameraCalibrator::setCalibrationFlag(bool radial8CoeffEnabled, bool tangentialParamEnabled) { // Set the flag used in cv::calibrateCamera() flag = 0; if (!tangentialParamEnabled) flag += cv::CALIB_ZERO_TANGENT_DIST; if (radial8CoeffEnabled) flag += cv::CALIB_RATIONAL_MODEL; } //! [compute_errors] double CameraCalibrator::computeReprojectionErrors(const std::vector<std::vector<cv::Point3f> >& objectPoints, const std::vector<std::vector<cv::Point2f> >& imagePoints, const std::vector<cv::Mat>& rvecs, const std::vector<cv::Mat>& tvecs, const cv::Mat& cameraMatrix, const cv::Mat& distCoeffs, std::vector<float>& perViewErrors) { std::vector<cv::Point2f> imagePoints2; size_t totalPoints = 0; double totalErr = 0, err; perViewErrors.resize(objectPoints.size()); for (size_t i = 0; i < objectPoints.size(); ++i) { cv::projectPoints(objectPoints[i], rvecs[i], tvecs[i], cameraMatrix, distCoeffs, imagePoints2); err = cv::norm(imagePoints[i], imagePoints2, cv::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); }