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459 lines
18 KiB
C++
459 lines
18 KiB
C++
/* This is sample from the OpenCV book. The copyright notice is below */
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/* *************** License:**************************
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Oct. 3, 2008
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Right to use this code in any way you want without warranty, support or any guarantee of it working.
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BOOK: It would be nice if you cited it:
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Learning OpenCV: Computer Vision with the OpenCV Library
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by Gary Bradski and Adrian Kaehler
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Published by O'Reilly Media, October 3, 2008
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AVAILABLE AT:
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http://www.amazon.com/Learning-OpenCV-Computer-Vision-Library/dp/0596516134
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Or: http://oreilly.com/catalog/9780596516130/
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ISBN-10: 0596516134 or: ISBN-13: 978-0596516130
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OPENCV WEBSITES:
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Homepage: http://opencv.org
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Online docs: http://docs.opencv.org
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GitHub: https://github.com/opencv/opencv/
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************************************************** */
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#include "opencv2/calib3d.hpp"
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#include "opencv2/imgcodecs.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/imgproc.hpp"
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#include "opencv2/objdetect/charuco_detector.hpp"
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#include <vector>
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#include <string>
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#include <algorithm>
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#include <iostream>
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#include <iterator>
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#include <stdio.h>
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#include <stdlib.h>
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#include <ctype.h>
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using namespace cv;
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using namespace std;
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static int print_help(char** argv)
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{
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cout <<
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" Given a list of chessboard or ChArUco images, the number of corners (nx, ny)\n"
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" on the chessboards and the number of squares (nx, ny) on ChArUco,\n"
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" and a flag: useCalibrated for \n"
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" calibrated (0) or\n"
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" uncalibrated \n"
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" (1: use stereoCalibrate(), 2: compute fundamental\n"
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" matrix separately) stereo. \n"
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" Calibrate the cameras and display the\n"
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" rectified results along with the computed disparity images. \n" << endl;
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cout << "Usage:\n " << argv[0] << " -w=<board_width default=9> -h=<board_height default=6>"
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<<" -t=<pattern type: chessboard or charucoboard default=chessboard> -s=<square_size default=1.0> -ms=<marker size default=0.5>"
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<<" -ad=<predefined aruco dictionary name default=DICT_4X4_50> -adf=<aruco dictionary file default=None>"
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<<" <image list XML/YML file default=stereo_calib.xml>\n" << endl;
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cout << "Available Aruco dictionaries: DICT_4X4_50, DICT_4X4_100, DICT_4X4_250, "
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<< "DICT_4X4_1000, DICT_5X5_50, DICT_5X5_100, DICT_5X5_250, DICT_5X5_1000, "
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<< "DICT_6X6_50, DICT_6X6_100, DICT_6X6_250, DICT_6X6_1000, DICT_7X7_50, "
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<< "DICT_7X7_100, DICT_7X7_250, DICT_7X7_1000, DICT_ARUCO_ORIGINAL, "
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<< "DICT_APRILTAG_16h5, DICT_APRILTAG_25h9, DICT_APRILTAG_36h10, DICT_APRILTAG_36h11\n";
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return 0;
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}
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static void
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StereoCalib(const vector<string>& imagelist, Size inputBoardSize, string type, float squareSize, float markerSize, cv::aruco::PredefinedDictionaryType arucoDict, string arucoDictFile, bool displayCorners = false, bool useCalibrated=true, bool showRectified=true)
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{
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if( imagelist.size() % 2 != 0 )
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{
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cout << "Error: the image list contains odd (non-even) number of elements\n";
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return;
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}
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const int maxScale = 2;
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// ARRAY AND VECTOR STORAGE:
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vector<vector<Point2f> > imagePoints[2];
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vector<vector<Point3f> > objectPoints;
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Size imageSize;
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int i, j, k, nimages = (int)imagelist.size()/2;
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imagePoints[0].resize(nimages);
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imagePoints[1].resize(nimages);
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vector<string> goodImageList;
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Size boardSizeInnerCorners, boardSizeUnits;
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if (type == "chessboard") {
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//chess board pattern boardSize is given in inner corners
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boardSizeInnerCorners = inputBoardSize;
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boardSizeUnits.height = inputBoardSize.height+1;
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boardSizeUnits.width = inputBoardSize.width+1;
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}
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else if (type == "charucoboard") {
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//ChArUco board pattern boardSize is given in squares units
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boardSizeUnits = inputBoardSize;
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boardSizeInnerCorners.width = inputBoardSize.width - 1;
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boardSizeInnerCorners.height = inputBoardSize.height - 1;
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}
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else {
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std::cout << "unknown pattern type " << type << "\n";
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return;
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}
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cv::aruco::Dictionary dictionary;
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if (arucoDictFile == "None") {
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dictionary = cv::aruco::getPredefinedDictionary(arucoDict);
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}
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else {
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cv::FileStorage dict_file(arucoDictFile, cv::FileStorage::Mode::READ);
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cv::FileNode fn(dict_file.root());
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dictionary.readDictionary(fn);
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}
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cv::aruco::CharucoBoard ch_board(boardSizeUnits, squareSize, markerSize, dictionary);
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cv::aruco::CharucoDetector ch_detector(ch_board);
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std::vector<int> markerIds;
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for( i = j = 0; i < nimages; i++ )
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{
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for( k = 0; k < 2; k++ )
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{
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const string& filename = imagelist[i*2+k];
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Mat img = imread(filename, IMREAD_GRAYSCALE);
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if(img.empty())
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break;
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if( imageSize == Size() )
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imageSize = img.size();
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else if( img.size() != imageSize )
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{
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cout << "The image " << filename << " has the size different from the first image size. Skipping the pair\n";
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break;
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}
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bool found = false;
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vector<Point2f>& corners = imagePoints[k][j];
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for( int scale = 1; scale <= maxScale; scale++ )
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{
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Mat timg;
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if( scale == 1 )
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timg = img;
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else
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resize(img, timg, Size(), scale, scale, INTER_LINEAR_EXACT);
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if (type == "chessboard") {
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found = findChessboardCorners(timg, boardSizeInnerCorners, corners,
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CALIB_CB_ADAPTIVE_THRESH | CALIB_CB_NORMALIZE_IMAGE);
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}
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else if (type == "charucoboard") {
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ch_detector.detectBoard(timg, corners, markerIds);
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found = corners.size() == (size_t) (boardSizeInnerCorners.height*boardSizeInnerCorners.width);
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}
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else {
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cout << "Error: unknown pattern " << type << "\n";
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return;
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}
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if( found )
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{
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if( scale > 1 )
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{
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Mat cornersMat(corners);
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cornersMat *= 1./scale;
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}
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break;
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}
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}
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if( displayCorners )
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{
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cout << filename << endl;
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Mat cimg, cimg1;
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cvtColor(img, cimg, COLOR_GRAY2BGR);
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drawChessboardCorners(cimg, boardSizeInnerCorners, corners, found);
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double sf = 640./MAX(img.rows, img.cols);
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resize(cimg, cimg1, Size(), sf, sf, INTER_LINEAR_EXACT);
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imshow("corners", cimg1);
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char c = (char)waitKey(500);
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if( c == 27 || c == 'q' || c == 'Q' ) //Allow ESC to quit
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exit(-1);
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}
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else
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putchar('.');
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if( !found )
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break;
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if (type == "chessboard") {
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cornerSubPix(img, corners, Size(11, 11), Size(-1, -1),
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TermCriteria(TermCriteria::COUNT + TermCriteria::EPS,
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30, 0.01));
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}
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}
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if( k == 2 )
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{
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goodImageList.push_back(imagelist[i*2]);
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goodImageList.push_back(imagelist[i*2+1]);
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j++;
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}
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}
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cout << j << " pairs have been successfully detected.\n";
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nimages = j;
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if( nimages < 2 )
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{
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cout << "Error: too little pairs to run the calibration\n";
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return;
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}
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imagePoints[0].resize(nimages);
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imagePoints[1].resize(nimages);
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objectPoints.resize(nimages);
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for( i = 0; i < nimages; i++ )
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{
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for( j = 0; j < boardSizeInnerCorners.height; j++ )
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for( k = 0; k < boardSizeInnerCorners.width; k++ )
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objectPoints[i].push_back(Point3f(k*squareSize, j*squareSize, 0));
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}
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cout << "Running stereo calibration ...\n";
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Mat cameraMatrix[2], distCoeffs[2];
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cameraMatrix[0] = initCameraMatrix2D(objectPoints,imagePoints[0],imageSize,0);
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cameraMatrix[1] = initCameraMatrix2D(objectPoints,imagePoints[1],imageSize,0);
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Mat R, T, E, F;
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double rms = stereoCalibrate(objectPoints, imagePoints[0], imagePoints[1],
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cameraMatrix[0], distCoeffs[0],
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cameraMatrix[1], distCoeffs[1],
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imageSize, R, T, E, F,
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CALIB_FIX_ASPECT_RATIO +
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CALIB_ZERO_TANGENT_DIST +
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CALIB_USE_INTRINSIC_GUESS +
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CALIB_SAME_FOCAL_LENGTH +
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CALIB_RATIONAL_MODEL +
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CALIB_FIX_K3 + CALIB_FIX_K4 + CALIB_FIX_K5,
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TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 100, 1e-5) );
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cout << "done with RMS error=" << rms << endl;
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// CALIBRATION QUALITY CHECK
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// because the output fundamental matrix implicitly
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// includes all the output information,
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// we can check the quality of calibration using the
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// epipolar geometry constraint: m2^t*F*m1=0
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double err = 0;
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int npoints = 0;
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vector<Vec3f> lines[2];
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for( i = 0; i < nimages; i++ )
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{
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int npt = (int)imagePoints[0][i].size();
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Mat imgpt[2];
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for( k = 0; k < 2; k++ )
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{
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imgpt[k] = Mat(imagePoints[k][i]);
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undistortPoints(imgpt[k], imgpt[k], cameraMatrix[k], distCoeffs[k], Mat(), cameraMatrix[k]);
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computeCorrespondEpilines(imgpt[k], k+1, F, lines[k]);
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}
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for( j = 0; j < npt; j++ )
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{
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double errij = fabs(imagePoints[0][i][j].x*lines[1][j][0] +
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imagePoints[0][i][j].y*lines[1][j][1] + lines[1][j][2]) +
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fabs(imagePoints[1][i][j].x*lines[0][j][0] +
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imagePoints[1][i][j].y*lines[0][j][1] + lines[0][j][2]);
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err += errij;
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}
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npoints += npt;
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}
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cout << "average epipolar err = " << err/npoints << endl;
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// save intrinsic parameters
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FileStorage fs("intrinsics.yml", FileStorage::WRITE);
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if( fs.isOpened() )
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{
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fs << "M1" << cameraMatrix[0] << "D1" << distCoeffs[0] <<
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"M2" << cameraMatrix[1] << "D2" << distCoeffs[1];
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fs.release();
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}
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else
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cout << "Error: can not save the intrinsic parameters\n";
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Mat R1, R2, P1, P2, Q;
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Rect validRoi[2];
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stereoRectify(cameraMatrix[0], distCoeffs[0],
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cameraMatrix[1], distCoeffs[1],
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imageSize, R, T, R1, R2, P1, P2, Q,
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CALIB_ZERO_DISPARITY, 1, imageSize, &validRoi[0], &validRoi[1]);
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fs.open("extrinsics.yml", FileStorage::WRITE);
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if( fs.isOpened() )
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{
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fs << "R" << R << "T" << T << "R1" << R1 << "R2" << R2 << "P1" << P1 << "P2" << P2 << "Q" << Q;
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fs.release();
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}
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else
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cout << "Error: can not save the extrinsic parameters\n";
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// OpenCV can handle left-right
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// or up-down camera arrangements
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bool isVerticalStereo = fabs(P2.at<double>(1, 3)) > fabs(P2.at<double>(0, 3));
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// COMPUTE AND DISPLAY RECTIFICATION
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if( !showRectified )
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return;
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Mat rmap[2][2];
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// IF BY CALIBRATED (BOUGUET'S METHOD)
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if( useCalibrated )
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{
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// we already computed everything
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}
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// OR ELSE HARTLEY'S METHOD
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else
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// use intrinsic parameters of each camera, but
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// compute the rectification transformation directly
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// from the fundamental matrix
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{
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vector<Point2f> allimgpt[2];
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for( k = 0; k < 2; k++ )
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{
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for( i = 0; i < nimages; i++ )
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std::copy(imagePoints[k][i].begin(), imagePoints[k][i].end(), back_inserter(allimgpt[k]));
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}
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F = findFundamentalMat(Mat(allimgpt[0]), Mat(allimgpt[1]), FM_8POINT, 0, 0);
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Mat H1, H2;
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stereoRectifyUncalibrated(Mat(allimgpt[0]), Mat(allimgpt[1]), F, imageSize, H1, H2, 3);
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R1 = cameraMatrix[0].inv()*H1*cameraMatrix[0];
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R2 = cameraMatrix[1].inv()*H2*cameraMatrix[1];
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P1 = cameraMatrix[0];
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P2 = cameraMatrix[1];
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}
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//Precompute maps for cv::remap()
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initUndistortRectifyMap(cameraMatrix[0], distCoeffs[0], R1, P1, imageSize, CV_16SC2, rmap[0][0], rmap[0][1]);
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initUndistortRectifyMap(cameraMatrix[1], distCoeffs[1], R2, P2, imageSize, CV_16SC2, rmap[1][0], rmap[1][1]);
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Mat canvas;
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double sf;
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int w, h;
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if( !isVerticalStereo )
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{
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sf = 600./MAX(imageSize.width, imageSize.height);
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w = cvRound(imageSize.width*sf);
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h = cvRound(imageSize.height*sf);
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canvas.create(h, w*2, CV_8UC3);
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}
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else
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{
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sf = 300./MAX(imageSize.width, imageSize.height);
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w = cvRound(imageSize.width*sf);
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h = cvRound(imageSize.height*sf);
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canvas.create(h*2, w, CV_8UC3);
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}
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for( i = 0; i < nimages; i++ )
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{
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for( k = 0; k < 2; k++ )
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{
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Mat img = imread(goodImageList[i*2+k], IMREAD_GRAYSCALE), rimg, cimg;
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remap(img, rimg, rmap[k][0], rmap[k][1], INTER_LINEAR);
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cvtColor(rimg, cimg, COLOR_GRAY2BGR);
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Mat canvasPart = !isVerticalStereo ? canvas(Rect(w*k, 0, w, h)) : canvas(Rect(0, h*k, w, h));
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resize(cimg, canvasPart, canvasPart.size(), 0, 0, INTER_AREA);
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if( useCalibrated )
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{
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Rect vroi(cvRound(validRoi[k].x*sf), cvRound(validRoi[k].y*sf),
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cvRound(validRoi[k].width*sf), cvRound(validRoi[k].height*sf));
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rectangle(canvasPart, vroi, Scalar(0,0,255), 3, 8);
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}
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}
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if( !isVerticalStereo )
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for( j = 0; j < canvas.rows; j += 16 )
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line(canvas, Point(0, j), Point(canvas.cols, j), Scalar(0, 255, 0), 1, 8);
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else
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for( j = 0; j < canvas.cols; j += 16 )
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line(canvas, Point(j, 0), Point(j, canvas.rows), Scalar(0, 255, 0), 1, 8);
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imshow("rectified", canvas);
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char c = (char)waitKey();
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if( c == 27 || c == 'q' || c == 'Q' )
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break;
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}
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}
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static bool readStringList( const string& filename, vector<string>& l )
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{
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l.resize(0);
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FileStorage fs(filename, FileStorage::READ);
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if( !fs.isOpened() )
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return false;
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FileNode n = fs.getFirstTopLevelNode();
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if( n.type() != FileNode::SEQ )
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return false;
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FileNodeIterator it = n.begin(), it_end = n.end();
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for( ; it != it_end; ++it )
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l.push_back((string)*it);
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return true;
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}
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int main(int argc, char** argv)
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{
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Size inputBoardSize;
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string imagelistfn;
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bool showRectified;
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cv::CommandLineParser parser(argc, argv, "{w|9|}{h|6|}{t|chessboard|}{s|1.0|}{ms|0.5|}{ad|DICT_4X4_50|}{adf|None|}{nr||}{help||}{@input|stereo_calib.xml|}");
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if (parser.has("help"))
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return print_help(argv);
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showRectified = !parser.has("nr");
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imagelistfn = samples::findFile(parser.get<string>("@input"));
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inputBoardSize.width = parser.get<int>("w");
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inputBoardSize.height = parser.get<int>("h");
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string type = parser.get<string>("t");
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float squareSize = parser.get<float>("s");
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float markerSize = parser.get<float>("ms");
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string arucoDictName = parser.get<string>("ad");
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string arucoDictFile = parser.get<string>("adf");
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cv::aruco::PredefinedDictionaryType arucoDict;
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if (arucoDictName == "DICT_4X4_50") { arucoDict = cv::aruco::DICT_4X4_50; }
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else if (arucoDictName == "DICT_4X4_100") { arucoDict = cv::aruco::DICT_4X4_100; }
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else if (arucoDictName == "DICT_4X4_250") { arucoDict = cv::aruco::DICT_4X4_250; }
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else if (arucoDictName == "DICT_4X4_1000") { arucoDict = cv::aruco::DICT_4X4_1000; }
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else if (arucoDictName == "DICT_5X5_50") { arucoDict = cv::aruco::DICT_5X5_50; }
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else if (arucoDictName == "DICT_5X5_100") { arucoDict = cv::aruco::DICT_5X5_100; }
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else if (arucoDictName == "DICT_5X5_250") { arucoDict = cv::aruco::DICT_5X5_250; }
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else if (arucoDictName == "DICT_5X5_1000") { arucoDict = cv::aruco::DICT_5X5_1000; }
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else if (arucoDictName == "DICT_6X6_50") { arucoDict = cv::aruco::DICT_6X6_50; }
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else if (arucoDictName == "DICT_6X6_100") { arucoDict = cv::aruco::DICT_6X6_100; }
|
|
else if (arucoDictName == "DICT_6X6_250") { arucoDict = cv::aruco::DICT_6X6_250; }
|
|
else if (arucoDictName == "DICT_6X6_1000") { arucoDict = cv::aruco::DICT_6X6_1000; }
|
|
else if (arucoDictName == "DICT_7X7_50") { arucoDict = cv::aruco::DICT_7X7_50; }
|
|
else if (arucoDictName == "DICT_7X7_100") { arucoDict = cv::aruco::DICT_7X7_100; }
|
|
else if (arucoDictName == "DICT_7X7_250") { arucoDict = cv::aruco::DICT_7X7_250; }
|
|
else if (arucoDictName == "DICT_7X7_1000") { arucoDict = cv::aruco::DICT_7X7_1000; }
|
|
else if (arucoDictName == "DICT_ARUCO_ORIGINAL") { arucoDict = cv::aruco::DICT_ARUCO_ORIGINAL; }
|
|
else if (arucoDictName == "DICT_APRILTAG_16h5") { arucoDict = cv::aruco::DICT_APRILTAG_16h5; }
|
|
else if (arucoDictName == "DICT_APRILTAG_25h9") { arucoDict = cv::aruco::DICT_APRILTAG_25h9; }
|
|
else if (arucoDictName == "DICT_APRILTAG_36h10") { arucoDict = cv::aruco::DICT_APRILTAG_36h10; }
|
|
else if (arucoDictName == "DICT_APRILTAG_36h11") { arucoDict = cv::aruco::DICT_APRILTAG_36h11; }
|
|
else {
|
|
cout << "incorrect name of aruco dictionary \n";
|
|
return 1;
|
|
}
|
|
|
|
if (!parser.check())
|
|
{
|
|
parser.printErrors();
|
|
return 1;
|
|
}
|
|
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(argv);
|
|
}
|
|
|
|
StereoCalib(imagelist, inputBoardSize, type, squareSize, markerSize, arucoDict, arucoDictFile, false, true, showRectified);
|
|
return 0;
|
|
}
|