Tutorials
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Victor Erukhimov
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doc/opencv_tutorials.pdf
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doc/opencv_tutorials.pdf
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doc/opencv_tutorials.tex
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doc/opencv_tutorials.tex
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\documentclass[11pt]{book}
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\usepackage{cite}
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\usepackage[pdftex]{graphicx}
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\usepackage{titlesec}
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\usepackage{listings}
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\usepackage{fancyvrb}
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\usepackage[svgnames]{xcolor}
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\usepackage{framed}
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\usepackage{amsmath}
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\usepackage{amssymb}
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\usepackage{bbm}
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\usepackage{hyperref}
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\usepackage{makeidx}
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\usepackage{color}
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\usepackage{verbatim}
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\setcounter{secnumdepth}{1}
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\definecolor{shadecolor}{gray}{0.95} % Background color of title bars
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\lstset{
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language=C,
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basicstyle=\small\ttfamily,
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backgroundcolor=\color{shadecolor}
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}
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\definecolor{cvlinkcolor}{rgb}{0.0 0.3 0.8}
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% taken from http://en.wikibooks.org/wiki/LaTeX/Hyperlinks
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\hypersetup{
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bookmarks=true, % show bookmarks bar?
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unicode=false, % non-Latin characters in Acrobat’s bookmarks
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%pdftoolbar=true, % show Acrobat’s toolbar?
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%pdfmenubar=true, % show Acrobat’s menu?
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%pdffitwindow=false, % window fit to page when opened
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%pdfstartview={FitH}, % fits the width of the page to the window
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%pdftitle={My title}, % title
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%pdfauthor={Author}, % author
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%pdfsubject={Subject}, % subject of the document
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%pdfcreator={Creator}, % creator of the document
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%pdfproducer={Producer}, % producer of the document
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%pdfkeywords={keywords}, % list of keywords
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%pdfnewwindow=true, % links in new window
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colorlinks=true, % false: boxed links; true: colored links
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linkcolor=cvlinkcolor, % color of internal links
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citecolor=cvlinkcolor, % color of links to bibliography
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filecolor=magenta, % color of file links
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urlcolor=cyan % color of external links
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}
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\makeindex
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\newcommand{\piRsquare}{\pi r^2} % This is my own macro !!!
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\usepackage{helvetica}
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\usepackage{ifthen}
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\usepackage{alltt}
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\usepackage{opencv}
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%%% Margins %%%
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\oddsidemargin 0.0in
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\evensidemargin 0.0in
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\textwidth 6.5in
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%\headheight 1.0in
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%\topmargin 0.5in
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%\textheight 9.0in
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%\footheight 1.0in
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%%%%%%%%%%%%%%%
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\title{OpenCV Tutorials} % used by \maketitle
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\author{v2.2} % used by \maketitle
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\date{February, 2011} % used by \maketitle
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\begin{document}
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\maketitle % automatic title!
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\setcounter{tocdepth}{8}
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\tableofcontents
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\titleformat{\subsection}
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{\titlerule
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\vspace{.8ex}%
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\normalfont\bfseries\Large}
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{\thesection.}{.5em}{}
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%%% Define these to get rid of warnings
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\def\genc{true}
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\def\genpy{true}
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\def\gencpp{true}
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\newif\ifC
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\newif\ifPy
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\newif\ifCpp
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\newif\ifCPy
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\Cfalse
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\Cpptrue
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\Pyfalse
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\CPyfalse
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\def\targetlang{cpp}
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\part{C++ API tutorials}
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\input{tutorials/opencv_tutorials_body}
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\addcontentsline{toc}{part}{Index}
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\printindex
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\end{document} % End of document.
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56
doc/tutorials/calib3d.tex
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doc/tutorials/calib3d.tex
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% %
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% C++ %
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% %
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\ifCpp
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\section{Camera calibration}
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The goal of this tutorial is to learn how to calibrate a camera given a set of chessboard images.
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\texttt{Test data}: use images in your data/chess folder.
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Compile opencv with samples by setting BUILD\_EXAMPLES to ON in cmake configuration.
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Go to bin folder and use \texttt{imagelist\_creator} to create an xml/yaml list of your images. Then, run \texttt{calibration} sample to get camera parameters. Use square size equal to 3cm.
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\section{Pose estimation}
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Now, let us write a code that detects a chessboard in a new image and finds its distance from the camera. You can apply the same method to any object with knwon 3d geometry that you can detect in an image.
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\texttt{Test data}: use chess\_test*.jpg images from your data folder.
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Create an empty console project. Load a test image:
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\begin{lstlisting}
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Mat img = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
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\end{lstlisting}
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Detect a chessboard in this image using findChessboard function.
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\begin{lstlisting}
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bool found = findChessboardCorners( img, boardSize, ptvec, CV_CALIB_CB_ADAPTIVE_THRESH );
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\end{lstlisting}
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Now, write a function that generates a \texttt{vector<Point3f>} array of 3d coordinates of a chessboard in any coordinate system. For simplicity, let us choose a system such that one of the chessboard corners is in the origin and the board is in the plane \(z = 0\).
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Read camera parameters from xml/yaml file:
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\begin{lstlisting}
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FileStorage fs(filename, FileStorage::READ);
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Mat intrinsics, distortion;
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fs["camera_matrix"] >> intrinsics;
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fs["distortion_coefficients"] >> distortion;
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\end{lstlisting}
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Now we are ready to find chessboard pose by running solvePnP:
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\begin{lstlisting}
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vector<Point3f> boardPoints;
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// fill the array
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...
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solvePnP(Mat(boardPoints), Mat(foundBoardCorners), cameraMatrix,
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distCoeffs, rvec, tvec, false);
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\end{lstlisting}
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Calculate reprojection error like it is done in \texttt{calibration} sample (see textttt{opencv/samples/cpp/calibration.cpp}, function \texttt{computeReprojectionErrors}).
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How to calculate the distance from the camera origin to any of the corners?
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\fi
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doc/tutorials/features2d.tex
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doc/tutorials/features2d.tex
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% %
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% C++ %
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% %
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\ifCpp
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\section{Detection of planar objects}
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The goal of this tutorial is to learn how to use features2d and calib3d modules for detecting known planar objects in scenes.
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\texttt{Test data}: use images in your data folder, for instance, box.png and box\_in\_scene.png.
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Create a new console project. Read two input images. Example:
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\begin{lstlisting}
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Mat img1 = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
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\end{lstlisting}
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Detect keypoints in both images. Example:
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\begin{lstlisting}
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// detecting keypoints
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FastFeatureDetector detector(15);
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vector<KeyPoint> keypoints1;
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detector.detect(img1, keypoints1);
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\end{lstlisting}
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Compute descriptors for each of the keypoints. Example:
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\begin{lstlisting}
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// computing descriptors
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SurfDescriptorExtractor extractor;
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Mat descriptors1;
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extractor.compute(img1, keypoints1, descriptors1);
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\end{lstlisting}
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Now, find the closest matches between descriptors from the first image to the second:
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\begin{lstlisting}
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// matching descriptors
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BruteForceMatcher<L2<float> > matcher;
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vector<DMatch> matches;
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matcher.match(descriptors1, descriptors2, matches);
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\end{lstlisting}
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Visualize the results:
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\begin{lstlisting}
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// drawing the results
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namedWindow("matches", 1);
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Mat img_matches;
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drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches);
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imshow("matches", img_matches);
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waitKey(0);
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\end{lstlisting}
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Find the homography transformation between two sets of points:
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\begin{lstlisting}
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vector<Point2f> points1, points2;
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// fill the arrays with the points
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....
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Mat H = findHomography(Mat(points1), Mat(points2), CV_RANSAC, ransacReprojThreshold);
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\end{lstlisting}
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Create a set of inlier matches and draw them. Use perspectiveTransform function to map points with homography:
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\begin{lstlisting}
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Mat points1Projected;
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perspectiveTransform(Mat(points1), points1Projected, H);
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\end{lstlisting}
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Use drawMatches for drawing inliers.
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\fi
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doc/tutorials/opencv_tutorials_body.tex
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doc/tutorials/opencv_tutorials_body.tex
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\chapter{Prerequisites}
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\renewcommand{\curModule}{Prerequisites}
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\input{tutorials/prerequisites}
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\chapter{Features2d}
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\renewcommand{\curModule}{Features2d}
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\input{tutorials/features2d}
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\chapter{Calib3d}
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\renewcommand{\curModule}{Calib3d}
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\input{tutorials/calib3d}
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doc/tutorials/prerequisites.tex
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doc/tutorials/prerequisites.tex
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% %
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% C++ %
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% %
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\ifCpp
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\section{Prerequisites}
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Download the latest release of opencv from \url{http://sourceforge.net/projects/opencvlibrary/}. You will need cmake and your favorite compiler environment in order to build opencv from sources. Please refer to the installation guide \url{http://opencv.willowgarage.com/wiki/InstallGuide} for detailed instructions.
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\fi
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