Tutorials

doc/tutorials/calib3d.tex

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+
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+%                                        C++                                         %
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+
+\ifCpp
+\section{Camera calibration}
+The goal of this tutorial is to learn how to calibrate a camera given a set of chessboard images. 
+
+\texttt{Test data}: use images in your data/chess folder. 
+
+Compile opencv with samples by setting BUILD\_EXAMPLES to ON in cmake configuration. 
+
+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. 
+
+\section{Pose estimation}
+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.
+
+\texttt{Test data}: use chess\_test*.jpg images from your data folder.
+
+Create an empty console project. Load a test image:
+\begin{lstlisting}
+Mat img = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
+\end{lstlisting}
+
+Detect a chessboard in this image using findChessboard function. 
+\begin{lstlisting}
+bool found = findChessboardCorners( img, boardSize, ptvec, CV_CALIB_CB_ADAPTIVE_THRESH );
+\end{lstlisting}
+
+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 \(z = 0\).
+
+Read camera parameters from xml/yaml file:
+\begin{lstlisting}
+FileStorage fs(filename, FileStorage::READ);
+Mat intrinsics, distortion;
+fs["camera_matrix"] >> intrinsics;
+fs["distortion_coefficients"] >> distortion;
+\end{lstlisting}
+
+Now we are ready to find chessboard pose by running solvePnP:
+\begin{lstlisting}
+vector<Point3f> boardPoints;
+// fill the array
+...
+
+solvePnP(Mat(boardPoints), Mat(foundBoardCorners), cameraMatrix,
+                     distCoeffs, rvec, tvec, false);
+\end{lstlisting}
+
+Calculate reprojection error like it is done in \texttt{calibration} sample (see textttt{opencv/samples/cpp/calibration.cpp}, function \texttt{computeReprojectionErrors}). 
+
+How to calculate the distance from the camera origin to any of the corners? 
+\fi

doc/tutorials/features2d.tex

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+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%                                                                                    %
+%                                        C++                                         %
+%                                                                                    %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\ifCpp
+\section{Detection of planar objects}
+The goal of this tutorial is to learn how to use features2d and calib3d modules for detecting known planar objects in scenes. 
+
+\texttt{Test data}: use images in your data folder, for instance, box.png and box\_in\_scene.png. 
+
+Create a new console project. Read two input images. Example:
+\begin{lstlisting}
+Mat img1 = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
+\end{lstlisting}
+
+Detect keypoints in both images. Example:
+\begin{lstlisting}
+// detecting keypoints
+FastFeatureDetector detector(15);
+vector<KeyPoint> keypoints1;
+detector.detect(img1, keypoints1);
+\end{lstlisting}
+
+Compute descriptors for each of the keypoints. Example:
+\begin{lstlisting}
+// computing descriptors
+SurfDescriptorExtractor extractor;
+Mat descriptors1;
+extractor.compute(img1, keypoints1, descriptors1);
+\end{lstlisting}
+
+Now, find the closest matches between descriptors from the first image to the second:
+\begin{lstlisting}
+// matching descriptors
+BruteForceMatcher<L2<float> > matcher;
+vector<DMatch> matches;
+matcher.match(descriptors1, descriptors2, matches);
+\end{lstlisting}
+
+Visualize the results:
+\begin{lstlisting}
+// drawing the results
+namedWindow("matches", 1);
+Mat img_matches;
+drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches);
+imshow("matches", img_matches);
+waitKey(0);
+\end{lstlisting}
+
+Find the homography transformation between two sets of points:
+\begin{lstlisting}
+vector<Point2f> points1, points2;
+// fill the arrays with the points
+....
+Mat H = findHomography(Mat(points1), Mat(points2), CV_RANSAC, ransacReprojThreshold);
+\end{lstlisting}
+
+Create a set of inlier matches and draw them. Use perspectiveTransform function to map points with homography:
+\begin{lstlisting}
+Mat points1Projected;
+perspectiveTransform(Mat(points1), points1Projected, H);
+\end{lstlisting}
+Use drawMatches for drawing inliers. 
+\fi

doc/tutorials/opencv_tutorials_body.tex

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+\chapter{Prerequisites}
+\renewcommand{\curModule}{Prerequisites}
+\input{tutorials/prerequisites}
+
+
+\chapter{Features2d}
+\renewcommand{\curModule}{Features2d}
+\input{tutorials/features2d}
+
+\chapter{Calib3d}
+\renewcommand{\curModule}{Calib3d}
+\input{tutorials/calib3d}

doc/tutorials/prerequisites.tex

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+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%                                                                                    %
+%                                        C++                                         %
+%                                                                                    %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\ifCpp
+\section{Prerequisites}
+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. 
+
+\fi