Open Source Computer Vision Library
https://opencv.org/
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56 lines
2.5 KiB
56 lines
2.5 KiB
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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 |