Add interactive calibration app
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Vladislav Sovrasov
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Interactive camera calibration application {#tutorial_interactive_calibration}
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==============================
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According to classical calibration technique user must collect all data first and when run @ref cv::calibrateCamera function
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to obtain camera parameters. If average re-projection error is huge or if estimated parameters seems to be wrong, process of
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selection or collecting data and starting of @ref cv::calibrateCamera repeats.
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Interactive calibration process assumes that after each new data portion user can see results and errors estimation, also
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he can delete last data portion and finally, when dataset for calibration is big enough starts process of auto data selection.
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Main application features
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------
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The sample application will:
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- Determine the distortion matrix and confidence interval for each element
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- Determine the camera matrix and confidence interval for each element
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- Take input from camera or video file
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- Read configuration from XML file
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- Save the results into XML file
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- Calculate re-projection error
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- Reject patterns views on sharp angles to prevent appear of ill-conditioned jacobian blocks
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- Auto switch calibration flags (fix aspect ratio and elements of distortion matrix if needed)
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- Auto detect when calibration is done by using several criteria
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- Auto capture of static patterns (user doesn't need press any keys to capture frame, just don't move pattern for a second)
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Supported patterns:
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- Black-white chessboard
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- Asymmetrical circle pattern
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- Dual asymmetrical circle pattern
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- chAruco (chessboard with Aruco markers)
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Description of parameters
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------
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Application has two groups of parameters: primary (passed through command line) and advances (passed through XML file).
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### Primary parameters:
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All of this parameters are passed to application through a command line.
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-[parameter]=[default value]: description
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- -v=[filename]: get video from filename, default input -- camera with id=0
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- -ci=[0]: get video from camera with specified id
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- -flip=[false]: vertical flip of input frames
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- -t=[circles]: pattern for calibration (circles, chessboard, dualCircles, chAruco)
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- -sz=[16.3]: distance between two nearest centers of circles or squares on calibration board
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- -dst=[295] distance between white and black parts of daulCircles pattern
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- -w=[width]: width of pattern (in corners or circles)
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- -h=[height]: height of pattern (in corners or circles)
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- -of=[camParams.xml]: output file name
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- -ft=[true]: auto tuning of calibration flags
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- -vis=[grid]: captured boards visualization (grid, window)
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- -d=[0.8]: delay between captures in seconds
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- -pf=[defaultConfig.xml]: advanced application parameters file
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### Advanced parameters:
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By default values of advanced parameters are stored in defaultConfig.xml
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@code{.xml}
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<?xml version="1.0"?>
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<opencv_storage>
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<charuco_dict>0</charuco_dict>
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<charuco_square_lenght>200</charuco_square_lenght>
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<charuco_marker_size>100</charuco_marker_size>
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<calibration_step>1</calibration_step>
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<max_frames_num>30</max_frames_num>
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<min_frames_num>10</min_frames_num>
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<solver_eps>1e-7</solver_eps>
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<solver_max_iters>30</solver_max_iters>
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<fast_solver>0</fast_solver>
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<frame_filter_conv_param>0.1</frame_filter_conv_param>
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<camera_resolution>1280 720</camera_resolution>
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</opencv_storage>
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@endcode
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- *charuco_dict*: name of special dictionary, which has been used for generation of chAruco pattern
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- *charuco_square_lenght*: size of square on chAruco board (in pixels)
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- *charuco_marker_size*: size of Aruco markers on chAruco board (in pixels)
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- *calibration_step*: interval in frames between launches of @ref cv::calibrateCamera
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- *max_frames_num*: if number of frames for calibration is greater then this value frames filter starts working.
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After filtration size of calibration dataset is equals to *max_frames_num*
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- *min_frames_num*: if number of frames is greater then this value turns on auto flags tuning, undistorted view and quality evaluation
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- *solver_eps*: precision of Levenberg-Marquardt solver in @ref cv::calibrateCamera
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- *solver_max_iters*: iterations limit of solver
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- *fast_solver*: if this value is nonzero and Lapack is found QR decomposition is used instead of SVD in solver.
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QR faster than SVD, but potentially less precise
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- *frame_filter_conv_param*: parameter which used in linear convolution of bicriterial frames filter
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- *camera_resolution*: resolution of camera which is used for calibration
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**Note:** *charuco_dict*, *charuco_square_lenght* and *charuco_marker_size* are used for chAruco pattern generation
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(see Aruco module description for details: [Aruco tutorials](https://github.com/Itseez/opencv_contrib/tree/master/modules/aruco/tutorials))
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Default chAruco pattern:
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Dual circles pattern
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------
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To make this pattern you need standard OpenCV circles pattern and binary inverted one.
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Place two patterns on one plane in order when all horizontal lines of circles in one pattern are
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continuations of similar lines in another.
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Measure distance between patterns as shown at picture below pass it as **dst** command line parameter. Also measure distance between centers of nearest circles and pass
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this value as **sz** command line parameter.
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This pattern is very sensitive to quality of production and measurements.
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Data filtration
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------
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When size of calibration dataset is greater then *max_frames_num* starts working
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data filter. It tries to remove "bad" frames from dataset. Filter removes the frame
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on which \f$loss\_function\f$ takes maximum.
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\f[loss\_function(i)=\alpha RMS(i)+(1-\alpha)reducedGridQuality(i)\f]
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**RMS** is an average re-projection error calculated for frame *i*, **reducedGridQuality**
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is scene coverage quality evaluation without frame *i*. \f$\alpha\f$ is equals to
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**frame_filter_conv_param**.
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Calibration process
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------
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To start calibration just run application. Place pattern ahead the camera and fixate pattern in some pose.
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After that wait for capturing (will be shown message like "Frame #i captured").
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Current focal distance and re-projection error will be shown at the main screen. Move pattern to the next position and repeat procedure. Try to cover image plane
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uniformly and don't show pattern on sharp angles to the image plane.
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If calibration seems to be successful (confidence intervals and average re-projection
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error are small, frame coverage quality and number of pattern views are big enough)
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application will show a message like on screen below.
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Hot keys:
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- Esc -- exit application
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- s -- save current data to XML file
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- r -- delete last frame
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- d -- delete all frames
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- u -- enable/disable applying of undistortion
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- v -- switch visualization mode
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Results
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------
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As result you will get camera parameters and confidence intervals for them.
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Example of output XML file:
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@code{.xml}
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<?xml version="1.0"?>
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<opencv_storage>
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<calibrationDate>"Thu 07 Apr 2016 04:23:03 PM MSK"</calibrationDate>
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<framesCount>21</framesCount>
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<cameraResolution>
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1280 720</cameraResolution>
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<cameraMatrix type_id="opencv-matrix">
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<rows>3</rows>
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<cols>3</cols>
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<dt>d</dt>
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<data>
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1.2519588293098975e+03 0. 6.6684948780852471e+02 0.
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1.2519588293098975e+03 3.6298123112613683e+02 0. 0. 1.</data></cameraMatrix>
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<cameraMatrix_std_dev type_id="opencv-matrix">
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<rows>4</rows>
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<cols>1</cols>
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<dt>d</dt>
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<data>
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0. 1.2887048808572649e+01 2.8536856683866230e+00
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2.8341737483430314e+00</data></cameraMatrix_std_dev>
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<dist_coeffs type_id="opencv-matrix">
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<rows>1</rows>
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<cols>5</cols>
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<dt>d</dt>
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<data>
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1.3569117181595716e-01 -8.2513063822554633e-01 0. 0.
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1.6412101575010554e+00</data></dist_coeffs>
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<dist_coeffs_std_dev type_id="opencv-matrix">
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<rows>5</rows>
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<cols>1</cols>
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<dt>d</dt>
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<data>
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1.5570675523402111e-02 8.7229075437543435e-02 0. 0.
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1.8382427901856876e-01</data></dist_coeffs_std_dev>
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<avg_reprojection_error>4.2691743074130178e-01</avg_reprojection_error>
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</opencv_storage>
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@endcode
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@@ -30,3 +30,14 @@ how to find out from the 2D images information about the 3D world.
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Real time pose estimation of a textured object using ORB features, FlannBased matcher, PnP
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approach plus Ransac and Linear Kalman Filter to reject possible bad poses.
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- @subpage tutorial_interactive_calibration
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*Compatibility:* \> OpenCV 3.1
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*Author:* Vladislav Sovrasov
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Camera calibration by using either the chessboard, chAruco, asymmetrical circle or dual asymmetrical circle
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pattern. Calibration process is continious, so you can see results after each new pattern shot.
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As an output you get average reprojection error, intrinsic camera parameters, distortion coefficients and
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confidence intervals for all of evaluated variables.
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