#Omni toolbox code#

The output parameters include camera parameters of two cameras and the relative pose of them. Stereo calibration is to calibrate two cameras together. For example, CALIB_FIX_SKEW+CALIB_FIX_K1 means fixing skew and K1.Ĭriteria is the stopping criteria during optimization, set it to be, for example, cv::TermCriteria( cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 200, 0.0001), which means using 200 iterations and stopping when relative change is smaller than 0.0001. Use 'plus' operator to set multiple features. Your can specify flags to fix parameters during calibration. The calibration supports some features, flags is a enumeration for some features, including: The returned value rms is the root mean square of reprojection errors. This is due to some images are failed in the initialization step so they are not used in the final optimization. idx is a CV_32S Mat that stores indices of images that are really used in calibration. The xi is a single value variable of Mei's model. K, xi, D are internal parameters and rvecs, tvecs are external parameters that store the pose of patterns.

#Omni toolbox code#

Use the following code to load them:ĭouble rms = cv::omnidir::calibrate(objectPoints, imagePoints, imgSize, K, xi, D, rvecs, tvecs, flags, critia, idx) The file opencv_contrib/modules/ccalib/tutorial/data/omni_calib_data.xml stores an example of objectPoints, imagePoints and imageSize.

Note the size of objectPoints and imagePoints must be the same because they are corresponding to each other.Īnother thing you should input is the size of images. Save 3D points in objectPoints, similar to imagePoints, it can be std::vector> or std::vector where cv::Mat is of type CV_32FC3. You can compute they for yourself if you know the physical size of your pattern. The type can also be std::vector where the cv::Mat is CV_32FC2.Īlso, the corresponding 3D points in world (pattern) coordinate are required. The type of imagePoints may be std::vector>, the first vector stores corners in each frame, the second vector stores corners in an individual frame. Save the positions of corners in images in a variable like imagePoints. For checkerboard, use OpenCV function cv::findChessboardCorners for circle grid, use cv::findCirclesGrid, for random pattern, use the randomPatternCornerFinder class in opencv_contrib/modules/ccalib/src/randomPattern.hpp. Next step is to extract corners from calibration pattern. A new pattern named random pattern can also be used, you can refer to opencv_contrib/modules/ccalib for more details.

Several kinds of patterns are supported by OpenCV, like checkerborad and circle grid. The first step to calibrate camera is to get a calibration pattern and take some photos.

comparison with fisheye model in opencv/calib3d/.

reconstruct 3D from two stereo images, with large filed of view.

rectify images so that large distoration is removed.

This tutorial will introduce the following parts of omnidirectional camera calibartion module:

Pollefeys, 'A Multiple-Camera System Calibration Toolbox Using A Feature Descriptor-Based Calibration Pattern', in IROS 2013. The implementation of the calibration part is based on Li's calibration toolbox:ī. The model is capable of modeling catadioptric cameras and fisheye cameras, which may both have very large field of view. Rives, Single view point omnidirectional camera calibration from planar grids, in ICRA 2007. The camera model is described in this paper:Ĭ. This module includes calibration, rectification and stereo reconstruction of omnidirectional camearas.