/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #include "precomp.hpp" #include "opencv2/video/video.hpp" namespace cv { /////////////////////////////////////////////////////////////////////////////////////////////////////////// CV_INIT_ALGORITHM(BackgroundSubtractorMOG, "BackgroundSubtractor.MOG", obj.info()->addParam(obj, "history", obj.history); obj.info()->addParam(obj, "nmixtures", obj.nmixtures); obj.info()->addParam(obj, "backgroundRatio", obj.backgroundRatio); obj.info()->addParam(obj, "noiseSigma", obj.noiseSigma)); /////////////////////////////////////////////////////////////////////////////////////////////////////////// CV_INIT_ALGORITHM(BackgroundSubtractorMOG2, "BackgroundSubtractor.MOG2", obj.info()->addParam(obj, "history", obj.history); obj.info()->addParam(obj, "nmixtures", obj.nmixtures); obj.info()->addParam(obj, "varThreshold", obj.varThreshold); obj.info()->addParam(obj, "detectShadows", obj.bShadowDetection)); /////////////////////////////////////////////////////////////////////////////////////////////////////////// CV_INIT_ALGORITHM(BackgroundSubtractorGMG, "BackgroundSubtractor.GMG", obj.info()->addParam(obj, "maxFeatures", obj.maxFeatures,false,0,0, "Maximum number of features to store in histogram. Harsh enforcement of sparsity constraint."); obj.info()->addParam(obj, "learningRate", obj.learningRate,false,0,0, "Adaptation rate of histogram. Close to 1, slow adaptation. Close to 0, fast adaptation, features forgotten quickly."); obj.info()->addParam(obj, "initializationFrames", obj.numInitializationFrames,false,0,0, "Number of frames to use to initialize histograms of pixels."); obj.info()->addParam(obj, "quantizationLevels", obj.quantizationLevels,false,0,0, "Number of discrete colors to be used in histograms. Up-front quantization."); obj.info()->addParam(obj, "backgroundPrior", obj.backgroundPrior,false,0,0, "Prior probability that each individual pixel is a background pixel."); obj.info()->addParam(obj, "smoothingRadius", obj.smoothingRadius,false,0,0, "Radius of smoothing kernel to filter noise from FG mask image."); obj.info()->addParam(obj, "decisionThreshold", obj.decisionThreshold,false,0,0, "Threshold for FG decision rule. Pixel is FG if posterior probability exceeds threshold.")); bool initModule_video(void) { bool all = true; all &= !BackgroundSubtractorMOG_info_auto.name().empty(); all &= !BackgroundSubtractorMOG2_info_auto.name().empty(); all &= !BackgroundSubtractorGMG_info_auto.name().empty(); return all; } }