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diff --git a/2.3-1/thirdparty/raspberrypi/includes/opencv2/video/background_segm.hpp b/2.3-1/thirdparty/raspberrypi/includes/opencv2/video/background_segm.hpp new file mode 100644 index 00000000..d2d068c6 --- /dev/null +++ b/2.3-1/thirdparty/raspberrypi/includes/opencv2/video/background_segm.hpp @@ -0,0 +1,263 @@ +/*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*/ + +#ifndef __OPENCV_BACKGROUND_SEGM_HPP__ +#define __OPENCV_BACKGROUND_SEGM_HPP__ + +#include "opencv2/core/core.hpp" +#include <list> +namespace cv +{ + +/*! + The Base Class for Background/Foreground Segmentation + + The class is only used to define the common interface for + the whole family of background/foreground segmentation algorithms. +*/ +class CV_EXPORTS_W BackgroundSubtractor : public Algorithm +{ +public: + //! the virtual destructor + virtual ~BackgroundSubtractor(); + //! the update operator that takes the next video frame and returns the current foreground mask as 8-bit binary image. + CV_WRAP_AS(apply) virtual void operator()(InputArray image, OutputArray fgmask, + double learningRate=0); + + //! computes a background image + virtual void getBackgroundImage(OutputArray backgroundImage) const; +}; + + +/*! + Gaussian Mixture-based Backbround/Foreground Segmentation Algorithm + + The class implements the following algorithm: + "An improved adaptive background mixture model for real-time tracking with shadow detection" + P. KadewTraKuPong and R. Bowden, + Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001." + http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf + +*/ +class CV_EXPORTS_W BackgroundSubtractorMOG : public BackgroundSubtractor +{ +public: + //! the default constructor + CV_WRAP BackgroundSubtractorMOG(); + //! the full constructor that takes the length of the history, the number of gaussian mixtures, the background ratio parameter and the noise strength + CV_WRAP BackgroundSubtractorMOG(int history, int nmixtures, double backgroundRatio, double noiseSigma=0); + //! the destructor + virtual ~BackgroundSubtractorMOG(); + //! the update operator + virtual void operator()(InputArray image, OutputArray fgmask, double learningRate=0); + + //! re-initiaization method + virtual void initialize(Size frameSize, int frameType); + + virtual AlgorithmInfo* info() const; + +protected: + Size frameSize; + int frameType; + Mat bgmodel; + int nframes; + int history; + int nmixtures; + double varThreshold; + double backgroundRatio; + double noiseSigma; +}; + + +/*! + The class implements the following algorithm: + "Improved adaptive Gausian mixture model for background subtraction" + Z.Zivkovic + International Conference Pattern Recognition, UK, August, 2004. + http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf +*/ +class CV_EXPORTS_W BackgroundSubtractorMOG2 : public BackgroundSubtractor +{ +public: + //! the default constructor + CV_WRAP BackgroundSubtractorMOG2(); + //! the full constructor that takes the length of the history, the number of gaussian mixtures, the background ratio parameter and the noise strength + CV_WRAP BackgroundSubtractorMOG2(int history, float varThreshold, bool bShadowDetection=true); + //! the destructor + virtual ~BackgroundSubtractorMOG2(); + //! the update operator + virtual void operator()(InputArray image, OutputArray fgmask, double learningRate=-1); + + //! computes a background image which are the mean of all background gaussians + virtual void getBackgroundImage(OutputArray backgroundImage) const; + + //! re-initiaization method + virtual void initialize(Size frameSize, int frameType); + + virtual AlgorithmInfo* info() const; + +protected: + Size frameSize; + int frameType; + Mat bgmodel; + Mat bgmodelUsedModes;//keep track of number of modes per pixel + int nframes; + int history; + int nmixtures; + //! here it is the maximum allowed number of mixture components. + //! Actual number is determined dynamically per pixel + double varThreshold; + // threshold on the squared Mahalanobis distance to decide if it is well described + // by the background model or not. Related to Cthr from the paper. + // This does not influence the update of the background. A typical value could be 4 sigma + // and that is varThreshold=4*4=16; Corresponds to Tb in the paper. + + ///////////////////////// + // less important parameters - things you might change but be carefull + //////////////////////// + float backgroundRatio; + // corresponds to fTB=1-cf from the paper + // TB - threshold when the component becomes significant enough to be included into + // the background model. It is the TB=1-cf from the paper. So I use cf=0.1 => TB=0. + // For alpha=0.001 it means that the mode should exist for approximately 105 frames before + // it is considered foreground + // float noiseSigma; + float varThresholdGen; + //correspondts to Tg - threshold on the squared Mahalan. dist. to decide + //when a sample is close to the existing components. If it is not close + //to any a new component will be generated. I use 3 sigma => Tg=3*3=9. + //Smaller Tg leads to more generated components and higher Tg might make + //lead to small number of components but they can grow too large + float fVarInit; + float fVarMin; + float fVarMax; + //initial variance for the newly generated components. + //It will will influence the speed of adaptation. A good guess should be made. + //A simple way is to estimate the typical standard deviation from the images. + //I used here 10 as a reasonable value + // min and max can be used to further control the variance + float fCT;//CT - complexity reduction prior + //this is related to the number of samples needed to accept that a component + //actually exists. We use CT=0.05 of all the samples. By setting CT=0 you get + //the standard Stauffer&Grimson algorithm (maybe not exact but very similar) + + //shadow detection parameters + bool bShadowDetection;//default 1 - do shadow detection + unsigned char nShadowDetection;//do shadow detection - insert this value as the detection result - 127 default value + float fTau; + // Tau - shadow threshold. The shadow is detected if the pixel is darker + //version of the background. Tau is a threshold on how much darker the shadow can be. + //Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow + //See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003. +}; + +/** + * Background Subtractor module. Takes a series of images and returns a sequence of mask (8UC1) + * images of the same size, where 255 indicates Foreground and 0 represents Background. + * This class implements an algorithm described in "Visual Tracking of Human Visitors under + * Variable-Lighting Conditions for a Responsive Audio Art Installation," A. Godbehere, + * A. Matsukawa, K. Goldberg, American Control Conference, Montreal, June 2012. + */ +class CV_EXPORTS BackgroundSubtractorGMG: public cv::BackgroundSubtractor +{ +public: + BackgroundSubtractorGMG(); + virtual ~BackgroundSubtractorGMG(); + virtual AlgorithmInfo* info() const; + + /** + * Validate parameters and set up data structures for appropriate image size. + * Must call before running on data. + * @param frameSize input frame size + * @param min minimum value taken on by pixels in image sequence. Usually 0 + * @param max maximum value taken on by pixels in image sequence. e.g. 1.0 or 255 + */ + void initialize(cv::Size frameSize, double min, double max); + + /** + * Performs single-frame background subtraction and builds up a statistical background image + * model. + * @param image Input image + * @param fgmask Output mask image representing foreground and background pixels + * @param learningRate Determines how quickly features are "forgotten" from histograms + */ + virtual void operator()(InputArray image, OutputArray fgmask, double learningRate=-1.0); + + /** + * Releases all inner buffers. + */ + void release(); + + //! Total number of distinct colors to maintain in histogram. + int maxFeatures; + //! Set between 0.0 and 1.0, determines how quickly features are "forgotten" from histograms. + double learningRate; + //! Number of frames of video to use to initialize histograms. + int numInitializationFrames; + //! Number of discrete levels in each channel to be used in histograms. + int quantizationLevels; + //! Prior probability that any given pixel is a background pixel. A sensitivity parameter. + double backgroundPrior; + //! Value above which pixel is determined to be FG. + double decisionThreshold; + //! Smoothing radius, in pixels, for cleaning up FG image. + int smoothingRadius; + //! Perform background model update + bool updateBackgroundModel; + +private: + double maxVal_; + double minVal_; + + cv::Size frameSize_; + int frameNum_; + + cv::Mat_<int> nfeatures_; + cv::Mat_<unsigned int> colors_; + cv::Mat_<float> weights_; + + cv::Mat buf_; +}; + +} + +#endif |