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Diffstat (limited to 'thirdparty/raspberrypi/includes/opencv2/video/background_segm.hpp')
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diff --git a/thirdparty/raspberrypi/includes/opencv2/video/background_segm.hpp b/thirdparty/raspberrypi/includes/opencv2/video/background_segm.hpp deleted file mode 100644 index d2d068c..0000000 --- a/thirdparty/raspberrypi/includes/opencv2/video/background_segm.hpp +++ /dev/null @@ -1,263 +0,0 @@ -/*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 |