mirror of
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Moved to Visual Studio 2015, OpenCV 3.2.0, x64
This commit is contained in:
MarekKowalski
@@ -7,11 +7,12 @@
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// copy or use the software.
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//
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//
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// License Agreement
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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@@ -40,223 +41,266 @@
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//
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//M*/
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#ifndef __OPENCV_BACKGROUND_SEGM_HPP__
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#define __OPENCV_BACKGROUND_SEGM_HPP__
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#ifndef OPENCV_BACKGROUND_SEGM_HPP
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#define OPENCV_BACKGROUND_SEGM_HPP
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#include "opencv2/core.hpp"
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#include "opencv2/core/core.hpp"
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#include <list>
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namespace cv
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{
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/*!
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The Base Class for Background/Foreground Segmentation
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//! @addtogroup video_motion
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//! @{
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The class is only used to define the common interface for
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the whole family of background/foreground segmentation algorithms.
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*/
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/** @brief Base class for background/foreground segmentation. :
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The class is only used to define the common interface for the whole family of background/foreground
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segmentation algorithms.
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*/
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class CV_EXPORTS_W BackgroundSubtractor : public Algorithm
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{
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public:
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//! the virtual destructor
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virtual ~BackgroundSubtractor();
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//! the update operator that takes the next video frame and returns the current foreground mask as 8-bit binary image.
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CV_WRAP_AS(apply) virtual void operator()(InputArray image, OutputArray fgmask,
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double learningRate=0);
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/** @brief Computes a foreground mask.
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//! computes a background image
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virtual void getBackgroundImage(OutputArray backgroundImage) const;
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@param image Next video frame.
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@param fgmask The output foreground mask as an 8-bit binary image.
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@param learningRate The value between 0 and 1 that indicates how fast the background model is
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learnt. Negative parameter value makes the algorithm to use some automatically chosen learning
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rate. 0 means that the background model is not updated at all, 1 means that the background model
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is completely reinitialized from the last frame.
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*/
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CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) = 0;
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/** @brief Computes a background image.
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@param backgroundImage The output background image.
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@note Sometimes the background image can be very blurry, as it contain the average background
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statistics.
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*/
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CV_WRAP virtual void getBackgroundImage(OutputArray backgroundImage) const = 0;
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};
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/*!
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Gaussian Mixture-based Backbround/Foreground Segmentation Algorithm
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/** @brief Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
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The class implements the following algorithm:
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"An improved adaptive background mixture model for real-time tracking with shadow detection"
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P. KadewTraKuPong and R. Bowden,
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Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001."
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http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
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*/
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class CV_EXPORTS_W BackgroundSubtractorMOG : public BackgroundSubtractor
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{
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public:
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//! the default constructor
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CV_WRAP BackgroundSubtractorMOG();
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//! the full constructor that takes the length of the history, the number of gaussian mixtures, the background ratio parameter and the noise strength
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CV_WRAP BackgroundSubtractorMOG(int history, int nmixtures, double backgroundRatio, double noiseSigma=0);
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//! the destructor
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virtual ~BackgroundSubtractorMOG();
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//! the update operator
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virtual void operator()(InputArray image, OutputArray fgmask, double learningRate=0);
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//! re-initiaization method
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virtual void initialize(Size frameSize, int frameType);
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virtual AlgorithmInfo* info() const;
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protected:
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Size frameSize;
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int frameType;
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Mat bgmodel;
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int nframes;
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int history;
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int nmixtures;
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double varThreshold;
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double backgroundRatio;
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double noiseSigma;
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};
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/*!
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The class implements the following algorithm:
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"Improved adaptive Gausian mixture model for background subtraction"
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Z.Zivkovic
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International Conference Pattern Recognition, UK, August, 2004.
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http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf
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*/
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The class implements the Gaussian mixture model background subtraction described in @cite Zivkovic2004
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and @cite Zivkovic2006 .
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*/
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class CV_EXPORTS_W BackgroundSubtractorMOG2 : public BackgroundSubtractor
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{
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public:
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//! the default constructor
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CV_WRAP BackgroundSubtractorMOG2();
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//! the full constructor that takes the length of the history, the number of gaussian mixtures, the background ratio parameter and the noise strength
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CV_WRAP BackgroundSubtractorMOG2(int history, float varThreshold, bool bShadowDetection=true);
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//! the destructor
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virtual ~BackgroundSubtractorMOG2();
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//! the update operator
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virtual void operator()(InputArray image, OutputArray fgmask, double learningRate=-1);
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/** @brief Returns the number of last frames that affect the background model
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*/
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CV_WRAP virtual int getHistory() const = 0;
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/** @brief Sets the number of last frames that affect the background model
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*/
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CV_WRAP virtual void setHistory(int history) = 0;
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//! computes a background image which are the mean of all background gaussians
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virtual void getBackgroundImage(OutputArray backgroundImage) const;
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/** @brief Returns the number of gaussian components in the background model
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*/
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CV_WRAP virtual int getNMixtures() const = 0;
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/** @brief Sets the number of gaussian components in the background model.
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//! re-initiaization method
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virtual void initialize(Size frameSize, int frameType);
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The model needs to be reinitalized to reserve memory.
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*/
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CV_WRAP virtual void setNMixtures(int nmixtures) = 0;//needs reinitialization!
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virtual AlgorithmInfo* info() const;
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/** @brief Returns the "background ratio" parameter of the algorithm
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protected:
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Size frameSize;
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int frameType;
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Mat bgmodel;
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Mat bgmodelUsedModes;//keep track of number of modes per pixel
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int nframes;
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int history;
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int nmixtures;
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//! here it is the maximum allowed number of mixture components.
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//! Actual number is determined dynamically per pixel
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double varThreshold;
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// threshold on the squared Mahalanobis distance to decide if it is well described
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// by the background model or not. Related to Cthr from the paper.
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// This does not influence the update of the background. A typical value could be 4 sigma
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// and that is varThreshold=4*4=16; Corresponds to Tb in the paper.
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If a foreground pixel keeps semi-constant value for about backgroundRatio\*history frames, it's
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considered background and added to the model as a center of a new component. It corresponds to TB
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parameter in the paper.
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*/
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CV_WRAP virtual double getBackgroundRatio() const = 0;
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/** @brief Sets the "background ratio" parameter of the algorithm
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*/
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CV_WRAP virtual void setBackgroundRatio(double ratio) = 0;
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/////////////////////////
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// less important parameters - things you might change but be carefull
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////////////////////////
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float backgroundRatio;
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// corresponds to fTB=1-cf from the paper
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// TB - threshold when the component becomes significant enough to be included into
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// the background model. It is the TB=1-cf from the paper. So I use cf=0.1 => TB=0.
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// For alpha=0.001 it means that the mode should exist for approximately 105 frames before
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// it is considered foreground
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// float noiseSigma;
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float varThresholdGen;
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//correspondts to Tg - threshold on the squared Mahalan. dist. to decide
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//when a sample is close to the existing components. If it is not close
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//to any a new component will be generated. I use 3 sigma => Tg=3*3=9.
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//Smaller Tg leads to more generated components and higher Tg might make
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//lead to small number of components but they can grow too large
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float fVarInit;
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float fVarMin;
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float fVarMax;
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//initial variance for the newly generated components.
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//It will will influence the speed of adaptation. A good guess should be made.
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//A simple way is to estimate the typical standard deviation from the images.
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//I used here 10 as a reasonable value
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// min and max can be used to further control the variance
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float fCT;//CT - complexity reduction prior
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//this is related to the number of samples needed to accept that a component
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//actually exists. We use CT=0.05 of all the samples. By setting CT=0 you get
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//the standard Stauffer&Grimson algorithm (maybe not exact but very similar)
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/** @brief Returns the variance threshold for the pixel-model match
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//shadow detection parameters
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bool bShadowDetection;//default 1 - do shadow detection
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unsigned char nShadowDetection;//do shadow detection - insert this value as the detection result - 127 default value
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float fTau;
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// Tau - shadow threshold. The shadow is detected if the pixel is darker
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//version of the background. Tau is a threshold on how much darker the shadow can be.
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//Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow
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//See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003.
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The main threshold on the squared Mahalanobis distance to decide if the sample is well described by
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the background model or not. Related to Cthr from the paper.
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*/
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CV_WRAP virtual double getVarThreshold() const = 0;
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/** @brief Sets the variance threshold for the pixel-model match
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*/
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CV_WRAP virtual void setVarThreshold(double varThreshold) = 0;
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/** @brief Returns the variance threshold for the pixel-model match used for new mixture component generation
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Threshold for the squared Mahalanobis distance that helps decide when a sample is close to the
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existing components (corresponds to Tg in the paper). If a pixel is not close to any component, it
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is considered foreground or added as a new component. 3 sigma =\> Tg=3\*3=9 is default. A smaller Tg
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value generates more components. A higher Tg value may result in a small number of components but
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they can grow too large.
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*/
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CV_WRAP virtual double getVarThresholdGen() const = 0;
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/** @brief Sets the variance threshold for the pixel-model match used for new mixture component generation
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*/
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CV_WRAP virtual void setVarThresholdGen(double varThresholdGen) = 0;
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/** @brief Returns the initial variance of each gaussian component
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*/
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CV_WRAP virtual double getVarInit() const = 0;
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/** @brief Sets the initial variance of each gaussian component
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*/
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CV_WRAP virtual void setVarInit(double varInit) = 0;
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CV_WRAP virtual double getVarMin() const = 0;
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CV_WRAP virtual void setVarMin(double varMin) = 0;
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CV_WRAP virtual double getVarMax() const = 0;
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CV_WRAP virtual void setVarMax(double varMax) = 0;
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/** @brief Returns the complexity reduction threshold
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This parameter defines the number of samples needed to accept to prove the component exists. CT=0.05
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is a default value for all the samples. By setting CT=0 you get an algorithm very similar to the
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standard Stauffer&Grimson algorithm.
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*/
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CV_WRAP virtual double getComplexityReductionThreshold() const = 0;
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/** @brief Sets the complexity reduction threshold
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*/
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CV_WRAP virtual void setComplexityReductionThreshold(double ct) = 0;
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/** @brief Returns the shadow detection flag
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If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorMOG2 for
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details.
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*/
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CV_WRAP virtual bool getDetectShadows() const = 0;
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/** @brief Enables or disables shadow detection
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*/
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CV_WRAP virtual void setDetectShadows(bool detectShadows) = 0;
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/** @brief Returns the shadow value
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Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0
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in the mask always means background, 255 means foreground.
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*/
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CV_WRAP virtual int getShadowValue() const = 0;
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/** @brief Sets the shadow value
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*/
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CV_WRAP virtual void setShadowValue(int value) = 0;
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/** @brief Returns the shadow threshold
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A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in
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the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel
|
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is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiarra,
|
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*Detecting Moving Shadows...*, IEEE PAMI,2003.
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*/
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CV_WRAP virtual double getShadowThreshold() const = 0;
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/** @brief Sets the shadow threshold
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*/
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CV_WRAP virtual void setShadowThreshold(double threshold) = 0;
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};
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/**
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* Background Subtractor module. Takes a series of images and returns a sequence of mask (8UC1)
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* images of the same size, where 255 indicates Foreground and 0 represents Background.
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* This class implements an algorithm described in "Visual Tracking of Human Visitors under
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* Variable-Lighting Conditions for a Responsive Audio Art Installation," A. Godbehere,
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* A. Matsukawa, K. Goldberg, American Control Conference, Montreal, June 2012.
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/** @brief Creates MOG2 Background Subtractor
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@param history Length of the history.
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@param varThreshold Threshold on the squared Mahalanobis distance between the pixel and the model
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to decide whether a pixel is well described by the background model. This parameter does not
|
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affect the background update.
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@param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the
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speed a bit, so if you do not need this feature, set the parameter to false.
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*/
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class CV_EXPORTS BackgroundSubtractorGMG: public cv::BackgroundSubtractor
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CV_EXPORTS_W Ptr<BackgroundSubtractorMOG2>
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createBackgroundSubtractorMOG2(int history=500, double varThreshold=16,
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bool detectShadows=true);
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/** @brief K-nearest neigbours - based Background/Foreground Segmentation Algorithm.
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The class implements the K-nearest neigbours background subtraction described in @cite Zivkovic2006 .
|
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Very efficient if number of foreground pixels is low.
|
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*/
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class CV_EXPORTS_W BackgroundSubtractorKNN : public BackgroundSubtractor
|
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{
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public:
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BackgroundSubtractorGMG();
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virtual ~BackgroundSubtractorGMG();
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virtual AlgorithmInfo* info() const;
|
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/** @brief Returns the number of last frames that affect the background model
|
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*/
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CV_WRAP virtual int getHistory() const = 0;
|
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/** @brief Sets the number of last frames that affect the background model
|
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*/
|
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CV_WRAP virtual void setHistory(int history) = 0;
|
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|
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/**
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* Validate parameters and set up data structures for appropriate image size.
|
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* Must call before running on data.
|
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* @param frameSize input frame size
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* @param min minimum value taken on by pixels in image sequence. Usually 0
|
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* @param max maximum value taken on by pixels in image sequence. e.g. 1.0 or 255
|
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/** @brief Returns the number of data samples in the background model
|
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*/
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CV_WRAP virtual int getNSamples() const = 0;
|
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/** @brief Sets the number of data samples in the background model.
|
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|
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The model needs to be reinitalized to reserve memory.
|
||||
*/
|
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CV_WRAP virtual void setNSamples(int _nN) = 0;//needs reinitialization!
|
||||
|
||||
/** @brief Returns the threshold on the squared distance between the pixel and the sample
|
||||
|
||||
The threshold on the squared distance between the pixel and the sample to decide whether a pixel is
|
||||
close to a data sample.
|
||||
*/
|
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void initialize(cv::Size frameSize, double min, double max);
|
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CV_WRAP virtual double getDist2Threshold() const = 0;
|
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/** @brief Sets the threshold on the squared distance
|
||||
*/
|
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CV_WRAP virtual void setDist2Threshold(double _dist2Threshold) = 0;
|
||||
|
||||
/**
|
||||
* 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
|
||||
/** @brief Returns the number of neighbours, the k in the kNN.
|
||||
|
||||
K is the number of samples that need to be within dist2Threshold in order to decide that that
|
||||
pixel is matching the kNN background model.
|
||||
*/
|
||||
virtual void operator()(InputArray image, OutputArray fgmask, double learningRate=-1.0);
|
||||
CV_WRAP virtual int getkNNSamples() const = 0;
|
||||
/** @brief Sets the k in the kNN. How many nearest neigbours need to match.
|
||||
*/
|
||||
CV_WRAP virtual void setkNNSamples(int _nkNN) = 0;
|
||||
|
||||
/**
|
||||
* Releases all inner buffers.
|
||||
/** @brief Returns the shadow detection flag
|
||||
|
||||
If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorKNN for
|
||||
details.
|
||||
*/
|
||||
void release();
|
||||
CV_WRAP virtual bool getDetectShadows() const = 0;
|
||||
/** @brief Enables or disables shadow detection
|
||||
*/
|
||||
CV_WRAP virtual void setDetectShadows(bool detectShadows) = 0;
|
||||
|
||||
//! 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;
|
||||
/** @brief Returns the shadow value
|
||||
|
||||
private:
|
||||
double maxVal_;
|
||||
double minVal_;
|
||||
Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0
|
||||
in the mask always means background, 255 means foreground.
|
||||
*/
|
||||
CV_WRAP virtual int getShadowValue() const = 0;
|
||||
/** @brief Sets the shadow value
|
||||
*/
|
||||
CV_WRAP virtual void setShadowValue(int value) = 0;
|
||||
|
||||
cv::Size frameSize_;
|
||||
int frameNum_;
|
||||
/** @brief Returns the shadow threshold
|
||||
|
||||
cv::Mat_<int> nfeatures_;
|
||||
cv::Mat_<unsigned int> colors_;
|
||||
cv::Mat_<float> weights_;
|
||||
|
||||
cv::Mat buf_;
|
||||
A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in
|
||||
the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel
|
||||
is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiarra,
|
||||
*Detecting Moving Shadows...*, IEEE PAMI,2003.
|
||||
*/
|
||||
CV_WRAP virtual double getShadowThreshold() const = 0;
|
||||
/** @brief Sets the shadow threshold
|
||||
*/
|
||||
CV_WRAP virtual void setShadowThreshold(double threshold) = 0;
|
||||
};
|
||||
|
||||
}
|
||||
/** @brief Creates KNN Background Subtractor
|
||||
|
||||
@param history Length of the history.
|
||||
@param dist2Threshold Threshold on the squared distance between the pixel and the sample to decide
|
||||
whether a pixel is close to that sample. This parameter does not affect the background update.
|
||||
@param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the
|
||||
speed a bit, so if you do not need this feature, set the parameter to false.
|
||||
*/
|
||||
CV_EXPORTS_W Ptr<BackgroundSubtractorKNN>
|
||||
createBackgroundSubtractorKNN(int history=500, double dist2Threshold=400.0,
|
||||
bool detectShadows=true);
|
||||
|
||||
//! @} video_motion
|
||||
|
||||
} // cv
|
||||
|
||||
#endif
|
||||
|
||||
Reference in New Issue
Block a user