Fixed windows build problems of BackgroundSubtractorGMG but code still need more work.
This commit is contained in:
Andrey Kamaev
@@ -50,7 +50,7 @@ namespace cv
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/*!
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The Base Class for Background/Foreground Segmentation
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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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@@ -70,13 +70,13 @@ public:
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/*!
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Gaussian Mixture-based Backbround/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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@@ -89,13 +89,13 @@ public:
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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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protected:
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Size frameSize;
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int frameType;
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Mat bgmodel;
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@@ -105,7 +105,7 @@ protected:
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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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/*!
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@@ -126,16 +126,16 @@ public:
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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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//! 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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//! 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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protected:
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Size frameSize;
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int frameType;
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Mat bgmodel;
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@@ -150,7 +150,7 @@ protected:
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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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/////////////////////////
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// less important parameters - things you might change but be carefull
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////////////////////////
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@@ -179,7 +179,7 @@ protected:
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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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//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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@@ -188,7 +188,7 @@ protected:
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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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};
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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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@@ -200,252 +200,250 @@ protected:
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class CV_EXPORTS BackgroundSubtractorGMG: public cv::BackgroundSubtractor
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{
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private:
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/**
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* A general flexible datatype.
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*
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* Used internally to enable background subtraction algorithm to be robust to any input Mat type.
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* Datatype can be char, unsigned char, int, unsigned int, long int, float, or double.
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*/
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union flexitype{
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char c;
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uchar uc;
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int i;
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unsigned int ui;
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long int li;
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float f;
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double d;
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/**
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* A general flexible datatype.
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*
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* Used internally to enable background subtraction algorithm to be robust to any input Mat type.
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* Datatype can be char, unsigned char, int, unsigned int, long int, float, or double.
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*/
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union flexitype{
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char c;
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uchar uc;
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int i;
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unsigned int ui;
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long int li;
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float f;
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double d;
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flexitype(){d = 0.0;} //!< Default constructor, set all bits of the union to 0.
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flexitype(char cval){c = cval;} //!< Char type constructor
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flexitype(){d = 0.0;} //!< Default constructor, set all bits of the union to 0.
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flexitype(char cval){c = cval;} //!< Char type constructor
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bool operator ==(flexitype& rhs)
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{
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return d == rhs.d;
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}
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bool operator ==(flexitype& rhs)
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{
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return d == rhs.d;
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}
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//! Char type assignment operator
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flexitype& operator =(char cval){
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if (this->c == cval){return *this;}
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c = cval; return *this;
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}
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flexitype(unsigned char ucval){uc = ucval;} //!< unsigned char type constructor
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//! Char type assignment operator
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flexitype& operator =(char cval){
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if (this->c == cval){return *this;}
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c = cval; return *this;
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}
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flexitype(unsigned char ucval){uc = ucval;} //!< unsigned char type constructor
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//! unsigned char type assignment operator
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flexitype& operator =(unsigned char ucval){
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if (this->uc == ucval){return *this;}
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uc = ucval; return *this;
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}
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flexitype(int ival){i = ival;} //!< int type constructor
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//! int type assignment operator
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flexitype& operator =(int ival){
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if (this->i == ival){return *this;}
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i = ival; return *this;
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}
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flexitype(unsigned int uival){ui = uival;} //!< unsigned int type constructor
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//! unsigned char type assignment operator
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flexitype& operator =(unsigned char ucval){
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if (this->uc == ucval){return *this;}
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uc = ucval; return *this;
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}
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flexitype(int ival){i = ival;} //!< int type constructor
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//! int type assignment operator
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flexitype& operator =(int ival){
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if (this->i == ival){return *this;}
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i = ival; return *this;
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}
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flexitype(unsigned int uival){ui = uival;} //!< unsigned int type constructor
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//! unsigned int type assignment operator
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flexitype& operator =(unsigned int uival){
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if (this->ui == uival){return *this;}
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ui = uival; return *this;
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}
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flexitype(float fval){f = fval;} //!< float type constructor
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//! float type assignment operator
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flexitype& operator =(float fval){
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if (this->f == fval){return *this;}
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f = fval; return *this;
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}
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flexitype(long int lival){li = lival;} //!< long int type constructor
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//! long int type assignment operator
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flexitype& operator =(long int lival){
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if (this->li == lival){return *this;}
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li = lival; return *this;
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}
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//! unsigned int type assignment operator
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flexitype& operator =(unsigned int uival){
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if (this->ui == uival){return *this;}
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ui = uival; return *this;
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}
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flexitype(float fval){f = fval;} //!< float type constructor
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//! float type assignment operator
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flexitype& operator =(float fval){
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if (this->f == fval){return *this;}
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f = fval; return *this;
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}
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flexitype(long int lival){li = lival;} //!< long int type constructor
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//! long int type assignment operator
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flexitype& operator =(long int lival){
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if (this->li == lival){return *this;}
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li = lival; return *this;
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}
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flexitype(double dval){d=dval;} //!< double type constructor
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//! double type assignment operator
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flexitype& operator =(double dval){
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if (this->d == dval){return *this;}
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d = dval; return *this;
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}
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};
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/**
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* Used internally to represent a single feature in a histogram.
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* Feature is a color and an associated likelihood (weight in the histogram).
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*/
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struct HistogramFeatureGMG
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{
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/**
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* Default constructor.
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* Initializes likelihood of feature to 0, color remains uninitialized.
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*/
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HistogramFeatureGMG(){likelihood = 0.0;}
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flexitype(double dval){d=dval;} //!< double type constructor
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//! double type assignment operator
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flexitype& operator =(double dval){
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if (this->d == dval){return *this;}
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d = dval; return *this;
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}
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};
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/**
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* Used internally to represent a single feature in a histogram.
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* Feature is a color and an associated likelihood (weight in the histogram).
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*/
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struct CV_EXPORTS HistogramFeatureGMG
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{
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/**
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* Default constructor.
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* Initializes likelihood of feature to 0, color remains uninitialized.
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*/
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HistogramFeatureGMG(){likelihood = 0.0;}
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/**
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* Copy constructor.
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* Required to use HistogramFeatureGMG in a std::vector
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* @see operator =()
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*/
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HistogramFeatureGMG(const HistogramFeatureGMG& orig){
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color = orig.color; likelihood = orig.likelihood;
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}
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/**
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* Copy constructor.
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* Required to use HistogramFeatureGMG in a std::vector
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* @see operator =()
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*/
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HistogramFeatureGMG(const HistogramFeatureGMG& orig){
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color = orig.color; likelihood = orig.likelihood;
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}
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/**
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* Assignment operator.
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* Required to use HistogramFeatureGMG in a std::vector
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*/
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HistogramFeatureGMG& operator =(const HistogramFeatureGMG& orig){
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color = orig.color; likelihood = orig.likelihood; return *this;
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}
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/**
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* Assignment operator.
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* Required to use HistogramFeatureGMG in a std::vector
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*/
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HistogramFeatureGMG& operator =(const HistogramFeatureGMG& orig){
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color = orig.color; likelihood = orig.likelihood; return *this;
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}
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/**
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* Tests equality of histogram features.
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* Equality is tested only by matching the color (feature), not the likelihood.
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* This operator is used to look up an observed feature in a histogram.
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*/
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bool operator ==(HistogramFeatureGMG &rhs);
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/**
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* Tests equality of histogram features.
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* Equality is tested only by matching the color (feature), not the likelihood.
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* This operator is used to look up an observed feature in a histogram.
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*/
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bool operator ==(HistogramFeatureGMG &rhs);
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//! Regardless of the image datatype, it is quantized and mapped to an integer and represented as a vector.
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vector<size_t> color;
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//! Regardless of the image datatype, it is quantized and mapped to an integer and represented as a vector.
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vector<size_t> color;
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//! Represents the weight of feature in the histogram.
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float likelihood;
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friend class PixelModelGMG;
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};
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//! Represents the weight of feature in the histogram.
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float likelihood;
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friend class PixelModelGMG;
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};
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/**
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* Representation of the statistical model of a single pixel for use in the background subtraction
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* algorithm.
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*/
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class PixelModelGMG
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{
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public:
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PixelModelGMG();
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virtual ~PixelModelGMG();
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/**
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* Representation of the statistical model of a single pixel for use in the background subtraction
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* algorithm.
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*/
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class CV_EXPORTS PixelModelGMG
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{
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public:
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PixelModelGMG();
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~PixelModelGMG();
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/**
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* Incorporate the last observed feature into the statistical model.
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*
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* @param learningRate The adaptation parameter for the histogram. -1.0 to use default. Value
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* should be between 0.0 and 1.0, the higher the value, the faster the
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* adaptation. 1.0 is limiting case where fast adaptation means no memory.
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*/
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void insertFeature(double learningRate = -1.0);
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/**
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* Incorporate the last observed feature into the statistical model.
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*
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* @param learningRate The adaptation parameter for the histogram. -1.0 to use default. Value
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* should be between 0.0 and 1.0, the higher the value, the faster the
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* adaptation. 1.0 is limiting case where fast adaptation means no memory.
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*/
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void insertFeature(double learningRate = -1.0);
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/**
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* Set the feature last observed, to save before incorporating it into the statistical
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* model with insertFeature().
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*
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* @param feature The feature (color) just observed.
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*/
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void setLastObservedFeature(BackgroundSubtractorGMG::HistogramFeatureGMG feature);
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/**
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* Set the upper limit for the number of features to store in the histogram. Use to adjust
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* memory requirements.
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*
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* @param max size_t representing the max number of features.
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*/
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void setMaxFeatures(size_t max) {
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maxFeatures = max; histogram.resize(max); histogram.clear();
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}
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/**
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* Normalize the histogram, so sum of weights of all features = 1.0
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*/
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void normalizeHistogram();
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/**
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* Return the weight of a feature in the histogram. If the feature is not represented in the
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* histogram, the weight returned is 0.0.
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*/
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double getLikelihood(HistogramFeatureGMG f);
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PixelModelGMG& operator *=(const float &rhs);
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//friend class BackgroundSubtractorGMG;
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//friend class HistogramFeatureGMG;
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protected:
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size_t numFeatures; //!< number of features in histogram
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size_t maxFeatures; //!< max allowable features in histogram
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std::list<HistogramFeatureGMG> histogram; //!< represents the histogram as a list of features
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HistogramFeatureGMG lastObservedFeature;
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//!< store last observed feature in case we need to add it to histogram
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};
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/**
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* Set the feature last observed, to save before incorporating it into the statistical
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* model with insertFeature().
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*
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* @param feature The feature (color) just observed.
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*/
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void setLastObservedFeature(BackgroundSubtractorGMG::HistogramFeatureGMG feature);
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/**
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* Set the upper limit for the number of features to store in the histogram. Use to adjust
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* memory requirements.
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*
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* @param max size_t representing the max number of features.
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*/
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void setMaxFeatures(size_t max) {
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maxFeatures = max; histogram.resize(max); histogram.clear();
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}
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/**
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* Normalize the histogram, so sum of weights of all features = 1.0
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*/
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void normalizeHistogram();
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/**
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* Return the weight of a feature in the histogram. If the feature is not represented in the
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* histogram, the weight returned is 0.0.
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*/
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double getLikelihood(HistogramFeatureGMG f);
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PixelModelGMG& operator *=(const float &rhs);
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//friend class BackgroundSubtractorGMG;
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//friend class HistogramFeatureGMG;
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private:
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size_t numFeatures; //!< number of features in histogram
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size_t maxFeatures; //!< max allowable features in histogram
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std::list<HistogramFeatureGMG> histogram; //!< represents the histogram as a list of features
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HistogramFeatureGMG lastObservedFeature;
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//!< store last observed feature in case we need to add it to histogram
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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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BackgroundSubtractorGMG();
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virtual ~BackgroundSubtractorGMG();
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virtual AlgorithmInfo* info() const;
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/**
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* Performs single-frame background subtraction and builds up a statistical background image
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* model.
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* @param image Input image
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* @param fgmask Output mask image representing foreground and background pixels
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*/
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virtual void operator()(InputArray image, OutputArray fgmask, double learningRate=-1.0);
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/**
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* Performs single-frame background subtraction and builds up a statistical background image
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* model.
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* @param image Input image
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* @param fgmask Output mask image representing foreground and background pixels
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*/
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virtual void operator()(InputArray image, OutputArray fgmask, double learningRate=-1.0);
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/**
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* Validate parameters and set up data structures for appropriate image type. Must call before
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* running on data.
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* @param image One sample image from dataset
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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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*/
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void initializeType(InputArray image, flexitype min, flexitype max);
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/**
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* Selectively update the background model. Only update background model for pixels identified
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* as background.
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* @param mask Mask image same size as images in sequence. Must be 8UC1 matrix, 255 for foreground
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* and 0 for background.
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*/
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void updateBackgroundModel(InputArray mask);
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/**
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* Retrieve the greyscale image representing the probability that each pixel is foreground given
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* the current estimated background model. Values are 0.0 (black) to 1.0 (white).
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* @param img The 32FC1 image representing per-pixel probabilities that the pixel is foreground.
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*/
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void getPosteriorImage(OutputArray img);
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/**
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* Validate parameters and set up data structures for appropriate image type. Must call before
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* running on data.
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* @param image One sample image from dataset
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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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*/
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void initializeType(InputArray image, flexitype min, flexitype max);
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/**
|
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* Selectively update the background model. Only update background model for pixels identified
|
||||
* as background.
|
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* @param mask Mask image same size as images in sequence. Must be 8UC1 matrix, 255 for foreground
|
||||
* and 0 for background.
|
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*/
|
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void updateBackgroundModel(InputArray mask);
|
||||
/**
|
||||
* Retrieve the greyscale image representing the probability that each pixel is foreground given
|
||||
* the current estimated background model. Values are 0.0 (black) to 1.0 (white).
|
||||
* @param img The 32FC1 image representing per-pixel probabilities that the pixel is foreground.
|
||||
*/
|
||||
void getPosteriorImage(OutputArray img);
|
||||
|
||||
protected:
|
||||
//! 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;
|
||||
//! 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;
|
||||
|
||||
double decisionThreshold; //!< value above which pixel is determined to be FG.
|
||||
int smoothingRadius; //!< smoothing radius, in pixels, for cleaning up FG image.
|
||||
double decisionThreshold; //!< value above which pixel is determined to be FG.
|
||||
int smoothingRadius; //!< smoothing radius, in pixels, for cleaning up FG image.
|
||||
|
||||
flexitype maxVal, minVal;
|
||||
flexitype maxVal, minVal;
|
||||
|
||||
/*
|
||||
* General Parameters
|
||||
*/
|
||||
size_t imWidth; //!< width of image.
|
||||
size_t imHeight; //!< height of image.
|
||||
size_t numPixels;
|
||||
/*
|
||||
* General Parameters
|
||||
*/
|
||||
size_t imWidth; //!< width of image.
|
||||
size_t imHeight; //!< height of image.
|
||||
size_t numPixels;
|
||||
|
||||
int imageDepth; //!< Depth of image, e.g. CV_8U
|
||||
unsigned int numChannels; //!< Number of channels in image.
|
||||
int imageDepth; //!< Depth of image, e.g. CV_8U
|
||||
unsigned int numChannels; //!< Number of channels in image.
|
||||
|
||||
bool isDataInitialized;
|
||||
//!< After general parameters are set, data structures must be initialized.
|
||||
bool isDataInitialized;
|
||||
//!< After general parameters are set, data structures must be initialized.
|
||||
|
||||
size_t elemSize; //!< store image mat element sizes
|
||||
size_t elemSize1;
|
||||
size_t elemSize; //!< store image mat element sizes
|
||||
size_t elemSize1;
|
||||
|
||||
/*
|
||||
* Data Structures
|
||||
*/
|
||||
vector<PixelModelGMG> pixels; //!< Probabilistic background models for each pixel in image.
|
||||
int frameNum; //!< Frame number counter, used to count frames in training mode.
|
||||
Mat posteriorImage; //!< Posterior probability image.
|
||||
Mat fgMaskImage; //!< Foreground mask image.
|
||||
/*
|
||||
* Data Structures
|
||||
*/
|
||||
vector<PixelModelGMG> pixels; //!< Probabilistic background models for each pixel in image.
|
||||
int frameNum; //!< Frame number counter, used to count frames in training mode.
|
||||
Mat posteriorImage; //!< Posterior probability image.
|
||||
Mat fgMaskImage; //!< Foreground mask image.
|
||||
};
|
||||
|
||||
bool initModule_BackgroundSubtractorGMG(void);
|
||||
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
Reference in New Issue
Block a user