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fixed some build problems

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This commit is contained in:
Vadim Pisarevsky 2010-12-28 21:15:58 +00:00
parent 0468bdeadd
commit 2dd0e85264
8 changed files with 359 additions and 227 deletions
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2
modules/calib3d/CMakeLists.txt
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@ -1 +1 @@
define_opencv_module(calib3d opencv_core opencv_imgproc opencv_highgui opencv_features2d)
define_opencv_module(calib3d opencv_core opencv_imgproc opencv_highgui opencv_features2d opencv_flann)

4
modules/core/include/opencv2/core/core.hpp
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@ -1779,7 +1779,7 @@ public:
{
MSize(int* _p);
Size operator()() const;
int operator[](int i) const;
const int& operator[](int i) const;
int& operator[](int i);
operator const int*() const;
bool operator == (const MSize& sz) const;
@ -1792,7 +1792,7 @@ public:
{
MStep();
MStep(size_t s);
size_t operator[](int i) const;
const size_t& operator[](int i) const;
size_t& operator[](int i);
operator size_t() const;
MStep& operator = (size_t s);

4
modules/core/include/opencv2/core/mat.hpp
View File

@ -679,7 +679,7 @@ inline Size Mat::MSize::operator()() const
CV_DbgAssert(p[-1] <= 2);
return Size(p[1], p[0]);
}
inline int Mat::MSize::operator[](int i) const { return p[i]; }
inline const int& Mat::MSize::operator[](int i) const { return p[i]; }
inline int& Mat::MSize::operator[](int i) { return p[i]; }
inline Mat::MSize::operator const int*() const { return p; }
@ -704,7 +704,7 @@ inline bool Mat::MSize::operator != (const MSize& sz) const
inline Mat::MStep::MStep() { p = buf; p[0] = p[1] = 0; }
inline Mat::MStep::MStep(size_t s) { p = buf; p[0] = s; p[1] = 0; }
inline size_t Mat::MStep::operator[](int i) const { return p[i]; }
inline const size_t& Mat::MStep::operator[](int i) const { return p[i]; }
inline size_t& Mat::MStep::operator[](int i) { return p[i]; }
inline Mat::MStep::operator size_t() const
{

2
modules/gtest/include/opencv2/gtest/gtestcv.hpp
View File

@ -33,7 +33,7 @@ CV_EXPORTS void randomSize(RNG& rng, int minDims, int maxDims, double maxSizeLog
CV_EXPORTS int randomType(RNG& rng, int typeMask, int minChannels, int maxChannels);
CV_EXPORTS Mat randomMat(RNG& rng, Size size, int type, bool useRoi);
CV_EXPORTS Mat randomMat(RNG& rng, const vector<int>& size, int type, bool useRoi);
CV_EXPORTS Mat add(const Mat& a, double alpha, const Mat& b, double beta,
CV_EXPORTS void add(const Mat& a, double alpha, const Mat& b, double beta,
Scalar gamma, Mat& c, int ctype, bool calcAbs);
CV_EXPORTS void convert(const Mat& src, Mat& dst, int dtype, double alpha, double beta);
CV_EXPORTS void copy(const Mat& src, Mat& dst, const Mat& mask=Mat());

86
modules/gtest/src/gtestcv.cpp
View File

@ -51,14 +51,15 @@ int randomType(RNG& rng, int typeMask, int minChannels, int maxChannels)
Mat randomMat(RNG& rng, Size size, int type, bool useRoi)
{
return Mat();
}
Mat randomMat(RNG& rng, const vector<int>& size, int type, bool useRoi)
{
return Mat();
}
Mat add(const Mat& _a, double alpha, const Mat& _b, double beta,
void add(const Mat& _a, double alpha, const Mat& _b, double beta,
Scalar gamma, Mat& c, int ctype, bool calcAbs)
{
Mat a = _a, b = _b;
@ -95,7 +96,7 @@ Mat add(const Mat& _a, double alpha, const Mat& _b, double beta,
NAryMatIterator it(arrays, planes, 3);
int i, nplanes = it.nplanes, cn=a.channels();
size_t total = planes[0].total(), maxsize = min(12*12*max(12/cn, 1), total);
size_t total = planes[0].total(), maxsize = std::min((size_t)12*12*std::max(12/cn, 1), total);
CV_Assert(planes[0].rows == 1);
buf[0].create(1, (int)maxsize, CV_64FC(cn));
@ -142,8 +143,8 @@ Mat add(const Mat& _a, double alpha, const Mat& _b, double beta,
}
static template<typename _Tp1, typename _Tp2> inline void
convert(const _Tp1* src, _Tp2* dst, size_t total, double alpha, double beta)
template<typename _Tp1, typename _Tp2> inline void
convert_(const _Tp1* src, _Tp2* dst, size_t total, double alpha, double beta)
{
size_t i;
if( alpha == 1 && beta == 0 )
@ -156,6 +157,37 @@ convert(const _Tp1* src, _Tp2* dst, size_t total, double alpha, double beta)
for( i = 0; i < total; i++ )
dst[i] = saturate_cast<_Tp2>(src[i]*alpha + beta);
}
template<typename _Tp> inline void
convertTo(const _Tp* src, void* dst, int dtype, size_t total, double alpha, double beta)
{
switch( CV_MAT_DEPTH(dtype) )
{
case CV_8U:
convert_(src, (uchar*)dst, total, alpha, beta);
break;
case CV_8S:
convert_(src, (schar*)dst, total, alpha, beta);
break;
case CV_16U:
convert_(src, (ushort*)dst, total, alpha, beta);
break;
case CV_16S:
convert_(src, (short*)dst, total, alpha, beta);
break;
case CV_32S:
convert_(src, (int*)dst, total, alpha, beta);
break;
case CV_32F:
convert_(src, (float*)dst, total, alpha, beta);
break;
case CV_64F:
convert_(src, (double*)dst, total, alpha, beta);
break;
default:
CV_Assert(0);
}
}
void convert(const Mat& src, Mat& dst, int dtype, double alpha, double beta)
{
@ -176,7 +208,7 @@ void convert(const Mat& src, Mat& dst, int dtype, double alpha, double beta)
Mat planes[2];
NAryMatIterator it(arrays, planes, 2);
size_t j, total = total = planes[0].total()*planes[0].channels();
size_t total = planes[0].total()*planes[0].channels();
int i, nplanes = it.nplanes;
for( i = 0; i < nplanes; i++, ++it)
@ -186,15 +218,27 @@ void convert(const Mat& src, Mat& dst, int dtype, double alpha, double beta)
switch( src.depth() )
{
case
}
for( j = 0; j < total; j++, sptr += elemSize, dptr += elemSize )
{
if( mptr[j] )
for( k = 0; k < elemSize; k++ )
dptr[k] = sptr[k];
case CV_8U:
convertTo((const uchar*)sptr, dptr, dtype, total, alpha, beta);
break;
case CV_8S:
convertTo((const schar*)sptr, dptr, dtype, total, alpha, beta);
break;
case CV_16U:
convertTo((const ushort*)sptr, dptr, dtype, total, alpha, beta);
break;
case CV_16S:
convertTo((const short*)sptr, dptr, dtype, total, alpha, beta);
break;
case CV_32S:
convertTo((const int*)sptr, dptr, dtype, total, alpha, beta);
break;
case CV_32F:
convertTo((const float*)sptr, dptr, dtype, total, alpha, beta);
break;
case CV_64F:
convertTo((const double*)sptr, dptr, dtype, total, alpha, beta);
break;
}
}
}
@ -246,7 +290,7 @@ void copy(const Mat& src, Mat& dst, const Mat& mask)
void set(Mat& dst, const Scalar& gamma, const Mat& mask)
{
double buf[12];
scalarToRawData(gama, &buf, dst.type(), dst.channels());
scalarToRawData(gamma, &buf, dst.type(), dst.channels());
const uchar* gptr = (const uchar*)&buf[0];
if(mask.empty())
@ -255,7 +299,7 @@ void set(Mat& dst, const Scalar& gamma, const Mat& mask)
Mat plane;
NAryMatIterator it(arrays, &plane, 1);
int i, nplanes = it.nplanes;
size_t j, k, elemSize = dst.elemSize(), planeSize = planes[0].total()*elemSize;
size_t j, k, elemSize = dst.elemSize(), planeSize = plane.total()*elemSize;
for( k = 1; k < elemSize; k++ )
if( gptr[k] != gptr[0] )
@ -274,7 +318,7 @@ void set(Mat& dst, const Scalar& gamma, const Mat& mask)
dptr[k] = gptr[k];
}
else
memcpy(dtr, dst.data, planeSize);
memcpy(dptr, dst.data, planeSize);
}
return;
}
@ -285,7 +329,7 @@ void set(Mat& dst, const Scalar& gamma, const Mat& mask)
Mat planes[2];
NAryMatIterator it(arrays, planes, 2);
size_t j, k, elemSize = src.elemSize(), total = planes[0].total();
size_t j, k, elemSize = dst.elemSize(), total = planes[0].total();
int i, nplanes = it.nplanes;
for( i = 0; i < nplanes; i++, ++it)
@ -303,7 +347,7 @@ void set(Mat& dst, const Scalar& gamma, const Mat& mask)
}
void minMaxFilter(const Mat& a, Mat& maxresult, const Mat& minresult, const Mat& kernel, Point anchor);
/*void minMaxFilter(const Mat& a, Mat& maxresult, const Mat& minresult, const Mat& kernel, Point anchor);
void filter2D(const Mat& src, Mat& dst, int ddepth, const Mat& kernel, Point anchor, double delta, int borderType);
void copyMakeBorder(const Mat& src, Mat& dst, int top, int bottom, int left, int right, int borderType, Scalar borderValue);
void minMaxLoc(const Mat& src, double* maxval, double* minval,
@ -314,6 +358,6 @@ bool cmpEps(const Mat& src1, const Mat& src2, int int_maxdiff, int flt_maxulp, v
void logicOp(const Mat& src1, const Mat& src2, Mat& dst, char c);
void logicOp(const Mat& src, const Scalar& s, Mat& dst, char c);
void compare(const Mat& src1, const Mat& src2, Mat& dst, int cmpop);
void compare(const Mat& src, const Scalar& s, Mat& dst, int cmpop);
void compare(const Mat& src, const Scalar& s, Mat& dst, int cmpop);*/
}

2
modules/objdetect/CMakeLists.txt
View File

@ -1 +1 @@
define_opencv_module(objdetect opencv_core opencv_imgproc opencv_highgui opencv_features2d opencv_calib3d)
define_opencv_module(objdetect opencv_core opencv_imgproc opencv_highgui opencv_features2d opencv_calib3d opencv_flann)

49
modules/video/include/opencv2/video/background_segm.hpp
View File

@ -399,6 +399,55 @@ public:
double noiseSigma;
};
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(double alphaT,
double sigma=15,
int nmixtures=5,
bool postFiltering=false,
double minArea=15,
bool detectShadows=true,
bool removeForeground=false,
double Tb=16,
double Tg=9,
double TB=0.9,
double CT=0.05,
uchar shadowOutputValue=127,
double tau=0.5);
//! the destructor
virtual ~BackgroundSubtractorMOG2();
//! the update operator
virtual void operator()(const Mat& image, Mat& fgmask, double learningRate=0);
//! re-initiaization method
virtual void initialize(Size frameSize,
double alphaT,
double sigma=15,
int nmixtures=5,
bool postFiltering=false,
double minArea=15,
bool detectShadows=true,
bool removeForeground=false,
double Tb=16,
double Tg=9,
double TB=0.9,
double CT=0.05,
uchar nShadowDetection=127,
double tau=0.5);
void* model;
};
}
#endif

437
modules/video/src/bgfg_gaussmix2.cpp
View File

@ -63,7 +63,7 @@
//
//
//
//Example usage as part of the CvBGStatModel:
// Example usage as part of the CvBGStatModel:
// CvBGStatModel* bg_model = cvCreateGaussianBGModel2( first_frame );
//
// //update for each frame
@ -76,10 +76,109 @@
//Date: 27-April-2005, Version:0.9
///////////*/
#include "cvaux.h"
#include "cvaux_mog2.h"
#include "precomp.hpp"
int _icvRemoveShadowGMM(long posPixel,
#define CV_BG_MODEL_MOG2 3 /* "Mixture of Gaussians 2". */
/* default parameters of gaussian background detection algorithm */
#define CV_BGFG_MOG2_STD_THRESHOLD 4.0f /* lambda=2.5 is 99% */
#define CV_BGFG_MOG2_WINDOW_SIZE 500 /* Learning rate; alpha = 1/CV_GBG_WINDOW_SIZE */
#define CV_BGFG_MOG2_BACKGROUND_THRESHOLD 0.9f /* threshold sum of weights for background test */
#define CV_BGFG_MOG2_STD_THRESHOLD_GENERATE 3.0f /* lambda=2.5 is 99% */
#define CV_BGFG_MOG2_NGAUSSIANS 5 /* = K = number of Gaussians in mixture */
#define CV_BGFG_MOG2_SIGMA_INIT 15.0f
#define CV_BGFG_MOG2_MINAREA 15.0f
/* additional parameters */
#define CV_BGFG_MOG2_CT 0.05f /* complexity reduction prior constant 0 - no reduction of number of components*/
#define CV_BGFG_MOG2_SHADOW_VALUE 127 /* value to use in the segmentation mask for shadows, sot 0 not to do shadow detection*/
#define CV_BGFG_MOG2_SHADOW_TAU 0.5f /* Tau - shadow threshold, see the paper for explanation*/
struct CvGaussBGStatModel2Params
{
bool bPostFiltering;//defult 1 - do postfiltering
double minArea; // for postfiltering
bool bShadowDetection;//default 1 - do shadow detection
bool bRemoveForeground;//default 0, set to 1 to remove foreground pixels from the image and return background image
bool bInit;//default 1, faster updates at start
/////////////////////////
//very important parameters - things you will change
////////////////////////
float fAlphaT;
//alpha - speed of update - if the time interval you want to average over is T
//set alpha=1/T. It is also usefull at start to make T slowly increase
//from 1 until the desired T
float fTb;
//Tb - threshold on the squared Mahalan. dist. 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 Tb=4*4=16;
/////////////////////////
//less important parameters - things you might change but be carefull
////////////////////////
float fTg;
//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 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 fSigma;
//initial standard deviation 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
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)
//even less important parameters
int nM;//max number of modes - const - 4 is usually enough
//shadow detection parameters
unsigned char nShadowDetection;//do shadow detection - insert this value as the detection result
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.
};
struct CvPBGMMGaussian
{
float sigma;
float muR;
float muG;
float muB;
float weight;
};
struct CvGaussBGStatModel2Data
{
int nWidth,nHeight,nSize,nNBands;//image info
// dynamic array for the mixture of Gaussians
std::vector<CvPBGMMGaussian> rGMM;
std::vector<uchar> rnUsedModes;//number of Gaussian components per pixel
};
//only foreground image is updated
//no filtering included
struct CvGaussBGModel2
{
CvGaussBGStatModel2Params params;
CvGaussBGStatModel2Data data;
int countFrames;
};
static int _icvRemoveShadowGMM(long posPixel,
float red, float green, float blue,
unsigned char nModes,
CvPBGMMGaussian* m_aGaussians,
@ -137,7 +236,7 @@ int _icvRemoveShadowGMM(long posPixel,
return 0;
}
int _icvUpdatePixelBackgroundGMM(long posPixel,
static int _icvUpdatePixelBackgroundGMM(long posPixel,
float red, float green, float blue,
unsigned char* pModesUsed,
CvPBGMMGaussian* m_aGaussians,
@ -341,7 +440,7 @@ int _icvUpdatePixelBackgroundGMM(long posPixel,
return bBackground;
}
void _icvReplacePixelBackgroundGMM(long pos,
static void _icvReplacePixelBackgroundGMM(long pos,
unsigned char* pData,
CvPBGMMGaussian* m_aGaussians)
{
@ -351,11 +450,11 @@ void _icvReplacePixelBackgroundGMM(long pos,
}
void icvUpdatePixelBackgroundGMM(CvGaussBGStatModel2Data* pGMMData,CvGaussBGStatModel2Params* pGMM, float m_fAlphaT, unsigned char* data,unsigned char* output)
static void icvUpdatePixelBackgroundGMM(CvGaussBGStatModel2Data* pGMMData,CvGaussBGStatModel2Params* pGMM, float m_fAlphaT, unsigned char* data,unsigned char* output)
{
int size=pGMMData->nSize;
unsigned char* pDataCurrent=data;
unsigned char* pUsedModes=pGMMData->rnUsedModes;
unsigned char* pUsedModes=&pGMMData->rnUsedModes[0];
unsigned char* pDataOutput=output;
//some constants
int m_nM=pGMM->nM;
@ -368,7 +467,7 @@ void icvUpdatePixelBackgroundGMM(CvGaussBGStatModel2Data* pGMMData,CvGaussBGStat
float m_fCT=pGMM->fCT;//CT - complexity reduction prior
float m_fPrune=-m_fAlphaT*m_fCT;
float m_fTau=pGMM->fTau;
CvPBGMMGaussian* m_aGaussians=pGMMData->rGMM;
CvPBGMMGaussian* m_aGaussians=&pGMMData->rGMM[0];
long posPixel=0;
bool m_bShadowDetection=pGMM->bShadowDetection;
unsigned char m_nShadowDetection=pGMM->nShadowDetection;
@ -427,214 +526,154 @@ void icvUpdatePixelBackgroundGMM(CvGaussBGStatModel2Data* pGMMData,CvGaussBGStat
}
}
//////////////////////////////////////////////
//implementation as part of the CvBGStatModel
static void CV_CDECL icvReleaseGaussianBGModel2( CvGaussBGModel2** bg_model );
static int CV_CDECL icvUpdateGaussianBGModel2( IplImage* curr_frame, CvGaussBGModel2* bg_model );
CV_IMPL CvBGStatModel*
cvCreateGaussianBGModel2( IplImage* first_frame, CvGaussBGStatModel2Params* parameters )
namespace cv
{
CvGaussBGModel2* bg_model = 0;
int w,h,size;
BackgroundSubtractorMOG2::BackgroundSubtractorMOG2()
{
model = 0;
initialize(Size(), 0);
}
BackgroundSubtractorMOG2::BackgroundSubtractorMOG2(double alphaT,
double sigma, int nmixtures, bool postFiltering, double minArea,
bool detectShadows, bool removeForeground, double Tb, double Tg,
double TB, double CT, uchar shadowValue, double tau)
{
model = 0;
initialize(Size(), alphaT, sigma, nmixtures, postFiltering, minArea,
detectShadows, removeForeground, Tb, Tg, TB, CT, shadowValue, tau);
}
CV_FUNCNAME( "cvCreateGaussianBGModel2" );
void BackgroundSubtractorMOG2::initialize(Size frameSize, double alphaT,
double sigma, int nmixtures, bool postFiltering, double minArea,
bool detectShadows, bool removeForeground, double Tb, double Tg,
double TB, double CT, uchar shadowValue, double tau)
{
if(!model)
model = new CvGaussBGModel2;
__BEGIN__;
CvGaussBGStatModel2Params params;
if( !CV_IS_IMAGE(first_frame) )
CV_ERROR( CV_StsBadArg, "Invalid or NULL first_frame parameter" );
if( !(first_frame->nChannels==3) )
CV_ERROR( CV_StsBadArg, "Need three channel image (RGB)" );
CV_CALL( bg_model = (CvGaussBGModel2*)cvAlloc( sizeof(*bg_model) ));
memset( bg_model, 0, sizeof(*bg_model) );
bg_model->type = CV_BG_MODEL_MOG2;
bg_model->release = (CvReleaseBGStatModel)icvReleaseGaussianBGModel2;
bg_model->update = (CvUpdateBGStatModel)icvUpdateGaussianBGModel2;
//init parameters
if( parameters == NULL )
{
/* These constants are defined in cvaux/include/cvaux.h: */
params.bRemoveForeground=0;
params.bShadowDetection = 1;
params.bPostFiltering=0;
params.minArea=CV_BGFG_MOG2_MINAREA;
//set parameters
// K - max number of Gaussians per pixel
params.nM = CV_BGFG_MOG2_NGAUSSIANS;//4;
// Tb - the threshold - n var
//pGMM->fTb = 4*4;
params.fTb = CV_BGFG_MOG2_STD_THRESHOLD*CV_BGFG_MOG2_STD_THRESHOLD;
// Tbf - the threshold
//pGMM->fTB = 0.9f;//1-cf from the paper
params.fTB = CV_BGFG_MOG2_BACKGROUND_THRESHOLD;
// Tgenerate - the threshold
params.fTg = CV_BGFG_MOG2_STD_THRESHOLD_GENERATE*CV_BGFG_MOG2_STD_THRESHOLD_GENERATE;//update the mode or generate new
//pGMM->fSigma= 11.0f;//sigma for the new mode
params.fSigma= CV_BGFG_MOG2_SIGMA_INIT;
// alpha - the learning factor
params.fAlphaT=1.0f/CV_BGFG_MOG2_WINDOW_SIZE;//0.003f;
// complexity reduction prior constant
params.fCT=CV_BGFG_MOG2_CT;//0.05f;
//shadow
// Shadow detection
params.nShadowDetection = CV_BGFG_MOG2_SHADOW_VALUE;//value 0 to turn off
params.fTau = CV_BGFG_MOG2_SHADOW_TAU;//0.5f;// Tau - shadow threshold
}
else
{
params = *parameters;
}
bg_model->params = params;
//allocate GMM data
w=first_frame->width;
h=first_frame->height;
size=w*h;
bg_model->data.nWidth=w;
bg_model->data.nHeight=h;
bg_model->data.nNBands=3;
bg_model->data.nSize=size;
//GMM for each pixel
bg_model->data.rGMM=(CvPBGMMGaussian*) malloc(size * params.nM * sizeof(CvPBGMMGaussian));
//used modes per pixel
bg_model->data.rnUsedModes = (unsigned char* ) malloc(size);
memset(bg_model->data.rnUsedModes,0,size);//no modes used
//prepare storages
CV_CALL( bg_model->background = cvCreateImage(cvSize(w,h), IPL_DEPTH_8U, first_frame->nChannels));
CV_CALL( bg_model->foreground = cvCreateImage(cvSize(w,h), IPL_DEPTH_8U, 1));
//for eventual filtering
CV_CALL( bg_model->storage = cvCreateMemStorage());
bg_model->countFrames = 0;
__END__;
if( cvGetErrStatus() < 0 )
{
CvBGStatModel* base_ptr = (CvBGStatModel*)bg_model;
CvGaussBGModel2* bg_model = (CvGaussBGModel2*)model;
if( bg_model && bg_model->release )
bg_model->release( &base_ptr );
else
cvFree( &bg_model );
bg_model = 0;
}
bg_model->params.bRemoveForeground=removeForeground;
bg_model->params.bShadowDetection = detectShadows;
bg_model->params.bPostFiltering = postFiltering;
bg_model->params.minArea = minArea;
bg_model->params.nM = nmixtures;
bg_model->params.fTb = Tb;
bg_model->params.fTB = TB;
bg_model->params.fTg = Tg;
bg_model->params.fSigma = sigma;
bg_model->params.fAlphaT = alphaT;
bg_model->params.fCT = CT;
bg_model->params.nShadowDetection = shadowValue;
bg_model->params.fTau = tau;
return (CvBGStatModel*)bg_model;
}
static void CV_CDECL
icvReleaseGaussianBGModel2( CvGaussBGModel2** _bg_model )
{
CV_FUNCNAME( "icvReleaseGaussianBGModel2" );
__BEGIN__;
int w = frameSize.width;
int h = frameSize.height;
int size = w*h;
if( !_bg_model )
CV_ERROR( CV_StsNullPtr, "" );
if( *_bg_model )
if( (bg_model->data.nWidth != w ||
bg_model->data.nHeight != h) &&
w > 0 && h > 0 )
{
CvGaussBGModel2* bg_model = *_bg_model;
free (bg_model->data.rGMM);
free (bg_model->data.rnUsedModes);
cvReleaseImage( &bg_model->background );
cvReleaseImage( &bg_model->foreground );
cvReleaseMemStorage(&bg_model->storage);
memset( bg_model, 0, sizeof(*bg_model) );
cvFree( _bg_model );
bg_model->data.nWidth=w;
bg_model->data.nHeight=h;
bg_model->data.nNBands=3;
bg_model->data.nSize=size;
//GMM for each pixel
bg_model->data.rGMM.resize(size * bg_model->params.nM);
}
__END__;
//used modes per pixel
bg_model->data.rnUsedModes.resize(0);
bg_model->data.rnUsedModes.resize(size, (uchar)0);
bg_model->params.bInit = true;
bg_model->countFrames = 0;
}
static int CV_CDECL
icvUpdateGaussianBGModel2( IplImage* curr_frame, CvGaussBGModel2* bg_model )
BackgroundSubtractorMOG2::~BackgroundSubtractorMOG2()
{
delete (CvGaussBGModel2*)model;
}
void BackgroundSubtractorMOG2::operator()(const Mat& image0, Mat& fgmask0, double learningRate)
{
CvGaussBGModel2* bg_model = (CvGaussBGModel2*)model;
CV_Assert(bg_model != 0);
Mat fgmask = fgmask0, image = image0;
CV_Assert( image.type() == CV_8UC1 || image.type() == CV_8UC3 );
if( learningRate <= 0 )
learningRate = bg_model->params.fAlphaT;
if( learningRate >= 1 )
{
learningRate = 1;
bg_model->params.bInit = true;
}
if( image.size() != Size(bg_model->data.nWidth, bg_model->data.nHeight) )
initialize(image.size(), learningRate, bg_model->params.fSigma,
bg_model->params.nM, bg_model->params.bPostFiltering,
bg_model->params.minArea, bg_model->params.bShadowDetection,
bg_model->params.bRemoveForeground,
bg_model->params.fTb, bg_model->params.fTg, bg_model->params.fTB,
bg_model->params.fCT, bg_model->params.nShadowDetection, bg_model->params.fTau);
//int i, j, k, n;
int region_count = 0;
CvSeq *first_seq = NULL, *prev_seq = NULL, *seq = NULL;
float alpha,alphaInit;
float alpha = (float)bg_model->params.fAlphaT;
bg_model->countFrames++;
alpha=bg_model->params.fAlphaT;
if (bg_model->params.bInit){
//faster initial updates
alphaInit=(1.0f/(2*bg_model->countFrames+1));
if (alphaInit>alpha)
{
alpha=alphaInit;
}
else
{
bg_model->params.bInit=0;
}
}
icvUpdatePixelBackgroundGMM(&bg_model->data,&bg_model->params,alpha,(unsigned char*)curr_frame->imageData,(unsigned char*)bg_model->foreground->imageData);
if (bg_model->params.bPostFiltering==1)
{
//foreground filtering
//filter small regions
cvClearMemStorage(bg_model->storage);
cvMorphologyEx( bg_model->foreground, bg_model->foreground, 0, 0, CV_MOP_OPEN, 1 );
cvMorphologyEx( bg_model->foreground, bg_model->foreground, 0, 0, CV_MOP_CLOSE, 1 );
cvFindContours( bg_model->foreground, bg_model->storage, &first_seq, sizeof(CvContour), CV_RETR_LIST );
for( seq = first_seq; seq; seq = seq->h_next )
if (bg_model->params.bInit)
{
CvContour* cnt = (CvContour*)seq;
if( cnt->rect.width * cnt->rect.height < bg_model->params.minArea )
{
//delete small contour
prev_seq = seq->h_prev;
if( prev_seq )
{
prev_seq->h_next = seq->h_next;
if( seq->h_next ) seq->h_next->h_prev = prev_seq;
}
else
{
first_seq = seq->h_next;
if( seq->h_next ) seq->h_next->h_prev = NULL;
}
}
//faster initial updates
float alphaInit = 1.0f/(2*bg_model->countFrames+1);
if( alphaInit > alpha )
alpha = alphaInit;
else
bg_model->params.bInit = false;
}
if( !image.isContinuous() || image.channels() != 3 )
{
image.release();
image.create(image0.size(), CV_8UC3);
if( image0.type() == image.type() )
image0.copyTo(image);
else
{
region_count++;
}
cvtColor(image0, image, CV_GRAY2BGR);
}
bg_model->foreground_regions = first_seq;
cvZero(bg_model->foreground);
cvDrawContours(bg_model->foreground, first_seq, CV_RGB(0, 0, 255), CV_RGB(0, 0, 255), 10, -1);
return region_count;
}
else
{
return 1;
}
if( !fgmask.isContinuous() )
fgmask.release();
fgmask.create(image.size(), CV_8UC1);
icvUpdatePixelBackgroundGMM(&bg_model->data,&bg_model->params,alpha,image.data,fgmask.data);
if (!bg_model->params.bPostFiltering)
return;
//foreground filtering: filter out small regions
morphologyEx(fgmask, fgmask, CV_MOP_OPEN, Mat());
morphologyEx(fgmask, fgmask, CV_MOP_CLOSE, Mat());
vector<vector<Point> > contours;
findContours(fgmask, contours, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);
fgmask = Scalar::all(0);
for( size_t i = 0; i < contours.size(); i++ )
{
if( boundingRect(Mat(contours[i])).area() < bg_model->params.minArea )
continue;
drawContours(fgmask, contours, (int)i, Scalar::all(255), -1, 8, vector<Vec4i>(), 1);
}
fgmask.copyTo(fgmask0);
}
}
/* End of file. */