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moved some old stuff to the legacy module; merge "compat_c.h" headers and moved to the legacy as well. moved implementation of many non-critical/obsolete inline functions and methods to .cpp to improve Opencv build time

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Vadim Pisarevsky
2010-11-26 17:58:20 +00:00
parent fbdb4f4ab5
commit 54ef4c08c2
32 changed files with 1661 additions and 1289 deletions
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modules/legacy/src/blobtrackingmsfgs.cpp Normal file
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#define SCALE_BASE 1.1
#define SCALE_RANGE 2
#define SCALE_NUM (2*SCALE_RANGE+1)
typedef float DefHistType;
#define DefHistTypeMat CV_32F
#define HIST_INDEX(_pData) (((_pData)[0]>>m_ByteShift) + (((_pData)[1]>>(m_ByteShift))<<m_BinBit)+((pImgData[2]>>m_ByteShift)<<(m_BinBit*2)))
void calcKernelEpanechnikov(CvMat* pK)
{ /* Allocate kernel for histogramm creation: */
int x,y;
int w = pK->width;
int h = pK->height;
float x0 = 0.5f*(w-1);
float y0 = 0.5f*(h-1);
for(y=0; y<h; ++y) for(x=0; x<w; ++x)
{
// float r2 = ((x-x0)*(x-x0)/(x0*x0)+(y-y0)*(y-y0)/(y0*y0));
float r2 = ((x-x0)*(x-x0)+(y-y0)*(y-y0))/((x0*x0)+(y0*y0));
CV_MAT_ELEM(pK[0],DefHistType, y, x) = (DefHistType)((r2<1)?(1-r2):0);
}
} /* Allocate kernel for histogram creation. */
class CvBlobTrackerOneMSFGS:public CvBlobTrackerOne
{
private:
/* Parameters: */
float m_FGWeight;
float m_Alpha;
CvSize m_ObjSize;
CvMat* m_KernelHistModel;
CvMat* m_KernelHistCandidate;
CvSize m_KernelMeanShiftSize;
CvMat* m_KernelMeanShiftK[SCALE_NUM];
CvMat* m_KernelMeanShiftG[SCALE_NUM];
CvMat* m_Weights;
int m_BinBit;
int m_ByteShift;
int m_BinNum;
int m_Dim;
int m_BinNumTotal;
CvMat* m_HistModel;
float m_HistModelVolume;
CvMat* m_HistCandidate;
float m_HistCandidateVolume;
CvMat* m_HistTemp;
CvBlob m_Blob;
void ReAllocHist(int Dim, int BinBit)
{
m_BinBit = BinBit;
m_ByteShift = 8-BinBit;
m_Dim = Dim;
m_BinNum = (1<<BinBit);
m_BinNumTotal = cvRound(pow((double)m_BinNum,(double)m_Dim));
if(m_HistModel) cvReleaseMat(&m_HistModel);
if(m_HistCandidate) cvReleaseMat(&m_HistCandidate);
if(m_HistTemp) cvReleaseMat(&m_HistTemp);
m_HistCandidate = cvCreateMat(1, m_BinNumTotal, DefHistTypeMat);
m_HistModel = cvCreateMat(1, m_BinNumTotal, DefHistTypeMat);
m_HistTemp = cvCreateMat(1, m_BinNumTotal, DefHistTypeMat);
cvZero(m_HistCandidate);
cvZero(m_HistModel);
m_HistModelVolume = 0.0f;
m_HistCandidateVolume = 0.0f;
}
void ReAllocKernel(int w, int h, float sigma=0.4)
{
double ScaleToObj = sigma*1.39;
int kernel_width = cvRound(w/ScaleToObj);
int kernel_height = cvRound(h/ScaleToObj);
int x,y,s;
assert(w>0);
assert(h>0);
m_ObjSize = cvSize(w,h);
m_KernelMeanShiftSize = cvSize(kernel_width,kernel_height);
/* Create kernels for histogram calculation: */
if(m_KernelHistModel) cvReleaseMat(&m_KernelHistModel);
m_KernelHistModel = cvCreateMat(h, w, DefHistTypeMat);
calcKernelEpanechnikov(m_KernelHistModel);
if(m_KernelHistCandidate) cvReleaseMat(&m_KernelHistCandidate);
m_KernelHistCandidate = cvCreateMat(kernel_height, kernel_width, DefHistTypeMat);
calcKernelEpanechnikov(m_KernelHistCandidate);
if(m_Weights) cvReleaseMat(&m_Weights);
m_Weights = cvCreateMat(kernel_height, kernel_width, CV_32F);
for(s=-SCALE_RANGE; s<=SCALE_RANGE; ++s)
{ /* Allocate kernel for meanshifts in space and scale: */
int si = s+SCALE_RANGE;
double cur_sigma = sigma * pow(SCALE_BASE,s);
double cur_sigma2 = cur_sigma*cur_sigma;
double x0 = 0.5*(kernel_width-1);
double y0 = 0.5*(kernel_height-1);
if(m_KernelMeanShiftK[si]) cvReleaseMat(&m_KernelMeanShiftK[si]);
if(m_KernelMeanShiftG[si]) cvReleaseMat(&m_KernelMeanShiftG[si]);
m_KernelMeanShiftK[si] = cvCreateMat(kernel_height, kernel_width, DefHistTypeMat);
m_KernelMeanShiftG[si] = cvCreateMat(kernel_height, kernel_width, DefHistTypeMat);
for(y=0; y<kernel_height; ++y)
{
DefHistType* pK = (DefHistType*)CV_MAT_ELEM_PTR_FAST( m_KernelMeanShiftK[si][0], y, 0, sizeof(DefHistType) );
DefHistType* pG = (DefHistType*)CV_MAT_ELEM_PTR_FAST( m_KernelMeanShiftG[si][0], y, 0, sizeof(DefHistType) );
for(x=0; x<kernel_width; ++x)
{
double r2 = ((x-x0)*(x-x0)/(x0*x0)+(y-y0)*(y-y0)/(y0*y0));
double sigma12 = cur_sigma2 / 2.56;
double sigma22 = cur_sigma2 * 2.56;
pK[x] = (DefHistType)(Gaussian2D(r2, sigma12)/sigma12 - Gaussian2D(r2, sigma22)/sigma22);
pG[x] = (DefHistType)(Gaussian2D(r2, cur_sigma2/1.6) - Gaussian2D(r2, cur_sigma2*1.6));
}
} /* Next line. */
}
} /* ReallocKernel */
inline double Gaussian2D(double x, double sigma2)
{
return (exp(-x/(2*sigma2)) / (2*3.1415926535897932384626433832795*sigma2) );
}
void calcHist(IplImage* pImg, IplImage* pMask, CvPoint Center, CvMat* pKernel, CvMat* pHist, DefHistType* pHistVolume)
{
int w = pKernel->width;
int h = pKernel->height;
DefHistType Volume = 0;
int x0 = Center.x - w/2;
int y0 = Center.y - h/2;
int x,y;
//cvZero(pHist);
cvSet(pHist,cvScalar(1.0/m_BinNumTotal)); /* no zero bins, all bins have very small value*/
Volume = 1;
if(m_Dim == 3)
{
for(y=0; y<h; ++y)
{
unsigned char* pImgData = NULL;
unsigned char* pMaskData = NULL;
DefHistType* pKernelData = NULL;
if((y0+y)>=pImg->height) continue;
if((y0+y)<0)continue;
pImgData = &CV_IMAGE_ELEM(pImg,unsigned char,y+y0,x0*3);
pMaskData = pMask?(&CV_IMAGE_ELEM(pMask,unsigned char,y+y0,x0)):NULL;
pKernelData = (DefHistType*)CV_MAT_ELEM_PTR_FAST(pKernel[0],y,0,sizeof(DefHistType));
for(x=0; x<w; ++x, pImgData+=3)
{
if((x0+x)>=pImg->width) continue;
if((x0+x)<0)continue;
if(pMaskData==NULL || pMaskData[x]>128)
{
DefHistType K = pKernelData[x];
int index = HIST_INDEX(pImgData);
assert(index >= 0 && index < pHist->cols);
Volume += K;
((DefHistType*)(pHist->data.ptr))[index] += K;
} /* Only masked pixels. */
} /* Next column. */
} /* Next row. */
} /* if m_Dim == 3. */
if(pHistVolume)pHistVolume[0] = Volume;
}; /* calcHist */
double calcBhattacharyya()
{
cvMul(m_HistCandidate,m_HistModel,m_HistTemp);
cvPow(m_HistTemp,m_HistTemp,0.5);
return cvSum(m_HistTemp).val[0] / sqrt(m_HistCandidateVolume*m_HistModelVolume);
} /* calcBhattacharyyaCoefficient */
void calcWeights(IplImage* pImg, IplImage* pImgFG, CvPoint Center)
{
cvZero(m_Weights);
/* Calculate new position: */
if(m_Dim == 3)
{
int x0 = Center.x - m_KernelMeanShiftSize.width/2;
int y0 = Center.y - m_KernelMeanShiftSize.height/2;
int x,y;
assert(m_Weights->width == m_KernelMeanShiftSize.width);
assert(m_Weights->height == m_KernelMeanShiftSize.height);
/* Calcualte shift vector: */
for(y=0; y<m_KernelMeanShiftSize.height; ++y)
{
unsigned char* pImgData = NULL;
unsigned char* pMaskData = NULL;
float* pWData = NULL;
if(y+y0 < 0 || y+y0 >= pImg->height) continue;
pImgData = &CV_IMAGE_ELEM(pImg,unsigned char,y+y0,x0*3);
pMaskData = pImgFG?(&CV_IMAGE_ELEM(pImgFG,unsigned char,y+y0,x0)):NULL;
pWData = (float*)CV_MAT_ELEM_PTR_FAST(m_Weights[0],y,0,sizeof(float));
for(x=0; x<m_KernelMeanShiftSize.width; ++x, pImgData+=3)
{
double V = 0;
double HM = 0;
double HC = 0;
int index;
if(x+x0 < 0 || x+x0 >= pImg->width) continue;
index = HIST_INDEX(pImgData);
assert(index >= 0 && index < m_BinNumTotal);
if(m_HistModelVolume>0)
HM = ((DefHistType*)m_HistModel->data.ptr)[index]/m_HistModelVolume;
if(m_HistCandidateVolume>0)
HC = ((DefHistType*)m_HistCandidate->data.ptr)[index]/m_HistCandidateVolume;
V = (HC>0)?sqrt(HM / HC):0;
V += m_FGWeight*(pMaskData?((pMaskData[x]/255.0f)):0);
pWData[x] = (float)MIN(V,100000);
} /* Next column. */
} /* Next row. */
} /* if m_Dim == 3. */
} /* calcWeights */
public:
CvBlobTrackerOneMSFGS()
{
int i;
m_FGWeight = 0;
m_Alpha = 0.0;
/* Add several parameters for external use: */
AddParam("FGWeight", &m_FGWeight);
CommentParam("FGWeight","Weight of FG mask using (0 - mask will not be used for tracking)");
AddParam("Alpha", &m_Alpha);
CommentParam("Alpha","Coefficient for model histogramm updating (0 - hist is not upated)");
m_BinBit=0;
m_Dim = 0;
m_HistModel = NULL;
m_HistCandidate = NULL;
m_HistTemp = NULL;
m_KernelHistModel = NULL;
m_KernelHistCandidate = NULL;
m_Weights = NULL;
for(i=0; i<SCALE_NUM; ++i)
{
m_KernelMeanShiftK[i] = NULL;
m_KernelMeanShiftG[i] = NULL;
}
ReAllocHist(3,5); /* 3D hist, each dimension has 2^5 bins. */
SetModuleName("MSFGS");
}
~CvBlobTrackerOneMSFGS()
{
int i;
if(m_HistModel) cvReleaseMat(&m_HistModel);
if(m_HistCandidate) cvReleaseMat(&m_HistCandidate);
if(m_HistTemp) cvReleaseMat(&m_HistTemp);
if(m_KernelHistModel) cvReleaseMat(&m_KernelHistModel);
for(i=0; i<SCALE_NUM; ++i)
{
if(m_KernelMeanShiftK[i]) cvReleaseMat(&m_KernelMeanShiftK[i]);
if(m_KernelMeanShiftG[i]) cvReleaseMat(&m_KernelMeanShiftG[i]);
}
}
/* Interface: */
virtual void Init(CvBlob* pBlobInit, IplImage* pImg, IplImage* pImgFG = NULL)
{
int w = cvRound(CV_BLOB_WX(pBlobInit));
int h = cvRound(CV_BLOB_WY(pBlobInit));
if(w<3)w=3;
if(h<3)h=3;
if(w>pImg->width)w=pImg->width;
if(h>pImg->height)h=pImg->height;
ReAllocKernel(w,h);
calcHist(pImg, pImgFG, cvPointFrom32f(CV_BLOB_CENTER(pBlobInit)), m_KernelHistModel, m_HistModel, &m_HistModelVolume);
m_Blob = pBlobInit[0];
};
virtual CvBlob* Process(CvBlob* pBlobPrev, IplImage* pImg, IplImage* pImgFG = NULL)
{
int iter;
if(pBlobPrev)
{
m_Blob = pBlobPrev[0];
}
for(iter=0; iter<10; ++iter)
{
// float newx=0,newy=0,sum=0;
float dx=0,dy=0,sum=0;
int x,y,si;
CvPoint Center = cvPoint(cvRound(m_Blob.x),cvRound(m_Blob.y));
CvSize Size = cvSize(cvRound(m_Blob.w),cvRound(m_Blob.h));
if(m_ObjSize.width != Size.width || m_ObjSize.height != Size.height)
{ /* Reallocate kernels: */
ReAllocKernel(Size.width,Size.height);
} /* Reallocate kernels. */
/* Mean shift in coordinate space: */
calcHist(pImg, NULL, Center, m_KernelHistCandidate, m_HistCandidate, &m_HistCandidateVolume);
calcWeights(pImg, pImgFG, Center);
for(si=1; si<(SCALE_NUM-1); ++si)
{
CvMat* pKernel = m_KernelMeanShiftK[si];
float sdx = 0, sdy=0, ssum=0;
int s = si-SCALE_RANGE;
float factor = (1.0f-( float(s)/float(SCALE_RANGE) )*( float(s)/float(SCALE_RANGE) ));
for(y=0; y<m_KernelMeanShiftSize.height; ++y)
for(x=0; x<m_KernelMeanShiftSize.width; ++x)
{
float W = *(float*)CV_MAT_ELEM_PTR_FAST(m_Weights[0],y,x,sizeof(float));
float K = *(float*)CV_MAT_ELEM_PTR_FAST(pKernel[0],y,x,sizeof(float));
float KW = K*W;
ssum += (float)fabs(KW);
sdx += KW*(x-m_KernelMeanShiftSize.width*0.5f);
sdy += KW*(y-m_KernelMeanShiftSize.height*0.5f);
} /* Next pixel. */
dx += sdx * factor;
dy += sdy * factor;
sum += ssum * factor;
} /* Next scale. */
if(sum > 0)
{
dx /= sum;
dy /= sum;
}
m_Blob.x += dx;
m_Blob.y += dy;
{ /* Mean shift in scale space: */
float news = 0;
float sum = 0;
float scale;
Center = cvPoint(cvRound(m_Blob.x),cvRound(m_Blob.y));
calcHist(pImg, NULL, Center, m_KernelHistCandidate, m_HistCandidate, &m_HistCandidateVolume);
calcWeights(pImg, pImgFG, Center);
//cvSet(m_Weights,cvScalar(1));
for(si=0; si<SCALE_NUM; si++)
{
double W = cvDotProduct(m_Weights, m_KernelMeanShiftG[si]);;
int s = si-SCALE_RANGE;
sum += (float)fabs(W);
news += (float)(s*W);
}
if(sum>0)
{
news /= sum;
}
scale = (float)pow((double)SCALE_BASE,(double)news);
m_Blob.w *= scale;
m_Blob.h *= scale;
} /* Mean shift in scale space. */
/* Check fo finish: */
if(fabs(dx)<0.1 && fabs(dy)<0.1) break;
} /* Next iteration. */
if(m_Alpha>0)
{ /* Update histogram: */
double Vol, WM, WC;
CvPoint Center = cvPoint(cvRound(m_Blob.x),cvRound(m_Blob.y));
calcHist(pImg, pImgFG, Center, m_KernelHistModel, m_HistCandidate, &m_HistCandidateVolume);
Vol = 0.5*(m_HistModelVolume + m_HistCandidateVolume);
WM = Vol*(1-m_Alpha)/m_HistModelVolume;
WC = Vol*(m_Alpha)/m_HistCandidateVolume;
cvAddWeighted(m_HistModel, WM, m_HistCandidate,WC,0,m_HistModel);
m_HistModelVolume = (float)cvSum(m_HistModel).val[0];
} /* Update histogram. */
return &m_Blob;
}; /* Process */
virtual void Release(){delete this;};
}; /*CvBlobTrackerOneMSFGS*/
CvBlobTrackerOne* cvCreateBlobTrackerOneMSFGS()
{
return (CvBlobTrackerOne*) new CvBlobTrackerOneMSFGS;
}
CvBlobTracker* cvCreateBlobTrackerMSFGS()
{
return cvCreateBlobTrackerList(cvCreateBlobTrackerOneMSFGS);
}