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 */
void Smooth(double **im, double **ims,int width,int height, int size, double theta){
double **kernel;
int i;
cout<<"g 01"<<flush<<endl;
kernel=new double*[size];
for (i=0;i<size;i++)
kernel[i]=new double[size];
cout<<"g 01"<<flush<<endl;
Gaussian2D(kernel,theta,size);
cout<<"g 01"<<flush<<endl;
Convolution2DPadded(im,ims,width,height,kernel,size,size);
cout<<"g 01"<<flush<<endl;
for(i=0;i<size;i++)
delete(kernel[i]);
delete(kernel);
}
