IppStatus BandPass_2D(Image2D &image_in, Image2D &image_bandpassed, const int feature_radius, const int hwhm_length)
{
//set status variable
IppStatus status;
Gaussian_Kernel GaussKernel(feature_radius, hwhm_length, image_in.get_width(), image_in.get_length());
Convolution_Kernel ConvolutionKernels(feature_radius, image_in.get_width(), image_in.get_length());
Tophat_Kernel TopHatKernel(feature_radius, image_in.get_width(), image_in.get_length());
int number_of_pixels = image_in.get_numberofpixels();
int step_size = image_in.get_stepsize();
//Create and initialize intermediate images
Image2D image_gauss_col(image_in.get_length(), image_in.get_width());
Image2D image_gauss_rowcol(image_in.get_length(), image_in.get_width());
Image2D image_tophat(image_in.get_length(), image_in.get_width());
//Gaussian kernel convolution
status = ippiFilterColumn_32f_C1R(image_in.get_image2D() + GaussKernel.get_offset(), step_size,
image_gauss_col.get_image2D() + GaussKernel.get_offset(), step_size,
GaussKernel.get_ROI_size(), GaussKernel.get_gaussian_kernel(),
GaussKernel.get_kernel_length(), GaussKernel.get_anchor_point());
status = ippiFilterRow_32f_C1R(image_gauss_col.get_image2D() + GaussKernel.get_offset(), step_size,
image_gauss_rowcol.get_image2D() + GaussKernel.get_offset(), step_size,
GaussKernel.get_ROI_size(), GaussKernel.get_gaussian_kernel(),
GaussKernel.get_kernel_length(), GaussKernel.get_anchor_point());
/*
//tophat kernel convolution/filterbox operation
status = ippiFilterBox_32f_C1R(image_in.get_image2D() + TopHatKernel.get_offset(), step_size,
image_tophat.get_image2D() + TopHatKernel.get_offset(), step_size,
TopHatKernel.get_ROI_size(), TopHatKernel.get_mask_size(),
TopHatKernel.get_anchor_point());
*/
//change by Eli Sloutskin: take away bias of square filtering kernel
status = ippiConvValid_32f_C1R(image_in.get_image2D(), step_size, image_in.get_ROIfull(),
ConvolutionKernels.get_circle_kernel(), ConvolutionKernels.get_kernel_step(), ConvolutionKernels.get_kernel_size(),
image_tophat.get_image2D() + ConvolutionKernels.get_offset(), step_size);
ippiDivC_32f_C1IR(3*feature_radius*feature_radius, image_tophat.get_image2D(),image_tophat.get_stepsize(),image_tophat.get_ROIfull());
//subtract the two images
status = ippiSub_32f_C1R(image_tophat.get_image2D() + TopHatKernel.get_offset(), step_size,
image_gauss_rowcol.get_image2D()+TopHatKernel.get_offset(), step_size,
image_bandpassed.get_image2D() + TopHatKernel.get_offset(), step_size,
TopHatKernel.get_ROI_size());
//cutoff values below zero
status = ippiThreshold_LTVal_32f_C1IR(image_bandpassed.get_image2D() + TopHatKernel.get_offset(), step_size,
TopHatKernel.get_ROI_size(),0,0);
return status;
}
void AtrousGabor::_process_level( IMAGE_PTR in, IMAGE_PTR out, int level) // can be used in place
{
const int filtersize = 5;
const int filtercenter = 2;
int r,c; //loop counters
int step = (int)pow(2.0f,level-1); //size of the increments between non zero filter coefs
int dim = step*(filtersize-1)+1; //dimension of the FIR filter at this scale
int mid = dim/2; //filter center
#ifdef USE_IPP
int stride;
IppiSize size = {m_i_colsext, m_i_linesext};
IppiSize horsize = {m_i_colsext-2*mid, m_i_linesext};
IppiSize versize = {m_i_colsext, m_i_linesext-2*mid};
Ipp32f *filter = ippiMalloc_32f_C1(dim,1,&stride);
ippiSet_32f_C1R(0.0f, temp, m_i_strideext, size);
for( r = 0; r < dim; r++ )
filter[r] = 0.0f;
filter[0] = filter[dim-1] = (Ipp32f)fc;
filter[step] = filter[dim-step-1] = (Ipp32f)fb;
filter[2*step] = (Ipp32f)fa;
ippiFilterRow_32f_C1R( in + mid, m_i_strideext, temp+mid, m_i_strideext, horsize, filter, dim, mid);
ippiFilterColumn_32f_C1R( temp + mid*m_i_stridepix, m_i_strideext,
out + mid*m_i_stridepix, m_i_strideext, versize, filter, dim, mid);
ippiFree(filter);
#else
int height = m_i_linesext;
int width = m_i_colsext;
int ws = m_i_stridepix;
//horizontal filtering
for(r=0; r < height; r++ )
{
//filtering the left border
for(c = 0; c < step; c++)
temp[r*ws+c] = fc*(in[r*ws]+in[r*ws+c+2*step]) +
fb*(in[r*ws]+in[r*ws+c+step]) +
fa*in[r*ws+c];
for(c = step; c < 2*step; c++)
temp[r*ws+c] = fc*(in[r*ws]+in[r*ws+c+2*step]) +
fb*(in[r*ws+c-step]+in[r*ws+c+step]) +
fa*in[r*ws+c];
//filtering the valid part
for(c = 2*step; c < width - 2*step; c++)
temp[r*ws+c] = fc*(in[r*ws+c-2*step]+in[r*ws+c+2*step]) +
fb*(in[r*ws+c-step]+in[r*ws+c+step]) +
fa*in[r*ws+c];
//filtering the right border
for(c = width-2*step; c < width-step; c++)
temp[r*ws+c] = fc*(in[r*ws+c-2*step]+in[r*ws+width-1]) +
fb*(in[r*ws+c-step]+in[r*ws+c+step]) +
fa*in[r*ws+c];
for(c = width-step; c < width; c++)
temp[r*ws+c] = fc*(in[r*ws+c-2*step]+in[r*ws+width-1]) +
fb*(in[r*ws+c-step]+in[r*ws+width-1]) +
fa*in[r*ws+c];
}
//vertical filtering
for(c=0; c < width; c++ )
{
//filtering the top border
for(r = 0; r < step; r++)
out[r*ws+c] = fc*(temp[c]+temp[(r+2*step)*ws+c]) +
fb*(temp[c]+temp[(r+step)*ws+c]) +
fa*temp[r*ws+c];
for(r = step; r < 2*step; r++)
out[r*ws+c] = fc*(temp[c]+temp[(r+2*step)*ws+c]) +
fb*(temp[(r-step)*ws+c]+temp[(r+step)*ws+c]) +
fa*temp[r*ws+c];
//filtering the valid part
for(r = 2*step; r < height - 2*step; r++)
out[r*ws+c] = fc*(temp[(r-2*step)*ws+c]+temp[(r+2*step)*ws+c]) +
fb*(temp[(r-step)*ws+c]+temp[(r+step)*ws+c]) +
fa*temp[r*ws+c];
//filtering the right border
for(r=height-2*step; r < height-step; r++)
out[r*ws+c] = fc*(temp[(r-2*step)*ws+c]+temp[(height-1)*ws+c]) +
fb*(temp[(r-step)*ws+c]+temp[(r+step)*ws+c]) +
fa*temp[r*ws+c];
for(r=height-step; r < height; r++)
out[r*ws+c] = fc*(temp[(r-2*step)*ws+c]+temp[(height-1)*ws+c]) +
fb*(temp[(r-step)*ws+c]+temp[(height-1)*ws+c]) +
fa*temp[r*ws+c];
}
#endif
return;
}
