Mat Kernel::extractDescriptors(Mat image){
int w = image.cols, h=image.rows, size=32, delta=16;
Mat result, temp, img;
int i = (patches/14)*delta, j = (patches%14)*delta;
if(filter=="DoG")
img = DoGFilter(image);
else if(filter=="CSDN")
img = CSDNFilter(image);
else if(filter=="Gaussian")
img = GaussianFilter(image);
else
cerr << "Error, no filter choosed" << endl;
img = img(Rect(i,j,size,size));
//img.convertTo(img, CV_32F); // Retirar isso depois
//dct(img,img); // Isso tambem
if(descriptor=="SIFT")
calcSIFTDescriptors(img,temp);
else if(descriptor=="MLBP")
calcLBPHistogram(img,temp);
else
cerr << "Error, no descriptor choosed" << endl;
normalize(temp,temp,1);
result = temp.t();
return result;
}
/*! \fn dogPGM(Pgm* pgmIn, Pgm* pgmOut, double sigma, int dim)
* \brief Filter the image \a pgmIn with a DoG filter. Store the result in \a pgmOut.
*
* Apply a DoG filter to \a pgmIn. Refer to \link DoGFilter() DoGFilter() function \endlink for further information.
* \param pgmIn Pointer to the input PGM image structure.
* \param pgmOut Pointer to the output PGM image structure.
* \param sigma The sigma of the external Gaussian.
* \param dim The number of rows and columns of the filter. If it is set to 0 then the dimension
* is the smallest odd number next to 6 \a sigma.
* \return 0 on success, -1 if either pgmIn or pgmOut are NULL.
*/
int dogPGM(Pgm* pgmIn, Pgm* pgmOut, double sigma, int dim)
{
if(!pgmIn)
{
fprintf(stderr, "Error! No input data. Please Check.\n");
return -1;
}
if(!pgmOut)
{
fprintf(stderr, "Error! No space to store the result. Please Check.\n");
return -1;
}
Filter* filter;
fprintf(stderr, "\nDoG filtering (sigma = %f)\n",sigma);
filter = DoGFilter(sigma, dim);
resetPGM(pgmOut);
convolution2DPGM(pgmIn, pgmOut, filter);
freeFilter(&filter);
return 0;
}
