I want to thank old-ufo for nudging me in the correct direction. I was hoping I won't have to reinvent the wheel by doing a quick matlab-->openCV conversion but guess this is the best solution I have for a quick solution.
NOTE - I did this for square kernels only (easy to modify otherwise, but I have no need for that so...). Maybe this can be written in a more elegant form but is a quick job I did so I can carry on with more pressing matters.
From main function:
int WinSize(7); int sigma(1); // can be changed to other odd-sized WinSize and different sigma values
cv::Mat h = fspecialLoG(WinSize,sigma);
And the actual function is:
// return NxN (square kernel) of Laplacian of Gaussian as is returned by Matlab's: fspecial(Winsize,sigma)
cv::Mat fspecialLoG(int WinSize, double sigma){
// I wrote this only for square kernels as I have no need for kernels that aren't square
cv::Mat xx (WinSize,WinSize,CV_64F);
for (int i=0;i<WinSize;i++){
for (int j=0;j<WinSize;j++){
xx.at<double>(j,i) = (i-(WinSize-1)/2)*(i-(WinSize-1)/2);
}
}
cv::Mat yy;
cv::transpose(xx,yy);
cv::Mat arg = -(xx+yy)/(2*pow(sigma,2));
cv::Mat h (WinSize,WinSize,CV_64F);
for (int i=0;i<WinSize;i++){
for (int j=0;j<WinSize;j++){
h.at<double>(j,i) = pow(exp(1),(arg.at<double>(j,i)));
}
}
double minimalVal, maximalVal;
minMaxLoc(h, &minimalVal, &maximalVal);
cv::Mat tempMask = (h>DBL_EPSILON*maximalVal)/255;
tempMask.convertTo(tempMask,h.type());
cv::multiply(tempMask,h,h);
if (cv::sum(h)[0]!=0){h=h/cv::sum(h)[0];}
cv::Mat h1 = (xx+yy-2*(pow(sigma,2))/(pow(sigma,4));
cv::multiply(h,h1,h1);
h = h1 - cv::sum(h1)[0]/(WinSize*WinSize);
return h;
}