void convolve(cv::Mat_<float> &im, const int axis, const float* kernel)
{
if(axis >= im.dims)
throw std::invalid_argument("Matrix dimension is too small to convolve along this axis");
assert(im.isContinuous());
Convolver co(im.size[axis]);
unsigned long int step = im.step1(axis);
//whatever the real dimension, we fall back to a 3d situation where the axis of interest is y
//and either x or z can be of size 1
int nbplanes = 1;
for(int d=0; d<axis; ++d)
nbplanes *= im.size[d];
int planestep = im.total()/nbplanes;
//for each plane
for(int i=0; i<nbplanes; ++i)
//for each line
for(size_t j=0; j<step; ++j)
co(reinterpret_cast<float*>(im.data) + i*planestep + j, step, kernel);
}
std::vector<float> get_spectrum_1d(const cv::Mat_<float> &im, const int axis, const bool windowing)
{
if(axis >= im.dims)
throw std::invalid_argument("Matrix dimension is too small to compute the spectrum along this axis");
assert(im.isContinuous());
Convolver co(im.size[axis]);
if(windowing)
co.set_hanning();
std::vector<float> spectrum(co.fourier_size());
std::vector<double> tot(co.fourier_size(), 0.0);
std::vector<std::complex<double> > totf(co.fourier_size(), 0.0);
unsigned long int step = im.step1(axis);
//whatever the real dimension, we fall back to a 3d situation where the axis of interest is y
//and either x or z can be of size 1
int nbplanes = 1;
for(int d=0; d<axis; ++d)
nbplanes *= im.size[d];
int planestep = im.total()/nbplanes;
//for each plane
for(int i=0; i<nbplanes; ++i)
{
//for each line
for(size_t j=0; j<step; ++j)
{
co.spectrum(reinterpret_cast<float* const>(im.data) + i*planestep + j, step, &spectrum[0]);
for(size_t u=0; u<spectrum.size(); ++u)
tot[u] += spectrum[u];
for(size_t u=0; u<spectrum.size(); ++u)
totf[u] += co.get_fourier()[u];
}
}
const double icount = 1.0 / (nbplanes * step);
for(size_t i=0; i<tot.size(); ++i)
spectrum[i] = tot[i]*icount - std::norm(totf[i]*icount);
return spectrum;
}