inline
__host__ __device__ dffast4
kernel_ffast4_feature<V>::new_state(const point2d<int>& n)
{
dffast4 b;
for(int i = 0; i < 4; i++)
b[i] = s1_(n.row() + 2 * c4[i][0],
n.col() + 2 * c4[i][1]);
for(int i = 0; i < 4; i++)
b[i+4] = s2_(n.row() / 2 + 2 * c4[i][0],
n.col() / 2 + 2 * c4[i][1]);
return b;
}
inline
__host__ __device__ float
kernel_ffast4_feature<V>::distance_linear(const dffast4& a,
const point2d<int>& n)
{
unsigned short d = 0;
for(int i = 0; i < 4; i++)
{
gl8u v = s1_(n.row() + 2* c4[i][0],
n.col() + 2* c4[i][1]);
d += ::abs(v - a[i]);
}
for(int i = 0; i < 4; i++)
{
gl8u v = s2_(n.row() / 2 + 2* c4[i][0],
n.col() / 2 + 2* c4[i][1]);
d += ::abs(v - a[4+i]);
}
return d / (255.f * 16.f);
// for(int i = 0; i < 16; i += 2)
// {
// float v = s1_(n.row() + circle_r3[i][0],
// n.col() + circle_r3[i][1]).x * 255.f;
// d += fabs(v - a[i/2]);
// }
// for(int i = 0; i < 16; i += 2)
// {
// float v = s2_(n.row() + 2 * circle_r3[i][0],
// n.col() + 2 * circle_r3[i][1]).x * 255.f;
// d += fabs(v - a[8+i/2]);
// }
// return d / (255.f * 16.f);
// return cuimg::distance_mean_linear(a, new_state(n));
}
void EstimateEMOS2<T,N>::iterate(
){
int l, m;
RectDomain<N> rect(this->est_.lbound(), this->est_.ubound());
this->old_ = this->est_;
int length = strata_.length(0);
int size = length - 2;
for ( l = 0; l < length; l++ )
{
estF_ = 0;
prev_ = this->est_;
// get image prediction which is a convolution of est with
// interpolation of psfs (multiplication and in Fourier domain)
for ( m = 0; m < size; m++ )
{
s1_ = 0;
if ( m == 0 )
{
s1_(strata_(m)) = this->est_(strata_(m));
}
else if ( m == size - 1 )
{
s1_(strata_(m+2)) = this->est_(strata_(m+2));
}
s1_(strata_(m+1)) = this->est_(strata_(m+1));
s2_ = s1_;
multiplyStratum(strata_(m+1), s1_, a_, true);
multiplyStratum(strata_(m+1), s2_, a_, false);
mirror(s1_, s_);
fftw_.plan(s_, sF_);
fftw_.execute();
estF_ += sF_ * psfsF_(m);
mirror(s2_, s_);
fftw_.plan(s_, sF_);
fftw_.execute();
estF_ += sF_ * psfsF_(m+1);
}
// convert back to space domain
fftw_.plan(estF_, s2_);
fftw_.execute();
s2_ /= s2_.size();
this->est_ = s2_(rect);
// get the ratio of image and prediction
s_ = where( this->est_ > epsilon_, this->img_/this->est_, this->img_/epsilon_);
mirror(s_, s2_);
fftw_.plan(s2_, estF_);
fftw_.execute();
// convolve the ratio with psf and multiply with old estimate
this->est_ = 0;
for ( m = 0; m < size; m++ )
{
sF_ = estF_ * conj(psfsF_(m));
// multiply with old estimate
fftw_.plan(sF_, s2_);
fftw_.execute();
s2_ /= s2_.size();
s_ = s2_(rect);
s_ *= prev_;
this->est_ += s_;
}
this->est_ *= scale_;
this->est_ = where( this->est_ > epsilon_, this->est_, 0);
}
}
