void readnextblock(std::istream& sin, libbase::matrix<S>& result,
int blocklength, const libbase::size_type<libbase::matrix>& txsize)
{
assertalways(blocklength == 0);
result.init(txsize);
result.serialize(sin);
}
void lut_interleaver<real>::inverse(const libbase::matrix<real>& in,
libbase::matrix<real>& out) const
{
const int tau = lut.size();
const int K = in.size().cols();
assertalways(in.size().rows() == tau);
out.init(in.size());
for (int t = 0; t < tau; t++)
if (lut(t) == fsm::tail)
for (int i = 0; i < K; i++)
out(t, i) = real(1.0 / K);
else
for (int i = 0; i < K; i++)
out(lut(t), i) = in(t, i);
}
void limitfilter<T>::process(const libbase::matrix<T>& in, libbase::matrix<T>& out) const
{
const int M = in.size().rows();
const int N = in.size().cols();
out.init(M, N);
for (int i = 0; i < M; i++)
for (int j = 0; j < N; j++)
if (in(i, j) < m_lo)
out(i, j) = m_lo;
else if (in(i, j) > m_hi)
out(i, j) = m_hi;
else
out(i, j) = in(i, j);
}
void variancefilter<T>::process(const libbase::matrix<T>& in,
libbase::matrix<T>& out) const
{
const int M = in.size().rows();
const int N = in.size().cols();
out.init(M, N);
for (int i = 0; i < M; i++)
for (int j = 0; j < N; j++)
{
// compute the variance of neighbouring pixels
libbase::rvstatistics r;
for (int ii = std::max(i - m_d, 0); ii <= std::min(i + m_d, M - 1); ii++)
for (int jj = std::max(j - m_d, 0); jj <= std::min(j + m_d, N - 1); jj++)
r.insert(double(in(ii, jj)));
out(i, j) = T(r.var());
}
}
void limitfilter<T>::process(libbase::matrix<T>& m) const
{
const int M = m.size().rows();
const int N = m.size().cols();
for (int i = 0; i < M; i++)
for (int j = 0; j < N; j++)
if (m(i, j) < m_lo)
m(i, j) = m_lo;
else if (m(i, j) > m_hi)
m(i, j) = m_hi;
}
