gjacobian.h

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#ifndef AIMS_SIGNALFILTER_GJACOBIAN_H
#define AIMS_SIGNALFILTER_GJACOBIAN_H

#include <aims/utility/converter_volume.h>
#include <aims/signalfilter/gslices.h>
#include <aims/signalfilter/glines.h>
#include <aims/signalfilter/gcolumns.h>


template< class T >
class GaussianJacobian
{
public:

  GaussianJacobian( float sx=1.0f, float sy=1.0f, float sz=1.0f );
  virtual ~GaussianJacobian() { }

  AimsVector< AimsData< float >, 3 >  doit( const AimsData<T>& );

private:

  float sigx;
  float sigy;
  float sigz;
};


template< class T > inline 
GaussianJacobian< T >::GaussianJacobian( float sx, float sy, float sz )
  : sigx( sx ), sigy( sy ), sigz( sz )
{
  ASSERT( sigx >= 0.1f && sigx <= 100.0f );
  ASSERT( sigy >= 0.1f && sigy <= 100.0f );
  ASSERT( sigz >= 0.1f && sigz <= 100.0f );
}


template< class T > inline AimsVector< AimsData< float >, 3 > 
GaussianJacobian< T >::doit( const AimsData< T >& data )
{
  float sx = sigx / data.sizeX();
  float sy = sigy / data.sizeY();
  float sz = sigz / data.sizeZ();

  carto::Converter< AimsData<T>, AimsData<float> > conv;
  AimsVector< AimsData< float >, 3 > res;

  AimsData< float> imaF;
  imaF=AimsData<float>( data.dimX(), data.dimY(), data.dimZ(),
                          data.dimT() );
  conv.convert( data, imaF );

  for ( int i=0; i<3; i++ )
          res[i]=imaF.clone();

  GaussianSlices gsli;
  GaussianLines glin;
  GaussianColumns gcol;

  // d / dx
  glin.doit( res[ 0 ], GCoef( sx, GCoef::gradient ) );
  gcol.doit( res[ 0 ], GCoef( sy ) );  // because default is smoothing
  gsli.doit( res[ 0 ], GCoef( sz ) );

  // d / dy
  glin.doit( res[ 1 ], GCoef( sx ) );
  gcol.doit( res[ 1 ], GCoef( sy, GCoef::gradient ) );
  gsli.doit( res[ 1 ], GCoef( sz ) );

  // d / dz
  glin.doit( res[ 2 ], GCoef( sx ) );
  gcol.doit( res[ 2 ], GCoef( sy ) );
  gsli.doit( res[ 2 ], GCoef( sz, GCoef::gradient ) );

  return res;
}

#endif