aimsalgo 6.0.0
Neuroimaging image processing
g3dsmooth.h
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33
34
35#ifndef AIMS_SIGNALFILTER_G3DSMOOTH_H
36#define AIMS_SIGNALFILTER_G3DSMOOTH_H
37
38#include <aims/utility/converter_volume.h>
42
43
46template< class T >
48{
49public:
50
51 Gaussian3DSmoothing( float sx=1.0f, float sy=1.0f, float sz=1.0f );
53
55
56private:
57
58 float sigx;
59 float sigy;
60 float sigz;
61};
62
63
64template< class T > inline
65Gaussian3DSmoothing< T >::Gaussian3DSmoothing( float sx, float sy, float sz )
66 : sigx( sx ), sigy( sy ), sigz( sz )
67{
68// ASSERT( sigx >= 0.1f && sigx <= 100.0f );
69// ASSERT( sigy >= 0.1f && sigy <= 100.0f );
70// ASSERT( sigz >= 0.1f && sigz <= 100.0f );
71}
72
73
74template< class T > inline
76 const carto::rc_ptr<carto::Volume< T > >& data )
77{
78 std::vector<float> vs = data->getVoxelSize();
79 float sx = sigx / vs[0];
80 float sy = sigy / vs[1];
81 float sz = sigz / vs[2];
82
84 carto::VolumeRef< float > dataF( data->getSize() );
85 dataF.setVoxelSize(data->getVoxelSize());
86 conv.convert( data, dataF );
87
88 GaussianSlices gsli;
89 gsli.doit( dataF, GCoef( sz ) );
90
91 GaussianLines glin;
92 glin.doit( dataF, GCoef( sx ) );
93
94 GaussianColumns gcol;
95 gcol.doit( dataF, GCoef( sy ) );
96
98 carto::VolumeRef<T> data3( data->getSize() );
99 conv2.convert( dataF, data3 );
100
101 return data3;
102}
103
104#endif
Definition gcoef.h:42
virtual ~Gaussian3DSmoothing()
Definition g3dsmooth.h:52
Gaussian3DSmoothing(float sx=1.0f, float sy=1.0f, float sz=1.0f)
Definition g3dsmooth.h:65
carto::VolumeRef< T > doit(const carto::rc_ptr< carto::Volume< T > > &)
Definition g3dsmooth.h:75
void doit(carto::rc_ptr< carto::Volume< float > > &)
void doit(carto::rc_ptr< carto::Volume< float > > &)
void doit(carto::rc_ptr< carto::Volume< float > > &)
virtual void convert(const INP &in, OUTP &out) const
void setVoxelSize(float vx, float vy=1., float vz=1., float vt=1.)
std::vector< float > getVoxelSize() const