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/************************************************************************/
/* */
/* Copyright 2003 by Christian-Dennis Rahn */
/* and Ullrich Koethe */
/* */
/* This file is part of the VIGRA computer vision library. */
/* The VIGRA Website is */
/* http://hci.iwr.uni-heidelberg.de/vigra/ */
/* Please direct questions, bug reports, and contributions to */
/* ullrich.koethe@iwr.uni-heidelberg.de or */
/* vigra@informatik.uni-hamburg.de */
/* */
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/* obtaining a copy of this software and associated documentation */
/* files (the "Software"), to deal in the Software without */
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/* copy, modify, merge, publish, distribute, sublicense, and/or */
/* sell copies of the Software, and to permit persons to whom the */
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/* The above copyright notice and this permission notice shall be */
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/************************************************************************/
#ifndef VIGRA_MULTI_CONVOLUTION_H
#define VIGRA_MULTI_CONVOLUTION_H
#include "separableconvolution.hxx"
#include "array_vector.hxx"
#include "multi_array.hxx"
#include "accessor.hxx"
#include "numerictraits.hxx"
#include "navigator.hxx"
#include "metaprogramming.hxx"
#include "multi_pointoperators.hxx"
#include "functorexpression.hxx"
namespace vigra
{
namespace detail
{
/********************************************************/
/* */
/* internalSeparableConvolveMultiArray */
/* */
/********************************************************/
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor, class KernelIterator>
void
internalSeparableConvolveMultiArrayTmp(
SrcIterator si, SrcShape const & shape, SrcAccessor src,
DestIterator di, DestAccessor dest, KernelIterator kit)
{
enum { N = 1 + SrcIterator::level };
typedef typename NumericTraits<typename DestAccessor::value_type>::RealPromote TmpType;
// temporay array to hold the current line to enable in-place operation
ArrayVector<TmpType> tmp( shape[0] );
typedef MultiArrayNavigator<SrcIterator, N> SNavigator;
typedef MultiArrayNavigator<DestIterator, N> DNavigator;
{ // only operate on first dimension here
SNavigator snav( si, shape, 0 );
DNavigator dnav( di, shape, 0 );
for( ; snav.hasMore(); snav++, dnav++ )
{
// first copy source to temp for maximum cache efficiency
copyLine( snav.begin(), snav.end(), src,
tmp.begin(), typename AccessorTraits<TmpType>::default_accessor() );
convolveLine( srcIterRange(tmp.begin(), tmp.end(),
typename AccessorTraits<TmpType>::default_const_accessor()),
destIter( dnav.begin(), dest ),
kernel1d( *kit ) );
}
++kit;
}
// operate on further dimensions
for( int d = 1; d < N; ++d, ++kit )
{
DNavigator dnav( di, shape, d );
tmp.resize( shape[d] );
for( ; dnav.hasMore(); dnav++ )
{
// first copy source to temp for maximum cache efficiency
copyLine( dnav.begin(), dnav.end(), dest,
tmp.begin(), typename AccessorTraits<TmpType>::default_accessor() );
convolveLine( srcIterRange(tmp.begin(), tmp.end(),
typename AccessorTraits<TmpType>::default_const_accessor()),
destIter( dnav.begin(), dest ),
kernel1d( *kit ) );
}
}
}
} // namespace detail
/** \addtogroup MultiArrayConvolutionFilters Convolution filters for multi-dimensional arrays.
These functions realize a separable convolution on an arbitrary dimensional
array that is specified by iterators (compatible to \ref MultiIteratorPage)
and shape objects. It can therefore be applied to a wide range of data structures
(\ref vigra::MultiArrayView, \ref vigra::MultiArray etc.).
*/
//@{
/********************************************************/
/* */
/* separableConvolveMultiArray */
/* */
/********************************************************/
/** \brief Separated convolution on multi-dimensional arrays.
This function computes a separated convolution on all dimensions
of the given multi-dimensional array. Both source and destination
arrays are represented by iterators, shape objects and accessors.
The destination array is required to already have the correct size.
There are two variants of this functions: one takes a single kernel
of type \ref vigra::Kernel1D which is then applied to all dimensions,
whereas the other requires an iterator referencing a sequence of
\ref vigra::Kernel1D objects, one for every dimension of the data.
Then the first kernel in this sequence is applied to the innermost
dimension (e.g. the x-dimension of an image), while the last is applied to the
outermost dimension (e.g. the z-dimension in a 3D image).
This function may work in-place, which means that <tt>siter == diter</tt> is allowed.
A full-sized internal array is only allocated if working on the destination
array directly would cause round-off errors (i.e. if
<tt>typeid(typename NumericTraits<typename DestAccessor::value_type>::RealPromote)
!= typeid(typename DestAccessor::value_type)</tt>.
<b> Declarations:</b>
pass arguments explicitly:
\code
namespace vigra {
// apply the same kernel to all dimensions
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor, class T>
void
separableConvolveMultiArray(SrcIterator siter, SrcShape const & shape, SrcAccessor src,
DestIterator diter, DestAccessor dest,
Kernel1D<T> const & kernel);
// apply each kernel from the sequence 'kernels' in turn
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor, class KernelIterator>
void
separableConvolveMultiArray(SrcIterator siter, SrcShape const & shape, SrcAccessor src,
DestIterator diter, DestAccessor dest,
KernelIterator kernels);
}
\endcode
use argument objects in conjunction with \ref ArgumentObjectFactories :
\code
namespace vigra {
// apply the same kernel to all dimensions
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor, class T>
void
separableConvolveMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
pair<DestIterator, DestAccessor> const & dest,
Kernel1D<T> const & kernel);
// apply each kernel from the sequence 'kernels' in turn
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor, class KernelIterator>
void
separableConvolveMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
pair<DestIterator, DestAccessor> const & dest,
KernelIterator kernels);
}
\endcode
<b> Usage:</b>
<b>\#include</b> \<<a href="multi__convolution_8hxx-source.html">vigra/multi_convolution.hxx</a>\>
\code
MultiArray<3, unsigned char>::size_type shape(width, height, depth);
MultiArray<3, unsigned char> source(shape);
MultiArray<3, float> dest(shape);
...
Kernel1D<float> gauss;
gauss.initGaussian(sigma);
// create 3 Gauss kernels, one for each dimension
ArrayVector<Kernel1D<float> > kernels(3, gauss);
// perform Gaussian smoothing on all dimensions
separableConvolveMultiArray(srcMultiArrayRange(source), destMultiArray(dest),
kernels.begin());
\endcode
\see vigra::Kernel1D, convolveLine()
*/
doxygen_overloaded_function(template <...> void separableConvolveMultiArray)
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor, class KernelIterator>
void
separableConvolveMultiArray( SrcIterator s, SrcShape const & shape, SrcAccessor src,
DestIterator d, DestAccessor dest, KernelIterator kernels )
{
typedef typename NumericTraits<typename DestAccessor::value_type>::RealPromote TmpType;
if(!IsSameType<TmpType, typename DestAccessor::value_type>::boolResult)
{
// need a temporary array to avoid rounding errors
MultiArray<SrcShape::static_size, TmpType> tmpArray(shape);
detail::internalSeparableConvolveMultiArrayTmp( s, shape, src,
tmpArray.traverser_begin(), typename AccessorTraits<TmpType>::default_accessor(), kernels );
copyMultiArray(srcMultiArrayRange(tmpArray), destIter(d, dest));
}
else
{
// work directly on the destination array
detail::internalSeparableConvolveMultiArrayTmp( s, shape, src, d, dest, kernels );
}
}
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor, class KernelIterator>
inline
void separableConvolveMultiArray(
triple<SrcIterator, SrcShape, SrcAccessor> const & source,
pair<DestIterator, DestAccessor> const & dest, KernelIterator kit )
{
separableConvolveMultiArray( source.first, source.second, source.third,
dest.first, dest.second, kit );
}
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor, class T>
inline void
separableConvolveMultiArray( SrcIterator s, SrcShape const & shape, SrcAccessor src,
DestIterator d, DestAccessor dest,
Kernel1D<T> const & kernel )
{
ArrayVector<Kernel1D<T> > kernels(shape.size(), kernel);
separableConvolveMultiArray( s, shape, src, d, dest, kernels.begin() );
}
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor, class T>
inline void
separableConvolveMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
pair<DestIterator, DestAccessor> const & dest,
Kernel1D<T> const & kernel )
{
ArrayVector<Kernel1D<T> > kernels(source.second.size(), kernel);
separableConvolveMultiArray( source.first, source.second, source.third,
dest.first, dest.second, kernels.begin() );
}
/********************************************************/
/* */
/* convolveMultiArrayOneDimension */
/* */
/********************************************************/
/** \brief Convolution along a single dimension of a multi-dimensional arrays.
This function computes a convolution along one dimension (specified by
the parameter <tt>dim</tt> of the given multi-dimensional array with the given
<tt>kernel</tt>. Both source and destination arrays are represented by
iterators, shape objects and accessors. The destination array is required to
already have the correct size.
This function may work in-place, which means that <tt>siter == diter</tt> is allowed.
<b> Declarations:</b>
pass arguments explicitly:
\code
namespace vigra {
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor, class T>
void
convolveMultiArrayOneDimension(SrcIterator siter, SrcShape const & shape, SrcAccessor src,
DestIterator diter, DestAccessor dest,
unsigned int dim, vigra::Kernel1D<T> const & kernel);
}
\endcode
use argument objects in conjunction with \ref ArgumentObjectFactories :
\code
namespace vigra {
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor, class T>
void
convolveMultiArrayOneDimension(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
pair<DestIterator, DestAccessor> const & dest,
unsigned int dim, vigra::Kernel1D<T> const & kernel);
}
\endcode
<b> Usage:</b>
<b>\#include</b> \<<a href="multi__convolution_8hxx-source.html">vigra/multi_convolution.hxx</a>\>
\code
MultiArray<3, unsigned char>::size_type shape(width, height, depth);
MultiArray<3, unsigned char> source(shape);
MultiArray<3, float> dest(shape);
...
Kernel1D<float> gauss;
gauss.initGaussian(sigma);
// perform Gaussian smoothing along dimensions 1 (height)
convolveMultiArrayOneDimension(srcMultiArrayRange(source), destMultiArray(dest), 1, gauss);
\endcode
\see separableConvolveMultiArray()
*/
doxygen_overloaded_function(template <...> void convolveMultiArrayOneDimension)
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor, class T>
void
convolveMultiArrayOneDimension(SrcIterator s, SrcShape const & shape, SrcAccessor src,
DestIterator d, DestAccessor dest,
unsigned int dim, vigra::Kernel1D<T> const & kernel )
{
enum { N = 1 + SrcIterator::level };
vigra_precondition( dim < N,
"convolveMultiArrayOneDimension(): The dimension number to convolve must be smaller "
"than the data dimensionality" );
typedef typename NumericTraits<typename DestAccessor::value_type>::RealPromote TmpType;
ArrayVector<TmpType> tmp( shape[dim] );
typedef MultiArrayNavigator<SrcIterator, N> SNavigator;
typedef MultiArrayNavigator<DestIterator, N> DNavigator;
SNavigator snav( s, shape, dim );
DNavigator dnav( d, shape, dim );
for( ; snav.hasMore(); snav++, dnav++ )
{
// first copy source to temp for maximum cache efficiency
copyLine( snav.begin(), snav.end(), src,
tmp.begin(), typename AccessorTraits<TmpType>::default_accessor() );
convolveLine( srcIterRange( tmp.begin(), tmp.end(), typename AccessorTraits<TmpType>::default_const_accessor()),
destIter( dnav.begin(), dest ),
kernel1d( kernel ) );
}
}
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor, class T>
inline void
convolveMultiArrayOneDimension(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
pair<DestIterator, DestAccessor> const & dest,
unsigned int dim, vigra::Kernel1D<T> const & kernel )
{
convolveMultiArrayOneDimension( source.first, source.second, source.third,
dest.first, dest.second, dim, kernel );
}
/********************************************************/
/* */
/* gaussianSmoothMultiArray */
/* */
/********************************************************/
/** \brief Isotropic Gaussian smoothing of a multi-dimensional arrays.
This function computes an isotropic convolution of the given multi-dimensional
array with a Gaussian filter at the given standard deviation <tt>sigma</tt>.
Both source and destination arrays are represented by
iterators, shape objects and accessors. The destination array is required to
already have the correct size. This function may work in-place, which means
that <tt>siter == diter</tt> is allowed. It is implemented by a call to
\ref separableConvolveMultiArray() with the appropriate kernel.
If the data are anisotropic (different pixel size along different dimensions)
you should call \ref separableConvolveMultiArray() directly with the appropriate
anisotropic Gaussians.
<b> Declarations:</b>
pass arguments explicitly:
\code
namespace vigra {
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
void
gaussianSmoothMultiArray(SrcIterator siter, SrcShape const & shape, SrcAccessor src,
DestIterator diter, DestAccessor dest,
double sigma);
}
\endcode
use argument objects in conjunction with \ref ArgumentObjectFactories :
\code
namespace vigra {
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
void
gaussianSmoothMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
pair<DestIterator, DestAccessor> const & dest,
double sigma);
}
\endcode
<b> Usage:</b>
<b>\#include</b> \<<a href="multi__convolution_8hxx-source.html">vigra/multi_convolution.hxx</a>\>
\code
MultiArray<3, unsigned char>::size_type shape(width, height, depth);
MultiArray<3, unsigned char> source(shape);
MultiArray<3, float> dest(shape);
...
// perform isotropic Gaussian smoothing at scale 'sigma'
gaussianSmoothMultiArray(srcMultiArrayRange(source), destMultiArray(dest), sigma);
\endcode
\see separableConvolveMultiArray()
*/
doxygen_overloaded_function(template <...> void gaussianSmoothMultiArray)
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
void
gaussianSmoothMultiArray( SrcIterator s, SrcShape const & shape, SrcAccessor src,
DestIterator d, DestAccessor dest, double sigma )
{
Kernel1D<double> gauss;
gauss.initGaussian( sigma );
separableConvolveMultiArray( s, shape, src, d, dest, gauss);
}
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
inline void
gaussianSmoothMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
pair<DestIterator, DestAccessor> const & dest,
double sigma )
{
gaussianSmoothMultiArray( source.first, source.second, source.third,
dest.first, dest.second, sigma );
}
/********************************************************/
/* */
/* gaussianGradientMultiArray */
/* */
/********************************************************/
/** \brief Calculate Gaussian gradient of a multi-dimensional arrays.
This function computes the Gaussian gradient of the given multi-dimensional
array with a sequence of first-derivative-of-Gaussian filters at the given
standard deviation <tt>sigma</tt> (differentiation is applied to each dimension
in turn, starting with the innermost dimension). Both source and destination arrays
are represented by iterators, shape objects and accessors. The destination array is
required to have a vector valued pixel type with as many elements as the number of
dimensions. This function is implemented by calls to
\ref separableConvolveMultiArray() with the appropriate kernels.
If the data are anisotropic (different pixel size along different dimensions)
you should call \ref separableConvolveMultiArray() directly with the appropriate
anisotropic Gaussian derivatives.
<b> Declarations:</b>
pass arguments explicitly:
\code
namespace vigra {
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
void
gaussianGradientMultiArray(SrcIterator siter, SrcShape const & shape, SrcAccessor src,
DestIterator diter, DestAccessor dest,
double sigma);
}
\endcode
use argument objects in conjunction with \ref ArgumentObjectFactories :
\code
namespace vigra {
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
void
gaussianGradientMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
pair<DestIterator, DestAccessor> const & dest,
double sigma);
}
\endcode
<b> Usage:</b>
<b>\#include</b> \<<a href="multi__convolution_8hxx-source.html">vigra/multi_convolution.hxx</a>\>
\code
MultiArray<3, unsigned char>::size_type shape(width, height, depth);
MultiArray<3, unsigned char> source(shape);
MultiArray<3, TinyVector<float, 3> > dest(shape);
...
// compute Gaussian gradient at scale sigma
gaussianGradientMultiArray(srcMultiArrayRange(source), destMultiArray(dest), sigma);
\endcode
<b> Required Interface:</b>
see \ref separableConvolveMultiArray(), in addition:
\code
int dimension = 0;
VectorElementAccessor<DestAccessor> elementAccessor(0, dest);
\endcode
\see separableConvolveMultiArray()
*/
doxygen_overloaded_function(template <...> void gaussianGradientMultiArray)
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
void
gaussianGradientMultiArray(SrcIterator si, SrcShape const & shape, SrcAccessor src,
DestIterator di, DestAccessor dest, double sigma )
{
typedef typename DestAccessor::value_type DestType;
typedef typename DestType::value_type DestValueType;
typedef typename NumericTraits<DestValueType>::RealPromote KernelType;
static const int N = SrcShape::static_size;
for(int k=0; k<N; ++k)
if(shape[k] <=0)
return;
vigra_precondition(N == dest.size(di),
"gaussianGradientMultiArray(): Wrong number of channels in output array.");
vigra_precondition(sigma > 0.0, "gaussianGradientMultiArray(): Scale must be positive.");
Kernel1D<KernelType> gauss, derivative;
gauss.initGaussian(sigma);
typedef VectorElementAccessor<DestAccessor> ElementAccessor;
// compute gradient components
for(int d = 0; d < N; ++d )
{
ArrayVector<Kernel1D<KernelType> > kernels(N, gauss);
kernels[d].initGaussianDerivative(sigma, 1);
separableConvolveMultiArray( si, shape, src, di, ElementAccessor(d, dest), kernels.begin());
}
}
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
inline void
gaussianGradientMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
pair<DestIterator, DestAccessor> const & dest, double sigma )
{
gaussianGradientMultiArray( source.first, source.second, source.third,
dest.first, dest.second, sigma );
}
/********************************************************/
/* */
/* symmetricGradientMultiArray */
/* */
/********************************************************/
/** \brief Calculate gradient of a multi-dimensional arrays using symmetric difference filters.
This function computes the gradient of the given multi-dimensional
array with a sequence of symmetric difference filters a (differentiation is applied
to each dimension in turn, starting with the innermost dimension). Both source and
destination arrays are represented by iterators, shape objects and accessors.
The destination array is required to have a vector valued pixel type with as many
elements as the number of dimensions. This function is implemented by calls to
\ref convolveMultiArrayOneDimension() with the symmetric difference kernel.
<b> Declarations:</b>
pass arguments explicitly:
\code
namespace vigra {
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
void
symmetricGradientMultiArray(SrcIterator siter, SrcShape const & shape, SrcAccessor src,
DestIterator diter, DestAccessor dest);
}
\endcode
use argument objects in conjunction with \ref ArgumentObjectFactories :
\code
namespace vigra {
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
void
symmetricGradientMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
pair<DestIterator, DestAccessor> const & dest);
}
\endcode
<b> Usage:</b>
<b>\#include</b> \<<a href="multi__convolution_8hxx-source.html">vigra/multi_convolution.hxx</a>\>
\code
MultiArray<3, unsigned char>::size_type shape(width, height, depth);
MultiArray<3, unsigned char> source(shape);
MultiArray<3, TinyVector<float, 3> > dest(shape);
...
// compute gradient
symmetricGradientMultiArray(srcMultiArrayRange(source), destMultiArray(dest));
\endcode
<b> Required Interface:</b>
see \ref convolveMultiArrayOneDimension(), in addition:
\code
int dimension = 0;
VectorElementAccessor<DestAccessor> elementAccessor(0, dest);
\endcode
\see convolveMultiArrayOneDimension()
*/
doxygen_overloaded_function(template <...> void symmetricGradientMultiArray)
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
void
symmetricGradientMultiArray(SrcIterator si, SrcShape const & shape, SrcAccessor src,
DestIterator di, DestAccessor dest)
{
typedef typename DestAccessor::value_type DestType;
typedef typename DestType::value_type DestValueType;
typedef typename NumericTraits<DestValueType>::RealPromote KernelType;
static const int N = SrcShape::static_size;
for(int k=0; k<N; ++k)
if(shape[k] <=0)
return;
vigra_precondition(N == dest.size(di),
"symmetricGradientMultiArray(): Wrong number of channels in output array.");
Kernel1D<KernelType> filter;
filter.initSymmetricGradient();
typedef VectorElementAccessor<DestAccessor> ElementAccessor;
// compute gradient components
for(int d = 0; d < N; ++d )
{
convolveMultiArrayOneDimension(si, shape, src,
di, ElementAccessor(d, dest),
d, filter);
}
}
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
inline void
symmetricGradientMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
pair<DestIterator, DestAccessor> const & dest )
{
symmetricGradientMultiArray(source.first, source.second, source.third,
dest.first, dest.second);
}
/********************************************************/
/* */
/* laplacianOfGaussianMultiArray */
/* */
/********************************************************/
/** \brief Calculate Laplacian of a N-dimensional arrays using Gaussian derivative filters.
This function computes the Laplacian the given N-dimensional
array with a sequence of second-derivative-of-Gaussian filters at the given
standard deviation <tt>sigma</tt>. Both source and destination arrays
are represented by iterators, shape objects and accessors. Both source and destination
arrays must have scalar value_type. This function is implemented by calls to
\ref separableConvolveMultiArray() with the appropriate kernels, followed by summation.
<b> Declarations:</b>
pass arguments explicitly:
\code
namespace vigra {
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
void
laplacianOfGaussianMultiArray(SrcIterator siter, SrcShape const & shape, SrcAccessor src,
DestIterator diter, DestAccessor dest,
double sigma);
}
\endcode
use argument objects in conjunction with \ref ArgumentObjectFactories :
\code
namespace vigra {
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
void
laplacianOfGaussianMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
pair<DestIterator, DestAccessor> const & dest,
double sigma);
}
\endcode
<b> Usage:</b>
<b>\#include</b> \<<a href="multi__convolution_8hxx-source.html">vigra/multi_convolution.hxx</a>\>
\code
MultiArray<3, float> source(shape);
MultiArray<3, float> laplacian(shape);
...
// compute Laplacian at scale sigma
laplacianOfGaussianMultiArray(srcMultiArrayRange(source), destMultiArray(laplacian), sigma);
\endcode
<b> Required Interface:</b>
see \ref separableConvolveMultiArray(), in addition:
\code
int dimension = 0;
VectorElementAccessor<DestAccessor> elementAccessor(0, dest);
\endcode
\see separableConvolveMultiArray()
*/
doxygen_overloaded_function(template <...> void laplacianOfGaussianMultiArray)
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
void
laplacianOfGaussianMultiArray(SrcIterator si, SrcShape const & shape, SrcAccessor src,
DestIterator di, DestAccessor dest, double sigma )
{
using namespace functor;
typedef typename DestAccessor::value_type DestType;
typedef typename NumericTraits<DestType>::RealPromote KernelType;
typedef typename AccessorTraits<KernelType>::default_accessor DerivativeAccessor;
static const int N = SrcShape::static_size;
vigra_precondition(sigma > 0.0, "laplacianOfGaussianMultiArray(): Scale must be positive.");
Kernel1D<KernelType> gauss;
gauss.initGaussian(sigma);
MultiArray<N, KernelType> derivative(shape);
// compute 2nd derivatives and sum them up
for(int d = 0; d < N; ++d )
{
ArrayVector<Kernel1D<KernelType> > kernels(N, gauss);
kernels[d].initGaussianDerivative(sigma, 2);
if(d == 0)
{
separableConvolveMultiArray( si, shape, src,
di, dest, kernels.begin());
}
else
{
separableConvolveMultiArray( si, shape, src,
derivative.traverser_begin(), DerivativeAccessor(),
kernels.begin());
combineTwoMultiArrays(di, shape, dest, derivative.traverser_begin(), DerivativeAccessor(),
di, dest, Arg1() + Arg2() );
}
}
}
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
inline void
laplacianOfGaussianMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
pair<DestIterator, DestAccessor> const & dest, double sigma )
{
laplacianOfGaussianMultiArray( source.first, source.second, source.third,
dest.first, dest.second, sigma );
}
/********************************************************/
/* */
/* hessianOfGaussianMultiArray */
/* */
/********************************************************/
/** \brief Calculate Hessian matrix of a N-dimensional arrays using Gaussian derivative filters.
This function computes the Hessian matrix the given scalar N-dimensional
array with a sequence of second-derivative-of-Gaussian filters at the given
standard deviation <tt>sigma</tt>. Both source and destination arrays
are represented by iterators, shape objects and accessors. The destination array must
have a vector valued element type with N*(N+1)/2 elements (it represents the
upper triangular part of the symmetric Hessian matrix). This function is implemented by calls to
\ref separableConvolveMultiArray() with the appropriate kernels.
<b> Declarations:</b>
pass arguments explicitly:
\code
namespace vigra {
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
void
hessianOfGaussianMultiArray(SrcIterator siter, SrcShape const & shape, SrcAccessor src,
DestIterator diter, DestAccessor dest,
double sigma);
}
\endcode
use argument objects in conjunction with \ref ArgumentObjectFactories :
\code
namespace vigra {
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
void
hessianOfGaussianMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
pair<DestIterator, DestAccessor> const & dest,
double sigma);
}
\endcode
<b> Usage:</b>
<b>\#include</b> \<<a href="multi__convolution_8hxx-source.html">vigra/multi_convolution.hxx</a>\>
\code
MultiArray<3, float> source(shape);
MultiArray<3, TinyVector<float, 6> > dest(shape);
...
// compute Hessian at scale sigma
hessianOfGaussianMultiArray(srcMultiArrayRange(source), destMultiArray(dest), sigma);
\endcode
<b> Required Interface:</b>
see \ref separableConvolveMultiArray(), in addition:
\code
int dimension = 0;
VectorElementAccessor<DestAccessor> elementAccessor(0, dest);
\endcode
\see separableConvolveMultiArray(), vectorToTensorMultiArray()
*/
doxygen_overloaded_function(template <...> void hessianOfGaussianMultiArray)
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
void
hessianOfGaussianMultiArray(SrcIterator si, SrcShape const & shape, SrcAccessor src,
DestIterator di, DestAccessor dest, double sigma )
{
typedef typename DestAccessor::value_type DestType;
typedef typename DestType::value_type DestValueType;
typedef typename NumericTraits<DestValueType>::RealPromote KernelType;
static const int N = SrcShape::static_size;
static const int M = N*(N+1)/2;
for(int k=0; k<N; ++k)
if(shape[k] <=0)
return;
vigra_precondition(M == dest.size(di),
"hessianOfGaussianMultiArray(): Wrong number of channels in output array.");
vigra_precondition(sigma > 0.0, "hessianOfGaussianMultiArray(): Scale must be positive.");
Kernel1D<KernelType> gauss;
gauss.initGaussian(sigma);
typedef VectorElementAccessor<DestAccessor> ElementAccessor;
// compute elements of the Hessian matrix
for(int b=0, i=0; i<N; ++i)
{
for(int j=i; j<N; ++j, ++b)
{
ArrayVector<Kernel1D<KernelType> > kernels(N, gauss);
if(i == j)
{
kernels[i].initGaussianDerivative(sigma, 2);
}
else
{
kernels[i].initGaussianDerivative(sigma, 1);
kernels[j].initGaussianDerivative(sigma, 1);
}
separableConvolveMultiArray(si, shape, src, di, ElementAccessor(b, dest),
kernels.begin());
}
}
}
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
inline void
hessianOfGaussianMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
pair<DestIterator, DestAccessor> const & dest, double sigma )
{
hessianOfGaussianMultiArray( source.first, source.second, source.third,
dest.first, dest.second, sigma );
}
namespace detail {
template<int N, class VectorType>
struct StructurTensorFunctor
{
typedef VectorType result_type;
typedef typename VectorType::value_type ValueType;
template <class T>
VectorType operator()(T const & in) const
{
VectorType res;
for(int b=0, i=0; i<N; ++i)
{
for(int j=i; j<N; ++j, ++b)
{
res[b] = detail::RequiresExplicitCast<ValueType>::cast(in[i]*in[j]);
}
}
return res;
}
};
} // namespace detail
/********************************************************/
/* */
/* structureTensorMultiArray */
/* */
/********************************************************/
/** \brief Calculate th structure tensor of a multi-dimensional arrays.
This function computes the gradient (outer product) tensor for each element
of the given N-dimensional array with first-derivative-of-Gaussian filters at
the given <tt>innerScale</tt>, followed by Gaussian smoothing at <tt>outerScale</tt>.
Both source and destination arrays are represented by iterators, shape objects and
accessors. The destination array must have a vector valued pixel type with
N*(N+1)/2 elements (it represents the upper triangular part of the symmetric
structure tensor matrix). If the source array is also vector valued, the
resulting structure tensor is the sum of the individual tensors for each channel.
This function is implemented by calls to
\ref separableConvolveMultiArray() with the appropriate kernels.
<b> Declarations:</b>
pass arguments explicitly:
\code
namespace vigra {
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
void
structureTensorMultiArray(SrcIterator siter, SrcShape const & shape, SrcAccessor src,
DestIterator diter, DestAccessor dest,
double innerScale, double outerScale);
}
\endcode
use argument objects in conjunction with \ref ArgumentObjectFactories :
\code
namespace vigra {
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
void
structureTensorMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
pair<DestIterator, DestAccessor> const & dest,
double innerScale, double outerScale);
}
\endcode
<b> Usage:</b>
<b>\#include</b> \<<a href="multi__convolution_8hxx-source.html">vigra/multi_convolution.hxx</a>\>
\code
MultiArray<3, RGBValue<float> > source(shape);
MultiArray<3, TinyVector<float, 6> > dest(shape);
...
// compute structure tensor at scales innerScale and outerScale
structureTensorMultiArray(srcMultiArrayRange(source), destMultiArray(dest), innerScale, outerScale);
\endcode
<b> Required Interface:</b>
see \ref separableConvolveMultiArray(), in addition:
\code
int dimension = 0;
VectorElementAccessor<DestAccessor> elementAccessor(0, dest);
\endcode
\see separableConvolveMultiArray(), vectorToTensorMultiArray()
*/
doxygen_overloaded_function(template <...> void structureTensorMultiArray)
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
void
structureTensorMultiArray(SrcIterator si, SrcShape const & shape, SrcAccessor src,
DestIterator di, DestAccessor dest,
double innerScale, double outerScale)
{
static const int N = SrcShape::static_size;
static const int M = N*(N+1)/2;
typedef typename DestAccessor::value_type DestType;
typedef typename DestType::value_type DestValueType;
typedef typename NumericTraits<DestValueType>::RealPromote KernelType;
typedef TinyVector<KernelType, N> GradientVector;
typedef typename AccessorTraits<GradientVector>::default_accessor GradientAccessor;
for(int k=0; k<N; ++k)
if(shape[k] <=0)
return;
vigra_precondition(M == dest.size(di),
"structureTensorMultiArray(): Wrong number of channels in output array.");
vigra_precondition(innerScale > 0.0 && outerScale >= 0.0,
"structureTensorMultiArray(): Scale must be positive.");
MultiArray<N, GradientVector> gradient(shape);
gaussianGradientMultiArray(si, shape, src,
gradient.traverser_begin(), GradientAccessor(),
innerScale);
transformMultiArray(gradient.traverser_begin(), shape, GradientAccessor(),
di, dest,
detail::StructurTensorFunctor<N, DestType>());
gaussianSmoothMultiArray(di, shape, dest, di, dest, outerScale);
}
template <class SrcIterator, class SrcShape, class SrcAccessor,
class DestIterator, class DestAccessor>
inline void
structureTensorMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
pair<DestIterator, DestAccessor> const & dest,
double innerScale, double outerScale)
{
structureTensorMultiArray( source.first, source.second, source.third,
dest.first, dest.second, innerScale, outerScale );
}
//@}
} //-- namespace vigra
#endif //-- VIGRA_MULTI_CONVOLUTION_H
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