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details vigra/multi_convolution.hxx VIGRA

00001 //-- -*- c++ -*-
00002 /************************************************************************/
00003 /*                                                                      */
00004 /*               Copyright 2003 by Christian-Dennis Rahn                */
00005 /*                        and Ullrich Koethe                            */
00006 /*       Cognitive Systems Group, University of Hamburg, Germany        */
00007 /*                                                                      */
00008 /*    This file is part of the VIGRA computer vision library.           */
00009 /*    ( Version 1.5.0, Dec 07 2006 )                                    */
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00027 /*    Software.                                                         */
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00039 
00040 #ifndef VIGRA_MULTI_CONVOLUTION_H
00041 #define VIGRA_MULTI_CONVOLUTION_H
00042 
00043 #include "separableconvolution.hxx"
00044 #include "array_vector.hxx"
00045 #include "multi_array.hxx"
00046 #include "accessor.hxx"
00047 #include "numerictraits.hxx"
00048 #include "navigator.hxx"
00049 #include "metaprogramming.hxx"
00050 #include "multi_pointoperators.hxx"
00051 
00052 
00053 namespace vigra
00054 {
00055 
00056 
00057 namespace detail
00058 {
00059 
00060 /********************************************************/
00061 /*                                                      */
00062 /*        internalSeparableConvolveMultiArray           */
00063 /*                                                      */
00064 /********************************************************/
00065 
00066 template <class SrcIterator, class SrcShape, class SrcAccessor,
00067           class DestIterator, class DestAccessor, class KernelIterator>
00068 void
00069 internalSeparableConvolveMultiArrayTmp(
00070                       SrcIterator si, SrcShape const & shape, SrcAccessor src,
00071                       DestIterator di, DestAccessor dest, KernelIterator kit)
00072 {
00073     enum { N = 1 + SrcIterator::level };
00074 
00075     typedef typename NumericTraits<typename DestAccessor::value_type>::RealPromote TmpType;
00076 
00077     // temporay array to hold the current line to enable in-place operation
00078     ArrayVector<TmpType> tmp( shape[0] );
00079 
00080     typedef MultiArrayNavigator<SrcIterator, N> SNavigator;
00081     typedef MultiArrayNavigator<DestIterator, N> DNavigator;
00082 
00083     { // only operate on first dimension here
00084         SNavigator snav( si, shape, 0 );
00085         DNavigator dnav( di, shape, 0 );
00086 
00087         for( ; snav.hasMore(); snav++, dnav++ )
00088         {
00089              // first copy source to temp for maximum cache efficiency
00090              copyLine( snav.begin(), snav.end(), src,
00091                        tmp.begin(), typename AccessorTraits<TmpType>::default_accessor() );
00092 
00093              convolveLine( srcIterRange(tmp.begin(), tmp.end(),
00094                                         typename AccessorTraits<TmpType>::default_const_accessor()),
00095                            destIter( dnav.begin(), dest ),
00096                            kernel1d( *kit ) );
00097         }
00098         ++kit;
00099     }
00100 
00101     // operate on further dimensions
00102     for( int d = 1; d < N; ++d, ++kit )
00103     {
00104         DNavigator dnav( di, shape, d );
00105 
00106         tmp.resize( shape[d] );
00107 
00108         for( ; dnav.hasMore(); dnav++ )
00109         {
00110              // first copy source to temp for maximum cache efficiency
00111              copyLine( dnav.begin(), dnav.end(), dest,
00112                        tmp.begin(), typename AccessorTraits<TmpType>::default_accessor() );
00113 
00114              convolveLine( srcIterRange(tmp.begin(), tmp.end(),
00115                                         typename AccessorTraits<TmpType>::default_const_accessor()),
00116                            destIter( dnav.begin(), dest ),
00117                            kernel1d( *kit ) );
00118         }
00119     }
00120 }
00121 
00122 
00123 } // namespace detail
00124 
00125 /** \addtogroup MultiArrayConvolutionFilters Convolution filters for multi-dimensional arrays.
00126 
00127     These functions realize a separable convolution on an arbitrary dimensional
00128     array that is specified by iterators (compatible to \ref MultiIteratorPage)
00129     and shape objects. It can therefore be applied to a wide range of data structures
00130     (\ref vigra::MultiArrayView, \ref vigra::MultiArray etc.).
00131 */
00132 //@{
00133 
00134 /********************************************************/
00135 /*                                                      */
00136 /*             separableConvolveMultiArray              */
00137 /*                                                      */
00138 /********************************************************/
00139 
00140 /** \brief Separated convolution on multi-dimensional arrays.
00141 
00142     This function computes a separated convolution on all dimensions
00143     of the given multi-dimensional array. Both source and destination
00144     arrays are represented by iterators, shape objects and accessors.
00145     The destination array is required to already have the correct size.
00146 
00147     There are two variants of this functions: one takes a single kernel
00148     of type \ref vigra::Kernel1D which is then applied to all dimensions,
00149     whereas the other requires an iterator referencing a sequence of
00150     \ref vigra::Kernel1D objects, one for every dimension of the data.
00151     Then the first kernel in this sequence is applied to the innermost
00152     dimension (e.g. the x-dimension of an image), while the last is applied to the
00153     outermost dimension (e.g. the z-dimension in a 3D image).
00154 
00155     This function may work in-place, which means that <tt>siter == diter</tt> is allowed.
00156     A full-sized internal array is only allocated if working on the destination
00157     array directly would cause round-off errors (i.e. if
00158     <tt>typeid(typename NumericTraits<typename DestAccessor::value_type>::RealPromote)
00159     != typeid(typename DestAccessor::value_type)</tt>.
00160 
00161     <b> Declarations:</b>
00162 
00163     pass arguments explicitly:
00164     \code
00165     namespace vigra {
00166         // apply the same kernel to all dimensions
00167         template <class SrcIterator, class SrcShape, class SrcAccessor,
00168                   class DestIterator, class DestAccessor, class T>
00169         void
00170         separableConvolveMultiArray(SrcIterator siter, SrcShape const & shape, SrcAccessor src,
00171                                     DestIterator diter, DestAccessor dest,
00172                                     Kernel1D<T> const & kernel);
00173 
00174         // apply each kernel from the sequence `kernels³ in turn
00175         template <class SrcIterator, class SrcShape, class SrcAccessor,
00176                   class DestIterator, class DestAccessor, class KernelIterator>
00177         void
00178         separableConvolveMultiArray(SrcIterator siter, SrcShape const & shape, SrcAccessor src,
00179                                     DestIterator diter, DestAccessor dest,
00180                                     KernelIterator kernels);
00181     }
00182     \endcode
00183 
00184     use argument objects in conjunction with \ref ArgumentObjectFactories:
00185     \code
00186     namespace vigra {
00187         // apply the same kernel to all dimensions
00188         template <class SrcIterator, class SrcShape, class SrcAccessor,
00189                   class DestIterator, class DestAccessor, class T>
00190         void
00191         separableConvolveMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
00192                                     pair<DestIterator, DestAccessor> const & dest,
00193                                     Kernel1D<T> const & kernel);
00194 
00195         // apply each kernel from the sequence `kernels³ in turn
00196         template <class SrcIterator, class SrcShape, class SrcAccessor,
00197                   class DestIterator, class DestAccessor, class KernelIterator>
00198         void
00199         separableConvolveMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
00200                                     pair<DestIterator, DestAccessor> const & dest,
00201                                     KernelIterator kernels);
00202     }
00203     \endcode
00204 
00205     <b> Usage:</b>
00206 
00207     <b>\#include</b> "<a href="multi__convolution_8hxx-source.html">vigra/multi_convolution.hxx</a>"
00208 
00209     \code
00210     MultiArray<3, unsigned char>::size_type shape(width, height, depth);
00211     MultiArray<3, unsigned char> source(shape);
00212     MultiArray<3, float> dest(shape);
00213     ...
00214     Kernel1D<float> gauss;
00215     gauss.initGaussian(sigma);
00216     // create 3 Gauss kernels, one for each dimension
00217     ArrayVector<Kernel1D<float> > kernels(3, gauss);
00218 
00219     // perform Gaussian smoothing on all dimensions
00220     separableConvolveMultiArray(srcMultiArrayRange(source), destMultiArray(dest), 
00221                                 kernels.begin());
00222     \endcode
00223 
00224     \see vigra::Kernel1D, convolveLine()
00225 */
00226 template <class SrcIterator, class SrcShape, class SrcAccessor,
00227           class DestIterator, class DestAccessor, class KernelIterator>
00228 void
00229 separableConvolveMultiArray( SrcIterator s, SrcShape const & shape, SrcAccessor src,
00230                              DestIterator d, DestAccessor dest, KernelIterator kernels )
00231 {
00232     typedef typename NumericTraits<typename DestAccessor::value_type>::RealPromote TmpType;
00233 
00234     if(!IsSameType<TmpType, typename DestAccessor::value_type>::boolResult)
00235     {
00236         // need a temporary array to avoid rounding errors
00237         MultiArray<SrcShape::static_size, TmpType> tmpArray(shape);
00238         detail::internalSeparableConvolveMultiArrayTmp( s, shape, src,
00239              tmpArray.traverser_begin(), typename AccessorTraits<TmpType>::default_accessor(), kernels );
00240         copyMultiArray(srcMultiArrayRange(tmpArray), destIter(d, dest));
00241     }
00242     else
00243     {
00244         // work directly on the destination array
00245         detail::internalSeparableConvolveMultiArrayTmp( s, shape, src, d, dest, kernels );
00246     }
00247 }
00248 
00249 template <class SrcIterator, class SrcShape, class SrcAccessor,
00250           class DestIterator, class DestAccessor, class KernelIterator>
00251 inline
00252 void separableConvolveMultiArray(
00253     triple<SrcIterator, SrcShape, SrcAccessor> const & source,
00254     pair<DestIterator, DestAccessor> const & dest, KernelIterator kit )
00255 {
00256     separableConvolveMultiArray( source.first, source.second, source.third,
00257                                  dest.first, dest.second, kit );
00258 }
00259 
00260 template <class SrcIterator, class SrcShape, class SrcAccessor,
00261           class DestIterator, class DestAccessor, class T>
00262 inline void
00263 separableConvolveMultiArray( SrcIterator s, SrcShape const & shape, SrcAccessor src,
00264                              DestIterator d, DestAccessor dest,
00265                              Kernel1D<T> const & kernel )
00266 {
00267     ArrayVector<Kernel1D<T> > kernels(shape.size(), kernel);
00268 
00269     separableConvolveMultiArray( s, shape, src, d, dest, kernels.begin() );
00270 }
00271 
00272 template <class SrcIterator, class SrcShape, class SrcAccessor,
00273           class DestIterator, class DestAccessor, class T>
00274 inline void
00275 separableConvolveMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
00276                             pair<DestIterator, DestAccessor> const & dest,
00277                             Kernel1D<T> const & kernel )
00278 {
00279     ArrayVector<Kernel1D<T> > kernels(source.second.size(), kernel);
00280 
00281     separableConvolveMultiArray( source.first, source.second, source.third,
00282                                  dest.first, dest.second, kernels.begin() );
00283 }
00284 
00285 /********************************************************/
00286 /*                                                      */
00287 /*            convolveMultiArrayOneDimension            */
00288 /*                                                      */
00289 /********************************************************/
00290 
00291 /** \brief Convolution along a single dimension of a multi-dimensional arrays.
00292 
00293     This function computes a convolution along one dimension (specified by
00294     the parameter <tt>dim</tt> of the given multi-dimensional array with the given
00295     <tt>kernel</tt>. Both source and destination arrays are represented by
00296     iterators, shape objects and accessors. The destination array is required to
00297     already have the correct size.
00298 
00299     This function may work in-place, which means that <tt>siter == diter</tt> is allowed.
00300 
00301     <b> Declarations:</b>
00302 
00303     pass arguments explicitly:
00304     \code
00305     namespace vigra {
00306         template <class SrcIterator, class SrcShape, class SrcAccessor,
00307                   class DestIterator, class DestAccessor, class T>
00308         void
00309         convolveMultiArrayOneDimension(SrcIterator siter, SrcShape const & shape, SrcAccessor src,
00310                                        DestIterator diter, DestAccessor dest,
00311                                        unsigned int dim, vigra::Kernel1D<T> const & kernel);
00312     }
00313     \endcode
00314 
00315     use argument objects in conjunction with \ref ArgumentObjectFactories:
00316     \code
00317     namespace vigra {
00318         template <class SrcIterator, class SrcShape, class SrcAccessor,
00319                   class DestIterator, class DestAccessor, class T>
00320         void
00321         convolveMultiArrayOneDimension(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
00322                                        pair<DestIterator, DestAccessor> const & dest,
00323                                        unsigned int dim, vigra::Kernel1D<T> const & kernel);
00324     }
00325     \endcode
00326 
00327     <b> Usage:</b>
00328 
00329     <b>\#include</b> "<a href="multi__convolution_8hxx-source.html">vigra/multi_convolution.hxx</a>"
00330 
00331     \code
00332     MultiArray<3, unsigned char>::size_type shape(width, height, depth);
00333     MultiArray<3, unsigned char> source(shape);
00334     MultiArray<3, float> dest(shape);
00335     ...
00336     Kernel1D<float> gauss;
00337     gauss.initGaussian(sigma);
00338 
00339     // perform Gaussian smoothing along dimensions 1 (height)
00340     convolveMultiArrayOneDimension(srcMultiArrayRange(source), destMultiArray(dest), 1, gauss);
00341     \endcode
00342 
00343     \see separableConvolveMultiArray()
00344 */
00345 template <class SrcIterator, class SrcShape, class SrcAccessor,
00346           class DestIterator, class DestAccessor, class T>
00347 void
00348 convolveMultiArrayOneDimension(SrcIterator s, SrcShape const & shape, SrcAccessor src,
00349                                DestIterator d, DestAccessor dest,
00350                                unsigned int dim, vigra::Kernel1D<T> const & kernel )
00351 {
00352     enum { N = 1 + SrcIterator::level };
00353     vigra_precondition( dim < N,
00354                         "convolveMultiArrayOneDimension(): The dimension number to convolve must be smaller "
00355                         "than the data dimensionality" );
00356 
00357     typedef typename NumericTraits<typename DestAccessor::value_type>::RealPromote TmpType;
00358     ArrayVector<TmpType> tmp( shape[dim] );
00359 
00360     typedef MultiArrayNavigator<SrcIterator, N> SNavigator;
00361     typedef MultiArrayNavigator<DestIterator, N> DNavigator;
00362 
00363     SNavigator snav( s, shape, dim );
00364     DNavigator dnav( d, shape, dim );
00365 
00366     for( ; snav.hasMore(); snav++, dnav++ )
00367     {
00368          // first copy source to temp for maximum cache efficiency
00369          copyLine( snav.begin(), snav.end(), src,
00370            tmp.begin(), typename AccessorTraits<TmpType>::default_accessor() );
00371 
00372          convolveLine( srcIterRange( tmp.begin(), tmp.end(), typename AccessorTraits<TmpType>::default_const_accessor()),
00373                        destIter( dnav.begin(), dest ),
00374                        kernel1d( kernel ) );
00375     }
00376 }
00377 
00378 template <class SrcIterator, class SrcShape, class SrcAccessor,
00379           class DestIterator, class DestAccessor, class T>
00380 inline void
00381 convolveMultiArrayOneDimension(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
00382                                pair<DestIterator, DestAccessor> const & dest,
00383                                unsigned int dim, vigra::Kernel1D<T> const & kernel )
00384 {
00385     convolveMultiArrayOneDimension( source.first, source.second, source.third,
00386                                    dest.first, dest.second, dim, kernel );
00387 }
00388 
00389 /********************************************************/
00390 /*                                                      */
00391 /*             gaussianSmoothMultiArray                 */
00392 /*                                                      */
00393 /********************************************************/
00394 
00395 /** \brief Isotropic Gaussian smoothing of a multi-dimensional arrays.
00396 
00397     This function computes an isotropic convolution of the given multi-dimensional
00398     array with a Gaussian filter at the given standard deviation <tt>sigma</tt>.
00399     Both source and destination arrays are represented by
00400     iterators, shape objects and accessors. The destination array is required to
00401     already have the correct size. This function may work in-place, which means
00402     that <tt>siter == diter</tt> is allowed. It is implemented by a call to
00403     \ref separableConvolveMultiArray() with the appropriate kernel.
00404     If the data are anisotropic (different pixel size along different dimensions)
00405     you should call \ref separableConvolveMultiArray() directly with the appropriate
00406     anisotropic Gaussians.
00407 
00408     <b> Declarations:</b>
00409 
00410     pass arguments explicitly:
00411     \code
00412     namespace vigra {
00413         template <class SrcIterator, class SrcShape, class SrcAccessor,
00414                   class DestIterator, class DestAccessor>
00415         void
00416         gaussianSmoothMultiArray(SrcIterator siter, SrcShape const & shape, SrcAccessor src,
00417                                  DestIterator diter, DestAccessor dest,
00418                                  double sigma);
00419     }
00420     \endcode
00421 
00422     use argument objects in conjunction with \ref ArgumentObjectFactories:
00423     \code
00424     namespace vigra {
00425         template <class SrcIterator, class SrcShape, class SrcAccessor,
00426                   class DestIterator, class DestAccessor>
00427         void
00428         gaussianSmoothMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
00429                                  pair<DestIterator, DestAccessor> const & dest,
00430                                  double sigma);
00431     }
00432     \endcode
00433 
00434     <b> Usage:</b>
00435 
00436     <b>\#include</b> "<a href="multi__convolution_8hxx-source.html">vigra/multi_convolution.hxx</a>"
00437 
00438     \code
00439     MultiArray<3, unsigned char>::size_type shape(width, height, depth);
00440     MultiArray<3, unsigned char> source(shape);
00441     MultiArray<3, float> dest(shape);
00442     ...
00443     // perform isotropic Gaussian smoothing at scale `sigma³
00444     gaussianSmoothMultiArray(srcMultiArrayRange(source), destMultiArray(dest), sigma);
00445     \endcode
00446 
00447     \see separableConvolveMultiArray()
00448 */
00449 template <class SrcIterator, class SrcShape, class SrcAccessor,
00450           class DestIterator, class DestAccessor>
00451 void
00452 gaussianSmoothMultiArray( SrcIterator s, SrcShape const & shape, SrcAccessor src,
00453                    DestIterator d, DestAccessor dest, double sigma )
00454 {
00455     typedef typename NumericTraits<typename DestAccessor::value_type>::RealPromote kernel_type;
00456     Kernel1D<kernel_type> gauss;
00457     gauss.initGaussian( sigma );
00458 
00459     separableConvolveMultiArray( s, shape, src, d, dest, gauss);
00460 }
00461 
00462 template <class SrcIterator, class SrcShape, class SrcAccessor,
00463           class DestIterator, class DestAccessor>
00464 inline void
00465 gaussianSmoothMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
00466                   pair<DestIterator, DestAccessor> const & dest,
00467                   double sigma )
00468 {
00469     gaussianSmoothMultiArray( source.first, source.second, source.third,
00470                               dest.first, dest.second, sigma );
00471 }
00472 
00473 /********************************************************/
00474 /*                                                      */
00475 /*             gaussianGradientMultiArray               */
00476 /*                                                      */
00477 /********************************************************/
00478 
00479 /** \brief Calculate Gaussian gradient of a multi-dimensional arrays.
00480 
00481     This function computes the Gaussian gradient of the given multi-dimensional
00482     array with a sequence of first-derivative-of-Gaussian filters at the given
00483     standard deviation <tt>sigma</tt> (differentiation is applied to each dimension
00484     in turn, starting with the innermost dimension). Both source and destination arrays
00485     are represented by iterators, shape objects and accessors. The destination array is
00486     required to have a vector valued pixel type with as many elements as the number of
00487     dimensions. This function is implemented by calls to
00488     \ref separableConvolveMultiArray() with the appropriate kernels.
00489     If the data are anisotropic (different pixel size along different dimensions)
00490     you should call \ref separableConvolveMultiArray() directly with the appropriate
00491     anisotropic Gaussian derivatives.
00492 
00493     <b> Declarations:</b>
00494 
00495     pass arguments explicitly:
00496     \code
00497     namespace vigra {
00498         template <class SrcIterator, class SrcShape, class SrcAccessor,
00499                   class DestIterator, class DestAccessor>
00500         void
00501         gaussianGradientMultiArray(SrcIterator siter, SrcShape const & shape, SrcAccessor src,
00502                                    DestIterator diter, DestAccessor dest,
00503                                    double sigma);
00504     }
00505     \endcode
00506 
00507     use argument objects in conjunction with \ref ArgumentObjectFactories:
00508     \code
00509     namespace vigra {
00510         template <class SrcIterator, class SrcShape, class SrcAccessor,
00511                   class DestIterator, class DestAccessor>
00512         void
00513         gaussianGradientMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
00514                                    pair<DestIterator, DestAccessor> const & dest,
00515                                    double sigma);
00516     }
00517     \endcode
00518 
00519     <b> Usage:</b>
00520 
00521     <b>\#include</b> "<a href="multi__convolution_8hxx-source.html">vigra/multi_convolution.hxx</a>"
00522 
00523     \code
00524     MultiArray<3, unsigned char>::size_type shape(width, height, depth);
00525     MultiArray<3, unsigned char> source(shape);
00526     MultiArray<3, TinyVector<float, 3> > dest(shape);
00527     ...
00528     // compute Gaussian gradient at scale sigma
00529     gaussianGradientMultiArray(srcMultiArrayRange(source), destMultiArray(dest), sigma);
00530     \endcode
00531 
00532     <b> Required Interface:</b>
00533 
00534     see \ref convolveImage(), in addition:
00535 
00536     \code
00537     int dimension = 0;
00538     VectorElementAccessor<DestAccessor> elementAccessor(0, dest);
00539     \endcode
00540 
00541     \see separableConvolveMultiArray()
00542 */
00543 template <class SrcIterator, class SrcShape, class SrcAccessor,
00544           class DestIterator, class DestAccessor>
00545 void
00546 gaussianGradientMultiArray( SrcIterator si, SrcShape const & shape, SrcAccessor src,
00547                 DestIterator di, DestAccessor dest, double sigma )
00548 {
00549     typedef typename DestAccessor::value_type DestType;
00550     typedef typename NumericTraits<typename DestType::value_type>::RealPromote kernel_type;
00551 
00552     Kernel1D<kernel_type> gauss, derivative;
00553     gauss.initGaussian(sigma);
00554     derivative.initGaussianDerivative(sigma, 1);
00555 
00556     typedef VectorElementAccessor<DestAccessor> ElementAccessor;
00557 
00558     // compute gradient components
00559     for(unsigned int d = 0; d < shape.size(); ++d )
00560     {
00561         ArrayVector<Kernel1D<kernel_type> > kernels(shape.size(), gauss);
00562         kernels[d] = derivative;
00563         separableConvolveMultiArray( si, shape, src, di, ElementAccessor(d, dest), kernels.begin());
00564     }
00565 }
00566 
00567 template <class SrcIterator, class SrcShape, class SrcAccessor,
00568           class DestIterator, class DestAccessor>
00569 inline void
00570 gaussianGradientMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
00571                   pair<DestIterator, DestAccessor> const & dest, double sigma )
00572 {
00573     gaussianGradientMultiArray( source.first, source.second, source.third,
00574                               dest.first, dest.second, sigma );
00575 }
00576 
00577 /********************************************************/
00578 /*                                                      */
00579 /*             symmetricGradientMultiArray              */
00580 /*                                                      */
00581 /********************************************************/
00582 
00583 /** \brief Calculate gradient of a multi-dimensional arrays using symmetric difference filters.
00584 
00585     This function computes the gradient of the given multi-dimensional
00586     array with a sequence of symmetric difference filters a (differentiation is applied
00587     to each dimension in turn, starting with the innermost dimension). Both source and
00588     destination arrays are represented by iterators, shape objects and accessors.
00589     The destination array is required to have a vector valued pixel type with as many
00590     elements as the number of dimensions. This function is implemented by calls to
00591     \ref convolveMultiArrayOneDimension() with the symmetric difference kernel.
00592 
00593     <b> Declarations:</b>
00594 
00595     pass arguments explicitly:
00596     \code
00597     namespace vigra {
00598         template <class SrcIterator, class SrcShape, class SrcAccessor,
00599                   class DestIterator, class DestAccessor>
00600         void
00601         symmetricGradientMultiArray(SrcIterator siter, SrcShape const & shape, SrcAccessor src,
00602                                     DestIterator diter, DestAccessor dest);
00603     }
00604     \endcode
00605 
00606     use argument objects in conjunction with \ref ArgumentObjectFactories:
00607     \code
00608     namespace vigra {
00609         template <class SrcIterator, class SrcShape, class SrcAccessor,
00610                   class DestIterator, class DestAccessor>
00611         void
00612         symmetricGradientMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
00613                                     pair<DestIterator, DestAccessor> const & dest);
00614     }
00615     \endcode
00616 
00617     <b> Usage:</b>
00618 
00619     <b>\#include</b> "<a href="multi__convolution_8hxx-source.html">vigra/multi_convolution.hxx</a>"
00620 
00621     \code
00622     MultiArray<3, unsigned char>::size_type shape(width, height, depth);
00623     MultiArray<3, unsigned char> source(shape);
00624     MultiArray<3, TinyVector<float, 3> > dest(shape);
00625     ...
00626     // compute gradient
00627     symmetricGradientMultiArray(srcMultiArrayRange(source), destMultiArray(dest));
00628     \endcode
00629 
00630     <b> Required Interface:</b>
00631 
00632     see \ref convolveImage(), in addition:
00633 
00634     \code
00635     int dimension = 0;
00636     VectorElementAccessor<DestAccessor> elementAccessor(0, dest);
00637     \endcode
00638 
00639     \see convolveMultiArrayOneDimension()
00640 */
00641 template <class SrcIterator, class SrcShape, class SrcAccessor,
00642           class DestIterator, class DestAccessor>
00643 void
00644 symmetricGradientMultiArray(SrcIterator si, SrcShape const & shape, SrcAccessor src,
00645                    DestIterator di, DestAccessor dest)
00646 {
00647     typedef typename DestAccessor::value_type DestType;
00648     typedef typename NumericTraits<typename DestType::value_type>::RealPromote kernel_type;
00649 
00650     Kernel1D<kernel_type> filter;
00651     filter.initSymmetricGradient();
00652 
00653     typedef VectorElementAccessor<DestAccessor> ElementAccessor;
00654 
00655     // compute gradient components
00656     for(unsigned int d = 0; d < shape.size(); ++d )
00657     {
00658         convolveMultiArrayOneDimension(si, shape, src,
00659                                        di, ElementAccessor(d, dest),
00660                                        d, filter);
00661     }
00662 }
00663 
00664 template <class SrcIterator, class SrcShape, class SrcAccessor,
00665           class DestIterator, class DestAccessor>
00666 inline void
00667 symmetricGradientMultiArray(triple<SrcIterator, SrcShape, SrcAccessor> const & source,
00668                             pair<DestIterator, DestAccessor> const & dest )
00669 {
00670     symmetricGradientMultiArray(source.first, source.second, source.third,
00671                                 dest.first, dest.second);
00672 }
00673 
00674 //@}
00675 
00676 } //-- namespace vigra
00677 
00678 
00679 #endif        //-- VIGRA_MULTI_CONVOLUTION_H

© Ullrich Köthe (koethe@informatik.uni-hamburg.de)
Cognitive Systems Group, University of Hamburg, Germany

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VIGRA 1.5.0 (7 Dec 2006)