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| vigra/resampling_convolution.hxx |
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    00001 /************************************************************************/
    00002 /*                                                                      */
    00003 /*               Copyright 1998-2004 by Ullrich Koethe                  */
    00004 /*       Cognitive Systems Group, University of Hamburg, Germany        */
    00005 /*                                                                      */
    00006 /*    This file is part of the VIGRA computer vision library.           */
    00007 /*    ( Version 1.4.0, Dec 21 2005 )                                    */
    00008 /*    The VIGRA Website is                                              */
    00009 /*        http://kogs-www.informatik.uni-hamburg.de/~koethe/vigra/      */
    00010 /*    Please direct questions, bug reports, and contributions to        */
    00011 /*        koethe@informatik.uni-hamburg.de          or                  */
    00012 /*        vigra@kogs1.informatik.uni-hamburg.de                         */
    00013 /*                                                                      */
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    00021 /*    conditions:                                                       */
    00022 /*                                                                      */
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    00024 /*    included in all copies or substantial portions of the             */
    00025 /*    Software.                                                         */
    00026 /*                                                                      */
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    00035 /*                                                                      */
    00036 /************************************************************************/
    00037
    00038 #ifndef VIGRA_RESAMPLING_CONVOLUTION_HXX
    00039 #define VIGRA_RESAMPLING_CONVOLUTION_HXX
    00040
    00041 #include <cmath>
    00042 #include "vigra/stdimage.hxx"
    00043 #include "vigra/array_vector.hxx"
    00044 #include "vigra/rational.hxx"
    00045 #include "vigra/functortraits.hxx"
    00046
    00047 namespace vigra {
    00048
    00049 namespace resampling_detail
    00050 {
    00051
    00052 struct MapTargetToSourceCoordinate
    00053 {
    00054     MapTargetToSourceCoordinate(Rational<int> const & samplingRatio,
    00055                                 Rational<int> const & offset)
    00056     : a(samplingRatio.denominator()*offset.denominator()),
    00057       b(samplingRatio.numerator()*offset.numerator()),
    00058       c(samplingRatio.numerator()*offset.denominator())
    00059     {}
    00060
    00061 //        the following funcions are more efficient realizations of:
    00062 //             rational_cast<T>(i / samplingRatio + offset);
    00063 //        we need efficiency because this may be called in the inner loop
    00064
    00065     int operator()(int i) const
    00066     {
    00067         return (i * a + b) / c;
    00068     }
    00069
    00070     double toDouble(int i) const
    00071     {
    00072         return double(i * a + b) / c;
    00073     }
    00074
    00075     Rational<int> toRational(int i) const
    00076     {
    00077         return Rational<int>(i * a + b, c);
    00078     }
    00079
    00080     int a, b, c;
    00081 };
    00082
    00083 } // namespace resampling_detail
    00084
    00085 template <>
    00086 class FunctorTraits<resampling_detail::MapTargetToSourceCoordinate>
    00087 : public FunctorTraitsBase<resampling_detail::MapTargetToSourceCoordinate>
    00088 {
    00089   public:
    00090     typedef VigraTrueType isUnaryFunctor;
    00091 };
    00092
    00093 template <class SrcIter, class SrcAcc,
    00094           class DestIter, class DestAcc,
    00095           class KernelArray,
    00096           class Functor>
    00097 void
    00098 resamplingConvolveLine(SrcIter s, SrcIter send, SrcAcc src,
    00099                        DestIter d, DestIter dend, DestAcc dest,
    00100                        KernelArray const & kernels,
    00101                        Functor mapTargetToSourceCoordinate)
    00102 {
    00103     typedef typename
    00104         NumericTraits<typename SrcAcc::value_type>::RealPromote
    00105         TmpType;
    00106     typedef typename KernelArray::value_type Kernel;
    00107     typedef typename Kernel::const_iterator KernelIter;
    00108
    00109     int wo = send - s;
    00110     int wn = dend - d;
    00111     int wo2 = 2*wo - 2;
    00112
    00113     int i;
    00114     typename KernelArray::const_iterator kernel = kernels.begin();
    00115     for(i=0; i<wn; ++i, ++d, ++kernel)
    00116     {
    00117         // use the kernels periodically
    00118         if(kernel == kernels.end())
    00119             kernel = kernels.begin();
    00120
    00121         // calculate current target point into source location
    00122         int is = mapTargetToSourceCoordinate(i);
    00123
    00124         TmpType sum = NumericTraits<TmpType>::zero();
    00125
    00126         int lbound = is - kernel->right(),
    00127             hbound = is - kernel->left();
    00128
    00129         KernelIter k = kernel->center() + kernel->right();
    00130         if(lbound < 0 || hbound >= wo)
    00131         {
    00132             vigra_precondition(-lbound < wo && wo2 - hbound >= 0,
    00133                 "resamplingConvolveLine(): kernel or offset larger than image.");
    00134             for(int m=lbound; m <= hbound; ++m, --k)
    00135             {
    00136                 int mm = (m < 0) ?
    00137                             -m :
    00138                             (m >= wo) ?
    00139                                 wo2 - m :
    00140                                 m;
    00141                 sum += *k * src(s, mm);
    00142             }
    00143         }
    00144         else
    00145         {
    00146             SrcIter ss = s + lbound;
    00147             SrcIter ssend = s + hbound;
    00148
    00149             for(; ss <= ssend; ++ss, --k)
    00150             {
    00151                 sum += *k * src(ss);
    00152             }
    00153         }
    00154
    00155         dest.set(sum, d);
    00156     }
    00157 }
    00158
    00159 template <class Kernel, class MapCoordinate, class KernelArray>
    00160 void
    00161 createResamplingKernels(Kernel const & kernel,
    00162              MapCoordinate const & mapCoordinate, KernelArray & kernels)
    00163 {
    00164     for(unsigned int idest = 0; idest < kernels.size(); ++idest)
    00165     {
    00166         int isrc = mapCoordinate(idest);
    00167         double idsrc = mapCoordinate.toDouble(idest);
    00168         double offset = idsrc - isrc;
    00169         double radius = kernel.radius();
    00170         int left = int(ceil(-radius - offset));
    00171         int right = int(floor(radius - offset));
    00172         kernels[idest].initExplicitly(left, right);
    00173
    00174         double x = left + offset;
    00175         for(int i = left; i <= right; ++i, ++x)
    00176             kernels[idest][i] = kernel(x);
    00177         kernels[idest].normalize(1.0, kernel.derivativeOrder(), offset);
    00178     }
    00179 }
    00180
    00181 /** \addtogroup ResamplingConvolutionFilters Resampling Convolution Filters
    00182
    00183     These functions implement the convolution operation when the source and target images
    00184     have different sizes. This is realized by accessing a continous kernel at the
    00185     appropriate non-integer positions. The technique is, for example, described in
    00186     D. Schumacher: <i>General Filtered Image Rescaling</i>, in: Graphics Gems III,
    00187     Academic Press, 1992.
    00188 */
    00189 //@{
    00190
    00191 /********************************************************/
    00192 /*                                                      */
    00193 /*                  resamplingConvolveX                 */
    00194 /*                                                      */
    00195 /********************************************************/
    00196
    00197 /** \brief Apply a resampling filter in the x-direction.
    00198
    00199     This function implements a convolution operation in x-direction
    00200     (i.e. applies a 1D filter to every row) where the width of the source
    00201     and destination images differ. This is typically used to avoid aliasing if
    00202     the image is scaled down, or to interpolate smoothly if the image is scaled up.
    00203     The target coordinates are transformed into source coordinates by
    00204
    00205     \code
    00206     xsource = (xtarget - offset) / samplingRatio
    00207     \endcode
    00208
    00209     The <tt>samplingRatio</tt> and <tt>offset</tt> must be given as \ref vigra::Rational
    00210     in order to avoid rounding errors in this transformation. It is required that for all
    00211     pixels of the target image, <tt>xsource</tt> remains within the range of the source
    00212     image (i.e. <tt>0 <= xsource <= sourceWidth-1</tt>. Since <tt>xsource</tt> is
    00213     in general not an integer, the <tt>kernel</tt> must be a functor that can be accessed at
    00214     arbitrary (<tt>double</tt>) coordinates. It must also provide a member function <tt>radius()</tt>
    00215     which specifies the support (non-zero interval) of the kernel. VIGRA already
    00216     provides a number of suitable functors, e.g. \ref vigra::Gaussian, \ref vigra::BSpline
    00217     \ref vigra::CatmullRomSpline, and \ref vigra::CoscotFunction. The function
    00218     \ref resizeImageSplineInterpolation() is implemented by means resamplingConvolveX() and
    00219     resamplingConvolveY().
    00220
    00221     <b> Declarations:</b>
    00222
    00223     pass arguments explicitly:
    00224     \code
    00225     namespace vigra {
    00226         template <class SrcIter, class SrcAcc,
    00227                   class DestIter, class DestAcc,
    00228                   class Kernel>
    00229         void
    00230         resamplingConvolveX(SrcIter sul, SrcIter slr, SrcAcc src,
    00231                             DestIter dul, DestIter dlr, DestAcc dest,
    00232                             Kernel const & kernel,
    00233                             Rational<int> const & samplingRatio, Rational<int> const & offset);
    00234     }
    00235     \endcode
    00236
    00237
    00238     use argument objects in conjunction with \ref ArgumentObjectFactories:
    00239     \code
    00240     namespace vigra {
    00241         template <class SrcIter, class SrcAcc,
    00242                   class DestIter, class DestAcc,
    00243                   class Kernel>
    00244         void
    00245         resamplingConvolveX(triple<SrcIter, SrcIter, SrcAcc> src,
    00246                             triple<DestIter, DestIter, DestAcc> dest,
    00247                             Kernel const & kernel,
    00248                             Rational<int> const & samplingRatio, Rational<int> const & offset);
    00249     }
    00250     \endcode
    00251
    00252     <b> Usage:</b>
    00253
    00254     <b>\#include</b> "<a href="resampling_convolution_8hxx-source.html">vigra/resampling_convolution.hxx</a>"
    00255
    00256
    00257     \code
    00258     Rational<int> ratio(2), offset(0);
    00259
    00260     FImage src(w,h),
    00261            dest(rational_cast<int>(ratio*w), h);
    00262
    00263     float sigma = 2.0;
    00264     Gaussian<float> smooth(sigma);
    00265     ...
    00266
    00267     // simpultaneously enlarge and smooth source image
    00268     resamplingConvolveX(srcImageRange(src), destImageRange(dest),
    00269                         smooth, ratio, offset);
    00270     \endcode
    00271
    00272     <b> Required Interface:</b>
    00273
    00274     \code
    00275     Kernel kernel;
    00276     int kernelRadius = kernel.radius();
    00277     double x = ...;  // must be <= radius()
    00278     double value = kernel(x);
    00279     \endcode
    00280 */
    00281 template <class SrcIter, class SrcAcc,
    00282           class DestIter, class DestAcc,
    00283           class Kernel>
    00284 void
    00285 resamplingConvolveX(SrcIter sul, SrcIter slr, SrcAcc src,
    00286                     DestIter dul, DestIter dlr, DestAcc dest,
    00287                     Kernel const & kernel,
    00288                     Rational<int> const & samplingRatio, Rational<int> const & offset)
    00289 {
    00290     int wold = slr.x - sul.x;
    00291     int wnew = dlr.x - dul.x;
    00292
    00293     vigra_precondition(!samplingRatio.is_inf() && samplingRatio > 0,
    00294                 "resamplingConvolveX(): sampling ratio must be > 0 and < infinity");
    00295     vigra_precondition(!offset.is_inf(),
    00296                 "resamplingConvolveX(): offset must be < infinity");
    00297
    00298     int period = lcm(samplingRatio.numerator(), samplingRatio.denominator());
    00299     resampling_detail::MapTargetToSourceCoordinate mapCoordinate(samplingRatio, offset);
    00300
    00301     ArrayVector<Kernel1D<double> > kernels(period);
    00302
    00303     createResamplingKernels(kernel, mapCoordinate, kernels);
    00304
    00305     for(; sul.y < slr.y; ++sul.y, ++dul.y)
    00306     {
    00307         typename SrcIter::row_iterator sr = sul.rowIterator();
    00308         typename DestIter::row_iterator dr = dul.rowIterator();
    00309         resamplingConvolveLine(sr, sr+wold, src, dr, dr+wnew, dest,
    00310                                kernels, mapCoordinate);
    00311     }
    00312 }
    00313
    00314 template <class SrcIter, class SrcAcc,
    00315           class DestIter, class DestAcc,
    00316           class Kernel>
    00317 inline void
    00318 resamplingConvolveX(triple<SrcIter, SrcIter, SrcAcc> src,
    00319                     triple<DestIter, DestIter, DestAcc> dest,
    00320                     Kernel const & kernel,
    00321                     Rational<int> const & samplingRatio, Rational<int> const & offset)
    00322 {
    00323     resamplingConvolveX(src.first, src.second, src.third,
    00324                         dest.first, dest.second, dest.third,
    00325                         kernel, samplingRatio, offset);
    00326 }
    00327
    00328 /********************************************************/
    00329 /*                                                      */
    00330 /*                  resamplingConvolveY                 */
    00331 /*                                                      */
    00332 /********************************************************/
    00333
    00334 /** \brief Apply a resampling filter in the y-direction.
    00335
    00336     This function implements a convolution operation in y-direction
    00337     (i.e. applies a 1D filter to every column) where the height of the source
    00338     and destination images differ. This is typically used to avoid aliasing if
    00339     the image is scaled down, or to interpolate smoothly if the image is scaled up.
    00340     The target coordinates are transformed into source coordinates by
    00341
    00342     \code
    00343     ysource = (ytarget - offset) / samplingRatio
    00344     \endcode
    00345
    00346     The <tt>samplingRatio</tt> and <tt>offset</tt> must be given as \ref vigra::Rational
    00347     in order to avoid rounding errors in this transformation. It is required that for all
    00348     pixels of the target image, <tt>ysource</tt> remains within the range of the source
    00349     image (i.e. <tt>0 <= ysource <= sourceHeight-1</tt>. Since <tt>ysource</tt> is
    00350     in general not an integer, the <tt>kernel</tt> must be a functor that can be accessed at
    00351     arbitrary (<tt>double</tt>) coordinates. It must also provide a member function <tt>radius()</tt>
    00352     which specifies the support (non-zero interval) of the kernel. VIGRA already
    00353     provides a number of suitable functors, e.g. \ref vigra::Gaussian, \ref vigra::BSpline
    00354     \ref vigra::CatmullRomSpline, and \ref vigra::CoscotFunction. The function
    00355     \ref resizeImageSplineInterpolation() is implemented by means resamplingConvolveX() and
    00356     resamplingConvolveY().
    00357
    00358     <b> Declarations:</b>
    00359
    00360     pass arguments explicitly:
    00361     \code
    00362     namespace vigra {
    00363         template <class SrcIter, class SrcAcc,
    00364                   class DestIter, class DestAcc,
    00365                   class Kernel>
    00366         void
    00367         resamplingConvolveY(SrcIter sul, SrcIter slr, SrcAcc src,
    00368                             DestIter dul, DestIter dlr, DestAcc dest,
    00369                             Kernel const & kernel,
    00370                             Rational<int> const & samplingRatio, Rational<int> const & offset);
    00371     }
    00372     \endcode
    00373
    00374
    00375     use argument objects in conjunction with \ref ArgumentObjectFactories:
    00376     \code
    00377     namespace vigra {
    00378         template <class SrcIter, class SrcAcc,
    00379                   class DestIter, class DestAcc,
    00380                   class Kernel>
    00381         void
    00382         resamplingConvolveY(triple<SrcIter, SrcIter, SrcAcc> src,
    00383                             triple<DestIter, DestIter, DestAcc> dest,
    00384                             Kernel const & kernel,
    00385                             Rational<int> const & samplingRatio, Rational<int> const & offset);
    00386     }
    00387     \endcode
    00388
    00389     <b> Usage:</b>
    00390
    00391     <b>\#include</b> "<a href="resampling_convolution_8hxx-source.html">vigra/resampling_convolution.hxx</a>"
    00392
    00393
    00394     \code
    00395     Rational<int> ratio(2), offset(0);
    00396
    00397     FImage src(w,h),
    00398            dest(w, rational_cast<int>(ratio*h));
    00399
    00400     float sigma = 2.0;
    00401     Gaussian<float> smooth(sigma);
    00402     ...
    00403
    00404     // simpultaneously enlarge and smooth source image
    00405     resamplingConvolveY(srcImageRange(src), destImageRange(dest),
    00406                         smooth, ratio, offset);
    00407     \endcode
    00408
    00409     <b> Required Interface:</b>
    00410
    00411     \code
    00412     Kernel kernel;
    00413     int kernelRadius = kernel.radius();
    00414     double y = ...;  // must be <= radius()
    00415     double value = kernel(y);
    00416     \endcode
    00417 */
    00418 template <class SrcIter, class SrcAcc,
    00419           class DestIter, class DestAcc,
    00420           class Kernel>
    00421 void
    00422 resamplingConvolveY(SrcIter sul, SrcIter slr, SrcAcc src,
    00423                     DestIter dul, DestIter dlr, DestAcc dest,
    00424                     Kernel const & kernel,
    00425                     Rational<int> const & samplingRatio, Rational<int> const & offset)
    00426 {
    00427     int hold = slr.y - sul.y;
    00428     int hnew = dlr.y - dul.y;
    00429
    00430     vigra_precondition(!samplingRatio.is_inf() && samplingRatio > 0,
    00431                 "resamplingConvolveY(): sampling ratio must be > 0 and < infinity");
    00432     vigra_precondition(!offset.is_inf(),
    00433                 "resamplingConvolveY(): offset must be < infinity");
    00434
    00435     int period = lcm(samplingRatio.numerator(), samplingRatio.denominator());
    00436
    00437     resampling_detail::MapTargetToSourceCoordinate mapCoordinate(samplingRatio, offset);
    00438
    00439     ArrayVector<Kernel1D<double> > kernels(period);
    00440
    00441     createResamplingKernels(kernel, mapCoordinate, kernels);
    00442
    00443     for(; sul.x < slr.x; ++sul.x, ++dul.x)
    00444     {
    00445         typename SrcIter::column_iterator sc = sul.columnIterator();
    00446         typename DestIter::column_iterator dc = dul.columnIterator();
    00447         resamplingConvolveLine(sc, sc+hold, src, dc, dc+hnew, dest,
    00448                                kernels, mapCoordinate);
    00449     }
    00450 }
    00451
    00452 template <class SrcIter, class SrcAcc,
    00453           class DestIter, class DestAcc,
    00454           class Kernel>
    00455 inline void
    00456 resamplingConvolveY(triple<SrcIter, SrcIter, SrcAcc> src,
    00457                     triple<DestIter, DestIter, DestAcc> dest,
    00458                     Kernel const & kernel,
    00459                     Rational<int> const & samplingRatio, Rational<int> const & offset)
    00460 {
    00461     resamplingConvolveY(src.first, src.second, src.third,
    00462                         dest.first, dest.second, dest.third,
    00463                         kernel, samplingRatio, offset);
    00464 }
    00465
    00466 /********************************************************/
    00467 /*                                                      */
    00468 /*               resamplingConvolveImage                */
    00469 /*                                                      */
    00470 /********************************************************/
    00471
    00472 /** \brief Apply two separable resampling filters successively, the first in x-direction,
    00473            the second in y-direction.
    00474
    00475     This function is a shorthand for the concatenation of a call to
    00476     \link ResamplingConvolutionFilters#resamplingConvolveX resamplingConvolveX\endlink()
    00477     and \link ResamplingConvolutionFilters#resamplingConvolveY resamplingConvolveY\endlink()
    00478     with the given kernels. See there for detailed documentation.
    00479
    00480     <b> Declarations:</b>
    00481
    00482     pass arguments explicitly:
    00483     \code
    00484     namespace vigra {
    00485         template <class SrcIterator, class SrcAccessor,
    00486                   class DestIterator, class DestAccessor,
    00487                   class KernelX, class KernelY>
    00488         void resamplingConvolveImage(SrcIterator sul,SrcIterator slr, SrcAccessor src,
    00489                            DestIterator dul, DestIterator dlr, DestAccessor dest,
    00490                            KernelX const & kx,
    00491                            Rational<int> const & samplingRatioX, Rational<int> const & offsetX,
    00492                            KernelY const & ky,
    00493                            Rational<int> const & samplingRatioY, Rational<int> const & offsetY);
    00494     }
    00495     \endcode
    00496
    00497
    00498     use argument objects in conjunction with \ref ArgumentObjectFactories:
    00499     \code
    00500     namespace vigra {
    00501         template <class SrcIterator, class SrcAccessor,
    00502                   class DestIterator, class DestAccessor,
    00503                   class KernelX, class KernelY>
    00504         void
    00505         resamplingConvolveImage(triple<SrcIterator, SrcIterator, SrcAccessor> src,
    00506                            triple<DestIterator, DestIterator, DestAccessor> dest,
    00507                            KernelX const & kx,
    00508                            Rational<int> const & samplingRatioX, Rational<int> const & offsetX,
    00509                            KernelY const & ky,
    00510                            Rational<int> const & samplingRatioY, Rational<int> const & offsetY);
    00511     }
    00512     \endcode
    00513
    00514     <b> Usage:</b>
    00515
    00516     <b>\#include</b> "<a href="resampling_convolution_8hxx-source.html">vigra/resampling_convolution.hxx</a>"
    00517
    00518
    00519     \code
    00520     Rational<int> xratio(2), yratio(3), offset(0);
    00521
    00522     FImage src(w,h),
    00523            dest(rational_cast<int>(xratio*w), rational_cast<int>(yratio*h));
    00524
    00525     float sigma = 2.0;
    00526     Gaussian<float> smooth(sigma);
    00527     ...
    00528
    00529     // simpultaneously enlarge and smooth source image
    00530     resamplingConvolveImage(srcImageRange(src), destImageRange(dest),
    00531                             smooth, xratio, offset,
    00532                             smooth, yratio, offset);
    00533
    00534     \endcode
    00535
    00536 */
    00537 template <class SrcIterator, class SrcAccessor,
    00538           class DestIterator, class DestAccessor,
    00539           class KernelX, class KernelY>
    00540 void resamplingConvolveImage(SrcIterator sul,SrcIterator slr, SrcAccessor src,
    00541                    DestIterator dul, DestIterator dlr, DestAccessor dest,
    00542                    KernelX const & kx,
    00543                    Rational<int> const & samplingRatioX, Rational<int> const & offsetX,
    00544                    KernelY const & ky,
    00545                    Rational<int> const & samplingRatioY, Rational<int> const & offsetY)
    00546 {
    00547     typedef typename
    00548         NumericTraits<typename SrcAccessor::value_type>::RealPromote
    00549         TmpType;
    00550
    00551     BasicImage<TmpType> tmp(dlr.x - dul.x, slr.y - sul.y);
    00552
    00553     resamplingConvolveX(srcIterRange(sul, slr, src),
    00554                         destImageRange(tmp),
    00555                         kx, samplingRatioX, offsetX);
    00556     resamplingConvolveY(srcImageRange(tmp),
    00557                         destIterRange(dul, dlr, dest),
    00558                         ky, samplingRatioY, offsetY);
    00559 }
    00560
    00561 template <class SrcIterator, class SrcAccessor,
    00562           class DestIterator, class DestAccessor,
    00563           class KernelX, class KernelY>
    00564 inline void
    00565 resamplingConvolveImage(triple<SrcIterator, SrcIterator, SrcAccessor> src,
    00566                    triple<DestIterator, DestIterator, DestAccessor> dest,
    00567                    KernelX const & kx,
    00568                    Rational<int> const & samplingRatioX, Rational<int> const & offsetX,
    00569                    KernelY const & ky,
    00570                    Rational<int> const & samplingRatioY, Rational<int> const & offsetY)
    00571 {
    00572     resamplingConvolveImage(src.first, src.second, src.third,
    00573                             dest.first, dest.second, dest.third,
    00574                             kx, samplingRatioX, offsetX,
    00575                             ky, samplingRatioY, offsetY);
    00576 }
    00577
    00578 } // namespace vigra
    00579
    00580
    00581 #endif /* VIGRA_RESAMPLING_CONVOLUTION_HXX */

© Ullrich Köthe (koethe@informatik.uni-hamburg.de) Cognitive Systems Group, University of Hamburg, Germany | html generated using doxygen and Python VIGRA 1.4.0 (21 Dec 2005)