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smooth.cxx |

Smooth an image using Recursive convolution functions functions: smooth.cxx
Usage: `smooth infile outfile`

    /************************************************************************/
    /*                                                                      */
    /*               Copyright 1998-2002 by 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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    /*    Software is furnished to do so, subject to the following          */
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    /*    Software.                                                         */
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    /*    OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND          */
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    /************************************************************************/

    #include <iostream>
    #include "vigra/stdimage.hxx"
    #include "vigra/convolution.hxx"
    #include "vigra/nonlineardiffusion.hxx"
    #include "vigra/impex.hxx"

    using namespace vigra;

    int main(int argc, char ** argv)
    {
        if(argc != 3)
        {
            std::cout << "Usage: " << argv[0] << " infile outfile" << std::endl;
            std::cout << "(supported formats: " << vigra::impexListFormats() << ")" << std::endl;

            return 1;
        }

        // Type of smoothing:
        int type;
        std::cout << "Type of smoothing (1 = Gauss, 2 = Exponential, 3 = nolinear) ? ";
        std::cin >> type;

        // input width of smoothing filter
        double scale;
        std::cout << "Amount of smoothing (operator scale) ? ";
        std::cin >> scale;

        double edge_threshold;
        if(type == 3)
        {
            std::cout << "Edge threshold ? ";
            std::cin >> edge_threshold;
        }

        try
        {
            vigra::ImageImportInfo info(argv[1]);

            if(info.isGrayscale())
            {
                vigra::BImage in(info.width(), info.height());
                vigra::BImage out(info.width(), info.height());

                importImage(info, destImage(in));

                switch(type)
                {
                  case 2:
                  {
                    // apply recursive filter (exponential filter) to gray image
                    recursiveSmoothX(srcImageRange(in), destImage(out), scale);
                    recursiveSmoothY(srcImageRange(out), destImage(out), scale);
                    break;
                  }
                  case 3:
                  {
                    // apply nonlinear diffusion to gray image
                    nonlinearDiffusion(srcImageRange(in), destImage(out),
                       vigra::DiffusivityFunctor<float>(edge_threshold), scale);
                    break;
                  }
                  default:
                  {
                    vigra::FImage tmp(info.width(), info.height());

                    // apply Gaussian filter to gray image
                    vigra::Kernel1D<double> gauss;
                    gauss.initGaussian(scale);
                    separableConvolveX(srcImageRange(in), destImage(tmp), kernel1d(gauss));
                    separableConvolveY(srcImageRange(tmp), destImage(out), kernel1d(gauss));
                  }
                }

                exportImage(srcImageRange(out), vigra::ImageExportInfo(argv[2]));
            }
            else
            {
                vigra::BRGBImage in(info.width(), info.height());
                vigra::BRGBImage out(info.width(), info.height());

                importImage(info, destImage(in));

                switch(type)
                {
                  case 2:
                  {
                    // apply recursive filter (exponential filter) to color image
                    recursiveSmoothX(srcImageRange(in), destImage(out), scale);
                    recursiveSmoothY(srcImageRange(out), destImage(out), scale);
                    break;
                  }
                  case 3:
                  {
                    // apply nonlinear diffusion to color image, one band at a time
                    VectorComponentValueAccessor<vigra::BRGBImage::value_type> bandAccessor(0);
                    for(int band = 0; band<3; ++band)
                    {
                        bandAccessor.setIndex(band);
                        nonlinearDiffusion(srcImageRange(in, bandAccessor), destImage(out, bandAccessor),
                               vigra::DiffusivityFunctor<float>(edge_threshold), scale);
                    }
                    break;
                  }
                  default:
                  {
                    vigra::FRGBImage tmp(info.width(), info.height());

                    // apply Gaussian filter to color image
                    vigra::Kernel1D<double> gauss;
                    gauss.initGaussian(scale);
                    separableConvolveX(srcImageRange(in), destImage(tmp), kernel1d(gauss));
                    separableConvolveY(srcImageRange(tmp), destImage(out), kernel1d(gauss));
                  }
                }

                exportImage(srcImageRange(out), vigra::ImageExportInfo(argv[2]));
            }
        }
        catch (vigra::StdException & e)
        {
            std::cout << e.what() << std::endl;
            return 1;
        }

        return 0;
    }

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany | html generated using doxygen and Python vigra 1.7.1 (3 Dec 2010)