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smooth.cxx VIGRA

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.                                                         */
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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 = nonlinear) ? ";
    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

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vigra 1.8.0 (20 Sep 2011)