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watershed.cxx |

Segment image by means of the watershed algorithm, using seededRegionGrowing()
Usage: `watershed infile outfile`

    /************************************************************************/
    /*                                                                      */
    /*               Copyright 1998-2002 by Ullrich Koethe                  */
    /*       Cognitive Systems Group, University of Hamburg, Germany        */
    /*                                                                      */
    /*    This file is part of the VIGRA computer vision library.           */
    /*    ( Version 1.6.0, Aug 13 2008 )                                    */
    /*    The VIGRA Website is                                              */
    /*        http://kogs-www.informatik.uni-hamburg.de/~koethe/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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    #include <iostream>
    #include "vigra/stdimage.hxx"
    #include "vigra/stdimagefunctions.hxx"
    #include "vigra/localminmax.hxx"
    #include "vigra/labelimage.hxx"
    #include "vigra/seededregiongrowing.hxx"
    #include "vigra/impex.hxx"

    using namespace vigra;

    // define a functor that calsulates the squared magnitude of the gradient
    // given the x- and y- components of the gradient
    struct GradientSquaredMagnitudeFunctor
    {
        float operator()(float const & g1, float const & g2) const
        {
            return g1 * g1 + g2 * g2;
        }

        float operator()(vigra::RGBValue<float> const & rg1, vigra::RGBValue<float> const & rg2) const
        {
            float g1 = rg1.squaredMagnitude();
            float g2 = rg2.squaredMagnitude();

            return g1 + g2;
        }
    };

    // generic implementation of the watershed algorithm
    template <class InImage, class OutImage>
    void watershedSegmentation(InImage & in, OutImage & out, double scale)
    {
        typedef typename vigra::NumericTraits<typename InImage::value_type>::RealPromote
            TmpType;

        int w = in.width();
        int h = in.height();

        vigra::BasicImage<TmpType> gradientx(w, h);
        vigra::BasicImage<TmpType> gradienty(w, h);
        vigra::FImage gradientmag(w, h);

        // calculate the x- and y-components of the image gradient at given scale
        recursiveFirstDerivativeX(srcImageRange(in), destImage(gradientx), scale);
        recursiveSmoothY(srcImageRange(gradientx), destImage(gradientx), scale);

        recursiveFirstDerivativeY(srcImageRange(in), destImage(gradienty), scale);
        recursiveSmoothX(srcImageRange(gradienty), destImage(gradienty), scale);

        // transform components into gradient magnitude
        combineTwoImages(srcImageRange(gradientx), srcImage(gradienty),
                         destImage(gradientmag), GradientSquaredMagnitudeFunctor());

        vigra::IImage labels(w, h);
        labels = 0;

        // find the local minima of the gradient magnitude
        // (might be larger than one pixel)
        extendedLocalMinima(srcImageRange(gradientmag), destImage(labels), 1);

        // label the minima just found
        int max_region_label =
            labelImageWithBackground(srcImageRange(labels), destImage(labels),
                                     false, 0);

        // create a statistics functor for region growing
        vigra::ArrayOfRegionStatistics<vigra::SeedRgDirectValueFunctor<float> >
                                              gradstat(max_region_label);

        // perform region growing, starting from the minima of the gradient magnitude;
        // as the feature (first input) image contains the gradient magnitude,
        // this calculates the catchment basin of each minimum
        seededRegionGrowing(srcImageRange(gradientmag), srcImage(labels),
                            destImage(labels), gradstat);

        // initialize a functor to determine the average gray-value or color
        // for each region (catchment basin) just found
        vigra::ArrayOfRegionStatistics<vigra::FindAverage<TmpType> >
                                              averages(max_region_label);

        // calculate the averages
        inspectTwoImages(srcImageRange(in), srcImage(labels), averages);

        // write the averages into the destination image (the functor 'averages'
        // acts as a look-up table)
        transformImage(srcImageRange(labels), destImage(out), averages);

        // mark the watersheds (region boundaries) black
        regionImageToEdgeImage(srcImageRange(labels), destImage(out),
                               vigra::NumericTraits<typename OutImage::value_type>::zero());
    }

    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;
        }

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

            // input width of gradient filter
            double scale = 1.0;
            std::cout << "Scale for gradient calculation ? ";
            std::cin >> scale;

            if(info.isGrayscale())
            {
                int w = info.width();
                int h = info.height();

                vigra::BImage in(w, h);
                importImage(info, destImage(in));

                vigra::BImage out(w, h);

                // perform watershed segmentation on gray image
                // note that the watershed algorithm usually results in an
                // oversegmentation (i.e., too many regions), but its boundary
                // localization is quite good
                watershedSegmentation(in, out, scale);

                std::cout << "Writing " << argv[2] << std::endl;
                exportImage(srcImageRange(out), vigra::ImageExportInfo(argv[2]));
            }
            else
            {
                int w = info.width();
                int h = info.height();

                vigra::BRGBImage in(w, h);
                importImage(info, destImage(in));

                vigra::BRGBImage out(w, h);

                // perform watershed segmentation on color image
                // note that the watershed algorithm usually results in an
                // oversegmentation (i.e., too many regions), but its boundary
                // localization is quite good
                watershedSegmentation(in, out, scale);

                std::cout << "Writing " << argv[2] << std::endl;
                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 1.6.0 (13 Aug 2008)