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watershed.cxx |

Segment image by means of the watershed algorithm, using seededRegionGrowing()
Usage: `example_watershed 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 */
/* */
/* Permission is hereby granted, free of charge, to any person */
/* obtaining a copy of this software and associated documentation */
/* files (the "Software"), to deal in the Software without */
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/* sell copies of the Software, and to permit persons to whom the */
/* Software is furnished to do so, subject to the following */
/* conditions: */
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/* The above copyright notice and this permission notice shall be */
/* included in all copies or substantial portions of the */
/* Software. */
/* */
/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND */
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/************************************************************************/
#include <iostream>
#include <vigra/multi_array.hxx>
#include <vigra/stdimagefunctions.hxx>
#include <vigra/multi_convolution.hxx>
#include <vigra/multi_watersheds.hxx>
#include <vigra/impex.hxx>
using namespace vigra;
template <class InImage, class OutImage>
void watershedSegmentation(InImage & in, OutImage & out, double scale)
{
// compute the gradient magnitude as a suitable boundary indicator
MultiArray<2, float> gradient(in.shape());
gaussianGradientMagnitude(in, gradient, scale);
// Compute watershed segmentation using a region growing algorithm with 4-neighborhood.
// Use option object to ask the watershed algorithm to compute seeds automatically at
// minima of the boundary indicator.
MultiArray<2, unsigned int> labeling(in.shape());
unsigned int max_region_label =
watershedsMultiArray(gradient, labeling, DirectNeighborhood,
WatershedOptions().seedOptions(SeedOptions().minima()));
// Initialize a functor to determine the average gray-value or color
// for each region (catchment basin) just found.
// define a temporary type that can hold floating point values
typedef typename NumericTraits<typename InImage::value_type>::RealPromote
TmpType;
ArrayOfRegionStatistics<FindAverage<TmpType> > averages(max_region_label);
// calculate the averages
inspectTwoImages(in, labeling, averages);
// write the averages into the destination image (the functor 'averages'
// acts as a look-up table)
transformImage(labeling, out, averages);
// mark the watersheds (region boundaries) black
regionImageToEdgeImage(labeling, out,
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: " << impexListFormats() << ")" << std::endl;
return 1;
}
try
{
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();
MultiArray<2, UInt8> in(w, h), out(w, h);
importImage(info, in);
// 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(out, ImageExportInfo(argv[2]));
}
else
{
int w = info.width();
int h = info.height();
MultiArray<2, RGBValue<UInt8> > in(w, h), out(w, h);
importImage(info, in);
// 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(out, ImageExportInfo(argv[2]));
}
}
catch (std::exception & 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 1.11.0 (Fri May 19 2017)