NICE_SemSeg/progs/testSemSegObliqueTrees.cpp

/**
 * @file testSemSegConvTrees.cpp
 * @brief test semantic segmentation routines of the ConvTree method
 * @author Sven Sickert
 * @date 10/20/2014

*/

#include "core/basics/StringTools.h"
#include "core/basics/ResourceStatistics.h"
#include "core/basics/Timer.h"
#include "core/image/Morph.h"

#include "semseg/semseg/SemSegObliqueTree.h"
#include "semseg/semseg/SemSegTools.h"

#include <fstream>
#include <vector>

using namespace OBJREC;

int main ( int argc, char **argv )
{
    // variables
    NICE::Config conf (argc, argv );
    NICE::ResourceStatistics rs;

    bool postProcessing = conf.gB( "SemSegObliqueTree", "post_process", false);
    MultiDataset md ( &conf );
    const ClassNames & classNames = md.getClassNames ( "train" );
    const LabeledSet *testFiles = md["test"];
    std::set<int> forbiddenClasses;
    classNames.getSelection ( conf.gS ( "analysis", "forbidden_classes", "" ),
                              forbiddenClasses );

    std::vector<bool> usedClasses ( classNames.numClasses(), true );
    for ( std::set<int>::const_iterator it = forbiddenClasses.begin();
          it != forbiddenClasses.end(); ++it)
    {
        usedClasses [ *it ] = false;
    }

    std::map<int,int> classMapping;
    int j = 0;
    for ( int i = 0; i < usedClasses.size(); i++ )
        if (usedClasses[i])
        {
            classMapping[i] = j;
            j++;
        }

    NICE::Matrix M ( classMapping.size(), classMapping.size() );
    M.set( 0 );

    // initialize semantic segmentation method
    SemanticSegmentation *semseg = NULL;

    // setup actual segmentation method
    semseg = new SemSegObliqueTree ( &conf, &classNames );

    // training
    std::cout << "\nTRAINING" << std::endl;
    std::cout << "########\n" << std::endl;
    semseg->train( &md );

    // testing
    NICE::Timer timer;
    std::cout << "\nCLASSIFICATION" << std::endl;
    std::cout << "##############\n" << std::endl;
    for (LabeledSet::const_iterator it = testFiles->begin(); it != testFiles->end(); it++)
    {
        for (std::vector<ImageInfo *>::const_iterator jt = it->second.begin();
            jt != it->second.end(); jt++)
        {
            ImageInfo & info = *(*jt);
            std::string file = info.img();

            NICE::ImageT<int> segresult, gtruth;
            if ( info.hasLocalizationInfo() )
            {
                const LocalizationResult *l_gt = info.localization();

                segresult.resize ( l_gt->xsize, l_gt->ysize );
                segresult.set( 0 );
                gtruth.resize( l_gt->xsize, l_gt->ysize );
                gtruth.set ( 0 );
                l_gt->calcLabeledImage ( gtruth, classNames.getBackgroundClass() );
            }
            else
            {
                std::cerr << "testSemSegConvTrees: WARNING: NO localization info found for "
                          << file << std::endl;
            }

            // actual testing
            NICE::MultiChannelImageT<double> probabilities;

            timer.start();
            semseg->semanticseg( file, segresult, probabilities );
            timer.stop();
            std::cout << "Time for Classification: " << timer.getLastAbsolute()
                      << "\n\n";

            // post processing results
            if (postProcessing)
            {
                std::cerr << "testSemSegConvTrees: WARNING: Post processing not yet supported."
                          << std::endl;
            }

            // updating confusion matrix
            SemSegTools::updateConfusionMatrix ( segresult, gtruth, M,
                forbiddenClasses, classMapping );

            // saving results to image file
            NICE::ColorImage rgb;
            NICE::ColorImage rgb_gt;
            NICE::ColorImage orig ( file );
            classNames.labelToRGB( segresult, rgb);
            classNames.labelToRGB( gtruth, rgb_gt);
            std::string fname = NICE::StringTools::baseName ( file, false );

            SemSegTools::saveResultsToImageFile(
                        &conf, "analysis", orig, rgb_gt, rgb, fname );
        }
    }

    // evaluation & analysis
    SemSegTools::computeClassificationStatistics( M, classNames, forbiddenClasses);

    // Cleaning up
    delete semseg;

}