NICE_SemSeg/semseg/SemanticSegmentation.cpp

/**
* @file SemanticSegmentation.cpp
* @brief abstract interface for semantic segmentation algorithms
* @author Erik Rodner and Sven Sickert
* @date 03/19/2009

*/
#include <iostream>

#include "SemanticSegmentation.h"
#include "vislearning/baselib/Preprocess.h"
#include "vislearning/baselib/Globals.h"
#include "core/image/MultiChannelImage3DT.h"
#include "core/basics/StringTools.h"

using namespace OBJREC;
using namespace std;
using namespace NICE;

SemanticSegmentation::SemanticSegmentation ()
{
  this->classNames = NULL;
  this->imagetype = IMAGETYPE_GRAY;
}

SemanticSegmentation::SemanticSegmentation ( const Config *conf,
    const ClassNames *classNames )
{
  this->classNames = classNames;

  Preprocess::Init ( conf );

  std::string imagetype_s = conf->gS ( "main", "imagetype", "gray" );
  if ( imagetype_s == "rgb" )
    this->imagetype = IMAGETYPE_RGB;
  else if ( imagetype_s == "gray" )
    this->imagetype = IMAGETYPE_GRAY;
  else
  {
    fprintf ( stderr, "SemanticSegmentation:: unknown image type option\n" );
    exit ( -1 );
  }
}

SemanticSegmentation::~SemanticSegmentation()
{
}


void SemanticSegmentation::convertLSetToSparseExamples ( Examples &examples, LabeledSetVector &lvec )
{
#ifdef DEBUG_PRINTS
  cout << "SemSegRegionBased::convertLSetToExamples starts" << endl;
#endif
  for ( map< int, vector<NICE::Vector *> >::iterator iter = lvec.begin(); iter != lvec.end(); ++iter )
  {
    for ( int j = 0; j < ( int ) iter->second.size(); j++ )
    {
      Vector &tmp = * ( iter->second[j] );
      int dim = tmp.size();
      SparseVector *vec = new SparseVector ( dim );
      for ( int j = 0; j < dim; j++ )
      {
        if ( tmp[j] != 0.0 )
        {
          ( *vec ) [j] = tmp[j];
        }
      }
      Example ex;
      ex.svec = vec;
      examples.push_back ( pair<int, Example> ( iter->first, ex ) );
    }
  }
  lvec.clear();
#ifdef DEBUG_PRINTS
  cout << "SemSegRegionBased::convertLSetToExamples finished" << endl;
#endif
}

void SemanticSegmentation::convertLSetToExamples ( Examples &examples, LabeledSetVector &lvec )
{
#ifdef DEBUG_PRINTS
  cout << "SemSegRegionBased::convertLSetToExamples starts" << endl;
#endif
  for ( map< int, vector<NICE::Vector *> >::iterator iter = lvec.begin(); iter != lvec.end(); ++iter )
  {
    for ( int j = 0; j < ( int ) iter->second.size(); j++ )
    {
      NICE::Vector *vec = new NICE::Vector ( * ( iter->second[j] ) );
      Example ex ( vec );
      examples.push_back ( pair<int, Example> ( iter->first, ex ) );
    }
  }
  lvec.clear();
#ifdef DEBUG_PRINTS
  cout << "SemSegRegionBased::convertLSetToExamples finished" << endl;
#endif
}

void SemanticSegmentation::convertExamplesToLSet ( Examples &examples, LabeledSetVector &lvec )
{
#ifdef DEBUG_PRINTS
  cout << "SemSegRegionBased::convertExamplesToLSet starts" << endl;
#endif
  lvec.clear();
  for ( int i = 0; i < ( int ) examples.size(); i++ )
  {
    if ( examples[i].second.vec != NULL )
    {
      lvec.add ( examples[i].first, *examples[i].second.vec );
      delete examples[i].second.vec;
      examples[i].second.vec = NULL;
    }
    else
    {
      if ( examples[i].second.svec != NULL )
      {
        throw ( "Transform SVEC to VEC not yet implemented" );
      }
      else
      {
        throw ( "no features for LabeledSet" );
      }
    }

  }
  examples.clear();
#ifdef DEBUG_PRINTS
  cout << "SemSegRegionBased::convertExamplesToLSet finished" << endl;
#endif
}

void SemanticSegmentation::convertExamplesToVVector ( VVector &feats, Examples &examples, vector<int> &label )
{
#ifdef DEBUG_PRINTS
  cout << "SemSegRegionBased::convertExamplesToVVector starts" << endl;
#endif
  feats.clear();
  label.clear();
  for ( int i = 0; i < ( int ) examples.size(); i++ )
  {
    label.push_back ( examples[i].first );
    feats.push_back ( *examples[i].second.vec );
    delete examples[i].second.vec;
    examples[i].second.vec = NULL;
  }
  examples.clear();
#ifdef DEBUG_PRINTS
  cout << "SemSegRegionBased::convertExamplesToVVector finished" << endl;
#endif
}

void SemanticSegmentation::convertVVectorToExamples ( VVector &feats, Examples &examples, vector<int> &label )
{
#ifdef DEBUG_PRINTS
  cout << "SemSegRegionBased::convertVVectorToExamples starts" << endl;
#endif
  for ( int i = 0; i < ( int ) feats.size(); i++ )
  {
    NICE::Vector *v = new NICE::Vector ( feats[i] );
    Example ex ( v );
    ex.position = 0; //TODO: hier mal was besseres überlegen, damit Klassifikator wieder Bildspezifisch lernt
    examples.push_back ( pair<int, Example> ( label[i], ex ) );
    feats[i].clear();
  }
  feats.clear();
  label.clear();
#ifdef DEBUG_PRINTS
  cout << "SemSegRegionBased::convertVVectorToExamples finished" << endl;
#endif
}

void SemanticSegmentation::getDepthVector ( const LabeledSet *Files,
                                            vector<int> & depthVec,
                                            const bool run3Dseg )
{
  std::string oldName;
  int zsize = 0;
  bool isInit = false;

  for (LabeledSet::const_iterator it = Files->begin(); it != Files->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();

      std::vector< std::string > list;
      StringTools::split ( file, '/', list );
      std::string filename = list.back();
      uint found = filename.find_last_of ( "_" );
      if (run3Dseg && found < filename.size() && found-3 > 0 )
      {
        std::string curName = filename.substr ( found-3,3 );
        if ( !isInit )
        {
          oldName = curName;
          isInit = true;
        }
        if ( curName.compare ( oldName ) == 0 ) // if strings match up
        {
          zsize++;
        }
        else
        {
          depthVec.push_back ( zsize );
          zsize = 1;
          oldName = curName;
        }
      }
      else
      {
        zsize = 1;
        depthVec.push_back ( zsize );
      }

    }
  }
  depthVec.push_back ( zsize );
}

void SemanticSegmentation::make3DImage ( const std::vector<std::string> & filelist,
    NICE::MultiChannelImage3DT<double> & imgData )
{
  bool isInit = false;
  for ( int it = 0; it < ( int ) filelist.size(); it++ )
  {
    if ( imagetype == IMAGETYPE_RGB )
    {
      NICE::ColorImage img;
      try
      {
        img.read ( filelist[it] );
      }
      catch ( ImageException iE )
      {
        fprintf ( stderr, "Failed to open color image file: %s\n", filelist[it].c_str() );
        fprintf ( stderr, "%s\n", iE.what() );
        exit ( -1 );
      }
      if ( !isInit )
      {
        imgData.reInit ( img.width(),img.height(),filelist.size(),3 );
        isInit = true;
      }
      for ( int y = 0; y < img.height(); y++ )
      {
        for ( int x = 0; x < img.width(); x++ )
        {
          for ( int r = 0; r < 3; r++ )
          {
            imgData.set ( x, y, it, img.getPixel ( x,y,r ), r );
          }
        }
      }
    }
    else
    {
      NICE::Image img;
      try
      {
        img.read ( filelist[it] );
      }
      catch ( ImageException iE )
      {
        fprintf ( stderr, "Failed to open image file: %s\n", filelist[it].c_str() );
        fprintf ( stderr, "%s\n", iE.what() );
        exit ( -1 );
      }
      if ( !isInit )
      {
        imgData.reInit ( img.width(),img.height(),filelist.size(),1 );
        isInit = true;
      }
      for ( int y = 0; y < img.height(); y++ )
      {
        for ( int x = 0; x < img.width(); x++ )
        {
          imgData.set ( x, y, it, img.getPixel ( x,y ), 0 );
        }
      }
    }
  }

  if ( imagetype == IMAGETYPE_GRAY )
  {
    imgData.equalizeHistogram( 0 );
#ifdef DEBUG_PRINTS
    for (int z = 0; z < imgData.depth(); z++)
    {
      NICE::Image im = imgData.getChannel( z, 0);
      im.write( filelist[z]+"_eq.pgm");
    }
#endif
  }
}

void SemanticSegmentation::getProbabilityMap ( const NICE::MultiChannelImage3DT<double> & prob )
{
  std::string s;

  for ( int cl = 0; cl < prob.channels(); cl++ )
    for ( int z = 0; z < prob.depth(); z++ )
    {
      NICE::ColorImage img( prob.width(),prob.height() );
      NICE::ImageT<double> m = prob.getChannelT(z, cl);
      imageToPseudoColor(m, img);

      std::stringstream out;
      out << "probmap_s" << z << "_c" << cl << ".ppm";
      s = out.str();
      img.write( s );

      //showImage(img, "Probability map");
      //getchar();
    }
}