NICE_SemSeg/semseg/SemanticSegmentation.cpp

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

*/
#include <iostream>

#include "SemanticSegmentation.h"
#include "vislearning/baselib/Preprocess.h"
#include "vislearning/baselib/Globals.h"
#include "semseg3d/image/MultiChannelImage3DT.h"
#include "core/basics/StringTools.h"

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

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
}

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" )
    imagetype = IMAGETYPE_RGB;
  else if ( imagetype_s == "gray" )
    imagetype = IMAGETYPE_GRAY;
  else {
    fprintf ( stderr, "SemanticSegmentation:: unknown image type option\n" );
    exit ( -1 );
  }
}

SemanticSegmentation::~SemanticSegmentation()
{
}

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

	LOOP_ALL_S( *Files )
  {
		EACH_INFO( classno, info );
		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("_");
		std::string curName = filename.substr(found-1,1);
		if ( curName.compare( oldName ) == 0 )
		{
			zsize++;
		}
		else
		{
			depthVec.push_back( zsize );
			zsize = 1;
			oldName = curName;
		}

	}
	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 = Preprocess::ReadImgAdvRGB( filelist[it] );
			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 = Preprocess::ReadImgAdv( filelist[it] );
			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);
				}
			}
		}
	}
}