minor fix, removed unneded includes

progs/evaluateCompleteBoWPipeline.cpp

@@ -33,131 +33,16 @@
 #include "vislearning/features/simplefeatures/BoWFeatureConverter.h"
 // 
 // vislearning -- local features
-// #include <vislearning/features/localfeatures/LFonHSG.h>
-// #include <vislearning/features/localfeatures/LFColorSande.h>
-// #include <vislearning/features/localfeatures/LFColorWeijer.h>
-// #include <vislearning/features/localfeatures/LFReadCache.h>
-// #include <vislearning/features/localfeatures/LFWriteCache.h>
 #include <vislearning/features/localfeatures/GenericLFSelection.h>
 //
 // vislearning -- clustering methods
 #include <vislearning/math/cluster/GenericClusterAlgorithmSelection.h>
-// #include <vislearning/math/cluster/ClusterAlgorithm.h>
-// #include <vislearning/math/cluster/RandomClustering.h>
-// #include <vislearning/math/cluster/KMeans.h>
-// #include <vislearning/math/cluster/KMedian.h>
-// #include <vislearning/math/cluster/GMM.h>
 //
 
 using namespace std;
 using namespace NICE;
 using namespace OBJREC;
 
-
-// LocalFeatureRepresentation * setFeatureExtractor( const Config * _conf )
-// {  
-//   LocalFeatureRepresentation * featureExtractor;
-//    
-//     //feature stuff
-//   // which OpponentSIFT implementation to use {NICE, VANDESANDE}
-//   std::string opSiftImpl;  
-//   opSiftImpl = _conf->gS ( "Descriptor", "implementation", "VANDESANDE" );
-//   // read features?
-//   bool readfeat;
-//   readfeat = _conf->gB ( "Descriptor", "read", true );
-//   // write features?
-//   bool writefeat;  
-//   writefeat = _conf->gB ( "Descriptor", "write", true );   
-//   
-//   // Welche Opponentsift Implementierung soll genutzt werden ?
-//   LocalFeatureRepresentation *cSIFT = NULL;
-//   LocalFeatureRepresentation *writeFeats = NULL;
-//   LocalFeatureRepresentation *readFeats = NULL;
-//   featureExtractor = NULL;
-//   if ( opSiftImpl == "NICE" )
-//   {
-//      cSIFT = new OBJREC::LFonHSG ( _conf, "HSG" );
-//   }
-//   else if ( opSiftImpl == "VANDESANDE" )
-//   {
-//     cSIFT = new OBJREC::LFColorSande ( _conf, "LFColorSande" );
-//   }
-//   else
-//   {
-//     fthrow ( Exception, "feattype: %s not yet supported" << opSiftImpl );
-//   }
-// 
-//   featureExtractor = cSIFT;
-//   
-//   if ( writefeat )
-//   {
-//     // write the features to a file, if there isn't any to read
-//     writeFeats = new LFWriteCache ( _conf, cSIFT );
-//     featureExtractor = writeFeats;
-//   }
-// 
-//   if ( readfeat )
-//   {
-//     // read the features from a file
-//     if ( writefeat )
-//     {
-//       readFeats = new LFReadCache ( _conf, writeFeats, -1 );
-//     }
-//     else
-//     {
-//       readFeats = new LFReadCache ( _conf, cSIFT, -1 );
-//     }
-//     featureExtractor = readFeats; 
-//   }  
-//   
-//   //only set feature stuff to NULL, deletion of the underlying object is done in the destructor
-//   if ( cSIFT != NULL )
-//     cSIFT = NULL;
-//   if ( writeFeats != NULL )
-//     writeFeats = NULL;
-//   if ( readFeats != NULL )
-//     readFeats  = NULL ;   
-//   
-//   return featureExtractor;
-// }
-// 
-// OBJREC::ClusterAlgorithm * setClusterAlgo( const Config * _conf )
-// { 
-//  std::string section ( "clusteringStuff" );
-//   // define the initial number of clusters our codebook shall contain
-//   int noClusters = _conf->gI(section, "noClusters", 10);
-//   
-//   // define the clustering algorithm to be used
-//   std::string clusterAlgoString = _conf->gS(section, "clusterAlgo", "kmeans");  
-//   
-//   OBJREC::ClusterAlgorithm * clusterAlgo;
-//   
-//   if (clusterAlgoString.compare("kmeans") == 0)
-//   {
-//     clusterAlgo = new OBJREC::KMeans(noClusters);
-//   }
-//   else if (clusterAlgoString.compare("kmedian") == 0)
-//   {
-//     clusterAlgo = new OBJREC::KMedian(noClusters);
-//   }  
-//   else if (clusterAlgoString.compare("GMM") == 0) 
-//   {
-//     clusterAlgo = new OBJREC::GMM( _conf, noClusters );
-//   }
-//   else if ( clusterAlgoString.compare("RandomClustering") == 0 )   
-//   {
-//     clusterAlgo = new OBJREC::RandomClustering( _conf, section );
-//   }
-//   else
-//   {
-//     std::cerr << "Unknown cluster algorithm selected, use random clustering instead" << std::endl;
-//     clusterAlgo = new OBJREC::RandomClustering( _conf, section );
-//   }    
-//   
-//   return clusterAlgo;
-// }
-
-
 /**
  a complete BoW pipeline
  
@@ -190,7 +75,6 @@ int main( int argc, char **argv )
   std::cerr << "FEATURE EXTRACTION FOR TRAINING IMAGES" << std::endl;
   
   OBJREC::LocalFeatureRepresentation * featureExtractor = OBJREC::GenericLFSelection::selectLocalFeatureRep ( conf, "features", OBJREC::GenericLFSelection::TRAINING );
-//   LocalFeatureRepresentation * featureExtractor = setFeatureExtractor( conf );
   
   //collect features in a single data structure
   NICE::VVector featuresFromAllTrainingImages;  
@@ -266,7 +150,6 @@ int main( int argc, char **argv )
   //**********************************************    
   
   std::cerr << "CODEBOOK CREATION" << std::endl;
-//   OBJREC::ClusterAlgorithm * clusterAlgo = setClusterAlgo( conf );
   
   OBJREC::ClusterAlgorithm * clusterAlgo = OBJREC::GenericClusterAlgorithmSelection::selectClusterAlgo ( conf );