NICE_VisLearning/featureLearning/progs/testFeatureLearning.cpp

/**
* @file testFeatureLearning.cpp
* @brief test the feature learning routines to incrementally increase / adapt the codebook currently used
* @author Alexander Freytag
* @date 11-04-2013
*/

#include <iostream>
#include <limits>

#include <core/basics/Config.h>
#include <core/basics/ResourceStatistics.h>
#include <core/vector/VectorT.h>

#include <vislearning/baselib/Globals.h>
#include <vislearning/baselib/ICETools.h>
#include <vislearning/cbaselib/MultiDataset.h>
#include <vislearning/cbaselib/Example.h>

#include "vislearning/featureLearning/FeatureLearningGeneric.h"
#include "vislearning/featureLearning/FeatureLearningClusterBased.h"


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


/**
 test feature learning routines
*/
int main( int argc, char **argv )
{
  std::set_terminate( __gnu_cxx::__verbose_terminate_handler );

  Config * conf = new Config ( argc, argv );
  
  bool showTrainingImages= conf->gB( "featureLearning", "showTrainingImages", false );
  bool showTestImages= conf->gB( "featureLearning", "showTestImages", false );
  
  ResourceStatistics rs;
  std::string resultdir;
  
  
  //**********************************************
  //
  //      READ INITIAL TRAINING SET TO COMPUTE
  //             AN INITIAL CODEBOOK
  //
  //**********************************************
  
  std::cerr << " READ INITIAL TRAINING SET TO COMPUTE AN INITIAL CODEBOOK" << std::endl;
  
  MultiDataset md( conf );
  const LabeledSet *trainFiles = md["train"];
  
  //**********************************************
  //
  //      SET UP THE FEATURE LEARNING ALGO
  //
  //********************************************** 
  
  OBJREC::FeatureLearningGeneric * featureLearning;
  featureLearning = new OBJREC::FeatureLearningClusterBased( conf, &md );
  
  //print computed cluster centers  -- this is NOT recommended :)
//   prototypes.store(std::cerr);
  
  //evaluate how well the training images are covered with our initial codebook
  //that is, compute these nice "novelty maps" per feature
  
  LOOP_ALL_S( *trainFiles )
  {
      EACH_INFO( classno, info );
      std::string filename = info.img();
      
      featureLearning->evaluateCurrentCodebook( filename );       
  }

  //**********************************************
  //
  //        FOR-LOOP OVER UNSEEN IMAGES
  //
  //       EXTRACT FEATURES, CLUSTER THEM, TAKE
  //       MOST "VALUABLE" CLUSTERS AS NEW 
  //    REPRESENTATIVES IN AN INCREASED CODEBOK
  //
  //**********************************************
  const LabeledSet *testFiles = md["test"];
  
  std::cerr << "start looping over all files" << std::endl;
  LOOP_ALL_S( *testFiles )
  {
      EACH_INFO( classno, info );
      std::string file = info.img();
      
      NICE::ColorImage orig( file );
      showImage( orig, "Input" );
  } //Loop over all test images
  
   return 0;
}