added minor program showing how to compute local features via LFSelection and MultiDataset

features/localfeatures/progs/computeLocalFeatures.cpp

@@ -0,0 +1,134 @@
+/**
+* @file computeLocalFeatures.cpp
+* @brief compute some features for training and testing (separate specifications possible)
+* @author Alexander Freytag
+* @date 14-06-2013
+*/
+
+//STL
+#include <iostream>
+#include <limits>
+
+//core -- basic stuff
+#include <core/basics/Config.h>
+#include <core/basics/ResourceStatistics.h>
+#include <core/basics/Timer.h>
+#include <core/image/Convert.h>
+#include <core/vector/VectorT.h>
+
+//vislearning -- basic stuff
+#include <vislearning/baselib/Globals.h>
+#include <vislearning/baselib/ICETools.h>
+#include <vislearning/cbaselib/MultiDataset.h>
+#include <vislearning/cbaselib/Example.h>
+#include <vislearning/cbaselib/ClassificationResult.h>
+#include <vislearning/cbaselib/ClassificationResults.h>
+// vislearning -- local features
+#include <vislearning/features/localfeatures/GenericLFSelection.h>
+//
+
+using namespace std;
+using namespace NICE;
+using namespace OBJREC;
+
+/**
+ a complete BoW pipeline
+ 
+ possibly, we can make use of objrec/progs/testClassifier.cpp
+*/
+int main( int argc, char **argv )
+{
+  std::set_terminate( __gnu_cxx::__verbose_terminate_handler );
+
+  NICE::Config * conf = new NICE::Config ( argc, argv );
+  
+  const std::string resultsfile = conf->gS( "main", "resultsfile", "/tmp/results.txt" );
+  
+  ResourceStatistics rs;
+  
+  // ========================================================================
+  //                            TRAINING STEP
+  // ========================================================================
+
+  MultiDataset md( conf );
+  const LabeledSet *trainFiles = md["train"];  
+   
+  //**********************************************
+  //
+  //     FEATURE EXTRACTION FOR TRAINING IMAGES
+  //
+  //**********************************************  
+  
+  std::cerr << "FEATURE EXTRACTION FOR TRAINING IMAGES" << std::endl;
+  
+  OBJREC::LocalFeatureRepresentation * featureExtractor = OBJREC::GenericLFSelection::selectLocalFeatureRep ( conf, "features", OBJREC::GenericLFSelection::TRAINING );
+  
+  
+  //TODO replace the nasty makro by a suitable for-loop to make it omp-ready (parallelization)
+  int imgCnt ( 0 );
+   
+  // the corresponding nasty makro: LOOP_ALL_S( *trainFiles )
+  for(LabeledSet::const_iterator classIt = trainFiles->begin() ; classIt != trainFiles->end() ; classIt++)
+  {
+    for ( std::vector<ImageInfo *>::const_iterator imgIt = classIt->second.begin(); 
+          imgIt != classIt->second.end(); 
+          imgIt++, imgCnt++
+        ) 
+    {
+      // the corresponding nasty makro: EACH_INFO( classno, info );
+      int classno ( classIt->first );
+      const ImageInfo imgInfo = *(*imgIt);      
+      
+      std::string filename = imgInfo.img();      
+      
+      NICE::ColorImage img( filename );
+  
+      //compute features
+      
+      //variables to store feature information
+      NICE::VVector features;
+      NICE::VVector positions;
+  
+      Globals::setCurrentImgFN ( filename );
+      featureExtractor->extractFeatures ( img, features, positions );  
+              
+    }
+  }
+  
+  
+  // ========================================================================
+  //                            TEST STEP
+  // ========================================================================
+  
+  const LabeledSet *testFiles = md["test"];
+
+  delete featureExtractor;
+  featureExtractor = OBJREC::GenericLFSelection::selectLocalFeatureRep ( conf, "features", OBJREC::GenericLFSelection::TESTING );  
+  
+  LOOP_ALL_S( *testFiles )
+  {
+      EACH_INFO( classno, info );
+      std::string filename = info.img();
+  
+      //**********************************************
+      //
+      //     FEATURE EXTRACTION FOR TEST IMAGES
+      //
+      //**********************************************  
+      
+      NICE::ColorImage img( filename );
+  
+      //compute features
+      
+      //variables to store feature information
+      NICE::VVector features;
+      NICE::VVector positions;
+  
+      Globals::setCurrentImgFN ( filename );
+      featureExtractor->extractFeatures ( img, features, positions );  
+
+  }
+
+  
+   return 0;
+}