bug fixed by setting probabilities to -inf; fixed using testWSize instead of trainWSize

semseg/SemSegNovelty.cpp

@@ -137,7 +137,7 @@ void SemSegNovelty::initFromConfig(const Config* conf, const string _confSection
   this->findMaximumUncert = conf->gB(_confSection, "findMaximumUncert", true);
   this->whs = conf->gI ( _confSection, "window_size", 10 );
   //distance to next descriptor during training
-  this->trainWsize = conf->gI ( _confSection, "train_window_size", 10 );
+  this->trainWSize = conf->gI ( _confSection, "train_window_size", 10 );
   //distance to next descriptor during testing
   this->testWSize = conf->gI (_confSection, "test_window_size", 10);
   // select your segmentation method here
@@ -375,9 +375,9 @@ void SemSegNovelty::train ( const MultiDataset *md )
 	feats.calcIntegral ( c );
       }
 
-      for ( int y = 0; y < ysize; y += trainWsize)
+      for ( int y = 0; y < ysize; y += trainWSize)
       {
-	for ( int x = 0; x < xsize; x += trainWsize )
+    for ( int x = 0; x < xsize; x += trainWSize )
 	{
 
 	  int classnoTmp = labels.getPixel ( x, y );
@@ -511,7 +511,7 @@ void SemSegNovelty::semanticseg ( CachedExample *ce, NICE::Image & segresult, NI
   ce->getImageSize ( xsize, ysize );
 
   probabilities.reInit( xsize, ysize, this->classNames->getMaxClassno() + 1);
-  probabilities.setAll ( 0.0 );
+  probabilities.setAll ( -std::numeric_limits<double>::max() );
    
   NICE::ColorImage img;
   try {
@@ -627,20 +627,23 @@ void SemSegNovelty::semanticseg ( CachedExample *ce, NICE::Image & segresult, NI
     int amountRegions = regionSeg->segRegions ( img, mask );
     
     //compute probs per region
-    std::vector<std::vector<double> > regionProb(amountRegions, std::vector<double>(probabilities.channels(),0.0));
+    std::vector<std::vector<double> > regionProb(amountRegions, std::vector<double>(probabilities.channels(), -std::numeric_limits<double>::max() ));
     std::vector<double> regionNoveltyMeasure (amountRegions, 0.0);
 
     std::vector<int> regionCounter(amountRegions, 0);
     std::vector<int> regionCounterNovelty(amountRegions, 0);
-    for ( int y = 0; y < ysize; y += trainWsize) //y++)
+    for ( int y = 0; y < ysize; y += testWSize) //y++)
     {
-      for (int x = 0; x < xsize; x += trainWsize) //x++)
+      for (int x = 0; x < xsize; x += testWSize) //x++)
       {
         int r = mask(x,y);
         regionCounter[r]++;
+
         for(int j = 0; j < probabilities.channels(); j++)
         {
-          regionProb[r][j] += probabilities ( x, y, j );
+            if( regionProb[r][j] == -std::numeric_limits<double>::max() )
+                regionProb[r][j] = 0.0f;
+            regionProb[r][j] += probabilities ( x, y, j );
         }
         
         if ( forbidden_classesActiveLearning.find( labels(x,y) ) == forbidden_classesActiveLearning.end() )
@@ -721,9 +724,9 @@ void SemSegNovelty::semanticseg ( CachedExample *ce, NICE::Image & segresult, NI
         //current most novel region of the image has "higher" novelty score then previous most novel region of all test images worked on so far
         // -> save new important features of this region
         Examples examples;
-        for ( int y = 0; y < ysize; y += trainWsize )
+        for ( int y = 0; y < ysize; y += testWSize )
         {
-          for ( int x = 0; x < xsize; x += trainWsize)
+          for ( int x = 0; x < xsize; x += testWSize)
           {
             if(mask(x,y) == maxUncertRegion)
             {
@@ -1116,7 +1119,7 @@ void SemSegNovelty::computeNoveltyByGPMean(  NICE::FloatImage & noveltyImage,
         }
 
         //check whether we found a class with higher smaller abs mean than the current minimum
-        if (abs(probabilities(x,y,j)) < minMeanAbs)
+        if (abs( cr.scores[j] ) < minMeanAbs)
         {
           minMeanAbs = abs(cr.scores[j]); 
         }
@@ -1663,9 +1666,9 @@ void SemSegNovelty::restore ( std::istream & is, int format )
         is >> tmp; // end of block 
         tmp = this->removeEndTag ( tmp );
       }      
-      else if ( tmp.compare("trainWsize") == 0 )
+      else if ( tmp.compare("trainWSize") == 0 )
       { 
-        is >> trainWsize;
+        is >> trainWSize;
         is >> tmp; // end of block 
         tmp = this->removeEndTag ( tmp );
       }
@@ -2021,7 +2024,7 @@ void SemSegNovelty::store ( std::ostream & os, int format ) const
     os << this->createStartTag( "featExtract" ) << std::endl; 
     
     os << this->createStartTag( "trainWsize" ) << std::endl;
-    os << this->trainWsize << std::endl;
+    os << this->trainWSize << std::endl;
     os << this->createStartTag( "trainWsize" ) << std::endl;  
     
     os << this->createStartTag( "whs" ) << std::endl;

semseg/SemSegNovelty.h

@@ -53,7 +53,7 @@ class SemSegNovelty : public SemanticSegmentation
     LocalFeatureColorWeijer *featExtract;     
     
     //! distance between features for training
-    int trainWsize;
+    int trainWSize;
     
     //! half of the window size for local features
     int whs;