faster

progs/testSemanticSegmentation.cpp

@@ -26,8 +26,6 @@
 
 #include <fstream>
 
-
-
 using namespace OBJREC;
 
 using namespace NICE;

semseg/SemSegContextTree.cpp

@@ -18,6 +18,9 @@
 
 #define BOUND(x,min,max) (((x)<(min))?(min):((x)>(max)?(max):(x)))
 #undef LOCALFEATS
+#undef WRITEGLOB
+#undef TEXTONMAP
+
 //#define LOCALFEATS
 
 using namespace OBJREC;
@@ -1287,7 +1290,9 @@ void SemSegContextTree::train ( const MultiDataset *md )
   vector<MultiChannelImageT<double> > allfeats;
   vector<MultiChannelImageT<unsigned short int> > currentfeats;
   vector<MatrixT<int> > labels;
+#ifdef TEXTONMAP
   vector<MultiChannelImageT<SparseVectorInt> > textonMap;
+#endif
   vector<MultiChannelImageT<SparseVectorInt> > integralTexton;
 
 
@@ -1333,8 +1338,10 @@ void SemSegContextTree::train ( const MultiDataset *md )
 
     currentfeats.push_back ( MultiChannelImageT<unsigned short int> ( xsize, ysize, nbTrees ) );
     currentfeats[imgcounter].setAll ( 0 );
+#ifdef TEXTONMAP
     textonMap.push_back ( MultiChannelImageT<SparseVectorInt> ( xsize / grid + 1, ysize / grid + 1, 1 ));
     integralTexton.push_back ( MultiChannelImageT<SparseVectorInt> ( xsize / grid + 1, ysize / grid + 1, 1 ));
+#endif
     
     labels.push_back ( tmpMat );
     
@@ -1586,7 +1593,9 @@ void SemSegContextTree::train ( const MultiDataset *md )
                       forest[tree][left].featcounter++;
                       SparseVectorInt v;
                       v.insert ( pair<int, double> ( leftu, weight ) );
+#ifdef TEXTONMAP
                       textonMap[iCounter] ( subx, suby ).add ( v );
+#endif
                     }
                     else
                     {
@@ -1597,7 +1606,9 @@ void SemSegContextTree::train ( const MultiDataset *md )
                       //feld im subsampled finden und in diesem rechts hochzählen
                       SparseVectorInt v;
                       v.insert ( pair<int, double> ( rightu, weight ) );
+#ifdef TEXTONMAP
                       textonMap[iCounter] ( subx, suby ).add ( v );
+#endif
                     }
                   }
                 }
@@ -1719,7 +1730,9 @@ void SemSegContextTree::train ( const MultiDataset *md )
     for ( int i = 0; i < imgcounter; i++ )
     {
       computeIntegralImage ( currentfeats[i], allfeats[i], integralImgs[i] );
+#ifdef TEXTONMAP
       computeIntegralImage ( textonMap[i], integralTexton[i] );
+#endif
     }
 
 #if 1
@@ -1729,7 +1742,6 @@ void SemSegContextTree::train ( const MultiDataset *md )
 #endif
   }
 
-#define WRITEGLOB
 #ifdef WRITEGLOB
   ofstream outstream("globtrain.feat");
   
@@ -1842,8 +1854,11 @@ void SemSegContextTree::semanticseg ( CachedExample *ce, NICE::Image & segresult
   //TODO: resize image?!
 
   MultiChannelImageT<double> feats;
+  
+#ifdef TEXTONMAP
   MultiChannelImageT<SparseVectorInt> textonMap ( xsize / grid + 1, ysize / grid + 1, 1 );
   MultiChannelImageT<SparseVectorInt> integralTexton ( xsize / grid + 1, ysize / grid + 1, 1 );
+#endif
 
 #ifdef LOCALFEATS
   lfcw->getFeats ( img, feats );
@@ -1918,14 +1933,18 @@ void SemSegContextTree::semanticseg ( CachedExample *ce, NICE::Image & segresult
               currentfeats.set ( x, y, forest[tree][t].left, tree );
               SparseVectorInt v;
               v.insert ( pair<int, double> ( forest[tree][forest[tree][t].left].nodeNumber, weight ) );
+#ifdef TEXTONMAP
               textonMap ( subx, suby ).add ( v );
+#endif
             }
             else
             {
               currentfeats.set ( x, y, forest[tree][t].right, tree );
               SparseVectorInt v;
               v.insert ( pair<int, double> ( forest[tree][forest[tree][t].right].nodeNumber, weight ) );
+#ifdef TEXTONMAP
               textonMap ( subx, suby ).add ( v );
+#endif
             }
 
             /*if ( x == xpos && y == ypos )
@@ -1951,7 +1970,9 @@ void SemSegContextTree::semanticseg ( CachedExample *ce, NICE::Image & segresult
     }
 
     computeIntegralImage ( currentfeats, feats, integralImg );
+#ifdef TEXTONMAP
     computeIntegralImage ( textonMap, integralTexton );
+#endif
   }
 
   cout << forest[0][currentfeats.get ( xpos, ypos, 0 ) ].dist << endl;