contex removed

semseg/SemSegContextTree.cpp

@@ -678,6 +678,8 @@ SemSegContextTree::~SemSegContextTree()
 
 double SemSegContextTree::getBestSplit( std::vector<NICE::MultiChannelImageT<double> > &feats, std::vector<NICE::MultiChannelImageT<int> > &currentfeats, std::vector<NICE::MultiChannelImageT<double> > &integralImgs, const std::vector<NICE::MatrixT<int> > &labels, int node, Operation *&splitop, double &splitval, const int &tree )
 {
+  Timer t;
+  t.start();
   int imgCount = 0, featdim = 0;
 
   try
@@ -747,7 +749,6 @@ double SemSegContextTree::getBestSplit( std::vector<NICE::MultiChannelImageT<dou
       }
     }
   }
-
   //cout << "size: " << selFeats.size() << endl;
   //getchar();
 
@@ -844,7 +845,7 @@ double SemSegContextTree::getBestSplit( std::vector<NICE::MultiChannelImageT<dou
       featsel.push_back( op );
     }
   }
-  
+
 #pragma omp parallel for private(mapit)
   for ( int f = 0; f < featsPerSplit; f++ )
   {
@@ -945,6 +946,7 @@ double SemSegContextTree::getBestSplit( std::vector<NICE::MultiChannelImageT<dou
       }
     }
   }
+ 
   //getchar();
   //splitop->writeInfos();
   //cout<< "ig: " << bestig << endl;
@@ -954,6 +956,8 @@ double SemSegContextTree::getBestSplit( std::vector<NICE::MultiChannelImageT<dou
    if(featsel[i] != splitop)
     delete featsel[i];
   }*/
+          
+ 
 #ifdef debug
   cout << "globent: " << globent <<  " bestig " << bestig << " splitval: " << splitval << endl;
 
@@ -1241,7 +1245,7 @@ void SemSegContextTree::train( const MultiDataset *md )
 
 #endif
 
-  int depth = 0;
+  depth = 0;
 
   for ( int t = 0; t < nbTrees; t++ )
   {
@@ -1262,6 +1266,9 @@ void SemSegContextTree::train( const MultiDataset *md )
 
   while ( !allleaf && depth < maxDepth )
   {
+#ifdef DEBUG
+    cout << "depth: " << depth << endl;
+#endif
     allleaf = true;
     vector<MultiChannelImageT<int> > lastfeats = currentfeats;
 
@@ -1287,7 +1294,7 @@ void SemSegContextTree::train( const MultiDataset *md )
         if ( !forest[tree][i].isleaf && forest[tree][i].left < 0 )
         {
 #if 0
-                    timer.stop();
+          timer.stop();
           cout << "time 1: " << timer.getLast() << endl;
           timer.start();
 #endif
@@ -1296,7 +1303,14 @@ void SemSegContextTree::train( const MultiDataset *md )
           double bestig = getBestSplit( allfeats, lastfeats, integralImgs, labels, i, splitfeat, splitval, tree );
 #if 0
           timer.stop();
-          cout << "time 2: " << timer.getLast() << endl;
+	  double tl = timer.getLast();
+          
+	  if(tl > 10.0)
+	  {
+	    cout << "time 2: " << tl << endl;
+	    cout << "slow split: " << splitfeat->writeInfos() << endl;
+	    getchar();
+	  }
           timer.start();
 #endif
           forest[tree][i].feat = splitfeat;
@@ -1322,6 +1336,7 @@ void SemSegContextTree::train( const MultiDataset *md )
           cout << "time 3: " << timer.getLast() << endl;
           timer.start();
 #endif
+
 #pragma omp parallel for
             for ( int iCounter = 0; iCounter < imgcounter; iCounter++ )
             {
@@ -1342,6 +1357,7 @@ void SemSegContextTree::train( const MultiDataset *md )
                     feat.integralImg = &integralImgs[iCounter];
                     double val = splitfeat->getVal( feat, x, y );
 
+#pragma omp critical
                     if ( val < splitval )
                     {
                       currentfeats[iCounter].set( x, y, left, tree );
@@ -1352,17 +1368,19 @@ void SemSegContextTree::train( const MultiDataset *md )
                     {
                       currentfeats[iCounter].set( x, y, right, tree );
                       forest[tree][right].dist[labelmap[labels[iCounter]( x, y )]]++;
+		      forest[tree][right].featcounter++;
                     }
                   }
                 }
               }
+              
             }
 #if 0
             timer.stop();
           cout << "time 4: " << timer.getLast() << endl;
           timer.start();
 #endif
-            forest[tree][right].featcounter = forest[tree][i].featcounter - forest[tree][left].featcounter;
+//            forest[tree][right].featcounter = forest[tree][i].featcounter - forest[tree][left].featcounter;
             
             double lcounter = 0.0, rcounter = 0.0;
 
@@ -1448,9 +1466,7 @@ void SemSegContextTree::train( const MultiDataset *md )
           timer.start();
 #endif
     }
-
-    //TODO: features neu berechnen!
-
+    
     //compute integral image
     int channels = classes + allfeats[0].channels();
 #if 0
@@ -1473,8 +1489,7 @@ void SemSegContextTree::train( const MultiDataset *md )
           timer.stop();
           cout << "time for part1: " << timer.getLast() << endl;
           timer.start();
-#endif     
-
+#endif
 
 #pragma omp parallel for
     for ( int i = 0; i < imgcounter; i++ )
@@ -1489,10 +1504,6 @@ void SemSegContextTree::train( const MultiDataset *md )
 
 #endif
     depth++;
-
-#ifdef DEBUG
-    cout << "depth: " << depth << endl;
-#endif
   }
 
 #ifdef DEBUG
@@ -1588,7 +1599,7 @@ void SemSegContextTree::semanticseg( CachedExample *ce, NICE::Image & segresult,
 
   currentfeats.setAll( 0 );
 
-  int depth = 0;
+  depth = 0;
 
   while ( !allleaf )
   {
@@ -1680,10 +1691,10 @@ void SemSegContextTree::semanticseg( CachedExample *ce, NICE::Image & segresult,
   else
   {
     //final labeling using segmentation
-    //TODO: segmentation
     Matrix regions;
     int regionNumber = segmentation->segRegions( img, regions );
     cout << "regions: " << regionNumber << endl;
+
     int dSize = forest[0][0].dist.size();
     vector<vector<double> > regionProbs( regionNumber, vector<double>( dSize, 0.0 ) );
     vector<int> bestlabels( regionNumber, 0 );

semseg/SemSegContextTree.h

@@ -185,6 +185,9 @@ class SemSegContextTree : public SemanticSegmentation
 
   /** maximal depth of tree */
   int maxDepth;
+  
+  /** current depth for training */
+  int depth;
 
   /** operations for pairwise features */
   std::vector<Operation*> ops;