ENH: use the class dictionary in Shark RF and store it in the serialised model

Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.h

@@ -157,6 +157,7 @@ private:
 
   shark::RFClassifier<unsigned int> m_RFModel;
   shark::RFTrainer<unsigned int> m_RFTrainer;
+  std::vector<unsigned int> m_ClassDictionary;
 
   unsigned int m_NumberOfTrees;
   unsigned int m_MTry;

Modules/Learning/Supervised/include/otbSharkRandomForestsMachineLearningModel.txx

@@ -75,6 +75,7 @@ SharkRandomForestsMachineLearningModel<TInputValue,TOutputValue>
 
   Shark::ListSampleToSharkVector(this->GetInputListSample(), features);
   Shark::ListSampleToSharkVector(this->GetTargetListSample(), class_labels);
+  Shark::NormalizeLabelsAndGetDictionary(class_labels, m_ClassDictionary);
   shark::ClassificationDataset TrainSamples = shark::createLabeledDataFromRange(features,class_labels);
 
   //Set parameters
@@ -130,7 +131,7 @@ SharkRandomForestsMachineLearningModel<TInputValue,TOutputValue>
   unsigned int res{0};
   m_RFModel.eval(samples, res);
   TargetSampleType target;
-  target[0] = static_cast<TOutputValue>(res);
+  target[0] = m_ClassDictionary[static_cast<TOutputValue>(res)];
   return target;
 }
 
@@ -154,9 +155,9 @@ SharkRandomForestsMachineLearningModel<TInputValue,TOutputValue>
   Shark::ListSampleRangeToSharkVector(input, features,startIndex,size);
   shark::Data<shark::RealVector> inputSamples = shark::createDataFromRange(features);
 
-  #ifdef _OPENMP
+#ifdef _OPENMP
   omp_set_num_threads(itk::MultiThreader::GetGlobalDefaultNumberOfThreads());
-  #endif
+#endif
   
   if(quality != ITK_NULLPTR)
     {
@@ -177,7 +178,7 @@ SharkRandomForestsMachineLearningModel<TInputValue,TOutputValue>
   for(const auto& p : prediction.elements())
     {
     TargetSampleType target;
-    target[0] = static_cast<TOutputValue>(p);
+    target[0] = m_ClassDictionary[static_cast<TOutputValue>(p)];
     targets->SetMeasurementVector(id,target);
     ++id;
     }
@@ -195,6 +196,12 @@ SharkRandomForestsMachineLearningModel<TInputValue,TOutputValue>
     }
   // Add comment with model file name
   ofs << "#" << m_RFModel.name() << std::endl;
+  ofs << m_ClassDictionary.size() << " ";
+  for(const auto& l : m_ClassDictionary)
+    {
+    ofs << l << " ";
+    }
+  ofs << std::endl;
   shark::TextOutArchive oa(ofs);
   m_RFModel.save(oa,0);
 }
@@ -221,6 +228,15 @@ SharkRandomForestsMachineLearningModel<TInputValue,TOutputValue>
       ifs.clear();
       ifs.seekg( 0, std::ios::beg );
       }
+    size_t nbLabels{0};
+    ifs >> nbLabels;
+    m_ClassDictionary.resize(nbLabels);
+    for(size_t i=0; i<nbLabels; ++i)
+      {
+      unsigned int label;
+      ifs >> label;
+      m_ClassDictionary[i]=label;
+      }
     shark::TextInArchive ia( ifs );
     m_RFModel.load( ia, 0 );
     }