ENH: set and normalize the user defined centroid in the application base

Modules/Applications/AppClassification/include/otbTrainSharkKMeans.hxx

@@ -22,6 +22,7 @@
 
 #include "otbLearningApplicationBase.h"
 #include "otbSharkKMeansMachineLearningModel.h"
+#include "otbStatisticsXMLFileReader.h"
 
 namespace otb
 {
@@ -45,9 +46,13 @@ void LearningApplicationBase<TInputValue, TOutputValue>::InitSharkKMeansParams()
   SetParameterDescription("classifier.sharkkm.k", "The number of classes used for the kmeans algorithm. Default set to 2 class");
   SetMinimumParameterIntValue("classifier.sharkkm.k", 2);
   
+  AddParameter(ParameterType_InputFilename, "classifier.sharkkm.centroidstats", "Statistics file");
+  SetParameterDescription("classifier.sharkkm.centroidstats", "A XML file containing mean and standard deviation to center"
+    "and reduce the centroids before classification, produced by ComputeImagesStatistics application.");
+  MandatoryOff("classifier.sharkkm.centroidstats");
   
   // Number of classes
-  AddParameter(ParameterType_String, "classifier.sharkkm.incentroid", "Number of classes for the kmeans algorithm");
+  AddParameter(ParameterType_String, "classifier.sharkkm.centroids", "Number of classes for the kmeans algorithm");
   SetParameterDescription("classifier.sharkkm.incentroid", "The number of classes used for the kmeans algorithm. Default set to 2 class");
   MandatoryOff("classifier.sharkkm.incentroid");
 }
@@ -66,7 +71,41 @@ void LearningApplicationBase<TInputValue, TOutputValue>::TrainSharkKMeans(
   classifier->SetInputListSample( trainingListSample );
   classifier->SetTargetListSample( trainingLabeledListSample );
   classifier->SetK( k );
-  classifier->SetCentroidFilename( GetParameterString( "classifier.sharkkm.incentroid") );
+
+  // Initialize centroids from file
+  if(HasValue("classifier.sharkkm.centroids"))
+  {
+    shark::Data<shark::RealVector> centroidData;
+    shark::importCSV(centroidData, GetParameterString( "classifier.sharkkm.centroidstats"), ' ');
+    if( HasValue( "classifier.sharkkm.centroids" ) )
+    {
+      auto statisticsReader = otb::StatisticsXMLFileReader< itk::VariableLengthVector<float> >::New();
+      statisticsReader->SetFileName(GetParameterString( "classifier.sharkkm.centroidstats" ));
+      auto meanMeasurementVector = statisticsReader->GetStatisticVectorByName("mean");
+      auto stddevMeasurementVector = statisticsReader->GetStatisticVectorByName("stddev");
+  
+      // Convert itk Variable Length Vector to shark Real Vector
+      shark::RealVector meanMeasurementRV(meanMeasurementVector.Size());
+      for (unsigned int i = 0; i<meanMeasurementVector.Size(); ++i)
+      {
+        // Substract the mean
+        meanMeasurementRV[i] = - meanMeasurementVector[i];
+      }
+  
+      shark::RealVector stddevMeasurementRV(stddevMeasurementVector.Size());
+      for (unsigned int i = 0; i<stddevMeasurementVector.Size(); ++i)
+      {
+        stddevMeasurementRV[i] = stddevMeasurementVector[i];
+      }
+      
+      shark::Normalizer<> normalizer(stddevMeasurementRV, meanMeasurementRV);
+
+      centroidData = normalizer(centroidData);
+    }
+    
+    classifier->SetCentroidsFromData( centroidData);
+  }
+  
   classifier->SetMaximumNumberOfIterations( nbMaxIter );
   classifier->Train();
   classifier->Save( modelPath );