ENH: Add Shark KMeans Model into the learning application.

Modules/Applications/AppClassification/include/otbLearningApplicationBase.h

@@ -49,6 +49,7 @@
 
 #ifdef OTB_USE_SHARK
 #include "otbSharkRandomForestsMachineLearningModel.h"
+#include "otbSharkKMeansMachineLearningModel.h"
 #endif
 
 namespace otb
@@ -139,6 +140,7 @@ public:
 
 #ifdef OTB_USE_SHARK
   typedef otb::SharkRandomForestsMachineLearningModel<InputValueType, OutputValueType> SharkRandomForestType;
+  typedef otb::SharkKMeansMachineLearningModel<InputValueType, OutputValueType> SharkKMeansType;
 #endif
   
 protected:
@@ -221,6 +223,10 @@ private:
   void TrainSharkRandomForests(typename ListSampleType::Pointer trainingListSample,
                                typename TargetListSampleType::Pointer trainingLabeledListSample,
                                std::string modelPath);
+  void InitSharkKMeansParams();
+  void TrainSharkKMeans(typename ListSampleType::Pointer trainingListSample,
+                        typename TargetListSampleType::Pointer trainingLabeledListSample,
+                        std::string modelPath);
 #endif
   //@}
 };
@@ -247,6 +253,7 @@ private:
 #endif
 #ifdef OTB_USE_SHARK
 #include "otbTrainSharkRandomForests.txx"
+#include "otbTrainSharkKMeans.txx"
 #endif
 #endif
 

Modules/Applications/AppClassification/include/otbLearningApplicationBase.txx

@@ -76,6 +76,7 @@ LearningApplicationBase<TInputValue,TOutputValue>
 
 #ifdef OTB_USE_SHARK
   InitSharkRandomForestsParams();
+  InitSharkKMeansParams();
 #endif
   
 }
@@ -147,6 +148,15 @@ LearningApplicationBase<TInputValue,TOutputValue>
     otbAppLogFATAL("Module SharkLearning is not installed. You should consider turning OTB_USE_SHARK on during cmake configuration.");
     #endif
     }
+  if(modelName == "sharkkm")
+    {
+    #ifdef OTB_USE_SHARK
+    TrainSharkKMeans( trainingListSample, trainingLabeledListSample, modelPath );
+    #else
+    otbAppLogFATAL("Module SharkLearning is not installed. You should consider turning OTB_USE_SHARK on during cmake configuration.");
+    #endif
+    }
+
   
   // OpenCV SVM implementation is buggy with linear kernel
   // Users should use the libSVM implementation instead.

Modules/Applications/AppClassification/include/otbTrainSharkKMeans.txx

+/*=========================================================================
+ Program:   ORFEO Toolbox
+ Language:  C++
+ Date:      $Date$
+ Version:   $Revision$
+
+
+ Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
+ See OTBCopyright.txt for details.
+
+
+ This software is distributed WITHOUT ANY WARRANTY; without even
+ the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
+ PURPOSE.  See the above copyright notices for more information.
+
+ =========================================================================*/
+#ifndef otbTrainSharkKMeans_txx
+#define otbTrainSharkKMeans_txx
+
+#include "otbLearningApplicationBase.h"
+
+namespace otb
+{
+namespace Wrapper
+{
+template<class TInputValue, class TOutputValue>
+void LearningApplicationBase<TInputValue, TOutputValue>::InitSharkKMeansParams()
+{
+  AddChoice( "classifier.sharkkm", "Shark kmeans classifier" );
+  SetParameterDescription( "classifier.sharkkm",
+                           "This group of parameters allows setting Shark kMeans classifier parameters. "
+                                   "See complete documentation here "
+                                   "\\url{http://image.diku.dk/shark/sphinx_pages/build/html/rest_sources/tutorials/algorithms/kmeans.html}.\n " );
+  //MaxNumberOfIterations
+  AddParameter( ParameterType_Int, "classifier.sharkkm.nbmaxiter",
+                "Maximum number of iteration for the kmeans algorithm." );
+  SetParameterInt( "classifier.sharkkm.nbmaxiter", 0 );
+  SetMinimumParameterIntValue( "classifier.sharkkm.nbmaxiter", 0 );
+  SetParameterDescription( "classifier.sharkkm.nbmaxiter",
+                           "The maximum number of iteration for the kmeans algorithm. Default set to unlimited." );
+
+  //MaxNumberOfIterations
+  AddParameter( ParameterType_Int, "classifier.sharkkm.k", "The number of class used for the kmeans algorithm." );
+  SetParameterInt( "classifier.sharkkm.k", 2 );
+  SetParameterDescription( "classifier.sharkkm.k",
+                           "The number of class used for the kmeans algorithm. Default set to 2 class" );
+  SetMinimumParameterIntValue( "classifier.sharkkm.k", 2 );
+}
+
+template<class TInputValue, class TOutputValue>
+void LearningApplicationBase<TInputValue, TOutputValue>::TrainSharkKMeans(
+        typename ListSampleType::Pointer trainingListSample,
+        typename TargetListSampleType::Pointer trainingLabeledListSample, std::string modelPath)
+{
+  unsigned int nbMaxIter = static_cast<unsigned int>(abs( GetParameterInt( "classifier.sharkkm.nbmaxiter" ) ));
+  unsigned int k = static_cast<unsigned int>(abs( GetParameterInt( "classifier.sharkkm.k" ) ));
+
+  typename SharkKMeansType::Pointer classifier = SharkKMeansType::New();
+  classifier->SetRegressionMode( this->m_RegressionFlag );
+  classifier->SetInputListSample( trainingListSample );
+  classifier->SetTargetListSample( trainingLabeledListSample );
+  classifier->SetK( k );
+  classifier->SetMaximumNumberOfIterations( nbMaxIter );
+  classifier->Train();
+  classifier->Save( modelPath );
+}
+
+} //end namespace wrapper
+} //end namespace otb
+
+#endif