REFAC: Rename Clustering to Unsupervised and update dependencies.

Modules/Applications/AppClassification/app/CMakeLists.txt

@@ -51,8 +51,8 @@ otb_create_application(
   LINK_LIBRARIES ${${otb-module}_LIBRARIES})
 
 otb_create_application(
-  NAME           TrainVectorClustering
-  SOURCES        otbTrainVectorClustering.cxx
+  NAME           TrainVectorUnsupervised
+  SOURCES        otbTrainVectorUnsupervised.cxx
   LINK_LIBRARIES ${${otb-module}_LIBRARIES})
 
 otb_create_application(
@@ -81,8 +81,8 @@ otb_create_application(
   LINK_LIBRARIES ${${otb-module}_LIBRARIES})
 
 otb_create_application(
-  NAME           TrainImagesClustering
-  SOURCES        otbTrainImagesClustering.cxx
+  NAME           TrainImagesUnsupervised
+  SOURCES        otbTrainImagesUnsupervised.cxx
   LINK_LIBRARIES ${${otb-module}_LIBRARIES})
 
 otb_create_application(

Modules/Applications/AppClassification/app/otbTrainImagesClustering.cxx → Modules/Applications/AppClassification/app/otbTrainImagesUnsupervised.cxx

@@ -5,10 +5,10 @@ namespace otb
 namespace Wrapper
 {
 
-class TrainImagesClustering : public TrainImagesBase
+class TrainImagesUnsupervised : public TrainImagesBase
 {
 public:
-  typedef TrainImagesClustering         Self;
+  typedef TrainImagesUnsupervised       Self;
   typedef TrainImagesBase               Superclass;
   typedef itk::SmartPointer<Self>       Pointer;
   typedef itk::SmartPointer<const Self> ConstPointer;
@@ -17,7 +17,7 @@ public:
 
   void DoInit() ITK_OVERRIDE
   {
-    SetName( "TrainImagesClustering" );
+    SetName( "TrainImagesUnsupervised" );
     SetDescription( "Train a classifier from multiple pairs of images and optional input training vector data." );
 
     // Documentation
@@ -168,4 +168,4 @@ private :
 }
 }
 
-OTB_APPLICATION_EXPORT( otb::Wrapper::TrainImagesClustering )
\ No newline at end of file
+OTB_APPLICATION_EXPORT( otb::Wrapper::TrainImagesUnsupervised )
\ No newline at end of file

Modules/Applications/AppClassification/app/otbTrainVectorClustering.cxx → Modules/Applications/AppClassification/app/otbTrainVectorUnsupervised.cxx

@@ -21,10 +21,10 @@ namespace otb
 namespace Wrapper
 {
 
-class TrainVectorClustering : public TrainVectorBase
+class TrainVectorUnsupervised : public TrainVectorBase
 {
 public:
-  typedef TrainVectorClustering Self;
+  typedef TrainVectorUnsupervised Self;
   typedef TrainVectorBase Superclass;
   typedef itk::SmartPointer<Self> Pointer;
   typedef itk::SmartPointer<const Self> ConstPointer;
@@ -37,7 +37,7 @@ public:
   typedef Superclass::TargetListSampleType TargetListSampleType;
 
 protected :
-  TrainVectorClustering() : TrainVectorBase()
+  TrainVectorUnsupervised() : TrainVectorBase()
   {
     m_ClassifierCategory = Unsupervised;
   }
@@ -45,10 +45,10 @@ protected :
 private:
   void DoTrainInit()
   {
-    SetName( "TrainVectorClustering" );
+    SetName( "TrainVectorUnsupervised" );
     SetDescription( "Train a classifier based on labeled or unlabeled geometries and a list of features to consider." );
 
-    SetDocName( "Train Vector Clustering" );
+    SetDocName( "Train Vector Unsupervised" );
     SetDocLongDescription( "This application trains a classifier based on "
                                    "labeled or unlabeled geometries and a list of features to consider for classification." );
     SetDocLimitations( " " );
@@ -83,4 +83,4 @@ private:
 }
 }
 
-OTB_APPLICATION_EXPORT( otb::Wrapper::TrainVectorClustering )
+OTB_APPLICATION_EXPORT( otb::Wrapper::TrainVectorUnsupervised )

Modules/Applications/AppClassification/include/otbTrainImagesBase.h

@@ -159,7 +159,7 @@ protected:
     if( supervised )
       AddApplication( "TrainVectorClassifier", "training", "Model training" );
     else
-      AddApplication( "TrainVectorClustering", "training", "Model training" );
+      AddApplication( "TrainVectorUnsupervised", "training", "Model training" );
 
     AddParameter( ParameterType_InputVectorDataList, "io.valid", "Validation Vector Data List" );
     SetParameterDescription( "io.valid", "A list of vector data to select the training samples." );

Modules/Applications/AppClassification/otb-module.cmake

@@ -11,6 +11,7 @@ otb_module(OTBAppClassification
     OTBMajorityVoting
     OTBVectorDataIO
     OTBSOM
+    OTBLearningBase
     OTBSupervised
     OTBUnsupervised
     OTBApplicationEngine

Modules/Applications/AppClassification/test/CMakeLists.txt

@@ -225,19 +225,19 @@ foreach(classifier ${classifierList})
 endforeach()
 
 
-#----------- TrainImagesClustering TESTS ----------------
+#----------- TrainImagesUnsupervised TESTS ----------------
 
 set(sharkkm_output_format ".txt")
 set(sharkkm_parameters "")
 
 if(OTB_USE_SHARK)
-  list(APPEND clusteringList "SHARKKM")
+  list(APPEND UnsupervisedList "SHARKKM")
 endif()
 
 list(APPEND classifier_without_baseline "SHARKKM")
 
 # Loop on classifiers
-foreach(classifier ${clusteringList})
+foreach(classifier ${UnsupervisedList})
   string(TOLOWER ${classifier} lclassifier)
 
   # Derive output file name
@@ -254,7 +254,7 @@ foreach(classifier ${clusteringList})
 
   otb_test_application(
     NAME     apTvClTrainMethod${classifier}ImagesClassifierQB1
-    APP      TrainImagesClustering
+    APP      TrainImagesUnsupervised
     OPTIONS  -io.il ${INPUTDATA}/Classification/QB_1_ortho${raster_input_format}
     -io.vd ${INPUTDATA}/Classification/VectorData_${${lclassifier}_input}QB1${vector_input_format}
     -io.imstat ${INPUTDATA}/Classification/clImageStatisticsQB1${stat_input_format}
@@ -275,7 +275,7 @@ foreach(classifier ${clusteringList})
 
   otb_test_application(
     NAME     apTvClTrainMethod${classifier}ImagesClassifierQB1_OutXML1
-    APP      TrainImagesClustering
+    APP      TrainImagesUnsupervised
     OPTIONS  -io.il ${INPUTDATA}/Classification/QB_1_ortho${raster_input_format}
     -io.vd ${INPUTDATA}/Classification/VectorData_${${lclassifier}_input}QB1${vector_input_format}
     -io.imstat ${INPUTDATA}/Classification/clImageStatisticsQB1${stat_input_format}
@@ -297,7 +297,7 @@ foreach(classifier ${clusteringList})
 
   otb_test_application(
     NAME     apTvClTrainMethod${classifier}ImagesClassifierQB1_InXML1
-    APP      TrainImagesClustering
+    APP      TrainImagesUnsupervised
     OPTIONS  -inxml ${INPUTDATA}/cl${classifier}_OutXML1.xml
     -io.il ${INPUTDATA}/Classification/QB_1_ortho${raster_input_format}
     -io.vd ${INPUTDATA}/Classification/VectorData_${${lclassifier}_input}QB1${vector_input_format}
@@ -1051,17 +1051,17 @@ if(OTB_USE_OPENCV)
     ${TEMP}/apTvClTrainVectorClassifierModel.rf)
 endif()
 
-#----------- TrainVectorClustering TESTS ----------------
+#----------- TrainVectorUnsupervised TESTS ----------------
 if(OTB_USE_SHARK)
-  otb_test_application(NAME apTvClTrainVectorClustering
-    APP  TrainVectorClustering
+  otb_test_application(NAME apTvClTrainVectorUnsupervised
+    APP  TrainVectorUnsupervised
     OPTIONS -io.vd ${INPUTDATA}/Classification/apTvClSampleExtractionOut.sqlite
     -feat value_0 value_1 value_2 value_3
     -classifier sharkkm
     -io.out ${TEMP}/apTvClTrainVectorClusteringModel.txt)
 
-  otb_test_application(NAME apTvClTrainVectorClusteringWithClass
-    APP  TrainVectorClustering
+  otb_test_application(NAME apTvClTrainVectorUnsupervisedWithClass
+    APP  TrainVectorUnsupervised
     OPTIONS -io.vd ${INPUTDATA}/Classification/apTvClSampleExtractionOut.sqlite
     -feat value_0 value_1 value_2 value_3
     -cfield class

Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.h

@@ -42,8 +42,6 @@
 #pragma GCC diagnostic pop
 #endif
 
-using namespace shark;
-
 /** \class SharkKMeansMachineLearningModel
  *  \brief Shark version of Random Forests algorithm
  *
@@ -64,10 +62,10 @@ class ITK_EXPORT SharkKMeansMachineLearningModel : public MachineLearningModel<T
 {
 public:
   /** Standard class typedefs. */
-  typedef SharkKMeansMachineLearningModel Self;
+  typedef SharkKMeansMachineLearningModel                 Self;
   typedef MachineLearningModel<TInputValue, TTargetValue> Superclass;
-  typedef itk::SmartPointer<Self> Pointer;
-  typedef itk::SmartPointer<const Self> ConstPointer;
+  typedef itk::SmartPointer<Self>                         Pointer;
+  typedef itk::SmartPointer<const Self>                   ConstPointer;
 
   typedef typename Superclass::InputValueType             InputValueType;
   typedef typename Superclass::InputSampleType            InputSampleType;
@@ -80,8 +78,8 @@ public:
   typedef typename Superclass::ConfidenceListSampleType   ConfidenceListSampleType;
 
 
-  typedef HardClusteringModel<RealVector> ClusteringModelType;
-  typedef ClusteringModelType::OutputType ClusteringOutputType;
+  typedef shark::HardClusteringModel<shark::RealVector>   ClusteringModelType;
+  typedef ClusteringModelType::OutputType                 ClusteringOutputType;
 
   /** Run-time type information (and related methods). */
   itkNewMacro( Self );
@@ -152,7 +150,7 @@ private:
 
 
   /** Centroids results form kMeans */
-  Centroids m_Centroids;
+  shark::Centroids m_Centroids;
 
 
   /** shark Model could be SoftClusteringModel or HardClusteringModel */

Modules/Learning/Unsupervised/include/otbSharkKMeansMachineLearningModel.txx

@@ -68,16 +68,16 @@ SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
 ::Train()
 {
   // Parse input data and convert to Shark Data
-  std::vector<RealVector> vector_data;
-  Shark::ListSampleToSharkVector( this->GetInputListSample(), vector_data );
-  Data<RealVector> data = createDataFromRange( vector_data );
+  std::vector<shark::RealVector> vector_data;
+  otb::Shark::ListSampleToSharkVector( this->GetInputListSample(), vector_data );
+  shark::Data<shark::RealVector> data = shark::createDataFromRange( vector_data );
 
   // Normalized input value if necessary
   if( m_Normalized )
     data = NormalizeData( data );
 
   // Use a Hard Clustering Model for classification
-  kMeans( data, m_K, m_Centroids, m_MaximumNumberOfIterations );
+  shark::kMeans( data, m_K, m_Centroids, m_MaximumNumberOfIterations );
   m_ClusteringModel = boost::shared_ptr<ClusteringModelType>(new ClusteringModelType( &m_Centroids ));
 }
 
@@ -99,7 +99,7 @@ typename SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
 SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
 ::DoPredict(const InputSampleType &value, ConfidenceValueType *quality) const
 {
-  RealVector data( value.Size());
+  shark::RealVector data( value.Size());
   for( size_t i = 0; i < value.Size(); i++ )
     {
     data.push_back( value[i] );
@@ -143,11 +143,11 @@ SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
     }
 
   // Convert input list of features to shark data format
-  std::vector<RealVector> features;
-  Shark::ListSampleRangeToSharkVector( input, features, startIndex, size );
-  Data<RealVector> inputSamples = shark::createDataFromRange( features );
+  std::vector<shark::RealVector> features;
+  otb::Shark::ListSampleRangeToSharkVector( input, features, startIndex, size );
+  shark::Data<shark::RealVector> inputSamples = shark::createDataFromRange( features );
 
-  Data<ClusteringOutputType> clusters;
+  shark::Data<ClusteringOutputType> clusters;
   try
     {
      clusters = ( *m_ClusteringModel )( inputSamples );
@@ -175,7 +175,6 @@ SharkKMeansMachineLearningModel<TInputValue, TOutputValue>
       quality->SetMeasurementVector( qid, static_cast<ConfidenceValueType>(1.) );
       }
     }
-
 }
 
 

Modules/Learning/Unsupervised/otb-module.cmake

@@ -16,7 +16,6 @@ otb_module(OTBUnsupervised
   OTBImageIO
   OTBImageBase
   OTBLearningBase
-  OTBSupervised
   OTBShark
 
   DESCRIPTION

Modules/Learning/Unsupervised/test/CMakeLists.txt

 otb_module_test()
 set(OTBUnsupervisedTests
   otbUnsupervisedTestDriver.cxx
-  otbMachineLearningClusteringModelCanRead.cxx
-  otbTrainMachineLearningClusteringModel.cxx
+  otbMachineLearningUnsupervisedModelCanRead.cxx
+  otbTrainMachineLearningUnsupervisedModel.cxx
   )
 
 # Tests Declaration