REFAC: Apply RFC 85 review

Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx

@@ -49,44 +49,42 @@ public:
   typedef ConfusionMatrixCalculatorType::MapOfIndicesType MapOfIndicesType;
   typedef ConfusionMatrixCalculatorType::ClassLabelType ClassLabelType;
 
-private:
-  void DoTrainInit()
+protected:
+  void DoInit()
   {
-    // Nothing to do here
+    TrainVectorBase::DoInit();
   }
 
-  void DoTrainUpdateParameters()
+  void DoUpdateParameters()
   {
-    // Nothing to do here
+    TrainVectorBase::DoUpdateParameters();
   }
 
-  void DoBeforeTrainExecute()
+  void DoExecute()
   {
     // Enforce the need of class field name in supervised mode
     if (GetClassifierCategory() == Supervised)
       {
-      featuresInfo.SetClassFieldNames( GetChoiceNames( "cfield" ), GetSelectedItems( "cfield" ) );
+      m_featuresInfo.SetClassFieldNames( GetChoiceNames( "cfield" ), GetSelectedItems( "cfield" ) );
 
-      if( featuresInfo.m_SelectedCFieldIdx.empty() )
+      if( m_featuresInfo.m_SelectedCFieldIdx.empty() )
         {
         otbAppLogFATAL( << "No field has been selected for data labelling!" );
         }
       }
-  }
 
-  void DoAfterTrainExecute()
-  {
+      TrainVectorBase::DoExecute();
 
-    if (GetClassifierCategory() == Supervised)
-      {
-      ConfusionMatrixCalculatorType::Pointer confMatCalc = ComputeConfusionMatrix( predictedList,
-                                                                                   classificationListSamples.labeledListSample );
-      WriteConfusionMatrix( confMatCalc );
-      }
-    else
-      {
-      // TODO Compute Contingency Table
-      }
+      if (GetClassifierCategory() == Supervised)
+        {
+        ConfusionMatrixCalculatorType::Pointer confMatCalc = ComputeConfusionMatrix( m_predictedList,
+                                                                                     m_classificationSamplesWithLabel.labeledListSample );
+        WriteConfusionMatrix( confMatCalc );
+        }
+      else
+        {
+        // TODO Compute Contingency Table
+        }
   }
 
 

Modules/Applications/AppClassification/include/otbTrainImagesBase.h

@@ -35,7 +35,7 @@ namespace Wrapper
 {
 
 /** \class TrainImagesBase
- * \brief Base class for the TrainImagesBaseClassifier and Clustering
+ * \brief Base class for the TrainImagesClassifier
  *
  * This class intends to hold common input/output parameters and
  * composite application connection for both supervised and unsupervised

Modules/Applications/AppClassification/include/otbTrainSharkKMeans.txx

@@ -35,11 +35,11 @@ void LearningApplicationBase<TInputValue, TOutputValue>::InitSharkKMeansParams()
                                    "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",
+  AddParameter( ParameterType_Int, "classifier.sharkkm.maxiter",
                 "Maximum number of iteration for the kmeans algorithm." );
-  SetParameterInt( "classifier.sharkkm.nbmaxiter", 10 );
-  SetMinimumParameterIntValue( "classifier.sharkkm.nbmaxiter", 0 );
-  SetParameterDescription( "classifier.sharkkm.nbmaxiter",
+  SetParameterInt( "classifier.sharkkm.maxiter", 10 );
+  SetMinimumParameterIntValue( "classifier.sharkkm.maxiter", 0 );
+  SetParameterDescription( "classifier.sharkkm.maxiter",
                            "The maximum number of iteration for the kmeans algorithm. 0=unlimited" );
 
   //MaxNumberOfIterations
@@ -55,7 +55,7 @@ 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 nbMaxIter = static_cast<unsigned int>(abs( GetParameterInt( "classifier.sharkkm.maxiter" ) ));
   unsigned int k = static_cast<unsigned int>(abs( GetParameterInt( "classifier.sharkkm.k" ) ));
 
   typename SharkKMeansType::Pointer classifier = SharkKMeansType::New();

Modules/Applications/AppClassification/include/otbTrainVectorBase.h

@@ -74,7 +74,7 @@ public:
 protected:
 
   /** Class used to store statistics Measurment (mean/stddev) */
-  class StatisticsMeasurement
+  class ShiftScaleParameters
   {
   public:
     MeasurementType meanMeasurementVector;
@@ -82,12 +82,12 @@ protected:
   };
 
   /** Class used to store a list of sample and the corresponding label */
-  class ListSamples
+  class SamplesWithLabel
   {
   public:
     ListSampleType::Pointer listSample;
     TargetListSampleType::Pointer labeledListSample;
-    ListSamples()
+    SamplesWithLabel()
     {
       listSample = ListSampleType::New();
       labeledListSample = TargetListSampleType::New();
@@ -137,7 +137,7 @@ protected:
    * \param measurement statics measurement (mean/stddev)
    * \param featuresInfo information about the features
    */
-  virtual void ExtractAllSamples(const StatisticsMeasurement &measurement);
+  virtual void ExtractAllSamples(const ShiftScaleParameters &measurement);
 
   /**
   * Extract the training sample list
@@ -145,7 +145,7 @@ protected:
   * \param featuresInfo information about the features
   * \return sample list used for training
   */
-  virtual ListSamples ExtractTrainingListSamples(const StatisticsMeasurement &measurement);
+  virtual SamplesWithLabel ExtractTrainingSamplesWithLabel(const ShiftScaleParameters &measurement);
 
   /**
    * Extract classification the sample list
@@ -153,7 +153,7 @@ protected:
    * \param featuresInfo information about the features
    * \return sample list used for classification
    */
-  virtual ListSamples ExtractClassificationListSamples(const StatisticsMeasurement &measurement);
+  virtual SamplesWithLabel ExtractClassificationSamplesWithLabel(const ShiftScaleParameters &measurement);
 
 
   /** Extract samples from input file for corresponding field name
@@ -164,8 +164,8 @@ protected:
    * \param nbFeatures the number of features.
    * \return the list of samples and their corresponding labels.
    */
-  ListSamples
-  ExtractListSamples(std::string parameterName, std::string parameterLayer, const StatisticsMeasurement &measurement);
+  SamplesWithLabel
+  ExtractSamplesWithLabel(std::string parameterName, std::string parameterLayer, const ShiftScaleParameters &measurement);
 
 
   /**
@@ -173,18 +173,12 @@ protected:
    * Otherwise mean is set to 0 and standard deviation to 1 for each Features.
    * \param nbFeatures
    */
-  StatisticsMeasurement ComputeStatistics(unsigned int nbFeatures);
+  ShiftScaleParameters ComputeStatistics(unsigned int nbFeatures);
 
-  ListSamples trainingListSamples;
-  ListSamples classificationListSamples;
-  TargetListSampleType::Pointer predictedList;
-  FeaturesInfo featuresInfo;
-
-private:
-  virtual void DoTrainInit() = 0;
-  virtual void DoBeforeTrainExecute() = 0;
-  virtual void DoAfterTrainExecute() = 0;
-  virtual void DoTrainUpdateParameters() = 0;
+  SamplesWithLabel m_trainingSamplesWithLabel;
+  SamplesWithLabel m_classificationSamplesWithLabel;
+  TargetListSampleType::Pointer m_predictedList;
+  FeaturesInfo m_featuresInfo;
 
   void DoInit() ITK_OVERRIDE;
   void DoUpdateParameters() ITK_OVERRIDE;
@@ -200,4 +194,3 @@ private:
 #endif
 
 #endif
-

Modules/Applications/AppClassification/include/otbTrainVectorBase.txx

@@ -102,7 +102,7 @@ void TrainVectorBase::DoInit()
 
   AddRANDParameter();
 
-  DoTrainInit();
+  DoInit();
 }
 
 void TrainVectorBase::DoUpdateParameters()
@@ -142,79 +142,75 @@ void TrainVectorBase::DoUpdateParameters()
       }
     }
 
-  DoTrainUpdateParameters();
+  DoUpdateParameters();
 }
 
 void TrainVectorBase::DoExecute()
 {
-  DoBeforeTrainExecute();
-
-  featuresInfo.SetFieldNames( GetChoiceNames( "feat" ), GetSelectedItems( "feat" ));
+  m_featuresInfo.SetFieldNames( GetChoiceNames( "feat" ), GetSelectedItems( "feat" ));
 
   // Check input parameters
-  if( featuresInfo.m_SelectedIdx.empty() )
+  if( m_featuresInfo.m_SelectedIdx.empty() )
     {
     otbAppLogFATAL( << "No features have been selected to train the classifier on!" );
     }
 
-  StatisticsMeasurement measurement = ComputeStatistics( featuresInfo.m_NbFeatures );
+  ShiftScaleParameters measurement = ComputeStatistics( m_featuresInfo.m_NbFeatures );
   ExtractAllSamples( measurement );
 
-  this->Train( trainingListSamples.listSample, trainingListSamples.labeledListSample, GetParameterString( "io.out" ) );
-
-  predictedList = TargetListSampleType::New();
-  this->Classify( classificationListSamples.listSample, predictedList, GetParameterString( "io.out" ) );
+  this->Train( m_trainingSamplesWithLabel.listSample, m_trainingSamplesWithLabel.labeledListSample, GetParameterString( "io.out" ) );
 
-  DoAfterTrainExecute();
+  m_predictedList = TargetListSampleType::New();
+  this->Classify( m_classificationSamplesWithLabel.listSample, m_predictedList, GetParameterString( "io.out" ) );
 }
 
 
-void TrainVectorBase::ExtractAllSamples(const StatisticsMeasurement &measurement)
+void TrainVectorBase::ExtractAllSamples(const ShiftScaleParameters &measurement)
 {
-  trainingListSamples = ExtractTrainingListSamples(measurement);
-  classificationListSamples = ExtractClassificationListSamples(measurement);
+  m_trainingSamplesWithLabel = ExtractTrainingSamplesWithLabel(measurement);
+  m_classificationSamplesWithLabel = ExtractClassificationSamplesWithLabel(measurement);
 }
 
-TrainVectorBase::ListSamples
-TrainVectorBase::ExtractTrainingListSamples(const StatisticsMeasurement &measurement)
+TrainVectorBase::SamplesWithLabel
+TrainVectorBase::ExtractTrainingSamplesWithLabel(const ShiftScaleParameters &measurement)
 {
-  return ExtractListSamples( "io.vd", "layer", measurement);
+  return ExtractSamplesWithLabel( "io.vd", "layer", measurement);
 }
 
-TrainVectorBase::ListSamples
-TrainVectorBase::ExtractClassificationListSamples(const StatisticsMeasurement &measurement)
+TrainVectorBase::SamplesWithLabel
+TrainVectorBase::ExtractClassificationSamplesWithLabel(const ShiftScaleParameters &measurement)
 {
   if(GetClassifierCategory() == Supervised)
     {
-    ListSamples tmpListSamples;
-    ListSamples validationListSamples = ExtractListSamples( "valid.vd", "valid.layer", measurement );
+    SamplesWithLabel tmpSamplesWithLabel;
+    SamplesWithLabel validationSamplesWithLabel = ExtractSamplesWithLabel( "valid.vd", "valid.layer", measurement );
     //Test the input validation set size
-    if( validationListSamples.labeledListSample->Size() != 0 )
+    if( validationSamplesWithLabel.labeledListSample->Size() != 0 )
       {
-      tmpListSamples.listSample = validationListSamples.listSample;
-      tmpListSamples.labeledListSample = validationListSamples.labeledListSample;
+      tmpSamplesWithLabel.listSample = validationSamplesWithLabel.listSample;
+      tmpSamplesWithLabel.labeledListSample = validationSamplesWithLabel.labeledListSample;
       }
     else
       {
       otbAppLogWARNING(
               "The validation set is empty. The performance estimation is done using the input training set in this case." );
-      tmpListSamples.listSample = trainingListSamples.listSample;
-      tmpListSamples.labeledListSample = trainingListSamples.labeledListSample;
+      tmpSamplesWithLabel.listSample = m_trainingSamplesWithLabel.listSample;
+      tmpSamplesWithLabel.labeledListSample = m_trainingSamplesWithLabel.labeledListSample;
       }
 
-    return tmpListSamples;
+    return tmpSamplesWithLabel;
     }
   else
     {
-    return trainingListSamples;
+    return m_trainingSamplesWithLabel;
     }
 }
 
 
-TrainVectorBase::StatisticsMeasurement
+TrainVectorBase::ShiftScaleParameters
 TrainVectorBase::ComputeStatistics(unsigned int nbFeatures)
 {
-  StatisticsMeasurement measurement = StatisticsMeasurement();
+  ShiftScaleParameters measurement = ShiftScaleParameters();
   if( HasValue( "io.stats" ) && IsParameterEnabled( "io.stats" ) )
     {
     StatisticsReader::Pointer statisticsReader = StatisticsReader::New();
@@ -234,51 +230,51 @@ TrainVectorBase::ComputeStatistics(unsigned int nbFeatures)
 }
 
 
-TrainVectorBase::ListSamples
-TrainVectorBase::ExtractListSamples(std::string parameterName, std::string parameterLayer,
-                                    const StatisticsMeasurement &measurement)
+TrainVectorBase::SamplesWithLabel
+TrainVectorBase::ExtractSamplesWithLabel(std::string parameterName, std::string parameterLayer,
+                                    const ShiftScaleParameters &measurement)
 {
-  ListSamples listSamples;
+  SamplesWithLabel samplesWithLabel;
   if( HasValue( parameterName ) && IsParameterEnabled( parameterName ) )
     {
     ListSampleType::Pointer input = ListSampleType::New();
     TargetListSampleType::Pointer target = TargetListSampleType::New();
-    input->SetMeasurementVectorSize( featuresInfo.m_NbFeatures );
+    input->SetMeasurementVectorSize( m_featuresInfo.m_NbFeatures );
 
-    std::vector<std::string> validFileList = this->GetParameterStringList( parameterName );
-    for( unsigned int k = 0; k < validFileList.size(); k++ )
+    std::vector<std::string> fileList = this->GetParameterStringList( parameterName );
+    for( unsigned int k = 0; k < fileList.size(); k++ )
       {
-      otbAppLogINFO( "Reading validation vector file " << k + 1 << "/" << validFileList.size() );
-      ogr::DataSource::Pointer source = ogr::DataSource::New( validFileList[k], ogr::DataSource::Modes::Read );
+      otbAppLogINFO( "Reading vector file " << k + 1 << "/" << fileList.size() );
+      ogr::DataSource::Pointer source = ogr::DataSource::New( fileList[k], ogr::DataSource::Modes::Read );
       ogr::Layer layer = source->GetLayer( static_cast<size_t>(this->GetParameterInt( parameterLayer )) );
       ogr::Feature feature = layer.ogr().GetNextFeature();
       bool goesOn = feature.addr() != 0;
       if( !goesOn )
         {
-        otbAppLogWARNING( "The layer " << GetParameterInt( parameterLayer ) << " of " << validFileList[k]
+        otbAppLogWARNING( "The layer " << GetParameterInt( parameterLayer ) << " of " << fileList[k]
                                        << " is empty, input is skipped." );
         continue;
         }
 
       // Check all needed fields are present :
       //   - check class field if we use supervised classification or if class field name is not empty
-      int cFieldIndex = feature.ogr().GetFieldIndex( featuresInfo.m_SelectedCFieldName.c_str() );
-      if( cFieldIndex < 0 && !featuresInfo.m_SelectedCFieldName.empty())
+      int cFieldIndex = feature.ogr().GetFieldIndex( m_featuresInfo.m_SelectedCFieldName.c_str() );
+      if( cFieldIndex < 0 && !m_featuresInfo.m_SelectedCFieldName.empty())
         {
-        otbAppLogFATAL( "The field name for class label (" << featuresInfo.m_SelectedCFieldName
+        otbAppLogFATAL( "The field name for class label (" << m_featuresInfo.m_SelectedCFieldName
                                                            << ") has not been found in the vector file "
-                                                           << validFileList[k] );
+                                                           << fileList[k] );
         }
 
       //   - check feature fields
-      std::vector<int> featureFieldIndex( featuresInfo.m_NbFeatures, -1 );
-      for( unsigned int i = 0; i < featuresInfo.m_NbFeatures; i++ )
+      std::vector<int> featureFieldIndex( m_featuresInfo.m_NbFeatures, -1 );
+      for( unsigned int i = 0; i < m_featuresInfo.m_NbFeatures; i++ )
         {
-        featureFieldIndex[i] = feature.ogr().GetFieldIndex( featuresInfo.m_SelectedNames[i].c_str() );
+        featureFieldIndex[i] = feature.ogr().GetFieldIndex( m_featuresInfo.m_SelectedNames[i].c_str() );
         if( featureFieldIndex[i] < 0 )
-          otbAppLogFATAL( "The field name for feature " << featuresInfo.m_SelectedNames[i]
+          otbAppLogFATAL( "The field name for feature " << m_featuresInfo.m_SelectedNames[i]
                                                         << " has not been found in the vector file "
-                                                        << validFileList[k] );
+                                                        << fileList[k] );
         }
 
 
@@ -286,8 +282,8 @@ TrainVectorBase::ExtractListSamples(std::string parameterName, std::string param
         {
         // Retrieve all the features for each field in the ogr layer.
         MeasurementType mv;
-        mv.SetSize( featuresInfo.m_NbFeatures );
-        for( unsigned int idx = 0; idx < featuresInfo.m_NbFeatures; ++idx )
+        mv.SetSize( m_featuresInfo.m_NbFeatures );
+        for( unsigned int idx = 0; idx < m_featuresInfo.m_NbFeatures; ++idx )
           mv[idx] = feature.ogr().GetFieldAsDouble( featureFieldIndex[idx] );
 
         input->PushBack( mv );
@@ -310,11 +306,11 @@ TrainVectorBase::ExtractListSamples(std::string parameterName, std::string param
     shiftScaleFilter->SetScales( measurement.stddevMeasurementVector );
     shiftScaleFilter->Update();
 
-    listSamples.listSample = shiftScaleFilter->GetOutput();
-    listSamples.labeledListSample = target;
+    samplesWithLabel.listSample = shiftScaleFilter->GetOutput();
+    samplesWithLabel.labeledListSample = target;
     }
 
-  return listSamples;
+  return samplesWithLabel;
 }
 
 
@@ -322,5 +318,3 @@ TrainVectorBase::ExtractListSamples(std::string parameterName, std::string param
 }
 
 #endif
-
-