ENH: add method for splitting training and validation set

Modules/Applications/AppClassification/app/otbTrainImagesRegression.cxx

@@ -42,7 +42,11 @@ public:
 
   /** Standard macro */
   itkTypeMacro(TrainImagesRegression, Superclass);
-
+  
+  /** filters typedefs*/
+  typedef otb::OGRDataToSamplePositionFilter<FloatVectorImageType, UInt8ImageType, otb::PeriodicSampler> PeriodicSamplerType;
+  typedef otb::SamplingRateCalculator::MapRateType MapRateType;
+  
 private:
   void DoInit() override
   {
@@ -351,6 +355,57 @@ private:
       ExecuteInternal("extraction");
     }
   }
+  
+  void SplitTrainingAndValidationSamples(FloatVectorImageType *image, std::string sampleFileName,
+                                                          std::string sampleTrainFileName,
+                                                          std::string sampleValidFileName,
+                                                          std::string ratesTrainFileName)
+  {
+    // Split between training and validation
+    ogr::DataSource::Pointer source = ogr::DataSource::New( sampleFileName, ogr::DataSource::Modes::Read );
+    ogr::DataSource::Pointer destTrain = ogr::DataSource::New( sampleTrainFileName, ogr::DataSource::Modes::Overwrite );
+    ogr::DataSource::Pointer destValid = ogr::DataSource::New( sampleValidFileName, ogr::DataSource::Modes::Overwrite );
+    // read sampling rates from ratesTrainOutputs
+    SamplingRateCalculator::Pointer rateCalculator = SamplingRateCalculator::New();
+    rateCalculator->Read( ratesTrainFileName );
+    // Compute sampling rates for train and valid
+    const otb::SamplingRateCalculator::MapRateType &inputRates = rateCalculator->GetRatesByClass();
+    otb::SamplingRateCalculator::MapRateType trainRates;
+    otb::SamplingRateCalculator::MapRateType validRates;
+    otb::SamplingRateCalculator::TripletType tpt;
+    for( MapRateType::const_iterator it = inputRates.begin(); it != inputRates.end(); ++it )
+      {
+      double vtr = GetParameterFloat( "sample.vtr" );
+      unsigned long total = std::min( it->second.Required, it->second.Tot );
+      unsigned long neededValid = static_cast<unsigned long>(( double ) total * vtr );
+      unsigned long neededTrain = total - neededValid;
+      tpt.Tot = total;
+      tpt.Required = neededTrain;
+      tpt.Rate = ( 1.0 - vtr );
+      trainRates[it->first] = tpt;
+      tpt.Tot = neededValid;
+      tpt.Required = neededValid;
+      tpt.Rate = 1.0;
+      validRates[it->first] = tpt;
+      }
+
+    // Use an otb::OGRDataToSamplePositionFilter with 2 outputs
+    PeriodicSamplerType::SamplerParameterType param;
+    param.Offset = 0;
+    param.MaxJitter = 0;
+    PeriodicSamplerType::Pointer splitter = PeriodicSamplerType::New();
+    splitter->SetInput( image );
+    splitter->SetOGRData( source );
+    splitter->SetOutputPositionContainerAndRates( destTrain, trainRates, 0 );
+    splitter->SetOutputPositionContainerAndRates( destValid, validRates, 1 );
+    splitter->SetFieldName( this->GetParameterStringList( "sample.vfn" )[0] );
+    splitter->SetLayerIndex( 0 );
+    splitter->SetOriginFieldName( std::string( "" ) );
+    splitter->SetSamplerParameters( param );
+    splitter->GetStreamer()->SetAutomaticTiledStreaming( static_cast<unsigned int>(this->GetParameterInt( "ram" )) );
+    AddProcess( splitter->GetStreamer(), "Split samples between training and validation..." );
+    splitter->Update();
+  }
 
   /** Configure and execute TrainVectorClassifier. Note that many parameters of TrainVectorClassifier
    * are shared with the main application during initialization. */