diff --git a/Modules/Applications/AppClassification/app/otbTrainImagesRegression.cxx b/Modules/Applications/AppClassification/app/otbTrainImagesRegression.cxx
index d17462be96ecd5b0a6d46e034716330915713beb..b88b73c90860c1f8d5a0f23cb916a23e6d54d3c8 100644
--- a/Modules/Applications/AppClassification/app/otbTrainImagesRegression.cxx
+++ b/Modules/Applications/AppClassification/app/otbTrainImagesRegression.cxx
@@ -160,6 +160,10 @@ private:
SetParameterDescription("sample.nv", "Number of validation samples.");
MandatoryOff("sample.nv");
+ AddParameter(ParameterType_Float, "sample.ratio", "Training and validation sample ratio");
+ SetParameterDescription("sample.ratio", "Ratio between training and validation samples.");
+ SetDefaultParameterFloat("sample.ratio", 0.5);
+
ShareParameter( "rand", "select.rand" );
ShareParameter( "ram", "polystat.ram" );
@@ -356,55 +360,65 @@ private:
}
}
- void SplitTrainingAndValidationSamples(FloatVectorImageType *image, std::string sampleFileName,
- std::string sampleTrainFileName,
- std::string sampleValidFileName,
- std::string ratesTrainFileName)
+ void SplitTrainingAndValidationSamples(const std::string & inputSampleFilePrefix)
{
- // 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 )
+ auto ImageList = GetParameterImageList("io.il");
+ const auto& inputSampleFiles = m_FileHandler[inputSampleFilePrefix + "samples"];
+ auto& trainSampleFiles = m_FileHandler["trainsamples"];
+ auto& validSampleFiles = m_FileHandler["validsamples"];
+ const auto& rateFiles = m_FileHandler[inputSampleFilePrefix + "rateFiles"];
+
+ for (unsigned int i = 0; i < ImageList->Size(); i++)
+ {
+ trainSampleFiles.push_back(GetParameterString("io.out") + "_trainsamples" + std::to_string(i) + ".shp");
+ validSampleFiles.push_back(GetParameterString("io.out") + "_validsamples" + std::to_string(i) + ".shp");
+
+ // Split between training and validation
+ auto image = ImageList->GetNthElement(i);
+ ogr::DataSource::Pointer source = ogr::DataSource::New( inputSampleFiles[i], ogr::DataSource::Modes::Read );
+ ogr::DataSource::Pointer destTrain = ogr::DataSource::New( trainSampleFiles[i], ogr::DataSource::Modes::Overwrite );
+ ogr::DataSource::Pointer destValid = ogr::DataSource::New( validSampleFiles[i], ogr::DataSource::Modes::Overwrite );
+ // read sampling rates from ratesTrainOutputs
+ SamplingRateCalculator::Pointer rateCalculator = SamplingRateCalculator::New();
+ rateCalculator->Read( rateFiles[i] );
+ // 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;
+ double vtr = GetParameterFloat( "sample.ratio" );
+ 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();
+ // 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( m_ClassFieldName );
+ 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
@@ -419,13 +433,13 @@ private:
{
featureNames.push_back(m_FeaturePrefix + std::to_string(i));
}
-
+
trainVectorRegression->SetParameterStringList("io.vd", trainSampleFileNameList);
trainVectorRegression->UpdateParameters();
trainVectorRegression->SetParameterString("cfield", m_PredictionFieldName);
trainVectorRegression->SetParameterStringList("feat", featureNames);
- if (IsParameterEnabled("io.valid") && HasValue("io.valid"))
+ if ((IsParameterEnabled("io.valid") && HasValue("io.valid")) || GetParameterFloat( "sample.ratio" ) >0)
{
trainVectorRegression->SetParameterStringList("valid.vd", m_FileHandler["validsamples"]);
}
@@ -484,7 +498,7 @@ private:
void DoExecute() override
{
SamplingParameters trainParams;
- trainParams.filePrefix = "train";
+ trainParams.filePrefix = "vd";
if (HasValue("sample.nt"))
trainParams.numberOfSamples = GetParameterInt("sample.nt");
@@ -502,17 +516,29 @@ private:
PerformSampling(trainParams);
+ // User validation data
if (IsParameterEnabled("io.valid") && HasValue("io.valid"))
{
+ m_FileHandler["trainsamples"] = m_FileHandler[trainParams.filePrefix + "samples"];
+
SamplingParameters validParams;
validParams.inputVectorList = GetParameterStringList("io.valid");
validParams.filePrefix = "valid";
if (HasValue("sample.nv"))
validParams.numberOfSamples = GetParameterInt("sample.nv");
-
+
PerformSampling(validParams);
}
-
+ // Split train and validation data
+ else if (GetParameterFloat( "sample.ratio" ) >0)
+ {
+ SplitTrainingAndValidationSamples( trainParams.filePrefix);
+ }
+ else
+ {
+ m_FileHandler["trainsamples"] = m_FileHandler[trainParams.filePrefix + "samples"];
+ }
+
otbAppLogINFO("Sampling Done.");
TrainModel();