From 078b7a5a0b4c25512c9d993251b558d1b1d2e54f Mon Sep 17 00:00:00 2001
From: Ludovic Hussonnois <ludovic.hussonnois@c-s.fr>
Date: Thu, 23 Feb 2017 16:55:15 +0100
Subject: [PATCH] ENH: Update TrainImagesClustering and Classifier to use
SampleSelection.
SampleSelection can now be used without input vector data.
---
.../app/otbTrainImagesClassifier.cxx | 2 +-
.../app/otbTrainImagesClustering.cxx | 93 +++++--------------
.../include/otbTrainImagesBase.h | 13 +--
3 files changed, 33 insertions(+), 75 deletions(-)
diff --git a/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx b/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx
index 3ed942dbc3..bb0f9176b2 100644
--- a/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx
+++ b/Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx
@@ -115,7 +115,7 @@ public:
ComputePolygonStatistics(imageList, validationVectorFileList, fileNames.polyStatValidOutputs);
ComputeSamplingRate(fileNames.polyStatValidOutputs, fileNames.rateValidOut, rates.fmv);
}
- SelectAndExtractValidationSamples(fileNames, imageList, validationVectorFileList, dedicatedValidation);
+ SelectAndExtractValidationSamples(fileNames, imageList, validationVectorFileList);
// Then train the model with extracted samples
diff --git a/Modules/Applications/AppClassification/app/otbTrainImagesClustering.cxx b/Modules/Applications/AppClassification/app/otbTrainImagesClustering.cxx
index fdabcd1b08..e4ffd3b5db 100644
--- a/Modules/Applications/AppClassification/app/otbTrainImagesClustering.cxx
+++ b/Modules/Applications/AppClassification/app/otbTrainImagesClustering.cxx
@@ -34,6 +34,14 @@ public:
InitSampling();
InitClassification( false );
+ AddParameter( ParameterType_Float, "sample.percent", "Percentage of samples extract in images for "
+ "training and validation when only images are provided." );
+ SetParameterDescription( "sample.percent", "Percentage of samples extract in images for "
+ "training and validation when only images are provided. This parameter is disable when vector data are provided" );
+ SetDefaultParameterFloat( "sample.percent", 100.0 );
+ SetMinimumParameterFloatValue( "sample.percent", 0.0 );
+ SetMaximumParameterFloatValue( "sample.percent", 100.0 );
+
// Doc example parameter settings
SetDocExampleParameterValue( "io.il", "QB_1_ortho.tif" );
SetDocExampleParameterValue( "io.vd", "VectorData_QB1.shp" );
@@ -51,8 +59,13 @@ public:
{
if( HasValue( "io.vd" ) )
{
+ MandatoryOff( "sample.percent" );
UpdatePolygonClassStatisticsParameters();
}
+ else
+ {
+ MandatoryOn( "sample.percent" );
+ }
}
void DoExecute() ITK_OVERRIDE
@@ -60,12 +73,12 @@ public:
TrainFileNamesHandler fileNames;
FloatVectorImageListType *imageList = GetParameterImageList( "io.il" );
bool HasInputVector = IsParameterEnabled( "io.vd" ) && HasValue( "io.vd" );
- std::vector<std::string> vectorFileList = GetVectorFileList( GetParameterString( "io.out" ), fileNames );
+ std::vector<std::string> vectorFileList = GetParameterStringList( "io.vd" );
unsigned long nbInputs = imageList->Size();
- if( nbInputs > vectorFileList.size() )
+ if( !vectorFileList.empty() && nbInputs > vectorFileList.size() )
{
otbAppLogFATAL( "Missing input vector data files to match number of images (" << nbInputs << ")." );
}
@@ -90,28 +103,29 @@ public:
SamplingRates rates = ComputeFinalMaximumSamplingRates( dedicatedValidation );
if( HasInputVector )
- {
+ {
// Select and Extract samples for training with computed statistics and rates
ComputePolygonStatistics( imageList, vectorFileList, fileNames.polyStatTrainOutputs );
ComputeSamplingRate( fileNames.polyStatTrainOutputs, fileNames.rateTrainOut, rates.fmt );
SelectAndExtractTrainSamples( fileNames, imageList, vectorFileList, SamplingStrategy::CLASS );
- }
+ }
else
- {
+ {
SelectAndExtractTrainSamples( fileNames, imageList, vectorFileList, SamplingStrategy::GEOMETRIC );
- }
+ }
// Select and Extract samples for validation with computed statistics and rates
// Validation samples could be empty if sample.vrt == 0 and if no dedicated validation are provided
- if( dedicatedValidation ) {
- ComputePolygonStatistics(imageList, validationVectorFileList, fileNames.polyStatValidOutputs);
- ComputeSamplingRate(fileNames.polyStatValidOutputs, fileNames.rateValidOut, rates.fmv);
+ if( dedicatedValidation )
+ {
+ ComputePolygonStatistics( imageList, validationVectorFileList, fileNames.polyStatValidOutputs );
+ ComputeSamplingRate( fileNames.polyStatValidOutputs, fileNames.rateValidOut, rates.fmv );
}
- SelectAndExtractValidationSamples(fileNames, imageList, validationVectorFileList, dedicatedValidation);
+ SelectAndExtractValidationSamples( fileNames, imageList, validationVectorFileList );
// Then train the model with extracted samples
- TrainModel( imageList, fileNames.sampleTrainOutputs, fileNames.sampleValidOutputs);
+ TrainModel( imageList, fileNames.sampleTrainOutputs, fileNames.sampleValidOutputs );
// cleanup
if( IsParameterEnabled( "cleanup" ) )
@@ -130,63 +144,6 @@ private :
UpdateInternalParameters( "polystat" );
}
-
- /**
- * Retrieve input vector data if provided otherwise generate a default vector shape file for each image.
- * \param output vector file path
- * \param fileNames
- * \return list of input vector data file names
- */
- std::vector<std::string> GetVectorFileList(std::string output, TrainFileNamesHandler &fileNames)
- {
- std::vector<std::string> vectorFileList;
- bool HasInputVector = IsParameterEnabled( "io.vd" ) && HasValue( "io.vd" );
-
- // Retrieve provided input vector data if available.
- if( !HasInputVector )
- {
- FloatVectorImageListType *imageList = GetParameterImageList( "io.il" );
- unsigned int nbInputs = static_cast<unsigned int>(imageList->Size());
-
- for( unsigned int i = 0; i < nbInputs; ++i )
- {
- std::string name = output + "_vector_" + std::to_string( i ) + ".shp";
- GenerateVectorDataFile( imageList->GetNthElement( i ), name );
- fileNames.tmpVectorFileList.push_back( name );
- }
- vectorFileList = fileNames.tmpVectorFileList;
- SetParameterStringList( "io.vd", vectorFileList, false );
- UpdatePolygonClassStatisticsParameters();
- GetInternalApplication( "polystat" )->SetParameterString( "field", "fid" );
- }
- else
- {
- vectorFileList = GetParameterStringList( "io.vd" );
- }
-
- return vectorFileList;
- }
-
-
-
- void GenerateVectorDataFile(const FloatVectorImageListType::ObjectPointerType &floatVectorImage, std::string name)
- {
- typedef otb::ImageToEnvelopeVectorDataFilter<FloatVectorImageType, VectorDataType> ImageToEnvelopeFilterType;
- typedef ImageToEnvelopeFilterType::OutputVectorDataType OutputVectorData;
- typedef otb::VectorDataFileWriter<OutputVectorData> VectorDataWriter;
-
- ImageToEnvelopeFilterType::Pointer imageToEnvelopeVectorData = ImageToEnvelopeFilterType::New();
- imageToEnvelopeVectorData->SetInput( floatVectorImage );
- imageToEnvelopeVectorData->SetOutputProjectionRef( floatVectorImage->GetProjectionRef().c_str() );
- OutputVectorData::Pointer vectorData = imageToEnvelopeVectorData->GetOutput();
-
- // write temporary generated vector file to disk.
- VectorDataWriter::Pointer vectorDataFileWriter = VectorDataWriter::New();
- vectorDataFileWriter->SetInput( vectorData );
- vectorDataFileWriter->SetFileName( name.c_str() );
- vectorDataFileWriter->Write();
- }
-
};
}
diff --git a/Modules/Applications/AppClassification/include/otbTrainImagesBase.h b/Modules/Applications/AppClassification/include/otbTrainImagesBase.h
index 4f5dd82d3c..4e9a0cf220 100644
--- a/Modules/Applications/AppClassification/include/otbTrainImagesBase.h
+++ b/Modules/Applications/AppClassification/include/otbTrainImagesBase.h
@@ -327,7 +327,6 @@ protected:
std::string statisticsFileName, std::string ratesFileName, SamplingStrategy strategy)
{
GetInternalApplication( "select" )->SetParameterInputImage( "in", image );
- GetInternalApplication( "select" )->SetParameterString( "vec", vectorFileName, false );
GetInternalApplication( "select" )->SetParameterString( "out", sampleFileName, false );
GetInternalApplication( "extraction" )->SetParameterString( "outfield", "prefix", false );
@@ -338,10 +337,12 @@ protected:
{
case GEOMETRIC:
GetInternalApplication( "select" )->SetParameterString( "sampler", "random", false );
- GetInternalApplication( "select" )->SetParameterString( "strategy", "all", false );
+ GetInternalApplication( "select" )->SetParameterString( "strategy", "percent", false );
+ GetInternalApplication( "select" )->SetParameterFloat("strategy.percent.p", GetParameterFloat("sample.percent"), false);
break;
case CLASS:
default:
+ GetInternalApplication( "select" )->SetParameterString( "vec", vectorFileName, false );
GetInternalApplication( "select" )->SetParameterString( "instats", statisticsFileName, false );
GetInternalApplication( "select" )->SetParameterString( "sampler", "periodic", false );
GetInternalApplication( "select" )->SetParameterInt( "sampler.periodic.jitter", 50 );
@@ -365,19 +366,19 @@ protected:
for( unsigned int i = 0; i < imageList->Size(); ++i )
{
- SelectAndExtractSamples( imageList->GetNthElement( i ), vectorFileNames[i], fileNames.sampleOutputs[i],
+ std::string vectorFileName = vectorFileNames.empty() ? "" : vectorFileNames[i];
+ SelectAndExtractSamples( imageList->GetNthElement( i ), vectorFileName, fileNames.sampleOutputs[i],
fileNames.polyStatTrainOutputs[i], fileNames.ratesTrainOutputs[i], strategy );
}
}
void SelectAndExtractValidationSamples(const TrainFileNamesHandler &fileNames, FloatVectorImageListType *imageList,
- const std::vector<std::string> &validationVectorFileList,
- bool dedicatedValidation)
+ const std::vector<std::string> &validationVectorFileList)
{
// In dedicated validation mode the by class sampling strategy and statistics are used.
// Otherwise simply split training to validation samples corresponding to sample.vtr percentage.
- if( dedicatedValidation )
+ if( !validationVectorFileList.empty() )
{
for( unsigned int i = 0; i < imageList->Size(); ++i )
{
--
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