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Commit 078b7a5a authored by Ludovic Hussonnois's avatar Ludovic Hussonnois
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ENH: Update TrainImagesClustering and Classifier to use SampleSelection. 

SampleSelection can now be used without input vector data.
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Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx
+ 1
− 1
View file @ 078b7a5a
......@@ -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
......
Modules/Applications/AppClassification/app/otbTrainImagesClustering.cxx
+ 25
− 68
View file @ 078b7a5a
......@@ -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();
}
};
}
......
Modules/Applications/AppClassification/include/otbTrainImagesBase.h
+ 7
− 6
View file @ 078b7a5a
......@@ -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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