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cbDimensionalityReductionTrainer.cxx 4.59 KiB
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#include "otbWrapperApplication.h"
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#include "otbWrapperApplicationFactory.h"
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#include "otbOGRDataSourceWrapper.h"
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#include "otbOGRFeatureWrapper.h"
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#include "itkVariableLengthVector.h"
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#include "otbShiftScaleSampleListFilter.h"
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#include "otbStatisticsXMLFileReader.h"
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//#include "otbSharkUtils.h"
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#include <fstream> // write the model file
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#include "DimensionalityReductionModelFactory.h"
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#include "cbLearningApplicationBaseDR.h"
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namespace otb
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{
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namespace Wrapper
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{
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class CbDimensionalityReductionTrainer : public cbLearningApplicationBaseDR<float,float>
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{
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public:
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	typedef CbDimensionalityReductionTrainer Self;
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	typedef cbLearningApplicationBaseDR<float, float> Superclass;
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	typedef itk::SmartPointer<Self> Pointer;
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	typedef itk::SmartPointer<const Self> ConstPointer;
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	itkNewMacro(Self);
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	itkTypeMacro(CbDimensionalityReductionTrainer, otb::Application);
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	typedef Superclass::SampleType              SampleType;
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	typedef Superclass::ListSampleType          ListSampleType;
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	typedef Superclass::SampleImageType         SampleImageType;
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	typedef double ValueType;
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	typedef itk::VariableLengthVector<ValueType> MeasurementType;
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	typedef otb::StatisticsXMLFileReader<SampleType> StatisticsReader;
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	typedef otb::Statistics::ShiftScaleSampleListFilter<ListSampleType, ListSampleType> ShiftScaleFilterType;
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	typedef otb::DimensionalityReductionModelFactory<ValueType, ValueType>  ModelFactoryType;
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private:
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	void DoInit()
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	{
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		SetName("CbDimensionalityReductionTrainer");
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		SetDescription("Trainer for the dimensionality reduction algorithms used in the cbDimensionalityReduction application.");
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		AddParameter(ParameterType_Group, "io", "Input and output data");
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		SetParameterDescription("io", "This group of parameters allows setting input and output data.");
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		AddParameter(ParameterType_InputVectorData, "io.vd", "Input Vector Data");
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		SetParameterDescription("io.vd", "Input geometries used for training (note : all geometries from the layer will be used)");
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		AddParameter(ParameterType_OutputFilename, "io.out", "Output model");
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		SetParameterDescription("io.out", "Output file containing the model estimated (.txt format).");
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		AddParameter(ParameterType_InputFilename, "io.stats", "Input XML image statistics file");
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		MandatoryOff("io.stats");
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		SetParameterDescription("io.stats", "XML file containing mean and variance of each feature.");
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		AddParameter(ParameterType_StringList, "feat", "Field names to be calculated."); //
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		SetParameterDescription("feat","List of field names in the input vector data used as features for training."); //
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Superclass::DoInit(); AddRAMParameter(); } void DoUpdateParameters() { } void DoExecute() { std::cout << "Appli Training!" << std::endl; std::string shapefile = GetParameterString("io.vd"); otb::ogr::DataSource::Pointer source = otb::ogr::DataSource::New(shapefile, otb::ogr::DataSource::Modes::Read); otb::ogr::Layer layer = source->GetLayer(0); ListSampleType::Pointer input = ListSampleType::New(); const int nbFeatures = GetParameterStringList("feat").size(); input->SetMeasurementVectorSize(nbFeatures); otb::ogr::Layer::const_iterator it = layer.cbegin(); otb::ogr::Layer::const_iterator itEnd = layer.cend(); for( ; it!=itEnd ; ++it) { MeasurementType mv; mv.SetSize(nbFeatures); for(int idx=0; idx < nbFeatures; ++idx) { mv[idx] = (*it)[GetParameterStringList("feat")[idx]].GetValue<double>(); } input->PushBack(mv); } MeasurementType meanMeasurementVector; MeasurementType stddevMeasurementVector; if (HasValue("io.stats") && IsParameterEnabled("io.stats")) { StatisticsReader::Pointer statisticsReader = StatisticsReader::New(); std::string XMLfile = GetParameterString("io.stats"); statisticsReader->SetFileName(XMLfile); meanMeasurementVector = statisticsReader->GetStatisticVectorByName("mean"); stddevMeasurementVector = statisticsReader->GetStatisticVectorByName("stddev"); } else { meanMeasurementVector.SetSize(nbFeatures); meanMeasurementVector.Fill(0.); stddevMeasurementVector.SetSize(nbFeatures); stddevMeasurementVector.Fill(1.); } ShiftScaleFilterType::Pointer trainingShiftScaleFilter = ShiftScaleFilterType::New(); trainingShiftScaleFilter->SetInput(input); trainingShiftScaleFilter->SetShifts(meanMeasurementVector); trainingShiftScaleFilter->SetScales(stddevMeasurementVector); trainingShiftScaleFilter->Update(); ListSampleType::Pointer trainingListSample= trainingShiftScaleFilter->GetOutput(); this->Train(trainingListSample,GetParameterString("io.out")); }
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}; } } OTB_APPLICATION_EXPORT(otb::Wrapper::CbDimensionalityReductionTrainer)