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#include "otbWrapperApplication.h"
#include "otbWrapperApplicationFactory.h"
#include "otbOGRDataSourceWrapper.h"
#include "otbOGRFeatureWrapper.h"
#include "itkVariableLengthVector.h"
#include "otbShiftScaleSampleListFilter.h"
#include "otbStatisticsXMLFileReader.h"
//#include "otbSharkUtils.h"
#include <fstream> // write the model file
#include "otbDimensionalityReductionModelFactory.h"
#include "otbTrainDimensionalityReductionApplicationBase.h"
namespace otb
{
namespace Wrapper
{
class TrainDimensionalityReduction : public TrainDimensionalityReductionApplicationBase<float,float>
{
public:
typedef TrainDimensionalityReduction Self;
typedef TrainDimensionalityReductionApplicationBase<float, float> Superclass;
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typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
itkNewMacro(Self);
itkTypeMacro(TrainDimensionalityReduction, otb::Application);
typedef Superclass::SampleType SampleType;
typedef Superclass::ListSampleType ListSampleType;
typedef Superclass::SampleImageType SampleImageType;
typedef float ValueType;
typedef itk::VariableLengthVector<ValueType> MeasurementType;
typedef otb::StatisticsXMLFileReader<SampleType> StatisticsReader;
typedef otb::Statistics::ShiftScaleSampleListFilter<ListSampleType, ListSampleType> ShiftScaleFilterType;
typedef otb::DimensionalityReductionModelFactory<ValueType, ValueType> ModelFactoryType;
private:
void DoInit()
{
SetName("TrainDimensionalityReduction");
SetDescription("Trainer for the dimensionality reduction algorithms used in the ImageDimensionalityReduction and VectorDimensionalityReduction applications.");
AddParameter(ParameterType_Group, "io", "Input and output data");
SetParameterDescription("io", "This group of parameters allows setting input and output data.");
AddParameter(ParameterType_InputVectorData, "io.vd", "Input Vector Data");
SetParameterDescription("io.vd", "Input geometries used for training (note : all geometries from the layer will be used)");
AddParameter(ParameterType_OutputFilename, "io.out", "Output model");
SetParameterDescription("io.out", "Output file containing the model estimated (.txt format).");
AddParameter(ParameterType_InputFilename, "io.stats", "Input XML image statistics file");
MandatoryOff("io.stats");
SetParameterDescription("io.stats", "XML file containing mean and variance of each feature.");
AddParameter(ParameterType_StringList, "feat", "Field names to be calculated."); //
SetParameterDescription("feat","List of field names in the input vector data used as features for training."); //
Superclass::DoInit();
AddRAMParameter();
}
void DoUpdateParameters()
{
}
void DoExecute()
{
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"));
}
};
}
}
OTB_APPLICATION_EXPORT(otb::Wrapper::TrainDimensionalityReduction)