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/*=========================================================================
Program: ORFEO Toolbox
Language: C++
Date: $Date$
Version: $Revision$
Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
See OTBCopyright.txt for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#include "itkMacro.h"
#include <iostream>
#include <cstdlib>
#include "otbSVMPointSetModelEstimator.h"
#include "itkPointSetToListSampleAdaptor.h"
#include "otbSVMClassifier.h"
int main(int itkNotUsed(argc), char* itkNotUsed(argv)[])
{
typedef float InputPixelType;
typedef std::vector<InputPixelType> InputVectorType;
typedef int LabelPixelType;
const unsigned int Dimension = 2;
typedef itk::PointSet<InputVectorType, Dimension> MeasurePointSetType;
typedef itk::PointSet<LabelPixelType, Dimension> LabelPointSetType;
MeasurePointSetType::Pointer mPSet = MeasurePointSetType::New();
LabelPointSetType::Pointer lPSet = LabelPointSetType::New();
typedef MeasurePointSetType::PointType MeasurePointType;
typedef LabelPointSetType::PointType LabelPointType;
typedef MeasurePointSetType::PointsContainer MeasurePointsContainer;
typedef LabelPointSetType::PointsContainer LabelPointsContainer;
MeasurePointsContainer::Pointer mCont = MeasurePointsContainer::New();
LabelPointsContainer::Pointer lCont = LabelPointsContainer::New();
/* We learn the y>x | y<x boundary*/
// srand((unsigned)time(0));
srand(0);
int lowest = 0;
int range = 1000;
unsigned int pointId;
for (pointId = 0; pointId < 500; pointId++)
{
MeasurePointType mP;
LabelPointType lP;
int x_coord = lowest + static_cast<int>(range * (rand() / (RAND_MAX + 1.0)));
int y_coord = lowest + static_cast<int>(range * (rand() / (RAND_MAX + 1.0)));
std::cout << "coords : " << x_coord << " " << y_coord << std::endl;
mP[0] = x_coord;
mP[1] = y_coord;
lP[0] = x_coord;
lP[1] = y_coord;
InputVectorType measure;
measure.push_back(static_cast<InputPixelType>((x_coord * 1.0 -
lowest) / range));
measure.push_back(static_cast<InputPixelType>((y_coord * 1.0 -
lowest) / range));
LabelPixelType label;
if (x_coord < y_coord) label = 0;
else label = 1;
std::cout << "Label : " << label << std::endl;
std::cout << "Measures : " << measure[0] << " " << measure[1] << std::endl;
mCont->InsertElement(pointId, mP);
mPSet->SetPointData(pointId, measure);
lCont->InsertElement(pointId, lP);
lPSet->SetPointData(pointId, label);
}
mPSet->SetPoints(mCont);
lPSet->SetPoints(lCont);
typedef otb::SVMPointSetModelEstimator<MeasurePointSetType,
LabelPointSetType> EstimatorType;
EstimatorType::Pointer estimator = EstimatorType::New();
estimator->SetInputPointSet(mPSet);
estimator->SetTrainingPointSet(lPSet);
estimator->Update();
std::cout << "Saving model" << std::endl;
estimator->SaveModel("model.svm");
// Build the test set
MeasurePointSetType::Pointer tPSet = MeasurePointSetType::New();
MeasurePointsContainer::Pointer tCont = MeasurePointsContainer::New();
for (pointId = 0; pointId < 100; pointId++)
{
MeasurePointType tP;
int x_coord = lowest + static_cast<int>(range * (rand() / (RAND_MAX + 1.0)));
int y_coord = lowest + static_cast<int>(range * (rand() / (RAND_MAX + 1.0)));
std::cout << "coords : " << x_coord << " " << y_coord << std::endl;
tP[0] = x_coord;
tP[1] = y_coord;
InputVectorType measure;
measure.push_back(static_cast<InputPixelType>((x_coord * 1.0 -
lowest) / range));
measure.push_back(static_cast<InputPixelType>((y_coord * 1.0 -
lowest) / range));
std::cout << "Measures : " << measure[0] << " " << measure[1] << std::endl;
tCont->InsertElement(pointId, tP);
tPSet->SetPointData(pointId, measure);
}
tPSet->SetPoints(tCont);
// Classify
typedef itk::Statistics::PointSetToListSampleAdaptor<MeasurePointSetType>
SampleType;
SampleType::Pointer sample = SampleType::New();
sample->SetPointSet(tPSet);
std::cout << "Sample set to Adaptor" << std::endl;
/** preparing classifier and decision rule object */
typedef otb::SVMModel<SampleType::MeasurementVectorType::ValueType,
LabelPixelType> ModelType;
ModelType::Pointer model = estimator->GetModel();
int numberOfClasses = model->GetNumberOfClasses();
std::cout << "Classification for " << numberOfClasses << " classes " <<
std::endl;
typedef otb::SVMClassifier<SampleType, LabelPixelType> ClassifierType;
ClassifierType::Pointer classifier = ClassifierType::New();
classifier->SetNumberOfClasses(numberOfClasses);
classifier->SetModel(model);
classifier->SetInput(sample.GetPointer());
classifier->Update();
/* Build the class map */
std::cout << "Output image creation" << std::endl;
std::cout << "classifier get output" << std::endl;
ClassifierType::OutputType* membershipSample =
classifier->GetOutput();
std::cout << "Sample iterators" << std::endl;
ClassifierType::OutputType::ConstIterator m_iter =
membershipSample->Begin();
ClassifierType::OutputType::ConstIterator m_last =
membershipSample->End();
double error = 0.0;
pointId = 0;
while (m_iter != m_last)
{
ClassifierType::ClassLabelType label = m_iter.GetClassLabel();
InputVectorType measure;
tPSet->GetPointData(pointId, &measure);
ClassifierType::ClassLabelType expectedLabel;
if (measure[0] < measure[1]) expectedLabel = 0;
else expectedLabel = 1;
double dist = fabs(measure[0] - measure[1]);
if (label != expectedLabel) error++;
std::cout << int(label) << "/" << int(expectedLabel) << " --- " << dist <<
std::endl;
++pointId;
++m_iter;
}
std::cout << "Error = " << error / pointId << std::endl;
return EXIT_SUCCESS;
}
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