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Cédric Traizet authored1df9f3f7
/* * Copyright (C) 2005-2019 Centre National d'Etudes Spatiales (CNES) * * This file is part of Orfeo Toolbox * * https://www.orfeo-toolbox.org/ * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */#ifndef otbTrainSharkKMeans_hxx#define otbTrainSharkKMeans_hxx#include "otbLearningApplicationBase.h"#include "otbSharkKMeansMachineLearningModel.h"#include "otbStatisticsXMLFileReader.h"namespace otb{namespace Wrapper{template<class TInputValue, class TOutputValue>void LearningApplicationBase<TInputValue, TOutputValue>::InitSharkKMeansParams(){ AddChoice( "classifier.sharkkm", "Shark kmeans classifier" ); SetParameterDescription("classifier.sharkkm", "http://image.diku.dk/shark/sphinx_pages/build/html/rest_sources/tutorials/algorithms/kmeans.html "); // MaxNumberOfIterations AddParameter(ParameterType_Int, "classifier.sharkkm.maxiter", "Maximum number of iterations for the kmeans algorithm"); SetParameterInt("classifier.sharkkm.maxiter", 10); SetMinimumParameterIntValue("classifier.sharkkm.maxiter", 0); SetParameterDescription("classifier.sharkkm.maxiter", "The maximum number of iterations for the kmeans algorithm. 0=unlimited"); // Number of classes AddParameter(ParameterType_Int, "classifier.sharkkm.k", "Number of classes for the kmeans algorithm"); SetParameterInt("classifier.sharkkm.k", 2); SetParameterDescription("classifier.sharkkm.k", "The number of classes used for the kmeans algorithm. Default set to 2 class"); SetMinimumParameterIntValue("classifier.sharkkm.k", 2); AddParameter( ParameterType_Group, "classifier.sharkkm.centroids", "Centroids IO parameters" ); SetParameterDescription( "classifier.sharkkm.centroids", "Group of parameters for centroids IO." ); // Input centroids AddParameter(ParameterType_InputFilename, "classifier.sharkkm.centroids.in", "User definied input centroids"); SetParameterDescription("classifier.sharkkm.centroids", "Text file containing input centroids."); MandatoryOff("classifier.sharkkm.centroids"); // Centroid statistics AddParameter(ParameterType_InputFilename, "classifier.sharkkm.centroids.stats", "Statistics file"); SetParameterDescription("classifier.sharkkm.centroids.stats", "A XML file containing mean and standard deviation to center" "and reduce the centroids before the KMeans algorithm, produced by ComputeImagesStatistics application."); MandatoryOff("classifier.sharkkm.centroids.stats"); // output centroids AddParameter(ParameterType_OutputFilename, "classifier.sharkkm.centroids.out", "Output centroids text file"); SetParameterDescription("classifier.sharkkm.centroids.out", "Output text file containing centroids after the kmean algorithm."); MandatoryOff("classifier.sharkkm.centroids.out");}7172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130
template<class TInputValue, class TOutputValue>
void LearningApplicationBase<TInputValue, TOutputValue>::TrainSharkKMeans(
typename ListSampleType::Pointer trainingListSample,
typename TargetListSampleType::Pointer trainingLabeledListSample, std::string modelPath)
{
unsigned int nbMaxIter = static_cast<unsigned int>(abs( GetParameterInt( "classifier.sharkkm.maxiter" ) ));
unsigned int k = static_cast<unsigned int>(abs( GetParameterInt( "classifier.sharkkm.k" ) ));
typedef otb::SharkKMeansMachineLearningModel<InputValueType, OutputValueType> SharkKMeansType;
typename SharkKMeansType::Pointer classifier = SharkKMeansType::New();
classifier->SetRegressionMode( this->m_RegressionFlag );
classifier->SetInputListSample( trainingListSample );
classifier->SetTargetListSample( trainingLabeledListSample );
classifier->SetK( k );
// Initialize centroids from file
if(IsParameterEnabled("classifier.sharkkm.centroids.in") && HasValue("classifier.sharkkm.centroids.in"))
{
shark::Data<shark::RealVector> centroidData;
shark::importCSV(centroidData, GetParameterString( "classifier.sharkkm.centroids.in"), ' ');
if( HasValue( "classifier.sharkkm.centroids.stats" ) )
{
auto statisticsReader = otb::StatisticsXMLFileReader< itk::VariableLengthVector<float> >::New();
statisticsReader->SetFileName(GetParameterString( "classifier.sharkkm.centroids.stats" ));
auto meanMeasurementVector = statisticsReader->GetStatisticVectorByName("mean");
auto stddevMeasurementVector = statisticsReader->GetStatisticVectorByName("stddev");
// Convert itk Variable Length Vector to shark Real Vector
shark::RealVector offsetRV(meanMeasurementVector.Size());
shark::RealVector scaleRV(stddevMeasurementVector.Size());
assert(meanMeasurementVector.Size()==stddevMeasurementVector.Size());
for (unsigned int i = 0; i<meanMeasurementVector.Size(); ++i)
{
scaleRV[i] = 1/stddevMeasurementVector[i];
// Substract the normalized mean
offsetRV[i] = - meanMeasurementVector[i]/stddevMeasurementVector[i];
}
shark::Normalizer<> normalizer(scaleRV, offsetRV);
centroidData = normalizer(centroidData);
}
classifier->SetCentroidsFromData( centroidData);
}
classifier->SetMaximumNumberOfIterations( nbMaxIter );
classifier->Train();
classifier->Save( modelPath );
if( HasValue( "classifier.sharkkm.centroids.out"))
classifier->ExportCentroids( GetParameterString( "classifier.sharkkm.centroids.out" ));
}
} //end namespace wrapper
} //end namespace otb
#endif