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otbTrainSharkKMeans.hxx 5.10 KiB
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/*
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 * Copyright (C) 2005-2019 Centre National d'Etudes Spatiales (CNES)
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 *
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 * This file is part of Orfeo Toolbox
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 *
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 *     https://www.orfeo-toolbox.org/
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 *
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 * Licensed under the Apache License, Version 2.0 (the "License");
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 * you may not use this file except in compliance with the License.
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 * You may obtain a copy of the License at
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 *
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 *     http://www.apache.org/licenses/LICENSE-2.0
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 *
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 * Unless required by applicable law or agreed to in writing, software
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 * distributed under the License is distributed on an "AS IS" BASIS,
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 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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 * See the License for the specific language governing permissions and
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 * limitations under the License.
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 */
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#ifndef otbTrainSharkKMeans_hxx
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#define otbTrainSharkKMeans_hxx
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#include "otbLearningApplicationBase.h"
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#include "otbSharkKMeansMachineLearningModel.h"
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#include "otbStatisticsXMLFileReader.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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template<class TInputValue, class TOutputValue>
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void LearningApplicationBase<TInputValue, TOutputValue>::InitSharkKMeansParams()
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{
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  AddChoice( "classifier.sharkkm", "Shark kmeans classifier" );
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  SetParameterDescription("classifier.sharkkm", "http://image.diku.dk/shark/sphinx_pages/build/html/rest_sources/tutorials/algorithms/kmeans.html ");
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  // MaxNumberOfIterations
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  AddParameter(ParameterType_Int, "classifier.sharkkm.maxiter", "Maximum number of iterations for the kmeans algorithm");
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  SetParameterInt("classifier.sharkkm.maxiter", 10);
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  SetMinimumParameterIntValue("classifier.sharkkm.maxiter", 0);
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  SetParameterDescription("classifier.sharkkm.maxiter", "The maximum number of iterations for the kmeans algorithm. 0=unlimited");
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  // Number of classes
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  AddParameter(ParameterType_Int, "classifier.sharkkm.k", "Number of classes for the kmeans algorithm");
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  SetParameterInt("classifier.sharkkm.k", 2);
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  SetParameterDescription("classifier.sharkkm.k", "The number of classes used for the kmeans algorithm. Default set to 2 class");
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  SetMinimumParameterIntValue("classifier.sharkkm.k", 2);
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  AddParameter(ParameterType_InputFilename, "classifier.sharkkm.centroidstats", "Statistics file");
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  SetParameterDescription("classifier.sharkkm.centroidstats", "A XML file containing mean and standard deviation to center"
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    "and reduce the centroids before classification, produced by ComputeImagesStatistics application.");
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  MandatoryOff("classifier.sharkkm.centroidstats");
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  // Number of classes
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  AddParameter(ParameterType_String, "classifier.sharkkm.centroids", "Number of classes for the kmeans algorithm");
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  SetParameterDescription("classifier.sharkkm.incentroid", "The number of classes used for the kmeans algorithm. Default set to 2 class");
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  MandatoryOff("classifier.sharkkm.incentroid");
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}
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template<class TInputValue, class TOutputValue>
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void LearningApplicationBase<TInputValue, TOutputValue>::TrainSharkKMeans(
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        typename ListSampleType::Pointer trainingListSample,
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        typename TargetListSampleType::Pointer trainingLabeledListSample, std::string modelPath)
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{
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  unsigned int nbMaxIter = static_cast<unsigned int>(abs( GetParameterInt( "classifier.sharkkm.maxiter" ) ));
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  unsigned int k = static_cast<unsigned int>(abs( GetParameterInt( "classifier.sharkkm.k" ) ));
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  typedef otb::SharkKMeansMachineLearningModel<InputValueType, OutputValueType> SharkKMeansType;
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  typename SharkKMeansType::Pointer classifier = SharkKMeansType::New();
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  classifier->SetRegressionMode( this->m_RegressionFlag );
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classifier->SetInputListSample( trainingListSample ); classifier->SetTargetListSample( trainingLabeledListSample ); classifier->SetK( k ); // Initialize centroids from file if(HasValue("classifier.sharkkm.centroids")) { shark::Data<shark::RealVector> centroidData; shark::importCSV(centroidData, GetParameterString( "classifier.sharkkm.centroidstats"), ' '); if( HasValue( "classifier.sharkkm.centroids" ) ) { auto statisticsReader = otb::StatisticsXMLFileReader< itk::VariableLengthVector<float> >::New(); statisticsReader->SetFileName(GetParameterString( "classifier.sharkkm.centroidstats" )); auto meanMeasurementVector = statisticsReader->GetStatisticVectorByName("mean"); auto stddevMeasurementVector = statisticsReader->GetStatisticVectorByName("stddev"); // Convert itk Variable Length Vector to shark Real Vector shark::RealVector meanMeasurementRV(meanMeasurementVector.Size()); for (unsigned int i = 0; i<meanMeasurementVector.Size(); ++i) { // Substract the mean meanMeasurementRV[i] = - meanMeasurementVector[i]; } shark::RealVector stddevMeasurementRV(stddevMeasurementVector.Size()); for (unsigned int i = 0; i<stddevMeasurementVector.Size(); ++i) { stddevMeasurementRV[i] = stddevMeasurementVector[i]; } shark::Normalizer<> normalizer(stddevMeasurementRV, meanMeasurementRV); centroidData = normalizer(centroidData); } classifier->SetCentroidsFromData( centroidData); } classifier->SetMaximumNumberOfIterations( nbMaxIter ); classifier->Train(); classifier->Save( modelPath ); } } //end namespace wrapper } //end namespace otb #endif