otbTrainSharkKMeans.hxx

/*
 * 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_InputFilename, "classifier.sharkkm.centroidstats", "Statistics file");
  SetParameterDescription("classifier.sharkkm.centroidstats", "A XML file containing mean and standard deviation to center"
    "and reduce the centroids before classification, produced by ComputeImagesStatistics application.");
  MandatoryOff("classifier.sharkkm.centroidstats");
  
  // Number of classes
  AddParameter(ParameterType_String, "classifier.sharkkm.centroids", "Number of classes for the kmeans algorithm");
  SetParameterDescription("classifier.sharkkm.incentroid", "The number of classes used for the kmeans algorithm. Default set to 2 class");
  MandatoryOff("classifier.sharkkm.incentroid");
}

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 );