• Manuel Grizonnet's avatar
    ENH: rename files with txx extension to hxx · 3a1fd1fc
    Manuel Grizonnet authored
    OTB followed since the beginning the ITK convention and use .txx extension for all
    template classes. Nevertheless, some development tools do not recognize .txx
    file extension. Other tool like GitHub can't do in-browser syntax highlighting for
    txx files I think.
    
    The root problem is the use of the txx which should be changed to hxx (or hpp).
    
    In 2011, after an in-depth discussion near April 20, 2011 on the
    Insight-Developers mailing list, ITK rename all txx files to hxx (and event
    prevent the push of .txx files with a pre-commit hook). It happens is major release v4.
    
    You can find some arguments in the discussion about the change and also in other
    projects related to ITK which applied the same modification, see for instance VXL:
    
    https://github.com/vxl/vxl/issues/209
    
    This commit apply now the same modification for OTB.
    
    I understand that it will change some habit for developers and don't bring new
    features but I think that in general it is better to stay align with ITK guidelines.
    
    In my opinion, it always facilitate the use of OTB and ITK together if we share
    when we can the same code architecture, directory organization, naming
    conventions...
    3a1fd1fc
otbLearningApplicationBase.hxx 8.75 KiB
/*
 * Copyright (C) 2005-2017 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 otbLearningApplicationBase_txx
#define otbLearningApplicationBase_txx
#include "otbLearningApplicationBase.h"
// only need this filter as a dummy process object
#include "otbRGBAPixelConverter.h"
namespace otb
namespace Wrapper
template <class TInputValue, class TOutputValue>
LearningApplicationBase<TInputValue,TOutputValue>
::LearningApplicationBase() : m_RegressionFlag(false)
template <class TInputValue, class TOutputValue>
LearningApplicationBase<TInputValue,TOutputValue>
::~LearningApplicationBase()
  ModelFactoryType::CleanFactories();
template <class TInputValue, class TOutputValue>
void
LearningApplicationBase<TInputValue,TOutputValue>
::DoInit()
  AddDocTag(Tags::Learning);
  // main choice parameter that will contain all machine learning options
  AddParameter(ParameterType_Choice, "classifier", "Classifier to use for the training");
  SetParameterDescription("classifier", "Choice of the classifier to use for the training.");
  InitSupervisedClassifierParams();
  m_SupervisedClassifier = GetChoiceKeys("classifier");
  InitUnsupervisedClassifierParams();
  std::vector<std::string> allClassifier = GetChoiceKeys("classifier");
  // Check for empty unsupervised classifier
  if( allClassifier.size() > m_UnsupervisedClassifier.size() )
    m_UnsupervisedClassifier.assign( allClassifier.begin() + m_SupervisedClassifier.size(), allClassifier.end() );
template <class TInputValue, class TOutputValue>
typename LearningApplicationBase<TInputValue,TOutputValue>::ClassifierCategory
LearningApplicationBase<TInputValue,TOutputValue>
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::GetClassifierCategory() { if( m_UnsupervisedClassifier.empty() ) { return Supervised; } else { bool foundUnsupervised = std::find( m_UnsupervisedClassifier.begin(), m_UnsupervisedClassifier.end(), GetParameterString( "classifier" ) ) != m_UnsupervisedClassifier.end(); return foundUnsupervised ? Unsupervised : Supervised; } } template <class TInputValue, class TOutputValue> void LearningApplicationBase<TInputValue,TOutputValue> ::InitSupervisedClassifierParams() { //Group LibSVM #ifdef OTB_USE_LIBSVM InitLibSVMParams(); #endif #ifdef OTB_USE_OPENCV // OpenCV SVM implementation is buggy with linear kernel // Users should use the libSVM implementation instead. // InitSVMParams(); if (!m_RegressionFlag) { InitBoostParams(); // Regression not supported } InitDecisionTreeParams(); InitGradientBoostedTreeParams(); InitNeuralNetworkParams(); if (!m_RegressionFlag) { InitNormalBayesParams(); // Regression not supported } InitRandomForestsParams(); InitKNNParams(); #endif #ifdef OTB_USE_SHARK InitSharkRandomForestsParams(); #endif } template <class TInputValue, class TOutputValue> void LearningApplicationBase<TInputValue,TOutputValue> ::InitUnsupervisedClassifierParams() { #ifdef OTB_USE_SHARK InitSharkKMeansParams(); #endif } template <class TInputValue, class TOutputValue> typename LearningApplicationBase<TInputValue,TOutputValue> ::TargetListSampleType::Pointer LearningApplicationBase<TInputValue,TOutputValue> ::Classify(typename ListSampleType::Pointer validationListSample, std::string modelPath) { // Setup fake reporter RGBAPixelConverter<int,int>::Pointer dummyFilter = RGBAPixelConverter<int,int>::New(); dummyFilter->SetProgress(0.0f);
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this->AddProcess(dummyFilter,"Classify..."); dummyFilter->InvokeEvent(itk::StartEvent()); // load a machine learning model from file and predict the input sample list ModelPointerType model = ModelFactoryType::CreateMachineLearningModel(modelPath, ModelFactoryType::ReadMode); if (model.IsNull()) { otbAppLogFATAL(<< "Error when loading model " << modelPath); } model->Load(modelPath); model->SetRegressionMode(this->m_RegressionFlag); typename TargetListSampleType::Pointer predictedList = model->PredictBatch(validationListSample, NULL); // update reporter dummyFilter->UpdateProgress(1.0f); dummyFilter->InvokeEvent(itk::EndEvent()); return predictedList; } template <class TInputValue, class TOutputValue> void LearningApplicationBase<TInputValue,TOutputValue> ::Train(typename ListSampleType::Pointer trainingListSample, typename TargetListSampleType::Pointer trainingLabeledListSample, std::string modelPath) { // Setup fake reporter RGBAPixelConverter<int,int>::Pointer dummyFilter = RGBAPixelConverter<int,int>::New(); dummyFilter->SetProgress(0.0f); this->AddProcess(dummyFilter,"Training model..."); dummyFilter->InvokeEvent(itk::StartEvent()); // get the name of the chosen machine learning model const std::string modelName = GetParameterString("classifier"); // call specific train function if (modelName == "libsvm") { #ifdef OTB_USE_LIBSVM TrainLibSVM(trainingListSample, trainingLabeledListSample, modelPath); #else otbAppLogFATAL("Module LIBSVM is not installed. You should consider turning OTB_USE_LIBSVM on during cmake configuration."); #endif } if(modelName == "sharkrf") { #ifdef OTB_USE_SHARK TrainSharkRandomForests(trainingListSample,trainingLabeledListSample,modelPath); #else otbAppLogFATAL("Module SharkLearning is not installed. You should consider turning OTB_USE_SHARK on during cmake configuration."); #endif } else if(modelName == "sharkkm") { #ifdef OTB_USE_SHARK TrainSharkKMeans( trainingListSample, trainingLabeledListSample, modelPath ); #else otbAppLogFATAL("Module SharkLearning is not installed. You should consider turning OTB_USE_SHARK on during cmake configuration."); #endif } else if (modelName == "svm") { #ifdef OTB_USE_OPENCV TrainSVM(trainingListSample, trainingLabeledListSample, modelPath); #else
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otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration."); #endif } else if (modelName == "boost") { #ifdef OTB_USE_OPENCV TrainBoost(trainingListSample, trainingLabeledListSample, modelPath); #else otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration."); #endif } else if (modelName == "dt") { #ifdef OTB_USE_OPENCV TrainDecisionTree(trainingListSample, trainingLabeledListSample, modelPath); #else otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration."); #endif } else if (modelName == "gbt") { #ifdef OTB_USE_OPENCV TrainGradientBoostedTree(trainingListSample, trainingLabeledListSample, modelPath); #else otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration."); #endif } else if (modelName == "ann") { #ifdef OTB_USE_OPENCV TrainNeuralNetwork(trainingListSample, trainingLabeledListSample, modelPath); #else otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration."); #endif } else if (modelName == "bayes") { #ifdef OTB_USE_OPENCV TrainNormalBayes(trainingListSample, trainingLabeledListSample, modelPath); #else otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration."); #endif } else if (modelName == "rf") { #ifdef OTB_USE_OPENCV TrainRandomForests(trainingListSample, trainingLabeledListSample, modelPath); #else otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration."); #endif } else if (modelName == "knn") { #ifdef OTB_USE_OPENCV TrainKNN(trainingListSample, trainingLabeledListSample, modelPath); #else otbAppLogFATAL("Module OPENCV is not installed. You should consider turning OTB_USE_OPENCV on during cmake configuration."); #endif } // update reporter dummyFilter->UpdateProgress(1.0f); dummyFilter->InvokeEvent(itk::EndEvent()); } } } #endif