libotb-dev 6.4.0+dfsg-1 / usr / include / OTB-6.4 / otbTrainImagesBase.h

/*
 * 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 otbTrainImagesBase_h
#define otbTrainImagesBase_h

#include "otbVectorDataFileWriter.h"
#include "otbWrapperCompositeApplication.h"
#include "otbWrapperApplicationFactory.h"

#include "otbStatisticsXMLFileWriter.h"
#include "otbImageToEnvelopeVectorDataFilter.h"
#include "otbSamplingRateCalculator.h"
#include "otbOGRDataToSamplePositionFilter.h"

namespace otb
{
namespace Wrapper
{

/** \class TrainImagesBase
 * \brief Base class for the TrainImagesClassifier
 *
 * This class intends to hold common input/output parameters and
 * composite application connection for both supervised and unsupervised
 * model training.
 *
 * \ingroup OTBAppClassification
 */
class TrainImagesBase : public CompositeApplication
{
public:
  /** Standard class typedefs. */
  typedef TrainImagesBase Self;
  typedef CompositeApplication Superclass;
  typedef itk::SmartPointer<Self> Pointer;
  typedef itk::SmartPointer<const Self> ConstPointer;

  /** Standard macro */
  itkTypeMacro( TrainImagesBase, Superclass )

  /** filters typedefs*/
  typedef otb::OGRDataToSamplePositionFilter<FloatVectorImageType, UInt8ImageType, otb::PeriodicSampler> PeriodicSamplerType;

  typedef otb::SamplingRateCalculator::MapRateType MapRateType;

protected:

  typedef enum
  {
    CLASS, GEOMETRIC
  } SamplingStrategy;
  struct SamplingRates;
  class TrainFileNamesHandler;

  /**
   * Initialize all the input and output parameter used for the train images
   */
  void InitIO();

  /**
   * Initialize sampling related application and parameters
   */
  void InitSampling();

  void ShareSamplingParameters();
  void ConnectSamplingParameters();
  void InitClassification();
  void ShareClassificationParams();
  void ConnectClassificationParams();

  /**
   * Compute polygon statistics given provided strategy with PolygonClassStatistics class
   * \param imageList list of input images
   * \param vectorFileNames list of input vector file names
   * \param statisticsFileNames list of out
   */
  void ComputePolygonStatistics(FloatVectorImageListType *imageList, const std::vector<std::string> &vectorFileNames,
                                const std::vector<std::string> &statisticsFileNames);

  /**
   * Compute final maximum training and validation
   * \param dedicatedValidation
   * \return SamplingRates final maximum training and final maximum validation
   */
  SamplingRates ComputeFinalMaximumSamplingRates(bool dedicatedValidation);


  /**
   * Compute rates using MultiImageSamplingRate application
   * \param statisticsFileNames
   * \param ratesFileName
   * \param maximum final maximum value computed by ComputeFinalMaximumSamplingRates
   * \sa ComputeFinalMaximumSamplingRates
   */
  void ComputeSamplingRate(const std::vector<std::string> &statisticsFileNames,
                           const std::string &ratesFileName,
                           long maximum);
  /**
   * Train the model with training and optional validation data samples
   * \param imageList list of input images
   * \param sampleTrainFileNames files names of the training samples
   * \param sampleValidationFileNames file names of the validation sample
   */
  void TrainModel(FloatVectorImageListType *imageList, const std::vector<std::string> &sampleTrainFileNames,
                  const std::vector<std::string> &sampleValidationFileNames);

  /**
   * Select samples by class or by geographic strategy
   * \param image
   * \param vectorFileName
   * \param sampleFileName
   * \param statisticsFileName
   * \param ratesFileName
   * \param strategy
   */
  void SelectAndExtractSamples(FloatVectorImageType *image, std::string vectorFileName, std::string sampleFileName,
                               std::string statisticsFileName, std::string ratesFileName, SamplingStrategy strategy,
                               std::string selectedField = "");
  /**
   * Select and extract samples with the SampleSelection and SampleExtraction application.
   * \param fileNames
   * \param imageList
   * \param vectorFileNames
   * \param strategy the strategy used for selection (by class or with geometry)
   * \param selectedFieldName
   */
  void SelectAndExtractTrainSamples(const TrainFileNamesHandler &fileNames, FloatVectorImageListType *imageList,
                                    std::vector<std::string> vectorFileNames, SamplingStrategy strategy,
                                    std::string selectedFieldName = "");


  /**
   * Function used to select validation samples based on a defined strategy (geometric in unsupervised mode)
   * and extract them. With dedicated validation the 'by class' sampling strategy and statistics are used.
   * Otherwise this function split training to validation samples corresponding to sample.vtr percentage.
   * or do nothing if this percentage is == 0
   * \param fileNames
   * \param imageList
   * \param validationVectorFileList optional validation vector file for each images
   */
  void SelectAndExtractValidationSamples(const TrainFileNamesHandler &fileNames, FloatVectorImageListType *imageList,
                                         const std::vector<std::string> &validationVectorFileList = std::vector<std::string>());

  /**
   * Function used to split all training samples from all images in a set of training and validation.
   * \param fileNames
   * \param imageList
   * \sa SplitTrainingAndValidationSamples
   */
  void SplitTrainingToValidationSamples(const TrainFileNamesHandler &fileNames, FloatVectorImageListType *imageList);

private:

  /**
   * Function used to split training samples in set of training and validation.
   * \param image input image
   * \param sampleFileName the input sample file name
   * \param sampleTrainFileName the input training file name
   * \param sampleValidFileName the input validation file name
   * \param ratesTrainFileName the rates file name
   */
  void SplitTrainingAndValidationSamples(FloatVectorImageType *image, std::string sampleFileName,
                                         std::string sampleTrainFileName, std::string sampleValidFileName,
                                         std::string ratesTrainFileName);


protected:

  struct SamplingRates
  {
    long int fmt;
    long int fmv;
  };

  /**
   * \class TrainFileNamesHandler
   * This class is used to store file names requires for the application's input and output.
   * And to clear temporary files generated by the applications
   * \ingroup OTBAppClassification
   */
  class TrainFileNamesHandler
  {
  public :
    void CreateTemporaryFileNames(std::string outModel, size_t nbInputs, bool dedicatedValidation)
    {

      if( dedicatedValidation )
        {
        rateTrainOut = outModel + "_ratesTrain.csv";
        }
      else
        {
        rateTrainOut = outModel + "_rates.csv";
        }

      rateValidOut = outModel + "_ratesValid.csv";
      for( unsigned int i = 0; i < nbInputs; i++ )
        {
        std::ostringstream oss;
        oss << i + 1;
        std::string strIndex( oss.str() );
        if( dedicatedValidation )
          {
          polyStatTrainOutputs.push_back( outModel + "_statsTrain_" + strIndex + ".xml" );
          polyStatValidOutputs.push_back( outModel + "_statsValid_" + strIndex + ".xml" );
          ratesTrainOutputs.push_back( outModel + "_ratesTrain_" + strIndex + ".csv" );
          ratesValidOutputs.push_back( outModel + "_ratesValid_" + strIndex + ".csv" );
          sampleOutputs.push_back( outModel + "_samplesTrain_" + strIndex + ".shp" );
          }
        else
          {
          polyStatTrainOutputs.push_back( outModel + "_stats_" + strIndex + ".xml" );
          ratesTrainOutputs.push_back( outModel + "_rates_" + strIndex + ".csv" );
          sampleOutputs.push_back( outModel + "_samples_" + strIndex + ".shp" );
          }
        sampleTrainOutputs.push_back( outModel + "_samplesTrain_" + strIndex + ".shp" );
        sampleValidOutputs.push_back( outModel + "_samplesValid_" + strIndex + ".shp" );
        }

    }

    void clear()
    {
      for( unsigned int i = 0; i < polyStatTrainOutputs.size(); i++ )
        RemoveFile( polyStatTrainOutputs[i] );
      for( unsigned int i = 0; i < polyStatValidOutputs.size(); i++ )
        RemoveFile( polyStatValidOutputs[i] );
      for( unsigned int i = 0; i < ratesTrainOutputs.size(); i++ )
        RemoveFile( ratesTrainOutputs[i] );
      for( unsigned int i = 0; i < ratesValidOutputs.size(); i++ )
        RemoveFile( ratesValidOutputs[i] );
      for( unsigned int i = 0; i < sampleOutputs.size(); i++ )
        RemoveFile( sampleOutputs[i] );
      for( unsigned int i = 0; i < sampleTrainOutputs.size(); i++ )
        RemoveFile( sampleTrainOutputs[i] );
      for( unsigned int i = 0; i < sampleValidOutputs.size(); i++ )
        RemoveFile( sampleValidOutputs[i] );
      for( unsigned int i = 0; i < tmpVectorFileList.size(); i++ )
        RemoveFile( tmpVectorFileList[i] );
    }

  public:
    std::vector<std::string> polyStatTrainOutputs;
    std::vector<std::string> polyStatValidOutputs;
    std::vector<std::string> ratesTrainOutputs;
    std::vector<std::string> ratesValidOutputs;
    std::vector<std::string> sampleOutputs;
    std::vector<std::string> sampleTrainOutputs;
    std::vector<std::string> sampleValidOutputs;
    std::vector<std::string> tmpVectorFileList;
    std::string rateValidOut;
    std::string rateTrainOut;

  private:
    bool RemoveFile(std::string &filePath)
    {
      bool res = true;
      if( itksys::SystemTools::FileExists( filePath.c_str() ) )
        {
        size_t posExt = filePath.rfind( '.' );
        if( posExt != std::string::npos && filePath.compare( posExt, std::string::npos, ".shp" ) == 0 )
          {
          std::string shxPath = filePath.substr( 0, posExt ) + std::string( ".shx" );
          std::string dbfPath = filePath.substr( 0, posExt ) + std::string( ".dbf" );
          std::string prjPath = filePath.substr( 0, posExt ) + std::string( ".prj" );
          RemoveFile( shxPath );
          RemoveFile( dbfPath );
          RemoveFile( prjPath );
          }
        res = itksys::SystemTools::RemoveFile( filePath.c_str() );
        if( !res )
          {
          //otbAppLogINFO( <<"Unable to remove file  "<<filePath );
          }
        }
      return res;
    }
  };

};

} // end namespace Wrapper
} // end namespace otb

#ifndef OTB_MANUAL_INSTANTIATION
#include "otbTrainImagesBase.txx"
#endif

#endif //otbTrainImagesBase_h