/*
 * 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 otbTrainVectorBase_hxx
#define otbTrainVectorBase_hxx

#include "otbTrainVectorBase.h"

namespace otb
{
namespace Wrapper
{

template <class TInputValue, class TOutputValue>
void TrainVectorBase<TInputValue, TOutputValue>::DoInit()
{
  // Common Parameters for all Learning Application
  this->AddParameter(ParameterType_Group, "io", "Input and output data");
  this->SetParameterDescription("io", "This group of parameters allows setting input and output data.");

  this->AddParameter(ParameterType_InputVectorDataList, "io.vd", "Input Vector Data");
  this->SetParameterDescription("io.vd", "Input geometries used for training (note: all geometries from the layer will be used)");

  this->AddParameter(ParameterType_InputFilename, "io.stats", "Input XML image statistics file");
  this->MandatoryOff("io.stats");
  this->SetParameterDescription("io.stats", "XML file containing mean and variance of each feature.");

  this->AddParameter(ParameterType_OutputFilename, "io.out", "Output model");
  this->SetParameterDescription("io.out", "Output file containing the model estimated (.txt format).");

  this->AddParameter(ParameterType_Int, "layer", "Layer Index");
  this->SetParameterDescription("layer", "Index of the layer to use in the input vector file.");
  this->MandatoryOff("layer");
  this->SetDefaultParameterInt("layer", 0);

  this->AddParameter(ParameterType_ListView, "feat", "Field names for training features");
  this->SetParameterDescription("feat", "List of field names in the input vector data to be used as features for training.");

  // Add validation data used to compute confusion matrix or contingency table
  this->AddParameter(ParameterType_Group, "valid", "Validation data");
  this->SetParameterDescription("valid", "This group of parameters defines validation data.");

  this->AddParameter(ParameterType_InputVectorDataList, "valid.vd", "Validation Vector Data");
  this->SetParameterDescription("valid.vd",
                                "Geometries used for validation "
                                "(must contain the same fields used for training, all geometries from the layer will be used)");
  this->MandatoryOff("valid.vd");

  this->AddParameter(ParameterType_Int, "valid.layer", "Layer Index");
  this->SetParameterDescription("valid.layer", "Index of the layer to use in the validation vector file.");
  this->MandatoryOff("valid.layer");
  this->SetDefaultParameterInt("valid.layer", 0);

  // Add class field if we used validation
  this->AddParameter(ParameterType_ListView, "cfield", "Field containing the class integer label for supervision");
  this->SetParameterDescription("cfield",
                                "Field containing the class id for supervision. "
                                "The values in this field shall be cast into integers. "
                                "Only geometries with this field available will be taken into account.");
  this->SetListViewSingleSelectionMode("cfield", true);

  this->AddParameter(ParameterType_Bool, "v", "Verbose mode");
  this->SetParameterDescription("v", "Verbose mode, display the contingency table result.");
  this->SetParameterInt("v", 1);

  // Doc example parameter settings
  this->SetDocExampleParameterValue("io.vd", "vectorData.shp");
  this->SetDocExampleParameterValue("io.stats", "meanVar.xml");
  this->SetDocExampleParameterValue("io.out", "svmModel.svm");
  this->SetDocExampleParameterValue("feat", "perimeter  area  width");
  this->SetDocExampleParameterValue("cfield", "predicted");


  // Add parameters for the classifier choice
  Superclass::DoInit();

  this->AddRANDParameter();
}

template <class TInputValue, class TOutputValue>
void TrainVectorBase<TInputValue, TOutputValue>::DoUpdateParameters()
{
  // if vector data is present and updated then reload fields
  if (this->HasValue("io.vd"))
  {
    std::vector<std::string> vectorFileList = this->GetParameterStringList("io.vd");
    ogr::DataSource::Pointer ogrDS          = ogr::DataSource::New(vectorFileList[0], ogr::DataSource::Modes::Read);
    ogr::Layer               layer          = ogrDS->GetLayer(static_cast<size_t>(this->GetParameterInt("layer")));
    ogr::Feature             feature        = layer.ogr().GetNextFeature();

    this->ClearChoices("feat");
    this->ClearChoices("cfield");

    for (int iField = 0; iField < feature.ogr().GetFieldCount(); iField++)
    {
      std::string key, item = feature.ogr().GetFieldDefnRef(iField)->GetNameRef();
      key                       = item;
      std::string::iterator end = std::remove_if(key.begin(), key.end(), [](char c) { return !std::isalnum(c); });
      std::transform(key.begin(), end, key.begin(), tolower);

      OGRFieldType fieldType = feature.ogr().GetFieldDefnRef(iField)->GetType();

      if (fieldType == OFTInteger || fieldType == OFTInteger64 || fieldType == OFTReal)
      {
        std::string tmpKey = "feat." + key.substr(0, static_cast<unsigned long>(end - key.begin()));
        this->AddChoice(tmpKey, item);
      }
      if (fieldType == OFTString || fieldType == OFTInteger || fieldType == OFTInteger64 || fieldType == OFTReal)
      {
        std::string tmpKey = "cfield." + key.substr(0, static_cast<unsigned long>(end - key.begin()));
        this->AddChoice(tmpKey, item);
      }
    }
  }
}

template <class TInputValue, class TOutputValue>
void TrainVectorBase<TInputValue, TOutputValue>::DoExecute()
{
  m_FeaturesInfo.SetFieldNames(this->GetChoiceNames("feat"), this->GetSelectedItems("feat"));

  // Check input parameters
  if (m_FeaturesInfo.m_SelectedIdx.empty())
  {
    otbAppLogFATAL(<< "No features have been selected to train the classifier on!");
  }

  ShiftScaleParameters measurement = GetStatistics(m_FeaturesInfo.m_NbFeatures);
  ExtractAllSamples(measurement);

  this->Train(m_TrainingSamplesWithLabel.listSample, m_TrainingSamplesWithLabel.labeledListSample, this->GetParameterString("io.out"));

  m_PredictedList = this->Classify(m_ClassificationSamplesWithLabel.listSample, this->GetParameterString("io.out"));
}

template <class TInputValue, class TOutputValue>
void TrainVectorBase<TInputValue, TOutputValue>::ExtractAllSamples(const ShiftScaleParameters& measurement)
{
  m_TrainingSamplesWithLabel       = ExtractTrainingSamplesWithLabel(measurement);
  m_ClassificationSamplesWithLabel = ExtractClassificationSamplesWithLabel(measurement);
}

template <class TInputValue, class TOutputValue>
typename TrainVectorBase<TInputValue, TOutputValue>::SamplesWithLabel
TrainVectorBase<TInputValue, TOutputValue>::ExtractTrainingSamplesWithLabel(const ShiftScaleParameters& measurement)
{
  return ExtractSamplesWithLabel("io.vd", "layer", measurement);
}

template <class TInputValue, class TOutputValue>
typename TrainVectorBase<TInputValue, TOutputValue>::SamplesWithLabel
TrainVectorBase<TInputValue, TOutputValue>::ExtractClassificationSamplesWithLabel(const ShiftScaleParameters& measurement)
{
  if (this->GetClassifierCategory() == Superclass::Supervised)
  {
    SamplesWithLabel tmpSamplesWithLabel;
    SamplesWithLabel validationSamplesWithLabel = ExtractSamplesWithLabel("valid.vd", "valid.layer", measurement);
    // Test the input validation set size
    if (validationSamplesWithLabel.labeledListSample->Size() != 0)
    {
      tmpSamplesWithLabel.listSample        = validationSamplesWithLabel.listSample;
      tmpSamplesWithLabel.labeledListSample = validationSamplesWithLabel.labeledListSample;
    }
    else
    {
      otbAppLogWARNING("The validation set is empty. The performance estimation is done using the input training set in this case.");
      tmpSamplesWithLabel.listSample        = m_TrainingSamplesWithLabel.listSample;
      tmpSamplesWithLabel.labeledListSample = m_TrainingSamplesWithLabel.labeledListSample;
    }

    return tmpSamplesWithLabel;
  }
  else
  {
    return m_TrainingSamplesWithLabel;
  }
}

template <class TInputValue, class TOutputValue>
typename TrainVectorBase<TInputValue, TOutputValue>::ShiftScaleParameters TrainVectorBase<TInputValue, TOutputValue>::GetStatistics(unsigned int nbFeatures)
{
  ShiftScaleParameters measurement = ShiftScaleParameters();
  if (this->HasValue("io.stats") && this->IsParameterEnabled("io.stats"))
  {
    typename StatisticsReader::Pointer statisticsReader = StatisticsReader::New();
    std::string                        XMLfile          = this->GetParameterString("io.stats");
    statisticsReader->SetFileName(XMLfile);
    measurement.meanMeasurementVector   = statisticsReader->GetStatisticVectorByName("mean");
    measurement.stddevMeasurementVector = statisticsReader->GetStatisticVectorByName("stddev");
  }
  else
  {
    measurement.meanMeasurementVector.SetSize(nbFeatures);
    measurement.meanMeasurementVector.Fill(0.);
    measurement.stddevMeasurementVector.SetSize(nbFeatures);
    measurement.stddevMeasurementVector.Fill(1.);
  }
  return measurement;
}

template <class TInputValue, class TOutputValue>
typename TrainVectorBase<TInputValue, TOutputValue>::SamplesWithLabel
TrainVectorBase<TInputValue, TOutputValue>::ExtractSamplesWithLabel(std::string parameterName, std::string parameterLayer,
                                                                    const ShiftScaleParameters& measurement)
{
  SamplesWithLabel samplesWithLabel;
  if (this->HasValue(parameterName) && this->IsParameterEnabled(parameterName))
  {
    typename ListSampleType::Pointer       input  = ListSampleType::New();
    typename TargetListSampleType::Pointer target = TargetListSampleType::New();
    input->SetMeasurementVectorSize(m_FeaturesInfo.m_NbFeatures);

    std::vector<std::string> fileList = this->GetParameterStringList(parameterName);
    for (unsigned int k = 0; k < fileList.size(); k++)
    {
      otbAppLogINFO("Reading vector file " << k + 1 << "/" << fileList.size());
      ogr::DataSource::Pointer source  = ogr::DataSource::New(fileList[k], ogr::DataSource::Modes::Read);
      ogr::Layer               layer   = source->GetLayer(static_cast<size_t>(this->GetParameterInt(parameterLayer)));
      ogr::Feature             feature = layer.ogr().GetNextFeature();
      bool                     goesOn  = feature.addr() != 0;
      if (!goesOn)
      {
        otbAppLogWARNING("The layer " << this->GetParameterInt(parameterLayer) << " of " << fileList[k] << " is empty, input is skipped.");
        continue;
      }

      // Check all needed fields are present :
      //   - check class field if we use supervised classification or if class field name is not empty
      int cFieldIndex = feature.ogr().GetFieldIndex(m_FeaturesInfo.m_SelectedCFieldName.c_str());
      if (cFieldIndex < 0 && !m_FeaturesInfo.m_SelectedCFieldName.empty())
      {
        otbAppLogFATAL("The field name for class label (" << m_FeaturesInfo.m_SelectedCFieldName << ") has not been found in the vector file " << fileList[k]);
      }

      //   - check feature fields
      std::vector<int> featureFieldIndex(m_FeaturesInfo.m_NbFeatures, -1);
      for (unsigned int i = 0; i < m_FeaturesInfo.m_NbFeatures; i++)
      {
        featureFieldIndex[i] = feature.ogr().GetFieldIndex(m_FeaturesInfo.m_SelectedNames[i].c_str());
        if (featureFieldIndex[i] < 0)
          otbAppLogFATAL("The field name for feature " << m_FeaturesInfo.m_SelectedNames[i] << " has not been found in the vector file " << fileList[k]);
      }


      while (goesOn)
      {
        // Retrieve all the features for each field in the ogr layer.
        MeasurementType mv;
        mv.SetSize(m_FeaturesInfo.m_NbFeatures);
        for (unsigned int idx = 0; idx < m_FeaturesInfo.m_NbFeatures; ++idx)
        {
          switch (feature[featureFieldIndex[idx]].GetType())
          {
          case OFTInteger:
            mv[idx] = static_cast<ValueType>(feature[featureFieldIndex[idx]].GetValue<int>());
            break;
          case OFTInteger64:
            mv[idx] = static_cast<ValueType>(feature[featureFieldIndex[idx]].GetValue<int>());
            break;
          case OFTReal:
            mv[idx] = static_cast<ValueType>(feature[featureFieldIndex[idx]].GetValue<double>());
            break;
          default:
            itkExceptionMacro(<< "incorrect field type: " << feature[featureFieldIndex[idx]].GetType() << ".");
          }
        }

        input->PushBack(mv);

        if (cFieldIndex >= 0 && ogr::Field(feature, cFieldIndex).HasBeenSet())
        {
          switch (feature[cFieldIndex].GetType())
          {
          case OFTInteger:
            target->PushBack(static_cast<ValueType>(feature[cFieldIndex].GetValue<int>()));
            break;
          case OFTInteger64:
            target->PushBack(static_cast<ValueType>(feature[cFieldIndex].GetValue<int>()));
            break;
          case OFTReal:
            target->PushBack(static_cast<ValueType>(feature[cFieldIndex].GetValue<double>()));
            break;
          case OFTString:
            target->PushBack(static_cast<ValueType>(std::stod(feature[cFieldIndex].GetValue<std::string>())));
            break;
          default:
            itkExceptionMacro(<< "incorrect field type: " << feature[featureFieldIndex[cFieldIndex]].GetType() << ".");
          }
        }
        else
          target->PushBack(0.);

        feature = layer.ogr().GetNextFeature();
        goesOn  = feature.addr() != 0;
      }
    }


    typename ShiftScaleFilterType::Pointer shiftScaleFilter = ShiftScaleFilterType::New();
    shiftScaleFilter->SetInput(input);
    shiftScaleFilter->SetShifts(measurement.meanMeasurementVector);
    shiftScaleFilter->SetScales(measurement.stddevMeasurementVector);
    shiftScaleFilter->Update();

    samplesWithLabel.listSample        = shiftScaleFilter->GetOutput();
    samplesWithLabel.labeledListSample = target;
    samplesWithLabel.listSample->DisconnectPipeline();
  }

  return samplesWithLabel;
}
}
}

#endif