otbImageDimensionalityReductionFilter.hxx

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/*
 * Copyright (C) 2005-2022 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 otbImageDimensionalityReductionFilter_hxx
#define otbImageDimensionalityReductionFilter_hxx
 
#include "otbImageDimensionalityReductionFilter.h"
#include "itkImageRegionIterator.h"
#include "itkProgressReporter.h"
 
namespace otb
{
template <class TInputImage, class TOutputImage, class TMaskImage>
ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>::ImageDimensionalityReductionFilter()
{
  this->SetNumberOfIndexedInputs(2);
  this->SetNumberOfRequiredInputs(1);
 
  this->SetNumberOfRequiredOutputs(2);
  this->SetNthOutput(0, TOutputImage::New());
  this->SetNthOutput(1, ConfidenceImageType::New());
  m_UseConfidenceMap = false;
  m_BatchMode        = true;
}
 
template <class TInputImage, class TOutputImage, class TMaskImage>
void ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>::SetInputMask(const MaskImageType* mask)
{
  this->itk::ProcessObject::SetNthInput(1, const_cast<MaskImageType*>(mask));
}
 
template <class TInputImage, class TOutputImage, class TMaskImage>
const typename ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>::MaskImageType*
ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>::GetInputMask()
{
  if (this->GetNumberOfInputs() < 2)
  {
    return nullptr;
  }
  return static_cast<const MaskImageType*>(this->itk::ProcessObject::GetInput(1));
}
 
template <class TInputImage, class TOutputImage, class TMaskImage>
typename ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>::ConfidenceImageType*
ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>::GetOutputConfidence()
{
  if (this->GetNumberOfOutputs() < 2)
  {
    return nullptr;
  }
  return static_cast<ConfidenceImageType*>(this->itk::ProcessObject::GetOutput(1));
}
 
template <class TInputImage, class TOutputImage, class TMaskImage>
void ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>::BeforeThreadedGenerateData()
{
  if (m_BatchMode)
  {
#ifdef _OPENMP
    // OpenMP will take care of threading
    this->SetNumberOfThreads(1);
#endif
  }
}
 
template <class TInputImage, class TOutputImage, class TMaskImage>
void ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>::ClassicThreadedGenerateData(const OutputImageRegionType& outputRegionForThread,
                                                                                                            itk::ThreadIdType threadId)
{
  // Get the input pointers
  InputImageConstPointerType inputPtr      = this->GetInput();
  MaskImageConstPointerType  inputMaskPtr  = this->GetInputMask();
  OutputImagePointerType     outputPtr     = this->GetOutput();
  ConfidenceImagePointerType confidencePtr = this->GetOutputConfidence();
 
  // Progress reporting
  itk::ProgressReporter progress(this, threadId, outputRegionForThread.GetNumberOfPixels());
 
  // Define iterators
  typedef itk::ImageRegionConstIterator<InputImageType> InputIteratorType;
  typedef itk::ImageRegionIterator<OutputImageType>     OutputIteratorType;
 
  InputIteratorType  inIt(inputPtr, outputRegionForThread);
  OutputIteratorType outIt(outputPtr, outputRegionForThread);
 
  // Walk the part of the image
  for (inIt.GoToBegin(), outIt.GoToBegin(); !inIt.IsAtEnd() && !outIt.IsAtEnd(); ++inIt, ++outIt)
  {
    // Classifify
    outIt.Set(m_Model->Predict(inIt.Get()));
    progress.CompletedPixel();
  }
}
 
template <class TInputImage, class TOutputImage, class TMaskImage>
void ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>::GenerateOutputInformation()
{
  Superclass::GenerateOutputInformation();
  if (!m_Model)
  {
    itkGenericExceptionMacro(<< "No model for dimensionality reduction");
  }
  this->GetOutput()->SetNumberOfComponentsPerPixel(m_Model->GetDimension());
}
 
template <class TInputImage, class TOutputImage, class TMaskImage>
void ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>::BatchThreadedGenerateData(const OutputImageRegionType& outputRegionForThread,
                                                                                                          itk::ThreadIdType threadId)
{
  // Get the input pointers
  InputImageConstPointerType inputPtr      = this->GetInput();
  MaskImageConstPointerType  inputMaskPtr  = this->GetInputMask();
  OutputImagePointerType     outputPtr     = this->GetOutput();
  ConfidenceImagePointerType confidencePtr = this->GetOutputConfidence();
 
  // Progress reporting
  itk::ProgressReporter progress(this, threadId, outputRegionForThread.GetNumberOfPixels());
 
  // Define iterators
  typedef itk::ImageRegionConstIterator<InputImageType> InputIteratorType;
  typedef itk::ImageRegionIterator<OutputImageType>     OutputIteratorType;
 
  InputIteratorType  inIt(inputPtr, outputRegionForThread);
  OutputIteratorType outIt(outputPtr, outputRegionForThread);
 
  typedef typename ModelType::InputSampleType      InputSampleType;
  typedef typename ModelType::InputListSampleType  InputListSampleType;
  typedef typename ModelType::TargetValueType      TargetValueType;
  typedef typename ModelType::TargetListSampleType TargetListSampleType;
 
  typename InputListSampleType::Pointer samples      = InputListSampleType::New();
  unsigned int                          num_features = inputPtr->GetNumberOfComponentsPerPixel();
  samples->SetMeasurementVectorSize(num_features);
  InputSampleType sample(num_features);
 
  // Fill the samples
  for (inIt.GoToBegin(); !inIt.IsAtEnd(); ++inIt)
  {
    typename InputImageType::PixelType pix = inIt.Get();
    for (size_t feat = 0; feat < num_features; ++feat)
    {
      sample[feat] = pix[feat];
    }
    samples->PushBack(sample);
  }
  // Make the batch prediction
  typename TargetListSampleType::Pointer labels;
 
  // This call is threadsafe
  labels = m_Model->PredictBatch(samples);
 
  // Set the output values
  typename TargetListSampleType::ConstIterator labIt = labels->Begin();
  for (outIt.GoToBegin(); !outIt.IsAtEnd(); ++outIt)
  {
    itk::VariableLengthVector<TargetValueType> labelValue;
    labelValue = labIt.GetMeasurementVector();
    ++labIt;
    outIt.Set(labelValue);
    progress.CompletedPixel();
  }
}
 
template <class TInputImage, class TOutputImage, class TMaskImage>
void ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>::ThreadedGenerateData(const OutputImageRegionType& outputRegionForThread,
                                                                                                     itk::ThreadIdType threadId)
{
  if (m_BatchMode)
  {
    this->BatchThreadedGenerateData(outputRegionForThread, threadId);
  }
  else
  {
    this->ClassicThreadedGenerateData(outputRegionForThread, threadId);
  }
}
 
template <class TInputImage, class TOutputImage, class TMaskImage>
void ImageDimensionalityReductionFilter<TInputImage, TOutputImage, TMaskImage>::PrintSelf(std::ostream& os, itk::Indent indent) const
{
  Superclass::PrintSelf(os, indent);
}
 
} // End namespace otb
#endif