/*=========================================================================
 *
 *  Copyright NumFOCUS
 *
 *  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
 *
 *         https://www.apache.org/licenses/LICENSE-2.0.txt
 *
 *  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.
 *
 *=========================================================================*/

// Software Guide : BeginLatex
//
// The statistics framework in ITK has been designed for managing
// multi-variate statistics in a natural way. The
// \subdoxygen{Statistics}{Histogram} class reflects this concept clearly
// since it is a N-variable joint histogram. This nature of the Histogram
// class is exploited in the following example in order to build the joint
// histogram of a color image encoded in RGB values.
//
// Note that the same treatment could be applied further to any vector image
// thanks to the generic programming approach used in the implementation of
// the statistical framework.
//
// The most relevant class in this example is the
// \subdoxygen{Statistics}{ImageToHistogramFilter}. This class will take
// care of adapting the \doxygen{Image} to a list of samples and then to a
// histogram filter. The user is only bound to provide the desired
// resolution on the histogram bins for each one of the image components.
//
// In this example we compute the joint histogram of the three channels of an
// RGB image. Our output histogram will be equivalent to a 3D array of bins.
// This histogram could be used further for feeding a segmentation method
// based on statistical pattern recognition. Such method was actually used
// during the generation of the image in the cover of the Software Guide.
//
// The first step is to include the header files for the histogram filter,
// the RGB pixel type and the Image.
//
// \index{itk::Statistics::ImageToHistogramFilter!header}
// \index{itk::RGBPixel!header}
// \index{itk::RGBPixel!Statistics}
//
// Software Guide : EndLatex


// Software Guide : BeginCodeSnippet
#include "itkImageToHistogramFilter.h"
#include "itkImage.h"
#include "itkRGBPixel.h"
// Software Guide : EndCodeSnippet

#include "itkImageFileReader.h"

int
main(int argc, char * argv[])
{

  if (argc < 3)
  {
    std::cerr << "Missing command line arguments" << std::endl;
    std::cerr << "Usage :  ImageHistogram4  inputRGBImageFileName ";
    std::cerr << " histogramFilename.raw" << std::endl;
    return EXIT_FAILURE;
  }


  // Software Guide : BeginLatex
  //
  // We declare now the type used for the components of the RGB pixel,
  // instantiate the type of the RGBPixel and instantiate the image type.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using PixelComponentType = unsigned char;

  using RGBPixelType = itk::RGBPixel<PixelComponentType>;

  constexpr unsigned int Dimension = 2;

  using RGBImageType = itk::Image<RGBPixelType, Dimension>;
  // Software Guide : EndCodeSnippet


  using ReaderType = itk::ImageFileReader<RGBImageType>;

  auto reader = ReaderType::New();

  reader->SetFileName(argv[1]);

  try
  {
    reader->Update();
  }
  catch (const itk::ExceptionObject & excp)
  {
    std::cerr << "Problem reading image file : " << argv[1] << std::endl;
    std::cerr << excp << std::endl;
    return EXIT_FAILURE;
  }


  // Software Guide : BeginLatex
  //
  // Using the type of the color image, and in general of any vector image, we
  // can now instantiate the type of the histogram filter class. We then use
  // that type for constructing an instance of the filter by invoking its
  // \code{New()} method and assigning the result to a smart pointer.
  //
  // Software Guide : EndLatex


  // Software Guide : BeginCodeSnippet
  using HistogramFilterType =
    itk::Statistics::ImageToHistogramFilter<RGBImageType>;

  auto histogramFilter = HistogramFilterType::New();
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // The resolution at which the statistics of each one of the color component
  // will be evaluated is defined by setting the number of bins along every
  // component in the joint histogram. For this purpose we take the
  // \code{HistogramSizeType} trait from the filter and use it to instantiate
  // a \code{size} variable. We set in this variable the number of bins to use
  // for each component of the color image.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using SizeType = HistogramFilterType::HistogramSizeType;

  SizeType size(3);

  size[0] = 256; // number of bins for the Red   channel
  size[1] = 256; // number of bins for the Green channel
  size[2] = 256; // number of bins for the Blue  channel

  histogramFilter->SetHistogramSize(size);
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // Finally, we must specify the upper and lower bounds for the histogram
  // using the \code{SetHistogramBinMinimum()} and
  // \code{SetHistogramBinMaximum()} methods.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using HistogramMeasurementVectorType =
    HistogramFilterType::HistogramMeasurementVectorType;

  HistogramMeasurementVectorType binMinimum(3);
  HistogramMeasurementVectorType binMaximum(3);

  binMinimum[0] = -0.5;
  binMinimum[1] = -0.5;
  binMinimum[2] = -0.5;

  binMaximum[0] = 255.5;
  binMaximum[1] = 255.5;
  binMaximum[2] = 255.5;

  histogramFilter->SetHistogramBinMinimum(binMinimum);
  histogramFilter->SetHistogramBinMaximum(binMaximum);
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // The input to the histogram filter is taken from the output of an image
  // reader. Of course, the output of any filter producing an RGB image could
  // have been used instead of a reader.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  histogramFilter->SetInput(reader->GetOutput());
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // The marginal scale is defined in the histogram filter. This value will
  // define the precision in the assignment of values to the histogram bins.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  histogramFilter->SetMarginalScale(10.0);
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // Finally, the computation of the histogram is triggered by invoking the
  // \code{Update()} method of the filter.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  histogramFilter->Update();
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // At this point, we can recover the histogram by calling the
  // \code{GetOutput()} method of the filter. The result is assigned to a
  // variable that is instantiated using the \code{HistogramType} trait of the
  // filter type.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using HistogramType = HistogramFilterType::HistogramType;

  const HistogramType * histogram = histogramFilter->GetOutput();
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // We can verify that the computed histogram has the requested size by
  // invoking its \code{Size()} method.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  const unsigned int histogramSize = histogram->Size();

  std::cout << "Histogram size " << histogramSize << std::endl;
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // The values of the histogram can now be saved into a file by walking
  // through all of the histogram bins and pushing them into a std::ofstream.
  //
  // Software Guide : EndLatex


  // Software Guide : BeginCodeSnippet
  std::ofstream histogramFile;
  histogramFile.open(argv[2]);

  HistogramType::ConstIterator itr = histogram->Begin();
  HistogramType::ConstIterator end = histogram->End();

  using AbsoluteFrequencyType = HistogramType::AbsoluteFrequencyType;

  while (itr != end)
  {
    const AbsoluteFrequencyType frequency = itr.GetFrequency();
    histogramFile.write((const char *)(&frequency), sizeof(frequency));

    if (frequency != 0)
    {
      HistogramType::IndexType index;
      index = histogram->GetIndex(itr.GetInstanceIdentifier());
      std::cout << "Index = " << index << ", Frequency = " << frequency
                << std::endl;
    }
    ++itr;
  }

  histogramFile.close();
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // Note that here the histogram is saved as a block of memory in a raw file.
  // At this point you can use visualization software in order to explore the
  // histogram in a display that would be equivalent to a scatter plot of the
  // RGB components of the input color image.
  //
  // Software Guide : EndLatex


  return EXIT_SUCCESS;
}
