Statistics/ImageHistogram3.cxx

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 *  Copyright Insight Software Consortium
 *
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
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// Software Guide : BeginLatex
//
// By now, you are probably thinking that the statistics framework in ITK is
// too complex for simply computing histograms from images. Here we illustrate
// that the benefit for this complexity is the power that these methods provide
// for dealing with more complex and realistic uses of image statistics than the
// trivial 256-bin histogram of 8-bit images that most software packages
// provide. One of such cases is the computation of histograms from
// multi-component images such as Vector images and color images.
//
// This example shows how to compute the histogram of an RGB image by using the
// helper class \code{ImageToHistogramFilter}.  In this first example we
// compute the histogram of each channel independently.
//
// We start by including the header of the
// \subdoxygen{Statistics}{ImageToHistogramFilter}, as well as the headers
// for the image class and the RGBPixel class.
//
// \index{itk::Statistics::ImageToHistogramFilter!header}
//
// 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 < 2 )
    {
    std::cerr << "Missing command line arguments" << std::endl;
    std::cerr << "Usage :  ImageHistogram3  inputRGBImageFileName " << std::endl;
    return -1;
    }


  // Software Guide : BeginLatex
  //
  // The type of the RGB image is defined by first instantiating a RGBPixel and
  // then using the image dimension specification.
  //
  // \index{itk::Statistics!Color Images}
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  typedef unsigned char                         PixelComponentType;

  typedef itk::RGBPixel< PixelComponentType >   RGBPixelType;

  const unsigned int                            Dimension = 2;

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


  typedef itk::ImageFileReader< RGBImageType >  ReaderType;

  ReaderType::Pointer reader = ReaderType::New();

  reader->SetFileName( argv[1] );

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


  // Software Guide : BeginLatex
  //
  // Using the RGB image type we can instantiate the type of the corresponding
  // histogram filter and construct one filter by invoking its \code{New()}
  // method.
  //
  // Software Guide : EndLatex

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

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


  // Software Guide : BeginLatex
  //
  // The parameters of the histogram must be defined now. Probably the most
  // important one is the arrangement of histogram bins. This is provided to the
  // histogram through a size array. The type of the array can be taken from the
  // traits of the \code{HistogramFilterType} type. We create one instance of
  // the size object and fill in its content. In this particular case, the three
  // components of the size array will correspond to the number of bins used for
  // each one of the RGB components in the color image. The following lines show
  // how to define a histogram on the red component of the image while
  // disregarding the green and blue components.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  typedef HistogramFilterType::HistogramSizeType   SizeType;

  SizeType size( 3 );

  size[0] = 255;        // number of bins for the Red   channel
  size[1] =   1;        // number of bins for the Green channel
  size[2] =   1;        // number of bins for the Blue  channel

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


  // Software Guide : BeginLatex
  //
  // The marginal scale must be defined in the filter. This will determine 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, we must specify the upper and lower bounds for the histogram. This
  // can either be done manually using the \code{SetHistogramBinMinimum()} and
  // \code{SetHistogramBinMaximum()} methods or it can be done automatically by
  // calling \code{SetHistogramAutoMinimumMaximum( true )}. Here we use the
  // manual method.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  HistogramFilterType::HistogramMeasurementVectorType lowerBound( 3 );
  HistogramFilterType::HistogramMeasurementVectorType upperBound( 3 );

  lowerBound[0] = 0;
  lowerBound[1] = 0;
  lowerBound[2] = 0;
  upperBound[0] = 256;
  upperBound[1] = 256;
  upperBound[2] = 256;

  histogramFilter->SetHistogramBinMinimum( lowerBound );
  histogramFilter->SetHistogramBinMaximum( upperBound );
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // The input of the filter is taken from an image reader, and the
  // computation of the histogram is triggered by invoking the \code{Update()}
  // method of the filter.
  //
  // \index{itk::Statistics::ImageToHistogramFilter!Update()}
  //
  // Software Guide : EndLatex

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

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


  // Software Guide : BeginLatex
  //
  // We can now access the results of the histogram computation by declaring a
  // pointer to histogram and getting its value from the filter using the
  // \code{GetOutput()} method. Note that here we use a \code{const
  // HistogramType} pointer instead of a const smart pointer because we are sure
  // that the filter is not going to be destroyed while we access the values
  // of the histogram. Depending on what you are doing, it may be safer to assign
  // the histogram to a const smart pointer as shown in previous examples.
  //
  // \index{itk::Statistics::ImageTohistogramFilter!GetOutput()}
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  typedef HistogramFilterType::HistogramType  HistogramType;

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


  // Software Guide : BeginLatex
  //
  // Just for the sake of exercising the experimental method~\cite{Popper2002}, we
  // verify that the resulting histogram actually have the size that we requested
  // when we configured the filter. This can be done by invoking the
  // \code{Size()} method of the histogram and printing out the result.
  //
  // \index{itk::Statistics::Histogram!Size()}
  //
  // 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
  //
  // Strictly speaking, the histogram computed here is the joint histogram of the
  // three RGB components. However, given that we set the resolution of the green
  // and blue channels to be just one bin, the histogram is in practice
  // representing just the red channel.  In the general case, we can alway access
  // the frequency of a particular channel in a joint histogram, thanks to the
  // fact that the histogram class offers a \code{GetFrequency()} method that
  // accepts a channel as argument. This is illustrated in the following lines of
  // code.
  //
  // \index{itk::Statistics::Histogram!GetFrequency()}
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  unsigned int channel = 0;  // red channel

  std::cout << "Histogram of the red component" << std::endl;

  for( unsigned int bin=0; bin < histogramSize; bin++ )
    {
    std::cout << "bin = " << bin << " frequency = ";
    std::cout << histogram->GetFrequency( bin, channel ) << std::endl;
    }
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // In order to reinforce the concepts presented above, we modify now the setup
  // of the histogram filter in order to compute the histogram of the green
  // channel instead of the red one. This is done by simply changing the number
  // of bins desired on each channel and invoking the computation of the
  // filter again by calling the \code{Update()} method.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  size[0] =   1;  // number of bins for the Red   channel
  size[1] = 255;  // number of bins for the Green channel
  size[2] =   1;  // number of bins for the Blue  channel

  histogramFilter->SetHistogramSize( size );

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


  // Software Guide : BeginLatex
  //
  // The result can be verified now by setting the desired channel to green and
  // invoking the \code{GetFrequency()} method.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  channel = 1;  // green channel

  std::cout << "Histogram of the green component" << std::endl;

  for( unsigned int bin=0; bin < histogramSize; bin++ )
    {
    std::cout << "bin = " << bin << " frequency = ";
    std::cout << histogram->GetFrequency( bin, channel ) << std::endl;
    }
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // To finalize the example, we do the same computation for the case of the blue
  // channel.
  //
  // Software Guide : EndLatex


  // Software Guide : BeginCodeSnippet
  size[0] =   1;  // number of bins for the Red   channel
  size[1] =   1;  // number of bins for the Green channel
  size[2] = 255;  // number of bins for the Blue  channel

  histogramFilter->SetHistogramSize( size );

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


  // Software Guide : BeginLatex
  //
  // and verify the output.
  //
  // Software Guide : EndLatex


  // Software Guide : BeginCodeSnippet
  channel = 2;  // blue channel

  std::cout << "Histogram of the blue component" << std::endl;

  for( unsigned int bin=0; bin < histogramSize; bin++ )
    {
    std::cout << "bin = " << bin << " frequency = ";
    std::cout << histogram->GetFrequency( bin, channel ) << std::endl;
    }
  // Software Guide : EndCodeSnippet

  return 0;

}