/*=========================================================================

  Program:   Insight Segmentation & Registration Toolkit
  Module:    $RCSfile: NeighborhoodIterators2.cxx,v $
  Language:  C++
  Date:      $Date: 2005/02/08 03:59:00 $
  Version:   $Revision: 1.22 $

  Copyright (c) Insight Software Consortium. All rights reserved.
  See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.

     This software is distributed WITHOUT ANY WARRANTY; without even 
     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR 
     PURPOSE.  See the above copyright notices for more information.

=========================================================================*/
#if defined(_MSC_VER)
#pragma warning ( disable : 4786 )
#endif
#include "itkImage.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkConstNeighborhoodIterator.h"
#include "itkImageRegionIterator.h"

// Software Guide : BeginLatex
//
// In this example, the Sobel edge-detection routine is rewritten using
// convolution filtering.  Convolution filtering is a standard image processing
// technique that can be implemented numerically as the inner product of all
// image neighborhoods with a convolution kernel \cite{Gonzalez1993}
// \cite{Castleman1996}.  In ITK, we use a class of objects called
// \emph{neighborhood operators} as convolution kernels and a special function
// object called \doxygen{NeighborhoodInnerProduct} to calculate inner
// products.
//
// The basic ITK convolution filtering routine is to step through the image
// with a neighborhood iterator and use NeighborhoodInnerProduct to
// find the inner product of each neighborhood with the desired kernel. The
// resulting values are written to an output image.  This example uses a
// neighborhood operator called the \doxygen{SobelOperator}, but all
// neighborhood operators can be convolved with images using this basic
// routine.  Other examples of neighborhood operators include derivative
// kernels, Gaussian kernels, and morphological
// operators. \doxygen{NeighborhoodOperatorImageFilter} is a generalization of
// the code in this section to ND images and arbitrary convolution kernels.
//
// We start writing this example by including the header files for the Sobel
// kernel and the inner product function.
//
// Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
#include "itkSobelOperator.h"
#include "itkNeighborhoodInnerProduct.h"
// Software Guide : EndCodeSnippet

int main( int argc, char ** argv )
{
  if ( argc < 4 )
    {
      std::cerr << "Missing parameters. " << std::endl;
      std::cerr << "Usage: " << std::endl;
      std::cerr << argv[0]
                << " inputImageFile outputImageFile direction"
                << std::endl;
      return -1;
    }

  typedef float PixelType;
  typedef itk::Image< PixelType, 2 >  ImageType;
  typedef itk::ImageFileReader< ImageType > ReaderType;

  typedef itk::ConstNeighborhoodIterator< ImageType > NeighborhoodIteratorType;
  typedef itk::ImageRegionIterator< ImageType>        IteratorType;

  ReaderType::Pointer reader = ReaderType::New();
  reader->SetFileName( argv[1] );
  try
    {
    reader->Update();
    }
  catch ( itk::ExceptionObject &err)
    {
    std::cout << "ExceptionObject caught !" << std::endl; 
    std::cout << err << std::endl; 
    return -1;
    }
  
  ImageType::Pointer output = ImageType::New();
  output->SetRegions(reader->GetOutput()->GetRequestedRegion());
  output->Allocate();
  
  IteratorType out(output, reader->GetOutput()->GetRequestedRegion());
  
// Software Guide : BeginLatex
//
// \index{convolution!kernels}
// \index{convolution!operators}
// \index{iterators!neighborhood!and convolution}
//
// Refer to the previous example for a description of reading the input image and
// setting up the output image and iterator.
//
// The following code creates a Sobel operator.  The Sobel operator requires
// a direction for its partial derivatives.  This direction is read from the command line.
// Changing the direction of the derivatives changes the bias of the edge
// detection, i.e. maximally vertical or maximally horizontal.
//
// Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
  itk::SobelOperator<PixelType, 2> sobelOperator;
  sobelOperator.SetDirection( ::atoi(argv[3]) );
  sobelOperator.CreateDirectional();
// Software Guide : EndCodeSnippet

// Software Guide : BeginLatex
//
// The neighborhood iterator is initialized as before, except that now it takes
// its radius directly from the radius of the Sobel operator.  The inner
// product function object is templated over image type and requires no 
// initialization.
//
// Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
  NeighborhoodIteratorType::RadiusType radius = sobelOperator.GetRadius();
  NeighborhoodIteratorType it( radius, reader->GetOutput(), 
                               reader->GetOutput()->GetRequestedRegion() );
  
  itk::NeighborhoodInnerProduct<ImageType> innerProduct;
// Software Guide : EndCodeSnippet

// Software Guide : BeginLatex
//
// Using the Sobel operator, inner product, and neighborhood iterator objects,
// we can now write a very simple \code{for} loop for performing convolution
// filtering.  As before, out-of-bounds pixel values are supplied automatically
// by the iterator.
//
// Software Guide : EndLatex
    
// Software Guide : BeginCodeSnippet
  for (it.GoToBegin(), out.GoToBegin(); !it.IsAtEnd(); ++it, ++out)
    {
    out.Set( innerProduct( it, sobelOperator ) );
    }
// Software Guide : EndCodeSnippet
  
// Software Guide : BeginLatex
//
// The output is rescaled and written as in the previous example.  Applying
// this example in the $x$ and $y$ directions produces the images at the center
// and right of Figure~\ref{fig:NeighborhoodExamples1}. Note that x-direction 
// operator produces the same output image as in the previous example.
// 
// Software Guide : EndLatex

  typedef unsigned char WritePixelType;
  typedef itk::Image< WritePixelType, 2 > WriteImageType;
  typedef itk::ImageFileWriter< WriteImageType > WriterType;
  
  typedef itk::RescaleIntensityImageFilter< 
               ImageType, WriteImageType > RescaleFilterType;

  RescaleFilterType::Pointer rescaler = RescaleFilterType::New();

  rescaler->SetOutputMinimum(   0 );
  rescaler->SetOutputMaximum( 255 );
  rescaler->SetInput(output);
  
  WriterType::Pointer writer = WriterType::New();
  writer->SetFileName( argv[2] );
  writer->SetInput(rescaler->GetOutput());
  try
    {
    writer->Update();
    }
  catch ( itk::ExceptionObject &err)
    {
    std::cout << "ExceptionObject caught !" << std::endl; 
    std::cout << err << std::endl; 
    return -1;   
    }

  return 0;
}
