// Example implementation of the confidence connected region growing algorithm
// using the Insight Toolkit
//
// Based off Examples/Segmentatoin/ConfidenceConnected.cxx
// Extended to be used with 3-D image data
//
// Matt Edman
//

#if defined(_MSC_VER)
#pragma warning ( disable : 4786 )
#endif

#ifdef __BORLANDC__
#define ITK_LEAN_AND_MEAN
#endif

#include "itkConfidenceConnectedImageFilter.h"
#include "itkImage.h"
#include "itkCastImageFilter.h"
#include "itkCurvatureFlowImageFilter.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"

// Define the constants most often tweaked in the algorithm
#define SMOOTHING_ITERATIONS    5
#define SMOOTHING_TIMESTEP      0.125
#define CONFIDENCE_NEIGHBORHOOD 2
#define CONFIDENCE_ITERATIONS   2


int
main(int argc, char *argv[])
{
  // Ensure we received the corrent number of arguments
  if (argc < 7)
    {
    std::cerr << "Missing parameters" << std::endl;
    std::cerr << "Usage: " << argv[0];
    std::cerr << " inputImage outputImage seedX seedY seedZ multiplier" 
              << std::endl;
    return 1;
    }

  // Setup the dimensions and type of image we're dealing with
  typedef float        InternalPixelType;
  const   unsigned int Dimension = 3;
  typedef itk::Image< InternalPixelType, Dimension >  InternalImageType;
  
  typedef unsigned char OutputPixelType;
  typedef itk::Image< OutputPixelType, Dimension > OutputImageType;

  typedef itk::CastImageFilter< InternalImageType, OutputImageType >
    CastingFilterType;
  CastingFilterType::Pointer caster = CastingFilterType::New();

  // Set up the input reader and output writer using the given command 
  // line arguments
  typedef  itk::ImageFileReader< InternalImageType > ReaderType;
  typedef  itk::ImageFileWriter< OutputImageType >   WriterType;
  ReaderType::Pointer reader = ReaderType::New();
  WriterType::Pointer writer = WriterType::New();
  reader->SetFileName(argv[1]);
  writer->SetFileName(argv[2]);
  
  // Construct a image smoothing filter
  typedef itk::CurvatureFlowImageFilter< InternalImageType, InternalImageType >
    CurvatureFlowImageFilterType;
  CurvatureFlowImageFilterType::Pointer smoothing =
                                CurvatureFlowImageFilterType::New();
  smoothing->SetNumberOfIterations(SMOOTHING_ITERATIONS);
  smoothing->SetTimeStep(SMOOTHING_TIMESTEP);
  
  // Construct the confidence connected filter
  typedef itk::ConfidenceConnectedImageFilter< InternalImageType, InternalImageType > 
    ConnectedFilterType;
  ConnectedFilterType::Pointer confidenceConnected = ConnectedFilterType::New();

  // Set up the pipeline
  smoothing->SetInput(reader->GetOutput());
  confidenceConnected->SetInput(smoothing->GetOutput());
  caster->SetInput(confidenceConnected->GetOutput());
  writer->SetInput(caster->GetOutput());

  // Specify our starting seed point and initiall neighborhood
  InternalImageType::IndexType index;
  index[0] = atoi(argv[3]);
  index[1] = atoi(argv[4]);
  index[2] = atoi(argv[5]);
  confidenceConnected->SetSeed(index);
  confidenceConnected->SetMultiplier(atof(argv[6]));
  confidenceConnected->SetInitialNeighborhoodRadius(CONFIDENCE_NEIGHBORHOOD);
  confidenceConnected->SetNumberOfIterations(CONFIDENCE_ITERATIONS);
  confidenceConnected->SetReplaceValue(255); // White pixels

  // Try to execute the filter or catch any thrown exceptions
  try 
    {
    writer->Update();
    } 
  catch(itk::ExceptionObject &ex)
    {
    std::cerr << "Exception caught !" << std::endl;
    std::cerr << ex << std::endl;
    return 1;
    }
  return 0;
}