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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile: itkImagePCAShapeModelEstimatorTest.cxx, v $
Language: C++
Date: $Date: 2008-02-03 04:05:34 $
Version: $Revision: 1.11 $
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.
=========================================================================*/
// Insight classes
#include "otbImage.h"
#include "itkVector.h"
#include "vnl/vnl_matrix_fixed.h"
#include "vnl/vnl_math.h"
#include "itkLightProcessObject.h"
#include "itkTextOutput.h"
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
#include "itkImagePCAShapeModelEstimator.h"
//Data definitions
#define IMGWIDTH 2
#define IMGHEIGHT 2
#define NDIMENSION 2
#define NUMTRAINIMAGES 3
#define NUMLARGESTPC 3
// class to support progress feeback
class ShowProgressObject
{
public:
ShowProgressObject(itk::LightProcessObject * o)
{
m_Process = o;
}
void ShowProgress()
{
std::cout << "Progress " << m_Process->GetProgress() << std::endl;
}
itk::LightProcessObject::Pointer m_Process;
};
int otbImagePCAShapeModelEstimatorTest(int itkNotUsed(argc), char* argv[])
{
/* const unsigned int numberOfPrincipalComponentsRequired(atoi(argv[1]));
const unsigned int numberOfTrainingImages(atoi(argv[2]));
std::vector<std::string> imagesfilenames;
std::vector<std::string> outputimagesfilenames;
int cpt(3);
for(; cpt<(numberOfTrainingImages+3); ++cpt)
{
imagesfilenames.push_back(argv[cpt]);
}
int cpt2(cpt);
for(; cpt<(numberOfPrincipalComponentsRequired+cpt2); ++cpt)
{
outputimagesfilenames.push_back(argv[cpt]);
}
*/
itk::OutputWindow::SetInstance(itk::TextOutput::New().GetPointer());
//------------------------------------------------------
//Create 3 simple test images with
//------------------------------------------------------
typedef otb::Image<double, NDIMENSION> InputImageType;
typedef otb::Image<double, NDIMENSION> OutputImageType;
typedef otb::ImageFileReader<InputImageType> ReaderType;
typedef otb::ImageFileWriter<OutputImageType> WriterType;
// Instantiating object
ReaderType::Pointer reader1 = ReaderType::New();
ReaderType::Pointer reader2 = ReaderType::New();
ReaderType::Pointer reader3 = ReaderType::New();
WriterType::Pointer writer1 = WriterType::New();
WriterType::Pointer writer2 = WriterType::New();
WriterType::Pointer writer3 = WriterType::New();
WriterType::Pointer writer4 = WriterType::New();
reader1->SetFileName(argv[1]);
reader2->SetFileName(argv[2]);
reader3->SetFileName(argv[3]);
writer1->SetFileName(argv[4]);
writer2->SetFileName(argv[5]);
writer3->SetFileName(argv[6]);
writer4->SetFileName(argv[7]);
//----------------------------------------------------------------------
// Test code for the Shape model estimator
//----------------------------------------------------------------------
//----------------------------------------------------------------------
//Set the image model estimator
//----------------------------------------------------------------------
typedef itk::ImagePCAShapeModelEstimator<InputImageType, OutputImageType>
ImagePCAShapeModelEstimatorType;
ImagePCAShapeModelEstimatorType::Pointer
applyPCAShapeEstimator = ImagePCAShapeModelEstimatorType::New();
//----------------------------------------------------------------------
//Set the parameters of the clusterer
//----------------------------------------------------------------------
applyPCAShapeEstimator->SetNumberOfTrainingImages(NUMTRAINIMAGES);
applyPCAShapeEstimator->SetNumberOfPrincipalComponentsRequired(NUMLARGESTPC + 1);
applyPCAShapeEstimator->SetNumberOfPrincipalComponentsRequired(NUMLARGESTPC);
applyPCAShapeEstimator->SetInput(0, reader1->GetOutput());
applyPCAShapeEstimator->SetInput(1, reader2->GetOutput());
applyPCAShapeEstimator->SetInput(2, reader3->GetOutput());
applyPCAShapeEstimator->Update();
writer1->SetInput(applyPCAShapeEstimator->GetOutput(0));
writer1->Update();
writer2->SetInput(applyPCAShapeEstimator->GetOutput(1));
writer2->Update();
writer3->SetInput(applyPCAShapeEstimator->GetOutput(2));
writer3->Update();
writer4->SetInput(applyPCAShapeEstimator->GetOutput(3));
writer4->Update();
//Test the printself function to increase coverage
applyPCAShapeEstimator->Print(std::cout);
//Exercise TypeMacro in superclass
typedef ImagePCAShapeModelEstimatorType::Superclass GenericEstimatorType;
std::cout << applyPCAShapeEstimator->GenericEstimatorType::GetNameOfClass() << std::endl;
//Print out the number of training images and the number of principal
//components
std::cout << "The number of training images are: " <<
applyPCAShapeEstimator->GetNumberOfTrainingImages() << std::endl;
std::cout << "The number of principal components desired are: " <<
applyPCAShapeEstimator->GetNumberOfPrincipalComponentsRequired() << std::endl;
//Print the eigen vectors
vnl_vector<double> eigenValues =
applyPCAShapeEstimator->GetEigenValues();
unsigned int numEigVal = eigenValues.size();
std::cout << "Number of returned eign-values: " << numEigVal << std::endl;
std::cout << "The " <<
applyPCAShapeEstimator->GetNumberOfPrincipalComponentsRequired() <<
" largest eigen values are:" << std::endl;
for (unsigned int i = 0; i < vnl_math_min(numEigVal, (unsigned int) NUMLARGESTPC); ++i)
{
std::cout << eigenValues[i] << std::endl;
}
//Print the MeanImage
/*
OutputImageType::Pointer outImage = applyPCAShapeEstimator->GetOutput( 0 );
OutputImageIterator outImageIt( outImage, outImage->GetBufferedRegion() );
outImageIt.GoToBegin();
std::cout << "The mean image is:" << std::endl;
while (!outImageIt.IsAtEnd() )
{
std::cout << (double)(outImageIt.Get()) << ";" << std::endl;
++outImageIt;
}
std::cout << " " << std::endl;
//Print the largest two eigen vectors
for (unsigned int j=1; j< NUMLARGESTPC + 1; ++j )
{
OutputImageType::Pointer outImage2 = applyPCAShapeEstimator->GetOutput( j );
OutputImageIterator outImage2It( outImage2, outImage2->GetBufferedRegion() );
outImage2It.GoToBegin();
std::cout << "" << std::endl;
std::cout << "The eigen vector number: " << j << " is:" << std::endl;
while (!outImage2It.IsAtEnd() )
{
std::cout << (double) (outImage2It.Get()) << ";" << std::endl;
++outImage2It;
}
std::cout << " " << std::endl;
}
*/
return EXIT_SUCCESS;
}
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