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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.
* You may obtain a copy of the License at
*
* http://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.
*
*=========================================================================*/
#include "itkImage.h"
#include "itkImageFileReader.h"
#include "itkBayesianClassifierImageFilter.h"
#include "itkBayesianClassifierInitializationImageFilter.h"
#include "itkImageFileWriter.h"
#include "itkImageToImageFilter.h"
#include "itkGradientAnisotropicDiffusionImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkPipelineMonitorImageFilter.h"
#include "itkTestingMacros.h"
template< typename TInputImage, typename TBayesianClassifierInitializer, typename TBayesianClassifierFilter >
int TestBayesianClassifierImageFilterWithNoPriors( typename TInputImage::Pointer image,
unsigned int numberOfClasses, unsigned int numberOfSmoothingIterations, char* outputFilename )
{
typedef TBayesianClassifierInitializer BayesianClassifierInitializerType;
typedef TBayesianClassifierFilter BayesianClassifierFilterType;
typename BayesianClassifierInitializerType::Pointer bayesianInitializer =
BayesianClassifierInitializerType::New();
bayesianInitializer->SetInput( image );
bayesianInitializer->SetNumberOfClasses( numberOfClasses );
typename BayesianClassifierFilterType::Pointer bayesianClassifier = BayesianClassifierFilterType::New();
bayesianClassifier->SetInput( bayesianInitializer->GetOutput() );
bayesianClassifier->SetNumberOfSmoothingIterations( numberOfSmoothingIterations );
TEST_SET_GET_VALUE( numberOfSmoothingIterations, bayesianClassifier->GetNumberOfSmoothingIterations() );
typedef typename BayesianClassifierFilterType::ExtractedComponentImageType ExtractedComponentImageType;
typedef itk::GradientAnisotropicDiffusionImageFilter<
ExtractedComponentImageType, ExtractedComponentImageType > SmoothingFilterType;
typename SmoothingFilterType::Pointer smoother = SmoothingFilterType::New();
smoother->SetNumberOfIterations( 1 );
smoother->SetTimeStep( 0.125 );
smoother->SetConductanceParameter( 3 );
bayesianClassifier->SetSmoothingFilter( smoother );
TEST_SET_GET_VALUE( smoother, bayesianClassifier->GetSmoothingFilter().GetPointer() );
typedef itk::PipelineMonitorImageFilter< TInputImage > MonitorFilterType;
typename MonitorFilterType::Pointer monitor = MonitorFilterType::New();
monitor->SetInput( bayesianClassifier->GetOutput() );
typedef typename BayesianClassifierFilterType::OutputImageType ClassifierOutputImageType;
typedef itk::Image< unsigned char, TInputImage::ImageDimension > OutputImageType;
typedef itk::RescaleIntensityImageFilter<
ClassifierOutputImageType, OutputImageType > RescalerType;
typename RescalerType::Pointer rescaler = RescalerType::New();
rescaler->SetInput( monitor->GetOutput() );
rescaler->SetOutputMinimum( 0 );
rescaler->SetOutputMaximum( 255 );
typedef itk::ImageFileWriter< OutputImageType > WriterType;
typename WriterType::Pointer writer = WriterType::New();
writer->SetFileName( outputFilename );
writer->SetInput( rescaler->GetOutput() );
TRY_EXPECT_NO_EXCEPTION( writer->Update() );
if (!monitor->VerifyAllInputCanNotStream())
{
std::cout << "Pipeline did not execute as expected!" << std::endl;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
template< typename TInputImage, typename TBayesianClassifierInitializer, typename TBayesianClassifierFilter >
int TestBayesianClassifierImageFilterWithPriors( typename TInputImage::Pointer image,
typename TBayesianClassifierFilter::PriorsImageType::Pointer priorsImage,
unsigned int numberOfClasses, unsigned int numberOfSmoothingIterations, char* outputFilename )
{
typedef TBayesianClassifierInitializer BayesianClassifierInitializerType;
typedef TBayesianClassifierFilter BayesianClassifierFilterType;
typename BayesianClassifierInitializerType::Pointer bayesianInitializer =
BayesianClassifierInitializerType::New();
bayesianInitializer->SetInput( image );
bayesianInitializer->SetNumberOfClasses( numberOfClasses );
typename BayesianClassifierFilterType::Pointer bayesianClassifier = BayesianClassifierFilterType::New();
bayesianClassifier->SetInput( bayesianInitializer->GetOutput() );
bayesianClassifier->SetPriors( priorsImage );
bayesianClassifier->SetNumberOfSmoothingIterations( numberOfSmoothingIterations );
TEST_SET_GET_VALUE( numberOfSmoothingIterations, bayesianClassifier->GetNumberOfSmoothingIterations() );
typedef typename BayesianClassifierFilterType::ExtractedComponentImageType ExtractedComponentImageType;
typedef itk::GradientAnisotropicDiffusionImageFilter<
ExtractedComponentImageType, ExtractedComponentImageType > SmoothingFilterType;
typename SmoothingFilterType::Pointer smoother = SmoothingFilterType::New();
smoother->SetNumberOfIterations( 1 );
smoother->SetTimeStep( 0.125 );
smoother->SetConductanceParameter( 3 );
bayesianClassifier->SetSmoothingFilter( smoother );
TEST_SET_GET_VALUE( smoother, bayesianClassifier->GetSmoothingFilter().GetPointer() );
typedef itk::PipelineMonitorImageFilter< TInputImage > MonitorFilterType;
typename MonitorFilterType::Pointer monitor = MonitorFilterType::New();
monitor->SetInput( bayesianClassifier->GetOutput() );
typedef typename BayesianClassifierFilterType::OutputImageType ClassifierOutputImageType;
typedef itk::Image< unsigned char, TInputImage::ImageDimension > OutputImageType;
typedef itk::RescaleIntensityImageFilter<
ClassifierOutputImageType, OutputImageType > RescalerType;
typename RescalerType::Pointer rescaler = RescalerType::New();
rescaler->SetInput( monitor->GetOutput() );
rescaler->SetOutputMinimum( 0 );
rescaler->SetOutputMaximum( 255 );
typedef itk::ImageFileWriter< OutputImageType > WriterType;
typename WriterType::Pointer writer = WriterType::New();
writer->SetFileName( outputFilename );
writer->SetInput( rescaler->GetOutput() );
TRY_EXPECT_NO_EXCEPTION( writer->Update() );
if (!monitor->VerifyAllInputCanNotStream())
{
std::cout << "Pipeline did not execute as expected!" << std::endl;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
int itkBayesianClassifierImageFilterTest( int argc, char* argv[] )
{
if( argc < 6 )
{
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0] << " inputImageFile outputImageFile numberOfClasses smoothingIterations testPriors" << std::endl;
return EXIT_FAILURE;
}
// Set up reader
const unsigned int Dimension = 2;
typedef unsigned char InputPixelType;
typedef itk::Image< InputPixelType, Dimension > InputImageType;
typedef itk::ImageFileReader< InputImageType > ReaderType;
typedef itk::BayesianClassifierInitializationImageFilter< InputImageType >
BayesianInitializerType;
BayesianInitializerType::Pointer bayesianInitializer = BayesianInitializerType::New();
EXERCISE_BASIC_OBJECT_METHODS( bayesianInitializer,
BayesianClassifierInitializationImageFilter, ImageToImageFilter );
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName( argv[1] );
TRY_EXPECT_NO_EXCEPTION( reader->Update() );
ReaderType::OutputImageType::Pointer inputImage = reader->GetOutput();
char* outputFilename = argv[2];
unsigned int numberOfClasses = atoi( argv[3] );
unsigned int numberOfSmoothingIterations = atoi( argv[4] );
bool testPriors = atoi( argv[5] );
typedef unsigned char LabelType;
typedef float PriorType;
typedef float PosteriorType;
typedef BayesianInitializerType::OutputImageType InitialLabelImageType;
typedef itk::BayesianClassifierImageFilter<
InitialLabelImageType, LabelType, PosteriorType, PriorType > BayesianClassifierFilterType;
BayesianClassifierFilterType::Pointer bayesianClassifier = BayesianClassifierFilterType::New();
EXERCISE_BASIC_OBJECT_METHODS( bayesianClassifier,
BayesianClassifierImageFilter, ImageToImageFilter );
bool testStatus = EXIT_SUCCESS;
if( !testPriors )
{
std::cout << "Running the filter with no Priors set..." << std::endl;
testStatus = TestBayesianClassifierImageFilterWithNoPriors<
ReaderType::OutputImageType, BayesianInitializerType, BayesianClassifierFilterType >( inputImage,
numberOfClasses, numberOfSmoothingIterations, outputFilename );
}
else
{
std::cout << "Running the filter with Priors set..." << std::endl;
typedef BayesianClassifierFilterType::PriorsImageType PriorsImageType;
const InputImageType * priorsInputImage = reader->GetOutput();
PriorsImageType::Pointer priorsImage = PriorsImageType::New();
priorsImage->CopyInformation( priorsInputImage );
priorsImage->SetRegions( inputImage->GetLargestPossibleRegion() );
priorsImage->SetNumberOfComponentsPerPixel(5);
priorsImage->Allocate(true);
testStatus =
TestBayesianClassifierImageFilterWithPriors<
ReaderType::OutputImageType, BayesianInitializerType, BayesianClassifierFilterType >(
inputImage, priorsImage, numberOfClasses, numberOfSmoothingIterations, outputFilename );
}
// TEST valid image type combinations.
// The hypothesis is that the vector element type for
// TestInitialLabelImageType must be the same as for TestPriorType
{
const unsigned int TestDimension = 2;
typedef unsigned char TestLabelType;
typedef float TestPosteriorType;
typedef float TestPriorType;
typedef itk::VectorImage< TestPriorType, TestDimension > TestInitialLabelImageType;
typedef itk::BayesianClassifierImageFilter<
TestInitialLabelImageType, TestLabelType, TestPosteriorType, TestPriorType > ClassifierFilterType;
ClassifierFilterType::Pointer filter = ClassifierFilterType::New();
if( filter.IsNull() )
{
return EXIT_FAILURE;
}
EXERCISE_BASIC_OBJECT_METHODS( filter, BayesianClassifierImageFilter, ImageToImageFilter );
}
{
const unsigned int TestDimension = 2;
typedef unsigned char TestLabelType;
typedef float TestPosteriorType;
typedef float TestPriorType;
typedef itk::VectorImage< double, TestDimension > TestInitialLabelImageType; // The element type MUST be the PriorType
typedef itk::BayesianClassifierImageFilter<
TestInitialLabelImageType, TestLabelType, TestPosteriorType, TestPriorType > ClassifierFilterType;
ClassifierFilterType::Pointer filter = ClassifierFilterType::New();
if( filter.IsNull() )
{
return EXIT_FAILURE;
}
EXERCISE_BASIC_OBJECT_METHODS( filter, BayesianClassifierImageFilter, ImageToImageFilter );
}
{
const unsigned int TestDimension = 2;
typedef unsigned char TestLabelType;
typedef float TestPosteriorType;
typedef double TestPriorType;
typedef itk::VectorImage< TestPriorType, TestDimension > TestInitialLabelImageType; // The element type MUST be the PriorType
typedef itk::BayesianClassifierImageFilter<
TestInitialLabelImageType, TestLabelType, TestPosteriorType, TestPriorType > ClassifierFilterType;
ClassifierFilterType::Pointer filter = ClassifierFilterType::New();
if( filter.IsNull() )
{
return EXIT_FAILURE;
}
EXERCISE_BASIC_OBJECT_METHODS( filter, BayesianClassifierImageFilter, ImageToImageFilter );
}
std::cout << "Test passed." << std::endl;
return testStatus;
}
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