ITK  4.6.0
Insight Segmentation and Registration Toolkit
Statistics/ImageHistogram1.cxx
/*=========================================================================
*
* 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.
*
*=========================================================================*/
// Software Guide : BeginLatex
//
// This example shows how to compute the histogram of a scalar image. Since
// the statistics framework classes operate on Samples and
// ListOfSamples, we need to introduce a class that will make the image look
// like a list of samples. This class is the
// \subdoxygen{Statistics}{ImageToListSampleAdaptor}. Once we have connected
// this adaptor to an image, we can proceed to use the
// \subdoxygen{Statistics}{SampleToHistogramFilter} in order to compute
// the histogram of the image.
//
// First, we need to include the headers for the
// \subdoxygen{Statistics}{ImageToListSampleAdaptor} and the \doxygen{Image} classes.
//
// \index{itk::Statistics::Scalar\-Image\-To\-List\-Adaptor!header}
// \index{Statistics!Images}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "itkImage.h"
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Now we include the headers for the \code{Histogram}, the
// \code{SampleToHistogramFilter}, and the reader that we will use for
// reading the image from a file.
//
// \index{itk::Statistics::List\-Sample\-To\-Histogram\-Generator!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "itkHistogram.h"
// Software Guide : EndCodeSnippet
int main( int argc, char * argv [] )
{
if( argc < 2 )
{
std::cerr << "Missing command line arguments" << std::endl;
std::cerr << "Usage : ImageHistogram1 inputImageFileName " << std::endl;
return -1;
}
// Software Guide : BeginLatex
//
// The image type must be defined using the typical pair of pixel type and
// dimension specification.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef unsigned char PixelType;
const unsigned int Dimension = 2;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Using the same image type we instantiate the type of the image reader that
// will provide the image source for our example.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName( argv[1] );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Now we introduce the central piece of this example, which is the use of the
// adaptor that will present the \doxygen{Image} as if it was a list of
// samples. We instantiate the type of the adaptor by using the actual image
// type. Then construct the adaptor by invoking its \code{New()} method and
// assigning the result to the corresponding smart pointer. Finally we connect
// the output of the image reader to the input of the adaptor.
//
// \index{itk::Statistics::Scalar\-Image\-To\-List\-Adaptor!instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
AdaptorType::Pointer adaptor = AdaptorType::New();
adaptor->SetImage( reader->GetOutput() );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// You must keep in mind that adaptors are not pipeline objects. This means
// that they do not propagate update calls. It is therefore your responsibility
// to make sure that you invoke the \code{Update()} method of the reader before
// you attempt to use the output of the adaptor. As usual, this must be done
// inside a try/catch block because the read operation can potentially throw
// exceptions.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
try
{
reader->Update();
}
catch( itk::ExceptionObject & excp )
{
std::cerr << "Problem reading image file : " << argv[1] << std::endl;
std::cerr << excp << std::endl;
return -1;
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// At this point, we are ready for instantiating the type of the histogram
// filter. We must first declare the type of histogram we wish to use.
// The adaptor type is also used as template parameter of the filter.
// Having instantiated this type, we proceed to create one filter
// by invoking its \code{New()} method.
//
// \index{itk::Statistics::Sample\-To\-Histogram\-Filter!instantiation}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef PixelType HistogramMeasurementType;
HistogramType;
AdaptorType,
HistogramType>
FilterType;
FilterType::Pointer filter = FilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We define now the characteristics of the Histogram that we want to compute.
// This typically includes the size of each one of the component, but given
// that in this simple example we are dealing with a scalar image, then our
// histogram will have a single component. For the sake of generality, however,
// we use the \code{HistogramType} as defined inside of the Generator type. We
// define also the marginal scale factor that will control the precision used
// when assigning values to histogram bins. Finally we invoke the
// \code{Update()} method in the filter.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const unsigned int numberOfComponents = 1;
HistogramType::SizeType size( numberOfComponents );
size.Fill( 255 );
filter->SetInput( adaptor );
filter->SetHistogramSize( size );
filter->SetMarginalScale( 10 );
HistogramType::MeasurementVectorType min( numberOfComponents );
HistogramType::MeasurementVectorType max( numberOfComponents );
min.Fill( 0 );
max.Fill( 255 );
filter->SetHistogramBinMinimum( min );
filter->SetHistogramBinMaximum( max );
filter->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Now we are ready for using the image histogram for any further processing.
// The histogram is obtained from the filter by invoking the
// \code{GetOutput()} method.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
HistogramType::ConstPointer histogram = filter->GetOutput();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// In this current example we simply print out the frequency values of all the
// bins in the image histogram.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const unsigned int histogramSize = histogram->Size();
std::cout << "Histogram size " << histogramSize << std::endl;
for( unsigned int bin=0; bin < histogramSize; bin++ )
{
std::cout << "bin = " << bin << " frequency = ";
std::cout << histogram->GetFrequency( bin, 0 ) <<std::endl;
}
// Software Guide : EndCodeSnippet
return 0;
}