ITK  4.13.0
Insight Segmentation and Registration Toolkit
Examples/Statistics/ImageHistogram4.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
//
// The statistics framework in ITK has been designed for managing multi-variate
// statistics in a natural way. The \subdoxygen{Statistics}{Histogram} class
// reflects this concept clearly since it is a N-variable joint histogram. This
// nature of the Histogram class is exploited in the following example in order
// to build the joint histogram of a color image encoded in RGB values.
//
// Note that the same treatment could be applied further to any vector image
// thanks to the generic programming approach used in the implementation of the
// statistical framework.
//
// The most relevant class in this example is the
// \subdoxygen{Statistics}{ImageToHistogramFilter}. This class will take
// care of adapting the \doxygen{Image} to a list of samples and then to a
// histogram filter. The user is only bound to provide the desired
// resolution on the histogram bins for each one of the image components.
//
// In this example we compute the joint histogram of the three channels of an
// RGB image. Our output histogram will be equivalent to a 3D array of bins.
// This histogram could be used further for feeding a segmentation method based
// on statistical pattern recognition. Such method was actually used during the
// generation of the image in the cover of the Software Guide.
//
// The first step is to include the header files for the histogram filter,
// the RGB pixel type and the Image.
//
// \index{itk::Statistics::ImageToHistogramFilter!header}
// \index{itk::RGBPixel!header}
// \index{itk::RGBPixel!Statistics}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "itkImage.h"
#include "itkRGBPixel.h"
// Software Guide : EndCodeSnippet
int main( int argc, char * argv [] )
{
if( argc < 3 )
{
std::cerr << "Missing command line arguments" << std::endl;
std::cerr << "Usage : ImageHistogram4 inputRGBImageFileName ";
std::cerr << " histogramFilename.raw" << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// We declare now the type used for the components of the RGB pixel,
// instantiate the type of the RGBPixel and instantiate the image type.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef unsigned char PixelComponentType;
const unsigned int Dimension = 2;
// Software Guide : EndCodeSnippet
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName( argv[1] );
try
{
reader->Update();
}
catch( itk::ExceptionObject & excp )
{
std::cerr << "Problem reading image file : " << argv[1] << std::endl;
std::cerr << excp << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Using the type of the color image, and in general of any vector image, we
// can now instantiate the type of the histogram filter class. We then use
// that type for constructing an instance of the filter by invoking its
// \code{New()} method and assigning the result to a smart pointer.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
RGBImageType > HistogramFilterType;
HistogramFilterType::Pointer histogramFilter =
HistogramFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The resolution at which the statistics of each one of the color component
// will be evaluated is defined by setting the number of bins along every
// component in the joint histogram. For this purpose we take the
// \code{HistogramSizeType} trait from the filter and use it to instantiate a
// \code{size} variable. We set in this variable the number of bins to use for
// each component of the color image.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef HistogramFilterType::HistogramSizeType SizeType;
SizeType size(3);
size[0] = 256; // number of bins for the Red channel
size[1] = 256; // number of bins for the Green channel
size[2] = 256; // number of bins for the Blue channel
histogramFilter->SetHistogramSize( size );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Finally, we must specify the upper and lower bounds for the histogram
// using the \code{SetHistogramBinMinimum()} and
// \code{SetHistogramBinMaximum()} methods.
//
// Software Guide : EndLatexex
// Software Guide : BeginCodeSnippet
typedef HistogramFilterType::HistogramMeasurementVectorType
HistogramMeasurementVectorType;
HistogramMeasurementVectorType binMinimum( 3 );
HistogramMeasurementVectorType binMaximum( 3 );
binMinimum[0] = -0.5;
binMinimum[1] = -0.5;
binMinimum[2] = -0.5;
binMaximum[0] = 255.5;
binMaximum[1] = 255.5;
binMaximum[2] = 255.5;
histogramFilter->SetHistogramBinMinimum( binMinimum );
histogramFilter->SetHistogramBinMaximum( binMaximum );
//Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The input to the histogram filter is taken from the output of an image
// reader. Of course, the output of any filter producing an RGB image could
// have been used instead of a reader.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
histogramFilter->SetInput( reader->GetOutput() );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The marginal scale is defined in the histogram filter. This value will
// define the precision in the assignment of values to the histogram bins.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
histogramFilter->SetMarginalScale( 10.0 );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Finally, the computation of the histogram is triggered by invoking the
// \code{Update()} method of the filter.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
histogramFilter->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// At this point, we can recover the histogram by calling the
// \code{GetOutput()} method of the filter. The result is assigned to a
// variable that is instantiated using the \code{HistogramType} trait of the
// filter type.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef HistogramFilterType::HistogramType HistogramType;
const HistogramType * histogram = histogramFilter->GetOutput();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We can verify that the computed histogram has the requested size by invoking
// its \code{Size()} method.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const unsigned int histogramSize = histogram->Size();
std::cout << "Histogram size " << histogramSize << std::endl;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The values of the histogram can now be saved into a file by walking through
// all of the histogram bins and pushing them into a std::ofstream.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
std::ofstream histogramFile;
histogramFile.open( argv[2] );
HistogramType::ConstIterator itr = histogram->Begin();
HistogramType::ConstIterator end = histogram->End();
typedef HistogramType::AbsoluteFrequencyType AbsoluteFrequencyType;
while( itr != end )
{
const AbsoluteFrequencyType frequency = itr.GetFrequency();
histogramFile.write( (const char *)(&frequency), sizeof(frequency) );
if (frequency != 0)
{
index = histogram->GetIndex(itr.GetInstanceIdentifier());
std::cout << "Index = " << index << ", Frequency = " << frequency
<< std::endl;
}
++itr;
}
histogramFile.close();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Note that here the histogram is saved as a block of memory in a raw file. At
// this point you can use visualization software in order to explore the
// histogram in a display that would be equivalent to a scatter plot of the RGB
// components of the input color image.
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}