ITK  4.6.0
Insight Segmentation and Registration Toolkit
Statistics/ImageHistogram3.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
//
// By now, you are probably thinking that the statistics framework in ITK is
// too complex for simply computing histograms from images. Here we illustrate
// that the benefit for this complexity is the power that these methods provide
// for dealing with more complex and realistic uses of image statistics than the
// trivial 256-bin histogram of 8-bit images that most software packages
// provide. One of such cases is the computation of histograms from
// multi-component images such as Vector images and color images.
//
// This example shows how to compute the histogram of an RGB image by using the
// helper class \code{ImageToHistogramFilter}. In this first example we
// compute the histogram of each channel independently.
//
// We start by including the header of the
// \subdoxygen{Statistics}{ImageToHistogramFilter}, as well as the headers
// for the image class and the RGBPixel class.
//
// \index{itk::Statistics::ImageToHistogramFilter!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "itkImage.h"
#include "itkRGBPixel.h"
// Software Guide : EndCodeSnippet
int main( int argc, char * argv [] )
{
if( argc < 2 )
{
std::cerr << "Missing command line arguments" << std::endl;
std::cerr << "Usage : ImageHistogram3 inputRGBImageFileName " << std::endl;
return -1;
}
// Software Guide : BeginLatex
//
// The type of the RGB image is defined by first instantiating a RGBPixel and
// then using the image dimension specification.
//
// \index{itk::Statistics!Color Images}
//
// 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 encoutered while reading image file : " << argv[1] << std::endl;
std::cerr << excp << std::endl;
return -1;
}
// Software Guide : BeginLatex
//
// Using the RGB image type we can instantiate the type of the corresponding
// histogram filter and construct one filter by invoking its \code{New()}
// method.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
RGBImageType > HistogramFilterType;
HistogramFilterType::Pointer histogramFilter =
HistogramFilterType::New();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The parameters of the histogram must be defined now. Probably the most
// important one is the arrangement of histogram bins. This is provided to the
// histogram through a size array. The type of the array can be taken from the
// traits of the \code{HistogramFilterType} type. We create one instance of
// the size object and fill in its content. In this particular case, the three
// components of the size array will correspond to the number of bins used for
// each one of the RGB components in the color image. The following lines show
// how to define a histogram on the red component of the image while
// disregarding the green and blue components.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef HistogramFilterType::HistogramSizeType SizeType;
SizeType size( 3 );
size[0] = 255; // number of bins for the Red channel
size[1] = 1; // number of bins for the Green channel
size[2] = 1; // number of bins for the Blue channel
histogramFilter->SetHistogramSize( size );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The marginal scale must be defined in the filter. This will determine 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, we must specify the upper and lower bounds for the histogram. This
// can either be done manually using the \code{SetHistogramBinMinimum()} and
// \code{SetHistogramBinMaximum()} methods or it can be done automatically by
// calling \code{SetHistogramAutoMinimumMaximum( true )}. Here we use the
// manual method.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
HistogramFilterType::HistogramMeasurementVectorType lowerBound( 3 );
HistogramFilterType::HistogramMeasurementVectorType upperBound( 3 );
lowerBound[0] = 0;
lowerBound[1] = 0;
lowerBound[2] = 0;
upperBound[0] = 256;
upperBound[1] = 256;
upperBound[2] = 256;
histogramFilter->SetHistogramBinMinimum( lowerBound );
histogramFilter->SetHistogramBinMaximum( upperBound );
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The input of the filter is taken from an image reader, and the
// computation of the histogram is triggered by invoking the \code{Update()}
// method of the filter.
//
// \index{itk::Statistics::ImageToHistogramFilter!Update()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
histogramFilter->SetInput( reader->GetOutput() );
histogramFilter->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We can now access the results of the histogram computation by declaring a
// pointer to histogram and getting its value from the filter using the
// \code{GetOutput()} method. Note that here we use a \code{const
// HistogramType} pointer instead of a const smart pointer because we are sure
// that the filter is not going to be destroyed while we access the values
// of the histogram. Depending on what you are doing, it may be safer to assign
// the histogram to a const smart pointer as shown in previous examples.
//
// \index{itk::Statistics::ImageTohistogramFilter!GetOutput()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef HistogramFilterType::HistogramType HistogramType;
const HistogramType * histogram = histogramFilter->GetOutput();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Just for the sake of exercising the experimental method~\cite{Popper2002}, we
// verify that the resulting histogram actually have the size that we requested
// when we configured the filter. This can be done by invoking the
// \code{Size()} method of the histogram and printing out the result.
//
// \index{itk::Statistics::Histogram!Size()}
//
// 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
//
// Strictly speaking, the histogram computed here is the joint histogram of the
// three RGB components. However, given that we set the resolution of the green
// and blue channels to be just one bin, the histogram is in practice
// representing just the red channel. In the general case, we can alway access
// the frequency of a particular channel in a joint histogram, thanks to the
// fact that the histogram class offers a \code{GetFrequency()} method that
// accepts a channel as argument. This is illustrated in the following lines of
// code.
//
// \index{itk::Statistics::Histogram!GetFrequency()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
unsigned int channel = 0; // red channel
std::cout << "Histogram of the red component" << std::endl;
for( unsigned int bin=0; bin < histogramSize; bin++ )
{
std::cout << "bin = " << bin << " frequency = ";
std::cout << histogram->GetFrequency( bin, channel ) << std::endl;
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// In order to reinforce the concepts presented above, we modify now the setup
// of the histogram filter in order to compute the histogram of the green
// channel instead of the red one. This is done by simply changing the number
// of bins desired on each channel and invoking the computation of the
// filter again by calling the \code{Update()} method.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
size[0] = 1; // number of bins for the Red channel
size[1] = 255; // number of bins for the Green channel
size[2] = 1; // number of bins for the Blue channel
histogramFilter->SetHistogramSize( size );
histogramFilter->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// The result can be verified now by setting the desired channel to green and
// invoking the \code{GetFrequency()} method.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
channel = 1; // green channel
std::cout << "Histogram of the green component" << std::endl;
for( unsigned int bin=0; bin < histogramSize; bin++ )
{
std::cout << "bin = " << bin << " frequency = ";
std::cout << histogram->GetFrequency( bin, channel ) << std::endl;
}
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// To finalize the example, we do the same computation for the case of the blue
// channel.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
size[0] = 1; // number of bins for the Red channel
size[1] = 1; // number of bins for the Green channel
size[2] = 255; // number of bins for the Blue channel
histogramFilter->SetHistogramSize( size );
histogramFilter->Update();
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// and verify the output.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
channel = 2; // blue channel
std::cout << "Histogram of the blue component" << std::endl;
for( unsigned int bin=0; bin < histogramSize; bin++ )
{
std::cout << "bin = " << bin << " frequency = ";
std::cout << histogram->GetFrequency( bin, channel ) << std::endl;
}
// Software Guide : EndCodeSnippet
return 0;
}