ITK  5.3.0
Insight Toolkit
Examples/Statistics/ImageHistogram3.cxx
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*
* Copyright NumFOCUS
*
* 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
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* http://www.apache.org/licenses/LICENSE-2.0.txt
*
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* See the License for the specific language governing permissions and
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// 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 EXIT_FAILURE;
}
// 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
using PixelComponentType = unsigned char;
using RGBPixelType = itk::RGBPixel<PixelComponentType>;
constexpr unsigned int Dimension = 2;
// Software Guide : EndCodeSnippet
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName(argv[1]);
try
{
reader->Update();
}
catch (const itk::ExceptionObject & excp)
{
std::cerr << "Problem encoutered while reading image file : " << argv[1]
<< std::endl;
std::cerr << excp << std::endl;
return EXIT_FAILURE;
}
// 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
using 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
using SizeType = HistogramFilterType::HistogramSizeType;
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
using HistogramType = HistogramFilterType::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 EXIT_SUCCESS;
}
itk::RGBPixel
Represent Red, Green and Blue components for color images.
Definition: itkRGBPixel.h:58
itkImageToHistogramFilter.h
itkRGBPixel.h
itkImageFileReader.h
itk::GTest::TypedefsAndConstructors::Dimension2::SizeType
ImageBaseType::SizeType SizeType
Definition: itkGTestTypedefsAndConstructors.h:49
itkImage.h
itk::ImageFileReader
Data source that reads image data from a single file.
Definition: itkImageFileReader.h:75
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:88
itk::Statistics::ImageToHistogramFilter
This class generates a histogram from an image.
Definition: itkImageToHistogramFilter.h:50
itk::GTest::TypedefsAndConstructors::Dimension2::Dimension
constexpr unsigned int Dimension
Definition: itkGTestTypedefsAndConstructors.h:44