ITK  5.3.0
Insight Toolkit
Examples/Statistics/ImageHistogram1.cxx
/*=========================================================================
*
* Copyright NumFOCUS
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* 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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*
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* 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
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*=========================================================================*/
// 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 EXIT_FAILURE;
}
// 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
using PixelType = unsigned char;
constexpr 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
using ReaderType = itk::ImageFileReader<ImageType>;
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 (const itk::ExceptionObject & excp)
{
std::cerr << "Problem reading image file : " << argv[1] << std::endl;
std::cerr << excp << std::endl;
return EXIT_FAILURE;
}
// 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
using HistogramMeasurementType = PixelType;
using 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
constexpr 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 EXIT_SUCCESS;
}
itk::Statistics::ImageToListSampleAdaptor
This class provides ListSample interface to ITK Image.
Definition: itkImageToListSampleAdaptor.h:54
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::Statistics::Histogram
This class stores measurement vectors in the context of n-dimensional histogram.
Definition: itkHistogram.h:77
itkSampleToHistogramFilter.h
itkHistogram.h
itk::Statistics::SampleToHistogramFilter
Computes the Histogram corresponding to a Sample.
Definition: itkSampleToHistogramFilter.h:68
itk::Image
Templated n-dimensional image class.
Definition: itkImage.h:88
itkImageToListSampleAdaptor.h
itk::GTest::TypedefsAndConstructors::Dimension2::Dimension
constexpr unsigned int Dimension
Definition: itkGTestTypedefsAndConstructors.h:44