STYLE: Use itk::ReadImage and itk::WriteImage in Examples/Statistics

Examples/Statistics/BayesianClassifier.cxx

@@ -75,10 +75,7 @@ main(int argc, char * argv[])
   constexpr unsigned int Dimension = 2;
   using InputPixelType = float;
   using InputImageType = itk::VectorImage<InputPixelType, Dimension>;
-  using ReaderType = itk::ImageFileReader<InputImageType>;
-
-  auto reader = ReaderType::New();
-  reader->SetFileName(membershipImageFileName);
+  const auto input = itk::ReadImage<InputImageType>(membershipImageFileName);
 
   using LabelType = unsigned char;
   using PriorType = float;
@@ -94,7 +91,7 @@ main(int argc, char * argv[])
   auto filter = ClassifierFilterType::New();
 
 
-  filter->SetInput(reader->GetOutput());
+  filter->SetInput(input);
 
   if (argv[3])
   {
@@ -130,19 +127,12 @@ main(int argc, char * argv[])
   rescaler->SetOutputMinimum(0);
   rescaler->SetOutputMaximum(255);
 
-  using WriterType = itk::ImageFileWriter<OutputImageType>;
-
-  auto writer = WriterType::New();
-  writer->SetFileName(labelMapImageFileName);
-
   //
   // Write labelmap to file
   //
-  writer->SetInput(rescaler->GetOutput());
-
   try
   {
-    writer->Update();
+    itk::WriteImage(rescaler->GetOutput(), labelMapImageFileName)
   }
   catch (const itk::ExceptionObject & excp)
   {

Examples/Statistics/BayesianClassifierInitializer.cxx

@@ -85,13 +85,9 @@ main(int argc, char * argv[])
     itk::BayesianClassifierInitializationImageFilter<ImageType>;
   auto bayesianInitializer = BayesianInitializerType::New();
 
-  using ReaderType = itk::ImageFileReader<ImageType>;
-  auto reader = ReaderType::New();
-  reader->SetFileName(argv[1]);
-
   try
   {
-    reader->Update();
+    const auto input = itk::ReadImage<ImageType>(argv[1]);
   }
   catch (const itk::ExceptionObject & excp)
   {
@@ -100,17 +96,11 @@ main(int argc, char * argv[])
     return EXIT_FAILURE;
   }
 
-  bayesianInitializer->SetInput(reader->GetOutput());
+  bayesianInitializer->SetInput(input);
   bayesianInitializer->SetNumberOfClasses(std::stoi(argv[3]));
 
   // TODO add test where we specify membership functions
 
-  using WriterType =
-    itk::ImageFileWriter<BayesianInitializerType::OutputImageType>;
-  auto writer = WriterType::New();
-  writer->SetInput(bayesianInitializer->GetOutput());
-  writer->SetFileName(argv[2]);
-
   try
   {
     bayesianInitializer->Update();
@@ -124,7 +114,7 @@ main(int argc, char * argv[])
 
   try
   {
-    writer->Update();
+    itk::WriteImage(bayesianInitializer->GetOutput(), argv[2])
   }
   catch (const itk::ExceptionObject & excp)
   {

Examples/Statistics/ImageEntropy1.cxx

@@ -84,15 +84,9 @@ main(int argc, char * argv[])
   using ImageType = itk::Image<PixelType, Dimension>;
   // Software Guide : EndCodeSnippet
 
-  using ReaderType = itk::ImageFileReader<ImageType>;
-
-  auto reader = ReaderType::New();
-
-  reader->SetFileName(argv[1]);
-
   try
   {
-    reader->Update();
+    const auto input = itk::ReadImage<ImageType>(argv[1]);
   }
   catch (const itk::ExceptionObject & excp)
   {
@@ -144,7 +138,7 @@ main(int argc, char * argv[])
   // Software Guide : EndLatex
 
   // Software Guide : BeginCodeSnippet
-  histogramGenerator->SetInput(reader->GetOutput());
+  histogramGenerator->SetInput(input);
 
   histogramGenerator->Compute();
   // Software Guide : EndCodeSnippet

Examples/Statistics/ImageHistogram1.cxx

@@ -84,17 +84,24 @@ main(int argc, char * argv[])
 
   // Software Guide : BeginLatex
   //
-  // Using the same image type we instantiate the type of the image reader
+  // Using the same image type we read the image file
   // that will provide the image source for our example.
+  // 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
-  using ReaderType = itk::ImageFileReader<ImageType>;
-
-  auto reader = ReaderType::New();
-
-  reader->SetFileName(argv[1]);
+  try
+  {
+    const auto input = itk::ReadImage<ImageType>(argv[1]);
+  }
+  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
@@ -104,7 +111,7 @@ main(int argc, char * argv[])
   // 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.
+  // specify the input to the adaptor.
   //
   // \index{itk::Statistics::Scalar\-Image\-To\-List\-Adaptor!instantiation}
   //
@@ -115,31 +122,7 @@ main(int argc, char * argv[])
 
   auto 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;
-  }
+  adaptor->SetImage(input);
   // Software Guide : EndCodeSnippet
 
   // Software Guide : BeginLatex

Examples/Statistics/ImageHistogram2.cxx

@@ -74,16 +74,9 @@ main(int argc, char * argv[])
   using ImageType = itk::Image<PixelType, Dimension>;
   // Software Guide : EndCodeSnippet
 
-
-  using ReaderType = itk::ImageFileReader<ImageType>;
-
-  auto reader = ReaderType::New();
-
-  reader->SetFileName(argv[1]);
-
   try
   {
-    reader->Update();
+    const auto input = itk::ReadImage<ImageType>(argv[1]);
   }
   catch (const itk::ExceptionObject & excp)
   {
@@ -119,7 +112,7 @@ main(int argc, char * argv[])
   // Software Guide : EndLatex
 
   // Software Guide : BeginCodeSnippet
-  histogramGenerator->SetInput(reader->GetOutput());
+  histogramGenerator->SetInput(input);
   // Software Guide : EndCodeSnippet
 
 

Examples/Statistics/ImageHistogram3.cxx

@@ -80,16 +80,9 @@ main(int argc, char * argv[])
   using RGBImageType = itk::Image<RGBPixelType, Dimension>;
   // Software Guide : EndCodeSnippet
 
-
-  using ReaderType = itk::ImageFileReader<RGBImageType>;
-
-  auto reader = ReaderType::New();
-
-  reader->SetFileName(argv[1]);
-
   try
   {
-    reader->Update();
+    const auto input = itk::ReadImage<RGBImageType>(argv[1]);
   }
   catch (const itk::ExceptionObject & excp)
   {
@@ -192,7 +185,7 @@ main(int argc, char * argv[])
   // Software Guide : EndLatex
 
   // Software Guide : BeginCodeSnippet
-  histogramFilter->SetInput(reader->GetOutput());
+  histogramFilter->SetInput(input);
 
   histogramFilter->Update();
   // Software Guide : EndCodeSnippet

Examples/Statistics/ImageHistogram4.cxx

@@ -89,16 +89,9 @@ main(int argc, char * argv[])
   using RGBImageType = itk::Image<RGBPixelType, Dimension>;
   // Software Guide : EndCodeSnippet
 
-
-  using ReaderType = itk::ImageFileReader<RGBImageType>;
-
-  auto reader = ReaderType::New();
-
-  reader->SetFileName(argv[1]);
-
   try
   {
-    reader->Update();
+    const auto input = itk::ReadImage<RGBImageType>(argv[1]);
   }
   catch (const itk::ExceptionObject & excp)
   {
@@ -187,7 +180,7 @@ main(int argc, char * argv[])
   // Software Guide : EndLatex
 
   // Software Guide : BeginCodeSnippet
-  histogramFilter->SetInput(reader->GetOutput());
+  histogramFilter->SetInput(input);
   // Software Guide : EndCodeSnippet
 
 

Examples/Statistics/ImageMutualInformation1.cxx

@@ -114,19 +114,14 @@ main(int argc, char * argv[])
 
   // Software Guide : BeginLatex
   //
-  // Using the image type we proceed to instantiate the readers for both input
-  // images. Then, we take their filenames from the command line arguments.
+  // Using the image type we proceed to read in the input images
+  // taking their filenames from the command line arguments.
   //
   // Software Guide : EndLatex
 
   // Software Guide : BeginCodeSnippet
-  using ReaderType = itk::ImageFileReader<ImageType>;
-
-  auto reader1 = ReaderType::New();
-  auto reader2 = ReaderType::New();
-
-  reader1->SetFileName(argv[1]);
-  reader2->SetFileName(argv[2]);
+  const auto input1 = itk::ReadImage<ImageType>(argv[1]);
+  const auto input2 = itk::ReadImage<ImageType>(argv[2]);
   // Software Guide : EndCodeSnippet
 
 
@@ -142,8 +137,8 @@ main(int argc, char * argv[])
 
   auto joinFilter = JoinFilterType::New();
 
-  joinFilter->SetInput1(reader1->GetOutput());
-  joinFilter->SetInput2(reader2->GetOutput());
+  joinFilter->SetInput1(input1);
+  joinFilter->SetInput2(input2);
   // Software Guide : EndCodeSnippet
 
 

Examples/Statistics/ScalarImageKmeansClassifier.cxx

@@ -61,9 +61,7 @@ main(int argc, char * argv[])
   // Software Guide : BeginLatex
   //
   // First we define the pixel type and dimension of the image that we intend
-  // to classify. With this image type we can also declare the
-  // \doxygen{ImageFileReader} needed for reading the input image, create one
-  // and set its input filename.
+  // to classify. With this image type we can also read the input image.
   //
   // Software Guide : EndLatex
 
@@ -72,10 +70,7 @@ main(int argc, char * argv[])
   constexpr unsigned int Dimension = 2;
 
   using ImageType = itk::Image<PixelType, Dimension>;
-
-  using ReaderType = itk::ImageFileReader<ImageType>;
-  auto reader = ReaderType::New();
-  reader->SetFileName(inputImageFileName);
+  const auto input = itk::ReadImage<ImageType>(inputImageFileName);
   // Software Guide : EndCodeSnippet
 
 
@@ -92,7 +87,7 @@ main(int argc, char * argv[])
 
   auto kmeansFilter = KMeansFilterType::New();
 
-  kmeansFilter->SetInput(reader->GetOutput());
+  kmeansFilter->SetInput(input);
 
   const unsigned int numberOfInitialClasses = std::stoi(argv[4]);
   // Software Guide : EndCodeSnippet
@@ -162,39 +157,17 @@ main(int argc, char * argv[])
   // The \doxygen{ScalarImageKmeansImageFilter} is predefined for producing an
   // 8 bits scalar image as output. This output image contains labels
   // associated to each one of the classes in the K-Means algorithm. In the
-  // following lines we use the \code{OutputImageType} in order to instantiate
-  // the type of a \doxygen{ImageFileWriter}. Then create one, and connect it
-  // to the output of the classification filter.
-  //
-  // Software Guide : EndLatex
-
-  // Software Guide : BeginCodeSnippet
-  using OutputImageType = KMeansFilterType::OutputImageType;
-
-  using WriterType = itk::ImageFileWriter<OutputImageType>;
-
-  auto writer = WriterType::New();
-
-  writer->SetInput(kmeansFilter->GetOutput());
-
-  writer->SetFileName(outputImageFileName);
-  // Software Guide : EndCodeSnippet
-
-
-  // Software Guide : BeginLatex
-  //
-  // We are now ready for triggering the execution of the pipeline. This is
-  // done by simply invoking the \code{Update()} method in the writer. This
-  // call will propagate the update request to the reader and then to the
-  // classifier.
+  // following lines we use the \code{OutputImageType}to write the output of
+  // the classification filter to file.
   //
   // Software Guide : EndLatex
 
 
   // Software Guide : BeginCodeSnippet
+  using OutputImageType = KMeansFilterType::OutputImageType;
   try
   {
-    writer->Update();
+    itk::WriteImage(kmeansFilter->GetOutput(), outputImageFileName)
   }
   catch (const itk::ExceptionObject & excp)
   {