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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
 *
 *         https://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.
 *
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#include "itkRawImageIO.h"
#include "itkNumericSeriesFileNames.h"
#include "itkImageSeriesReader.h"
#include "itkImageFileWriter.h"
#include "itkSimpleFilterWatcher.h"
#include "itkShrinkImageFilter.h"
#include "itkComposeImageFilter.h"
#include "itkExtractImageFilter.h"
#include "itkShrinkImageFilter.h"
#include "itkMeanImageFilter.h"

// The Insight Toolkit was originally motivated by a need for software
// tools to segment and register the National Library of Medicine’s
// Visible Human Project data sets. The data is freely available
// through NLM’s website [3]. The original Visible Male cryosectional
// images are non-interlaced 24-bit RGB pixels with a resolution of
// 2048x1216 pixels by 1871 slices with a physical spacing of
// approximately 0.33 mm in slice and 1.0 mm between slices. These
// dimensions results in about 13 gigabytes of data, which is an
// appropriate size to demonstrate streaming. The following are two
// examples of streaming which shows all three IO classes capable of
// streaming along with the two types of streaming supported by the
// writer.
//
// A coronal slice is a classic view of the Visible Male. The
// following is an example that reads the entire raw dataset and
// generates that classic image:

int
main(int argc, char * argv[])
{
  if (argc < 3)
  {
    std::cerr << "Missing Parameters " << std::endl;
    std::cerr << "Usage: " << argv[0];
    std::cerr << " visibleHumanPath  outputImageFile" << std::endl;
    return EXIT_FAILURE;
  }

  std::string visibleHumanPath = argv[1];
  std::string outputImageFile = argv[2];

  using RGBPixelType = itk::RGBPixel<unsigned char>;
  using PixelType = unsigned char;
  using ImageType = itk::Image<PixelType, 3>;
  using RGB3DImageType = itk::Image<RGBPixelType, 3>;
  using RGB2DImageType = itk::Image<RGBPixelType, 2>;

  // generate the names of the decompressed Visible Male images
  using NameGeneratorType = itk::NumericSeriesFileNames;
  auto nameGenerator = NameGeneratorType::New();
  nameGenerator->SetSeriesFormat(visibleHumanPath + "a_vm%04d.raw");
  nameGenerator->SetStartIndex(1001);
  nameGenerator->SetEndIndex(2878);
  nameGenerator->SetIncrementIndex(1);

  // create an ImageIO for the red channel
  using ImageIOType = itk::RawImageIO<PixelType, 2>;
  auto rimageio = ImageIOType::New();
  rimageio->SetDimensions(0, 2048);
  rimageio->SetDimensions(1, 1216);
  rimageio->SetSpacing(0, .33);
  rimageio->SetSpacing(1, .33);
  rimageio->SetHeaderSize(rimageio->GetImageSizeInPixels() * 0);


  // create an ImageIO for the green channel
  auto gimageio = ImageIOType::New();
  gimageio->SetDimensions(0, 2048);
  gimageio->SetDimensions(1, 1216);
  gimageio->SetSpacing(0, .33);
  gimageio->SetSpacing(1, .33);
  gimageio->SetHeaderSize(gimageio->GetImageSizeInPixels() * 1);


  // create an ImageIO for the blue channel
  auto bimageio = ImageIOType::New();
  bimageio->SetDimensions(0, 2048);
  bimageio->SetDimensions(1, 1216);
  bimageio->SetSpacing(0, .33);
  bimageio->SetSpacing(1, .33);
  bimageio->SetHeaderSize(bimageio->GetImageSizeInPixels() * 2);

  using SeriesReaderType = itk::ImageSeriesReader<ImageType>;
  auto rreader = SeriesReaderType::New();
  rreader->SetFileNames(nameGenerator->GetFileNames());
  rreader->SetImageIO(rimageio);
  // the z-spacing will default to be correctly 1mm

  auto greader = SeriesReaderType::New();
  greader->SetFileNames(nameGenerator->GetFileNames());
  greader->SetImageIO(gimageio);

  auto breader = SeriesReaderType::New();
  breader->SetFileNames(nameGenerator->GetFileNames());
  breader->SetImageIO(bimageio);

  using ComposeRGBFilterType =
    itk::ComposeImageFilter<ImageType, RGB3DImageType>;
  auto composeRGB = ComposeRGBFilterType::New();
  composeRGB->SetInput1(rreader->GetOutput());
  composeRGB->SetInput2(greader->GetOutput());
  composeRGB->SetInput3(breader->GetOutput());

  // this filter is needed if square pixels are needed
  //   const int xyShrinkFactor = 3;
  //   using ShrinkImageFilterType = itk::ShrinkImageFilter<  RGB3DImageType,
  //   RGB3DImageType >; auto shrinker =
  //   ShrinkImageFilterType::New(); shrinker->SetInput(
  //   composeRGB->GetOutput() ); shrinker->SetShrinkFactors(  xyShrinkFactor
  //   ); shrinker->SetShrinkFactor( 2, 1 );

  // update output information to know propagate the size of the largest
  // possible region
  composeRGB->UpdateOutputInformation();
  RGB3DImageType::RegionType coronalSlice =
    composeRGB->GetOutput()->GetLargestPossibleRegion();
  coronalSlice.SetIndex(1, 448);
  coronalSlice.SetSize(1, 0);

  // another interesting view
  //   RGB3DImageType::RegionType sagittalSlice =
  //   shrinker->GetOutput()->GetLargestPossibleRegion();
  //   sagittalSlice.SetIndex( 0, 1024 ); sagittalSlice.SetSize( 0, 0 );

  // create a 2D coronal slice from the volume
  using ExtractFilterType =
    itk::ExtractImageFilter<RGB3DImageType, RGB2DImageType>;
  auto extract = ExtractFilterType::New();
  // Note on direction cosines: Because our plane is in the xz-plane,
  // the default submatrix would be invalid, so we must use the identity
  extract->SetDirectionCollapseToIdentity();
  extract->InPlaceOn();
  extract->SetInput(composeRGB->GetOutput());
  extract->SetExtractionRegion(coronalSlice);


  using ImageWriterType = itk::ImageFileWriter<RGB2DImageType>;
  auto writer = ImageWriterType::New();
  writer->SetFileName(outputImageFile);

  // this line is a request for the number of regions
  // the image will be broken into
  writer->SetNumberOfStreamDivisions(200);
  writer->SetInput(extract->GetOutput());

  itk::SimpleFilterWatcher watcher1(writer, "stream writing");


  try
  {
    // update by streaming
    writer->Update();
  }
  catch (const itk::ExceptionObject & err)
  {
    std::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
  }

  // This example creates a RawImageIO and ImageSeriesReader for each
  // color channel in the data. Notice that there are no special methods
  // that are needed to enable streaming; it will just respond correctly
  // to requests from the pipeline. In the ComposeImageFilter, the
  // channels are composited into a single color image. Then the
  // information is updated to initialize the coronal slice region to be
  // extracted. The final filter, ImageFileWriter, writes out the file
  // as a Meta Image type, which fully supports IO streaming.
  //
  // The most interesting aspect of this example is not the filters
  // used, but how ITK’s pipeline manages its execution. The final
  // output image is 2048 by 1878 pixels. The ImageFileWriter breaks
  // this 2D image into 200 separate regions, which have the size of
  // about 2048 by 10 pixels; each region is streamed and processes
  // through the pipeline. The writer makes 200 calls to its ImageIO
  // object to write the individual regions. The extractor converts this
  // 2D region into a 3D region of 2048 by 1 by 10 pixels, which is
  // propagated to the ImageSeriesReader. Then the reader reads the
  // entire slice, but only copies the requested sub-region to its
  // output. This pipeline is so efficient because very little data is
  // actually processed at any one stage of the pipeline due to
  // streaming IO.


  return EXIT_SUCCESS;
}