Node Emscripten for watershed segmentation, unexpected pixel type



I have following cxx code which does watershed segmentation, wasi is working now, but the node, inspect debugging, etc. not working, I have build node-emscripten debug, and node-inspect, node-emscripten-debug.

code >>

//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {VisibleWomanEyeSlice.png}
//    OUTPUTS: {WatershedSegmentation1Output1.png}
//    ARGUMENTS:    2 10 0 0.05 1
//  Software Guide : EndCommandLineArgs
//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {VisibleWomanEyeSlice.png}
//    OUTPUTS: {WatershedSegmentation1Output2.png}
//    ARGUMENTS:    2 10 0.001 0.15 0
//  Software Guide : EndCommandLineArgs

// Software Guide : BeginLatex
// The following example illustrates how to preprocess and segment images
// using the \doxygen{WatershedImageFilter}. Note that the care with which
// the data are preprocessed will greatly affect the quality of your result.
// Typically, the best results are obtained by preprocessing the original
// image with an edge-preserving diffusion filter, such as one of the
// anisotropic diffusion filters, or the bilateral image filter.  As
// noted in Section~\ref{sec:AboutWatersheds}, the height function used as
// input should be created such that higher positive values correspond to
// object boundaries.  A suitable height function for many applications can
// be generated as the gradient magnitude of the image to be segmented.
// The \doxygen{VectorGradientMagnitudeAnisotropicDiffusionImageFilter} class
// is used to smooth the image and the
// \doxygen{VectorGradientMagnitudeImageFilter} is used to generate the
// height function.  We begin by including all preprocessing filter header
// files and the header file for the WatershedImageFilter.  We
// use the vector versions of these filters because the input dataset is a
// color image.
// Software Guide : EndLatex

#include "itkPipeline.h"
#include "itkInputImage.h"
#include "itkOutputImage.h"

#include "itkRGBPixel.h"
#include "itkImage.h"

#include <iostream>

// Software Guide : BeginCodeSnippet
#include "itkVectorGradientAnisotropicDiffusionImageFilter.h"
#include "itkVectorGradientMagnitudeImageFilter.h"
#include "itkWatershedImageFilter.h"
// Software Guide : EndCodeSnippet

#include "itkCastImageFilter.h"
#include "itkScalarToRGBPixelFunctor.h"
#include "itkMedianImageFilter.h"

namespace itk
/** \class myRGBPixel
 * \brief Extends RGBPixel with operator <=
 * This class overrides the <= and < operators to use Luminance as a sorting
 * value.
template <typename TComponent = unsigned short>
class myRGBPixel : public RGBPixel<TComponent>
  using Self = myRGBPixel;
  using Superclass = RGBPixel<TComponent>;

  using RGBPixel<TComponent>::operator=;

  operator<=(const Self & r) const
    return (this->GetLuminance() <= r.GetLuminance());
  operator<(const Self & r) const
    return (this->GetLuminance() < r.GetLuminance());
} // namespace itk

int main(int argc, char * argv[])

constexpr unsigned int Dimension = 2;
//sag i think 3D --- constexpr unsigned int VDimension = 3;
constexpr unsigned int VDimension = 3;

using MyPixelType = itk::myRGBPixel<unsigned char>;
using MyImageType = itk::Image<MyPixelType, Dimension>;

using RGBPixelType = itk::RGBPixel<unsigned char>;
using RGBImageType = itk::Image<RGBPixelType, Dimension>;
using VectorPixelType = itk::Vector<float, VDimension>;
using VectorImageType = itk::Image<VectorPixelType, Dimension>;
using LabeledImageType = itk::Image<itk::IdentifierType, Dimension>;
using ScalarImageType = itk::Image<float, Dimension>;

using InputImageType = itk::wasm::InputImage<MyImageType>;
using OutputImageType = itk::wasm::OutputImage<RGBImageType>;

    //initialization of variables
    unsigned int conductanceTerm = 2;
    unsigned int diffusionIterations = 10;
    double lowerThreshold = 0.0;
    double outputScaleLevel = 0.05;
    unsigned int gradientMode = 1;

    itk::wasm::Pipeline pipeline("Segmentation-watershed", "Segment input image using Watershed method itk::wasm::pipeline", argc, argv);

    //Add input image argument
    InputImageType inputImage;
    pipeline.add_option("InputImage", inputImage, "the input file name")->required()->type_name("INPUT_IMAGE");

    //Add output image argument
    OutputImageType outputImage;
    pipeline.add_option("OuptputImage", outputImage, "the output file name")->required()->type_name("OUTPUT_IMAGE");

    //Add conductanceTerm value argument
    pipeline.add_option("-c,--conductanceTerm", conductanceTerm, "the conductanceTerm value");

    //Add diffusion iterations value
    pipeline.add_option("-d,--diffusionIterations", diffusionIterations, "the diffusionIterations value");

    pipeline.add_option("-l,--lowerThreshold", lowerThreshold, "the lowerThreshold value");

    //Add outputScaleLevel value
    pipeline.add_option("-o,--outputScaleLevel", outputScaleLevel, "the outputScaleLevel value");

    //Add gradientMode value
    pipeline.add_option("-g,--gradientMode", gradientMode, "the gradientMode value");

    //parse the pipeline input

    // Create and setup a median filter
    using FilterType = itk::MedianImageFilter<MyImageType, MyImageType>;
    auto medianFilter = FilterType::New();

    FilterType::InputSizeType radius;


    // Cast the custom myRBGPixel's to RGBPixel's

    using CastType = itk::CastImageFilter<MyImageType,RGBImageType>;
    auto cast = CastType::New();

    // Software Guide : BeginCodeSnippet
    //using FileReaderType = itk::ImageFileReader<RGBImageType>;
    using CastFilterType = itk::CastImageFilter<RGBImageType, VectorImageType>;
    using DiffusionFilterType =
    using GradientMagnitudeFilterType =
    using WatershedFilterType = itk::WatershedImageFilter<ScalarImageType>;
// Software Guide : EndCodeSnippet

auto caster = CastFilterType::New();

// Software Guide : BeginLatex
  // Next we instantiate the filters and set their parameters.  The first
  // step in the image processing pipeline is diffusion of the color input
  // image using an anisotropic diffusion filter.  For this class of filters,
  // the CFL condition requires that the time step be no more than 0.25 for
  // two-dimensional images, and no more than 0.125 for three-dimensional
  // images.  The number of iterations and the conductance term will be taken
  // from the command line. See
  // Section~\ref{sec:EdgePreservingSmoothingFilters} for more information on
  // the ITK anisotropic diffusion filters.
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  auto diffusion = DiffusionFilterType::New();
  // Software Guide : EndCodeSnippet

//sag check
    // Software Guide : BeginLatex
    // The ITK gradient magnitude filter for vector-valued images can optionally
    // take several parameters.  Here we allow only enabling or disabling
    // of principal component analysis.
    // Software Guide : EndLatex

    // Software Guide : BeginCodeSnippet
    auto gradient = GradientMagnitudeFilterType::New();
    // Software Guide : BeginLatex
    // Finally we set up the watershed filter.  There are two parameters.
    // \code{Level} controls watershed depth, and \code{Threshold} controls the
    // lower thresholding of the input.  Both parameters are set as a
    // percentage (0.0 - 1.0) of the maximum depth in the input image.
    // Software Guide : EndLatex

    // Software Guide : BeginCodeSnippet
    auto watershed = WatershedFilterType::New();
    // Software Guide : EndCodeSnippet
//sag check

    // Software Guide : BeginLatex
    // The output of WatershedImageFilter is an image of unsigned long integer
    // labels, where a label denotes membership of a pixel in a particular
    // segmented region.  This format is not practical for visualization, so
    // for the purposes of this example, we will convert it to RGB pixels.  RGB
    // images have the advantage that they can be saved as a simple png file
    // and viewed using any standard image viewer software.  The
    // \subdoxygen{Functor}{ScalarToRGBPixelFunctor} class is a special
    // function object designed to hash a scalar value into an
    // \doxygen{RGBPixel}. Plugging this functor into the
    // \doxygen{UnaryFunctorImageFilter} creates an image filter which
    // converts scalar images to RGB images.
    // Software Guide : EndLatex

    // Software Guide : BeginCodeSnippet

    using ColormapFunctorType =
    itk::Functor::ScalarToRGBPixelFunctor<unsigned long>;
    using ColormapFilterType =
    auto colormapper = ColormapFilterType::New();
    // Software Guide : EndCodeSnippet

    // Software Guide : BeginLatex
    // The filters are connected into a single pipeline, with readers and
    // writers at each end.
    // Software Guide : EndLatex

    //  Software Guide : BeginCodeSnippet



    using CasterUpdateType = itk::CastImageFilter<RGBImageType, RGBImageType>;
    auto castUpdate = CasterUpdateType::New();


        std::cout << "after filter->Update();" << std::endl;
    catch (const itk::ExceptionObject & e)
        std::cerr << e << std::endl;
        return EXIT_FAILURE;

    // Software Guide : EndCodeSnippet

    return EXIT_SUCCESS;

i am getting unexpected pixel type error.

  1. WASI works fine with this code please check see if i am doing anything un-webassembly. for watersheding image.
  2. node emscripten, though, is complaling about unexpected pixel type which i cannot figure out what is it. I even commented out most of the code except the pipeline part upto the ITK_WASM_PARSE(pipeline). Any pixel types above this line could be problematic?
  3. by the way i have used itk exasmples to write the warshed segmentation. Median filter for RGB IMAGE is another one of the examples.

screen shot of the console in node.js WSL2 ubuntu-22.04 is following

I cannot step into the c++ code though?!

watersheded image WASI

I dont understand how an CT/MRI/radiology/dental/microscope image could be colored?

appreciate the help matt


Hi @sag,

You may want to convert the image to a pixel type supported by your pipeline with castImage,