Pipeline Inputs and Outputs#


This example dives deeper into itk::wasm::Pipeline image inputs and outputs and how they are handled. We will create a pipeline to smooth an image with a median filter, run the Wasm from the command line, in Node.js.

Make sure to complete the Hello Pipeline! example before you start your filtering journey.

Write the code#

First, let’s create a new directory to house our project.

mkdir inputs-outputs
cd inputs-outputs

Let’s write some code! Populate inputs-outputs.cxx with the headers we need:

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

#include "itkImage.h"
#include "itkMedianImageFilter.h"

The itkImage.h header is ITK’s standard n-dimensional image data structure and the itkMedianImageFilter.h also comes from ITK.

The itkPipeline.h, itkInputImage.h, and itkOutputImage.h headers come from the itk-wasm WebAssemblyInterface ITK module. These will help process arguments, injest input images, and produce output images, respectively.

Next, create a standard main C command line interface function and an itk::wasm::Pipeline:

int main(int argc, char * argv[]) {
  // Create the pipeline for parsing arguments. Provide a description.
  itk::wasm::Pipeline pipeline("median-filter", "Smooth an image with a median filter", argc, argv);

  return EXIT_SUCCESS;

Add options to the pipeline that define our inputs, outputs, and processing parameters.

  itk::wasm::Pipeline pipeline("median-filter", "Smooth an image with a median filter", argc, argv);

  constexpr unsigned int Dimension = 2;
  using PixelType = unsigned char;
  using ImageType = itk::Image<PixelType, Dimension>;

  // Add a flag to specify the radius of the median filter.
  unsigned int radius = 1;
  pipeline.add_option("-r,--radius", radius, "Kernel radius in pixels");

  // Add a input image argument.
  using InputImageType = itk::wasm::InputImage<ImageType>;
  InputImageType inputImage;
  pipeline.add_option("input-image", inputImage,
    "The input image")->required()->type_name("INPUT_IMAGE");

  // Add an output image argument.
  using OutputImageType = itk::wasm::OutputImage<ImageType>;
  OutputImageType outputImage;
  pipeline.add_option("output-image", outputImage,
    "The output image")->required()->type_name("OUTPUT_IMAGE");

The inputImage variable is populated from the filesystem if built as a native executable or a WASI binary run from the command line. When running in the browser or in a wrapped language, inputImage is read from WebAssembly memory without file IO.

When the program completes, outputImage is written to the filesystem if built as a native executable or a WASI binary run from the command line. When running in the browser or in a wrapped language, outputImage is read from WebAssembly memory without file IO.

Parse the command line arguments with the ITK_WASM_PARSE macro:

  pipeline.add_option("output-image", outputImage,
    "The output image")->required()->type_name("OUTPUT_IMAGE");


The -h and --help flags are automatically generated from pipeline arguments to print usage information.

inputs-outputs help

Finally, process our data:

  using FilterType = itk::MedianImageFilter< ImageType, ImageType >;
  auto filter = FilterType::New();

Set the output image before the program completes:


  return EXIT_SUCCESS;

Next, provide a CMake build configuration at CMakeLists.txt:

cmake_minimum_required(VERSION 3.16)

# Use C++17 or newer with itk-wasm

# We always want to build against the WebAssemblyInterface module.
  ITKSmoothing # provides itkMedianImageFilter.h
# WASI or native binaries
  # WebAssemblyInterface supports the .iwi, .iwi.cbor itk-wasm format.
  # We can list other ITK IO modules to build against to support other
  # formats when building native executable or WASI WebAssembly.
  # However, this will bloat the size of the WASI WebAssembly binary, so
  # add them judiciously.
    # ITKImageIO # Adds support for all available image IO modules
find_package(ITK REQUIRED
  COMPONENTS ${itk_components}

add_executable(inputs-outputs inputs-outputs.cxx)
target_link_libraries(inputs-outputs PUBLIC ${ITK_LIBRARIES})

Create WebAssembly binary#

Build the WASI binary:

npx itk-wasm -b wasi-build -i itkwasm/wasi build

Try running on an example image.

Run WebAssembly binary#

npx itk-wasm -b wasi-build run inputs-outputs.wasi.wasm -- -- --radius 2 cthead1.png smoothed.png

The input image:

input image

has been smoothed:


Run in Node.js#

To run in the Node.js JavaScript environment, first build with the Emscripten toolchain.

npx itk-wasm build

In our Node.js JavaScript script, we will load the file with dedicated image IO WebAssembly modules. These are provided by the itk-image-io package.

npm install -g itk-image-io

Next, let’s create a script to call our pipeline, index.mjs. Start the script with our imports.

import path from 'path'
import { runPipelineNode,
         InterfaceTypes } from 'itk-wasm'

To switch from filesystem to WebAssembly memory IO, pass the --memory-io flag. This flag is supported by all itk::wasm::Pipeline’s. Skip the two node ./index.mjs arguments from Node invocation.

const args = ['--memory-io'].concat(process.argv.slice(2))

When using memory IO, interface types, such as images, are specified in the pipeline arguments with integer strings. Inputs and output integer identifiers both start counting from zero.

// Assume we have input and output images as the last arguments
const inputFile = args[args.length-2]
const inputImage = await readImageLocalFile(inputFile)
// '0' is the index of the first input corresponding to the `inputs` array below
args[args.length-2] = '0'

const outputFile = args[args.length-1]
// '0' is the index of the first output corresponding to the `desiredOutputs` below
args[args.length-1] = '0'

Input images can be read with readImageLocalFile. We specify the type and value of the pipeline input interface types. With pipeline outputs, only the type is specified.

const inputs = [
  { type: InterfaceTypes.Image, data: inputImage }
const desiredOutputs = [
  { type: InterfaceTypes.Image }

Run the pipeline.

// Path to the Emscripten WebAssembly module without extensions
const pipelinePath = path.resolve('emscripten-build', 'inputs-outputs')
const { stdout, stderr, outputs } = await runPipelineNode(pipelinePath, args, desiredOutputs, inputs)

And handle the outputs.

await writeImageLocalFile(outputs[0].data, outputFile)

Invoke the script.

npx node ./index.mjs --radius 2 ./cthead1.png smoothed.png

Congratulations! You just executed a C++ pipeline capable of processsing a scientific image in Node.js. πŸŽ‰