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Install the Library

Kinetix ML should be installed as a node module using the command below.

yarn add kml-pipe-ts

There is a known issue with Tensorflow JS dependencies when installing using the npm command so please use yarn. They are for the most part interoperable.

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Initialization

First start by importing the module.

import { KMLPipeline } from 'kml-pipe-ts';

Create the pipeline object using your API Key and project name.

var pipeline = new KMLPipeline("[Your Project Name]", "[Your API Key]");

Initialize the pipeline.

await pipeline.initialize();

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Execution

To execute the pipeline you need to run the execute method with the required input data.

await pipeline.execute([]) // the [] should be replaced with your list of inputs

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Webcam Inference

The pipeline will need a data source for each of its inputs. A common input is image data for nodes such as PoseDetection2D. For these nodes you should first create a video source element.

<video id="webcam" autoplay />

Now we need to make a function that we can call for every frame

let outputs = []
let processing = false
async function runInference() {
  if (!processing) {
      processing = true;
      outputs = await pipe.execute([videoSource]);
      processing = false;
    }
  document.getElementById("webcam").requestVideoFrameCallback(runInference); // run inference on every frame
};

Then, to use your webcam, run the following function which will ask the user for webcam permission, start playing the video, and start inference.

async function startWebcam() {
    const stream = await navigator.mediaDevices.getUserMedia({ video: true });
    document.getElementById("webcam").srcObject = stream;
    await document.getElementById("webcam").play();
    document.getElementById("webcam").requestVideoFrameCallback(runInference);
};