Edge Impulse Docs

Edge Impulse Documentation

Welcome to the Edge Impulse documentation. You'll find comprehensive guides and documentation to help you start working with Edge Impulse as quickly as possible, as well as support if you get stuck. Let's jump right in!

Through WebAssembly (Node.js)

Impulses can be deployed as a WebAssembly library. This packages all your signal processing blocks, configuration and learning blocks up into a single package. You can include this package in web pages, or as part of your Node.js application. This allows you to run your impulse locally, without any compilation. In this tutorial you'll export an impulse, and build a Node.js application to classify sensor data. You can also load this library from a web page, see Through WebAssembly (browser).

Prerequisites

Make sure you followed the Continuous motion recognition tutorial, and have a trained impulse. Also install the following software:

  • Node.js - to build the application.

Deploying your impulse

Head over to your Edge Impulse project, and go to Deployment. From here you can create the full library which contains the impulse and all external required libraries. Select WebAssembly and then click Build to create the library. Download and unzip the .zip file.

Then, create a new file called run-impulse.js in the same folder, and add:

// Load the inferencing WebAssembly module
const Module = require('./edge-impulse-standalone');

// Classifier module
let classifierInitialized = false;
Module.onRuntimeInitialized = function() {
    classifierInitialized = true;
};

class EdgeImpulseClassifier {
    _initialized = false;

    init() {
        if (classifierInitialized === true) return Promise.resolve();

        return new Promise((resolve) => {
            Module.onRuntimeInitialized = () => {
                resolve();
                classifierInitialized = true;
            };
        });
    }

    classify(rawData, debug = false) {
        if (!classifierInitialized) throw new Error('Module is not initialized');

        const obj = this._arrayToHeap(rawData);
        let ret = Module.run_classifier(obj.buffer.byteOffset, rawData.length, debug);
        Module._free(obj.ptr);

        if (ret.result !== 0) {
            throw new Error('Classification failed (err code: ' + ret.result + ')');
        }

        let jsResult = {
            anomaly: ret.anomaly,
            results: []
        };

        for (let cx = 0; cx < ret.classification.size(); cx++) {
            let c = ret.classification.get(cx);
            jsResult.results.push({ label: c.label, value: c.value });
        }

        return jsResult;
    }

    _arrayToHeap(data) {
        let typedArray = new Float32Array(data);
        let numBytes = typedArray.length * typedArray.BYTES_PER_ELEMENT;
        let ptr = Module._malloc(numBytes);
        let heapBytes = new Uint8Array(Module.HEAPU8.buffer, ptr, numBytes);
        heapBytes.set(new Uint8Array(typedArray.buffer));
        return { ptr: ptr, buffer: heapBytes };
    }
}

if (!process.argv[2]) {
    return console.error('Requires one parameter (a comma-separated list of raw features)');
}

// Initialize the classifier, and invoke with the argument passed in
let classifier = new EdgeImpulseClassifier();
classifier.init().then(() => {
    let result = classifier.classify(process.argv[2].trim().split(',').map(n => Number(n)));

    console.log(result);
}).catch(err => {
    console.error('Failed to initialize classifier', err);
});

Running the impulse

With the project ready it's time to verify that the application works. Head back to the studio and click on Live classification. Then load a validation sample, and click on a row under 'Detailed result'.

Selecting the row with timestamp '320' under 'Detailed result'.

To verify that the local application classifies the same, we need the raw features for this timestamp. To do so click on the 'Copy to clipboard' button next to 'Raw features'. This will copy the raw values from this validation file, before any signal processing or inferencing happened.

Copying the raw features

Then invoke the local application by passing these features as an argument to the application. Open a terminal or command prompt and run:

node run-impulse.js "-19.8800, -0.6900, 8.2300, -17.6600, -1.1300, 5.9700, ..."

This will run the signal processing pipeline, and then classify the output:

{
  anomaly: 0.133557,
  results: [
    { label: 'idle', value: 0.015319 },
    { label: 'snake', value: 0.000444 },
    { label: 'updown', value: 0.006182 },
    { label: 'wave', value: 0.978056 }
  ]
}

Which matches the values we just saw in the studio. You now have your impulse running locally!

Updated 26 days ago

Through WebAssembly (Node.js)


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