Using TensorFlow.js with MobileNet models for image classification on Node.js

README.md

USAGE:

$ node mobilenet-node.js ./model image.jpg

mobilenet-node.js

const tf = require('@tensorflow/tfjs')
const mobilenet = require('@tensorflow-models/mobilenet');
require('@tensorflow/tfjs-node')

const fs = require('fs');
const jpeg = require('jpeg-js');

const NUMBER_OF_CHANNELS = 3

const readImage = path => {
  const buf = fs.readFileSync(path)
  const pixels = jpeg.decode(buf, true)
  return pixels
}

const imageByteArray = (image, numChannels) => {
  const pixels = image.data
  const numPixels = image.width * image.height;
  const values = new Int32Array(numPixels * numChannels);

  for (let i = 0; i < numPixels; i++) {
    for (let channel = 0; channel < numChannels; ++channel) {
      values[i * numChannels + channel] = pixels[i * 4 + channel];
    }
  }

  return values
}

const imageToInput = (image, numChannels) => {
  const values = imageByteArray(image, numChannels)
  const outShape = [image.height, image.width, numChannels];
  const input = tf.tensor3d(values, outShape, 'int32');

  return input
}

const loadModel = async path => {
  const mn = new mobilenet.MobileNet("1.00", "1.00");
  mn.path = `file://${path}`
  await mn.load()
  return mn
}

const classify = async (model, path) => {
  const image = readImage(path)
  const input = imageToInput(image, NUMBER_OF_CHANNELS)

  const  mn_model = await loadModel(model)
  const predictions = await mn_model.classify(input)

  console.log('classification results:', predictions)
}

if (process.argv.length !== 4) throw new Error('incorrect arguments: node script.js <MODEL> <IMAGE_FILE>')

classify(process.argv[2], process.argv[3])

package.json

{
  "name": "tf-js",
  "version": "1.0.0",
  "main": "script.js",
  "license": "MIT",
  "dependencies": {
    "@tensorflow-models/mobilenet": "^1.0.1",
    "@tensorflow/tfjs": "^1.2.2",
    "@tensorflow/tfjs-node": "^1.2.3",
    "jpeg-js": "^0.3.5"
  }
}