script.js

global.fetch = require('node-fetch');
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, 1);
  //mn.path = `file://${path}`
  mn.path = `https://storage.googleapis.com/tfjs-models/tfjs/mobilenet_v1_1.0_224/model.json`
  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])