Image Prediction on tfjs-node (with model made by Teachable Machine Image)

image-predict-on-tfjs-node.js

const tf = require('@tensorflow/tfjs-node');
const Jimp = require('jimp');

// Directory path for model files (model.json, metadata.json, weights.bin)
// NOTE: It can be obtained from [Export Model] -> [Tensorflow.js] -> [Download my model]
//        on https://teachablemachine.withgoogle.com/train/image
const MODEL_DIR_PATH = `${__dirname}`;

// Path for image file to predict class
const IMAGE_FILE_PATH = `${__dirname}/example.jpg`;

(async () => {

  const labels = require(`${MODEL_DIR_PATH}/metadata.json`).labels;

  const model = await tf.loadLayersModel(`file://${MODEL_DIR_PATH}/model.json`);
  model.summary();

  const image = await Jimp.read(IMAGE_FILE_PATH);
  image.cover(224, 224, Jimp.HORIZONTAL_ALIGN_CENTER | Jimp.VERTICAL_ALIGN_MIDDLE);

  const NUM_OF_CHANNELS = 3;
  let values = new Float32Array(224 * 224 * NUM_OF_CHANNELS);

  let i = 0;
  image.scan(0, 0, image.bitmap.width, image.bitmap.height, (x, y, idx) => {
    const pixel = Jimp.intToRGBA(image.getPixelColor(x, y));
    pixel.r = pixel.r / 127.0 - 1;
    pixel.g = pixel.g / 127.0 - 1;
    pixel.b = pixel.b / 127.0 - 1;
    pixel.a = pixel.a / 127.0 - 1;
    values[i * NUM_OF_CHANNELS + 0] = pixel.r;
    values[i * NUM_OF_CHANNELS + 1] = pixel.g;
    values[i * NUM_OF_CHANNELS + 2] = pixel.b;
    i++;
  });

  const outShape = [224, 224, NUM_OF_CHANNELS];
  let img_tensor = tf.tensor3d(values, outShape, 'float32');
  img_tensor = img_tensor.expandDims(0);

  const predictions = await model.predict(img_tensor).dataSync();

  for (let i = 0; i < predictions.length; i++) {
    const label = labels[i];
    const probability = predictions[i];
    console.log(`${label}: ${probability}`);
  }

})();

package.json

{
  "name": "image-predict-on-tfjs-node",
  "description": "Prediction using tfjs-node (with model made by Teachable Machine Image)",
  "scripts": {
    "start": "node image-predict-on-tfjs-node.js"
  },
  "engines": {
    "node": "14"
  },
  "dependencies": {
    "jimp": "^0.12.1",
    "@tensorflow/tfjs-node": "^1.3.1",
  },
  "devDependencies": {
  },
  "private": true
}

I wrote this code with reference to the followings. If there are any mistakes, please let me know.
https://github.com/googlecreativelab/teachablemachine-community/blob/e429499338b69c87c3338d4735299eccb40b8c14/snippets/markdown/image/tensorflow/keras.md

Thanks a million! I've been searching for this solution for a week! Hurrah!! It is the best solution that I have found

Great code sample, thank you!

Hello,
Could you please explain what are lines 19 to 40 for, and if there's any simpler way to get the result of an image ?
Thanks