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');

// Path for model file
// NOTE: It can be obtained from [Export Model] -> [Tensorflow.js] -> [Download my model]
//        on https://teachablemachine.withgoogle.com/train/image
const MODEL_FILE = `${__dirname}/model.json`;

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

(async () => {

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

  const image = await Jimp.read(`${IMAGE_FILE}`);
  image.cover(224, 224, Jimp.HORIZONTAL_ALIGN_CENTER | Jimp.VERTICAL_ALIGN_MIDDLE);

  image.scan(0, 0, image.bitmap.width, image.bitmap.height, (x, y, idx) => {
    const pixel = Jimp.intToRGBA(image.getPixelColor(x, y));
    pixel.r = Math.max(0.0, pixel.r / 127.0 - 1);
    pixel.g = Math.max(0.0, pixel.g / 127.0 - 1);
    pixel.b = Math.max(0.0, pixel.b / 127.0 - 1);
    pixel.a = Math.max(0.0, pixel.a / 127.0 - 1);
    image.setPixelColor(Jimp.rgbaToInt(pixel.r, pixel.g, pixel.b, pixel.a), x, y);
  });

  const buffer = await image.getBufferAsync(Jimp.MIME_JPEG);

  let img_tensor = tf.node.decodeImage(new Uint8Array(buffer), 3);
  img_tensor = img_tensor.expandDims(0);

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

})();

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
}