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Viewing file:     data.js (2.27 KB)      -rw-r--r--

const tf = require("@tensorflow/tfjs-node-gpu");
const fs = require("fs");
const path = require("path");

const TRAIN_IMAGES_DIR = "./data/train";
const TEST_IMAGES_DIR = "./data/test";

function loadImages(dataDir) {
  const images = [];
  const labels = [];

  var files = fs.readdirSync(dataDir);

  for (let i = 0; i < files.length; i++) {
    console.log(files[i]);
    if (!files[i].toLocaleLowerCase().endsWith(".png")) {
      continue;
    }

    var filePath = path.join(dataDir, files[i]);

    var buffer = fs.readFileSync(filePath);
    var imageTensor = tf.node
      .decodeImage(buffer)
      .resizeNearestNeighbor([96, 96])
      .toFloat()
      .div(tf.scalar(255.0))
      .expandDims();
    images.push(imageTensor);

    // here we assume every folder has file with name n_flower.png
    var daisy = files[i].toLocaleLowerCase().endsWith("daisy.png");
    var dandelion = files[i].toLocaleLowerCase().endsWith("dandelion.png");
    var rose = files[i].toLocaleLowerCase().endsWith("rose.png");
    var sunflower = files[i].toLocaleLowerCase().endsWith("sunflower.png");
    var tulip = files[i].toLocaleLowerCase().endsWith("tulip.png");

    if (daisy == true) {
      labels.push(1);
    } else if (dandelion == true) {
      labels.push(2);
    } else if (rose == true) {
      labels.push(3);
    } else if (sunflower == true) {
      labels.push(4);
    } else {
      labels.push(0);
    }
  }
  console.log("Labels are");
  console.log(labels);
  return [images, labels];
}

/** Helper class to handle loading training and test data. */
class FlowerDataset {
  constructor() {
    this.trainData = [];
    this.testData = [];
  }

  /** Loads training and test data. */
  loadData() {
    console.log("Loading images...");
    this.trainData = loadImages(TRAIN_IMAGES_DIR);
    this.testData = loadImages(TEST_IMAGES_DIR);
    console.log("Images loaded successfully.");
  }

  getTrainData() {
    return {
      images: tf.concat(this.trainData[0]),
      labels: tf.oneHot(tf.tensor1d(this.trainData[1], "int32"), 5).toFloat(), // here 5 is class
    };
  }

  getTestData() {
    return {
      images: tf.concat(this.testData[0]),
      labels: tf.oneHot(tf.tensor1d(this.testData[1], "int32"), 5).toFloat(),
    };
  }
}

module.exports = new FlowerDataset();
console.log("All done.");