//

// This software is released under the MIT License.

// https://opensource.org/licenses/MIT

​

/* ===

ml5 Example

Multiple Image classification using MobileNet and p5.js

=== */

​

// Initialize the Image Classifier method using MobileNet

let classifier;

​

let img;

let currentIndex = 0;

const allImages = [];

const predictions = [];

​

function preload() {

  classifier = ml5.imageClassifier("MobileNet");

  data = loadJSON("assets/data.json");

}

​

function setup() {

  createCanvas(400, 400);

  background(0);

  appendImages();

  loadImage(allImages[currentIndex], imageReady);

}

​

function appendImages() {

  for (i = 0; i < data.images.length; i += 1) {

    imgPath = data.images[i];

    allImages.push(`images/dataset/${imgPath}`);

  }

}

​

// When the image has been loaded,

// get a prediction for that image

function imageReady(img) {

  image(img, 0, 0);

  classifier.classify(img, gotResult);

}

​

function savePredictions() {

  predictionsJSON = {

    predictions,

  };

  saveJSON(predictionsJSON, "predictions.json");

}

​

// When we get the results

function gotResult(err, results) {

  // If there is an error, show in the console

  if (err) {

    console.error(err);

  }

​

  information = {

    name: allImages[currentIndex],

    result: results,

  };

​

  predictions.push(information);

  createDiv(`Label: ${results[0].label}`);

  createDiv(`Confidence: ${nf(results[0].confidence, 0, 2)}`);

  currentIndex += 1;

  if (currentIndex <= allImages.length - 1) {

    loadImage(allImages[currentIndex], imageReady);

  } else {

    savePredictions();

  }

}

​