let myClassifier;

let myResults;

let myVoice;

let myEmojis = {

  'monitor': '🖥️',

  'water bottle': '🍼',

  'ipod': '📱'

}

​

​

function setup() {

  createCanvas(640, 480);

  myVideo = createCapture(VIDEO);

  myVideo.hide();

  myClassifier = ml5.imageClassifier('MobileNet', modelReady);

  textSize(48)

  fill(225, 120, 0)

  textAlign(CENTER)

  myVoice = new p5.Speech();

}

​

​

function modelReady() {

  console.log(myClassifier);

  myClassifier.classify(myVideo, gotResult);

  myVoice.listVoices();

}

​

function gotResult(error, results) {

  if (error) {

    console.log(error)

  }

  if (results) {

    myResults = results;

    myClassifier.classify(myVideo, gotResult); 

    const mySentence = 'I see ${myResults[0].label}';

    myVoice.setVoice('Grandpa (English (UK))');

    myVoice.setPitch(1.5);

    myVoice.setRate(0.5);

    myVoice.speak(mySentence);

    }

}

​

function draw() {

  image(myVideo, 0, 0, width, height)

  if (myResults) {

    text(myResults[0].label, width/2, height/2)

    if (myEmojis[myResults[0].label]) {

      drawEmojis(myEmojis[myResults[0].label], width/2, height/2)

    }

  }

}

​

function drawEmojis(emoji) {

  for (let i = 0; i < 20; i++){

    for (let j = 0; j < 20; j++){

      //text(myEmojis[myResults[0].label], i * 32, j * 24)

      textSize(random() * 200);

      text(myEmojis[myResults[0].label], random() * 640, random() * 480);

    }

  }

}