let classifier;

let predictions = [];

​

function setup() {

  createCanvas(640, 480);

​

  // capture the webcam and store it in 'video'

  video = createCapture(VIDEO);

  video.size(width, height);

  //hide the html video element

  video.hide();

  // start the classifier

  classifier = ml5.imageClassifier('MobileNet', video, modelReady);

}

​

function modelReady() {

  console.log("model ready");

  classifyVideo()

}

​

// Get a prediction for the current video frame

function classifyVideo() {

  classifier.classify(gotResults);

}

​

function gotResults(err, results) {

  predictions = results;

  classifyVideo();

}

​

function draw() {

  background(220);

  image(video, 0, 0, width, height);

​

  if(predictions.length > 0){

    textSize(30)

      text(predictions[0].label, 10, height/2)

  }

}

​