let classifier;

let label = "";

let trainingCompleted = false;

let y = 0;

​

function setup() {

  createCanvas(640, 480);

  capture = createCapture(VIDEO);

  capture.hide();

​

  classifier = ml5.imageClassifier("MobileNet", capture, modelReady);

}

​

function modelReady() {

  console.log("Model is ready!");

  classifier.classify(gotResults);

}

​

function draw() {

  background(255);

  image(capture, 0, 0, 640, 480);

​

  fill(0);

  textSize(32);

  textAlign(CENTER, CENTER);

  text(label, width / 2, height - 16);

​

  strokeWeight(4);

  stroke(0, 255, 0);

  line(0, y, width, y);

​

  if (trainingCompleted) {

    fill(0, 255, 0);

    noStroke();

    ellipse(width - 32, 32, 64);

  }

}

​

function gotResults(error, results) {

  if (error) {

    console.error(error);

  } else {

    label = results[0].label;

    classifier.classify(gotResults);

  }

}

​

function mousePressed() {

  if (!trainingCompleted) {

    classifier.addImage(label);

    y = y + 48;

    if (y > height) {

      trainingCompleted = true;

    }

  } else {

    classifier.train((lossValue) => {

      if (lossValue) {

        console.error(lossValue);

      } else {

        console.log("Training complete");

        classifier.classify(gotResults);

      }

    });

  }

}

​