let regressor;

let video;

let loss;

let slider;

let addSample;

let samples;

let positionX;

let t;

​

var boxSize = 100;

var lineThick = 3;

​

function rotateDraw(depth) {

  push();

  translate(400, 0, 200);

  noFill();

  stroke(255,100,100);

  strokeWeight(lineThick);

  rotateZ(frameCount * .001);

  rotateX(frameCount * .001);

  rotateY(frameCount * .001);

  box(boxSize, boxSize,depth);

  pop();

}

​

function setup() {

  createCanvas(1024, 768, WEBGL);

  ortho();

  video = createCapture(VIDEO);

  video.hide();

  featureExtractor = ml5.featureExtractor('MobileNet', modelReady);

  regressor = featureExtractor.regression(video);

  createButtons();

  positionX = 140;

  samples = 0;

  t = 0;

}

​

function draw() {

  background(255);

  for (var i=0; i<width; i++) {

    stroke(255 * noise(0.025 * i, t), 255 * slider.value(), 150*slider.value());

    line(i, 0, i, height);

  }

  t += (0.1 * slider.value());

  texture(video);

    translate(-250, -250, 0);

    plane(500);

  rotateDraw(depth=255 * slider.value());

  translate(0,0,-500)

}

​

// A function to be called when the model has been loaded

function modelReady() {

  select('#loading').html('Model loaded!');

}

​

// Classify the current frame.

function predict() {

  regressor.predict(gotResults);

}

​

// A util function to create UI buttons

function createButtons() {

  slider = select('#slider');

  // When the Dog button is pressed, add the current frame

  // from the video with a label of "dog" to the classifier

  addSample = select('#addSample');

  addSample.mousePressed(function() {

    regressor.addImage(slider.value());

    select('#amountOfSamples').html(samples++);

  });

​

  // Train Button

  train = select('#train');

  train.mousePressed(function() {

    regressor.train(function(lossValue) {

      if (lossValue) {

        loss = lossValue;

        select('#loss').html('Loss: ' + loss);

      } else {

        select('#loss').html('Done Training! Final Loss: ' + loss);

      }

    });

  });

​

  // Predict Button

  buttonPredict = select('#buttonPredict');

  buttonPredict.mousePressed(predict);

}

​

// Show the results

function gotResults(err, result) {

  if (err) {

    console.error(err);

  }

  positionX = map(result, 0, 1, 0, width);

  slider.value(result);

  predict();

}

​

​