// Intro to ML for the Arts, IMA Fall 2021

// https://github.com/ml5js/Intro-ML-Arts-IMA-F21

​

// Classifier Variable

let classifier;

// Model URL

const imageModelURL = 'https://teachablemachine.withgoogle.com/models/w_i1E4QDp/';

​

// Video

let video;

let flippedVideo;

// To store the classification

let label = "";

​

// Data from the file

let data = [];

let images = {};

let texts = {};

​

// Load the model and data first

function preload() {

  classifier = ml5.imageClassifier(imageModelURL + 'model.json');

  data = loadStrings('data.txt', parseData);

}

​

function setup() {

  createCanvas(320, 260);

  // Create the video

  video = createCapture(VIDEO);

  video.size(320, 240);

  video.hide();

​

  flippedVideo = ml5.flipImage(video);

  // Start classifying

  classifyVideo();

}

​

function draw() {

  background(0);

  // Draw the video

  //image(flippedVideo, 0, 0);

​

  // Draw the label

  fill(255);

  textSize(16);

  textAlign(CENTER);

  text(label, width / 2, height - 4);

​

  // Display corresponding image and text

  if (images[label] && texts[label]) {

    let img = loadImage(images[label], img => {

      image(img, 0, 0, 320, 240);

    });

    text(texts[label], width / 2, height - 20);

  }

}

​

// Get a prediction for the current video frame

function classifyVideo() {

  flippedVideo = ml5.flipImage(video);

  classifier.classify(flippedVideo, gotResult);

}

​

// When we get a result

function gotResult(error, results) {

  if (error) {

    console.error(error);

    return;

  }

  label = results[0].label;

  classifyVideo();

}

​

// Parse data from the file

function parseData(data) {

  data.forEach(line => {

    let [label, imageUrl, text] = line.split(',');

    images[label] = imageUrl;

    texts[label] = text;

  });

}

​