// Coding Challenge

// The Coding Train / Daniel Shiffman

// https://thecodingtrain.com/CodingChallenges/158-shape-classifier.html

// https://youtu.be/3MqJzMvHE3E

​

// Generate Dataset: https://github.com/CodingTrain/website/tree/gh-pages/CodingChallenges/CC_158_Shape_Classifier/dataset

// Generate Dataset (port): https://editor.p5js.org/codingtrain/sketches/7leVIzy5l

// Training: https://github.com/CodingTrain/website/tree/gh-pages/CodingChallenges/CC_158_Shape_Classifier/training

// Mouse: https://editor.p5js.org/codingtrain/sketches/JgLVfCS4E

// Webcam: https://editor.p5js.org/codingtrain/sketches/2hZGBkqqq

​

let circles = [];

let squares = [];

let triangles = [];

​

function preload() {

  for (let i = 0; i < 8; i++) {

    let index = nf(i + 1, 4, 0);

    circles[i] = loadImage(`testing/circle${index}.png`);

    squares[i] = loadImage(`testing/square${index}.png`);

    triangles[i] = loadImage(`testing/triangle${index}.png`);

  }

}

​

let shapeClassifier;

​

function setup() {

  createCanvas(400, 400);

  //background(0);

  //image(squares[0], 0, 0, width, height);

​

  let options = {

    inputs: [64, 64, 4],

    task: 'imageClassification',

    debug: true

  };

  shapeClassifier = ml5.neuralNetwork(options);

​

  for (let i = 0; i < circles.length; i++) {

    shapeClassifier.addData({ image: circles[i] }, { label: 'circle' });

    shapeClassifier.addData({ image: squares[i] }, { label: 'square' });

    shapeClassifier.addData({ image: triangles[i] }, { label: 'triangle' });

  }

  shapeClassifier.normalizeData();

  shapeClassifier.train({ epochs: 50 }, finishedTraining);

}

​

function finishedTraining() {

  console.log('finished training!');

  shapeClassifier.save();

}