let video;

let hands = [];

let targetLabel;

let brain;

let state = 'waiting';

​

function preload() {

  // Load the handPose model

  handPose = ml5.handPose({ flipped: true });

}

​

function setup() {

  createCanvas(640, 480);

​

  video = createCapture(VIDEO, { flipped: true });

  video.size(640, 480);

  video.hide();

​

  handPose.detectStart(video, gotHands);

​

  let options = {

    inputs: 42,

    outputs: 3,

    task: "classification",

    debug: true,

  };

  brain = ml5.neuralNetwork(options);

  brain.loadData("shapes.json", trainModel);

}

​

function trainModel() {

  brain.normalizeData();

  brain.train({ epochs: 100 }, finished);

}

​

function finished() {

  console.log("model trained");

  brain.save("shapes_trained");

}

​

function gotHands(results) {

  // save the output to the hands variable

  hands = results;

}

​

function draw() {

  image(video, 0, 0, width, height);

​

  if (hands.length > 0) {

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

      let hand = hands[i];

      let inputs = [];

      for (let j = 0; j < hand.keypoints.length; j++) {

        let keypoint = hand.keypoints[j];

        let x = keypoint.x;

        let y = keypoint.y;

        inputs.push(x);

        inputs.push(y);

        noStroke();

        circle(x, y, 10);

      }

      let target = [targetLabel];

      brain.addData(inputs, target);

      // console.log(inputs);

    }

  }

}

​