let poseNet;

let pose;

let skeleton;

​

let brain;

let state = "waiting";

let targetLabel;

​

function setup() {

  createCanvas(640, 480);

​

  video = createCapture(VIDEO);

  video.hide();

​

  poseNet = ml5.poseNet(video, modelLoaded);

  poseNet.on("pose", gotPoses);

​

  let options = {

    inputs: 34,

    outputs: 4,

    task: "classification",

    debug: true,

  };

  brain = ml5.neuralNetwork(options);

}

​

function keyPressed() {

  if(key == 's'){

    brain.saveData();

  }

​

  targetLabel = key;

  console.log(targetLabel);

  setTimeout(function () {

    console.log("collecting");

    state = "collecting";

    setTimeout(function(){

      console.log('not collecting');

      state = 'waiting';

    }, 10000);

  }, 10000);

}

​

function modelLoaded() {

  console.log("poseNet ready gan!");

}

​

function gotPoses(poses) {

  if (poses.length > 0) {

    pose = poses[0].pose;

    skeleton = poses[0].skeleton;

​

    let inputs = [];

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

      let x = pose.keypoints[i].position.x;

      let y = pose.keypoints[i].position.y;

      inputs.push(x);

      inputs.push(y);

    }

    let target = [targetLabel];

    brain.addData(inputs, target);

  }

}

​

function draw() {

  translate(video.width, 0);

  scale(-1, 1);

  image(video, 0, 0, video.width, video.height);

​

  if (pose) {

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

      let x = pose.keypoints[i].position.x;

      let y = pose.keypoints[i].position.y;

      fill(0, 255, 0);

      noStroke();

      circle(x, y, 20);

    }

​

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

      let a = skeleton[i][0];

      let b = skeleton[i][1];

      strokeWeight(4);

      stroke(255);

      line(a.position.x, a.position.y, b.position.x, b.position.y);

    }

  }

}

​