// The Coding Train / Daniel Shiffman

// https://thecodingtrain.com/Courses/ml5-beginners-guide/7.3-pose-regression.html

// https://youtu.be/lob74HqHYJ0

​

// All code: https://editor.p5js.org/codingtrain/sketches/JI_j-PiLk

​

// Separated into three sketches

// 1: Data Collection: https://editor.p5js.org/codingtrain/sketches/Fe1ZNKw1Z

// 2: Model Training: https://editor.p5js.org/codingtrain/sketches/KLrIligVq

// 3: Model Deployment: https://editor.p5js.org/codingtrain/sketches/nejAYCA6N

​

let video;

let poseNet;

let pose;

let skeleton;

​

let brain;

​

let rSlider, gSlider, bSlider;

​

function setup() {

  createCanvas(640, 480);

​

  rSlider = createSlider(0, 255, 0);

  gSlider = createSlider(0, 255, 0);

  bSlider = createSlider(0, 255, 0);

​

  video = createCapture(VIDEO);

  video.hide();

  poseNet = ml5.poseNet(video, modelLoaded);

  poseNet.on('pose', gotPoses);

​

  let options = {

    inputs: 34,

    outputs: 3,

    task: 'regression',

    debug: true

  }

  brain = ml5.neuralNetwork(options);

  const modelInfo = {

    model: 'model/model.json',

    metadata: 'model/model_meta.json',

    weights: 'model/model.weights.bin',

  };

  brain.load(modelInfo, brainLoaded);

}

​

function brainLoaded() {

  console.log('pose predicting ready!');

  predictColor();

}

​

function predictColor() {

  if (pose) {

    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);

    }

    brain.predict(inputs, gotResult);

  } else {

    setTimeout(predictColor, 100);

  }

}

​

function gotResult(error, results) {

  console.log(results);

  let r = results[0].value;

  let g = results[1].value;

  let b = results[2].value;

  rSlider.value(r);

  gSlider.value(g);

  bSlider.value(b);

  predictColor();

}

​

​

​

​

​

​

function gotPoses(poses) {

  // console.log(poses); 

  if (poses.length > 0) {

    pose = poses[0].pose;

    skeleton = poses[0].skeleton;

  }

}

​

​

function modelLoaded() {

  console.log('poseNet ready');

}

​

function draw() {

  push();

  translate(video.width, 0);

  scale(-1, 1);

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

​

  if (pose) {

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

      let a = skeleton[i][0];

      let b = skeleton[i][1];

      strokeWeight(2);

      stroke(0);

​

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

    }

    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);

      stroke(255);

      ellipse(x, y, 16, 16);

    }

  }

  pop();

​

  let r = rSlider.value();

  let g = gSlider.value();

  let b = bSlider.value();

  background(r, g, b, 100);

​

}