​

let video;

let poseNet;

let pose;

let skeleton;

​

let brain;

let targetLabel;

​

let poseLabel = "circle";

​

function setup() {

  createCanvas(640, 480);

  video = createCapture(VIDEO);

  video.hide();

  poseNet = ml5.bodyPose(video, gotPoses);

  // poseNet.on("pose", );

  rectMode(CENTER);

  ellipseMode(CENTER);

​

  let options = {

    inputs: 34,

    //     outputs: circle, triangle, rectangle

    outputs: 3,

    task: "classification",

    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);

  // brain.loadData('shapes.json', trainModel);

}

​

function brainLoaded() {

  console.log("pose classification ready!");

  classifyPose();

}

​

function classifyPose() {

  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.classify(inputs, gotResult);

  } else {

    setTimeout(classifyPose, 100);

  }

}

​

function gotResult(error, results) {

  poseLabel = results[0].label;

​

  console.log(results);

  console.log(results[0].label);

  classifyPose();

}

​

function trainModel() {

  brain.normalizeData();

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

}

​

function finished() {

  console.log("model trained");

  brain.save();

}

​

function modelLoaded() {

  console.log("poseNet ready");

}

​

function gotPoses(poses) {

  // console.log(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() {

  push();

  translate(video.width, 0);

  scale(-1, 1);

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

​

  if (pose) {

    // The line that connects each keypoint

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

      let a = skeleton[i][0];

      let b = skeleton[i][1];

      strokeWeight(2);

      stroke(255);

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

    }

​

    //     The seven keypoints

    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);

      stroke(255);

      ellipse(x, y, 16, 16);

    }

  }

  pop();

​

  //   Draw the shapes

  if (poseLabel == "t") {

    fill(255, 0, 255, 80);

    noStroke();

    triangle(100, height- 100, width / 2, 100, width-100, height-100);

  }

​

  if (poseLabel == "c") {

    fill(255, 0, 255, 80);

    noStroke();

    circle(width / 2, height / 2, 400);

  }

​

  if (poseLabel == "r") {

    fill(255, 0, 255, 80);

    noStroke();

    rect(width / 2, height / 2, 400);

  }

}

​