let poseNet;

​

function preload() {

  // load an image for pose detection

  img = loadImage('data/runner.jpg');

}

​

function setup() {

  createCanvas(640, 360);

  image(img, 0, 0);

  poseNet = ml5.poseNet(modelReady);

}

​

// when poseNet is ready, do the detection

function modelReady() {

  select('#status').html('Model Loaded');

  // If/When a pose is detected, poseNet.on('pose', ...) will be listening for the detection results 

  poseNet.on('pose', function (poses) {

    if (poses.length > 0) {

      drawSkeleton(poses);

      drawKeypoints(poses);

    }

  });

  // When the model is ready, run the singlePose() function...

  poseNet.singlePose(img);

}

​

// The following comes from https://ml5js.org/docs/posenet-webcam

// A function to draw ellipses over the detected keypoints

function drawKeypoints(poses) {

  // Loop through all the poses detected

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

    // For each pose detected, loop through all the keypoints

    let pose = poses[i].pose;

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

      // A keypoint is an object describing a body part (like rightArm or leftShoulder)

      let keypoint = pose.keypoints[j];

      // Only draw an ellipse is the pose probability is bigger than 0.2

      if (keypoint.score > 0.2) {

        fill(255);

        stroke(20);

        strokeWeight(4);

        ellipse(round(keypoint.position.x), round(keypoint.position.y), 8, 8);

      }

    }

  }

}

​

// A function to draw the skeletons

function drawSkeleton(poses) {

  // Loop through all the skeletons detected

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

    let skeleton = poses[i].skeleton;

    // For every skeleton, loop through all body connections

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

      let partA = skeleton[j][0];

      let partB = skeleton[j][1];

      stroke(255);

      strokeWeight(1);

      line(partA.position.x, partA.position.y, partB.position.x, partB.position.y);

    }

  }

}