04.04 ml5.js

<!-- include ml5.js library from Content Delivery Network -->
<script src="https://unpkg.com/ml5@latest/dist/ml5.min.js"></script>

// show the predictions array in the console if it is not empty and then stop the program after one detections
if (predictions.length > 0) {
  console.log(predictions);
  noLoop();
}

// example from ml5.js documentation
// https://learn.ml5js.org/#/reference/handpose
// https://editor.p5js.org/ml5/sketches/Handpose_Webcam

let handpose;
let video;
let predictions = [];

function setup() {
  createCanvas(640, 480);
  video = createCapture(VIDEO);
  video.size(width, height);

  handpose = ml5.handpose(video, modelReady);

  // This sets up an event that fills the global variable "predictions"
  // with an array every time new hand poses are detected
  handpose.on("predict", (results) => {
    predictions = results;
  });

  // Hide the video element, and just show the canvas
  video.hide();
}

function modelReady() {
  console.log("Model ready!");
}

function draw() {
  image(video, 0, 0, width, height);

  // We can call both functions to draw all keypoints and the skeletons
  drawKeypoints();
}

// A function to draw ellipses over the detected keypoints
function drawKeypoints() {
  for (let i = 0; i < predictions.length; i += 1) {
    const prediction = predictions[i];
    for (let j = 0; j < prediction.landmarks.length; j += 1) {
      const keypoint = prediction.landmarks[j];
      fill(0, 255, 0);
      noStroke();
      ellipse(keypoint[0], keypoint[1], 10, 10);
    }
  }
}

const options = {
  flipHorizontal: false, // boolean value for if the video should be flipped, defaults to false
  maxContinuousChecks: Infinity, // How many frames to go without running the bounding box detector. Defaults to infinity, but try a lower value if the detector is consistently producing bad predictions.
  detectionConfidence: 0.8, // Threshold for discarding a prediction. Defaults to 0.8.
  scoreThreshold: 0.75, // A threshold for removing multiple (likely duplicate) detections based on a "non-maximum suppression" algorithm. Defaults to 0.75
  iouThreshold: 0.3, // A float representing the threshold for deciding whether boxes overlap too much in non-maximum suppression. Must be between [0, 1]. Defaults to 0.3.
};

let facemesh;
let video;
let predictions = [];

function setup() {
  createCanvas(640, 480);
  video = createCapture(VIDEO);
  video.size(width, height);

  facemesh = ml5.facemesh(video, modelReady);

  // This sets up an event that fills the global variable "predictions"
  // with an array every time new predictions are made
  facemesh.on("predict", (results) => {
    predictions = results;
  });

  // Hide the video element, and just show the canvas
  video.hide();
}

function modelReady() {
  console.log("Model ready!");
}

function draw() {
  image(video, 0, 0, width, height);

  // We can call both functions to draw all keypoints
  drawKeypoints();
}

// A function to draw ellipses over the detected keypoints
function drawKeypoints() {
  for (let i = 0; i < predictions.length; i += 1) {
    const keypoints = predictions[i].scaledMesh;

    // Draw facial keypoints.
    for (let j = 0; j < keypoints.length; j += 1) {
      const [x, y] = keypoints[j];

      fill(0, 255, 0);
      ellipse(x, y, 5, 5);
    }
  }
}

// Example Sketch from ml5.js
// https://learn.ml5js.org/#/reference/posenet
// https://editor.p5js.org/ml5/sketches/PoseNet_webcam

// Copyright (c) 2019 ml5
//
// This software is released under the MIT License.
// https://opensource.org/licenses/MIT

/* ===
ml5 Example
PoseNet example using p5.js
=== */

let video;
let poseNet;
let poses = [];

function setup() {
  createCanvas(640, 480);
  video = createCapture(VIDEO);
  video.size(width, height);

  // Create a new poseNet method with a single detection
  poseNet = ml5.poseNet(video, modelReady);
  // This sets up an event that fills the global variable "poses"
  // with an array every time new poses are detected
  poseNet.on("pose", function (results) {
    poses = results;
  });
  // Hide the video element, and just show the canvas
  video.hide();
}

function modelReady() {
  select("#status").html("Model Loaded");
}

function draw() {
  image(video, 0, 0, width, height);

  // We can call both functions to draw all keypoints and the skeletons
  drawKeypoints();
  drawSkeleton();
}

// A function to draw ellipses over the detected keypoints
function drawKeypoints() {
  // 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, 0, 0);
        noStroke();
        ellipse(keypoint.position.x, keypoint.position.y, 10, 10);
      }
    }
  }
}

// A function to draw the skeletons
function drawSkeleton() {
  // 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, 0, 0);
      line(
        partA.position.x,
        partA.position.y,
        partB.position.x,
        partB.position.y
      );
    }
  }
}