human

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Human Library

AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition,
Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis,
Age & Gender & Emotion Prediction, Gaze Tracking, Gesture Recognition, Body Segmentation


Highlights

Jump to Quick Start


Compatibility


Releases

Check out Simple Live Demo fully annotated app as a good start starting point (html)(code)

Check out Main Live Demo app for advanced processing of of webcam, video stream or images static images with all possible tunable options


Browser Demos

All browser demos are self-contained without any external dependencies

NodeJS Demos

NodeJS demos may require extra dependencies which are used to decode inputs
See header of each demo to see its dependencies as they are not automatically installed with Human

Project pages

Wiki pages

Additional notes


See issues and discussions for list of known limitations and planned enhancements

Suggestions are welcome!


App Examples

Visit Examples gallery for more examples


Options

All options as presented in the demo application…
demo/index.html


Results Browser:
[ Demo -> Display -> Show Results ]


Advanced Examples

  1. Face Similarity Matching:
    Extracts all faces from provided input images,
    sorts them by similarity to selected face
    and optionally matches detected face with database of known people to guess their names

    demo/facematch

  1. Face Detect:
    Extracts all detect faces from loaded images on-demand and highlights face details on a selected face

    demo/facedetect

  1. Face ID:
    Performs validation check on a webcam input to detect a real face and matches it to known faces stored in database

    demo/faceid


  1. 3D Rendering:

    human-motion


  1. VR Model Tracking:

    human-three-vrm
    human-bjs-vrm

  1. Human as OS native application:

    human-electron


468-Point Face Mesh Defails:
(view in full resolution to see keypoints)


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Quick Start

Simply load Human (IIFE version) directly from a cloud CDN in your HTML file:
(pick one: jsdelirv, unpkg or cdnjs)

<!DOCTYPE HTML>
<script src="https://cdn.jsdelivr.net/npm/@vladmandic/human/dist/human.js"></script>
<script src="https://unpkg.dev/@vladmandic/human/dist/human.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/human/3.0.0/human.js"></script>

For details, including how to use Browser ESM version or NodeJS version of Human, see Installation


Code Examples

Simple app that uses Human to process video input and
draw output on screen using internal draw helper functions

// create instance of human with simple configuration using default values
const config = { backend: 'webgl' };
const human = new Human.Human(config);
// select input HTMLVideoElement and output HTMLCanvasElement from page
const inputVideo = document.getElementById('video-id');
const outputCanvas = document.getElementById('canvas-id');

function detectVideo() {
  // perform processing using default configuration
  human.detect(inputVideo).then((result) => {
    // result object will contain detected details
    // as well as the processed canvas itself
    // so lets first draw processed frame on canvas
    human.draw.canvas(result.canvas, outputCanvas);
    // then draw results on the same canvas
    human.draw.face(outputCanvas, result.face);
    human.draw.body(outputCanvas, result.body);
    human.draw.hand(outputCanvas, result.hand);
    human.draw.gesture(outputCanvas, result.gesture);
    // and loop immediate to the next frame
    requestAnimationFrame(detectVideo);
    return result;
  });
}

detectVideo();

or using async/await:

// create instance of human with simple configuration using default values
const config = { backend: 'webgl' };
const human = new Human(config); // create instance of Human
const inputVideo = document.getElementById('video-id');
const outputCanvas = document.getElementById('canvas-id');

async function detectVideo() {
  const result = await human.detect(inputVideo); // run detection
  human.draw.all(outputCanvas, result); // draw all results
  requestAnimationFrame(detectVideo); // run loop
}

detectVideo(); // start loop

or using Events:

// create instance of human with simple configuration using default values
const config = { backend: 'webgl' };
const human = new Human(config); // create instance of Human
const inputVideo = document.getElementById('video-id');
const outputCanvas = document.getElementById('canvas-id');

human.events.addEventListener('detect', () => { // event gets triggered when detect is complete
  human.draw.all(outputCanvas, human.result); // draw all results
});

function detectVideo() {
  human.detect(inputVideo) // run detection
    .then(() => requestAnimationFrame(detectVideo)); // upon detect complete start processing of the next frame
}

detectVideo(); // start loop

or using interpolated results for smooth video processing by separating detection and drawing loops:

const human = new Human(); // create instance of Human
const inputVideo = document.getElementById('video-id');
const outputCanvas = document.getElementById('canvas-id');
let result;

async function detectVideo() {
  result = await human.detect(inputVideo); // run detection
  requestAnimationFrame(detectVideo); // run detect loop
}

async function drawVideo() {
  if (result) { // check if result is available
    const interpolated = human.next(result); // get smoothened result using last-known results
    human.draw.all(outputCanvas, interpolated); // draw the frame
  }
  requestAnimationFrame(drawVideo); // run draw loop
}

detectVideo(); // start detection loop
drawVideo(); // start draw loop

or same, but using built-in full video processing instead of running manual frame-by-frame loop:

const human = new Human(); // create instance of Human
const inputVideo = document.getElementById('video-id');
const outputCanvas = document.getElementById('canvas-id');

async function drawResults() {
  const interpolated = human.next(); // get smoothened result using last-known results
  human.draw.all(outputCanvas, interpolated); // draw the frame
  requestAnimationFrame(drawResults); // run draw loop
}

human.video(inputVideo); // start detection loop which continously updates results
drawResults(); // start draw loop

or using built-in webcam helper methods that take care of video handling completely:

const human = new Human(); // create instance of Human
const outputCanvas = document.getElementById('canvas-id');

async function drawResults() {
  const interpolated = human.next(); // get smoothened result using last-known results
  human.draw.canvas(outputCanvas, human.webcam.element); // draw current webcam frame
  human.draw.all(outputCanvas, interpolated); // draw the frame detectgion results
  requestAnimationFrame(drawResults); // run draw loop
}

await human.webcam.start({ crop: true });
human.video(human.webcam.element); // start detection loop which continously updates results
drawResults(); // start draw loop

And for even better results, you can run detection in a separate web worker thread


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Inputs

Human library can process all known input types:

Additionally, HTMLVideoElement, HTMLMediaElement can be a standard <video> tag that links to:


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Detailed Usage


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TypeDefs

Human is written using TypeScript strong typing and ships with full TypeDefs for all classes defined by the library bundled in types/human.d.ts and enabled by default

Note: This does not include embedded tfjs
If you want to use embedded tfjs inside Human (human.tf namespace) and still full typedefs, add this code:

import type * as tfjs from ‘@vladmandic/human/dist/tfjs.esm’;
const tf = human.tf as typeof tfjs;

This is not enabled by default as Human does not ship with full TFJS TypeDefs due to size considerations
Enabling tfjs TypeDefs as above creates additional project (dev-only as only types are required) dependencies as defined in @vladmandic/human/dist/tfjs.esm.d.ts:

@tensorflow/tfjs-core, @tensorflow/tfjs-converter, @tensorflow/tfjs-backend-wasm, @tensorflow/tfjs-backend-webgl


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Default models

Default models in Human library are:

Note that alternative models are provided and can be enabled via configuration
For example, body pose detection by default uses MoveNet Lightning, but can be switched to MultiNet Thunder for higher precision or Multinet MultiPose for multi-person detection or even PoseNet, BlazePose or EfficientPose depending on the use case

For more info, see Configuration Details and List of Models


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Diagnostics


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Human library is written in TypeScript 5.1 using TensorFlow/JS 4.10 and conforming to latest JavaScript ECMAScript version 2022 standard

Build target for distributables is JavaScript EMCAScript version 2018


For details see Wiki Pages
and API Specification


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