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Vladimir Mandic edited this page Nov 26, 2021 · 49 revisions

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


JavaScript module using TensorFlow/JS Machine Learning library

  • Browser:
    Compatible with both desktop and mobile platforms
    Compatible with CPU, WebGL, WASM backends
    Compatible with WebWorker execution
  • NodeJS:
    Compatible with both software tfjs-node and
    GPU accelerated backends tfjs-node-gpu using CUDA libraries

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

  • To start video detection, simply press Play
  • To process images, simply drag & drop in your Browser window
  • Note: For optimal performance, select only models you'd like to use
  • Note: If you have modern GPU, WebGL (default) backend is preferred, otherwise select WASM backend

Releases

Demos

Browser Demos

  • Full [Live] [Details]: Main browser demo app that showcases all Human capabilities
  • Simple [Live] [Details]: Simple demo in WebCam processing demo in TypeScript
  • Face Match [Live] [Details]: Extract faces from images, calculates face descriptors and simmilarities and matches them to known database
  • Face ID [Live] [Details]: Runs multiple checks to validate webcam input before performing face match to faces in IndexDB
  • Multi-thread [Live] [Details]: Runs each human module in a separate web worker for highest possible performance
  • 3D Analysis [Live] [Details]: 3D tracking and visualization of heead, face, eye, body and hand
  • Virtual Avatar [Live] [Details]: VR model with head, face, eye, body and hand tracking

NodeJS Demos

  • Main [Details]: Process images from files, folders or URLs using native methods
  • Canvas [Details]: Process image from file or URL and draw results to a new image file using node-canvas
  • Video [Details]: Processing of video input using ffmpeg
  • WebCam [Details]: Processing of webcam screenshots using fswebcam
  • Events [Details]: Showcases usage of Human eventing to get notifications on processing
  • Similarity [Details]: Compares two input images for similarity of detected faces
  • Face Match [Details]: Parallel processing of face match in multiple child worker threads
  • Multiple Workers [Details]: Runs multiple parallel human by dispaching them to pool of pre-created worker processes

Project pages

Wiki pages

Additional notes


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

Suggestions are welcome!


Examples

Visit Examples galery for more examples
https://vladmandic.github.io/human/samples/samples.html

samples


Options

All options as presented in the demo application...

demo/index.html

Options visible in demo


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

Face Matching


  1. Face3D OpenGL Rendering:

human-motion

Face3D Body3D Hand3D


  1. VR Model Tracking:

human-vrmmotion

VRM


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

FaceMesh



Quick Start

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

<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/2.1.5/human.js"></script>

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


Inputs

Human library can process all known input types:

  • Image, ImageData, ImageBitmap, Canvas, OffscreenCanvas, Tensor,
  • HTMLImageElement, HTMLCanvasElement, HTMLVideoElement, HTMLMediaElement

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

  • WebCam on user's system
  • Any supported video type
    For example: .mp4, .avi, etc.
  • Additional video types supported via HTML5 Media Source Extensions
    Live streaming examples:
    • HLS (HTTP Live Streaming) using hls.js
    • DASH (Dynamic Adaptive Streaming over HTTP) using dash.js
  • WebRTC media track using built-in support

Example

Example 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(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);
  });
}

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); // calculate next interpolated frame
    human.draw.all(outputCanvas, interpolated); // draw the frame
  }
  requestAnimationFrame(drawVideo); // run draw loop
}

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

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



Default models

Default models in Human library are:

  • Face Detection: MediaPipe BlazeFace Back variation
  • Face Mesh: MediaPipe FaceMesh
  • Face Iris Analysis: MediaPipe Iris
  • Face Description: HSE FaceRes
  • Emotion Detection: Oarriaga Emotion
  • Body Analysis: MoveNet Lightning variation
  • Hand Analysis: HandTrack & MediaPipe HandLandmarks
  • Body Segmentation: Google Selfie
  • Object Detection: CenterNet with MobileNet v3

Note that alternative models are provided and can be enabled via configuration
For example, PoseNet model can be switched for BlazePose, EfficientPose or MoveNet model depending on the use case

For more info, see Configuration Details and List of Models



Diagnostics



Human library is written in TypeScript 4.5
Conforming to latest JavaScript ECMAScript version 2021 standard
Build target is JavaScript EMCAScript version 2018


For details see Wiki Pages
and API Specification

Human Library Wiki Pages
3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking, Age & Gender Prediction, Emotion Prediction & Gesture Recognition

Clone this wiki locally