# Human Library

## 3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking, Age & Gender Prediction & Emotion Prediction

- [**Documentation**](https://github.com/vladmandic/human#readme)
- [**Code Repository**](https://github.com/vladmandic/human)
- [**NPM Package**](https://www.npmjs.com/package/@vladmandic/human)
- [**Issues Tracker**](https://github.com/vladmandic/human/issues)
- [**Live Demo**](https://vladmandic.github.io/human/demo/demo-esm.html)

Compatible with Browser, WebWorker and NodeJS execution!

*This is a pre-release project, see [issues](https://github.com/vladmandic/human/issues) for list of known limitations*  

*Suggestions are welcome!*

<hr>

## Examples

**Using static images:**  
![Example Using Image](assets/screenshot1.jpg)

**Using webcam:**  
![Example Using WebCam](assets/screenshot2.jpg)

<hr>

## Installation

**Important**  
*The packaged (IIFE and ESM) version of `Human` includes `TensorFlow/JS (TFJS) 2.6.0` library which can be accessed via `human.tf`*  
*You should NOT manually load another instance of `tfjs`, but if you do, be aware of possible version conflicts*  

There are multiple ways to use `Human` library, pick one that suits you:

### Included

- `dist/human.js`: IIFE format minified bundle with TFJS for Browsers
- `dist/human.esm.js`: ESM format minified bundle with TFJS for Browsers
- `dist/human.esm-nobundle.js`: ESM format non-minified bundle without TFJS for Browsers
- `dist/human.cjs`: CommonJS format minified bundle with TFJS for NodeJS
- `dist/human-nobundle.cjs`: CommonJS format non-minified bundle without TFJS for NodeJS

All versions include `sourcemap`

Defaults:
```json
  {
    "main": "dist/human.cjs",
    "module": "dist/human.esm.js",
    "browser": "dist/human.esm.js",
  }
```

### 1. [IIFE](https://developer.mozilla.org/en-US/docs/Glossary/IIFE) script

*Simplest way for usage within Browser*

Simply download `dist/human.js`, include it in your `HTML` file & it's ready to use.

```html
  <script src="dist/human.js"><script>
``` 

IIFE script auto-registers global namespace `human` within global `Window` object  
This way you can also use `Human` library within embbedded `<script>` tag within your `html` page for all-in-one approach  

### 2. [ESM](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Statements/import) module

*Recommended for usage within `Browser`*  

#### 2.1 With Bundler

If you're using bundler *(such as rollup, webpack, esbuild)* to package your client application, you can import ESM version of `Human` library which supports full tree shaking  

Install with:
```shell
  npm install @vladmandic/human
```
```js
  import human from '@vladmandic/human'; // points to @vladmandic/human/dist/human.esm.js
```

Or if you prefer to package your version of `tfjs`, you can use `nobundle` version

Install with:
```shell
  npm install @vladmandic/human @tensorflow/tfjs-node
```
```js
  import tf from '@tensorflow/tfjs'
  import human from '@vladmandic/human/dist/human.esm-nobundle.js'; // same functionality as default import, but without tfjs bundled
```

#### 2.2 Using Script Module
You could use same syntax within your main `JS` file if it's imported with `<script type="module">`  

```html
  <script src="./index.js" type="module">
```
and then in your `index.js`

```js
  import * as tf from `https://cdnjs.cloudflare.com/ajax/libs/tensorflow/2.6.0/tf.es2017.min.js`; // load tfjs directly from CDN link
  import human from 'dist/human.esm.js'; // for direct import must use path to module, not package name
```

### 3. [NPM](https://www.npmjs.com/) module

*Recommended for `NodeJS` projects that will execute in the backend*  

Entry point is bundle in CJS format `dist/human.node.js`  
You also need to install and include `tfjs-node` or `tfjs-node-gpu` in your project so it can register an optimized backend  

Install with:
```shell
  npm install @vladmandic/human
```
And then use with:
```js
  const human = require('@vladmandic/human'); // points to @vladmandic/human/dist/human.cjs
```
or
```shell
  npm install @vladmandic/human @tensorflow/tfjs-node
```
And then use with:
```js
  const tf = require('@tensorflow/tfjs-node'); // can also use '@tensorflow/tfjs-node-gpu' if you have environment with CUDA extensions
  const human = require('@vladmandic/human/dist/human-nobundle.cjs');
```


Since NodeJS projects load `weights` from local filesystem instead of using `http` calls, you must modify default configuration to include correct paths with `file://` prefix  
For example:
```js
const config = {
  body: { enabled: true, modelPath: 'file://models/posenet/model.json' },
}
```

Note that when using `Human` in NodeJS, you must load and parse the image *before* you pass it for detection  
For example:
```js
  const buffer = fs.readFileSync(input);
  const image = tf.node.decodeImage(buffer);
  const result = human.detect(image, config);
  image.dispose();
```

### Weights

Pretrained model weights are includes in `./models`  
Default configuration uses relative paths to you entry script pointing to `../models`  
If your application resides in a different folder, modify `modelPath` property in configuration of each module  

<hr>

## Demo

Demos are included in `/demo`:

Browser:
- `demo-esm`: Full demo using Browser with ESM module, includes selectable backends and webworkers
- `demo-iife`: Older demo using Browser with IIFE module

NodeJS:
- `demo-node`: Demo using NodeJS with CJS module  
  This is a very simple demo as althought `Human` library is compatible with NodeJS execution  
  and is able to load images and models from local filesystem,  

<hr>

## Usage

`Human` library does not require special initialization.
All configuration is done in a single JSON object and all model weights will be dynamically loaded upon their first usage(and only then, `Human` will not load weights that it doesn't need according to configuration).

There is only *ONE* method you need:

```js
  import * as tf from '@tensorflow/tfjs';
  import human from '@vladmandic/human';

  // 'image': can be of any type of an image object: HTMLImage, HTMLVideo, HTMLMedia, Canvas, Tensor4D
  // 'options': optional parameter used to override any options present in default configuration
  const result = await human.detect(image, options?)
```

or if you want to use promises

```js
  human.detect(image, options?).then((result) => {
    // your code
  })
```

Additionally, `Human` library exposes several classes:

```js
  human.config   // access to configuration object, normally set as parameter to detect()
  human.defaults // read-only view of default configuration object
  human.models   // dynamically maintained list of object of any loaded models
  human.tf       // instance of tfjs used by human
```

<hr>

## Configuration 

Below is output of `human.defaults` object  
Any property can be overriden by passing user object during `human.detect()`  
Note that user object and default configuration are merged using deep-merge, so you do not need to redefine entire configuration  

Configurtion object is large, but typically you only need to modify few values:

- `enabled`: Choose which models to use
- `skipFrames`: Must be set to 0 for static images
- `modelPath`: Update as needed to reflect your application's relative path


```js
export default {
  backend: 'webgl',          // select tfjs backend to use
  console: true,             // enable debugging output to console
  face: {
    enabled: true,           // controls if specified modul is enabled
                             // face.enabled is required for all face models: detector, mesh, iris, age, gender, emotion
                             // note: module is not loaded until it is required
    detector: {
      modelPath: '../models/blazeface/back/model.json', // can be 'tfhub', 'front' or 'back'.
                                                        // 'front' is optimized for large faces such as front-facing camera and 'back' is optimized for distanct faces.
      inputSize: 256,        // fixed value: 128 for front and 'tfhub' and 'front' and 256 for 'back'
      maxFaces: 10,          // maximum number of faces detected in the input, should be set to the minimum number for performance
      skipFrames: 10,        // how many frames to go without re-running the face bounding box detector
                             // if model is running st 25 FPS, we can re-use existing bounding box for updated face mesh analysis
                             // as face probably hasn't moved much in short time (10 * 1/25 = 0.25 sec)
      minConfidence: 0.5,    // threshold for discarding a prediction
      iouThreshold: 0.3,     // threshold for deciding whether boxes overlap too much in non-maximum suppression
      scoreThreshold: 0.7,   // threshold for deciding when to remove boxes based on score in non-maximum suppression
    },
    mesh: {
      enabled: true,
      modelPath: '../models/facemesh/model.json',
      inputSize: 192,        // fixed value
    },
    iris: {
      enabled: true,
      modelPath: '../models/iris/model.json',
      enlargeFactor: 2.3,    // empiric tuning
      inputSize: 64,         // fixed value
    },
    age: {
      enabled: true,
      modelPath: '../models/ssrnet-age/imdb/model.json', // can be 'imdb' or 'wiki'
                                                         // which determines training set for model
      inputSize: 64,         // fixed value
      skipFrames: 10,        // how many frames to go without re-running the detector
    },
    gender: {
      enabled: true,
      minConfidence: 0.8,    // threshold for discarding a prediction
      modelPath: '../models/ssrnet-gender/imdb/model.json',
    },
    emotion: {
      enabled: true,
      inputSize: 64,         // fixed value
      minConfidence: 0.5,    // threshold for discarding a prediction
      skipFrames: 10,        // how many frames to go without re-running the detector
      useGrayscale: true,    // convert image to grayscale before prediction or use highest channel
      modelPath: '../models/emotion/model.json',
    },
  },
  body: {
    enabled: true,
    modelPath: '../models/posenet/model.json',
    inputResolution: 257,    // fixed value
    outputStride: 16,        // fixed value
    maxDetections: 10,       // maximum number of people detected in the input, should be set to the minimum number for performance
    scoreThreshold: 0.7,     // threshold for deciding when to remove boxes based on score in non-maximum suppression
    nmsRadius: 20,           // radius for deciding points are too close in non-maximum suppression
  },
  hand: {
    enabled: true,
    inputSize: 256,          // fixed value
    skipFrames: 10,          // how many frames to go without re-running the hand bounding box detector
                             // if model is running st 25 FPS, we can re-use existing bounding box for updated hand skeleton analysis
                             // as face probably hasn't moved much in short time (10 * 1/25 = 0.25 sec)
    minConfidence: 0.5,      // threshold for discarding a prediction
    iouThreshold: 0.3,       // threshold for deciding whether boxes overlap too much in non-maximum suppression
    scoreThreshold: 0.7,     // threshold for deciding when to remove boxes based on score in non-maximum suppression
    enlargeFactor: 1.65,     // empiric tuning as skeleton prediction prefers hand box with some whitespace
    maxHands: 10,            // maximum number of hands detected in the input, should be set to the minimum number for performance
    detector: {
      anchors: '../models/handdetect/anchors.json',
      modelPath: '../models/handdetect/model.json',
    },
    skeleton: {
      modelPath: '../models/handskeleton/model.json',
    },
  },
};
```

<hr>

## Outputs

Result of `humand.detect()` is a single object that includes data for all enabled modules and all detected objects:

```js
result = {
  version:         // <string> version string of the human library
  face:            // <array of detected objects>
  [
    {
      confidence,  // <number>
      box,         // <array [x, y, width, height]>
      mesh,        // <array of 3D points [x, y, z]> 468 base points & 10 iris points
      annotations, // <list of object { landmark: array of points }> 32 base annotated landmarks & 2 iris annotations
      iris,        // <number> relative distance of iris to camera, multiple by focal lenght to get actual distance
      age,         // <number> estimated age
      gender,      // <string> 'male', 'female'
    }
  ],
  body:            // <array of detected objects>
  [
    {
      score,       // <number>,
      keypoints,   // <array of 2D landmarks [ score, landmark, position [x, y] ]> 17 annotated landmarks
    }
  ],
  hand:            // <array of detected objects>
  [
    {
      confidence,  // <number>,
      box,         // <array [x, y, width, height]>,
      landmarks,   // <array of 3D points [x, y,z]> 21 points
      annotations, // <array of 3D landmarks [ landmark: <array of points> ]> 5 annotated landmakrs
    }
  ],
  emotion:         // <array of emotions>
  [
    {
      score,       // <number> probabily of emotion
      emotion,     // <string> 'angry', 'discust', 'fear', 'happy', 'sad', 'surpise', 'neutral'
    }
  ],
  performance = {  // performance data of last execution for each module measuredin miliseconds
    body,
    hand,
    face,
    agegender,
    emotion,
    total,
  }
}
```

<hr>

## Build

If you want to modify the library and perform a full rebuild:  

*clone repository, install dependencies, check for errors and run full rebuild from which creates bundles from `/src` into `/dist`:*

```shell
git clone https://github.com/vladmandic/human
cd human
npm install # installs all project dependencies
npm run lint
npm run build
```

Project is written in pure `JavaScript` [ECMAScript version 2020](https://www.ecma-international.org/ecma-262/11.0/index.html)  

Only project depdendency is [@tensorflow/tfjs](https://github.com/tensorflow/tfjs)
Development dependencies are [eslint](https://github.com/eslint) used for code linting and [esbuild](https://github.com/evanw/esbuild) used for IIFE and ESM script bundling  

<hr>

## Performance

Performance will vary depending on your hardware, but also on number of resolution of input video/image, enabled modules as well as their parameters  

For example, on a desktop with a low-end nVidia GTX1050 it can perform multiple face detections at 60+ FPS, but drops to 10 FPS on a medium complex images if all modules are enabled  

Performance per module:

- Enabled all: 10 FPS
- Face Detect: 80 FPS (standalone)
- Face Geometry: 30 FPS (includes face detect)
- Face Iris: 25 FPS (includes face detect and face geometry)
- Age: 60 FPS (includes face detect)
- Gender: 60 FPS (includes face detect)
- Emotion: 60 FPS (includes face detect)
- Hand: 40 FPS (standalone)
- Body: 50 FPS (standalone)

Library can also be used on mobile devices  

<hr>

## Credits

- Face Detection: [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)
- Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)
- Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)
- Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)
- Body Pose Detection: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)
- Age & Gender Prediction: [**SSR-Net**](https://github.com/shamangary/SSR-Net)
- Emotion Prediction: [**Oarriaga**](https://github.com/oarriaga/face_classification)

<hr>

## Todo

- Tweak default models, parameters and factorization for age/gender/emotion/blazeface
- Add sample images
