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# Human Library
## 3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking, Age & Gender Prediction, Emotion Prediction & Gesture Recognition
- [**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)
- [**Change Log**](./CHANGELOG.md)
- [**Live Demo**](https://vladmandic.github.io/human/demo/index.html)
Compatible with *Browser*, *WebWorker* and *NodeJS* execution
Compatible with *CPU*, *WebGL*, *WASM* and *WebGPU* backends
(and maybe with React-Native as it doesn't use any DOM objects)
*This is a pre-release project, see [issues](https://github.com/vladmandic/human/issues) for list of known limitations and planned enhancements*
*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) 2.7.0` 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 bundle with TFJS for Browsers
- `dist/human.esm.js`: ESM format bundle with TFJS for Browsers
- `dist/human.esm-nobundle.js`: ESM format bundle without TFJS for Browsers
- `dist/human.node.js`: CommonJS format bundle with TFJS for NodeJS
- `dist/human.node-nobundle.js`: CommonJS format bundle without TFJS for NodeJS
All versions include `sourcemap` *(.map)* and build `manifest` *(.json)*
While `Human` is in pre-release mode, all bundles are non-minified
Defaults:
```json
{
"main": "dist/human.node.js",
"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
Which you can use to create instance of `human` library:
```js
const human = new Human();
```
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 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 Human from 'dist/human.esm.js'; // for direct import must use path to module, not package name
const human = new Human();
```
#### **2.2 With Bundler**
If you're using bundler *(such as rollup, webpack, parcel, browserify, 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
// you can also force-load specific version
// for example: `@vladmandic/human/dist/human.esm-nobundle.js`
const human = new Human();
```
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
const human = new Human();
```
### 3. [NPM](https://www.npmjs.com/) module
*Recommended for `NodeJS` projects that will execute in the backend*
Entry point is bundle in CommonJS 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 @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').default; // points to @vladmandic/human/dist/human.node.js
const human = new Human();
```
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' },
}
```
### 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**:
- `index.html`: Full demo using Browser with ESM module, includes selectable backends and webworkers
it loads `dist/demo-browser-index.js` which is built from sources in `demo`, starting with `demo/browser`
alternatively you can load `demo/browser.js` directly
*If you want to test `wasm` or `webgpu` backends, enable loading in `index.html`*
**NodeJS**:
- `node.js`: Demo using NodeJS with CommonJS 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
// 'image': can be of any type of an image object: HTMLImage, HTMLVideo, HTMLMedia, Canvas, Tensor4D
// 'config': optional parameter used to override any options present in default configuration
// configuration is fully dynamic and can change between different calls to 'detect()'
const result = await human.detect(image, config?)
```
or if you want to use promises
```js
human.detect(image, config?).then((result) => {
// your code
})
```
Additionally, `Human` library exposes several objects and methods:
```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
human.state // <string> describing current operation in progress
// progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'
human.load(config) // explicitly call load method that loads configured models
// if you want to pre-load them instead of on-demand loading during 'human.detect()'
```
Note that when using `Human` library in `NodeJS`, you must load and parse the image *before* you pass it for detection and dispose it afterwards
Input format is `Tensor4D[1, width, height, 3]` of type `float32`
For example:
```js
const imageFile = '../assets/sample1.jpg';
const buffer = fs.readFileSync(imageFile);
const decoded = tf.node.decodeImage(buffer);
const casted = decoded.toFloat();
const image = casted.expandDims(0);
decoded.dispose();
casted.dispose();
logger.log('Processing:', image.shape);
const human = new Human.Human();
const result = await human.detect(image, config);
image.dispose();
```
<hr>
## Configuration
Detailed configuration options are explained below, but they are best seen in the menus present in the `demo` application:
![Menus](assets/screenshot-menu.png)
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
All configuration details can be changed in real-time!
```js
config = {
backend: 'webgl', // select tfjs backend to use
console: true, // enable debugging output to console
async: false, // execute enabled models in parallel
// this disables per-model performance data but slightly increases performance
// cannot be used if profiling is enabled
profile: false, // enable tfjs profiling
// this has significant performance impact, only enable for debugging purposes
// currently only implemented for age,gender,emotion models
deallocate: false, // aggresively deallocate gpu memory after each usage
// only valid for webgl backend and only during first call, cannot be changed unless library is reloaded
// this has significant performance impact, only enable on low-memory devices
scoped: false, // enable scoped runs
// some models *may* have memory leaks, this wrapps everything in a local scope at a cost of performance
// typically not needed
videoOptimized: true, // perform additional optimizations when input is video, must be disabled for images
filter: { // note: image filters are only available in Browser environments and not in NodeJS as they require WebGL for processing
enabled: true, // enable image pre-processing filters
return: true, // return processed canvas imagedata in result
width: 0, // resize input width
height: 0, // resize input height
// usefull on low-performance devices to reduce the size of processed input
// if both width and height are set to 0, there is no resizing
// if just one is set, second one is scaled automatically
// if both are set, values are used as-is
brightness: 0, // range: -1 (darken) to 1 (lighten)
contrast: 0, // range: -1 (reduce contrast) to 1 (increase contrast)
sharpness: 0, // range: 0 (no sharpening) to 1 (maximum sharpening)
blur: 0, // range: 0 (no blur) to N (blur radius in pixels)
saturation: 0, // range: -1 (reduce saturation) to 1 (increase saturation)
hue: 0, // range: 0 (no change) to 360 (hue rotation in degrees)
negative: false, // image negative
sepia: false, // image sepia colors
vintage: false, // image vintage colors
kodachrome: false, // image kodachrome colors
technicolor: false, // image technicolor colors
polaroid: false, // image polaroid camera effect
pixelate: 0, // range: 0 (no pixelate) to N (number of pixels to pixelate)
},
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 '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 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
// only used for video inputs, ignored for static inputs
// if model is running st 25 FPS, we can re-use existing bounding box for updated face mesh analysis
// as the 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, only used for video inputs
},
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, only used for video inputs
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
// only used for video inputs
// if model is running st 25 FPS, we can re-use existing bounding box for updated hand skeleton analysis
// as the hand 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: {
modelPath: '../models/handdetect/model.json',
},
skeleton: {
modelPath: '../models/handskeleton/model.json',
},
},
gesture: {
enabled: true, // enable simple gesture recognition
// takes processed data and based on geometry detects simple gestures
// easily expandable via code, see `src/gesture.js`
},
};
```
Any user configuration 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
- `modelPath`: Update as needed to reflect your application's relative path
for example,
```js
const myConfig = {
backend: 'wasm',
filter: { enabled: false },
}
const result = await human.detect(image, myConfig)
```
<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'
}
],
gesture: // object containing parsed gestures
{
face, // <array of string>
body, // <array of string>
hand, // <array of string>
}
performance = { // performance data of last execution for each module measuredin miliseconds
backend, // time to initialize tf backend, valid only during backend startup
load, // time to load models, valid only during model load
image, // time for image processing
gesture, // gesture analysis time
body, // model time
hand, // model time
face, // model time
agegender, // model time
emotion, // model time
total, // end to end time
}
}
```
<hr>
## Profile
If `config.profile` is enabled, call to `human.profile()` will return detailed profiling data from the last detect invokation.
example:
```js
result = {
{age: {…}, gender: {…}, emotion: {…}}
age:
timeKernelOps: 53.78892800000002
newBytes: 4
newTensors: 1
numKernelOps: 341
peakBytes: 46033948
largestKernelOps: Array(5)
0: {name: "Reshape", bytesAdded: 107648, totalBytesSnapshot: 46033948, tensorsAdded: 1, totalTensorsSnapshot: 1149, …}
1: {name: "Reshape", bytesAdded: 0, totalBytesSnapshot: 45818652, tensorsAdded: 1, totalTensorsSnapshot: 1147, …}
2: {name: "Reshape", bytesAdded: 0, totalBytesSnapshot: 45633996, tensorsAdded: 1, totalTensorsSnapshot: 1148, …}
3: {name: "Reshape", bytesAdded: 0, totalBytesSnapshot: 45389376, tensorsAdded: 1, totalTensorsSnapshot: 1154, …}
4: {name: "Reshape", bytesAdded: 53824, totalBytesSnapshot: 45381776, tensorsAdded: 1, totalTensorsSnapshot: 1155, …}
slowestKernelOps: Array(5)
0: {name: "_FusedMatMul", bytesAdded: 12, totalBytesSnapshot: 44802280, tensorsAdded: 1, totalTensorsSnapshot: 1156, …}
1: {name: "_FusedMatMul", bytesAdded: 4, totalBytesSnapshot: 44727564, tensorsAdded: 1, totalTensorsSnapshot: 1152, …}
2: {name: "_FusedMatMul", bytesAdded: 12, totalBytesSnapshot: 44789100, tensorsAdded: 1, totalTensorsSnapshot: 1157, …}
3: {name: "Add", bytesAdded: 4, totalBytesSnapshot: 44788748, tensorsAdded: 1, totalTensorsSnapshot: 1158, …}
4: {name: "Add", bytesAdded: 4, totalBytesSnapshot: 44788748, tensorsAdded: 1, totalTensorsSnapshot: 1158, …}
}
```
## 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
```
This will rebuild library itself (all variations) as well as demo
Project is written in pure `JavaScript` [ECMAScript version 2020](https://www.ecma-international.org/ecma-262/11.0/index.html)
Build target is `JavaScript` **EMCAScript version 2018**
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, it can perform multiple face detections at 60+ FPS, but drops to ~15 FPS on a medium complex images if all modules are enabled
### Performance per module on a **notebook** with nVidia GTX1050 GPU on a FullHD input:
- Enabled all: 15 FPS
- Image filters: 80 FPS (standalone)
- Gesture: 80 FPS (standalone)
- Face Detect: 80 FPS (standalone)
- Face Geometry: 30 FPS (includes face detect)
- Face Iris: 30 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)
### Performance per module on a **smartphone** with Snapdragon 855 on a FullHD input:
- Enabled all: 5 FPS
- Image filters: 30 FPS (standalone)
- Gesture: 30 FPS (standalone)
- Face Detect: 20 FPS (standalone)
- Face Geometry: 10 FPS (includes face detect)
- Face Iris: 5 FPS (includes face detect and face geometry)
- Age: 20 FPS (includes face detect)
- Gender: 20 FPS (includes face detect)
- Emotion: 20 FPS (includes face detect)
- Hand: 40 FPS (standalone)
- Body: 10 FPS (standalone)
For performance details, see output of `result.performance` object during after running inference
<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)
- Image Filters: [**WebGLImageFilter**](https://github.com/phoboslab/WebGLImageFilter)
<hr>
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