mirror of https://github.com/vladmandic/human
282 lines
9.2 KiB
Markdown
282 lines
9.2 KiB
Markdown
# Human: 3D Face Detection, Body Pose, Hand & Finger Tracking, Iris Tracking and Age & Gender Prediction
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**Documentation**: <https://github.com/vladmandic/human#readme>
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**Code Repository**: <https://github.com/vladmandic/human>
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**Package**: <https://www.npmjs.com/package/@vladmandic/human>
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**Live Demo**: <https://vladmandic.github.io/human/demo/demo-esm.html>
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*Suggestions are welcome!*
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<hr>
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## Credits
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This is an amalgamation of multiple existing models:
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- Face Detection: [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)
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- Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)
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- Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)
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- Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)
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- Body Pose Detection: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)
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- Age & Gender Prediction: [**SSR-Net**](https://github.com/shamangary/SSR-Net)
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<hr>
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## Installation
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**Important**
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*The packaged (IIFE and ESM) version of `Human` includes `TensorFlow/JS (TFJS) 2.6.0` library which can be accessed via `human.tf`*
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*You should NOT manually load another instance of `tfjs`, but if you do, be aware of possible version conflicts*
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There are multiple ways to use `Human` library, pick one that suits you:
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### 1. [IIFE](https://developer.mozilla.org/en-US/docs/Glossary/IIFE) script
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*Simplest way for usage within Browser*
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Simply download `dist/human.js`, include it in your `HTML` file & it's ready to use.
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```html
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<script src="dist/human.js"><script>
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```
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IIFE script auto-registers global namespace `human` within global `Window` object
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This way you can also use `Human` library within embbedded `<script>` tag within your `html` page for all-in-one approach
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IIFE script is distributed in minified form with attached sourcemap
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### 2. [ESM](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Statements/import) module
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*Recommended for usage within `Browser`*
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#### 2.1 With Bundler
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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
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```js
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import human from 'dist/human.esm.js';
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```
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#### 2.2 Using Script Module
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You could use same syntax within your main `JS` file if it's imported with `<script type="module">`
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```html
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<script src="./index.js" type="module">
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```
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and then in your `index.js`
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```js
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import human from 'dist/human.esm.js';
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```
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ESM script is distributed in minified form with attached sourcemap
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### 3. [NPM](https://www.npmjs.com/) module
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*Recommended for `NodeJS` projects*
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Entry point is actual source `build/src/index.js`
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You also need to install and includ `tfjs` in your project
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Install with:
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```shell
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npm install @tensorflow/tfjs @vladmandic/human
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```
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And then use with:
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```js
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import * as tf from '@tensorflow/tfjs';
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import human from '@vladmandic/Human';
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```
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### Weights
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Pretrained model weights are includes in `./models`
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Default configuration uses relative paths to you entry script pointing to `../models`
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If your application resides in a different folder, modify `modelPath` property in configuration of each module
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<hr>
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## Demo
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Demos are included in `/demo`:
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- `demo-esm`: Demo using ESM module
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- `demo-iife`: Demo using IIFE module
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Both demos are identical, they just illustrate different ways to load `Human` library
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<hr>
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## Usage
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`Human` library does not require special initialization.
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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).
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There is only *ONE* method you need:
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```js
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import * as tf from '@tensorflow/tfjs';
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import human from '@vladmandic/human';
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// 'image': can be of any type of an image object: HTMLImage, HTMLVideo, HTMLMedia, Canvas, Tensor4D
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// 'options': optional parameter used to override any options present in default configuration
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const results = await human.detect(image, options?)
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```
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Additionally, `Human` library exposes several classes:
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```js
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human.defaults // default configuration object
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human.models // dynamically maintained object of any loaded models
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human.tf // instance of tfjs used by human
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```
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<hr>
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## Configuration
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Below is output of `human.defaults` object
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Any property can be overriden by passing user object during `human.detect()`
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Note that user object and default configuration are merged using deep-merge, so you do not need to redefine entire configuration
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```js
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human.defaults = {
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face: {
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enabled: true,
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detector: {
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modelPath: '../models/human/blazeface/model.json',
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maxFaces: 10,
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skipFrames: 5,
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minConfidence: 0.8,
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iouThreshold: 0.3,
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scoreThreshold: 0.75,
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},
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mesh: {
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enabled: true,
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modelPath: '../models/human/facemesh/model.json',
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},
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iris: {
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enabled: true,
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modelPath: '../models/human/iris/model.json',
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},
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age: {
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enabled: true,
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modelPath: '../models/human/ssrnet-imdb-age/model.json',
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skipFrames: 5,
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},
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gender: {
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enabled: true,
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modelPath: '../models/human/ssrnet-imdb-gender/model.json',
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},
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},
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body: {
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enabled: true,
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modelPath: '../models/human/posenet/model.json',
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maxDetections: 5,
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scoreThreshold: 0.75,
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nmsRadius: 20,
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},
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hand: {
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enabled: true,
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skipFrames: 5,
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minConfidence: 0.8,
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iouThreshold: 0.3,
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scoreThreshold: 0.75,
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detector: {
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anchors: '../models/human/handdetect/anchors.json',
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modelPath: '../models/human/handdetect/model.json',
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},
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skeleton: {
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modelPath: '../models/human/handskeleton/model.json',
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},
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},
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};
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```
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Where:
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- `enabled`: controls if specified modul is enabled (note: module is not loaded until it is required)
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- `modelPath`: path to specific pre-trained model weights
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- `maxFaces`, `maxDetections`: how many faces or people are we trying to analyze. limiting number in busy scenes will result in higher performance
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- `skipFrames`: how many frames to skip before re-running bounding box detection (e.g., face position does not move fast within a video, so it's ok to use previously detected face position and just run face geometry analysis)
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- `minConfidence`: threshold for discarding a prediction
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- `iouThreshold`: threshold for deciding whether boxes overlap too much in non-maximum suppression
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- `scoreThreshold`: threshold for deciding when to remove boxes based on score in non-maximum suppression
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- `nmsRadius`: radius for deciding points are too close in non-maximum suppression
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<hr>
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## Outputs
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Result of `humand.detect()` is a single object that includes data for all enabled modules and all detected objects:
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```js
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result = {
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face: // <array of detected objects>
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[
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{
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confidence: // <number>
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box: // <array [x, y, width, height]>
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mesh: // <array of 3D points [x, y, z]> (468 base points & 10 iris points)
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annotations: // <list of object { landmark: array of points }> (32 base annotated landmarks & 2 iris annotations)
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iris: // <number> (relative distance of iris to camera, multiple by focal lenght to get actual distance)
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age: // <number> (estimated age)
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gender: // <string> (male or female)
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}
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],
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body: // <array of detected objects>
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[
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{
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score: // <number>,
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keypoints: // <array of 2D landmarks [ score, landmark, position [x, y] ]> (17 annotated landmarks)
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}
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],
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hand: // <array of detected objects>
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[
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{
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confidence: // <number>,
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box: // <array [x, y, width, height]>,
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landmarks: // <array of 3D points [x, y,z]> (21 points)
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annotations: // <array of 3D landmarks [ landmark: <array of points> ]> (5 annotated landmakrs)
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}
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]
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}
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```
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<hr>
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## Build
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If you want to modify the library and perform a full rebuild:
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*clone repository, install dependencies, check for errors and run full rebuild from which creates bundles from `/src` into `/dist`:*
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```shell
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git clone https://github.com/vladmandic/human
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cd human
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npm install # installs all project dependencies
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npm run lint
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npm run build
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```
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Project is written in pure `JavaScript`, [ECMAScript version 2020](https://www.ecma-international.org/ecma-262/11.0/index.html)
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Only project depdendency is [@tensorflow/tfjs](https://github.com/tensorflow/tfjs), the rest are devDependencies tools: [eslint](https://github.com/eslint) used for linting and [esbuild](https://github.com/evanw/esbuild) used for bundling
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<hr>
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## Performance
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Performance will vary depending on your hardware, but also on number of resolution of input video/image, enabled modules as well as their parameters
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For example, on a desktop with a low-end nVidia GTX1050 it can perform multiple face detections at 50+ FPS, but drops to 5-10 FPS on a medium complex images if all modules are enabled
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Library can also be used on mobile devices
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<hr>
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## Todo
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- Improve detection of smaller faces
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- Fix memory leak in face detector
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