Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/auduno/clmtrackr

Javascript library for precise tracking of facial features via Constrained Local Models
https://github.com/auduno/clmtrackr

Last synced: 3 days ago
JSON representation

Javascript library for precise tracking of facial features via Constrained Local Models

Awesome Lists containing this project

README 

        
clmtrackr

======



[![npm version](https://img.shields.io/npm/v/clmtrackr.svg)](https://www.npmjs.com/package/clmtrackr)



![tracked face](https://auduno.github.io/clmtrackr/examples/media/clmtrackr_03.jpg)



**clmtrackr** is a javascript library for fitting facial models to faces in videos or images. It currently is an implementation of *constrained local models* fitted by *regularized landmark mean-shift*, as described in [Jason M. Saragih's paper](http://dl.acm.org/citation.cfm?id=1938021). **clmtrackr** tracks a face and outputs the coordinate positions of the face model as an array, following the numbering of the model below:



[![facemodel_numbering](https://auduno.github.io/clmtrackr/examples/media/facemodel_numbering_new_small.png)](https://auduno.github.io/clmtrackr/examples/media/facemodel_numbering_new.png)



[Reference](http://auduno.github.io/clmtrackr/docs/reference.html) - [Overview](https://www.auduno.com/2014/01/05/fitting-faces/)



The library provides some generic face models that were trained on [the MUCT database](http://www.milbo.org/muct/) and some additional self-annotated images. Check out [clmtools](https://github.com/auduno/clmtools) for building your own models.



For tracking in video, it is recommended to use a browser with WebGL support, though the library should work on any modern browser.



For some more information about Constrained Local Models, take a look at Xiaoguang Yan's [excellent tutorial](https://sites.google.com/site/xgyanhome/home/projects/clm-implementation/ConstrainedLocalModel-tutorial%2Cv0.7.pdf?attredirects=0), which was of great help in implementing this library.



### Examples ###



* [Tracking in image](https://auduno.github.io/clmtrackr/examples/clm_image.html)

* [Tracking in video](https://auduno.github.io/clmtrackr/examples/clm_video.html)

* [Face substitution](https://auduno.github.io/clmtrackr/examples/facesubstitution.html)

* [Face masking](https://auduno.github.io/clmtrackr/examples/face_mask.html)

* [Realtime face deformation](https://auduno.github.io/clmtrackr/examples/facedeform.html)

* [Emotion detection](https://auduno.github.io/clmtrackr/examples/clm_emotiondetection.html)

* [Caricature](https://auduno.github.io/clmtrackr/examples/caricature.html)



### Usage ###



Download the minified library [clmtrackr.js](https://github.com/auduno/clmtrackr/raw/dev/build/clmtrackr.js), and include it in your webpage.



```html

/* clmtrackr libraries */



```



The following code initiates the clmtrackr with the default model (see the [reference](http://auduno.github.io/clmtrackr/docs/reference.html) for some alternative models), and starts the tracker running on a video element.



```html



  



  var videoInput = document.getElementById('inputVideo');

  

  var ctracker = new clm.tracker();

  ctracker.init();

  ctracker.start(videoInput);



```



You can now get the positions of the tracked facial features as an array via ```getCurrentPosition()```:



```html



  function positionLoop() {

    requestAnimationFrame(positionLoop);

    var positions = ctracker.getCurrentPosition();

    // positions = [[x_0, y_0], [x_1,y_1], ... ]

    // do something with the positions ...

  }

  positionLoop();



```



You can also use the built in function ```draw()``` to draw the tracked facial model on a canvas :



```html



  var canvasInput = document.getElementById('drawCanvas');

  var cc = canvasInput.getContext('2d');

  function drawLoop() {

    requestAnimationFrame(drawLoop);

    cc.clearRect(0, 0, canvasInput.width, canvasInput.height);

    ctracker.draw(canvasInput);

  }

  drawLoop();



```



See the complete example [here](https://auduno.github.io/clmtrackr/examples/example.html).



### Development ###



First, install [node.js](http://nodejs.org/) with npm.



In the root directory of clmtrackr, run `npm install` then run `npm run build`. This will create `clmtrackr.js` and `clmtrackr.module.js` in `build` folder.



To test the examples locally, you need to run a local server. One easy way to do this is to install `http-server`, a small node.js utility: `npm install -g http-server`. Then run `http-server` in the root of clmtrackr and go to `https://localhost:8080/examples` in your browser.



### License ###



**clmtrackr** is distributed under the [MIT License](http://www.opensource.org/licenses/MIT)