LJ/www/README.md

= Web Interface

You can load index.html file from your browser or have a webserver of your choice pointing to this directory.

Webserver is mandatory if you want :

- to remotely control LJ : imagine LJ can be installed in a dedicated computer/container with no easy access.
- to use the face tracking, say from a smartphone. That's Lasercam (a clmtrackr plugin). 

== Simu

A laser simulator. Choose lasernumber and it will display redis points for current scene/lasernumber




== 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 */
<script src="js/clmtrackr.js"></script>
```

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
<video id="inputVideo" width="400" height="300" autoplay loop>
  <source src="./media/somevideo.ogv" type="video/ogg"/>
</video>
<script type="text/javascript">
  var videoInput = document.getElementById('inputVideo');
  
  var ctracker = new clm.tracker();
  ctracker.init();
  ctracker.start(videoInput);
</script>
```

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

```html
<script type="text/javascript">
  function positionLoop() {
    requestAnimationFrame(positionLoop);
    var positions = ctracker.getCurrentPosition();
    // positions = [[x_0, y_0], [x_1,y_1], ... ]
    // do something with the positions ...
  }
  positionLoop();
</script>
```

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

```html
<canvas id="drawCanvas" width="400" height="300"></canvas>
<script type="text/javascript">
  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();
</script>
```

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)