forked from protonphoton/LJ
113 lines
5.0 KiB
Markdown
113 lines
5.0 KiB
Markdown
= Web Interface
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You can load index.html file from your browser or have a webserver of your choice pointing to this directory.
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Webserver is mandatory if you want :
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- to remotely control LJ : imagine LJ can be installed in a dedicated computer/container with no easy access.
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- to use the face tracking, say from a smartphone. That's Lasercam (a clmtrackr plugin).
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== Simu
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A laser simulator. Choose lasernumber and it will display redis points for current scene/lasernumber
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== clmtrackr
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[](https://www.npmjs.com/package/clmtrackr)
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**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:
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[](https://auduno.github.io/clmtrackr/examples/media/facemodel_numbering_new.png)
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[Reference](http://auduno.github.io/clmtrackr/docs/reference.html) - [Overview](https://www.auduno.com/2014/01/05/fitting-faces/)
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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.
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For tracking in video, it is recommended to use a browser with WebGL support, though the library should work on any modern browser.
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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.
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### Examples ###
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* [Tracking in image](https://auduno.github.io/clmtrackr/examples/clm_image.html)
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* [Tracking in video](https://auduno.github.io/clmtrackr/examples/clm_video.html)
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* [Face substitution](https://auduno.github.io/clmtrackr/examples/facesubstitution.html)
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* [Face masking](https://auduno.github.io/clmtrackr/examples/face_mask.html)
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* [Realtime face deformation](https://auduno.github.io/clmtrackr/examples/facedeform.html)
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* [Emotion detection](https://auduno.github.io/clmtrackr/examples/clm_emotiondetection.html)
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* [Caricature](https://auduno.github.io/clmtrackr/examples/caricature.html)
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### Usage ###
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Download the minified library [clmtrackr.js](https://github.com/auduno/clmtrackr/raw/dev/build/clmtrackr.js), and include it in your webpage.
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```html
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/* clmtrackr libraries */
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<script src="js/clmtrackr.js"></script>
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```
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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.
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```html
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<video id="inputVideo" width="400" height="300" autoplay loop>
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<source src="./media/somevideo.ogv" type="video/ogg"/>
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</video>
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<script type="text/javascript">
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var videoInput = document.getElementById('inputVideo');
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var ctracker = new clm.tracker();
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ctracker.init();
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ctracker.start(videoInput);
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</script>
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```
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You can now get the positions of the tracked facial features as an array via ```getCurrentPosition()```:
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```html
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<script type="text/javascript">
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function positionLoop() {
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requestAnimationFrame(positionLoop);
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var positions = ctracker.getCurrentPosition();
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// positions = [[x_0, y_0], [x_1,y_1], ... ]
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// do something with the positions ...
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}
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positionLoop();
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</script>
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```
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You can also use the built in function ```draw()``` to draw the tracked facial model on a canvas :
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```html
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<canvas id="drawCanvas" width="400" height="300"></canvas>
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<script type="text/javascript">
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var canvasInput = document.getElementById('drawCanvas');
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var cc = canvasInput.getContext('2d');
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function drawLoop() {
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requestAnimationFrame(drawLoop);
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cc.clearRect(0, 0, canvasInput.width, canvasInput.height);
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ctracker.draw(canvasInput);
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}
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drawLoop();
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</script>
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```
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See the complete example [here](https://auduno.github.io/clmtrackr/examples/example.html).
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### Development ###
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First, install [node.js](http://nodejs.org/) with npm.
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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.
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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.
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### License ###
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**clmtrackr** is distributed under the [MIT License](http://www.opensource.org/licenses/MIT)
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