Discover the new easier way to develop Kurento video applications

JavaScript Module - Plate Detector Filter

This web application consists of a WebRTC video communication in mirror (loopback) with a plate detector filter element.

Running this example

First of all, install Kurento Media Server: Installation Guide. Start the media server and leave it running in the background.

Note

If you will run this tutorial from a remote machine (i.e. not from localhost), then you need to configure Secure WebSocket (wss://) in Kurento Media Server. For instructions, check Securing Kurento Media Server.

This is not an issue if you will run both KMS and the tutorial demo locally, because browsers (at least Chrome at the time of this writing) allow connecting to insecure WebSockets from HTTPS pages, as long as everything happens in localhost.

Install Node.js, Bower, and a web server in your system:

curl -sL https://deb.nodesource.com/setup_8.x | sudo -E bash -
sudo apt-get install -y nodejs
sudo npm install -g bower
sudo npm install -g http-server

Here, we suggest using the simple Node.js http-server, but you could use any other web server.

Note

You need to configure the web server with HTTPS. For more information, check Configure JavaScript applications to use HTTPS.

You also need the source code of this demo; clone it from GitHub, then start the web server:

git clone https://github.com/Kurento/kurento-tutorial-js.git
cd kurento-tutorial-js/kurento-platedetector
git checkout 6.12.0
bower install
http-server -p 8443 --ssl --cert keys/server.crt --key keys/server.key

Finally, access the web application by using a WebRTC-capable browser (Firefox, Chrome) to open the appropriate URL:

  • If KMS is running in your local machine:

    https://localhost:8443/
    
  • If KMS is running in a remote server:

    https://localhost:8443/index.html?ws_uri=wss://<KmsIp>:<KmsPort>/kurento
    

Understanding this example

This application uses computer vision and augmented reality techniques to detect a plate in a WebRTC stream on optical character recognition (OCR).

The interface of the application (an HTML web page) is composed by two HTML5 video tags: one for the video camera stream (the local client-side stream) and other for the mirror (the remote stream). The video camera stream is sent to Kurento Media Server, which processes and sends it back to the client as a remote stream. To implement this, we need to create a Media Pipeline composed by the following Media Element s:

WebRTC with plateDetector filter Media Pipeline

WebRTC with plateDetector filter Media Pipeline

The complete source code of this demo can be found in GitHub.

This example is a modified version of the Magic Mirror tutorial. In this case, this demo uses a PlateDetector instead of FaceOverlay filter. An screenshot of the running example is shown in the following picture:

Plate detector demo in action

Plate detector demo in action

Note

Modules can have options. For configuring these options, you’ll need to get the constructor for them. In Javascript and Node, you have to use kurentoClient.getComplexType(‘qualifiedName’) . There is an example in the code.

The following snippet shows how the media pipeline is implemented in the Java server-side code of the demo. An important issue in this code is that a listener is added to the PlateDetectorFilter object (addPlateDetectedListener). This way, each time a plate is detected in the stream, a message is sent to the client side. As shown in the screenshot below, this event is printed in the console of the GUI.

...
kurentoClient.register('kurento-module-platedetector')
...

kurentoClient(args.ws_uri, function(error, client) {
  if (error) return onError(error);

  client.create('MediaPipeline', function(error, _pipeline) {
    if (error) return onError(error);

    pipeline = _pipeline;

    console.log("Got MediaPipeline");

    pipeline.create('WebRtcEndpoint', function(error, webRtc) {
      if (error) return onError(error);

      console.log("Got WebRtcEndpoint");

      setIceCandidateCallbacks(webRtcPeer, webRtc, onError)

      webRtc.processOffer(sdpOffer, function(error, sdpAnswer) {
        if (error) return onError(error);

        console.log("SDP answer obtained. Processing...");

        webRtc.gatherCandidates(onError);
        webRtcPeer.processAnswer(sdpAnswer);
      });

      pipeline.create('platedetector.PlateDetectorFilter', function(error, filter) {
        if (error) return onError(error);

        console.log("Got Filter");

        filter.on('PlateDetected', function (data){
          console.log("License plate detected " + data.plate);
        });

        client.connect(webRtc, filter, webRtc, function(error) {
          if (error) return onError(error);

          console.log("WebRtcEndpoint --> filter --> WebRtcEndpoint");
        });
      });
    });
  });
});

Note

The TURN and STUN servers to be used can be configured simple adding the parameter ice_servers to the application URL, as follows:

https://localhost:8443/index.html?ice_servers=[{"urls":"stun:stun1.example.net"},{"urls":"stun:stun2.example.net"}]
https://localhost:8443/index.html?ice_servers=[{"urls":"turn:turn.example.org","username":"user","credential":"myPassword"}]

Dependencies

The dependencies of this demo has to be obtained using Bower. The definition of these dependencies are defined in the bower.json file, as follows:

"dependencies": {
   "kurento-client": "6.12.0",
   "kurento-utils": "6.12.0"
   "kurento-module-pointerdetector": "6.12.0"
}

To get these dependencies, just run the following shell command:

bower install

Note

We are in active development. You can find the latest versions at Bower.