B4J Question Face recognition server with B4J?

public class WebsocketServer extends Server {

   private final static Logger LOG = Logger.getLogger(WebsocketServer.class);

   public WebsocketServer(int port) {
       SelectChannelConnector connector = new SelectChannelConnector();
       connector.setPort(port);
       addConnector(connector);

       WebSocketHandler wsHandler = new WebSocketHandler() {
           public WebSocket doWebSocketConnect(HttpServletRequest request,    String protocol) {
               return new FaceDetectWebSocket();
           }
       };
       setHandler(wsHandler);
   }

   /**
    * Simple innerclass that is used to handle websocket connections.
    *
    * @author jos
    */
   private static class FaceDetectWebSocket implements WebSocket,
           WebSocket.OnBinaryMessage, WebSocket.OnTextMessage {

       private Connection connection;
       private FaceDetection faceDetection = new FaceDetection();

       public FaceDetectWebSocket() {
           super();
       }

       /**
        * On open we set the connection locally, and enable
        * binary support
        */
       public void onOpen(Connection connection) {
           this.connection = connection;
           this.connection.setMaxBinaryMessageSize(1024 * 512);
       }

       /**
        * Cleanup if needed. Not used for this example
        */
       public void onClose(int code, String message) {}

       /**
        * When we receive a binary message we assume it is an image. We then run this
        * image through our face detection algorithm and send back the response.
        */
       public void onMessage(byte[] data, int offset, int length) {

           ByteArrayOutputStream bOut = new ByteArrayOutputStream();
           bOut.write(data, offset, length);
           try {
               byte[] result = faceDetection.convert(bOut.toByteArray());               
               this.connection.sendMessage(result, 0, result.length);
           } catch (IOException e) {
               LOG.error("Error in facedetection, ignoring message:" + e.getMessage());
           }
       }


   }

   /**
    * Start the server on port 999
    */
   public static void main(String[] args) throws Exception {
       WebsocketServer server = new WebsocketServer(9999);
       server.start();
       server.join();
   }   
}


public class FaceDetection {

   private static final String CASCADE_FILE = "resources/haarcascade_frontalface_alt.xml";

   private int minsize = 20;
   private int group = 0;
   private double scale = 1.1;

   /**
    * Based on FaceDetection example from JavaCV.
    */
   public byte[] convert(byte[] imageData) throws IOException {
       // create image from supplied bytearray
       IplImage originalImage = cvDecodeImage(cvMat(1, imageData.length,CV_8UC1, new BytePointer(imageData)));

       // Convert to grayscale for recognition
       IplImage grayImage = IplImage.create(originalImage.width(), originalImage.height(), IPL_DEPTH_8U, 1);
       cvCvtColor(originalImage, grayImage, CV_BGR2GRAY);

       // storage is needed to store information during detection
       CvMemStorage storage = CvMemStorage.create();

       // Configuration to use in analysis
       CvHaarClassifierCascade cascade = new CvHaarClassifierCascade(cvLoad(CASCADE_FILE));

       // We detect the faces.
       CvSeq faces = cvHaarDetectObjects(grayImage, cascade, storage, scale, group, minsize);

       // We iterate over the discovered faces and draw yellow rectangles around them.
       for (int i = 0; i < faces.total(); i++) {
           CvRect r = new CvRect(cvGetSeqElem(faces, i));
           cvRectangle(originalImage, cvPoint(r.x(), r.y()),
                   cvPoint(r.x() + r.width(), r.y() + r.height()),
                   CvScalar.YELLOW, 1, CV_AA, 0);
       }

       // convert the resulting image back to an array
       ByteArrayOutputStream bout = new ByteArrayOutputStream();
       BufferedImage imgb = originalImage.getBufferedImage();
       ImageIO.write(imgb, "png", bout);
       return bout.toByteArray();
   }
}