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Code Block
titlepgm.dfdl.xsd
<?xml version="1.0" encoding="UTF-8"?>

<!--
Load image data from a PGM file and represent the data as a sequence of pixels in row major order.
-->

<xs:schema xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:dfdl="http://www.ogf.org/dfdl/dfdl-1.0/" xmlns:ex="http://example.com" targetNamespace="http://example.com">
  <xs:include schemaLocation="xsd/built-in-formats.xsd"/>

  <xs:annotation>
    <xs:appinfo source="http://www.ogf.org/dfdl/">
      <dfdl:format ref="ex:daffodilTest1" separator="" initiator="" terminator="" leadingSkip='0' textTrimKind="none" initiatedContent="no"
        alignment="implicit" alignmentUnits="bits" trailingSkip="0" ignoreCase="no" separatorPolicy="suppressed" 
        separatorPosition="infix" occursCountKind="parsed" emptyValueDelimiterPolicy="both" representation="text" 
        textNumberRep="standard" lengthKind="delimited" encoding="ASCII"/>
    </xs:appinfo>
  </xs:annotation>

  <xs:element name="file">
    <xs:complexType>
      <xs:sequence>

        <xs:element name="header" dfdl:lengthKind="implicit" maxOccurs="1">
          <xs:complexType>
            <xs:sequence dfdl:sequenceKind="ordered" dfdl:separator="%NL;" dfdl:separatorPosition="postfix">
              <xs:element name="type" type="xs:string"/>
              <xs:element name="dimensions" maxOccurs="1" dfdl:occursCountKind="implicit">
                <xs:complexType>
                  <xs:sequence dfdl:sequenceKind="ordered" dfdl:separator="%SP;">
                    <xs:element name="width" type="xs:integer"/>
                    <xs:element name="height" type="xs:integer"/>
                  </xs:sequence>
                </xs:complexType>
              </xs:element>
              <xs:element name="depth" type="xs:integer"/>
            </xs:sequence>
          </xs:complexType>
        </xs:element>

        <xs:element name="pixels" dfdl:lengthKind="implicit" maxOccurs="1">
          <xs:complexType>
            <xs:sequence dfdl:separator="%SP; %NL; %SP;%NL;" dfdl:separatorPosition="postfix" dfdl:separatorSuppressionPolicy="anyEmpty">
              <xs:element name="pixel" type="xs:integer" maxOccurs="unbounded" dfdl:occursCountKind="expression"
                dfdl:occursCount="{../../ex:header/ex:dimensions/ex:width * ../../ex:header/ex:dimensions/ex:height }"/>
            </xs:sequence>
          </xs:complexType>
        </xs:element>

      </xs:sequence>
    </xs:complexType>
  </xs:element>

</xs:schema>

...

Medici extractors typically serve to automatically extract some new kind of information from a file's content when it is uploaded into Medici.  These extractors do this by connecting to a shared RabbitMQ bus.  When a new file is uploaded to Medici it is announced on this bus.  Extractors that can handle a file of the type posted on the bus are triggered and the data they in turn create is returned to Medici as derived data to be associated with that file.  The extractors themselves can be implemented in a variety of languages. Examples of these extractors in different languages can be found in the extractors-templates code repository.

Anchor
Java
Java
Java

An extractor must establish a connection with the Medici RabbitMQ bus, handle incoming messages, start jobs based on received messages, and ultimatley carry out a job on a given file.  The example below simply counts the number of words in a document and returns this information as a piece of metadata to be associated with the file.

Code Block
themeEmacs
languagejava
titleConnecting to RabbitMQ
protected void startExtractor(String rabbitMQUsername, String rabbitMQpassword)
{
	try{ 
 		//Open channel and declare exchange and consumer
		ConnectionFactory factory = new ConnectionFactory();
		factory.setHost(serverAddr);
		factory.setUsername(rabbitMQUsername);
		factory.setPassword(rabbitMQpassword);
		Connection connection = factory.newConnection();

 		final Channel channel = connection.createChannel();
		channel.exchangeDeclare(EXCHANGE_NAME, "topic", true);

		channel.queueDeclare(QUEUE_NAME,DURABLE,EXCLUSIVE,AUTO_DELETE,null);
		channel.queueBind(QUEUE_NAME, EXCHANGE_NAME, "*.file.text.plain.#");
 
 		this.channel = channel;

 		// create listener
		channel.basicConsume(QUEUE_NAME, false, CONSUMER_TAG, new DefaultConsumer(channel) {
 			@Override
 			public void handleDelivery(String consumerTag, Envelope envelope, AMQP.BasicProperties properties, byte[] body) throws IOException {
				messageReceived = new String(body);
 				long deliveryTag = envelope.getDeliveryTag();
 				// (process the message components here ...)
				System.out.println(" {x} Received '" + messageReceived + "'");
 
				replyProps = new AMQP.BasicProperties.Builder().correlationId(properties.getCorrelationId()).build();
				replyTo = properties.getReplyTo();
 
				processMessageReceived();
				System.out.println(" [x] Done");
				channel.basicAck(deliveryTag, false);
			}
		});

 		// start listening 
		System.out.println(" [*] Waiting for messages. To exit press CTRL+C");

  		while (true) {
			Thread.sleep(1000);
		}
	}catch(Exception e){
		e.printStackTrace();
		System.exit(1);
	} 
}
Code Block
languagejava
titleProcessing Messages
protected void processMessageReceived()
{
  try {
    try {
      ExampleJavaExtractorService extrServ = new ExampleJavaExtractorService(this);
      jobReceived = getRepresentation(messageReceived, ExtractionJob.class);        
    
      File textFile = extrServ.processJob(jobReceived);
      jobReceived.setFlag("wasText");
      log.info("Word count extraction complete. Returning word count file as intermediate result.");
      sendStatus(jobReceived.getId(), this.getClass().getSimpleName(), "Word count extraction complete. Returning word count file as intermediate result.", log);
      uploadIntermediate(textFile, "text/plain", log);
      textFile.delete();
        
      sendStatus(jobReceived.getId(), this.getClass().getSimpleName(), "DONE.", log);
    } catch (Exception ioe) {
      log.error("Could not finish extraction job.", ioe);
      sendStatus(jobReceived.getId(), this.getClass().getSimpleName(), "Could not finish extraction job.", log);
      sendStatus(jobReceived.getId(), this.getClass().getSimpleName(), "DONE.", log);
    }
  } catch(Exception e) {
      e.printStackTrace();
      System.exit(1);
  } 
}
Code Block
languagejava
titleProcessing Jobs
public File processJob(ExtractionJob receivedMsg) throws Exception 
{    
  log.info("Downloading text file with ID "+ receivedMsg.getIntermediateId() +" from " + receivedMsg.getHost());
  callingExtractor.sendStatus(receivedMsg.getId(), callingExtractor.getClass().getSimpleName(), "Downloading text file.", log);
      
  DefaultHttpClient httpclient = new DefaultHttpClient();
  HttpGet httpGet = new HttpGet(receivedMsg.getHost() +"api/files/"+ receivedMsg.getIntermediateId()+"?key="+playserverKey);    
  HttpResponse fileResponse = httpclient.execute(httpGet);
  log.info(fileResponse.getStatusLine());

  if(fileResponse.getStatusLine().toString().indexOf("200") == -1){
    throw new IOException("File not found.");
  }

  HttpEntity fileEntity = fileResponse.getEntity();
  InputStream fileIs = fileEntity.getContent();
  
  Header[] hdrs = fileResponse.getHeaders("content-disposition");
  String contentDisp = hdrs[0].toString();
  
  String fileName = contentDisp.substring(contentDisp.indexOf("filename=")+9);
  File tempFile = File.createTempFile(fileName.substring(0, fileName.lastIndexOf(".")),      fileName.substring(fileName.lastIndexOf(".")).toLowerCase());
  OutputStream fileOs = new FileOutputStream(tempFile);   
  IOUtils.copy(fileIs,fileOs);
  fileIs.close();
  fileOs.close();
      
  EntityUtils.consume(fileEntity); 
      
  log.info("Download complete. Initiating word count generation");
  
  File textFile = processFile(tempFile, receivedMsg.getId());  
  return textFile;       
}
Code Block
languagejava
titleProcessing Files
private File processFile(File tempFile, String originalFileId) throws Exception
{
    Runtime r = Runtime.getRuntime();
    Process p;     // Process tracks one external native process
      
    String tempDir = System.getProperty("java.io.tmpdir");

	if (new Character(tempDir.charAt(tempDir.length()-1)).toString().equals(System.getProperty("file.separator")) == false){
      tempDir = tempDir + System.getProperty("file.separator");
    }
      
    String processCmd = "";
    String operSystem = System.getProperty("os.name").toLowerCase();

	// TODO: windows impl
    if(operSystem.indexOf("nix") >= 0 || operSystem.indexOf("nux") >= 0 || operSystem.indexOf("aix") > 0 ){
      "wc -w " + tempDir +  tempFile.getName();
    }  
      
    p = r.exec(processCmd, null, new File(tempDir));
    StreamGobbler outputGobbler = new StreamGobbler(p.getInputStream(), "INFO", log);
    StreamGobbler errorGobbler = new StreamGobbler(p.getErrorStream(),"ERROR", log);
    outputGobbler.start();
    errorGobbler.start();
    p.waitFor();
      
    File outFile = new File(tempDir + tempFile.getName().substring(0, tempFile.getName().lastIndexOf(".")) + ".txt");
    tempFile.delete();

	if(!Files.exists(outFile.toPath()))
      throw new Exception("File not processed correctly. File is possibly corrupt.");
      
  	return outFile;  
}

...

Java extractors can be created using the amqp-client jar file. This allows you to connect to the RabitMQ bus and received messages. The easiest way to get up and running is to use maven with java to add all required dependencies. An example of an extractor written in java can be found at Medici Extractor in Java.

Anchor
Python
Python
Python

Python extractors will often be based on the packages pika and requests. This allows you to connect to the RabittMQ message bus and easily send requests to medici. A complete example of the python extractor can be found at Medici Extractor in Python

Calling R Scripts from Python

Coming soon...

Anchor
Versus
Versus
Versus Extractors

Versus extractors serve to extract a signature from a file's content.  These signatures, effectively a hash for the data, are typically numerical vectors which capture some semantically meaningful aspect of the content so that two such signatures can then be compared using some distance measure.  Within Versus extractors operate on a data structure representing the content of a file, produced a Versus adapter, and the returned signatures compared by either a Versus similarity or distance measure.  The combination of these adapters, extractors, and measures in turn compose a comparison which can be used for relating files according their contents.

Anchor
Java Measure
Java Measure
Java

The main class sets up the comparison, this is done by adding the two files that need to be compared, as well as the adapter to load the file, the extractor to extract a feature from the file, and a measurement to compare the two features.

Code Block
languagejava
titleMain
    static public void main(String[] args) {         
        PairwiseComparison comparison = new PairwiseComparison();
        comparison.setId(UUID.randomUUID().toString());
        comparison.setFirstDataset(new File("data/test1.txt"));
        comparison.setSecondDataset(new File("data/test2.txt"));
        comparison.setAdapterId(TextAdapter.class.getName());
        comparison.setExtractorId(TextHistogramExtractor.class.getName());
        comparison.setMeasureId(LabelHistogramEuclidianDistanceMeasure.class.getName());

        ExecutionEngine ee = new ExecutionEngine(); 
        ee.submit(comparison, new ComparisonStatusHandler()
Code Block
languagecpp
titleConnecting to RabbitMQ
#include <amqpcpp.h>

namespace CPPExample
{
  class RabbitMQConnectionHandler : public AMQP::ConnectionHandler {
      /**
      *  Method that is called by the AMQP library every time it has data
      *  available that should be sent to RabbitMQ. 
      *  @param  connection  pointer to the main connection object  
      *  @param  data        memory buffer with the data that should be sent to RabbitMQ
      *  @param  size        size of the buffer
      */
     virtual void onData(AMQP::Connection *connection, const char *data, size_t size)
     {
         // @todo 
         //  Add your own implementation, for example by doing a call to the
         //  send() system call. But be aware that the send() call may not
         //  send all data at once, so you also need to take care of buffering
         //  the bytes that could not immediately be sent, and try to send 
         //  them again when the socket becomes writable again
     }

      /**
      *  Method that is called by the AMQP library when the login attempt 
      *  succeeded. After this method has been called, the connection is ready 
      *  to use.
      *  @param  connection      The connection that can now be used
      */
      virtual void onConnected(Connection *connection)
      {
         // @todo
         //  add your own implementation, for example by creating a channel 
         //  instance, and start publishing or consuming
      }

      /**
      *  Method that is called by the AMQP library when a fatal error occurs
      *  on the connection, for example because data received from RabbitMQ
      *  could not be recognized.
      *  @param  connection      The connection on which the error occured
      *  @param  message         A human readable error message
      */
      virtual void onError(Connection *connection, const std::string &message)
      {
        // @todo
        //  add your own implementation, for example by reporting the error
        //  to the user of your program, log the error, and destruct the 
        //  connection object because it is no longer in a usable state
      }
  };

}
Code Block
languagecpp
titleReceiver
namespace CPPExample 
{
  /**
   *  Parse data that was recevied from RabbitMQ
   *  
   *  Every time that data comes in from RabbitMQ, you should call this method to parse
   *  the incoming data, and let it handle by the AMQP-CPP library. This method returns the number
   *  of bytes that were processed.
   *
   *  If not all bytes could be processed because it only contained a partial frame, you should
   *  call this same method later on when more data is available. The AMQP-CPP library does not do
   *  any buffering, so it is up to the caller to ensure that the old data is also passed in that
   *  later call.
   *
   *  @param  buffer      buffer to decode
   *  @param  size        size of the buffer to decode
   *  @return             number of bytes that were processed
   */
  size_t parse(char *buffer, size_t size)
  {
     return _implementation.parse(buffer, size);
  }
}
Code Block
languagecpp
titleInstantiating a Connection Handler
// create an instance of your own connection handler
RabbitMQConnectionHandler myHandler;

// create a AMQP connection object
AMQP::Connection connection(&myHandler, Login("guest","guest"), "/");

// and create a channel
AMQP::Channel channel(&connection);

// use the channel object to call the AMQP method you like
channel.declareExchange("my-exchange", AMQP::fanout);
channel.declareQueue("my-queue");
channel.bindQueue("my-exchange", "my-queue");

...

Code Block
themeEmacs
languagepy
titleInstantiating the logger and starting the extractor
def main():
 global logger

  # name of receiver
  receiver='ExamplePythonExtractor'

  # configure the logging system
  logging.basicConfig(format="%(asctime)-15s %(name)-10s %(levelname)-7s : %(message)s", level=logging.WARN)
  logger = logging.getLogger(receiver)
  logger.setLevel(logging.DEBUG)
 
  if len(sys.argv) != 4:
    logger.info("Input RabbitMQ username, followed by RabbitMQ password and Medici REST API key.")
    sys.exit()
 
  global playserverKey
  playserverKey = sys.argv[3]
  global exchange_name
  exchange_name = sys.argv[4]
Code Block
themeEmacs
languagepy
titleConnecting to RabbitMQ
# connect to rabbitmq using input username and password 
credentials = pika.PlainCredentials(sys.argv[1], sys.argv[2])
parameters = pika.ConnectionParameters(credentials=credentials)
connection = pika.BlockingConnection(parameters)
 
 # connect to channel
channel = connection.channel()

 # declare the exchange
channel.exchange_declare(exchange='medici', exchange_type='topic', durable=True)

 # declare the queue
channel.queue_declare(queue=receiver, durable=True)

 # connect queue and exchange
channel.queue_bind(queue=receiver, exchange='medici', routing_key='*.file.text.plain')

 # create listener
channel.basic_consume(on_message, queue=receiver, no_ack=False)

 # start listening
logger.info("Waiting for messages. To exit press CTRL+C")
 try:
   channel.start_consuming()
 except KeyboardInterrupt:
   channel.stop_consuming()

# close connection
connection.close()
Code Block
languagepy
titleProcessing Messages
def on_message(channel, method, header, body):
	global logger
	statusreport = {}
	inputfile=None
	try:
		# parse body back from json
		jbody=json.loads(body)

		host=jbody['host']
		fileid=jbody['id']
		intermediatefileid=jbody['intermediateId']
		if not (host.endswith('/')):
			host += '/'
		
		# for status reports
		statusreport['file_id'] = fileid
		statusreport['extractor_id'] = 'wordCount' 

		# print what we are doing
		logger.debug("[%s] started processing", fileid)

		# fetch data
		statusreport['status'] = 'Downloading file.'
		statusreport['start'] = time.strftime('%Y-%m-%dT%H:%M:%S')
		channel.basic_publish(exchange='',
							routing_key=header.reply_to,
							properties=pika.BasicProperties(correlation_id = \
														header.correlation_id),
							body=json.dumps(statusreport)) 
		url=host + 'api/files/' + intermediatefileid + '?key=' + playserverKey
		r=requests.get(url, stream=True)
		r.raise_for_status()
		(fd, inputfile)=tempfile.mkstemp()
		with os.fdopen(fd, "w") as f:
			for chunk in r.iter_content(chunk_size=10*1024):
				f.write(chunk)

		# create word count
		statusreport['status'] = 'Creating word count.'
		statusreport['start'] = time.strftime('%Y-%m-%dT%H:%M:%S')
		channel.basic_publish(exchange='',
							routing_key=header.reply_to,
							properties=pika.BasicProperties(correlation_id = \
														header.correlation_id),
							body=json.dumps(statusreport))
		create_word_count(inputfile, ext, host, fileid)

		# Ack
		channel.basic_ack(method.delivery_tag)
		logger.debug("[%s] finished processing", fileid)
	except subprocess.CalledProcessError as e:
		logger.exception("[%s] error processing [exit code=%d]\n%s", fileid, e.returncode, e.output)
		statusreport['status'] = 'Error processing.'
		statusreport['start'] = time.strftime('%Y-%m-%dT%H:%M:%S') 
		channel.basic_publish(exchange='',
                routing_key=header.reply_to,
                properties=pika.BasicProperties(correlation_id = \
                                                header.correlation_id),
                body=json.dumps(statusreport)) 
	except:
		logger.exception("[%s] error processing", fileid)
		statusreport['status'] = 'Error processing.'
		statusreport['start'] = time.strftime('%Y-%m-%dT%H:%M:%S') 
		channel.basic_publish(exchange='',
                routing_key=header.reply_to,
                properties=pika.BasicProperties(correlation_id = \
                                                header.correlation_id),
                body=json.dumps(statusreport))		
	finally:
		statusreport['status'] = 'DONE.'
		statusreport['start'] = time.strftime('%Y-%m-%dT%H:%M:%S')
		channel.basic_publish(exchange='',
							routing_key=header.reply_to,
							properties=pika.BasicProperties(correlation_id = \
														header.correlation_id),
							body=json.dumps(statusreport))
		if inputfile is not None:
			try:
				os.remove(inputfile)
			except OSError:
				pass
			except UnboundLocalError:
				pass
Code Block
languagepy
titleExample Job: Word Count
def create_word_count(inputfile, ext, host, fileid):
	global logger

	(fd, inputfile)=tempfile.mkstemp(suffix='.' + ext)
	try:
		# make syscall to wc
		subprocess.check_output(['wc', inputfile], stderr=subprocess.STDOUT)

		if(os.path.getsize(wcfile) == 0):
			raise Exception("File is empty.")

		# upload word count file

Calling R Scripts from Python

Coming soon...

...

Versus extractors serve to extract a signature from a file's content.  These signatures, effectively a hash for the data, are typically numerical vectors which capture some semantically meaningful aspect of the content so that two such signatures can then be compared using some distance measure.  Within Versus extractors operate on a data structure representing the content of a file, produced a Versus adapter, and the returned signatures compared by either a Versus similarity or distance measure.  The combination of these adapters, extractors, and measures in turn compose a comparison which can be used for relating files according their contents.

...

Code Block
languagejava
titlePDF Adapter
 public class PDFAdapter implements FileLoader, HasRGBPixels, HasText, HasLineGraphics {
    private File         file;
    private double[][][] pixels;
    private List<String> words;
    private List<Path2D> graphics;

    static public void main(String[] args) {
        List<Double> weights = new ArrayList<Double>();
        List<PairwiseComparison> comparisons = new ArrayList<PairwiseComparison>();
        
        PairwiseComparison comparison = new PairwiseComparison();
        comparison.setId(UUID.randomUUID().toString());
        comparison.setFirstDataset(new File("data/test1.pdf"));
        comparison.setSecondDataset(new File("data/test2.pdf"));
        comparison.setAdapterId(PDFAdapter.class.getName());
        comparison.setExtractorId(TextHistogramExtractor.class.getName());
        comparison.setMeasureId(LabelHistogramEuclidianDistanceMeasure.class.getName());
        comparisons.add(comparison);
        weights.add(0.7);
        
        comparison = new PairwiseComparison();
        comparison.setId(UUID.randomUUID().toString());
        comparison.setFirstDataset(new File("data/test1.pdf"));
        comparison.setSecondDataset(new File("data/test2.pdf"));
        comparison.setAdapterId(PDFAdapter.class.getName());
        comparison.setExtractorId(TextHistogramExtractor.class.getName());
        comparison.setMeasureId(LabelHistogramEuclidianDistanceMeasure.class.getName());
        comparisons.add(comparison);
        weights.add(0.2);
        
        comparison = new PairwiseComparison();
        comparison.setId(UUID.randomUUID().toString());
        comparison.setFirstDataset(new File("data/test1.pdf"));
        comparison.setSecondDataset(new File("data/test2.pdf"));
        comparison.setAdapterId(PDFAdapter.class.getName());
        comparison.setExtractorId(TextHistogramExtractor.class.getName());
        comparison.setMeasureId(LabelHistogramEuclidianDistanceMeasure.class.getName());
        comparisons.add(comparison);
        weights.add(0.1);

        ComprehensiveEngine engine = new ComprehensiveEngine();
        Double d = engine.compute(comparisons, weights);
        System.out.println(d);
        System.exit(0);
        
        ExecutionEngine ee = new ExecutionEngine();
        ee.submit(comparison, new ComparisonStatusHandler() {
            @Override
            public void onStarted() {
                System.out.println("STARTED : ");
            }

            @Override
            public void onFailed(String msg, Throwable e) {
                System.out.println("FAILED  : " + msg);
                e.printStackTrace();
                System.exit(0);
            }

            @Override
            public void onDone(double value) {
                System.out.println("DONE    : " + value);
                System.exit(0);
            }

            @Override
            public void onAborted(String msg) {
                System.out.println("ABORTED : " + msg);
                System.exit(0);
            }
        });
    }

    public PDFAdapter() {
    }

    // ----------------------------------------------------------------------
    // FileLoader
    // ----------------------------------------------------------------------

    @Override
    public void load(File file) {
        this.file = file;
    }

    @Override
    public String getName() {
        return "PDF Document";
    }

    @Override
    public List<String> getSupportedMediaTypes() {
        List<String> mediaTypes = new ArrayList<String>();
        mediaTypes.add("application/pdf");
        return mediaTypes;
    }

    // ----------------------------------------------------------------------
    // HasRGBPixels
    // ----------------------------------------------------------------------

    @Override
    public double getRGBPixel(int row, int column, int band) {
        if ((pixels == null) && (getRGBPixels() == null)) {
            return Double.NaN;
        } else {
            return pixels[row][column][band];
        }
    }

    @Override
    public double[][][] getRGBPixels() {
        if (pixels == null) {
            // create monster array.
            try {
                loadImages();
            } catch (IOException e) {
                e.printStackTrace();
                return null;
            }
        }
        return pixels;
    }

    private void loadImages() throws IOException {
        PDFParser parser = new PDFParser(new FileInputStream(file), PDFParser.EXTRACT_IMAGES);

        // get all images in the pdf document
        List<PDFObjectImage> images = new ArrayList<PDFObjectImage>();
        for (int i = 0; i < parser.getPageCount(); i++) {
            parser.parse(i);
            for (PDFObject po : parser.getObjects()) {
                if (po instanceof PDFObjectImage) {
                    PDFObjectImage poi = (PDFObjectImage) po;
                    images.add(poi);
                }
            }
        }

        // create a virtual image that is all the images combined
        // first column is the image number
        // second column is pixel (col + row*width)
        // third column is RGB value
        pixels = new double[images.size()][][];
        for (int i = 0; i < images.size(); i++) {
            PDFObjectImage poi = images.get(i);
            int w = poi.getImage().getWidth();
            int h = poi.getImage().getHeight();
            int[] rgb = poi.getImage().getRGB(0, 0, w, h, null, 0, w);
            pixels[i] = new double[rgb.length][3];
            for (int j = 0; j < rgb.length; j++) {
                pixels[i][j][0] = (rgb[j] & 0xff0000) >> 16;
                pixels[i][j][1] = (rgb[j] & 0x00ff00) >> 8;
                pixels[i][j][2] = (rgb[j] & 0x0000ff) >> 0;
            }
        }

        // close the parser
        parser.close();
    }

    // ----------------------------------------------------------------------
    // HasText
    // ----------------------------------------------------------------------

    @Override
    public List<String> getWords() {
        if (words == null) {
            words = new ArrayList<String>();

            try {
                PDFParser parser = new PDFParser(new FileInputStream(file), PDFParser.EXTRACT_TEXT);
                PDFGroupingText textgroup = new PDFGroupingText(PDFGroupingText.REMOVE_EMPTY_LINES);

                for (int i = 0; i < parser.getPageCount(); i++) {
                    parser.parse(i);
                    for (PDFObject po : textgroup.group(parser.getObjects())) {
            @Override
            public ifvoid onStarted(po instanceof PDFObjectText) {
                System.out.println("STARTED : ");
            for (String s : ((PDFObjectText) po).getText().split("\\W+")) { //$NON-NLS-1$}

            @Override
            public void onFailed(String msg, Throwable e) {
               if (!s.isEmpty()) {
     System.out.println("FAILED  : " + msg);
                e.printStackTrace();
                wordsSystem.addexit(s0);
            }

            @Override
        }
    public void onDone(double value) {
                System.out.println("DONE    }
: " + value);
                System.exit(0);
     }
       }

             }@Override
            public void onAborted(String msg) }
{
                parserSystem.out.close(println("ABORTED : " + msg);
            } catch (IOException e) {
    System.exit(0);
            }
        e.printStackTrace(});
    }

The text adapter will take a text file, and load all the file, splitting the text into words and return a list of all words in the text. The words are still in the right order, and it is possible to read the original information of the file by reading the words in the order as they are returned by getWords().

Code Block
languagejava
titleText Adapter
public class TextAdapter implements FileLoader, HasText {
   }

 private File      }

   file;
    private returnList<String> words;

    public TextAdapter() {}

    // ----------------------------------------------------------------------
    // HasLineGraphicsFileLoader
    // ----------------------------------------------------------------------

    @Override
    public List<Path2D>void getLineGraphicsload(File file) {
        this.file = file;
    if (graphics == null}

    @Override
    public String getName() {
        return "Text Document";
    }

    @Override
   graphics =public newList<String> ArrayList<Path2D>getSupportedMediaTypes(); {

        List<String> mediaTypes = new  try {
ArrayList<String>();
        mediaTypes.add("text/*");
        return mediaTypes;
   PDFParser parser}

 = new PDFParser(new FileInputStream(file), PDFParser.EXTRACT_GRAPHICS); // ----------------------------------------------------------------------
                PDFGroupingGraphics textgroup = new PDFGroupingGraphics();
// HasText
            // ----------------------------------------------------------------------
    for@Override
 (int i = 0;public i < parser.getPageCount(); i++List<String> getWords() {
        if (words == null) {
        parser.parse(i);
    words = new ArrayList<String>();
             for (PDFObject po : textgroup.group(parser.getObjects())) try {
                BufferedReader br = new BufferedReader(new FileReader(file));
   if (po instanceof PDFObjectGraphics) {
         String line;
                  graphics.addwhile(((PDFObjectGraphics) po).getPath());line = br.readLine()) != null) {
                    String[] w =  }line.split(" ");
                    }words.addAll(Arrays.asList(w));
                }

                parserbr.close();
            } catch (IOException e) {
                e.printStackTrace();
            }
        }

        return graphics;
    }
}words;
    }
 }

The extractor will take the words returned by the adapter and count the occurrence of each word. At this point we are left with a histogram with all words and how often they occur in the text, we can no longer read the text since the information about the order of the words is lost.

Code Block
languagejava
titleText Histogram Extractor
 publicpublic class TextHistogramExtractor implements Extractor 
{
    @Override
    public Adapter newAdapter() {
        throw (new RuntimeException("Not supported."));
    }

    @Override
    public String getName() {
        return "Text Histogram Extractor";
    }

    @Override
    public Set<Class<? extends Adapter>> supportedAdapters() {
        Set<Class<? extends Adapter>> adapters = new HashSet<Class<? extends Adapter>>();
        adapters.add(HasText.class);
        return adapters;
    }

    @Override
    public Class<? extends Descriptor> getFeatureType() {
        return LabelHistogramDescriptor.class;
    }

    @Override
    public Descriptor extract(Adapter adapter) throws Exception {
        if (adapter instanceof HasText) {
            LabelHistogramDescriptor desc = new LabelHistogramDescriptor();

            for for (String word : ((HasText) adapter).getWords()) {
                desc.increaseBin(word);
            }

            return return desc;
        } else {
            throw new UnsupportedTypeException();
        }
    }
    
    @Override
    public boolean hasPreview(){
        return false;
    }
    
    @Override
    public String previewName(){
        return null;
    }
}

To compare two texts we use the euclidian distance measure of two histograms. First we normalize each histogram, so we can compare a large text with a small text, next we compare each big of the two histograms. If the bin is missing from either histogram it is assumed to have a value of 0.

Code Block
languagejava
titleEuclidian Distance Measure
public class LabelHistogramEuclidianDistanceMeasure implements Measure 
{

    @Override
    public SimilarityPercentage normalize(Similarity similarity) {
        return new SimilarityPercentage(1 - similarity.getValue());
    }

    @Override
    public String getFeatureType() {
        return LabelHistogramDescriptor.class.getName();
    }

    @Override
    public String getName() {
        return "Histogram Distance";
    }

    @Override
    public Class<LabelHistogramEuclidianDistanceMeasure> getType() {
        return LabelHistogramEuclidianDistanceMeasure.class;
    }

    // correlation

    @Override
    public Similarity compare(Descriptor desc1, Descriptor desc2) throws Exception {
        if ((desc1 instanceof LabelHistogramDescriptor) && (desc2 instanceof LabelHistogramDescriptor)) {
            LabelHistogramDescriptor lhd1 = (LabelHistogramDescriptor) desc1;
            LabelHistogramDescriptor lhd2 = (LabelHistogramDescriptor) desc2;

            // get all possible labels
            Set<String> labels = new HashSet<String>();
            labels.addAll(lhd1.getLabels());
            labels.addAll(lhd2.getLabels());

            // normalize
            lhd1.normalize();
            lhd2.normalize();
            
            // compute distance
            double sum = 0;

            for for (String s : labels) {
                Double b1 = lhd1.getBin(s);
                Double b2 = lhd2.getBin(s);
             
   if             if (b1 == null) {
                    sum += b2 * b2;
                } else if (b2 == null) {
                    sum += b1 * b1;
                } else {
                    sum += (b1 - b2) * (b1 - b2);
                }
            }

            return new SimilarityNumber(Math.sqrt(sum), 0, 1, 0);
        } else {
            throw new UnsupportedTypeException();
        }
    }
}