Example usage for org.apache.lucene.document StoredField StoredField

Introduction

In this page you can find the example usage for org.apache.lucene.document StoredField StoredField.

Prototype

public StoredField(String name, double value) 

Source Link

Document

Create a stored-only field with the given double value.

Usage

From source file:net.semanticmetadata.lire.imageanalysis.OpponentHistogramTest.java

License:Open Source License

public void testFromString() {
    String q = "0-0-0-0-0-12-8-0-0-62-5a-0-0-0-0-0-0-0-0-0-0-48-8-0-0-29-2b-0-0-0-0-0-0-0-0-0-0-58-4-0-0-b-7f-0-0-0-0-0-0-0-0-0-0-16-b-0-0-2-72-0-0-0-0-0";
    String[] tmp = q.split("-");
    byte[] tmpByte = new byte[tmp.length];
    for (int i = 0; i < tmp.length; i++) {
        tmpByte[i] = Byte.parseByte(tmp[i], 16);
    }/*from w w  w. j a  v  a2s  .  c om*/
    OpponentHistogram o = new OpponentHistogram();
    o.setByteArrayRepresentation(tmpByte);
    Document d = new Document();
    d.add(new StoredField("featOpHist", o.getByteArrayRepresentation()));
    d.add(new TextField("featOpHist" + "_hash",
            SerializationUtils.arrayToString(BitSampling.generateHashes(o.getDoubleHistogram())),
            Field.Store.YES));
}

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From source file:net.semanticmetadata.lire.impl.docbuilder.GenericDocumentBuilder.java

License:Open Source License

public Field[] createDescriptorFields(BufferedImage image) {
    Field[] result;/*from  www.j a va2 s . c  om*/
    if (hashingEnabled)
        result = new Field[2];
    else
        result = new Field[1];
    String featureString = "";
    assert (image != null);
    BufferedImage bimg = image;
    // Scaling image is especially with the correlogram features very important!
    // All images are scaled to guarantee a certain upper limit for indexing.
    if (Math.max(image.getHeight(), image.getWidth()) > MAX_IMAGE_DIMENSION) {
        bimg = ImageUtils.scaleImage(image, MAX_IMAGE_DIMENSION);
    }
    Document doc = null;
    try {
        logger.finer("Starting extraction from image [" + descriptorClass.getName() + "].");
        LireFeature lireFeature = null;

        lireFeature = descriptorClass.newInstance();

        lireFeature.extract(bimg);
        //            featureString = vd.getStringRepresentation();
        logger.fine("Extraction finished [" + descriptorClass.getName() + "].");

        // TODO: Stored field is compressed and upon search decompression takes a lot of time (> 50% with a small index with 50k images). Find something else ...
        result[0] = new StoredField(fieldName, lireFeature.getByteArrayRepresentation());

        // if BitSampling is an issue we add a field with the given hashFunctionsFileName and the suffix "hash":
        if (hashingEnabled) {
            // TODO: check eventually if there is a more compressed string version of the integers. i.e. the hex string
            if (lireFeature.getDoubleHistogram().length <= 3100) {
                int[] hashes;
                if (hashingMode == HashingMode.BitSampling) {
                    hashes = BitSampling.generateHashes(lireFeature.getDoubleHistogram());
                } else {
                    hashes = LocalitySensitiveHashing.generateHashes(lireFeature.getDoubleHistogram());
                }
                result[1] = new TextField(fieldName + HASH_FIELD_SUFFIX,
                        SerializationUtils.arrayToString(hashes), Field.Store.YES);
            } else
                System.err.println("Could not create hashes, feature vector too long: "
                        + lireFeature.getDoubleHistogram().length + " (" + lireFeature.getClass().getName()
                        + ")");
        }
    } catch (InstantiationException e) {
        e.printStackTrace();
    } catch (IllegalAccessException e) {
        e.printStackTrace();
    }
    return result;
}

---

From source file:net.semanticmetadata.lire.impl.docbuilder.MSERDocumentBuilder.java

License:Open Source License

public Document createDocument(BufferedImage image, ImageInfo imageInfo) {
    Document doc = null;/*  w w w .  j  a v a  2 s  . c  om*/
    try {
        // convert to grey ...
        BufferedImage image1 = convertImageToGrey(image);
        // extract features from image:
        List<MSERFeature> features = extractor.computeMSERFeatures(image1);

        ImageUtils.invertImage(image1);
        // invert grey
        features.addAll(extractor.computeMSERFeatures(image1));

        // create new document:
        doc = new Document();
        if (features.size() < 1) {
            System.err.println("No MSER features found for " + imageInfo.getTitle());
            //            } else {
            //                System.out.println("features.size() = " + features.size());
        }
        for (Iterator<MSERFeature> fit = features.iterator(); fit.hasNext();) {
            MSERFeature f = fit.next();
            boolean skip = false;
            // add each feature to the document:
            // check first if NaN!!
            for (int j = 0; j < f.descriptor.length; j++) {
                if (Double.isNaN(f.descriptor[j]))
                    skip = true;
                break;
            }

            if (!skip)
                doc.add(new StoredField(DocumentBuilder.FIELD_NAME_MSER, f.getByteArrayRepresentation()));
            else {
                //                    System.err.println("Found NaN in features in file " + identifier + ". ");
            }
        }
    } catch (Exception e) {
        e.printStackTrace();
    }

    DocumentUtils.appendImageInfoFields(doc, imageInfo);

    return doc;
}

---

From source file:net.semanticmetadata.lire.impl.docbuilder.SiftDocumentBuilder.java

License:Open Source License

@Override
public Field[] createDescriptorFields(BufferedImage image) {
    Field[] result = null;/*  w ww .j a v  a2s.c  o m*/
    try {
        // extract features from image:
        List<Feature> features = extractor.computeSiftFeatures(image);
        result = new Field[features.size()];
        int count = 0;
        // create new document:
        for (Iterator<Feature> fit = features.iterator(); fit.hasNext();) {
            Feature f = fit.next();
            result[count] = new StoredField(DocumentBuilder.FIELD_NAME_SIFT, f.getByteArrayRepresentation());
            count++;
        }
    } catch (IOException e) {
        logger.severe(e.getMessage());
    }
    return result;
}

---

From source file:net.semanticmetadata.lire.impl.docbuilder.SiftDocumentBuilder.java

License:Open Source License

public Document createDocument(BufferedImage image, ImageInfo imageInfo) {
    Document doc = null;//w ww  .  ja v  a  2  s .c  o m
    try {
        // extract features from image:
        List<Feature> features = extractor.computeSiftFeatures(image);
        // create new document:
        doc = new Document();
        for (Iterator<Feature> fit = features.iterator(); fit.hasNext();) {
            Feature f = fit.next();
            // add each feature to the document:
            doc.add(new StoredField(DocumentBuilder.FIELD_NAME_SIFT, f.getByteArrayRepresentation()));
        }
    } catch (IOException e) {
        logger.severe(e.getMessage());
    }

    DocumentUtils.appendImageInfoFields(doc, imageInfo);

    return doc;
}

---

From source file:net.semanticmetadata.lire.impl.docbuilder.SurfDocumentBuilder.java

License:Open Source License

@Override
public Field[] createDescriptorFields(BufferedImage image) {
    Field[] result = null;/*from   w ww  .j  av  a 2  s.  c o  m*/
    Surf s = new Surf(image);
    List<SURFInterestPoint> interestPoints = s.getFreeOrientedInterestPoints();
    result = new Field[interestPoints.size()];
    int count = 0;
    for (Iterator<SURFInterestPoint> sipi = interestPoints.iterator(); sipi.hasNext();) {
        SURFInterestPoint sip = sipi.next();
        SurfFeature sf = new SurfFeature(sip);
        result[count] = (new StoredField(DocumentBuilder.FIELD_NAME_SURF, sf.getByteArrayRepresentation()));
        count++;
    }
    return result;
}

---

From source file:net.semanticmetadata.lire.impl.docbuilder.SurfDocumentBuilder.java

License:Open Source License

public Document createDocument(BufferedImage image, ImageInfo imageInfo) {
    Document doc = null;//from  w ww . ja va 2s .  c o  m
    Surf s = new Surf(image);
    List<SURFInterestPoint> interestPoints = s.getFreeOrientedInterestPoints();
    doc = new Document();
    for (Iterator<SURFInterestPoint> sipi = interestPoints.iterator(); sipi.hasNext();) {
        SURFInterestPoint sip = sipi.next();
        SurfFeature sf = new SurfFeature(sip);
        doc.add(new StoredField(DocumentBuilder.FIELD_NAME_SURF, sf.getByteArrayRepresentation()));
    }

    DocumentUtils.appendImageInfoFields(doc, imageInfo);
    return doc;
}

---

From source file:net.semanticmetadata.lire.impl.GenericDocumentBuilder.java

License:Open Source License

public Field[] createDescriptorFields(BufferedImage image) {
    Field[] result;/*from www. ja  va2s  .com*/
    if (hashingEnabled)
        result = new Field[2];
    else
        result = new Field[1];
    String featureString = "";
    assert (image != null);
    BufferedImage bimg = image;
    // Scaling image is especially with the correlogram features very important!
    // All images are scaled to guarantee a certain upper limit for indexing.
    if (Math.max(image.getHeight(), image.getWidth()) > MAX_IMAGE_DIMENSION) {
        bimg = ImageUtils.scaleImage(image, MAX_IMAGE_DIMENSION);
    }
    Document doc = null;
    try {
        logger.finer("Starting extraction from image [" + descriptorClass.getName() + "].");
        LireFeature lireFeature = null;

        lireFeature = descriptorClass.newInstance();

        lireFeature.extract(bimg);
        //            featureString = vd.getStringRepresentation();
        logger.fine("Extraction finished [" + descriptorClass.getName() + "].");

        // TODO: Stored field is compressed and upon search decompression takes a lot of time (> 50% with a small index with 50k images). Find something else ...
        result[0] = new StoredField(fieldName, new BytesRef(lireFeature.getByteArrayRepresentation()));

        // if BitSampling is an issue we add a field with the given hashFunctionsFileName and the suffix "hash":
        if (hashingEnabled) {
            // TODO: check eventually if there is a more compressed string version of the integers. i.e. the hex string
            if (lireFeature.getDoubleHistogram().length <= 3100) {
                int[] hashes;
                if (hashingMode == HashingMode.BitSampling) {
                    hashes = BitSampling.generateHashes(lireFeature.getDoubleHistogram());
                } else {
                    hashes = LocalitySensitiveHashing.generateHashes(lireFeature.getDoubleHistogram());
                }
                result[1] = new TextField(fieldName + HASH_FIELD_SUFFIX,
                        SerializationUtils.arrayToString(hashes), Field.Store.YES);
            } else
                System.err.println("Could not create hashes, feature vector too long: "
                        + lireFeature.getDoubleHistogram().length + " (" + lireFeature.getClass().getName()
                        + ")");
        }
    } catch (InstantiationException e) {
        e.printStackTrace();
    } catch (IllegalAccessException e) {
        e.printStackTrace();
    }
    return result;
}

---

From source file:net.semanticmetadata.lire.impl.MSERDocumentBuilder.java

License:Open Source License

public Document createDocument(BufferedImage image, String identifier) {
    Document doc = null;//from  w w w  .j a  v a  2 s.  co  m
    try {
        // convert to grey ...
        BufferedImage image1 = convertImageToGrey(image);
        // extract features from image:
        List<MSERFeature> features = extractor.computeMSERFeatures(image1);

        ImageUtils.invertImage(image1);
        // invert grey
        features.addAll(extractor.computeMSERFeatures(image1));

        // create new document:
        doc = new Document();
        if (features.size() < 1) {
            System.err.println("No MSER features found for " + identifier);
            //            } else {
            //                System.out.println("features.size() = " + features.size());
        }
        for (Iterator<MSERFeature> fit = features.iterator(); fit.hasNext();) {
            MSERFeature f = fit.next();
            boolean skip = false;
            // add each feature to the document:
            // check first if NaN!!
            for (int j = 0; j < f.getDoubleHistogram().length; j++) {
                if (Double.isNaN(f.getDoubleHistogram()[j]))
                    skip = true;
                break;
            }

            if (!skip)
                doc.add(new StoredField(DocumentBuilder.FIELD_NAME_MSER, f.getByteArrayRepresentation()));
            else {
                //                    System.err.println("Found NaN in features in file " + identifier + ". ");
            }
        }
        if (identifier != null) {
            doc.add(new StringField(DocumentBuilder.FIELD_NAME_IDENTIFIER, identifier, Field.Store.YES));
        }
    } catch (Exception e) {
        e.printStackTrace();
    }
    return doc;
}

---

From source file:net.semanticmetadata.lire.impl.SiftDocumentBuilder.java

License:Open Source License

public Document createDocument(BufferedImage image, String identifier) {
    Document doc = null;/*w  w  w.j  a  v a2 s .c  o  m*/
    try {
        // extract features from image:
        List<Feature> features = extractor.computeSiftFeatures(image);
        // create new document:
        doc = new Document();
        for (Iterator<Feature> fit = features.iterator(); fit.hasNext();) {
            Feature f = fit.next();
            // add each feature to the document:
            doc.add(new StoredField(DocumentBuilder.FIELD_NAME_SIFT, f.getByteArrayRepresentation()));
        }
        if (identifier != null)
            doc.add(new StringField(DocumentBuilder.FIELD_NAME_IDENTIFIER, identifier, Field.Store.YES));
    } catch (IOException e) {
        logger.severe(e.getMessage());
    }
    return doc;
}

---

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