コード例 #1
0
ファイル: Analyzer.java プロジェクト: kappalabs/OHS
 /**
  * Resize input image so that its size matches the optimal image size boundary.
  *
  * @param image The input image to be resized.
  * @return The resized image matching the optimal size boundary.
  */
 public static BufferedImage resize(BufferedImage image) {
   double divider =
       Math.max(
           (double) image.getWidth() / settingsManager.getOptimalWidth(),
           (double) image.getHeight() / settingsManager.getOptimalHeight());
   return resize(image, (int) (image.getWidth() / divider), (int) (image.getHeight() / divider));
 }
コード例 #2
0
ファイル: Analyzer.java プロジェクト: kappalabs/OHS
 /**
  * Determines if given photo contains night picture.
  *
  * @param photo The photo to analyze.
  * @return True if the given photo contains night picture, false otherwise.
  */
 public static boolean isNight(Photo photo) {
   BufferedImage img = ((SImage) photo.sImage).toBufferedImage();
   Random rand = new Random();
   int x, y;
   double souc = 0;
   for (int i = 0; i < settingsManager.getRandomPhotoSamplesNumber(); i++) {
     x = rand.nextInt(img.getWidth());
     y = rand.nextInt(img.getHeight());
     souc += argbToIntensity(img.getRGB(x, y));
   }
   double val = (souc / settingsManager.getRandomPhotoSamplesNumber());
   return val < settingsManager.getNightTreshold();
 }
コード例 #3
0
ファイル: Analyzer.java プロジェクト: kappalabs/OHS
  /**
   * For given two photos, count their similarity. The result is from interval [0;1], 1 means
   * perfect match, 0 totaly different.
   *
   * @param photo1 First photo.
   * @param photo2 Second photo.
   * @return Similarity of given photos from interval [0;1];
   * @throws com.kappa_labs.ohunter.lib.net.OHException When Photos are wrongly set.
   */
  public static synchronized float computeSimilarity(Photo photo1, Photo photo2)
      throws OHException {
    float ret = 0;

    /* Scale the images to provide the best results */
    try {
      photo1.sImage = photo1._sImage = new SImage(photo1.sImage);
      photo2.sImage = photo2._sImage = new SImage(photo2.sImage);

      ((SImage) photo1.sImage).setImage(resize(((SImage) photo1._sImage).toBufferedImage()));
      ((SImage) photo2.sImage).setImage(resize(((SImage) photo2._sImage).toBufferedImage()));
    } catch (Exception e) {
      LOGGER.log(Level.WARNING, "Could not acquire photos from client: {0}", e);
      throw new OHException("Could not acquire photos!", OHException.EXType.OTHER);
    }

    /* Count average from few attempts */
    final int numRepeats = settingsManager.getSimilarityNumberOfRepeats();
    int iteration = numRepeats;
    while (iteration-- > 0) {
      /* Perform segmentation */
      Segment[] segments1 = Segmenter.findSegments(photo1);
      Segment[] segments2 = Segmenter.findSegments(photo2);
      LOGGER.log(
          Level.FINER,
          "... got {0} segments from first and {1} segments" + " in the second photo",
          new Object[] {segments1.length, segments2.length});

      /* Create new Problem from given counted segments */
      Problem problem = new Problem();
      problem.distr1 = prepareDistribution(segments1, photo1);
      problem.distr2 = prepareDistribution(segments2, photo2);

      /* Solve the EMP linear problem and return the final result */
      EMDSolver empm = new EMDSolver(problem);
      float act = Math.max(0f, Math.min(1f, (float) empm.countValue()));
      LOGGER.finer(String.format(" - similarity: %.1f%%", 100 - act * 100));
      ret += act;
    }
    ret /= numRepeats;

    return 1f - ret;
  }
コード例 #4
0
ファイル: Analyzer.java プロジェクト: kappalabs/OHS
/** Class providing a method for measuring the similarity of two given images. */
public class Analyzer {

  private static final Logger LOGGER = Logger.getLogger(Analyzer.class.getName());

  private static final SettingsManager settingsManager = SettingsManager.getInstance();

  private Analyzer() {
    /* Analyzer cannot be instantiated from outside of this class */
  }

  /**
   * For given two photos, count their similarity. The result is from interval [0;1], 1 means
   * perfect match, 0 totaly different.
   *
   * @param photo1 First photo.
   * @param photo2 Second photo.
   * @return Similarity of given photos from interval [0;1];
   * @throws com.kappa_labs.ohunter.lib.net.OHException When Photos are wrongly set.
   */
  public static synchronized float computeSimilarity(Photo photo1, Photo photo2)
      throws OHException {
    float ret = 0;

    /* Scale the images to provide the best results */
    try {
      photo1.sImage = photo1._sImage = new SImage(photo1.sImage);
      photo2.sImage = photo2._sImage = new SImage(photo2.sImage);

      ((SImage) photo1.sImage).setImage(resize(((SImage) photo1._sImage).toBufferedImage()));
      ((SImage) photo2.sImage).setImage(resize(((SImage) photo2._sImage).toBufferedImage()));
    } catch (Exception e) {
      LOGGER.log(Level.WARNING, "Could not acquire photos from client: {0}", e);
      throw new OHException("Could not acquire photos!", OHException.EXType.OTHER);
    }

    /* Count average from few attempts */
    final int numRepeats = settingsManager.getSimilarityNumberOfRepeats();
    int iteration = numRepeats;
    while (iteration-- > 0) {
      /* Perform segmentation */
      Segment[] segments1 = Segmenter.findSegments(photo1);
      Segment[] segments2 = Segmenter.findSegments(photo2);
      LOGGER.log(
          Level.FINER,
          "... got {0} segments from first and {1} segments" + " in the second photo",
          new Object[] {segments1.length, segments2.length});

      /* Create new Problem from given counted segments */
      Problem problem = new Problem();
      problem.distr1 = prepareDistribution(segments1, photo1);
      problem.distr2 = prepareDistribution(segments2, photo2);

      /* Solve the EMP linear problem and return the final result */
      EMDSolver empm = new EMDSolver(problem);
      float act = Math.max(0f, Math.min(1f, (float) empm.countValue()));
      LOGGER.finer(String.format(" - similarity: %.1f%%", 100 - act * 100));
      ret += act;
    }
    ret /= numRepeats;

    return 1f - ret;
  }

  /**
   * Resize input image so that its size matches the optimal image size boundary.
   *
   * @param image The input image to be resized.
   * @return The resized image matching the optimal size boundary.
   */
  public static BufferedImage resize(BufferedImage image) {
    double divider =
        Math.max(
            (double) image.getWidth() / settingsManager.getOptimalWidth(),
            (double) image.getHeight() / settingsManager.getOptimalHeight());
    return resize(image, (int) (image.getWidth() / divider), (int) (image.getHeight() / divider));
  }

  /**
   * Resize given image to specified dimensions.
   *
   * @param image Image for modification.
   * @param width Desired new image width.
   * @param height Desired new image height.
   * @return Given image scaled to given dimensions.
   */
  public static BufferedImage resize(BufferedImage image, int width, int height) {
    BufferedImage resized = new BufferedImage(width, height, BufferedImage.TYPE_INT_RGB);
    Graphics2D _g2d = resized.createGraphics();
    _g2d.drawImage(image, 0, 0, width, height, null);
    _g2d.dispose();

    return resized;
  }

  private static float[] toHSB(float[] moment) {
    float[] rgb = CIELab.getInstance().toRGB(moment);
    return Color.RGBtoHSB((int) (rgb[0] * 255), (int) (rgb[1] * 255), (int) (rgb[2] * 255), null);
  }

  private static List<DistrPair> prepareDistribution(Segment[] segments, Photo photo) {
    List<DistrPair> distribution = new ArrayList<>();
    int area = photo.getWidth() * photo.getHeight();
    double sum = 0;
    for (Segment seg : segments) {
      DistrPair dp = new DistrPair();
      //            dp.weight = (double)seg.getSumPixels() / area;
      /* Original method uses sqrt */
      dp.weight = Math.sqrt((double) seg.getSumPixels() / area);
      sum += dp.weight;
      Vector vect = new Vector(14);
      Addterator<Float> addter = vect.addterator();

      /* Color moments - 9 elements in total */
      float[] mean_hsb = seg.getMean();
      float[] stdev_hsb = seg.getStdDeviation();
      float[] skew_hsb = seg.getSkewness();
      for (int i = 0; i < Segment.MODEL_NUM_ELEMENTS; i++) {
        addter.add(mean_hsb[i]);
        addter.add(stdev_hsb[i]);
        addter.add(skew_hsb[i]);
      }
      /* The other elements - another 5 of them */
      int o_width = seg.getRight() - seg.getLeft();
      o_width = Math.max(o_width, 1);
      int o_height = seg.getBottom() - seg.getTop();
      o_height = Math.max(o_height, 1);
      int o_area = o_width * o_height;
      /* 5 elements characterizing the shape of one segment */
      addter.add((float) Math.log((double) o_width / o_height + 1));
      addter.add((float) Math.log((double) o_area / area + 1));
      addter.add((float) seg.getSumPixels() / o_area);
      addter.add((float) seg.getX() / photo.getWidth());
      addter.add((float) seg.getY() / photo.getHeight());

      dp.vector = vect;
      distribution.add(dp);
    }
    /* Scale the sum of weights to be 1 */
    for (DistrPair distrPair : distribution) {
      distrPair.weight /= sum;
    }
    return distribution;
  }

  /**
   * Determines if given photo contains night picture.
   *
   * @param photo The photo to analyze.
   * @return True if the given photo contains night picture, false otherwise.
   */
  public static boolean isNight(Photo photo) {
    BufferedImage img = ((SImage) photo.sImage).toBufferedImage();
    Random rand = new Random();
    int x, y;
    double souc = 0;
    for (int i = 0; i < settingsManager.getRandomPhotoSamplesNumber(); i++) {
      x = rand.nextInt(img.getWidth());
      y = rand.nextInt(img.getHeight());
      souc += argbToIntensity(img.getRGB(x, y));
    }
    double val = (souc / settingsManager.getRandomPhotoSamplesNumber());
    return val < settingsManager.getNightTreshold();
  }

  /**
   * Convert argb 4-byte color value to intensity value.
   *
   * @param argb Input 4-byte value.
   * @return The intensity of given color.
   */
  private static double argbToIntensity(int argb) {
    int ret = 0;
    ret += (argb & 0xff) * 0.0722;
    ret += ((argb & 0xff00) >> 8) * 0.7152;
    ret += ((argb & 0xff0000) >> 16) * 0.226;
    return ret;
  }
}