/**
* 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));
}
/**
* 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();
}
/**
* 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;
}
/** 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;
}
}
