com.opensearchserver.textextractor.util.ImagePHash Maven / Gradle / Ivy
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package com.opensearchserver.textextractor.util;
import java.awt.Graphics2D;
import java.awt.color.ColorSpace;
import java.awt.image.BufferedImage;
import java.awt.image.ColorConvertOp;
/*
* pHash-like image hash.
* Author: Elliot Shepherd ([email protected]
* Based On: http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html
*/
public class ImagePHash {
private int size = 32;
private int smallerSize = 8;
public ImagePHash() {
initCoefficients();
}
public ImagePHash(int size, int smallerSize) {
this.size = size;
this.smallerSize = smallerSize;
initCoefficients();
}
public int distance(String s1, String s2) {
int counter = 0;
for (int k = 0; k < s1.length(); k++) {
if (s1.charAt(k) != s2.charAt(k)) {
counter++;
}
}
return counter;
}
// Returns a 'binary string' (like. 001010111011100010) which is easy to do
// a hamming distance on.
public String getHash(BufferedImage img) {
/*
* 1. Reduce size. Like Average Hash, pHash starts with a small image.
* However, the image is larger than 8x8; 32x32 is a good size. This is
* really done to simplify the DCT computation and not because it is
* needed to reduce the high frequencies.
*/
img = resize(img, size, size);
/*
* 2. Reduce color. The image is reduced to a grayscale just to further
* simplify the number of computations.
*/
img = grayscale(img);
double[][] vals = new double[size][size];
for (int x = 0; x < img.getWidth(); x++) {
for (int y = 0; y < img.getHeight(); y++) {
vals[x][y] = getBlue(img, x, y);
}
}
/*
* 3. Compute the DCT. The DCT separates the image into a collection of
* frequencies and scalars. While JPEG uses an 8x8 DCT, this algorithm
* uses a 32x32 DCT.
*/
// long start = System.currentTimeMillis();
double[][] dctVals = applyDCT(vals);
// System.out.println("DCT: " + (System.currentTimeMillis() - start));
/*
* 4. Reduce the DCT. This is the magic step. While the DCT is 32x32,
* just keep the top-left 8x8. Those represent the lowest frequencies in
* the picture.
*/
/*
* 5. Compute the average value. Like the Average Hash, compute the mean
* DCT value (using only the 8x8 DCT low-frequency values and excluding
* the first term since the DC coefficient can be significantly
* different from the other values and will throw off the average).
*/
double total = 0;
for (int x = 0; x < smallerSize; x++) {
for (int y = 0; y < smallerSize; y++) {
total += dctVals[x][y];
}
}
total -= dctVals[0][0];
double avg = total / (double) ((smallerSize * smallerSize) - 1);
/*
* 6. Further reduce the DCT. This is the magic step. Set the 64 hash
* bits to 0 or 1 depending on whether each of the 64 DCT values is
* above or below the average value. The result doesn't tell us the
* actual low frequencies; it just tells us the very-rough relative
* scale of the frequencies to the mean. The result will not vary as
* long as the overall structure of the image remains the same; this can
* survive gamma and color histogram adjustments without a problem.
*/
StringBuilder hash = new StringBuilder();
for (int x = 0; x < smallerSize; x++) {
for (int y = 0; y < smallerSize; y++) {
if (x != 0 && y != 0) {
hash.append(dctVals[x][y] > avg ? "1" : "0");
}
}
}
return hash.toString();
}
private BufferedImage resize(BufferedImage image, int width, int height) {
BufferedImage resizedImage = new BufferedImage(width, height,
BufferedImage.TYPE_INT_ARGB);
Graphics2D g = resizedImage.createGraphics();
g.drawImage(image, 0, 0, width, height, null);
g.dispose();
return resizedImage;
}
private ColorConvertOp colorConvert = new ColorConvertOp(
ColorSpace.getInstance(ColorSpace.CS_GRAY), null);
private BufferedImage grayscale(BufferedImage img) {
colorConvert.filter(img, img);
return img;
}
private static int getBlue(BufferedImage img, int x, int y) {
return (img.getRGB(x, y)) & 0xff;
}
// DCT function stolen from
// http://stackoverflow.com/questions/4240490/problems-with-dct-and-idct-algorithm-in-java
private double[] c;
private void initCoefficients() {
c = new double[size];
for (int i = 1; i < size; i++) {
c[i] = 1;
}
c[0] = 1 / Math.sqrt(2.0);
}
private double[][] applyDCT(double[][] f) {
int N = size;
double[][] F = new double[N][N];
for (int u = 0; u < N; u++) {
for (int v = 0; v < N; v++) {
double sum = 0.0;
for (int i = 0; i < N; i++) {
for (int j = 0; j < N; j++) {
sum += Math
.cos(((2 * i + 1) / (2.0 * N)) * u * Math.PI)
* Math.cos(((2 * j + 1) / (2.0 * N)) * v
* Math.PI) * (f[i][j]);
}
}
sum *= ((c[u] * c[v]) / 4.0);
F[u][v] = sum;
}
}
return F;
}
}