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OpenSearchServer is a powerful, enterprise-class, search engine program. Using the web user interface, the crawlers (web, file, database, ...) and the REST/RESTFul API you will be able to integrate quickly and easily advanced full-text search capabilities in your application. OpenSearchServer runs on Windows and Linux/Unix/BSD.
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package com.jaeksoft.searchlib.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;
	}

}