import java.awt.Graphics2D; import java.awt.color.ColorSpace; import java.awt.image.BufferedImage; import java.awt.image.ColorConvertOp; import java.io.InputStream; import javax.imageio.ImageIO; /* * pHash-like image hash. * Author: Elliot Shepherd (elliot@jarofworms.com * 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(InputStream is) throws Exception { BufferedImage img = ImageIO.read(is); /* 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. */ String hash = ""; for (int x = 0; x < smallerSize; x++) { for (int y = 0; y < smallerSize; y++) { if (x != 0 && y != 0) { hash += (dctVals[x][y] > avg?"1":"0"); } } } return hash; } 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