相似图片查找感知哈希算法（phash）实现

import java.awt.Graphics2D;import java.awt.color.ColorSpace;import java.awt.image.BufferedImage;import java.awt.image.ColorConvertOp;import java.io.File;import java.io.FileInputStream;import java.io.FileNotFoundException;import java.io.InputStream;import javax.imageio.ImageIO;/** function: 用汉明距离进行图片相似度检测的Java实现* pHash-like image hash.* Author: Sun Huaqiang* 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();}private ImagePHash(int size, int smallerSize) {this.size = size;this.smallerSize = smallerSize;initCoefficients();}private 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.private String getHash(InputStream is) throws Exception {BufferedImage img = ImageIO.read(is);/* * 1. Reduce size(缩小尺寸). 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(简化色彩). 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(计算DCT). The DCT(Discrete Cosine Transform,离散余弦转换) * 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_COST_TIME: " + (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-javaprivate 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;}/** * * @param img1 * @param img2 * @param tv * @return boolean */public boolean imgChk(String img1, String img2, int tv) {ImagePHash p = new ImagePHash();String image1;String image2;try {image1 = p.getHash(new FileInputStream(new File(img1)));image2 = p.getHash(new FileInputStream(new File(img2)));int dt = p.distance(image1, image2);System.out.println("[" + img1 + "] : [" + img2 + "] Score is " + dt);if (dt <= tv)return true;} catch (FileNotFoundException e) {e.printStackTrace();} catch (Exception e) {e.printStackTrace();}return false;}public static void main(String[] args) {ImagePHash p = new ImagePHash();String targetImage = "/Users/jjs/Documents/workspace/SimilarPhotoHunter/origin/meiliwu.jpg";String compareImage = "/Users/jjs/Documents/workspace/SimilarPhotoHunter/images/";System.out.println(p.imgChk(targetImage, compareImage + "美丽屋文字.jpeg", 10));System.out.println(p.imgChk(targetImage, compareImage + "美丽屋去水印.jpeg", 10));System.out.println(p.imgChk(targetImage, compareImage + "美丽屋美化.jpeg", 10));System.out.println(p.imgChk(targetImage, compareImage + "google.gif", 10));System.out.println(p.imgChk(targetImage, compareImage + "ohter_word.jpg", 10));System.out.println(p.imgChk(targetImage, compareImage + "similar_pic.jpg", 10));System.out.println(p.imgChk(targetImage, compareImage + "origin.jpg", 10));}}