此算法中的DCT变换是从 http://blog.csdn.net/luoweifu/article/details/8214959抄过来的，因为这种需要大量的复杂的数学运算，我看不来，完全不懂...都已经还给老师了...
package com.imageretrieval.features;
import com.imageretrieval.utils.ImageUtil;
/**
* pHash
* 参考链接：http://blog.csdn.net/zouxy09/article/details/17471401
* http://blog.csdn.net/luoweifu/article/details/8220992
* @author VenyoWang
*
*/
public class PHash {
public static void main(String[] args) {
String hash = getFeatureValue("");
String hash1 = getFeatureValue("");
System.out.println(hash);
System.out.println(hash1);
System.out.println(calculateSimilarity(hash, hash1));
}
public static String getFeatureValue(String imagePath) {
// 缩小尺寸，简化色彩
int[][] grayMatrix = getGrayPixel(imagePath, 32, 32);
// 计算DCT
grayMatrix = DCT(grayMatrix, 32);
// 缩小DCT，计算平均值
int[][] newMatrix = new int[8][8];
double average = 0;
for(int i = 0; i < 8; i++){
for(int j = 0; j < 8; j++){
newMatrix[i][j] = grayMatrix[i][j];
average += grayMatrix[i][j];
}
}
average /= 64.0;
// 计算hash值
String hash = "";
for(int i = 0; i < 8; i++){
for(int j = 0; j < 8; j++){
if(newMatrix[i][j] < average){
hash += '0';
}
else{
hash += '1';
}
}
}
return hash;
}
public static int[][] getGrayPixel(String imagePath, int width, int height) {
BufferedImage bi = null;
try {
bi = resizeImage(imagePath, width, height, BufferedImage.TYPE_INT_RGB);
} catch (Exception e) {
e.printStackTrace();
return null;
}
int minx = bi.getMinX();
int miny = bi.getMinY();
int[][] matrix = new int[width - minx][height - miny];
for (int i = minx; i < width; i++) {
for (int j = miny; j < height; j++) {
int pixel = bi.getRGB(i, j);
int red = (pixel & 0xff0000) >> 16;
int green = (pixel & 0xff00) >> 8;
int blue = (pixel & 0xff);
int gray = (int) (red * 0.3 + green * 0.59 + blue * 0.11);
matrix[i][j] = gray;
}
}
return matrix;
}
public static BufferedImage resizeImage(String srcImgPath, int width, int height, int imageType)
throws IOException {
File srcFile = new File(srcImgPath);
BufferedImage srcImg = ImageIO.read(srcFile);
BufferedImage buffImg = null;
buffImg = new BufferedImage(width, height, imageType);
buffImg.getGraphics().drawImage(srcImg.getScaledInstance(width, height, Image.SCALE_SMOOTH), 0, 0, null);
return buffImg;
}
/**
* 用于计算pHash的相似度
* 相似度为1时，图片最相似
* @param str1
* @param str2
* @return
*/
public static double calculateSimilarity(String str1, String str2) {
int num = 0;
for(int i = 0; i < 64; i++){
if(str1.charAt(i) == str2.charAt(i)){
num++;
}
}
return ((double)num) / 64.0;
}
/**
* 离散余弦变换
* @author luoweifu
*
* @param pix
* 原图像的数据矩阵
* @param n
* 原图像(n*n)的高或宽
* @return 变换后的矩阵数组
*/
public static int[][] DCT(int[][] pix, int n) {
double[][] iMatrix = new double[n][n];
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
iMatrix[i][j] = (double) (pix[i][j]);
}
}
double[][] quotient = coefficient(n); // 求系数矩阵
double[][] quotientT = transposingMatrix(quotient, n); // 转置系数矩阵
double[][] temp = new double[n][n];
temp = matrixMultiply(quotient, iMatrix, n);
iMatrix = matrixMultiply(temp, quotientT, n);
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
pix[i][j] = (int) (iMatrix[i][j]);
}
}
return pix;
}
/**
* 求离散余弦变换的系数矩阵
* @author luoweifu
*
* @param n
* n*n矩阵的大小
* @return 系数矩阵
*/
private static double[][] coefficient(int n) {
double[][] coeff = new double[n][n];
double sqrt = 1.0 / Math.sqrt(n);
for (int i = 0; i < n; i++) {
coeff[0][i] = sqrt;
}
for (int i = 1; i < n; i++) {
for (int j = 0; j < n; j++) {
coeff[i][j] = Math.sqrt(2.0 / n) * Math.cos(i * Math.PI * (j + 0.5) / (double) n);
}
}
return coeff;
}
/**
* 矩阵转置
* @author luoweifu
*
* @param matrix
* 原矩阵
* @param n
* 矩阵(n*n)的高或宽
* @return 转置后的矩阵
*/
private static double[][] transposingMatrix(double[][] matrix, int n) {
double nMatrix[][] = new double[n][n];
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
nMatrix[i][j] = matrix[j][i];
}
}
return nMatrix;
}
/**
* 矩阵相乘
* @author luoweifu
*
* @param A
* 矩阵A
* @param B
* 矩阵B
* @param n
* 矩阵的大小n*n
* @return 结果矩阵
*/
private static double[][] matrixMultiply(double[][] A, double[][] B, int n) {
double nMatrix[][] = new double[n][n];
int t = 0;
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
t = 0;
for (int k = 0; k < n; k++) {
t += A[i][k] * B[k][j];
}
nMatrix[i][j] = t;
}
}
return nMatrix;
}
}