判断图像的相似性主要用于图像的去重，一种验证相似性的思路是先将图像进行缩放至指定尺寸，然后进行灰度处理，去掉颜色特征，最后对处理后的图像计算哈希值，通过比对不同图像的哈希值的汉明距离来判断图像是否相似，下面我们直接上代码：
package com.jianggujin.image;
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.IOException;
import javax.imageio.ImageIO;
/**
* 图片相似性
*
* @author jianggujin
*
*/
public class ImageSimilarity {
public static int size = 32;
public static int smallerSize = 8;
// DCT function stolen from
// http://stackoverflow.com/questions/4240490/problems-with-dct-and-idct-algorithm-in-java
private static double[] c;
static {
c = new double[size];
for (int i = 1; i < size; i++) {
c[i] = 1;
}
c[0] = 1 / Math.sqrt(2.0);
}
/**
* 通过汉明距离计算相似度
*
* @param hash1
* @param hash2
* @return
*/
public static double calSimilarity(String hash1, String hash2) {
return calSimilarity(getHammingDistance(hash1, hash2));
}
/**
* 通过汉明距离计算相似度
*
* @param hammingDistance
* @return
*/
public static double calSimilarity(int hammingDistance) {
int length = size * size;
double similarity = (length - hammingDistance) / (double) length;
// 使用指数曲线调整相似度结果
similarity = Math.pow(similarity, 2);
return similarity;
}
/**
* 通过汉明距离计算相似度
*
* @param image1
* @param image2
* @return
* @throws IOException
*/
public static double calSimilarity(File image1, File image2) throws IOException {
return calSimilarity(getHammingDistance(image1, image2));
}
/**
* 获得汉明距离
*
* @param hash1
* @param hash2
* @return
*/
public static int getHammingDistance(String hash1, String hash2) {
int counter = 0;
for (int k = 0; k < hash1.length(); k++) {
if (hash1.charAt(k) != hash2.charAt(k)) {
counter++;
}
}
return counter;
}
/**
* 获得汉明距离
*
* @param image1
* @param image2
* @return
* @throws IOException
*/
public static int getHammingDistance(File image1, File image2) throws IOException {
return getHammingDistance(getHash(image1), getHash(image2));
}
/**
* 返回二进制字符串，类似“001010111011100010”，可用于计算汉明距离
*
* @param imageFile
* @return
* @throws IOException
* @throws Exception
*/
public static String getHash(File imageFile) throws IOException {
BufferedImage img = ImageIO.read(imageFile);
/*
* 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 static 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 static BufferedImage grayscale(BufferedImage img) {
new ColorConvertOp(ColorSpace.getInstance(ColorSpace.CS_GRAY), null).filter(img, img);
return img;
}
private static int getBlue(BufferedImage img, int x, int y) {
return (img.getRGB(x, y)) & 0xff;
}
private static 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;
}
}我们可以找两张图像进行测试：
package test;
import java.io.File;
import org.junit.Test;
import com.jianggujin.image.ImageSimilarity;
public class ImageSimilarityTest {
@Test
public void test() throws Exception {
// 获取图像
File imageFile1 = new File("1.jpg");
File imageFile2 = new File("2.jpg");
System.err.println(ImageSimilarity.calSimilarity(imageFile1, imageFile2));
System.err.println(ImageSimilarity.calSimilarity(imageFile1, imageFile1));
}
}我使用的测试图像的结果为：
0.9632349014282227
1.0
我们可以按照需求对最后的结果进行判断，值越大，相似度越高