import javax.imageio.ImageIO;
import java.awt.*;
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.InputStream;
public class ImagePHash {
public static void main(String[] args) {
try {
ImagePHash imagePHash = new ImagePHash();
String file = "C:\\Users\\1.png";
String file2 = "C:\\Users\\1.png";
String pHashString = imagePHash.getHash(new FileInputStream(new File(file)));
String pHashString2 = imagePHash.getHash(new FileInputStream(new File(file2)));
// 差异值(0代表两张图片完全一样)
System.out.println(imagePHash.distance(pHashString, pHashString2));
} catch (Exception e) {
e.printStackTrace();
}
}
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;
}
public String getHash(InputStream is) throws Exception {
BufferedImage img = ImageIO.read(is);
/*
* 1.缩小尺寸 pHash以小图片开始,但图片大于8*8,32*32是最好的。这样做的目的是简化了DCT的计算,而不是减小频率。
*/
img = resize(img, size, size);
/*
* 2. 简化色彩 将图片转化成灰度图像,进一步简化计算量。
*/
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.计算DCT DCT是把图片分解频率聚集和梯状形,虽然JPEG使用8*8的DCT变换,在这里使用32*32的DCT变换。
*/
double[][] dctVals = applyDCT(vals);
/*
* 4. 计算平均值 如同均值哈希一样,计算DCT的均值,
*/
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);
/*
* 5. 进一步减小DCT 这是最主要的一步,根据8*8的DCT矩阵, 设置0或1的64位的hash值,大于
* 等于DCT均值的设为”1”,小于DCT均值的设为“0”。结果并不能告诉我们真
* 实性的低频率,只能粗略地告诉我们相对于平均值频率的相对比例。只要图 片的整体结构保持不变,hash结果值就不变。能够避免伽马校正或颜色直方
* 图被调整带来的影响。
*/
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;
}
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;
}
}
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转载自blog.csdn.net/AAA17864308253/article/details/79457056
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