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;
/* * 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://*.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 < 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);
}
public static void main(String[] args) {
ImagePHash p = new ImagePHash();
String image1;
String image2;
try {
image1 = p.getHash(new FileInputStream(new File(
"C:/Users/zirong.lzr/Desktop/1.png")));
image2 = p.getHash(new FileInputStream(new File(
"C:/Users/zirong.lzr/Desktop/3.png")));
System.out.println("1:1 Score is " + p.distance(image1, image2));
//
image1 = p.getHash(new FileInputStream(new File(
//
"C:/Users/june/Desktop/1.jpg")));
//
image2 = p.getHash(new FileInputStream(new File(
//
"C:/Users/june/Desktop/2.jpg")));
//
System.out.println("1:2 Score is " + p.distance(image1, image2));
//
image1 = p.getHash(new FileInputStream(new File(
//
"C:/Users/june/Desktop/1.jpg")));
//
image2 = p.getHash(new FileInputStream(new File(
//
"C:/Users/june/Desktop/3.jpg")));
//
System.out.println("1:3 Score is " + p.distance(image1, image2));
//
image1 = p.getHash(new FileInputStream(new File(
//
"C:/Users/june/Desktop/2.jpg")));
//
image2 = p.getHash(new FileInputStream(new File(
//
"C:/Users/june/Desktop/3.jpg")));
//
System.out.println("2:3 Score is " + p.distance(image1, image2));
//
image1 = p.getHash(new FileInputStream(new File(
//
"C:/Users/june/Desktop/4.jpg")));
//
image2 = p.getHash(new FileInputStream(new File(
//
"C:/Users/june/Desktop/5.jpg")));
//
System.out.println("4:5 Score is " + p.distance(image1, image2));
} catch (FileNotFoundException e) {
e.printStackTrace();
} catch (Exception e) {
e.printStackTrace();
}
}
}