java 图片相似度算法

利用直方图原理实现图像内容相似度比较、均值哈希实现图像内容相似度比较、汉明距离算法实现图像内容相似度比较

直方图原理实现图像内容相似度比较算法

import javax.imageio.*;
import java.awt.image.*;
import java.awt.*;
import java.io.*;
 
public class PhotoDigest {
    public static void main(String[] args) throws Exception {
        float percent = compare(getData("/Users/sun/Downloads/1.jpg"),
                getData("/Users/sun/Downloads/2.jpg"));
        if (percent == 0) {
            System.out.println("无法比较");
        } else {
            System.out.println("两张图片的相似度为：" + percent + "%");
        }
    }
 
    public static int[] getData(String name) {
        try {
            BufferedImage img = ImageIO.read(new File(name));
            BufferedImage slt = new BufferedImage(100, 100,
                    BufferedImage.TYPE_INT_RGB);
            slt.getGraphics().drawImage(img, 0, 0, 100, 100, null);
            // ImageIO.write(slt,"jpeg",new File("slt.jpg"));
            int[] data = new int[256];
            for (int x = 0; x < slt.getWidth(); x++) {
                for (int y = 0; y < slt.getHeight(); y++) {
                    int rgb = slt.getRGB(x, y);
                    Color myColor = new Color(rgb);
                    int r = myColor.getRed();
                    int g = myColor.getGreen();
                    int b = myColor.getBlue();
                    data[(r + g + b) / 3]++;
                }
            }
            // data 就是所谓图形学当中的直方图的概念
            return data;
        } catch (Exception exception) {
            System.out.println("有文件没有找到,请检查文件是否存在或路径是否正确");
            return null;
        }
    }
 
    public static float compare(int[] s, int[] t) {
        try {
            float result = 0F;
            for (int i = 0; i < 256; i++) {
                int abs = Math.abs(s[i] - t[i]);
                int max = Math.max(s[i], t[i]);
                result += (1 - ((float) abs / (max == 0 ? 1 : max)));
            }
            return (result / 256) * 100;
        } catch (Exception exception) {
            return 0;
        }
    }
}

均值哈希实现图像内容相似度比较算法

import java.awt.Graphics;
import java.awt.Image;
import java.awt.color.ColorSpace;
import java.awt.image.BufferedImage;
import java.awt.image.ColorConvertOp;
import java.io.File;
import java.io.IOException;
import java.util.Arrays;
 
import javax.imageio.ImageIO;
 
/**
 * 均值哈希实现图像指纹比较
 * 
 */
public final class FingerPrint {
 
    public static void main(String[] args) {
        try {
            FingerPrint fp1 = new FingerPrint(ImageIO.read(new File(
                    "/Users/sun/Downloads/1.jpg")));
            FingerPrint fp2 = new FingerPrint(ImageIO.read(new File(
                    "/Users/sun/Downloads/2.jpg")));
            System.out.println(fp1.toString(true));
            System.out.printf("sim=%f", fp1.compare(fp2));
        } catch (IOException e) {
            e.printStackTrace();
        }
    }
 
    /**
     * 图像指纹的尺寸,将图像resize到指定的尺寸，来计算哈希数组
     */
    private static final int HASH_SIZE = 16;
    /**
     * 保存图像指纹的二值化矩阵
     */
    private final byte[] binaryzationMatrix;
 
    public FingerPrint(byte[] hashValue) {
        if (hashValue.length != HASH_SIZE * HASH_SIZE)
            throw new IllegalArgumentException(String.format(
                    "length of hashValue must be %d", HASH_SIZE * HASH_SIZE));
        this.binaryzationMatrix = hashValue;
    }
 
    public FingerPrint(String hashValue) {
        this(toBytes(hashValue));
    }
 
    public FingerPrint(BufferedImage src) {
        this(hashValue(src));
    }
 
    private static byte[] hashValue(BufferedImage src) {
        BufferedImage hashImage = resize(src, HASH_SIZE, HASH_SIZE);
        byte[] matrixGray = (byte[]) toGray(hashImage).getData()
                .getDataElements(0, 0, HASH_SIZE, HASH_SIZE, null);
        return binaryzation(matrixGray);
    }
 
    /**
     * 从压缩格式指纹创建{@link FingerPrint}对象
     * 
     * @param compactValue
     * @return
     */
    public static FingerPrint createFromCompact(byte[] compactValue) {
        return new FingerPrint(uncompact(compactValue));
    }
 
    public static boolean validHashValue(byte[] hashValue) {
        if (hashValue.length != HASH_SIZE)
            return false;
        for (byte b : hashValue) {
            if (0 != b && 1 != b)
                return false;
        }
        return true;
    }
 
    public static boolean validHashValue(String hashValue) {
        if (hashValue.length() != HASH_SIZE)
            return false;
        for (int i = 0; i < hashValue.length(); ++i) {
            if ('0' != hashValue.charAt(i) && '1' != hashValue.charAt(i))
                return false;
        }
        return true;
    }
 
    public byte[] compact() {
        return compact(binaryzationMatrix);
    }
 
    /**
     * 指纹数据按位压缩
     * 
     * @param hashValue
     * @return
     */
    private static byte[] compact(byte[] hashValue) {
        byte[] result = new byte[(hashValue.length + 7) >> 3];
        byte b = 0;
        for (int i = 0; i < hashValue.length; ++i) {
            if (0 == (i & 7)) {
                b = 0;
            }
            if (1 == hashValue[i]) {
                b |= 1 << (i & 7);
            } else if (hashValue[i] != 0)
                throw new IllegalArgumentException(
                        "invalid hashValue,every element must be 0 or 1");
            if (7 == (i & 7) || i == hashValue.length - 1) {
                result[i >> 3] = b;
            }
        }
        return result;
    }
 
    /**
     * 压缩格式的指纹解压缩
     * 
     * @param compactValue
     * @return
     */
    private static byte[] uncompact(byte[] compactValue) {
        byte[] result = new byte[compactValue.length << 3];
        for (int i = 0; i < result.length; ++i) {
            if ((compactValue[i >> 3] & (1 << (i & 7))) == 0)
                result[i] = 0;
            else
                result[i] = 1;
        }
        return result;
    }
 
    /**
     * 字符串类型的指纹数据转为字节数组
     * 
     * @param hashValue
     * @return
     */
    private static byte[] toBytes(String hashValue) {
        hashValue = hashValue.replaceAll("\\s", "");
        byte[] result = new byte[hashValue.length()];
        for (int i = 0; i < result.length; ++i) {
            char c = hashValue.charAt(i);
            if ('0' == c)
                result[i] = 0;
            else if ('1' == c)
                result[i] = 1;
            else
                throw new IllegalArgumentException("invalid hashValue String");
        }
        return result;
    }
 
    /**
     * 缩放图像到指定尺寸
     * 
     * @param src
     * @param width
     * @param height
     * @return
     */
    private static BufferedImage resize(Image src, int width, int height) {
        BufferedImage result = new BufferedImage(width, height,
                BufferedImage.TYPE_3BYTE_BGR);
        Graphics g = result.getGraphics();
        try {
            g.drawImage(
                    src.getScaledInstance(width, height, Image.SCALE_SMOOTH),
                    0, 0, null);
        } finally {
            g.dispose();
        }
        return result;
    }
 
    /**
     * 计算均值
     * 
     * @param src
     * @return
     */
    private static int mean(byte[] src) {
        long sum = 0;
        // 将数组元素转为无符号整数
        for (byte b : src)
            sum += (long) b & 0xff;
        return (int) (Math.round((float) sum / src.length));
    }
 
    /**
     * 二值化处理
     * 
     * @param src
     * @return
     */
    private static byte[] binaryzation(byte[] src) {
        byte[] dst = src.clone();
        int mean = mean(src);
        for (int i = 0; i < dst.length; ++i) {
            // 将数组元素转为无符号整数再比较
            dst[i] = (byte) (((int) dst[i] & 0xff) >= mean ? 1 : 0);
        }
        return dst;
 
    }
 
    /**
     * 转灰度图像
     * 
     * @param src
     * @return
     */
    private static BufferedImage toGray(BufferedImage src) {
        if (src.getType() == BufferedImage.TYPE_BYTE_GRAY) {
            return src;
        } else {
            // 图像转灰
            BufferedImage grayImage = new BufferedImage(src.getWidth(),
                    src.getHeight(), BufferedImage.TYPE_BYTE_GRAY);
            new ColorConvertOp(ColorSpace.getInstance(ColorSpace.CS_GRAY), null)
                    .filter(src, grayImage);
            return grayImage;
        }
    }
 
    @Override
    public String toString() {
        return toString(true);
    }
 
    /**
     * @param multiLine
     *            是否分行
     * @return
     */
    public String toString(boolean multiLine) {
        StringBuffer buffer = new StringBuffer();
        int count = 0;
        for (byte b : this.binaryzationMatrix) {
            buffer.append(0 == b ? '0' : '1');
            if (multiLine && ++count % HASH_SIZE == 0)
                buffer.append('\n');
        }
        return buffer.toString();
    }
 
    @Override
    public boolean equals(Object obj) {
        if (obj instanceof FingerPrint) {
            return Arrays.equals(this.binaryzationMatrix,
                    ((FingerPrint) obj).binaryzationMatrix);
        } else
            return super.equals(obj);
    }
 
    /**
     * 与指定的压缩格式指纹比较相似度
     * 
     * @param compactValue
     * @return
     * @see #compare(FingerPrint)
     */
    public float compareCompact(byte[] compactValue) {
        return compare(createFromCompact(compactValue));
    }
 
    /**
     * @param hashValue
     * @return
     * @see #compare(FingerPrint)
     */
    public float compare(String hashValue) {
        return compare(new FingerPrint(hashValue));
    }
 
    /**
     * 与指定的指纹比较相似度
     * 
     * @param hashValue
     * @return
     * @see #compare(FingerPrint)
     */
    public float compare(byte[] hashValue) {
        return compare(new FingerPrint(hashValue));
    }
 
    /**
     * 与指定图像比较相似度
     * 
     * @param image2
     * @return
     * @see #compare(FingerPrint)
     */
    public float compare(BufferedImage image2) {
        return compare(new FingerPrint(image2));
    }
 
    /**
     * 比较指纹相似度
     * 
     * @param src
     * @return
     * @see #compare(byte[], byte[])
     */
    public float compare(FingerPrint src) {
        if (src.binaryzationMatrix.length != this.binaryzationMatrix.length)
            throw new IllegalArgumentException(
                    "length of hashValue is mismatch");
        return compare(binaryzationMatrix, src.binaryzationMatrix);
    }
 
    /**
     * 判断两个数组相似度，数组长度必须一致否则抛出异常
     * 
     * @param f1
     * @param f2
     * @return 返回相似度(0.0~1.0)
     */
    private static float compare(byte[] f1, byte[] f2) {
        if (f1.length != f2.length)
            throw new IllegalArgumentException("mismatch FingerPrint length");
        int sameCount = 0;
        for (int i = 0; i < f1.length; ++i) {
            if (f1[i] == f2[i])
                ++sameCount;
        }
        return (float) sameCount / f1.length;
    }
 
    public static float compareCompact(byte[] f1, byte[] f2) {
        return compare(uncompact(f1), uncompact(f2));
    }
 
    public static float compare(BufferedImage image1, BufferedImage image2) {
        return new FingerPrint(image1).compare(new FingerPrint(image2));
    }
}

汉明距离算法实现图像内容相似度比较算法

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 ([email protected] 
 * 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://stackoverflow.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(
                    "/Users/sun/Downloads/1.jpg")));
            image2 = p.getHash(new FileInputStream(new File(
                    "/Users/sun/Downloads/11.png")));
            System.out.println("1:1 Score is " + p.distance(image1, image2));
 
        } catch (FileNotFoundException e) {
            e.printStackTrace();
        } catch (Exception e) {
            e.printStackTrace();
        }
 
    }
}

结果说明：汉明距离越大表明图片差异越大，如果不相同的数据位不超过5，就说明两张图片很相似；如果大于10，就说明这是两张不同的图片。

《三种基于感知哈希算法的相似图像检索技术》

根据Neal Krawetz博士的解释，原理非常简单易懂。我们可以用一个快速算法，就达到基本的效果。

这里的关键技术叫做"感知哈希算法"（Perceptual hash algorithm），它的作用是对每张图片生成一个"指纹"（fingerprint）字符串，然后比较不同图片的指纹。结果越接近，就说明图片越相似。

下面是一个最简单的实现：

第一步，缩小尺寸。

将图片缩小到8x8的尺寸，总共64个像素。这一步的作用是去除图片的细节，只保留结构、明暗等基本信息，摒弃不同尺寸、比例带来的图片差异。

第二步，简化色彩。

将缩小后的图片，转为64级灰度。也就是说，所有像素点总共只有64种颜色。

第三步，计算平均值。

计算所有64个像素的灰度平均值。

第四步，比较像素的灰度。

将每个像素的灰度，与平均值进行比较。大于或等于平均值，记为1；小于平均值，记为0。

第五步，计算哈希值。

将上一步的比较结果，组合在一起，就构成了一个64位的整数，这就是这张图片的指纹。组合的次序并不重要，只要保证所有图片都采用同样次序就行了。

得到指纹以后，就可以对比不同的图片，看看64位中有多少位是不一样的。在理论上，这等同于计算"汉明距离"（Hamming distance）。如果不相同的数据位不超过5，就说明两张图片很相似；如果大于10，就说明这是两张不同的图片。

具体的代码实现，可以参见Wote用python语言写的imgHash.py。代码很短，只有53行。使用的时候，第一个参数是基准图片，第二个参数是用来比较的其他图片所在的目录，返回结果是两张图片之间不相同的数据位数量（汉明距离）。

这种算法的优点是简单快速，不受图片大小缩放的影响，缺点是图片的内容不能变更。如果在图片上加几个文字，它就认不出来了。所以，它的最佳用途是根据缩略图，找出原图。

实际应用中，往往采用更强大的pHash算法和SIFT算法，它们能够识别图片的变形。只要变形程度不超过25%，它们就能匹配原图。这些算法虽然更复杂，但是原理与上面的简便算法是一样的，就是先将图片转化成Hash字符串，然后再进行比较。

转载自：https://www.sunjs.com/article/detail/24dd9a9e436e489185430c4c45034d69.html