添加 直方图算法计算图片相识度

src/main/java/image/similarity/ImageHistogram.java

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+package image.similarity;
+
+import java.awt.image.BufferedImage;
+import java.io.File;
+import java.io.IOException;
+import java.net.URL;
+
+import javax.imageio.ImageIO;
+
+/**
+ * @desc  相似图片识别（直方图）
+ */
+public class ImageHistogram {
+
+	private int redBins;
+	private int greenBins;
+	private int blueBins;
+
+	public ImageHistogram() {
+		redBins = greenBins = blueBins = 4;
+	}
+
+	private float[] filter(BufferedImage src) {
+		int width = src.getWidth();
+		int height = src.getHeight();
+
+		int[] inPixels = new int[width * height];
+		float[] histogramData = new float[redBins * greenBins * blueBins];
+		getRGB(src, 0, 0, width, height, inPixels);
+		int index = 0;
+		int redIdx = 0, greenIdx = 0, blueIdx = 0;
+		int singleIndex = 0;
+		float total = 0;
+		for (int row = 0; row < height; row++) {
+			int tr = 0, tg = 0, tb = 0;
+			for (int col = 0; col < width; col++) {
+				index = row * width + col;
+				tr = (inPixels[index] >> 16) & 0xff;
+				tg = (inPixels[index] >> 8) & 0xff;
+				tb = inPixels[index] & 0xff;
+				redIdx = (int) getBinIndex(redBins, tr, 255);
+				greenIdx = (int) getBinIndex(greenBins, tg, 255);
+				blueIdx = (int) getBinIndex(blueBins, tb, 255);
+				singleIndex = redIdx + greenIdx * redBins + blueIdx * redBins * greenBins;
+				histogramData[singleIndex] += 1;
+				total += 1;
+			}
+		}
+
+		// start to normalize the histogram data
+		for (int i = 0; i < histogramData.length; i++) {
+			histogramData[i] = histogramData[i] / total;
+		}
+
+		return histogramData;
+	}
+
+	private float getBinIndex(int binCount, int color, int colorMaxValue) {
+		float binIndex = (((float) color) / ((float) colorMaxValue)) * ((float) binCount);
+		if (binIndex >= binCount)
+			binIndex = binCount - 1;
+		return binIndex;
+	}
+
+	private int[] getRGB(BufferedImage image, int x, int y, int width, int height, int[] pixels) {
+		int type = image.getType();
+		if (type == BufferedImage.TYPE_INT_ARGB || type == BufferedImage.TYPE_INT_RGB)
+			return (int[]) image.getRaster().getDataElements(x, y, width, height, pixels);
+		return image.getRGB(x, y, width, height, pixels, 0, width);
+	}
+
+	/**
+	 * Bhattacharyya Coefficient
+	 * http://www.cse.yorku.ca/~kosta/CompVis_Notes/bhattacharyya.pdf
+	 * 
+	 * @return	返回值大于等于0.8可以简单判断这两张图片内容一致
+	 * @throws IOException 
+	 */
+	public double match(File srcFile, File canFile) throws IOException {
+		float[] sourceData = this.filter(ImageIO.read(srcFile));
+		float[] candidateData = this.filter(ImageIO.read(canFile));
+		return calcSimilarity(sourceData, candidateData);
+	}
+
+	/**
+	 * @return	返回值大于等于0.8可以简单判断这两张图片内容一致
+	 * @throws IOException
+	 */
+	public double match(URL srcUrl, URL canUrl) throws IOException {
+		float[] sourceData = this.filter(ImageIO.read(srcUrl));
+		float[] candidateData = this.filter(ImageIO.read(canUrl));
+		return calcSimilarity(sourceData, candidateData);
+	}
+
+	private double calcSimilarity(float[] sourceData, float[] candidateData) {
+		double[] mixedData = new double[sourceData.length];
+		for (int i = 0; i < sourceData.length; i++) {
+			mixedData[i] = Math.sqrt(sourceData[i] * candidateData[i]);
+		}
+
+		// The values of Bhattacharyya Coefficient ranges from 0 to 1,
+		double similarity = 0;
+		for (int i = 0; i < mixedData.length; i++) {
+			similarity += mixedData[i];
+		}
+
+		// The degree of similarity
+		return similarity;
+	}
+
+}

src/test/java/image/similarity/ImgHistogramTest.java

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+package image.similarity;
+
+import java.io.File;
+import java.io.IOException;
+import java.net.URL;
+
+import org.junit.Assert;
+
+import junit.framework.TestCase;
+
+public class ImgHistogramTest extends TestCase {
+
+	ImageHistogram histogram = null;
+
+	public void setUp() {
+		histogram = new ImageHistogram();
+	}
+
+	public void testImageHistogram() {
+		try {
+			double score = histogram.match(new File("doc/imgs/1.jpg"), new File("doc/imgs/1.jpg"));
+			System.out.println("img1-->img1::::score : " + score);
+			Assert.assertTrue(score >= 0.8);
+
+			score = histogram.match(new File("doc/imgs/1.jpg"), new File("doc/imgs/2.jpg"));
+			System.out.println("img1-->img2::::score : " + score);
+			Assert.assertTrue(score >= 0.8);
+
+			score = histogram.match(new File("doc/imgs/1.jpg"), new File("doc/imgs/3.jpg"));
+			System.out.println("img1-->img3::::score : " + score);
+			Assert.assertTrue(score >= 0.8);
+
+			score = histogram.match(new File("doc/imgs/1.jpg"), new File("doc/imgs/4.jpg"));
+			System.out.println("img1-->img4::::score : " + score);
+			Assert.assertTrue(score < 0.8);
+
+			score = histogram.match(new File("doc/imgs/5.jpg"), new File("doc/imgs/6.jpg"));
+			System.out.println("img5-->img6::::score : " + score);
+			Assert.assertTrue(score < 0.8); // incorrect
+
+			score = histogram.match(new File("doc/imgs/1.jpg"), new File("doc/imgs/6.jpg"));
+			System.out.println("img1-->img6::::score : " + score);
+			Assert.assertTrue(score < 0.8);
+
+			String srcUrl = "http://oarfc773f.bkt.clouddn.com/100000094nzslsdnswbb_1_1_r.jpg";
+			score = histogram.match(new URL(srcUrl), new URL("https://img3.doubanio.com/lpic/s27140981.jpg"));
+			System.out.println("url::::score:" + score);
+			Assert.assertTrue(score < 0.8);	// incorrect
+
+			score = histogram.match(new URL(srcUrl), new URL("https://img3.doubanio.com/lpic/s8966044.jpg"));
+			System.out.println("url::::score:" + score);
+			Assert.assertTrue(score < 0.8);	// incorrect
+
+		} catch (IOException e) {
+			e.printStackTrace();
+			Assert.assertFalse(false);
+		}
+
+	}
+
+}