学习目标

找出目标图集中相似度最高的图片；

开发环境

JDK 8， OpenCV 2.3.14， Windows 7 64位；

测试图

测试图片

源码

package com.dotions.opencv;

import java.util.Arrays;
import java.util.HashMap;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;

import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfDMatch;
import org.opencv.core.MatOfKeyPoint;
import org.opencv.features2d.DMatch;
import org.opencv.features2d.DescriptorExtractor;
import org.opencv.features2d.DescriptorMatcher;
import org.opencv.features2d.FeatureDetector;
import org.opencv.highgui.Highgui;

/**
 * @author Scott 2018-02-05
 */
public class TestImageSearch {

    /**
     * @param args
     */
    public static void main(String[] args) {
        // 声明系统库
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);

        String f1 = "C:\\Users\\demo\\Pictures\\test\\CA4517-1-1.jpg";
        String f2 = "C:\\Users\\demo\\Pictures\\test\\CB4943-5.jpg";
        String f3 = "C:\\Users\\demo\\Pictures\\test\\CA4517-1-2.jpg";
        String f4 = "C:\\Users\\demo\\Pictures\\test\\CB4943-4.jpg";
        String f5 = "C:\\Users\\demo\\Pictures\\test\\CA4517-1-3.jpg";

        String url = find(f1, Arrays.asList(f2, f3, f4, f5));
        
        System.out.println("原图为：" + f1);
        System.out.println("最相似的图片为：" + url);
    }

    /**
     * 找出最相似的图片
     * @param base 原图
     * @param imgs 目标图集
     * @return 最相似的图片
     */
    public static String find(String base, List<String> imgs) {
        FeatureDetector detector = FeatureDetector.create(FeatureDetector.SURF);
        DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.SURF);
        DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.FLANNBASED);

        Mat baseDesc = getDescriptors(detector, extractor, base);

        Mat tempDesc;
        String resultImage = null;
        double minScore = Double.MAX_VALUE;
        double score;
        for (String f : imgs) {
            tempDesc = getDescriptors(detector, extractor, f);
            score = computeScore(baseDesc, tempDesc, matcher);

            if (score < minScore) {
                minScore = score;
                resultImage = f;
            }
        }
        return resultImage;
    }

    public static Mat getDescriptors(FeatureDetector fd, DescriptorExtractor de, String fname) {
        Mat src = Highgui.imread(fname);
        MatOfKeyPoint kp = new MatOfKeyPoint();
        fd.detect(src, kp);
        Mat desc = new Mat();
        de.compute(src, kp, desc);
        return desc;
    }
    /**
     * 计算相似度（此处用方差来作为衡量标准，可以用其他算法替换）
     * */
    public static double computeScore(Mat desc1, Mat desc2, DescriptorMatcher dm) {
        MatOfDMatch mdm = new MatOfDMatch();
        dm.match(desc1, desc2, mdm);

        double maxDist = Double.MIN_VALUE;
        double minDist = Double.MAX_VALUE;

        DMatch[] mats = mdm.toArray();
        double dist = 0.0d;
        for (int i = 0; i < mats.length; i++) {
            dist = mats[i].distance;
            if (dist < minDist)
                minDist = dist;
            if (dist > maxDist)
                maxDist = dist;
        }

        List<DMatch> goodMatches = new LinkedList<>();
        for (int i = 0; i < mats.length; i++) {
            dist = mats[i].distance;
            if (dist < 5 * minDist) {
                goodMatches.add(mats[i]);
            }
        }

        List<Float> list = goodMatches.stream().map(m -> m.distance).collect(Collectors.toList());
        Float[] dists = list.toArray(new Float[] {});

        double score = computeScore(dists);
        System.out.println("maxDist=" + maxDist);
        System.out.println("minDist=" + minDist);
        System.out.println("score=" + score);
        System.out.println("--------------------------------");
        return score;
    }

    /**
     * 计算数组的方差
     */
    public static double computeScore(Float[] dists) {
        double sum = 0.0d;
        for (int i = 0; i < dists.length; i++) {
            sum += dists[i];
        }
        double avg = sum / dists.length;
        double dvar = 0.0d;
        for (int i = 0; i < dists.length; i++) {
            dvar += (dists[i] - avg) * (dists[i] - avg);
        }
        return dvar;
    }

}

运行结果

maxDist=0.6122338175773621
minDist=0.039984580129384995
score=14.781858758643258
--------------------------------
maxDist=0.7934221625328064
minDist=0.0831444263458252
score=94.25050941872964
--------------------------------
maxDist=0.8908746838569641
minDist=0.08397696167230606
score=74.58133620484614
--------------------------------
maxDist=0.8740571141242981
minDist=0.08881661295890808
score=92.69089855851176
--------------------------------
原图为：C:\Users\demo\Pictures\test\1.jpg
最相似的图片为：C:\Users\demo\Pictures\test\2.jpg