安卓利用openCV实现两张图片的相似度对比的详细代码及过程

实现两张图片的相似度对比可以使用OpenCV中的ORB算法来提取图片的特征点，然后使用Brute-Force匹配算法来匹配特征点，最后计算匹配点的数量来表示两张图片的相似度。以下是详细的代码及过程：

1. 导入OpenCV库

import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfDMatch;
import org.opencv.core.MatOfKeyPoint;
import org.opencv.core.Point;
import org.opencv.features2d.DescriptorExtractor;
import org.opencv.features2d.FeatureDetector;
import org.opencv.features2d.Features2d;
import org.opencv.features2d.FlannBasedMatcher;
import org.opencv.features2d.KeyPoint;
import org.opencv.features2d.Matcher;
import org.opencv.highgui.HighGui;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;

2. 加载图片

// 加载图片
Mat img1 = Imgcodecs.imread("path/to/image1.jpg");
Mat img2 = Imgcodecs.imread("path/to/image2.jpg");

3. 提取特征点

// 创建ORB特征点检测器和描述子提取器
FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);
DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.ORB);

// 检测特征点并提取描述子
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
Mat descriptors1 = new Mat();
Mat descriptors2 = new Mat();
detector.detect(img1, keypoints1);
detector.detect(img2, keypoints2);
extractor.compute(img1, keypoints1, descriptors1);
extractor.compute(img2, keypoints2, descriptors2);

4. 匹配特征点

// 创建Brute-Force匹配器
Matcher matcher = new BFMatcher();

// 匹配特征点
MatOfDMatch matches = new MatOfDMatch();
matcher.match(descriptors1, descriptors2, matches);

5. 计算相似度

// 计算匹配点数量
double similarity = matches.toArray().length;

// 显示匹配结果
Mat outputImg = new Mat();
Features2d.drawMatches(img1, keypoints1, img2, keypoints2, matches, outputImg);
HighGui.imshow("Matches", outputImg);
HighGui.waitKey();

完整代码如下：

import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfDMatch;
import org.opencv.core.MatOfKeyPoint;
import org.opencv.core.Point;
import org.opencv.features2d.DescriptorExtractor;
import org.opencv.features2d.FeatureDetector;
import org.opencv.features2d.Features2d;
import org.opencv.features2d.FlannBasedMatcher;
import org.opencv.features2d.KeyPoint;
import org.opencv.features2d.Matcher;
import org.opencv.highgui.HighGui;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;

public class ImageComparator {
    public static void main(String[] args) {
        // 加载OpenCV库
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);

        // 加载图片
        Mat img1 = Imgcodecs.imread("path/to/image1.jpg");
        Mat img2 = Imgcodecs.imread("path/to/image2.jpg");

        // 创建ORB特征点检测器和描述子提取器
        FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);
        DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.ORB);

        // 检测特征点并提取描述子
        MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
        MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
        Mat descriptors1 = new Mat();
        Mat descriptors2 = new Mat();
        detector.detect(img1, keypoints1);
        detector.detect(img2, keypoints2);
        extractor.compute(img1, keypoints1, descriptors1);
        extractor.compute(img2, keypoints2, descriptors2);

        // 创建Brute-Force匹配器
        Matcher matcher = new BFMatcher();

        // 匹配特征点
        MatOfDMatch matches = new MatOfDMatch();
        matcher.match(descriptors1, descriptors2, matches);

        // 计算匹配点数量
        double similarity = matches.toArray().length;

        // 显示匹配结果
        Mat outputImg = new Mat();
        Features2d.drawMatches(img1, keypoints1, img2, keypoints2, matches, outputImg);
        HighGui.imshow("Matches", outputImg);
        HighGui.waitKey();
    }
}