Question

我试图使用OpenCV的DescriptorMatcher匹配2张相反的图像而没有运气。图片为：http://i61.tinypic.com/28whu0g.jpg（从左到右）和http://i61.tinypic.com/x35vte.jpg（从右到左）。

我的代码很像我在StackOverflow和网络上看到的很多例子，但我总是得不到匹配。

        String firstImageSourcePath = "RTL_IMAGE_PATH";
        String secondImageSourcePath = "LTR_IMAGE_PATH";

        Mat firstImageSrcImgMat = Highgui.imread(firstImageSourcePath);
        Mat secondImageSrcImgMat = Highgui.imread(firstImageSourcePath);

        if (firstImageSrcImgMat.empty() || secondImageSrcImgMat.empty()) {
            System.out.println("Failed to load images");
            return;
        }

        System.out.println("Loaded image at " + firstImageSourcePath + " and " + secondImageSourcePath);

        FeatureDetector featureDetector = FeatureDetector.create(FeatureDetector.BRISK);

        MatOfKeyPoint firstImgMatOfKeyPoints = new MatOfKeyPoint();
        MatOfKeyPoint secondImgMatOfKeyPoints = new MatOfKeyPoint();

        featureDetector.detect(firstImageSrcImgMat, firstImgMatOfKeyPoints);
        featureDetector.detect(secondImageSrcImgMat, secondImgMatOfKeyPoints);

        System.out.println("Detected " + firstImgMatOfKeyPoints.size() + " and " + secondImgMatOfKeyPoints + " blobs in the images");

        List<KeyPoint> firstImgKeyPoints = firstImgMatOfKeyPoints.toList();
        List<KeyPoint> secondImgKeyPoints = secondImgMatOfKeyPoints.toList();

        System.out.println("First Image key points: " + firstImgKeyPoints);
        System.out.println("Second Image key points: " + secondImgKeyPoints);

        Mat firstImgDescriptors = new Mat();
        Mat secondImgDescriptors = new Mat();

        DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.BRISK); 
        extractor.compute(firstImageSrcImgMat, firstImgMatOfKeyPoints, firstImgDescriptors);
        extractor.compute(secondImageSrcImgMat, secondImgMatOfKeyPoints, secondImgDescriptors);

        System.out.println("descriptorsA.size() : " + firstImgDescriptors.size());
        System.out.println("descriptorsB.size() : " + secondImgDescriptors.size());

        MatOfDMatch matches = new MatOfDMatch();

        DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMINGLUT); // BRUTEFORCE_HAMMINGLUT
        matcher.match(firstImgDescriptors, secondImgDescriptors, matches);

        System.out.println("matches.size() : " + matches.size());
        System.out.println("matches : " + matches);

        MatOfDMatch matchesFiltered = new MatOfDMatch();

        List<DMatch> matchesList = matches.toList();
        List<DMatch> bestMatches = new ArrayList<DMatch>();

        Double max_dist = 0.0;
        Double min_dist = 100.0;

        for (int i = 0; i < matchesList.size(); i++) {
            Double dist = (double) matchesList.get(i).distance;

            if (dist > 0)
                System.out.println("dist : " + dist);

            if (dist < min_dist && dist != 0) {
                min_dist = dist;
            }

            if (dist > max_dist) {
                max_dist = dist;
            }

        }

        System.out.println("max_dist : " + max_dist);
        System.out.println("min_dist : " + min_dist);

        if (min_dist > 50) {
            System.out.println("No match found, min_dist under minimum value");
            return;
        }

        double threshold = 3 * min_dist;
        double threshold2 = 2 * min_dist;

        if (threshold > 75) {
            threshold = 75;
        } else if (threshold2 >= max_dist) {
            threshold = min_dist * 1.1;
        } else if (threshold >= max_dist) {
            threshold = threshold2 * 1.4;
        }

        System.out.println("Threshold : " + threshold);

        for (int i = 0; i < matchesList.size(); i++) {
            Double dist = (double) matchesList.get(i).distance;

            if (dist < threshold) {
                bestMatches.add(matches.toList().get(i));
                System.out.println(String.format(i + " best match added : %s", dist));
            }
        }

        matchesFiltered.fromList(bestMatches);

        System.out.println("matchesFiltered.size() : " + matchesFiltered.size());

        if (matchesFiltered.rows() >= 1) {
            System.out.println("match found");
        } else {
            System.out.println("match not found");
        }

任何暗示我做错了什么？

Answer 1

正如@ Iwillnotexist-Idonotexist所说，第一个问题是你申请的门槛。尝试使用不依赖于描述符之间距离的阈值，因为某些描述符比其他描述符更具辨别力。我认为这会给你更好的结果。我建议你使用D. Lowe在SIFT论文中提出的比率测试。请查看第7.1节：http://cs.ubc.ca/~lowe/papers/ijcv04.pdf

第二个问题是您使用BRISK来检测图像中的功能。这个OpenCV实现有bug（你可以在这里查看：http://code.opencv.org/issues/3976）所以尝试使用另一个FeatureDetector，如FAST，ORB等...（描述符很好所以你可以继续使用它）

我最终测试了你的照片，我设法用不同的探测器/描述符获得了一些结果：（没有匹配的关键点 - ＆gt;黄色）

BRISK探测器和描述符： BRISK/BRISK

左图关键点：74
右图关键点：86
匹配：3（即使检测器坏了，我也有匹配）

使用BRISK作为描述符的ORB检测器： ORB/BRISK

左图关键点：499
右图关键点：500
匹配：26

ORB检测器和描述符 using ORB

左图关键点：841
右图关键点：907
匹配：43

使用比率测试获得所有结果以去除错误匹配。我希望这有帮助！

编辑：

BruteForceMatcher<Hamming> matcher;
vector< vector<DMatch> > matches;
vector <DMatch> goodMatches;
matcher.knnMatch(imgDescriptors1, imgDescriptors2, matches, 2);
// Ratio Test
for (unsigned int matchIdx = 0; matchIdx < matches.size(); ++matchIdx) 
{
    const float ratio = 0.8; // As in Lowe's paper (can be tuned)
    if (matches[matchIdx][0].distance < ratio * matches[matchIdx][1].distance)
    {
        goodMatches.push_back(matches[matchIdx][0]);
    }
}

Answer 2

Java版本是：

DescriptorMatcher descriptorMatcher;
descriptorMatcher=DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING );
MatOfDMatch good_matches;
good_matches = new MatOfDMatch();

LinkedList<MatOfDMatch> dmatchesListOfMat = new LinkedList<>();
descriptorMatcher.knnMatch(imgDescriptors1, imgDescriptors2, dmatchesListOfMat, 2);

LinkedList<DMatch> good_matchesList = new LinkedList<>();
for (int matchIndx = 0; matchIndx < dmatchesListOfMat.size() ; matchIndx++) {
    double ratio = 0.8;
    if (dmatchesListOfMat.get(matchIndx).toArray()[0].distance  < ratio * dmatchesListOfMat.get(matchIndx).toArray()[1].distance) {
        good_matchesList.addLast(dmatchesListOfMat.get(matchIndx).toArray()[0]);
    }
}
good_matches.fromList(good_matchesList);

OpenCV - Java - 使用DescriptorMatcher与2个相反的图像不匹配

2 个答案: