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我尝试这样做，我发现这个。他的问题不是我想问的，但我想做同样的事情。我可以在图像上找到特征并绘制特征描述符，但是对象周围的边界框很奇怪。对不起，我不能在这里发布我的结果，出来的线不是矩形，不是围绕对象这里是我的结果，我做错了什么还是有其他方法可以做到？

对不起我的英语不好，谢谢你的帮助

    private void Featrue_found(){       

    MatOfKeyPoint templateKeypoints = new MatOfKeyPoint();
    MatOfKeyPoint keypoints = new MatOfKeyPoint();
    MatOfDMatch matches = new MatOfDMatch();

    Object = new Mat(CvType.CV_32FC2);
    Object = Highgui.imread(Environment.getExternalStorageDirectory()+ "/Android/data/" + getApplicationContext().getPackageName() + "/Files/Object.jpg", Highgui.CV_LOAD_IMAGE_UNCHANGED);
    Resource = new Mat(CvType.CV_32FC2);
    Resource = Highgui.imread(Environment.getExternalStorageDirectory()+ "/Android/data/" + getApplicationContext().getPackageName() + "/Files/Resource.jpg", Highgui.CV_LOAD_IMAGE_UNCHANGED);
    Mat imageOut = Resource.clone();        


    FeatureDetector myFeatures = FeatureDetector.create(FeatureDetector.ORB);       
    myFeatures.detect(Resource, keypoints);
    myFeatures.detect(Object, templateKeypoints);

    DescriptorExtractor Extractor = DescriptorExtractor.create(DescriptorExtractor.ORB);
    Mat descriptors1 = new Mat();
    Mat descriptors2 = new Mat();
    Extractor.compute(Resource, keypoints, descriptors1);
    Extractor.compute(Resource, templateKeypoints, descriptors2);

    //add Feature descriptors
    DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE);
    matcher.match(descriptors1, descriptors2, matches);

    List<DMatch> matches_list = matches.toList();

    MatOfDMatch good_matches = new MatOfDMatch();
    double max_dist = 0; double min_dist = 100;

    //-- Quick calculation of max and min distances between keypoints
    for( int i = 0; i < descriptors1.rows(); i++ )
    { 
        double dist = matches_list.get(i).distance;
        if( dist < min_dist )
            min_dist = dist;
        if( dist > max_dist )
            max_dist = dist;
    }

    //-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
    for( int i = 0; i < descriptors1.rows(); i++ )
    { 
        if( matches_list.get(i).distance < 3*min_dist ){
            MatOfDMatch temp = new MatOfDMatch();
            temp.fromArray(matches.toArray()[i]);
             good_matches.push_back(temp);
        }
    }

    MatOfByte drawnMatches = new MatOfByte();       
    Features2d.drawMatches(Resource, keypoints, Object, templateKeypoints, good_matches, imageOut, Scalar.all(-1), Color_Red,  drawnMatches, Features2d.NOT_DRAW_SINGLE_POINTS);
    //no Feature descriptors
    //Features2d.drawMatches(Resource, keypoints, Object, templateKeypoints, matches, imageOut);

    LinkedList<Point> objList = new LinkedList<Point>();
    LinkedList<Point> sceneList = new LinkedList<Point>();
    List<DMatch> good_matches_list = good_matches.toList();

    List<KeyPoint> keypoints_objectList = templateKeypoints.toList();
    List<KeyPoint> keypoints_sceneList = keypoints.toList();

    for(int i = 0; i<good_matches_list.size(); i++)
    {
        objList.addLast(keypoints_objectList.get(good_matches_list.get(i).queryIdx).pt);
        sceneList.addLast(keypoints_sceneList.get(good_matches_list.get(i).trainIdx).pt);
    }

    MatOfPoint2f obj = new MatOfPoint2f();
    obj.fromList(objList);

    MatOfPoint2f scene = new MatOfPoint2f();

    scene.fromList(sceneList);
    //findHomography 
    Mat hg = Calib3d.findHomography(obj, scene);

    Mat obj_corners = new Mat(4,1,CvType.CV_32FC2);
    Mat scene_corners = new Mat(4,1,CvType.CV_32FC2);

    obj_corners.put(0, 0, new double[] {0,0});
    obj_corners.put(1, 0, new double[] {Object.cols(),0});
    obj_corners.put(2, 0, new double[] {Object.cols(),Object.rows()});
    obj_corners.put(3, 0, new double[] {0,Object.rows()});
    //obj_corners:input
    Core.perspectiveTransform(obj_corners, scene_corners, hg);

    Core.line(imageOut, new Point(scene_corners.get(0,0)), new Point(scene_corners.get(1,0)), new Scalar(0, 255, 0),4);
    Core.line(imageOut, new Point(scene_corners.get(1,0)), new Point(scene_corners.get(2,0)), new Scalar(0, 255, 0),4);
    Core.line(imageOut, new Point(scene_corners.get(2,0)), new Point(scene_corners.get(3,0)), new Scalar(0, 255, 0),4);
    Core.line(imageOut, new Point(scene_corners.get(3,0)), new Point(scene_corners.get(0,0)), new Scalar(0, 255, 0),4);

    Highgui.imwrite(Environment.getExternalStorageDirectory()+ "/Android/data/" + getApplicationContext().getPackageName() + "/Files/result_match.jpg", imageOut);


}
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1 回答 1

0

在您的情况下，匹配数不够。匹配也不正确。为了获得最佳结果，我建议在单应性计算中使用 RANSAC 并更改良好匹配的阈值。

    if( matches_list.get(i).distance < 3*min_dist ){
        MatOfDMatch temp = new MatOfDMatch();
        temp.fromArray(matches.toArray()[i]);
         good_matches.push_back(temp);
    }

将 3*min_dist 设为 4*min_dist 并检查是否存在良好的特征匹配。如果不是，那么您应该选择其他一些特征检测器（SURF、SIFT、哈里森）哈里森可能会给出嘈杂的结果，但它会产生最大数量的特征点。或者，如果这些都不起作用，则可以使用更好的图像质量图像。

于 2016-08-31T11:07:21.023 回答