opencv-java 特征匹配SURF算法

网上一直搜索opencv的相关匹配，模板匹配对尺寸要求太高了，而且识别率不算太高，网上大多数都是有关C的资料，java下载的opencv也不支持SURF算法，后来搞了半天解析opencv，终于把带有SURF的jar包下载下来了！下面晒一下成果！！

        //-- 步骤1：使用SURF Detector检测关键点，计算描述符
        double hessianThreshold = 400;
        int nOctaves = 4;
        int nOctaveLayers = 3;
        boolean extended = false;
        boolean upright = false;
        SURF detector = SURF.create(hessianThreshold, nOctaves, nOctaveLayers, extended, upright);
        MatOfKeyPoint keypointsObject = new MatOfKeyPoint();
        MatOfKeyPoint keypointsScene = new MatOfKeyPoint();
        Mat descriptorsObject = new Mat();
        Mat descriptorsScene = new Mat();
        detector.detectAndCompute(imgObject, new Mat(), keypointsObject, descriptorsObject);
        detector.detectAndCompute(imgScene, new Mat(), keypointsScene, descriptorsScene);

//-- 步骤2：将描述符向量与基于FLANN的匹配器进行匹配
        // 由于SURF是浮点描述符，因此使用NORM_L2
        DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.FLANNBASED);
        List knnMatches = new ArrayList<>();
        matcher.knnMatch(descriptorsObject, descriptorsScene, knnMatches, 2);
        //-- 使用Lowe比率测试过滤匹配项
        float ratioThresh = 0.75f;
        List listOfGoodMatches = new ArrayList<>();
        for (int i = 0; i < knnMatches.size(); i++) {
            if (knnMatches.get(i).rows() > 1) {
                DMatch[] matches = knnMatches.get(i).toArray();
                if (matches[0].distance < ratioThresh * matches[1].distance) {
                    listOfGoodMatches.add(matches[0]);
                }
            }
        }
        MatOfDMatch goodMatches = new MatOfDMatch();
        goodMatches.fromList(listOfGoodMatches);
        //-- 匹配
        Mat imgMatches = new Mat();
        Features2d.drawMatches(imgObject, keypointsObject, imgScene, keypointsScene, goodMatches, imgMatches, Scalar.all(-1),
                Scalar.all(-1), new MatOfByte(), Features2d.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS);
        //-- 定位对象
        List obj = new ArrayList<>();
        List scene = new ArrayList<>();
        List listOfKeypointsObject = keypointsObject.toList();
        List listOfKeypointsScene = keypointsScene.toList();
        for (int i = 0; i < listOfGoodMatches.size(); i++) {
            //-- 从良好的匹配中获取关键点
            obj.add(listOfKeypointsObject.get(listOfGoodMatches.get(i).queryIdx).pt);
            scene.add(listOfKeypointsScene.get(listOfGoodMatches.get(i).trainIdx).pt);
        }
        MatOfPoint2f objMat = new MatOfPoint2f();
        MatOfPoint2f sceneMat = new MatOfPoint2f();
        objMat.fromList(obj);
        sceneMat.fromList(scene);
        double ransacReprojThreshold = 3.0;
        Mat H = Calib3d.findHomography( objMat, sceneMat, Calib3d.RANSAC, ransacReprojThreshold );
        //-- 从image_1（要“检测”的对象）获取角
        Mat objCorners = new Mat(4, 1, CvType.CV_32FC2), sceneCorners = new Mat();
        float[] objCornersData = new float[(int) (objCorners.total() * objCorners.channels())];
        objCorners.get(0, 0, objCornersData);
        objCornersData[0] = 0;
        objCornersData[1] = 0;
        objCornersData[2] = imgObject.cols();
        objCornersData[3] = 0;
        objCornersData[4] = imgObject.cols();
        objCornersData[5] = imgObject.rows();
        objCornersData[6] = 0;
        objCornersData[7] = imgObject.rows();
        objCorners.put(0, 0, objCornersData);
        Core.perspectiveTransform(objCorners, sceneCorners, H);
        float[] sceneCornersData = new float[(int) (sceneCorners.total() * sceneCorners.channels())];
        sceneCorners.get(0, 0, sceneCornersData);
        //-- 在角之间绘制线（场景中的映射对象-image_2）
        Imgproc.line(imgMatches, new Point(sceneCornersData[0] + imgObject.cols(), sceneCornersData[1]),
                new Point(sceneCornersData[2] + imgObject.cols(), sceneCornersData[3]), new Scalar(0, 255, 0), 4);
        Imgproc.line(imgMatches, new Point(sceneCornersData[2] + imgObject.cols(), sceneCornersData[3]),
                new Point(sceneCornersData[4] + imgObject.cols(), sceneCornersData[5]), new Scalar(0, 255, 0), 4);
        Imgproc.line(imgMatches, new Point(sceneCornersData[4] + imgObject.cols(), sceneCornersData[5]),
                new Point(sceneCornersData[6] + imgObject.cols(), sceneCornersData[7]), new Scalar(0, 255, 0), 4);
        Imgproc.line(imgMatches, new Point(sceneCornersData[6] + imgObject.cols(), sceneCornersData[7]),
                new Point(sceneCornersData[0] + imgObject.cols(), sceneCornersData[1]), new Scalar(0, 255, 0), 4);