feat: 切换后端至PaddleOCR-NCNN，切换工程为CMake

1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试
2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程
3.重整权利声明文件，重整代码工程，确保最小化侵权风险

Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake
Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c

3rdparty/opencv-4.5.4/modules/features2d/misc/java/test/BRIEFDescriptorExtractorTest.java

@@ -0,0 +1,102 @@
+package org.opencv.test.features2d;
+
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.MatOfKeyPoint;
+import org.opencv.core.Point;
+import org.opencv.core.Scalar;
+import org.opencv.core.KeyPoint;
+import org.opencv.test.OpenCVTestCase;
+import org.opencv.test.OpenCVTestRunner;
+import org.opencv.imgproc.Imgproc;
+import org.opencv.features2d.Feature2D;
+
+public class BRIEFDescriptorExtractorTest extends OpenCVTestCase {
+
+    Feature2D extractor;
+    int matSize;
+
+    private Mat getTestImg() {
+        Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
+        Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
+
+        return cross;
+    }
+
+    @Override
+    protected void setUp() throws Exception {
+        super.setUp();
+        extractor = createClassInstance(XFEATURES2D+"BriefDescriptorExtractor", DEFAULT_FACTORY, null, null);
+        matSize = 100;
+    }
+
+    public void testComputeListOfMatListOfListOfKeyPointListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testComputeMatListOfKeyPointMat() {
+        KeyPoint point = new KeyPoint(55.775577545166016f, 44.224422454833984f, 16, 9.754629f, 8617.863f, 1, -1);
+        MatOfKeyPoint keypoints = new MatOfKeyPoint(point);
+        Mat img = getTestImg();
+        Mat descriptors = new Mat();
+
+        extractor.compute(img, keypoints, descriptors);
+
+        Mat truth = new Mat(1, 32, CvType.CV_8UC1) {
+            {
+                put(0, 0, 96, 0, 76, 24, 47, 182, 68, 137,
+                          149, 195, 67, 16, 187, 224, 74, 8,
+                          82, 169, 87, 70, 44, 4, 192, 56,
+                          13, 128, 44, 106, 146, 72, 194, 245);
+            }
+        };
+
+        assertMatEqual(truth, descriptors);
+    }
+
+    public void testCreate() {
+        assertNotNull(extractor);
+    }
+
+    public void testDescriptorSize() {
+        assertEquals(32, extractor.descriptorSize());
+    }
+
+    public void testDescriptorType() {
+        assertEquals(CvType.CV_8U, extractor.descriptorType());
+    }
+
+    public void testEmpty() {
+//        assertFalse(extractor.empty());
+        fail("Not yet implemented"); // BRIEF does not override empty() method
+    }
+
+    public void testRead() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+        writeFile(filename, "%YAML:1.0\n---\ndescriptorSize: 64\n");
+
+        extractor.read(filename);
+
+        assertEquals(64, extractor.descriptorSize());
+    }
+
+    public void testWrite() {
+        String filename = OpenCVTestRunner.getTempFileName("xml");
+
+        extractor.write(filename);
+
+        String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n<descriptorSize>32</descriptorSize>\n</opencv_storage>\n";
+        assertEquals(truth, readFile(filename));
+    }
+
+    public void testWriteYml() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+
+        extractor.write(filename);
+
+        String truth = "%YAML:1.0\n---\ndescriptorSize: 32\n";
+        assertEquals(truth, readFile(filename));
+    }
+
+}

3rdparty/opencv-4.5.4/modules/features2d/misc/java/test/BruteForceDescriptorMatcherTest.java

@@ -0,0 +1,304 @@
+package org.opencv.test.features2d;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.MatOfDMatch;
+import org.opencv.core.MatOfKeyPoint;
+import org.opencv.core.Point;
+import org.opencv.core.Scalar;
+import org.opencv.core.DMatch;
+import org.opencv.features2d.DescriptorMatcher;
+import org.opencv.features2d.BFMatcher;
+import org.opencv.core.KeyPoint;
+import org.opencv.test.OpenCVTestCase;
+import org.opencv.test.OpenCVTestRunner;
+import org.opencv.imgproc.Imgproc;
+import org.opencv.features2d.Feature2D;
+
+public class BruteForceDescriptorMatcherTest extends OpenCVTestCase {
+
+    DescriptorMatcher matcher;
+    int matSize;
+    DMatch[] truth;
+
+    private Mat getMaskImg() {
+        return new Mat(5, 2, CvType.CV_8U, new Scalar(0)) {
+            {
+                put(0, 0, 1, 1, 1, 1);
+            }
+        };
+    }
+
+    private Mat getQueryDescriptors() {
+        Mat img = getQueryImg();
+        MatOfKeyPoint keypoints = new MatOfKeyPoint();
+        Mat descriptors = new Mat();
+
+        Feature2D detector = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
+        Feature2D extractor = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
+
+        setProperty(detector, "hessianThreshold", "double", 8000);
+        setProperty(detector, "nOctaves", "int", 3);
+        setProperty(detector, "nOctaveLayers", "int", 4);
+        setProperty(detector, "upright", "boolean", false);
+
+        detector.detect(img, keypoints);
+        extractor.compute(img, keypoints, descriptors);
+
+        return descriptors;
+    }
+
+    private Mat getQueryImg() {
+        Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Imgproc.line(cross, new Point(30, matSize / 2), new Point(matSize - 31, matSize / 2), new Scalar(100), 3);
+        Imgproc.line(cross, new Point(matSize / 2, 30), new Point(matSize / 2, matSize - 31), new Scalar(100), 3);
+
+        return cross;
+    }
+
+    private Mat getTrainDescriptors() {
+        Mat img = getTrainImg();
+        MatOfKeyPoint keypoints = new MatOfKeyPoint(new KeyPoint(50, 50, 16, 0, 20000, 1, -1), new KeyPoint(42, 42, 16, 160, 10000, 1, -1));
+        Mat descriptors = new Mat();
+
+        Feature2D extractor = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
+
+        extractor.compute(img, keypoints, descriptors);
+
+        return descriptors;
+    }
+
+    private Mat getTrainImg() {
+        Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
+        Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
+
+        return cross;
+    }
+
+    protected void setUp() throws Exception {
+        super.setUp();
+        matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE);
+        matSize = 100;
+
+        truth = new DMatch[] {
+                new DMatch(0, 0, 0, 0.6159003f),
+                new DMatch(1, 1, 0, 0.9177120f),
+                new DMatch(2, 1, 0, 0.3112163f),
+                new DMatch(3, 1, 0, 0.2925074f),
+                new DMatch(4, 1, 0, 0.26520672f)
+                };
+    }
+
+    // https://github.com/opencv/opencv/issues/11268
+    public void testConstructor()
+    {
+        BFMatcher self_created_matcher = new BFMatcher();
+        Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
+        self_created_matcher.add(Arrays.asList(train));
+        assertTrue(!self_created_matcher.empty());
+    }
+
+    public void testAdd() {
+        matcher.add(Arrays.asList(new Mat()));
+        assertFalse(matcher.empty());
+    }
+
+    public void testClear() {
+        matcher.add(Arrays.asList(new Mat()));
+
+        matcher.clear();
+
+        assertTrue(matcher.empty());
+    }
+
+    public void testClone() {
+        Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
+        Mat truth = train.clone();
+        matcher.add(Arrays.asList(train));
+
+        DescriptorMatcher cloned = matcher.clone();
+
+        assertNotNull(cloned);
+
+        List<Mat> descriptors = cloned.getTrainDescriptors();
+        assertEquals(1, descriptors.size());
+        assertMatEqual(truth, descriptors.get(0));
+    }
+
+    public void testCloneBoolean() {
+        matcher.add(Arrays.asList(new Mat()));
+
+        DescriptorMatcher cloned = matcher.clone(true);
+
+        assertNotNull(cloned);
+        assertTrue(cloned.empty());
+    }
+
+    public void testCreate() {
+        assertNotNull(matcher);
+    }
+
+    public void testEmpty() {
+        assertTrue(matcher.empty());
+    }
+
+    public void testGetTrainDescriptors() {
+        Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
+        Mat truth = train.clone();
+        matcher.add(Arrays.asList(train));
+
+        List<Mat> descriptors = matcher.getTrainDescriptors();
+
+        assertEquals(1, descriptors.size());
+        assertMatEqual(truth, descriptors.get(0));
+    }
+
+    public void testIsMaskSupported() {
+        assertTrue(matcher.isMaskSupported());
+    }
+
+    public void testKnnMatchMatListOfListOfDMatchInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatListOfListOfDMatchIntListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatListOfListOfDMatchIntListOfMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatMatListOfListOfDMatchInt() {
+        final int k = 3;
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        List<MatOfDMatch> matches = new ArrayList<MatOfDMatch>();
+        matcher.knnMatch(query, train, matches, k);
+        /*
+        Log.d("knnMatch", "train = " + train);
+        Log.d("knnMatch", "query = " + query);
+
+        matcher.add(train);
+        matcher.knnMatch(query, matches, k);
+        */
+        assertEquals(query.rows(), matches.size());
+        for(int i = 0; i<matches.size(); i++)
+        {
+            MatOfDMatch vdm = matches.get(i);
+            //Log.d("knn", "vdm["+i+"]="+vdm.dump());
+            assertTrue(Math.min(k, train.rows()) >= vdm.total());
+            for(DMatch dm : vdm.toArray())
+            {
+                assertEquals(dm.queryIdx, i);
+            }
+        }
+    }
+
+    public void testKnnMatchMatMatListOfListOfDMatchIntMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatMatListOfListOfDMatchIntMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testMatchMatListOfDMatch() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        MatOfDMatch matches = new MatOfDMatch();
+        matcher.add(Arrays.asList(train));
+
+        matcher.match(query, matches);
+
+        assertArrayDMatchEquals(truth, matches.toArray(), EPS);
+    }
+
+    public void testMatchMatListOfDMatchListOfMat() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        Mat mask = getMaskImg();
+        MatOfDMatch matches = new MatOfDMatch();
+        matcher.add(Arrays.asList(train));
+
+        matcher.match(query, matches, Arrays.asList(mask));
+
+        assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
+    }
+
+    public void testMatchMatMatListOfDMatch() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        MatOfDMatch matches = new MatOfDMatch();
+
+        matcher.match(query, train, matches);
+
+        assertArrayDMatchEquals(truth, matches.toArray(), EPS);
+
+        // OpenCVTestRunner.Log("matches found: " + matches.size());
+        // for (DMatch m : matches)
+        // OpenCVTestRunner.Log(m.toString());
+    }
+
+    public void testMatchMatMatListOfDMatchMat() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        Mat mask = getMaskImg();
+        MatOfDMatch matches = new MatOfDMatch();
+
+        matcher.match(query, train, matches, mask);
+
+        assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
+    }
+
+    public void testRadiusMatchMatListOfListOfDMatchFloat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatListOfListOfDMatchFloatListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatListOfListOfDMatchFloatListOfMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatMatListOfListOfDMatchFloat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatMatListOfListOfDMatchFloatMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatMatListOfListOfDMatchFloatMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testRead() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+        writeFile(filename, "%YAML:1.0\n---\n");
+
+        matcher.read(filename);
+        assertTrue(true);// BruteforceMatcher has no settings
+    }
+
+    public void testTrain() {
+        matcher.train();// BruteforceMatcher does not need to train
+    }
+
+    public void testWrite() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+
+        matcher.write(filename);
+
+        String truth = "%YAML:1.0\n---\n";
+        assertEquals(truth, readFile(filename));
+    }
+
+}

3rdparty/opencv-4.5.4/modules/features2d/misc/java/test/BruteForceHammingDescriptorMatcherTest.java

@@ -0,0 +1,262 @@
+package org.opencv.test.features2d;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.MatOfDMatch;
+import org.opencv.core.MatOfKeyPoint;
+import org.opencv.core.Point;
+import org.opencv.core.Scalar;
+import org.opencv.core.DMatch;
+import org.opencv.features2d.DescriptorMatcher;
+import org.opencv.features2d.FastFeatureDetector;
+import org.opencv.test.OpenCVTestCase;
+import org.opencv.test.OpenCVTestRunner;
+import org.opencv.imgproc.Imgproc;
+import org.opencv.features2d.Feature2D;
+
+public class BruteForceHammingDescriptorMatcherTest extends OpenCVTestCase {
+
+    DescriptorMatcher matcher;
+    int matSize;
+    DMatch[] truth;
+
+    private Mat getMaskImg() {
+        return new Mat(4, 4, CvType.CV_8U, new Scalar(0)) {
+            {
+                put(0, 0, 1, 1, 1, 1, 1, 1, 1, 1);
+            }
+        };
+    }
+
+    private Mat getQueryDescriptors() {
+        return getTestDescriptors(getQueryImg());
+    }
+
+    private Mat getQueryImg() {
+        Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Imgproc.line(img, new Point(40, matSize - 40), new Point(matSize - 50, 50), new Scalar(0), 8);
+        return img;
+    }
+
+    private Mat getTestDescriptors(Mat img) {
+        MatOfKeyPoint keypoints = new MatOfKeyPoint();
+        Mat descriptors = new Mat();
+
+        Feature2D detector = FastFeatureDetector.create();
+        Feature2D extractor = createClassInstance(XFEATURES2D+"BriefDescriptorExtractor", DEFAULT_FACTORY, null, null);
+
+        detector.detect(img, keypoints);
+        extractor.compute(img, keypoints, descriptors);
+
+        return descriptors;
+    }
+
+    private Mat getTrainDescriptors() {
+        return getTestDescriptors(getTrainImg());
+    }
+
+    private Mat getTrainImg() {
+        Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Imgproc.line(img, new Point(40, 40), new Point(matSize - 40, matSize - 40), new Scalar(0), 8);
+        return img;
+    }
+
+    protected void setUp() throws Exception {
+        super.setUp();
+        matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);
+        matSize = 100;
+
+        truth = new DMatch[] {
+                new DMatch(0, 0, 0, 51),
+                new DMatch(1, 2, 0, 42),
+                new DMatch(2, 1, 0, 40),
+                new DMatch(3, 3, 0, 53) };
+    }
+
+    public void testAdd() {
+        matcher.add(Arrays.asList(new Mat()));
+        assertFalse(matcher.empty());
+    }
+
+    public void testClear() {
+        matcher.add(Arrays.asList(new Mat()));
+
+        matcher.clear();
+
+        assertTrue(matcher.empty());
+    }
+
+    public void testClone() {
+        Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
+        Mat truth = train.clone();
+        matcher.add(Arrays.asList(train));
+
+        DescriptorMatcher cloned = matcher.clone();
+
+        assertNotNull(cloned);
+
+        List<Mat> descriptors = cloned.getTrainDescriptors();
+        assertEquals(1, descriptors.size());
+        assertMatEqual(truth, descriptors.get(0));
+    }
+
+    public void testCloneBoolean() {
+        matcher.add(Arrays.asList(new Mat()));
+
+        DescriptorMatcher cloned = matcher.clone(true);
+
+        assertNotNull(cloned);
+        assertTrue(cloned.empty());
+    }
+
+    public void testCreate() {
+        assertNotNull(matcher);
+    }
+
+    public void testEmpty() {
+        assertTrue(matcher.empty());
+    }
+
+    public void testGetTrainDescriptors() {
+        Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
+        Mat truth = train.clone();
+        matcher.add(Arrays.asList(train));
+
+        List<Mat> descriptors = matcher.getTrainDescriptors();
+
+        assertEquals(1, descriptors.size());
+        assertMatEqual(truth, descriptors.get(0));
+    }
+
+    public void testIsMaskSupported() {
+        assertTrue(matcher.isMaskSupported());
+    }
+
+    public void testKnnMatchMatListOfListOfDMatchInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatListOfListOfDMatchIntListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatListOfListOfDMatchIntListOfMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatMatListOfListOfDMatchInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatMatListOfListOfDMatchIntMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatMatListOfListOfDMatchIntMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testMatchMatListOfDMatch() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        MatOfDMatch matches = new MatOfDMatch();
+        matcher.add(Arrays.asList(train));
+
+        matcher.match(query, matches);
+
+        assertListDMatchEquals(Arrays.asList(truth), matches.toList(), EPS);
+    }
+
+    public void testMatchMatListOfDMatchListOfMat() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        Mat mask = getMaskImg();
+        MatOfDMatch matches = new MatOfDMatch();
+        matcher.add(Arrays.asList(train));
+
+        matcher.match(query, matches, Arrays.asList(mask));
+
+        assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
+    }
+
+    public void testMatchMatMatListOfDMatch() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        MatOfDMatch matches = new MatOfDMatch();
+
+        matcher.match(query, train, matches);
+
+        assertListDMatchEquals(Arrays.asList(truth), matches.toList(), EPS);
+    }
+
+    public void testMatchMatMatListOfDMatchMat() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        Mat mask = getMaskImg();
+        MatOfDMatch matches = new MatOfDMatch();
+
+        matcher.match(query, train, matches, mask);
+
+        assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
+    }
+
+    public void testRadiusMatchMatListOfListOfDMatchFloat() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        ArrayList<MatOfDMatch> matches = new ArrayList<MatOfDMatch>();
+
+        matcher.radiusMatch(query, train, matches, 50.f);
+
+        assertEquals(4, matches.size());
+        assertTrue(matches.get(0).empty());
+        assertMatEqual(matches.get(1), new MatOfDMatch(truth[1]), EPS);
+        assertMatEqual(matches.get(2), new MatOfDMatch(truth[2]), EPS);
+        assertTrue(matches.get(3).empty());
+    }
+
+    public void testRadiusMatchMatListOfListOfDMatchFloatListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatListOfListOfDMatchFloatListOfMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatMatListOfListOfDMatchFloat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatMatListOfListOfDMatchFloatMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatMatListOfListOfDMatchFloatMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testRead() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+        writeFile(filename, "%YAML:1.0\n---\n");
+
+        matcher.read(filename);
+        assertTrue(true);// BruteforceMatcher has no settings
+    }
+
+    public void testTrain() {
+        matcher.train();// BruteforceMatcher does not need to train
+    }
+
+    public void testWrite() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+
+        matcher.write(filename);
+
+        String truth = "%YAML:1.0\n---\n";
+        assertEquals(truth, readFile(filename));
+    }
+
+}

3rdparty/opencv-4.5.4/modules/features2d/misc/java/test/BruteForceHammingLUTDescriptorMatcherTest.java

@@ -0,0 +1,257 @@
+package org.opencv.test.features2d;
+
+import java.util.Arrays;
+import java.util.List;
+
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.MatOfDMatch;
+import org.opencv.core.MatOfKeyPoint;
+import org.opencv.core.Point;
+import org.opencv.core.Scalar;
+import org.opencv.core.DMatch;
+import org.opencv.features2d.DescriptorMatcher;
+import org.opencv.features2d.FastFeatureDetector;
+import org.opencv.test.OpenCVTestCase;
+import org.opencv.test.OpenCVTestRunner;
+import org.opencv.imgproc.Imgproc;
+import org.opencv.features2d.Feature2D;
+
+public class BruteForceHammingLUTDescriptorMatcherTest extends OpenCVTestCase {
+
+    DescriptorMatcher matcher;
+    int matSize;
+    DMatch[] truth;
+
+    private Mat getMaskImg() {
+        return new Mat(4, 4, CvType.CV_8U, new Scalar(0)) {
+            {
+                put(0, 0, 1, 1, 1, 1, 1, 1, 1, 1);
+            }
+        };
+    }
+
+    private Mat getQueryDescriptors() {
+        return getTestDescriptors(getQueryImg());
+    }
+
+    private Mat getQueryImg() {
+        Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Imgproc.line(img, new Point(40, matSize - 40), new Point(matSize - 50, 50), new Scalar(0), 8);
+        return img;
+    }
+
+    private Mat getTestDescriptors(Mat img) {
+        MatOfKeyPoint keypoints = new MatOfKeyPoint();
+        Mat descriptors = new Mat();
+
+        Feature2D detector = FastFeatureDetector.create();
+        Feature2D extractor = createClassInstance(XFEATURES2D+"BriefDescriptorExtractor", DEFAULT_FACTORY, null, null);
+
+        detector.detect(img, keypoints);
+        extractor.compute(img, keypoints, descriptors);
+
+        return descriptors;
+    }
+
+    private Mat getTrainDescriptors() {
+        return getTestDescriptors(getTrainImg());
+    }
+
+    private Mat getTrainImg() {
+        Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Imgproc.line(img, new Point(40, 40), new Point(matSize - 40, matSize - 40), new Scalar(0), 8);
+        return img;
+    }
+
+    protected void setUp() throws Exception {
+        super.setUp();
+        matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMINGLUT);
+        matSize = 100;
+
+        truth = new DMatch[] {
+                new DMatch(0, 0, 0, 51),
+                new DMatch(1, 2, 0, 42),
+                new DMatch(2, 1, 0, 40),
+                new DMatch(3, 3, 0, 53) };
+    }
+
+    public void testAdd() {
+        matcher.add(Arrays.asList(new Mat()));
+        assertFalse(matcher.empty());
+    }
+
+    public void testClear() {
+        matcher.add(Arrays.asList(new Mat()));
+
+        matcher.clear();
+
+        assertTrue(matcher.empty());
+    }
+
+    public void testClone() {
+        Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
+        Mat truth = train.clone();
+        matcher.add(Arrays.asList(train));
+
+        DescriptorMatcher cloned = matcher.clone();
+
+        assertNotNull(cloned);
+
+        List<Mat> descriptors = cloned.getTrainDescriptors();
+        assertEquals(1, descriptors.size());
+        assertMatEqual(truth, descriptors.get(0));
+    }
+
+    public void testCloneBoolean() {
+        matcher.add(Arrays.asList(new Mat()));
+
+        DescriptorMatcher cloned = matcher.clone(true);
+
+        assertNotNull(cloned);
+        assertTrue(cloned.empty());
+    }
+
+    public void testCreate() {
+        assertNotNull(matcher);
+    }
+
+    public void testEmpty() {
+        assertTrue(matcher.empty());
+    }
+
+    public void testGetTrainDescriptors() {
+        Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
+        Mat truth = train.clone();
+        matcher.add(Arrays.asList(train));
+
+        List<Mat> descriptors = matcher.getTrainDescriptors();
+
+        assertEquals(1, descriptors.size());
+        assertMatEqual(truth, descriptors.get(0));
+    }
+
+    public void testIsMaskSupported() {
+        assertTrue(matcher.isMaskSupported());
+    }
+
+    public void testKnnMatchMatListOfListOfDMatchInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatListOfListOfDMatchIntListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatListOfListOfDMatchIntListOfMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatMatListOfListOfDMatchInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatMatListOfListOfDMatchIntMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatMatListOfListOfDMatchIntMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testMatchMatListOfDMatch() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        MatOfDMatch matches = new MatOfDMatch();
+        matcher.add(Arrays.asList(train));
+
+        matcher.match(query, matches);
+
+        assertArrayDMatchEquals(truth, matches.toArray(), EPS);
+    }
+
+    public void testMatchMatListOfDMatchListOfMat() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        Mat mask = getMaskImg();
+        MatOfDMatch matches = new MatOfDMatch();
+        matcher.add(Arrays.asList(train));
+
+        matcher.match(query, matches, Arrays.asList(mask));
+
+        assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
+    }
+
+    public void testMatchMatMatListOfDMatch() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        MatOfDMatch matches = new MatOfDMatch();
+
+        matcher.match(query, train, matches);
+
+        /*
+        OpenCVTestRunner.Log("matches found: " + matches.size());
+        for (DMatch m : matches.toArray())
+            OpenCVTestRunner.Log(m.toString());
+        */
+
+        assertArrayDMatchEquals(truth, matches.toArray(), EPS);
+    }
+
+    public void testMatchMatMatListOfDMatchMat() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        Mat mask = getMaskImg();
+        MatOfDMatch matches = new MatOfDMatch();
+
+        matcher.match(query, train, matches, mask);
+
+        assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
+    }
+
+    public void testRadiusMatchMatListOfListOfDMatchFloat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatListOfListOfDMatchFloatListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatListOfListOfDMatchFloatListOfMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatMatListOfListOfDMatchFloat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatMatListOfListOfDMatchFloatMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatMatListOfListOfDMatchFloatMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testRead() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+        writeFile(filename, "%YAML:1.0\n---\n");
+
+        matcher.read(filename);
+        assertTrue(true);// BruteforceMatcher has no settings
+    }
+
+    public void testTrain() {
+        matcher.train();// BruteforceMatcher does not need to train
+    }
+
+    public void testWrite() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+
+        matcher.write(filename);
+
+        String truth = "%YAML:1.0\n---\n";
+        assertEquals(truth, readFile(filename));
+    }
+
+}

3rdparty/opencv-4.5.4/modules/features2d/misc/java/test/BruteForceL1DescriptorMatcherTest.java

@@ -0,0 +1,268 @@
+package org.opencv.test.features2d;
+
+import java.util.Arrays;
+import java.util.List;
+
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.MatOfDMatch;
+import org.opencv.core.MatOfKeyPoint;
+import org.opencv.core.Point;
+import org.opencv.core.Scalar;
+import org.opencv.core.DMatch;
+import org.opencv.features2d.DescriptorMatcher;
+import org.opencv.core.KeyPoint;
+import org.opencv.test.OpenCVTestCase;
+import org.opencv.test.OpenCVTestRunner;
+import org.opencv.imgproc.Imgproc;
+import org.opencv.features2d.Feature2D;
+
+public class BruteForceL1DescriptorMatcherTest extends OpenCVTestCase {
+
+    DescriptorMatcher matcher;
+    int matSize;
+    DMatch[] truth;
+
+    private Mat getMaskImg() {
+        return new Mat(5, 2, CvType.CV_8U, new Scalar(0)) {
+            {
+                put(0, 0, 1, 1, 1, 1);
+            }
+        };
+    }
+
+    private Mat getQueryDescriptors() {
+        Mat img = getQueryImg();
+        MatOfKeyPoint keypoints = new MatOfKeyPoint();
+        Mat descriptors = new Mat();
+
+        Feature2D detector = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
+        Feature2D extractor = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
+
+        setProperty(detector, "extended", "boolean", true);
+        setProperty(detector, "hessianThreshold", "double", 8000);
+        setProperty(detector, "nOctaveLayers", "int", 2);
+        setProperty(detector, "nOctaves", "int", 3);
+        setProperty(detector, "upright", "boolean", false);
+
+        detector.detect(img, keypoints);
+        extractor.compute(img, keypoints, descriptors);
+
+        return descriptors;
+    }
+
+    private Mat getQueryImg() {
+        Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Imgproc.line(cross, new Point(30, matSize / 2), new Point(matSize - 31, matSize / 2), new Scalar(100), 3);
+        Imgproc.line(cross, new Point(matSize / 2, 30), new Point(matSize / 2, matSize - 31), new Scalar(100), 3);
+
+        return cross;
+    }
+
+    private Mat getTrainDescriptors() {
+        Mat img = getTrainImg();
+        MatOfKeyPoint keypoints = new MatOfKeyPoint(new KeyPoint(50, 50, 16, 0, 20000, 1, -1), new KeyPoint(42, 42, 16, 160, 10000, 1, -1));
+        Mat descriptors = new Mat();
+
+        Feature2D extractor = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
+
+        extractor.compute(img, keypoints, descriptors);
+
+        return descriptors;
+    }
+
+    private Mat getTrainImg() {
+        Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
+        Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
+
+        return cross;
+    }
+
+    protected void setUp() throws Exception {
+        super.setUp();
+        matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_L1);
+        matSize = 100;
+
+        truth = new DMatch[] {
+                new DMatch(0, 0, 0, 3.0710702f),
+                new DMatch(1, 1, 0, 3.562016f),
+                new DMatch(2, 1, 0, 1.3682679f),
+                new DMatch(3, 1, 0, 1.3012862f),
+                new DMatch(4, 1, 0, 1.1852086f)
+                };
+    }
+
+    public void testAdd() {
+        matcher.add(Arrays.asList(new Mat()));
+        assertFalse(matcher.empty());
+    }
+
+    public void testClear() {
+        matcher.add(Arrays.asList(new Mat()));
+
+        matcher.clear();
+
+        assertTrue(matcher.empty());
+    }
+
+    public void testClone() {
+        Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
+        Mat truth = train.clone();
+        matcher.add(Arrays.asList(train));
+
+        DescriptorMatcher cloned = matcher.clone();
+
+        assertNotNull(cloned);
+
+        List<Mat> descriptors = cloned.getTrainDescriptors();
+        assertEquals(1, descriptors.size());
+        assertMatEqual(truth, descriptors.get(0));
+    }
+
+    public void testCloneBoolean() {
+        matcher.add(Arrays.asList(new Mat()));
+
+        DescriptorMatcher cloned = matcher.clone(true);
+
+        assertNotNull(cloned);
+        assertTrue(cloned.empty());
+    }
+
+    public void testCreate() {
+        assertNotNull(matcher);
+    }
+
+    public void testEmpty() {
+        assertTrue(matcher.empty());
+    }
+
+    public void testGetTrainDescriptors() {
+        Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
+        Mat truth = train.clone();
+        matcher.add(Arrays.asList(train));
+
+        List<Mat> descriptors = matcher.getTrainDescriptors();
+
+        assertEquals(1, descriptors.size());
+        assertMatEqual(truth, descriptors.get(0));
+    }
+
+    public void testIsMaskSupported() {
+        assertTrue(matcher.isMaskSupported());
+    }
+
+    public void testKnnMatchMatListOfListOfDMatchInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatListOfListOfDMatchIntListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatListOfListOfDMatchIntListOfMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatMatListOfListOfDMatchInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatMatListOfListOfDMatchIntMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatMatListOfListOfDMatchIntMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testMatchMatListOfDMatch() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        MatOfDMatch matches = new MatOfDMatch();
+        matcher.add(Arrays.asList(train));
+
+        matcher.match(query, matches);
+
+        assertArrayDMatchEquals(truth, matches.toArray(), EPS);
+    }
+
+    public void testMatchMatListOfDMatchListOfMat() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        Mat mask = getMaskImg();
+        MatOfDMatch matches = new MatOfDMatch();
+        matcher.add(Arrays.asList(train));
+
+        matcher.match(query, matches, Arrays.asList(mask));
+
+        assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
+    }
+
+    public void testMatchMatMatListOfDMatch() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        MatOfDMatch matches = new MatOfDMatch();
+
+        matcher.match(query, train, matches);
+
+        assertArrayDMatchEquals(truth, matches.toArray(), EPS);
+    }
+
+    public void testMatchMatMatListOfDMatchMat() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        Mat mask = getMaskImg();
+        MatOfDMatch matches = new MatOfDMatch();
+
+        matcher.match(query, train, matches, mask);
+
+        assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
+    }
+
+    public void testRadiusMatchMatListOfListOfDMatchFloat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatListOfListOfDMatchFloatListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatListOfListOfDMatchFloatListOfMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatMatListOfListOfDMatchFloat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatMatListOfListOfDMatchFloatMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatMatListOfListOfDMatchFloatMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testRead() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+        writeFile(filename, "%YAML:1.0\n---\n");
+
+        matcher.read(filename);
+        assertTrue(true);// BruteforceMatcher has no settings
+    }
+
+    public void testTrain() {
+        matcher.train();// BruteforceMatcher does not need to train
+    }
+
+    public void testWrite() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+
+        matcher.write(filename);
+
+        String truth = "%YAML:1.0\n---\n";
+        assertEquals(truth, readFile(filename));
+    }
+
+}

3rdparty/opencv-4.5.4/modules/features2d/misc/java/test/BruteForceSL2DescriptorMatcherTest.java

@@ -0,0 +1,280 @@
+package org.opencv.test.features2d;
+
+import java.util.Arrays;
+import java.util.List;
+
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.MatOfDMatch;
+import org.opencv.core.MatOfKeyPoint;
+import org.opencv.core.Point;
+import org.opencv.core.Scalar;
+import org.opencv.core.DMatch;
+import org.opencv.features2d.DescriptorMatcher;
+import org.opencv.core.KeyPoint;
+import org.opencv.test.OpenCVTestCase;
+import org.opencv.test.OpenCVTestRunner;
+import org.opencv.imgproc.Imgproc;
+import org.opencv.features2d.Feature2D;
+
+public class BruteForceSL2DescriptorMatcherTest extends OpenCVTestCase {
+
+    DescriptorMatcher matcher;
+    int matSize;
+    DMatch[] truth;
+
+    private Mat getMaskImg() {
+        return new Mat(5, 2, CvType.CV_8U, new Scalar(0)) {
+            {
+                put(0, 0, 1, 1, 1, 1);
+            }
+        };
+    }
+
+    /*
+    private float sqr(float val){
+        return val * val;
+    }
+    */
+
+    private Mat getQueryDescriptors() {
+        Mat img = getQueryImg();
+        MatOfKeyPoint keypoints = new MatOfKeyPoint();
+        Mat descriptors = new Mat();
+
+        Feature2D detector = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
+        Feature2D extractor = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
+
+        setProperty(detector, "hessianThreshold", "double", 8000);
+        setProperty(detector, "nOctaves", "int", 3);
+        setProperty(detector, "nOctaveLayers", "int", 4);
+        setProperty(detector, "upright", "boolean", false);
+
+        detector.detect(img, keypoints);
+        extractor.compute(img, keypoints, descriptors);
+
+        return descriptors;
+    }
+
+    private Mat getQueryImg() {
+        Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Imgproc.line(cross, new Point(30, matSize / 2), new Point(matSize - 31, matSize / 2), new Scalar(100), 3);
+        Imgproc.line(cross, new Point(matSize / 2, 30), new Point(matSize / 2, matSize - 31), new Scalar(100), 3);
+
+        return cross;
+    }
+
+    private Mat getTrainDescriptors() {
+        Mat img = getTrainImg();
+        MatOfKeyPoint keypoints = new MatOfKeyPoint(new KeyPoint(50, 50, 16, 0, 20000, 1, -1), new KeyPoint(42, 42, 16, 160, 10000, 1, -1));
+        Mat descriptors = new Mat();
+
+        Feature2D extractor = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
+
+        extractor.compute(img, keypoints, descriptors);
+
+        return descriptors;
+    }
+
+    private Mat getTrainImg() {
+        Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
+        Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
+
+        return cross;
+    }
+
+    protected void setUp() throws Exception {
+        super.setUp();
+        matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_SL2);
+        matSize = 100;
+
+        truth = new DMatch[] {
+                new DMatch(0, 0, 0, 0.37933317f),
+                new DMatch(1, 1, 0, 0.8421953f),
+                new DMatch(2, 1, 0, 0.0968556f),
+                new DMatch(3, 1, 0, 0.0855606f),
+                new DMatch(4, 1, 0, 0.07033461f)
+                };
+    }
+
+    public void testAdd() {
+        matcher.add(Arrays.asList(new Mat()));
+        assertFalse(matcher.empty());
+    }
+
+    public void testClear() {
+        matcher.add(Arrays.asList(new Mat()));
+
+        matcher.clear();
+
+        assertTrue(matcher.empty());
+    }
+
+    public void testClone() {
+        Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
+        Mat truth = train.clone();
+        matcher.add(Arrays.asList(train));
+
+        DescriptorMatcher cloned = matcher.clone();
+
+        assertNotNull(cloned);
+
+        List<Mat> descriptors = cloned.getTrainDescriptors();
+        assertEquals(1, descriptors.size());
+        assertMatEqual(truth, descriptors.get(0));
+    }
+
+    public void testCloneBoolean() {
+        matcher.add(Arrays.asList(new Mat()));
+
+        DescriptorMatcher cloned = matcher.clone(true);
+
+        assertNotNull(cloned);
+        assertTrue(cloned.empty());
+    }
+
+    public void testCreate() {
+        assertNotNull(matcher);
+    }
+
+    public void testEmpty() {
+        assertTrue(matcher.empty());
+    }
+
+    public void testGetTrainDescriptors() {
+        Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
+        Mat truth = train.clone();
+        matcher.add(Arrays.asList(train));
+
+        List<Mat> descriptors = matcher.getTrainDescriptors();
+
+        assertEquals(1, descriptors.size());
+        assertMatEqual(truth, descriptors.get(0));
+    }
+
+    public void testIsMaskSupported() {
+        assertTrue(matcher.isMaskSupported());
+    }
+
+    public void testKnnMatchMatListOfListOfDMatchInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatListOfListOfDMatchIntListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatListOfListOfDMatchIntListOfMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatMatListOfListOfDMatchInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatMatListOfListOfDMatchIntMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatMatListOfListOfDMatchIntMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testMatchMatListOfDMatch() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        MatOfDMatch matches = new MatOfDMatch();
+        matcher.add(Arrays.asList(train));
+
+        matcher.match(query, matches);
+        OpenCVTestRunner.Log(matches);
+        OpenCVTestRunner.Log(matches);
+        OpenCVTestRunner.Log(matches);
+
+        assertArrayDMatchEquals(truth, matches.toArray(), EPS);
+    }
+
+    public void testMatchMatListOfDMatchListOfMat() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        Mat mask = getMaskImg();
+        MatOfDMatch matches = new MatOfDMatch();
+        matcher.add(Arrays.asList(train));
+
+        matcher.match(query, matches, Arrays.asList(mask));
+
+        assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
+    }
+
+    public void testMatchMatMatListOfDMatch() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        MatOfDMatch matches = new MatOfDMatch();
+
+        matcher.match(query, train, matches);
+
+        assertArrayDMatchEquals(truth, matches.toArray(), EPS);
+
+        // OpenCVTestRunner.Log("matches found: " + matches.size());
+        // for (DMatch m : matches)
+        // OpenCVTestRunner.Log(m.toString());
+    }
+
+    public void testMatchMatMatListOfDMatchMat() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        Mat mask = getMaskImg();
+        MatOfDMatch matches = new MatOfDMatch();
+
+        matcher.match(query, train, matches, mask);
+
+        assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
+    }
+
+    public void testRadiusMatchMatListOfListOfDMatchFloat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatListOfListOfDMatchFloatListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatListOfListOfDMatchFloatListOfMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatMatListOfListOfDMatchFloat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatMatListOfListOfDMatchFloatMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatMatListOfListOfDMatchFloatMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testRead() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+        writeFile(filename, "%YAML:1.0\n---\n");
+
+        matcher.read(filename);
+        assertTrue(true);// BruteforceMatcher has no settings
+    }
+
+    public void testTrain() {
+        matcher.train();// BruteforceMatcher does not need to train
+    }
+
+    public void testWrite() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+
+        matcher.write(filename);
+
+        String truth = "%YAML:1.0\n---\n";
+        assertEquals(truth, readFile(filename));
+    }
+
+}

3rdparty/opencv-4.5.4/modules/features2d/misc/java/test/DENSEFeatureDetectorTest.java

@@ -0,0 +1,39 @@
+package org.opencv.test.features2d;
+
+import org.opencv.test.OpenCVTestCase;
+
+public class DENSEFeatureDetectorTest extends OpenCVTestCase {
+
+    public void testCreate() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectListOfMatListOfListOfKeyPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfKeyPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfKeyPointMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testEmpty() {
+        fail("Not yet implemented");
+    }
+
+    public void testRead() {
+        fail("Not yet implemented");
+    }
+
+    public void testWrite() {
+        fail("Not yet implemented");
+    }
+
+}

3rdparty/opencv-4.5.4/modules/features2d/misc/java/test/FASTFeatureDetectorTest.java

@@ -0,0 +1,150 @@
+package org.opencv.test.features2d;
+
+import java.util.Arrays;
+
+import org.opencv.core.Core;
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.MatOfKeyPoint;
+import org.opencv.core.Point;
+import org.opencv.core.Scalar;
+import org.opencv.features2d.Feature2D;
+import org.opencv.features2d.FastFeatureDetector;
+import org.opencv.core.KeyPoint;
+import org.opencv.test.OpenCVTestCase;
+import org.opencv.test.OpenCVTestRunner;
+import org.opencv.imgproc.Imgproc;
+
+public class FASTFeatureDetectorTest extends OpenCVTestCase {
+
+    Feature2D detector;
+    KeyPoint[] truth;
+
+    private Mat getMaskImg() {
+        Mat mask = new Mat(100, 100, CvType.CV_8U, new Scalar(255));
+        Mat right = mask.submat(0, 100, 50, 100);
+        right.setTo(new Scalar(0));
+        return mask;
+    }
+
+    private Mat getTestImg() {
+        Mat img = new Mat(100, 100, CvType.CV_8U, new Scalar(255));
+        Imgproc.line(img, new Point(30, 30), new Point(70, 70), new Scalar(0), 8);
+        return img;
+    }
+
+    @Override
+    protected void setUp() throws Exception {
+        super.setUp();
+        detector = FastFeatureDetector.create();
+        truth = new KeyPoint[] { new KeyPoint(32, 27, 7, -1, 254, 0, -1), new KeyPoint(27, 32, 7, -1, 254, 0, -1), new KeyPoint(73, 68, 7, -1, 254, 0, -1),
+                new KeyPoint(68, 73, 7, -1, 254, 0, -1) };
+    }
+
+    public void testCreate() {
+        assertNotNull(detector);
+    }
+
+    public void testDetectListOfMatListOfListOfKeyPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfKeyPoint() {
+        Mat img = getTestImg();
+        MatOfKeyPoint keypoints = new MatOfKeyPoint();
+
+        detector.detect(img, keypoints);
+
+        assertListKeyPointEquals(Arrays.asList(truth), keypoints.toList(), EPS);
+
+        // OpenCVTestRunner.Log("points found: " + keypoints.size());
+        // for (KeyPoint kp : keypoints)
+        // OpenCVTestRunner.Log(kp.toString());
+    }
+
+    public void testDetectMatListOfKeyPointMat() {
+        Mat img = getTestImg();
+        Mat mask = getMaskImg();
+        MatOfKeyPoint keypoints = new MatOfKeyPoint();
+
+        detector.detect(img, keypoints, mask);
+
+        assertListKeyPointEquals(Arrays.asList(truth[0], truth[1]), keypoints.toList(), EPS);
+    }
+
+    public void testEmpty() {
+//        assertFalse(detector.empty());
+        fail("Not yet implemented"); //FAST does not override empty() method
+    }
+
+    public void testRead() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+
+        writeFile(filename, "%YAML:1.0\n---\nthreshold: 130\nnonmaxSuppression: 1\n");
+        detector.read(filename);
+
+        MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
+
+        detector.detect(grayChess, keypoints1);
+
+        writeFile(filename, "%YAML:1.0\n---\nthreshold: 150\nnonmaxSuppression: 1\n");
+        detector.read(filename);
+
+        MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
+
+        detector.detect(grayChess, keypoints2);
+
+        assertTrue(keypoints2.total() <= keypoints1.total());
+    }
+
+    public void testReadYml() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+
+        writeFile(filename,
+                "<?xml version=\"1.0\"?>\n<opencv_storage>\n<threshold>130</threshold>\n<nonmaxSuppression>1</nonmaxSuppression>\n</opencv_storage>\n");
+        detector.read(filename);
+
+        MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
+
+        detector.detect(grayChess, keypoints1);
+
+        writeFile(filename,
+                "<?xml version=\"1.0\"?>\n<opencv_storage>\n<threshold>150</threshold>\n<nonmaxSuppression>1</nonmaxSuppression>\n</opencv_storage>\n");
+        detector.read(filename);
+
+        MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
+
+        detector.detect(grayChess, keypoints2);
+
+        assertTrue(keypoints2.total() <= keypoints1.total());
+    }
+
+    public void testWrite() {
+        String filename = OpenCVTestRunner.getTempFileName("xml");
+
+        detector.write(filename);
+
+//        String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n<name>Feature2D.FAST</name>\n<nonmaxSuppression>1</nonmaxSuppression>\n<threshold>10</threshold>\n<type>2</type>\n</opencv_storage>\n";
+        String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n</opencv_storage>\n";
+        String data = readFile(filename);
+        //Log.d("qqq", "\"" + data + "\"");
+        assertEquals(truth, data);
+    }
+
+    public void testWriteYml() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+
+        detector.write(filename);
+
+//        String truth = "%YAML:1.0\n---\nname: \"Feature2D.FAST\"\nnonmaxSuppression: 1\nthreshold: 10\ntype: 2\n";
+        String truth = "%YAML:1.0\n---\n";
+        String data = readFile(filename);
+
+        //Log.d("qqq", "\"" + data + "\"");
+        assertEquals(truth, data);
+    }
+}

3rdparty/opencv-4.5.4/modules/features2d/misc/java/test/Features2dTest.java

@@ -0,0 +1,172 @@
+package org.opencv.test.features2d;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+
+import org.opencv.calib3d.Calib3d;
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.MatOfInt;
+import org.opencv.core.MatOfDMatch;
+import org.opencv.core.MatOfKeyPoint;
+import org.opencv.core.MatOfPoint2f;
+import org.opencv.core.Point;
+import org.opencv.core.Range;
+import org.opencv.core.Scalar;
+import org.opencv.core.DMatch;
+import org.opencv.features2d.DescriptorMatcher;
+import org.opencv.features2d.Features2d;
+import org.opencv.core.KeyPoint;
+import org.opencv.imgcodecs.Imgcodecs;
+import org.opencv.test.OpenCVTestCase;
+import org.opencv.test.OpenCVTestRunner;
+import org.opencv.features2d.Feature2D;
+
+public class Features2dTest extends OpenCVTestCase {
+
+    public void testDrawKeypointsMatListOfKeyPointMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testDrawKeypointsMatListOfKeyPointMatScalar() {
+        fail("Not yet implemented");
+    }
+
+    public void testDrawKeypointsMatListOfKeyPointMatScalarInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testDrawMatches2MatListOfKeyPointMatListOfKeyPointListOfListOfDMatchMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testDrawMatches2MatListOfKeyPointMatListOfKeyPointListOfListOfDMatchMatScalar() {
+        fail("Not yet implemented");
+    }
+
+    public void testDrawMatches2MatListOfKeyPointMatListOfKeyPointListOfListOfDMatchMatScalarScalar() {
+        fail("Not yet implemented");
+    }
+
+    public void testDrawMatches2MatListOfKeyPointMatListOfKeyPointListOfListOfDMatchMatScalarScalarListOfListOfByte() {
+        fail("Not yet implemented");
+    }
+
+    public void testDrawMatches2MatListOfKeyPointMatListOfKeyPointListOfListOfDMatchMatScalarScalarListOfListOfByteInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testDrawMatchesMatListOfKeyPointMatListOfKeyPointListOfDMatchMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testDrawMatchesMatListOfKeyPointMatListOfKeyPointListOfDMatchMatScalar() {
+        fail("Not yet implemented");
+    }
+
+    public void testDrawMatchesMatListOfKeyPointMatListOfKeyPointListOfDMatchMatScalarScalar() {
+        fail("Not yet implemented");
+    }
+
+    public void testDrawMatchesMatListOfKeyPointMatListOfKeyPointListOfDMatchMatScalarScalarListOfByte() {
+        fail("Not yet implemented");
+    }
+
+    public void testDrawMatchesMatListOfKeyPointMatListOfKeyPointListOfDMatchMatScalarScalarListOfByteInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testPTOD()
+    {
+        String detectorCfg = "%YAML:1.0\n---\nhessianThreshold: 4000.\noctaves: 3\noctaveLayers: 4\nupright: 0\n";
+        String extractorCfg = "%YAML:1.0\n---\nnOctaves: 4\nnOctaveLayers: 2\nextended: 0\nupright: 0\n";
+
+        Feature2D detector = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
+        Feature2D extractor = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
+        DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE);
+
+        String detectorCfgFile = OpenCVTestRunner.getTempFileName("yml");
+        writeFile(detectorCfgFile, detectorCfg);
+        detector.read(detectorCfgFile);
+
+        String extractorCfgFile = OpenCVTestRunner.getTempFileName("yml");
+        writeFile(extractorCfgFile, extractorCfg);
+        extractor.read(extractorCfgFile);
+
+        Mat imgTrain = Imgcodecs.imread(OpenCVTestRunner.LENA_PATH, Imgcodecs.IMREAD_GRAYSCALE);
+        Mat imgQuery = imgTrain.submat(new Range(0, imgTrain.rows() - 100), Range.all());
+
+        MatOfKeyPoint trainKeypoints = new MatOfKeyPoint();
+        MatOfKeyPoint queryKeypoints = new MatOfKeyPoint();
+
+        detector.detect(imgTrain, trainKeypoints);
+        detector.detect(imgQuery, queryKeypoints);
+
+        // OpenCVTestRunner.Log("Keypoints found: " + trainKeypoints.size() +
+        // ":" + queryKeypoints.size());
+
+        Mat trainDescriptors = new Mat();
+        Mat queryDescriptors = new Mat();
+
+        extractor.compute(imgTrain, trainKeypoints, trainDescriptors);
+        extractor.compute(imgQuery, queryKeypoints, queryDescriptors);
+
+        MatOfDMatch matches = new MatOfDMatch();
+
+        matcher.add(Arrays.asList(trainDescriptors));
+        matcher.match(queryDescriptors, matches);
+
+        // OpenCVTestRunner.Log("Matches found: " + matches.size());
+
+        DMatch adm[] = matches.toArray();
+        List<Point> lp1 = new ArrayList<Point>(adm.length);
+        List<Point> lp2 = new ArrayList<Point>(adm.length);
+        KeyPoint tkp[] = trainKeypoints.toArray();
+        KeyPoint qkp[] = queryKeypoints.toArray();
+        for (int i = 0; i < adm.length; i++) {
+            DMatch dm = adm[i];
+            lp1.add(tkp[dm.trainIdx].pt);
+            lp2.add(qkp[dm.queryIdx].pt);
+        }
+
+        MatOfPoint2f points1 = new MatOfPoint2f(lp1.toArray(new Point[0]));
+        MatOfPoint2f points2 = new MatOfPoint2f(lp2.toArray(new Point[0]));
+
+        Mat hmg = Calib3d.findHomography(points1, points2, Calib3d.RANSAC, 3);
+
+        assertMatEqual(Mat.eye(3, 3, CvType.CV_64F), hmg, EPS);
+
+        Mat outimg = new Mat();
+        Features2d.drawMatches(imgQuery, queryKeypoints, imgTrain, trainKeypoints, matches, outimg);
+        String outputPath = OpenCVTestRunner.getOutputFileName("PTODresult.png");
+        Imgcodecs.imwrite(outputPath, outimg);
+        // OpenCVTestRunner.Log("Output image is saved to: " + outputPath);
+    }
+
+    public void testDrawKeypoints()
+    {
+        Mat outImg = Mat.ones(11, 11, CvType.CV_8U);
+
+        MatOfKeyPoint kps = new MatOfKeyPoint(new KeyPoint(5, 5, 1));  // x, y, size
+        Features2d.drawKeypoints(new Mat(), kps, outImg, new Scalar(255),
+                                 Features2d.DrawMatchesFlags_DRAW_OVER_OUTIMG);
+
+        Mat ref = new MatOfInt(new int[] {
+            1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,
+            1,   1,   1,   1,  15,  54,  15,   1,   1,   1,   1,
+            1,   1,   1,  76, 217, 217, 221,  81,   1,   1,   1,
+            1,   1, 100, 224, 111,  57, 115, 225, 101,   1,   1,
+            1,  44, 215, 100,   1,   1,   1, 101, 214,  44,   1,
+            1,  54, 212,  57,   1,   1,   1,  55, 212,  55,   1,
+            1,  40, 215, 104,   1,   1,   1, 105, 215,  40,   1,
+            1,   1, 102, 221, 111,  55, 115, 222, 103,   1,   1,
+            1,   1,   1,  76, 218, 217, 220,  81,   1,   1,   1,
+            1,   1,   1,   1,  15,  55,  15,   1,   1,   1,   1,
+            1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1
+        }).reshape(1, 11);
+        ref.convertTo(ref, CvType.CV_8U);
+
+        assertMatEqual(ref, outImg);
+    }
+}

3rdparty/opencv-4.5.4/modules/features2d/misc/java/test/FlannBasedDescriptorMatcherTest.java

@@ -0,0 +1,389 @@
+package org.opencv.test.features2d;
+
+import java.util.Arrays;
+import java.util.List;
+
+import org.opencv.core.CvException;
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.MatOfDMatch;
+import org.opencv.core.MatOfKeyPoint;
+import org.opencv.core.Point;
+import org.opencv.core.Scalar;
+import org.opencv.core.DMatch;
+import org.opencv.features2d.DescriptorMatcher;
+import org.opencv.features2d.FlannBasedMatcher;
+import org.opencv.core.KeyPoint;
+import org.opencv.test.OpenCVTestCase;
+import org.opencv.test.OpenCVTestRunner;
+import org.opencv.imgproc.Imgproc;
+import org.opencv.features2d.Feature2D;
+
+public class FlannBasedDescriptorMatcherTest extends OpenCVTestCase {
+
+    static final String xmlParamsDefault = "<?xml version=\"1.0\"?>\n"
+            + "<opencv_storage>\n"
+            + "<format>3</format>\n"
+            + "<indexParams>\n"
+            + "  <_>\n"
+            + "    <name>algorithm</name>\n"
+            + "    <type>9</type>\n"  // FLANN_INDEX_TYPE_ALGORITHM
+            + "    <value>1</value></_>\n"
+            + "  <_>\n"
+            + "    <name>trees</name>\n"
+            + "    <type>4</type>\n"
+            + "    <value>4</value></_></indexParams>\n"
+            + "<searchParams>\n"
+            + "  <_>\n"
+            + "    <name>checks</name>\n"
+            + "    <type>4</type>\n"
+            + "    <value>32</value></_>\n"
+            + "  <_>\n"
+            + "    <name>eps</name>\n"
+            + "    <type>5</type>\n"
+            + "    <value>0.</value></_>\n"
+            + "  <_>\n"
+            + "    <name>explore_all_trees</name>\n"
+            + "    <type>8</type>\n"
+            + "    <value>0</value></_>\n"
+            + "  <_>\n"
+            + "    <name>sorted</name>\n"
+            + "    <type>8</type>\n"  // FLANN_INDEX_TYPE_BOOL
+            + "    <value>1</value></_></searchParams>\n"
+            + "</opencv_storage>\n";
+    static final String ymlParamsDefault = "%YAML:1.0\n---\n"
+            + "format: 3\n"
+            + "indexParams:\n"
+            + "   -\n"
+            + "      name: algorithm\n"
+            + "      type: 9\n"  // FLANN_INDEX_TYPE_ALGORITHM
+            + "      value: 1\n"
+            + "   -\n"
+            + "      name: trees\n"
+            + "      type: 4\n"
+            + "      value: 4\n"
+            + "searchParams:\n"
+            + "   -\n"
+            + "      name: checks\n"
+            + "      type: 4\n"
+            + "      value: 32\n"
+            + "   -\n"
+            + "      name: eps\n"
+            + "      type: 5\n"
+            + "      value: 0.\n"
+            + "   -\n"
+            + "      name: explore_all_trees\n"
+            + "      type: 8\n"
+            + "      value: 0\n"
+            + "   -\n"
+            + "      name: sorted\n"
+            + "      type: 8\n"  // FLANN_INDEX_TYPE_BOOL
+            + "      value: 1\n";
+    static final String ymlParamsModified = "%YAML:1.0\n---\n"
+            + "format: 3\n"
+            + "indexParams:\n"
+            + "   -\n"
+            + "      name: algorithm\n"
+            + "      type: 9\n"  // FLANN_INDEX_TYPE_ALGORITHM
+            + "      value: 6\n"// this line is changed!
+            + "   -\n"
+            + "      name: trees\n"
+            + "      type: 4\n"
+            + "      value: 4\n"
+            + "searchParams:\n"
+            + "   -\n"
+            + "      name: checks\n"
+            + "      type: 4\n"
+            + "      value: 32\n"
+            + "   -\n"
+            + "      name: eps\n"
+            + "      type: 5\n"
+            + "      value: 4.\n"// this line is changed!
+            + "   -\n"
+            + "      name: explore_all_trees\n"
+            + "      type: 8\n"
+            + "      value: 1\n"// this line is changed!
+            + "   -\n"
+            + "      name: sorted\n"
+            + "      type: 8\n"    // FLANN_INDEX_TYPE_BOOL
+            + "      value: 1\n";
+
+    DescriptorMatcher matcher;
+
+    int matSize;
+
+    DMatch[] truth;
+
+    private Mat getMaskImg() {
+        return new Mat(5, 2, CvType.CV_8U, new Scalar(0)) {
+            {
+                put(0, 0, 1, 1, 1, 1);
+            }
+        };
+    }
+
+    private Mat getQueryDescriptors() {
+        Mat img = getQueryImg();
+        MatOfKeyPoint keypoints = new MatOfKeyPoint();
+        Mat descriptors = new Mat();
+
+        Feature2D detector = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
+        Feature2D extractor = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
+
+        setProperty(detector, "hessianThreshold", "double", 8000);
+        setProperty(detector, "nOctaves", "int", 3);
+        setProperty(detector, "upright", "boolean", false);
+
+        detector.detect(img, keypoints);
+        extractor.compute(img, keypoints, descriptors);
+
+        return descriptors;
+    }
+
+    private Mat getQueryImg() {
+        Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Imgproc.line(cross, new Point(30, matSize / 2), new Point(matSize - 31, matSize / 2), new Scalar(100), 3);
+        Imgproc.line(cross, new Point(matSize / 2, 30), new Point(matSize / 2, matSize - 31), new Scalar(100), 3);
+
+        return cross;
+    }
+
+    private Mat getTrainDescriptors() {
+        Mat img = getTrainImg();
+        MatOfKeyPoint keypoints = new MatOfKeyPoint(new KeyPoint(50, 50, 16, 0, 20000, 1, -1), new KeyPoint(42, 42, 16, 160, 10000, 1, -1));
+        Mat descriptors = new Mat();
+
+        Feature2D extractor = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
+
+        extractor.compute(img, keypoints, descriptors);
+
+        return descriptors;
+    }
+
+    private Mat getTrainImg() {
+        Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
+        Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
+
+        return cross;
+    }
+
+    protected void setUp() throws Exception {
+        super.setUp();
+        matcher = DescriptorMatcher.create(DescriptorMatcher.FLANNBASED);
+        matSize = 100;
+        truth = new DMatch[] {
+                new DMatch(0, 0, 0, 0.6159003f),
+                new DMatch(1, 1, 0, 0.9177120f),
+                new DMatch(2, 1, 0, 0.3112163f),
+                new DMatch(3, 1, 0, 0.2925075f),
+                new DMatch(4, 1, 0, 0.26520672f)
+                };
+    }
+
+    // https://github.com/opencv/opencv/issues/11268
+    public void testConstructor()
+    {
+        FlannBasedMatcher self_created_matcher = new FlannBasedMatcher();
+        Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
+        self_created_matcher.add(Arrays.asList(train));
+        assertTrue(!self_created_matcher.empty());
+    }
+
+    public void testAdd() {
+        matcher.add(Arrays.asList(new Mat()));
+        assertFalse(matcher.empty());
+    }
+
+    public void testClear() {
+        matcher.add(Arrays.asList(new Mat()));
+
+        matcher.clear();
+
+        assertTrue(matcher.empty());
+    }
+
+    public void testClone() {
+        Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
+        matcher.add(Arrays.asList(train));
+
+        try {
+            matcher.clone();
+            fail("Expected CvException (CV_StsNotImplemented)");
+        } catch (CvException cverr) {
+            // expected
+        }
+    }
+
+    public void testCloneBoolean() {
+        matcher.add(Arrays.asList(new Mat()));
+
+        DescriptorMatcher cloned = matcher.clone(true);
+
+        assertNotNull(cloned);
+        assertTrue(cloned.empty());
+    }
+
+    public void testCreate() {
+        assertNotNull(matcher);
+    }
+
+    public void testEmpty() {
+        assertTrue(matcher.empty());
+    }
+
+    public void testGetTrainDescriptors() {
+        Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
+        Mat truth = train.clone();
+        matcher.add(Arrays.asList(train));
+
+        List<Mat> descriptors = matcher.getTrainDescriptors();
+
+        assertEquals(1, descriptors.size());
+        assertMatEqual(truth, descriptors.get(0));
+    }
+
+    public void testIsMaskSupported() {
+        assertFalse(matcher.isMaskSupported());
+    }
+
+    public void testKnnMatchMatListOfListOfDMatchInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatListOfListOfDMatchIntListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatListOfListOfDMatchIntListOfMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatMatListOfListOfDMatchInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatMatListOfListOfDMatchIntMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testKnnMatchMatMatListOfListOfDMatchIntMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testMatchMatListOfDMatch() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        MatOfDMatch matches = new MatOfDMatch();
+        matcher.add(Arrays.asList(train));
+        matcher.train();
+
+        matcher.match(query, matches);
+
+        assertArrayDMatchEquals(truth, matches.toArray(), EPS);
+    }
+
+    public void testMatchMatListOfDMatchListOfMat() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        Mat mask = getMaskImg();
+        MatOfDMatch matches = new MatOfDMatch();
+        matcher.add(Arrays.asList(train));
+        matcher.train();
+
+        matcher.match(query, matches, Arrays.asList(mask));
+
+        assertArrayDMatchEquals(truth, matches.toArray(), EPS);
+    }
+
+    public void testMatchMatMatListOfDMatch() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        MatOfDMatch matches = new MatOfDMatch();
+
+        matcher.match(query, train, matches);
+
+        assertArrayDMatchEquals(truth, matches.toArray(), EPS);
+
+        // OpenCVTestRunner.Log(matches.toString());
+        // OpenCVTestRunner.Log(matches);
+    }
+
+    public void testMatchMatMatListOfDMatchMat() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        Mat mask = getMaskImg();
+        MatOfDMatch matches = new MatOfDMatch();
+
+        matcher.match(query, train, matches, mask);
+
+        assertListDMatchEquals(Arrays.asList(truth), matches.toList(), EPS);
+    }
+
+    public void testRadiusMatchMatListOfListOfDMatchFloat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatListOfListOfDMatchFloatListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatListOfListOfDMatchFloatListOfMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatMatListOfListOfDMatchFloat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatMatListOfListOfDMatchFloatMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testRadiusMatchMatMatListOfListOfDMatchFloatMatBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testRead() {
+        String filenameR = OpenCVTestRunner.getTempFileName("yml");
+        String filenameW = OpenCVTestRunner.getTempFileName("yml");
+        writeFile(filenameR, ymlParamsModified);
+
+        matcher.read(filenameR);
+        matcher.write(filenameW);
+
+        assertEquals(ymlParamsModified, readFile(filenameW));
+    }
+
+    public void testTrain() {
+        Mat train = getTrainDescriptors();
+        matcher.add(Arrays.asList(train));
+        matcher.train();
+    }
+
+    public void testTrainNoData() {
+        try {
+            matcher.train();
+            fail("Expected CvException - FlannBasedMatcher::train should fail on empty train set");
+        } catch (CvException cverr) {
+            // expected
+        }
+    }
+
+    public void testWrite() {
+        String filename = OpenCVTestRunner.getTempFileName("xml");
+
+        matcher.write(filename);
+
+        assertEquals(xmlParamsDefault, readFile(filename));
+    }
+
+    public void testWriteYml() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+
+        matcher.write(filename);
+
+        assertEquals(ymlParamsDefault, readFile(filename));
+    }
+
+}

3rdparty/opencv-4.5.4/modules/features2d/misc/java/test/GFTTFeatureDetectorTest.java

@@ -0,0 +1,39 @@
+package org.opencv.test.features2d;
+
+import org.opencv.test.OpenCVTestCase;
+
+public class GFTTFeatureDetectorTest extends OpenCVTestCase {
+
+    public void testCreate() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectListOfMatListOfListOfKeyPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfKeyPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfKeyPointMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testEmpty() {
+        fail("Not yet implemented");
+    }
+
+    public void testRead() {
+        fail("Not yet implemented");
+    }
+
+    public void testWrite() {
+        fail("Not yet implemented");
+    }
+
+}

3rdparty/opencv-4.5.4/modules/features2d/misc/java/test/HARRISFeatureDetectorTest.java

@@ -0,0 +1,39 @@
+package org.opencv.test.features2d;
+
+import org.opencv.test.OpenCVTestCase;
+
+public class HARRISFeatureDetectorTest extends OpenCVTestCase {
+
+    public void testCreate() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectListOfMatListOfListOfKeyPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfKeyPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfKeyPointMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testEmpty() {
+        fail("Not yet implemented");
+    }
+
+    public void testRead() {
+        fail("Not yet implemented");
+    }
+
+    public void testWrite() {
+        fail("Not yet implemented");
+    }
+
+}

3rdparty/opencv-4.5.4/modules/features2d/misc/java/test/MSERFeatureDetectorTest.java

@@ -0,0 +1,39 @@
+package org.opencv.test.features2d;
+
+import org.opencv.test.OpenCVTestCase;
+
+public class MSERFeatureDetectorTest extends OpenCVTestCase {
+
+    public void testCreate() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectListOfMatListOfListOfKeyPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfKeyPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfKeyPointMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testEmpty() {
+        fail("Not yet implemented");
+    }
+
+    public void testRead() {
+        fail("Not yet implemented");
+    }
+
+    public void testWrite() {
+        fail("Not yet implemented");
+    }
+
+}

3rdparty/opencv-4.5.4/modules/features2d/misc/java/test/ORBDescriptorExtractorTest.java

@@ -0,0 +1,124 @@
+package org.opencv.test.features2d;
+
+import org.opencv.core.Core;
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.MatOfKeyPoint;
+import org.opencv.core.Point;
+import org.opencv.core.Scalar;
+import org.opencv.core.KeyPoint;
+import org.opencv.features2d.ORB;
+import org.opencv.test.OpenCVTestCase;
+import org.opencv.test.OpenCVTestRunner;
+import org.opencv.imgproc.Imgproc;
+
+public class ORBDescriptorExtractorTest extends OpenCVTestCase {
+
+    ORB extractor;
+    int matSize;
+
+    public static void assertDescriptorsClose(Mat expected, Mat actual, int allowedDistance) {
+        double distance = Core.norm(expected, actual, Core.NORM_HAMMING);
+        assertTrue("expected:<" + allowedDistance + "> but was:<" + distance + ">", distance <= allowedDistance);
+    }
+
+    private Mat getTestImg() {
+        Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
+        Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
+
+        return cross;
+    }
+
+    @Override
+    protected void setUp() throws Exception {
+        super.setUp();
+        extractor = ORB.create();
+        matSize = 100;
+    }
+
+    public void testComputeListOfMatListOfListOfKeyPointListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testComputeMatListOfKeyPointMat() {
+        KeyPoint point = new KeyPoint(55.775577545166016f, 44.224422454833984f, 16, 9.754629f, 8617.863f, 1, -1);
+        MatOfKeyPoint keypoints = new MatOfKeyPoint(point);
+        Mat img = getTestImg();
+        Mat descriptors = new Mat();
+
+        extractor.compute(img, keypoints, descriptors);
+
+        Mat truth = new Mat(1, 32, CvType.CV_8UC1) {
+            {
+                put(0, 0,
+                        6, 74, 6, 129, 2, 130, 56, 0, 44, 132, 66, 165, 172, 6, 3, 72, 102, 61, 171, 214, 0, 144, 65, 232, 4, 32, 138, 131, 4, 21, 37, 217);
+            }
+        };
+        assertDescriptorsClose(truth, descriptors, 1);
+    }
+
+    public void testCreate() {
+        assertNotNull(extractor);
+    }
+
+    public void testDescriptorSize() {
+        assertEquals(32, extractor.descriptorSize());
+    }
+
+    public void testDescriptorType() {
+        assertEquals(CvType.CV_8U, extractor.descriptorType());
+    }
+
+    public void testEmpty() {
+//        assertFalse(extractor.empty());
+        fail("Not yet implemented"); // ORB does not override empty() method
+    }
+
+    public void testRead() {
+        KeyPoint point = new KeyPoint(55.775577545166016f, 44.224422454833984f, 16, 9.754629f, 8617.863f, 1, -1);
+        MatOfKeyPoint keypoints = new MatOfKeyPoint(point);
+        Mat img = getTestImg();
+        Mat descriptors = new Mat();
+
+//        String filename = OpenCVTestRunner.getTempFileName("yml");
+//        writeFile(filename, "%YAML:1.0\n---\nscaleFactor: 1.1\nnLevels: 3\nfirstLevel: 0\nedgeThreshold: 31\npatchSize: 31\n");
+//        extractor.read(filename);
+        extractor = ORB.create(500, 1.1f, 3, 31, 0, 2, ORB.HARRIS_SCORE, 31, 20);
+
+        extractor.compute(img, keypoints, descriptors);
+
+        Mat truth = new Mat(1, 32, CvType.CV_8UC1) {
+            {
+                put(0, 0,
+                        6, 10, 22, 5, 2, 130, 56, 0, 44, 164, 66, 165, 140, 6, 1, 72, 38, 61, 163, 210, 0, 208, 1, 104, 4, 32, 74, 131, 0, 37, 37, 67);
+            }
+        };
+        assertDescriptorsClose(truth, descriptors, 1);
+    }
+
+    public void testWrite() {
+        String filename = OpenCVTestRunner.getTempFileName("xml");
+
+        extractor.write(filename);
+
+//        String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n<name>Feature2D.ORB</name>\n<WTA_K>2</WTA_K>\n<edgeThreshold>31</edgeThreshold>\n<firstLevel>0</firstLevel>\n<nFeatures>500</nFeatures>\n<nLevels>8</nLevels>\n<patchSize>31</patchSize>\n<scaleFactor>1.2000000476837158e+00</scaleFactor>\n<scoreType>0</scoreType>\n</opencv_storage>\n";
+        String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n</opencv_storage>\n";
+        String actual = readFile(filename);
+        actual = actual.replaceAll("e\\+000", "e+00"); // NOTE: workaround for different platforms double representation
+        assertEquals(truth, actual);
+    }
+
+    public void testWriteYml() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+
+        extractor.write(filename);
+
+//        String truth = "%YAML:1.0\n---\nname: \"Feature2D.ORB\"\nWTA_K: 2\nedgeThreshold: 31\nfirstLevel: 0\nnFeatures: 500\nnLevels: 8\npatchSize: 31\nscaleFactor: 1.2000000476837158e+00\nscoreType: 0\n";
+        String truth = "%YAML:1.0\n---\n";
+        String actual = readFile(filename);
+        actual = actual.replaceAll("e\\+000", "e+00"); // NOTE: workaround for different platforms double representation
+        assertEquals(truth, actual);
+    }
+
+}

3rdparty/opencv-4.5.4/modules/features2d/misc/java/test/ORBFeatureDetectorTest.java

@@ -0,0 +1,39 @@
+package org.opencv.test.features2d;
+
+import org.opencv.test.OpenCVTestCase;
+
+public class ORBFeatureDetectorTest extends OpenCVTestCase {
+
+    public void testCreate() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectListOfMatListOfListOfKeyPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfKeyPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfKeyPointMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testEmpty() {
+        fail("Not yet implemented");
+    }
+
+    public void testRead() {
+        fail("Not yet implemented");
+    }
+
+    public void testWrite() {
+        fail("Not yet implemented");
+    }
+
+}

3rdparty/opencv-4.5.4/modules/features2d/misc/java/test/SIFTDescriptorExtractorTest.java

@@ -0,0 +1,110 @@
+package org.opencv.test.features2d;
+
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.MatOfKeyPoint;
+import org.opencv.core.Point;
+import org.opencv.core.Scalar;
+import org.opencv.core.KeyPoint;
+import org.opencv.features2d.SIFT;
+import org.opencv.test.OpenCVTestCase;
+import org.opencv.test.OpenCVTestRunner;
+import org.opencv.imgproc.Imgproc;
+import org.opencv.features2d.Feature2D;
+
+public class SIFTDescriptorExtractorTest extends OpenCVTestCase {
+
+    Feature2D extractor;
+    KeyPoint keypoint;
+    int matSize;
+    Mat truth;
+
+    private Mat getTestImg() {
+        Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
+        Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
+
+        return cross;
+    }
+
+    @Override
+    protected void setUp() throws Exception {
+        super.setUp();
+        extractor = SIFT.create();
+        keypoint = new KeyPoint(55.775577545166016f, 44.224422454833984f, 16, 9.754629f, 8617.863f, 1, -1);
+        matSize = 100;
+        truth = new Mat(1, 128, CvType.CV_32FC1) {
+            {
+                put(0, 0,
+                          0, 0, 0, 1, 3, 0, 0, 0, 15, 23, 22, 20, 24, 2, 0, 0, 7, 8, 2, 0,
+                          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 27, 16, 13, 2, 0, 0, 117,
+                          86, 79, 68, 117, 42, 5, 5, 79, 60, 117, 25, 9, 2, 28, 19, 11, 13,
+                          20, 2, 0, 0, 5, 8, 0, 0, 76, 58, 34, 31, 97, 16, 95, 49, 117, 92,
+                          117, 112, 117, 76, 117, 54, 117, 25, 29, 22, 117, 117, 16, 11, 14,
+                          1, 0, 0, 22, 26, 0, 0, 0, 0, 1, 4, 15, 2, 47, 8, 0, 0, 82, 56, 31,
+                          17, 81, 12, 0, 0, 26, 23, 18, 23, 0, 0, 0, 0, 0, 0, 0, 0
+                   );
+            }
+        };
+    }
+
+    public void testComputeListOfMatListOfListOfKeyPointListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testComputeMatListOfKeyPointMat() {
+        MatOfKeyPoint keypoints = new MatOfKeyPoint(keypoint);
+        Mat img = getTestImg();
+        Mat descriptors = new Mat();
+
+        extractor.compute(img, keypoints, descriptors);
+
+        assertMatEqual(truth, descriptors, EPS);
+    }
+
+    public void testCreate() {
+        assertNotNull(extractor);
+    }
+
+    public void testDescriptorSize() {
+        assertEquals(128, extractor.descriptorSize());
+    }
+
+    public void testDescriptorType() {
+        assertEquals(CvType.CV_32F, extractor.descriptorType());
+    }
+
+    public void testEmpty() {
+//        assertFalse(extractor.empty());
+        fail("Not yet implemented"); //SIFT does not override empty() method
+    }
+
+    public void testRead() {
+        fail("Not yet implemented");
+    }
+
+    public void testWrite() {
+        String filename = OpenCVTestRunner.getTempFileName("xml");
+
+        extractor.write(filename);
+
+//        String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n<name>Feature2D.SIFT</name>\n<contrastThreshold>4.0000000000000001e-02</contrastThreshold>\n<edgeThreshold>10.</edgeThreshold>\n<nFeatures>0</nFeatures>\n<nOctaveLayers>3</nOctaveLayers>\n<sigma>1.6000000000000001e+00</sigma>\n</opencv_storage>\n";
+        String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n</opencv_storage>\n";
+        String actual = readFile(filename);
+        actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
+        assertEquals(truth, actual);
+    }
+
+    public void testWriteYml() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+
+        extractor.write(filename);
+
+//        String truth = "%YAML:1.0\n---\nname: \"Feature2D.SIFT\"\ncontrastThreshold: 4.0000000000000001e-02\nedgeThreshold: 10.\nnFeatures: 0\nnOctaveLayers: 3\nsigma: 1.6000000000000001e+00\n";
+        String truth = "%YAML:1.0\n---\n";
+        String actual = readFile(filename);
+        actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
+        assertEquals(truth, actual);
+    }
+
+}

3rdparty/opencv-4.5.4/modules/features2d/misc/java/test/SIFTFeatureDetectorTest.java

@@ -0,0 +1,39 @@
+package org.opencv.test.features2d;
+
+import org.opencv.test.OpenCVTestCase;
+
+public class SIFTFeatureDetectorTest extends OpenCVTestCase {
+
+    public void testCreate() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectListOfMatListOfListOfKeyPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfKeyPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfKeyPointMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testEmpty() {
+        fail("Not yet implemented");
+    }
+
+    public void testRead() {
+        fail("Not yet implemented");
+    }
+
+    public void testWrite() {
+        fail("Not yet implemented");
+    }
+
+}

3rdparty/opencv-4.5.4/modules/features2d/misc/java/test/SIMPLEBLOBFeatureDetectorTest.java

@@ -0,0 +1,118 @@
+package org.opencv.test.features2d;
+
+import java.util.Arrays;
+
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.MatOfKeyPoint;
+import org.opencv.core.Point;
+import org.opencv.core.Scalar;
+import org.opencv.core.KeyPoint;
+import org.opencv.test.OpenCVTestCase;
+import org.opencv.test.OpenCVTestRunner;
+import org.opencv.imgproc.Imgproc;
+import org.opencv.features2d.Feature2D;
+import org.opencv.features2d.SimpleBlobDetector;
+
+public class SIMPLEBLOBFeatureDetectorTest extends OpenCVTestCase {
+
+    Feature2D detector;
+    int matSize;
+    KeyPoint[] truth;
+
+    private Mat getMaskImg() {
+        Mat mask = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Mat right = mask.submat(0, matSize, matSize / 2, matSize);
+        right.setTo(new Scalar(0));
+        return mask;
+    }
+
+    private Mat getTestImg() {
+
+        int center = matSize / 2;
+        int offset = 40;
+
+        Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Imgproc.circle(img, new Point(center - offset, center), 24, new Scalar(0), -1);
+        Imgproc.circle(img, new Point(center + offset, center), 20, new Scalar(50), -1);
+        Imgproc.circle(img, new Point(center, center - offset), 18, new Scalar(100), -1);
+        Imgproc.circle(img, new Point(center, center + offset), 14, new Scalar(150), -1);
+        Imgproc.circle(img, new Point(center, center), 10, new Scalar(200), -1);
+        return img;
+    }
+
+    @Override
+    protected void setUp() throws Exception {
+        super.setUp();
+        detector = SimpleBlobDetector.create();
+        matSize = 200;
+        truth = new KeyPoint[] {
+                new KeyPoint( 140, 100, 41.036568f, -1, 0, 0, -1),
+                new KeyPoint( 60, 100, 48.538486f, -1, 0, 0, -1),
+                new KeyPoint(100, 60, 36.769554f, -1, 0, 0, -1),
+                new KeyPoint(100, 140, 28.635643f, -1, 0, 0, -1),
+                new KeyPoint(100, 100, 20.880613f, -1, 0, 0, -1)
+            };
+    }
+
+    public void testCreate() {
+        assertNotNull(detector);
+    }
+
+    public void testDetectListOfMatListOfListOfKeyPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfKeyPoint() {
+        Mat img = getTestImg();
+        MatOfKeyPoint keypoints = new MatOfKeyPoint();
+
+        detector.detect(img, keypoints);
+
+        assertListKeyPointEquals(Arrays.asList(truth), keypoints.toList(), EPS);
+    }
+
+    public void testDetectMatListOfKeyPointMat() {
+        Mat img = getTestImg();
+        Mat mask = getMaskImg();
+        MatOfKeyPoint keypoints = new MatOfKeyPoint();
+
+        detector.detect(img, keypoints, mask);
+
+        assertListKeyPointEquals(Arrays.asList(truth[1]), keypoints.toList(), EPS);
+    }
+
+    public void testEmpty() {
+//        assertFalse(detector.empty());
+        fail("Not yet implemented");
+    }
+
+    public void testRead() {
+        Mat img = getTestImg();
+
+        MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
+        detector.detect(img, keypoints1);
+
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+        writeFile(filename, "%YAML:1.0\nthresholdStep: 10\nminThreshold: 50\nmaxThreshold: 220\nminRepeatability: 2\nfilterByArea: true\nminArea: 800\nmaxArea: 5000\n");
+        detector.read(filename);
+
+        MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
+        detector.detect(img, keypoints2);
+
+        assertTrue(keypoints2.total() <= keypoints1.total());
+    }
+
+    public void testWrite() {
+        String filename = OpenCVTestRunner.getTempFileName("xml");
+
+        detector.write(filename);
+
+        String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n<format>3</format>\n<thresholdStep>10.</thresholdStep>\n<minThreshold>50.</minThreshold>\n<maxThreshold>220.</maxThreshold>\n<minRepeatability>2</minRepeatability>\n<minDistBetweenBlobs>10.</minDistBetweenBlobs>\n<filterByColor>1</filterByColor>\n<blobColor>0</blobColor>\n<filterByArea>1</filterByArea>\n<minArea>25.</minArea>\n<maxArea>5000.</maxArea>\n<filterByCircularity>0</filterByCircularity>\n<minCircularity>8.0000001192092896e-01</minCircularity>\n<maxCircularity>3.4028234663852886e+38</maxCircularity>\n<filterByInertia>1</filterByInertia>\n<minInertiaRatio>1.0000000149011612e-01</minInertiaRatio>\n<maxInertiaRatio>3.4028234663852886e+38</maxInertiaRatio>\n<filterByConvexity>1</filterByConvexity>\n<minConvexity>9.4999998807907104e-01</minConvexity>\n<maxConvexity>3.4028234663852886e+38</maxConvexity>\n</opencv_storage>\n";
+        assertEquals(truth, readFile(filename));
+    }
+}

3rdparty/opencv-4.5.4/modules/features2d/misc/java/test/STARFeatureDetectorTest.java

@@ -0,0 +1,133 @@
+package org.opencv.test.features2d;
+
+import java.util.Arrays;
+
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.MatOfKeyPoint;
+import org.opencv.core.Point;
+import org.opencv.core.Scalar;
+import org.opencv.core.KeyPoint;
+import org.opencv.test.OpenCVTestCase;
+import org.opencv.test.OpenCVTestRunner;
+import org.opencv.imgproc.Imgproc;
+import org.opencv.features2d.Feature2D;
+
+public class STARFeatureDetectorTest extends OpenCVTestCase {
+
+    Feature2D detector;
+    int matSize;
+    KeyPoint[] truth;
+
+    private Mat getMaskImg() {
+        Mat mask = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Mat right = mask.submat(0, matSize, matSize / 2, matSize);
+        right.setTo(new Scalar(0));
+        return mask;
+    }
+
+    private Mat getTestImg() {
+        Scalar color = new Scalar(0);
+        int center = matSize / 2;
+        int radius = 6;
+        int offset = 40;
+
+        Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Imgproc.circle(img, new Point(center - offset, center), radius, color, -1);
+        Imgproc.circle(img, new Point(center + offset, center), radius, color, -1);
+        Imgproc.circle(img, new Point(center, center - offset), radius, color, -1);
+        Imgproc.circle(img, new Point(center, center + offset), radius, color, -1);
+        Imgproc.circle(img, new Point(center, center), radius, color, -1);
+        return img;
+    }
+
+    protected void setUp() throws Exception {
+        super.setUp();
+        detector = createClassInstance(XFEATURES2D+"StarDetector", DEFAULT_FACTORY, null, null);
+        matSize = 200;
+        truth = new KeyPoint[] {
+                new KeyPoint( 95,  80, 22, -1, 31.5957f, 0, -1),
+                new KeyPoint(105,  80, 22, -1, 31.5957f, 0, -1),
+                new KeyPoint( 80,  95, 22, -1, 31.5957f, 0, -1),
+                new KeyPoint(120,  95, 22, -1, 31.5957f, 0, -1),
+                new KeyPoint(100, 100,  8, -1, 30.f,     0, -1),
+                new KeyPoint( 80, 105, 22, -1, 31.5957f, 0, -1),
+                new KeyPoint(120, 105, 22, -1, 31.5957f, 0, -1),
+                new KeyPoint( 95, 120, 22, -1, 31.5957f, 0, -1),
+                new KeyPoint(105, 120, 22, -1, 31.5957f, 0, -1)
+            };
+    }
+
+    public void testCreate() {
+        assertNotNull(detector);
+    }
+
+    public void testDetectListOfMatListOfListOfKeyPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfKeyPoint() {
+        Mat img = getTestImg();
+        MatOfKeyPoint keypoints = new MatOfKeyPoint();
+
+        detector.detect(img, keypoints);
+
+        assertListKeyPointEquals(Arrays.asList(truth), keypoints.toList(), EPS);
+    }
+
+    public void testDetectMatListOfKeyPointMat() {
+        Mat img = getTestImg();
+        Mat mask = getMaskImg();
+        MatOfKeyPoint keypoints = new MatOfKeyPoint();
+
+        detector.detect(img, keypoints, mask);
+
+        assertListKeyPointEquals(Arrays.asList(truth[0], truth[2], truth[5], truth[7]), keypoints.toList(), EPS);
+    }
+
+    public void testEmpty() {
+//        assertFalse(detector.empty());
+        fail("Not yet implemented");
+    }
+
+    public void testRead() {
+        Mat img = getTestImg();
+
+        MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
+        detector.detect(img, keypoints1);
+
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+        writeFile(filename, "%YAML:1.0\n---\nmaxSize: 45\nresponseThreshold: 150\nlineThresholdProjected: 10\nlineThresholdBinarized: 8\nsuppressNonmaxSize: 5\n");
+        detector.read(filename);
+
+        MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
+        detector.detect(img, keypoints2);
+
+        assertTrue(keypoints2.total() <= keypoints1.total());
+    }
+
+    public void testWrite() {
+        String filename = OpenCVTestRunner.getTempFileName("xml");
+
+        detector.write(filename);
+
+//        String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n<name>Feature2D.STAR</name>\n<lineThresholdBinarized>8</lineThresholdBinarized>\n<lineThresholdProjected>10</lineThresholdProjected>\n<maxSize>45</maxSize>\n<responseThreshold>30</responseThreshold>\n<suppressNonmaxSize>5</suppressNonmaxSize>\n</opencv_storage>\n";
+        String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n</opencv_storage>\n";
+        assertEquals(truth, readFile(filename));
+    }
+
+    public void testWriteYml() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+
+        detector.write(filename);
+
+//        String truth = "%YAML:1.0\n---\nname: \"Feature2D.STAR\"\nlineThresholdBinarized: 8\nlineThresholdProjected: 10\nmaxSize: 45\nresponseThreshold: 30\nsuppressNonmaxSize: 5\n";
+        String truth = "%YAML:1.0\n---\n";
+        assertEquals(truth, readFile(filename));
+    }
+
+}

3rdparty/opencv-4.5.4/modules/features2d/misc/java/test/SURFDescriptorExtractorTest.java

@@ -0,0 +1,119 @@
+package org.opencv.test.features2d;
+
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.MatOfKeyPoint;
+import org.opencv.core.Point;
+import org.opencv.core.Scalar;
+import org.opencv.core.KeyPoint;
+import org.opencv.test.OpenCVTestCase;
+import org.opencv.test.OpenCVTestRunner;
+import org.opencv.imgproc.Imgproc;
+import org.opencv.features2d.Feature2D;
+
+public class SURFDescriptorExtractorTest extends OpenCVTestCase {
+
+    Feature2D extractor;
+    int matSize;
+
+    private Mat getTestImg() {
+        Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
+        Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
+
+        return cross;
+    }
+
+    @Override
+    protected void setUp() throws Exception {
+        super.setUp();
+
+        Class[] cParams = {double.class, int.class, int.class, boolean.class, boolean.class};
+        Object[] oValues = {100, 2, 4, true, false};
+        extractor = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, cParams, oValues);
+
+        matSize = 100;
+    }
+
+    public void testComputeListOfMatListOfListOfKeyPointListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testComputeMatListOfKeyPointMat() {
+        KeyPoint point = new KeyPoint(55.775577545166016f, 44.224422454833984f, 16, 9.754629f, 8617.863f, 1, -1);
+        MatOfKeyPoint keypoints = new MatOfKeyPoint(point);
+        Mat img = getTestImg();
+        Mat descriptors = new Mat();
+
+        extractor.compute(img, keypoints, descriptors);
+
+        Mat truth = new Mat(1, 128, CvType.CV_32FC1) {
+            {
+                put(0, 0,
+                          0, 0, 0, 0, 0, 0, 0, 0, 0.058821894, 0.058821894, -0.045962855, 0.046261817, 0.0085156476,
+                          0.0085754395, -0.0064509804, 0.0064509804, 0.00044069235, 0.00044069235, 0, 0, 0.00025723741,
+                          0.00025723741, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00025723741, 0.00025723741, -0.00044069235,
+                          0.00044069235, 0, 0, 0.36278215, 0.36278215, -0.24688604, 0.26173124, 0.052068226, 0.052662034,
+                          -0.032815345, 0.032815345, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.0064523756,
+                          0.0064523756, 0.0082002236, 0.0088908644, -0.059001274, 0.059001274, 0.045789491, 0.04648013,
+                          0.11961588, 0.22789426, -0.01322381, 0.18291828, -0.14042182, 0.23973691, 0.073782086, 0.23769434,
+                          -0.027880307, 0.027880307, 0.049587864, 0.049587864, -0.33991757, 0.33991757, 0.21437603, 0.21437603,
+                          -0.0020763327, 0.0020763327, 0.006245892, 0.006245892, -0.04067041, 0.04067041, 0.019361559,
+                          0.019361559, 0, 0, -0.0035977389, 0.0035977389, 0, 0, -0.00099993451, 0.00099993451, 0.040670406,
+                          0.040670406, -0.019361559, 0.019361559, 0.006245892, 0.006245892, -0.0020763327, 0.0020763327,
+                          -0.00034532088, 0.00034532088, 0, 0, 0, 0, 0.00034532088, 0.00034532088, -0.00099993451,
+                          0.00099993451, 0, 0, 0, 0, 0.0035977389, 0.0035977389
+                    );
+            }
+        };
+
+        assertMatEqual(truth, descriptors, EPS);
+    }
+
+    public void testCreate() {
+        assertNotNull(extractor);
+    }
+
+    public void testDescriptorSize() {
+        assertEquals(128, extractor.descriptorSize());
+    }
+
+    public void testDescriptorType() {
+        assertEquals(CvType.CV_32F, extractor.descriptorType());
+    }
+
+    public void testEmpty() {
+//        assertFalse(extractor.empty());
+        fail("Not yet implemented");
+    }
+
+    public void testRead() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+        writeFile(filename, "%YAML:1.0\n---\nnOctaves: 4\nnOctaveLayers: 2\nextended: 1\nupright: 0\n");
+
+        extractor.read(filename);
+
+        assertEquals(128, extractor.descriptorSize());
+    }
+
+    public void testWrite() {
+        String filename = OpenCVTestRunner.getTempFileName("xml");
+
+        extractor.write(filename);
+
+//        String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n<name>Feature2D.SURF</name>\n<extended>1</extended>\n<hessianThreshold>100.</hessianThreshold>\n<nOctaveLayers>2</nOctaveLayers>\n<nOctaves>4</nOctaves>\n<upright>0</upright>\n</opencv_storage>\n";
+        String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n</opencv_storage>\n";
+        assertEquals(truth, readFile(filename));
+    }
+
+    public void testWriteYml() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+
+        extractor.write(filename);
+
+//        String truth = "%YAML:1.0\n---\nname: \"Feature2D.SURF\"\nextended: 1\nhessianThreshold: 100.\nnOctaveLayers: 2\nnOctaves: 4\nupright: 0\n";
+        String truth = "%YAML:1.0\n---\n";
+        assertEquals(truth, readFile(filename));
+    }
+
+}

3rdparty/opencv-4.5.4/modules/features2d/misc/java/test/SURFFeatureDetectorTest.java

@@ -0,0 +1,175 @@
+package org.opencv.test.features2d;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.Comparator;
+import java.util.List;
+
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.MatOfKeyPoint;
+import org.opencv.core.Point;
+import org.opencv.core.Scalar;
+import org.opencv.core.KeyPoint;
+import org.opencv.test.OpenCVTestCase;
+import org.opencv.test.OpenCVTestRunner;
+import org.opencv.imgproc.Imgproc;
+import org.opencv.features2d.Feature2D;
+
+public class SURFFeatureDetectorTest extends OpenCVTestCase {
+
+    Feature2D detector;
+    int matSize;
+    KeyPoint[] truth;
+
+    private Mat getMaskImg() {
+        Mat mask = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Mat right = mask.submat(0, matSize, matSize / 2, matSize);
+        right.setTo(new Scalar(0));
+        return mask;
+    }
+
+    private Mat getTestImg() {
+        Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
+        Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
+
+        return cross;
+    }
+
+    private void order(List<KeyPoint> points) {
+        Collections.sort(points, new Comparator<KeyPoint>() {
+            public int compare(KeyPoint p1, KeyPoint p2) {
+                if (p1.angle < p2.angle)
+                    return -1;
+                if (p1.angle > p2.angle)
+                    return 1;
+                return 0;
+            }
+        });
+    }
+
+    @Override
+    protected void setUp() throws Exception {
+        super.setUp();
+        detector = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, null, null);
+        matSize = 100;
+        truth = new KeyPoint[] {
+                new KeyPoint(55.775578f, 55.775578f, 16, 80.245735f, 8617.8633f, 0, -1),
+                new KeyPoint(44.224422f, 55.775578f, 16, 170.24574f, 8617.8633f, 0, -1),
+                new KeyPoint(44.224422f, 44.224422f, 16, 260.24573f, 8617.8633f, 0, -1),
+                new KeyPoint(55.775578f, 44.224422f, 16, 350.24573f, 8617.8633f, 0, -1)
+            };
+    }
+
+    public void testCreate() {
+        assertNotNull(detector);
+    }
+
+    public void testDetectListOfMatListOfListOfKeyPoint() {
+
+        setProperty(detector, "hessianThreshold", "double", 8000);
+        setProperty(detector, "nOctaves", "int", 3);
+        setProperty(detector, "nOctaveLayers", "int", 4);
+        setProperty(detector, "upright", "boolean", false);
+
+        List<MatOfKeyPoint> keypoints = new ArrayList<MatOfKeyPoint>();
+        Mat cross = getTestImg();
+        List<Mat> crosses = new ArrayList<Mat>(3);
+        crosses.add(cross);
+        crosses.add(cross);
+        crosses.add(cross);
+
+        detector.detect(crosses, keypoints);
+
+        assertEquals(3, keypoints.size());
+
+        for (MatOfKeyPoint mkp : keypoints) {
+            List<KeyPoint> lkp = mkp.toList();
+            order(lkp);
+            assertListKeyPointEquals(Arrays.asList(truth), lkp, EPS);
+        }
+    }
+
+    public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfKeyPoint() {
+
+        setProperty(detector, "hessianThreshold", "double", 8000);
+        setProperty(detector, "nOctaves", "int", 3);
+        setProperty(detector, "nOctaveLayers", "int", 4);
+        setProperty(detector, "upright", "boolean", false);
+
+        MatOfKeyPoint keypoints = new MatOfKeyPoint();
+        Mat cross = getTestImg();
+
+        detector.detect(cross, keypoints);
+
+        List<KeyPoint> lkp = keypoints.toList();
+        order(lkp);
+        assertListKeyPointEquals(Arrays.asList(truth), lkp, EPS);
+    }
+
+    public void testDetectMatListOfKeyPointMat() {
+
+        setProperty(detector, "hessianThreshold", "double", 8000);
+        setProperty(detector, "nOctaves", "int", 3);
+        setProperty(detector, "nOctaveLayers", "int", 4);
+        setProperty(detector, "upright", "boolean", false);
+
+        Mat img = getTestImg();
+        Mat mask = getMaskImg();
+        MatOfKeyPoint keypoints = new MatOfKeyPoint();
+
+        detector.detect(img, keypoints, mask);
+
+        List<KeyPoint> lkp = keypoints.toList();
+        order(lkp);
+        assertListKeyPointEquals(Arrays.asList(truth[1], truth[2]), lkp, EPS);
+    }
+
+    public void testEmpty() {
+//        assertFalse(detector.empty());
+        fail("Not yet implemented");
+    }
+
+    public void testRead() {
+        Mat cross = getTestImg();
+
+        MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
+        detector.detect(cross, keypoints1);
+
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+        writeFile(filename, "%YAML:1.0\n---\nhessianThreshold: 8000.\noctaves: 3\noctaveLayers: 4\nupright: 0\n");
+        detector.read(filename);
+
+        MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
+        detector.detect(cross, keypoints2);
+
+        assertTrue(keypoints2.total() <= keypoints1.total());
+    }
+
+    public void testWrite() {
+        String filename = OpenCVTestRunner.getTempFileName("xml");
+
+        detector.write(filename);
+
+//        String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n<name>Feature2D.SURF</name>\n<extended>0</extended>\n<hessianThreshold>100.</hessianThreshold>\n<nOctaveLayers>3</nOctaveLayers>\n<nOctaves>4</nOctaves>\n<upright>0</upright>\n</opencv_storage>\n";
+        String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n</opencv_storage>\n";
+        assertEquals(truth, readFile(filename));
+    }
+
+    public void testWriteYml() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+
+        detector.write(filename);
+
+//        String truth = "%YAML:1.0\n---\nname: \"Feature2D.SURF\"\nextended: 0\nhessianThreshold: 100.\nnOctaveLayers: 3\nnOctaves: 4\nupright: 0\n";
+        String truth = "%YAML:1.0\n---\n";
+        assertEquals(truth, readFile(filename));
+    }
+
+}