Merge remote-tracking branch 'upstream/3.4' into merge-3.4

modules/features2d/include/opencv2/features2d.hpp

@@ -341,7 +341,7 @@ public:
     but it is a little faster to compute.
     @param patchSize size of the patch used by the oriented BRIEF descriptor. Of course, on smaller
     pyramid layers the perceived image area covered by a feature will be larger.
-    @param fastThreshold
+    @param fastThreshold the fast threshold
      */
     CV_WRAP static Ptr<ORB> create(int nfeatures=500, float scaleFactor=1.2f, int nlevels=8, int edgeThreshold=31,
         int firstLevel=0, int WTA_K=2, ORB::ScoreType scoreType=ORB::HARRIS_SCORE, int patchSize=31, int fastThreshold=20);

modules/features2d/misc/java/gen_dict.json

@@ -1,7 +1,4 @@
 {
-    "class_ignore_list" : [
-        "SimpleBlobDetector"
-    ],
     "type_dict" : {
         "Feature2D": {
             "j_type": "Feature2D",

modules/features2d/misc/java/test/SIMPLEBLOBFeatureDetectorTest.java

@@ -1,11 +1,62 @@
 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() {
-        fail("Not yet implemented");
+        assertNotNull(detector);
     }
 
     public void testDetectListOfMatListOfListOfKeyPoint() {
@@ -17,23 +68,51 @@ public class SIMPLEBLOBFeatureDetectorTest extends OpenCVTestCase {
     }
 
     public void testDetectMatListOfKeyPoint() {
-        fail("Not yet implemented");
+        Mat img = getTestImg();
+        MatOfKeyPoint keypoints = new MatOfKeyPoint();
+
+        detector.detect(img, keypoints);
+
+        assertListKeyPointEquals(Arrays.asList(truth), keypoints.toList(), EPS);
     }
 
     public void testDetectMatListOfKeyPointMat() {
-        fail("Not yet implemented");
+        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() {
-        fail("Not yet implemented");
+        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() {
-        fail("Not yet implemented");
-    }
+        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));
+    }
 }