JavaAPI: fixed typo; some constants are moved to private section; added tests for...

diff --git a/modules/java/android_test/src/org/opencv/test/features2d/BruteForceHammingDescriptorMatcherTest.java b/modules/java/android_test/src/org/opencv/test/features2d/BruteForceHammingDescriptorMatcherTest.java
new file mode 100644 (file)
index 0000000..5324f70
--- /dev/null
+++ b/modules/java/android_test/src/org/opencv/test/features2d/BruteForceHammingDescriptorMatcherTest.java
@@ -0,0 +1,203 @@
+package org.opencv.test.features2d;
+
+import org.opencv.core.Core;
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.Point;
+import org.opencv.core.Scalar;
+import org.opencv.features2d.DMatch;
+import org.opencv.features2d.DescriptorExtractor;
+import org.opencv.features2d.DescriptorMatcher;
+import org.opencv.features2d.FeatureDetector;
+import org.opencv.features2d.KeyPoint;
+import org.opencv.test.OpenCVTestCase;
+import org.opencv.test.OpenCVTestRunner;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+
+public class BruteForceHammingDescriptorMatcherTest extends OpenCVTestCase {
+
+    DescriptorMatcher matcher;
+    int matSize;
+    DMatch[] truth;
+
+    protected void setUp() throws Exception {
+        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) };
+        super.setUp();
+    }
+
+    private Mat getTrainImg() {
+        Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Core.line(img, new Point(40, 40), new Point(matSize - 40, matSize - 40), new Scalar(0), 8);
+        return img;
+    }
+    
+    private Mat getQueryImg() {
+        Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Core.line(img, new Point(40, matSize - 40), new Point(matSize - 50, 50), new Scalar(0), 8);
+        return img;
+    }
+    
+    private Mat getTestDescriptors(Mat img) {
+        List<KeyPoint> keypoints = new ArrayList<KeyPoint>();
+        Mat descriptors = new Mat();
+
+        FeatureDetector detector = FeatureDetector.create(FeatureDetector.FAST);
+        DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.BRIEF);
+
+        detector.detect(img, keypoints);
+        extractor.compute(img, keypoints, descriptors);
+        
+        return descriptors;
+    }
+
+    private Mat getQueryDescriptors() {
+        return getTestDescriptors(getQueryImg());
+    }
+    
+    private Mat getTrainDescriptors() {
+        return getTestDescriptors(getTrainImg());
+    }
+
+    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);
+            }
+        };
+    }
+
+    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 testCloneBoolean() {
+        matcher.add(Arrays.asList(new Mat()));
+
+        DescriptorMatcher cloned = matcher.clone(true);
+
+        assertNotNull(cloned);
+        assertTrue(cloned.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 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 testMatchMatMatListOfDMatchMat() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        Mat mask = getMaskImg();
+        List<DMatch> matches = new ArrayList<DMatch>();
+        
+        matcher.match(query, train, matches, mask);
+
+        assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches, EPS);
+    }
+
+    public void testMatchMatMatListOfDMatch() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        List<DMatch> matches = new ArrayList<DMatch>();
+        
+        matcher.match(query, train, matches);
+        
+        assertListDMatchEquals(Arrays.asList(truth), matches, EPS);
+    }
+
+    public void testMatchMatListOfDMatchListOfMat() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        Mat mask = getMaskImg();
+        List<DMatch> matches = new ArrayList<DMatch>();
+        matcher.add(Arrays.asList(train));
+
+        matcher.match(query, matches, Arrays.asList(mask));
+
+        assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches, EPS);
+    }
+
+    public void testMatchMatListOfDMatch() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        List<DMatch> matches = new ArrayList<DMatch>();
+        matcher.add(Arrays.asList(train));
+        
+        matcher.match(query, matches);
+        
+        assertListDMatchEquals(Arrays.asList(truth), matches, EPS);
+    }
+
+    public void testRead() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+        writeFile(filename, "%YAML:1.0\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";
+        assertEquals(truth, readFile(filename));
+    }
+
+}

diff --git a/modules/java/android_test/src/org/opencv/test/features2d/BruteForceHammingLUTDescriptorMatcherTest.java b/modules/java/android_test/src/org/opencv/test/features2d/BruteForceHammingLUTDescriptorMatcherTest.java
new file mode 100644 (file)
index 0000000..48b3c57
--- /dev/null
+++ b/modules/java/android_test/src/org/opencv/test/features2d/BruteForceHammingLUTDescriptorMatcherTest.java
@@ -0,0 +1,207 @@
+package org.opencv.test.features2d;
+
+import org.opencv.core.Core;
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.Point;
+import org.opencv.core.Scalar;
+import org.opencv.features2d.DMatch;
+import org.opencv.features2d.DescriptorExtractor;
+import org.opencv.features2d.DescriptorMatcher;
+import org.opencv.features2d.FeatureDetector;
+import org.opencv.features2d.KeyPoint;
+import org.opencv.test.OpenCVTestCase;
+import org.opencv.test.OpenCVTestRunner;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+
+public class BruteForceHammingLUTDescriptorMatcherTest extends OpenCVTestCase {
+
+    DescriptorMatcher matcher;
+    int matSize;
+    DMatch[] truth;
+
+    protected void setUp() throws Exception {
+        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) };
+        super.setUp();
+    }
+
+    private Mat getTrainImg() {
+        Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Core.line(img, new Point(40, 40), new Point(matSize - 40, matSize - 40), new Scalar(0), 8);
+        return img;
+    }
+    
+    private Mat getQueryImg() {
+        Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
+        Core.line(img, new Point(40, matSize - 40), new Point(matSize - 50, 50), new Scalar(0), 8);
+        return img;
+    }
+    
+    private Mat getTestDescriptors(Mat img) {
+        List<KeyPoint> keypoints = new ArrayList<KeyPoint>();
+        Mat descriptors = new Mat();
+
+        FeatureDetector detector = FeatureDetector.create(FeatureDetector.FAST);
+        DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.BRIEF);
+
+        detector.detect(img, keypoints);
+        extractor.compute(img, keypoints, descriptors);
+        
+        return descriptors;
+    }
+
+    private Mat getQueryDescriptors() {
+        return getTestDescriptors(getQueryImg());
+    }
+    
+    private Mat getTrainDescriptors() {
+        return getTestDescriptors(getTrainImg());
+    }
+
+    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);
+            }
+        };
+    }
+
+    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 testCloneBoolean() {
+        matcher.add(Arrays.asList(new Mat()));
+
+        DescriptorMatcher cloned = matcher.clone(true);
+
+        assertNotNull(cloned);
+        assertTrue(cloned.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 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 testMatchMatMatListOfDMatchMat() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        Mat mask = getMaskImg();
+        List<DMatch> matches = new ArrayList<DMatch>();
+        
+        matcher.match(query, train, matches, mask);
+
+        assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches, EPS);
+    }
+
+    public void testMatchMatMatListOfDMatch() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        List<DMatch> matches = new ArrayList<DMatch>();
+        
+        matcher.match(query, train, matches);
+        
+      OpenCVTestRunner.Log("matches found: " + matches.size());
+      for (DMatch m : matches)
+          OpenCVTestRunner.Log(m.toString());
+        
+        assertListDMatchEquals(Arrays.asList(truth), matches, EPS);
+    }
+
+    public void testMatchMatListOfDMatchListOfMat() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        Mat mask = getMaskImg();
+        List<DMatch> matches = new ArrayList<DMatch>();
+        matcher.add(Arrays.asList(train));
+
+        matcher.match(query, matches, Arrays.asList(mask));
+
+        assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches, EPS);
+    }
+
+    public void testMatchMatListOfDMatch() {
+        Mat train = getTrainDescriptors();
+        Mat query = getQueryDescriptors();
+        List<DMatch> matches = new ArrayList<DMatch>();
+        matcher.add(Arrays.asList(train));
+        
+        matcher.match(query, matches);
+        
+        assertListDMatchEquals(Arrays.asList(truth), matches, EPS);
+    }
+
+    public void testRead() {
+        String filename = OpenCVTestRunner.getTempFileName("yml");
+        writeFile(filename, "%YAML:1.0\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";
+        assertEquals(truth, readFile(filename));
+    }
+
+}

diff --git a/modules/java/gen_java.py b/modules/java/gen_java.py
index 665adff..3915f30 100644 (file)
--- a/modules/java/gen_java.py
+++ b/modules/java/gen_java.py
@@ -100,6 +100,10 @@ const_private_list = (
     "CV_INPAINT_.+",\r
     "CV_RETR_.+",\r
     "CV_CHAIN_APPROX_.+",\r
+    "OPPONENTEXTRACTOR",\r
+    "GRIDRETECTOR",\r
+    "PYRAMIDDETECTOR",\r
+    "DYNAMICDETECTOR",\r
 )\r
 \r
 # { Module : { public : [[name, val],...], private : [[]...] } }\r

diff --git a/modules/java/src/cpp/features2d_manual.hpp b/modules/java/src/cpp/features2d_manual.hpp
index 4f1eff3..6fc25e6 100644 (file)
--- a/modules/java/src/cpp/features2d_manual.hpp
+++ b/modules/java/src/cpp/features2d_manual.hpp
@@ -245,10 +245,10 @@ public:
 \r
         OPPONENTEXTRACTOR = 1000,\r
 \r
-        OPPENENT_SIFT  = OPPONENTEXTRACTOR + SIFT,\r
-        OPPENENT_SURF  = OPPONENTEXTRACTOR + SURF,\r
-        OPPENENT_ORB   = OPPONENTEXTRACTOR + ORB,\r
-        OPPENENT_BRIEF = OPPONENTEXTRACTOR + BRIEF\r
+        OPPONENT_SIFT  = OPPONENTEXTRACTOR + SIFT,\r
+        OPPONENT_SURF  = OPPONENTEXTRACTOR + SURF,\r
+        OPPONENT_ORB   = OPPONENTEXTRACTOR + ORB,\r
+        OPPONENT_BRIEF = OPPONENTEXTRACTOR + BRIEF\r
     };\r
 \r
     //supported SIFT, SURF, ORB, BRIEF, Opponent(XXXX)\r