content: 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));
      }
  
  }
  