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