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In read function, check before if node is empty to avoid erasing default value in case of missing parameter. Add getters/setters to complete cpp/java/python API (needed for Java Tests.) fix warning due to double to float conversion in freak
177 lines
6.1 KiB
Java
177 lines
6.1 KiB
Java
package org.opencv.test.features2d;
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import java.util.ArrayList;
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import java.util.Arrays;
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import java.util.Collections;
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import java.util.Comparator;
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import java.util.List;
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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.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.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.xfeatures2d.SURF;
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public class SURFFeatureDetectorTest extends OpenCVTestCase {
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SURF detector;
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int matSize;
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KeyPoint[] truth;
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private Mat getMaskImg() {
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Mat mask = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
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Mat right = mask.submat(0, matSize, matSize / 2, matSize);
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right.setTo(new Scalar(0));
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return mask;
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}
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private Mat getTestImg() {
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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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private void order(List<KeyPoint> points) {
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Collections.sort(points, new Comparator<KeyPoint>() {
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public int compare(KeyPoint p1, KeyPoint p2) {
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if (p1.angle < p2.angle)
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return -1;
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if (p1.angle > p2.angle)
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return 1;
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return 0;
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}
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});
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}
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@Override
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protected void setUp() throws Exception {
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super.setUp();
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detector = createClassInstance(XFEATURES2D + "SURF", DEFAULT_FACTORY, null, null);
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matSize = 100;
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truth = new KeyPoint[] {
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new KeyPoint(55.775578f, 55.775578f, 16, 80.245735f, 8617.8633f, 0, -1),
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new KeyPoint(44.224422f, 55.775578f, 16, 170.24574f, 8617.8633f, 0, -1),
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new KeyPoint(44.224422f, 44.224422f, 16, 260.24573f, 8617.8633f, 0, -1),
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new KeyPoint(55.775578f, 44.224422f, 16, 350.24573f, 8617.8633f, 0, -1)
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};
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}
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public void testCreate() {
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assertNotNull(detector);
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}
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public void testDetectListOfMatListOfListOfKeyPoint() {
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setProperty(detector, "hessianThreshold", "double", 8000);
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setProperty(detector, "nOctaves", "int", 3);
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setProperty(detector, "nOctaveLayers", "int", 4);
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setProperty(detector, "upright", "boolean", false);
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setProperty(detector, "extended", "boolean", true);
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List<MatOfKeyPoint> keypoints = new ArrayList<MatOfKeyPoint>();
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Mat cross = getTestImg();
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List<Mat> crosses = new ArrayList<Mat>(3);
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crosses.add(cross);
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crosses.add(cross);
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crosses.add(cross);
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detector.detect(crosses, keypoints);
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assertEquals(3, keypoints.size());
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for (MatOfKeyPoint mkp : keypoints) {
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List<KeyPoint> lkp = mkp.toList();
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order(lkp);
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assertListKeyPointEquals(Arrays.asList(truth), lkp, EPS);
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}
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}
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public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
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fail("Not yet implemented");
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}
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public void testDetectMatListOfKeyPoint() {
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setProperty(detector, "hessianThreshold", "double", 8000);
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setProperty(detector, "nOctaves", "int", 3);
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setProperty(detector, "nOctaveLayers", "int", 4);
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setProperty(detector, "upright", "boolean", false);
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MatOfKeyPoint keypoints = new MatOfKeyPoint();
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Mat cross = getTestImg();
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detector.detect(cross, keypoints);
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List<KeyPoint> lkp = keypoints.toList();
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order(lkp);
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assertListKeyPointEquals(Arrays.asList(truth), lkp, EPS);
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}
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public void testDetectMatListOfKeyPointMat() {
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setProperty(detector, "hessianThreshold", "double", 8000);
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setProperty(detector, "nOctaves", "int", 3);
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setProperty(detector, "nOctaveLayers", "int", 4);
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setProperty(detector, "upright", "boolean", false);
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setProperty(detector, "extended", "boolean", true);
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Mat img = getTestImg();
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Mat mask = getMaskImg();
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MatOfKeyPoint keypoints = new MatOfKeyPoint();
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detector.detect(img, keypoints, mask);
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List<KeyPoint> lkp = keypoints.toList();
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order(lkp);
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assertListKeyPointEquals(Arrays.asList(truth[1], truth[2]), lkp, EPS);
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}
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public void testEmpty() {
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// assertFalse(detector.empty());
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fail("Not yet implemented");
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}
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public void testReadYml() {
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Mat cross = getTestImg();
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MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
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detector.detect(cross, keypoints1);
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String filename = OpenCVTestRunner.getTempFileName("xml");
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writeFile(filename, "<?xml version=\"1.0\"?>\n<opencv_storage>\n<name>Feature2D.SURF</name>\n<hessianThreshold>8000.</hessianThreshold>\n<extended>1</extended>\n<upright>0</upright>\n<nOctaves>3</nOctaves>\n<nOctaveLayers>4</nOctaveLayers>\n</opencv_storage>\n");
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detector.read(filename);
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assertEquals(128, detector.descriptorSize());
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assertEquals(8000., detector.getHessianThreshold());
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assertEquals(true, detector.getExtended());
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assertEquals(false, detector.getUpright());
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assertEquals(3, detector.getNOctaves());
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assertEquals(4, detector.getNOctaveLayers());
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MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
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detector.detect(cross, keypoints2);
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assertTrue(keypoints2.total() <= keypoints1.total());
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}
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public void testWriteYml() {
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String filename = OpenCVTestRunner.getTempFileName("yml");
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detector.write(filename);
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String truth = "%YAML:1.0\n---\nname: \"Feature2D.SURF\"\nhessianThreshold: 100.\nextended: 0\nupright: 0\nnOctaves: 4\nnOctaveLayers: 3\n";
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String actual = readFile(filename);
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actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
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assertEquals(truth, actual);
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}
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}
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