vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e
This commit is contained in:
Gitea Mirror Bot
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package org.opencv.test.features;
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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.xfeatures2d.AgastFeatureDetector;
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public class AGASTFeatureDetectorTest extends OpenCVTestCase {
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AgastFeatureDetector detector;
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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 = AgastFeatureDetector.create(); // default (10,true,3)
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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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fail("Not yet implemented");
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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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fail("Not yet implemented");
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}
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public void testDetectMatListOfKeyPointMat() {
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fail("Not yet implemented");
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}
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public void testEmpty() {
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fail("Not yet implemented");
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}
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public void testRead() {
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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.AgastFeatureDetector</name>\n<threshold>11</threshold>\n<nonmaxSuppression>0</nonmaxSuppression>\n<type>2</type>\n</opencv_storage>\n");
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detector.read(filename);
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assertEquals(11, detector.getThreshold());
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assertEquals(false, detector.getNonmaxSuppression());
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assertEquals(2, detector.getType());
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}
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public void testReadYml() {
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String filename = OpenCVTestRunner.getTempFileName("yml");
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writeFile(filename, "%YAML:1.0\n---\nname: \"Feature2D.AgastFeatureDetector\"\nthreshold: 11\nnonmaxSuppression: 0\ntype: 2\n");
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detector.read(filename);
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assertEquals(11, detector.getThreshold());
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assertEquals(false, detector.getNonmaxSuppression());
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assertEquals(2, detector.getType());
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}
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public void testWrite() {
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String filename = OpenCVTestRunner.getTempFileName("xml");
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detector.write(filename);
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String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n<name>Feature2D.AgastFeatureDetector</name>\n<threshold>10</threshold>\n<nonmaxSuppression>1</nonmaxSuppression>\n<type>3</type>\n</opencv_storage>\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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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 1.2\n---\nname: \"Feature2D.AgastFeatureDetector\"\nthreshold: 10\nnonmaxSuppression: true\ntype: 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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@@ -0,0 +1,67 @@
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package org.opencv.test.features;
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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.xfeatures2d.AKAZE;
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public class AKAZEDescriptorExtractorTest extends OpenCVTestCase {
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AKAZE extractor;
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@Override
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protected void setUp() throws Exception {
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super.setUp();
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extractor = AKAZE.create(); // default (5,0,3,0.001f,4,4,1)
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}
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public void testCreate() {
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assertNotNull(extractor);
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}
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public void testDetectListOfMatListOfListOfKeyPoint() {
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fail("Not yet implemented");
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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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fail("Not yet implemented");
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}
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public void testDetectMatListOfKeyPointMat() {
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fail("Not yet implemented");
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}
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public void testEmpty() {
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fail("Not yet implemented");
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}
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public void testReadYml() {
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String filename = OpenCVTestRunner.getTempFileName("yml");
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writeFile(filename, "%YAML:1.0\n---\nformat: 3\nname: \"Feature2D.AKAZE\"\ndescriptor: 4\ndescriptor_channels: 2\ndescriptor_size: 32\nthreshold: 0.125\noctaves: 3\nsublevels: 5\ndiffusivity: 2\n");
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extractor.read(filename);
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assertEquals(4, extractor.getDescriptorType());
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assertEquals(2, extractor.getDescriptorChannels());
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assertEquals(32, extractor.getDescriptorSize());
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assertEquals(0.125, extractor.getThreshold());
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assertEquals(3, extractor.getNOctaves());
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assertEquals(5, extractor.getNOctaveLayers());
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assertEquals(2, extractor.getDiffusivity());
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}
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public void testWriteYml() {
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String filename = OpenCVTestRunner.getTempFileName("yml");
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extractor.write(filename);
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String truth = "%YAML 1.2\n---\nformat: 3\nname: \"Feature2D.AKAZE\"\ndescriptor: 5\ndescriptor_channels: 3\ndescriptor_size: 0\nthreshold: 0.0010000000474974513\noctaves: 4\nsublevels: 4\ndiffusivity: 1\nmax_points: -1\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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@@ -0,0 +1,48 @@
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package org.opencv.test.features;
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import org.opencv.core.Core;
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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.features.ORB;
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import org.opencv.features.DescriptorMatcher;
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import org.opencv.xfeatures2d.BOWImgDescriptorExtractor;
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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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public class BOWImgDescriptorExtractorTest extends OpenCVTestCase {
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ORB extractor;
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DescriptorMatcher matcher;
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int matSize;
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public static void assertDescriptorsClose(Mat expected, Mat actual, int allowedDistance) {
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double distance = Core.norm(expected, actual, Core.NORM_HAMMING);
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assertTrue("expected:<" + allowedDistance + "> but was:<" + distance + ">", distance <= allowedDistance);
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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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@Override
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protected void setUp() throws Exception {
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super.setUp();
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extractor = ORB.create();
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matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE);
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matSize = 100;
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}
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public void testCreate() {
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BOWImgDescriptorExtractor bow = new BOWImgDescriptorExtractor(extractor, matcher);
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}
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}
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@@ -0,0 +1,102 @@
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package org.opencv.test.features;
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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.features.Feature2D;
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public class BRIEFDescriptorExtractorTest extends OpenCVTestCase {
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Feature2D extractor;
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int matSize;
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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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@Override
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protected void setUp() throws Exception {
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super.setUp();
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extractor = createClassInstance(XFEATURES2D+"BriefDescriptorExtractor", DEFAULT_FACTORY, null, null);
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matSize = 100;
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}
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public void testComputeListOfMatListOfListOfKeyPointListOfMat() {
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fail("Not yet implemented");
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}
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public void testComputeMatListOfKeyPointMat() {
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KeyPoint point = new KeyPoint(55.775577545166016f, 44.224422454833984f, 16, 9.754629f, 8617.863f, 1, -1);
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MatOfKeyPoint keypoints = new MatOfKeyPoint(point);
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Mat img = getTestImg();
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Mat descriptors = new Mat();
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extractor.compute(img, keypoints, descriptors);
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Mat truth = new Mat(1, 32, CvType.CV_8UC1) {
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{
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put(0, 0, 96, 0, 76, 24, 47, 182, 68, 137,
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149, 195, 67, 16, 187, 224, 74, 8,
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82, 169, 87, 70, 44, 4, 192, 56,
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13, 128, 44, 106, 146, 72, 194, 245);
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}
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};
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assertMatEqual(truth, descriptors);
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}
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public void testCreate() {
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assertNotNull(extractor);
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}
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public void testDescriptorSize() {
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assertEquals(32, extractor.descriptorSize());
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}
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public void testDescriptorType() {
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assertEquals(CvType.CV_8U, extractor.descriptorType());
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}
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public void testEmpty() {
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// assertFalse(extractor.empty());
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fail("Not yet implemented"); // BRIEF does not override empty() method
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}
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public void testRead() {
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String filename = OpenCVTestRunner.getTempFileName("yml");
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writeFile(filename, "%YAML:1.0\n---\ndescriptorSize: 64\n");
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extractor.read(filename);
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assertEquals(64, extractor.descriptorSize());
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}
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public void testWrite() {
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String filename = OpenCVTestRunner.getTempFileName("xml");
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extractor.write(filename);
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String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n<name>Feature2D.BRIEF</name>\n<descriptorSize>32</descriptorSize>\n<use_orientation>0</use_orientation>\n</opencv_storage>\n";
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assertEquals(truth, readFile(filename));
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}
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public void testWriteYml() {
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String filename = OpenCVTestRunner.getTempFileName("yml");
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extractor.write(filename);
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String truth = "%YAML 1.2\n---\nname: \"Feature2D.BRIEF\"\ndescriptorSize: 32\nuse_orientation: false\n";
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assertEquals(truth, readFile(filename));
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}
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}
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@@ -0,0 +1,63 @@
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package org.opencv.test.features;
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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.xfeatures2d.BRISK;
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public class BRISKDescriptorExtractorTest extends OpenCVTestCase {
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BRISK extractor;
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@Override
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protected void setUp() throws Exception {
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super.setUp();
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extractor = BRISK.create(); // default (30,3,1)
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}
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public void testCreate() {
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assertNotNull(extractor);
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}
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public void testDetectListOfMatListOfListOfKeyPoint() {
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fail("Not yet implemented");
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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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fail("Not yet implemented");
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}
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public void testDetectMatListOfKeyPointMat() {
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fail("Not yet implemented");
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}
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public void testEmpty() {
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fail("Not yet implemented");
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}
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public void testReadYml() {
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String filename = OpenCVTestRunner.getTempFileName("yml");
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writeFile(filename, "%YAML:1.0\n---\nname: \"Feature2D.BRISK\"\nthreshold: 31\noctaves: 4\npatternScale: 1.1\n");
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extractor.read(filename);
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assertEquals(31, extractor.getThreshold());
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assertEquals(4, extractor.getOctaves());
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assertEquals(1.1f, extractor.getPatternScale());
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}
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public void testWriteYml() {
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String filename = OpenCVTestRunner.getTempFileName("yml");
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extractor.write(filename);
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String truth = "%YAML 1.2\n---\nname: \"Feature2D.BRISK\"\nthreshold: 30\noctaves: 3\npatternScale: 1.\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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@@ -0,0 +1,67 @@
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package org.opencv.test.features;
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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.xfeatures2d.DAISY;
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public class DAISYDescriptorExtractorTest extends OpenCVTestCase {
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DAISY extractor;
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@Override
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protected void setUp() throws Exception {
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super.setUp();
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extractor = DAISY.create(); // default (15, 3, 8, 8, 100, noArray, true, false)
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}
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public void testCreate() {
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assertNotNull(extractor);
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}
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public void testDetectListOfMatListOfListOfKeyPoint() {
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fail("Not yet implemented");
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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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fail("Not yet implemented");
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}
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public void testDetectMatListOfKeyPointMat() {
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fail("Not yet implemented");
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}
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public void testEmpty() {
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fail("Not yet implemented");
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}
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public void testReadYml() {
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String filename = OpenCVTestRunner.getTempFileName("yml");
|
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writeFile(filename, "%YAML:1.0\n---\nname: \"Feature2D.DAISY\"\nradius: 16.\nq_radius: 4\nq_theta: 9\nq_hist: 10\nnorm_type: 101\nenable_interpolation: 0\nuse_orientation: 1\n");
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extractor.read(filename);
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assertEquals(16.0f, extractor.getRadius());
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assertEquals(4, extractor.getQRadius());
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assertEquals(9, extractor.getQTheta());
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assertEquals(10, extractor.getQHist());
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assertEquals(101, extractor.getNorm());
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assertEquals(false, extractor.getInterpolation());
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assertEquals(true, extractor.getUseOrientation());
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}
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public void testWriteYml() {
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String filename = OpenCVTestRunner.getTempFileName("yml");
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extractor.write(filename);
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String truth = "%YAML 1.2\n---\nname: \"Feature2D.DAISY\"\nradius: 15.\nq_radius: 3\nq_theta: 8\nq_hist: 8\nnorm_type: 100\nenable_interpolation: true\nuse_orientation: false\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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||||
}
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@@ -0,0 +1,64 @@
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package org.opencv.test.features;
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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.xfeatures2d.FREAK;
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public class FREAKDescriptorExtractorTest extends OpenCVTestCase {
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FREAK extractor;
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@Override
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protected void setUp() throws Exception {
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||||
super.setUp();
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extractor = FREAK.create(); // default (true,true,22,4)
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||||
}
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||||
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||||
public void testCreate() {
|
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assertNotNull(extractor);
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPointMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testReadYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(filename, "%YAML:1.0\n---\nname: \"Feature2D.FREAK\"\norientationNormalized: 0\nscaleNormalized: 0\npatternScale: 23.\nnOctaves: 5\n");
|
||||
|
||||
extractor.read(filename);
|
||||
|
||||
assertEquals(false, extractor.getOrientationNormalized());
|
||||
assertEquals(false, extractor.getScaleNormalized());
|
||||
assertEquals(23.0, extractor.getPatternScale());
|
||||
assertEquals(5, extractor.getNOctaves());
|
||||
}
|
||||
|
||||
public void testWriteYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
extractor.write(filename);
|
||||
|
||||
String truth = "%YAML 1.2\n---\nname: \"Feature2D.FREAK\"\norientationNormalized: true\nscaleNormalized: true\npatternScale: 22.\nnOctaves: 4\n";
|
||||
String actual = readFile(filename);
|
||||
actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
|
||||
assertEquals(truth, actual);
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,65 @@
|
||||
package org.opencv.test.features;
|
||||
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.xfeatures2d.HarrisLaplaceFeatureDetector;
|
||||
|
||||
public class HARRISFeatureDetectorTest extends OpenCVTestCase {
|
||||
|
||||
HarrisLaplaceFeatureDetector detector;
|
||||
|
||||
@Override
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
detector = HarrisLaplaceFeatureDetector.create(); // default constructor have (6, 0.01, 0.01, 5000, 4)
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(detector);
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPointMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testReadYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
writeFile(filename, "%YAML:1.0\n---\nname: \"Feature2D.HARRIS-LAPLACE\"\nnumOctaves: 5\ncorn_thresh: 0.02\nDOG_thresh: 0.03\nmaxCorners: 4000\nnum_layers: 2\n");
|
||||
detector.read(filename);
|
||||
|
||||
assertEquals(5, detector.getNumOctaves());
|
||||
assertEquals(0.02f, detector.getCornThresh());
|
||||
assertEquals(0.03f, detector.getDOGThresh());
|
||||
assertEquals(4000, detector.getMaxCorners());
|
||||
assertEquals(2, detector.getNumLayers());
|
||||
}
|
||||
|
||||
public void testWriteYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
detector.write(filename);
|
||||
|
||||
String truth = "%YAML:1.0\n---\nname: \"Feature2D.HARRIS-LAPLACE\"\nnumOctaves: 6\ncorn_thresh: 9.9999997764825821e-03\nDOG_thresh: 9.9999997764825821e-03\nmaxCorners: 5000\nnum_layers: 4\n";
|
||||
String actual = readFile(filename);
|
||||
actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
|
||||
assertEquals(truth, actual);
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,66 @@
|
||||
package org.opencv.test.features;
|
||||
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.xfeatures2d.KAZE;
|
||||
|
||||
public class KAZEDescriptorExtractorTest extends OpenCVTestCase {
|
||||
|
||||
KAZE extractor;
|
||||
|
||||
@Override
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
extractor = KAZE.create(); // default (false,false,0.001f,4,4,1)
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(extractor);
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPointMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testReadYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(filename, "%YAML:1.0\n---\nformat: 3\nname: \"Feature2D.KAZE\"\nextended: 1\nupright: 1\nthreshold: 0.125\noctaves: 3\nsublevels: 5\ndiffusivity: 2\n");
|
||||
|
||||
extractor.read(filename);
|
||||
|
||||
assertEquals(true, extractor.getExtended());
|
||||
assertEquals(true, extractor.getUpright());
|
||||
assertEquals(0.125, extractor.getThreshold());
|
||||
assertEquals(3, extractor.getNOctaves());
|
||||
assertEquals(5, extractor.getNOctaveLayers());
|
||||
assertEquals(2, extractor.getDiffusivity());
|
||||
}
|
||||
|
||||
public void testWriteYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
extractor.write(filename);
|
||||
|
||||
String truth = "%YAML 1.2\n---\nformat: 3\nname: \"Feature2D.KAZE\"\nextended: 0\nupright: 0\nthreshold: 0.0010000000474974513\noctaves: 4\nsublevels: 4\ndiffusivity: 1\n";
|
||||
String actual = readFile(filename);
|
||||
actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
|
||||
assertEquals(truth, actual);
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,64 @@
|
||||
package org.opencv.test.features;
|
||||
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.xfeatures2d.LATCH;
|
||||
|
||||
public class LATCHDescriptorExtractorTest extends OpenCVTestCase {
|
||||
|
||||
LATCH extractor;
|
||||
|
||||
@Override
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
extractor = LATCH.create(); // default (32,true,3,2.0)
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(extractor);
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPointMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testReadYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(filename, "%YAML:1.0\n---\nname: \"Feature2D.LATCH\"\ndescriptorSize: 64\nrotationInvariance: 0\nhalf_ssd_size: 5\nsigma: 3.\n");
|
||||
|
||||
extractor.read(filename);
|
||||
|
||||
assertEquals(64, extractor.getBytes());
|
||||
assertEquals(false, extractor.getRotationInvariance());
|
||||
assertEquals(5, extractor.getHalfSSDsize());
|
||||
assertEquals(3.0, extractor.getSigma());
|
||||
}
|
||||
|
||||
public void testWriteYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
extractor.write(filename);
|
||||
|
||||
String truth = "%YAML 1.2\n---\nname: \"Feature2D.LATCH\"\ndescriptorSize: 32\nrotationInvariance: true\nhalf_ssd_size: 3\nsigma: 2.\n";
|
||||
String actual = readFile(filename);
|
||||
actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
|
||||
assertEquals(truth, actual);
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,62 @@
|
||||
package org.opencv.test.features;
|
||||
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.xfeatures2d.LUCID;
|
||||
|
||||
public class LUCIDDescriptorExtractorTest extends OpenCVTestCase {
|
||||
|
||||
LUCID extractor;
|
||||
|
||||
@Override
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
extractor = LUCID.create(); // default (1,2)
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(extractor);
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPointMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testReadYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(filename, "%YAML:1.0\n---\nname: \"Feature2D.LUCID\"\nlucid_kernel: 2\nblur_kernel: 3\n");
|
||||
|
||||
extractor.read(filename);
|
||||
|
||||
assertEquals(2, extractor.getLucidKernel());
|
||||
assertEquals(3, extractor.getBlurKernel());
|
||||
}
|
||||
|
||||
public void testWriteYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
extractor.write(filename);
|
||||
|
||||
String truth = "%YAML 1.2\n---\nname: \"Feature2D.LUCID\"\nlucid_kernel: 1\nblur_kernel: 2\n";
|
||||
String actual = readFile(filename);
|
||||
actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
|
||||
assertEquals(truth, actual);
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,69 @@
|
||||
package org.opencv.test.features;
|
||||
|
||||
import org.opencv.test.OpenCVTestCase;
|
||||
import org.opencv.test.OpenCVTestRunner;
|
||||
import org.opencv.xfeatures2d.MSDDetector;
|
||||
|
||||
public class MSDFeatureDetectorTest extends OpenCVTestCase {
|
||||
|
||||
MSDDetector detector;
|
||||
|
||||
@Override
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
detector = MSDDetector.create(); // default (3,5,5,0,250.4,',1.25,-1,false)
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(detector);
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPointMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testReadYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(filename, "%YAML:1.0\n---\nname: \"Feature2D.MSD\"\npatch_radius: 4\nsearch_area_radius: 6\nnms_radius: 7\nnms_scale_radius: 1\nth_saliency: 251.\nkNN: 2\nscale_factor: 1.26\nn_scales: 3\ncompute_orientation: 1\n");
|
||||
|
||||
detector.read(filename);
|
||||
|
||||
assertEquals(4, detector.getPatchRadius());
|
||||
assertEquals(6, detector.getSearchAreaRadius());
|
||||
assertEquals(7, detector.getNmsRadius());
|
||||
assertEquals(1, detector.getNmsScaleRadius());
|
||||
assertEquals(251.0f, detector.getThSaliency());
|
||||
assertEquals(2, detector.getKNN());
|
||||
assertEquals(1.26f, detector.getScaleFactor());
|
||||
assertEquals(3, detector.getNScales());
|
||||
assertEquals(true, detector.getComputeOrientation());
|
||||
}
|
||||
|
||||
public void testWriteYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
detector.write(filename);
|
||||
|
||||
String truth = "%YAML 1.2\n---\nname: \"Feature2D.MSD\"\npatch_radius: 3\nsearch_area_radius: 5\nnms_radius: 5\nnms_scale_radius: 0\nth_saliency: 250.\nkNN: 4\nscale_factor: 1.25\nn_scales: -1\ncompute_orientation: false\n";
|
||||
String actual = readFile(filename);
|
||||
actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
|
||||
assertEquals(truth, actual);
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,128 @@
|
||||
package org.opencv.test.features;
|
||||
|
||||
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.xfeatures2d.StarDetector;
|
||||
|
||||
public class STARFeatureDetectorTest extends OpenCVTestCase {
|
||||
|
||||
StarDetector 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() {
|
||||
Scalar color = new Scalar(0);
|
||||
int center = matSize / 2;
|
||||
int radius = 6;
|
||||
int offset = 40;
|
||||
|
||||
Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
|
||||
Imgproc.circle(img, new Point(center - offset, center), radius, color, -1);
|
||||
Imgproc.circle(img, new Point(center + offset, center), radius, color, -1);
|
||||
Imgproc.circle(img, new Point(center, center - offset), radius, color, -1);
|
||||
Imgproc.circle(img, new Point(center, center + offset), radius, color, -1);
|
||||
Imgproc.circle(img, new Point(center, center), radius, color, -1);
|
||||
return img;
|
||||
}
|
||||
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
detector = createClassInstance(XFEATURES2D+"StarDetector", DEFAULT_FACTORY, null, null);
|
||||
matSize = 200;
|
||||
truth = new KeyPoint[] {
|
||||
new KeyPoint( 95, 80, 22, -1, 31.5957f, 0, -1),
|
||||
new KeyPoint(105, 80, 22, -1, 31.5957f, 0, -1),
|
||||
new KeyPoint( 80, 95, 22, -1, 31.5957f, 0, -1),
|
||||
new KeyPoint(120, 95, 22, -1, 31.5957f, 0, -1),
|
||||
new KeyPoint(100, 100, 8, -1, 30.f, 0, -1),
|
||||
new KeyPoint( 80, 105, 22, -1, 31.5957f, 0, -1),
|
||||
new KeyPoint(120, 105, 22, -1, 31.5957f, 0, -1),
|
||||
new KeyPoint( 95, 120, 22, -1, 31.5957f, 0, -1),
|
||||
new KeyPoint(105, 120, 22, -1, 31.5957f, 0, -1)
|
||||
};
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(detector);
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPoint() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPoint() {
|
||||
Mat img = getTestImg();
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint();
|
||||
|
||||
detector.detect(img, keypoints);
|
||||
|
||||
assertListKeyPointEquals(Arrays.asList(truth), keypoints.toList(), EPS);
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPointMat() {
|
||||
Mat img = getTestImg();
|
||||
Mat mask = getMaskImg();
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint();
|
||||
|
||||
detector.detect(img, keypoints, mask);
|
||||
|
||||
assertListKeyPointEquals(Arrays.asList(truth[0], truth[2], truth[5], truth[7]), keypoints.toList(), EPS);
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
// assertFalse(detector.empty());
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testReadYml() {
|
||||
Mat img = getTestImg();
|
||||
|
||||
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
|
||||
detector.detect(img, keypoints1);
|
||||
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(filename, "%YAML:1.0\n---\nname: \"Feature2D.STAR\"\nmaxSize: 45\nresponseThreshold: 150\nlineThresholdProjected: 10\nlineThresholdBinarized: 8\nsuppressNonmaxSize: 5\n");
|
||||
detector.read(filename);
|
||||
|
||||
assertEquals(45, detector.getMaxSize());
|
||||
assertEquals(150, detector.getResponseThreshold());
|
||||
assertEquals(10, detector.getLineThresholdProjected());
|
||||
assertEquals(8, detector.getLineThresholdBinarized());
|
||||
assertEquals(5, detector.getSuppressNonmaxSize());
|
||||
|
||||
MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
|
||||
detector.detect(img, keypoints2);
|
||||
|
||||
assertTrue(keypoints2.total() <= keypoints1.total());
|
||||
}
|
||||
|
||||
public void testWriteYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
detector.write(filename);
|
||||
|
||||
String truth = "%YAML 1.2\n---\nname: \"Feature2D.STAR\"\nmaxSize: 45\nresponseThreshold: 30\nlineThresholdProjected: 10\nlineThresholdBinarized: 8\nsuppressNonmaxSize: 5\n";
|
||||
assertEquals(truth, readFile(filename));
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,115 @@
|
||||
package org.opencv.test.features;
|
||||
|
||||
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.xfeatures2d.SURF;
|
||||
|
||||
public class SURFDescriptorExtractorTest extends OpenCVTestCase {
|
||||
|
||||
SURF extractor;
|
||||
int matSize;
|
||||
|
||||
private Mat getTestImg() {
|
||||
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;
|
||||
}
|
||||
|
||||
@Override
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
|
||||
Class[] cParams = {double.class, int.class, int.class, boolean.class, boolean.class};
|
||||
Object[] oValues = {100, 2, 4, true, false};
|
||||
extractor = createClassInstance(XFEATURES2D+"SURF", DEFAULT_FACTORY, cParams, oValues);
|
||||
|
||||
matSize = 100;
|
||||
}
|
||||
|
||||
public void testComputeListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testComputeMatListOfKeyPointMat() {
|
||||
KeyPoint point = new KeyPoint(55.775577545166016f, 44.224422454833984f, 16, 9.754629f, 8617.863f, 1, -1);
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint(point);
|
||||
Mat img = getTestImg();
|
||||
Mat descriptors = new Mat();
|
||||
|
||||
extractor.compute(img, keypoints, descriptors);
|
||||
|
||||
Mat truth = new Mat(1, 128, CvType.CV_32FC1) {
|
||||
{
|
||||
put(0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0.058821894, 0.058821894, -0.045962855, 0.046261817, 0.0085156476,
|
||||
0.0085754395, -0.0064509804, 0.0064509804, 0.00044069235, 0.00044069235, 0, 0, 0.00025723741,
|
||||
0.00025723741, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00025723741, 0.00025723741, -0.00044069235,
|
||||
0.00044069235, 0, 0, 0.36278215, 0.36278215, -0.24688604, 0.26173124, 0.052068226, 0.052662034,
|
||||
-0.032815345, 0.032815345, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.0064523756,
|
||||
0.0064523756, 0.0082002236, 0.0088908644, -0.059001274, 0.059001274, 0.045789491, 0.04648013,
|
||||
0.11961588, 0.22789426, -0.01322381, 0.18291828, -0.14042182, 0.23973691, 0.073782086, 0.23769434,
|
||||
-0.027880307, 0.027880307, 0.049587864, 0.049587864, -0.33991757, 0.33991757, 0.21437603, 0.21437603,
|
||||
-0.0020763327, 0.0020763327, 0.006245892, 0.006245892, -0.04067041, 0.04067041, 0.019361559,
|
||||
0.019361559, 0, 0, -0.0035977389, 0.0035977389, 0, 0, -0.00099993451, 0.00099993451, 0.040670406,
|
||||
0.040670406, -0.019361559, 0.019361559, 0.006245892, 0.006245892, -0.0020763327, 0.0020763327,
|
||||
-0.00034532088, 0.00034532088, 0, 0, 0, 0, 0.00034532088, 0.00034532088, -0.00099993451,
|
||||
0.00099993451, 0, 0, 0, 0, 0.0035977389, 0.0035977389
|
||||
);
|
||||
}
|
||||
};
|
||||
|
||||
assertMatEqual(truth, descriptors, EPS);
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(extractor);
|
||||
}
|
||||
|
||||
public void testDescriptorSize() {
|
||||
assertEquals(128, extractor.descriptorSize());
|
||||
}
|
||||
|
||||
public void testDescriptorType() {
|
||||
assertEquals(CvType.CV_32F, extractor.descriptorType());
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
// assertFalse(extractor.empty());
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testReadYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
writeFile(filename, "%YAML:1.0\n---\nname: \"Feature2D.SURF\"\nhessianThreshold: 100.\nextended: 1\nupright: 0\nnOctaves: 2\nnOctaveLayers: 4\n");
|
||||
|
||||
extractor.read(filename);
|
||||
|
||||
assertEquals(128, extractor.descriptorSize());
|
||||
assertEquals(true, extractor.getExtended());
|
||||
assertEquals(false, extractor.getUpright());
|
||||
assertEquals(2, extractor.getNOctaves());
|
||||
assertEquals(4, extractor.getNOctaveLayers());
|
||||
assertEquals(100., extractor.getHessianThreshold());
|
||||
}
|
||||
|
||||
public void testWriteYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
extractor.write(filename);
|
||||
|
||||
String truth = "%YAML 1.2\n---\nname: \"Feature2D.SURF\"\nhessianThreshold: 100.\nextended: true\nupright: false\nnOctaves: 2\nnOctaveLayers: 4\n";
|
||||
String actual = readFile(filename);
|
||||
actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
|
||||
assertEquals(truth, actual);
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,176 @@
|
||||
package org.opencv.test.features;
|
||||
|
||||
import java.util.ArrayList;
|
||||
import java.util.Arrays;
|
||||
import java.util.Collections;
|
||||
import java.util.Comparator;
|
||||
import java.util.List;
|
||||
|
||||
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.xfeatures2d.SURF;
|
||||
|
||||
public class SURFFeatureDetectorTest extends OpenCVTestCase {
|
||||
|
||||
SURF 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() {
|
||||
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;
|
||||
}
|
||||
|
||||
private void order(List<KeyPoint> points) {
|
||||
Collections.sort(points, new Comparator<KeyPoint>() {
|
||||
public int compare(KeyPoint p1, KeyPoint p2) {
|
||||
if (p1.angle < p2.angle)
|
||||
return -1;
|
||||
if (p1.angle > p2.angle)
|
||||
return 1;
|
||||
return 0;
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
@Override
|
||||
protected void setUp() throws Exception {
|
||||
super.setUp();
|
||||
detector = createClassInstance(XFEATURES2D + "SURF", DEFAULT_FACTORY, null, null);
|
||||
matSize = 100;
|
||||
truth = new KeyPoint[] {
|
||||
new KeyPoint(55.775578f, 55.775578f, 16, 80.245735f, 8617.8633f, 0, -1),
|
||||
new KeyPoint(44.224422f, 55.775578f, 16, 170.24574f, 8617.8633f, 0, -1),
|
||||
new KeyPoint(44.224422f, 44.224422f, 16, 260.24573f, 8617.8633f, 0, -1),
|
||||
new KeyPoint(55.775578f, 44.224422f, 16, 350.24573f, 8617.8633f, 0, -1)
|
||||
};
|
||||
}
|
||||
|
||||
public void testCreate() {
|
||||
assertNotNull(detector);
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPoint() {
|
||||
|
||||
setProperty(detector, "hessianThreshold", "double", 8000);
|
||||
setProperty(detector, "nOctaves", "int", 3);
|
||||
setProperty(detector, "nOctaveLayers", "int", 4);
|
||||
setProperty(detector, "upright", "boolean", false);
|
||||
setProperty(detector, "extended", "boolean", true);
|
||||
|
||||
List<MatOfKeyPoint> keypoints = new ArrayList<MatOfKeyPoint>();
|
||||
Mat cross = getTestImg();
|
||||
List<Mat> crosses = new ArrayList<Mat>(3);
|
||||
crosses.add(cross);
|
||||
crosses.add(cross);
|
||||
crosses.add(cross);
|
||||
|
||||
detector.detect(crosses, keypoints);
|
||||
|
||||
assertEquals(3, keypoints.size());
|
||||
|
||||
for (MatOfKeyPoint mkp : keypoints) {
|
||||
List<KeyPoint> lkp = mkp.toList();
|
||||
order(lkp);
|
||||
assertListKeyPointEquals(Arrays.asList(truth), lkp, EPS);
|
||||
}
|
||||
}
|
||||
|
||||
public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPoint() {
|
||||
|
||||
setProperty(detector, "hessianThreshold", "double", 8000);
|
||||
setProperty(detector, "nOctaves", "int", 3);
|
||||
setProperty(detector, "nOctaveLayers", "int", 4);
|
||||
setProperty(detector, "upright", "boolean", false);
|
||||
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint();
|
||||
Mat cross = getTestImg();
|
||||
|
||||
detector.detect(cross, keypoints);
|
||||
|
||||
List<KeyPoint> lkp = keypoints.toList();
|
||||
order(lkp);
|
||||
assertListKeyPointEquals(Arrays.asList(truth), lkp, EPS);
|
||||
}
|
||||
|
||||
public void testDetectMatListOfKeyPointMat() {
|
||||
|
||||
setProperty(detector, "hessianThreshold", "double", 8000);
|
||||
setProperty(detector, "nOctaves", "int", 3);
|
||||
setProperty(detector, "nOctaveLayers", "int", 4);
|
||||
setProperty(detector, "upright", "boolean", false);
|
||||
setProperty(detector, "extended", "boolean", true);
|
||||
|
||||
Mat img = getTestImg();
|
||||
Mat mask = getMaskImg();
|
||||
MatOfKeyPoint keypoints = new MatOfKeyPoint();
|
||||
|
||||
detector.detect(img, keypoints, mask);
|
||||
|
||||
List<KeyPoint> lkp = keypoints.toList();
|
||||
order(lkp);
|
||||
assertListKeyPointEquals(Arrays.asList(truth[1], truth[2]), lkp, EPS);
|
||||
}
|
||||
|
||||
public void testEmpty() {
|
||||
// assertFalse(detector.empty());
|
||||
fail("Not yet implemented");
|
||||
}
|
||||
|
||||
public void testReadYml() {
|
||||
Mat cross = getTestImg();
|
||||
|
||||
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
|
||||
detector.detect(cross, keypoints1);
|
||||
|
||||
String filename = OpenCVTestRunner.getTempFileName("xml");
|
||||
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");
|
||||
|
||||
detector.read(filename);
|
||||
|
||||
assertEquals(128, detector.descriptorSize());
|
||||
assertEquals(8000., detector.getHessianThreshold());
|
||||
assertEquals(true, detector.getExtended());
|
||||
assertEquals(false, detector.getUpright());
|
||||
assertEquals(3, detector.getNOctaves());
|
||||
assertEquals(4, detector.getNOctaveLayers());
|
||||
|
||||
MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
|
||||
detector.detect(cross, keypoints2);
|
||||
|
||||
assertTrue(keypoints2.total() <= keypoints1.total());
|
||||
}
|
||||
|
||||
public void testWriteYml() {
|
||||
String filename = OpenCVTestRunner.getTempFileName("yml");
|
||||
|
||||
detector.write(filename);
|
||||
|
||||
String truth = "%YAML 1.2\n---\nname: \"Feature2D.SURF\"\nhessianThreshold: 100.\nextended: false\nupright: false\nnOctaves: 4\nnOctaveLayers: 3\n";
|
||||
String actual = readFile(filename);
|
||||
actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
|
||||
assertEquals(truth, actual);
|
||||
}
|
||||
|
||||
}
|
||||
@@ -0,0 +1,9 @@
|
||||
{
|
||||
"whitelist":
|
||||
{
|
||||
"BRISK": ["create", "getDefaultName"],
|
||||
"AgastFeatureDetector": ["create", "setThreshold", "getThreshold", "setNonmaxSuppression", "getNonmaxSuppression", "setType", "getType", "getDefaultName"],
|
||||
"KAZE": ["create", "setExtended", "getExtended", "setUpright", "getUpright", "setThreshold", "getThreshold", "setNOctaves", "getNOctaves", "setNOctaveLayers", "getNOctaveLayers", "setDiffusivity", "getDiffusivity", "getDefaultName"],
|
||||
"AKAZE": ["create", "setDescriptorType", "getDescriptorType", "setDescriptorSize", "getDescriptorSize", "setDescriptorChannels", "getDescriptorChannels", "setThreshold", "getThreshold", "setNOctaves", "getNOctaves", "setNOctaveLayers", "getNOctaveLayers", "setDiffusivity", "getDiffusivity", "getDefaultName"]
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,20 @@
|
||||
{
|
||||
"class_ignore_list" : [
|
||||
"SURF_CUDA"
|
||||
],
|
||||
"AdditionalImports" : {
|
||||
"*" : [ "\"xfeatures2d.hpp\"" ]
|
||||
},
|
||||
"func_arg_fix" : {
|
||||
"DAISY" : {
|
||||
"create" : { "norm" : { "ctype" : "NormalizationType",
|
||||
"defval" : "cv::xfeatures2d::DAISY::NRM_NONE"} }
|
||||
},
|
||||
"PCTSignatures" : {
|
||||
"(PCTSignatures*)create:(NSArray<Point2f*>*)initSamplingPoints initSeedCount:(int)initSeedCount" : { "create" : {"name" : "create2"} }
|
||||
}
|
||||
},
|
||||
"enum_fix" : {
|
||||
"AgastFeatureDetector" : { "DetectorType": "AgastDetectorType" }
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,2 @@
|
||||
// Compatibility
|
||||
#include "shadow_sift.hpp"
|
||||
@@ -0,0 +1,12 @@
|
||||
#ifdef HAVE_OPENCV_XFEATURES2D
|
||||
|
||||
#include "opencv2/xfeatures2d.hpp"
|
||||
using namespace cv::xfeatures2d;
|
||||
|
||||
typedef DAISY::NormalizationType DAISY_NormalizationType;
|
||||
|
||||
typedef AKAZE::DescriptorType AKAZE_DescriptorType;
|
||||
typedef AgastFeatureDetector::DetectorType AgastFeatureDetector_DetectorType;
|
||||
typedef KAZE::DiffusivityType KAZE_DiffusivityType;
|
||||
|
||||
#endif
|
||||
@@ -0,0 +1,20 @@
|
||||
// Compatibility
|
||||
// SIFT is moved to the main repository
|
||||
|
||||
namespace cv {
|
||||
namespace xfeatures2d {
|
||||
|
||||
/** Use cv.SIFT_create() instead */
|
||||
CV_WRAP static inline
|
||||
Ptr<cv::SIFT> SIFT_create(int nfeatures = 0, int nOctaveLayers = 3,
|
||||
double contrastThreshold = 0.04, double edgeThreshold = 10,
|
||||
double sigma = 1.6)
|
||||
{
|
||||
CV_LOG_ONCE_WARNING(NULL, "DEPRECATED: cv.xfeatures2d.SIFT_create() is deprecated due SIFT tranfer to the main repository. "
|
||||
"https://github.com/opencv/opencv/issues/16736"
|
||||
);
|
||||
|
||||
return SIFT::create(nfeatures, nOctaveLayers, contrastThreshold, edgeThreshold, sigma);
|
||||
}
|
||||
|
||||
}} // namespace
|
||||
@@ -0,0 +1,48 @@
|
||||
#!/usr/bin/env python
|
||||
import os
|
||||
import cv2 as cv
|
||||
import numpy as np
|
||||
|
||||
from tests_common import NewOpenCVTests, unittest
|
||||
|
||||
class xfeatures2d_test(NewOpenCVTests):
|
||||
def setUp(self):
|
||||
super(xfeatures2d_test, self).setUp()
|
||||
if not cv.cuda.getCudaEnabledDeviceCount():
|
||||
self.skipTest("No CUDA-capable device is detected")
|
||||
|
||||
@unittest.skipIf('OPENCV_TEST_DATA_PATH' not in os.environ,
|
||||
"OPENCV_TEST_DATA_PATH is not defined")
|
||||
def test_surf(self):
|
||||
img_path = os.environ['OPENCV_TEST_DATA_PATH'] + "/gpu/features2d/aloe.png"
|
||||
hessianThreshold = 100
|
||||
nOctaves = 3
|
||||
nOctaveLayers = 2
|
||||
extended = False
|
||||
keypointsRatio = 0.05
|
||||
upright = False
|
||||
|
||||
npMat = cv.cvtColor(cv.imread(img_path),cv.COLOR_BGR2GRAY)
|
||||
cuMat = cv.cuda_GpuMat(npMat)
|
||||
|
||||
try:
|
||||
cuSurf = cv.cuda_SURF_CUDA.create(hessianThreshold,nOctaves,nOctaveLayers,extended,keypointsRatio,upright)
|
||||
surf = cv.xfeatures2d_SURF.create(hessianThreshold,nOctaves,nOctaveLayers,extended,upright)
|
||||
except cv.error as e:
|
||||
self.assertEqual(e.code, cv.Error.StsNotImplemented)
|
||||
self.skipTest("OPENCV_ENABLE_NONFREE is not enabled in this build.")
|
||||
|
||||
cuKeypoints = cuSurf.detect(cuMat,cv.cuda_GpuMat())
|
||||
keypointsHost = cuSurf.downloadKeypoints(cuKeypoints)
|
||||
keypoints = surf.detect(npMat)
|
||||
self.assertTrue(len(keypointsHost) == len(keypoints))
|
||||
|
||||
cuKeypoints, cuDescriptors = cuSurf.detectWithDescriptors(cuMat,cv.cuda_GpuMat(),cuKeypoints,useProvidedKeypoints=True)
|
||||
keypointsHost = cuSurf.downloadKeypoints(cuKeypoints)
|
||||
descriptorsHost = cuDescriptors.download()
|
||||
keypoints, descriptors = surf.compute(npMat,keypoints)
|
||||
|
||||
self.assertTrue(len(keypointsHost) == len(keypoints) and descriptorsHost.shape == descriptors.shape)
|
||||
|
||||
if __name__ == '__main__':
|
||||
NewOpenCVTests.bootstrap()
|
||||
@@ -0,0 +1,36 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Python 2/3 compatibility
|
||||
from __future__ import print_function
|
||||
|
||||
import os
|
||||
import numpy as np
|
||||
import cv2 as cv
|
||||
|
||||
from tests_common import NewOpenCVTests
|
||||
|
||||
class MSDDetector_test(NewOpenCVTests):
|
||||
|
||||
def test_create(self):
|
||||
|
||||
msd = cv.xfeatures2d.MSDDetector_create()
|
||||
self.assertFalse(msd is None)
|
||||
|
||||
img1 = np.zeros((100, 100, 3), dtype=np.uint8)
|
||||
kp1_ = msd.detect(img1, None)
|
||||
|
||||
class matchLOGOS_test(NewOpenCVTests):
|
||||
|
||||
def test_basic(self):
|
||||
|
||||
frame = self.get_sample('python/images/baboon.png', cv.IMREAD_COLOR)
|
||||
detector = cv.xfeatures2d.AKAZE_create(threshold = 0.003)
|
||||
|
||||
keypoints1, descrs1 = detector.detectAndCompute(frame, None)
|
||||
keypoints2, descrs2 = detector.detectAndCompute(frame, None)
|
||||
matches1to2 = cv.xfeatures2d.matchLOGOS(keypoints1, keypoints2, range(len(keypoints1)), range(len(keypoints2)))
|
||||
self.assertFalse(matches1to2 is None)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
NewOpenCVTests.bootstrap()
|
||||
@@ -0,0 +1,24 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# Python 2/3 compatibility
|
||||
from __future__ import print_function
|
||||
|
||||
import os
|
||||
import numpy as np
|
||||
import cv2 as cv
|
||||
|
||||
from tests_common import NewOpenCVTests
|
||||
|
||||
class sift_compatibility_test(NewOpenCVTests):
|
||||
|
||||
def test_create(self):
|
||||
|
||||
sift = cv.xfeatures2d.SIFT_create()
|
||||
self.assertFalse(sift is None)
|
||||
|
||||
img1 = np.zeros((100, 100, 3), dtype=np.uint8)
|
||||
kp1_, des1_ = sift.detectAndCompute(img1, None)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
NewOpenCVTests.bootstrap()
|
||||
Reference in New Issue
Block a user