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JavaAPI: fixed typo; some constants are moved to private section; added tests for BruteForceMatcher-Hamming
This commit is contained in:
Andrey Kamaev
parent
cc6b7edf95
commit
6944c0dba4
@ -0,0 +1,203 @@
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package org.opencv.test.features2d;
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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.Point;
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import org.opencv.core.Scalar;
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import org.opencv.features2d.DMatch;
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import org.opencv.features2d.DescriptorExtractor;
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import org.opencv.features2d.DescriptorMatcher;
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import org.opencv.features2d.FeatureDetector;
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import org.opencv.features2d.KeyPoint;
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import org.opencv.test.OpenCVTestCase;
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import org.opencv.test.OpenCVTestRunner;
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import java.util.ArrayList;
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import java.util.Arrays;
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import java.util.List;
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public class BruteForceHammingDescriptorMatcherTest extends OpenCVTestCase {
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DescriptorMatcher matcher;
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int matSize;
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DMatch[] truth;
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protected void setUp() throws Exception {
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matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);
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matSize = 100;
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truth = new DMatch[] {
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new DMatch (0, 0, 0, 51),
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new DMatch (1, 2, 0, 42),
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new DMatch (2, 1, 0, 40),
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new DMatch (3, 3, 0, 53) };
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super.setUp();
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}
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private Mat getTrainImg() {
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Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
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Core.line(img, new Point(40, 40), new Point(matSize - 40, matSize - 40), new Scalar(0), 8);
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return img;
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}
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private Mat getQueryImg() {
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Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
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Core.line(img, new Point(40, matSize - 40), new Point(matSize - 50, 50), new Scalar(0), 8);
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return img;
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}
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private Mat getTestDescriptors(Mat img) {
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List<KeyPoint> keypoints = new ArrayList<KeyPoint>();
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Mat descriptors = new Mat();
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FeatureDetector detector = FeatureDetector.create(FeatureDetector.FAST);
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DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.BRIEF);
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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 getQueryDescriptors() {
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return getTestDescriptors(getQueryImg());
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}
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private Mat getTrainDescriptors() {
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return getTestDescriptors(getTrainImg());
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}
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private Mat getMaskImg() {
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return new Mat(4, 4, CvType.CV_8U, new Scalar(0)) {
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{
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put(0, 0, 1, 1, 1, 1, 1, 1, 1, 1);
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}
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};
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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 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 testClone() {
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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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DescriptorMatcher cloned = matcher.clone();
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assertNotNull(cloned);
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List<Mat> descriptors = cloned.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 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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assertTrue(matcher.isMaskSupported());
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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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List<DMatch> matches = new ArrayList<DMatch>();
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matcher.match(query, train, matches, mask);
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assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches, 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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List<DMatch> matches = new ArrayList<DMatch>();
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matcher.match(query, train, matches);
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assertListDMatchEquals(Arrays.asList(truth), matches, 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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List<DMatch> matches = new ArrayList<DMatch>();
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matcher.add(Arrays.asList(train));
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matcher.match(query, matches, Arrays.asList(mask));
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assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches, EPS);
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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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List<DMatch> matches = new ArrayList<DMatch>();
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matcher.add(Arrays.asList(train));
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matcher.match(query, matches);
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assertListDMatchEquals(Arrays.asList(truth), matches, EPS);
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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");
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matcher.read(filename);
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assertTrue(true);// BruteforceMatcher has no settings
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}
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public void testTrain() {
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matcher.train();// BruteforceMatcher does not need to train
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}
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public void testWrite() {
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String filename = OpenCVTestRunner.getTempFileName("yml");
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matcher.write(filename);
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String truth = "%YAML:1.0\n";
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assertEquals(truth, readFile(filename));
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}
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}
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@ -0,0 +1,207 @@
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package org.opencv.test.features2d;
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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.Point;
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import org.opencv.core.Scalar;
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import org.opencv.features2d.DMatch;
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import org.opencv.features2d.DescriptorExtractor;
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import org.opencv.features2d.DescriptorMatcher;
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import org.opencv.features2d.FeatureDetector;
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import org.opencv.features2d.KeyPoint;
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import org.opencv.test.OpenCVTestCase;
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import org.opencv.test.OpenCVTestRunner;
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import java.util.ArrayList;
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import java.util.Arrays;
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import java.util.List;
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public class BruteForceHammingLUTDescriptorMatcherTest extends OpenCVTestCase {
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DescriptorMatcher matcher;
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int matSize;
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DMatch[] truth;
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protected void setUp() throws Exception {
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matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMINGLUT);
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matSize = 100;
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truth = new DMatch[] {
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new DMatch (0, 0, 0, 51),
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new DMatch (1, 2, 0, 42),
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new DMatch (2, 1, 0, 40),
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new DMatch (3, 3, 0, 53) };
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super.setUp();
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}
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private Mat getTrainImg() {
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Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
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Core.line(img, new Point(40, 40), new Point(matSize - 40, matSize - 40), new Scalar(0), 8);
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return img;
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}
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private Mat getQueryImg() {
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Mat img = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
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Core.line(img, new Point(40, matSize - 40), new Point(matSize - 50, 50), new Scalar(0), 8);
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return img;
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}
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private Mat getTestDescriptors(Mat img) {
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List<KeyPoint> keypoints = new ArrayList<KeyPoint>();
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Mat descriptors = new Mat();
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FeatureDetector detector = FeatureDetector.create(FeatureDetector.FAST);
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DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.BRIEF);
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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 getQueryDescriptors() {
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return getTestDescriptors(getQueryImg());
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}
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private Mat getTrainDescriptors() {
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return getTestDescriptors(getTrainImg());
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}
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private Mat getMaskImg() {
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return new Mat(4, 4, CvType.CV_8U, new Scalar(0)) {
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{
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put(0, 0, 1, 1, 1, 1, 1, 1, 1, 1);
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}
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};
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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 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 testClone() {
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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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DescriptorMatcher cloned = matcher.clone();
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assertNotNull(cloned);
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List<Mat> descriptors = cloned.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 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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assertTrue(matcher.isMaskSupported());
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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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List<DMatch> matches = new ArrayList<DMatch>();
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matcher.match(query, train, matches, mask);
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assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches, 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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List<DMatch> matches = new ArrayList<DMatch>();
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matcher.match(query, train, matches);
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OpenCVTestRunner.Log("matches found: " + matches.size());
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for (DMatch m : matches)
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OpenCVTestRunner.Log(m.toString());
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assertListDMatchEquals(Arrays.asList(truth), matches, 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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List<DMatch> matches = new ArrayList<DMatch>();
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matcher.add(Arrays.asList(train));
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matcher.match(query, matches, Arrays.asList(mask));
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assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches, EPS);
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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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List<DMatch> matches = new ArrayList<DMatch>();
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matcher.add(Arrays.asList(train));
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matcher.match(query, matches);
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assertListDMatchEquals(Arrays.asList(truth), matches, EPS);
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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");
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matcher.read(filename);
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assertTrue(true);// BruteforceMatcher has no settings
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}
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public void testTrain() {
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matcher.train();// BruteforceMatcher does not need to train
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}
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public void testWrite() {
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String filename = OpenCVTestRunner.getTempFileName("yml");
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matcher.write(filename);
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String truth = "%YAML:1.0\n";
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assertEquals(truth, readFile(filename));
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}
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}
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@ -100,6 +100,10 @@ const_private_list = (
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"CV_INPAINT_.+",
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"CV_RETR_.+",
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"CV_CHAIN_APPROX_.+",
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"OPPONENTEXTRACTOR",
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"GRIDRETECTOR",
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"PYRAMIDDETECTOR",
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"DYNAMICDETECTOR",
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)
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# { Module : { public : [[name, val],...], private : [[]...] } }
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@ -245,10 +245,10 @@ public:
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OPPONENTEXTRACTOR = 1000,
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OPPENENT_SIFT = OPPONENTEXTRACTOR + SIFT,
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OPPENENT_SURF = OPPONENTEXTRACTOR + SURF,
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OPPENENT_ORB = OPPONENTEXTRACTOR + ORB,
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OPPENENT_BRIEF = OPPONENTEXTRACTOR + BRIEF
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OPPONENT_SIFT = OPPONENTEXTRACTOR + SIFT,
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OPPONENT_SURF = OPPONENTEXTRACTOR + SURF,
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OPPONENT_ORB = OPPONENTEXTRACTOR + ORB,
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OPPONENT_BRIEF = OPPONENTEXTRACTOR + BRIEF
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};
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//supported SIFT, SURF, ORB, BRIEF, Opponent(XXXX)
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