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Open Source Computer Vision Library
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281 lines
8.3 KiB
281 lines
8.3 KiB
package org.opencv.test.features2d; |
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import java.util.Arrays; |
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import java.util.List; |
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import org.opencv.core.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.MatOfDMatch; |
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import org.opencv.core.MatOfKeyPoint; |
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import org.opencv.core.Point; |
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import org.opencv.core.Scalar; |
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import org.opencv.core.DMatch; |
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import org.opencv.features2d.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.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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public class BruteForceSL2DescriptorMatcherTest extends OpenCVTestCase { |
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DescriptorMatcher matcher; |
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int matSize; |
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DMatch[] truth; |
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private Mat getMaskImg() { |
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return new Mat(5, 2, CvType.CV_8U, new Scalar(0)) { |
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{ |
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put(0, 0, 1, 1, 1, 1); |
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} |
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}; |
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} |
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/* |
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private float sqr(float val){ |
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return val * val; |
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} |
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*/ |
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private Mat getQueryDescriptors() { |
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Mat img = getQueryImg(); |
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MatOfKeyPoint keypoints = new MatOfKeyPoint(); |
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Mat descriptors = new Mat(); |
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FeatureDetector detector = FeatureDetector.create(FeatureDetector.SURF); |
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DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.SURF); |
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String filename = OpenCVTestRunner.getTempFileName("yml"); |
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writeFile(filename, "%YAML:1.0\nhessianThreshold: 8000.\noctaves: 3\noctaveLayers: 4\nupright: 0\n"); |
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detector.read(filename); |
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detector.detect(img, keypoints); |
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extractor.compute(img, keypoints, descriptors); |
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return descriptors; |
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} |
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private Mat getQueryImg() { |
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Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255)); |
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Imgproc.line(cross, new Point(30, matSize / 2), new Point(matSize - 31, matSize / 2), new Scalar(100), 3); |
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Imgproc.line(cross, new Point(matSize / 2, 30), new Point(matSize / 2, matSize - 31), new Scalar(100), 3); |
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return cross; |
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} |
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private Mat getTrainDescriptors() { |
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Mat img = getTrainImg(); |
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MatOfKeyPoint keypoints = new MatOfKeyPoint(new KeyPoint(50, 50, 16, 0, 20000, 1, -1), new KeyPoint(42, 42, 16, 160, 10000, 1, -1)); |
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Mat descriptors = new Mat(); |
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DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.SURF); |
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extractor.compute(img, keypoints, descriptors); |
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return descriptors; |
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} |
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private Mat getTrainImg() { |
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Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255)); |
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Imgproc.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2); |
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Imgproc.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2); |
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return cross; |
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} |
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protected void setUp() throws Exception { |
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super.setUp(); |
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matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_SL2); |
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matSize = 100; |
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truth = new DMatch[] { |
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new DMatch(0, 0, 0, 0.3858146f), |
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new DMatch(1, 1, 0, 0.8421953f), |
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new DMatch(2, 1, 0, 0.0968556f), |
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new DMatch(3, 1, 0, 0.0855606f), |
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new DMatch(4, 1, 0, 0.8666080f) |
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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 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 testCloneBoolean() { |
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matcher.add(Arrays.asList(new Mat())); |
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DescriptorMatcher cloned = matcher.clone(true); |
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assertNotNull(cloned); |
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assertTrue(cloned.empty()); |
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} |
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public void testCreate() { |
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assertNotNull(matcher); |
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} |
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public void testEmpty() { |
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assertTrue(matcher.empty()); |
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} |
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public void testGetTrainDescriptors() { |
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Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123)); |
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Mat truth = train.clone(); |
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matcher.add(Arrays.asList(train)); |
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List<Mat> descriptors = matcher.getTrainDescriptors(); |
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assertEquals(1, descriptors.size()); |
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assertMatEqual(truth, descriptors.get(0)); |
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} |
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public void testIsMaskSupported() { |
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assertTrue(matcher.isMaskSupported()); |
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} |
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public void testKnnMatchMatListOfListOfDMatchInt() { |
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fail("Not yet implemented"); |
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} |
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public void testKnnMatchMatListOfListOfDMatchIntListOfMat() { |
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fail("Not yet implemented"); |
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} |
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public void testKnnMatchMatListOfListOfDMatchIntListOfMatBoolean() { |
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fail("Not yet implemented"); |
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} |
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public void testKnnMatchMatMatListOfListOfDMatchInt() { |
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fail("Not yet implemented"); |
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} |
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public void testKnnMatchMatMatListOfListOfDMatchIntMat() { |
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fail("Not yet implemented"); |
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} |
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public void testKnnMatchMatMatListOfListOfDMatchIntMatBoolean() { |
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fail("Not yet implemented"); |
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} |
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public void testMatchMatListOfDMatch() { |
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Mat train = getTrainDescriptors(); |
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Mat query = getQueryDescriptors(); |
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MatOfDMatch matches = new MatOfDMatch(); |
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matcher.add(Arrays.asList(train)); |
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matcher.match(query, matches); |
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OpenCVTestRunner.Log(matches); |
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OpenCVTestRunner.Log(matches); |
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OpenCVTestRunner.Log(matches); |
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assertArrayDMatchEquals(truth, matches.toArray(), EPS); |
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} |
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public void testMatchMatListOfDMatchListOfMat() { |
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Mat train = getTrainDescriptors(); |
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Mat query = getQueryDescriptors(); |
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Mat mask = getMaskImg(); |
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MatOfDMatch matches = new MatOfDMatch(); |
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matcher.add(Arrays.asList(train)); |
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matcher.match(query, matches, Arrays.asList(mask)); |
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assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS); |
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} |
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public void testMatchMatMatListOfDMatch() { |
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Mat train = getTrainDescriptors(); |
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Mat query = getQueryDescriptors(); |
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MatOfDMatch matches = new MatOfDMatch(); |
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matcher.match(query, train, matches); |
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assertArrayDMatchEquals(truth, matches.toArray(), EPS); |
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// OpenCVTestRunner.Log("matches found: " + matches.size()); |
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// for (DMatch m : matches) |
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// OpenCVTestRunner.Log(m.toString()); |
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} |
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public void testMatchMatMatListOfDMatchMat() { |
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Mat train = getTrainDescriptors(); |
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Mat query = getQueryDescriptors(); |
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Mat mask = getMaskImg(); |
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MatOfDMatch matches = new MatOfDMatch(); |
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matcher.match(query, train, matches, mask); |
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assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS); |
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} |
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public void testRadiusMatchMatListOfListOfDMatchFloat() { |
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fail("Not yet implemented"); |
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} |
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public void testRadiusMatchMatListOfListOfDMatchFloatListOfMat() { |
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fail("Not yet implemented"); |
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} |
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public void testRadiusMatchMatListOfListOfDMatchFloatListOfMatBoolean() { |
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fail("Not yet implemented"); |
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} |
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public void testRadiusMatchMatMatListOfListOfDMatchFloat() { |
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fail("Not yet implemented"); |
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} |
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public void testRadiusMatchMatMatListOfListOfDMatchFloatMat() { |
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fail("Not yet implemented"); |
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} |
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public void testRadiusMatchMatMatListOfListOfDMatchFloatMatBoolean() { |
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fail("Not yet implemented"); |
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} |
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public void testRead() { |
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String 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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