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Modified java wrapping mechanism

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This commit is contained in:
Maksim Shabunin
2015-02-19 16:17:19 +03:00
parent 5850a9b8c3
commit 457123027e
167 changed files with 376 additions and 1254 deletions
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modules/features2d/misc/java/test/BruteForceDescriptorMatcherTest.java Normal file
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package org.opencv.test.features2d;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.MatOfDMatch;
import org.opencv.core.MatOfKeyPoint;
import org.opencv.core.Point;
import org.opencv.core.Scalar;
import org.opencv.core.DMatch;
import org.opencv.features2d.DescriptorExtractor;
import org.opencv.features2d.DescriptorMatcher;
import org.opencv.features2d.FeatureDetector;
import org.opencv.core.KeyPoint;
import org.opencv.test.OpenCVTestCase;
import org.opencv.test.OpenCVTestRunner;
import org.opencv.imgproc.Imgproc;
public class BruteForceDescriptorMatcherTest extends OpenCVTestCase {
DescriptorMatcher matcher;
int matSize;
DMatch[] truth;
private Mat getMaskImg() {
return new Mat(5, 2, CvType.CV_8U, new Scalar(0)) {
{
put(0, 0, 1, 1, 1, 1);
}
};
}
private Mat getQueryDescriptors() {
Mat img = getQueryImg();
MatOfKeyPoint keypoints = new MatOfKeyPoint();
Mat descriptors = new Mat();
FeatureDetector detector = FeatureDetector.create(FeatureDetector.SURF);
DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.SURF);
String filename = OpenCVTestRunner.getTempFileName("yml");
writeFile(filename, "%YAML:1.0\nhessianThreshold: 8000.\noctaves: 3\noctaveLayers: 4\nupright: 0\n");
detector.read(filename);
detector.detect(img, keypoints);
extractor.compute(img, keypoints, descriptors);
return descriptors;
}
private Mat getQueryImg() {
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
Imgproc.line(cross, new Point(30, matSize / 2), new Point(matSize - 31, matSize / 2), new Scalar(100), 3);
Imgproc.line(cross, new Point(matSize / 2, 30), new Point(matSize / 2, matSize - 31), new Scalar(100), 3);
return cross;
}
private Mat getTrainDescriptors() {
Mat img = getTrainImg();
MatOfKeyPoint keypoints = new MatOfKeyPoint(new KeyPoint(50, 50, 16, 0, 20000, 1, -1), new KeyPoint(42, 42, 16, 160, 10000, 1, -1));
Mat descriptors = new Mat();
DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.SURF);
extractor.compute(img, keypoints, descriptors);
return descriptors;
}
private Mat getTrainImg() {
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;
}
protected void setUp() throws Exception {
super.setUp();
matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE);
matSize = 100;
truth = new DMatch[] {
new DMatch(0, 0, 0, 0.6211397f),
new DMatch(1, 1, 0, 0.9177120f),
new DMatch(2, 1, 0, 0.3112163f),
new DMatch(3, 1, 0, 0.2925074f),
new DMatch(4, 1, 0, 0.9309178f)
};
}
public void testAdd() {
matcher.add(Arrays.asList(new Mat()));
assertFalse(matcher.empty());
}
public void testClear() {
matcher.add(Arrays.asList(new Mat()));
matcher.clear();
assertTrue(matcher.empty());
}
public void testClone() {
Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
Mat truth = train.clone();
matcher.add(Arrays.asList(train));
DescriptorMatcher cloned = matcher.clone();
assertNotNull(cloned);
List<Mat> descriptors = cloned.getTrainDescriptors();
assertEquals(1, descriptors.size());
assertMatEqual(truth, descriptors.get(0));
}
public void testCloneBoolean() {
matcher.add(Arrays.asList(new Mat()));
DescriptorMatcher cloned = matcher.clone(true);
assertNotNull(cloned);
assertTrue(cloned.empty());
}
public void testCreate() {
assertNotNull(matcher);
}
public void testEmpty() {
assertTrue(matcher.empty());
}
public void testGetTrainDescriptors() {
Mat train = new Mat(1, 1, CvType.CV_8U, new Scalar(123));
Mat truth = train.clone();
matcher.add(Arrays.asList(train));
List<Mat> descriptors = matcher.getTrainDescriptors();
assertEquals(1, descriptors.size());
assertMatEqual(truth, descriptors.get(0));
}
public void testIsMaskSupported() {
assertTrue(matcher.isMaskSupported());
}
public void testKnnMatchMatListOfListOfDMatchInt() {
fail("Not yet implemented");
}
public void testKnnMatchMatListOfListOfDMatchIntListOfMat() {
fail("Not yet implemented");
}
public void testKnnMatchMatListOfListOfDMatchIntListOfMatBoolean() {
fail("Not yet implemented");
}
public void testKnnMatchMatMatListOfListOfDMatchInt() {
final int k = 3;
Mat train = getTrainDescriptors();
Mat query = getQueryDescriptors();
List<MatOfDMatch> matches = new ArrayList<MatOfDMatch>();
matcher.knnMatch(query, train, matches, k);
/*
Log.d("knnMatch", "train = " + train);
Log.d("knnMatch", "query = " + query);
matcher.add(train);
matcher.knnMatch(query, matches, k);
*/
assertEquals(query.rows(), matches.size());
for(int i = 0; i<matches.size(); i++)
{
MatOfDMatch vdm = matches.get(i);
//Log.d("knn", "vdm["+i+"]="+vdm.dump());
assertTrue(Math.min(k, train.rows()) >= vdm.total());
for(DMatch dm : vdm.toArray())
{
assertEquals(dm.queryIdx, i);
}
}
}
public void testKnnMatchMatMatListOfListOfDMatchIntMat() {
fail("Not yet implemented");
}
public void testKnnMatchMatMatListOfListOfDMatchIntMatBoolean() {
fail("Not yet implemented");
}
public void testMatchMatListOfDMatch() {
Mat train = getTrainDescriptors();
Mat query = getQueryDescriptors();
MatOfDMatch matches = new MatOfDMatch();
matcher.add(Arrays.asList(train));
matcher.match(query, matches);
assertArrayDMatchEquals(truth, matches.toArray(), EPS);
}
public void testMatchMatListOfDMatchListOfMat() {
Mat train = getTrainDescriptors();
Mat query = getQueryDescriptors();
Mat mask = getMaskImg();
MatOfDMatch matches = new MatOfDMatch();
matcher.add(Arrays.asList(train));
matcher.match(query, matches, Arrays.asList(mask));
assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
}
public void testMatchMatMatListOfDMatch() {
Mat train = getTrainDescriptors();
Mat query = getQueryDescriptors();
MatOfDMatch matches = new MatOfDMatch();
matcher.match(query, train, matches);
assertArrayDMatchEquals(truth, matches.toArray(), EPS);
// OpenCVTestRunner.Log("matches found: " + matches.size());
// for (DMatch m : matches)
// OpenCVTestRunner.Log(m.toString());
}
public void testMatchMatMatListOfDMatchMat() {
Mat train = getTrainDescriptors();
Mat query = getQueryDescriptors();
Mat mask = getMaskImg();
MatOfDMatch matches = new MatOfDMatch();
matcher.match(query, train, matches, mask);
assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches.toList(), EPS);
}
public void testRadiusMatchMatListOfListOfDMatchFloat() {
fail("Not yet implemented");
}
public void testRadiusMatchMatListOfListOfDMatchFloatListOfMat() {
fail("Not yet implemented");
}
public void testRadiusMatchMatListOfListOfDMatchFloatListOfMatBoolean() {
fail("Not yet implemented");
}
public void testRadiusMatchMatMatListOfListOfDMatchFloat() {
fail("Not yet implemented");
}
public void testRadiusMatchMatMatListOfListOfDMatchFloatMat() {
fail("Not yet implemented");
}
public void testRadiusMatchMatMatListOfListOfDMatchFloatMatBoolean() {
fail("Not yet implemented");
}
public void testRead() {
String filename = OpenCVTestRunner.getTempFileName("yml");
writeFile(filename, "%YAML:1.0\n");
matcher.read(filename);
assertTrue(true);// BruteforceMatcher has no settings
}
public void testTrain() {
matcher.train();// BruteforceMatcher does not need to train
}
public void testWrite() {
String filename = OpenCVTestRunner.getTempFileName("yml");
matcher.write(filename);
String truth = "%YAML:1.0\n";
assertEquals(truth, readFile(filename));
}
}