提交 2d820d23 编写于 作者: A Andrey Kamaev

Java API: added tests for BruteForceMatcher (L1)

上级 472820d8
......@@ -66,10 +66,6 @@ public class BruteForceDescriptorMatcherTest extends OpenCVTestCase {
detector.read(filename);
detector.detect(img, keypoints);
OpenCVTestRunner.Log("points found: " + keypoints.size());
for (KeyPoint kp : keypoints)
OpenCVTestRunner.Log(kp.toString());
extractor.compute(img, keypoints, descriptors);
return descriptors;
......
package org.opencv.test.features2d;
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.Point;
import org.opencv.core.Scalar;
import org.opencv.features2d.DMatch;
import org.opencv.features2d.DescriptorExtractor;
import org.opencv.features2d.DescriptorMatcher;
import org.opencv.features2d.FeatureDetector;
import org.opencv.features2d.KeyPoint;
import org.opencv.test.OpenCVTestCase;
import org.opencv.test.OpenCVTestRunner;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
public class BruteForceL1DescriptorMatcherTest extends OpenCVTestCase {
DescriptorMatcher matcher;
int matSize;
DMatch[] truth;
protected void setUp() throws Exception {
matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_L1);
matSize = 100;
truth = new DMatch[] {
new DMatch(0, 0, 0, 3.175296f),
new DMatch(1, 1, 0, 3.5954158f),
new DMatch(2, 1, 0, 1.2537984f),
new DMatch(3, 1, 0, 3.5761614f),
new DMatch(4, 1, 0, 1.3250958f) };
super.setUp();
}
private Mat getTrainImg() {
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
Core.line(cross, new Point(20, matSize / 2), new Point(matSize - 21, matSize / 2), new Scalar(100), 2);
Core.line(cross, new Point(matSize / 2, 20), new Point(matSize / 2, matSize - 21), new Scalar(100), 2);
return cross;
}
private Mat getQueryImg() {
Mat cross = new Mat(matSize, matSize, CvType.CV_8U, new Scalar(255));
Core.line(cross, new Point(30, matSize / 2), new Point(matSize - 31, matSize / 2), new Scalar(100), 3);
Core.line(cross, new Point(matSize / 2, 30), new Point(matSize / 2, matSize - 31), new Scalar(100), 3);
return cross;
}
private Mat getQueryDescriptors() {
Mat img = getQueryImg();
List<KeyPoint> keypoints = new ArrayList<KeyPoint>();
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 getTrainDescriptors() {
Mat img = getTrainImg();
List<KeyPoint> keypoints = Arrays.asList(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 getMaskImg() {
return new Mat(5, 2, CvType.CV_8U, new Scalar(0)) {
{
put(0,0, 1, 1, 1, 1);
}
};
}
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 testCloneBoolean() {
matcher.add(Arrays.asList(new Mat()));
DescriptorMatcher cloned = matcher.clone(true);
assertNotNull(cloned);
assertTrue(cloned.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 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 testMatchMatMatListOfDMatchMat() {
Mat train = getTrainDescriptors();
Mat query = getQueryDescriptors();
Mat mask = getMaskImg();
List<DMatch> matches = new ArrayList<DMatch>();
matcher.match(query, train, matches, mask);
assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches, EPS);
}
public void testMatchMatMatListOfDMatch() {
Mat train = getTrainDescriptors();
Mat query = getQueryDescriptors();
List<DMatch> matches = new ArrayList<DMatch>();
matcher.match(query, train, matches);
assertListDMatchEquals(Arrays.asList(truth), matches, EPS);
}
public void testMatchMatListOfDMatchListOfMat() {
Mat train = getTrainDescriptors();
Mat query = getQueryDescriptors();
Mat mask = getMaskImg();
List<DMatch> matches = new ArrayList<DMatch>();
matcher.add(Arrays.asList(train));
matcher.match(query, matches, Arrays.asList(mask));
assertListDMatchEquals(Arrays.asList(truth[0], truth[1]), matches, EPS);
}
public void testMatchMatListOfDMatch() {
Mat train = getTrainDescriptors();
Mat query = getQueryDescriptors();
List<DMatch> matches = new ArrayList<DMatch>();
matcher.add(Arrays.asList(train));
matcher.match(query, matches);
assertListDMatchEquals(Arrays.asList(truth), matches, EPS);
}
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));
}
}
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册