opencv/modules/features2d/misc/java/test/STARFeatureDetectorTest.java

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package org.opencv.test.features2d;
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import java.util.Arrays;
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
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import org.opencv.core.MatOfKeyPoint;
import org.opencv.core.Point;
import org.opencv.core.Scalar;
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 STARFeatureDetectorTest extends OpenCVTestCase {
FeatureDetector 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 = FeatureDetector.create(FeatureDetector.STAR);
matSize = 200;
truth = new KeyPoint[] {
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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)
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};
}
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();
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MatOfKeyPoint keypoints = new MatOfKeyPoint();
detector.detect(img, keypoints);
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assertListKeyPointEquals(Arrays.asList(truth), keypoints.toList(), EPS);
}
public void testDetectMatListOfKeyPointMat() {
Mat img = getTestImg();
Mat mask = getMaskImg();
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MatOfKeyPoint keypoints = new MatOfKeyPoint();
detector.detect(img, keypoints, mask);
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assertListKeyPointEquals(Arrays.asList(truth[0], truth[2], truth[5], truth[7]), keypoints.toList(), EPS);
}
public void testEmpty() {
assertFalse(detector.empty());
}
public void testRead() {
Mat img = getTestImg();
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MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
detector.detect(img, keypoints1);
String filename = OpenCVTestRunner.getTempFileName("yml");
writeFile(filename, "%YAML:1.0\nmaxSize: 45\nresponseThreshold: 150\nlineThresholdProjected: 10\nlineThresholdBinarized: 8\nsuppressNonmaxSize: 5\n");
detector.read(filename);
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MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
detector.detect(img, keypoints2);
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assertTrue(keypoints2.total() <= keypoints1.total());
}
public void testWrite() {
String filename = OpenCVTestRunner.getTempFileName("xml");
detector.write(filename);
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String truth = "<?xml version=\"1.0\"?>\n<opencv_storage>\n<name>Feature2D.STAR</name>\n<lineThresholdBinarized>8</lineThresholdBinarized>\n<lineThresholdProjected>10</lineThresholdProjected>\n<maxSize>45</maxSize>\n<responseThreshold>30</responseThreshold>\n<suppressNonmaxSize>5</suppressNonmaxSize>\n</opencv_storage>\n";
assertEquals(truth, readFile(filename));
}
public void testWriteYml() {
String filename = OpenCVTestRunner.getTempFileName("yml");
detector.write(filename);
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String truth = "%YAML:1.0\nname: \"Feature2D.STAR\"\nlineThresholdBinarized: 8\nlineThresholdProjected: 10\nmaxSize: 45\nresponseThreshold: 30\nsuppressNonmaxSize: 5\n";
assertEquals(truth, readFile(filename));
}
}