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opencv_contrib/modules/xfeatures2d/misc/java/test/SURFFeatureDetectorTest.java
Augustin Manecy a8864db902 Add read/write functions to xfeatures2d and normalize naming convention
In read function, check before if node is empty to avoid erasing default value in case of missing parameter.

Add getters/setters to complete cpp/java/python API (needed for Java Tests.)

fix warning due to double to float conversion in freak
2022-10-24 13:12:31 +03:00

177 lines
6.1 KiB
Java

package org.opencv.test.features2d;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.Comparator;
import java.util.List;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.MatOfKeyPoint;
import org.opencv.core.Point;
import org.opencv.core.Scalar;
import org.opencv.core.KeyPoint;
import org.opencv.test.OpenCVTestCase;
import org.opencv.test.OpenCVTestRunner;
import org.opencv.imgproc.Imgproc;
import org.opencv.xfeatures2d.SURF;
public class SURFFeatureDetectorTest extends OpenCVTestCase {
SURF 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() {
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;
}
private void order(List<KeyPoint> points) {
Collections.sort(points, new Comparator<KeyPoint>() {
public int compare(KeyPoint p1, KeyPoint p2) {
if (p1.angle < p2.angle)
return -1;
if (p1.angle > p2.angle)
return 1;
return 0;
}
});
}
@Override
protected void setUp() throws Exception {
super.setUp();
detector = createClassInstance(XFEATURES2D + "SURF", DEFAULT_FACTORY, null, null);
matSize = 100;
truth = new KeyPoint[] {
new KeyPoint(55.775578f, 55.775578f, 16, 80.245735f, 8617.8633f, 0, -1),
new KeyPoint(44.224422f, 55.775578f, 16, 170.24574f, 8617.8633f, 0, -1),
new KeyPoint(44.224422f, 44.224422f, 16, 260.24573f, 8617.8633f, 0, -1),
new KeyPoint(55.775578f, 44.224422f, 16, 350.24573f, 8617.8633f, 0, -1)
};
}
public void testCreate() {
assertNotNull(detector);
}
public void testDetectListOfMatListOfListOfKeyPoint() {
setProperty(detector, "hessianThreshold", "double", 8000);
setProperty(detector, "nOctaves", "int", 3);
setProperty(detector, "nOctaveLayers", "int", 4);
setProperty(detector, "upright", "boolean", false);
setProperty(detector, "extended", "boolean", true);
List<MatOfKeyPoint> keypoints = new ArrayList<MatOfKeyPoint>();
Mat cross = getTestImg();
List<Mat> crosses = new ArrayList<Mat>(3);
crosses.add(cross);
crosses.add(cross);
crosses.add(cross);
detector.detect(crosses, keypoints);
assertEquals(3, keypoints.size());
for (MatOfKeyPoint mkp : keypoints) {
List<KeyPoint> lkp = mkp.toList();
order(lkp);
assertListKeyPointEquals(Arrays.asList(truth), lkp, EPS);
}
}
public void testDetectListOfMatListOfListOfKeyPointListOfMat() {
fail("Not yet implemented");
}
public void testDetectMatListOfKeyPoint() {
setProperty(detector, "hessianThreshold", "double", 8000);
setProperty(detector, "nOctaves", "int", 3);
setProperty(detector, "nOctaveLayers", "int", 4);
setProperty(detector, "upright", "boolean", false);
MatOfKeyPoint keypoints = new MatOfKeyPoint();
Mat cross = getTestImg();
detector.detect(cross, keypoints);
List<KeyPoint> lkp = keypoints.toList();
order(lkp);
assertListKeyPointEquals(Arrays.asList(truth), lkp, EPS);
}
public void testDetectMatListOfKeyPointMat() {
setProperty(detector, "hessianThreshold", "double", 8000);
setProperty(detector, "nOctaves", "int", 3);
setProperty(detector, "nOctaveLayers", "int", 4);
setProperty(detector, "upright", "boolean", false);
setProperty(detector, "extended", "boolean", true);
Mat img = getTestImg();
Mat mask = getMaskImg();
MatOfKeyPoint keypoints = new MatOfKeyPoint();
detector.detect(img, keypoints, mask);
List<KeyPoint> lkp = keypoints.toList();
order(lkp);
assertListKeyPointEquals(Arrays.asList(truth[1], truth[2]), lkp, EPS);
}
public void testEmpty() {
// assertFalse(detector.empty());
fail("Not yet implemented");
}
public void testReadYml() {
Mat cross = getTestImg();
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
detector.detect(cross, keypoints1);
String filename = OpenCVTestRunner.getTempFileName("xml");
writeFile(filename, "<?xml version=\"1.0\"?>\n<opencv_storage>\n<name>Feature2D.SURF</name>\n<hessianThreshold>8000.</hessianThreshold>\n<extended>1</extended>\n<upright>0</upright>\n<nOctaves>3</nOctaves>\n<nOctaveLayers>4</nOctaveLayers>\n</opencv_storage>\n");
detector.read(filename);
assertEquals(128, detector.descriptorSize());
assertEquals(8000., detector.getHessianThreshold());
assertEquals(true, detector.getExtended());
assertEquals(false, detector.getUpright());
assertEquals(3, detector.getNOctaves());
assertEquals(4, detector.getNOctaveLayers());
MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
detector.detect(cross, keypoints2);
assertTrue(keypoints2.total() <= keypoints1.total());
}
public void testWriteYml() {
String filename = OpenCVTestRunner.getTempFileName("yml");
detector.write(filename);
String truth = "%YAML:1.0\n---\nname: \"Feature2D.SURF\"\nhessianThreshold: 100.\nextended: 0\nupright: 0\nnOctaves: 4\nnOctaveLayers: 3\n";
String actual = readFile(filename);
actual = actual.replaceAll("e([+-])0(\\d\\d)", "e$1$2"); // NOTE: workaround for different platforms double representation
assertEquals(truth, actual);
}
}