mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
366 lines
11 KiB
366 lines
11 KiB
package org.opencv.test.features2d; |
|
|
|
import java.util.Arrays; |
|
import java.util.List; |
|
|
|
import org.opencv.core.Core; |
|
import org.opencv.core.CvException; |
|
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 FlannBasedDescriptorMatcherTest extends OpenCVTestCase { |
|
|
|
static final String xmlParamsDefault = "<?xml version=\"1.0\"?>\n" |
|
+ "<opencv_storage>\n" |
|
+ "<indexParams>\n" |
|
+ " <_>\n" |
|
+ " <name>algorithm</name>\n" |
|
+ " <type>23</type>\n" |
|
+ " <value>1</value></_>\n" |
|
+ " <_>\n" |
|
+ " <name>trees</name>\n" |
|
+ " <type>4</type>\n" |
|
+ " <value>4</value></_></indexParams>\n" |
|
+ "<searchParams>\n" |
|
+ " <_>\n" |
|
+ " <name>checks</name>\n" |
|
+ " <type>4</type>\n" |
|
+ " <value>32</value></_>\n" |
|
+ " <_>\n" |
|
+ " <name>eps</name>\n" |
|
+ " <type>5</type>\n" |
|
+ " <value>0.</value></_>\n" |
|
+ " <_>\n" |
|
+ " <name>sorted</name>\n" |
|
+ " <type>15</type>\n" |
|
+ " <value>1</value></_></searchParams>\n" |
|
+ "</opencv_storage>\n"; |
|
static final String ymlParamsDefault = "%YAML:1.0\n" |
|
+ "indexParams:\n" |
|
+ " -\n" |
|
+ " name: algorithm\n" |
|
+ " type: 23\n" |
|
+ " value: 1\n" |
|
+ " -\n" |
|
+ " name: trees\n" |
|
+ " type: 4\n" |
|
+ " value: 4\n" |
|
+ "searchParams:\n" |
|
+ " -\n" |
|
+ " name: checks\n" |
|
+ " type: 4\n" |
|
+ " value: 32\n" |
|
+ " -\n" |
|
+ " name: eps\n" |
|
+ " type: 5\n" |
|
+ " value: 0.\n" |
|
+ " -\n" |
|
+ " name: sorted\n" |
|
+ " type: 15\n" |
|
+ " value: 1\n"; |
|
static final String ymlParamsModified = "%YAML:1.0\n" |
|
+ "indexParams:\n" |
|
+ " -\n" |
|
+ " name: algorithm\n" |
|
+ " type: 23\n" |
|
+ " value: 6\n"// this line is changed! |
|
+ " -\n" |
|
+ " name: trees\n" |
|
+ " type: 4\n" |
|
+ " value: 4\n" |
|
+ "searchParams:\n" |
|
+ " -\n" |
|
+ " name: checks\n" |
|
+ " type: 4\n" |
|
+ " value: 32\n" |
|
+ " -\n" |
|
+ " name: eps\n" |
|
+ " type: 5\n" |
|
+ " value: 4.\n"// this line is changed! |
|
+ " -\n" |
|
+ " name: sorted\n" |
|
+ " type: 15\n" |
|
+ " value: 1\n"; |
|
|
|
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.FLANNBASED); |
|
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, 0.2925075f), |
|
new DMatch(4, 1, 0, 0.9309179f) |
|
}; |
|
} |
|
|
|
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)); |
|
matcher.add(Arrays.asList(train)); |
|
|
|
try { |
|
matcher.clone(); |
|
fail("Expected CvException (CV_StsNotImplemented)"); |
|
} catch (CvException cverr) { |
|
// expected |
|
} |
|
} |
|
|
|
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() { |
|
assertFalse(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() { |
|
fail("Not yet implemented"); |
|
} |
|
|
|
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.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.train(); |
|
|
|
matcher.match(query, matches, Arrays.asList(mask)); |
|
|
|
assertArrayDMatchEquals(truth, matches.toArray(), 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.toString()); |
|
// OpenCVTestRunner.Log(matches); |
|
} |
|
|
|
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), 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 filenameR = OpenCVTestRunner.getTempFileName("yml"); |
|
String filenameW = OpenCVTestRunner.getTempFileName("yml"); |
|
writeFile(filenameR, ymlParamsModified); |
|
|
|
matcher.read(filenameR); |
|
matcher.write(filenameW); |
|
|
|
assertEquals(ymlParamsModified, readFile(filenameW)); |
|
} |
|
|
|
public void testTrain() { |
|
Mat train = getTrainDescriptors(); |
|
matcher.add(Arrays.asList(train)); |
|
matcher.train(); |
|
} |
|
|
|
public void testTrainNoData() { |
|
try { |
|
matcher.train(); |
|
fail("Expected CvException - FlannBasedMatcher::train should fail on empty train set"); |
|
} catch (CvException cverr) { |
|
// expected |
|
} |
|
} |
|
|
|
public void testWrite() { |
|
String filename = OpenCVTestRunner.getTempFileName("xml"); |
|
|
|
matcher.write(filename); |
|
|
|
assertEquals(xmlParamsDefault, readFile(filename)); |
|
} |
|
|
|
public void testWriteYml() { |
|
String filename = OpenCVTestRunner.getTempFileName("yml"); |
|
|
|
matcher.write(filename); |
|
|
|
assertEquals(ymlParamsDefault, readFile(filename)); |
|
} |
|
|
|
}
|
|
|