use of boofcv.abst.feature.detect.extract.ConfigExtract in project BoofCV by lessthanoptimal.
the class DebugSiftDetectorApp method main.
public static void main(String[] args) {
BufferedImage input = UtilImageIO.loadImage(UtilIO.pathExample("sunflowers.jpg"));
// BufferedImage input = UtilImageIO.loadImage(UtilIO.pathExample("shapes/shapes01.png");
GrayF32 gray = ConvertBufferedImage.convertFromSingle(input, null, GrayF32.class);
NonMaxSuppression nonmax = FactoryFeatureExtractor.nonmax(new ConfigExtract(3, 1, 1, true, true, true));
NonMaxLimiter extractor = new NonMaxLimiter(nonmax, 400);
SiftScaleSpace imageSS = new SiftScaleSpace(-1, 5, 3, 2.75);
SiftDetector alg = new SiftDetector(imageSS, 10, extractor);
alg.process(gray);
System.out.println("total features found: " + alg.getDetections().size());
VisualizeFeatures.drawScalePoints(input.createGraphics(), alg.getDetections().toList(), 1);
// ListDisplayPanel dog = new ListDisplayPanel();
// for( int i = 0; i < alg.getScaleSpace().getDog().length; i++ ) {
// int scale = i % (alg.getScaleSpace().getNumScales()-1);
// int octave = i / (alg.getScaleSpace().getNumScales()-1);
//
// BufferedImage img = VisualizeImageData.colorizeSign(alg.getScaleSpace().getDog()[i],null,-1);
// dog.addImage(img,octave+" "+scale);
// }
//
// ListDisplayPanel ss = new ListDisplayPanel();
// for( int i = 0; i < alg.getScaleSpace().getScale().length; i++ ) {
// int scale = i % alg.getScaleSpace().getNumScales();
// int octave = i / alg.getScaleSpace().getNumScales();
//
// BufferedImage img = VisualizeImageData.grayMagnitude(alg.getScaleSpace().getScale()[i],null,255);
// ss.addImage(img,octave+" "+scale);
// }
// ShowImages.showWindow(dog, "Octave DOG");
// ShowImages.showWindow(ss, "Octave Scales");
ShowImages.showWindow(input, "Found Features", true);
System.out.println("Done");
}
use of boofcv.abst.feature.detect.extract.ConfigExtract in project BoofCV by lessthanoptimal.
the class TestGeneralFeatureDetector method testDetection.
/**
* Several basic detection tests
*/
@Test
public void testDetection() {
// use a real extractor
NonMaxSuppression extractor;
HelperIntensity intensity = new HelperIntensity(false, false, false);
// add several features while avoiding the image border
intensity.img.set(1, 1, 10);
intensity.img.set(7, 1, 10);
intensity.img.set(1, 5, 10);
intensity.img.set(7, 5, 10);
intensity.img.set(1, 9, 10);
intensity.img.set(6, 9, 10);
// add a couple of minimums
intensity.img.set(2, 5, -10);
intensity.img.set(6, 5, -10);
// configure it to only detect positive features
intensity.minimums = false;
extractor = FactoryFeatureExtractor.nonmax(new ConfigExtract(1, 0.001f, 1, true, false, true));
GeneralFeatureDetector<GrayF32, GrayF32> detector = new GeneralFeatureDetector<>(intensity, extractor);
detector.process(new GrayF32(width, height), null, null, null, null, null);
assertEquals(6, detector.getMaximums().size());
assertEquals(0, detector.getMinimums().size());
// try detecting the negative features too
intensity.minimums = true;
extractor = FactoryFeatureExtractor.nonmax(new ConfigExtract(1, 0.001f, 1, true, true, true));
detector = new GeneralFeatureDetector<>(intensity, extractor);
detector.process(new GrayF32(width, height), null, null, null, null, null);
assertEquals(6, detector.getMaximums().size());
assertEquals(2, detector.getMinimums().size());
// call it twice to make sure everything is reset correctly
detector.process(new GrayF32(width, height), null, null, null, null, null);
assertEquals(6, detector.getMaximums().size());
assertEquals(2, detector.getMinimums().size());
}
use of boofcv.abst.feature.detect.extract.ConfigExtract in project BoofCV by lessthanoptimal.
the class TestHoughTransformLineFootOfNorm method obviousLines.
private <D extends ImageGray<D>> void obviousLines(Class<D> derivType) {
GrayU8 binary = new GrayU8(width, height);
D derivX = GeneralizedImageOps.createSingleBand(derivType, width, height);
D derivY = GeneralizedImageOps.createSingleBand(derivType, width, height);
for (int i = 0; i < height; i++) {
binary.set(5, i, 1);
GeneralizedImageOps.set(derivX, 5, i, 20);
}
NonMaxSuppression extractor = FactoryFeatureExtractor.nonmax(new ConfigExtract(4, 2, 0, true));
HoughTransformLineFootOfNorm alg = new HoughTransformLineFootOfNorm(extractor, 2);
alg.transform(derivX, derivY, binary);
FastQueue<LineParametric2D_F32> lines = alg.extractLines();
assertEquals(1, lines.size());
LineParametric2D_F32 l = lines.get(0);
assertEquals(l.p.x, 5, 0.1);
// normalize the line for easier evaluation
l.slope.x /= l.slope.norm();
l.slope.y /= l.slope.norm();
assertEquals(0, Math.abs(l.slope.x), 0);
assertEquals(1, Math.abs(l.slope.y), 0.1);
}
use of boofcv.abst.feature.detect.extract.ConfigExtract in project BoofCV by lessthanoptimal.
the class TestGeneralFeatureDetector method ignoreBorder.
/**
* If the intensity image has an ignore border is that border request actually followed?
*/
@Test
public void ignoreBorder() {
HelperIntensity intensity = new HelperIntensity(false, false, false);
intensity.ignoreBorder = 2;
intensity.minimums = true;
// add several features inside the image
intensity.img.set(3, 3, 10);
intensity.img.set(5, 5, 10);
intensity.img.set(6, 7, 10);
intensity.img.set(4, 7, -10);
intensity.img.set(6, 8, -10);
// add some along the border
intensity.img.set(5, 1, 10);
intensity.img.set(5, 11, 10);
intensity.img.set(0, 5, 10);
intensity.img.set(1, 1, 10);
intensity.img.set(9, 5, 10);
intensity.img.set(9, 4, -10);
// use a real extractor
NonMaxSuppression extractor = FactoryFeatureExtractor.nonmax(new ConfigExtract(1, 0.001f, 1, true, true, true));
GeneralFeatureDetector<GrayF32, GrayF32> detector = new GeneralFeatureDetector<>(intensity, extractor);
detector.process(new GrayF32(width, height), null, null, null, null, null);
// only features inside the image should be found
assertEquals(3, detector.getMaximums().size());
assertEquals(2, detector.getMinimums().size());
}
use of boofcv.abst.feature.detect.extract.ConfigExtract in project BoofCV by lessthanoptimal.
the class VisualizeStereoVisualOdometryApp method createStereoDepth.
private StereoVisualOdometry<I> createStereoDepth(int whichAlg) {
Class derivType = GImageDerivativeOps.getDerivativeType(imageType);
StereoDisparitySparse<I> disparity = FactoryStereoDisparity.regionSparseWta(2, 150, 3, 3, 30, -1, true, imageType);
PkltConfig kltConfig = new PkltConfig();
kltConfig.templateRadius = 3;
kltConfig.pyramidScaling = new int[] { 1, 2, 4, 8 };
if (whichAlg == 0) {
ConfigGeneralDetector configDetector = new ConfigGeneralDetector(600, 3, 1);
PointTrackerTwoPass<I> tracker = FactoryPointTrackerTwoPass.klt(kltConfig, configDetector, imageType, derivType);
return FactoryVisualOdometry.stereoDepth(1.5, 120, 2, 200, 50, false, disparity, tracker, imageType);
} else if (whichAlg == 1) {
ConfigGeneralDetector configExtract = new ConfigGeneralDetector(600, 3, 1);
GeneralFeatureDetector detector = FactoryPointTracker.createShiTomasi(configExtract, derivType);
DescribeRegionPoint describe = FactoryDescribeRegionPoint.brief(null, imageType);
ScoreAssociateHamming_B score = new ScoreAssociateHamming_B();
AssociateDescription2D<TupleDesc_B> associate = new AssociateDescTo2D<>(FactoryAssociation.greedy(score, 150, true));
PointTrackerTwoPass tracker = FactoryPointTrackerTwoPass.dda(detector, describe, associate, null, 1, imageType);
return FactoryVisualOdometry.stereoDepth(1.5, 80, 3, 200, 50, false, disparity, tracker, imageType);
} else if (whichAlg == 2) {
PointTracker<I> tracker = FactoryPointTracker.combined_ST_SURF_KLT(new ConfigGeneralDetector(600, 3, 0), kltConfig, 50, null, null, imageType, derivType);
PointTrackerTwoPass<I> twopass = new PointTrackerToTwoPass<>(tracker);
return FactoryVisualOdometry.stereoDepth(1.5, 80, 3, 200, 50, false, disparity, twopass, imageType);
} else if (whichAlg == 3) {
ConfigGeneralDetector configDetector = new ConfigGeneralDetector(600, 3, 1);
PointTracker<I> trackerLeft = FactoryPointTracker.klt(kltConfig, configDetector, imageType, derivType);
PointTracker<I> trackerRight = FactoryPointTracker.klt(kltConfig, configDetector, imageType, derivType);
DescribeRegionPoint describe = FactoryDescribeRegionPoint.surfFast(null, imageType);
return FactoryVisualOdometry.stereoDualTrackerPnP(90, 2, 1.5, 1.5, 200, 50, trackerLeft, trackerRight, describe, imageType);
} else if (whichAlg == 4) {
// GeneralFeatureIntensity intensity =
// FactoryIntensityPoint.hessian(HessianBlobIntensity.Type.TRACE,defaultType);
GeneralFeatureIntensity intensity = FactoryIntensityPoint.shiTomasi(1, false, imageType);
NonMaxSuppression nonmax = FactoryFeatureExtractor.nonmax(new ConfigExtract(2, 50, 0, true, false, true));
GeneralFeatureDetector general = new GeneralFeatureDetector(intensity, nonmax);
general.setMaxFeatures(600);
DetectorInterestPointMulti detector = new GeneralToInterestMulti(general, 2, imageType, derivType);
// DescribeRegionPoint describe = FactoryDescribeRegionPoint.brief(new ConfigBrief(true),defaultType);
// DescribeRegionPoint describe = FactoryDescribeRegionPoint.pixelNCC(5,5,defaultType);
DescribeRegionPoint describe = FactoryDescribeRegionPoint.surfFast(null, imageType);
DetectDescribeMulti detDescMulti = new DetectDescribeMultiFusion(detector, null, describe);
return FactoryVisualOdometry.stereoQuadPnP(1.5, 0.5, 75, Double.MAX_VALUE, 300, 50, detDescMulti, imageType);
} else {
throw new RuntimeException("Unknown selection");
}
}
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