Examples with AssociateDescTo2D boofcv.abst.feature.associate.AssociateDescTo2D used on opensource projects

Example 1 with AssociateDescTo2D

use of boofcv.abst.feature.associate.AssociateDescTo2D in project BoofCV by lessthanoptimal.

the class TestDdaManagerGeneralPoint method createTracker.

@Override
public PointTracker<GrayF32> createTracker() {
    DescribePointBrief<GrayF32> brief = FactoryDescribePointAlgs.brief(FactoryBriefDefinition.gaussian2(new Random(123), 16, 512), FactoryBlurFilter.gaussian(ImageType.single(GrayF32.class), 0, 4));
    GeneralFeatureDetector<GrayF32, GrayF32> corner = FactoryDetectPoint.createShiTomasi(new ConfigGeneralDetector(-1, 2, 0), false, GrayF32.class);
    ScoreAssociateHamming_B score = new ScoreAssociateHamming_B();
    AssociateDescription2D<TupleDesc_B> association = new AssociateDescTo2D<>(FactoryAssociation.greedy(score, 400, true));
    DescribeRegionPoint<GrayF32, TupleDesc_B> describe = new WrapDescribeBrief<>(brief, GrayF32.class);
    EasyGeneralFeatureDetector<GrayF32, GrayF32> easy = new EasyGeneralFeatureDetector<>(corner, GrayF32.class, GrayF32.class);
    DdaManagerGeneralPoint<GrayF32, GrayF32, TupleDesc_B> manager;
    manager = new DdaManagerGeneralPoint<>(easy, describe, 2);
    DetectDescribeAssociate<GrayF32, TupleDesc_B> tracker = new DetectDescribeAssociate<>(manager, association, false);
    return tracker;
}

Example 2 with AssociateDescTo2D

use of boofcv.abst.feature.associate.AssociateDescTo2D in project BoofCV by lessthanoptimal.

the class TestDdaManagerDetectDescribePoint method createTracker.

@Override
public PointTracker<GrayF32> createTracker() {
    DescribePointBrief<GrayF32> brief = FactoryDescribePointAlgs.brief(FactoryBriefDefinition.gaussian2(new Random(123), 16, 512), FactoryBlurFilter.gaussian(ImageType.single(GrayF32.class), 0, 4));
    GeneralFeatureDetector<GrayF32, GrayF32> corner = FactoryDetectPoint.createShiTomasi(new ConfigGeneralDetector(100, 2, 0, 0, true), false, GrayF32.class);
    InterestPointDetector<GrayF32> detector = FactoryInterestPoint.wrapPoint(corner, 1, GrayF32.class, GrayF32.class);
    ScoreAssociateHamming_B score = new ScoreAssociateHamming_B();
    AssociateDescription2D<TupleDesc_B> association = new AssociateDescTo2D<>(FactoryAssociation.greedy(score, 400, true));
    DetectDescribeFusion<GrayF32, TupleDesc_B> fused = new DetectDescribeFusion<>(detector, null, new WrapDescribeBrief<>(brief, GrayF32.class));
    DdaManagerDetectDescribePoint<GrayF32, TupleDesc_B> manager;
    manager = new DdaManagerDetectDescribePoint<>(fused);
    DetectDescribeAssociate<GrayF32, TupleDesc_B> tracker = new DetectDescribeAssociate<>(manager, association, false);
    return tracker;
}

Example 3 with AssociateDescTo2D

use of boofcv.abst.feature.associate.AssociateDescTo2D in project BoofCV by lessthanoptimal.

the class FactoryVisualOdometry method stereoQuadPnP.

/**
 * Stereo visual odometry which uses the two most recent stereo observations (total of four views) to estimate
 * motion.
 *
 * @see VisOdomQuadPnP
 *
 * @param inlierPixelTol Pixel tolerance for RANSAC inliers - Euclidean distance
 * @param epipolarPixelTol Feature association tolerance in pixels.
 * @param maxDistanceF2F Maximum allowed distance between two features in pixels
 * @param maxAssociationError Maxium error between two features when associating.
 * @param ransacIterations Number of iterations RANSAC will perform
 * @param refineIterations Number of refinement iterations
 * @param detector Which feature detector to use
 * @param imageType Type of input image
 */
public static <T extends ImageGray<T>, Desc extends TupleDesc> StereoVisualOdometry<T> stereoQuadPnP(double inlierPixelTol, double epipolarPixelTol, double maxDistanceF2F, double maxAssociationError, int ransacIterations, int refineIterations, DetectDescribeMulti<T, Desc> detector, Class<T> imageType) {
    EstimateNofPnP pnp = FactoryMultiView.computePnP_N(EnumPNP.P3P_FINSTERWALDER, -1);
    DistanceModelMonoPixels<Se3_F64, Point2D3D> distanceMono = new PnPDistanceReprojectionSq();
    PnPStereoDistanceReprojectionSq distanceStereo = new PnPStereoDistanceReprojectionSq();
    PnPStereoEstimator pnpStereo = new PnPStereoEstimator(pnp, distanceMono, 0);
    ModelManagerSe3_F64 manager = new ModelManagerSe3_F64();
    EstimatorToGenerator<Se3_F64, Stereo2D3D> generator = new EstimatorToGenerator<>(pnpStereo);
    // euclidean error squared from left + right images
    double ransacTOL = 2 * inlierPixelTol * inlierPixelTol;
    ModelMatcher<Se3_F64, Stereo2D3D> motion = new Ransac<>(2323, manager, generator, distanceStereo, ransacIterations, ransacTOL);
    RefinePnPStereo refinePnP = null;
    if (refineIterations > 0) {
        refinePnP = new PnPStereoRefineRodrigues(1e-12, refineIterations);
    }
    Class<Desc> descType = detector.getDescriptionType();
    ScoreAssociation<Desc> scorer = FactoryAssociation.defaultScore(descType);
    AssociateDescription2D<Desc> assocSame;
    if (maxDistanceF2F > 0)
        assocSame = new AssociateMaxDistanceNaive<>(scorer, true, maxAssociationError, maxDistanceF2F);
    else
        assocSame = new AssociateDescTo2D<>(FactoryAssociation.greedy(scorer, maxAssociationError, true));
    AssociateStereo2D<Desc> associateStereo = new AssociateStereo2D<>(scorer, epipolarPixelTol, descType);
    TriangulateTwoViewsCalibrated triangulate = FactoryMultiView.triangulateTwoGeometric();
    associateStereo.setThreshold(maxAssociationError);
    VisOdomQuadPnP<T, Desc> alg = new VisOdomQuadPnP<>(detector, assocSame, associateStereo, triangulate, motion, refinePnP);
    return new WrapVisOdomQuadPnP<>(alg, refinePnP, associateStereo, distanceStereo, distanceMono, imageType);
}