Examples with DetectDescribePoint boofcv.abst.feature.detdesc.DetectDescribePoint used on opensource projects

Example 6 with DetectDescribePoint

use of boofcv.abst.feature.detdesc.DetectDescribePoint in project BoofCV by lessthanoptimal.

the class ExampleDetectDescribe method main.

public static void main(String[] args) {
    Class imageType = GrayF32.class;
    DetectDescribePoint detDesc = createFromPremade(imageType);
    // DetectDescribePoint detDesc = createFromComponents(imageType);
    // Might as well have this example do something useful, like associate two images
    ScoreAssociation scorer = FactoryAssociation.defaultScore(detDesc.getDescriptionType());
    AssociateDescription associate = FactoryAssociation.greedy(scorer, Double.MAX_VALUE, true);
    // load and match images
    ExampleAssociatePoints app = new ExampleAssociatePoints(detDesc, associate, imageType);
    BufferedImage imageA = UtilImageIO.loadImage(UtilIO.pathExample("stitch/kayak_01.jpg"));
    BufferedImage imageB = UtilImageIO.loadImage(UtilIO.pathExample("stitch/kayak_03.jpg"));
    app.associate(imageA, imageB);
}

Example 7 with DetectDescribePoint

use of boofcv.abst.feature.detdesc.DetectDescribePoint in project BoofCV by lessthanoptimal.

the class ExampleImageStitching method computeTransform.

/**
 * Using abstracted code, find a transform which minimizes the difference between corresponding features
 * in both images.  This code is completely model independent and is the core algorithms.
 */
public static <T extends ImageGray<T>, FD extends TupleDesc> Homography2D_F64 computeTransform(T imageA, T imageB, DetectDescribePoint<T, FD> detDesc, AssociateDescription<FD> associate, ModelMatcher<Homography2D_F64, AssociatedPair> modelMatcher) {
    // get the length of the description
    List<Point2D_F64> pointsA = new ArrayList<>();
    FastQueue<FD> descA = UtilFeature.createQueue(detDesc, 100);
    List<Point2D_F64> pointsB = new ArrayList<>();
    FastQueue<FD> descB = UtilFeature.createQueue(detDesc, 100);
    // extract feature locations and descriptions from each image
    describeImage(imageA, detDesc, pointsA, descA);
    describeImage(imageB, detDesc, pointsB, descB);
    // Associate features between the two images
    associate.setSource(descA);
    associate.setDestination(descB);
    associate.associate();
    // create a list of AssociatedPairs that tell the model matcher how a feature moved
    FastQueue<AssociatedIndex> matches = associate.getMatches();
    List<AssociatedPair> pairs = new ArrayList<>();
    for (int i = 0; i < matches.size(); i++) {
        AssociatedIndex match = matches.get(i);
        Point2D_F64 a = pointsA.get(match.src);
        Point2D_F64 b = pointsB.get(match.dst);
        pairs.add(new AssociatedPair(a, b, false));
    }
    // find the best fit model to describe the change between these images
    if (!modelMatcher.process(pairs))
        throw new RuntimeException("Model Matcher failed!");
    // return the found image transform
    return modelMatcher.getModelParameters().copy();
}