Examples with FastHessianFeatureDetector boofcv.alg.feature.detect.interest.FastHessianFeatureDetector used on opensource projects

Example 1 with FastHessianFeatureDetector

use of boofcv.alg.feature.detect.interest.FastHessianFeatureDetector in project BoofCV by lessthanoptimal.

the class DetectFastHessianApp method doStuff.

private static <T extends ImageGray<T>> void doStuff(Class<T> imageType, BufferedImage input) {
    T workImage = ConvertBufferedImage.convertFromSingle(input, null, imageType);
    NonMaxSuppression extractor = FactoryFeatureExtractor.nonmax(new ConfigExtract(5, 1, 5, true));
    FastHessianFeatureDetector<T> det = new FastHessianFeatureDetector<>(extractor, NUM_FEATURES, 2, 9, 4, 4, 6);
    T integral = GIntegralImageOps.transform(workImage, null);
    det.detect(integral);
    System.out.println("total features found: " + det.getFoundPoints().size());
    VisualizeFeatures.drawScalePoints(input.createGraphics(), det.getFoundPoints(), BoofDefaults.SURF_SCALE_TO_RADIUS);
    ShowImages.showWindow(input, "Found Features: " + imageType.getSimpleName(), true);
}

Example 2 with FastHessianFeatureDetector

use of boofcv.alg.feature.detect.interest.FastHessianFeatureDetector in project BoofCV by lessthanoptimal.

the class ExampleFeatureSurf method harder.

/**
 * Configured exactly the same as the easy example above, but require a lot more code and a more in depth
 * understanding of how SURF works and is configured.  Instead of TupleDesc_F64, SurfFeature are computed in
 * this case.  They are almost the same as TupleDesc_F64, but contain the Laplacian's sign which can be used
 * to speed up association. That is an example of how using less generalized interfaces can improve performance.
 *
 * @param image Input image type. DOES NOT NEED TO BE GrayF32, GrayU8 works too
 */
public static <II extends ImageGray<II>> void harder(GrayF32 image) {
    // SURF works off of integral images
    Class<II> integralType = GIntegralImageOps.getIntegralType(GrayF32.class);
    // define the feature detection algorithm
    NonMaxSuppression extractor = FactoryFeatureExtractor.nonmax(new ConfigExtract(2, 0, 5, true));
    FastHessianFeatureDetector<II> detector = new FastHessianFeatureDetector<>(extractor, 200, 2, 9, 4, 4, 6);
    // estimate orientation
    OrientationIntegral<II> orientation = FactoryOrientationAlgs.sliding_ii(null, integralType);
    DescribePointSurf<II> descriptor = FactoryDescribePointAlgs.<II>surfStability(null, integralType);
    // compute the integral image of 'image'
    II integral = GeneralizedImageOps.createSingleBand(integralType, image.width, image.height);
    GIntegralImageOps.transform(image, integral);
    // detect fast hessian features
    detector.detect(integral);
    // tell algorithms which image to process
    orientation.setImage(integral);
    descriptor.setImage(integral);
    List<ScalePoint> points = detector.getFoundPoints();
    List<BrightFeature> descriptions = new ArrayList<>();
    for (ScalePoint p : points) {
        // estimate orientation
        orientation.setObjectRadius(p.scale * BoofDefaults.SURF_SCALE_TO_RADIUS);
        double angle = orientation.compute(p.x, p.y);
        // extract the SURF description for this region
        BrightFeature desc = descriptor.createDescription();
        descriptor.describe(p.x, p.y, angle, p.scale, desc);
        // save everything for processing later on
        descriptions.add(desc);
    }
    System.out.println("Found Features: " + points.size());
    System.out.println("First descriptor's first value: " + descriptions.get(0).value[0]);
}