Examples with ConfigExtract boofcv.abst.feature.detect.extract.ConfigExtract used on opensource projects

Example 16 with ConfigExtract

use of boofcv.abst.feature.detect.extract.ConfigExtract in project BoofCV by lessthanoptimal.

the class ExampleNonMaximumSupression method renderNonMax.

public static BufferedImage renderNonMax(GrayF32 intensity, int radius, float threshold) {
    // Create and configure the feature detector
    NonMaxSuppression nonmax = FactoryFeatureExtractor.nonmax(new ConfigExtract(radius, threshold));
    // We will only searching for the maximums.  Other variants will look for minimums or will exclude previous
    // candidate detections from being detected twice
    QueueCorner maximums = new QueueCorner();
    nonmax.process(intensity, null, null, null, maximums);
    // Visualize the intensity image
    BufferedImage output = new BufferedImage(intensity.width, intensity.height, BufferedImage.TYPE_INT_RGB);
    VisualizeImageData.colorizeSign(intensity, output, -1);
    // render each maximum with a circle
    Graphics2D g2 = output.createGraphics();
    g2.setColor(Color.blue);
    for (int i = 0; i < maximums.size(); i++) {
        Point2D_I16 c = maximums.get(i);
        VisualizeFeatures.drawCircle(g2, c.x, c.y, radius);
    }
    return output;
}

Example 17 with ConfigExtract

use of boofcv.abst.feature.detect.extract.ConfigExtract 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]);
}

Example 18 with ConfigExtract

use of boofcv.abst.feature.detect.extract.ConfigExtract in project BoofCV by lessthanoptimal.

the class TestSiftDetector method createDetector.

private SiftDetector createDetector() {
    SiftScaleSpace ss = new SiftScaleSpace(-1, 5, 3, 1.6);
    NonMaxSuppression nonmax = FactoryFeatureExtractor.nonmax(new ConfigExtract(1, 0, 1, true, true, true));
    NonMaxLimiter limiter = new NonMaxLimiter(nonmax, 1000);
    return new SiftDetector(ss, 10, limiter);
}

Example 19 with ConfigExtract

use of boofcv.abst.feature.detect.extract.ConfigExtract in project BoofCV by lessthanoptimal.

the class TestHoughTransformLinePolar method obviousLines.

/**
 * See if it can detect an obvious line in the image
 */
@Test
public void obviousLines() {
    GrayU8 image = new GrayU8(width, height);
    for (int i = 0; i < height; i++) {
        image.set(5, i, 1);
    }
    NonMaxSuppression extractor = FactoryFeatureExtractor.nonmax(new ConfigExtract(4, 5, 0, true));
    HoughTransformLinePolar alg = new HoughTransformLinePolar(extractor, 40, 180);
    alg.transform(image);
    FastQueue<LineParametric2D_F32> lines = alg.extractLines();
    assertTrue(lines.size() > 0);
    for (int i = 0; i < lines.size(); i++) {
        LineParametric2D_F32 l = lines.get(i);
        assertEquals(l.p.x, 5, 0.1);
        assertEquals(Math.abs(l.slope.x), 0, 1e-4);
        assertEquals(Math.abs(l.slope.y), 1, 0.1);
    }
}

Example 20 with ConfigExtract

use of boofcv.abst.feature.detect.extract.ConfigExtract in project BoofCV by lessthanoptimal.

the class GeneralTemplateMatchTests method checkExpected.

private void checkExpected(Point2D_I32... points) {
    // I'm being lazy, update this in the future
    assertFalse(alg.isBorderProcessed());
    // only process the regions which are not considered the border
    int x0 = alg.getBorderX0();
    int y0 = alg.getBorderY0();
    // solutions should be local maximums
    NonMaxSuppression extractor = FactoryFeatureExtractor.nonmax(new ConfigExtract(2, -Float.MAX_VALUE, 0, true));
    QueueCorner found = new QueueCorner(10);
    extractor.process(alg.getIntensity(), null, null, null, found);
    assertTrue(found.size >= points.length);
    // search for all the expected matches
    for (Point2D_I32 expected : points) {
        int numMatches = 0;
        for (Point2D_I16 f : found.toList()) {
            double d = UtilPoint2D_F64.distance(f.x - x0, f.y - y0, expected.x, expected.y);
            if (d <= 1)
                numMatches++;
        }
        assertEquals(1, numMatches);
    }
}