Examples with DogArray_F64 org.ddogleg.struct.DogArray_F64 used on opensource projects

Example 6 with DogArray_F64

use of org.ddogleg.struct.DogArray_F64 in project BoofCV by lessthanoptimal.

the class WrapAssociateGreedy method associate.

@Override
public void associate() {
    if (listSrc == null)
        throw new IllegalArgumentException("source features not specified");
    if (listDst == null)
        throw new IllegalArgumentException("destination features not specified");
    unassocSrc.reset();
    alg.associate(listSrc, listDst);
    DogArray_I32 pairs = alg.getPairs();
    DogArray_F64 score = alg.getFitQuality();
    matches.reset();
    for (int i = 0; i < listSrc.size; i++) {
        int dst = pairs.data[i];
        if (dst >= 0)
            matches.grow().setTo(i, dst, score.data[i]);
        else
            unassocSrc.add(i);
    }
}

Example 7 with DogArray_F64

use of org.ddogleg.struct.DogArray_F64 in project BoofCV by lessthanoptimal.

the class CompleteSift_MT method describeDetections.

@Override
protected void describeDetections(List<SiftDetector.SiftPoint> detections) {
    // if there are very few features don't spawn threads
    if (minimumDetectionsThread >= detections.size())
        super.describeDetections(detections);
    BoofConcurrency.loopBlocks(0, detections.size(), helpers, (helper, idx0, idx1) -> {
        final OrientationHistogramSift<GrayF32> orientation = helper.orientation;
        final DescribePointSift<GrayF32> describe = helper.describe;
        helper.reset();
        for (int detIdx = idx0; detIdx < idx1; detIdx++) {
            SiftDetector.SiftPoint p = detections.get(detIdx);
            // Gradient image is offset by one
            GrayF32 derivX = gradient.getDerivX(p.octaveIdx, (byte) (p.scaleIdx - 1));
            GrayF32 derivY = gradient.getDerivY(p.octaveIdx, (byte) (p.scaleIdx - 1));
            orientation.setImageGradient(derivX, derivY);
            describe.setImageGradient(derivX, derivY);
            double pixelScaleToInput = scaleSpace.pixelScaleCurrentToInput(p.octaveIdx);
            // adjust the image for the down sampling in each octave
            double localX = p.pixel.x / pixelScaleToInput;
            double localY = p.pixel.y / pixelScaleToInput;
            double localSigma = p.scale / pixelScaleToInput;
            // find potential orientations first
            orientation.process(localX, localY, localSigma);
            // describe each feature
            DogArray_F64 angles = orientation.getOrientations();
            for (int angleIdx = 0; angleIdx < angles.size; angleIdx++) {
                describe.process(localX, localY, localSigma, angles.get(angleIdx), helper.features.grow());
                helper.orientations.add(angles.get(angleIdx));
                helper.locations.add(p);
            }
        }
    });
    // Stitch results from all the threads back together
    for (int i = 0; i < helpers.size(); i++) {
        ThreadHelper helper = helpers.get(i);
        locations.addAll(helper.locations);
        combinedFeatures.addAll(helper.features);
        orientations.addAll(helper.orientations);
    }
}

Example 8 with DogArray_F64

use of org.ddogleg.struct.DogArray_F64 in project BoofCV by lessthanoptimal.

the class ShapeFittingOps method averageCircle_I32.

/**
 * Computes a circle which has it's center at the mean position of the provided points and radius is equal to the
 * average distance of each point from the center. While fast to compute the provided circle is not a best
 * fit circle by any reasonable metric, except for special cases.
 *
 * @param points (Input) Set of unordered points. Not modified.
 * @param optional (Optional) Used internally to store the distance of each point from the center. Can be null.
 * @param outputStorage (Output/Optional) Storage for results. If null then a new circle instance will be returned.
 * @return The found circle fit.
 */
public static FitData<Circle2D_F64> averageCircle_I32(List<Point2D_I32> points, DogArray_F64 optional, @Nullable FitData<Circle2D_F64> outputStorage) {
    if (outputStorage == null) {
        outputStorage = new FitData<>(new Circle2D_F64());
    }
    if (optional == null) {
        optional = new DogArray_F64();
    }
    Circle2D_F64 circle = outputStorage.shape;
    int N = points.size();
    // find center of the circle by computing the mean x and y
    int sumX = 0, sumY = 0;
    for (int i = 0; i < N; i++) {
        Point2D_I32 p = points.get(i);
        sumX += p.x;
        sumY += p.y;
    }
    optional.reset();
    double centerX = circle.center.x = sumX / (double) N;
    double centerY = circle.center.y = sumY / (double) N;
    double meanR = 0;
    for (int i = 0; i < N; i++) {
        Point2D_I32 p = points.get(i);
        double dx = p.x - centerX;
        double dy = p.y - centerY;
        double r = Math.sqrt(dx * dx + dy * dy);
        optional.push(r);
        meanR += r;
    }
    meanR /= N;
    circle.radius = meanR;
    // compute radius variance
    double variance = 0;
    for (int i = 0; i < N; i++) {
        double diff = optional.get(i) - meanR;
        variance += diff * diff;
    }
    outputStorage.error = variance / N;
    return outputStorage;
}

Example 9 with DogArray_F64

use of org.ddogleg.struct.DogArray_F64 in project BoofCV by lessthanoptimal.

the class FiducialDetection method parseImage.

void parseImage(int index, String[] args) {
    boolean robust = true;
    List<String> paths = new ArrayList<>();
    DogArray_F64 sizes = new DogArray_F64();
    double borderWidth = 0.25;
    for (; index < args.length; index++) {
        String arg = args[index];
        if (!arg.startsWith("--")) {
            throw new RuntimeException("Expected flags for image fiducial");
        }
        splitFlag(arg);
        if (flagName.compareToIgnoreCase("Robust") == 0) {
            robust = Boolean.parseBoolean(parameters);
        } else if (flagName.compareToIgnoreCase("Image") == 0) {
            String[] words = parameters.split(":");
            if (words.length != 2)
                throw new RuntimeException("Expected two for width and image path");
            sizes.add(Double.parseDouble(words[0]));
            paths.add(words[1]);
        } else if (flagName.compareToIgnoreCase("Border") == 0) {
            borderWidth = Double.parseDouble(parameters);
        } else {
            throw new RuntimeException("Unknown image option " + flagName);
        }
    }
    if (paths.isEmpty())
        throw new RuntimeException("Need to specify patterns");
    System.out.println("image: robust = " + robust + " total patterns = " + paths.size() + " border = " + borderWidth);
    ConfigFiducialImage config = new ConfigFiducialImage();
    config.borderWidthFraction = borderWidth;
    ConfigThreshold configThreshold;
    if (robust)
        configThreshold = ConfigThreshold.local(ThresholdType.LOCAL_MEAN, 21);
    else
        configThreshold = ConfigThreshold.fixed(DEFAULT_THRESHOLD);
    SquareImage_to_FiducialDetector<GrayU8> detector = FactoryFiducial.squareImage(config, configThreshold, GrayU8.class);
    for (int i = 0; i < paths.size(); i++) {
        BufferedImage buffered = UtilImageIO.loadImage(paths.get(i));
        if (buffered == null)
            throw new RuntimeException("Can't find pattern " + paths.get(i));
        GrayU8 pattern = new GrayU8(buffered.getWidth(), buffered.getHeight());
        ConvertBufferedImage.convertFrom(buffered, pattern);
        detector.addPatternImage(pattern, 125, sizes.get(i));
    }
    this.detector = detector;
}

Example 10 with DogArray_F64

use of org.ddogleg.struct.DogArray_F64 in project BoofCV by lessthanoptimal.

the class TestShapeFittingOps method averageCircle_I32.

@Test
void averageCircle_I32() {
    List<Point2D_I32> points = new ArrayList<>();
    points.add(new Point2D_I32(0, 0));
    points.add(new Point2D_I32(10, 0));
    points.add(new Point2D_I32(5, 5));
    points.add(new Point2D_I32(5, -5));
    FitData<Circle2D_F64> found = ShapeFittingOps.averageCircle_I32(points, null, null);
    assertEquals(5, found.shape.center.x, 1e-5);
    assertEquals(0, found.shape.center.y, 1e-5);
    assertEquals(5, found.shape.radius, 1e-5);
    assertEquals(0, found.error, 1e-5);
    // Pass in storage and see if it fails
    found.error = 23;
    found.shape.center.x = 3;
    DogArray_F64 optional = new DogArray_F64();
    optional.push(4);
    ShapeFittingOps.averageCircle_I32(points, optional, found);
    assertEquals(5, found.shape.center.x, 1e-5);
    assertEquals(0, found.shape.center.y, 1e-5);
    assertEquals(5, found.shape.radius, 1e-5);
    assertEquals(0, found.error, 1e-5);
    // now make it no longer a perfect fit
    points.get(0).x = -1;
    found = ShapeFittingOps.averageCircle_I32(points, null, null);
    assertTrue(found.error > 0);
}