Examples with TupleDesc boofcv.struct.feature.TupleDesc used on opensource projects

Example 1 with TupleDesc

use of boofcv.struct.feature.TupleDesc in project BoofCV by lessthanoptimal.

the class VisualizeRegionDescriptionApp method updateTargetDescription.

/**
 * Extracts the target description and updates the panel.  Should only be called from a swing thread
 */
private void updateTargetDescription() {
    if (targetPt != null) {
        TupleDesc feature = describe.createDescription();
        describe.process(targetPt.x, targetPt.y, targetOrientation, targetRadius, feature);
        tuplePanel.setDescription(feature);
    } else {
        tuplePanel.setDescription(null);
    }
    tuplePanel.repaint();
}

Example 2 with TupleDesc

use of boofcv.struct.feature.TupleDesc in project BoofCV by lessthanoptimal.

the class TupleDescPanel method paintComponent.

@Override
public synchronized void paintComponent(Graphics g) {
    super.paintComponent(g);
    Graphics2D g2 = (Graphics2D) g;
    @Nullable TupleDesc desc = this.desc;
    if (desc == null) {
        g2.setColor(Color.WHITE);
        g2.fillRect(0, 0, getWidth(), getHeight());
    } else {
        int h = getHeight();
        int w = getWidth();
        int m = h / 2;
        int[] x = new int[desc.size()];
        int[] y = new int[desc.size()];
        // find the maximum magnitude of any of the elements
        double max = 0;
        for (int i = 0; i < desc.size(); i++) {
            double d = desc.getDouble(i);
            if (max < Math.abs(d)) {
                max = Math.abs(d);
            }
        }
        // draw a normalized histogram plot
        double stepX = 1.0 / desc.size();
        for (int i = 0; i < desc.size(); i++) {
            x[i] = (int) (w * i * stepX);
            y[i] = (int) ((m * desc.getDouble(i) / max) + m);
        }
        g2.setColor(Color.GRAY);
        g2.drawLine(0, m, w, m);
        g2.setStroke(new BasicStroke(2));
        g2.setColor(Color.RED);
        g2.drawPolyline(x, y, x.length);
        // print out the magnitude
        g2.setColor(Color.BLACK);
        String s = String.format("%4.1e", max);
        g2.drawString(s, 0, 20);
        g2.setColor(Color.BLUE);
        for (int i = 0; i < desc.size(); i++) {
            int r = 1;
            w = r * 2 + 1;
            g2.fillOval(x[i] - r, y[i] - r, w, w);
        }
    }
}

Example 3 with TupleDesc

use of boofcv.struct.feature.TupleDesc in project BoofCV by lessthanoptimal.

the class VisualizeAssociationMatchesApp method extractImageFeatures.

private void extractImageFeatures(Planar<T> color, T gray, FastQueue<TupleDesc> descs, List<Point2D_F64> locs) {
    detector.detect(gray);
    if (describe.getImageType().getFamily() == ImageType.Family.GRAY)
        describe.setImage(gray);
    else
        describe.setImage(color);
    orientation.setImage(gray);
    if (detector.hasScale()) {
        for (int i = 0; i < detector.getNumberOfFeatures(); i++) {
            double yaw = 0;
            Point2D_F64 pt = detector.getLocation(i);
            double radius = detector.getRadius(i);
            if (describe.requiresOrientation()) {
                orientation.setObjectRadius(radius);
                yaw = orientation.compute(pt.x, pt.y);
            }
            TupleDesc d = descs.grow();
            if (describe.process(pt.x, pt.y, yaw, radius, d)) {
                locs.add(pt.copy());
            } else {
                descs.removeTail();
            }
        }
    } else {
        orientation.setObjectRadius(10);
        for (int i = 0; i < detector.getNumberOfFeatures(); i++) {
            double yaw = 0;
            Point2D_F64 pt = detector.getLocation(i);
            if (describe.requiresOrientation()) {
                yaw = orientation.compute(pt.x, pt.y);
            }
            TupleDesc d = descs.grow();
            if (describe.process(pt.x, pt.y, yaw, 1, d)) {
                locs.add(pt.copy());
            } else {
                descs.removeTail();
            }
        }
    }
}

Example 4 with TupleDesc

use of boofcv.struct.feature.TupleDesc 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);
}

Example 5 with TupleDesc

use of boofcv.struct.feature.TupleDesc in project BoofCV by lessthanoptimal.

the class FactoryVisualOdometry method stereoDualTrackerPnP.

/**
 * Creates a stereo visual odometry algorithm that independently tracks features in left and right camera.
 *
 * @see VisOdomDualTrackPnP
 *
 * @param thresholdAdd When the number of inliers is below this number new features are detected
 * @param thresholdRetire When a feature has not been in the inlier list for this many ticks it is dropped
 * @param inlierPixelTol Tolerance in pixels for defining an inlier during robust model matching.  Typically 1.5
 * @param epipolarPixelTol Tolerance in pixels for enforcing the epipolar constraint
 * @param ransacIterations Number of iterations performed by RANSAC.  Try 300 or more.
 * @param refineIterations Number of iterations done during non-linear optimization.  Try 50 or more.
 * @param trackerLeft Tracker used for left camera
 * @param trackerRight Tracker used for right camera
 * @param imageType Type of image being processed
 * @return Stereo visual odometry algorithm.
 */
public static <T extends ImageGray<T>, Desc extends TupleDesc> StereoVisualOdometry<T> stereoDualTrackerPnP(int thresholdAdd, int thresholdRetire, double inlierPixelTol, double epipolarPixelTol, int ransacIterations, int refineIterations, PointTracker<T> trackerLeft, PointTracker<T> trackerRight, DescribeRegionPoint<T, Desc> descriptor, 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);
    // Pixel tolerance for RANSAC inliers - 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;
    Class<Desc> descType = descriptor.getDescriptionType();
    ScoreAssociation<Desc> scorer = FactoryAssociation.defaultScore(descType);
    AssociateStereo2D<Desc> associateStereo = new AssociateStereo2D<>(scorer, epipolarPixelTol, descType);
    // need to make sure associations are unique
    AssociateDescription2D<Desc> associateUnique = associateStereo;
    if (!associateStereo.uniqueDestination() || !associateStereo.uniqueSource()) {
        associateUnique = new EnforceUniqueByScore.Describe2D<>(associateStereo, true, true);
    }
    if (refineIterations > 0) {
        refinePnP = new PnPStereoRefineRodrigues(1e-12, refineIterations);
    }
    TriangulateTwoViewsCalibrated triangulate = FactoryMultiView.triangulateTwoGeometric();
    VisOdomDualTrackPnP<T, Desc> alg = new VisOdomDualTrackPnP<>(thresholdAdd, thresholdRetire, epipolarPixelTol, trackerLeft, trackerRight, descriptor, associateUnique, triangulate, motion, refinePnP);
    return new WrapVisOdomDualTrackPnP<>(pnpStereo, distanceMono, distanceStereo, associateStereo, alg, refinePnP, imageType);
}