Examples with Estimate1ofEpipolar boofcv.abst.geo.Estimate1ofEpipolar used on opensource projects

Example 1 with Estimate1ofEpipolar

use of boofcv.abst.geo.Estimate1ofEpipolar in project BoofCV by lessthanoptimal.

the class TestRemovePerspectiveDistortion method applyForwardTransform.

private void applyForwardTransform(GrayF32 expected, GrayF32 input, Point2D_F64 topLeft, Point2D_F64 topRight, Point2D_F64 bottomRight, Point2D_F64 bottomLeft) {
    Estimate1ofEpipolar computeHomography = FactoryMultiView.computeHomographyDLT(true);
    ArrayList<AssociatedPair> associatedPairs = new ArrayList<>();
    associatedPairs.add(new AssociatedPair(topLeft, new Point2D_F64(0, 0)));
    associatedPairs.add(new AssociatedPair(topRight, new Point2D_F64(expected.width - 1, 0)));
    associatedPairs.add(new AssociatedPair(bottomRight, new Point2D_F64(expected.width - 1, expected.height - 1)));
    associatedPairs.add(new AssociatedPair(bottomLeft, new Point2D_F64(0, expected.height - 1)));
    DMatrixRMaj H = new DMatrixRMaj(3, 3);
    computeHomography.process(associatedPairs, H);
    FMatrixRMaj H32 = new FMatrixRMaj(3, 3);
    ConvertMatrixData.convert(H, H32);
    new FDistort(expected, input).transform(new PointTransformHomography_F32(H32)).apply();
}

Example 2 with Estimate1ofEpipolar

use of boofcv.abst.geo.Estimate1ofEpipolar in project BoofCV by lessthanoptimal.

the class BenchmarkStabilityFundamental method evaluateMinimal.

public void evaluateMinimal(GeoModelEstimatorN<DMatrixRMaj, AssociatedPair> estimatorN) {
    DistanceEpipolarConstraint distance = new DistanceEpipolarConstraint();
    Estimate1ofEpipolar estimator = new EstimateNto1ofEpipolar(estimatorN, distance, 1);
    scores = new ArrayList<>();
    int failed = 0;
    int numSamples = estimator.getMinimumPoints();
    Random rand = new Random(234);
    DMatrixRMaj F = new DMatrixRMaj(3, 3);
    for (int i = 0; i < 50; i++) {
        List<AssociatedPair> pairs = new ArrayList<>();
        // create a unique set of pairs
        while (pairs.size() < numSamples) {
            AssociatedPair p = observations.get(rand.nextInt(observations.size()));
            if (!pairs.contains(p)) {
                pairs.add(p);
            }
        }
        if (!estimator.process(pairs, F)) {
            failed++;
            continue;
        }
        // normalize the scale of F
        CommonOps_DDRM.scale(1.0 / CommonOps_DDRM.elementMaxAbs(F), F);
        double totalScore = 0;
        // score against all observations
        for (AssociatedPair p : observations) {
            double score = Math.abs(GeometryMath_F64.innerProd(p.p2, F, p.p1));
            if (Double.isNaN(score))
                System.out.println("Score is NaN");
            scores.add(score);
            totalScore += score;
        }
    // System.out.println("  score["+i+"] = "+totalScore);
    }
    Collections.sort(scores);
    System.out.printf(" Failures %3d  Score:  50%% = %6.3e  95%% = %6.3e\n", failed, scores.get(scores.size() / 2), scores.get((int) (scores.size() * 0.95)));
}

Example 3 with Estimate1ofEpipolar

use of boofcv.abst.geo.Estimate1ofEpipolar in project BoofCV by lessthanoptimal.

the class ExampleFundamentalMatrix method robustFundamental.

/**
 * Given a set of noisy observations, compute the Fundamental matrix while removing
 * the noise.
 *
 * @param matches List of associated features between the two images
 * @param inliers List of feature pairs that were determined to not be noise.
 * @return The found fundamental matrix.
 */
public static DMatrixRMaj robustFundamental(List<AssociatedPair> matches, List<AssociatedPair> inliers) {
    // used to create and copy new instances of the fit model
    ModelManager<DMatrixRMaj> managerF = new ModelManagerEpipolarMatrix();
    // Select which linear algorithm is to be used.  Try playing with the number of remove ambiguity points
    Estimate1ofEpipolar estimateF = FactoryMultiView.computeFundamental_1(EnumFundamental.LINEAR_7, 2);
    // Wrapper so that this estimator can be used by the robust estimator
    GenerateEpipolarMatrix generateF = new GenerateEpipolarMatrix(estimateF);
    // How the error is measured
    DistanceFromModelResidual<DMatrixRMaj, AssociatedPair> errorMetric = new DistanceFromModelResidual<>(new FundamentalResidualSampson());
    // Use RANSAC to estimate the Fundamental matrix
    ModelMatcher<DMatrixRMaj, AssociatedPair> robustF = new Ransac<>(123123, managerF, generateF, errorMetric, 6000, 0.1);
    // Estimate the fundamental matrix while removing outliers
    if (!robustF.process(matches))
        throw new IllegalArgumentException("Failed");
    // save the set of features that were used to compute the fundamental matrix
    inliers.addAll(robustF.getMatchSet());
    // Improve the estimate of the fundamental matrix using non-linear optimization
    DMatrixRMaj F = new DMatrixRMaj(3, 3);
    ModelFitter<DMatrixRMaj, AssociatedPair> refine = FactoryMultiView.refineFundamental(1e-8, 400, EpipolarError.SAMPSON);
    if (!refine.fitModel(inliers, robustF.getModelParameters(), F))
        throw new IllegalArgumentException("Failed");
    // Return the solution
    return F;
}

Example 4 with Estimate1ofEpipolar

use of boofcv.abst.geo.Estimate1ofEpipolar in project BoofCV by lessthanoptimal.

the class TestBaseDetectFiducialSquare method render.

/**
 * Draws a distorted pattern onto the output
 */
public static void render(GrayU8 pattern, Quadrilateral_F64 where, GrayU8 output) {
    int w = pattern.width;
    int h = pattern.height;
    ArrayList<AssociatedPair> associatedPairs = new ArrayList<>();
    associatedPairs.add(new AssociatedPair(where.a, new Point2D_F64(0, 0)));
    associatedPairs.add(new AssociatedPair(where.b, new Point2D_F64(w, 0)));
    associatedPairs.add(new AssociatedPair(where.c, new Point2D_F64(w, h)));
    associatedPairs.add(new AssociatedPair(where.d, new Point2D_F64(0, h)));
    Estimate1ofEpipolar computeHomography = FactoryMultiView.computeHomographyDLT(true);
    DMatrixRMaj H = new DMatrixRMaj(3, 3);
    computeHomography.process(associatedPairs, H);
    // Create the transform for distorting the image
    FMatrixRMaj H32 = new FMatrixRMaj(3, 3);
    ConvertMatrixData.convert(H, H32);
    PointTransformHomography_F32 homography = new PointTransformHomography_F32(H32);
    PixelTransform2_F32 pixelTransform = new PointToPixelTransform_F32(homography);
    // Apply distortion and show the results
    DistortImageOps.distortSingle(pattern, output, pixelTransform, InterpolationType.BILINEAR, BorderType.SKIP);
// ShowImages.showWindow(output, "Rendered");
// try {Thread.sleep(10000);} catch (InterruptedException e) {}
}

Example 5 with Estimate1ofEpipolar

use of boofcv.abst.geo.Estimate1ofEpipolar in project BoofCV by lessthanoptimal.

the class BenchmarkRuntimeRefineHomography method runAll.

public void runAll() {
    System.out.println("=========  Profile numFeatures " + NUM_POINTS);
    System.out.println();
    double tol = 1e-16;
    int MAX_ITER = 200;
    init(NUM_POINTS, PIXELS, true);
    Estimate1ofEpipolar computeAlg = FactoryMultiView.computeHomographyDLT(true);
    computeAlg.process(pairs, H);
    H.data[0] += 0.1;
    H.data[4] -= 0.15;
    H.data[7] -= 0.2;
    ProfileOperation.printOpsPerSec(new Refine("Simple", refineHomography(tol, MAX_ITER, EpipolarError.SIMPLE)), TEST_TIME);
    ProfileOperation.printOpsPerSec(new Refine("Sampson", refineHomography(tol, MAX_ITER, EpipolarError.SAMPSON)), TEST_TIME);
    ProfileOperation.printOpsPerSec(new Linear4(), TEST_TIME);
    System.out.println();
    System.out.println("Done");
}