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

Example 1 with AssociatedTripleIndex

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

the class ExampleTrifocalStereoUncalibrated method main.

public static void main(String[] args) {
    String name = "rock_leaves_";
    // String name = "mono_wall_";
    // String name = "minecraft_cave1_";
    // String name = "minecraft_distant_";
    // String name = "bobcats_";
    // String name = "chicken_";
    // String name = "turkey_";
    // String name = "rockview_";
    // String name = "pebbles_";
    // String name = "books_";
    // String name = "skull_";
    // String name = "triflowers_";
    BufferedImage buff01 = UtilImageIO.loadImageNotNull(UtilIO.pathExample("triple/" + name + "01.jpg"));
    BufferedImage buff02 = UtilImageIO.loadImageNotNull(UtilIO.pathExample("triple/" + name + "02.jpg"));
    BufferedImage buff03 = UtilImageIO.loadImageNotNull(UtilIO.pathExample("triple/" + name + "03.jpg"));
    Planar<GrayU8> color01 = ConvertBufferedImage.convertFrom(buff01, true, ImageType.pl(3, GrayU8.class));
    Planar<GrayU8> color02 = ConvertBufferedImage.convertFrom(buff02, true, ImageType.pl(3, GrayU8.class));
    Planar<GrayU8> color03 = ConvertBufferedImage.convertFrom(buff03, true, ImageType.pl(3, GrayU8.class));
    GrayU8 image01 = ConvertImage.average(color01, null);
    GrayU8 image02 = ConvertImage.average(color02, null);
    GrayU8 image03 = ConvertImage.average(color03, null);
    // using SURF features. Robust and fairly fast to compute
    DetectDescribePoint<GrayU8, TupleDesc_F64> detDesc = FactoryDetectDescribe.surfStable(new ConfigFastHessian(0, 4, 1000, 1, 9, 4, 2), null, null, GrayU8.class);
    // Associate features across all three views using previous example code
    var associateThree = new ExampleAssociateThreeView();
    associateThree.initialize(detDesc);
    associateThree.detectFeatures(image01, 0);
    associateThree.detectFeatures(image02, 1);
    associateThree.detectFeatures(image03, 2);
    System.out.println("features01.size = " + associateThree.features01.size);
    System.out.println("features02.size = " + associateThree.features02.size);
    System.out.println("features03.size = " + associateThree.features03.size);
    int width = image01.width, height = image01.height;
    System.out.println("Image Shape " + width + " x " + height);
    double cx = width / 2;
    double cy = height / 2;
    // The self calibration step requires that the image coordinate system be in the image center
    associateThree.locations01.forEach(p -> p.setTo(p.x - cx, p.y - cy));
    associateThree.locations02.forEach(p -> p.setTo(p.x - cx, p.y - cy));
    associateThree.locations03.forEach(p -> p.setTo(p.x - cx, p.y - cy));
    // Converting data formats for the found features into what can be processed by SFM algorithms
    // Notice how the image center is subtracted from the coordinates? In many cases a principle point
    // of zero is assumed. This is a reasonable assumption in almost all modern cameras. Errors in
    // the principle point tend to materialize as translations and are non fatal.
    // Associate features in the three views using image information alone
    DogArray<AssociatedTripleIndex> associatedIdx = associateThree.threeViewPairwiseAssociate();
    // Convert the matched indexes into AssociatedTriple which contain the actual pixel coordinates
    var associated = new DogArray<>(AssociatedTriple::new);
    associatedIdx.forEach(p -> associated.grow().setTo(associateThree.locations01.get(p.a), associateThree.locations02.get(p.b), associateThree.locations03.get(p.c)));
    System.out.println("Total Matched Triples = " + associated.size);
    var model = new TrifocalTensor();
    List<AssociatedTriple> inliers = ExampleComputeTrifocalTensor.computeTrifocal(associated, model);
    System.out.println("Remaining after RANSAC " + inliers.size());
    // Show remaining associations from RANSAC
    var triplePanel = new AssociatedTriplePanel();
    triplePanel.setPixelOffset(cx, cy);
    triplePanel.setImages(buff01, buff02, buff03);
    triplePanel.setAssociation(inliers);
    ShowImages.showWindow(triplePanel, "Associations", true);
    // estimate using all the inliers
    // No need to re-scale the input because the estimator automatically adjusts the input on its own
    var configTri = new ConfigTrifocal();
    configTri.which = EnumTrifocal.ALGEBRAIC_7;
    configTri.converge.maxIterations = 100;
    Estimate1ofTrifocalTensor trifocalEstimator = FactoryMultiView.trifocal_1(configTri);
    if (!trifocalEstimator.process(inliers, model))
        throw new RuntimeException("Estimator failed");
    model.print();
    DMatrixRMaj P1 = CommonOps_DDRM.identity(3, 4);
    DMatrixRMaj P2 = new DMatrixRMaj(3, 4);
    DMatrixRMaj P3 = new DMatrixRMaj(3, 4);
    MultiViewOps.trifocalToCameraMatrices(model, P2, P3);
    // Most of the time this refinement step makes little difference, but in some edges cases it appears
    // to help convergence
    System.out.println("Refining projective camera matrices");
    RefineThreeViewProjective refineP23 = FactoryMultiView.threeViewRefine(null);
    if (!refineP23.process(inliers, P2, P3, P2, P3))
        throw new RuntimeException("Can't refine P2 and P3!");
    var selfcalib = new SelfCalibrationLinearDualQuadratic(1.0);
    selfcalib.addCameraMatrix(P1);
    selfcalib.addCameraMatrix(P2);
    selfcalib.addCameraMatrix(P3);
    var listPinhole = new ArrayList<CameraPinhole>();
    GeometricResult result = selfcalib.solve();
    if (GeometricResult.SOLVE_FAILED != result) {
        for (int i = 0; i < 3; i++) {
            Intrinsic c = selfcalib.getIntrinsics().get(i);
            CameraPinhole p = new CameraPinhole(c.fx, c.fy, 0, 0, 0, width, height);
            listPinhole.add(p);
        }
    } else {
        System.out.println("Self calibration failed!");
        for (int i = 0; i < 3; i++) {
            CameraPinhole p = new CameraPinhole(width / 2, width / 2, 0, 0, 0, width, height);
            listPinhole.add(p);
        }
    }
    // parameter
    for (int i = 0; i < 3; i++) {
        CameraPinhole r = listPinhole.get(i);
        System.out.println("fx=" + r.fx + " fy=" + r.fy + " skew=" + r.skew);
    }
    System.out.println("Projective to metric");
    // convert camera matrix from projective to metric
    // storage for rectifying homography
    var H = new DMatrixRMaj(4, 4);
    if (!MultiViewOps.absoluteQuadraticToH(selfcalib.getQ(), H))
        throw new RuntimeException("Projective to metric failed");
    var K = new DMatrixRMaj(3, 3);
    var worldToView = new ArrayList<Se3_F64>();
    for (int i = 0; i < 3; i++) {
        worldToView.add(new Se3_F64());
    }
    // ignore K since we already have that
    MultiViewOps.projectiveToMetric(P1, H, worldToView.get(0), K);
    MultiViewOps.projectiveToMetric(P2, H, worldToView.get(1), K);
    MultiViewOps.projectiveToMetric(P3, H, worldToView.get(2), K);
    // scale is arbitrary. Set max translation to 1
    adjustTranslationScale(worldToView);
    // Construct bundle adjustment data structure
    var structure = new SceneStructureMetric(false);
    structure.initialize(3, 3, inliers.size());
    var observations = new SceneObservations();
    observations.initialize(3);
    for (int i = 0; i < listPinhole.size(); i++) {
        BundlePinholeSimplified bp = new BundlePinholeSimplified();
        bp.f = listPinhole.get(i).fx;
        structure.setCamera(i, false, bp);
        structure.setView(i, i, i == 0, worldToView.get(i));
    }
    for (int i = 0; i < inliers.size(); i++) {
        AssociatedTriple t = inliers.get(i);
        observations.getView(0).add(i, (float) t.p1.x, (float) t.p1.y);
        observations.getView(1).add(i, (float) t.p2.x, (float) t.p2.y);
        observations.getView(2).add(i, (float) t.p3.x, (float) t.p3.y);
        structure.connectPointToView(i, 0);
        structure.connectPointToView(i, 1);
        structure.connectPointToView(i, 2);
    }
    // Initial estimate for point 3D locations
    triangulatePoints(structure, observations);
    ConfigLevenbergMarquardt configLM = new ConfigLevenbergMarquardt();
    configLM.dampeningInitial = 1e-3;
    configLM.hessianScaling = false;
    ConfigBundleAdjustment configSBA = new ConfigBundleAdjustment();
    configSBA.configOptimizer = configLM;
    // Create and configure the bundle adjustment solver
    BundleAdjustment<SceneStructureMetric> bundleAdjustment = FactoryMultiView.bundleSparseMetric(configSBA);
    // prints out useful debugging information that lets you know how well it's converging
    // bundleAdjustment.setVerbose(System.out,0);
    // convergence criteria
    bundleAdjustment.configure(1e-6, 1e-6, 100);
    bundleAdjustment.setParameters(structure, observations);
    bundleAdjustment.optimize(structure);
    // See if the solution is physically possible. If not fix and run bundle adjustment again
    checkBehindCamera(structure, observations, bundleAdjustment);
    // It's very difficult to find the best solution due to the number of local minimum. In the three view
    // case it's often the problem that a small translation is virtually identical to a small rotation.
    // Convergence can be improved by considering that possibility
    // Now that we have a decent solution, prune the worst outliers to improve the fit quality even more
    var pruner = new PruneStructureFromSceneMetric(structure, observations);
    pruner.pruneObservationsByErrorRank(0.7);
    pruner.pruneViews(10);
    pruner.pruneUnusedMotions();
    pruner.prunePoints(1);
    bundleAdjustment.setParameters(structure, observations);
    bundleAdjustment.optimize(structure);
    System.out.println("Final Views");
    for (int i = 0; i < 3; i++) {
        BundlePinholeSimplified cp = structure.getCameras().get(i).getModel();
        Vector3D_F64 T = structure.getParentToView(i).T;
        System.out.printf("[ %d ] f = %5.1f T=%s\n", i, cp.f, T.toString());
    }
    System.out.println("\n\nComputing Stereo Disparity");
    BundlePinholeSimplified cp = structure.getCameras().get(0).getModel();
    var intrinsic01 = new CameraPinholeBrown();
    intrinsic01.fsetK(cp.f, cp.f, 0, cx, cy, width, height);
    intrinsic01.fsetRadial(cp.k1, cp.k2);
    cp = structure.getCameras().get(1).getModel();
    var intrinsic02 = new CameraPinholeBrown();
    intrinsic02.fsetK(cp.f, cp.f, 0, cx, cy, width, height);
    intrinsic02.fsetRadial(cp.k1, cp.k2);
    Se3_F64 leftToRight = structure.getParentToView(1);
    // TODO dynamic max disparity
    computeStereoCloud(image01, image02, color01, color02, intrinsic01, intrinsic02, leftToRight, 0, 250);
}

Example 2 with AssociatedTripleIndex

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

the class AssociateThreeByPairs method pruneMatches.

/**
 * Removes by swapping all elements with a 'c' index of -1
 */
private void pruneMatches() {
    int index = 0;
    while (index < matches.size) {
        AssociatedTripleIndex a = matches.get(index);
        // not matched. Remove it from the list by copying that last element over it
        if (a.c == -1) {
            a.setTo(matches.get(matches.size - 1));
            matches.size--;
        } else {
            index++;
        }
    }
}

Example 3 with AssociatedTripleIndex

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

the class TestAssociateThreeByPairs method failOnAtoB.

/**
 * A->B is bad.
 */
@Test
void failOnAtoB() {
    DogArray<TupleDesc_F64> featuresA = UtilFeature.createArrayF64(1);
    DogArray<TupleDesc_F64> featuresB = UtilFeature.createArrayF64(1);
    DogArray<TupleDesc_F64> featuresC = UtilFeature.createArrayF64(1);
    DogArray_I32 featuresSetA = new DogArray_I32();
    DogArray_I32 featuresSetB = new DogArray_I32();
    DogArray_I32 featuresSetC = new DogArray_I32();
    featuresB.grow().setTo(234234234);
    featuresC.grow().setTo(2344234);
    featuresC.grow().setTo(99234234);
    for (int i = 0; i < 10; i++) {
        featuresA.grow().setTo(i);
        featuresB.grow().setTo(i + 0.12);
        featuresC.grow().setTo(i + 0.3);
    }
    // there is only one set
    featuresSetA.resize(featuresA.size);
    featuresSetA.fill(0);
    featuresSetB.resize(featuresB.size);
    featuresSetB.fill(0);
    featuresSetC.resize(featuresC.size);
    featuresSetC.fill(0);
    double maxError = 0.1 * 0.1 + 0.00000001;
    ScoreAssociation<TupleDesc_F64> score = FactoryAssociation.defaultScore(TupleDesc_F64.class);
    AssociateDescription<TupleDesc_F64> associate = FactoryAssociation.greedy(new ConfigAssociateGreedy(true, maxError), score);
    AssociateThreeByPairs<TupleDesc_F64> alg = new AssociateThreeByPairs<>(associate);
    alg.initialize(1);
    alg.setFeaturesA(featuresA, featuresSetA);
    alg.setFeaturesB(featuresB, featuresSetB);
    alg.setFeaturesC(featuresC, featuresSetC);
    alg.associate();
    DogArray<AssociatedTripleIndex> matches = alg.getMatches();
    assertEquals(0, matches.size);
}

Example 4 with AssociatedTripleIndex

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

the class ExampleAssociateThreeView method main.

public static void main(String[] args) {
    String name = "rock_leaves_";
    GrayU8 gray01 = UtilImageIO.loadImage(UtilIO.pathExample("triple/" + name + "01.jpg"), GrayU8.class);
    GrayU8 gray02 = UtilImageIO.loadImage(UtilIO.pathExample("triple/" + name + "02.jpg"), GrayU8.class);
    GrayU8 gray03 = UtilImageIO.loadImage(UtilIO.pathExample("triple/" + name + "03.jpg"), GrayU8.class);
    // Using SURF features. Robust and fairly fast to compute
    DetectDescribePoint<GrayU8, TupleDesc_F64> detDesc = FactoryDetectDescribe.surfStable(new ConfigFastHessian(0, 4, 1000, 1, 9, 4, 2), null, null, GrayU8.class);
    ExampleAssociateThreeView example = new ExampleAssociateThreeView();
    example.initialize(detDesc);
    // Compute and describe features inside the image
    example.detectFeatures(gray01, 0);
    example.detectFeatures(gray02, 1);
    example.detectFeatures(gray03, 2);
    System.out.println("features01.size = " + example.features01.size);
    System.out.println("features02.size = " + example.features02.size);
    System.out.println("features03.size = " + example.features03.size);
    // Find features for an association ring across all the views. This removes most false positives.
    DogArray<AssociatedTripleIndex> associatedIdx = example.threeViewPairwiseAssociate();
    // Convert the matched indexes into AssociatedTriple which contain the actual pixel coordinates
    var associated = new DogArray<>(AssociatedTriple::new);
    associatedIdx.forEach(p -> associated.grow().setTo(example.locations01.get(p.a), example.locations02.get(p.b), example.locations03.get(p.c)));
    System.out.println("Total Matched Triples = " + associated.size);
    // Show remaining associations from RANSAC
    var triplePanel = new AssociatedTriplePanel();
    triplePanel.setImages(UtilImageIO.loadImageNotNull(UtilIO.pathExample("triple/" + name + "01.jpg")), UtilImageIO.loadImageNotNull(UtilIO.pathExample("triple/" + name + "02.jpg")), UtilImageIO.loadImageNotNull(UtilIO.pathExample("triple/" + name + "03.jpg")));
    triplePanel.setAssociation(associated.toList());
    ShowImages.showWindow(triplePanel, "Associations", true);
}

Example 5 with AssociatedTripleIndex

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

the class TestAssociateThreeByPairs method failOnBtoC.

/**
 * A->B is good. B->C is bad.
 */
@Test
void failOnBtoC() {
    DogArray<TupleDesc_F64> featuresA = UtilFeature.createArrayF64(1);
    DogArray<TupleDesc_F64> featuresB = UtilFeature.createArrayF64(1);
    DogArray<TupleDesc_F64> featuresC = UtilFeature.createArrayF64(1);
    DogArray_I32 featuresSetA = new DogArray_I32();
    DogArray_I32 featuresSetB = new DogArray_I32();
    DogArray_I32 featuresSetC = new DogArray_I32();
    featuresB.grow().setTo(234234234);
    featuresC.grow().setTo(2344234);
    featuresC.grow().setTo(99234234);
    for (int i = 0; i < 10; i++) {
        featuresA.grow().setTo(i);
        featuresB.grow().setTo(i + 0.1);
        featuresC.grow().setTo(i + 0.22);
    }
    // there is only one set
    featuresSetA.resize(featuresA.size);
    featuresSetA.fill(0);
    featuresSetB.resize(featuresB.size);
    featuresSetB.fill(0);
    featuresSetC.resize(featuresC.size);
    featuresSetC.fill(0);
    double maxError = 0.1 * 0.1 + 0.00000001;
    ScoreAssociation<TupleDesc_F64> score = FactoryAssociation.defaultScore(TupleDesc_F64.class);
    AssociateDescription<TupleDesc_F64> associate = FactoryAssociation.greedy(new ConfigAssociateGreedy(true, maxError), score);
    AssociateThreeByPairs<TupleDesc_F64> alg = new AssociateThreeByPairs<>(associate);
    alg.initialize(1);
    alg.setFeaturesA(featuresA, featuresSetA);
    alg.setFeaturesB(featuresB, featuresSetB);
    alg.setFeaturesC(featuresC, featuresSetC);
    alg.associate();
    DogArray<AssociatedTripleIndex> matches = alg.getMatches();
    assertEquals(0, matches.size);
}