Examples with Point4D_F64 georegression.struct.point.Point4D_F64 used on opensource projects

Example 26 with Point4D_F64

use of georegression.struct.point.Point4D_F64 in project BoofCV by lessthanoptimal.

the class CommonTriangulationChecks method convertH.

protected Point4D_F64 convertH(Point3D_F64 X) {
    double scale = rand.nextGaussian();
    if (Math.abs(scale) < 1e-5)
        scale = 0.001;
    Point4D_F64 P = new Point4D_F64();
    P.x = X.x * scale;
    P.y = X.y * scale;
    P.z = X.z * scale;
    P.w = scale;
    return P;
}

Example 27 with Point4D_F64

use of georegression.struct.point.Point4D_F64 in project BoofCV by lessthanoptimal.

the class TestTriangulateProjectiveLinearDLT method triangulate_projective_noise.

/**
 * Add a tinny bit of noise and see if it blows up
 */
@Test
void triangulate_projective_noise() {
    createScene();
    TriangulateProjectiveLinearDLT alg = new TriangulateProjectiveLinearDLT();
    Point4D_F64 foundX = new Point4D_F64();
    obsPixels.get(0).x += 0.01;
    obsPixels.get(0).y -= 0.01;
    alg.triangulate(obsPixels, cameraMatrices, foundX);
    // project the found coordinate back on to each image
    Point2D_F64 foundPixel = new Point2D_F64();
    for (int i = 0; i < cameraMatrices.size(); i++) {
        DMatrixRMaj P = cameraMatrices.get(i);
        Point2D_F64 expected = obsPixels.get(i);
        GeometryMath_F64.mult(P, foundX, foundPixel);
        assertEquals(0, expected.distance(foundPixel), 0.03);
    }
}

Example 28 with Point4D_F64

use of georegression.struct.point.Point4D_F64 in project BoofCV by lessthanoptimal.

the class ExampleMultiViewSparseReconstruction method visualizeSparseCloud.

/**
 * To visualize the results we will render a sparse point cloud along with the location of each camera in the
 * scene.
 */
public void visualizeSparseCloud() {
    checkTrue(scene.isHomogenous());
    List<Point3D_F64> cloudXyz = new ArrayList<>();
    Point4D_F64 world = new Point4D_F64();
    // NOTE: By default the colors found below are not used. Look before to see why and how to turn them on.
    // 
    // Colorize the cloud by reprojecting the images. The math is straight forward but there's a lot of book
    // keeping that needs to be done due to the scene data structure. A class is provided to make this process easy
    var imageLookup = new LookUpImageFilesByIndex(imageFiles);
    var colorize = new ColorizeMultiViewStereoResults<>(new LookUpColorRgbFormats.PL_U8(), imageLookup);
    DogArray_I32 rgb = new DogArray_I32();
    rgb.resize(scene.points.size);
    colorize.processScenePoints(scene, // String encodes the image's index
    (viewIdx) -> viewIdx + "", // Assign the RGB color
    (pointIdx, r, g, b) -> rgb.set(pointIdx, (r << 16) | (g << 8) | b));
    // Convert the structure into regular 3D points from homogenous
    for (int i = 0; i < scene.points.size; i++) {
        scene.points.get(i).get(world);
        // array would be out of sync. Let's just throw it far far away then.
        if (world.w == 0.0)
            cloudXyz.add(new Point3D_F64(0, 0, Double.MAX_VALUE));
        else
            cloudXyz.add(new Point3D_F64(world.x / world.w, world.y / world.w, world.z / world.w));
    }
    PointCloudViewer viewer = VisualizeData.createPointCloudViewer();
    viewer.setFog(true);
    // We just did a bunch of work to look up the true color of points, however for sparse data it's easy to see
    // the structure with psuedo color. Comment out the line below to see the true color.
    viewer.setColorizer(new TwoAxisRgbPlane.Z_XY(1.0).fperiod(40));
    viewer.setDotSize(1);
    viewer.setTranslationStep(0.15);
    viewer.addCloud((idx, p) -> p.setTo(cloudXyz.get(idx)), rgb::get, rgb.size);
    viewer.setCameraHFov(UtilAngle.radian(60));
    SwingUtilities.invokeLater(() -> {
        // Show where the cameras are
        BoofSwingUtil.visualizeCameras(scene, viewer);
        // Size the window and show it to the user
        viewer.getComponent().setPreferredSize(new Dimension(600, 600));
        ShowImages.showWindow(viewer.getComponent(), "Refined Scene", true);
        var copy = new DogArray<>(Point3dRgbI_F64::new);
        viewer.copyCloud(copy);
        try (var out = new FileOutputStream("saved_cloud.ply")) {
            PointCloudIO.save3D(PointCloudIO.Format.PLY, PointCloudReader.wrapF64RGB(copy.toList()), true, out);
        } catch (IOException e) {
            e.printStackTrace();
        }
    });
}

Example 29 with Point4D_F64

use of georegression.struct.point.Point4D_F64 in project BoofCV by lessthanoptimal.

the class TestCompatibleProjectiveHomography method refineWorld.

@Test
void refineWorld() {
    createScene(20, false);
    // Find the homography
    DMatrixRMaj H = new DMatrixRMaj(4, 4);
    CompatibleProjectiveHomography alg = new CompatibleProjectiveHomography();
    List<Point3D_F64> world3a = new ArrayList<>();
    List<Point3D_F64> world3b = new ArrayList<>();
    for (int i = 0; i < worldPts.size(); i++) {
        Point4D_F64 a = worldPts.get(i);
        Point4D_F64 b = sceneB.get(i);
        world3a.add(new Point3D_F64(a.x / a.w, a.y / a.w, a.z / a.w));
        world3b.add(new Point3D_F64(b.x / b.w, b.y / b.w, b.z / b.w));
    }
    // Get the initial value of H
    assertTrue(alg.fitPoints(worldPts, sceneB, H));
    // break the model
    H.data[3] += 0.1;
    H.data[7] -= 0.8;
    // Refine it
    // alg.lm.setVerbose(System.out,0);
    alg.refineWorld(world3a, world3b, H);
    checkCamerasH(H, 1e-5);
}

Example 30 with Point4D_F64

use of georegression.struct.point.Point4D_F64 in project BoofCV by lessthanoptimal.

the class TestCompatibleProjectiveHomography method createScene.

@Override
public void createScene(int numFeatures, boolean planar) {
    super.createScene(numFeatures, planar);
    // Triangulate 3D points in another projective
    TrifocalTensor T = MultiViewOps.createTrifocal(cameras.get(0), cameras.get(1), cameras.get(2), null);
    DMatrixRMaj P1 = new DMatrixRMaj(3, 4);
    DMatrixRMaj P2 = new DMatrixRMaj(3, 4);
    DMatrixRMaj P3 = new DMatrixRMaj(3, 4);
    MultiViewOps.trifocalToCameraMatrices(T, P2, P3);
    CommonOps_DDRM.setIdentity(P1);
    TriangulateNViewsProjective triangulator = FactoryMultiView.triangulateNViewProj(ConfigTriangulation.GEOMETRIC());
    List<Point2D_F64> observations = new ArrayList<>();
    for (int i = 0; i < 3; i++) {
        observations.add(new Point2D_F64());
    }
    camerasB = new ArrayList<>();
    camerasB.add(P1);
    camerasB.add(P2);
    camerasB.add(P3);
    sceneB = new ArrayList<>();
    for (int i = 0; i < numFeatures; i++) {
        AssociatedTriple t = triples.get(i);
        observations.get(0).setTo(t.p1);
        observations.get(1).setTo(t.p2);
        observations.get(2).setTo(t.p3);
        Point4D_F64 location = new Point4D_F64();
        assertTrue(triangulator.triangulate(observations, camerasB, location));
        sceneB.add(location);
    }
}