Examples with Point2Transform2_F64 boofcv.struct.distort.Point2Transform2_F64 used on opensource projects

Example 41 with Point2Transform2_F64

use of boofcv.struct.distort.Point2Transform2_F64 in project BoofCV by lessthanoptimal.

the class FiducialDetectorPnP method setLensDistortion.

@Override
public void setLensDistortion(@Nullable LensDistortionNarrowFOV distortion, int width, int height) {
    if (distortion != null) {
        this.hasCameraModel = true;
        this.lensDistortion = distortion;
        this.pixelToNorm = lensDistortion.undistort_F64(true, false);
        Point2Transform2_F64 normToPixel = lensDistortion.distort_F64(false, true);
        stability.setTransforms(pixelToNorm, normToPixel);
    } else {
        this.hasCameraModel = false;
        this.lensDistortion = null;
        this.pixelToNorm = null;
    }
}

Example 42 with Point2Transform2_F64

use of boofcv.struct.distort.Point2Transform2_F64 in project BoofCV by lessthanoptimal.

the class MicroQrCodeDetectorPnP method setLensDistortion.

@Override
public void setLensDistortion(@Nullable LensDistortionNarrowFOV distortion, int width, int height) {
    super.setLensDistortion(distortion, width, height);
    if (distortion == null) {
        poseUtils.setLensDistortion(null, null);
        // Yes this shouldn't be hard coded to that type. It feels dirty adding lens distortion to the
        // generic QR Code detector... deal with this later if there is more than one detector
        ((MicroQrCodePreciseDetector) detector).setLensDistortion(width, height, null);
    } else {
        Point2D_F64 test = new Point2D_F64();
        Point2Transform2_F64 undistToDist = distortion.distort_F64(true, true);
        undistToDist.compute(0, 0, test);
        poseUtils.setLensDistortion(distortion.undistort_F64(true, false), undistToDist);
        // If there's no actual distortion don't undistort the image while processing. Faster this way
        if (test.norm() <= UtilEjml.TEST_F32) {
            ((MicroQrCodePreciseDetector) detector).setLensDistortion(width, height, null);
        } else {
            ((MicroQrCodePreciseDetector) detector).setLensDistortion(width, height, distortion);
        }
    }
}

Example 43 with Point2Transform2_F64

use of boofcv.struct.distort.Point2Transform2_F64 in project BoofCV by lessthanoptimal.

the class ExampleMultiBaselineStereo method main.

public static void main(String[] args) {
    // Compute a sparse reconstruction. This will give us intrinsic and extrinsic for all views
    var example = new ExampleMultiViewSparseReconstruction();
    // Specifies the "center" frame to use
    int centerViewIdx = 15;
    example.compute("tree_snow_01.mp4", true);
    // example.compute("ditch_02.mp4", true);
    // example.compute("holiday_display_01.mp4"", true);
    // example.compute("log_building_02.mp4"", true);
    // example.compute("drone_park_01.mp4", false);
    // example.compute("stone_sign.mp4", true);
    // We need a way to load images based on their ID. In this particular case the ID encodes the array index.
    var imageLookup = new LookUpImageFilesByIndex(example.imageFiles);
    // Next we tell it which view to use as the "center", which acts as the common view for all disparity images.
    // The process of selecting the best views to use as centers is a problem all it's own. To keep things
    // we just pick a frame.
    SceneWorkingGraph.View center = example.working.getAllViews().get(centerViewIdx);
    // The final scene refined by bundle adjustment is created by the Working graph. However the 3D relationship
    // between views is contained in the pairwise graph. A View in the working graph has a reference to the view
    // in the pairwise graph. Using that we will find all connected views that have a 3D relationship
    var pairedViewIdxs = new DogArray_I32();
    var sbaIndexToImageID = new TIntObjectHashMap<String>();
    // This relationship between pairwise and working graphs might seem (and is) a bit convoluted. The Pairwise
    // graph is the initial crude sketch of what might be connected. The working graph is an intermediate
    // data structure for computing the metric scene. SBA is a refinement of the working graph.
    // Iterate through all connected views in the pairwise graph and mark their indexes in the working graph
    center.pview.connections.forEach((m) -> {
        // if there isn't a 3D relationship just skip it
        if (!m.is3D)
            return;
        String connectedID = m.other(center.pview).id;
        SceneWorkingGraph.View connected = example.working.views.get(connectedID);
        // Make sure the pairwise view exists in the working graph too
        if (connected == null)
            return;
        // Add this view to the index to name/ID lookup table
        sbaIndexToImageID.put(connected.index, connectedID);
        // Note that this view is one which acts as the second image in the stereo pair
        pairedViewIdxs.add(connected.index);
    });
    // Add the center camera image to the ID look up table
    sbaIndexToImageID.put(centerViewIdx, center.pview.id);
    // Configure there stereo disparity algorithm which is used
    var configDisparity = new ConfigDisparityBMBest5();
    configDisparity.validateRtoL = 1;
    configDisparity.texture = 0.5;
    configDisparity.regionRadiusX = configDisparity.regionRadiusY = 4;
    configDisparity.disparityRange = 120;
    // This is the actual MBS algorithm mentioned previously. It selects the best disparity for each pixel
    // in the original image using a median filter.
    var multiBaseline = new MultiBaselineStereoIndependent<>(imageLookup, ImageType.SB_U8);
    multiBaseline.setStereoDisparity(FactoryStereoDisparity.blockMatchBest5(configDisparity, GrayU8.class, GrayF32.class));
    // Print out verbose debugging and profile information
    multiBaseline.setVerbose(System.out, null);
    multiBaseline.setVerboseProfiling(System.out);
    // Improve stereo by removing small regions, which tends to be noise. Consider adjusting the region size.
    multiBaseline.setDisparitySmoother(FactoryStereoDisparity.removeSpeckle(null, GrayF32.class));
    // Print out debugging information from the smoother
    // Objects.requireNonNull(multiBaseline.getDisparitySmoother()).setVerbose(System.out,null);
    // Creates a list where you can switch between different images/visualizations
    var listDisplay = new ListDisplayPanel();
    listDisplay.setPreferredSize(new Dimension(1000, 300));
    ShowImages.showWindow(listDisplay, "Intermediate Results", true);
    // We will display intermediate results as they come in
    multiBaseline.setListener((leftView, rightView, rectLeft, rectRight, disparity, mask, parameters, rect) -> {
        // Visualize the rectified stereo pair. You can interact with this window and verify
        // that the y-axis is  aligned
        var rectified = new RectifiedPairPanel(true);
        rectified.setImages(ConvertBufferedImage.convertTo(rectLeft, null), ConvertBufferedImage.convertTo(rectRight, null));
        // Cleans up the disparity image by zeroing out pixels that are outside the original image bounds
        RectifyImageOps.applyMask(disparity, mask, 0);
        // Display the colorized disparity
        BufferedImage colorized = VisualizeImageData.disparity(disparity, null, parameters.disparityRange, 0);
        SwingUtilities.invokeLater(() -> {
            listDisplay.addItem(rectified, "Rectified " + leftView + " " + rightView);
            listDisplay.addImage(colorized, leftView + " " + rightView);
        });
    });
    // Process the images and compute a single combined disparity image
    if (!multiBaseline.process(example.scene, center.index, pairedViewIdxs, sbaIndexToImageID::get)) {
        throw new RuntimeException("Failed to fuse stereo views");
    }
    // Extract the point cloud from the fused disparity image
    GrayF32 fusedDisparity = multiBaseline.getFusedDisparity();
    DisparityParameters fusedParam = multiBaseline.getFusedParam();
    BufferedImage colorizedDisp = VisualizeImageData.disparity(fusedDisparity, null, fusedParam.disparityRange, 0);
    ShowImages.showWindow(colorizedDisp, "Fused Disparity");
    // Now compute the point cloud it represents and the color of each pixel.
    // For the fused image, instead of being in rectified image coordinates it's in the original image coordinates
    // this makes extracting color much easier.
    var cloud = new DogArray<>(Point3D_F64::new);
    var cloudRgb = new DogArray_I32(cloud.size);
    // Load the center image in color
    var colorImage = new InterleavedU8(1, 1, 3);
    imageLookup.loadImage(center.pview.id, colorImage);
    // Since the fused image is in the original (i.e. distorted) pixel coordinates and is not rectified,
    // that needs to be taken in account by undistorting the image to create the point cloud.
    CameraPinholeBrown intrinsic = BundleAdjustmentOps.convert(example.scene.cameras.get(center.cameraIdx).model, colorImage.width, colorImage.height, null);
    Point2Transform2_F64 pixel_to_norm = new LensDistortionBrown(intrinsic).distort_F64(true, false);
    MultiViewStereoOps.disparityToCloud(fusedDisparity, fusedParam, new PointToPixelTransform_F64(pixel_to_norm), (pixX, pixY, x, y, z) -> {
        cloud.grow().setTo(x, y, z);
        cloudRgb.add(colorImage.get24(pixX, pixY));
    });
    // Configure the point cloud viewer
    PointCloudViewer pcv = VisualizeData.createPointCloudViewer();
    pcv.setCameraHFov(UtilAngle.radian(70));
    pcv.setTranslationStep(0.15);
    pcv.addCloud(cloud.toList(), cloudRgb.data);
    // pcv.setColorizer(new SingleAxisRgb.Z().fperiod(30.0));
    JComponent viewer = pcv.getComponent();
    viewer.setPreferredSize(new Dimension(600, 600));
    ShowImages.showWindow(viewer, "Point Cloud", true);
    System.out.println("Done");
}

Example 44 with Point2Transform2_F64

use of boofcv.struct.distort.Point2Transform2_F64 in project BoofCV by lessthanoptimal.

the class ExamplePnP method createObservations.

/**
 * Generates synthetic observations randomly in front of the camera. Observations are in normalized image
 * coordinates and not pixels!  See {@link PerspectiveOps#convertPixelToNorm} for how to go from pixels
 * to normalized image coordinates.
 */
public List<Point2D3D> createObservations(Se3_F64 worldToCamera, int total) {
    Se3_F64 cameraToWorld = worldToCamera.invert(null);
    // transform from pixel coordinates to normalized pixel coordinates, which removes lens distortion
    Point2Transform2_F64 pixelToNorm = LensDistortionFactory.narrow(intrinsic).undistort_F64(true, false);
    List<Point2D3D> observations = new ArrayList<>();
    Point2D_F64 norm = new Point2D_F64();
    for (int i = 0; i < total; i++) {
        // randomly pixel a point inside the image
        double x = rand.nextDouble() * intrinsic.width;
        double y = rand.nextDouble() * intrinsic.height;
        // Convert to normalized image coordinates because that's what PNP needs.
        // it can't process pixel coordinates
        pixelToNorm.compute(x, y, norm);
        // Randomly pick a depth and compute 3D coordinate
        double Z = rand.nextDouble() + 4;
        double X = norm.x * Z;
        double Y = norm.y * Z;
        // Change the point's reference frame from camera to world
        Point3D_F64 cameraPt = new Point3D_F64(X, Y, Z);
        Point3D_F64 worldPt = new Point3D_F64();
        SePointOps_F64.transform(cameraToWorld, cameraPt, worldPt);
        // Save the perfect noise free observation
        Point2D3D o = new Point2D3D();
        o.getLocation().setTo(worldPt);
        o.getObservation().setTo(norm.x, norm.y);
        observations.add(o);
    }
    return observations;
}

Example 45 with Point2Transform2_F64

use of boofcv.struct.distort.Point2Transform2_F64 in project BoofCV by lessthanoptimal.

the class ExampleStereoTwoViewsOneCamera method drawInliers.

/**
 * Draw inliers for debugging purposes. Need to convert from normalized to pixel coordinates.
 */
public static void drawInliers(BufferedImage left, BufferedImage right, CameraPinholeBrown intrinsic, List<AssociatedPair> normalized) {
    Point2Transform2_F64 n_to_p = LensDistortionFactory.narrow(intrinsic).distort_F64(false, true);
    List<AssociatedPair> pixels = new ArrayList<>();
    for (AssociatedPair n : normalized) {
        AssociatedPair p = new AssociatedPair();
        n_to_p.compute(n.p1.x, n.p1.y, p.p1);
        n_to_p.compute(n.p2.x, n.p2.y, p.p2);
        pixels.add(p);
    }
    // display the results
    AssociationPanel panel = new AssociationPanel(20);
    panel.setAssociation(pixels);
    panel.setImages(left, right);
    ShowImages.showWindow(panel, "Inlier Features", true);
}