Examples with LensDistortionBrown boofcv.alg.distort.brown.LensDistortionBrown used on opensource projects

Example 16 with LensDistortionBrown

use of boofcv.alg.distort.brown.LensDistortionBrown in project BoofCV by lessthanoptimal.

the class TestBaseDetectFiducialSquare method checkDetectRender.

private void checkDetectRender(int width, int height, CameraPinholeBrown intrinsic, boolean applyLens) {
    SimulatePlanarWorld simulator = new SimulatePlanarWorld();
    simulator.setCamera(intrinsic);
    double simulatedTargetWidth = 0.4;
    Se3_F64 markerToWorld = SpecialEuclideanOps_F64.eulerXyz(0, 0, 0.32, 0, Math.PI, 0, null);
    GrayF32 pattern = new GrayF32(100, 100);
    ImageMiscOps.fill(pattern, 0);
    ImageMiscOps.fillRectangle(pattern, 255, 25, 25, 50, 50);
    simulator.setBackground(255);
    simulator.resetScene();
    simulator.addSurface(markerToWorld, simulatedTargetWidth, pattern);
    simulator.render();
    // ShowImages.showWindow(simulator.getOutput(),"Simulated");
    // BoofMiscOps.sleep(10000);
    GrayU8 grayU8 = new GrayU8(width, height);
    ConvertImage.convert(simulator.getOutput(), grayU8);
    DetectCorner detector = new DetectCorner();
    if (applyLens)
        detector.configure(new LensDistortionBrown(intrinsic), width, height, false);
    detector.process(grayU8);
    assertEquals(1, detector.getFound().size);
}

Example 17 with LensDistortionBrown

use of boofcv.alg.distort.brown.LensDistortionBrown in project BoofCV by lessthanoptimal.

the class ExampleFiducialHamming method main.

public static void main(String[] args) {
    String directory = UtilIO.pathExample("fiducial/square_hamming/aruco_25h7");
    // load the lens distortion parameters and the input image
    CameraPinholeBrown param = CalibrationIO.load(new File(directory, "intrinsic.yaml"));
    LensDistortionNarrowFOV lensDistortion = new LensDistortionBrown(param);
    // You need to create a different configuration for each dictionary type
    ConfigHammingMarker configMarker = ConfigHammingMarker.loadDictionary(HammingDictionary.ARUCO_MIP_25h7);
    FiducialDetector<GrayF32> detector = FactoryFiducial.squareHamming(configMarker, /*detector*/
    null, GrayF32.class);
    // Provide it lens parameters so that a 3D pose estimate is possible
    detector.setLensDistortion(lensDistortion, param.width, param.height);
    // Load and process all example images
    ListDisplayPanel gui = new ListDisplayPanel();
    for (int imageID = 1; imageID <= 3; imageID++) {
        String name = String.format("image%02d.jpg", imageID);
        System.out.println("processing: " + name);
        // Load the image
        BufferedImage buffered = UtilImageIO.loadImageNotNull(new File(directory, name).getPath());
        // Convert to a BoofCV format
        GrayF32 input = ConvertBufferedImage.convertFrom(buffered, (GrayF32) null);
        // Run the detector
        detector.detect(input);
        // Render a 3D compute on top of all detections
        Graphics2D g2 = buffered.createGraphics();
        var targetToSensor = new Se3_F64();
        var locationPixel = new Point2D_F64();
        var bounds = new Polygon2D_F64();
        for (int i = 0; i < detector.totalFound(); i++) {
            detector.getCenter(i, locationPixel);
            detector.getBounds(i, bounds);
            g2.setColor(new Color(50, 50, 255));
            g2.setStroke(new BasicStroke(10));
            VisualizeShapes.drawPolygon(bounds, true, 1.0, g2);
            if (detector.hasID())
                System.out.println("Target ID = " + detector.getId(i));
            if (detector.hasMessage())
                System.out.println("Message   = " + detector.getMessage(i));
            System.out.println("2D Image Location = " + locationPixel);
            if (detector.is3D()) {
                detector.getFiducialToCamera(i, targetToSensor);
                System.out.println("3D Location:");
                System.out.println(targetToSensor);
                VisualizeFiducial.drawCube(targetToSensor, param, detector.getWidth(i), 3, g2);
                VisualizeFiducial.drawLabelCenter(targetToSensor, param, "" + detector.getId(i), g2);
            } else {
                VisualizeFiducial.drawLabel(locationPixel, "" + detector.getId(i), g2);
            }
        }
        gui.addImage(buffered, name, ScaleOptions.ALL);
    }
    ShowImages.showWindow(gui, "Example Fiducial Hamming", true);
}

Example 18 with LensDistortionBrown

use of boofcv.alg.distort.brown.LensDistortionBrown in project BoofCV by lessthanoptimal.

the class ExampleFiducialBinary method main.

public static void main(String[] args) {
    String directory = UtilIO.pathExample("fiducial/binary");
    // load the lens distortion parameters and the input image
    CameraPinholeBrown param = CalibrationIO.load(new File(directory, "intrinsic.yaml"));
    var lensDistortion = new LensDistortionBrown(param);
    BufferedImage input = UtilImageIO.loadImageNotNull(directory, "image0000.jpg");
    // BufferedImage input = UtilImageIO.loadImageNotNull(directory, "image0001.jpg");
    // BufferedImage input = UtilImageIO.loadImageNotNull(directory, "image0002.jpg");
    GrayF32 original = ConvertBufferedImage.convertFrom(input, true, ImageType.single(GrayF32.class));
    // Detect the fiducial
    FiducialDetector<GrayF32> detector = FactoryFiducial.squareBinary(new ConfigFiducialBinary(0.1), ConfigThreshold.local(ThresholdType.LOCAL_MEAN, 21), GrayF32.class);
    // new ConfigFiducialBinary(0.1), ConfigThreshold.fixed(100),GrayF32.class);
    detector.setLensDistortion(lensDistortion, param.width, param.height);
    detector.detect(original);
    // print the results
    Graphics2D g2 = input.createGraphics();
    var targetToSensor = new Se3_F64();
    var locationPixel = new Point2D_F64();
    var bounds = new Polygon2D_F64();
    for (int i = 0; i < detector.totalFound(); i++) {
        detector.getCenter(i, locationPixel);
        detector.getBounds(i, bounds);
        g2.setColor(new Color(50, 50, 255));
        g2.setStroke(new BasicStroke(10));
        VisualizeShapes.drawPolygon(bounds, true, 1.0, g2);
        if (detector.hasID())
            System.out.println("Target ID = " + detector.getId(i));
        if (detector.hasMessage())
            System.out.println("Message   = " + detector.getMessage(i));
        System.out.println("2D Image Location = " + locationPixel);
        if (detector.is3D()) {
            detector.getFiducialToCamera(i, targetToSensor);
            System.out.println("3D Location:");
            System.out.println(targetToSensor);
            VisualizeFiducial.drawCube(targetToSensor, param, detector.getWidth(i), 3, g2);
            VisualizeFiducial.drawLabelCenter(targetToSensor, param, "" + detector.getId(i), g2);
        } else {
            VisualizeFiducial.drawLabel(locationPixel, "" + detector.getId(i), g2);
        }
    }
    ShowImages.showWindow(input, "Fiducials", true);
}

Example 19 with LensDistortionBrown

use of boofcv.alg.distort.brown.LensDistortionBrown in project BoofCV by lessthanoptimal.

the class ColorizeMultiViewStereoResults method processMvsCloud.

/**
 * Extracts color information for the point cloud on a view by view basis.
 *
 * @param scene (Input) Geometric description of the scene
 * @param mvs (Input) Contains the 3D point cloud
 * @param indexColor (Output) RGB values are passed through to this function.
 */
public void processMvsCloud(SceneStructureMetric scene, MultiViewStereoFromKnownSceneStructure<?> mvs, BoofLambdas.IndexRgbConsumer indexColor) {
    // Get a list of views that were used as "centers"
    List<ViewInfo> centers = mvs.getListCenters();
    // Get the point cloud
    DogArray<Point3D_F64> cloud = mvs.getDisparityCloud().getCloud();
    // Step through each "center" view
    for (int centerIdx = 0; centerIdx < centers.size(); centerIdx++) {
        ViewInfo center = centers.get(centerIdx);
        if (!lookupImages.loadImage(center.relations.id, image))
            throw new RuntimeException("Couldn't find image: " + center.relations.id);
        // Which points came from this view/center
        int idx0 = mvs.getDisparityCloud().viewPointIdx.get(centerIdx);
        int idx1 = mvs.getDisparityCloud().viewPointIdx.get(centerIdx + 1);
        // Setup the camera projection model using bundle adjustment model directly
        BundleAdjustmentOps.convert(scene.getViewCamera(center.metric).model, image.width, image.height, intrinsic);
        Point2Transform2_F64 norm_to_pixel = new LensDistortionBrown(intrinsic).distort_F64(false, true);
        // Get the transform from world/cloud to this view
        scene.getWorldToView(center.metric, world_to_view, tmp);
        // Grab the colorized points from this view
        colorizer.process3(image, cloud.toList(), idx0, idx1, world_to_view, norm_to_pixel, indexColor);
    }
}

Example 20 with LensDistortionBrown

use of boofcv.alg.distort.brown.LensDistortionBrown 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");
}