Examples with GrayU8 boofcv.struct.image.GrayU8 used on opensource projects

Example 66 with GrayU8

use of boofcv.struct.image.GrayU8 in project BoofCV by lessthanoptimal.

the class ExampleStereoDisparity3D method main.

public static void main(String[] args) {
    // ------------- Compute Stereo Correspondence
    // Load camera images and stereo camera parameters
    String calibDir = UtilIO.pathExample("calibration/stereo/Bumblebee2_Chess/");
    String imageDir = UtilIO.pathExample("stereo/");
    StereoParameters param = CalibrationIO.load(new File(calibDir, "stereo.yaml"));
    // load and convert images into a BoofCV format
    BufferedImage origLeft = UtilImageIO.loadImage(imageDir, "chair01_left.jpg");
    BufferedImage origRight = UtilImageIO.loadImage(imageDir, "chair01_right.jpg");
    GrayU8 distLeft = ConvertBufferedImage.convertFrom(origLeft, (GrayU8) null);
    GrayU8 distRight = ConvertBufferedImage.convertFrom(origRight, (GrayU8) null);
    // re-scale input images
    GrayU8 scaledLeft = new GrayU8((int) (distLeft.width * scale), (int) (distLeft.height * scale));
    GrayU8 scaledRight = new GrayU8((int) (distRight.width * scale), (int) (distRight.height * scale));
    new FDistort(distLeft, scaledLeft).scaleExt().apply();
    new FDistort(distRight, scaledRight).scaleExt().apply();
    // Don't forget to adjust camera parameters for the change in scale!
    PerspectiveOps.scaleIntrinsic(param.left, scale);
    PerspectiveOps.scaleIntrinsic(param.right, scale);
    // rectify images and compute disparity
    GrayU8 rectLeft = new GrayU8(scaledLeft.width, scaledLeft.height);
    GrayU8 rectRight = new GrayU8(scaledRight.width, scaledRight.height);
    RectifyCalibrated rectAlg = ExampleStereoDisparity.rectify(scaledLeft, scaledRight, param, rectLeft, rectRight);
    // GrayU8 disparity = ExampleStereoDisparity.denseDisparity(rectLeft, rectRight, 3,minDisparity, maxDisparity);
    GrayF32 disparity = ExampleStereoDisparity.denseDisparitySubpixel(rectLeft, rectRight, 3, minDisparity, maxDisparity);
    // ------------- Convert disparity image into a 3D point cloud
    // The point cloud will be in the left cameras reference frame
    DMatrixRMaj rectK = rectAlg.getCalibrationMatrix();
    DMatrixRMaj rectR = rectAlg.getRectifiedRotation();
    // used to display the point cloud
    PointCloudViewer viewer = new PointCloudViewer(rectK, 10);
    viewer.setPreferredSize(new Dimension(rectLeft.width, rectLeft.height));
    // extract intrinsic parameters from rectified camera
    double baseline = param.getBaseline();
    double fx = rectK.get(0, 0);
    double fy = rectK.get(1, 1);
    double cx = rectK.get(0, 2);
    double cy = rectK.get(1, 2);
    // Iterate through each pixel in disparity image and compute its 3D coordinate
    Point3D_F64 pointRect = new Point3D_F64();
    Point3D_F64 pointLeft = new Point3D_F64();
    for (int y = 0; y < disparity.height; y++) {
        for (int x = 0; x < disparity.width; x++) {
            double d = disparity.unsafe_get(x, y) + minDisparity;
            // skip over pixels were no correspondence was found
            if (d >= rangeDisparity)
                continue;
            // Coordinate in rectified camera frame
            pointRect.z = baseline * fx / d;
            pointRect.x = pointRect.z * (x - cx) / fx;
            pointRect.y = pointRect.z * (y - cy) / fy;
            // rotate into the original left camera frame
            GeometryMath_F64.multTran(rectR, pointRect, pointLeft);
            // add pixel to the view for display purposes and sets its gray scale value
            int v = rectLeft.unsafe_get(x, y);
            viewer.addPoint(pointLeft.x, pointLeft.y, pointLeft.z, v << 16 | v << 8 | v);
        }
    }
    // display the results.  Click and drag to change point cloud camera
    BufferedImage visualized = VisualizeImageData.disparity(disparity, null, minDisparity, maxDisparity, 0);
    ShowImages.showWindow(visualized, "Disparity");
    ShowImages.showWindow(viewer, "Point Cloud");
}

Example 67 with GrayU8

use of boofcv.struct.image.GrayU8 in project BoofCV by lessthanoptimal.

the class ExampleStereoTwoViewsOneCamera method rectifyImages.

/**
 * Remove lens distortion and rectify stereo images
 *
 * @param distortedLeft  Input distorted image from left camera.
 * @param distortedRight Input distorted image from right camera.
 * @param leftToRight    Camera motion from left to right
 * @param intrinsic      Intrinsic camera parameters
 * @param rectifiedLeft  Output rectified image for left camera.
 * @param rectifiedRight Output rectified image for right camera.
 * @param rectifiedK     Output camera calibration matrix for rectified camera
 */
public static void rectifyImages(GrayU8 distortedLeft, GrayU8 distortedRight, Se3_F64 leftToRight, CameraPinholeRadial intrinsic, GrayU8 rectifiedLeft, GrayU8 rectifiedRight, DMatrixRMaj rectifiedK) {
    RectifyCalibrated rectifyAlg = RectifyImageOps.createCalibrated();
    // original camera calibration matrices
    DMatrixRMaj K = PerspectiveOps.calibrationMatrix(intrinsic, (DMatrixRMaj) null);
    rectifyAlg.process(K, new Se3_F64(), K, leftToRight);
    // rectification matrix for each image
    DMatrixRMaj rect1 = rectifyAlg.getRect1();
    DMatrixRMaj rect2 = rectifyAlg.getRect2();
    // New calibration matrix,
    rectifiedK.set(rectifyAlg.getCalibrationMatrix());
    // Adjust the rectification to make the view area more useful
    RectifyImageOps.allInsideLeft(intrinsic, rect1, rect2, rectifiedK);
    // undistorted and rectify images
    FMatrixRMaj rect1_F32 = new FMatrixRMaj(3, 3);
    FMatrixRMaj rect2_F32 = new FMatrixRMaj(3, 3);
    ConvertMatrixData.convert(rect1, rect1_F32);
    ConvertMatrixData.convert(rect2, rect2_F32);
    ImageDistort<GrayU8, GrayU8> distortLeft = RectifyImageOps.rectifyImage(intrinsic, rect1_F32, BorderType.SKIP, distortedLeft.getImageType());
    ImageDistort<GrayU8, GrayU8> distortRight = RectifyImageOps.rectifyImage(intrinsic, rect2_F32, BorderType.SKIP, distortedRight.getImageType());
    distortLeft.apply(distortedLeft, rectifiedLeft);
    distortRight.apply(distortedRight, rectifiedRight);
}

Example 68 with GrayU8

use of boofcv.struct.image.GrayU8 in project BoofCV by lessthanoptimal.

the class ExampleBackgroundRemovalMoving method main.

public static void main(String[] args) {
    // Example with a moving camera.  Highlights why motion estimation is sometimes required
    String fileName = UtilIO.pathExample("tracking/chipmunk.mjpeg");
    // Camera has a bit of jitter in it.  Static kinda works but motion reduces false positives
    // String fileName = UtilIO.pathExample("background/horse_jitter.mp4");
    // Comment/Uncomment to switch input image type
    ImageType imageType = ImageType.single(GrayF32.class);
    // ImageType imageType = ImageType.il(3, InterleavedF32.class);
    // ImageType imageType = ImageType.il(3, InterleavedU8.class);
    // Configure the feature detector
    ConfigGeneralDetector confDetector = new ConfigGeneralDetector();
    confDetector.threshold = 10;
    confDetector.maxFeatures = 300;
    confDetector.radius = 6;
    // Use a KLT tracker
    PointTracker tracker = FactoryPointTracker.klt(new int[] { 1, 2, 4, 8 }, confDetector, 3, GrayF32.class, null);
    // This estimates the 2D image motion
    ImageMotion2D<GrayF32, Homography2D_F64> motion2D = FactoryMotion2D.createMotion2D(500, 0.5, 3, 100, 0.6, 0.5, false, tracker, new Homography2D_F64());
    ConfigBackgroundBasic configBasic = new ConfigBackgroundBasic(30, 0.005f);
    // Configuration for Gaussian model.  Note that the threshold changes depending on the number of image bands
    // 12 = gray scale and 40 = color
    ConfigBackgroundGaussian configGaussian = new ConfigBackgroundGaussian(12, 0.001f);
    configGaussian.initialVariance = 64;
    configGaussian.minimumDifference = 5;
    // Note that GMM doesn't interpolate the input image. Making it harder to model object edges.
    // However it runs faster because of this.
    ConfigBackgroundGmm configGmm = new ConfigBackgroundGmm();
    configGmm.initialVariance = 1600;
    configGmm.significantWeight = 1e-1f;
    // Comment/Uncomment to switch background mode
    BackgroundModelMoving background = FactoryBackgroundModel.movingBasic(configBasic, new PointTransformHomography_F32(), imageType);
    // FactoryBackgroundModel.movingGaussian(configGaussian, new PointTransformHomography_F32(), imageType);
    // FactoryBackgroundModel.movingGmm(configGmm,new PointTransformHomography_F32(), imageType);
    background.setUnknownValue(1);
    MediaManager media = DefaultMediaManager.INSTANCE;
    SimpleImageSequence video = media.openVideo(fileName, background.getImageType());
    // media.openCamera(null,640,480,background.getImageType());
    // ====== Initialize Images
    // storage for segmented image.  Background = 0, Foreground = 1
    GrayU8 segmented = new GrayU8(video.getNextWidth(), video.getNextHeight());
    // Grey scale image that's the input for motion estimation
    GrayF32 grey = new GrayF32(segmented.width, segmented.height);
    // coordinate frames
    Homography2D_F32 firstToCurrent32 = new Homography2D_F32();
    Homography2D_F32 homeToWorld = new Homography2D_F32();
    homeToWorld.a13 = grey.width / 2;
    homeToWorld.a23 = grey.height / 2;
    // Create a background image twice the size of the input image.  Tell it that the home is in the center
    background.initialize(grey.width * 2, grey.height * 2, homeToWorld);
    BufferedImage visualized = new BufferedImage(segmented.width, segmented.height, BufferedImage.TYPE_INT_RGB);
    ImageGridPanel gui = new ImageGridPanel(1, 2);
    gui.setImages(visualized, visualized);
    ShowImages.showWindow(gui, "Detections", true);
    double fps = 0;
    // smoothing factor for FPS
    double alpha = 0.01;
    while (video.hasNext()) {
        ImageBase input = video.next();
        long before = System.nanoTime();
        GConvertImage.convert(input, grey);
        if (!motion2D.process(grey)) {
            throw new RuntimeException("Should handle this scenario");
        }
        Homography2D_F64 firstToCurrent64 = motion2D.getFirstToCurrent();
        ConvertMatrixData.convert(firstToCurrent64, firstToCurrent32);
        background.segment(firstToCurrent32, input, segmented);
        background.updateBackground(firstToCurrent32, input);
        long after = System.nanoTime();
        fps = (1.0 - alpha) * fps + alpha * (1.0 / ((after - before) / 1e9));
        VisualizeBinaryData.renderBinary(segmented, false, visualized);
        gui.setImage(0, 0, (BufferedImage) video.getGuiImage());
        gui.setImage(0, 1, visualized);
        gui.repaint();
        System.out.println("FPS = " + fps);
        try {
            Thread.sleep(5);
        } catch (InterruptedException e) {
        }
    }
}

Example 69 with GrayU8

use of boofcv.struct.image.GrayU8 in project BoofCV by lessthanoptimal.

the class ExampleClassifySceneKnn method computeHistograms.

/**
 * For all the images in the training data set it computes a {@link HistogramScene}.  That data structure
 * contains the word histogram and the scene that the histogram belongs to.
 */
private List<HistogramScene> computeHistograms(FeatureToWordHistogram_F64 featuresToHistogram) {
    List<String> scenes = getScenes();
    // Processed results which will be passed into the k-NN algorithm
    List<HistogramScene> memory;
    memory = new ArrayList<>();
    for (int sceneIndex = 0; sceneIndex < scenes.size(); sceneIndex++) {
        String scene = scenes.get(sceneIndex);
        System.out.println("   " + scene);
        List<String> imagePaths = train.get(scene);
        for (String path : imagePaths) {
            GrayU8 image = UtilImageIO.loadImage(path, GrayU8.class);
            // reset before processing a new image
            featuresToHistogram.reset();
            describeImage.process(image);
            for (TupleDesc_F64 d : describeImage.getDescriptions()) {
                featuresToHistogram.addFeature(d);
            }
            featuresToHistogram.process();
            // The histogram is already normalized so that it sums up to 1.  This provides invariance
            // against the overall number of features changing.
            double[] histogram = featuresToHistogram.getHistogram();
            // Create the data structure used by the KNN classifier
            HistogramScene imageHist = new HistogramScene(NUMBER_OF_WORDS);
            imageHist.setHistogram(histogram);
            imageHist.type = sceneIndex;
            memory.add(imageHist);
        }
    }
    return memory;
}

Example 70 with GrayU8

use of boofcv.struct.image.GrayU8 in project BoofCV by lessthanoptimal.

the class ExampleClassifySceneKnn method computeClusters.

/**
 * Extract dense features across the training set.  Then clusters are found within those features.
 */
private AssignCluster<double[]> computeClusters() {
    System.out.println("Image Features");
    // computes features in the training image set
    List<TupleDesc_F64> features = new ArrayList<>();
    for (String scene : train.keySet()) {
        List<String> imagePaths = train.get(scene);
        System.out.println("   " + scene);
        for (String path : imagePaths) {
            GrayU8 image = UtilImageIO.loadImage(path, GrayU8.class);
            describeImage.process(image);
            // the descriptions will get recycled on the next call, so create a copy
            for (TupleDesc_F64 d : describeImage.getDescriptions()) {
                features.add(d.copy());
            }
        }
    }
    // add the features to the overall list which the clusters will be found inside of
    for (int i = 0; i < features.size(); i++) {
        cluster.addReference(features.get(i));
    }
    System.out.println("Clustering");
    // Find the clusters.  This can take a bit
    cluster.process(NUMBER_OF_WORDS);
    UtilIO.save(cluster.getAssignment(), CLUSTER_FILE_NAME);
    return cluster.getAssignment();
}