Examples with ConfigGeneralDetector boofcv.abst.feature.detect.interest.ConfigGeneralDetector used on opensource projects

Example 1 with ConfigGeneralDetector

use of boofcv.abst.feature.detect.interest.ConfigGeneralDetector 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 2 with ConfigGeneralDetector

use of boofcv.abst.feature.detect.interest.ConfigGeneralDetector in project BoofCV by lessthanoptimal.

the class ExamplePointFeatureTracker method createKLT.

/**
 * A simple way to create a Kanade-Lucas-Tomasi (KLT) tracker.
 */
public void createKLT() {
    PkltConfig config = new PkltConfig();
    config.templateRadius = 3;
    config.pyramidScaling = new int[] { 1, 2, 4, 8 };
    tracker = FactoryPointTracker.klt(config, new ConfigGeneralDetector(600, 6, 1), imageType, derivType);
}

Example 3 with ConfigGeneralDetector

use of boofcv.abst.feature.detect.interest.ConfigGeneralDetector in project BoofCV by lessthanoptimal.

the class ExampleTrackingKlt method main.

public static void main(String[] args) {
    // tune the tracker for the image size and visual appearance
    ConfigGeneralDetector configDetector = new ConfigGeneralDetector(-1, 8, 1);
    PkltConfig configKlt = new PkltConfig(3, new int[] { 1, 2, 4, 8 });
    PointTracker<GrayF32> tracker = FactoryPointTracker.klt(configKlt, configDetector, GrayF32.class, null);
    // Open a webcam at a resolution close to 640x480
    Webcam webcam = UtilWebcamCapture.openDefault(640, 480);
    // Create the panel used to display the image and feature tracks
    ImagePanel gui = new ImagePanel();
    gui.setPreferredSize(webcam.getViewSize());
    ShowImages.showWindow(gui, "KLT Tracker", true);
    int minimumTracks = 100;
    while (true) {
        BufferedImage image = webcam.getImage();
        GrayF32 gray = ConvertBufferedImage.convertFrom(image, (GrayF32) null);
        tracker.process(gray);
        List<PointTrack> tracks = tracker.getActiveTracks(null);
        // Spawn tracks if there are too few
        if (tracks.size() < minimumTracks) {
            tracker.spawnTracks();
            tracks = tracker.getActiveTracks(null);
            minimumTracks = tracks.size() / 2;
        }
        // Draw the tracks
        Graphics2D g2 = image.createGraphics();
        for (PointTrack t : tracks) {
            VisualizeFeatures.drawPoint(g2, (int) t.x, (int) t.y, Color.RED);
        }
        gui.setImageUI(image);
    }
}

Example 4 with ConfigGeneralDetector

use of boofcv.abst.feature.detect.interest.ConfigGeneralDetector in project BoofCV by lessthanoptimal.

the class VisualizeStereoVisualOdometryApp method createStereoDepth.

private StereoVisualOdometry<I> createStereoDepth(int whichAlg) {
    Class derivType = GImageDerivativeOps.getDerivativeType(imageType);
    StereoDisparitySparse<I> disparity = FactoryStereoDisparity.regionSparseWta(2, 150, 3, 3, 30, -1, true, imageType);
    PkltConfig kltConfig = new PkltConfig();
    kltConfig.templateRadius = 3;
    kltConfig.pyramidScaling = new int[] { 1, 2, 4, 8 };
    if (whichAlg == 0) {
        ConfigGeneralDetector configDetector = new ConfigGeneralDetector(600, 3, 1);
        PointTrackerTwoPass<I> tracker = FactoryPointTrackerTwoPass.klt(kltConfig, configDetector, imageType, derivType);
        return FactoryVisualOdometry.stereoDepth(1.5, 120, 2, 200, 50, false, disparity, tracker, imageType);
    } else if (whichAlg == 1) {
        ConfigGeneralDetector configExtract = new ConfigGeneralDetector(600, 3, 1);
        GeneralFeatureDetector detector = FactoryPointTracker.createShiTomasi(configExtract, derivType);
        DescribeRegionPoint describe = FactoryDescribeRegionPoint.brief(null, imageType);
        ScoreAssociateHamming_B score = new ScoreAssociateHamming_B();
        AssociateDescription2D<TupleDesc_B> associate = new AssociateDescTo2D<>(FactoryAssociation.greedy(score, 150, true));
        PointTrackerTwoPass tracker = FactoryPointTrackerTwoPass.dda(detector, describe, associate, null, 1, imageType);
        return FactoryVisualOdometry.stereoDepth(1.5, 80, 3, 200, 50, false, disparity, tracker, imageType);
    } else if (whichAlg == 2) {
        PointTracker<I> tracker = FactoryPointTracker.combined_ST_SURF_KLT(new ConfigGeneralDetector(600, 3, 0), kltConfig, 50, null, null, imageType, derivType);
        PointTrackerTwoPass<I> twopass = new PointTrackerToTwoPass<>(tracker);
        return FactoryVisualOdometry.stereoDepth(1.5, 80, 3, 200, 50, false, disparity, twopass, imageType);
    } else if (whichAlg == 3) {
        ConfigGeneralDetector configDetector = new ConfigGeneralDetector(600, 3, 1);
        PointTracker<I> trackerLeft = FactoryPointTracker.klt(kltConfig, configDetector, imageType, derivType);
        PointTracker<I> trackerRight = FactoryPointTracker.klt(kltConfig, configDetector, imageType, derivType);
        DescribeRegionPoint describe = FactoryDescribeRegionPoint.surfFast(null, imageType);
        return FactoryVisualOdometry.stereoDualTrackerPnP(90, 2, 1.5, 1.5, 200, 50, trackerLeft, trackerRight, describe, imageType);
    } else if (whichAlg == 4) {
        // GeneralFeatureIntensity intensity =
        // FactoryIntensityPoint.hessian(HessianBlobIntensity.Type.TRACE,defaultType);
        GeneralFeatureIntensity intensity = FactoryIntensityPoint.shiTomasi(1, false, imageType);
        NonMaxSuppression nonmax = FactoryFeatureExtractor.nonmax(new ConfigExtract(2, 50, 0, true, false, true));
        GeneralFeatureDetector general = new GeneralFeatureDetector(intensity, nonmax);
        general.setMaxFeatures(600);
        DetectorInterestPointMulti detector = new GeneralToInterestMulti(general, 2, imageType, derivType);
        // DescribeRegionPoint describe = FactoryDescribeRegionPoint.brief(new ConfigBrief(true),defaultType);
        // DescribeRegionPoint describe = FactoryDescribeRegionPoint.pixelNCC(5,5,defaultType);
        DescribeRegionPoint describe = FactoryDescribeRegionPoint.surfFast(null, imageType);
        DetectDescribeMulti detDescMulti = new DetectDescribeMultiFusion(detector, null, describe);
        return FactoryVisualOdometry.stereoQuadPnP(1.5, 0.5, 75, Double.MAX_VALUE, 300, 50, detDescMulti, imageType);
    } else {
        throw new RuntimeException("Unknown selection");
    }
}

Example 5 with ConfigGeneralDetector

use of boofcv.abst.feature.detect.interest.ConfigGeneralDetector in project BoofCV by lessthanoptimal.

the class ExampleVideoMosaic method main.

public static void main(String[] args) {
    // Configure the feature detector
    ConfigGeneralDetector confDetector = new ConfigGeneralDetector();
    confDetector.threshold = 1;
    confDetector.maxFeatures = 300;
    confDetector.radius = 3;
    // Use a KLT tracker
    PointTracker<GrayF32> tracker = FactoryPointTracker.klt(new int[] { 1, 2, 4, 8 }, confDetector, 3, GrayF32.class, GrayF32.class);
    // This estimates the 2D image motion
    // An Affine2D_F64 model also works quite well.
    ImageMotion2D<GrayF32, Homography2D_F64> motion2D = FactoryMotion2D.createMotion2D(220, 3, 2, 30, 0.6, 0.5, false, tracker, new Homography2D_F64());
    // wrap it so it output color images while estimating motion from gray
    ImageMotion2D<Planar<GrayF32>, Homography2D_F64> motion2DColor = new PlToGrayMotion2D<>(motion2D, GrayF32.class);
    // This fuses the images together
    StitchingFromMotion2D<Planar<GrayF32>, Homography2D_F64> stitch = FactoryMotion2D.createVideoStitch(0.5, motion2DColor, ImageType.pl(3, GrayF32.class));
    // Load an image sequence
    MediaManager media = DefaultMediaManager.INSTANCE;
    String fileName = UtilIO.pathExample("mosaic/airplane01.mjpeg");
    SimpleImageSequence<Planar<GrayF32>> video = media.openVideo(fileName, ImageType.pl(3, GrayF32.class));
    Planar<GrayF32> frame = video.next();
    // shrink the input image and center it
    Homography2D_F64 shrink = new Homography2D_F64(0.5, 0, frame.width / 4, 0, 0.5, frame.height / 4, 0, 0, 1);
    shrink = shrink.invert(null);
    // The mosaic will be larger in terms of pixels but the image will be scaled down.
    // To change this into stabilization just make it the same size as the input with no shrink.
    stitch.configure(frame.width, frame.height, shrink);
    // process the first frame
    stitch.process(frame);
    // Create the GUI for displaying the results + input image
    ImageGridPanel gui = new ImageGridPanel(1, 2);
    gui.setImage(0, 0, new BufferedImage(frame.width, frame.height, BufferedImage.TYPE_INT_RGB));
    gui.setImage(0, 1, new BufferedImage(frame.width, frame.height, BufferedImage.TYPE_INT_RGB));
    gui.setPreferredSize(new Dimension(3 * frame.width, frame.height * 2));
    ShowImages.showWindow(gui, "Example Mosaic", true);
    boolean enlarged = false;
    // process the video sequence one frame at a time
    while (video.hasNext()) {
        frame = video.next();
        if (!stitch.process(frame))
            throw new RuntimeException("You should handle failures");
        // if the current image is close to the image border recenter the mosaic
        StitchingFromMotion2D.Corners corners = stitch.getImageCorners(frame.width, frame.height, null);
        if (nearBorder(corners.p0, stitch) || nearBorder(corners.p1, stitch) || nearBorder(corners.p2, stitch) || nearBorder(corners.p3, stitch)) {
            stitch.setOriginToCurrent();
            // only enlarge the image once
            if (!enlarged) {
                enlarged = true;
                // double the image size and shift it over to keep it centered
                int widthOld = stitch.getStitchedImage().width;
                int heightOld = stitch.getStitchedImage().height;
                int widthNew = widthOld * 2;
                int heightNew = heightOld * 2;
                int tranX = (widthNew - widthOld) / 2;
                int tranY = (heightNew - heightOld) / 2;
                Homography2D_F64 newToOldStitch = new Homography2D_F64(1, 0, -tranX, 0, 1, -tranY, 0, 0, 1);
                stitch.resizeStitchImage(widthNew, heightNew, newToOldStitch);
                gui.setImage(0, 1, new BufferedImage(widthNew, heightNew, BufferedImage.TYPE_INT_RGB));
            }
            corners = stitch.getImageCorners(frame.width, frame.height, null);
        }
        // display the mosaic
        ConvertBufferedImage.convertTo(frame, gui.getImage(0, 0), true);
        ConvertBufferedImage.convertTo(stitch.getStitchedImage(), gui.getImage(0, 1), true);
        // draw a red quadrilateral around the current frame in the mosaic
        Graphics2D g2 = gui.getImage(0, 1).createGraphics();
        g2.setColor(Color.RED);
        g2.drawLine((int) corners.p0.x, (int) corners.p0.y, (int) corners.p1.x, (int) corners.p1.y);
        g2.drawLine((int) corners.p1.x, (int) corners.p1.y, (int) corners.p2.x, (int) corners.p2.y);
        g2.drawLine((int) corners.p2.x, (int) corners.p2.y, (int) corners.p3.x, (int) corners.p3.y);
        g2.drawLine((int) corners.p3.x, (int) corners.p3.y, (int) corners.p0.x, (int) corners.p0.y);
        gui.repaint();
        // throttle the speed just in case it's on a fast computer
        BoofMiscOps.pause(50);
    }
}