Examples with ConfigBackgroundGaussian boofcv.factory.background.ConfigBackgroundGaussian used on opensource projects

Example 1 with ConfigBackgroundGaussian

use of boofcv.factory.background.ConfigBackgroundGaussian in project narchy by automenta.

the class ExampleBackgroundRemovalStationary method main.

public static void main(String[] args) {
    WebCam c = new WebCam();
    Tex output = new Tex();
    SpaceGraph.window(new Gridding(c.view(), output.view()), 800, 800);
    // String fileName = UtilIO.pathExample("background/horse_jitter.mp4"); // degraded performance because of jitter
    // String fileName = UtilIO.pathExample("tracking/chipmunk.mjpeg"); // Camera moves.  Stationary will fail here
    // 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);
    // 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(40, 0.0005f);
    configGaussian.initialVariance = 100;
    configGaussian.minimumDifference = 10f;
    // Comment/Uncomment to switch algorithms
    BackgroundModelStationary background = // FactoryBackgroundModel.stationaryBasic(new ConfigBackgroundBasic(35, 0.005f), imageType);
    FactoryBackgroundModel.stationaryGaussian(configGaussian, imageType);
    // Declare storage for segmented image.  1 = moving foreground and 0 = background
    GrayU8 segmented = new GrayU8(c.width, c.height);
    GrayF32 input = new GrayF32(c.width, c.height);
    BufferedImage segmentedVis = new BufferedImage(c.width, c.height, BufferedImage.TYPE_INT_RGB);
    new Loop(10f) {

        @Override
        public boolean next() {
            BufferedImage img = c.image;
            if (img != null) {
                ConvertBufferedImage.convertFrom(img, input, true);
                // long before = System.nanoTime();
                background.segment(input, segmented);
                background.updateBackground(input);
                byte[] b = segmented.data;
                for (int i = 0; i < b.length; i++) {
                    if (b[i] != 0)
                        b[i] = 127;
                }
                output.update(// segmented
                ConvertBufferedImage.convertTo(segmented, segmentedVis));
            }
            // }
            return true;
        }
    };
}

Example 2 with ConfigBackgroundGaussian

use of boofcv.factory.background.ConfigBackgroundGaussian 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
    ConfigPointDetector configDetector = new ConfigPointDetector();
    configDetector.type = PointDetectorTypes.SHI_TOMASI;
    configDetector.general.maxFeatures = 300;
    configDetector.general.radius = 6;
    configDetector.general.threshold = 10;
    // Use a KLT tracker
    PointTracker tracker = FactoryPointTracker.klt(4, configDetector, 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.getWidth(), video.getHeight());
    // 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);
        BoofMiscOps.sleep(5);
    }
}