Boofcv研究：動態目標追蹤

public class RemovalMoving {
    public static void main(String[] args) {
        String fileName = UtilIO.pathExample("D:\\JavaProject\\Boofcv\\example\\tracking\\chipmunk.mjpeg");
        ImageType imageType = ImageType.single(GrayF32.class);      
        //ImageType imageType = ImageType.il(3, InterleavedF32.class);
 

        //ImageType imageType = ImageType.il(3, InterleavedU8.class);

        ConfigGeneralDetector confDetector = new ConfigGeneralDetector();
        confDetector.threshold = 10;
        confDetector.maxFeatures = 300;
        confDetector.radius = 6;

        //  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;

        // Comment/Uncomment to switch background mode
        BackgroundModelMoving background =
                FactoryBackgroundModel.movingBasic(configBasic, new PointTransformHomography_F32(), imageType);
//              FactoryBackgroundModel.movingGaussian(configGaussian, new PointTransformHomography_F32(), imageType);


        MediaManager media = DefaultMediaManager.INSTANCE;
        SimpleImageSequence video =
                media.openVideo(fileName, background.getImageType());
                media.openCamera(null,640,480,background.getImageType());


        // 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;
        double alpha = 0.01; // smoothing factor for FPS

        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();
            UtilHomography.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) {}
        }
    }
}