@Override
public PointTracker<GrayF32> createTracker() {
DescribePointBrief<GrayF32> brief =
FactoryDescribePointAlgs.brief(
FactoryBriefDefinition.gaussian2(new Random(123), 16, 512),
FactoryBlurFilter.gaussian(GrayF32.class, 0, 4));
GeneralFeatureDetector<GrayF32, GrayF32> corner =
FactoryDetectPoint.createShiTomasi(
new ConfigGeneralDetector(100, 2, 0), false, GrayF32.class);
InterestPointDetector<GrayF32> detector =
FactoryInterestPoint.wrapPoint(corner, 1, GrayF32.class, GrayF32.class);
ScoreAssociateHamming_B score = new ScoreAssociateHamming_B();
AssociateDescription<TupleDesc_B> association = FactoryAssociation.greedy(score, 400, true);
PointTracker<GrayF32> pointTracker =
FactoryPointTracker.combined(
detector,
null,
new WrapDescribeBrief<>(brief, GrayF32.class),
association,
null,
20,
GrayF32.class);
return pointTracker;
}
protected MonocularPlaneVisualOdometry<ImageUInt8> createAlgorithm() {
PkltConfig config = new PkltConfig();
config.pyramidScaling = new int[] {1, 2, 4, 8};
config.templateRadius = 3;
ConfigGeneralDetector configDetector = new ConfigGeneralDetector(600, 3, 1);
PointTracker<ImageUInt8> tracker =
FactoryPointTracker.klt(config, configDetector, ImageUInt8.class, ImageSInt16.class);
return FactoryVisualOdometry.monoPlaneInfinity(
50, 2, 1.5, 300, tracker, ImageType.single(ImageUInt8.class));
}
private StitchingFromMotion2D<GrayU8, Affine2D_F64> createStabilization() {
ConfigGeneralDetector config = new ConfigGeneralDetector();
config.maxFeatures = 150;
config.threshold = 40;
config.radius = 3;
PointTracker<GrayU8> tracker =
FactoryPointTracker.klt(new int[] {1, 2, 4}, config, 3, GrayU8.class, GrayS16.class);
ImageMotion2D<GrayU8, Affine2D_F64> motion =
FactoryMotion2D.createMotion2D(
100, 1.5, 2, 40, 0.5, 0.6, false, tracker, new Affine2D_F64());
return FactoryMotion2D.createVideoStitch(0.2, motion, ImageType.single(GrayU8.class));
}
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;
// 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());
// ====== 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;
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) {
}
}
}