Java ConfigFastHessian类代码示例

import boofcv.abst.feature.detect.interest.ConfigFastHessian; //导入依赖的package包/类
/**
 * Given two input images create and display an image where the two have been overlayed on top of each other.
 */
public static <T extends ImageSingleBand>
void stitch( BufferedImage imageA , BufferedImage imageB , Class<T> imageType )
{
	T inputA = ConvertBufferedImage.convertFromSingle(imageA, null, imageType);
	T inputB = ConvertBufferedImage.convertFromSingle(imageB, null, imageType);

	// Detect using the standard SURF feature descriptor and describer
	DetectDescribePoint detDesc = FactoryDetectDescribe.surfStable(
			new ConfigFastHessian(1, 2, 200, 1, 9, 4, 4), null,null, ImageDataType.single(imageType));
	ScoreAssociation<SurfFeature> scorer = FactoryAssociation.scoreEuclidean(SurfFeature.class,true);
	AssociateDescription<SurfFeature> associate = FactoryAssociation.greedy(scorer,2,true);

	// fit the images using a homography.  This works well for rotations and distant objects.
	GenerateHomographyLinear modelFitter = new GenerateHomographyLinear(true);
	DistanceHomographySq distance = new DistanceHomographySq();

	ModelMatcher<Homography2D_F64,AssociatedPair> modelMatcher =
			new Ransac<Homography2D_F64,AssociatedPair>(123,modelFitter,distance,60,9);

	Homography2D_F64 H = computeTransform(inputA, inputB, detDesc, associate, modelMatcher);

	renderStitching(imageA,imageB,H);
}
 

import boofcv.abst.feature.detect.interest.ConfigFastHessian; //导入依赖的package包/类
/**
 * Creates a tracker which detects Fast-Hessian features and describes them with SURF using the faster variant
 * of SURF.
 *
 * @see DescribePointSurf
 * @see boofcv.abst.feature.tracker.DdaManagerDetectDescribePoint
 *
 * @param configDetector Configuration for SURF detector
 * @param configDescribe Configuration for SURF descriptor
 * @param configOrientation Configuration for orientation
 * @param imageType      Type of image the input is.
 * @return SURF based tracker.
 */
// TODO remove maxTracks?  Use number of detected instead
public static <I extends ImageSingleBand>
PointTracker<I> dda_FH_SURF_Fast(
									  ConfigFastHessian configDetector ,
									  ConfigSurfDescribe.Speed configDescribe ,
									  ConfigAverageIntegral configOrientation ,
									  Class<I> imageType)
{
	ScoreAssociation<TupleDesc_F64> score = FactoryAssociation.scoreEuclidean(TupleDesc_F64.class, true);
	AssociateSurfBasic assoc = new AssociateSurfBasic(FactoryAssociation.greedy(score, 5, true));

	AssociateDescription2D<SurfFeature> generalAssoc =
			new AssociateDescTo2D<SurfFeature>(new WrapAssociateSurfBasic(assoc));

	DetectDescribePoint<I,SurfFeature> fused =
			FactoryDetectDescribe.surfFast(configDetector, configDescribe, configOrientation,
					ImageDataType.single(imageType));

	DdaManagerDetectDescribePoint<I,SurfFeature> manager = new DdaManagerDetectDescribePoint<I,SurfFeature>(fused);

	return new DetectDescribeAssociate<I,SurfFeature>(manager, generalAssoc,false);
}
 

import boofcv.abst.feature.detect.interest.ConfigFastHessian; //导入依赖的package包/类
/**
 * Creates a tracker which detects Fast-Hessian features and describes them with SURF using the faster variant
 * of SURF.
 *
 * @see DescribePointSurf
 * @see boofcv.abst.feature.tracker.DdaManagerDetectDescribePoint
 *
 * @param configDetector Configuration for SURF detector
 * @param configDescribe Configuration for SURF descriptor
 * @param configOrientation Configuration for orientation
 * @param imageType      Type of image the input is.
 * @return SURF based tracker.
 */
// TODO remove maxTracks?  Use number of detected instead
public static <I extends ImageSingleBand>
PointTracker<I> dda_FH_SURF_Stable(
										ConfigFastHessian configDetector ,
										ConfigSurfDescribe.Stablility configDescribe ,
										ConfigSlidingIntegral configOrientation ,
										Class<I> imageType)
{
	ScoreAssociation<TupleDesc_F64> score = FactoryAssociation.scoreEuclidean(TupleDesc_F64.class, true);
	AssociateSurfBasic assoc = new AssociateSurfBasic(FactoryAssociation.greedy(score, 5, true));

	AssociateDescription2D<SurfFeature> generalAssoc =
			new AssociateDescTo2D<SurfFeature>(new WrapAssociateSurfBasic(assoc));

	DetectDescribePoint<I,SurfFeature> fused =
			FactoryDetectDescribe.surfStable(configDetector,configDescribe,configOrientation,
					ImageDataType.single(imageType));

	DdaManagerDetectDescribePoint<I,SurfFeature> manager = new DdaManagerDetectDescribePoint<I,SurfFeature>(fused);

	return new DetectDescribeAssociate<I,SurfFeature>(manager, generalAssoc,false);
}
 

import boofcv.abst.feature.detect.interest.ConfigFastHessian; //导入依赖的package包/类
/**
 * Creates a tracker which detects Fast-Hessian features, describes them with SURF, nominally tracks them using KLT.
 *
 * @see DescribePointSurf
 * @see boofcv.abst.feature.tracker.DdaManagerDetectDescribePoint
 *
 * @param trackRadius Size of feature being tracked by KLT
 * @param pyramidScalingKlt Image pyramid used for KLT
 * @param reactivateThreshold Tracks are reactivated after this many have been dropped.  Try 10% of maxMatches
 * @param configDetector Configuration for SURF detector
 * @param configDescribe Configuration for SURF descriptor
 * @param configOrientation Configuration for region orientation
 * @param imageType      Type of image the input is.
 * @param <I>            Input image type.
 * @return SURF based tracker.
 */
public static <I extends ImageSingleBand>
PointTracker<I> combined_FH_SURF_KLT(int trackRadius,
												  int[] pyramidScalingKlt ,
												  int reactivateThreshold ,
												  ConfigFastHessian configDetector ,
												  ConfigSurfDescribe.Stablility configDescribe ,
												  ConfigSlidingIntegral configOrientation ,
												  Class<I> imageType) {

	ScoreAssociation<TupleDesc_F64> score = FactoryAssociation.defaultScore(TupleDesc_F64.class);
	AssociateSurfBasic assoc = new AssociateSurfBasic(FactoryAssociation.greedy(score, 100000, true));

	AssociateDescription<SurfFeature> generalAssoc = new WrapAssociateSurfBasic(assoc);

	DetectDescribePoint<I,SurfFeature> fused =
			FactoryDetectDescribe.surfStable(configDetector, configDescribe, configOrientation,
					ImageDataType.single(imageType));

	return combined(fused,generalAssoc,trackRadius,pyramidScalingKlt,reactivateThreshold,
			imageType);
}
 

import boofcv.abst.feature.detect.interest.ConfigFastHessian; //导入依赖的package包/类
public static <T extends ImageSingleBand, D extends ImageSingleBand>
	void perform( String fileName , Class<T> imageType , Class<D> derivType )
	{
		SimpleImageSequence<T> sequence = BoofVideoManager.loadManagerDefault().load(fileName, ImageDataType.single(imageType));

		int maxCorners = 200;
		int radius = 2;

		// if null then no orientation will be computed
		OrientationImageAverage<T> orientation = null;
		orientation = FactoryOrientationAlgs.nogradient(radius,imageType);

		InterestPointDetector<T> detector;

		detector = FactoryInterestPoint.fastHessian(new ConfigFastHessian(1, 2, 100, 2, 9, 4, 4));
//		FeatureScaleSpace<T,D> feature = FactoryInterestPointAlgs.hessianScaleSpace(radius,1,maxCorners,imageType,derivType);
//		detector = FactoryInterestPoint.wrapDetector(feature,new double[]{1,2,4,6,8,12},imageType);

		VideoDetectInterestPoints<T> display = new VideoDetectInterestPoints<T>(sequence, detector,orientation);

		display.process();
	}
 

import boofcv.abst.feature.detect.interest.ConfigFastHessian; //导入依赖的package包/类
public DetectFeaturePointSOApp(Class<T> imageType, Class<D> derivType) {
	super(1);
	this.imageType = imageType;

	FeatureLaplacePyramid<T, D> flss = FactoryInterestPointAlgs.hessianLaplace(radius, thresh, maxScaleFeatures, imageType, derivType);
	addAlgorithm(0, "Hess Lap SS", FactoryInterestPoint.wrapDetector(flss, scales, false, imageType));
	FeatureLaplacePyramid<T, D> flp = FactoryInterestPointAlgs.hessianLaplace(radius, thresh, maxScaleFeatures, imageType, derivType);
	addAlgorithm(0, "Hess Lap P", FactoryInterestPoint.wrapDetector(flp, scales, true,imageType));
	addAlgorithm(0, "FastHessian", FactoryInterestPoint.<T>fastHessian(
			new ConfigFastHessian(thresh, 2, maxScaleFeatures, 2, 9, 4, 4)));
	if( imageType == ImageFloat32.class )
		addAlgorithm(0, "SIFT", FactoryInterestPoint.siftDetector(null,new ConfigSiftDetector(2,10,maxScaleFeatures,5)));

	JPanel viewArea = new JPanel(new BorderLayout());
	corruptPanel = new ImageCorruptPanel();
	corruptPanel.setListener(this);
	panel = new ImagePanel();

	viewArea.add(corruptPanel, BorderLayout.WEST);
	viewArea.add(panel, BorderLayout.CENTER);
	setMainGUI(viewArea);
}
 

import boofcv.abst.feature.detect.interest.ConfigFastHessian; //导入依赖的package包/类
public VisualizeAssociationAlgorithmsApp( Class<T> imageType ) {
		super(1);
		this.imageType = imageType;

		detector = (DetectDescribePoint) FactoryDetectDescribe.surfStable(
				new ConfigFastHessian(5, 4, 200, 1, 9, 4, 4), null, null, ImageDataType.single(ImageFloat32.class));
//		detector = (DetectDescribePoint) FactoryDetectDescribe.sift(4,1,false,200);

		int DOF = detector.createDescription().size();

		ScoreAssociation<TupleDesc_F64> score = FactoryAssociation.scoreEuclidean(TupleDesc_F64.class,true);

		addAlgorithm(0, "Greedy", FactoryAssociation.greedy(score, Double.MAX_VALUE, false));
		addAlgorithm(0, "Greedy Backwards", FactoryAssociation.greedy(score, Double.MAX_VALUE, true));
		addAlgorithm(0, "K-D Tree BBF", FactoryAssociation.kdtree(DOF, 75));
		addAlgorithm(0, "Random Forest", FactoryAssociation.kdRandomForest(DOF, 75, 10, 5, 1233445565));

		image0 = GeneralizedImageOps.createSingleBand(imageType, 1, 1);
		image1 = GeneralizedImageOps.createSingleBand(imageType, 1, 1);

		setMainGUI(panel);
	}
 

import boofcv.abst.feature.detect.interest.ConfigFastHessian; //导入依赖的package包/类
/**
 * Detects key points inside the image and computes descriptions at those points.
 */
protected double[][] extractFeaturesInternal(BufferedImage image) {
	ImageFloat32 boofcvImage = ConvertBufferedImage.convertFromSingle(image, null, ImageFloat32.class);

	// create the SURF detector and descriptor in BoofCV v0.15
	ConfigFastHessian conf = new ConfigFastHessian(detectThreshold, 2, maxFeaturesPerScale, 2, 9, 4, 4);
	DetectDescribePoint<ImageFloat32, SurfFeature> surf = FactoryDetectDescribe.surfStable(conf, null,
			null, ImageFloat32.class);
	// specify the image to process
	surf.detect(boofcvImage);
	int numPoints = surf.getNumberOfFeatures();
	double[][] descriptions = new double[numPoints][SURFLength];
	for (int i = 0; i < numPoints; i++) {
		descriptions[i] = surf.getDescription(i).getValue();
	}
	return descriptions;
}
 

import boofcv.abst.feature.detect.interest.ConfigFastHessian; //导入依赖的package包/类
public <T extends ImageFloat32>
Bitmap detect( Bitmap image, Class<T> imageType , int nPuntos) {

    if(nPuntos != 0){           //There is a problem with BoofCV if we put 0 points
        T input = ConvertBitmap.bitmapToGray(image, null, imageType, null);

        // Create a Fast Hessian detector from the SURF paper.
        // Other detectors can be used in this example too.

        InterestPointDetector<T> detector = FactoryInterestPoint.fastHessian(
                new ConfigFastHessian(30, 2, nPuntos, 2, 9, 3, 4));

        // find interest points in the image
        detector.detect(input);

        Paint paintMax;
        paintMax = new Paint();
        paintMax.setColor(Color.RED);
        paintMax.setStyle(Paint.Style.FILL);

        Canvas canvas = new Canvas(image);


        for(int i = 0; i<detector.getNumberOfFeatures();i++){
            canvas.drawCircle((float) detector.getLocation(i).getX(), (float) detector.getLocation(i).getY(), 3, paintMax);
            points.add(new Point(detector.getLocation(i).getX(), detector.getLocation(i).getY()));
        }
    }
    return image;

}
 

import boofcv.abst.feature.detect.interest.ConfigFastHessian; //导入依赖的package包/类
/**
 * Returns SURF descriptors for an image using the settings above. Uses the BoofCV stable SURF algorithm.
 *
 * @param image Image for which to obtain the SURF descriptors.
 * @return
 */
public static DetectDescribePoint<GrayF32, BrightFeature> getStableSurf(BufferedImage image) {
     /* Obtain raw SURF descriptors using the configuration above (FH-9 according to [1]). */
    GrayF32 gray = ConvertBufferedImage.convertFromSingle(image, null, GrayF32.class);
    ConfigFastHessian config = new ConfigFastHessian(0, 2, FH_MAX_FEATURES_PER_SCALE, FH_INITIAL_SAMPLE_SIZE, FH_INITIAL_SIZE, FH_NUMBER_SCALES_PER_OCTAVE, FH_NUMBER_OF_OCTAVES);
    DetectDescribePoint<GrayF32, BrightFeature> surf = FactoryDetectDescribe.surfStable(config, null, null, GrayF32.class);
    surf.detect(gray);
    return surf;
}
 

import boofcv.abst.feature.detect.interest.ConfigFastHessian; //导入依赖的package包/类
/**
 * Returns SURF descriptors for an image using the settings above. Uses the BoofCV fast SURF algorithm,
 * which yields less images but operates a bit faster.
 *
 * @param image Image for which to obtain the SURF descriptors.
 * @return
 */
public static DetectDescribePoint<GrayF32, BrightFeature> getFastSurf(BufferedImage image) {
     /* Obtain raw SURF descriptors using the configuration above (FH-9 according to [1]). */
    GrayF32 gray = ConvertBufferedImage.convertFromSingle(image, null, GrayF32.class);
    ConfigFastHessian config = new ConfigFastHessian(0, 2, FH_MAX_FEATURES_PER_SCALE, FH_INITIAL_SAMPLE_SIZE, FH_INITIAL_SIZE, FH_NUMBER_SCALES_PER_OCTAVE, FH_NUMBER_OF_OCTAVES);
    DetectDescribePoint<GrayF32, BrightFeature> surf = FactoryDetectDescribe.surfFast(config, null, null, GrayF32.class);
    surf.detect(gray);
    return surf;
}
 

import boofcv.abst.feature.detect.interest.ConfigFastHessian; //导入依赖的package包/类
@Override
public void init(RunConfig config) throws InvalidTestFormatException {
	super.init(config);

	File file = new File(GR.getGoldenDir(), goldenFileName);
	try {
		InputStream inImageStream = MTTestResourceManager.openFileAsInputStream(file.getPath());
		image = UtilImageIO.loadPGM_U8(inImageStream, (ImageUInt8) null);
		detector = FactoryInterestPoint.fastHessian(new ConfigFastHessian(
				10, 2, 100, 2, 9, 3, 4));
	} catch (IOException e) {
		throw new GoldenFileNotFoundException(file, this.getClass());
	}
}
 

import boofcv.abst.feature.detect.interest.ConfigFastHessian; //导入依赖的package包/类
public DetectDescribePoint<ImageFloat32,SurfFeature> getDesc( BufferedImage src ) {
	ImageFloat32 image = convertImage(src);
	
       // create the detector and descriptors
       DetectDescribePoint<ImageFloat32,SurfFeature> surf = FactoryDetectDescribe.
                       surfFast(new ConfigFastHessian(0, 2, 200, 2, 9, 4, 4), null, null, ImageFloat32.class);

        // specify the image to process
       surf.detect(image);
       
       return surf;
}
 

import boofcv.abst.feature.detect.interest.ConfigFastHessian; //导入依赖的package包/类
public static <T extends ImageSingleBand>
void detect( BufferedImage image , Class<T> imageType ) {
	T input = ConvertBufferedImage.convertFromSingle(image, null, imageType);

	// Create a Fast Hessian detector from the SURF paper.
	// Other detectors can be used in this example too.
	InterestPointDetector<T> detector = FactoryInterestPoint.fastHessian(
			new ConfigFastHessian(10, 2, 100, 2, 9, 3, 4));

	// find interest points in the image
	detector.detect(input);

	// Show the features
	displayResults(image, detector);
}
 

import boofcv.abst.feature.detect.interest.ConfigFastHessian; //导入依赖的package包/类
/**
	 * For some features, there are pre-made implementations of DetectDescribePoint.  This has only been done
	 * in situations where there was a performance advantage or that it was a very common combination.
	 */
	public static <T extends ImageSingleBand, TD extends TupleDesc>
	DetectDescribePoint<T, TD> createFromPremade( Class<T> imageType ) {
		return (DetectDescribePoint)FactoryDetectDescribe.surfStable(
				new ConfigFastHessian(1, 2, 200, 1, 9, 4, 4), null,null, ImageDataType.single(ImageFloat32.class));
		// note that SIFT only supports ImageFloat32
//		if( imageType == ImageFloat32.class )
//			return (DetectDescribePoint)FactoryDetectDescribe.sift(null,new ConfigSiftDetector(2,0,200,5),null,null);
//		else
//			throw new RuntimeException("Unsupported image type");
	}