Example SURF Feature

From BoofCV

Speeded Up Robust Feature (SURF) is a region descriptor and interest point detector. Two different ways of using SURF are demonstrated in this example. The easy way uses a high level interface that is easy to work with, but sacrifices flexibility. The harder way directly creates the SURF classes, is more complex, and requires a better understanding of how the code works.

Example File: ExampleFeatureSurf.java


  • SURF
  • Region Descriptor
  • Interest Point

Example Code

 * Example of how to use SURF detector and descriptors in BoofCV. 
 * @author Peter Abeles
public class ExampleFeatureSurf {

	 * Use generalized interfaces for working with SURF.  This removes much of the drudgery, but also reduces flexibility
	 * and slightly increases memory and computational requirements.
	 *  @param image Input image type. DOES NOT NEED TO BE GrayF32, GrayU8 works too
	public static void easy( GrayF32 image ) {
		// create the detector and descriptors
		DetectDescribePoint<GrayF32,BrightFeature> surf = FactoryDetectDescribe.
				surfStable(new ConfigFastHessian(0, 2, 200, 2, 9, 4, 4), null, null,GrayF32.class);

		 // specify the image to process

		System.out.println("Found Features: "+surf.getNumberOfFeatures());
		System.out.println("First descriptor's first value: "+surf.getDescription(0).value[0]);

	 * Configured exactly the same as the easy example above, but require a lot more code and a more in depth
	 * understanding of how SURF works and is configured.  Instead of TupleDesc_F64, SurfFeature are computed in
	 * this case.  They are almost the same as TupleDesc_F64, but contain the Laplacian's sign which can be used
	 * to speed up association. That is an example of how using less generalized interfaces can improve performance.
	 * @param image Input image type. DOES NOT NEED TO BE GrayF32, GrayU8 works too
	public static <II extends ImageGray<II>> void harder(GrayF32 image ) {
		// SURF works off of integral images
		Class<II> integralType = GIntegralImageOps.getIntegralType(GrayF32.class);
		// define the feature detection algorithm
		NonMaxSuppression extractor =
				FactoryFeatureExtractor.nonmax(new ConfigExtract(2, 0, 5, true));
		FastHessianFeatureDetector<II> detector =
				new FastHessianFeatureDetector<>(extractor, 200, 2, 9, 4, 4, 6);

		// estimate orientation
		OrientationIntegral<II> orientation = 
				FactoryOrientationAlgs.sliding_ii(null, integralType);

		DescribePointSurf<II> descriptor = FactoryDescribePointAlgs.<II>surfStability(null,integralType);
		// compute the integral image of 'image'
		II integral = GeneralizedImageOps.createSingleBand(integralType,image.width,image.height);
		GIntegralImageOps.transform(image, integral);

		// detect fast hessian features
		// tell algorithms which image to process

		List<ScalePoint> points = detector.getFoundPoints();

		List<BrightFeature> descriptions = new ArrayList<>();

		for( ScalePoint p : points ) {
			// estimate orientation
			orientation.setObjectRadius( p.scale*BoofDefaults.SURF_SCALE_TO_RADIUS);
			double angle = orientation.compute(p.x,p.y);
			// extract the SURF description for this region
			BrightFeature desc = descriptor.createDescription();

			// save everything for processing later on
		System.out.println("Found Features: "+points.size());
		System.out.println("First descriptor's first value: "+descriptions.get(0).value[0]);

	public static void main( String args[] ) {

		// Need to turn off concurrency since the order in which feature are returned
		// is not determininistic if turned on
		BoofConcurrency.USE_CONCURRENT = false;

		GrayF32 image = UtilImageIO.loadImage(UtilIO.pathExample("particles01.jpg"), GrayF32.class);
		// run each example