/** Associates the features together. */ public void associate() { // initialize data structures matches.reset(); unassociatedSrc.reset(); // find and add the matches assoc.setSource((FastQueue) srcPositive); assoc.setDestination((FastQueue) dstPositive); assoc.associate(); FastQueue<AssociatedIndex> m = assoc.getMatches(); for (int i = 0; i < m.size; i++) { AssociatedIndex a = m.data[i]; int globalSrcIndex = srcPositive.data[a.src].index; int globalDstIndex = dstPositive.data[a.dst].index; matches.grow().setAssociation(globalSrcIndex, globalDstIndex, a.fitScore); } GrowQueue_I32 un = assoc.getUnassociatedSource(); for (int i = 0; i < un.size; i++) { unassociatedSrc.add(srcPositive.data[un.get(i)].index); } assoc.setSource((FastQueue) srcNegative); assoc.setDestination((FastQueue) dstNegative); assoc.associate(); m = assoc.getMatches(); for (int i = 0; i < m.size; i++) { AssociatedIndex a = m.data[i]; int globalSrcIndex = srcNegative.data[a.src].index; int globalDstIndex = dstNegative.data[a.dst].index; matches.grow().setAssociation(globalSrcIndex, globalDstIndex, a.fitScore); } un = assoc.getUnassociatedSource(); for (int i = 0; i < un.size; i++) { unassociatedSrc.add(srcNegative.data[un.get(i)].index); } }
/** * Splits the set of input features into positive and negative laplacian lists. Keep track of the * feature's index in the original input list. This is the index that needs to be returned. */ private void sort(FastQueue<SurfFeature> input, FastQueue<Helper> pos, FastQueue<Helper> neg) { pos.reset(); neg.reset(); for (int i = 0; i < input.size; i++) { SurfFeature f = input.get(i); if (f.laplacianPositive) { pos.grow().wrap(f, i); } else { neg.grow().wrap(f, i); } } }
private FastQueue<TupleDesc_F64> createData(double... values) { FastQueue<TupleDesc_F64> ret = new FastQueue<TupleDesc_F64>(10, TupleDesc_F64.class, true) { @Override protected TupleDesc_F64 createInstance() { return new TupleDesc_F64(1); } }; for (int i = 0; i < values.length; i++) { ret.grow().set(values[i]); } return ret; }
/** * Processes the image and extracts SIFT features * * @param input input image */ public void process(ImageFloat32 input) { features.reset(); featureScales.reset(); featureAngles.reset(); location.reset(); ss.constructPyramid(input); ss.computeFeatureIntensity(); ss.computeDerivatives(); detector.process(ss); orientation.setScaleSpace(ss); describe.setScaleSpace(ss); FastQueue<ScalePoint> found = detector.getFoundPoints(); for (int i = 0; i < found.size; i++) { ScalePoint sp = found.data[i]; orientation.process(sp.x, sp.y, sp.scale); GrowQueue_F64 angles = orientation.getOrientations(); int imageIndex = orientation.getImageIndex(); double pixelScale = orientation.getPixelScale(); for (int j = 0; j < angles.size; j++) { SurfFeature desc = features.grow(); double yaw = angles.data[j]; describe.process(sp.x, sp.y, sp.scale, yaw, imageIndex, pixelScale, desc); desc.laplacianPositive = sp.white; featureScales.push(sp.scale); featureAngles.push(yaw); location.grow().set(sp.x, sp.y); } } }