/** 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);
}
}
}