Methods to filter a vector of keypoints.

A keypoint is a data structure for salient point detectors.

The struct stores a keypoint, i.e. a point feature found by one of many available keypoint detectors, such as Harris corner detector, cv.FAST, cv.StarDetector, cv.SURF, cv.SIFT, cv.FeatureDetector etc.

The keypoint is characterized by the 2D position, scale (proportional to the diameter of the neighborhood that needs to be taken into account), orientation and some other parameters. The keypoint neighborhood is then analyzed by another algorithm that builds a descriptor (usually represented as a feature vector). The keypoints representing the same object in different images can then be matched using cv.DescriptorMatcher or another method.

The keypoint struct contains the following fields: * pt coordinates of the keypoint [x,y]. * size diameter of the meaningful keypoint neighborhood. * angle computed orientation of the keypoint (-1 if not applicable); it's in [0,360) degrees and measured relative to image coordinate system, ie in clockwise. * response the response by which the most strong keypoints have been selected. Can be used for the further sorting or subsampling. * octave octave (pyramid layer) from which the keypoint has been extracted. * class_id object class (if the keypoints need to be clustered by an object they belong to).