HarrisFeature

class HarrisFeature(kp: list | ndarray | None = None, des: ndarray | None = None, scale: bool = False, orient: bool = False, image: ndarray | None = None)

Create set of Harris corner features

Create set of 2D point features

Parameters:

• kp (list of N elements, optional) – list of opencv.KeyPoint objects, one per feature, defaults to None

• des (ndarray(N,M), optional) – Feature descriptor, each is an M-vector, defaults to None

• scale (bool, optional) – features have an inherent scale, defaults to False

• orient (bool, optional) – features have an inherent orientation, defaults to False

A BaseFeature2D object:

• has a length, the number of feature points it contains

• can be sliced to extract a subset of features

This object behaves like a list, allowing indexing, slicing and iteration over individual features. It also supports a number of convenience methods.

Note

OpenCV consider feature points as opencv.KeyPoint objects and the descriptors as a multirow NumPy array. This class provides a more convenient abstraction.

Methods

distance(other[, metric])	Distance between feature sets
draw(image, *args[, ax, filled, color, ...])	Draw features into image
draw2(image[, color, type])
drawKeypoints(image[, drawing, isift, ...])	Render keypoints into image
drawMatches(im1, sift1, im2, sift2, matches, ...)
filter(**kwargs)	Filter features
gridify(nbins, nfeat)	Sort features into grid
list()	List matches
match(other[, ratio, crosscheck, metric, ...])	Match point features
plot(*args[, ax, filled, color, alpha, ...])	Plot features using Matplotlib
sort([by, descending, inplace])	Sort features
subset([N])	Select subset of features
support(images[, N])	Find support region
table()	Print features in tabular form

Properties

descriptor	Descriptor of feature
id	Image id for feature point
octave	Octave of feature
orientation	Orientation of feature
p	Feature coordinates
scale	Scale of feature
strength	Strength of feature
u	Horizontal coordinate of feature point
v	Vertical coordinate of feature point