DAISYFeature.match#

DAISYFeature.match(other: BaseFeature2D, ratio: float = 0.75, crosscheck: bool = False, metric: str | None = None, sort: bool = True, top: int | None = None, thresh: float | None = None) → FeatureMatch[source]#

Match point features

Parameters:

  • other (BaseFeature2D) – set of feature points

  • ratio (float, optional) – parameter for Lowe’s ratio test, defaults to 0.75

  • crosscheck (bool, optional) – perform left-right cross check, defaults to False

  • metric (str, optional) – distance metric, one of: ‘L1’, ‘L2’, ‘hamming’, ‘hamming2’. Defaults to None, which auto-selects ‘hamming’ for binary descriptors (eg. ORB, BRISK, AKAZE) and ‘L2’ otherwise (eg. SIFT).

  • sort (bool, optional) – sort features by strength, defaults to True

Raises:

ValueError – bad metric name provided

Returns:

set of candidate matches

Return type:

FeatureMatch instance

Return a match object that contains pairs of putative corresponding points. If crosscheck is True the ratio test is disabled

Note

Binary descriptors (eg. ORB, BRISK, AKAZE) are uint8 arrays, for which ‘L2’ distance is close to meaningless – it will silently return very few matches rather than raising an error, which is why this is auto-selected rather than left for the caller to always remember.

Example:

>>> from machinevisiontoolbox import Image
>>> orb1 = Image.Read("eiffel-1.png").ORB()
>>> orb2 = Image.Read("eiffel-2.png").ORB()
>>> m = orb1.match(orb2)
>>> len(m)
82
Seealso:

FeatureMatch distance