@@ -3,14 +3,14 @@ Common Interfaces of Generic Descriptor Matchers
.. highlight:: cpp
Matchers of keypoint descriptors in OpenCV have wrappers with common interface that enables to switch easily
Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to easily switch
between different algorithms solving the same problem. This section is devoted to matching descriptors
that cannot be represented as vectors in a multidimensional space. ``GenericDescriptorMatcher`` is a more generic interface for descriptors. It does not make any assumptions about descriptor representation.
Every descriptor with
:func:`DescriptorExtractor` interface has a wrapper with ``GenericDescriptorMatcher`` interface (see
:func:`VectorDescriptorMatcher` ).
There are descriptors such as One way descriptor and Ferns that have ``GenericDescriptorMatcher`` interface implemented, but do not support
:func:`DescriptorExtractor` .
that cannot be represented as vectors in a multidimensional space. ``GenericDescriptorMatcher`` is a more generic interface for descriptors. It does not make any assumptions about descriptor representation.
Every descriptor with the
:ref:`DescriptorExtractor` interface has a wrapper with the ``GenericDescriptorMatcher`` interface (see
:ref:`VectorDescriptorMatcher` ).
There are descriptors such as the One-way descriptor and Ferns that have the ``GenericDescriptorMatcher`` interface implemented but do not support
:ref:`DescriptorExtractor` .
.. index:: GenericDescriptorMatcher
...
...
@@ -18,7 +18,7 @@ GenericDescriptorMatcher
------------------------
.. c:type:: GenericDescriptorMatcher
Abstract interface for a keypoint descriptor extracting and matching. There is :func:`DescriptorExtractor` and :func:`DescriptorMatcher` for these purposes too, but their interfaces are intended for descriptors represented as vectors in a multidimensional space. ``GenericDescriptorMatcher`` is a more generic interface for descriptors. :func:`DescriptorMatcher`,``GenericDescriptorMatcher`` has two groups of match methods: for matching keypoints of one image with other image or with image set. ::
Abstract interface for extracting and matching a keypoint descriptor. There are aslo :ref:`DescriptorExtractor` and :ref:`DescriptorMatcher` for these purposes but their interfaces are intended for descriptors represented as vectors in a multidimensional space. ``GenericDescriptorMatcher`` is a more generic interface for descriptors. :ref:`DescriptorMatcher` and``GenericDescriptorMatcher`` have two groups of match methods: for matching keypoints of an image with another image or with an image set. ::
class GenericDescriptorMatcher
{
...
...
@@ -45,7 +45,7 @@ Abstract interface for a keypoint descriptor extracting and matching. There is :
vector<KeyPoint>& queryKeypoints );
/*
* Group of methods to match keypoints from image pair.
* Group of methods to match keypoints from an image pair.
Adds images and keypoints from them to the train collection (descriptors are supposed to be calculated here). If train collection is not empty new image and keypoints from them will be added to existing data.
Adds images and their keypoints to a train collection (descriptors are supposed to be calculated here). If the train collection is not empty, a new image and its keypoints is added to existing data.??
:param images: Image collection.
:param keypoints: Point collection. Assumes that ``keypoints[i]`` are keypoints
detected in an image ``images[i]`` .
:param keypoints: Point collection. It is assumed that ``keypoints[i]`` are keypoints detected in the image ``images[i]`` .
Classifies query keypoints under keypoints of one train image qiven as input argument (first version of the method) or train image collection that set using (second version).
Classifies query keypoints under keypoints of a train image qiven as an input argument (first version of the method) or a train image collection (second version).??
Find best match for query keypoints to the training set. In first version of method one train image and keypoints detected on it - are input arguments. In second version query keypoints are matched to training collectin that set using . As in the mask can be set.
Finds the best match for query keypoints to the training set. In the first version of the method, a train image and keypoints detected on it are input arguments. In the second version, query keypoints are matched to a training collection set using ??. As in the mask can be set.??
:param queryKeypoints: Keypoints detected in ``queryImage`` .
:param trainImage: Train image. This will not be added to train image collection
stored in class object.
:param trainImage: Train image. It is not added to a train image collection stored in the class object.
:param trainKeypoints: Keypoints detected in ``trainImage`` . They will not be added to train points collection
stored in class object.
:param trainKeypoints: Keypoints detected in ``trainImage`` . They are not added to a train points collection stored in the class object.
:param matches: Matches. If some query descriptor (keypoint) masked out in ``mask`` no match will be added for this descriptor.
So ``matches`` size may be less query keypoints count.
:param matches: Matches. If a query descriptor (keypoint) is masked out in ``mask`` , match is added for this descriptor. So, ``matches`` size may be smaller than the query keypoints count.
:param mask: Mask specifying permissible matches between input query and train keypoints.
:param masks: The set of masks. Each ``masks[i]`` specifies permissible matches between input query keypoints
and stored train keypointss from i-th image.
:param masks: Set of masks. Each ``masks[i]`` specifies permissible matches between input query keypoints and stored train keypoints from the i-th image.
Find the knn best matches for each keypoint from a query set with train keypoints. Found knn (or less if not possible) matches are returned in distance increasing order. Details see in and.
Finds the knn best matches for each keypoint from a query set with train keypoints. Found knn (or less if not possible) matches are returned in the distance increasing order. See details in ??.
Find the best matches for each query keypoint which have distance less than given threshold. Found matches are returned in distance increasing order. Details see in and .
Finds the best matches for each query keypoint that has a distance smaller than the given threshold. Found matches are returned in the distance increasing order. See details see in ??.
