Finds the best match for each descriptor from a query set with train descriptors. Query descriptors are supposed to be of keypoints detected on the same query image. In the first variant of this method, train descriptors are set as an input argument and are supposed to be of keypoints detected on the same train image. In the second variant of the method, train descriptors collection that was set using ``addmethod`` is used. Optional mask (or masks) can be set to describe which descriptors can be matched. ``queryDescriptors[i]`` can be matched with ``trainDescriptors[j]`` only if ``mask.at<uchar>(i,j)`` is non-zero.?? what does "of keypoints" mean?
Finds the best match for each descriptor from a query set with train descriptors. Query descriptors are supposed to belong?? to keypoints detected on the same query image. In the first variant of this method, train descriptors are set as an input argument and are supposed to belong to keypoints detected on the same train image. In the second variant of the method, train descriptors collection that was set by ``addmethod`` is used. Optional mask (or masks) can be set to describe descriptors that can be matched. ``queryDescriptors[i]`` can be matched with ``trainDescriptors[j]`` only if ``mask.at<uchar>(i,j)`` is non-zero.??
:param mask: Mask specifying permissible matches between an input query and train matrices of descriptors.
:param masks: Set of masks. Each ``masks[i]`` specifies permissible matches between input query descriptors and stored train descriptors from the i-th image ``trainDescCollection[i]``.
:param masks: Set of masks. Each ``masks[i]`` specifies permissible matches between input query descriptors and stored train descriptors from the i-th image ``trainDescCollection[i]``.
Finds the best matches for each query descriptor that has a distance smaller than the given threshold. Found matches are returned in the distance increasing order. See the details about query and train descriptors in ??.
Now the following matcher types are supported: ``"BruteForce"`` (it uses ``L2`` ), ``"BruteForce-L1"`` ,``"BruteForce-Hamming"`` ,``"BruteForce-HammingLUT"`` , and ``"FlannBased"`` .
Now the following matcher types are supported: ``BruteForce`` (it uses ``L2`` ), ``BruteForce-L1`` ,``BruteForce-Hamming`` ,``BruteForce-HammingLUT`` , and ``FlannBased`` .
.. index:: BruteForceMatcher
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@@ -258,7 +256,7 @@ Brute-force descriptor matcher. For each descriptor in the first set, this match
}
For efficiency, ``BruteForceMatcher`` is used as a template for the distance metric??. For float descriptors, a common choice is ``L2<float>`` . The following class of supported distances is used: ::
For efficiency, ``BruteForceMatcher`` is used as a template for the distance metric??. For float descriptors, ``L2<float>`` is a common choice. The following distances are supported: ::
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. ::
Abstract interface for extracting and matching a keypoint descriptor. There are also :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. ::
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 flags: Flags setting drawing features. Possible ``flags`` bit values are defined by ``DrawMatchesFlags``.
This function draws matches of keypints from two images in the output image. Match is a line connecting two keypoints (circles). The structure ``DrawMatchesFlags`` is defined as follows:
This function draws matches of keypoints from two images in the output image. Match is a line connecting two keypoints (circles). The structure ``DrawMatchesFlags`` is defined as follows:
Similarly to ``getSignature``, but it uses a threshold for removing all signature elements below the threshold so that the signature is compressed.
Returns a signature for an image patch similarly to ``getSignature`` but uses a threshold for removing all signature elements below the threshold so that the signature is compressed.
:param patch: Image patch to calculate signature for
:param patch: Image patch to calculate the nsignature for.
:param sig: Output signature (array dimension is ``reduced_num_dim)``
:param sig: Output signature (array dimension is ``reduced_num_dim)`` .
:param thresh: The threshold that is used for compressing the signature
:param thresh: Threshold that is used for compressing the signature.
Applies quantization to the current randomized tree.
:param num_quant_bits: Number of bits are used for quantization
:param num_quant_bits: Number of bits used for quantization.
The example below demonstrates the usage of ``RTreeClassifier`` for feature matching. There are test and train images and features are extracted from both with SURF. Output is
The example below demonstrates the usage of ``RTreeClassifier`` for matching the features. The features are extracted from the test and train images with SURF. Output is
:math:`best\_corr` and
:math:`best\_corr\_idx` arrays that keep the best probabilities and corresponding features indices for every train feature. ::
:param pointIdxsOfClusters: Indices of keypoints that belong to the cluster. This means that ``pointIdxsOfClusters[i]`` are keypoint indices that belong to the ``i`` -th cluster (word of vocabulary) returned if it is non-zero.
