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Fixed hundreds of documentation problems
This commit is contained in:
Andrey Kamaev
@@ -13,7 +13,7 @@ descriptor extractors inherit the
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DescriptorExtractor
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-------------------
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.. ocv:class:: DescriptorExtractor
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.. ocv:class:: DescriptorExtractor : public Algorithm
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Abstract base class for computing descriptors for image keypoints. ::
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@@ -107,7 +107,7 @@ for example: ``"OpponentSIFT"`` .
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OpponentColorDescriptorExtractor
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--------------------------------
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.. ocv:class:: OpponentColorDescriptorExtractor
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.. ocv:class:: OpponentColorDescriptorExtractor : public DescriptorExtractor
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Class adapting a descriptor extractor to compute descriptors in the Opponent Color Space
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(refer to Van de Sande et al., CGIV 2008 *Color Descriptors for Object Category Recognition*).
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@@ -132,7 +132,7 @@ them into a single color descriptor. ::
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BriefDescriptorExtractor
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------------------------
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.. ocv:class:: BriefDescriptorExtractor
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.. ocv:class:: BriefDescriptorExtractor : public DescriptorExtractor
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Class for computing BRIEF descriptors described in a paper of Calonder M., Lepetit V.,
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Strecha C., Fua P. *BRIEF: Binary Robust Independent Elementary Features* ,
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@@ -11,7 +11,7 @@ descriptor matchers inherit the
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DMatch
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------
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.. ocv:class:: DMatch
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.. ocv:struct:: DMatch
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Class for matching keypoint descriptors: query descriptor index,
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train descriptor index, train image index, and distance between descriptors. ::
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@@ -40,7 +40,7 @@ train descriptor index, train image index, and distance between descriptors. ::
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DescriptorMatcher
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-----------------
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.. ocv:class:: DescriptorMatcher
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.. ocv:class:: DescriptorMatcher : public Algorithm
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Abstract base class for matching keypoint descriptors. It has two groups
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of match methods: for matching descriptors of an image with another image or
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@@ -111,7 +111,7 @@ Returns a constant link to the train descriptor collection ``trainDescCollection
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.. ocv:function:: const vector<Mat>& DescriptorMatcher::getTrainDescriptors() const
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@@ -167,7 +167,7 @@ Finds the best match for each descriptor from a query set.
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:param masks: Set of masks. Each ``masks[i]`` specifies permissible matches between the input query descriptors and stored train descriptors from the i-th image ``trainDescCollection[i]``.
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In the first variant of this method, the train descriptors are passed as an input argument. In the second variant of the method, train descriptors collection that was set by ``DescriptorMatcher::add`` is used. Optional mask (or masks) can be passed to specify which query and training descriptors can be matched. Namely, ``queryDescriptors[i]`` can be matched with ``trainDescriptors[j]`` only if ``mask.at<uchar>(i,j)`` is non-zero.
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In the first variant of this method, the train descriptors are passed as an input argument. In the second variant of the method, train descriptors collection that was set by ``DescriptorMatcher::add`` is used. Optional mask (or masks) can be passed to specify which query and training descriptors can be matched. Namely, ``queryDescriptors[i]`` can be matched with ``trainDescriptors[j]`` only if ``mask.at<uchar>(i,j)`` is non-zero.
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@@ -193,7 +193,7 @@ Finds the k best matches for each descriptor from a query set.
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:param compactResult: Parameter used when the mask (or masks) is not empty. If ``compactResult`` is false, the ``matches`` vector has the same size as ``queryDescriptors`` rows. If ``compactResult`` is true, the ``matches`` vector does not contain matches for fully masked-out query descriptors.
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These extended variants of :ocv:func:`DescriptorMatcher::match` methods find several best matches for each query descriptor. The matches are returned in the distance increasing order. See :ocv:func:`DescriptorMatcher::match` for the details about query and train descriptors.
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These extended variants of :ocv:func:`DescriptorMatcher::match` methods find several best matches for each query descriptor. The matches are returned in the distance increasing order. See :ocv:func:`DescriptorMatcher::match` for the details about query and train descriptors.
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@@ -218,7 +218,7 @@ For each query descriptor, finds the training descriptors not farther than the s
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:param compactResult: Parameter used when the mask (or masks) is not empty. If ``compactResult`` is false, the ``matches`` vector has the same size as ``queryDescriptors`` rows. If ``compactResult`` is true, the ``matches`` vector does not contain matches for fully masked-out query descriptors.
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:param maxDistance: Threshold for the distance between matched descriptors.
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For each query descriptor, the methods find such training descriptors that the distance between the query descriptor and the training descriptor is equal or smaller than ``maxDistance``. Found matches are returned in the distance increasing order.
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@@ -227,7 +227,7 @@ DescriptorMatcher::clone
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----------------------------
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Clones the matcher.
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.. ocv:function:: Ptr<DescriptorMatcher> DescriptorMatcher::clone( bool emptyTrainData ) const
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.. ocv:function:: Ptr<DescriptorMatcher> DescriptorMatcher::clone( bool emptyTrainData=false )
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:param emptyTrainData: If ``emptyTrainData`` is false, the method creates a deep copy of the object, that is, copies both parameters and train data. If ``emptyTrainData`` is true, the method creates an object copy with the current parameters but with empty train data.
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@@ -241,15 +241,15 @@ Creates a descriptor matcher of a given type with the default parameters (using
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:param descriptorMatcherType: Descriptor matcher type. Now the following matcher types are supported:
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*
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*
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``BruteForce`` (it uses ``L2`` )
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*
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*
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``BruteForce-L1``
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*
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*
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``BruteForce-Hamming``
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*
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*
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``BruteForce-Hamming(2)``
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*
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*
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``FlannBased``
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@@ -258,7 +258,7 @@ Creates a descriptor matcher of a given type with the default parameters (using
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BFMatcher
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-----------------
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.. ocv:class::BFMatcher
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.. ocv:class::BFMatcher : public DescriptorMatcher
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Brute-force descriptor matcher. For each descriptor in the first set, this matcher finds the closest descriptor in the second set by trying each one. This descriptor matcher supports masking permissible matches of descriptor sets. ::
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@@ -267,16 +267,16 @@ BFMatcher::BFMatcher
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--------------------
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Brute-force matcher constructor.
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.. ocv:function:: BFMatcher::BFMatcher( int distanceType, bool crossCheck=false )
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.. ocv:function:: BFMatcher::BFMatcher( int normType, bool crossCheck=false )
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:param distanceType: One of ``NORM_L1``, ``NORM_L2``, ``NORM_HAMMING``, ``NORM_HAMMING2``. ``L1`` and ``L2`` norms are preferable choices for SIFT and SURF descriptors, ``NORM_HAMMING`` should be used with ORB and BRIEF, ``NORM_HAMMING2`` should be used with ORB when ``WTA_K==3`` or ``4`` (see ORB::ORB constructor description).
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:param crossCheck: If it is false, this is will be default BFMatcher behaviour when it finds the k nearest neighbors for each query descriptor. If ``crossCheck==true``, then the ``knnMatch()`` method with ``k=1`` will only return pairs ``(i,j)`` such that for ``i-th`` query descriptor the ``j-th`` descriptor in the matcher's collection is the nearest and vice versa, i.e. the ``BFMathcher`` will only return consistent pairs. Such technique usually produces best results with minimal number of outliers when there are enough matches. This is alternative to the ratio test, used by D. Lowe in SIFT paper.
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FlannBasedMatcher
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-----------------
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.. ocv:class:: FlannBasedMatcher
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.. ocv:class:: FlannBasedMatcher : public DescriptorMatcher
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Flann-based descriptor matcher. This matcher trains :ocv:class:`flann::Index_` on a train descriptor collection and calls its nearest search methods to find the best matches. So, this matcher may be faster when matching a large train collection than the brute force matcher. ``FlannBasedMatcher`` does not support masking permissible matches of descriptor sets because ``flann::Index`` does not support this. ::
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@@ -48,7 +48,7 @@ The keypoint constructors
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.. ocv:function:: KeyPoint::KeyPoint(float x, float y, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1)
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.. ocv:pyfunction:: cv2.KeyPoint(x, y, _size[, _angle[, _response[, _octave[, _class_id]]]]) -> <KeyPoint object>
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.. ocv:pyfunction:: cv2.KeyPoint([x, y, _size[, _angle[, _response[, _octave[, _class_id]]]]]) -> <KeyPoint object>
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:param x: x-coordinate of the keypoint
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@@ -69,7 +69,7 @@ The keypoint constructors
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FeatureDetector
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---------------
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.. ocv:class:: FeatureDetector
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.. ocv:class:: FeatureDetector : public Algorithm
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Abstract base class for 2D image feature detectors. ::
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@@ -156,7 +156,7 @@ for example: ``"GridFAST"``, ``"PyramidSTAR"`` .
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FastFeatureDetector
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-------------------
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.. ocv:class:: FastFeatureDetector
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.. ocv:class:: FastFeatureDetector : public FeatureDetector
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Wrapping class for feature detection using the
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:ocv:func:`FAST` method. ::
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@@ -252,7 +252,7 @@ Wrapping class for feature detection using the
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DenseFeatureDetector
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--------------------
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.. ocv:class:: DenseFeatureDetector
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.. ocv:class:: DenseFeatureDetector : public FeatureDetector
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Class for generation of image features which are distributed densely and regularly over the image. ::
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@@ -279,7 +279,7 @@ The detector generates several levels (in the amount of ``featureScaleLevels``)
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SimpleBlobDetector
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-------------------
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.. ocv:class:: SimpleBlobDetector
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.. ocv:class:: SimpleBlobDetector : public FeatureDetector
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Class for extracting blobs from an image. ::
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@@ -344,7 +344,7 @@ Default values of parameters are tuned to extract dark circular blobs.
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GridAdaptedFeatureDetector
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--------------------------
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.. ocv:class:: GridAdaptedFeatureDetector
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.. ocv:class:: GridAdaptedFeatureDetector : public FeatureDetector
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Class adapting a detector to partition the source image into a grid and detect points in each cell. ::
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@@ -369,7 +369,7 @@ Class adapting a detector to partition the source image into a grid and detect p
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PyramidAdaptedFeatureDetector
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-----------------------------
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.. ocv:class:: PyramidAdaptedFeatureDetector
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.. ocv:class:: PyramidAdaptedFeatureDetector : public FeatureDetector
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Class adapting a detector to detect points over multiple levels of a Gaussian pyramid. Consider using this class for detectors that are not inherently scaled. ::
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@@ -387,7 +387,7 @@ Class adapting a detector to detect points over multiple levels of a Gaussian py
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DynamicAdaptedFeatureDetector
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-----------------------------
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.. ocv:class:: DynamicAdaptedFeatureDetector
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.. ocv:class:: DynamicAdaptedFeatureDetector : public FeatureDetector
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Adaptively adjusting detector that iteratively detects features until the desired number is found. ::
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@@ -431,7 +431,7 @@ DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector
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----------------------------------------------------------------
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The constructor
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.. ocv:function:: DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>& adjuster, int min_features, int max_features, int max_iters )
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.. ocv:function:: DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>& adjaster, int min_features=400, int max_features=500, int max_iters=5 )
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:param adjuster: :ocv:class:`AdjusterAdapter` that detects features and adjusts parameters.
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@@ -443,7 +443,7 @@ The constructor
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AdjusterAdapter
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---------------
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.. ocv:class:: AdjusterAdapter
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.. ocv:class:: AdjusterAdapter : public FeatureDetector
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Class providing an interface for adjusting parameters of a feature detector. This interface is used by :ocv:class:`DynamicAdaptedFeatureDetector` . It is a wrapper for :ocv:class:`FeatureDetector` that enables adjusting parameters after feature detection. ::
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@@ -522,7 +522,7 @@ Creates an adjuster adapter by name
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FastAdjuster
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------------
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.. ocv:class:: FastAdjuster
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.. ocv:class:: FastAdjuster : public AdjusterAdapter
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:ocv:class:`AdjusterAdapter` for :ocv:class:`FastFeatureDetector`. This class decreases or increases the threshold value by 1. ::
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@@ -535,7 +535,7 @@ FastAdjuster
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StarAdjuster
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------------
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.. ocv:class:: StarAdjuster
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.. ocv:class:: StarAdjuster : public AdjusterAdapter
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:ocv:class:`AdjusterAdapter` for :ocv:class:`StarFeatureDetector`. This class adjusts the ``responseThreshhold`` of ``StarFeatureDetector``. ::
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@@ -3,7 +3,7 @@ Common Interfaces of Generic Descriptor Matchers
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.. highlight:: cpp
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Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to easily switch
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Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to easily switch
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between different algorithms solving the same problem. This section is devoted to matching descriptors
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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.
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Every descriptor with the
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@@ -130,7 +130,7 @@ GenericDescriptorMatcher::isMaskSupported
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---------------------------------------------
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Returns ``true`` if a generic descriptor matcher supports masking permissible matches.
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.. ocv:function:: void GenericDescriptorMatcher::isMaskSupported()
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.. ocv:function:: bool GenericDescriptorMatcher::isMaskSupported()
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@@ -151,12 +151,12 @@ Classifies keypoints from a query set.
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:param trainKeypoints: Keypoints from a train image.
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The method classifies each keypoint from a query set. The first variant of the method takes a train image and its keypoints as an input argument. The second variant uses the internally stored training collection that can be built using the ``GenericDescriptorMatcher::add`` method.
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The methods do the following:
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#.
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Call the ``GenericDescriptorMatcher::match`` method to find correspondence between the query set and the training set.
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#.
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Set the ``class_id`` field of each keypoint from the query set to ``class_id`` of the corresponding keypoint from the training set.
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@@ -195,7 +195,7 @@ Finds the ``k`` best matches for each query keypoint.
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.. ocv:function:: void GenericDescriptorMatcher::knnMatch( const Mat& queryImage, vector<KeyPoint>& queryKeypoints, const Mat& trainImage, vector<KeyPoint>& trainKeypoints, vector<vector<DMatch> >& matches, int k, const Mat& mask=Mat(), bool compactResult=false ) const
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.. ocv:function:: void GenericDescriptorMatcher::knnMatch( const Mat& queryImage, vector<KeyPoint>& queryKeypoints, vector<vector<DMatch> >& matches, int k, const vector<Mat>& masks=vector<Mat>(), bool compactResult=false )
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The methods are extended variants of ``GenericDescriptorMatch::match``. The parameters are similar, and the semantics is similar to ``DescriptorMatcher::knnMatch``. But this class does not require explicitly computed keypoint descriptors.
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@@ -231,7 +231,7 @@ GenericDescriptorMatcher::clone
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-----------------------------------
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Clones the matcher.
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.. ocv:function:: Ptr<GenericDescriptorMatcher> GenericDescriptorMatcher::clone( bool emptyTrainData ) const
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.. ocv:function:: Ptr<GenericDescriptorMatcher> GenericDescriptorMatcher::clone( bool emptyTrainData=false ) const
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:param emptyTrainData: If ``emptyTrainData`` is false, the method creates a deep copy of the object, that is, copies
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both parameters and train data. If ``emptyTrainData`` is true, the method creates an object copy with the current parameters
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@@ -240,7 +240,7 @@ Clones the matcher.
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VectorDescriptorMatcher
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-----------------------
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.. ocv:class:: VectorDescriptorMatcher
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.. ocv:class:: VectorDescriptorMatcher : public GenericDescriptorMatcher
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Class used for matching descriptors that can be described as vectors in a finite-dimensional space. ::
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@@ -7,9 +7,9 @@ drawMatches
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---------------
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Draws the found matches of keypoints from two images.
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.. ocv:function:: void drawMatches( const Mat& img1, const vector<KeyPoint>& keypoints1, const Mat& img2, const vector<KeyPoint>& keypoints2, const vector<DMatch>& matches1to2, Mat& outImg, const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1), const vector<char>& matchesMask=vector<char>(), int flags=DrawMatchesFlags::DEFAULT )
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.. ocv:function:: void drawMatches( const Mat& img1, const vector<KeyPoint>& keypoints1, const Mat& img2, const vector<KeyPoint>& keypoints2, const vector<DMatch>& matches1to2, Mat& outImg, const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1), const vector<char>& matchesMask=vector<char>(), int flags=DrawMatchesFlags::DEFAULT )
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.. ocv:function:: void drawMatches( const Mat& img1, const vector<KeyPoint>& keypoints1, const Mat& img2, const vector<KeyPoint>& keypoints2, const vector<vector<DMatch> >& matches1to2, Mat& outImg, const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1), const vector<vector<char>>& matchesMask= vector<vector<char> >(), int flags=DrawMatchesFlags::DEFAULT )
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.. ocv:function:: void drawMatches( const Mat& img1, const vector<KeyPoint>& keypoints1, const Mat& img2, const vector<KeyPoint>& keypoints2, const vector<vector<DMatch> >& matches1to2, Mat& outImg, const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1), const vector<vector<char> >& matchesMask=vector<vector<char> >(), int flags=DrawMatchesFlags::DEFAULT )
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:param img1: First source image.
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@@ -31,7 +31,7 @@ Draws the found matches of keypoints from two images.
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:param matchesMask: Mask determining which matches are drawn. If the mask is empty, all matches are drawn.
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:param flags: Flags setting drawing features. Possible ``flags`` bit values are defined by ``DrawMatchesFlags``.
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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:
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.. code-block:: cpp
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@@ -65,7 +65,7 @@ drawKeypoints
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-----------------
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Draws keypoints.
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.. ocv:function:: void drawKeypoints( const Mat& image, const vector<KeyPoint>& keypoints, Mat& outImg, const Scalar& color=Scalar::all(-1), int flags=DrawMatchesFlags::DEFAULT )
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.. ocv:function:: void drawKeypoints( const Mat& image, const vector<KeyPoint>& keypoints, Mat& outImage, const Scalar& color=Scalar::all(-1), int flags=DrawMatchesFlags::DEFAULT )
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:param image: Source image.
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@@ -24,7 +24,7 @@ Detects corners using the FAST algorithm by [Rosten06]_.
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MSER
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----
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.. ocv:class:: MSER
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.. ocv:class:: MSER : public FeatureDetector
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Maximally stable extremal region extractor. ::
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@@ -50,7 +50,7 @@ http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions). Also see http:/
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ORB
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---
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.. ocv:class:: ORB
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.. ocv:class:: ORB : public Feature2D
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Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor, described in [RRKB11]_. The algorithm uses FAST in pyramids to detect stable keypoints, selects the strongest features using FAST or Harris response, finds their orientation using first-order moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or k-tuples) are rotated according to the measured orientation).
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@@ -60,39 +60,37 @@ ORB::ORB
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--------
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The ORB constructor
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.. ocv:function:: ORB::ORB()
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.. ocv:function:: ORB::ORB(int nfeatures = 500, float scaleFactor = 1.2f, int nlevels = 8, int edgeThreshold = 31, int firstLevel = 0, int WTA_K=2, int scoreType=HARRIS_SCORE, int patchSize=31)
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:param nfeatures: The maximum number of features to retain.
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:param scaleFactor: Pyramid decimation ratio, greater than 1. ``scaleFactor==2`` means the classical pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor will mean that to cover certain scale range you will need more pyramid levels and so the speed will suffer.
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:param nlevels: The number of pyramid levels. The smallest level will have linear size equal to ``input_image_linear_size/pow(scaleFactor, nlevels)``.
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:param edgeThreshold: This is size of the border where the features are not detected. It should roughly match the ``patchSize`` parameter.
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:param firstLevel: It should be 0 in the current implementation.
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|
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:param WTA_K: The number of points that produce each element of the oriented BRIEF descriptor. The default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 random points (of course, those point coordinates are random, but they are generated from the pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, denoted as ``NORM_HAMMING2`` (2 bits per bin). When ``WTA_K=4``, we take 4 random points to compute each bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
|
||||
|
||||
|
||||
:param scoreType: The default HARRIS_SCORE means that Harris algorithm is used to rank features (the score is written to ``KeyPoint::score`` and is used to retain best ``nfeatures`` features); FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, but it is a little faster to compute.
|
||||
|
||||
|
||||
:param patchSize: size of the patch used by the oriented BRIEF descriptor. Of course, on smaller pyramid layers the perceived image area covered by a feature will be larger.
|
||||
|
||||
ORB::operator()
|
||||
---------------
|
||||
Finds keypoints in an image and computes their descriptors
|
||||
|
||||
|
||||
.. ocv:function:: void ORB::operator()(InputArray image, InputArray mask, vector<KeyPoint>& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false ) const
|
||||
|
||||
:param image: The input 8-bit grayscale image.
|
||||
|
||||
|
||||
:param mask: The operation mask.
|
||||
|
||||
|
||||
:param keypoints: The output vector of keypoints.
|
||||
|
||||
|
||||
:param descriptors: The output descriptors. Pass ``cv::noArray()`` if you do not need it.
|
||||
|
||||
|
||||
:param useProvidedKeypoints: If it is true, then the method will use the provided vector of keypoints instead of detecting them.
|
||||
|
||||
|
||||
@@ -54,7 +54,7 @@ BOWTrainer::descripotorsCount
|
||||
---------------------------------
|
||||
Returns the count of all descriptors stored in the training set.
|
||||
|
||||
.. ocv:function:: const vector<Mat>& BOWTrainer::descripotorsCount() const
|
||||
.. ocv:function:: int BOWTrainer::descripotorsCount() const
|
||||
|
||||
|
||||
|
||||
@@ -72,7 +72,7 @@ The vocabulary consists of cluster centers. So, this method returns the vocabula
|
||||
|
||||
BOWKMeansTrainer
|
||||
----------------
|
||||
.. ocv:class:: BOWKMeansTrainer
|
||||
.. ocv:class:: BOWKMeansTrainer : public BOWTrainer
|
||||
|
||||
:ocv:func:`kmeans` -based class to train visual vocabulary using the *bag of visual words* approach.
|
||||
::
|
||||
|
||||
@@ -276,7 +276,7 @@ public:
|
||||
enum { kBytes = 32, HARRIS_SCORE=0, FAST_SCORE=1 };
|
||||
|
||||
explicit ORB(int nfeatures = 500, float scaleFactor = 1.2f, int nlevels = 8, int edgeThreshold = 31,
|
||||
int firstLevel = 0, int WTA_K=2, int scoreType=0, int patchSize=31 );
|
||||
int firstLevel = 0, int WTA_K=2, int scoreType=HARRIS_SCORE, int patchSize=31 );
|
||||
|
||||
// returns the descriptor size in bytes
|
||||
int descriptorSize() const;
|
||||
@@ -1158,9 +1158,9 @@ public:
|
||||
const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );
|
||||
|
||||
// Reads matcher object from a file node
|
||||
virtual void read( const FileNode& );
|
||||
virtual void read( const FileNode& fn );
|
||||
// Writes matcher object to a file storage
|
||||
virtual void write( FileStorage& ) const;
|
||||
virtual void write( FileStorage& fs ) const;
|
||||
|
||||
// Return true if matching object is empty (e.g. feature detector or descriptor matcher are empty)
|
||||
virtual bool empty() const;
|
||||
|
||||
Reference in New Issue
Block a user