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Code Packages Releases Wiki Activity

the first round of cleaning up the RST docs

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This commit is contained in:
Vadim Pisarevsky
2011-02-28 21:26:43 +00:00
parent eb8c0b8b4b
commit 4bb893aa9f
48 changed files with 1664 additions and 1649 deletions
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modules/features2d/doc/common_interfaces_of_descriptor_extractors.rst
+15 -15
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@@ -15,7 +15,7 @@ descriptor extractors inherit
DescriptorExtractor
-------------------
.. ctype:: DescriptorExtractor
.. c:type:: DescriptorExtractor
Abstract base class for computing descriptors for image keypoints. ::
@@ -51,9 +51,9 @@ descriptors as a
.. index:: DescriptorExtractor::compute
cv::DescriptorExtractor::compute
DescriptorExtractor::compute
--------------------------------
.. cfunction:: void DescriptorExtractor::compute( const Mat\& image, vector<KeyPoint>\& keypoints, Mat\& descriptors ) const
.. c:function:: void DescriptorExtractor::compute( const Mat\& image, vector<KeyPoint>\& keypoints, Mat\& descriptors ) const
Compute the descriptors for a set of keypoints detected in an image (first variant)
or image set (second variant).
@@ -64,7 +64,7 @@ or image set (second variant).
:param descriptors: The descriptors. Row i is the descriptor for keypoint i.
.. cfunction:: void DescriptorExtractor::compute( const vector<Mat>\& images, vector<vector<KeyPoint> >\& keypoints, vector<Mat>\& descriptors ) const
.. c:function:: void DescriptorExtractor::compute( const vector<Mat>\& images, vector<vector<KeyPoint> >\& keypoints, vector<Mat>\& descriptors ) const
* **images** The image set.
@@ -77,9 +77,9 @@ or image set (second variant).
.. index:: DescriptorExtractor::read
cv::DescriptorExtractor::read
DescriptorExtractor::read
-----------------------------
.. cfunction:: void DescriptorExtractor::read( const FileNode\& fn )
.. c:function:: void DescriptorExtractor::read( const FileNode\& fn )
Read descriptor extractor object from file node.
@@ -87,9 +87,9 @@ cv::DescriptorExtractor::read
.. index:: DescriptorExtractor::write
cv::DescriptorExtractor::write
DescriptorExtractor::write
------------------------------
.. cfunction:: void DescriptorExtractor::write( FileStorage\& fs ) const
.. c:function:: void DescriptorExtractor::write( FileStorage\& fs ) const
Write descriptor extractor object to file storage.
@@ -97,10 +97,10 @@ cv::DescriptorExtractor::write
.. index:: DescriptorExtractor::create
cv::DescriptorExtractor::create
DescriptorExtractor::create
-------------------------------
:func:`DescriptorExtractor`
.. cfunction:: Ptr<DescriptorExtractor> DescriptorExtractor::create( const string\& descriptorExtractorType )
.. c:function:: Ptr<DescriptorExtractor> DescriptorExtractor::create( const string\& descriptorExtractorType )
Descriptor extractor factory that creates of given type with
default parameters (rather using default constructor).
@@ -123,7 +123,7 @@ e.g. ``"OpponentSIFT"`` , etc.
SiftDescriptorExtractor
-----------------------
.. ctype:: SiftDescriptorExtractor
.. c:type:: SiftDescriptorExtractor
Wrapping class for descriptors computing using
:func:`SIFT` class. ::
@@ -155,7 +155,7 @@ Wrapping class for descriptors computing using
SurfDescriptorExtractor
-----------------------
.. ctype:: SurfDescriptorExtractor
.. c:type:: SurfDescriptorExtractor
Wrapping class for descriptors computing using
:func:`SURF` class. ::
@@ -181,7 +181,7 @@ Wrapping class for descriptors computing using
CalonderDescriptorExtractor
---------------------------
.. ctype:: CalonderDescriptorExtractor
.. c:type:: CalonderDescriptorExtractor
Wrapping class for descriptors computing using
:func:`RTreeClassifier` class. ::
@@ -207,7 +207,7 @@ Wrapping class for descriptors computing using
OpponentColorDescriptorExtractor
--------------------------------
.. ctype:: OpponentColorDescriptorExtractor
.. c:type:: OpponentColorDescriptorExtractor
Adapts a descriptor extractor to compute descripors in Opponent Color Space
(refer to van de Sande et al., CGIV 2008 "Color Descriptors for Object Category Recognition").
@@ -235,7 +235,7 @@ them into a single color descriptor. ::
BriefDescriptorExtractor
------------------------
.. ctype:: BriefDescriptorExtractor
.. c:type:: BriefDescriptorExtractor
Class for computing BRIEF descriptors described in paper of Calonder M., Lepetit V.,
Strecha C., Fua P.: ''BRIEF: Binary Robust Independent Elementary Features.''
modules/features2d/doc/common_interfaces_of_descriptor_matchers.rst
+29 -29
View File
@@ -15,7 +15,7 @@ descriptor matchers inherit
DMatch
------
.. ctype:: DMatch
.. c:type:: DMatch
Match between two keypoint descriptors: query descriptor index,
train descriptor index, train image index and distance between descriptors. ::
@@ -48,7 +48,7 @@ train descriptor index, train image index and distance between descriptors. ::
DescriptorMatcher
-----------------
.. ctype:: DescriptorMatcher
.. c:type:: DescriptorMatcher
Abstract base class for matching keypoint descriptors. It has two groups
of match methods: for matching descriptors of one image with other image or
@@ -106,9 +106,9 @@ with image set. ::
.. index:: DescriptorMatcher::add
cv::DescriptorMatcher::add
DescriptorMatcher::add
-------------------------- ````
.. cfunction:: void add( const vector<Mat>\& descriptors )
.. c:function:: void add( const vector<Mat>\& descriptors )
Add descriptors to train descriptor collection. If collection trainDescCollectionis not empty
the new descriptors are added to existing train descriptors.
@@ -118,41 +118,41 @@ the new descriptors are added to existing train descriptors.
.. index:: DescriptorMatcher::getTrainDescriptors
cv::DescriptorMatcher::getTrainDescriptors
DescriptorMatcher::getTrainDescriptors
------------------------------------------ ````
.. cfunction:: const vector<Mat>\& getTrainDescriptors() const
.. c:function:: const vector<Mat>\& getTrainDescriptors() const
Returns constant link to the train descriptor collection (i.e. trainDescCollection).
.. index:: DescriptorMatcher::clear
cv::DescriptorMatcher::clear
DescriptorMatcher::clear
----------------------------
.. cfunction:: void DescriptorMatcher::clear()
.. c:function:: void DescriptorMatcher::clear()
Clear train descriptor collection.
.. index:: DescriptorMatcher::empty
cv::DescriptorMatcher::empty
DescriptorMatcher::empty
----------------------------
.. cfunction:: bool DescriptorMatcher::empty() const
.. c:function:: bool DescriptorMatcher::empty() const
Return true if there are not train descriptors in collection.
.. index:: DescriptorMatcher::isMaskSupported
cv::DescriptorMatcher::isMaskSupported
DescriptorMatcher::isMaskSupported
--------------------------------------
.. cfunction:: bool DescriptorMatcher::isMaskSupported()
.. c:function:: bool DescriptorMatcher::isMaskSupported()
Returns true if descriptor matcher supports masking permissible matches.
.. index:: DescriptorMatcher::train
cv::DescriptorMatcher::train
DescriptorMatcher::train
----------------------------
.. cfunction:: void DescriptorMatcher::train()
.. c:function:: void DescriptorMatcher::train()
Train descriptor matcher (e.g. train flann index). In all methods to match the method train()
is run every time before matching. Some descriptor matchers (e.g. BruteForceMatcher) have empty
@@ -161,9 +161,9 @@ trains flann::Index)
.. index:: DescriptorMatcher::match
cv::DescriptorMatcher::match
DescriptorMatcher::match
---------------------------- ```` ```` ```` ````
.. cfunction:: void DescriptorMatcher::match( const Mat\& queryDescriptors, const Mat\& trainDescriptors, vector<DMatch>\& matches, const Mat\& mask=Mat() ) const
.. c:function:: void DescriptorMatcher::match( const Mat\& queryDescriptors, const Mat\& trainDescriptors, vector<DMatch>\& matches, const Mat\& mask=Mat() ) const
Find the best match for each descriptor from a query set with train descriptors.
Supposed that the query descriptors are of keypoints detected on the same query image.
@@ -172,7 +172,7 @@ supposed that they are of keypoints detected on the same train image. In second
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.
.. cfunction:: void DescriptorMatcher::match( const Mat\& queryDescriptors, vector<DMatch>\& matches, const vector<Mat>\& masks=vector<Mat>() )
.. c:function:: void DescriptorMatcher::match( const Mat\& queryDescriptors, vector<DMatch>\& matches, const vector<Mat>\& masks=vector<Mat>() )
:param queryDescriptors: Query set of descriptors.
@@ -189,16 +189,16 @@ Optional mask (or masks) can be set to describe which descriptors can be matched
.. index:: DescriptorMatcher::knnMatch
cv::DescriptorMatcher::knnMatch
DescriptorMatcher::knnMatch
-------------------------------
:func:`DescriptorMatcher::match`
.. cfunction:: void DescriptorMatcher::knnMatch( const Mat\& queryDescriptors, const Mat\& trainDescriptors, vector<vector<DMatch> >\& matches, int k, const Mat\& mask=Mat(), bool compactResult=false ) const
.. c:function:: void DescriptorMatcher::knnMatch( const Mat\& queryDescriptors, const Mat\& trainDescriptors, vector<vector<DMatch> >\& matches, int k, const Mat\& mask=Mat(), bool compactResult=false ) const
Find the k best matches for each descriptor from a query set with train descriptors.
Found k (or less if not possible) matches are returned in distance increasing order.
Details about query and train descriptors see in .
.. cfunction:: void DescriptorMatcher::knnMatch( const Mat\& queryDescriptors, vector<vector<DMatch> >\& matches, int k, const vector<Mat>\& masks=vector<Mat>(), bool compactResult=false )
.. c:function:: void DescriptorMatcher::knnMatch( const Mat\& queryDescriptors, vector<vector<DMatch> >\& matches, int k, const vector<Mat>\& masks=vector<Mat>(), bool compactResult=false )
:param queryDescriptors, trainDescriptors, mask, masks: See in :func:`DescriptorMatcher::match` .
@@ -210,16 +210,16 @@ Details about query and train descriptors see in .
.. index:: DescriptorMatcher::radiusMatch
cv::DescriptorMatcher::radiusMatch
DescriptorMatcher::radiusMatch
----------------------------------
:func:`DescriptorMatcher::match`
.. cfunction:: void DescriptorMatcher::radiusMatch( const Mat\& queryDescriptors, const Mat\& trainDescriptors, vector<vector<DMatch> >\& matches, float maxDistance, const Mat\& mask=Mat(), bool compactResult=false ) const
.. c:function:: void DescriptorMatcher::radiusMatch( const Mat\& queryDescriptors, const Mat\& trainDescriptors, vector<vector<DMatch> >\& matches, float maxDistance, const Mat\& mask=Mat(), bool compactResult=false ) const
Find the best matches for each query descriptor which have distance less than given threshold.
Found matches are returned in distance increasing order. Details about query and train
descriptors see in .
.. cfunction:: void DescriptorMatcher::radiusMatch( const Mat\& queryDescriptors, vector<vector<DMatch> >\& matches, float maxDistance, const vector<Mat>\& masks=vector<Mat>(), bool compactResult=false )
.. c:function:: void DescriptorMatcher::radiusMatch( const Mat\& queryDescriptors, vector<vector<DMatch> >\& matches, float maxDistance, const vector<Mat>\& masks=vector<Mat>(), bool compactResult=false )
:param queryDescriptors, trainDescriptors, mask, masks: See in :func:`DescriptorMatcher::match` .
@@ -229,9 +229,9 @@ descriptors see in .
.. index:: DescriptorMatcher::clone
cv::DescriptorMatcher::clone
DescriptorMatcher::clone
----------------------------
.. cfunction:: Ptr<DescriptorMatcher> \\DescriptorMatcher::clone( bool emptyTrainData ) const
.. c:function:: Ptr<DescriptorMatcher> \\DescriptorMatcher::clone( bool emptyTrainData ) const
Clone the matcher.
@@ -241,10 +241,10 @@ cv::DescriptorMatcher::clone
.. index:: DescriptorMatcher::create
cv::DescriptorMatcher::create
DescriptorMatcher::create
-----------------------------
:func:`DescriptorMatcher`
.. cfunction:: Ptr<DescriptorMatcher> DescriptorMatcher::create( const string\& descriptorMatcherType )
.. c:function:: Ptr<DescriptorMatcher> DescriptorMatcher::create( const string\& descriptorMatcherType )
Descriptor matcher factory that creates of
given type with default parameters (rather using default constructor).
@@ -259,7 +259,7 @@ Now the following matcher types are supported: ``"BruteForce"`` (it uses ``L2``
BruteForceMatcher
-----------------
.. ctype:: BruteForceMatcher
.. c:type:: BruteForceMatcher
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
@@ -348,7 +348,7 @@ For float descriptors, a common choice would be ``L2<float>`` . Class of support
FlannBasedMatcher
-----------------
.. ctype:: FlannBasedMatcher
.. c:type:: FlannBasedMatcher
Flann based descriptor matcher. This matcher trains
:func:`flann::Index` on
modules/features2d/doc/common_interfaces_of_feature_detectors.rst
+32 -32
View File
@@ -14,7 +14,7 @@ inherit
KeyPoint
--------
.. ctype:: KeyPoint
.. c:type:: KeyPoint
Data structure for salient point detectors. ::
@@ -73,7 +73,7 @@ Data structure for salient point detectors. ::
FeatureDetector
---------------
.. ctype:: FeatureDetector
.. c:type:: FeatureDetector
Abstract base class for 2D image feature detectors. ::
@@ -101,9 +101,9 @@ Abstract base class for 2D image feature detectors. ::
.. index:: FeatureDetector::detect
cv::FeatureDetector::detect
FeatureDetector::detect
---------------------------
.. cfunction:: void FeatureDetector::detect( const Mat\& image, vector<KeyPoint>\& keypoints, const Mat\& mask=Mat() ) const
.. c:function:: void FeatureDetector::detect( const Mat\& image, vector<KeyPoint>\& keypoints, const Mat\& mask=Mat() ) const
Detect keypoints in an image (first variant) or image set (second variant).
@@ -114,7 +114,7 @@ cv::FeatureDetector::detect
:param mask: Mask specifying where to look for keypoints (optional). Must be a char matrix
with non-zero values in the region of interest.
.. cfunction:: void FeatureDetector::detect( const vector<Mat>\& images, vector<vector<KeyPoint> >\& keypoints, const vector<Mat>\& masks=vector<Mat>() ) const
.. c:function:: void FeatureDetector::detect( const vector<Mat>\& images, vector<vector<KeyPoint> >\& keypoints, const vector<Mat>\& masks=vector<Mat>() ) const
* **images** Images set.
@@ -125,9 +125,9 @@ cv::FeatureDetector::detect
.. index:: FeatureDetector::read
cv::FeatureDetector::read
FeatureDetector::read
-------------------------
.. cfunction:: void FeatureDetector::read( const FileNode\& fn )
.. c:function:: void FeatureDetector::read( const FileNode\& fn )
Read feature detector object from file node.
@@ -135,9 +135,9 @@ cv::FeatureDetector::read
.. index:: FeatureDetector::write
cv::FeatureDetector::write
FeatureDetector::write
--------------------------
.. cfunction:: void FeatureDetector::write( FileStorage\& fs ) const
.. c:function:: void FeatureDetector::write( FileStorage\& fs ) const
Write feature detector object to file storage.
@@ -145,10 +145,10 @@ cv::FeatureDetector::write
.. index:: FeatureDetector::create
cv::FeatureDetector::create
FeatureDetector::create
---------------------------
:func:`FeatureDetector`
.. cfunction:: Ptr<FeatureDetector> FeatureDetector::create( const string\& detectorType )
.. c:function:: Ptr<FeatureDetector> FeatureDetector::create( const string\& detectorType )
Feature detector factory that creates of given type with
default parameters (rather using default constructor).
@@ -176,7 +176,7 @@ e.g. ``"GridFAST"``,``"PyramidSTAR"`` , etc.
FastFeatureDetector
-------------------
.. ctype:: FastFeatureDetector
.. c:type:: FastFeatureDetector
Wrapping class for feature detection using
:func:`FAST` method. ::
@@ -198,7 +198,7 @@ Wrapping class for feature detection using
GoodFeaturesToTrackDetector
---------------------------
.. ctype:: GoodFeaturesToTrackDetector
.. c:type:: GoodFeaturesToTrackDetector
Wrapping class for feature detection using
:func:`goodFeaturesToTrack` function. ::
@@ -241,7 +241,7 @@ Wrapping class for feature detection using
MserFeatureDetector
-------------------
.. ctype:: MserFeatureDetector
.. c:type:: MserFeatureDetector
Wrapping class for feature detection using
:func:`MSER` class. ::
@@ -267,7 +267,7 @@ Wrapping class for feature detection using
StarFeatureDetector
-------------------
.. ctype:: StarFeatureDetector
.. c:type:: StarFeatureDetector
Wrapping class for feature detection using
:func:`StarDetector` class. ::
@@ -291,7 +291,7 @@ Wrapping class for feature detection using
SiftFeatureDetector
-------------------
.. ctype:: SiftFeatureDetector
.. c:type:: SiftFeatureDetector
Wrapping class for feature detection using
:func:`SIFT` class. ::
@@ -320,7 +320,7 @@ Wrapping class for feature detection using
SurfFeatureDetector
-------------------
.. ctype:: SurfFeatureDetector
.. c:type:: SurfFeatureDetector
Wrapping class for feature detection using
:func:`SURF` class. ::
@@ -343,7 +343,7 @@ Wrapping class for feature detection using
GridAdaptedFeatureDetector
--------------------------
.. ctype:: GridAdaptedFeatureDetector
.. c:type:: GridAdaptedFeatureDetector
Adapts a detector to partition the source image into a grid and detect
points in each cell. ::
@@ -374,7 +374,7 @@ points in each cell. ::
PyramidAdaptedFeatureDetector
-----------------------------
.. ctype:: PyramidAdaptedFeatureDetector
.. c:type:: PyramidAdaptedFeatureDetector
Adapts a detector to detect points over multiple levels of a Gaussian
pyramid. Useful for detectors that are not inherently scaled. ::
@@ -397,7 +397,7 @@ pyramid. Useful for detectors that are not inherently scaled. ::
DynamicAdaptedFeatureDetector
-----------------------------
.. ctype:: DynamicAdaptedFeatureDetector
.. c:type:: DynamicAdaptedFeatureDetector
An adaptively adjusting detector that iteratively detects until the desired number
of features are found.
@@ -442,9 +442,9 @@ Here is a sample of how to create a DynamicAdaptedFeatureDetector. ::
.. index:: DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector
cv::DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector
DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector
----------------------------------------------------------------
.. cfunction:: DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>\& adjaster, int min_features, int max_features, int max_iters )
.. c:function:: DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>\& adjaster, int min_features, int max_features, int max_iters )
DynamicAdaptedFeatureDetector constructor.
@@ -464,7 +464,7 @@ cv::DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector
AdjusterAdapter
---------------
.. ctype:: AdjusterAdapter
.. c:type:: AdjusterAdapter
A feature detector parameter adjuster interface, this is used by the
:func:`DynamicAdaptedFeatureDetector` and is a wrapper for
@@ -485,9 +485,9 @@ See
.. index:: AdjusterAdapter::tooFew
cv::AdjusterAdapter::tooFew
AdjusterAdapter::tooFew
---------------------------
.. cfunction:: virtual void tooFew(int min, int n_detected) = 0
.. c:function:: virtual void tooFew(int min, int n_detected) = 0
Too few features were detected so, adjust the detector parameters accordingly - so that the next
detection detects more features.
@@ -506,9 +506,9 @@ An example implementation of this is ::
.. index:: AdjusterAdapter::tooMany
cv::AdjusterAdapter::tooMany
AdjusterAdapter::tooMany
----------------------------
.. cfunction:: virtual void tooMany(int max, int n_detected) = 0
.. c:function:: virtual void tooMany(int max, int n_detected) = 0
Too many features were detected so, adjust the detector parameters accordingly - so that the next
detection detects less features.
@@ -527,9 +527,9 @@ An example implementation of this is ::
.. index:: AdjusterAdapter::good
cv::AdjusterAdapter::good
AdjusterAdapter::good
-------------------------
.. cfunction:: virtual bool good() const = 0
.. c:function:: virtual bool good() const = 0
Are params maxed out or still valid? Returns false if the parameters can't be adjusted any more.
@@ -547,7 +547,7 @@ An example implementation of this is ::
FastAdjuster
------------
.. ctype:: FastAdjuster
.. c:type:: FastAdjuster
An
:func:`AdjusterAdapter` for the
@@ -568,7 +568,7 @@ threshhold by 1 ::
StarAdjuster
------------
.. ctype:: StarAdjuster
.. c:type:: StarAdjuster
An
:func:`AdjusterAdapter` for the
@@ -588,7 +588,7 @@ StarFeatureDetector. ::
SurfAdjuster
------------
.. ctype:: SurfAdjuster
.. c:type:: SurfAdjuster
An
:func:`AdjusterAdapter` for the
modules/features2d/doc/common_interfaces_of_generic_descriptor_matchers.rst
+34 -34
View File
@@ -18,7 +18,7 @@ There are descriptors such as One way descriptor and Ferns that have ``GenericDe
GenericDescriptorMatcher
------------------------
.. ctype:: GenericDescriptorMatcher
.. c:type:: GenericDescriptorMatcher
Abstract interface for a keypoint descriptor extracting and matching.
There is
@@ -92,9 +92,9 @@ with image set. ::
.. index:: GenericDescriptorMatcher::add
cv::GenericDescriptorMatcher::add
GenericDescriptorMatcher::add
---------------------------------
.. cfunction:: void GenericDescriptorMatcher::add( const vector<Mat>\& images, vector<vector<KeyPoint> >\& keypoints )
.. c:function:: void GenericDescriptorMatcher::add( const vector<Mat>\& images, vector<vector<KeyPoint> >\& keypoints )
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
@@ -107,56 +107,56 @@ existing data.
.. index:: GenericDescriptorMatcher::getTrainImages
cv::GenericDescriptorMatcher::getTrainImages
GenericDescriptorMatcher::getTrainImages
--------------------------------------------
.. cfunction:: const vector<Mat>\& GenericDescriptorMatcher::getTrainImages() const
.. c:function:: const vector<Mat>\& GenericDescriptorMatcher::getTrainImages() const
Returns train image collection.
.. index:: GenericDescriptorMatcher::getTrainKeypoints
cv::GenericDescriptorMatcher::getTrainKeypoints
GenericDescriptorMatcher::getTrainKeypoints
-----------------------------------------------
.. cfunction:: const vector<vector<KeyPoint> >\& GenericDescriptorMatcher::getTrainKeypoints() const
.. c:function:: const vector<vector<KeyPoint> >\& GenericDescriptorMatcher::getTrainKeypoints() const
Returns train keypoints collection.
.. index:: GenericDescriptorMatcher::clear
cv::GenericDescriptorMatcher::clear
GenericDescriptorMatcher::clear
-----------------------------------
.. cfunction:: void GenericDescriptorMatcher::clear()
.. c:function:: void GenericDescriptorMatcher::clear()
Clear train collection (iamges and keypoints).
.. index:: GenericDescriptorMatcher::train
cv::GenericDescriptorMatcher::train
GenericDescriptorMatcher::train
-----------------------------------
.. cfunction:: void GenericDescriptorMatcher::train()
.. c:function:: void GenericDescriptorMatcher::train()
Train the object, e.g. tree-based structure to extract descriptors or
to optimize descriptors matching.
.. index:: GenericDescriptorMatcher::isMaskSupported
cv::GenericDescriptorMatcher::isMaskSupported
GenericDescriptorMatcher::isMaskSupported
---------------------------------------------
.. cfunction:: void GenericDescriptorMatcher::isMaskSupported()
.. c:function:: void GenericDescriptorMatcher::isMaskSupported()
Returns true if generic descriptor matcher supports masking permissible matches.
.. index:: GenericDescriptorMatcher::classify
cv::GenericDescriptorMatcher::classify
GenericDescriptorMatcher::classify
--------------------------------------
:func:`GenericDescriptorMatcher::add`
.. cfunction:: void GenericDescriptorMatcher::classify( const Mat\& queryImage, vector<KeyPoint>\& queryKeypoints, const Mat\& trainImage, vector<KeyPoint>\& trainKeypoints ) const
.. c:function:: void GenericDescriptorMatcher::classify( const Mat\& queryImage, vector<KeyPoint>\& queryKeypoints, const Mat\& trainImage, vector<KeyPoint>\& trainKeypoints ) const
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).
.. cfunction:: void GenericDescriptorMatcher::classify( const Mat\& queryImage, vector<KeyPoint>\& queryKeypoints )
.. c:function:: void GenericDescriptorMatcher::classify( const Mat\& queryImage, vector<KeyPoint>\& queryKeypoints )
:param queryImage: The query image.
@@ -168,16 +168,16 @@ cv::GenericDescriptorMatcher::classify
.. index:: GenericDescriptorMatcher::match
cv::GenericDescriptorMatcher::match
GenericDescriptorMatcher::match
-----------------------------------
:func:`GenericDescriptorMatcher::add` :func:`DescriptorMatcher::match`
.. cfunction:: void GenericDescriptorMatcher::match( const Mat\& queryImage, vector<KeyPoint>\& queryKeypoints, const Mat\& trainImage, vector<KeyPoint>\& trainKeypoints, vector<DMatch>\& matches, const Mat\& mask=Mat() ) const
.. c:function:: void GenericDescriptorMatcher::match( const Mat\& queryImage, vector<KeyPoint>\& queryKeypoints, const Mat\& trainImage, vector<KeyPoint>\& trainKeypoints, vector<DMatch>\& matches, const Mat\& mask=Mat() ) const
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.
.. cfunction:: void GenericDescriptorMatcher::match( const Mat\& queryImage, vector<KeyPoint>\& queryKeypoints, vector<DMatch>\& matches, const vector<Mat>\& masks=vector<Mat>() )
.. c:function:: void GenericDescriptorMatcher::match( const Mat\& queryImage, vector<KeyPoint>\& queryKeypoints, vector<DMatch>\& matches, const vector<Mat>\& masks=vector<Mat>() )
:param queryImage: Query image.
@@ -199,50 +199,50 @@ query keypoints are matched to training collectin that set using . As in the mas
.. index:: GenericDescriptorMatcher::knnMatch
cv::GenericDescriptorMatcher::knnMatch
GenericDescriptorMatcher::knnMatch
--------------------------------------
:func:`GenericDescriptorMatcher::match` :func:`DescriptorMatcher::knnMatch`
.. cfunction:: 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
.. c: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
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 .
.. cfunction:: void GenericDescriptorMatcher::knnMatch( const Mat\& queryImage, vector<KeyPoint>\& queryKeypoints, vector<vector<DMatch> >\& matches, int k, const vector<Mat>\& masks=vector<Mat>(), bool compactResult=false )
.. c: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 )
.. index:: GenericDescriptorMatcher::radiusMatch
cv::GenericDescriptorMatcher::radiusMatch
GenericDescriptorMatcher::radiusMatch
-----------------------------------------
:func:`GenericDescriptorMatcher::match` :func:`DescriptorMatcher::radiusMatch`
.. cfunction:: void GenericDescriptorMatcher::radiusMatch( const Mat\& queryImage, vector<KeyPoint>\& queryKeypoints, const Mat\& trainImage, vector<KeyPoint>\& trainKeypoints, vector<vector<DMatch> >\& matches, float maxDistance, const Mat\& mask=Mat(), bool compactResult=false ) const
.. c:function:: void GenericDescriptorMatcher::radiusMatch( const Mat\& queryImage, vector<KeyPoint>\& queryKeypoints, const Mat\& trainImage, vector<KeyPoint>\& trainKeypoints, vector<vector<DMatch> >\& matches, float maxDistance, const Mat\& mask=Mat(), bool compactResult=false ) const
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 .
.. cfunction:: void GenericDescriptorMatcher::radiusMatch( const Mat\& queryImage, vector<KeyPoint>\& queryKeypoints, vector<vector<DMatch> >\& matches, float maxDistance, const vector<Mat>\& masks=vector<Mat>(), bool compactResult=false )
.. c:function:: void GenericDescriptorMatcher::radiusMatch( const Mat\& queryImage, vector<KeyPoint>\& queryKeypoints, vector<vector<DMatch> >\& matches, float maxDistance, const vector<Mat>\& masks=vector<Mat>(), bool compactResult=false )
.. index:: GenericDescriptorMatcher::read
cv::GenericDescriptorMatcher::read
GenericDescriptorMatcher::read
----------------------------------
.. cfunction:: void GenericDescriptorMatcher::read( const FileNode\& fn )
.. c:function:: void GenericDescriptorMatcher::read( const FileNode\& fn )
Reads matcher object from a file node.
.. index:: GenericDescriptorMatcher::write
cv::GenericDescriptorMatcher::write
GenericDescriptorMatcher::write
-----------------------------------
.. cfunction:: void GenericDescriptorMatcher::write( FileStorage\& fs ) const
.. c:function:: void GenericDescriptorMatcher::write( FileStorage\& fs ) const
Writes match object to a file storage
.. index:: GenericDescriptorMatcher::clone
cv::GenericDescriptorMatcher::clone
GenericDescriptorMatcher::clone
-----------------------------------
.. cfunction:: Ptr<GenericDescriptorMatcher>\\GenericDescriptorMatcher::clone( bool emptyTrainData ) const
.. c:function:: Ptr<GenericDescriptorMatcher>\\GenericDescriptorMatcher::clone( bool emptyTrainData ) const
Clone the matcher.
@@ -256,7 +256,7 @@ cv::GenericDescriptorMatcher::clone
OneWayDescriptorMatcher
-----------------------
.. ctype:: OneWayDescriptorMatcher
.. c:type:: OneWayDescriptorMatcher
Wrapping class for computing, matching and classification of descriptors using
:func:`OneWayDescriptorBase` class. ::
@@ -317,7 +317,7 @@ Wrapping class for computing, matching and classification of descriptors using
FernDescriptorMatcher
---------------------
.. ctype:: FernDescriptorMatcher
.. c:type:: FernDescriptorMatcher
Wrapping class for computing, matching and classification of descriptors using
:func:`FernClassifier` class. ::
@@ -376,7 +376,7 @@ Wrapping class for computing, matching and classification of descriptors using
VectorDescriptorMatcher
-----------------------
.. ctype:: VectorDescriptorMatcher
.. c:type:: VectorDescriptorMatcher
Class used for matching descriptors that can be described as vectors in a finite-dimensional space. ::
modules/features2d/doc/drawing_function_of_keypoints_and_matches.rst
+5 -5
View File
@@ -5,14 +5,14 @@ Drawing Function of Keypoints and Matches
.. index:: drawMatches
cv::drawMatches
drawMatches
---------------
.. cfunction:: 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 )
.. c: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 )
This function draws matches of keypints from two images on output image.
Match is a line connecting two keypoints (circles).
.. cfunction:: 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 )
.. c: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 )
:param img1: First source image.
@@ -60,9 +60,9 @@ Match is a line connecting two keypoints (circles).
.. index:: drawKeypoints
cv::drawKeypoints
drawKeypoints
-----------------
.. cfunction:: void drawKeypoints( const Mat\& image, const vector<KeyPoint>\& keypoints, Mat\& outImg, const Scalar\& color=Scalar::all(-1), int flags=DrawMatchesFlags::DEFAULT )
.. c:function:: void drawKeypoints( const Mat\& image, const vector<KeyPoint>\& keypoints, Mat\& outImg, const Scalar\& color=Scalar::all(-1), int flags=DrawMatchesFlags::DEFAULT )
Draw keypoints.
modules/features2d/doc/feature_detection_and_description.rst
+61 -69
View File
@@ -5,9 +5,9 @@ Feature detection and description
.. index:: FAST
cv::FAST
FAST
--------
.. cfunction:: void FAST( const Mat\& image, vector<KeyPoint>\& keypoints, int threshold, bool nonmaxSupression=true )
.. c:function:: void FAST( const Mat& image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSupression=true )
Detects corners using FAST algorithm by E. Rosten (''Machine learning for high-speed corner detection'', 2006).
@@ -26,7 +26,7 @@ cv::FAST
MSER
----
.. ctype:: MSER
.. c:type:: MSER
Maximally-Stable Extremal Region Extractor ::
@@ -45,11 +45,9 @@ Maximally-Stable Extremal Region Extractor ::
// the optional mask marks the area where MSERs are searched for
void operator()( const Mat& image, vector<vector<Point> >& msers, const Mat& mask ) const;
};
..
The class encapsulates all the parameters of MSER (see
http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions
) extraction algorithm.
http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions) extraction algorithm.
.. index:: StarDetector
@@ -57,7 +55,7 @@ http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions
StarDetector
------------
.. ctype:: StarDetector
.. c:type:: StarDetector
Implements Star keypoint detector ::
@@ -86,7 +84,6 @@ Implements Star keypoint detector ::
// finds keypoints in an image
void operator()(const Mat& image, vector<KeyPoint>& keypoints) const;
};
..
The class implements a modified version of CenSurE keypoint detector described in
Agrawal08
@@ -97,7 +94,7 @@ Agrawal08
SIFT
----
.. ctype:: SIFT
.. c:type:: SIFT
Class for extracting keypoints and computing descriptors using approach named Scale Invariant Feature Transform (SIFT). ::
@@ -179,7 +176,7 @@ Class for extracting keypoints and computing descriptors using approach named Sc
protected:
...
};
..
.. index:: SURF
@@ -187,14 +184,14 @@ Class for extracting keypoints and computing descriptors using approach named Sc
SURF
----
.. ctype:: SURF
.. c:type:: SURF
Class for extracting Speeded Up Robust Features from an image. ::
class SURF : public CvSURFParams
{
public:
// default constructor
// c:function::default constructor
SURF();
// constructor that initializes all the algorithm parameters
SURF(double _hessianThreshold, int _nOctaves=4,
@@ -213,11 +210,8 @@ Class for extracting Speeded Up Robust Features from an image. ::
vector<float>& descriptors,
bool useProvidedKeypoints=false) const;
};
..
The class ``SURF`` implements Speeded Up Robust Features descriptor
Bay06
.
The class ``SURF`` implements Speeded Up Robust Features descriptor Bay06.
There is fast multi-scale Hessian keypoint detector that can be used to find the keypoints
(which is the default option), but the descriptors can be also computed for the user-specified keypoints.
The function can be used for object tracking and localization, image stitching etc. See the ``find_obj.cpp`` demo in OpenCV samples directory.
@@ -228,7 +222,7 @@ The function can be used for object tracking and localization, image stitching e
RandomizedTree
--------------
.. ctype:: RandomizedTree
.. c:type:: RandomizedTree
The class contains base structure for ``RTreeClassifier`` ::
@@ -241,10 +235,10 @@ The class contains base structure for ``RTreeClassifier`` ::
~RandomizedTree();
void train(std::vector<BaseKeypoint> const& base_set,
cv::RNG &rng, int depth, int views,
RNG &rng, int depth, int views,
size_t reduced_num_dim, int num_quant_bits);
void train(std::vector<BaseKeypoint> const& base_set,
cv::RNG &rng, PatchGenerator &make_patch, int depth,
RNG &rng, PatchGenerator &make_patch, int depth,
int views, size_t reduced_num_dim, int num_quant_bits);
// following two funcs are EXPERIMENTAL
@@ -281,11 +275,11 @@ The class contains base structure for ``RTreeClassifier`` ::
uchar **posteriors2_; // 16-bytes aligned posteriors
std::vector<int> leaf_counts_;
void createNodes(int num_nodes, cv::RNG &rng);
void createNodes(int num_nodes, RNG &rng);
void allocPosteriorsAligned(int num_leaves, int num_classes);
void freePosteriors(int which);
// which: 1=posteriors_, 2=posteriors2_, 3=both
void init(int classes, int depth, cv::RNG &rng);
void init(int classes, int depth, RNG &rng);
void addExample(int class_id, uchar* patch_data);
void finalize(size_t reduced_num_dim, int num_quant_bits);
int getIndex(uchar* patch_data) const;
@@ -297,19 +291,18 @@ The class contains base structure for ``RTreeClassifier`` ::
void compressLeaves(size_t reduced_num_dim);
void estimateQuantPercForPosteriors(float perc[2]);
};
..
.. index:: RandomizedTree::train
cv::RandomizedTree::train
RandomizedTree::train
-------------------------
.. cfunction:: void train(std::vector<BaseKeypoint> const\& base_set, cv::RNG \&rng, PatchGenerator \&make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits)
.. c:function:: void train(std::vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits)
Trains a randomized tree using input set of keypoints
.. cfunction:: void train(std::vector<BaseKeypoint> const\& base_set, cv::RNG \&rng, PatchGenerator \&make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits)
.. c:function:: void train(std::vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits)
{Vector of ``BaseKeypoint`` type. Contains keypoints from the image are used for training}
{Vector of ``BaseKeypoint`` type. Contains keypoints from the image are used for training}
{Random numbers generator is used for training}
{Patch generator is used for training}
{Maximum tree depth}
@@ -319,13 +312,13 @@ cv::RandomizedTree::train
.. index:: RandomizedTree::read
cv::RandomizedTree::read
RandomizedTree::read
------------------------
.. cfunction:: read(const char* file_name, int num_quant_bits)
.. c:function:: read(const char* file_name, int num_quant_bits)
Reads pre-saved randomized tree from file or stream
.. cfunction:: read(std::istream \&is, int num_quant_bits)
.. c:function:: read(std::istream \&is, int num_quant_bits)
:param file_name: Filename of file contains randomized tree data
@@ -335,13 +328,13 @@ cv::RandomizedTree::read
.. index:: RandomizedTree::write
cv::RandomizedTree::write
RandomizedTree::write
-------------------------
.. cfunction:: void write(const char* file_name) const
.. c:function:: void write(const char* file_name) const
Writes current randomized tree to a file or stream
.. cfunction:: void write(std::ostream \&os) const
.. c:function:: void write(std::ostream \&os) const
:param file_name: Filename of file where randomized tree data will be stored
@@ -349,9 +342,9 @@ cv::RandomizedTree::write
.. index:: RandomizedTree::applyQuantization
cv::RandomizedTree::applyQuantization
RandomizedTree::applyQuantization
-------------------------------------
.. cfunction:: void applyQuantization(int num_quant_bits)
.. c:function:: void applyQuantization(int num_quant_bits)
Applies quantization to the current randomized tree
@@ -363,7 +356,7 @@ cv::RandomizedTree::applyQuantization
RTreeNode
---------
.. ctype:: RTreeNode
.. c:type:: RTreeNode
The class contains base structure for ``RandomizedTree`` ::
@@ -384,7 +377,6 @@ The class contains base structure for ``RandomizedTree`` ::
return patch_data[offset1] > patch_data[offset2];
}
};
..
.. index:: RTreeClassifier
@@ -392,7 +384,7 @@ The class contains base structure for ``RandomizedTree`` ::
RTreeClassifier
---------------
.. ctype:: RTreeClassifier
.. c:type:: RTreeClassifier
The class contains ``RTreeClassifier`` . It represents calonder descriptor which was originally introduced by Michael Calonder ::
@@ -405,7 +397,7 @@ The class contains ``RTreeClassifier`` . It represents calonder descriptor which
RTreeClassifier();
void train(std::vector<BaseKeypoint> const& base_set,
cv::RNG &rng,
RNG &rng,
int num_trees = RTreeClassifier::DEFAULT_TREES,
int depth = DEFAULT_DEPTH,
int views = DEFAULT_VIEWS,
@@ -413,7 +405,7 @@ The class contains ``RTreeClassifier`` . It represents calonder descriptor which
int num_quant_bits = DEFAULT_NUM_QUANT_BITS,
bool print_status = true);
void train(std::vector<BaseKeypoint> const& base_set,
cv::RNG &rng,
RNG &rng,
PatchGenerator &make_patch,
int num_trees = RTreeClassifier::DEFAULT_TREES,
int depth = DEFAULT_DEPTH,
@@ -457,17 +449,16 @@ The class contains ``RTreeClassifier`` . It represents calonder descriptor which
int original_num_classes_;
bool keep_floats_;
};
..
.. index:: RTreeClassifier::train
cv::RTreeClassifier::train
RTreeClassifier::train
--------------------------
.. cfunction:: void train(std::vector<BaseKeypoint> const\& base_set, cv::RNG \&rng, int num_trees = RTreeClassifier::DEFAULT_TREES, int depth = DEFAULT_DEPTH, int views = DEFAULT_VIEWS, size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM, int num_quant_bits = DEFAULT_NUM_QUANT_BITS, bool print_status = true)
.. c:function:: void train(vector<BaseKeypoint> const& base_set, RNG& rng, int num_trees = RTreeClassifier::DEFAULT_TREES, int depth = DEFAULT_DEPTH, int views = DEFAULT_VIEWS, size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM, int num_quant_bits = DEFAULT_NUM_QUANT_BITS, bool print_status = true)
Trains a randomized tree classificator using input set of keypoints
.. cfunction:: void train(std::vector<BaseKeypoint> const\& base_set, cv::RNG \&rng, PatchGenerator \&make_patch, int num_trees = RTreeClassifier::DEFAULT_TREES, int depth = DEFAULT_DEPTH, int views = DEFAULT_VIEWS, size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM, int num_quant_bits = DEFAULT_NUM_QUANT_BITS, bool print_status = true)
.. c:function:: void train(vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int num_trees = RTreeClassifier::DEFAULT_TREES, int depth = DEFAULT_DEPTH, int views = DEFAULT_VIEWS, size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM, int num_quant_bits = DEFAULT_NUM_QUANT_BITS, bool print_status = true)
{Vector of ``BaseKeypoint`` type. Contains keypoints from the image are used for training}
{Random numbers generator is used for training}
@@ -481,34 +472,35 @@ cv::RTreeClassifier::train
.. index:: RTreeClassifier::getSignature
cv::RTreeClassifier::getSignature
RTreeClassifier::getSignature
---------------------------------
.. cfunction:: void getSignature(IplImage *patch, uchar *sig)
.. c:function:: void getSignature(IplImage *patch, uchar *sig)
Returns signature for image patch
.. cfunction:: void getSignature(IplImage *patch, float *sig)
.. c:function:: void getSignature(IplImage *patch, float *sig)
{Image patch to calculate signature for}
{Output signature (array dimension is ``reduced_num_dim)`` }
.. index:: RTreeClassifier::getSparseSignature
cv::RTreeClassifier::getSparseSignature
--------------------------------------- ````
.. cfunction:: void getSparseSignature(IplImage *patch, float *sig, float thresh)
RTreeClassifier::getSparseSignature
---------------------------------------
.. c:function:: void getSparseSignature(IplImage *patch, float *sig, float thresh)
The function is simular to getSignaturebut uses the threshold for removing all signature elements less than the threshold. So that the signature is compressed
{Image patch to calculate signature for}
{Output signature (array dimension is ``reduced_num_dim)`` }
{Output signature (array dimension is ``reduced_num_dim)``}
{The threshold that is used for compressing the signature}
.. index:: RTreeClassifier::countNonZeroElements
cv::RTreeClassifier::countNonZeroElements
RTreeClassifier::countNonZeroElements
-----------------------------------------
.. cfunction:: static int countNonZeroElements(float *vec, int n, double tol=1e-10)
.. c:function:: static int countNonZeroElements(float *vec, int n, double tol=1e-10)
The function returns the number of non-zero elements in the input array.
@@ -520,13 +512,13 @@ cv::RTreeClassifier::countNonZeroElements
.. index:: RTreeClassifier::read
cv::RTreeClassifier::read
RTreeClassifier::read
-------------------------
.. cfunction:: read(const char* file_name)
.. c:function:: read(const char* file_name)
Reads pre-saved RTreeClassifier from file or stream
.. cfunction:: read(std::istream \&is)
.. c:function:: read(std::istream& is)
:param file_name: Filename of file contains randomized tree data
@@ -534,13 +526,13 @@ cv::RTreeClassifier::read
.. index:: RTreeClassifier::write
cv::RTreeClassifier::write
RTreeClassifier::write
--------------------------
.. cfunction:: void write(const char* file_name) const
.. c:function:: void write(const char* file_name) const
Writes current RTreeClassifier to a file or stream
.. cfunction:: void write(std::ostream \&os) const
.. c:function:: void write(std::ostream \&os) const
:param file_name: Filename of file where randomized tree data will be stored
@@ -548,9 +540,9 @@ cv::RTreeClassifier::write
.. index:: RTreeClassifier::setQuantization
cv::RTreeClassifier::setQuantization
RTreeClassifier::setQuantization
------------------------------------
.. cfunction:: void setQuantization(int num_quant_bits)
.. c:function:: void setQuantization(int num_quant_bits)
Applies quantization to the current randomized tree
@@ -569,26 +561,26 @@ Below there is an example of ``RTreeClassifier`` usage for feature matching. The
cvExtractSURF( train_image, 0, &objectKeypoints, &objectDescriptors,
storage, params );
cv::RTreeClassifier detector;
int patch_width = cv::PATCH_SIZE;
iint patch_height = cv::PATCH_SIZE;
vector<cv::BaseKeypoint> base_set;
RTreeClassifier detector;
int patch_width = PATCH_SIZE;
iint patch_height = PATCH_SIZE;
vector<BaseKeypoint> base_set;
int i=0;
CvSURFPoint* point;
for (i=0;i<(n_points > 0 ? n_points : objectKeypoints->total);i++)
{
point=(CvSURFPoint*)cvGetSeqElem(objectKeypoints,i);
base_set.push_back(
cv::BaseKeypoint(point->pt.x,point->pt.y,train_image));
BaseKeypoint(point->pt.x,point->pt.y,train_image));
}
//Detector training
cv::RNG rng( cvGetTickCount() );
cv::PatchGenerator gen(0,255,2,false,0.7,1.3,-CV_PI/3,CV_PI/3,
RNG rng( cvGetTickCount() );
PatchGenerator gen(0,255,2,false,0.7,1.3,-CV_PI/3,CV_PI/3,
-CV_PI/3,CV_PI/3);
printf("RTree Classifier training...n");
detector.train(base_set,rng,gen,24,cv::DEFAULT_DEPTH,2000,
detector.train(base_set,rng,gen,24,DEFAULT_DEPTH,2000,
(int)base_set.size(), detector.DEFAULT_NUM_QUANT_BITS);
printf("Donen");
@@ -643,5 +635,5 @@ Below there is an example of ``RTreeClassifier`` usage for feature matching. The
}
cvResetImageROI(test_image);
}
..
..
modules/features2d/doc/object_categorization.rst
+24 -24
View File
@@ -12,7 +12,7 @@ are described in this section.
BOWTrainer
----------
.. ctype:: BOWTrainer
.. c:type:: BOWTrainer
Abstract base class for training ''bag of visual words'' vocabulary from a set of descriptors.
See e.g. ''Visual Categorization with Bags of Keypoints'' of Gabriella Csurka, Christopher R. Dance,
@@ -40,9 +40,9 @@ Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. ::
.. index:: BOWTrainer::add
cv::BOWTrainer::add
BOWTrainer::add
------------------- ````
.. cfunction:: void BOWTrainer::add( const Mat\& descriptors )
.. c:function:: void BOWTrainer::add( const Mat\& descriptors )
Add descriptors to training set. The training set will be clustered using clustermethod to construct vocabulary.
@@ -50,31 +50,31 @@ cv::BOWTrainer::add
.. index:: BOWTrainer::getDescriptors
cv::BOWTrainer::getDescriptors
BOWTrainer::getDescriptors
------------------------------
.. cfunction:: const vector<Mat>\& BOWTrainer::getDescriptors() const
.. c:function:: const vector<Mat>\& BOWTrainer::getDescriptors() const
Returns training set of descriptors.
.. index:: BOWTrainer::descripotorsCount
cv::BOWTrainer::descripotorsCount
BOWTrainer::descripotorsCount
---------------------------------
.. cfunction:: const vector<Mat>\& BOWTrainer::descripotorsCount() const
.. c:function:: const vector<Mat>\& BOWTrainer::descripotorsCount() const
Returns count of all descriptors stored in the training set.
.. index:: BOWTrainer::cluster
cv::BOWTrainer::cluster
BOWTrainer::cluster
-----------------------
.. cfunction:: Mat BOWTrainer::cluster() const
.. c:function:: Mat BOWTrainer::cluster() const
Cluster train descriptors. Vocabulary consists from cluster centers. So this method
returns vocabulary. In first method variant the stored in object train descriptors will be
clustered, in second variant -- input descriptors will be clustered.
.. cfunction:: Mat BOWTrainer::cluster( const Mat\& descriptors ) const
.. c:function:: Mat BOWTrainer::cluster( const Mat\& descriptors ) const
:param descriptors: Descriptors to cluster. Each row of ``descriptors`` matrix is a one descriptor. Descriptors will not be added
to the inner train descriptor set.
@@ -85,7 +85,7 @@ clustered, in second variant -- input descriptors will be clustered.
BOWKMeansTrainer
----------------
.. ctype:: BOWKMeansTrainer
.. c:type:: BOWKMeansTrainer
:func:`kmeans` based class to train visual vocabulary using the ''bag of visual words'' approach. ::
@@ -115,7 +115,7 @@ arguments.
BOWImgDescriptorExtractor
-------------------------
.. ctype:: BOWImgDescriptorExtractor
.. c:type:: BOWImgDescriptorExtractor
Class to compute image descriptor using ''bad of visual words''. In few,
such computing consists from the following steps:
@@ -149,9 +149,9 @@ Class to compute image descriptor using ''bad of visual words''. In few,
.. index:: BOWImgDescriptorExtractor::BOWImgDescriptorExtractor
cv::BOWImgDescriptorExtractor::BOWImgDescriptorExtractor
BOWImgDescriptorExtractor::BOWImgDescriptorExtractor
--------------------------------------------------------
.. cfunction:: BOWImgDescriptorExtractor::BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>\& dextractor, const Ptr<DescriptorMatcher>\& dmatcher )
.. c:function:: BOWImgDescriptorExtractor::BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>\& dextractor, const Ptr<DescriptorMatcher>\& dmatcher )
Constructor.
@@ -163,9 +163,9 @@ cv::BOWImgDescriptorExtractor::BOWImgDescriptorExtractor
.. index:: BOWImgDescriptorExtractor::setVocabulary
cv::BOWImgDescriptorExtractor::setVocabulary
BOWImgDescriptorExtractor::setVocabulary
--------------------------------------------
.. cfunction:: void BOWImgDescriptorExtractor::setVocabulary( const Mat\& vocabulary )
.. c:function:: void BOWImgDescriptorExtractor::setVocabulary( const Mat\& vocabulary )
Method to set visual vocabulary.
@@ -174,17 +174,17 @@ cv::BOWImgDescriptorExtractor::setVocabulary
.. index:: BOWImgDescriptorExtractor::getVocabulary
cv::BOWImgDescriptorExtractor::getVocabulary
BOWImgDescriptorExtractor::getVocabulary
--------------------------------------------
.. cfunction:: const Mat\& BOWImgDescriptorExtractor::getVocabulary() const
.. c:function:: const Mat\& BOWImgDescriptorExtractor::getVocabulary() const
Returns set vocabulary.
.. index:: BOWImgDescriptorExtractor::compute
cv::BOWImgDescriptorExtractor::compute
BOWImgDescriptorExtractor::compute
--------------------------------------
.. cfunction:: void BOWImgDescriptorExtractor::compute( const Mat\& image, vector<KeyPoint>\& keypoints, Mat\& imgDescriptor, vector<vector<int> >* pointIdxsOfClusters=0, Mat* descriptors=0 )
.. c:function:: void BOWImgDescriptorExtractor::compute( const Mat\& image, vector<KeyPoint>\& keypoints, Mat\& imgDescriptor, vector<vector<int> >* pointIdxsOfClusters=0, Mat* descriptors=0 )
Compute image descriptor using set visual vocabulary.
@@ -201,17 +201,17 @@ cv::BOWImgDescriptorExtractor::compute
.. index:: BOWImgDescriptorExtractor::descriptorSize
cv::BOWImgDescriptorExtractor::descriptorSize
BOWImgDescriptorExtractor::descriptorSize
---------------------------------------------
.. cfunction:: int BOWImgDescriptorExtractor::descriptorSize() const
.. c:function:: int BOWImgDescriptorExtractor::descriptorSize() const
Returns image discriptor size, if vocabulary was set, and 0 otherwise.
.. index:: BOWImgDescriptorExtractor::descriptorType
cv::BOWImgDescriptorExtractor::descriptorType
BOWImgDescriptorExtractor::descriptorType
---------------------------------------------
.. cfunction:: int BOWImgDescriptorExtractor::descriptorType() const
.. c:function:: int BOWImgDescriptorExtractor::descriptorType() const
Returns image descriptor type.