OpenCV reference manual (C++ part only for now) is now produced directly from RST, not from TeX.
This commit is contained in:
Vadim Pisarevsky
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Common Interfaces of Descriptor Extractors
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==========================================
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.. highlight:: cpp
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Extractors of keypoint descriptors in OpenCV have wrappers with common interface that enables to switch easily
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between different algorithms solving the same problem. This section is devoted to computing descriptors
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that are represented as vectors in a multidimensional space. All objects that implement ''vector''
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descriptor extractors inherit
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:func:`DescriptorExtractor`
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interface.
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.. index:: DescriptorExtractor
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.. _DescriptorExtractor:
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DescriptorExtractor
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-------------------
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`id=0.00924308242838 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/DescriptorExtractor>`__
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.. ctype:: DescriptorExtractor
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Abstract base class for computing descriptors for image keypoints.
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::
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class CV_EXPORTS DescriptorExtractor
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{
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public:
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virtual ~DescriptorExtractor();
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void compute( const Mat& image, vector<KeyPoint>& keypoints,
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Mat& descriptors ) const;
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void compute( const vector<Mat>& images, vector<vector<KeyPoint> >& keypoints,
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vector<Mat>& descriptors ) const;
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virtual void read( const FileNode& );
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virtual void write( FileStorage& ) const;
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virtual int descriptorSize() const = 0;
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virtual int descriptorType() const = 0;
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static Ptr<DescriptorExtractor> create( const string& descriptorExtractorType );
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protected:
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...
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};
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..
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In this interface we assume a keypoint descriptor can be represented as a
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dense, fixed-dimensional vector of some basic type. Most descriptors used
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in practice follow this pattern, as it makes it very easy to compute
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distances between descriptors. Therefore we represent a collection of
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descriptors as a
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:func:`Mat`
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, where each row is one keypoint descriptor.
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.. index:: DescriptorExtractor::compute
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cv::DescriptorExtractor::compute
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--------------------------------
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`id=0.622580160404 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/DescriptorExtractor%3A%3Acompute>`__
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.. cfunction:: void DescriptorExtractor::compute( const Mat\& image, vector<KeyPoint>\& keypoints, Mat\& descriptors ) const
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Compute the descriptors for a set of keypoints detected in an image (first variant)
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or image set (second variant).
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:param image: The image.
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:param keypoints: The keypoints. Keypoints for which a descriptor cannot be computed are removed.
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:param descriptors: The descriptors. Row i is the descriptor for keypoint i.
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.. cfunction:: void DescriptorExtractor::compute( const vector<Mat>\& images, vector<vector<KeyPoint> >\& keypoints, vector<Mat>\& descriptors ) const
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* **images** The image set.
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* **keypoints** Input keypoints collection. keypoints[i] is keypoints
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detected in images[i]. Keypoints for which a descriptor
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can not be computed are removed.
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* **descriptors** Descriptor collection. descriptors[i] are descriptors computed for
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a set keypoints[i].
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.. index:: DescriptorExtractor::read
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cv::DescriptorExtractor::read
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-----------------------------
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`id=0.708176779821 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/DescriptorExtractor%3A%3Aread>`__
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.. cfunction:: void DescriptorExtractor::read( const FileNode\& fn )
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Read descriptor extractor object from file node.
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:param fn: File node from which detector will be read.
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.. index:: DescriptorExtractor::write
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cv::DescriptorExtractor::write
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------------------------------
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`id=0.206682397054 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/DescriptorExtractor%3A%3Awrite>`__
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.. cfunction:: void DescriptorExtractor::write( FileStorage\& fs ) const
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Write descriptor extractor object to file storage.
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:param fs: File storage in which detector will be written.
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.. index:: DescriptorExtractor::create
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cv::DescriptorExtractor::create
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-------------------------------
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`id=0.923714079643 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/DescriptorExtractor%3A%3Acreate>`__
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:func:`DescriptorExtractor`
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.. cfunction:: Ptr<DescriptorExtractor> DescriptorExtractor::create( const string\& descriptorExtractorType )
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Descriptor extractor factory that creates of given type with
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default parameters (rather using default constructor).
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:param descriptorExtractorType: Descriptor extractor type.
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Now the following descriptor extractor types are supported:
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\
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``"SIFT"``
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--
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:func:`SiftFeatureDetector`
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,
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\
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``"SURF"``
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--
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:func:`SurfFeatureDetector`
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,
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\
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``"BRIEF"``
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--
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:func:`BriefFeatureDetector`
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.
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\
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Also combined format is supported: descriptor extractor adapter name (
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``"Opponent"``
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--
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:func:`OpponentColorDescriptorExtractor`
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) + descriptor extractor name (see above),
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e.g.
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``"OpponentSIFT"``
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, etc.
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.. index:: SiftDescriptorExtractor
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.. _SiftDescriptorExtractor:
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SiftDescriptorExtractor
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-----------------------
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`id=0.676546819501 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/SiftDescriptorExtractor>`__
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.. ctype:: SiftDescriptorExtractor
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Wrapping class for descriptors computing using
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:func:`SIFT`
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class.
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::
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class SiftDescriptorExtractor : public DescriptorExtractor
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{
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public:
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SiftDescriptorExtractor(
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const SIFT::DescriptorParams& descriptorParams=SIFT::DescriptorParams(),
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const SIFT::CommonParams& commonParams=SIFT::CommonParams() );
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SiftDescriptorExtractor( double magnification, bool isNormalize=true,
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bool recalculateAngles=true, int nOctaves=SIFT::CommonParams::DEFAULT_NOCTAVES,
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int nOctaveLayers=SIFT::CommonParams::DEFAULT_NOCTAVE_LAYERS,
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int firstOctave=SIFT::CommonParams::DEFAULT_FIRST_OCTAVE,
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int angleMode=SIFT::CommonParams::FIRST_ANGLE );
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virtual void read (const FileNode &fn);
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virtual void write (FileStorage &fs) const;
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virtual int descriptorSize() const;
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virtual int descriptorType() const;
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protected:
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...
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}
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..
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.. index:: SurfDescriptorExtractor
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.. _SurfDescriptorExtractor:
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SurfDescriptorExtractor
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-----------------------
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`id=0.638581739296 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/SurfDescriptorExtractor>`__
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.. ctype:: SurfDescriptorExtractor
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Wrapping class for descriptors computing using
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:func:`SURF`
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class.
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::
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class SurfDescriptorExtractor : public DescriptorExtractor
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{
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public:
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SurfDescriptorExtractor( int nOctaves=4,
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int nOctaveLayers=2, bool extended=false );
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virtual void read (const FileNode &fn);
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virtual void write (FileStorage &fs) const;
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virtual int descriptorSize() const;
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virtual int descriptorType() const;
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protected:
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...
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}
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..
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.. index:: CalonderDescriptorExtractor
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.. _CalonderDescriptorExtractor:
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CalonderDescriptorExtractor
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---------------------------
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`id=0.301561509204 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/CalonderDescriptorExtractor>`__
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.. ctype:: CalonderDescriptorExtractor
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Wrapping class for descriptors computing using
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:func:`RTreeClassifier`
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class.
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::
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template<typename T>
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class CalonderDescriptorExtractor : public DescriptorExtractor
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{
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public:
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CalonderDescriptorExtractor( const string& classifierFile );
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virtual void read( const FileNode &fn );
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virtual void write( FileStorage &fs ) const;
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virtual int descriptorSize() const;
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virtual int descriptorType() const;
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protected:
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...
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}
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..
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.. index:: OpponentColorDescriptorExtractor
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.. _OpponentColorDescriptorExtractor:
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OpponentColorDescriptorExtractor
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--------------------------------
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`id=0.081563051622 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/OpponentColorDescriptorExtractor>`__
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.. ctype:: OpponentColorDescriptorExtractor
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Adapts a descriptor extractor to compute descripors in 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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Input RGB image is transformed in Opponent Color Space. Then unadapted descriptor extractor
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(set in constructor) computes descriptors on each of the three channel and concatenate
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them into a single color descriptor.
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::
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class OpponentColorDescriptorExtractor : public DescriptorExtractor
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{
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public:
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OpponentColorDescriptorExtractor( const Ptr<DescriptorExtractor>& dextractor );
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virtual void read( const FileNode& );
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virtual void write( FileStorage& ) const;
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virtual int descriptorSize() const;
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virtual int descriptorType() const;
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protected:
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...
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};
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..
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.. index:: BriefDescriptorExtractor
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.. _BriefDescriptorExtractor:
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BriefDescriptorExtractor
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------------------------
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`id=0.207875021385 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BriefDescriptorExtractor>`__
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.. ctype:: BriefDescriptorExtractor
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Class for computing BRIEF descriptors described in paper of Calonder M., Lepetit V.,
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Strecha C., Fua P.: ''BRIEF: Binary Robust Independent Elementary Features.''
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11th European Conference on Computer Vision (ECCV), Heraklion, Crete. LNCS Springer, September 2010.
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::
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class BriefDescriptorExtractor : public DescriptorExtractor
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{
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public:
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static const int PATCH_SIZE = 48;
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static const int KERNEL_SIZE = 9;
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// bytes is a length of descriptor in bytes. It can be equal 16, 32 or 64 bytes.
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BriefDescriptorExtractor( int bytes = 32 );
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virtual void read( const FileNode& );
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virtual void write( FileStorage& ) const;
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virtual int descriptorSize() const;
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virtual int descriptorType() const;
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protected:
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...
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};
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..
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@@ -0,0 +1,637 @@
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Common Interfaces of Descriptor Matchers
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========================================
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.. highlight:: cpp
|
||||
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||||
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Matchers of keypoint descriptors in OpenCV have wrappers with common interface that enables to switch easily
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||||
between different algorithms solving the same problem. This section is devoted to matching descriptors
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that are represented as vectors in a multidimensional space. All objects that implement ''vector''
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descriptor matchers inherit
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:func:`DescriptorMatcher`
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interface.
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.. index:: DMatch
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.. _DMatch:
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DMatch
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------
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`id=0.193402930617 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/DMatch>`__
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.. ctype:: DMatch
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Match between two keypoint descriptors: query descriptor index,
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train descriptor index, train image index and distance between descriptors.
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::
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struct DMatch
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{
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DMatch() : queryIdx(-1), trainIdx(-1), imgIdx(-1),
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distance(std::numeric_limits<float>::max()) {}
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DMatch( int _queryIdx, int _trainIdx, float _distance ) :
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queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(-1),
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distance(_distance) {}
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DMatch( int _queryIdx, int _trainIdx, int _imgIdx, float _distance ) :
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queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(_imgIdx),
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distance(_distance) {}
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int queryIdx; // query descriptor index
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int trainIdx; // train descriptor index
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int imgIdx; // train image index
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float distance;
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// less is better
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bool operator<( const DMatch &m ) const;
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};
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..
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.. index:: DescriptorMatcher
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.. _DescriptorMatcher:
|
||||
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||||
DescriptorMatcher
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||||
-----------------
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||||
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`id=0.0185035556985 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/DescriptorMatcher>`__
|
||||
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||||
.. ctype:: DescriptorMatcher
|
||||
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||||
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||||
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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 one image with other image or
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||||
with image set.
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||||
|
||||
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||||
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||||
|
||||
::
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||||
|
||||
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||||
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||||
class DescriptorMatcher
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||||
{
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||||
public:
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||||
virtual ~DescriptorMatcher();
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||||
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||||
virtual void add( const vector<Mat>& descriptors );
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||||
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||||
const vector<Mat>& getTrainDescriptors() const;
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||||
virtual void clear();
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||||
bool empty() const;
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||||
virtual bool isMaskSupported() const = 0;
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||||
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||||
virtual void train();
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||||
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||||
/*
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||||
* Group of methods to match descriptors from image pair.
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*/
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||||
void match( const Mat& queryDescriptors, const Mat& trainDescriptors,
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||||
vector<DMatch>& matches, const Mat& mask=Mat() ) const;
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||||
void knnMatch( const Mat& queryDescriptors, const Mat& trainDescriptors,
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vector<vector<DMatch> >& matches, int k,
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const Mat& mask=Mat(), bool compactResult=false ) const;
|
||||
void radiusMatch( const Mat& queryDescriptors, const Mat& trainDescriptors,
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||||
vector<vector<DMatch> >& matches, float maxDistance,
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const Mat& mask=Mat(), bool compactResult=false ) const;
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||||
/*
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||||
* Group of methods to match descriptors from one image to image set.
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||||
*/
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||||
void match( const Mat& queryDescriptors, vector<DMatch>& matches,
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||||
const vector<Mat>& masks=vector<Mat>() );
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||||
void knnMatch( const Mat& queryDescriptors, vector<vector<DMatch> >& matches,
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||||
int k, const vector<Mat>& masks=vector<Mat>(),
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||||
bool compactResult=false );
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||||
void radiusMatch( const Mat& queryDescriptors, vector<vector<DMatch> >& matches,
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||||
float maxDistance, const vector<Mat>& masks=vector<Mat>(),
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||||
bool compactResult=false );
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||||
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||||
virtual void read( const FileNode& );
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||||
virtual void write( FileStorage& ) const;
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||||
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||||
virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const = 0;
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||||
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||||
static Ptr<DescriptorMatcher> create( const string& descriptorMatcherType );
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||||
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||||
protected:
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||||
vector<Mat> trainDescCollection;
|
||||
...
|
||||
};
|
||||
|
||||
|
||||
..
|
||||
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||||
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||||
.. index:: DescriptorMatcher::add
|
||||
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||||
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||||
cv::DescriptorMatcher::add
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||||
--------------------------
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||||
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||||
`id=0.549221986718 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/DescriptorMatcher%3A%3Aadd>`__
|
||||
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||||
|
||||
````
|
||||
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||||
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||||
.. cfunction:: 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.
|
||||
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||||
|
||||
|
||||
|
||||
|
||||
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||||
:param descriptors: Descriptors to add. Each ``descriptors[i]`` is a set of descriptors
|
||||
from the same (one) train image.
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: DescriptorMatcher::getTrainDescriptors
|
||||
|
||||
|
||||
cv::DescriptorMatcher::getTrainDescriptors
|
||||
------------------------------------------
|
||||
|
||||
`id=0.354691082433 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/DescriptorMatcher%3A%3AgetTrainDescriptors>`__
|
||||
|
||||
|
||||
````
|
||||
|
||||
|
||||
.. cfunction:: const vector<Mat>\& getTrainDescriptors() const
|
||||
|
||||
Returns constant link to the train descriptor collection (i.e. trainDescCollection).
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: DescriptorMatcher::clear
|
||||
|
||||
|
||||
cv::DescriptorMatcher::clear
|
||||
----------------------------
|
||||
|
||||
`id=0.776403699262 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/DescriptorMatcher%3A%3Aclear>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: void DescriptorMatcher::clear()
|
||||
|
||||
Clear train descriptor collection.
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: DescriptorMatcher::empty
|
||||
|
||||
|
||||
cv::DescriptorMatcher::empty
|
||||
----------------------------
|
||||
|
||||
`id=0.186730120991 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/DescriptorMatcher%3A%3Aempty>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: bool DescriptorMatcher::empty() const
|
||||
|
||||
Return true if there are not train descriptors in collection.
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: DescriptorMatcher::isMaskSupported
|
||||
|
||||
|
||||
cv::DescriptorMatcher::isMaskSupported
|
||||
--------------------------------------
|
||||
|
||||
`id=0.4880242426 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/DescriptorMatcher%3A%3AisMaskSupported>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: bool DescriptorMatcher::isMaskSupported()
|
||||
|
||||
Returns true if descriptor matcher supports masking permissible matches.
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: DescriptorMatcher::train
|
||||
|
||||
|
||||
cv::DescriptorMatcher::train
|
||||
----------------------------
|
||||
|
||||
`id=0.708209257367 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/DescriptorMatcher%3A%3Atrain>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: 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
|
||||
implementation of this method, other matchers realy train their inner structures (e.g. FlannBasedMatcher
|
||||
trains flann::Index)
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: DescriptorMatcher::match
|
||||
|
||||
|
||||
cv::DescriptorMatcher::match
|
||||
----------------------------
|
||||
|
||||
`id=0.803878673329 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/DescriptorMatcher%3A%3Amatch>`__
|
||||
|
||||
|
||||
````
|
||||
````
|
||||
````
|
||||
````
|
||||
|
||||
|
||||
.. cfunction:: 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.
|
||||
In first variant of this method train descriptors are set as input argument and
|
||||
supposed that they are of keypoints detected on the same train image. In 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.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: void DescriptorMatcher::match( const Mat\& queryDescriptors, vector<DMatch>\& matches, const vector<Mat>\& masks=vector<Mat>() )
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:param queryDescriptors: Query set of descriptors.
|
||||
|
||||
|
||||
:param trainDescriptors: Train set of descriptors. This will not be added to train descriptors collection
|
||||
stored in class object.
|
||||
|
||||
|
||||
:param matches: Matches. If some query descriptor masked out in ``mask`` no match will be added for this descriptor.
|
||||
So ``matches`` size may be less query descriptors count.
|
||||
|
||||
|
||||
:param mask: Mask specifying permissible matches between input query and train matrices of descriptors.
|
||||
|
||||
|
||||
:param masks: The set of masks. Each ``masks[i]`` specifies permissible matches between input query descriptors
|
||||
and stored train descriptors from i-th image (i.e. ``trainDescCollection[i])`` .
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: DescriptorMatcher::knnMatch
|
||||
|
||||
|
||||
cv::DescriptorMatcher::knnMatch
|
||||
-------------------------------
|
||||
|
||||
`id=0.510078848403 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/DescriptorMatcher%3A%3AknnMatch>`__
|
||||
|
||||
|
||||
: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
|
||||
|
||||
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 )
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:param queryDescriptors, trainDescriptors, mask, masks: See in :func:`DescriptorMatcher::match` .
|
||||
|
||||
|
||||
:param matches: Mathes. Each ``matches[i]`` is k or less matches for the same query descriptor.
|
||||
|
||||
|
||||
:param k: Count of best matches will be found per each query descriptor (or less if it's not possible).
|
||||
|
||||
|
||||
:param compactResult: It's used when mask (or masks) is not empty. If ``compactResult`` is false ``matches`` vector will have the same size as ``queryDescriptors`` rows. If ``compactResult``
|
||||
is true ``matches`` vector will not contain matches for fully masked out query descriptors.
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: DescriptorMatcher::radiusMatch
|
||||
|
||||
|
||||
cv::DescriptorMatcher::radiusMatch
|
||||
----------------------------------
|
||||
|
||||
`id=0.763278154174 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/DescriptorMatcher%3A%3AradiusMatch>`__
|
||||
|
||||
|
||||
: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
|
||||
|
||||
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 )
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:param queryDescriptors, trainDescriptors, mask, masks: See in :func:`DescriptorMatcher::match` .
|
||||
|
||||
|
||||
:param matches, compactResult: See in :func:`DescriptorMatcher::knnMatch` .
|
||||
|
||||
|
||||
:param maxDistance: The threshold to found match distances.
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: DescriptorMatcher::clone
|
||||
|
||||
|
||||
cv::DescriptorMatcher::clone
|
||||
----------------------------
|
||||
|
||||
`id=0.743679534249 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/DescriptorMatcher%3A%3Aclone>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: Ptr<DescriptorMatcher> \\DescriptorMatcher::clone( bool emptyTrainData ) const
|
||||
|
||||
Clone the matcher.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:param emptyTrainData: If emptyTrainData is false the method create deep copy of the object, i.e. copies
|
||||
both parameters and train data. If emptyTrainData is true the method create object copy with current parameters
|
||||
but with empty train data..
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: DescriptorMatcher::create
|
||||
|
||||
|
||||
cv::DescriptorMatcher::create
|
||||
-----------------------------
|
||||
|
||||
`id=0.681869512138 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/DescriptorMatcher%3A%3Acreate>`__
|
||||
|
||||
|
||||
:func:`DescriptorMatcher`
|
||||
|
||||
|
||||
.. cfunction:: Ptr<DescriptorMatcher> DescriptorMatcher::create( const string\& descriptorMatcherType )
|
||||
|
||||
Descriptor matcher factory that creates of
|
||||
given type with default parameters (rather using default constructor).
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:param descriptorMatcherType: Descriptor matcher type.
|
||||
|
||||
|
||||
|
||||
Now the following matcher types are supported:
|
||||
``"BruteForce"``
|
||||
(it uses
|
||||
``L2``
|
||||
),
|
||||
``"BruteForce-L1"``
|
||||
,
|
||||
``"BruteForce-Hamming"``
|
||||
,
|
||||
``"BruteForce-HammingLUT"``
|
||||
,
|
||||
``"FlannBased"``
|
||||
.
|
||||
|
||||
|
||||
.. index:: BruteForceMatcher
|
||||
|
||||
.. _BruteForceMatcher:
|
||||
|
||||
BruteForceMatcher
|
||||
-----------------
|
||||
|
||||
`id=0.47821275438 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BruteForceMatcher>`__
|
||||
|
||||
.. ctype:: 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
|
||||
permissible matches between descriptor sets.
|
||||
|
||||
|
||||
|
||||
|
||||
::
|
||||
|
||||
|
||||
|
||||
template<class Distance>
|
||||
class BruteForceMatcher : public DescriptorMatcher
|
||||
{
|
||||
public:
|
||||
BruteForceMatcher( Distance d = Distance() );
|
||||
virtual ~BruteForceMatcher();
|
||||
|
||||
virtual bool isMaskSupported() const;
|
||||
virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const;
|
||||
protected:
|
||||
...
|
||||
}
|
||||
|
||||
|
||||
..
|
||||
|
||||
For efficiency, BruteForceMatcher is templated on the distance metric.
|
||||
For float descriptors, a common choice would be
|
||||
``L2<float>``
|
||||
. Class of supported distances are:
|
||||
|
||||
|
||||
|
||||
|
||||
::
|
||||
|
||||
|
||||
|
||||
template<typename T>
|
||||
struct Accumulator
|
||||
{
|
||||
typedef T Type;
|
||||
};
|
||||
|
||||
template<> struct Accumulator<unsigned char> { typedef unsigned int Type; };
|
||||
template<> struct Accumulator<unsigned short> { typedef unsigned int Type; };
|
||||
template<> struct Accumulator<char> { typedef int Type; };
|
||||
template<> struct Accumulator<short> { typedef int Type; };
|
||||
|
||||
/*
|
||||
* Squared Euclidean distance functor
|
||||
*/
|
||||
template<class T>
|
||||
struct L2
|
||||
{
|
||||
typedef T ValueType;
|
||||
typedef typename Accumulator<T>::Type ResultType;
|
||||
|
||||
ResultType operator()( const T* a, const T* b, int size ) const;
|
||||
};
|
||||
|
||||
/*
|
||||
* Manhattan distance (city block distance) functor
|
||||
*/
|
||||
template<class T>
|
||||
struct CV_EXPORTS L1
|
||||
{
|
||||
typedef T ValueType;
|
||||
typedef typename Accumulator<T>::Type ResultType;
|
||||
|
||||
ResultType operator()( const T* a, const T* b, int size ) const;
|
||||
...
|
||||
};
|
||||
|
||||
/*
|
||||
* Hamming distance (city block distance) functor
|
||||
*/
|
||||
struct HammingLUT
|
||||
{
|
||||
typedef unsigned char ValueType;
|
||||
typedef int ResultType;
|
||||
|
||||
ResultType operator()( const unsigned char* a, const unsigned char* b,
|
||||
int size ) const;
|
||||
...
|
||||
};
|
||||
|
||||
struct Hamming
|
||||
{
|
||||
typedef unsigned char ValueType;
|
||||
typedef int ResultType;
|
||||
|
||||
ResultType operator()( const unsigned char* a, const unsigned char* b,
|
||||
int size ) const;
|
||||
...
|
||||
};
|
||||
|
||||
|
||||
..
|
||||
|
||||
|
||||
.. index:: FlannBasedMatcher
|
||||
|
||||
.. _FlannBasedMatcher:
|
||||
|
||||
FlannBasedMatcher
|
||||
-----------------
|
||||
|
||||
`id=0.721140850904 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/FlannBasedMatcher>`__
|
||||
|
||||
.. ctype:: FlannBasedMatcher
|
||||
|
||||
|
||||
|
||||
Flann based descriptor matcher. This matcher trains
|
||||
:func:`flann::Index`
|
||||
on
|
||||
train descriptor collection and calls it's nearest search methods to find best matches.
|
||||
So this matcher may be faster in cases of matching to large train collection than
|
||||
brute force matcher.
|
||||
``FlannBasedMatcher``
|
||||
does not support masking permissible
|
||||
matches between descriptor sets, because
|
||||
:func:`flann::Index`
|
||||
does not
|
||||
support this.
|
||||
|
||||
|
||||
|
||||
|
||||
::
|
||||
|
||||
|
||||
|
||||
class FlannBasedMatcher : public DescriptorMatcher
|
||||
{
|
||||
public:
|
||||
FlannBasedMatcher(
|
||||
const Ptr<flann::IndexParams>& indexParams=new flann::KDTreeIndexParams(),
|
||||
const Ptr<flann::SearchParams>& searchParams=new flann::SearchParams() );
|
||||
|
||||
virtual void add( const vector<Mat>& descriptors );
|
||||
virtual void clear();
|
||||
|
||||
virtual void train();
|
||||
virtual bool isMaskSupported() const;
|
||||
|
||||
virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const;
|
||||
protected:
|
||||
...
|
||||
};
|
||||
|
||||
|
||||
..
|
||||
|
||||
1045
modules/features2d/doc/common_interfaces_of_feature_detectors.rst
Normal file
1045
modules/features2d/doc/common_interfaces_of_feature_detectors.rst
Normal file
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,677 @@
|
||||
Common Interfaces of Generic Descriptor Matchers
|
||||
================================================
|
||||
|
||||
.. highlight:: cpp
|
||||
|
||||
|
||||
Matchers of keypoint descriptors in OpenCV have wrappers with common interface that enables to switch easily
|
||||
between different algorithms solving the same problem. This section is devoted to matching descriptors
|
||||
that can not be represented as vectors in a multidimensional space.
|
||||
``GenericDescriptorMatcher``
|
||||
is a more generic interface for descriptors. It does not make any assumptions about descriptor representation.
|
||||
Every descriptor with
|
||||
:func:`DescriptorExtractor`
|
||||
interface has a wrapper with
|
||||
``GenericDescriptorMatcher``
|
||||
interface (see
|
||||
:func:`VectorDescriptorMatcher`
|
||||
).
|
||||
There are descriptors such as One way descriptor and Ferns that have
|
||||
``GenericDescriptorMatcher``
|
||||
interface implemented, but do not support
|
||||
:func:`DescriptorExtractor`
|
||||
.
|
||||
|
||||
|
||||
.. index:: GenericDescriptorMatcher
|
||||
|
||||
.. _GenericDescriptorMatcher:
|
||||
|
||||
GenericDescriptorMatcher
|
||||
------------------------
|
||||
|
||||
`id=0.973387347242 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/GenericDescriptorMatcher>`__
|
||||
|
||||
.. ctype:: GenericDescriptorMatcher
|
||||
|
||||
|
||||
|
||||
Abstract interface for a keypoint descriptor extracting and matching.
|
||||
There is
|
||||
:func:`DescriptorExtractor`
|
||||
and
|
||||
:func:`DescriptorMatcher`
|
||||
for these purposes too, but their interfaces are intended for descriptors
|
||||
represented as vectors in a multidimensional space.
|
||||
``GenericDescriptorMatcher``
|
||||
is a more generic interface for descriptors.
|
||||
As
|
||||
:func:`DescriptorMatcher`
|
||||
,
|
||||
``GenericDescriptorMatcher``
|
||||
has two groups
|
||||
of match methods: for matching keypoints of one image with other image or
|
||||
with image set.
|
||||
|
||||
|
||||
|
||||
|
||||
::
|
||||
|
||||
|
||||
|
||||
class GenericDescriptorMatcher
|
||||
{
|
||||
public:
|
||||
GenericDescriptorMatcher();
|
||||
virtual ~GenericDescriptorMatcher();
|
||||
|
||||
virtual void add( const vector<Mat>& images,
|
||||
vector<vector<KeyPoint> >& keypoints );
|
||||
|
||||
const vector<Mat>& getTrainImages() const;
|
||||
const vector<vector<KeyPoint> >& getTrainKeypoints() const;
|
||||
virtual void clear();
|
||||
|
||||
virtual void train() = 0;
|
||||
|
||||
virtual bool isMaskSupported() = 0;
|
||||
|
||||
void classify( const Mat& queryImage,
|
||||
vector<KeyPoint>& queryKeypoints,
|
||||
const Mat& trainImage,
|
||||
vector<KeyPoint>& trainKeypoints ) const;
|
||||
void classify( const Mat& queryImage,
|
||||
vector<KeyPoint>& queryKeypoints );
|
||||
|
||||
/*
|
||||
* Group of methods to match keypoints from image pair.
|
||||
*/
|
||||
void match( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
|
||||
const Mat& trainImage, vector<KeyPoint>& trainKeypoints,
|
||||
vector<DMatch>& matches, const Mat& mask=Mat() ) const;
|
||||
void 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;
|
||||
void 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;
|
||||
/*
|
||||
* Group of methods to match keypoints from one image to image set.
|
||||
*/
|
||||
void match( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
|
||||
vector<DMatch>& matches, const vector<Mat>& masks=vector<Mat>() );
|
||||
void knnMatch( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
|
||||
vector<vector<DMatch> >& matches, int k,
|
||||
const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );
|
||||
void radiusMatch( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
|
||||
vector<vector<DMatch> >& matches, float maxDistance,
|
||||
const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );
|
||||
|
||||
virtual void read( const FileNode& );
|
||||
virtual void write( FileStorage& ) const;
|
||||
|
||||
virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const = 0;
|
||||
|
||||
protected:
|
||||
...
|
||||
};
|
||||
|
||||
|
||||
..
|
||||
|
||||
|
||||
.. index:: GenericDescriptorMatcher::add
|
||||
|
||||
|
||||
cv::GenericDescriptorMatcher::add
|
||||
---------------------------------
|
||||
|
||||
`id=0.507600777855 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/GenericDescriptorMatcher%3A%3Aadd>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: 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
|
||||
existing data.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:param images: Image collection.
|
||||
|
||||
|
||||
:param keypoints: Point collection. Assumes that ``keypoints[i]`` are keypoints
|
||||
detected in an image ``images[i]`` .
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: GenericDescriptorMatcher::getTrainImages
|
||||
|
||||
|
||||
cv::GenericDescriptorMatcher::getTrainImages
|
||||
--------------------------------------------
|
||||
|
||||
`id=0.520364236881 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/GenericDescriptorMatcher%3A%3AgetTrainImages>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: const vector<Mat>\& GenericDescriptorMatcher::getTrainImages() const
|
||||
|
||||
Returns train image collection.
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: GenericDescriptorMatcher::getTrainKeypoints
|
||||
|
||||
|
||||
cv::GenericDescriptorMatcher::getTrainKeypoints
|
||||
-----------------------------------------------
|
||||
|
||||
`id=0.179197628979 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/GenericDescriptorMatcher%3A%3AgetTrainKeypoints>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: const vector<vector<KeyPoint> >\& GenericDescriptorMatcher::getTrainKeypoints() const
|
||||
|
||||
Returns train keypoints collection.
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: GenericDescriptorMatcher::clear
|
||||
|
||||
|
||||
cv::GenericDescriptorMatcher::clear
|
||||
-----------------------------------
|
||||
|
||||
`id=0.163507435554 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/GenericDescriptorMatcher%3A%3Aclear>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: void GenericDescriptorMatcher::clear()
|
||||
|
||||
Clear train collection (iamges and keypoints).
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: GenericDescriptorMatcher::train
|
||||
|
||||
|
||||
cv::GenericDescriptorMatcher::train
|
||||
-----------------------------------
|
||||
|
||||
`id=0.270072381935 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/GenericDescriptorMatcher%3A%3Atrain>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: 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
|
||||
---------------------------------------------
|
||||
|
||||
`id=0.208711469863 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/GenericDescriptorMatcher%3A%3AisMaskSupported>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: void GenericDescriptorMatcher::isMaskSupported()
|
||||
|
||||
Returns true if generic descriptor matcher supports masking permissible matches.
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: GenericDescriptorMatcher::classify
|
||||
|
||||
|
||||
cv::GenericDescriptorMatcher::classify
|
||||
--------------------------------------
|
||||
|
||||
`id=0.550844968727 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/GenericDescriptorMatcher%3A%3Aclassify>`__
|
||||
|
||||
|
||||
:func:`GenericDescriptorMatcher::add`
|
||||
|
||||
|
||||
.. cfunction:: 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 )
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:param queryImage: The query image.
|
||||
|
||||
|
||||
:param queryKeypoints: Keypoints from the query image.
|
||||
|
||||
|
||||
:param trainImage: The train image.
|
||||
|
||||
|
||||
:param trainKeypoints: Keypoints from the train image.
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: GenericDescriptorMatcher::match
|
||||
|
||||
|
||||
cv::GenericDescriptorMatcher::match
|
||||
-----------------------------------
|
||||
|
||||
`id=0.91509902003 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/GenericDescriptorMatcher%3A%3Amatch>`__
|
||||
|
||||
|
||||
: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
|
||||
|
||||
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>() )
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:param queryImage: Query image.
|
||||
|
||||
|
||||
:param queryKeypoints: Keypoints detected in ``queryImage`` .
|
||||
|
||||
|
||||
:param trainImage: Train image. This will not be added to train image collection
|
||||
stored in class object.
|
||||
|
||||
|
||||
:param trainKeypoints: Keypoints detected in ``trainImage`` . They will not be added to train points collection
|
||||
stored in class object.
|
||||
|
||||
|
||||
:param matches: Matches. If some query descriptor (keypoint) masked out in ``mask``
|
||||
no match will be added for this descriptor.
|
||||
So ``matches`` size may be less query keypoints count.
|
||||
|
||||
|
||||
:param mask: Mask specifying permissible matches between input query and train keypoints.
|
||||
|
||||
|
||||
:param masks: The set of masks. Each ``masks[i]`` specifies permissible matches between input query keypoints
|
||||
and stored train keypointss from i-th image.
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: GenericDescriptorMatcher::knnMatch
|
||||
|
||||
|
||||
cv::GenericDescriptorMatcher::knnMatch
|
||||
--------------------------------------
|
||||
|
||||
`id=0.828361496735 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/GenericDescriptorMatcher%3A%3AknnMatch>`__
|
||||
|
||||
|
||||
: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
|
||||
|
||||
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 )
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: GenericDescriptorMatcher::radiusMatch
|
||||
|
||||
|
||||
cv::GenericDescriptorMatcher::radiusMatch
|
||||
-----------------------------------------
|
||||
|
||||
`id=0.732845229707 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/GenericDescriptorMatcher%3A%3AradiusMatch>`__
|
||||
|
||||
|
||||
: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
|
||||
|
||||
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 )
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: GenericDescriptorMatcher::read
|
||||
|
||||
|
||||
cv::GenericDescriptorMatcher::read
|
||||
----------------------------------
|
||||
|
||||
`id=0.937930388921 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/GenericDescriptorMatcher%3A%3Aread>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: void GenericDescriptorMatcher::read( const FileNode\& fn )
|
||||
|
||||
Reads matcher object from a file node.
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: GenericDescriptorMatcher::write
|
||||
|
||||
|
||||
cv::GenericDescriptorMatcher::write
|
||||
-----------------------------------
|
||||
|
||||
`id=0.509497773169 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/GenericDescriptorMatcher%3A%3Awrite>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: void GenericDescriptorMatcher::write( FileStorage\& fs ) const
|
||||
|
||||
Writes match object to a file storage
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: GenericDescriptorMatcher::clone
|
||||
|
||||
|
||||
cv::GenericDescriptorMatcher::clone
|
||||
-----------------------------------
|
||||
|
||||
`id=0.864304581549 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/GenericDescriptorMatcher%3A%3Aclone>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: Ptr<GenericDescriptorMatcher>\\GenericDescriptorMatcher::clone( bool emptyTrainData ) const
|
||||
|
||||
Clone the matcher.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:param emptyTrainData: If emptyTrainData is false the method create deep copy of the object, i.e. copies
|
||||
both parameters and train data. If emptyTrainData is true the method create object copy with current parameters
|
||||
but with empty train data.
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: OneWayDescriptorMatcher
|
||||
|
||||
.. _OneWayDescriptorMatcher:
|
||||
|
||||
OneWayDescriptorMatcher
|
||||
-----------------------
|
||||
|
||||
`id=0.295296902287 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/OneWayDescriptorMatcher>`__
|
||||
|
||||
.. ctype:: OneWayDescriptorMatcher
|
||||
|
||||
|
||||
|
||||
Wrapping class for computing, matching and classification of descriptors using
|
||||
:func:`OneWayDescriptorBase`
|
||||
class.
|
||||
|
||||
|
||||
|
||||
|
||||
::
|
||||
|
||||
|
||||
|
||||
class OneWayDescriptorMatcher : public GenericDescriptorMatcher
|
||||
{
|
||||
public:
|
||||
class Params
|
||||
{
|
||||
public:
|
||||
static const int POSE_COUNT = 500;
|
||||
static const int PATCH_WIDTH = 24;
|
||||
static const int PATCH_HEIGHT = 24;
|
||||
static float GET_MIN_SCALE() { return 0.7f; }
|
||||
static float GET_MAX_SCALE() { return 1.5f; }
|
||||
static float GET_STEP_SCALE() { return 1.2f; }
|
||||
|
||||
Params( int poseCount = POSE_COUNT,
|
||||
Size patchSize = Size(PATCH_WIDTH, PATCH_HEIGHT),
|
||||
string pcaFilename = string(),
|
||||
string trainPath = string(), string trainImagesList = string(),
|
||||
float minScale = GET_MIN_SCALE(), float maxScale = GET_MAX_SCALE(),
|
||||
float stepScale = GET_STEP_SCALE() );
|
||||
|
||||
int poseCount;
|
||||
Size patchSize;
|
||||
string pcaFilename;
|
||||
string trainPath;
|
||||
string trainImagesList;
|
||||
|
||||
float minScale, maxScale, stepScale;
|
||||
};
|
||||
|
||||
OneWayDescriptorMatcher( const Params& params=Params() );
|
||||
virtual ~OneWayDescriptorMatcher();
|
||||
|
||||
void initialize( const Params& params, const Ptr<OneWayDescriptorBase>& base=Ptr<OneWayDescriptorBase>() );
|
||||
|
||||
// Clears keypoints storing in collection and OneWayDescriptorBase
|
||||
virtual void clear();
|
||||
|
||||
virtual void train();
|
||||
|
||||
virtual bool isMaskSupported();
|
||||
|
||||
virtual void read( const FileNode &fn );
|
||||
virtual void write( FileStorage& fs ) const;
|
||||
|
||||
virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;
|
||||
protected:
|
||||
...
|
||||
};
|
||||
|
||||
|
||||
..
|
||||
|
||||
|
||||
.. index:: FernDescriptorMatcher
|
||||
|
||||
.. _FernDescriptorMatcher:
|
||||
|
||||
FernDescriptorMatcher
|
||||
---------------------
|
||||
|
||||
`id=0.410971973421 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/FernDescriptorMatcher>`__
|
||||
|
||||
.. ctype:: FernDescriptorMatcher
|
||||
|
||||
|
||||
|
||||
Wrapping class for computing, matching and classification of descriptors using
|
||||
:func:`FernClassifier`
|
||||
class.
|
||||
|
||||
|
||||
|
||||
|
||||
::
|
||||
|
||||
|
||||
|
||||
class FernDescriptorMatcher : public GenericDescriptorMatcher
|
||||
{
|
||||
public:
|
||||
class Params
|
||||
{
|
||||
public:
|
||||
Params( int nclasses=0,
|
||||
int patchSize=FernClassifier::PATCH_SIZE,
|
||||
int signatureSize=FernClassifier::DEFAULT_SIGNATURE_SIZE,
|
||||
int nstructs=FernClassifier::DEFAULT_STRUCTS,
|
||||
int structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
|
||||
int nviews=FernClassifier::DEFAULT_VIEWS,
|
||||
int compressionMethod=FernClassifier::COMPRESSION_NONE,
|
||||
const PatchGenerator& patchGenerator=PatchGenerator() );
|
||||
|
||||
Params( const string& filename );
|
||||
|
||||
int nclasses;
|
||||
int patchSize;
|
||||
int signatureSize;
|
||||
int nstructs;
|
||||
int structSize;
|
||||
int nviews;
|
||||
int compressionMethod;
|
||||
PatchGenerator patchGenerator;
|
||||
|
||||
string filename;
|
||||
};
|
||||
|
||||
FernDescriptorMatcher( const Params& params=Params() );
|
||||
virtual ~FernDescriptorMatcher();
|
||||
|
||||
virtual void clear();
|
||||
|
||||
virtual void train();
|
||||
|
||||
virtual bool isMaskSupported();
|
||||
|
||||
virtual void read( const FileNode &fn );
|
||||
virtual void write( FileStorage& fs ) const;
|
||||
|
||||
virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;
|
||||
|
||||
protected:
|
||||
...
|
||||
};
|
||||
|
||||
|
||||
..
|
||||
|
||||
|
||||
.. index:: VectorDescriptorMatcher
|
||||
|
||||
.. _VectorDescriptorMatcher:
|
||||
|
||||
VectorDescriptorMatcher
|
||||
-----------------------
|
||||
|
||||
`id=0.89575693039 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/VectorDescriptorMatcher>`__
|
||||
|
||||
.. ctype:: VectorDescriptorMatcher
|
||||
|
||||
|
||||
|
||||
Class used for matching descriptors that can be described as vectors in a finite-dimensional space.
|
||||
|
||||
|
||||
|
||||
|
||||
::
|
||||
|
||||
|
||||
|
||||
class CV_EXPORTS VectorDescriptorMatcher : public GenericDescriptorMatcher
|
||||
{
|
||||
public:
|
||||
VectorDescriptorMatcher( const Ptr<DescriptorExtractor>& extractor, const Ptr<DescriptorMatcher>& matcher );
|
||||
virtual ~VectorDescriptorMatcher();
|
||||
|
||||
virtual void add( const vector<Mat>& imgCollection,
|
||||
vector<vector<KeyPoint> >& pointCollection );
|
||||
virtual void clear();
|
||||
virtual void train();
|
||||
virtual bool isMaskSupported();
|
||||
|
||||
virtual void read( const FileNode& fn );
|
||||
virtual void write( FileStorage& fs ) const;
|
||||
|
||||
virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;
|
||||
|
||||
protected:
|
||||
...
|
||||
};
|
||||
|
||||
|
||||
..
|
||||
|
||||
Example of creating:
|
||||
|
||||
|
||||
|
||||
::
|
||||
|
||||
|
||||
|
||||
VectorDescriptorMatcher matcher( new SurfDescriptorExtractor,
|
||||
new BruteForceMatcher<L2<float> > );
|
||||
|
||||
|
||||
..
|
||||
|
||||
@@ -0,0 +1,140 @@
|
||||
Drawing Function of Keypoints and Matches
|
||||
=========================================
|
||||
|
||||
.. highlight:: cpp
|
||||
|
||||
|
||||
|
||||
.. index:: drawMatches
|
||||
|
||||
|
||||
cv::drawMatches
|
||||
---------------
|
||||
|
||||
`id=0.919261687295 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/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 )
|
||||
|
||||
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 )
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:param img1: First source image.
|
||||
|
||||
|
||||
:param keypoints1: Keypoints from first source image.
|
||||
|
||||
|
||||
:param img2: Second source image.
|
||||
|
||||
|
||||
:param keypoints2: Keypoints from second source image.
|
||||
|
||||
|
||||
:param matches: Matches from first image to second one, i.e. ``keypoints1[i]``
|
||||
has corresponding point ``keypoints2[matches[i]]`` .
|
||||
|
||||
|
||||
:param outImg: Output image. Its content depends on ``flags`` value
|
||||
what is drawn in output image. See below possible ``flags`` bit values.
|
||||
|
||||
|
||||
:param matchColor: Color of matches (lines and connected keypoints).
|
||||
If ``matchColor==Scalar::all(-1)`` color will be generated randomly.
|
||||
|
||||
|
||||
:param singlePointColor: Color of single keypoints (circles), i.e. keypoints not having the matches.
|
||||
If ``singlePointColor==Scalar::all(-1)`` color will be generated randomly.
|
||||
|
||||
|
||||
:param matchesMask: Mask determining which matches will be drawn. If mask is empty all matches will be drawn.
|
||||
|
||||
|
||||
:param flags: Each bit of ``flags`` sets some feature of drawing.
|
||||
Possible ``flags`` bit values is defined by ``DrawMatchesFlags`` , see below.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
::
|
||||
|
||||
|
||||
|
||||
struct DrawMatchesFlags
|
||||
{
|
||||
enum{ DEFAULT = 0, // Output image matrix will be created (Mat::create),
|
||||
// i.e. existing memory of output image may be reused.
|
||||
// Two source image, matches and single keypoints
|
||||
// will be drawn.
|
||||
// For each keypoint only the center point will be
|
||||
// drawn (without the circle around keypoint with
|
||||
// keypoint size and orientation).
|
||||
DRAW_OVER_OUTIMG = 1, // Output image matrix will not be
|
||||
// created (Mat::create). Matches will be drawn
|
||||
// on existing content of output image.
|
||||
NOT_DRAW_SINGLE_POINTS = 2, // Single keypoints will not be drawn.
|
||||
DRAW_RICH_KEYPOINTS = 4 // For each keypoint the circle around
|
||||
// keypoint with keypoint size and orientation will
|
||||
// be drawn.
|
||||
};
|
||||
};
|
||||
|
||||
|
||||
..
|
||||
|
||||
|
||||
.. index:: drawKeypoints
|
||||
|
||||
|
||||
cv::drawKeypoints
|
||||
-----------------
|
||||
|
||||
`id=0.694314481427 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/drawKeypoints>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: void drawKeypoints( const Mat\& image, const vector<KeyPoint>\& keypoints, Mat\& outImg, const Scalar\& color=Scalar::all(-1), int flags=DrawMatchesFlags::DEFAULT )
|
||||
|
||||
Draw keypoints.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:param image: Source image.
|
||||
|
||||
|
||||
:param keypoints: Keypoints from source image.
|
||||
|
||||
|
||||
:param outImg: Output image. Its content depends on ``flags`` value
|
||||
what is drawn in output image. See possible ``flags`` bit values.
|
||||
|
||||
|
||||
:param color: Color of keypoints
|
||||
|
||||
.
|
||||
|
||||
:param flags: Each bit of ``flags`` sets some feature of drawing.
|
||||
Possible ``flags`` bit values is defined by ``DrawMatchesFlags`` ,
|
||||
see above in :func:`drawMatches` .
|
||||
|
||||
|
||||
|
||||
975
modules/features2d/doc/feature_detection_and_description.rst
Normal file
975
modules/features2d/doc/feature_detection_and_description.rst
Normal file
@@ -0,0 +1,975 @@
|
||||
Feature detection and description
|
||||
=================================
|
||||
|
||||
.. highlight:: cpp
|
||||
|
||||
|
||||
|
||||
.. index:: FAST
|
||||
|
||||
|
||||
cv::FAST
|
||||
--------
|
||||
|
||||
`id=0.180338558353 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/FAST>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: 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).
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:param image: The image. Keypoints (corners) will be detected on this.
|
||||
|
||||
|
||||
:param keypoints: Keypoints detected on the image.
|
||||
|
||||
|
||||
:param threshold: Threshold on difference between intensity of center pixel and
|
||||
pixels on circle around this pixel. See description of the algorithm.
|
||||
|
||||
|
||||
:param nonmaxSupression: If it is true then non-maximum supression will be applied to detected corners (keypoints).
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: MSER
|
||||
|
||||
.. _MSER:
|
||||
|
||||
MSER
|
||||
----
|
||||
|
||||
`id=0.0333368188128 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/MSER>`__
|
||||
|
||||
.. ctype:: MSER
|
||||
|
||||
|
||||
|
||||
Maximally-Stable Extremal Region Extractor
|
||||
|
||||
|
||||
|
||||
|
||||
::
|
||||
|
||||
|
||||
|
||||
class MSER : public CvMSERParams
|
||||
{
|
||||
public:
|
||||
// default constructor
|
||||
MSER();
|
||||
// constructor that initializes all the algorithm parameters
|
||||
MSER( int _delta, int _min_area, int _max_area,
|
||||
float _max_variation, float _min_diversity,
|
||||
int _max_evolution, double _area_threshold,
|
||||
double _min_margin, int _edge_blur_size );
|
||||
// runs the extractor on the specified image; returns the MSERs,
|
||||
// each encoded as a contour (vector<Point>, see findContours)
|
||||
// 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.
|
||||
|
||||
|
||||
.. index:: StarDetector
|
||||
|
||||
.. _StarDetector:
|
||||
|
||||
StarDetector
|
||||
------------
|
||||
|
||||
`id=0.378812518152 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/StarDetector>`__
|
||||
|
||||
.. ctype:: StarDetector
|
||||
|
||||
|
||||
|
||||
Implements Star keypoint detector
|
||||
|
||||
|
||||
|
||||
|
||||
::
|
||||
|
||||
|
||||
|
||||
class StarDetector : CvStarDetectorParams
|
||||
{
|
||||
public:
|
||||
// default constructor
|
||||
StarDetector();
|
||||
// the full constructor initialized all the algorithm parameters:
|
||||
// maxSize - maximum size of the features. The following
|
||||
// values of the parameter are supported:
|
||||
// 4, 6, 8, 11, 12, 16, 22, 23, 32, 45, 46, 64, 90, 128
|
||||
// responseThreshold - threshold for the approximated laplacian,
|
||||
// used to eliminate weak features. The larger it is,
|
||||
// the less features will be retrieved
|
||||
// lineThresholdProjected - another threshold for the laplacian to
|
||||
// eliminate edges
|
||||
// lineThresholdBinarized - another threshold for the feature
|
||||
// size to eliminate edges.
|
||||
// The larger the 2 threshold, the more points you get.
|
||||
StarDetector(int maxSize, int responseThreshold,
|
||||
int lineThresholdProjected,
|
||||
int lineThresholdBinarized,
|
||||
int suppressNonmaxSize);
|
||||
|
||||
// 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
|
||||
|
||||
.. index:: SIFT
|
||||
|
||||
.. _SIFT:
|
||||
|
||||
SIFT
|
||||
----
|
||||
|
||||
`id=0.385373212311 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/SIFT>`__
|
||||
|
||||
.. ctype:: SIFT
|
||||
|
||||
|
||||
|
||||
Class for extracting keypoints and computing descriptors using approach named Scale Invariant Feature Transform (SIFT).
|
||||
|
||||
|
||||
|
||||
|
||||
::
|
||||
|
||||
|
||||
|
||||
class CV_EXPORTS SIFT
|
||||
{
|
||||
public:
|
||||
struct CommonParams
|
||||
{
|
||||
static const int DEFAULT_NOCTAVES = 4;
|
||||
static const int DEFAULT_NOCTAVE_LAYERS = 3;
|
||||
static const int DEFAULT_FIRST_OCTAVE = -1;
|
||||
enum{ FIRST_ANGLE = 0, AVERAGE_ANGLE = 1 };
|
||||
|
||||
CommonParams();
|
||||
CommonParams( int _nOctaves, int _nOctaveLayers, int _firstOctave,
|
||||
int _angleMode );
|
||||
int nOctaves, nOctaveLayers, firstOctave;
|
||||
int angleMode;
|
||||
};
|
||||
|
||||
struct DetectorParams
|
||||
{
|
||||
static double GET_DEFAULT_THRESHOLD()
|
||||
{ return 0.04 / SIFT::CommonParams::DEFAULT_NOCTAVE_LAYERS / 2.0; }
|
||||
static double GET_DEFAULT_EDGE_THRESHOLD() { return 10.0; }
|
||||
|
||||
DetectorParams();
|
||||
DetectorParams( double _threshold, double _edgeThreshold );
|
||||
double threshold, edgeThreshold;
|
||||
};
|
||||
|
||||
struct DescriptorParams
|
||||
{
|
||||
static double GET_DEFAULT_MAGNIFICATION() { return 3.0; }
|
||||
static const bool DEFAULT_IS_NORMALIZE = true;
|
||||
static const int DESCRIPTOR_SIZE = 128;
|
||||
|
||||
DescriptorParams();
|
||||
DescriptorParams( double _magnification, bool _isNormalize,
|
||||
bool _recalculateAngles );
|
||||
double magnification;
|
||||
bool isNormalize;
|
||||
bool recalculateAngles;
|
||||
};
|
||||
|
||||
SIFT();
|
||||
//! sift-detector constructor
|
||||
SIFT( double _threshold, double _edgeThreshold,
|
||||
int _nOctaves=CommonParams::DEFAULT_NOCTAVES,
|
||||
int _nOctaveLayers=CommonParams::DEFAULT_NOCTAVE_LAYERS,
|
||||
int _firstOctave=CommonParams::DEFAULT_FIRST_OCTAVE,
|
||||
int _angleMode=CommonParams::FIRST_ANGLE );
|
||||
//! sift-descriptor constructor
|
||||
SIFT( double _magnification, bool _isNormalize=true,
|
||||
bool _recalculateAngles = true,
|
||||
int _nOctaves=CommonParams::DEFAULT_NOCTAVES,
|
||||
int _nOctaveLayers=CommonParams::DEFAULT_NOCTAVE_LAYERS,
|
||||
int _firstOctave=CommonParams::DEFAULT_FIRST_OCTAVE,
|
||||
int _angleMode=CommonParams::FIRST_ANGLE );
|
||||
SIFT( const CommonParams& _commParams,
|
||||
const DetectorParams& _detectorParams = DetectorParams(),
|
||||
const DescriptorParams& _descriptorParams = DescriptorParams() );
|
||||
|
||||
//! returns the descriptor size in floats (128)
|
||||
int descriptorSize() const { return DescriptorParams::DESCRIPTOR_SIZE; }
|
||||
//! finds the keypoints using SIFT algorithm
|
||||
void operator()(const Mat& img, const Mat& mask,
|
||||
vector<KeyPoint>& keypoints) const;
|
||||
//! finds the keypoints and computes descriptors for them using SIFT algorithm.
|
||||
//! Optionally it can compute descriptors for the user-provided keypoints
|
||||
void operator()(const Mat& img, const Mat& mask,
|
||||
vector<KeyPoint>& keypoints,
|
||||
Mat& descriptors,
|
||||
bool useProvidedKeypoints=false) const;
|
||||
|
||||
CommonParams getCommonParams () const { return commParams; }
|
||||
DetectorParams getDetectorParams () const { return detectorParams; }
|
||||
DescriptorParams getDescriptorParams () const { return descriptorParams; }
|
||||
protected:
|
||||
...
|
||||
};
|
||||
|
||||
|
||||
..
|
||||
|
||||
|
||||
.. index:: SURF
|
||||
|
||||
.. _SURF:
|
||||
|
||||
SURF
|
||||
----
|
||||
|
||||
`id=0.43149154692 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/SURF>`__
|
||||
|
||||
.. ctype:: SURF
|
||||
|
||||
|
||||
|
||||
Class for extracting Speeded Up Robust Features from an image.
|
||||
|
||||
|
||||
|
||||
|
||||
::
|
||||
|
||||
|
||||
|
||||
class SURF : public CvSURFParams
|
||||
{
|
||||
public:
|
||||
// default constructor
|
||||
SURF();
|
||||
// constructor that initializes all the algorithm parameters
|
||||
SURF(double _hessianThreshold, int _nOctaves=4,
|
||||
int _nOctaveLayers=2, bool _extended=false);
|
||||
// returns the number of elements in each descriptor (64 or 128)
|
||||
int descriptorSize() const;
|
||||
// detects keypoints using fast multi-scale Hessian detector
|
||||
void operator()(const Mat& img, const Mat& mask,
|
||||
vector<KeyPoint>& keypoints) const;
|
||||
// detects keypoints and computes the SURF descriptors for them;
|
||||
// output vector "descriptors" stores elements of descriptors and has size
|
||||
// equal descriptorSize()*keypoints.size() as each descriptor is
|
||||
// descriptorSize() elements of this vector.
|
||||
void operator()(const Mat& img, const Mat& mask,
|
||||
vector<KeyPoint>& keypoints,
|
||||
vector<float>& descriptors,
|
||||
bool useProvidedKeypoints=false) const;
|
||||
};
|
||||
|
||||
|
||||
..
|
||||
|
||||
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.
|
||||
|
||||
|
||||
.. index:: RandomizedTree
|
||||
|
||||
.. _RandomizedTree:
|
||||
|
||||
RandomizedTree
|
||||
--------------
|
||||
|
||||
`id=0.539311466248 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RandomizedTree>`__
|
||||
|
||||
.. ctype:: RandomizedTree
|
||||
|
||||
|
||||
|
||||
The class contains base structure for
|
||||
``RTreeClassifier``
|
||||
|
||||
|
||||
|
||||
::
|
||||
|
||||
|
||||
|
||||
class CV_EXPORTS RandomizedTree
|
||||
{
|
||||
public:
|
||||
friend class RTreeClassifier;
|
||||
|
||||
RandomizedTree();
|
||||
~RandomizedTree();
|
||||
|
||||
void train(std::vector<BaseKeypoint> const& base_set,
|
||||
cv::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,
|
||||
int views, size_t reduced_num_dim, int num_quant_bits);
|
||||
|
||||
// following two funcs are EXPERIMENTAL
|
||||
//(do not use unless you know exactly what you do)
|
||||
static void quantizeVector(float *vec, int dim, int N, float bnds[2],
|
||||
int clamp_mode=0);
|
||||
static void quantizeVector(float *src, int dim, int N, float bnds[2],
|
||||
uchar *dst);
|
||||
|
||||
// patch_data must be a 32x32 array (no row padding)
|
||||
float* getPosterior(uchar* patch_data);
|
||||
const float* getPosterior(uchar* patch_data) const;
|
||||
uchar* getPosterior2(uchar* patch_data);
|
||||
|
||||
void read(const char* file_name, int num_quant_bits);
|
||||
void read(std::istream &is, int num_quant_bits);
|
||||
void write(const char* file_name) const;
|
||||
void write(std::ostream &os) const;
|
||||
|
||||
int classes() { return classes_; }
|
||||
int depth() { return depth_; }
|
||||
|
||||
void discardFloatPosteriors() { freePosteriors(1); }
|
||||
|
||||
inline void applyQuantization(int num_quant_bits)
|
||||
{ makePosteriors2(num_quant_bits); }
|
||||
|
||||
private:
|
||||
int classes_;
|
||||
int depth_;
|
||||
int num_leaves_;
|
||||
std::vector<RTreeNode> nodes_;
|
||||
float **posteriors_; // 16-bytes aligned posteriors
|
||||
uchar **posteriors2_; // 16-bytes aligned posteriors
|
||||
std::vector<int> leaf_counts_;
|
||||
|
||||
void createNodes(int num_nodes, cv::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 addExample(int class_id, uchar* patch_data);
|
||||
void finalize(size_t reduced_num_dim, int num_quant_bits);
|
||||
int getIndex(uchar* patch_data) const;
|
||||
inline float* getPosteriorByIndex(int index);
|
||||
inline uchar* getPosteriorByIndex2(int index);
|
||||
inline const float* getPosteriorByIndex(int index) const;
|
||||
void convertPosteriorsToChar();
|
||||
void makePosteriors2(int num_quant_bits);
|
||||
void compressLeaves(size_t reduced_num_dim);
|
||||
void estimateQuantPercForPosteriors(float perc[2]);
|
||||
};
|
||||
|
||||
|
||||
..
|
||||
|
||||
|
||||
.. index:: RandomizedTree::train
|
||||
|
||||
|
||||
cv::RandomizedTree::train
|
||||
-------------------------
|
||||
|
||||
`id=0.360469298211 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RandomizedTree%3A%3Atrain>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. 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)
|
||||
|
||||
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)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
{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}
|
||||
|
||||
{Number of dimensions are used in compressed signature}
|
||||
{Number of bits are used for quantization}
|
||||
|
||||
|
||||
|
||||
.. index:: RandomizedTree::read
|
||||
|
||||
|
||||
cv::RandomizedTree::read
|
||||
------------------------
|
||||
|
||||
`id=0.663893576705 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RandomizedTree%3A%3Aread>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: 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)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:param file_name: Filename of file contains randomized tree data
|
||||
|
||||
|
||||
:param is: Input stream associated with file contains randomized tree data
|
||||
|
||||
{Number of bits are used for quantization}
|
||||
|
||||
|
||||
|
||||
.. index:: RandomizedTree::write
|
||||
|
||||
|
||||
cv::RandomizedTree::write
|
||||
-------------------------
|
||||
|
||||
`id=0.640726433619 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RandomizedTree%3A%3Awrite>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: void write(const char* file_name) const
|
||||
|
||||
Writes current randomized tree to a file or stream
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: void write(std::ostream \&os) const
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:param file_name: Filename of file where randomized tree data will be stored
|
||||
|
||||
|
||||
:param is: Output stream associated with file where randomized tree data will be stored
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: RandomizedTree::applyQuantization
|
||||
|
||||
|
||||
cv::RandomizedTree::applyQuantization
|
||||
-------------------------------------
|
||||
|
||||
`id=0.113364904421 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RandomizedTree%3A%3AapplyQuantization>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: void applyQuantization(int num_quant_bits)
|
||||
|
||||
Applies quantization to the current randomized tree
|
||||
|
||||
|
||||
|
||||
|
||||
{Number of bits are used for quantization}
|
||||
|
||||
|
||||
|
||||
.. index:: RTreeNode
|
||||
|
||||
.. _RTreeNode:
|
||||
|
||||
RTreeNode
|
||||
---------
|
||||
|
||||
`id=0.718763052087 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RTreeNode>`__
|
||||
|
||||
.. ctype:: RTreeNode
|
||||
|
||||
|
||||
|
||||
The class contains base structure for
|
||||
``RandomizedTree``
|
||||
|
||||
|
||||
|
||||
::
|
||||
|
||||
|
||||
|
||||
struct RTreeNode
|
||||
{
|
||||
short offset1, offset2;
|
||||
|
||||
RTreeNode() {}
|
||||
|
||||
RTreeNode(uchar x1, uchar y1, uchar x2, uchar y2)
|
||||
: offset1(y1*PATCH_SIZE + x1),
|
||||
offset2(y2*PATCH_SIZE + x2)
|
||||
{}
|
||||
|
||||
//! Left child on 0, right child on 1
|
||||
inline bool operator() (uchar* patch_data) const
|
||||
{
|
||||
return patch_data[offset1] > patch_data[offset2];
|
||||
}
|
||||
};
|
||||
|
||||
|
||||
..
|
||||
|
||||
|
||||
.. index:: RTreeClassifier
|
||||
|
||||
.. _RTreeClassifier:
|
||||
|
||||
RTreeClassifier
|
||||
---------------
|
||||
|
||||
`id=0.477872539921 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RTreeClassifier>`__
|
||||
|
||||
.. ctype:: RTreeClassifier
|
||||
|
||||
|
||||
|
||||
The class contains
|
||||
``RTreeClassifier``
|
||||
. It represents calonder descriptor which was originally introduced by Michael Calonder
|
||||
|
||||
|
||||
|
||||
|
||||
::
|
||||
|
||||
|
||||
|
||||
class CV_EXPORTS RTreeClassifier
|
||||
{
|
||||
public:
|
||||
static const int DEFAULT_TREES = 48;
|
||||
static const size_t DEFAULT_NUM_QUANT_BITS = 4;
|
||||
|
||||
RTreeClassifier();
|
||||
|
||||
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);
|
||||
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);
|
||||
|
||||
// sig must point to a memory block of at least
|
||||
//classes()*sizeof(float|uchar) bytes
|
||||
void getSignature(IplImage *patch, uchar *sig);
|
||||
void getSignature(IplImage *patch, float *sig);
|
||||
void getSparseSignature(IplImage *patch, float *sig,
|
||||
float thresh);
|
||||
|
||||
static int countNonZeroElements(float *vec, int n, double tol=1e-10);
|
||||
static inline void safeSignatureAlloc(uchar **sig, int num_sig=1,
|
||||
int sig_len=176);
|
||||
static inline uchar* safeSignatureAlloc(int num_sig=1,
|
||||
int sig_len=176);
|
||||
|
||||
inline int classes() { return classes_; }
|
||||
inline int original_num_classes()
|
||||
{ return original_num_classes_; }
|
||||
|
||||
void setQuantization(int num_quant_bits);
|
||||
void discardFloatPosteriors();
|
||||
|
||||
void read(const char* file_name);
|
||||
void read(std::istream &is);
|
||||
void write(const char* file_name) const;
|
||||
void write(std::ostream &os) const;
|
||||
|
||||
std::vector<RandomizedTree> trees_;
|
||||
|
||||
private:
|
||||
int classes_;
|
||||
int num_quant_bits_;
|
||||
uchar **posteriors_;
|
||||
ushort *ptemp_;
|
||||
int original_num_classes_;
|
||||
bool keep_floats_;
|
||||
};
|
||||
|
||||
|
||||
..
|
||||
|
||||
|
||||
.. index:: RTreeClassifier::train
|
||||
|
||||
|
||||
cv::RTreeClassifier::train
|
||||
--------------------------
|
||||
|
||||
`id=0.173927228061 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RTreeClassifier%3A%3Atrain>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. 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)
|
||||
|
||||
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)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
{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}
|
||||
{Number of randomized trees used in RTreeClassificator}
|
||||
{Maximum tree depth}
|
||||
|
||||
{Number of dimensions are used in compressed signature}
|
||||
{Number of bits are used for quantization}
|
||||
{Print current status of training on the console}
|
||||
|
||||
|
||||
|
||||
.. index:: RTreeClassifier::getSignature
|
||||
|
||||
|
||||
cv::RTreeClassifier::getSignature
|
||||
---------------------------------
|
||||
|
||||
`id=0.90043980708 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RTreeClassifier%3A%3AgetSignature>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: void getSignature(IplImage *patch, uchar *sig)
|
||||
|
||||
Returns signature for image patch
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: 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
|
||||
---------------------------------------
|
||||
|
||||
`id=0.692099737961 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RTreeClassifier%3A%3AgetSparseSignature>`__
|
||||
|
||||
|
||||
````
|
||||
|
||||
|
||||
.. cfunction:: 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)``
|
||||
}
|
||||
{The threshold that is used for compressing the signature}
|
||||
|
||||
|
||||
|
||||
.. index:: RTreeClassifier::countNonZeroElements
|
||||
|
||||
|
||||
cv::RTreeClassifier::countNonZeroElements
|
||||
-----------------------------------------
|
||||
|
||||
`id=0.553226961988 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RTreeClassifier%3A%3AcountNonZeroElements>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: static int countNonZeroElements(float *vec, int n, double tol=1e-10)
|
||||
|
||||
The function returns the number of non-zero elements in the input array.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:param vec: Input vector contains float elements
|
||||
|
||||
|
||||
:param n: Input vector size
|
||||
|
||||
{The threshold used for elements counting. We take all elements are less than
|
||||
``tol``
|
||||
as zero elements}
|
||||
|
||||
|
||||
|
||||
.. index:: RTreeClassifier::read
|
||||
|
||||
|
||||
cv::RTreeClassifier::read
|
||||
-------------------------
|
||||
|
||||
`id=0.648907224792 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RTreeClassifier%3A%3Aread>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: read(const char* file_name)
|
||||
|
||||
Reads pre-saved RTreeClassifier from file or stream
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: read(std::istream \&is)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:param file_name: Filename of file contains randomized tree data
|
||||
|
||||
|
||||
:param is: Input stream associated with file contains randomized tree data
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: RTreeClassifier::write
|
||||
|
||||
|
||||
cv::RTreeClassifier::write
|
||||
--------------------------
|
||||
|
||||
`id=0.340545032412 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RTreeClassifier%3A%3Awrite>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: void write(const char* file_name) const
|
||||
|
||||
Writes current RTreeClassifier to a file or stream
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: void write(std::ostream \&os) const
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:param file_name: Filename of file where randomized tree data will be stored
|
||||
|
||||
|
||||
:param is: Output stream associated with file where randomized tree data will be stored
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: RTreeClassifier::setQuantization
|
||||
|
||||
|
||||
cv::RTreeClassifier::setQuantization
|
||||
------------------------------------
|
||||
|
||||
`id=0.788175788924 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RTreeClassifier%3A%3AsetQuantization>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: void setQuantization(int num_quant_bits)
|
||||
|
||||
Applies quantization to the current randomized tree
|
||||
|
||||
|
||||
|
||||
|
||||
{Number of bits are used for quantization}
|
||||
|
||||
|
||||
Below there is an example of
|
||||
``RTreeClassifier``
|
||||
usage for feature matching. There are test and train images and we extract features from both with SURF. Output is
|
||||
:math:`best\_corr`
|
||||
and
|
||||
:math:`best\_corr\_idx`
|
||||
arrays which keep the best probabilities and corresponding features indexes for every train feature.
|
||||
|
||||
|
||||
|
||||
|
||||
::
|
||||
|
||||
|
||||
|
||||
CvMemStorage* storage = cvCreateMemStorage(0);
|
||||
CvSeq *objectKeypoints = 0, *objectDescriptors = 0;
|
||||
CvSeq *imageKeypoints = 0, *imageDescriptors = 0;
|
||||
CvSURFParams params = cvSURFParams(500, 1);
|
||||
cvExtractSURF( test_image, 0, &imageKeypoints, &imageDescriptors,
|
||||
storage, params );
|
||||
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;
|
||||
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));
|
||||
}
|
||||
|
||||
//Detector training
|
||||
cv::RNG rng( cvGetTickCount() );
|
||||
cv::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,
|
||||
(int)base_set.size(), detector.DEFAULT_NUM_QUANT_BITS);
|
||||
printf("Donen");
|
||||
|
||||
float* signature = new float[detector.original_num_classes()];
|
||||
float* best_corr;
|
||||
int* best_corr_idx;
|
||||
if (imageKeypoints->total > 0)
|
||||
{
|
||||
best_corr = new float[imageKeypoints->total];
|
||||
best_corr_idx = new int[imageKeypoints->total];
|
||||
}
|
||||
|
||||
for(i=0; i < imageKeypoints->total; i++)
|
||||
{
|
||||
point=(CvSURFPoint*)cvGetSeqElem(imageKeypoints,i);
|
||||
int part_idx = -1;
|
||||
float prob = 0.0f;
|
||||
|
||||
CvRect roi = cvRect((int)(point->pt.x) - patch_width/2,
|
||||
(int)(point->pt.y) - patch_height/2,
|
||||
patch_width, patch_height);
|
||||
cvSetImageROI(test_image, roi);
|
||||
roi = cvGetImageROI(test_image);
|
||||
if(roi.width != patch_width || roi.height != patch_height)
|
||||
{
|
||||
best_corr_idx[i] = part_idx;
|
||||
best_corr[i] = prob;
|
||||
}
|
||||
else
|
||||
{
|
||||
cvSetImageROI(test_image, roi);
|
||||
IplImage* roi_image =
|
||||
cvCreateImage(cvSize(roi.width, roi.height),
|
||||
test_image->depth, test_image->nChannels);
|
||||
cvCopy(test_image,roi_image);
|
||||
|
||||
detector.getSignature(roi_image, signature);
|
||||
for (int j = 0; j< detector.original_num_classes();j++)
|
||||
{
|
||||
if (prob < signature[j])
|
||||
{
|
||||
part_idx = j;
|
||||
prob = signature[j];
|
||||
}
|
||||
}
|
||||
|
||||
best_corr_idx[i] = part_idx;
|
||||
best_corr[i] = prob;
|
||||
|
||||
|
||||
if (roi_image)
|
||||
cvReleaseImage(&roi_image);
|
||||
}
|
||||
cvResetImageROI(test_image);
|
||||
}
|
||||
|
||||
|
||||
|
||||
..
|
||||
|
||||
14
modules/features2d/doc/features2d.rst
Normal file
14
modules/features2d/doc/features2d.rst
Normal file
@@ -0,0 +1,14 @@
|
||||
*********************
|
||||
2D Features Framework
|
||||
*********************
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
|
||||
feature_detection_and_description
|
||||
common_interfaces_of_feature_detectors
|
||||
common_interfaces_of_descriptor_extractors
|
||||
common_interfaces_of_descriptor_matchers
|
||||
common_interfaces_of_generic_descriptor_matchers
|
||||
drawing_function_of_keypoints_and_matches
|
||||
object_categorization
|
||||
408
modules/features2d/doc/object_categorization.rst
Normal file
408
modules/features2d/doc/object_categorization.rst
Normal file
@@ -0,0 +1,408 @@
|
||||
Object Categorization
|
||||
=====================
|
||||
|
||||
.. highlight:: cpp
|
||||
|
||||
|
||||
Some approaches based on local 2D features and used to object categorization
|
||||
are described in this section.
|
||||
|
||||
|
||||
.. index:: BOWTrainer
|
||||
|
||||
.. _BOWTrainer:
|
||||
|
||||
BOWTrainer
|
||||
----------
|
||||
|
||||
`id=0.926370937775 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWTrainer>`__
|
||||
|
||||
.. ctype:: 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,
|
||||
Lixin Fan, Jutta Willamowski, Cedric Bray, 2004.
|
||||
|
||||
|
||||
|
||||
|
||||
::
|
||||
|
||||
|
||||
|
||||
class BOWTrainer
|
||||
{
|
||||
public:
|
||||
BOWTrainer(){}
|
||||
virtual ~BOWTrainer(){}
|
||||
|
||||
void add( const Mat& descriptors );
|
||||
const vector<Mat>& getDescriptors() const;
|
||||
int descripotorsCount() const;
|
||||
|
||||
virtual void clear();
|
||||
|
||||
virtual Mat cluster() const = 0;
|
||||
virtual Mat cluster( const Mat& descriptors ) const = 0;
|
||||
|
||||
protected:
|
||||
...
|
||||
};
|
||||
|
||||
|
||||
..
|
||||
|
||||
|
||||
.. index:: BOWTrainer::add
|
||||
|
||||
|
||||
cv::BOWTrainer::add
|
||||
-------------------
|
||||
|
||||
`id=0.849162389183 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWTrainer%3A%3Aadd>`__
|
||||
|
||||
|
||||
````
|
||||
|
||||
|
||||
.. cfunction:: void BOWTrainer::add( const Mat\& descriptors )
|
||||
|
||||
Add descriptors to training set. The training set will be clustered using clustermethod to construct vocabulary.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:param descriptors: Descriptors to add to training set. Each row of ``descriptors``
|
||||
matrix is a one descriptor.
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: BOWTrainer::getDescriptors
|
||||
|
||||
|
||||
cv::BOWTrainer::getDescriptors
|
||||
------------------------------
|
||||
|
||||
`id=0.999824242082 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWTrainer%3A%3AgetDescriptors>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: const vector<Mat>\& BOWTrainer::getDescriptors() const
|
||||
|
||||
Returns training set of descriptors.
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: BOWTrainer::descripotorsCount
|
||||
|
||||
|
||||
cv::BOWTrainer::descripotorsCount
|
||||
---------------------------------
|
||||
|
||||
`id=0.497913292449 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWTrainer%3A%3AdescripotorsCount>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: const vector<Mat>\& BOWTrainer::descripotorsCount() const
|
||||
|
||||
Returns count of all descriptors stored in the training set.
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: BOWTrainer::cluster
|
||||
|
||||
|
||||
cv::BOWTrainer::cluster
|
||||
-----------------------
|
||||
|
||||
`id=0.560094315089 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWTrainer%3A%3Acluster>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: 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
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
: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.
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: BOWKMeansTrainer
|
||||
|
||||
.. _BOWKMeansTrainer:
|
||||
|
||||
BOWKMeansTrainer
|
||||
----------------
|
||||
|
||||
`id=0.588500098443 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWKMeansTrainer>`__
|
||||
|
||||
.. ctype:: BOWKMeansTrainer
|
||||
|
||||
|
||||
|
||||
:func:`kmeans`
|
||||
based class to train visual vocabulary using the ''bag of visual words'' approach.
|
||||
|
||||
|
||||
|
||||
|
||||
::
|
||||
|
||||
|
||||
|
||||
class BOWKMeansTrainer : public BOWTrainer
|
||||
{
|
||||
public:
|
||||
BOWKMeansTrainer( int clusterCount, const TermCriteria& termcrit=TermCriteria(),
|
||||
int attempts=3, int flags=KMEANS_PP_CENTERS );
|
||||
virtual ~BOWKMeansTrainer(){}
|
||||
|
||||
// Returns trained vocabulary (i.e. cluster centers).
|
||||
virtual Mat cluster() const;
|
||||
virtual Mat cluster( const Mat& descriptors ) const;
|
||||
|
||||
protected:
|
||||
...
|
||||
};
|
||||
|
||||
|
||||
..
|
||||
|
||||
To gain an understanding of constructor parameters see
|
||||
:func:`kmeans`
|
||||
function
|
||||
arguments.
|
||||
|
||||
|
||||
|
||||
.. index:: BOWImgDescriptorExtractor
|
||||
|
||||
.. _BOWImgDescriptorExtractor:
|
||||
|
||||
BOWImgDescriptorExtractor
|
||||
-------------------------
|
||||
|
||||
`id=0.166378792557 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWImgDescriptorExtractor>`__
|
||||
|
||||
.. ctype:: BOWImgDescriptorExtractor
|
||||
|
||||
|
||||
|
||||
Class to compute image descriptor using ''bad of visual words''. In few,
|
||||
such computing consists from the following steps:
|
||||
1. Compute descriptors for given image and it's keypoints set,
|
||||
\
|
||||
2. Find nearest visual words from vocabulary for each keypoint descriptor,
|
||||
\
|
||||
3. Image descriptor is a normalized histogram of vocabulary words encountered in the image. I.e.
|
||||
|
||||
``i``
|
||||
-bin of the histogram is a frequency of
|
||||
``i``
|
||||
-word of vocabulary in the given image.
|
||||
|
||||
|
||||
|
||||
|
||||
::
|
||||
|
||||
|
||||
|
||||
class BOWImgDescriptorExtractor
|
||||
{
|
||||
public:
|
||||
BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>& dextractor,
|
||||
const Ptr<DescriptorMatcher>& dmatcher );
|
||||
virtual ~BOWImgDescriptorExtractor(){}
|
||||
|
||||
void setVocabulary( const Mat& vocabulary );
|
||||
const Mat& getVocabulary() const;
|
||||
void compute( const Mat& image, vector<KeyPoint>& keypoints,
|
||||
Mat& imgDescriptor,
|
||||
vector<vector<int> >* pointIdxsOfClusters=0,
|
||||
Mat* descriptors=0 );
|
||||
int descriptorSize() const;
|
||||
int descriptorType() const;
|
||||
|
||||
protected:
|
||||
...
|
||||
};
|
||||
|
||||
|
||||
..
|
||||
|
||||
|
||||
.. index:: BOWImgDescriptorExtractor::BOWImgDescriptorExtractor
|
||||
|
||||
|
||||
cv::BOWImgDescriptorExtractor::BOWImgDescriptorExtractor
|
||||
--------------------------------------------------------
|
||||
|
||||
`id=0.355574799377 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWImgDescriptorExtractor%3A%3ABOWImgDescriptorExtractor>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: BOWImgDescriptorExtractor::BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>\& dextractor, const Ptr<DescriptorMatcher>\& dmatcher )
|
||||
|
||||
Constructor.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:param dextractor: Descriptor extractor that will be used to compute descriptors
|
||||
for input image and it's keypoints.
|
||||
|
||||
|
||||
:param dmatcher: Descriptor matcher that will be used to find nearest word of trained vocabulary to
|
||||
each keupoints descriptor of the image.
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: BOWImgDescriptorExtractor::setVocabulary
|
||||
|
||||
|
||||
cv::BOWImgDescriptorExtractor::setVocabulary
|
||||
--------------------------------------------
|
||||
|
||||
`id=0.592484692408 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWImgDescriptorExtractor%3A%3AsetVocabulary>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: void BOWImgDescriptorExtractor::setVocabulary( const Mat\& vocabulary )
|
||||
|
||||
Method to set visual vocabulary.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:param vocabulary: Vocabulary (can be trained using inheritor of :func:`BOWTrainer` ).
|
||||
Each row of vocabulary is a one visual word (cluster center).
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: BOWImgDescriptorExtractor::getVocabulary
|
||||
|
||||
|
||||
cv::BOWImgDescriptorExtractor::getVocabulary
|
||||
--------------------------------------------
|
||||
|
||||
`id=0.0185667539631 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWImgDescriptorExtractor%3A%3AgetVocabulary>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: const Mat\& BOWImgDescriptorExtractor::getVocabulary() const
|
||||
|
||||
Returns set vocabulary.
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: BOWImgDescriptorExtractor::compute
|
||||
|
||||
|
||||
cv::BOWImgDescriptorExtractor::compute
|
||||
--------------------------------------
|
||||
|
||||
`id=0.558308680471 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWImgDescriptorExtractor%3A%3Acompute>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: 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.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
:param image: The image. Image descriptor will be computed for this.
|
||||
|
||||
|
||||
:param keypoints: Keypoints detected in the input image.
|
||||
|
||||
|
||||
:param imgDescriptor: This is output, i.e. computed image descriptor.
|
||||
|
||||
|
||||
:param pointIdxsOfClusters: Indices of keypoints which belong to the cluster, i.e.
|
||||
``pointIdxsOfClusters[i]`` is keypoint indices which belong
|
||||
to the ``i-`` cluster (word of vocabulary) (returned if it is not 0.)
|
||||
|
||||
|
||||
:param descriptors: Descriptors of the image keypoints (returned if it is not 0.)
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: BOWImgDescriptorExtractor::descriptorSize
|
||||
|
||||
|
||||
cv::BOWImgDescriptorExtractor::descriptorSize
|
||||
---------------------------------------------
|
||||
|
||||
`id=0.758326749957 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWImgDescriptorExtractor%3A%3AdescriptorSize>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: int BOWImgDescriptorExtractor::descriptorSize() const
|
||||
|
||||
Returns image discriptor size, if vocabulary was set, and 0 otherwise.
|
||||
|
||||
|
||||
|
||||
|
||||
.. index:: BOWImgDescriptorExtractor::descriptorType
|
||||
|
||||
|
||||
cv::BOWImgDescriptorExtractor::descriptorType
|
||||
---------------------------------------------
|
||||
|
||||
`id=0.940227909801 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWImgDescriptorExtractor%3A%3AdescriptorType>`__
|
||||
|
||||
|
||||
|
||||
|
||||
.. cfunction:: int BOWImgDescriptorExtractor::descriptorType() const
|
||||
|
||||
Returns image descriptor type.
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user