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639 lines
24 KiB
ReStructuredText
639 lines
24 KiB
ReStructuredText
Feature Detection and Description
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=================================
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.. highlight:: cpp
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.. index:: FAST
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FAST
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--------
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.. c:function:: void FAST( const Mat& image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSupression=true )
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Detects corners using the FAST algorithm by E. Rosten (*Machine learning for high-speed corner detection*, 2006).
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:param image: Image where keypoints (corners) are detected.
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:param keypoints: Keypoints detected on the image.
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:param threshold: Threshold on difference between intensity of the central pixel and pixels on a circle around this pixel. See the algorithm description below.
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:param nonmaxSupression: If it is true, non-maximum supression is applied to detected corners (keypoints).
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.. index:: MSER
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.. _MSER:
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MSER
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----
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.. c:type:: MSER
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Maximally (or Most??) stable extremal region extractor ::
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class MSER : public CvMSERParams
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{
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public:
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// default constructor
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MSER();
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// constructor that initializes all the algorithm parameters
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MSER( int _delta, int _min_area, int _max_area,
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float _max_variation, float _min_diversity,
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int _max_evolution, double _area_threshold,
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double _min_margin, int _edge_blur_size );
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// runs the extractor on the specified image; returns the MSERs,
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// each encoded as a contour (vector<Point>, see findContours)
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// the optional mask marks the area where MSERs are searched for
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void operator()( const Mat& image, vector<vector<Point> >& msers, const Mat& mask ) const;
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};
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The class encapsulates all the parameters of the MSER extraction algorithm (see
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http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions).
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.. index:: StarDetector
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.. _StarDetector:
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StarDetector
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------------
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.. c:type:: StarDetector
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Class implementing the Star keypoint detector ::
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class StarDetector : CvStarDetectorParams
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{
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public:
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// default constructor
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StarDetector();
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// the full constructor that initializes all the algorithm parameters:
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// maxSize - maximum size of the features. The following
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// values of the parameter are supported:
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// 4, 6, 8, 11, 12, 16, 22, 23, 32, 45, 46, 64, 90, 128
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// responseThreshold - threshold for the approximated laplacian,
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// used to eliminate weak features. The larger it is,
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// the less features will be retrieved
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// lineThresholdProjected - another threshold for the laplacian to
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// eliminate edges
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// lineThresholdBinarized - another threshold for the feature
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// size to eliminate edges.
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// The larger the 2nd threshold, the more points you get.
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StarDetector(int maxSize, int responseThreshold,
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int lineThresholdProjected,
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int lineThresholdBinarized,
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int suppressNonmaxSize);
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// finds keypoints in an image
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void operator()(const Mat& image, vector<KeyPoint>& keypoints) const;
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};
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The class implements a modified version of the CenSurE keypoint detector described in
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Agrawal08??.
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.. index:: SIFT
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.. _SIFT:
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SIFT
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----
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.. c:type:: SIFT
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Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) approach ::
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class CV_EXPORTS SIFT
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{
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public:
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struct CommonParams
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{
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static const int DEFAULT_NOCTAVES = 4;
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static const int DEFAULT_NOCTAVE_LAYERS = 3;
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static const int DEFAULT_FIRST_OCTAVE = -1;
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enum{ FIRST_ANGLE = 0, AVERAGE_ANGLE = 1 };
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CommonParams();
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CommonParams( int _nOctaves, int _nOctaveLayers, int _firstOctave,
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int _angleMode );
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int nOctaves, nOctaveLayers, firstOctave;
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int angleMode;
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};
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struct DetectorParams
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{
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static double GET_DEFAULT_THRESHOLD()
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{ return 0.04 / SIFT::CommonParams::DEFAULT_NOCTAVE_LAYERS / 2.0; }
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static double GET_DEFAULT_EDGE_THRESHOLD() { return 10.0; }
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DetectorParams();
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DetectorParams( double _threshold, double _edgeThreshold );
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double threshold, edgeThreshold;
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};
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struct DescriptorParams
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{
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static double GET_DEFAULT_MAGNIFICATION() { return 3.0; }
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static const bool DEFAULT_IS_NORMALIZE = true;
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static const int DESCRIPTOR_SIZE = 128;
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DescriptorParams();
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DescriptorParams( double _magnification, bool _isNormalize,
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bool _recalculateAngles );
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double magnification;
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bool isNormalize;
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bool recalculateAngles;
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};
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SIFT();
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//! sift-detector constructor
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SIFT( double _threshold, double _edgeThreshold,
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int _nOctaves=CommonParams::DEFAULT_NOCTAVES,
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int _nOctaveLayers=CommonParams::DEFAULT_NOCTAVE_LAYERS,
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int _firstOctave=CommonParams::DEFAULT_FIRST_OCTAVE,
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int _angleMode=CommonParams::FIRST_ANGLE );
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//! sift-descriptor constructor
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SIFT( double _magnification, bool _isNormalize=true,
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bool _recalculateAngles = true,
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int _nOctaves=CommonParams::DEFAULT_NOCTAVES,
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int _nOctaveLayers=CommonParams::DEFAULT_NOCTAVE_LAYERS,
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int _firstOctave=CommonParams::DEFAULT_FIRST_OCTAVE,
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int _angleMode=CommonParams::FIRST_ANGLE );
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SIFT( const CommonParams& _commParams,
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const DetectorParams& _detectorParams = DetectorParams(),
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const DescriptorParams& _descriptorParams = DescriptorParams() );
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//! returns the descriptor size in floats (128)
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int descriptorSize() const { return DescriptorParams::DESCRIPTOR_SIZE; }
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//! finds the keypoints using the SIFT algorithm
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void operator()(const Mat& img, const Mat& mask,
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vector<KeyPoint>& keypoints) const;
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//! finds the keypoints and computes descriptors for them using SIFT algorithm.
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//! Optionally it can compute descriptors for the user-provided keypoints
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void operator()(const Mat& img, const Mat& mask,
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vector<KeyPoint>& keypoints,
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Mat& descriptors,
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bool useProvidedKeypoints=false) const;
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CommonParams getCommonParams () const { return commParams; }
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DetectorParams getDetectorParams () const { return detectorParams; }
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DescriptorParams getDescriptorParams () const { return descriptorParams; }
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protected:
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...
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};
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.. index:: SURF
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.. _SURF:
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SURF
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----
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.. c:type:: SURF
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Class for extracting Speeded Up Robust Features from an image ::
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class SURF : public CvSURFParams
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{
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public:
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// c:function::default constructor
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SURF();
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// constructor that initializes all the algorithm parameters
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SURF(double _hessianThreshold, int _nOctaves=4,
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int _nOctaveLayers=2, bool _extended=false);
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// returns the number of elements in each descriptor (64 or 128)
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int descriptorSize() const;
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// detects keypoints using fast multi-scale Hessian detector
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void operator()(const Mat& img, const Mat& mask,
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vector<KeyPoint>& keypoints) const;
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// detects keypoints and computes the SURF descriptors for them;
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// output vector "descriptors" stores elements of descriptors and has size
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// equal descriptorSize()*keypoints.size() as each descriptor is
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// descriptorSize() elements of this vector.
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void operator()(const Mat& img, const Mat& mask,
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vector<KeyPoint>& keypoints,
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vector<float>& descriptors,
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bool useProvidedKeypoints=false) const;
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};
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The class implements the Speeded Up Robust Features descriptor Bay06.
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There is a fast multi-scale Hessian keypoint detector that can be used to find keypoints
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(which is the default option). But the descriptors can be also computed for the user-specified keypoints.
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The function?? can be used for object tracking and localization, image stitching, and so on. See the ``find_obj.cpp`` demo in OpenCV samples directory.
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.. index:: RandomizedTree
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.. _RandomizedTree:
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RandomizedTree
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--------------
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.. c:type:: RandomizedTree
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Class containing a base structure for ``RTreeClassifier`` ::
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class CV_EXPORTS RandomizedTree
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{
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public:
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friend class RTreeClassifier;
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RandomizedTree();
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~RandomizedTree();
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void train(std::vector<BaseKeypoint> const& base_set,
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RNG &rng, int depth, int views,
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size_t reduced_num_dim, int num_quant_bits);
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void train(std::vector<BaseKeypoint> const& base_set,
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RNG &rng, PatchGenerator &make_patch, int depth,
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int views, size_t reduced_num_dim, int num_quant_bits);
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// next two functions are EXPERIMENTAL
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//(do not use unless you know exactly what you do)
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static void quantizeVector(float *vec, int dim, int N, float bnds[2],
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int clamp_mode=0);
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static void quantizeVector(float *src, int dim, int N, float bnds[2],
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uchar *dst);
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// patch_data must be a 32x32 array (no row padding)
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float* getPosterior(uchar* patch_data);
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const float* getPosterior(uchar* patch_data) const;
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uchar* getPosterior2(uchar* patch_data);
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void read(const char* file_name, int num_quant_bits);
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void read(std::istream &is, int num_quant_bits);
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void write(const char* file_name) const;
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void write(std::ostream &os) const;
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int classes() { return classes_; }
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int depth() { return depth_; }
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void discardFloatPosteriors() { freePosteriors(1); }
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inline void applyQuantization(int num_quant_bits)
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{ makePosteriors2(num_quant_bits); }
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private:
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int classes_;
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int depth_;
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int num_leaves_;
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std::vector<RTreeNode> nodes_;
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float **posteriors_; // 16-byte aligned posteriors
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uchar **posteriors2_; // 16-byte aligned posteriors
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std::vector<int> leaf_counts_;
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void createNodes(int num_nodes, RNG &rng);
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void allocPosteriorsAligned(int num_leaves, int num_classes);
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void freePosteriors(int which);
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// which: 1=posteriors_, 2=posteriors2_, 3=both
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void init(int classes, int depth, RNG &rng);
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void addExample(int class_id, uchar* patch_data);
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void finalize(size_t reduced_num_dim, int num_quant_bits);
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int getIndex(uchar* patch_data) const;
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inline float* getPosteriorByIndex(int index);
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inline uchar* getPosteriorByIndex2(int index);
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inline const float* getPosteriorByIndex(int index) const;
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void convertPosteriorsToChar();
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void makePosteriors2(int num_quant_bits);
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void compressLeaves(size_t reduced_num_dim);
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void estimateQuantPercForPosteriors(float perc[2]);
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};
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.. index:: RandomizedTree::train
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RandomizedTree::train
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-------------------------
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.. c:function:: void train(std::vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits)
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Trains a randomized tree using an input set of keypoints.
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.. c:function:: void train(std::vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits)
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{Vector of ``BaseKeypoint`` type. Contains keypoints from the image that are used for training}??
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{Random numbers generator is used for training}??
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{Patch generator is used for training}??
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{Maximum tree depth}??
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{Number of dimensions are used in compressed signature}??
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{Number of bits are used for quantization}??
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.. index:: RandomizedTree::read
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RandomizedTree::read
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------------------------
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.. c:function:: read(const char* file_name, int num_quant_bits)
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Reads a pre-saved randomized tree from a file or stream. ?? is it applied to the 1st func only?
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.. c:function:: read(std::istream \&is, int num_quant_bits)
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:param file_name: Name of the file that contains randomized tree data.
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:param is: Input stream associated with the file that contains randomized tree data.
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{Number of bits are used for quantization}??
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.. index:: RandomizedTree::write
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RandomizedTree::write
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-------------------------
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.. c:function:: void write(const char* file_name) const
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Writes the current randomized tree to a file or stream.
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.. c:function:: void write(std::ostream \&os) const
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:param file_name: Name of the file where randomized tree data is stored.
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:param is: Output stream associated with the file where randomized tree data is stored.
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.. index:: RandomizedTree::applyQuantization
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RandomizedTree::applyQuantization
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-------------------------------------
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.. c:function:: void applyQuantization(int num_quant_bits)
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Applies quantization to the current randomized tree.
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{Number of bits are used for quantization}??
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.. index:: RTreeNode
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.. _RTreeNode:
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RTreeNode
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---------
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.. c:type:: RTreeNode
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Class containing a base structure for ``RandomizedTree`` ::
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struct RTreeNode
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{
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short offset1, offset2;
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RTreeNode() {}
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RTreeNode(uchar x1, uchar y1, uchar x2, uchar y2)
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: offset1(y1*PATCH_SIZE + x1),
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offset2(y2*PATCH_SIZE + x2)
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{}
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//! Left child on 0, right child on 1
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inline bool operator() (uchar* patch_data) const
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{
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return patch_data[offset1] > patch_data[offset2];
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}
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};
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.. index:: RTreeClassifier
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.. _RTreeClassifier:
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RTreeClassifier
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---------------
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.. c:type:: RTreeClassifier
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Class containing ``RTreeClassifier`` . It represents the Calonder descriptor that was originally introduced by Michael Calonder ::
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class CV_EXPORTS RTreeClassifier
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{
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public:
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static const int DEFAULT_TREES = 48;
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static const size_t DEFAULT_NUM_QUANT_BITS = 4;
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RTreeClassifier();
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void train(std::vector<BaseKeypoint> const& base_set,
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RNG &rng,
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int num_trees = RTreeClassifier::DEFAULT_TREES,
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int depth = DEFAULT_DEPTH,
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int views = DEFAULT_VIEWS,
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size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM,
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int num_quant_bits = DEFAULT_NUM_QUANT_BITS,
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bool print_status = true);
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void train(std::vector<BaseKeypoint> const& base_set,
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RNG &rng,
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PatchGenerator &make_patch,
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int num_trees = RTreeClassifier::DEFAULT_TREES,
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int depth = DEFAULT_DEPTH,
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int views = DEFAULT_VIEWS,
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size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM,
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int num_quant_bits = DEFAULT_NUM_QUANT_BITS,
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bool print_status = true);
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// sig must point to a memory block of at least
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//classes()*sizeof(float|uchar) bytes
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void getSignature(IplImage *patch, uchar *sig);
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void getSignature(IplImage *patch, float *sig);
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void getSparseSignature(IplImage *patch, float *sig,
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float thresh);
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static int countNonZeroElements(float *vec, int n, double tol=1e-10);
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static inline void safeSignatureAlloc(uchar **sig, int num_sig=1,
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int sig_len=176);
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static inline uchar* safeSignatureAlloc(int num_sig=1,
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int sig_len=176);
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inline int classes() { return classes_; }
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inline int original_num_classes()
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{ return original_num_classes_; }
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void setQuantization(int num_quant_bits);
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void discardFloatPosteriors();
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void read(const char* file_name);
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void read(std::istream &is);
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void write(const char* file_name) const;
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void write(std::ostream &os) const;
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std::vector<RandomizedTree> trees_;
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private:
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int classes_;
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int num_quant_bits_;
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uchar **posteriors_;
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ushort *ptemp_;
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int original_num_classes_;
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bool keep_floats_;
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};
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.. index:: RTreeClassifier::train
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RTreeClassifier::train
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--------------------------
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.. c:function:: void train(vector<BaseKeypoint> const& base_set, RNG& rng, int num_trees = RTreeClassifier::DEFAULT_TREES, int depth = DEFAULT_DEPTH, int views = DEFAULT_VIEWS, size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM, int num_quant_bits = DEFAULT_NUM_QUANT_BITS, bool print_status = true)
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Trains a randomized tree classifier using an input set of keypoints.
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.. c:function:: void train(vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int num_trees = RTreeClassifier::DEFAULT_TREES, int depth = DEFAULT_DEPTH, int views = DEFAULT_VIEWS, size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM, int num_quant_bits = DEFAULT_NUM_QUANT_BITS, bool print_status = true)
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{Vector of ``BaseKeypoint`` type. Contains image keypoints used for training}??
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{Random-number generator is used for training}??
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{Patch generator is used for training}??
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{Number of randomized trees used in RTreeClassificator}??
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{Maximum tree depth}??
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{Number of dimensions are used in compressed signature}??
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{Number of bits are used for quantization}??
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{Print current status of training on the console}??
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.. index:: RTreeClassifier::getSignature
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RTreeClassifier::getSignature
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---------------------------------
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.. c:function:: void getSignature(IplImage *patch, uchar *sig)
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Returns a signature for an image patch.
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.. c:function:: void getSignature(IplImage *patch, float *sig)
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{Image patch to calculate signature for}
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{Output signature (array dimension is ``reduced_num_dim)`` }
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.. index:: RTreeClassifier::getSparseSignature
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RTreeClassifier::getSparseSignature
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---------------------------------------
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.. c:function:: void getSparseSignature(IplImage *patch, float *sig, float thresh)
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Similarly to ``getSignaturebut`` , uses a threshold for removing all signature elements below the threshold so that the signature is compressed.??
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{Image patch to calculate signature for}??
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{Output signature (array dimension is ``reduced_num_dim)``}??
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{The threshold that is used for compressing the signature}??
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.. index:: RTreeClassifier::countNonZeroElements
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RTreeClassifier::countNonZeroElements
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-----------------------------------------
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.. c:function:: static int countNonZeroElements(float *vec, int n, double tol=1e-10)
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Returns the number of non-zero elements in an input array.
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:param vec: Input vector containing float elements.
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:param n: Input vector size.
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{The threshold used for counting elements. We take all elements are less than ``tol`` as zero elements}??
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.. index:: RTreeClassifier::read
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RTreeClassifier::read
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-------------------------
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.. c:function:: read(const char* file_name)
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Reads a pre-saved ``RTreeClassifier`` from a file or stream.
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.. c:function:: read(std::istream& is)
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:param file_name: Name of the file that contains randomized tree data.
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:param is: Input stream associated with the file that contains randomized tree data.
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.. index:: RTreeClassifier::write
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RTreeClassifier::write
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--------------------------
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.. c:function:: void write(const char* file_name) const
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Writes the current RTreeClassifier to a file or stream.
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.. c:function:: void write(std::ostream \&os) const
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:param file_name: Name of the file where randomized tree data is stored.
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:param is: Output stream associated with the file where randomized tree data is stored.
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.. index:: RTreeClassifier::setQuantization
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RTreeClassifier::setQuantization
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------------------------------------
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.. c:function:: void setQuantization(int num_quant_bits)
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Applies quantization to the current randomized tree.
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{Number of bits are used for quantization}??
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The example below demonstrates the usage of ``RTreeClassifier`` for feature matching. There are test and train images and features are extracted from both with SURF. Output is
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:math:`best\_corr` and
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:math:`best\_corr\_idx` arrays that keep the best probabilities and corresponding features indices for every train feature. ::
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CvMemStorage* storage = cvCreateMemStorage(0);
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CvSeq *objectKeypoints = 0, *objectDescriptors = 0;
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CvSeq *imageKeypoints = 0, *imageDescriptors = 0;
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CvSURFParams params = cvSURFParams(500, 1);
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cvExtractSURF( test_image, 0, &imageKeypoints, &imageDescriptors,
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storage, params );
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cvExtractSURF( train_image, 0, &objectKeypoints, &objectDescriptors,
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storage, params );
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RTreeClassifier detector;
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int patch_width = PATCH_SIZE;
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iint patch_height = PATCH_SIZE;
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vector<BaseKeypoint> base_set;
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int i=0;
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CvSURFPoint* point;
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for (i=0;i<(n_points > 0 ? n_points : objectKeypoints->total);i++)
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{
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point=(CvSURFPoint*)cvGetSeqElem(objectKeypoints,i);
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base_set.push_back(
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BaseKeypoint(point->pt.x,point->pt.y,train_image));
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}
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//Detector training
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RNG rng( cvGetTickCount() );
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PatchGenerator gen(0,255,2,false,0.7,1.3,-CV_PI/3,CV_PI/3,
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-CV_PI/3,CV_PI/3);
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printf("RTree Classifier training...n");
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detector.train(base_set,rng,gen,24,DEFAULT_DEPTH,2000,
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(int)base_set.size(), detector.DEFAULT_NUM_QUANT_BITS);
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printf("Donen");
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float* signature = new float[detector.original_num_classes()];
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float* best_corr;
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int* best_corr_idx;
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if (imageKeypoints->total > 0)
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{
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best_corr = new float[imageKeypoints->total];
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best_corr_idx = new int[imageKeypoints->total];
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}
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for(i=0; i < imageKeypoints->total; i++)
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{
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point=(CvSURFPoint*)cvGetSeqElem(imageKeypoints,i);
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int part_idx = -1;
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float prob = 0.0f;
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CvRect roi = cvRect((int)(point->pt.x) - patch_width/2,
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(int)(point->pt.y) - patch_height/2,
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patch_width, patch_height);
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cvSetImageROI(test_image, roi);
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roi = cvGetImageROI(test_image);
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if(roi.width != patch_width || roi.height != patch_height)
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{
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best_corr_idx[i] = part_idx;
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best_corr[i] = prob;
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}
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else
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{
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cvSetImageROI(test_image, roi);
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IplImage* roi_image =
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cvCreateImage(cvSize(roi.width, roi.height),
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test_image->depth, test_image->nChannels);
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cvCopy(test_image,roi_image);
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detector.getSignature(roi_image, signature);
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for (int j = 0; j< detector.original_num_classes();j++)
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{
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if (prob < signature[j])
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{
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part_idx = j;
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prob = signature[j];
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}
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}
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best_corr_idx[i] = part_idx;
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best_corr[i] = prob;
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if (roi_image)
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cvReleaseImage(&roi_image);
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}
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cvResetImageROI(test_image);
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}
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..
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