211c112b91
- enable a border_threshold just like for SIFt so that calling ORB, or descriptor after feature gives the same number of features
715 lines
26 KiB
ReStructuredText
715 lines
26 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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.. cpp:class:: MSER
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Maximally 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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.. cpp:class:: 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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.. cpp:class:: 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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.. cpp:class:: 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
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[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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(default option). But the descriptors can be also computed for the user-specified keypoints.
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The algorithm 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:: ORB
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.. _ORB:
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ORB
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----
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.. cpp:class:: ORB
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Class for extracting ORB features and descriptors from an image ::
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class ORB
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{
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public:
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/** The patch sizes that can be used (only one right now) */
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struct CommonParams
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{
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enum { DEFAULT_N_LEVELS = 3, DEFAULT_FIRST_LEVEL = 0};
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/** default constructor */
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CommonParams(float scale_factor = 1.2f, unsigned int n_levels = DEFAULT_N_LEVELS,
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int edge_threshold = 31, unsigned int first_level = DEFAULT_FIRST_LEVEL);
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void read(const FileNode& fn);
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void write(FileStorage& fs) const;
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/** Coefficient by which we divide the dimensions from one scale pyramid level to the next */
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float scale_factor_;
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/** The number of levels in the scale pyramid */
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unsigned int n_levels_;
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/** The level at which the image is given
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* if 1, that means we will also look at the image scale_factor_ times bigger
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*/
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unsigned int first_level_;
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/** How far from the boundary the points should be */
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int edge_threshold_;
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};
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// c:function::default constructor
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ORB();
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// constructor that initializes all the algorithm parameters
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ORB( const CommonParams detector_params );
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// returns the number of elements in each descriptor (32 bytes)
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int descriptorSize() const;
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// detects keypoints using ORB
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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 ORB keypoints and computes the ORB 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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cv::Mat& descriptors,
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bool useProvidedKeypoints=false) const;
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};
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The class implements ORB
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.. index:: RandomizedTree
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.. _RandomizedTree:
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RandomizedTree
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--------------
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.. cpp:class:: 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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:param base_set: Vector of the ``BaseKeypoint`` type. It contains image keypoints used for training.
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:param rng: Random-number generator used for training.
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:param make_patch: Patch generator used for training.
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:param depth: Maximum tree depth.
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:param views: Number of random views of each keypoint neighborhood to generate.
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:param reduced_num_dim: Number of dimensions used in the compressed signature.
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:param num_quant_bits: Number of bits 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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.. c:function:: read(std::istream &is, int num_quant_bits)
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Reads a pre-saved randomized tree from a file or stream.
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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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:param num_quant_bits: Number of bits 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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:param num_quant_bits: Number of bits 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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.. cpp:class:: 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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.. cpp:class:: 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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:param base_set: Vector of the ``BaseKeypoint`` type. It contains image keypoints used for training.
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:param rng: Random-number generator used for training.
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:param make_patch: Patch generator used for training.
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:param num_trees: Number of randomized trees used in ``RTreeClassificator`` .
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:param depth: Maximum tree depth.
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:param views: Number of random views of each keypoint neighborhood to generate.
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:param reduced_num_dim: Number of dimensions used in the compressed signature.
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:param num_quant_bits: Number of bits used for quantization.
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:param print_status: Current status of training printed 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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:param patch: Image patch to calculate the signature for.
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:param sig: 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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Returns a signature for an image patch similarly to ``getSignature`` but uses a threshold for removing all signature elements below the threshold so that the signature is compressed.
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:param patch: Image patch to calculate the signature for.
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:param sig: Output signature (array dimension is ``reduced_num_dim)`` .
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:param thresh: 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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:param tol: Threshold used for counting elements. All elements less than ``tol`` are considered 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 os: 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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:param num_quant_bits: Number of bits used for quantization.
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The example below demonstrates the usage of ``RTreeClassifier`` for matching the features. The features are extracted from the test and train images with SURF. Output is
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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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