Feature Detection and Description ================================= SIFT ---- .. ocv:class:: SIFT Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) approach. :: class CV_EXPORTS SIFT { public: struct CommonParams { static const int DEFAULT_NOCTAVES = 4; static const int DEFAULT_NOCTAVE_LAYERS = 3; static const int DEFAULT_FIRST_OCTAVE = -1; enum{ FIRST_ANGLE = 0, AVERAGE_ANGLE = 1 }; CommonParams(); CommonParams( int _nOctaves, int _nOctaveLayers, int _firstOctave, int _angleMode ); int nOctaves, nOctaveLayers, firstOctave; int angleMode; }; struct DetectorParams { static double GET_DEFAULT_THRESHOLD() { return 0.04 / SIFT::CommonParams::DEFAULT_NOCTAVE_LAYERS / 2.0; } static double GET_DEFAULT_EDGE_THRESHOLD() { return 10.0; } DetectorParams(); DetectorParams( double _threshold, double _edgeThreshold ); double threshold, edgeThreshold; }; struct DescriptorParams { static double GET_DEFAULT_MAGNIFICATION() { return 3.0; } static const bool DEFAULT_IS_NORMALIZE = true; static const int DESCRIPTOR_SIZE = 128; DescriptorParams(); DescriptorParams( double _magnification, bool _isNormalize, bool _recalculateAngles ); double magnification; bool isNormalize; bool recalculateAngles; }; SIFT(); //! sift-detector constructor SIFT( double _threshold, double _edgeThreshold, int _nOctaves=CommonParams::DEFAULT_NOCTAVES, int _nOctaveLayers=CommonParams::DEFAULT_NOCTAVE_LAYERS, int _firstOctave=CommonParams::DEFAULT_FIRST_OCTAVE, int _angleMode=CommonParams::FIRST_ANGLE ); //! sift-descriptor constructor SIFT( double _magnification, bool _isNormalize=true, bool _recalculateAngles = true, int _nOctaves=CommonParams::DEFAULT_NOCTAVES, int _nOctaveLayers=CommonParams::DEFAULT_NOCTAVE_LAYERS, int _firstOctave=CommonParams::DEFAULT_FIRST_OCTAVE, int _angleMode=CommonParams::FIRST_ANGLE ); SIFT( const CommonParams& _commParams, const DetectorParams& _detectorParams = DetectorParams(), const DescriptorParams& _descriptorParams = DescriptorParams() ); //! returns the descriptor size in floats (128) int descriptorSize() const { return DescriptorParams::DESCRIPTOR_SIZE; } //! finds the keypoints using the SIFT algorithm void operator()(const Mat& img, const Mat& mask, vector& keypoints) const; //! finds the keypoints and computes descriptors for them using SIFT algorithm. //! Optionally it can compute descriptors for the user-provided keypoints void operator()(const Mat& img, const Mat& mask, vector& keypoints, Mat& descriptors, bool useProvidedKeypoints=false) const; CommonParams getCommonParams () const { return commParams; } DetectorParams getDetectorParams () const { return detectorParams; } DescriptorParams getDescriptorParams () const { return descriptorParams; } protected: ... }; SURF ---- .. ocv:class:: SURF Class for extracting Speeded Up Robust Features from an image [Bay06]_. The class is derived from ``CvSURFParams`` structure, which specifies the algorithm parameters: .. ocv:member:: int extended * 0 means that the basic descriptors (64 elements each) shall be computed * 1 means that the extended descriptors (128 elements each) shall be computed .. ocv:member:: int upright * 0 means that detector computes orientation of each feature. * 1 means that the orientation is not computed (which is much, much faster). For example, if you match images from a stereo pair, or do image stitching, the matched features likely have very similar angles, and you can speed up feature extraction by setting ``upright=1``. .. ocv:member:: double hessianThreshold Threshold for the keypoint detector. Only features, whose hessian is larger than ``hessianThreshold`` are retained by the detector. Therefore, the larger the value, the less keypoints you will get. A good default value could be from 300 to 500, depending from the image contrast. .. ocv:member:: int nOctaves The number of a gaussian pyramid octaves that the detector uses. It is set to 4 by default. If you want to get very large features, use the larger value. If you want just small features, decrease it. .. ocv:member:: int nOctaveLayers The number of images within each octave of a gaussian pyramid. It is set to 2 by default. .. [Bay06] Bay, H. and Tuytelaars, T. and Van Gool, L. "SURF: Speeded Up Robust Features", 9th European Conference on Computer Vision, 2006 SURF::SURF ---------- The SURF extractor constructors. .. ocv:function:: SURF::SURF() .. ocv:function:: SURF::SURF(double hessianThreshold, int nOctaves=4, int nOctaveLayers=2, bool extended=false, bool upright=false) .. ocv:pyfunction:: cv2.SURF(_hessianThreshold[, _nOctaves[, _nOctaveLayers[, _extended[, _upright]]]]) -> :param hessianThreshold: Threshold for hessian keypoint detector used in SURF. :param nOctaves: Number of pyramid octaves the keypoint detector will use. :param nOctaveLayers: Number of octave layers within each octave. :param extended: Extended descriptor flag (true - use extended 128-element descriptors; false - use 64-element descriptors). :param upright: Up-right or rotated features flag (true - do not compute orientation of features; false - compute orientation). SURF::operator() ---------------- Detects keypoints and computes SURF descriptors for them. .. ocv:function:: void SURF::operator()(const Mat& image, const Mat& mask, vector& keypoints) .. ocv:function:: void SURF::operator()(const Mat& image, const Mat& mask, vector& keypoints, vector& descriptors, bool useProvidedKeypoints=false) .. ocv:pyfunction:: cv2.SURF.detect(img, mask) -> keypoints .. ocv:pyfunction:: cv2.SURF.detect(img, mask[, useProvidedKeypoints]) -> keypoints, descriptors .. ocv:cfunction:: void cvExtractSURF( const CvArr* image, const CvArr* mask, CvSeq** keypoints, CvSeq** descriptors, CvMemStorage* storage, CvSURFParams params ) .. ocv:pyoldfunction:: cv.ExtractSURF(image, mask, storage, params)-> (keypoints, descriptors) :param image: Input 8-bit grayscale image :param mask: Optional input mask that marks the regions where we should detect features. :param keypoints: The input/output vector of keypoints :param descriptors: The output concatenated vectors of descriptors. Each descriptor is 64- or 128-element vector, as returned by ``SURF::descriptorSize()``. So the total size of ``descriptors`` will be ``keypoints.size()*descriptorSize()``. :param useProvidedKeypoints: Boolean flag. If it is true, the keypoint detector is not run. Instead, the provided vector of keypoints is used and the algorithm just computes their descriptors. :param storage: Memory storage for the output keypoints and descriptors in OpenCV 1.x API. :param params: SURF algorithm parameters in OpenCV 1.x API. The function is parallelized with the TBB library.