174 lines
7.7 KiB
ReStructuredText
174 lines
7.7 KiB
ReStructuredText
Feature Detection and Description
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=================================
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SIFT
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----
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.. ocv: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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SURF
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----
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.. ocv:class:: SURF
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Class for extracting Speeded Up Robust Features from an image [Bay06]_. The class is derived from ``CvSURFParams`` structure, which specifies the algorithm parameters:
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.. ocv:member:: int extended
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* 0 means that the basic descriptors (64 elements each) shall be computed
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* 1 means that the extended descriptors (128 elements each) shall be computed
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.. ocv:member:: int upright
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* 0 means that detector computes orientation of each feature.
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* 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``.
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.. ocv:member:: double hessianThreshold
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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.
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.. ocv:member:: int nOctaves
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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.
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.. ocv:member:: int nOctaveLayers
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The number of images within each octave of a gaussian pyramid. It is set to 2 by default.
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.. [Bay06] Bay, H. and Tuytelaars, T. and Van Gool, L. "SURF: Speeded Up Robust Features", 9th European Conference on Computer Vision, 2006
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SURF::SURF
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----------
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The SURF extractor constructors.
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.. ocv:function:: SURF::SURF()
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.. ocv:function:: SURF::SURF(double hessianThreshold, int nOctaves=4, int nOctaveLayers=2, bool extended=false, bool upright=false)
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.. ocv:pyfunction:: cv2.SURF(_hessianThreshold[, _nOctaves[, _nOctaveLayers[, _extended[, _upright]]]]) -> <SURF object>
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:param hessianThreshold: Threshold for hessian keypoint detector used in SURF.
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:param nOctaves: Number of pyramid octaves the keypoint detector will use.
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:param nOctaveLayers: Number of octave layers within each octave.
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:param extended: Extended descriptor flag (true - use extended 128-element descriptors; false - use 64-element descriptors).
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:param upright: Up-right or rotated features flag (true - do not compute orientation of features; false - compute orientation).
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SURF::operator()
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----------------
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Detects keypoints and computes SURF descriptors for them.
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.. ocv:function:: void SURF::operator()(const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints)
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.. ocv:function:: void SURF::operator()(const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints, vector<float>& descriptors, bool useProvidedKeypoints=false)
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.. ocv:pyfunction:: cv2.SURF.detect(img, mask) -> keypoints
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.. ocv:pyfunction:: cv2.SURF.detect(img, mask[, useProvidedKeypoints]) -> keypoints, descriptors
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.. ocv:cfunction:: void cvExtractSURF( const CvArr* image, const CvArr* mask, CvSeq** keypoints, CvSeq** descriptors, CvMemStorage* storage, CvSURFParams params )
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.. ocv:pyoldfunction:: cv.ExtractSURF(image, mask, storage, params)-> (keypoints, descriptors)
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:param image: Input 8-bit grayscale image
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:param mask: Optional input mask that marks the regions where we should detect features.
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:param keypoints: The input/output vector of keypoints
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: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()``.
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: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.
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:param storage: Memory storage for the output keypoints and descriptors in OpenCV 1.x API.
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:param params: SURF algorithm parameters in OpenCV 1.x API.
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The function is parallelized with the TBB library.
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