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252 lines
12 KiB
ReStructuredText
252 lines
12 KiB
ReStructuredText
Feature Detection and Description
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=================================
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.. highlight:: cpp
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.. note::
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* An example explaining keypoint detection and description can be found at opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp
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FAST
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----
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Detects corners using the FAST algorithm
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.. ocv:function:: void FAST( InputArray image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSuppression=true )
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.. ocv:function:: void FAST( InputArray image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSuppression, int type )
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.. ocv:pyfunction:: cv2.FastFeatureDetector([, threshold[, nonmaxSuppression]]) -> <FastFeatureDetector object>
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.. ocv:pyfunction:: cv2.FastFeatureDetector(threshold, nonmaxSuppression, type) -> <FastFeatureDetector object>
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.. ocv:pyfunction:: cv2.FastFeatureDetector.detect(image[, mask]) -> keypoints
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:param image: grayscale 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 of a circle around this pixel.
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:param nonmaxSuppression: if true, non-maximum suppression is applied to detected corners (keypoints).
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:param type: one of the three neighborhoods as defined in the paper: ``FastFeatureDetector::TYPE_9_16``, ``FastFeatureDetector::TYPE_7_12``, ``FastFeatureDetector::TYPE_5_8``
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Detects corners using the FAST algorithm by [Rosten06]_.
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.. note:: In Python API, types are given as ``cv2.FAST_FEATURE_DETECTOR_TYPE_5_8``, ``cv2.FAST_FEATURE_DETECTOR_TYPE_7_12`` and ``cv2.FAST_FEATURE_DETECTOR_TYPE_9_16``. For corner detection, use ``cv2.FAST.detect()`` method.
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.. [Rosten06] E. Rosten. Machine Learning for High-speed Corner Detection, 2006.
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MSER
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----
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.. ocv:class:: MSER : public FeatureDetector
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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). Also see http://code.opencv.org/projects/opencv/wiki/MSER for useful comments and parameters description.
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.. note::
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* (Python) A complete example showing the use of the MSER detector can be found at opencv_source_code/samples/python2/mser.py
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ORB
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---
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.. ocv:class:: ORB : public Feature2D
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Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor, described in [RRKB11]_. The algorithm uses FAST in pyramids to detect stable keypoints, selects the strongest features using FAST or Harris response, finds their orientation using first-order moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or k-tuples) are rotated according to the measured orientation).
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.. [RRKB11] Ethan Rublee, Vincent Rabaud, Kurt Konolige, Gary R. Bradski: ORB: An efficient alternative to SIFT or SURF. ICCV 2011: 2564-2571.
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ORB::ORB
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--------
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The ORB constructor
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.. ocv:function:: ORB::ORB(int nfeatures = 500, float scaleFactor = 1.2f, int nlevels = 8, int edgeThreshold = 31, int firstLevel = 0, int WTA_K=2, int scoreType=ORB::HARRIS_SCORE, int patchSize=31)
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.. ocv:pyfunction:: cv2.ORB([, nfeatures[, scaleFactor[, nlevels[, edgeThreshold[, firstLevel[, WTA_K[, scoreType[, patchSize]]]]]]]]) -> <ORB object>
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:param nfeatures: The maximum number of features to retain.
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:param scaleFactor: Pyramid decimation ratio, greater than 1. ``scaleFactor==2`` means the classical pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor will mean that to cover certain scale range you will need more pyramid levels and so the speed will suffer.
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:param nlevels: The number of pyramid levels. The smallest level will have linear size equal to ``input_image_linear_size/pow(scaleFactor, nlevels)``.
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:param edgeThreshold: This is size of the border where the features are not detected. It should roughly match the ``patchSize`` parameter.
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:param firstLevel: It should be 0 in the current implementation.
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:param WTA_K: The number of points that produce each element of the oriented BRIEF descriptor. The default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 random points (of course, those point coordinates are random, but they are generated from the pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, denoted as ``NORM_HAMMING2`` (2 bits per bin). When ``WTA_K=4``, we take 4 random points to compute each bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
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:param scoreType: The default HARRIS_SCORE means that Harris algorithm is used to rank features (the score is written to ``KeyPoint::score`` and is used to retain best ``nfeatures`` features); FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, but it is a little faster to compute.
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:param patchSize: size of the patch used by the oriented BRIEF descriptor. Of course, on smaller pyramid layers the perceived image area covered by a feature will be larger.
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ORB::operator()
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---------------
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Finds keypoints in an image and computes their descriptors
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.. ocv:function:: void ORB::operator()(InputArray image, InputArray mask, vector<KeyPoint>& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false ) const
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.. ocv:pyfunction:: cv2.ORB.detect(image[, mask]) -> keypoints
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.. ocv:pyfunction:: cv2.ORB.compute(image, keypoints[, descriptors]) -> keypoints, descriptors
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.. ocv:pyfunction:: cv2.ORB.detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) -> keypoints, descriptors
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:param image: The input 8-bit grayscale image.
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:param mask: The operation mask.
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:param keypoints: The output vector of keypoints.
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:param descriptors: The output descriptors. Pass ``cv::noArray()`` if you do not need it.
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:param useProvidedKeypoints: If it is true, then the method will use the provided vector of keypoints instead of detecting them.
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BRISK
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-----
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.. ocv:class:: BRISK : public Feature2D
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Class implementing the BRISK keypoint detector and descriptor extractor, described in [LCS11]_.
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.. [LCS11] Stefan Leutenegger, Margarita Chli and Roland Siegwart: BRISK: Binary Robust Invariant Scalable Keypoints. ICCV 2011: 2548-2555.
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BRISK::BRISK
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------------
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The BRISK constructor
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.. ocv:function:: BRISK::BRISK(int thresh=30, int octaves=3, float patternScale=1.0f)
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.. ocv:pyfunction:: cv2.BRISK([, thresh[, octaves[, patternScale]]]) -> <BRISK object>
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:param thresh: FAST/AGAST detection threshold score.
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:param octaves: detection octaves. Use 0 to do single scale.
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:param patternScale: apply this scale to the pattern used for sampling the neighbourhood of a keypoint.
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BRISK::BRISK
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------------
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The BRISK constructor for a custom pattern
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.. ocv:function:: BRISK::BRISK(std::vector<float> &radiusList, std::vector<int> &numberList, float dMax=5.85f, float dMin=8.2f, std::vector<int> indexChange=std::vector<int>())
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.. ocv:pyfunction:: cv2.BRISK(radiusList, numberList[, dMax[, dMin[, indexChange]]]) -> <BRISK object>
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:param radiusList: defines the radii (in pixels) where the samples around a keypoint are taken (for keypoint scale 1).
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:param numberList: defines the number of sampling points on the sampling circle. Must be the same size as radiusList..
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:param dMax: threshold for the short pairings used for descriptor formation (in pixels for keypoint scale 1).
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:param dMin: threshold for the long pairings used for orientation determination (in pixels for keypoint scale 1).
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:param indexChanges: index remapping of the bits.
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BRISK::operator()
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-----------------
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Finds keypoints in an image and computes their descriptors
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.. ocv:function:: void BRISK::operator()(InputArray image, InputArray mask, vector<KeyPoint>& keypoints, OutputArray descriptors, bool useProvidedKeypoints=false ) const
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.. ocv:pyfunction:: cv2.BRISK.detect(image[, mask]) -> keypoints
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.. ocv:pyfunction:: cv2.BRISK.compute(image, keypoints[, descriptors]) -> keypoints, descriptors
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.. ocv:pyfunction:: cv2.BRISK.detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) -> keypoints, descriptors
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:param image: The input 8-bit grayscale image.
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:param mask: The operation mask.
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:param keypoints: The output vector of keypoints.
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:param descriptors: The output descriptors. Pass ``cv::noArray()`` if you do not need it.
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:param useProvidedKeypoints: If it is true, then the method will use the provided vector of keypoints instead of detecting them.
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KAZE
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----
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.. ocv:class:: KAZE : public Feature2D
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Class implementing the KAZE keypoint detector and descriptor extractor, described in [ABD12]_. ::
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class CV_EXPORTS_W KAZE : public Feature2D
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{
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public:
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CV_WRAP KAZE();
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CV_WRAP explicit KAZE(bool extended, bool upright, float threshold = 0.001f,
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int octaves = 4, int sublevels = 4, int diffusivity = DIFF_PM_G2);
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};
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.. note:: AKAZE descriptor can only be used with KAZE or AKAZE keypoints
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.. [ABD12] KAZE Features. Pablo F. Alcantarilla, Adrien Bartoli and Andrew J. Davison. In European Conference on Computer Vision (ECCV), Fiorenze, Italy, October 2012.
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KAZE::KAZE
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----------
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The KAZE constructor
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.. ocv:function:: KAZE::KAZE(bool extended, bool upright, float threshold, int octaves, int sublevels, int diffusivity)
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:param extended: Set to enable extraction of extended (128-byte) descriptor.
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:param upright: Set to enable use of upright descriptors (non rotation-invariant).
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:param threshold: Detector response threshold to accept point
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:param octaves: Maximum octave evolution of the image
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:param sublevels: Default number of sublevels per scale level
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:param diffusivity: Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or DIFF_CHARBONNIER
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AKAZE
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-----
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.. ocv:class:: AKAZE : public Feature2D
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Class implementing the AKAZE keypoint detector and descriptor extractor, described in [ANB13]_. ::
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class CV_EXPORTS_W AKAZE : public Feature2D
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{
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public:
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CV_WRAP AKAZE();
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CV_WRAP explicit AKAZE(int descriptor_type, int descriptor_size = 0, int descriptor_channels = 3,
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float threshold = 0.001f, int octaves = 4, int sublevels = 4, int diffusivity = DIFF_PM_G2);
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};
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.. note:: AKAZE descriptor can only be used with KAZE or AKAZE keypoints
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.. [ANB13] Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces. Pablo F. Alcantarilla, Jesús Nuevo and Adrien Bartoli. In British Machine Vision Conference (BMVC), Bristol, UK, September 2013.
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AKAZE::AKAZE
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------------
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The AKAZE constructor
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.. ocv:function:: AKAZE::AKAZE(int descriptor_type, int descriptor_size, int descriptor_channels, float threshold, int octaves, int sublevels, int diffusivity)
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:param descriptor_type: Type of the extracted descriptor: DESCRIPTOR_KAZE, DESCRIPTOR_KAZE_UPRIGHT, DESCRIPTOR_MLDB or DESCRIPTOR_MLDB_UPRIGHT.
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:param descriptor_size: Size of the descriptor in bits. 0 -> Full size
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:param descriptor_channels: Number of channels in the descriptor (1, 2, 3)
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:param threshold: Detector response threshold to accept point
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:param octaves: Maximum octave evolution of the image
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:param sublevels: Default number of sublevels per scale level
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:param diffusivity: Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or DIFF_CHARBONNIER
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SIFT
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----
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.. ocv:class:: SIFT : public Feature2D
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The SIFT algorithm has been moved to opencv_contrib/xfeatures2d module. |