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579 lines
22 KiB
ReStructuredText
579 lines
22 KiB
ReStructuredText
Feature Detection and Description
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=================================
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.. highlight:: 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( const Mat& image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSupression=true )
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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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Detects corners using the FAST algorithm by 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
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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://opencv.willowgarage.com/wiki/documentation/cpp/features2d/MSER for usefull comments and parameters description.
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StarDetector
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------------
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.. ocv:class:: StarDetector
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Class implementing the ``Star`` keypoint detector, a modified version of the ``CenSurE`` keypoint detector described in [Agrawal08]_.
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.. [Agrawal08] Agrawal, M. and Konolige, K. and Blas, M.R. "CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching", ECCV08, 2008
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StarDetector::StarDetector
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--------------------------
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The Star Detector constructor
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.. ocv:function:: StarDetector::StarDetector()
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.. ocv:function:: StarDetector::StarDetector(int maxSize, int responseThreshold, int lineThresholdProjected, int lineThresholdBinarized, int suppressNonmaxSize)
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.. ocv:pyfunction:: cv2.StarDetector(maxSize, responseThreshold, lineThresholdProjected, lineThresholdBinarized, suppressNonmaxSize) -> <StarDetector object>
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:param maxSize: maximum size of the features. The following values are supported: 4, 6, 8, 11, 12, 16, 22, 23, 32, 45, 46, 64, 90, 128. In the case of a different value the result is undefined.
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:param responseThreshold: threshold for the approximated laplacian, used to eliminate weak features. The larger it is, the less features will be retrieved
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:param lineThresholdProjected: another threshold for the laplacian to eliminate edges
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:param lineThresholdBinarized: yet another threshold for the feature size to eliminate edges. The larger the 2nd threshold, the more points you get.
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StarDetector::operator()
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------------------------
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Finds keypoints in an image
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.. ocv:function:: void StarDetector::operator()(const Mat& image, vector<KeyPoint>& keypoints)
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.. ocv:pyfunction:: cv2.StarDetector.detect(image) -> keypoints
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.. ocv:cfunction:: CvSeq* cvGetStarKeypoints( const CvArr* image, CvMemStorage* storage, CvStarDetectorParams params=cvStarDetectorParams() )
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.. ocv:pyoldfunction:: cv.GetStarKeypoints(image, storage, params)-> keypoints
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:param image: The input 8-bit grayscale image
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:param keypoints: The output vector of keypoints
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:param storage: The memory storage used to store the keypoints (OpenCV 1.x API only)
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:param params: The algorithm parameters stored in ``CvStarDetectorParams`` (OpenCV 1.x API only)
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ORB
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----
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.. ocv: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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// constructor that initializes all the algorithm parameters
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// n_features is the number of desired features
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ORB(size_t n_features = 500, const CommonParams & detector_params = CommonParams());
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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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RandomizedTree
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--------------
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.. ocv: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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RandomizedTree::train
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-------------------------
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Trains a randomized tree using an input set of keypoints.
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.. ocv: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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.. ocv: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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RandomizedTree::read
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------------------------
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Reads a pre-saved randomized tree from a file or stream.
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.. ocv:function:: read(const char* file_name, int num_quant_bits)
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.. ocv: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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:param num_quant_bits: Number of bits used for quantization.
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RandomizedTree::write
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-------------------------
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Writes the current randomized tree to a file or stream.
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.. ocv:function:: void write(const char* file_name) const
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.. ocv: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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RandomizedTree::applyQuantization
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-------------------------------------
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.. ocv: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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RTreeNode
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---------
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.. ocv: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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RTreeClassifier
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---------------
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.. ocv:class:: RTreeClassifier
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Class containing ``RTreeClassifier``. It represents the Calonder descriptor 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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RTreeClassifier::train
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--------------------------
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Trains a randomized tree classifier using an input set of keypoints.
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.. ocv: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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.. ocv: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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RTreeClassifier::getSignature
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---------------------------------
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Returns a signature for an image patch.
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.. ocv:function:: void getSignature(IplImage *patch, uchar *sig)
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.. ocv: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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RTreeClassifier::getSparseSignature
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---------------------------------------
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Returns a sparse signature for an image patch
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.. ocv:function:: void getSparseSignature(IplImage *patch, float *sig, float thresh)
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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 used for compressing the signature.
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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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RTreeClassifier::countNonZeroElements
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-----------------------------------------
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Returns the number of non-zero elements in an input array.
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.. ocv:function:: static int countNonZeroElements(float *vec, int n, double tol=1e-10)
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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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RTreeClassifier::read
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-------------------------
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Reads a pre-saved ``RTreeClassifier`` from a file or stream.
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.. ocv:function:: read(const char* file_name)
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.. ocv: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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RTreeClassifier::write
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--------------------------
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Writes the current ``RTreeClassifier`` to a file or stream.
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.. ocv:function:: void write(const char* file_name) const
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.. ocv: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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RTreeClassifier::setQuantization
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------------------------------------
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Applies quantization to the current randomized tree.
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.. ocv:function:: void setQuantization(int num_quant_bits)
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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++)
|
|
{
|
|
point=(CvSURFPoint*)cvGetSeqElem(objectKeypoints,i);
|
|
base_set.push_back(
|
|
BaseKeypoint(point->pt.x,point->pt.y,train_image));
|
|
}
|
|
|
|
//Detector training
|
|
RNG rng( cvGetTickCount() );
|
|
PatchGenerator gen(0,255,2,false,0.7,1.3,-CV_PI/3,CV_PI/3,
|
|
-CV_PI/3,CV_PI/3);
|
|
|
|
printf("RTree Classifier training...n");
|
|
detector.train(base_set,rng,gen,24,DEFAULT_DEPTH,2000,
|
|
(int)base_set.size(), detector.DEFAULT_NUM_QUANT_BITS);
|
|
printf("Donen");
|
|
|
|
float* signature = new float[detector.original_num_classes()];
|
|
float* best_corr;
|
|
int* best_corr_idx;
|
|
if (imageKeypoints->total > 0)
|
|
{
|
|
best_corr = new float[imageKeypoints->total];
|
|
best_corr_idx = new int[imageKeypoints->total];
|
|
}
|
|
|
|
for(i=0; i < imageKeypoints->total; i++)
|
|
{
|
|
point=(CvSURFPoint*)cvGetSeqElem(imageKeypoints,i);
|
|
int part_idx = -1;
|
|
float prob = 0.0f;
|
|
|
|
CvRect roi = cvRect((int)(point->pt.x) - patch_width/2,
|
|
(int)(point->pt.y) - patch_height/2,
|
|
patch_width, patch_height);
|
|
cvSetImageROI(test_image, roi);
|
|
roi = cvGetImageROI(test_image);
|
|
if(roi.width != patch_width || roi.height != patch_height)
|
|
{
|
|
best_corr_idx[i] = part_idx;
|
|
best_corr[i] = prob;
|
|
}
|
|
else
|
|
{
|
|
cvSetImageROI(test_image, roi);
|
|
IplImage* roi_image =
|
|
cvCreateImage(cvSize(roi.width, roi.height),
|
|
test_image->depth, test_image->nChannels);
|
|
cvCopy(test_image,roi_image);
|
|
|
|
detector.getSignature(roi_image, signature);
|
|
for (int j = 0; j< detector.original_num_classes();j++)
|
|
{
|
|
if (prob < signature[j])
|
|
{
|
|
part_idx = j;
|
|
prob = signature[j];
|
|
}
|
|
}
|
|
|
|
best_corr_idx[i] = part_idx;
|
|
best_corr[i] = prob;
|
|
|
|
if (roi_image)
|
|
cvReleaseImage(&roi_image);
|
|
}
|
|
cvResetImageROI(test_image);
|
|
}
|
|
|
|
..
|