Feature Detection and Description ================================= .. highlight:: cpp FAST -------- Detects corners using the FAST algorithm .. ocv:function:: void FAST( const Mat& image, vector& keypoints, int threshold, bool nonmaxSupression=true ) :param image: Image where keypoints (corners) are detected. :param keypoints: Keypoints detected on the image. :param threshold: Threshold on difference between intensity of the central pixel and pixels on a circle around this pixel. See the algorithm description below. :param nonmaxSupression: If it is true, non-maximum supression is applied to detected corners (keypoints). Detects corners using the FAST algorithm by E. Rosten (*Machine Learning for High-speed Corner Detection*, 2006). MSER ---- .. ocv:class:: MSER Maximally stable extremal region extractor. :: class MSER : public CvMSERParams { public: // default constructor MSER(); // constructor that initializes all the algorithm parameters MSER( int _delta, int _min_area, int _max_area, float _max_variation, float _min_diversity, int _max_evolution, double _area_threshold, double _min_margin, int _edge_blur_size ); // runs the extractor on the specified image; returns the MSERs, // each encoded as a contour (vector, see findContours) // the optional mask marks the area where MSERs are searched for void operator()( const Mat& image, vector >& msers, const Mat& mask ) const; }; The class encapsulates all the parameters of the MSER extraction algorithm (see 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. StarDetector ------------ .. ocv:class:: StarDetector Class implementing the ``Star`` keypoint detector, a modified version of the ``CenSurE`` keypoint detector described in [Agrawal08]_. .. [Agrawal08] Agrawal, M. and Konolige, K. and Blas, M.R. "CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching", ECCV08, 2008 StarDetector::StarDetector -------------------------- The Star Detector constructor .. ocv:function:: StarDetector::StarDetector() .. ocv:function:: StarDetector::StarDetector(int maxSize, int responseThreshold, int lineThresholdProjected, int lineThresholdBinarized, int suppressNonmaxSize) .. ocv:pyfunction:: cv2.StarDetector(maxSize, responseThreshold, lineThresholdProjected, lineThresholdBinarized, suppressNonmaxSize) -> :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. :param responseThreshold: threshold for the approximated laplacian, used to eliminate weak features. The larger it is, the less features will be retrieved :param lineThresholdProjected: another threshold for the laplacian to eliminate edges :param lineThresholdBinarized: yet another threshold for the feature size to eliminate edges. The larger the 2nd threshold, the more points you get. StarDetector::operator() ------------------------ Finds keypoints in an image .. ocv:function:: void StarDetector::operator()(const Mat& image, vector& keypoints) .. ocv:pyfunction:: cv2.StarDetector.detect(image) -> keypoints .. ocv:cfunction:: CvSeq* cvGetStarKeypoints( const CvArr* image, CvMemStorage* storage, CvStarDetectorParams params=cvStarDetectorParams() ) .. ocv:pyoldfunction:: cv.GetStarKeypoints(image, storage, params)-> keypoints :param image: The input 8-bit grayscale image :param keypoints: The output vector of keypoints :param storage: The memory storage used to store the keypoints (OpenCV 1.x API only) :param params: The algorithm parameters stored in ``CvStarDetectorParams`` (OpenCV 1.x API only) ORB ---- .. ocv:class:: ORB Class for extracting ORB features and descriptors from an image. :: class ORB { public: /** The patch sizes that can be used (only one right now) */ struct CommonParams { enum { DEFAULT_N_LEVELS = 3, DEFAULT_FIRST_LEVEL = 0}; /** default constructor */ CommonParams(float scale_factor = 1.2f, unsigned int n_levels = DEFAULT_N_LEVELS, int edge_threshold = 31, unsigned int first_level = DEFAULT_FIRST_LEVEL); void read(const FileNode& fn); void write(FileStorage& fs) const; /** Coefficient by which we divide the dimensions from one scale pyramid level to the next */ float scale_factor_; /** The number of levels in the scale pyramid */ unsigned int n_levels_; /** The level at which the image is given * if 1, that means we will also look at the image scale_factor_ times bigger */ unsigned int first_level_; /** How far from the boundary the points should be */ int edge_threshold_; }; // constructor that initializes all the algorithm parameters // n_features is the number of desired features ORB(size_t n_features = 500, const CommonParams & detector_params = CommonParams()); // returns the number of elements in each descriptor (32 bytes) int descriptorSize() const; // detects keypoints using ORB void operator()(const Mat& img, const Mat& mask, vector& keypoints) const; // detects ORB keypoints and computes the ORB descriptors for them; // output vector "descriptors" stores elements of descriptors and has size // equal descriptorSize()*keypoints.size() as each descriptor is // descriptorSize() elements of this vector. void operator()(const Mat& img, const Mat& mask, vector& keypoints, cv::Mat& descriptors, bool useProvidedKeypoints=false) const; }; The class implements ORB. RandomizedTree -------------- .. ocv:class:: RandomizedTree Class containing a base structure for ``RTreeClassifier``. :: class CV_EXPORTS RandomizedTree { public: friend class RTreeClassifier; RandomizedTree(); ~RandomizedTree(); void train(std::vector const& base_set, RNG &rng, int depth, int views, size_t reduced_num_dim, int num_quant_bits); void train(std::vector const& base_set, RNG &rng, PatchGenerator &make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits); // next two functions are EXPERIMENTAL //(do not use unless you know exactly what you do) static void quantizeVector(float *vec, int dim, int N, float bnds[2], int clamp_mode=0); static void quantizeVector(float *src, int dim, int N, float bnds[2], uchar *dst); // patch_data must be a 32x32 array (no row padding) float* getPosterior(uchar* patch_data); const float* getPosterior(uchar* patch_data) const; uchar* getPosterior2(uchar* patch_data); void read(const char* file_name, int num_quant_bits); void read(std::istream &is, int num_quant_bits); void write(const char* file_name) const; void write(std::ostream &os) const; int classes() { return classes_; } int depth() { return depth_; } void discardFloatPosteriors() { freePosteriors(1); } inline void applyQuantization(int num_quant_bits) { makePosteriors2(num_quant_bits); } private: int classes_; int depth_; int num_leaves_; std::vector nodes_; float **posteriors_; // 16-byte aligned posteriors uchar **posteriors2_; // 16-byte aligned posteriors std::vector leaf_counts_; void createNodes(int num_nodes, RNG &rng); void allocPosteriorsAligned(int num_leaves, int num_classes); void freePosteriors(int which); // which: 1=posteriors_, 2=posteriors2_, 3=both void init(int classes, int depth, RNG &rng); void addExample(int class_id, uchar* patch_data); void finalize(size_t reduced_num_dim, int num_quant_bits); int getIndex(uchar* patch_data) const; inline float* getPosteriorByIndex(int index); inline uchar* getPosteriorByIndex2(int index); inline const float* getPosteriorByIndex(int index) const; void convertPosteriorsToChar(); void makePosteriors2(int num_quant_bits); void compressLeaves(size_t reduced_num_dim); void estimateQuantPercForPosteriors(float perc[2]); }; RandomizedTree::train ------------------------- Trains a randomized tree using an input set of keypoints. .. ocv:function:: void train(std::vector const& base_set, RNG& rng, PatchGenerator& make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits) .. ocv:function:: void train(std::vector const& base_set, RNG& rng, PatchGenerator& make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits) :param base_set: Vector of the ``BaseKeypoint`` type. It contains image keypoints used for training. :param rng: Random-number generator used for training. :param make_patch: Patch generator used for training. :param depth: Maximum tree depth. :param views: Number of random views of each keypoint neighborhood to generate. :param reduced_num_dim: Number of dimensions used in the compressed signature. :param num_quant_bits: Number of bits used for quantization. RandomizedTree::read ------------------------ Reads a pre-saved randomized tree from a file or stream. .. ocv:function:: read(const char* file_name, int num_quant_bits) .. ocv:function:: read(std::istream &is, int num_quant_bits) :param file_name: Name of the file that contains randomized tree data. :param is: Input stream associated with the file that contains randomized tree data. :param num_quant_bits: Number of bits used for quantization. RandomizedTree::write ------------------------- Writes the current randomized tree to a file or stream. .. ocv:function:: void write(const char* file_name) const .. ocv:function:: void write(std::ostream &os) const :param file_name: Name of the file where randomized tree data is stored. :param os: Output stream associated with the file where randomized tree data is stored. RandomizedTree::applyQuantization ------------------------------------- .. ocv:function:: void applyQuantization(int num_quant_bits) Applies quantization to the current randomized tree. :param num_quant_bits: Number of bits used for quantization. RTreeNode --------- .. ocv:class:: RTreeNode Class containing a base structure for ``RandomizedTree``. :: struct RTreeNode { short offset1, offset2; RTreeNode() {} RTreeNode(uchar x1, uchar y1, uchar x2, uchar y2) : offset1(y1*PATCH_SIZE + x1), offset2(y2*PATCH_SIZE + x2) {} //! Left child on 0, right child on 1 inline bool operator() (uchar* patch_data) const { return patch_data[offset1] > patch_data[offset2]; } }; RTreeClassifier --------------- .. ocv:class:: RTreeClassifier Class containing ``RTreeClassifier``. It represents the Calonder descriptor originally introduced by Michael Calonder. :: class CV_EXPORTS RTreeClassifier { public: static const int DEFAULT_TREES = 48; static const size_t DEFAULT_NUM_QUANT_BITS = 4; RTreeClassifier(); void train(std::vector 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); void train(std::vector 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); // sig must point to a memory block of at least //classes()*sizeof(float|uchar) bytes void getSignature(IplImage *patch, uchar *sig); void getSignature(IplImage *patch, float *sig); void getSparseSignature(IplImage *patch, float *sig, float thresh); static int countNonZeroElements(float *vec, int n, double tol=1e-10); static inline void safeSignatureAlloc(uchar **sig, int num_sig=1, int sig_len=176); static inline uchar* safeSignatureAlloc(int num_sig=1, int sig_len=176); inline int classes() { return classes_; } inline int original_num_classes() { return original_num_classes_; } void setQuantization(int num_quant_bits); void discardFloatPosteriors(); void read(const char* file_name); void read(std::istream &is); void write(const char* file_name) const; void write(std::ostream &os) const; std::vector trees_; private: int classes_; int num_quant_bits_; uchar **posteriors_; ushort *ptemp_; int original_num_classes_; bool keep_floats_; }; RTreeClassifier::train -------------------------- Trains a randomized tree classifier using an input set of keypoints. .. ocv:function:: void train(vector 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) .. ocv:function:: void train(vector 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) :param base_set: Vector of the ``BaseKeypoint`` type. It contains image keypoints used for training. :param rng: Random-number generator used for training. :param make_patch: Patch generator used for training. :param num_trees: Number of randomized trees used in ``RTreeClassificator`` . :param depth: Maximum tree depth. :param views: Number of random views of each keypoint neighborhood to generate. :param reduced_num_dim: Number of dimensions used in the compressed signature. :param num_quant_bits: Number of bits used for quantization. :param print_status: Current status of training printed on the console. RTreeClassifier::getSignature --------------------------------- Returns a signature for an image patch. .. ocv:function:: void getSignature(IplImage *patch, uchar *sig) .. ocv:function:: void getSignature(IplImage *patch, float *sig) :param patch: Image patch to calculate the signature for. :param sig: Output signature (array dimension is ``reduced_num_dim)`` . RTreeClassifier::getSparseSignature --------------------------------------- Returns a sparse signature for an image patch .. ocv:function:: void getSparseSignature(IplImage *patch, float *sig, float thresh) :param patch: Image patch to calculate the signature for. :param sig: Output signature (array dimension is ``reduced_num_dim)`` . :param thresh: Threshold used for compressing the signature. 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. RTreeClassifier::countNonZeroElements ----------------------------------------- Returns the number of non-zero elements in an input array. .. ocv:function:: static int countNonZeroElements(float *vec, int n, double tol=1e-10) :param vec: Input vector containing float elements. :param n: Input vector size. :param tol: Threshold used for counting elements. All elements less than ``tol`` are considered as zero elements. RTreeClassifier::read ------------------------- Reads a pre-saved ``RTreeClassifier`` from a file or stream. .. ocv:function:: read(const char* file_name) .. ocv:function:: read(std::istream& is) :param file_name: Name of the file that contains randomized tree data. :param is: Input stream associated with the file that contains randomized tree data. RTreeClassifier::write -------------------------- Writes the current ``RTreeClassifier`` to a file or stream. .. ocv:function:: void write(const char* file_name) const .. ocv:function:: void write(std::ostream &os) const :param file_name: Name of the file where randomized tree data is stored. :param os: Output stream associated with the file where randomized tree data is stored. RTreeClassifier::setQuantization ------------------------------------ Applies quantization to the current randomized tree. .. ocv:function:: void setQuantization(int num_quant_bits) :param num_quant_bits: Number of bits used for quantization. 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 :math:`best\_corr` and :math:`best\_corr\_idx` arrays that keep the best probabilities and corresponding features indices for every train feature. :: CvMemStorage* storage = cvCreateMemStorage(0); CvSeq *objectKeypoints = 0, *objectDescriptors = 0; CvSeq *imageKeypoints = 0, *imageDescriptors = 0; CvSURFParams params = cvSURFParams(500, 1); cvExtractSURF( test_image, 0, &imageKeypoints, &imageDescriptors, storage, params ); cvExtractSURF( train_image, 0, &objectKeypoints, &objectDescriptors, storage, params ); RTreeClassifier detector; int patch_width = PATCH_SIZE; iint patch_height = PATCH_SIZE; vector base_set; int i=0; CvSURFPoint* point; 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); } ..