Feature detection and description ================================= .. highlight:: cpp .. index:: FAST cv::FAST -------- .. cfunction:: void FAST( const Mat\& image, vector\& keypoints, int threshold, bool nonmaxSupression=true ) Detects corners using FAST algorithm by E. Rosten (''Machine learning for high-speed corner detection'', 2006). :param image: The image. Keypoints (corners) will be detected on this. :param keypoints: Keypoints detected on the image. :param threshold: Threshold on difference between intensity of center pixel and pixels on circle around this pixel. See description of the algorithm. :param nonmaxSupression: If it is true then non-maximum supression will be applied to detected corners (keypoints). .. index:: MSER .. _MSER: MSER ---- .. ctype:: 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 MSER (see http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions ) extraction algorithm. .. index:: StarDetector .. _StarDetector: StarDetector ------------ .. ctype:: StarDetector Implements Star keypoint detector :: class StarDetector : CvStarDetectorParams { public: // default constructor StarDetector(); // the full constructor initialized all the algorithm parameters: // maxSize - maximum size of the features. The following // values of the parameter are supported: // 4, 6, 8, 11, 12, 16, 22, 23, 32, 45, 46, 64, 90, 128 // responseThreshold - threshold for the approximated laplacian, // used to eliminate weak features. The larger it is, // the less features will be retrieved // lineThresholdProjected - another threshold for the laplacian to // eliminate edges // lineThresholdBinarized - another threshold for the feature // size to eliminate edges. // The larger the 2 threshold, the more points you get. StarDetector(int maxSize, int responseThreshold, int lineThresholdProjected, int lineThresholdBinarized, int suppressNonmaxSize); // finds keypoints in an image void operator()(const Mat& image, vector& keypoints) const; }; .. The class implements a modified version of CenSurE keypoint detector described in Agrawal08 .. index:: SIFT .. _SIFT: SIFT ---- .. ctype:: SIFT Class for extracting keypoints and computing descriptors using approach named Scale Invariant Feature Transform (SIFT). :: 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 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: ... }; .. .. index:: SURF .. _SURF: SURF ---- .. ctype:: SURF Class for extracting Speeded Up Robust Features from an image. :: class SURF : public CvSURFParams { public: // default constructor SURF(); // constructor that initializes all the algorithm parameters SURF(double _hessianThreshold, int _nOctaves=4, int _nOctaveLayers=2, bool _extended=false); // returns the number of elements in each descriptor (64 or 128) int descriptorSize() const; // detects keypoints using fast multi-scale Hessian detector void operator()(const Mat& img, const Mat& mask, vector& keypoints) const; // detects keypoints and computes the SURF 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, vector& descriptors, bool useProvidedKeypoints=false) const; }; .. The class ``SURF`` implements Speeded Up Robust Features descriptor Bay06 . There is fast multi-scale Hessian keypoint detector that can be used to find the keypoints (which is the default option), but the descriptors can be also computed for the user-specified keypoints. The function can be used for object tracking and localization, image stitching etc. See the ``find_obj.cpp`` demo in OpenCV samples directory. .. index:: RandomizedTree .. _RandomizedTree: RandomizedTree -------------- .. ctype:: RandomizedTree The class contains base structure for ``RTreeClassifier`` :: class CV_EXPORTS RandomizedTree { public: friend class RTreeClassifier; RandomizedTree(); ~RandomizedTree(); void train(std::vector const& base_set, cv::RNG &rng, int depth, int views, size_t reduced_num_dim, int num_quant_bits); void train(std::vector const& base_set, cv::RNG &rng, PatchGenerator &make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits); // following two funcs 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-bytes aligned posteriors uchar **posteriors2_; // 16-bytes aligned posteriors std::vector leaf_counts_; void createNodes(int num_nodes, cv::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, cv::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]); }; .. .. index:: RandomizedTree::train cv::RandomizedTree::train ------------------------- .. cfunction:: void train(std::vector const\& base_set, cv::RNG \&rng, PatchGenerator \&make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits) Trains a randomized tree using input set of keypoints .. cfunction:: void train(std::vector const\& base_set, cv::RNG \&rng, PatchGenerator \&make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits) {Vector of ``BaseKeypoint`` type. Contains keypoints from the image are used for training} {Random numbers generator is used for training} {Patch generator is used for training} {Maximum tree depth} {Number of dimensions are used in compressed signature} {Number of bits are used for quantization} .. index:: RandomizedTree::read cv::RandomizedTree::read ------------------------ .. cfunction:: read(const char* file_name, int num_quant_bits) Reads pre-saved randomized tree from file or stream .. cfunction:: read(std::istream \&is, int num_quant_bits) :param file_name: Filename of file contains randomized tree data :param is: Input stream associated with file contains randomized tree data {Number of bits are used for quantization} .. index:: RandomizedTree::write cv::RandomizedTree::write ------------------------- .. cfunction:: void write(const char* file_name) const Writes current randomized tree to a file or stream .. cfunction:: void write(std::ostream \&os) const :param file_name: Filename of file where randomized tree data will be stored :param is: Output stream associated with file where randomized tree data will be stored .. index:: RandomizedTree::applyQuantization cv::RandomizedTree::applyQuantization ------------------------------------- .. cfunction:: void applyQuantization(int num_quant_bits) Applies quantization to the current randomized tree {Number of bits are used for quantization} .. index:: RTreeNode .. _RTreeNode: RTreeNode --------- .. ctype:: RTreeNode The class contains 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]; } }; .. .. index:: RTreeClassifier .. _RTreeClassifier: RTreeClassifier --------------- .. ctype:: RTreeClassifier The class contains ``RTreeClassifier`` . It represents calonder descriptor which was 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, cv::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, cv::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_; }; .. .. index:: RTreeClassifier::train cv::RTreeClassifier::train -------------------------- .. cfunction:: void train(std::vector const\& base_set, cv::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) Trains a randomized tree classificator using input set of keypoints .. cfunction:: void train(std::vector const\& base_set, cv::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) {Vector of ``BaseKeypoint`` type. Contains keypoints from the image are used for training} {Random numbers generator is used for training} {Patch generator is used for training} {Number of randomized trees used in RTreeClassificator} {Maximum tree depth} {Number of dimensions are used in compressed signature} {Number of bits are used for quantization} {Print current status of training on the console} .. index:: RTreeClassifier::getSignature cv::RTreeClassifier::getSignature --------------------------------- .. cfunction:: void getSignature(IplImage *patch, uchar *sig) Returns signature for image patch .. cfunction:: void getSignature(IplImage *patch, float *sig) {Image patch to calculate signature for} {Output signature (array dimension is ``reduced_num_dim)`` } .. index:: RTreeClassifier::getSparseSignature cv::RTreeClassifier::getSparseSignature --------------------------------------- ```` .. cfunction:: void getSparseSignature(IplImage *patch, float *sig, float thresh) The function is simular to getSignaturebut uses the threshold for removing all signature elements less than the threshold. So that the signature is compressed {Image patch to calculate signature for} {Output signature (array dimension is ``reduced_num_dim)`` } {The threshold that is used for compressing the signature} .. index:: RTreeClassifier::countNonZeroElements cv::RTreeClassifier::countNonZeroElements ----------------------------------------- .. cfunction:: static int countNonZeroElements(float *vec, int n, double tol=1e-10) The function returns the number of non-zero elements in the input array. :param vec: Input vector contains float elements :param n: Input vector size {The threshold used for elements counting. We take all elements are less than ``tol`` as zero elements} .. index:: RTreeClassifier::read cv::RTreeClassifier::read ------------------------- .. cfunction:: read(const char* file_name) Reads pre-saved RTreeClassifier from file or stream .. cfunction:: read(std::istream \&is) :param file_name: Filename of file contains randomized tree data :param is: Input stream associated with file contains randomized tree data .. index:: RTreeClassifier::write cv::RTreeClassifier::write -------------------------- .. cfunction:: void write(const char* file_name) const Writes current RTreeClassifier to a file or stream .. cfunction:: void write(std::ostream \&os) const :param file_name: Filename of file where randomized tree data will be stored :param is: Output stream associated with file where randomized tree data will be stored .. index:: RTreeClassifier::setQuantization cv::RTreeClassifier::setQuantization ------------------------------------ .. cfunction:: void setQuantization(int num_quant_bits) Applies quantization to the current randomized tree {Number of bits are used for quantization} Below there is an example of ``RTreeClassifier`` usage for feature matching. There are test and train images and we extract features from both with SURF. Output is :math:`best\_corr` and :math:`best\_corr\_idx` arrays which keep the best probabilities and corresponding features indexes 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 ); cv::RTreeClassifier detector; int patch_width = cv::PATCH_SIZE; iint patch_height = cv::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( cv::BaseKeypoint(point->pt.x,point->pt.y,train_image)); } //Detector training cv::RNG rng( cvGetTickCount() ); cv::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,cv::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); } ..