Feature Detection and Description ================================= .. highlight:: cpp 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]); }; .. note:: * : PYTHON : An example using Randomized Tree training for letter recognition can be found at opencv_source_code/samples/python2/letter_recog.py RandomizedTree::train ------------------------- Trains a randomized tree using an input set of keypoints. .. ocv:function:: void RandomizedTree::train( vector const& base_set, RNG & rng, int depth, int views, size_t reduced_num_dim, int num_quant_bits ) .. ocv:function:: void RandomizedTree::train( 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. .. note:: * : An example on training a Random Tree Classifier for letter recognition can be found at opencv_source_code\samples\cpp\letter_recog.cpp RandomizedTree::read ------------------------ Reads a pre-saved randomized tree from a file or stream. .. ocv:function:: RandomizedTree::read(const char* file_name, int num_quant_bits) .. ocv:function:: RandomizedTree::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 RandomizedTree::write(const char* file_name) const .. ocv:function:: void RandomizedTree::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 RandomizedTree::applyQuantization(int num_quant_bits) Applies quantization to the current randomized tree. :param num_quant_bits: Number of bits used for quantization. RTreeNode --------- .. ocv:struct:: 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 RTreeClassifier::train( vector const& base_set, RNG & rng, int num_trees=RTreeClassifier::DEFAULT_TREES, int depth=RandomizedTree::DEFAULT_DEPTH, int views=RandomizedTree::DEFAULT_VIEWS, size_t reduced_num_dim=RandomizedTree::DEFAULT_REDUCED_NUM_DIM, int num_quant_bits=DEFAULT_NUM_QUANT_BITS ) .. ocv:function:: void RTreeClassifier::train( vector const& base_set, RNG & rng, PatchGenerator & make_patch, int num_trees=RTreeClassifier::DEFAULT_TREES, int depth=RandomizedTree::DEFAULT_DEPTH, int views=RandomizedTree::DEFAULT_VIEWS, size_t reduced_num_dim=RandomizedTree::DEFAULT_REDUCED_NUM_DIM, int num_quant_bits=DEFAULT_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 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. RTreeClassifier::getSignature --------------------------------- Returns a signature for an image patch. .. ocv:function:: void RTreeClassifier::getSignature(IplImage *patch, uchar *sig) .. ocv:function:: void RTreeClassifier::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 RTreeClassifier::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 RTreeClassifier::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:: void RTreeClassifier::read(const char* file_name) .. ocv:function:: void RTreeClassifier::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 RTreeClassifier::write(const char* file_name) const .. ocv:function:: void RTreeClassifier::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 RTreeClassifier::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); } ..