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Open Source Computer Vision Library
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434 lines
16 KiB
434 lines
16 KiB
Feature Detection and Description |
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.. highlight:: cpp |
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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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.. Sample code:: |
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* : PYTHON : An example using Randomized Tree training for letter recognition can be found at opencv_source_code/samples/python2/letter_recog.py |
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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 RandomizedTree::train( vector<BaseKeypoint> const& base_set, RNG & rng, int depth, int views, size_t reduced_num_dim, int num_quant_bits ) |
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.. ocv:function:: void RandomizedTree::train( 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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.. Sample code:: |
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* : An example on training a Random Tree Classifier for letter recognition can be found at opencv_source_code\samples\cpp\letter_recog.cpp |
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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:: RandomizedTree::read(const char* file_name, int num_quant_bits) |
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.. ocv:function:: RandomizedTree::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 RandomizedTree::write(const char* file_name) const |
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.. ocv:function:: void RandomizedTree::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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RandomizedTree::applyQuantization |
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------------------------------------- |
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.. ocv:function:: void RandomizedTree::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:struct:: 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 RTreeClassifier::train( vector<BaseKeypoint> 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 ) |
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.. ocv:function:: void RTreeClassifier::train( vector<BaseKeypoint> 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 ) |
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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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RTreeClassifier::getSignature |
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--------------------------------- |
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Returns a signature for an image patch. |
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.. ocv:function:: void RTreeClassifier::getSignature(IplImage *patch, uchar *sig) |
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.. ocv:function:: void RTreeClassifier::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 RTreeClassifier::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 RTreeClassifier::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:: void RTreeClassifier::read(const char* file_name) |
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.. ocv:function:: void RTreeClassifier::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 RTreeClassifier::write(const char* file_name) const |
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.. ocv:function:: void RTreeClassifier::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 RTreeClassifier::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++) |
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{ |
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point=(CvSURFPoint*)cvGetSeqElem(objectKeypoints,i); |
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base_set.push_back( |
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BaseKeypoint(point->pt.x,point->pt.y,train_image)); |
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} |
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//Detector training |
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RNG rng( cvGetTickCount() ); |
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PatchGenerator gen(0,255,2,false,0.7,1.3,-CV_PI/3,CV_PI/3, |
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-CV_PI/3,CV_PI/3); |
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printf("RTree Classifier training...n"); |
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detector.train(base_set,rng,gen,24,DEFAULT_DEPTH,2000, |
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(int)base_set.size(), detector.DEFAULT_NUM_QUANT_BITS); |
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printf("Donen"); |
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float* signature = new float[detector.original_num_classes()]; |
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float* best_corr; |
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int* best_corr_idx; |
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if (imageKeypoints->total > 0) |
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{ |
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best_corr = new float[imageKeypoints->total]; |
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best_corr_idx = new int[imageKeypoints->total]; |
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} |
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for(i=0; i < imageKeypoints->total; i++) |
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{ |
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point=(CvSURFPoint*)cvGetSeqElem(imageKeypoints,i); |
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int part_idx = -1; |
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float prob = 0.0f; |
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CvRect roi = cvRect((int)(point->pt.x) - patch_width/2, |
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(int)(point->pt.y) - patch_height/2, |
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patch_width, patch_height); |
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cvSetImageROI(test_image, roi); |
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roi = cvGetImageROI(test_image); |
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if(roi.width != patch_width || roi.height != patch_height) |
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{ |
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best_corr_idx[i] = part_idx; |
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best_corr[i] = prob; |
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} |
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else |
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{ |
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cvSetImageROI(test_image, roi); |
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IplImage* roi_image = |
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cvCreateImage(cvSize(roi.width, roi.height), |
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test_image->depth, test_image->nChannels); |
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cvCopy(test_image,roi_image); |
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detector.getSignature(roi_image, signature); |
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for (int j = 0; j< detector.original_num_classes();j++) |
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{ |
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if (prob < signature[j]) |
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{ |
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part_idx = j; |
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prob = signature[j]; |
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} |
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} |
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best_corr_idx[i] = part_idx; |
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best_corr[i] = prob; |
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if (roi_image) |
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cvReleaseImage(&roi_image); |
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} |
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cvResetImageROI(test_image); |
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} |
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..
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