Alexander Shishkov
13 years ago
parent
c9dacaa254
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78ef76165e
7 changed files with 588 additions and 571 deletions
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Common Interfaces of Descriptor Extractors |
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========================================== |
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.. highlight:: cpp |
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Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to easily switch |
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between different algorithms solving the same problem. This section is devoted to computing descriptors |
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represented as vectors in a multidimensional space. All objects that implement the ``vector`` |
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descriptor extractors inherit the |
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:ocv:class:`DescriptorExtractor` interface. |
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CalonderDescriptorExtractor |
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--------------------------- |
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.. ocv:class:: CalonderDescriptorExtractor |
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Wrapping class for computing descriptors by using the |
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:ocv:class:`RTreeClassifier` class. :: |
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template<typename T> |
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class CalonderDescriptorExtractor : public DescriptorExtractor |
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{ |
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public: |
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CalonderDescriptorExtractor( const string& classifierFile ); |
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virtual void read( const FileNode &fn ); |
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virtual void write( FileStorage &fs ) const; |
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virtual int descriptorSize() const; |
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virtual int descriptorType() const; |
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protected: |
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... |
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} |
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Common Interfaces of Generic Descriptor Matchers |
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================================================ |
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.. highlight:: cpp |
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OneWayDescriptorMatcher |
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----------------------- |
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.. ocv:class:: OneWayDescriptorMatcher |
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Wrapping class for computing, matching, and classifying descriptors using the |
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:ocv:class:`OneWayDescriptorBase` class. :: |
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class OneWayDescriptorMatcher : public GenericDescriptorMatcher |
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{ |
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public: |
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class Params |
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{ |
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public: |
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static const int POSE_COUNT = 500; |
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static const int PATCH_WIDTH = 24; |
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static const int PATCH_HEIGHT = 24; |
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static float GET_MIN_SCALE() { return 0.7f; } |
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static float GET_MAX_SCALE() { return 1.5f; } |
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static float GET_STEP_SCALE() { return 1.2f; } |
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Params( int poseCount = POSE_COUNT, |
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Size patchSize = Size(PATCH_WIDTH, PATCH_HEIGHT), |
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string pcaFilename = string(), |
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string trainPath = string(), string trainImagesList = string(), |
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float minScale = GET_MIN_SCALE(), float maxScale = GET_MAX_SCALE(), |
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float stepScale = GET_STEP_SCALE() ); |
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int poseCount; |
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Size patchSize; |
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string pcaFilename; |
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string trainPath; |
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string trainImagesList; |
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float minScale, maxScale, stepScale; |
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}; |
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OneWayDescriptorMatcher( const Params& params=Params() ); |
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virtual ~OneWayDescriptorMatcher(); |
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void initialize( const Params& params, const Ptr<OneWayDescriptorBase>& base=Ptr<OneWayDescriptorBase>() ); |
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// Clears keypoints stored in collection and OneWayDescriptorBase |
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virtual void clear(); |
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virtual void train(); |
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virtual bool isMaskSupported(); |
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virtual void read( const FileNode &fn ); |
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virtual void write( FileStorage& fs ) const; |
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virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const; |
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protected: |
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... |
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}; |
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FernDescriptorMatcher |
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--------------------- |
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.. ocv:class:: FernDescriptorMatcher |
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Wrapping class for computing, matching, and classifying descriptors using the |
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:ocv:class:`FernClassifier` class. :: |
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class FernDescriptorMatcher : public GenericDescriptorMatcher |
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{ |
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public: |
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class Params |
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{ |
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public: |
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Params( int nclasses=0, |
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int patchSize=FernClassifier::PATCH_SIZE, |
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int signatureSize=FernClassifier::DEFAULT_SIGNATURE_SIZE, |
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int nstructs=FernClassifier::DEFAULT_STRUCTS, |
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int structSize=FernClassifier::DEFAULT_STRUCT_SIZE, |
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int nviews=FernClassifier::DEFAULT_VIEWS, |
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int compressionMethod=FernClassifier::COMPRESSION_NONE, |
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const PatchGenerator& patchGenerator=PatchGenerator() ); |
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Params( const string& filename ); |
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int nclasses; |
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int patchSize; |
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int signatureSize; |
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int nstructs; |
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int structSize; |
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int nviews; |
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int compressionMethod; |
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PatchGenerator patchGenerator; |
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string filename; |
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}; |
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FernDescriptorMatcher( const Params& params=Params() ); |
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virtual ~FernDescriptorMatcher(); |
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virtual void clear(); |
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virtual void train(); |
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virtual bool isMaskSupported(); |
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virtual void read( const FileNode &fn ); |
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virtual void write( FileStorage& fs ) const; |
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virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const; |
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protected: |
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... |
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}; |
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@ -0,0 +1,433 @@ |
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Feature Detection and Description |
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================================= |
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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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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 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 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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|
------------------------------------ |
||||||
|
Applies quantization to the current randomized tree. |
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|
.. ocv:function:: void setQuantization(int num_quant_bits) |
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|
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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 |
||||||
|
: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; |
||||||
|
CvSURFParams params = cvSURFParams(500, 1); |
||||||
|
cvExtractSURF( test_image, 0, &imageKeypoints, &imageDescriptors, |
||||||
|
storage, params ); |
||||||
|
cvExtractSURF( train_image, 0, &objectKeypoints, &objectDescriptors, |
||||||
|
storage, params ); |
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|
|
||||||
|
RTreeClassifier detector; |
||||||
|
int patch_width = PATCH_SIZE; |
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|
iint patch_height = PATCH_SIZE; |
||||||
|
vector<BaseKeypoint> 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); |
||||||
|
} |
||||||
|
|
||||||
|
.. |
||||||
Loading…
Reference in new issue