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moved part of f2d docs to the legacy module
This commit is contained in:
Alexander Shishkov
parent
c9dacaa254
commit
78ef76165e
@ -183,29 +183,6 @@ Wrapping class for computing descriptors by using the
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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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OpponentColorDescriptorExtractor
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--------------------------------
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.. ocv:class:: OpponentColorDescriptorExtractor
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@ -238,122 +238,6 @@ Clones the matcher.
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but with empty train data.
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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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VectorDescriptorMatcher
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-----------------------
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.. ocv:class:: VectorDescriptorMatcher
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@ -144,435 +144,4 @@ Class for extracting ORB features and descriptors from an image. ::
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The class implements ORB.
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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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------------------------------------
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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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: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 );
|
||||
cvExtractSURF( train_image, 0, &objectKeypoints, &objectDescriptors,
|
||||
storage, params );
|
||||
|
||||
RTreeClassifier detector;
|
||||
int patch_width = PATCH_SIZE;
|
||||
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);
|
||||
}
|
||||
|
||||
..
|
||||
..
|
||||
@ -0,0 +1,33 @@
|
||||
Common Interfaces of Descriptor Extractors
|
||||
==========================================
|
||||
|
||||
.. highlight:: cpp
|
||||
|
||||
Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to easily switch
|
||||
between different algorithms solving the same problem. This section is devoted to computing descriptors
|
||||
represented as vectors in a multidimensional space. All objects that implement the ``vector``
|
||||
descriptor extractors inherit the
|
||||
:ocv:class:`DescriptorExtractor` interface.
|
||||
|
||||
|
||||
|
||||
CalonderDescriptorExtractor
|
||||
---------------------------
|
||||
.. ocv:class:: CalonderDescriptorExtractor
|
||||
|
||||
Wrapping class for computing descriptors by using the
|
||||
:ocv:class:`RTreeClassifier` class. ::
|
||||
|
||||
template<typename T>
|
||||
class CalonderDescriptorExtractor : public DescriptorExtractor
|
||||
{
|
||||
public:
|
||||
CalonderDescriptorExtractor( const string& classifierFile );
|
||||
|
||||
virtual void read( const FileNode &fn );
|
||||
virtual void write( FileStorage &fs ) const;
|
||||
virtual int descriptorSize() const;
|
||||
virtual int descriptorType() const;
|
||||
protected:
|
||||
...
|
||||
}
|
||||
@ -0,0 +1,118 @@
|
||||
Common Interfaces of Generic Descriptor Matchers
|
||||
================================================
|
||||
|
||||
.. highlight:: cpp
|
||||
|
||||
OneWayDescriptorMatcher
|
||||
-----------------------
|
||||
.. ocv:class:: OneWayDescriptorMatcher
|
||||
|
||||
Wrapping class for computing, matching, and classifying descriptors using the
|
||||
:ocv:class:`OneWayDescriptorBase` class. ::
|
||||
|
||||
class OneWayDescriptorMatcher : public GenericDescriptorMatcher
|
||||
{
|
||||
public:
|
||||
class Params
|
||||
{
|
||||
public:
|
||||
static const int POSE_COUNT = 500;
|
||||
static const int PATCH_WIDTH = 24;
|
||||
static const int PATCH_HEIGHT = 24;
|
||||
static float GET_MIN_SCALE() { return 0.7f; }
|
||||
static float GET_MAX_SCALE() { return 1.5f; }
|
||||
static float GET_STEP_SCALE() { return 1.2f; }
|
||||
|
||||
Params( int poseCount = POSE_COUNT,
|
||||
Size patchSize = Size(PATCH_WIDTH, PATCH_HEIGHT),
|
||||
string pcaFilename = string(),
|
||||
string trainPath = string(), string trainImagesList = string(),
|
||||
float minScale = GET_MIN_SCALE(), float maxScale = GET_MAX_SCALE(),
|
||||
float stepScale = GET_STEP_SCALE() );
|
||||
|
||||
int poseCount;
|
||||
Size patchSize;
|
||||
string pcaFilename;
|
||||
string trainPath;
|
||||
string trainImagesList;
|
||||
|
||||
float minScale, maxScale, stepScale;
|
||||
};
|
||||
|
||||
OneWayDescriptorMatcher( const Params& params=Params() );
|
||||
virtual ~OneWayDescriptorMatcher();
|
||||
|
||||
void initialize( const Params& params, const Ptr<OneWayDescriptorBase>& base=Ptr<OneWayDescriptorBase>() );
|
||||
|
||||
// Clears keypoints stored in collection and OneWayDescriptorBase
|
||||
virtual void clear();
|
||||
|
||||
virtual void train();
|
||||
|
||||
virtual bool isMaskSupported();
|
||||
|
||||
virtual void read( const FileNode &fn );
|
||||
virtual void write( FileStorage& fs ) const;
|
||||
|
||||
virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;
|
||||
protected:
|
||||
...
|
||||
};
|
||||
|
||||
|
||||
|
||||
|
||||
FernDescriptorMatcher
|
||||
---------------------
|
||||
.. ocv:class:: FernDescriptorMatcher
|
||||
|
||||
Wrapping class for computing, matching, and classifying descriptors using the
|
||||
:ocv:class:`FernClassifier` class. ::
|
||||
|
||||
class FernDescriptorMatcher : public GenericDescriptorMatcher
|
||||
{
|
||||
public:
|
||||
class Params
|
||||
{
|
||||
public:
|
||||
Params( int nclasses=0,
|
||||
int patchSize=FernClassifier::PATCH_SIZE,
|
||||
int signatureSize=FernClassifier::DEFAULT_SIGNATURE_SIZE,
|
||||
int nstructs=FernClassifier::DEFAULT_STRUCTS,
|
||||
int structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
|
||||
int nviews=FernClassifier::DEFAULT_VIEWS,
|
||||
int compressionMethod=FernClassifier::COMPRESSION_NONE,
|
||||
const PatchGenerator& patchGenerator=PatchGenerator() );
|
||||
|
||||
Params( const string& filename );
|
||||
|
||||
int nclasses;
|
||||
int patchSize;
|
||||
int signatureSize;
|
||||
int nstructs;
|
||||
int structSize;
|
||||
int nviews;
|
||||
int compressionMethod;
|
||||
PatchGenerator patchGenerator;
|
||||
|
||||
string filename;
|
||||
};
|
||||
|
||||
FernDescriptorMatcher( const Params& params=Params() );
|
||||
virtual ~FernDescriptorMatcher();
|
||||
|
||||
virtual void clear();
|
||||
|
||||
virtual void train();
|
||||
|
||||
virtual bool isMaskSupported();
|
||||
|
||||
virtual void read( const FileNode &fn );
|
||||
virtual void write( FileStorage& fs ) const;
|
||||
|
||||
virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;
|
||||
|
||||
protected:
|
||||
...
|
||||
};
|
||||
|
||||
433
modules/legacy/doc/feature_detection_and_description.rst
Normal file
433
modules/legacy/doc/feature_detection_and_description.rst
Normal file
@ -0,0 +1,433 @@
|
||||
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<BaseKeypoint> const& base_set,
|
||||
RNG &rng, int depth, int views,
|
||||
size_t reduced_num_dim, int num_quant_bits);
|
||||
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);
|
||||
|
||||
// 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<RTreeNode> nodes_;
|
||||
float **posteriors_; // 16-byte aligned posteriors
|
||||
uchar **posteriors2_; // 16-byte aligned posteriors
|
||||
std::vector<int> leaf_counts_;
|
||||
|
||||
void createNodes(int num_nodes, RNG &rng);
|
||||
void allocPosteriorsAligned(int num_leaves, int num_classes);
|
||||
void freePosteriors(int which);
|
||||
// which: 1=posteriors_, 2=posteriors2_, 3=both
|
||||
void init(int classes, int depth, RNG &rng);
|
||||
void addExample(int class_id, uchar* patch_data);
|
||||
void finalize(size_t reduced_num_dim, int num_quant_bits);
|
||||
int getIndex(uchar* patch_data) const;
|
||||
inline float* getPosteriorByIndex(int index);
|
||||
inline uchar* getPosteriorByIndex2(int index);
|
||||
inline const float* getPosteriorByIndex(int index) const;
|
||||
void convertPosteriorsToChar();
|
||||
void makePosteriors2(int num_quant_bits);
|
||||
void compressLeaves(size_t reduced_num_dim);
|
||||
void estimateQuantPercForPosteriors(float perc[2]);
|
||||
};
|
||||
|
||||
|
||||
|
||||
RandomizedTree::train
|
||||
-------------------------
|
||||
Trains a randomized tree using an input set of keypoints.
|
||||
|
||||
.. ocv:function:: void train(std::vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits)
|
||||
|
||||
.. ocv:function:: void train(std::vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits)
|
||||
|
||||
:param base_set: Vector of the ``BaseKeypoint`` type. It contains image keypoints used for training.
|
||||
|
||||
:param rng: Random-number generator used for training.
|
||||
|
||||
:param make_patch: Patch generator used for training.
|
||||
|
||||
:param depth: Maximum tree depth.
|
||||
|
||||
:param views: Number of random views of each keypoint neighborhood to generate.
|
||||
|
||||
:param reduced_num_dim: Number of dimensions used in the compressed signature.
|
||||
|
||||
:param num_quant_bits: Number of bits used for quantization.
|
||||
|
||||
|
||||
|
||||
RandomizedTree::read
|
||||
------------------------
|
||||
Reads a pre-saved randomized tree from a file or stream.
|
||||
|
||||
.. ocv:function:: read(const char* file_name, int num_quant_bits)
|
||||
|
||||
.. ocv:function:: read(std::istream &is, int num_quant_bits)
|
||||
|
||||
:param file_name: Name of the file that contains randomized tree data.
|
||||
|
||||
:param is: Input stream associated with the file that contains randomized tree data.
|
||||
|
||||
:param num_quant_bits: Number of bits used for quantization.
|
||||
|
||||
|
||||
|
||||
RandomizedTree::write
|
||||
-------------------------
|
||||
Writes the current randomized tree to a file or stream.
|
||||
|
||||
.. ocv:function:: void write(const char* file_name) const
|
||||
|
||||
.. ocv:function:: void write(std::ostream &os) const
|
||||
|
||||
:param file_name: Name of the file where randomized tree data is stored.
|
||||
|
||||
:param os: Output stream associated with the file where randomized tree data is stored.
|
||||
|
||||
|
||||
|
||||
RandomizedTree::applyQuantization
|
||||
-------------------------------------
|
||||
.. ocv:function:: void applyQuantization(int num_quant_bits)
|
||||
|
||||
Applies quantization to the current randomized tree.
|
||||
|
||||
:param num_quant_bits: Number of bits used for quantization.
|
||||
|
||||
|
||||
RTreeNode
|
||||
---------
|
||||
.. ocv:class:: RTreeNode
|
||||
|
||||
Class containing a base structure for ``RandomizedTree``. ::
|
||||
|
||||
struct RTreeNode
|
||||
{
|
||||
short offset1, offset2;
|
||||
|
||||
RTreeNode() {}
|
||||
|
||||
RTreeNode(uchar x1, uchar y1, uchar x2, uchar y2)
|
||||
: offset1(y1*PATCH_SIZE + x1),
|
||||
offset2(y2*PATCH_SIZE + x2)
|
||||
{}
|
||||
|
||||
//! Left child on 0, right child on 1
|
||||
inline bool operator() (uchar* patch_data) const
|
||||
{
|
||||
return patch_data[offset1] > patch_data[offset2];
|
||||
}
|
||||
};
|
||||
|
||||
|
||||
|
||||
RTreeClassifier
|
||||
---------------
|
||||
.. ocv:class:: RTreeClassifier
|
||||
|
||||
Class containing ``RTreeClassifier``. It represents the Calonder descriptor originally introduced by Michael Calonder. ::
|
||||
|
||||
class CV_EXPORTS RTreeClassifier
|
||||
{
|
||||
public:
|
||||
static const int DEFAULT_TREES = 48;
|
||||
static const size_t DEFAULT_NUM_QUANT_BITS = 4;
|
||||
|
||||
RTreeClassifier();
|
||||
|
||||
void train(std::vector<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);
|
||||
void train(std::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);
|
||||
|
||||
// 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<RandomizedTree> trees_;
|
||||
|
||||
private:
|
||||
int classes_;
|
||||
int num_quant_bits_;
|
||||
uchar **posteriors_;
|
||||
ushort *ptemp_;
|
||||
int original_num_classes_;
|
||||
bool keep_floats_;
|
||||
};
|
||||
|
||||
|
||||
|
||||
RTreeClassifier::train
|
||||
--------------------------
|
||||
Trains a randomized tree classifier using an input set of keypoints.
|
||||
|
||||
.. ocv:function:: void train(vector<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)
|
||||
|
||||
.. 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)
|
||||
|
||||
:param base_set: Vector of the ``BaseKeypoint`` type. It contains image keypoints used for training.
|
||||
|
||||
:param rng: Random-number generator used for training.
|
||||
|
||||
:param make_patch: Patch generator used for training.
|
||||
|
||||
:param num_trees: Number of randomized trees used in ``RTreeClassificator`` .
|
||||
|
||||
:param depth: Maximum tree depth.
|
||||
|
||||
:param views: Number of random views of each keypoint neighborhood to generate.
|
||||
|
||||
:param reduced_num_dim: Number of dimensions used in the compressed signature.
|
||||
|
||||
:param num_quant_bits: Number of bits used for quantization.
|
||||
|
||||
:param print_status: Current status of training printed on the console.
|
||||
|
||||
|
||||
|
||||
RTreeClassifier::getSignature
|
||||
---------------------------------
|
||||
Returns a signature for an image patch.
|
||||
|
||||
.. ocv:function:: void getSignature(IplImage *patch, uchar *sig)
|
||||
|
||||
.. ocv:function:: void getSignature(IplImage *patch, float *sig)
|
||||
|
||||
:param patch: Image patch to calculate the signature for.
|
||||
:param sig: Output signature (array dimension is ``reduced_num_dim)`` .
|
||||
|
||||
|
||||
|
||||
RTreeClassifier::getSparseSignature
|
||||
---------------------------------------
|
||||
Returns a sparse signature for an image patch
|
||||
|
||||
.. ocv:function:: void getSparseSignature(IplImage *patch, float *sig, float thresh)
|
||||
|
||||
:param patch: Image patch to calculate the signature for.
|
||||
|
||||
:param sig: Output signature (array dimension is ``reduced_num_dim)`` .
|
||||
|
||||
:param thresh: Threshold used for compressing the signature.
|
||||
|
||||
Returns a signature for an image patch similarly to ``getSignature`` but uses a threshold for removing all signature elements below the threshold so that the signature is compressed.
|
||||
|
||||
|
||||
RTreeClassifier::countNonZeroElements
|
||||
-----------------------------------------
|
||||
Returns the number of non-zero elements in an input array.
|
||||
|
||||
.. ocv:function:: static int countNonZeroElements(float *vec, int n, double tol=1e-10)
|
||||
|
||||
:param vec: Input vector containing float elements.
|
||||
|
||||
:param n: Input vector size.
|
||||
|
||||
:param tol: Threshold used for counting elements. All elements less than ``tol`` are considered as zero elements.
|
||||
|
||||
|
||||
|
||||
RTreeClassifier::read
|
||||
-------------------------
|
||||
Reads a pre-saved ``RTreeClassifier`` from a file or stream.
|
||||
|
||||
.. ocv:function:: read(const char* file_name)
|
||||
|
||||
.. ocv:function:: read(std::istream& is)
|
||||
|
||||
:param file_name: Name of the file that contains randomized tree data.
|
||||
|
||||
:param is: Input stream associated with the file that contains randomized tree data.
|
||||
|
||||
|
||||
|
||||
RTreeClassifier::write
|
||||
--------------------------
|
||||
Writes the current ``RTreeClassifier`` to a file or stream.
|
||||
|
||||
.. ocv:function:: void write(const char* file_name) const
|
||||
|
||||
.. ocv:function:: void write(std::ostream &os) const
|
||||
|
||||
:param file_name: Name of the file where randomized tree data is stored.
|
||||
|
||||
:param os: Output stream associated with the file where randomized tree data is stored.
|
||||
|
||||
|
||||
|
||||
RTreeClassifier::setQuantization
|
||||
------------------------------------
|
||||
Applies quantization to the current randomized tree.
|
||||
|
||||
.. ocv:function:: void setQuantization(int num_quant_bits)
|
||||
|
||||
:param num_quant_bits: Number of bits used for quantization.
|
||||
|
||||
The example below demonstrates the usage of ``RTreeClassifier`` for matching the features. The features are extracted from the test and train images with SURF. Output is
|
||||
:math:`best\_corr` and
|
||||
:math:`best\_corr\_idx` arrays that keep the best probabilities and corresponding features indices for every train feature. ::
|
||||
|
||||
CvMemStorage* storage = cvCreateMemStorage(0);
|
||||
CvSeq *objectKeypoints = 0, *objectDescriptors = 0;
|
||||
CvSeq *imageKeypoints = 0, *imageDescriptors = 0;
|
||||
CvSURFParams params = cvSURFParams(500, 1);
|
||||
cvExtractSURF( test_image, 0, &imageKeypoints, &imageDescriptors,
|
||||
storage, params );
|
||||
cvExtractSURF( train_image, 0, &objectKeypoints, &objectDescriptors,
|
||||
storage, params );
|
||||
|
||||
RTreeClassifier detector;
|
||||
int patch_width = PATCH_SIZE;
|
||||
iint patch_height = PATCH_SIZE;
|
||||
vector<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);
|
||||
}
|
||||
|
||||
..
|
||||
@ -10,3 +10,6 @@ legacy. Deprecated stuff
|
||||
motion_analysis
|
||||
expectation_maximization
|
||||
planar_subdivisions
|
||||
feature_detection_and_description
|
||||
common_interfaces_of_descriptor_extractors
|
||||
common_interfaces_of_generic_descriptor_matchers
|
||||
|
||||
Loading…
Reference in New Issue
Block a user