// Copyright (C) 2013 Davis E. King (davis@dlib.net) // License: Boost Software License See LICENSE.txt for the full license. #undef DLIB_BINNED_VECTOR_FEATUrES_ABSTRACT_Hh_ #ifdef DLIB_BINNED_VECTOR_FEATUrES_ABSTRACT_Hh_ #include "../lsh/projection_hash_abstract.h" #include <vector> #include "../matrix.h" namespace dlib{// ---------------------------------------------------------------------------------------- template < typename feature_extractor, typename hash_function_type_ = projection_hash > classbinned_vector_feature_image: noncopyable{/*! REQUIREMENTS ON feature_extractor - must be an object with an interface compatible with dlib::hog_image REQUIREMENTS ON hash_function_type_ - must be an object with an interface compatible with projection_hash INITIAL VALUE - size() == 0 WHAT THIS OBJECT REPRESENTS This object is a tool for performing image feature extraction. In particular, it wraps another image feature extractor and converts the wrapped image feature vectors into a high dimensional sparse vector. For example, if the lower level feature extractor outputs the vector [3,4,5] and this vector is hashed into the second bin of four bins then the output sparse vector is: [0,0,0,0, 3,4,5,1, 0,0,0,0, 0,0,0,0]. That is, the output vector has a dimensionality that is equal to the number of hash bins times the dimensionality of the lower level vector plus one. The value in the extra dimension concatenated onto the end of the vector is always a constant value of of 1 and serves as a bias value. This means that, if there are N hash bins, these vectors are capable of representing N different linear functions, each operating on the vectors that fall into their corresponding hash bin. THREAD SAFETY Concurrent access to an instance of this object is not safe and should be protected by a mutex lock except for the case where you are copying the configuration (via copy_configuration()) of a binned_vector_feature_image object to many other threads. In this case, it is safe to copy the configuration of a shared object so long as no other operations are performed on it. NOTATION let BASE_FE denote the base feature_extractor object contained inside the binned_vector_feature_image. !*/ public: typedef feature_extractor feature_extractor_type; typedef hash_function_type_ hash_function_type; typedef std::vector<std::pair<unsignedint,double> > descriptor_type;binned_vector_feature_image( ); /*! ensures - this object is properly initialized !*/voidclear( ); /*! ensures - this object will have its initial value !*/voidset_hash( const hash_function_type& hash ); /*! ensures - #get_hash() == hash !*/ const hash_function_type&get_hash( ) const; /*! ensures - returns the hash function used by this object to hash base feature vectors into integers. !*/voidcopy_configuration( const feature_extractor& item ); /*! ensures - performs BASE_FE.copy_configuration(item) !*/voidcopy_configuration( const binned_vector_feature_image& item ); /*! ensures - copies all the state information of item into *this, except for state information populated by load(). More precisely, given two binned_vector_feature_image objects H1 and H2, the following sequence of instructions should always result in both of them having the exact same state. H2.copy_configuration(H1); H1.load(img); H2.load(img); !*/ template < typename image_type >voidload( const image_type& img ); /*! requires - image_type == any type that can be supplied to feature_extractor::load() ensures - performs BASE_FE.load(img) i.e. does feature extraction. The features can be accessed using operator() as defined below. !*/size_tsize( ) const; /*! ensures - returns BASE_FE.size() !*/longnr( ) const; /*! ensures - returns BASE_FE.nr() !*/longnc( ) const; /*! ensures - returns BASE_FE.nc() !*/longget_num_dimensions( ) const; /*! ensures - returns the dimensionality of the feature vectors returned by operator(). In this case, this is the number of hash bins times the dimensionality of the features produced by BASE_FE plus one. That is, this function returns get_hash().num_hash_bins()*(BASE_FE.get_num_dimensions()+1) !*/ const descriptor_type&operator() (longrow,longcol ) const; /*! requires - 0 <= row < nr() - 0 <= col < nc() - It must be legal to evaluate expressions of the form: get_hash()(BASE_FE(row,col)) (e.g. the hash function must be properly configured to process the feature vectors produced by the base feature extractor) ensures - hashes BASE_FE(row,col) and returns the resulting sparse vector. In particular, we return a vector that is a copy of BASE_FE(row,col) that has been shifted into the part of the sparse vector indicated by the hash function. It will also have a constant bias value of 1 appended to it. - To be precise, this function returns a sparse vector V such that: - V.size() == BASE_FE.get_num_dimensions()+1 - let IDX = get_hash()(BASE_FE(row,col)) - for i where 0 <= i < BASE_FE.get_num_dimensions(): - V[i].first == IDX*(BASE_FE.get_num_dimensions()+1) + i - V[i].second == BASE_FE(row,col)(i) - V[BASE_FE.get_num_dimensions()].first == IDX*(BASE_FE.get_num_dimensions()+1) + BASE_FE.get_num_dimensions() - V[BASE_FE.get_num_dimensions()].second == 1 !*/ const rectangleget_block_rect(longrow,longcol ) const; /*! ensures - returns BASE_FE.get_block_rect(row,col) I.e. returns a rectangle that tells you what part of the original image is associated with a particular feature vector. !*/ const pointimage_to_feat_space( const point& p ) const; /*! ensures - returns BASE_FE.image_to_feat_space(p) I.e. Each local feature is extracted from a certain point in the input image. This function returns the identity of the local feature corresponding to the image location p. Or in other words, let P == image_to_feat_space(p), then (*this)(P.y(),P.x()) == the local feature closest to, or centered at, the point p in the input image. Note that some image points might not have corresponding feature locations. E.g. border points or points outside the image. In these cases the returned point will be outside get_rect(*this). !*/ const rectangleimage_to_feat_space( const rectangle& rect ) const; /*! ensures - returns BASE_FE.image_to_feat_space(rect) I.e. returns rectangle(image_to_feat_space(rect.tl_corner()), image_to_feat_space(rect.br_corner())); (i.e. maps a rectangle from image space to feature space) !*/ const pointfeat_to_image_space( const point& p ) const; /*! ensures - returns BASE_FE.feat_to_image_space(p) I.e. returns the location in the input image space corresponding to the center of the local feature at point p. In other words, this function computes the inverse of image_to_feat_space(). Note that it may only do so approximately, since more than one image location might correspond to the same local feature. That is, image_to_feat_space() might not be invertible so this function gives the closest possible result. !*/ const rectanglefeat_to_image_space( const rectangle& rect ) const; /*! ensures - returns BASE_FE.feat_to_image_space(rect) I.e. return rectangle(feat_to_image_space(rect.tl_corner()), feat_to_image_space(rect.br_corner())); (i.e. maps a rectangle from feature space to image space) !*/}; // ---------------------------------------------------------------------------------------- template < typename T, typename U >voidserialize( const binned_vector_feature_image<T,U>& item, std::ostream& out ); /*! provides serialization support !*/ // ---------------------------------------------------------------------------------------- template < typename T, typename U >voiddeserialize( binned_vector_feature_image<T,U>& item, std::istream& in ); /*! provides deserialization support !*/ // ----------------------------------------------------------------------------------------}#endif // DLIB_BINNED_VECTOR_FEATUrES_ABSTRACT_Hh_