Object Categorization ===================== .. highlight:: cpp This section describes approaches based on local 2D features and used to categorize objects. .. index:: BOWTrainer .. _BOWTrainer: BOWTrainer ---------- .. cpp:class:: BOWTrainer Abstract base class for training the *bag of visual words* vocabulary from a set of descriptors. For details, see, for example, *Visual Categorization with Bags of Keypoints* by Gabriella Csurka, Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. :: class BOWTrainer { public: BOWTrainer(){} virtual ~BOWTrainer(){} void add( const Mat& descriptors ); const vector& getDescriptors() const; int descripotorsCount() const; virtual void clear(); virtual Mat cluster() const = 0; virtual Mat cluster( const Mat& descriptors ) const = 0; protected: ... }; .. index:: BOWTrainer::add BOWTrainer::add ------------------- .. c:function:: void BOWTrainer::add( const Mat& descriptors ) Adds descriptors to a training set. The training set is clustered using ``clustermethod`` to construct the vocabulary. :param descriptors: Descriptors to add to a training set. Each row of the ``descriptors`` matrix is a descriptor. .. index:: BOWTrainer::getDescriptors BOWTrainer::getDescriptors ------------------------------ .. c:function:: const vector& BOWTrainer::getDescriptors() const Returns a training set of descriptors. .. index:: BOWTrainer::descripotorsCount BOWTrainer::descripotorsCount --------------------------------- .. c:function:: const vector& BOWTrainer::descripotorsCount() const Returns the count of all descriptors stored in the training set. .. index:: BOWTrainer::cluster BOWTrainer::cluster ----------------------- .. c:function:: Mat BOWTrainer::cluster() const Clusters train descriptors. The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first variant of the method, train descriptors stored in the object are clustered. In the second variant, input descriptors are clustered. .. c:function:: Mat BOWTrainer::cluster( const Mat& descriptors ) const :param descriptors: Descriptors to cluster. Each row of the ``descriptors`` matrix is a descriptor. Descriptors are not added to the inner train descriptor set. .. index:: BOWKMeansTrainer .. _BOWKMeansTrainer: BOWKMeansTrainer ---------------- .. cpp:class:: BOWKMeansTrainer :ref:`kmeans` -based class to train visual vocabulary using the *bag of visual words* approach :: class BOWKMeansTrainer : public BOWTrainer { public: BOWKMeansTrainer( int clusterCount, const TermCriteria& termcrit=TermCriteria(), int attempts=3, int flags=KMEANS_PP_CENTERS ); virtual ~BOWKMeansTrainer(){} // Returns trained vocabulary (i.e. cluster centers). virtual Mat cluster() const; virtual Mat cluster( const Mat& descriptors ) const; protected: ... }; To understand constructor parameters, see :ref:`kmeans` function arguments. .. index:: BOWImgDescriptorExtractor .. _BOWImgDescriptorExtractor: BOWImgDescriptorExtractor ------------------------- .. cpp:class:: BOWImgDescriptorExtractor Class to compute an image descriptor using the ''bag of visual words''. Such a computation consists of the following steps: #. Compute descriptors for a given image and its keypoints set. #. Find the nearest visual words from the vocabulary for each keypoint descriptor. #. Compute the bag-of-words image descriptor as is a normalized histogram of vocabulary words encountered in the image. The ``i``-th bin of the histogram is a frequency of ``i``-th word of the vocabulary in the given image. Here is the class declaration :: class BOWImgDescriptorExtractor { public: BOWImgDescriptorExtractor( const Ptr& dextractor, const Ptr& dmatcher ); virtual ~BOWImgDescriptorExtractor(){} void setVocabulary( const Mat& vocabulary ); const Mat& getVocabulary() const; void compute( const Mat& image, vector& keypoints, Mat& imgDescriptor, vector >* pointIdxsOfClusters=0, Mat* descriptors=0 ); int descriptorSize() const; int descriptorType() const; protected: ... }; .. index:: BOWImgDescriptorExtractor::BOWImgDescriptorExtractor BOWImgDescriptorExtractor::BOWImgDescriptorExtractor -------------------------------------------------------- .. c:function:: BOWImgDescriptorExtractor::BOWImgDescriptorExtractor( const Ptr& dextractor, const Ptr& dmatcher ) The class constructor. :param dextractor: Descriptor extractor that is used to compute descriptors for an input image and its keypoints. :param dmatcher: Descriptor matcher that is used to find the nearest word of the trained vocabulary for each keypoint descriptor of the image. .. index:: BOWImgDescriptorExtractor::setVocabulary BOWImgDescriptorExtractor::setVocabulary -------------------------------------------- .. c:function:: void BOWImgDescriptorExtractor::setVocabulary( const Mat& vocabulary ) Sets a visual vocabulary. :param vocabulary: Vocabulary (can be trained using the inheritor of :ref:`BOWTrainer` ). Each row of the vocabulary is a visual word (cluster center). .. index:: BOWImgDescriptorExtractor::getVocabulary BOWImgDescriptorExtractor::getVocabulary -------------------------------------------- .. c:function:: const Mat& BOWImgDescriptorExtractor::getVocabulary() const Returns the set vocabulary. .. index:: BOWImgDescriptorExtractor::compute BOWImgDescriptorExtractor::compute -------------------------------------- .. c:function:: void BOWImgDescriptorExtractor::compute( const Mat& image, vector& keypoints, Mat& imgDescriptor, vector >* pointIdxsOfClusters=0, Mat* descriptors=0 ) Computes an image descriptor using the set visual vocabulary. :param image: Image, for which the descriptor is computed. :param keypoints: Keypoints detected in the input image. :param imgDescriptor: Computed output image descriptor. :param pointIdxsOfClusters: Indices of keypoints that belong to the cluster. This means that ``pointIdxsOfClusters[i]`` are keypoint indices that belong to the ``i`` -th cluster (word of vocabulary) returned if it is non-zero. :param descriptors: Descriptors of the image keypoints that are returned if they are non-zero. .. index:: BOWImgDescriptorExtractor::descriptorSize BOWImgDescriptorExtractor::descriptorSize --------------------------------------------- .. c:function:: int BOWImgDescriptorExtractor::descriptorSize() const Returns an image discriptor size if the vocabulary is set. Otherwise, it returns 0. .. index:: BOWImgDescriptorExtractor::descriptorType BOWImgDescriptorExtractor::descriptorType --------------------------------------------- .. c:function:: int BOWImgDescriptorExtractor::descriptorType() const Returns an image descriptor type.