Object Categorization
=====================

.. highlight:: cpp


Some approaches based on local 2D features and used to object categorization 
are described in this section.


.. index:: BOWTrainer

.. _BOWTrainer:

BOWTrainer
----------

`id=0.624981548431 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWTrainer>`__

.. ctype:: BOWTrainer



Abstract base class for training ''bag of visual words'' vocabulary from a set of descriptors. 
See e.g. ''Visual Categorization with Bags of Keypoints'' of 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<Mat>& 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


cv::BOWTrainer::add
-------------------

`id=0.55981141615 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWTrainer%3A%3Aadd>`__


````


.. cfunction:: void BOWTrainer::add( const Mat\& descriptors )

    Add descriptors to training set. The training set will be clustered using clustermethod to construct vocabulary.





    
    :param descriptors: Descriptors to add to training set. Each row of  ``descriptors`` 
                                                matrix is a one descriptor. 
    
    
    

.. index:: BOWTrainer::getDescriptors


cv::BOWTrainer::getDescriptors
------------------------------

`id=0.197213441608 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWTrainer%3A%3AgetDescriptors>`__




.. cfunction:: const vector<Mat>\& BOWTrainer::getDescriptors() const

    Returns training set of descriptors.




.. index:: BOWTrainer::descripotorsCount


cv::BOWTrainer::descripotorsCount
---------------------------------

`id=0.589567188232 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWTrainer%3A%3AdescripotorsCount>`__




.. cfunction:: const vector<Mat>\& BOWTrainer::descripotorsCount() const

    Returns count of all descriptors stored in the training set.




.. index:: BOWTrainer::cluster


cv::BOWTrainer::cluster
-----------------------

`id=0.499465812452 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWTrainer%3A%3Acluster>`__




.. cfunction:: Mat BOWTrainer::cluster() const

    Cluster train descriptors. Vocabulary consists from cluster centers. So this method 
returns vocabulary. In first method variant the stored in object train descriptors will be 
clustered, in second variant -- input descriptors will be clustered.





.. cfunction:: Mat BOWTrainer::cluster( const Mat\& descriptors ) const





    
    :param descriptors: Descriptors to cluster. Each row of  ``descriptors`` 
                                                matrix is a one descriptor. Descriptors will not be added 
                                                to the inner train descriptor set. 
    
    
    

.. index:: BOWKMeansTrainer

.. _BOWKMeansTrainer:

BOWKMeansTrainer
----------------

`id=0.056318363983 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWKMeansTrainer>`__

.. ctype:: BOWKMeansTrainer



:func:`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 gain an understanding of constructor parameters see 
:func:`kmeans`
function 
arguments.



.. index:: BOWImgDescriptorExtractor

.. _BOWImgDescriptorExtractor:

BOWImgDescriptorExtractor
-------------------------

`id=0.0305574768224 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWImgDescriptorExtractor>`__

.. ctype:: BOWImgDescriptorExtractor



Class to compute image descriptor using ''bad of visual words''. In few, 
 such computing consists from the following steps: 
 1. Compute descriptors for given image and it's keypoints set, 
\
2. Find nearest visual words from vocabulary for each keypoint descriptor, 
\
3. Image descriptor is a normalized histogram of vocabulary words encountered in the image. I.e. 
 
``i``
-bin of the histogram is a frequency of 
``i``
-word of vocabulary in the given image.




::


    
    class BOWImgDescriptorExtractor
    {
    public:
        BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>& dextractor,
                                   const Ptr<DescriptorMatcher>& dmatcher );
        virtual ~BOWImgDescriptorExtractor(){}
    
        void setVocabulary( const Mat& vocabulary );
        const Mat& getVocabulary() const;
        void compute( const Mat& image, vector<KeyPoint>& keypoints, 
                      Mat& imgDescriptor, 
                      vector<vector<int> >* pointIdxsOfClusters=0, 
                      Mat* descriptors=0 );
        int descriptorSize() const;
        int descriptorType() const;
    
    protected:
        ...
    };
    

..


.. index:: BOWImgDescriptorExtractor::BOWImgDescriptorExtractor


cv::BOWImgDescriptorExtractor::BOWImgDescriptorExtractor
--------------------------------------------------------

`id=0.204827459102 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWImgDescriptorExtractor%3A%3ABOWImgDescriptorExtractor>`__




.. cfunction:: BOWImgDescriptorExtractor::BOWImgDescriptorExtractor(           const Ptr<DescriptorExtractor>\& dextractor,          const Ptr<DescriptorMatcher>\& dmatcher )

    Constructor.





    
    :param dextractor: Descriptor extractor that will be used to compute descriptors 
                                           for input image and it's keypoints. 
    
    
    :param dmatcher: Descriptor matcher that will be used to find nearest word of trained vocabulary to
                                         each keupoints descriptor of the image. 
    
    
    

.. index:: BOWImgDescriptorExtractor::setVocabulary


cv::BOWImgDescriptorExtractor::setVocabulary
--------------------------------------------

`id=0.137316738477 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWImgDescriptorExtractor%3A%3AsetVocabulary>`__




.. cfunction:: void BOWImgDescriptorExtractor::setVocabulary( const Mat\& vocabulary )

    Method to set visual vocabulary.





    
    :param vocabulary: Vocabulary (can be trained using inheritor of  :func:`BOWTrainer` ). 
                                           Each row of vocabulary is a one visual word (cluster center). 
    
    
    

.. index:: BOWImgDescriptorExtractor::getVocabulary


cv::BOWImgDescriptorExtractor::getVocabulary
--------------------------------------------

`id=0.100098837965 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWImgDescriptorExtractor%3A%3AgetVocabulary>`__




.. cfunction:: const Mat\& BOWImgDescriptorExtractor::getVocabulary() const

    Returns set vocabulary.




.. index:: BOWImgDescriptorExtractor::compute


cv::BOWImgDescriptorExtractor::compute
--------------------------------------

`id=0.913461508095 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWImgDescriptorExtractor%3A%3Acompute>`__




.. cfunction:: void BOWImgDescriptorExtractor::compute( const Mat\& image,           vector<KeyPoint>\& keypoints, Mat\& imgDescriptor,           vector<vector<int> >* pointIdxsOfClusters=0,           Mat* descriptors=0 )

    Compute image descriptor using set visual vocabulary.





    
    :param image: The image. Image descriptor will be computed for this. 
    
    
    :param keypoints: Keypoints detected in the input image. 
    
    
    :param imgDescriptor: This is output, i.e. computed image descriptor. 
    
    
    :param pointIdxsOfClusters: Indices of keypoints which belong to the cluster, i.e. 
                                 ``pointIdxsOfClusters[i]``  is keypoint indices which belong 
                                to the  ``i-`` cluster (word of vocabulary) (returned if it is not 0.) 
    
    
    :param descriptors: Descriptors of the image keypoints (returned if it is not 0.) 
    
    
    

.. index:: BOWImgDescriptorExtractor::descriptorSize


cv::BOWImgDescriptorExtractor::descriptorSize
---------------------------------------------

`id=0.813385359449 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWImgDescriptorExtractor%3A%3AdescriptorSize>`__




.. cfunction:: int BOWImgDescriptorExtractor::descriptorSize() const

    Returns image discriptor size, if vocabulary was set, and 0 otherwise.




.. index:: BOWImgDescriptorExtractor::descriptorType


cv::BOWImgDescriptorExtractor::descriptorType
---------------------------------------------

`id=0.500242248636 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/BOWImgDescriptorExtractor%3A%3AdescriptorType>`__




.. cfunction:: int BOWImgDescriptorExtractor::descriptorType() const

    Returns image descriptor type.