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