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https://github.com/opencv/opencv_contrib.git
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6e7d162e5a
I've changed Rect to Rect2d in Tracker::update(), Tracker::init() and all related methods (including documentation). This allows to initialize trackers with double-valued rectangles, thus adding versality. Besides, trackers also can output double-valued rectangles, which may be beneficial in some scenarios. However, it remains to change UML diagrams in documentation to tracker module, as they still display methods above with old signatures.
344 lines
9.0 KiB
ReStructuredText
344 lines
9.0 KiB
ReStructuredText
Common Interfaces of TrackerFeatureSet
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======================================
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.. highlight:: cpp
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TrackerFeatureSet
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-----------------
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Class that manages the extraction and selection of features
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[AAM]_ Feature Extraction and Feature Set Refinement (Feature Processing and Feature Selection). See table I and section III C
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[AMVOT]_ Appearance modelling -> Visual representation (Table II, section 3.1 - 3.2)
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.. ocv:class:: TrackerFeatureSet
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TrackerFeatureSet class::
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class CV_EXPORTS_W TrackerFeatureSet
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{
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public:
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TrackerFeatureSet();
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~TrackerFeatureSet();
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void extraction( const std::vector<Mat>& images );
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void selection();
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void removeOutliers();
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bool addTrackerFeature( String trackerFeatureType );
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bool addTrackerFeature( Ptr<TrackerFeature>& feature );
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const std::vector<std::pair<String, Ptr<TrackerFeature> > >& getTrackerFeature() const;
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const std::vector<Mat>& getResponses() const;
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};
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TrackerFeatureSet is an aggregation of :ocv:class:`TrackerFeature`
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.. seealso::
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:ocv:class:`TrackerFeature`
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TrackerFeatureSet::extraction
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-----------------------------
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Extract features from the images collection
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.. ocv:function:: void TrackerFeatureSet::extraction( const std::vector<Mat>& images )
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:param images: The input images
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TrackerFeatureSet::selection
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----------------------------
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Identify most effective features for all feature types (optional)
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.. ocv:function:: void TrackerFeatureSet::selection()
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TrackerFeatureSet::removeOutliers
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---------------------------------
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Remove outliers for all feature types (optional)
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.. ocv:function:: void TrackerFeatureSet::removeOutliers()
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TrackerFeatureSet::addTrackerFeature
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------------------------------------
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Add TrackerFeature in the collection. Return true if TrackerFeature is added, false otherwise
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.. ocv:function:: bool TrackerFeatureSet::addTrackerFeature( String trackerFeatureType )
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:param trackerFeatureType: The TrackerFeature name
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.. ocv:function:: bool TrackerFeatureSet::addTrackerFeature( Ptr<TrackerFeature>& feature )
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:param feature: The TrackerFeature class
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The modes available now:
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* ``"HAAR"`` -- Haar Feature-based
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The modes that will be available soon:
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* ``"HOG"`` -- Histogram of Oriented Gradients features
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* ``"LBP"`` -- Local Binary Pattern features
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* ``"FEATURE2D"`` -- All types of Feature2D
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Example ``TrackerFeatureSet::addTrackerFeature`` : ::
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//sample usage:
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Ptr<TrackerFeature> trackerFeature = new TrackerFeatureHAAR( HAARparameters );
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featureSet->addTrackerFeature( trackerFeature );
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//or add CSC sampler with default parameters
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//featureSet->addTrackerFeature( "HAAR" );
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.. note:: If you use the second method, you must initialize the TrackerFeature
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TrackerFeatureSet::getTrackerFeature
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------------------------------------
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Get the TrackerFeature collection (TrackerFeature name, TrackerFeature pointer)
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.. ocv:function:: const std::vector<std::pair<String, Ptr<TrackerFeature> > >& TrackerFeatureSet::getTrackerFeature() const
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TrackerFeatureSet::getResponses
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-------------------------------
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Get the responses
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.. ocv:function:: const std::vector<Mat>& TrackerFeatureSet::getResponses() const
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.. note:: Be sure to call extraction before getResponses
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Example ``TrackerFeatureSet::getResponses`` : ::
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//get the patches from sampler
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std::vector<Mat> detectSamples = sampler->getSamples();
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if( detectSamples.empty() )
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return false;
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//features extraction
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featureSet->extraction( detectSamples );
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//get responses
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std::vector<Mat> response = featureSet->getResponses();
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TrackerFeature
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--------------
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Abstract base class for TrackerFeature that represents the feature.
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.. ocv:class:: TrackerFeature
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TrackerFeature class::
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class CV_EXPORTS_W TrackerFeature
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{
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public:
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virtual ~TrackerFeature();
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static Ptr<TrackerFeature> create( const String& trackerFeatureType );
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void compute( const std::vector<Mat>& images, Mat& response );
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virtual void selection( Mat& response, int npoints ) = 0;
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String getClassName() const;
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};
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TrackerFeature::create
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----------------------
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Create TrackerFeature by tracker feature type
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.. ocv:function:: static Ptr<TrackerFeature> TrackerFeature::create( const String& trackerFeatureType )
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:param trackerFeatureType: The TrackerFeature name
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The modes available now:
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* ``"HAAR"`` -- Haar Feature-based
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The modes that will be available soon:
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* ``"HOG"`` -- Histogram of Oriented Gradients features
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* ``"LBP"`` -- Local Binary Pattern features
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* ``"FEATURE2D"`` -- All types of Feature2D
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TrackerFeature::compute
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-----------------------
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Compute the features in the images collection
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.. ocv:function:: void TrackerFeature::compute( const std::vector<Mat>& images, Mat& response )
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:param images: The images
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:param response: The output response
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TrackerFeature::selection
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-------------------------
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Identify most effective features
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.. ocv:function:: void TrackerFeature::selection( Mat& response, int npoints )
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:param response: Collection of response for the specific TrackerFeature
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:param npoints: Max number of features
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.. note:: This method modifies the response parameter
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TrackerFeature::getClassName
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----------------------------
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Get the name of the specific TrackerFeature
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.. ocv:function:: String TrackerFeature::getClassName() const
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Specialized TrackerFeature
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==========================
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In [AAM]_ table I and section III C are described the most known features type. At moment only :ocv:class:`TrackerFeatureHAAR` is implemented.
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TrackerFeatureHAAR : TrackerFeature
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-----------------------------------
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TrackerFeature based on HAAR features, used by TrackerMIL and many others algorithms
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.. ocv:class:: TrackerFeatureHAAR
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TrackerFeatureHAAR class::
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class CV_EXPORTS_W TrackerFeatureHAAR : TrackerFeature
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{
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public:
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TrackerFeatureHAAR( const TrackerFeatureHAAR::Params ¶meters = TrackerFeatureHAAR::Params() );
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~TrackerFeatureHAAR();
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void selection( Mat& response, int npoints );
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bool extractSelected( const std::vector<int> selFeatures, const std::vector<Mat>& images, Mat& response );
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std::vector<std::pair<float, float> >& getMeanSigmaPairs();
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bool swapFeature( int source, int target );
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bool swapFeature( int id, CvHaarEvaluator::FeatureHaar& feature );
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CvHaarEvaluator::FeatureHaar& getFeatureAt( int id );
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};
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.. note:: HAAR features implementation is copied from apps/traincascade and modified according to MIL implementation
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TrackerFeatureHAAR::Params
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--------------------------
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.. ocv:struct:: TrackerFeatureHAAR::Params
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List of TrackerFeatureHAAR parameters::
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struct CV_EXPORTS Params
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{
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Params();
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int numFeatures; // # of rects
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Size rectSize; // rect size
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bool isIntegral; // true if input images are integral, false otherwise
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};
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TrackerFeatureHAAR::TrackerFeatureHAAR
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--------------------------------------
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Constructor
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.. ocv:function:: TrackerFeatureHAAR::TrackerFeatureHAAR( const TrackerFeatureHAAR::Params ¶meters = TrackerFeatureHAAR::Params() )
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:param parameters: TrackerFeatureHAAR parameters :ocv:struct:`TrackerFeatureHAAR::Params`
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TrackerFeatureHAAR::selection
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-----------------------------
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Identify most effective features
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.. ocv:function:: void TrackerFeatureHAAR::selection( Mat& response, int npoints )
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:param response: Collection of response for the specific TrackerFeature
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:param npoints: Max number of features
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.. note:: This method modifies the response parameter
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TrackerFeatureHAAR::extractSelected
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-----------------------------------
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Compute the features only for the selected indices in the images collection
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.. ocv:function:: bool TrackerFeatureHAAR::extractSelected( const std::vector<int> selFeatures, const std::vector<Mat>& images, Mat& response )
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:param selFeatures: indices of selected features
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:param images: The images
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:param response: Collection of response for the specific TrackerFeature
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TrackerFeatureHAAR::getMeanSigmaPairs
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-------------------------------------
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Get the list of mean/sigma. Return the list of mean/sigma
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.. ocv:function:: std::vector<std::pair<float, float> >& TrackerFeatureHAAR::getMeanSigmaPairs()
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TrackerFeatureHAAR::swapFeature
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-------------------------------
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Swap the feature in position source with the feature in position target
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.. ocv:function:: bool TrackerFeatureHAAR::swapFeature( int source, int target )
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:param source: The source position
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:param target: The target position
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Swap the feature in position id with the feature input
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.. ocv:function:: bool TrackerFeatureHAAR::swapFeature( int id, CvHaarEvaluator::FeatureHaar& feature )
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:param id: The position
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:param feature: The feature
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TrackerFeatureHAAR::getFeatureAt
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--------------------------------
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Get the feature in position id
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.. ocv:function:: CvHaarEvaluator::FeatureHaar& TrackerFeatureHAAR::getFeatureAt( int id )
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:param id: The position
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TrackerFeatureHOG
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-----------------
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TODO To be implemented
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TrackerFeatureLBP
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-----------------
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TODO To be implemented
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TrackerFeatureFeature2d
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-----------------------
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TODO To be implemented
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