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#ifndef __OPENCV_SALIENCY_BASE_CLASSES_HPP__
#define __OPENCV_SALIENCY_BASE_CLASSES_HPP__
#include "opencv2/core.hpp"
#include <opencv2/core/persistence.hpp>
#include "opencv2/imgproc.hpp"
#include <iostream>
#include <sstream>
#include <complex>
namespace cv
{
namespace saliency
{
//! @addtogroup saliency
//! @{
/************************************ Saliency Base Class ************************************/
class CV_EXPORTS_W Saliency : public virtual Algorithm
{
public:
/**
* \brief Destructor
*/
virtual ~Saliency();
/**
* \brief Create Saliency by saliency type.
*/
static Ptr<Saliency> create( const String& saliencyType );
/**
* \brief Compute the saliency
* \param image The image.
* \param saliencyMap The computed saliency map.
* \return true if the saliency map is computed, false otherwise
*/
CV_WRAP bool computeSaliency( InputArray image, OutputArray saliencyMap );
/**
* \brief Get the name of the specific saliency type
* \return The name of the tracker initializer
*/
CV_WRAP String getClassName() const;
protected:
virtual bool computeSaliencyImpl( InputArray image, OutputArray saliencyMap ) = 0;
String className;
};
/************************************ Static Saliency Base Class ************************************/
class CV_EXPORTS_W StaticSaliency : public virtual Saliency
{
public:
/** @brief This function perform a binary map of given saliency map. This is obtained in this
way:
In a first step, to improve the definition of interest areas and facilitate identification of
targets, a segmentation by clustering is performed, using *K-means algorithm*. Then, to gain a
binary representation of clustered saliency map, since values of the map can vary according to
the characteristics of frame under analysis, it is not convenient to use a fixed threshold. So,
*Otsu’s algorithm* is used, which assumes that the image to be thresholded contains two classes
of pixels or bi-modal histograms (e.g. foreground and back-ground pixels); later on, the
algorithm calculates the optimal threshold separating those two classes, so that their
intra-class variance is minimal.
@param _saliencyMap the saliency map obtained through one of the specialized algorithms
@param _binaryMap the binary map
*/
CV_WRAP bool computeBinaryMap( InputArray _saliencyMap, OutputArray _binaryMap );
protected:
virtual bool computeSaliencyImpl( InputArray image, OutputArray saliencyMap )=0;
};
/************************************ Motion Saliency Base Class ************************************/
class CV_EXPORTS_W MotionSaliency : public virtual Saliency
{
protected:
virtual bool computeSaliencyImpl( InputArray image, OutputArray saliencyMap )=0;
};
/************************************ Objectness Base Class ************************************/
class CV_EXPORTS_W Objectness : public virtual Saliency
{
protected:
virtual bool computeSaliencyImpl( InputArray image, OutputArray saliencyMap )=0;
};
//! @}
} /* namespace saliency */
} /* namespace cv */
#endif
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