csu3_am335x/linux-devkit/sysroots/armv7ahf-neon-linux-gnueabi/usr/include/opencv2/saliency/saliencyBaseClasses.hpp

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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 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
   */
  bool computeSaliency( InputArray image, OutputArray saliencyMap );

  /**
   * \brief Get the name of the specific saliency type
   * \return The name of the tracker initializer
   */
  String getClassName() const;

 protected:

  virtual bool computeSaliencyImpl( InputArray image, OutputArray saliencyMap ) = 0;
  String className;
};

/************************************ Static Saliency Base Class ************************************/
class CV_EXPORTS 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
     */
  bool computeBinaryMap( const Mat& saliencyMap, Mat& binaryMap );
 protected:
  virtual bool computeSaliencyImpl( InputArray image, OutputArray saliencyMap )=0;

};

/************************************ Motion Saliency Base Class ************************************/
class CV_EXPORTS MotionSaliency : public virtual Saliency
{

 protected:
  virtual bool computeSaliencyImpl( InputArray image, OutputArray saliencyMap )=0;

};

/************************************ Objectness Base Class ************************************/
class CV_EXPORTS Objectness : public virtual Saliency
{

 protected:
  virtual bool computeSaliencyImpl( InputArray image, OutputArray saliencyMap )=0;

};

//! @}

} /* namespace saliency */
} /* namespace cv */

#endif