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e3995236ed
replaced "const InputArray" with "InputArray" to avoid warnings about "const const _InputArray&"
145 lines
5.1 KiB
C++
145 lines
5.1 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2014, OpenCV Foundation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#ifndef __OPENCV_SALIENCY_BASE_CLASSES_HPP__
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#define __OPENCV_SALIENCY_BASE_CLASSES_HPP__
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#include "opencv2/core.hpp"
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#include <opencv2/core/persistence.hpp>
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#include "opencv2/imgproc.hpp"
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#include <iostream>
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#include <sstream>
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#include <complex>
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namespace cv
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{
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namespace saliency
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{
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//! @addtogroup saliency
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//! @{
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/************************************ Saliency Base Class ************************************/
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class CV_EXPORTS Saliency : public virtual Algorithm
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{
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public:
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/**
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* \brief Destructor
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*/
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virtual ~Saliency();
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/**
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* \brief Create Saliency by saliency type.
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*/
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static Ptr<Saliency> create( const String& saliencyType );
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/**
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* \brief Compute the saliency
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* \param image The image.
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* \param saliencyMap The computed saliency map.
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* \return true if the saliency map is computed, false otherwise
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*/
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bool computeSaliency( InputArray image, OutputArray saliencyMap );
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/**
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* \brief Get the name of the specific saliency type
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* \return The name of the tracker initializer
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*/
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String getClassName() const;
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protected:
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virtual bool computeSaliencyImpl( InputArray image, OutputArray saliencyMap ) = 0;
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String className;
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};
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/************************************ Static Saliency Base Class ************************************/
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class CV_EXPORTS StaticSaliency : public virtual Saliency
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{
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public:
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/** @brief This function perform a binary map of given saliency map. This is obtained in this
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way:
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In a first step, to improve the definition of interest areas and facilitate identification of
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targets, a segmentation by clustering is performed, using *K-means algorithm*. Then, to gain a
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binary representation of clustered saliency map, since values of the map can vary according to
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the characteristics of frame under analysis, it is not convenient to use a fixed threshold. So,
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*Otsu’s algorithm* is used, which assumes that the image to be thresholded contains two classes
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of pixels or bi-modal histograms (e.g. foreground and back-ground pixels); later on, the
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algorithm calculates the optimal threshold separating those two classes, so that their
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intra-class variance is minimal.
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@param saliencyMap the saliency map obtained through one of the specialized algorithms
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@param binaryMap the binary map
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*/
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bool computeBinaryMap( const Mat& saliencyMap, Mat& binaryMap );
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protected:
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virtual bool computeSaliencyImpl( InputArray image, OutputArray saliencyMap )=0;
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};
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/************************************ Motion Saliency Base Class ************************************/
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class CV_EXPORTS MotionSaliency : public virtual Saliency
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{
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protected:
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virtual bool computeSaliencyImpl( InputArray image, OutputArray saliencyMap )=0;
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};
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/************************************ Objectness Base Class ************************************/
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class CV_EXPORTS Objectness : public virtual Saliency
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{
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protected:
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virtual bool computeSaliencyImpl( InputArray image, OutputArray saliencyMap )=0;
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};
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//! @}
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} /* namespace saliency */
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} /* namespace cv */
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#endif
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