1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
|
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2014, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#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
|
