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
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
/*
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
(3-clause BSD License)
Copyright (C) 2013, 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:
* Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
* Redistributions 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.
* Neither the names of the copyright holders nor the names of the contributors
may 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 copyright holders 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.
*/
#ifndef __OPENCV_XOBJDETECT_XOBJDETECT_HPP__
#define __OPENCV_XOBJDETECT_XOBJDETECT_HPP__
#include <opencv2/core.hpp>
#include <vector>
#include <string>
namespace cv
{
namespace xobjdetect
{
/* Compute channel pyramid for acf features
image — image, for which channels should be computed
channels — output array for computed channels
*/
void computeChannels(InputArray image, OutputArrayOfArrays channels);
class CV_EXPORTS ACFFeatureEvaluator : public Algorithm
{
public:
/* Set channels for feature evaluation */
virtual void setChannels(InputArrayOfArrays channels) = 0;
/* Set window position */
virtual void setPosition(Size position) = 0;
/* Evaluate feature with given index for current channels
and window position */
virtual int evaluate(size_t feature_ind) const = 0;
/* Evaluate all features for current channels and window position
Returns matrix-column of features
*/
virtual void evaluateAll(OutputArray feature_values) const = 0;
};
/* Construct evaluator, set features to evaluate */
CV_EXPORTS Ptr<ACFFeatureEvaluator>
createACFFeatureEvaluator(const std::vector<Point3i>& features);
/* Generate acf features
window_size — size of window in which features should be evaluated
count — number of features to generate.
Max number of features is min(count, # possible distinct features)
Returns vector of distinct acf features
*/
std::vector<Point3i>
generateFeatures(Size window_size, int count = INT_MAX);
struct CV_EXPORTS WaldBoostParams
{
int weak_count;
float alpha;
WaldBoostParams(): weak_count(100), alpha(0.01f)
{}
};
class CV_EXPORTS WaldBoost : public Algorithm
{
public:
/* Train WaldBoost cascade for given data
data — matrix of feature values, size M x N, one feature per row
labels — matrix of sample class labels, size 1 x N. Labels can be from
{-1, +1}
Returns feature indices chosen for cascade.
Feature enumeration starts from 0
*/
virtual std::vector<int> train(const Mat& data,
const Mat& labels) = 0;
/* Predict object class given object that can compute object features
feature_evaluator — object that can compute features by demand
Returns confidence_value — measure of confidense that object
is from class +1
*/
virtual float predict(
const Ptr<ACFFeatureEvaluator>& feature_evaluator) const = 0;
};
CV_EXPORTS Ptr<WaldBoost>
createWaldBoost(const WaldBoostParams& params = WaldBoostParams());
struct CV_EXPORTS ICFDetectorParams
{
int feature_count;
int weak_count;
int model_n_rows;
int model_n_cols;
double overlap;
ICFDetectorParams(): feature_count(UINT_MAX), weak_count(100),
model_n_rows(40), model_n_cols(40), overlap(0.0)
{}
};
class CV_EXPORTS ICFDetector
{
public:
/* Train detector
image_filenames — filenames of images for training
labelling — vector of object bounding boxes per every image
params — parameters for detector training
*/
void train(const std::vector<std::string>& image_filenames,
const std::vector<std::vector<cv::Rect> >& labelling,
ICFDetectorParams params = ICFDetectorParams());
/* Save detector in file, return true on success, false otherwise */
bool save(const std::string& filename);
};
} /* namespace xobjdetect */
} /* namespace cv */
#endif /* __OPENCV_XOBJDETECT_XOBJDETECT_HPP__ */