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
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
|
// Copyright (C) 2011 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#undef DLIB_SETUP_HAShED_FEATURES_ABSTRACT_Hh_
#ifdef DLIB_SETUP_HAShED_FEATURES_ABSTRACT_Hh_
#include "scan_image_pyramid_abstract.h"
#include "scan_image_boxes_abstract.h"
#include "../lsh/projection_hash_abstract.h"
#include "../image_keypoint/hashed_feature_image_abstract.h"
#include "../image_keypoint/binned_vector_feature_image_abstract.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
class image_hash_construction_failure : public error
{
/*!
WHAT THIS OBJECT REPRESENTS
This is the exception object used by the routines in this file.
!*/
};
// ----------------------------------------------------------------------------------------
template <
typename image_scanner
>
void use_uniform_feature_weights (
image_scanner& scanner
);
/*!
requires
- image_scanner should be either scan_image_pyramid or scan_image_boxes and
should use the hashed_feature_image as its local feature extractor.
ensures
- #scanner.get_feature_extractor().uses_uniform_feature_weights() == true
(i.e. Make the scanner's feature extractor use the uniform feature weighting
scheme)
!*/
// ----------------------------------------------------------------------------------------
template <
typename image_scanner
>
void use_relative_feature_weights (
image_scanner& scanner
);
/*!
requires
- image_scanner should be either scan_image_pyramid or scan_image_boxes and
should use the hashed_feature_image as its local feature extractor.
ensures
- #scanner.get_feature_extractor().uses_uniform_feature_weights() == false
(i.e. Make the scanner's feature extractor use the relative feature weighting
scheme)
!*/
// ----------------------------------------------------------------------------------------
template <
typename image_array,
typename pyramid,
typename feature_extractor
template <typename fe, typename hash> class feature_image
>
void setup_hashed_features (
scan_image_pyramid<pyramid, feature_image<feature_extractor, projection_hash> >& scanner,
const image_array& images,
const feature_extractor& fe,
int bits,
unsigned long num_samples = 200000
);
/*!
requires
- 0 < bits <= 32
- num_samples > 1
- images.size() > 0
- it must be valid to pass images[0] into scanner.load().
(also, image_array must be an implementation of dlib/array/array_kernel_abstract.h)
- feature_image == must be either hashed_feature_image, binned_vector_feature_image,
or a type with a compatible interface.
ensures
- Creates a projection_hash suitable for hashing the feature vectors produced by
fe and then configures scanner to use this hash function.
- The hash function will map vectors into integers in the range [0, pow(2,bits))
- The hash function will be setup so that it hashes a random sample of num_samples
vectors from fe such that each bin ends up with roughly the same number of
elements in it.
throws
- image_hash_construction_failure
This exception is thrown if there is a problem creating the projection_hash.
This should only happen the images are so small they contain less than 2
feature vectors.
!*/
// ----------------------------------------------------------------------------------------
template <
typename image_array,
typename pyramid,
typename feature_extractor
template <typename fe, typename hash> class feature_image
>
void setup_hashed_features (
scan_image_pyramid<pyramid, feature_image<feature_extractor, projection_hash> >& scanner,
const image_array& images,
int bits,
unsigned long num_samples = 200000
);
/*!
requires
- 0 < bits <= 32
- num_samples > 1
- images.size() > 0
- it must be valid to pass images[0] into scanner.load().
(also, image_array must be an implementation of dlib/array/array_kernel_abstract.h)
- feature_image == must be either hashed_feature_image, binned_vector_feature_image,
or a type with a compatible interface.
ensures
- performs: setup_hashed_features(scanner, images, feature_extractor(), bits, num_samples)
throws
- image_hash_construction_failure
This exception is thrown if there is a problem creating the projection_hash.
This should only happen the images are so small they contain less than 2
feature vectors.
!*/
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
template <
typename image_array,
typename feature_extractor,
template <typename fe, typename hash> class feature_image
typename box_generator
>
void setup_hashed_features (
scan_image_boxes<feature_image<feature_extractor, projection_hash>,box_generator>& scanner,
const image_array& images,
const feature_extractor& fe,
int bits,
unsigned long num_samples = 200000
);
/*!
requires
- 0 < bits <= 32
- num_samples > 1
- images.size() > 0
- it must be valid to pass images[0] into scanner.load().
(also, image_array must be an implementation of dlib/array/array_kernel_abstract.h)
- feature_image == must be either hashed_feature_image, binned_vector_feature_image,
or a type with a compatible interface.
ensures
- Creates a projection_hash suitable for hashing the feature vectors produced by
fe and then configures scanner to use this hash function.
- The hash function will map vectors into integers in the range [0, pow(2,bits))
- The hash function will be setup so that it hashes a random sample of num_samples
vectors from fe such that each bin ends up with roughly the same number of
elements in it.
throws
- image_hash_construction_failure
This exception is thrown if there is a problem creating the projection_hash.
This should only happen the images are so small they contain less than 2
feature vectors.
!*/
// ----------------------------------------------------------------------------------------
template <
typename image_array,
typename feature_extractor,
template <typename fe, typename hash> class feature_image
typename box_generator
>
void setup_hashed_features (
scan_image_boxes<feature_image<feature_extractor, projection_hash>,box_generator>& scanner,
const image_array& images,
int bits,
unsigned long num_samples = 200000
);
/*!
requires
- 0 < bits <= 32
- num_samples > 1
- images.size() > 0
- it must be valid to pass images[0] into scanner.load().
(also, image_array must be an implementation of dlib/array/array_kernel_abstract.h)
- feature_image == must be either hashed_feature_image, binned_vector_feature_image,
or a type with a compatible interface.
ensures
- performs: setup_hashed_features(scanner, images, feature_extractor(), bits, num_samples)
throws
- image_hash_construction_failure
This exception is thrown if there is a problem creating the projection_hash.
This should only happen the images are so small they contain less than 2
feature vectors.
!*/
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_SETUP_HAShED_FEATURES_ABSTRACT_Hh_
|
