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
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
// Copyright (C) 2014 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_SHAPE_PREDICTOR_H__
#define DLIB_SHAPE_PREDICTOR_H__
#include "dlib/string.h"
#include "dlib/geometry.h"
#include "dlib/data_io/load_image_dataset.h"
#include "dlib/image_processing.h"
using namespace std;
namespace dlib
{
// ----------------------------------------------------------------------------------------
struct shape_predictor_training_options
{
shape_predictor_training_options()
{
be_verbose = false;
cascade_depth = 10;
tree_depth = 4;
num_trees_per_cascade_level = 500;
nu = 0.1;
oversampling_amount = 20;
oversampling_translation_jitter = 0;
feature_pool_size = 400;
lambda_param = 0.1;
num_test_splits = 20;
feature_pool_region_padding = 0;
random_seed = "";
num_threads = 0;
landmark_relative_padding_mode = true;
}
bool be_verbose;
unsigned long cascade_depth;
unsigned long tree_depth;
unsigned long num_trees_per_cascade_level;
double nu;
unsigned long oversampling_amount;
double oversampling_translation_jitter;
unsigned long feature_pool_size;
double lambda_param;
unsigned long num_test_splits;
double feature_pool_region_padding;
std::string random_seed;
bool landmark_relative_padding_mode;
// not serialized
unsigned long num_threads;
};
inline void serialize (
const shape_predictor_training_options& item,
std::ostream& out
)
{
try
{
serialize("shape_predictor_training_options_v2", out);
serialize(item.be_verbose,out);
serialize(item.cascade_depth,out);
serialize(item.tree_depth,out);
serialize(item.num_trees_per_cascade_level,out);
serialize(item.nu,out);
serialize(item.oversampling_amount,out);
serialize(item.oversampling_translation_jitter,out);
serialize(item.feature_pool_size,out);
serialize(item.lambda_param,out);
serialize(item.num_test_splits,out);
serialize(item.feature_pool_region_padding,out);
serialize(item.random_seed,out);
serialize(item.landmark_relative_padding_mode,out);
}
catch (serialization_error& e)
{
throw serialization_error(e.info + "\n while serializing an object of type shape_predictor_training_options");
}
}
inline void deserialize (
shape_predictor_training_options& item,
std::istream& in
)
{
try
{
check_serialized_version("shape_predictor_training_options_v2", in);
deserialize(item.be_verbose,in);
deserialize(item.cascade_depth,in);
deserialize(item.tree_depth,in);
deserialize(item.num_trees_per_cascade_level,in);
deserialize(item.nu,in);
deserialize(item.oversampling_amount,in);
deserialize(item.oversampling_translation_jitter,in);
deserialize(item.feature_pool_size,in);
deserialize(item.lambda_param,in);
deserialize(item.num_test_splits,in);
deserialize(item.feature_pool_region_padding,in);
deserialize(item.random_seed,in);
deserialize(item.landmark_relative_padding_mode,in);
}
catch (serialization_error& e)
{
throw serialization_error(e.info + "\n while deserializing an object of type shape_predictor_training_options");
}
}
inline string print_shape_predictor_training_options(const shape_predictor_training_options& o)
{
std::ostringstream sout;
sout << "shape_predictor_training_options("
<< "be_verbose=" << o.be_verbose << ", "
<< "cascade_depth=" << o.cascade_depth << ", "
<< "tree_depth=" << o.tree_depth << ", "
<< "num_trees_per_cascade_level=" << o.num_trees_per_cascade_level << ", "
<< "nu=" << o.nu << ", "
<< "oversampling_amount=" << o.oversampling_amount << ", "
<< "oversampling_translation_jitter=" << o.oversampling_translation_jitter << ", "
<< "feature_pool_size=" << o.feature_pool_size << ", "
<< "lambda_param=" << o.lambda_param << ", "
<< "num_test_splits=" << o.num_test_splits << ", "
<< "feature_pool_region_padding=" << o.feature_pool_region_padding << ", "
<< "random_seed=" << o.random_seed << ", "
<< "num_threads=" << o.num_threads << ", "
<< "landmark_relative_padding_mode=" << o.landmark_relative_padding_mode
<< ")";
return sout.str();
}
// ----------------------------------------------------------------------------------------
namespace impl
{
inline bool contains_any_detections (
const std::vector<std::vector<full_object_detection> >& detections
)
{
for (unsigned long i = 0; i < detections.size(); ++i)
{
if (detections[i].size() != 0)
return true;
}
return false;
}
}
// ----------------------------------------------------------------------------------------
template <typename image_array>
inline shape_predictor train_shape_predictor_on_images (
image_array& images,
std::vector<std::vector<full_object_detection> >& detections,
const shape_predictor_training_options& options
)
{
if (options.lambda_param <= 0)
throw error("Invalid lambda_param value given to train_shape_predictor(), lambda_param must be > 0.");
if (!(0 < options.nu && options.nu <= 1))
throw error("Invalid nu value given to train_shape_predictor(). It is required that 0 < nu <= 1.");
if (options.feature_pool_region_padding <= -0.5)
throw error("Invalid feature_pool_region_padding value given to train_shape_predictor(), feature_pool_region_padding must be > -0.5.");
if (images.size() != detections.size())
throw error("The list of images must have the same length as the list of detections.");
if (!impl::contains_any_detections(detections))
throw error("Error, the training dataset does not have any labeled object detections in it.");
shape_predictor_trainer trainer;
trainer.set_cascade_depth(options.cascade_depth);
trainer.set_tree_depth(options.tree_depth);
trainer.set_num_trees_per_cascade_level(options.num_trees_per_cascade_level);
trainer.set_nu(options.nu);
trainer.set_random_seed(options.random_seed);
trainer.set_oversampling_amount(options.oversampling_amount);
trainer.set_oversampling_translation_jitter(options.oversampling_translation_jitter);
trainer.set_feature_pool_size(options.feature_pool_size);
trainer.set_feature_pool_region_padding(options.feature_pool_region_padding);
trainer.set_lambda(options.lambda_param);
trainer.set_num_test_splits(options.num_test_splits);
trainer.set_num_threads(options.num_threads);
if (options.landmark_relative_padding_mode)
trainer.set_padding_mode(shape_predictor_trainer::landmark_relative);
else
trainer.set_padding_mode(shape_predictor_trainer::bounding_box_relative);
if (options.be_verbose)
{
std::cout << "Training with cascade depth: " << options.cascade_depth << std::endl;
std::cout << "Training with tree depth: " << options.tree_depth << std::endl;
std::cout << "Training with " << options.num_trees_per_cascade_level << " trees per cascade level."<< std::endl;
std::cout << "Training with nu: " << options.nu << std::endl;
std::cout << "Training with random seed: " << options.random_seed << std::endl;
std::cout << "Training with oversampling amount: " << options.oversampling_amount << std::endl;
std::cout << "Training with oversampling translation jitter: " << options.oversampling_translation_jitter << std::endl;
std::cout << "Training with landmark_relative_padding_mode: " << options.landmark_relative_padding_mode << std::endl;
std::cout << "Training with feature pool size: " << options.feature_pool_size << std::endl;
std::cout << "Training with feature pool region padding: " << options.feature_pool_region_padding << std::endl;
std::cout << "Training with " << options.num_threads << " threads." << std::endl;
std::cout << "Training with lambda_param: " << options.lambda_param << std::endl;
std::cout << "Training with " << options.num_test_splits << " split tests."<< std::endl;
trainer.be_verbose();
}
shape_predictor predictor = trainer.train(images, detections);
return predictor;
}
inline void train_shape_predictor (
const std::string& dataset_filename,
const std::string& predictor_output_filename,
const shape_predictor_training_options& options
)
{
dlib::array<array2d<unsigned char> > images;
std::vector<std::vector<full_object_detection> > objects;
load_image_dataset(images, objects, dataset_filename);
shape_predictor predictor = train_shape_predictor_on_images(images, objects, options);
serialize(predictor_output_filename) << predictor;
if (options.be_verbose)
std::cout << "Training complete, saved predictor to file " << predictor_output_filename << std::endl;
}
// ----------------------------------------------------------------------------------------
template <typename image_array>
inline double test_shape_predictor_with_images (
image_array& images,
std::vector<std::vector<full_object_detection> >& detections,
std::vector<std::vector<double> >& scales,
const shape_predictor& predictor
)
{
if (images.size() != detections.size())
throw error("The list of images must have the same length as the list of detections.");
if (scales.size() > 0 && scales.size() != images.size())
throw error("The list of scales must have the same length as the list of detections.");
if (scales.size() > 0)
return test_shape_predictor(predictor, images, detections, scales);
else
return test_shape_predictor(predictor, images, detections);
}
inline double test_shape_predictor_py (
const std::string& dataset_filename,
const std::string& predictor_filename
)
{
// Load the images, no scales can be provided
dlib::array<array2d<unsigned char> > images;
// This interface cannot take the scales parameter.
std::vector<std::vector<double> > scales;
std::vector<std::vector<full_object_detection> > objects;
load_image_dataset(images, objects, dataset_filename);
// Load the shape predictor
shape_predictor predictor;
deserialize(predictor_filename) >> predictor;
return test_shape_predictor_with_images(images, objects, scales, predictor);
}
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_SHAPE_PREDICTOR_H__