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
/*
This is the program that created the http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2 model file.
*/
#include <dlib/image_processing/frontal_face_detector.h>
#include <dlib/image_processing.h>
#include <dlib/console_progress_indicator.h>
#include <dlib/data_io.h>
#include <dlib/statistics.h>
#include <iostream>
using namespace dlib;
using namespace std;
// ----------------------------------------------------------------------------------------
std::vector<std::vector<double> > get_interocular_distances (
const std::vector<std::vector<full_object_detection> >& objects
);
/*!
ensures
- returns an object D such that:
- D[i][j] == the distance, in pixels, between the eyes for the face represented
by objects[i][j].
!*/
// ----------------------------------------------------------------------------------------
template <
typename image_array_type,
typename T
>
void add_image_left_right_flips_5points (
image_array_type& images,
std::vector<std::vector<T> >& objects
)
{
// make sure requires clause is not broken
DLIB_ASSERT( images.size() == objects.size(),
"\t void add_image_left_right_flips()"
<< "\n\t Invalid inputs were given to this function."
<< "\n\t images.size(): " << images.size()
<< "\n\t objects.size(): " << objects.size()
);
typename image_array_type::value_type temp;
std::vector<T> rects;
const unsigned long num = images.size();
for (unsigned long j = 0; j < num; ++j)
{
const point_transform_affine tran = flip_image_left_right(images[j], temp);
rects.clear();
for (unsigned long i = 0; i < objects[j].size(); ++i)
{
rects.push_back(impl::tform_object(tran, objects[j][i]));
DLIB_CASSERT(rects.back().num_parts() == 5);
swap(rects.back().part(0), rects.back().part(2));
swap(rects.back().part(1), rects.back().part(3));
}
images.push_back(temp);
objects.push_back(rects);
}
}
// ----------------------------------------------------------------------------------------
int main(int argc, char** argv)
{
try
{
if (argc != 2)
{
cout << "give the path to the training data folder" << endl;
return 0;
}
const std::string faces_directory = argv[1];
dlib::array<array2d<unsigned char> > images_train, images_test;
std::vector<std::vector<full_object_detection> > faces_train, faces_test;
std::vector<std::string> parts_list;
load_image_dataset(images_train, faces_train, faces_directory+"/train_cleaned.xml", parts_list);
load_image_dataset(images_test, faces_test, faces_directory+"/test_cleaned.xml");
add_image_left_right_flips_5points(images_train, faces_train);
add_image_left_right_flips_5points(images_test, faces_test);
add_image_rotations(linspace(-20,20,3)*pi/180.0,images_train, faces_train);
cout << "num training images: "<< images_train.size() << endl;
for (auto& part : parts_list)
cout << part << endl;
shape_predictor_trainer trainer;
trainer.set_oversampling_amount(40);
trainer.set_num_test_splits(150);
trainer.set_feature_pool_size(800);
trainer.set_num_threads(4);
trainer.set_cascade_depth(15);
trainer.be_verbose();
// Now finally generate the shape model
shape_predictor sp = trainer.train(images_train, faces_train);
serialize("shape_predictor_5_face_landmarks.dat") << sp;
cout << "mean training error: "<<
test_shape_predictor(sp, images_train, faces_train, get_interocular_distances(faces_train)) << endl;
cout << "mean testing error: "<<
test_shape_predictor(sp, images_test, faces_test, get_interocular_distances(faces_test)) << endl;
}
catch (exception& e)
{
cout << "\nexception thrown!" << endl;
cout << e.what() << endl;
}
}
// ----------------------------------------------------------------------------------------
double interocular_distance (
const full_object_detection& det
)
{
dlib::vector<double,2> l, r;
// left eye
l = (det.part(0) + det.part(1))/2;
// right eye
r = (det.part(2) + det.part(3))/2;
return length(l-r);
}
std::vector<std::vector<double> > get_interocular_distances (
const std::vector<std::vector<full_object_detection> >& objects
)
{
std::vector<std::vector<double> > temp(objects.size());
for (unsigned long i = 0; i < objects.size(); ++i)
{
for (unsigned long j = 0; j < objects[i].size(); ++j)
{
temp[i].push_back(interocular_distance(objects[i][j]));
}
}
return temp;
}
// ----------------------------------------------------------------------------------------