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/*
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;
}
// ----------------------------------------------------------------------------------------
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