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我试图使用dlib来使用默认数据集（/dlib-19.0/examples/faces/training_with_face_landmarks.xml）和默认训练样本（train_shape_predictor_ex.cpp）训练形状预测器。使用默认数据集和训练的形状预测器的准确性较低
所以我想训练形状的预测，这将是完全一样的默认形状预测（shape_predictor_68_face_landmarks.dat），因为我用同样的数据集和相同的训练码。但我得到一些问题。
训练结束后，我得到我的.dat文件与16.6mb（但默认的dlib预测器shape_predictor_68_face_landmarks.dat有99.7mb）。 在测试我的.dat文件（16.6mb）后，我得到的准确度较低，但在测试默认.dat文件（shape_predictor_68_face_landmarks.dat,16.6mb）之后，我获得了高精度。
我的形状预测： 
shape_predictor_68_face_landmarks.dat： 
培训：
#include <QCoreApplication>
#include <dlib/image_processing.h>
#include <dlib/data_io.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
);
int main(int argc, char *argv[])
{
QCoreApplication a(argc, argv);
try
{
const std::string faces_directory = "/home/user/Documents/dlib-19.0/examples/faces/";
dlib::array<array2d<unsigned char> > images_train;
std::vector<std::vector<full_object_detection> > faces_train;
load_image_dataset(images_train, faces_train, faces_directory+"training_with_face_landmarks.xml");
shape_predictor_trainer trainer;
trainer.set_oversampling_amount(300);
trainer.set_nu(0.05);
trainer.set_tree_depth(2);
trainer.be_verbose();
shape_predictor sp = trainer.train(images_train, faces_train);
cout << "mean training error: "<<
test_shape_predictor(sp, images_train, faces_train, get_interocular_distances(faces_train)) << endl;
serialize(faces_directory+"sp_default_settings.dat") << sp;
}
catch (exception& e)
{
cout << "\nexception thrown!" << endl;
cout << e.what() << endl;
}
return a.exec();
}
double interocular_distance (
const full_object_detection& det
)
{
dlib::vector<double,2> l, r;
double cnt = 0;
// Find the center of the left eye by averaging the points around
// the eye.
for (unsigned long i = 36; i <= 41; ++i)
{
l += det.part(i);
++cnt;
}
l /= cnt;
// Find the center of the right eye by averaging the points around
// the eye.
cnt = 0;
for (unsigned long i = 42; i <= 47; ++i)
{
r += det.part(i);
++cnt;
}
r /= cnt;
// Now return the distance between the centers of the eyes
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;
}
测试：
#include <QCoreApplication>
#include <dlib/image_processing/frontal_face_detector.h>
#include <dlib/image_processing/render_face_detections.h>
#include <dlib/image_processing.h>
#include <dlib/gui_widgets.h>
#include <dlib/image_io.h>
#include <dlib/data_io.h>
#include <iostream>
using namespace dlib;
using namespace std;
int main(int argc, char *argv[])
{
QCoreApplication a(argc, argv);
try
{
// We need a face detector. We will use this to get bounding boxes for
// each face in an image.
frontal_face_detector detector = get_frontal_face_detector();
// And we also need a shape_predictor. This is the tool that will predict face
// landmark positions given an image and face bounding box. Here we are just
// loading the model from the shape_predictor_68_face_landmarks.dat file you gave
// as a command line argument.
shape_predictor sp;
deserialize("/home/user/Downloads/muct-master/samples/sp_default_settings.dat") >> sp;
string srcDir = "/home/user/Downloads/muct-master/samples/selection/";
string dstDir = "/home/user/Downloads/muct-master/samples/my_results_default/";
std::vector<string> vecOfImg;
vecOfImg.push_back("i001qa-mn.jpg");
vecOfImg.push_back("i002ra-mn.jpg");
vecOfImg.push_back("i003ra-fn.jpg");
vecOfImg.push_back("i003sa-fn.jpg");
vecOfImg.push_back("i004qa-mn.jpg");
vecOfImg.push_back("i004ra-mn.jpg");
vecOfImg.push_back("i005ra-fn.jpg");
vecOfImg.push_back("i006ra-mn.jpg");
vecOfImg.push_back("i007qa-fn.jpg");
vecOfImg.push_back("i008ra-mn.jpg");
vecOfImg.push_back("i009qa-mn.jpg");
vecOfImg.push_back("i009ra-mn.jpg");
vecOfImg.push_back("i009sa-mn.jpg");
vecOfImg.push_back("i010qa-mn.jpg");
vecOfImg.push_back("i010sa-mn.jpg");
vecOfImg.push_back("i011qa-mn.jpg");
vecOfImg.push_back("i011ra-mn.jpg");
vecOfImg.push_back("i012ra-mn.jpg");
vecOfImg.push_back("i012sa-mn.jpg");
vecOfImg.push_back("i014qa-fn.jpg");
for(int imgC = 0; imgC < vecOfImg.size(); imgC++){
array2d<rgb_pixel> img;
load_image(img, srcDir + vecOfImg.at(imgC));
// Make the image larger so we can detect small faces.
pyramid_up(img);
// Now tell the face detector to give us a list of bounding boxes
// around all the faces in the image.
std::vector<rectangle> dets = detector(img);
cout << "Number of faces detected: " << dets.size() << endl;
// Now we will go ask the shape_predictor to tell us the pose of
// each face we detected.
std::vector<full_object_detection> shapes;
for (unsigned long j = 0; j < dets.size(); ++j)
{
full_object_detection shape = sp(img, dets[j]);
cout << "number of parts: "<< shape.num_parts() << endl;
cout << "pixel position of first part: " << shape.part(0) << endl;
cout << "pixel position of second part: " << shape.part(1) << endl;
for(unsigned long i = 0; i < shape.num_parts(); i++){
draw_solid_circle(img, shape.part(i), 2, rgb_pixel(100,255,100));
}
save_jpeg(img, dstDir + vecOfImg.at(imgC));
// You get the idea, you can get all the face part locations if
// you want them. Here we just store them in shapes so we can
// put them on the screen.
shapes.push_back(shape);
}
}
}
catch (exception& e)
{
cout << "\nexception thrown!" << endl;
cout << e.what() << endl;
}
return a.exec();
}
是什么违约和我的训练和测试，如果我使用之间的区别默认数据集和示例莱？我如何将shape形状预测器训练成shape_predictor_68_face_landmarks.dat？
即使你在sourceforge页面上提出了一个问题（但没有得到答案），那里仍然有很多信息，很确定这个问题已经被讨论过:) –