具有默认数据集和训练的形状预测器准确性低 [英] Low accuracy of shape predictor with default dataset and training
问题描述
我正在尝试使用dlib来训练具有默认数据集(/dlib-19.0/examples/faces/training_with_face_landmarks.xml)和默认训练样本(train_shape_predictor_ex.cpp)的形状预测器.
I'm trying to use dlib to train shape predictor with default dataset(/dlib-19.0/examples/faces/training_with_face_landmarks.xml) and default train sample(train_shape_predictor_ex.cpp).
所以我想训练形状预测器，它与默认形状预测器( shape_predictor_68_face_landmarks.dat )完全一样，因为我使用了相同的数据集和相同的训练代码.一些问题.
训练后，我的.dat文件的大小为16.6mb(但是默认的dlib预测变量shape_predictor_68_face_landmarks.dat的大小为99.7mb).
测试我的.dat文件(16.6mb)后，我的准确度较低，但是测试了默认.dat文件(shape_predictor_68_face_landmarks.dat，16.6mb)后，我的准确度很高.
After training I get my .dat file with 16.6mb (but default dlib predictor shape_predictor_68_face_landmarks.dat has 99.7mb).
After testing my .dat file (16.6mb) I get low accuracy, but after testing default .dat file (shape_predictor_68_face_landmarks.dat, 16.6mb) I get hight accuracy.
我的形状预测器:
shape_predictor_68_face_landmarks.dat:
My shape predictor:
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();
}
如果我使用默认数据集和示例，那么默认值与我的培训和测试之间有什么区别?如何将形状预测器训练为shape_predictor_68_face_landmarks.dat?
What is difference between default and my training and testing if I used default dataset and examples? How did I can train shape predictor as shape_predictor_68_face_landmarks.dat?
推荐答案
示例数据集(/dlib-19.0/examples/faces/training_with_face_landmarks.xml)太小，无法训练出高质量的模型.并不是dlib随附的模型受过训练.
The example dataset (/dlib-19.0/examples/faces/training_with_face_landmarks.xml) is way too small to train a high quality model. It isn't what the model that comes with dlib is trained on.
示例使用小的数据集来使示例快速运行.所有示例的重点是说明dlib API，而不是有用的程序.它们只是文档.取决于您如何使用dlib API做一些有趣的事情.
The examples use a small dataset to make the examples run fast. The point of all the examples is to explain the dlib API, not to be useful programs. They are just documentation. It's up to you to do something interesting with the dlib API.
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