kithara支持的Dlib19.17库
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kithara支持的Dlib19.17库
// Copyright (C) 2013 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_FRONTAL_FACE_DETECTOr_Hh_
#define DLIB_FRONTAL_FACE_DETECTOr_Hh_
#include "frontal_face_detector_abstract.h"
#include "../image_processing/object_detector.h"
#include "../image_processing/scan_fhog_pyramid.h"
#include
#include "../compress_stream.h"
#include "../base64.h"
namespace dlib
{
typedef object_detector > > frontal_face_detector;
inline const std::string get_serialized_frontal_faces();
inline frontal_face_detector get_frontal_face_detector()
{
std::istringstream sin(get_serialized_frontal_faces());
frontal_face_detector detector;
deserialize(detector, sin);
return detector;
}
// ----------------------------------------------------------------------------------------
/*
It is built out of 5 HOG filters. A front looking, left looking, right looking,
front looking but rotated left, and finally a front looking but rotated right one.
Moreover, here is the training log and parameters used to generate the filters:
The front detector:
trained on mirrored set of labeled_faces_in_the_wild/frontal_faces.xml
upsampled each image by 2:1
used pyramid_down<6>
loss per missed target: 1
epsilon: 0.05
padding: 0
detection window size: 80 80
C: 700
nuclear norm regularizer: 9
cell_size: 8
num filters: 78
num images: 4748
Train detector (precision,recall,AP): 0.999793 0.895517 0.895368
singular value threshold: 0.15
The left detector:
trained on labeled_faces_in_the_wild/left_faces.xml
upsampled each image by 2:1
used pyramid_down<6>
loss per missed target: 2
epsilon: 0.05
padding: 0
detection window size: 80 80
C: 250
nuclear norm regularizer: 8
cell_size: 8
num filters: 63
num images: 493
Train detector (precision,recall,AP): 0.991803 0.86019 0.859486
singular value threshold: 0.15
The right detector:
trained left-right flip of labeled_faces_in_the_wild/left_faces.xml
upsampled each image by 2:1
used pyramid_down<6>
loss per missed target: 2
epsilon: 0.05
padding: 0
detection window size: 80 80
C: 250
nuclear norm regularizer: 8
cell_size: 8
num filters: 66
num images: 493
Train detector (precision,recall,AP): 0.991781 0.85782 0.857341
singular value threshold: 0.19
The front-rotate-left detector:
trained on mirrored set of labeled_faces_in_the_wild/frontal_faces.xml
upsampled each image by 2:1
used pyramid_down<6>
rotated left 27 degrees
loss per missed target: 1
epsilon: 0.05
padding: 0
detection window size: 80 80
C: 700
nuclear norm regularizer: 9
cell_size: 8
num images: 4748
singular value threshold: 0.12
The front-rotate-right detector:
trained on mirrored set of labeled_faces_in_the_wild/frontal_faces.xml
upsampled each image by 2:1
used pyramid_down<6>
rotated right 27 degrees
loss per missed target: 1
epsilon: 0.05
padding: 0
detection window size: 80 80
C: 700
nuclear norm regularizer: 9
cell_size: 8
num filters: 89
num images: 4748
Train detector (precision,recall,AP): 1 0.897369 0.897369
singular value threshold: 0.15
*/
inline const std::string get_serialized_frontal_faces()
{
dlib::base64 base64_coder;
dlib::compress_stream::kernel_1ea compressor;
std::ostringstream sout;
std::istringstream sin;
// The base64 encoded data from the file 'object_detector.dat' we want to decode and return.
sout << "AW2B5ZIvv09mlKLVYjKqbJC05yeR2KsCpPGEGOgn2QlwM92S4UT4HgQkV0V9WqYRf6xETTSVKz7Z";
sout << "YcJ84Jc4C3+VdPgZDhV+LDt6qAt3OI4nA9zN4Y9cCIb6ivlETkN/JMmapbOAUW2mrSzDif5zjAaq";
sout << "+NFvw/5V0Jciopw9tR6nYtV41unWGvyyfsO9CcqvDy81QIydToHh0a7UaL0jCtA2DYzkViDufxyv";
sout << "Kpsn4xMyiU0haM1ge3UktIO48io/gSzjEKu0YYAffbD2YO1IE34tUH15Z3Z9NjkBFxTytDgrMxk8";
sout << "i9MYq+Nl9nS421aogmec3ugExJYjLZMHs4KAk71jvG8vtJyJEA3qyLY6lvONt98gzQwGQ9+2B6de";
sout << "ocb/DDJUza6mvudHQNJBYraR4gCWcIn9gFu2rJiRHf4IiqP4GEB3B1zKiHfJRo9jZbhxQUitAxAx";
sout << "U/E2SuuHGZDilqK9AJ4K41RAudraxF9li/Bs4f+CK3G8Z/c97P7WLVekJL2ws+MsCdL9ObHE5ePD";
sout << "uLLQWBy5NUbgPVM6HEnhnOiZk3rA4DYNqbABy3uemablAln9BLGkk4wrm2UcicacnzY8Aq054Ttb";
sout << "3CCTcG4SOSPfePl/7T1M6Uy1hOesp5MpXfUR8gBKr4466dbdXCDHSahI05gra6NzxkOpOo2mOqBg";
sout << "LYNGZUkHK4tdRyyD12N1MH+nJiMJbgk+qj54t5i3AuEr/71HTRXoTT8AEYbvc9y4f2WAlliQYXPn";
sout << "O2Uaza3lKYrH7mFjKMNhLfvrezy9fe+1asbSlRKelnU3eY4lhD6fTVJjXqZypBfMnfmGQQJ0Q7g5";
sout << "1Z/9GzpRyZnPSzQljtJgzVp8Gk0z3fuKiXPO9g+s4XL2cEuxBOFij0KGTy4eNitM0gcPc6xzp3tz";
sout << "6Wv0W2h7w4h+V8Bzvyn8ag1sbEO0G1Lf2BrDVM9+pNxFoWFxYHqdoOmJPVvb8PRQqoC5bkqhplFr";
sout << "TR5l3XsQedgwsnkadxNZQ3MbRJyo0JU0kvV1cfphLcn24MIIKqAnw3daXqbJaba+oCUep5GTuzI7";
sout << "nad7ykHNN0iFkgYXMmXJl+F5TsS8y+izuHlXAX6wX1qRVzWJwCpM5oVVG/5eYTzg0J9C1bCcNyHL";
sout << "2w5TJFYrD8bq3O+Y3fiO5LJ8F5/vsu2EBUMi1+eP1WfsTwd6N9jFtF5gA5sHX3zI925aDqVx9byr";
sout << "j4X5yr68p5P6f8wSLL8jzW8i4a0yP3zXlqN6QQDY1ssfNsMf43tOTtmbBlmxviL2egs4gvadD7Gd";
sout << "fRNowL71P3mkqRmnrnihlI01NbDl+Trzsh3EOn43PRC9nl8yo+fYVH8GqS8JGy1xOw4G479vOifI";
sout << "9GC4BGnSDJdKgSnBwI1AJQ2TT8EZ//56lkRlgusg25TwC7uQ1zreeL6baYdgfXSggx3ULdNDGl5o";
sout << "ftRK9LDaop6XvB6I0ITsLYvAoGP/5sHfttDj6HlQW/LlzkSPmzY/FtV6h6bE+k1gG7BANrQjwOW5";
sout << "sfHNYadD1v4zIFdt2su3docGbGP/iDMvM+BmYIBP86zX5eIlTYwDmxXht95T6GCCjS/XuMMy12hd";
sout << "Fdb6lm1O42ieM4KQ/2EOFy3Ij+YOIapzYA6p6Jz9dtINpCojgUHyo6xc4HTNnEKRy+YN+awhb1l2";
sout << "FJdy2/QI3xGVNNTnWcQrsvjGZb/Z3VaZUltrIbnCeEZOeOCM0TxkBEhqFfI3qwMx8PUj+imUlTDM";
sout << "7N+p5sxmKLliHHovOO32ajBTKUSI9IMQzf3QY6dZDts4JkMYQ1xc6lpm679s1KMVVrWuOqiAU5Vs";
sout << "qehfnl+oMRngi0G0BnMne45CjU5RECvhg+Vkkxx0kAp38+9pY3XiO/DuyIxpOSPip2o0+9rZLF1Z";
sout << "cAUGnG85CFEXl96wpxvqVlIULUV2+pNJxdU+q1MkCsxDeXrvfjhEAJpPE38dUb3t4blsNUZ3wJ2w";
sout << "s6cXe0nEPWNkZlmEsXcFpw5zHe0Gd7YpXigz7Z+IVhvplpv686TJiLTpVPW2T1uJvSmMuG/FqvT5";
sout << "JIIMg2of1ydicw5EbWrqhIUzllX3l0u00gFziPmKAioiqCxjWojd9l3Q0Q6IsaZAH+WzV2xFabbY";
sout << "4b8SwoFvhe4qnUQLFdOSTbzeDIKP9B8bSiQwbjUBg3jYEWUrMz+eR9lpGu8603vChIEXaTxyMrO5";
sout << "SCeaVOgPE77potDoSUV1hsoW7ZqGCFH+AGyVTohitS0iqZbIxC7+7rnVP8XfXw5YpSajF94z2TSd";
sout << "jW0KpmuCZ88DTCPFamf5zh917qp/PzQOGTdalr+Ov+ogvrJraDnoE+ONWrdHqBm7Adgn8/wy5vzX";
sout << "fNu1AT14eYrEmWmXvt6JDAbBYqP8Aw8b1QRZff11MblUh0IpztedWhifGy/RFJUN0/e66Mh0cKeF";
sout << "plmK6NqchTzOQMKJVq9jxdyurcjcA0uu4dVJ1XXkAtxBim2J2m0zcwX/+HcRe9VbeNehmDbUC49o";
sout << "ktNvrwbbB1IUV/c0MNCruV359DVINXskQTK12g2X5qprOLW+YPO6CnTFpJRsiFBoLllF1sUTjROH";
sout << "SrHHRYp3W5t5gqfT4afBxmtTmpJEG0oG4eNfMhxEhQ7HjoVhahOM6px9Be9S+4ca/w+zII7NnUkY";
sout << "Iaas+FW7vhOIDOiV82SpJqBjdY9eIP//XGR1DFQKI5cLKmT2/DF8tB9XcqTgmVWNMVt9Xw21CaeR";
sout << "eYeoWvLHlm8o7ahtJCSQ0iHypTZMA16wdJ5IJD5WoYd50rUn58RBa9sTXT/t/KhxJfG5OWXl55eq";
sout << "abYojSlluFyvFSk7Z/wu/EqFUEBD8r4OIrlJCMZl6kKy4E
资源文件列表:
dlib.zip 大约有1168个文件