0.引言
自己在下载dlib官网给的example代码时，一开始不知道怎么使用，在一番摸索之后弄明白怎么使用了；
现分享下 face_detector.py 和 face_landmark_detection.py 这两个py的使用方法；
1.简介
python: 3.6.3
dlib: 19.7
利用dlib的特征提取器，进行人脸 矩形框 的特征提取：
dets
=
dlib
.
get_frontal_face_detector
(
img
)
利用dlib的68点特征预测器，进行人脸 68点 特征提取：
predictor
=
dlib
.
shape_predictor
(
"shape_predictor_68_face_landmarks.dat"
)
shape
=
predictor
(
img
,
dets
[
0
])
效果：
(a) face_detector.py
b) face_landmark_detection.py
2.py文件功能介绍
face_detector.py :
识别出图片文件中一张或多张人脸，并用矩形框框出标识出人脸；
link:
face_landmark_detection.py ：在face_detector.py的识别人脸基础上，识别出人脸部的具体特征部位：下巴轮廓、眉毛、眼睛、嘴巴，同样用标记标识出面部特征；
link:
2.1. face_detector.py
官网给的face_detector.py
#!/usr/bin/python
The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
This example program shows how to find frontal human faces in an image. In
particular, it shows how you can take a list of images from the command
line and display each on the screen with red boxes overlaid on each human
face.
The examples/faces folder contains some jpg images of people. You can run
this program on them and see the detections by executing the
following command:
./face_detector.py ../examples/faces/*.jpg
This face detector is made using the now classic Histogram of Oriented
Gradients (HOG) feature combined with a linear classifier, an image
pyramid, and sliding window detection scheme. This type of object detector
is fairly general and capable of detecting many types of semi-rigid objects
in addition to human faces. Therefore, if you are interested in making
your own object detectors then read the train_object_detector.py example
program.
COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
You can install dlib using the command:
pip install dlib
Alternatively, if you want to compile dlib yourself then go into the dlib
root folder and run:
python setup.py install
or
python setup.py install --yes USE_AVX_INSTRUCTIONS
if you have a CPU that supports AVX instructions, since this makes some
things run faster.
Compiling dlib should work on any operating system so long as you have
CMake and boost-python installed. On Ubuntu, this can be done easily by
running the command:
sudo apt-get install libboost-python-dev cmake
Also note that this example requires scikit-image which can be installed
via the command:
pip install scikit-image
Or downloaded from http://scikit-image.org/download.html.
import
sys
import
dlib
from
skimage
import
io
detector
=
dlib
.
get_frontal_face_detector
()
win
=
dlib
.
image_window
()
for
f
in
sys
.
argv
[
1
:]:
print
(
"Processing file: {}"
.
format
(
f
))
img
=
io
.
imread
(
f
)
The 1 in the second argument indicates that we should upsample the image
1 time. This will make everything bigger and allow us to detect more
faces.
dets
=
detector
(
img
,
1
)
print
(
"Number of faces detected: {}"
.
format
(
len
(
dets
)))
for
i
,
d
in
enumerate
(
dets
):
print
(
"Detection {}: Left: {} Top: {} Right: {} Bottom: {}"
.
format
(
i
,
d
.
left
(),
d
.
top
(),
d
.
right
(),
d
.
bottom
()))
win
.
clear_overlay
()
win
.
set_image
(
img
)
win
.
add_overlay
(
dets
)
dlib
.
hit_enter_to_continue
()
Finally, if you really want to you can ask the detector to tell you the score
for each detection. The score is bigger for more confident detections.
The third argument to run is an optional adjustment to the detection threshold,
where a negative value will return more detections and a positive value fewer.
Also, the idx tells you which of the face sub-detectors matched. This can be
used to broadly identify faces in different orientations.
if
(
len
(
sys
.
argv
[
1
:])
0
):
img
=
io
.
imread
(
sys
.
argv
[
1
])
dets
,
scores
,
idx
=
detector
.
run
(
img
,
1
,
-
1
)
for
i
,
d
in
enumerate
(
dets
):
print
(
"Detection {}, score: {}, face_type:{}"
.
format
(
d
,
scores
[
i
],
idx
[
i
]))
为了方便理解，修改增加注释之后的 face_detector.py
import
dlib
from
skimage
import
io
使用特征提取器frontal_face_detector
detector
=
dlib
.
get_frontal_face_detector
()
path是图片所在路径
path
=
"F:/code/python/P_dlib_face/pic/"
img
=
io
.
imread
(
path
+
"1.jpg"
)
特征提取器的实例化
dets
=
detector
(
img
)
print
(
"人脸数："
,
len
(
dets
))
输出人脸矩形的四个坐标点
for
i
,
d
in
enumerate
(
dets
):
print
(
"第"
,
i
,
"个人脸d的坐标："
,
"left:"
,
d
.
left
(),
"right:"
,
d
.
right
(),
"top:"
,
d
.
top
(),
"bottom:"
,
d
.
bottom
())
绘制图片
win
=
dlib
.
image_window
()
清除覆盖
#win.clear_overlay()
win
.
set_image
(
img
)
将生成的矩阵覆盖上
win
.
add_overlay
(
dets
)
保持图像
dlib
.
hit_enter_to_continue
()
对test.jpg进行人脸检测：
结果：
图片窗口结果：
输出结果：
人脸数：
1
第
0
个人脸：
left
:
79
right
:
154
top
:
47
bottom
:
121
Hit
enter to
continue
对于多个人脸的检测结果：
2.2 face_landmark_detection.py
官网给的 face_detector.py
#!/usr/bin/python
The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
This example program shows how to find frontal human faces in an image and
estimate their pose. The pose takes the form of 68 landmarks. These are
points on the face such as the corners of the mouth, along the eyebrows, on
the eyes, and so forth.
The face detector we use is made using the classic Histogram of Oriented
Gradients (HOG) feature combined with a linear classifier, an image pyramid,
and sliding window detection scheme. The pose estimator was created by
using dlib's implementation of the paper:
One Millisecond Face Alignment with an Ensemble of Regression Trees by
Vahid Kazemi and Josephine Sullivan, CVPR 2014
and was trained on the iBUG 300-W face landmark dataset (see
):
C. Sagonas, E. Antonakos, G, Tzimiropoulos, S. Zafeiriou, M. Pantic.
300 faces In-the-wild challenge: Database and results.
Image and Vision Computing (IMAVIS), Special Issue on Facial Landmark Localisation "In-The-Wild". 2016.
You can get the trained model file from:
Note that the license for the iBUG 300-W dataset excludes commercial use.
So you should contact Imperial College London to find out if it's OK for
you to use this model file in a commercial product.
Also, note that you can train your own models using dlib's machine learning
tools. See train_shape_predictor.py to see an example.
COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
You can install dlib using the command:
pip install dlib
Alternatively, if you want to compile dlib yourself then go into the dlib
root folder and run:
python setup.py install
or
python setup.py install --yes USE_AVX_INSTRUCTIONS
if you have a CPU that supports AVX instructions, since this makes some
things run faster.
Compiling dlib should work on any operating system so long as you have
CMake and boost-python installed. On Ubuntu, this can be done easily by
running the command:
sudo apt-get install libboost-python-dev cmake
Also note that this example requires scikit-image which can be installed
via the command:
pip install scikit-image
Or downloaded from http://scikit-image.org/download.html.
import
sys
import
os
import
dlib
import
glob
from
skimage
import
io
if
len
(
sys
.
argv
)
!=
3
:
print
(
"Give the path to the trained shape predictor model as the first "
"argument and then the directory containing the facial images.\n"
"For example, if you are in the python_examples folder then "
"execute this program by running:\n"
" ./face_landmark_detection.py shape_predictor_68_face_landmarks.dat ../examples/faces\n"
"You can download a trained facial shape predictor from:\n"
"
)
exit
()
predictor_path
=
sys
.
argv
[
1
]
faces_folder_path
=
sys
.
argv
[
2
]
detector
=
dlib
.
get_frontal_face_detector
()
predictor
=
dlib
.
shape_predictor
(
predictor_path
)
win
=
dlib
.
image_window
()
for
f
in
glob
.
glob
(
os
.
path
.
join
(
faces_folder_path
,
"*.jpg"
)):
print
(
"Processing file: {}"
.
format
(
f
))
img
=
io
.
imread
(
f
)
win
.
clear_overlay
()
win
.
set_image
(
img
)
Ask the detector to find the bounding boxes of each face. The 1 in the
second argument indicates that we should upsample the image 1 time. This
will make everything bigger and allow us to detect more faces.
dets
=
detector
(
img
,
1
)
print
(
"Number of faces detected: {}"
.
format
(
len
(
dets
)))
for
k
,
d
in
enumerate
(
dets
):
print
(
"Detection {}: Left: {} Top: {} Right: {} Bottom: {}"
.
format
(
k
,
d
.
left
(),
d
.
top
(),
d
.
right
(),
d
.
bottom
()))
Get the landmarks/parts for the face in box d.
shape
=
predictor
(
img
,
d
)
print
(
"Part 0: {}, Part 1: {} ..."
.
format
(
shape
.
part
(
0
),
shape
.
part
(
1
)))
Draw the face landmarks on the screen.
win
.
add_overlay
(
shape
)
win
.
add_overlay
(
dets
)
dlib
.
hit_enter_to_continue
()
修改：
绘制两个overlay，矩阵框 和 面部特征
import
dlib
from
skimage
import
io
使用特征提取器frontal_face_detector
detector
=
dlib
.
get_frontal_face_detector
()
dlib的68点模型
path_pre
=
"F:/code/python/P_dlib_face/"
predictor
=
dlib
.
shape_predictor
(
path_pre
+
"shape_predictor_68_face_landmarks.dat"
)
图片所在路径
path_pic
=
"F:/code/python/P_dlib_face/pic/"
img
=
io
.
imread
(
path_pic
+
"1.jpg"
)
生成dlib的图像窗口
win
=
dlib
.
image_window
()
win
.
clear_overlay
()
win
.
set_image
(
img
)
特征提取器的实例化
dets
=
detector
(
img
,
1
)
print
(
"人脸数："
,
len
(
dets
))
for
k
,
d
in
enumerate
(
dets
):
print
(
"第"
,
k
,
"个人脸d的坐标："
,
"left:"
,
d
.
left
(),
"right:"
,
d
.
right
(),
"top:"
,
d
.
top
(),
"bottom:"
,
d
.
bottom
())
利用预测器预测
shape
=
predictor
(
img
,
d
)
绘制面部轮廓
win
.
add_overlay
(
shape
)
绘制矩阵轮廓
win
.
add_overlay
(
dets
)
保持图像
dlib
.
hit_enter_to_continue
()
结果：
人脸数：
1
第
0
个人脸
d
的坐标：
left
:
79
right
:
154
top
:
47
bottom
:
121
图片窗口结果：
蓝色的是绘制的
win
.
add_overlay
(
shape
)
红色的是绘制的
win
.
add_overlay
(
dets
)
对于多张人脸的检测结果：
官网例程中是利用sys.argv[]读取命令行输入，其实为了方便我把文件路径写好了，如果对于sys.argv[]有疑惑，可以参照下面的总结：
- 关于sys.argv[]的使用:
( 如果对于代码中 sys.argv[] 的使用不了解可以参考这里 )
用来获取cmd命令行参数，例如 获取cmd命令输入“python test.py XXXXX” 的XXXXX参数，可以用于cmd下读取用户输入的文件路径；
如果不明白可以在python代码内直接 img = imread("F:/*****/test.jpg") 代替 img = imread(sys.argv[1]) 读取图片；
用代码实例来帮助理解：
1.（sys.argv[0]，指的是代码文件本身在的路径）
test1.py:
import
sys
a
=
sys
.
argv
[
0
]
print
(
a
)
cmd input:
python test1
.
py
cmd output:
test1
.
py
2.（sys.argv[1]，cmd输入获取的参数字符串中，第一个字符）
test2.py:
import
sys
a
=
sys
.
argv
[
1
]
print
(
a
)
cmd input:
python test2
.
py what
is
your name
（sys.argv[1:]，cmd输入获取的参数字符串中，从第一个字符开始到结束）
test3.py:
