Python 3 利用 Dlib 19.7 进行人脸识别

内容简介：Python 3 利用 Dlib 19.7 进行人脸识别

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：

1  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：

1 predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
2 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: http://dlib.net/cnn_face_detector.py.html

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: http://dlib.net/face_landmark_detection.py.html

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.  OnUbuntu, 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：

1 人脸数： 1
2 第 0 个人脸： left: 79 right: 154 top: 47 bottom: 121
3 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

#  https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/): 

#      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:

#  http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2.

#  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"

"    http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2")

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：

1 人脸数： 1
2 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:

1  import sys
2  a=sys.argv[0]
3  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:

1 import sys
2 a=sys.argv[1]
3 print(a)

cmd input:

python test2.py what is your name

cmd output:

what

3. （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:

1 import sys
2 a=sys.argv[1:]
3 print(a)

cmd input:

python test3.py what is your name

cmd output:

[“what”,“is”,“your”,“name”]

3.（sys.argv[2]，cmd输入获取的参数字符串中，第二个字符）

test4.py:

1 import sys
2 a=sys.argv[2]
3 print(a)

cmd input:

python test4.py what is your name

cmd output:

"is"

本文永久更新链接地址 ： http://www.linuxidc.com/Linux/2018-01/150532.htm

以上所述就是小编给大家介绍的《Python 3 利用 Dlib 19.7 进行人脸识别》，希望对大家有所帮助，如果大家有任何疑问请给我留言，小编会及时回复大家的。在此也非常感谢大家对 码农网 的支持！

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