学习Python3 Dlib19.7进行人脸面部识别

概述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；

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：

@H_404_34@

(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. 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 sysimport dlibfrom skimage import iodetector = 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 dlibfrom skimage import io# 使用特征提取器frontal_face_detectordetector = 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:","right:","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: 121Hit 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,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 sysimport osimport dlibimport globfrom skimage import ioif 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.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 dlibfrom skimage import io# 使用特征提取器frontal_face_detectordetector = 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,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 sysa=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 sysa=sys.argv[1]print(a) 

cmd input:

python test2.py what is your name

cmd output:　

what

（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:

import sysa=sys.argv[1:]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:

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

cmd input:

python test4.py what is your name

cmd output:

"is"

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