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: 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]))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:", 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: 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, 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 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.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 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, "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 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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