0.引言

自己在下載dlib官網給的example代碼時，一開始不知道怎么使用，在一番摸索之后弄明白怎么使用了；

現分享下 face_detector.py 和 face_landmark_detection.py 這兩個py的使用方法；

1.簡介

python:　　3.6.3

dlib:　　　 19.7

利用dlib的特征提取器，進行人臉 矩形框 的特征提取：　　

dets = dlib.get_frontal_face_detector(img)

利用dlib的68點特征預測器，進行人臉 68點 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文件功能介紹

face_detector.py :

識別出圖片文件中一張或多張人臉，并用矩形框框出標識出人臉；

link: http://dlib.net/cnn_face_detector.py.html

face_landmark_detection.py ：在face_detector.py的識別人臉基礎上，識別出人臉部的具體特征部位：下巴輪廓、眉毛、眼睛、嘴巴，同樣用標記標識出面部特征；

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

2.1. face_detector.py

官網給的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]))