學習Python3 Dlib19.7進行人臉臉部辨識，python3dlib19.7

學習Python3 Dlib19.7進行人臉臉部辨識，python3dlib19.7

0.引言

自己在下載dlib官網給的example代碼時，一開始不知道怎麼使用，在一番摸索之後弄明白怎麼使用了；

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的特徵提取器，進行人臉 矩形框 的特徵提取：　　

dets = dlib.get_frontal_face_detector(img)

利用dlib的68點特徵預測器，進行人臉 68點 特徵提取：

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檔案功能介紹

face_detector.py :

識別出圖片檔案中一張或多張人臉，並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徵部位：下巴輪廓、眉毛、眼睛、嘴巴，同樣用標記標識出面部特徵；

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]))

為了方便理解，修改增加註釋之後的 face_detector.py

import dlibfrom skimage import io# 使用特徵提取器frontal_face_detectordetector = dlib.get_frontal_face_detector()# path是圖片所在路徑path = "F:/code/python/P_dlib_face/pic/"img = io.imread(path+"1.jpg")# 特徵提取器的執行個體化dets = detector(img)print("人臉數：", len(dets))# 輸出人臉矩形的四個座標點for i, d in enumerate(dets):  print("第", i, "個人臉d的座標：",     "left:", d.left(),     "right:", d.right(),     "top:", d.top(),     "bottom:", d.bottom())# 繪製圖片win = dlib.image_window()# 清除覆蓋#win.clear_overlay()win.set_image(img)# 將產生的矩陣覆蓋上win.add_overlay(dets)# 保持映像dlib.hit_enter_to_continue()

對test.jpg進行臉部偵測：　

結果：

圖片視窗結果：

輸出結果：　　　

人臉數： 1第 0 個人臉： left: 79 right: 154 top: 47 bottom: 121Hit enter to continue

對於多個人臉的檢測結果：

2.2 face_landmark_detection.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 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：

繪製兩個overlay，矩陣框 和 面部特徵

import dlibfrom skimage import io# 使用特徵提取器frontal_face_detectordetector = dlib.get_frontal_face_detector()# dlib的68點模型path_pre = "F:/code/python/P_dlib_face/"predictor = dlib.shape_predictor(path_pre+"shape_predictor_68_face_landmarks.dat")# 圖片所在路徑path_pic = "F:/code/python/P_dlib_face/pic/"img = io.imread(path_pic+"1.jpg")# 產生dlib的映像視窗win = dlib.image_window()win.clear_overlay()win.set_image(img)# 特徵提取器的執行個體化dets = detector(img, 1)print("人臉數：", len(dets))for k, d in enumerate(dets):    print("第", k, "個人臉d的座標：",       "left:", d.left(),       "right:", d.right(),       "top:", d.top(),       "bottom:", d.bottom())    # 利用預測器預測    shape = predictor(img, d)    # 繪製面部輪廓    win.add_overlay(shape)# 繪製矩陣輪廓win.add_overlay(dets)# 保持映像dlib.hit_enter_to_continue()

結果：

人臉數： 1第 0 個人臉d的座標： left: 79 right: 154 top: 47 bottom: 121

圖片視窗結果：

藍色的是繪製的　　win.add_overlay(shape)紅色的是繪製的　　win.add_overlay(dets)

對於多張人臉的檢測結果：

官網常式中是利用sys.argv[]讀取命令列輸入，其實為了方便我把檔案路徑寫好了，如果對於sys.argv[]有疑惑，可以參照下面的總結： 

* 關於sys.argv[]的使用:

( 如果對於代碼中 sys.argv[] 的使用不瞭解可以參考這裡 )

用來擷取cmd命令列參數，例如 擷取cmd命令輸入“python test.py XXXXX” 的XXXXX參數，可以用於cmd下讀取使用者輸入的檔案路徑；

如果不明白可以在python代碼內直接 img = imread("F:/*****/test.jpg") 代替 img = imread(sys.argv[1]) 讀取圖片；

用代碼執行個體來協助理解：

1.（sys.argv[0]，指的是代碼檔案本身在的路徑）

test1.py:

import sysa=sys.argv[0]print(a) 

cmd input:

python test1.py

cmd output:

test1.py

2.（sys.argv[1]，cmd輸入擷取的參數字串中，第一個字元）

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輸入擷取的參數字串中，從第一個字元開始到結束）

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輸入擷取的參數字串中，第二個字元）

test4.py:

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

cmd input:

python test4.py what is your name

cmd output:

"is"