Python利用Dlib库进行人脸识别

 1 #!/usr/bin/python
 2 # The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
 3 #
 4 #   This example program shows how to find frontal human faces in an image.  In
 5 #   particular, it shows how you can take a list of images from the command
 6 #   line and display each on the screen with red boxes overlaid on each human
 7 #   face.
 8 #
 9 #   The examples/faces folder contains some jpg images of people.  You can run
10 #   this program on them and see the detections by executing the
11 #   following command:
12 #       ./face_detector.py ../examples/faces/*.jpg
13 #
14 #   This face detector is made using the now classic Histogram of Oriented
15 #   Gradients (HOG) feature combined with a linear classifier, an image
16 #   pyramid, and sliding window detection scheme.  This type of object detector
17 #   is fairly general and capable of detecting many types of semi-rigid objects
18 #   in addition to human faces.  Therefore, if you are interested in making
19 #   your own object detectors then read the train_object_detector.py example
20 #   program.  
21 #
22 #
23 # COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
24 #   You can install dlib using the command:
25 #       pip install dlib
26 #
27 #   Alternatively, if you want to compile dlib yourself then go into the dlib
28 #   root folder and run:
29 #       python setup.py install
30 #   or
31 #       python setup.py install --yes USE_AVX_INSTRUCTIONS
32 #   if you have a CPU that supports AVX instructions, since this makes some
33 #   things run faster.  
34 #
35 #   Compiling dlib should work on any operating system so long as you have
36 #   CMake and boost-python installed.  On Ubuntu, this can be done easily by
37 #   running the command:
38 #       sudo apt-get install libboost-python-dev cmake
39 #
40 #   Also note that this example requires scikit-image which can be installed
41 #   via the command:
42 #       pip install scikit-image
43 #   Or downloaded from http://scikit-image.org/download.html. 
44 
45 import sys
46 
47 import dlib
48 from skimage import io
49 
50 
51 detector = dlib.get_frontal_face_detector()
52 win = dlib.image_window()
53 
54 for f in sys.argv[1:]:
55     print("Processing file: {}".format(f))
56     img = io.imread(f)
57     # The 1 in the second argument indicates that we should upsample the image
58     # 1 time.  This will make everything bigger and allow us to detect more
59     # faces.
60     dets = detector(img, 1)
61     print("Number of faces detected: {}".format(len(dets)))
62     for i, d in enumerate(dets):
63         print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
64             i, d.left(), d.top(), d.right(), d.bottom()))
65 
66     win.clear_overlay()
67     win.set_image(img)
68     win.add_overlay(dets)
69     dlib.hit_enter_to_continue()
70 
71 
72 # Finally, if you really want to you can ask the detector to tell you the score
73 # for each detection.  The score is bigger for more confident detections.
74 # The third argument to run is an optional adjustment to the detection threshold,
75 # where a negative value will return more detections and a positive value fewer.
76 # Also, the idx tells you which of the face sub-detectors matched.  This can be
77 # used to broadly identify faces in different orientations.
78 if (len(sys.argv[1:]) > 0):
79     img = io.imread(sys.argv[1])
80     dets, scores, idx = detector.run(img, 1, -1)
81     for i, d in enumerate(dets):
82         print("Detection {}, score: {}, face_type:{}".format(
83             d, scores[i], idx[i]))

 1 import sys
 2 
 3 import dlib
 4 from skimage import io
 5 
 6 #使用dlib自带的frontal_face_detector()函数作为特征提取器
 7 detector = dlib.get_frontal_face_detector()
 8 
 9 #使用dlib的图片窗口
10 win = dlib.image_window()
11 
12 #sys.argv用来获取命令行参数，[0]表示代码本身文件路径，参数1开始向后依次是获取图片路径
13 for f in sys.argv[1:]:
14     #输出目前处理的图片地址
15     print("Processing file: {}".format(f))
16     #使用skimage的io读取图片
17     img = io.imread(f)
18      #使用detector进行人脸检测，dets为人脸个数
19     dets = detector(img, 1)
20     print("Number of faces detected: {}".format(len(dets)))
21     for i, d in enumerate(dets):
22         print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
23         i, d.left(), d.top(), d.right(), d.bottom()))
24 
25     #绘制图片
26     win.clear_overlay()
27     win.set_image(img)
28     win.add_overlay(dets)
29     dlib.hit_enter_to_continue()
30 
31 
32 # Finally, if you really want to you can ask the detector to tell you the score
33 # for each detection.  The score is bigger for more confident detections.
34 # The third argument to run is an optional adjustment to the detection threshold,
35 # where a negative value will return more detections and a positive value fewer.
36 # Also, the idx tells you which of the face sub-detectors matched.  This can be
37 # used to broadly identify faces in different orientations.
38 if (len(sys.argv[1:]) > 0):
39     img = io.imread(sys.argv[1])
40     dets, scores, idx = detector.run(img, 1, -1)
41     for i, d in enumerate(dets):
42         print("Detection {}, score: {}, face_type:{}".format(d, scores[i], idx[i]))

python face_detector.py test.jpg

1 F:\code\******\face_test>python face_detector.py test.jpg
2 Processing file: test.jpg
3 Number of faces detected: 1
4 Detection 0: Left: 79 Top: 47 Right: 154 Bottom: 121
5 Hit enter to continue
6 Detection [(79, 47) (154, 121)], score: 2.5174034275544996, face_type:0.0

  1 #!/usr/bin/python
  2 # The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
  3 #
  4 #   This example program shows how to find frontal human faces in an image and
  5 #   estimate their pose.  The pose takes the form of 68 landmarks.  These are
  6 #   points on the face such as the corners of the mouth, along the eyebrows, on
  7 #   the eyes, and so forth.
  8 #
  9 #   The face detector we use is made using the classic Histogram of Oriented
 10 #   Gradients (HOG) feature combined with a linear classifier, an image pyramid,
 11 #   and sliding window detection scheme.  The pose estimator was created by
 12 #   using dlib‘s implementation of the paper:
 13 #      One Millisecond Face Alignment with an Ensemble of Regression Trees by
 14 #      Vahid Kazemi and Josephine Sullivan, CVPR 2014
 15 #   and was trained on the iBUG 300-W face landmark dataset (see
 16 #   https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/):  
 17 #      C. Sagonas, E. Antonakos, G, Tzimiropoulos, S. Zafeiriou, M. Pantic. 
 18 #      300 faces In-the-wild challenge: Database and results. 
 19 #      Image and Vision Computing (IMAVIS), Special Issue on Facial Landmark Localisation "In-The-Wild". 2016.
 20 #   You can get the trained model file from:
 21 #   http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2.
 22 #   Note that the license for the iBUG 300-W dataset excludes commercial use.
 23 #   So you should contact Imperial College London to find out if it‘s OK for
 24 #   you to use this model file in a commercial product.
 25 #
 26 #
 27 #   Also, note that you can train your own models using dlib‘s machine learning
 28 #   tools. See train_shape_predictor.py to see an example.
 29 #
 30 #
 31 # COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
 32 #   You can install dlib using the command:
 33 #       pip install dlib
 34 #
 35 #   Alternatively, if you want to compile dlib yourself then go into the dlib
 36 #   root folder and run:
 37 #       python setup.py install
 38 #   or
 39 #       python setup.py install --yes USE_AVX_INSTRUCTIONS
 40 #   if you have a CPU that supports AVX instructions, since this makes some
 41 #   things run faster.  
 42 #
 43 #   Compiling dlib should work on any operating system so long as you have
 44 #   CMake and boost-python installed.  On Ubuntu, this can be done easily by
 45 #   running the command:
 46 #       sudo apt-get install libboost-python-dev cmake
 47 #
 48 #   Also note that this example requires scikit-image which can be installed
 49 #   via the command:
 50 #       pip install scikit-image
 51 #   Or downloaded from http://scikit-image.org/download.html. 
 52 
 53 import sys
 54 import os
 55 import dlib
 56 import glob
 57 from skimage import io
 58 
 59 if len(sys.argv) != 3:
 60     print(
 61         "Give the path to the trained shape predictor model as the first "
 62         "argument and then the directory containing the facial images.\n"
 63         "For example, if you are in the python_examples folder then "
 64         "execute this program by running:\n"
 65         "    ./face_landmark_detection.py shape_predictor_68_face_landmarks.dat ../examples/faces\n"
 66         "You can download a trained facial shape predictor from:\n"
 67         "    http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2")
 68     exit()
 69 
 70 predictor_path = sys.argv[1]
 71 faces_folder_path = sys.argv[2]
 72 
 73 detector = dlib.get_frontal_face_detector()
 74 predictor = dlib.shape_predictor(predictor_path)
 75 win = dlib.image_window()
 76 
 77 for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
 78     print("Processing file: {}".format(f))
 79     img = io.imread(f)
 80 
 81     win.clear_overlay()
 82     win.set_image(img)
 83 
 84     # Ask the detector to find the bounding boxes of each face. The 1 in the
 85     # second argument indicates that we should upsample the image 1 time. This
 86     # will make everything bigger and allow us to detect more faces.
 87     dets = detector(img, 1)
 88     print("Number of faces detected: {}".format(len(dets)))
 89     for k, d in enumerate(dets):
 90         print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
 91             k, d.left(), d.top(), d.right(), d.bottom()))
 92         # Get the landmarks/parts for the face in box d.
 93         shape = predictor(img, d)
 94         print("Part 0: {}, Part 1: {} ...".format(shape.part(0),
 95                                                   shape.part(1)))
 96         # Draw the face landmarks on the screen.
 97         win.add_overlay(shape)
 98 
 99     win.add_overlay(dets)
100     dlib.hit_enter_to_continue()

 1 import sys
 2 import os
 3 import dlib
 4 import glob
 5 from skimage import io
 6 
 7 detector = dlib.get_frontal_face_detector()
 8 
 9 #使用预测器，此处为预测器的路径，预测器在下载的example文件夹里面，　　*****修改此处****
10 predictor = dlib.shape_predictor("F:/code/******/shape_predictor_68_face_landmarks.dat")
11 
12 #使用dlib的图片窗口
13 win = dlib.image_window()
14 
15 #1.读取图片test2.jpg的路径,　　　　******修改此处*****
16 img = io.imread("F:/code/*****/test2.jpg")
17 
18 #2.或者还是利用cmd参数输入读取路径：
19 #img=io.imread(sys.argv[1])
20     
21 win.clear_overlay()
22 win.set_image(img)
23 
24 dets = detector(img, 1)
25 print("Number of faces detected: {}".format(len(dets)))
26 
27 for k, d in enumerate(dets):
28         print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
29             k, d.left(), d.top(), d.right(), d.bottom()))
30         # Get the landmarks/parts for the face in box d.
31         shape = predictor(img, d)
32         print("Part 0: {}, Part 1: {} ...".format(shape.part(0),
33                                                   shape.part(1)))
34         # Draw the face landmarks on the screen.
35         win.add_overlay(shape)
36 
37 win.add_overlay(dets)
38 dlib.hit_enter_to_continue()

1 F:\code\python\test2017110601\python_examples\face_test>python face_landmark_detection.py
2 Number of faces detected: 1
3 Detection 0: Left: 200 Top: 142 Right: 468 Bottom: 409
4 Part 0: (195, 233), Part 1: (197, 267) ...
5 Hit enter to continue

1  import sys
2  a=sys.argv[0]
3  print(a) 

 python test1.py

test1.py

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

python test2.py what is your name

what

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

python test3.py what is your name

 [“what”,“is”,“your”,“name”]

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

python test4.py what is your name

"is"