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.开发环境
python: 3.6.3
dlib: 19.7
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；
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；
2.1. face_detector.py
官网给的face_detector.py
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]))
为了方便理解，修改增加注释之后的 face_detector.py
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]))
打开cmd命令提示符，cd到face_detector.py所在目录，然后输入
python face_detector.py test.jpg
对test.jpg进行人脸检测，test.jpg需要和py文件放在同一目录下；
结果：
图片窗口结果：
cmd输出结果：
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
对于多个人脸的检测结果：
2.2 face_landmark_detection.py
官网给的 face_detector.py
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()
修改：
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()
结果：
cmd输出：
可以看到Number of faces detected：1，即检测到人脸数为1
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
图片窗口结果：
对于多张人脸的检测结果：
* 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:
1 import sys 2 a=sys.argv[0] 3 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:
1 import sys 2 a=sys.argv[1] 3 print(a)
cmd input:
python test2.py what is your name
cmd output:
what
3. （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:
1 import sys 2 a=sys.argv[1:] 3 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:
1 import sys 2 a=sys.argv[2] 3 print(a)
cmd input:
python test4.py what is your name
cmd output:
"is"
时间： 2024-10-08 19:10:02