Python 3.6.3 利用Dlib 19.7库进行人脸识别

detector = dlib.get_frontal_face_detector()

predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

shape = predictor(img, dets[0])

 #!/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 installed.  On Ubuntu, this can be done easily by running the

 #   command:

 #       sudo apt-get install 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 sys

 import dlib

 from skimage import io

 detector = 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]))

 # created at 2017-11-27

 # updated at 2018-09-06

 # Author:   coneypo

 # Dlib:     http://dlib.net/

 # Blog:     http://www.cnblogs.com/AdaminXie/

 # Github:   https://github.com/coneypo/Dlib_examples

 import dlib

 from skimage import io

 # 使用 Dlib 的正面人脸检测器 frontal_face_detector

 detector = dlib.get_frontal_face_detector()

 # 图片所在路径

 img = io.imread("../imgs/faces_2.jpeg")

 # 生成 Dlib 的图像窗口

 win = dlib.image_window()

 win.set_image(img)

 # 使用detector检测器来检测图像中的人脸

 faces = detector(img, 1)

 print(type(faces[0]), '\n')

 print("人脸数 / faces in all：", len(faces))

 for i, d in enumerate(faces):

     print("第", i+1, "个人脸的矩形框坐标：",

           "left:", d.left(), '\t', "right:", d.right(), '\t', "top:", d.top(),'\t',  "bottom:", d.bottom())

 # 绘制矩阵轮廓

 win.add_overlay(faces)

 # 保持图像

 dlib.hit_enter_to_continue()

人脸数 / faces in all：

第  个人脸的矩形框坐标： left:       right:       top:       bottom:

Hit enter to continue

 # created at 2017-11-27

 # updated at 2018-09-06

 # Author:   coneypo

 # Dlib:     http://dlib.net/

 # Blog:     http://www.cnblogs.com/AdaminXie/

 # Github:   https://github.com/coneypo/Dlib_examples

 # create object of OpenCv

 # use OpenCv to read and show images

 import dlib

 import cv2

 # 使用 Dlib 的正面人脸检测器 frontal_face_detector

 detector = dlib.get_frontal_face_detector()

 # 图片所在路径

 # read image

 img = cv2.imread("imgs/faces_2.jpeg")

 # 使用 detector 检测器来检测图像中的人脸

 # use detector of Dlib to detector faces

 faces = detector(img, 1)

 print("人脸数 / Faces in all: ", len(faces))

 # Traversal every face

 for i, d in enumerate(faces):

     print("第", i+1, "个人脸的矩形框坐标：",

           "left:", d.left(), "right:", d.right(), "top:", d.top(), "bottom:", d.bottom())

     cv2.rectangle(img, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]), (0, 255, 255), 2)

 cv2.namedWindow("img", 2)

 cv2.imshow("img", img)

 cv2.waitKey(0)

 #!/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 installed.  On Ubuntu, this can be done easily by running the

 #   command:

 #       sudo apt-get install 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 sys

 import os

 import dlib

 import glob

 from skimage import io

 if 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()

 # created at 2017-11-27

 # updated at 2018-09-06

 # Author:   coneypo

 # Dlib:     http://dlib.net/

 # Blog:     http://www.cnblogs.com/AdaminXie/

 # Github:   https://github.com/coneypo/Dlib_examples

 import dlib

 from skimage import io

 # 使用 Dlib 的正面人脸检测器 frontal_face_detector

 detector = dlib.get_frontal_face_detector()

 # Dlib 的 68点模型

 predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

 # 图片所在路径

 img = io.imread("imgs/faces_2.jpeg")

 # 生成 Dlib 的图像窗口

 win = dlib.image_window()

 win.set_image(img)

 # 使用 detector 检测器来检测图像中的人脸

 faces = detector(img, 1)

 print("人脸数：", len(faces))

 for i, d in enumerate(faces):

     print("第", i+1, "个人脸的矩形框坐标：",

           "left:", d.left(), "right:", d.right(), "top:", d.top(), "bottom:", d.bottom())

     # 使用predictor来计算面部轮廓

     shape = predictor(img, faces[i])

     # 绘制面部轮廓

     win.add_overlay(shape)

 # 绘制矩阵轮廓

 win.add_overlay(faces)

 # 保持图像

 dlib.hit_enter_to_continue()

人脸数： 1

第 1 个人脸的矩形框坐标： left: 63 right: 384 top: 206 bottom: 527

Hit enter to continue

>>> python test.py parameter_1

python test.py what is your name

import sys

print(sys.argv[0])

# test.py

print(sys.argv[1])

# what

print(sys.argv[2])

# is

print(sys.argv[1:])

# [“what”,“is”,“your”,“name”]