学习Python3 Dlib19.7进行人脸面部识别
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.简介
python: 3.6.3
dlib: 19.7
利用dlib的特征提取器,进行人脸 矩形框 的特征提取:
dets = dlib.get_frontal_face_detector(img)
利用dlib的68点特征预测器,进行人脸 68点 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;
link: http://dlib.net/cnn_face_detector.py.html
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;
link: http://dlib.net/face_landmark_detection.py.html
2.1. face_detector.py
官网给的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 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]))
import dlib from skimage import io # 使用特征提取器frontal_face_detector detector = dlib.get_frontal_face_detector() # path是图片所在路径 path = "F:/code/python/P_dlib_face/pic/" img = io.imread(path+"1.jpg") # 特征提取器的实例化 dets = detector(img) print("人脸数:", len(dets)) # 输出人脸矩形的四个坐标点 for i, d in enumerate(dets): print("第", i, "个人脸d的坐标:", "left:", d.left(), "right:", d.right(), "top:", d.top(), "bottom:", d.bottom()) # 绘制图片 win = dlib.image_window() # 清除覆盖 #win.clear_overlay() win.set_image(img) # 将生成的矩阵覆盖上 win.add_overlay(dets) # 保持图像 dlib.hit_enter_to_continue()
对test.jpg进行人脸检测:
结果:
图片窗口结果:
输出结果:
人脸数: 1 第 0 个人脸: left: 79 right: 154 top: 47 bottom: 121 Hit enter to continue
对于多个人脸的检测结果:
2.2 face_landmark_detection.py
官网给的 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 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()
修改:
绘制两个overlay,矩阵框 和 面部特征
import dlib from skimage import io # 使用特征提取器frontal_face_detector detector = dlib.get_frontal_face_detector() # dlib的68点模型 path_pre = "F:/code/python/P_dlib_face/" predictor = dlib.shape_predictor(path_pre+"shape_predictor_68_face_landmarks.dat") # 图片所在路径 path_pic = "F:/code/python/P_dlib_face/pic/" img = io.imread(path_pic+"1.jpg") # 生成dlib的图像窗口 win = dlib.image_window() win.clear_overlay() win.set_image(img) # 特征提取器的实例化 dets = detector(img, 1) print("人脸数:", len(dets)) for k, d in enumerate(dets): print("第", k, "个人脸d的坐标:", "left:", d.left(), "right:", d.right(), "top:", d.top(), "bottom:", d.bottom()) # 利用预测器预测 shape = predictor(img, d) # 绘制面部轮廓 win.add_overlay(shape) # 绘制矩阵轮廓 win.add_overlay(dets) # 保持图像 dlib.hit_enter_to_continue()
结果:
人脸数: 1 第 0 个人脸d的坐标: left: 79 right: 154 top: 47 bottom: 121
图片窗口结果:
蓝色的是绘制的 win.add_overlay(shape) 红色的是绘制的 win.add_overlay(dets)
对于多张人脸的检测结果:
官网例程中是利用sys.argv[]读取命令行输入,其实为了方便我把文件路径写好了,如果对于sys.argv[]有疑惑,可以参照下面的总结:
* 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:
import sys a=sys.argv[0] 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:
import sys a=sys.argv[1] print(a)
cmd input:
python test2.py what is your name
cmd output:
what
(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:
import sys a=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输入获取的参数字符串中,第二个字符)
test4.py:
import sys a=sys.argv[2] print(a)
cmd input:
python test4.py what is your name
cmd output:
"is"
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