学习Python3 Dlib19.7进行人脸面部识别

 #!/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-600 # #  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("P<em style="color:transparent">来源[email protected]搞@^&代*@码)网</em>rocessing 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-600") # 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()

 #!/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-600")): 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()

 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-600") # 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()

 蓝色的是绘制的　　win.add_overlay(shape) 红色的是绘制的　　win.add_overlay(dets)