python dlib学习（二）：人脸特征点标定

前言

上次介绍了人脸检测的程序（python dlib学习（一）：人脸检测），这次介绍人脸特征点标定。dlib提供了训练好的模型，可以识别人脸的68个特征点。
下载链接：http://pan.baidu.com/s/1i46vPu1。

程序

还是直接上代码，注释在程序中。用到了python-opencv、dlib。

# -*- coding: utf-8 -*-import sysimport dlibimport cv2import oscurrent_path = os.getcwd()  # 获取当前路径predictor_path = current_path + "\\model\\shape_predictor_68_face_landmarks.dat"  # shape_predictor_68_face_landmarks.dat是进行人脸标定的模型，它是基于HOG特征的，这里是他所在的路径face_directory_path = current_path + "\\faces\\"    # 存放人脸图片的路径detector = dlib.get_frontal_face_detector() #获取人脸分类器predictor = dlib.shape_predictor(predictor_path)    # 获取人脸检测器# 传入的命令行参数for f in sys.argv[1:]:    # 图片路径，目录+文件名    face_path = face_directory_path + f    # opencv 读取图片，并显示    img = cv2.imread(f, cv2.IMREAD_COLOR)    # 摘自官方文档：    # image is a numpy ndarray containing either an 8bit grayscale or RGB image.    # opencv读入的图片默认是bgr格式，我们需要将其转换为rgb格式；都是numpy的ndarray类。    b, g, r = cv2.split(img)    # 分离三个颜色通道    img2 = cv2.merge([r, g, b])   # 融合三个颜色通道生成新图片    dets = detector(img, 1) #使用detector进行人脸检测 dets为返回的结果    print("Number of faces detected: {}".format(len(dets)))   # 打印识别到的人脸个数    # enumerate是一个Python的内置方法，用于遍历索引    # index是序号；face是dets中取出的dlib.rectangle类的对象，包含了人脸的区域等信息    # left()、top()、right()、bottom()都是dlib.rectangle类的方法，对应矩形四条边的位置    for index, face in enumerate(dets):        print('face {}; left {}; top {}; right {}; bottom {}'.format(index, face.left(), face.top(), face.right(), face.bottom()))        # 这里不需要画出人脸的框了        # left = face.left()        # top = face.top()        # right = face.right()        # bottom = face.bottom()        # cv2.rectangle(img, (left, top), (right, bottom), (0, 255, 0), 3)        # cv2.namedWindow(f, cv2.WINDOW_AUTOSIZE)        # cv2.imshow(f, img)        shape = predictor(img, face)  # 寻找人脸的68个标定点         # print(shape)        # print(shape.num_parts)        # 遍历所有点，打印出其坐标，并用蓝色的圈表示出来        for index, pt in enumerate(shape.parts()):            print('Part {}: {}'.format(index, pt))            pt_pos = (pt.x, pt.y)            cv2.circle(img, pt_pos, 2, (255, 0, 0), 1)        # 在新窗口中显示        cv2.namedWindow(f, cv2.WINDOW_AUTOSIZE)        cv2.imshow(f, img)# 等待按键，随后退出，销毁窗口k = cv2.waitKey(0)cv2.destroyAllWindows()

还有一点补充的：
我的文件夹结构是这样的：

faces中存放图片，运行程序时指定名字，会到这个文件夹中读取图片。

model文件夹中存放模型。

运行结果

官方例程

最后给出官方例程，也可以参考官方例程。

#!/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.##   This face detector 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.##   Also, note that you can train your own models using dlib's machine learning#   tools. See train_shape_predictor.py to see an example.##   You can get the shape_predictor_68_face_landmarks.dat file from:#   http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2## 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 sysimport osimport dlibimport globfrom skimage import ioif 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()