版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/hongbin_xu/article/details/78348086
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 sys
import dlib
import cv2
import os
current_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 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()