1. 安装python

参考http://blog.csdn.net/liuxiaoheng1992/article/details/54407589 这篇blog给出的python

python setup.py install

提示我没有权限，所以我试着加上sudo

sudo python setup.py install

安装成功
可以在终端运行查看是否能够顺利导入：

python
>>>import dlib

2. 运行face landmark官网的demo

官网人脸特征点检测demo代码如下：

#!/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()

根据代码提示，输入：

cd dlib
cd python_examples
./face_landmark_detection.py shape_predictor_68_face_landmarks.dat ../examples/faces   #shape_predictor_68_face_landmarks.dat放置位置自选，这样写就意味着shape_predictor_68_face_landmarks.dat放置再python_examples

demo运行结果如下：

3. 新建.py文件

如果要自己新建.py文件运行的话，如下操作：

新建一个fr.py的文件，将官网给的demo代码copy进去

此时fr.py还未被编译？，然后在该目录下输入：

python fr.py
chmod +x fr.py

变成如下：

这样就可以用了。

看下我们的结果，face_landmark.py我们的python文件：
输入：

./face_landmark.py shape_predictor_68_face_landmarks.dat ./image

这里的./image是放置图片的文件夹，不需要带.jpg后缀，看原文代码就知道。

faces_folder_path = sys.argv[2]

for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")):

问题

解决办法：缺少动态连接库.so–cannot open shared object file: No such file or directory

sudo ln -s /usr/local/anaconda2/lib/libmkl_rt.so /usr/lib

解决啦。

解决办法：
发现pip install scikit-image没用，提示我已经安装过了。

然后我好加上sudo试试：

可以了。

问题：

解决办法：在~/.bashrc中加上：

export LD_LIBRARY_PATH="/usr/local/anaconda2/lib:$LD_LIBRARY_PATH"

博主试过，在/etc/profile中加上上面指令，没用，而且会和我的cuda-7.5/lib发生冲突，就是重启或者注销系统的时候进不去系统，怀疑就是路径发生冲突了，/etc/profile是全局的，而~/.bashrc是局部的。所以当我添加到~/.bashrc里，就可以使用了。
参考：Intel MKL FATAL ERROR: Cannot load libmkl_avx.so or libmkl_def.so.