说明：本人在安装过程中遇到各种各样的问题，经过多次折腾，终于完成dlib在python中的配置，在此过程中多谢各位大牛写的文章，在此对自己的配置过程进行进行记录与说明，希望对后来人具有一定的参考。
1、下载python3.5.4，下载地址: https://www.python.org/downloads/
目前python有多个版本，作者最初装的3.7版本，但一直为配置成功，故降低版本，选择3.5版本。
2、下载dlib的whl文件，whl可以直接安装，无需编译，在查询的大部分资料中都提示说需要采用CMake和VS2015以上版本进行编译，dlib下载地址：http://dlib.net/files/
3、安装python，注意添加环境路径
4、验证python安装是否成功，
5、安装dlib-19.4.0-cp35-cp35m-win_amd64.whl
6、测试dlib，在dlib官网下载测试文件，下载example，如下：
import sys
import os
import dlib
import glob
from skimage import io
current_path = os.getcwd() # 获取当前路径
predictor_path =current_path+"\\shape_predictor_68_face_landmarks.dat"
face_rec_model_path = current_path+"\\dlib_face_recognition_resnet_model_v1.dat"
faces_folder_path = current_path+"\\image"
# Load all the modelswe need: a detector to find the faces, a shape predictor
# to find face landmarks so we can precisely localize the face, and finally the
# face recognition model.
detector =dlib.get_frontal_face_detector()
sp = dlib.shape_predictor(predictor_path)
facerec = dlib.face_recognition_model_v1(face_rec_model_path)
# Now process all the images
for f in glob.glob(os.path.join(faces_folder_path,"*.jpg")):
print("Processing file: {}".format(f))
img = io.imread(f)
win = dlib.image_window()
win.clear_overlay()
win.set_image(img)
# Ask the detector to find the bounding boxes of eachface. The 1 in the
# second argument indicates that weshould upsample the image 1 time. This
# will make everything bigger andallow us to detect more faces.
dets = detector(img, 1)
print("Number of faces detected: {}".format(len(dets)))
shape2=sp(img,dets[0])
win.add_overlay(shape2)
win.add_overlay(dets)
# Now process each face we found.
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 = sp(img,d)
# Draw the face landmarks on the screen so we can seewhat face is currently being processed.
#win.clear_overlay()
#win.add_overlay(d)
win.add_overlay(shape)
# Compute the 128D vector that describes the face in imgidentified by
# shape. In general, if two face descriptor vectorshave a Euclidean
# distance between them less than0.6 then they are from the same
# person, otherwise they are fromdifferent people. Here we just print
# the vector to the screen.
face_descriptor = facerec.compute_face_descriptor(img,shape)
print(face_descriptor)
dlib.hit_enter_to_continue()
7、运行该文件，错误如下，
安装scikit-image，pip installscikit-image
8、安装完成后提示缺少scipy库，pip install scipy安装该库。
9、安装完成后重新运行程序，结果如下：
配置过程如有问题请咨询qq：3511353029
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博客：https://blog.csdn.net/zz_z123/article/details/79859954