最近看了一篇很有意思的文章 http://matthewearl.github.io/2015/07/28/switching-eds-with-python/ ，本来想自己复现一下，后来发现自己太菜，用了一整天只完成了不到一半，最近要找工作了，看书看的有点烦，本来想写个有趣的代码放松下。哎。

开始正文。原作者用的是dlib的库完成关键点检测，试着装了一下，没装成，那就不用了。本来想用自己的库，后来想了下自己封装的太烂了，还是改用别人的吧，这样程序的大小也会小很多，训练好的文件还是比较大的。

首先去Face++注册一个账号，然后创建应用(得到访问的密钥)，这里直接截图了。

我用的是Python 接口，去github下载SDK  https://github.com/FacePlusPlus/facepp-python-sdk/tree/v2.0

修改apikey.cfg的内容，可以运行hello.py的历程。

注意服务器的地址不要选错，还有就是官方的历程里是这样的，API_KEY = ‘‘,替换直接的密钥时记得把<>也删掉，不然也会报错。

SDK的facepp.py文件的350行左右修改一下，添加 ‘/detection/landmark‘,这句，不然的话人脸关键点检测的接口无法调用。

这里稍微吐槽一下，Face++的SDK写的真不怎么好，很多地方不够详细，而且程序有时会因为网络问题出现bug。想上传自己的图片也找不到接口，官网只给了这么几句，

感觉解释的太简单了吧，SDK里也没有见到有相关的本地图片上传接口。

说了这么多，展示一下效果，然后贴代码。

这是人类关键点检测的效果。

由于开始选的两个脸对齐的比较好，所以换了一张，这里完成的效果是把第二幅图片的人脸缩放、旋转和第一张人脸对其。之后的操作就是裁剪、覆盖了。

下面贴一下代码，只为完成了一半(文章里写的这么多)，后面的部分不太熟悉，要找工作了，没心情写代码。。。

这里建议大家自己注册一个账号，每个账号的开发者版本同时有3个线程，如果我这里修改了密钥程序应该会报错，这里可能程序无法运行。

#!/usr/bin/env python2

# -*- coding: utf-8 -*-

# You need to register your App first, and enter you API key/secret.

# 您需要先注册一个App，并将得到的API key和API secret写在这里。API_KEY = 'de12a860239680fb8aba7c8283efffd9'

API_SECRET = '61MzhMy_j_L8T1-JAzVjlBSsKqy2pUap'

# Import system libraries and define helper functions

# 导入系统库并定义辅助函数

import time

import os

import cv2

import numpy

import urllib

import re

from facepp import API

ALIGN_POINTS = list(range(0,25))

OVERLAY_POINTS=list(range(0,25))

RIGHT_EYE_POINTS = list(range(2, 6))

LEFT_EYE_POINTS = list(range(4, 8))

FEATHER_AMOUNT = 11

SCALE_FACTOR = 1

COLOUR_CORRECT_BLUR_FRAC = 0.6

def encode(obj):

if type(obj) is unicode:

return obj.encode('utf-8')

if type(obj) is dict:

return {encode(k): encode(v) for (k, v) in obj.iteritems()}

if type(obj) is list:

return [encode(i) for i in obj]

return obj

def getPoints(text):

a=encode(text)

a=str(a)

#print a

x = re.findall(r'\'x\':......',a)

for i in range(len(x)):

x[i]=re.findall(r'\d+\.\d\d',x[i])

y = re.findall(r'\'y\':......',a)

for i in range(len(y)):

y[i]=re.findall(r'\d+\.\d\d',y[i])

xy =zip(x,y)

return xy

def drawPoints(img,xy): #画点，用于检测程序运行情况

Img = img

tmp = numpy.array(img)

h,w,c = tmp.shape

for i,j in xy:

xp=float(i[0])*w/100.

yp=float(j[0])*h/100.

point = (int(xp),int(yp))

cv2.circle(Img,point,1,(0,255,0))

return Img

def get_landmarks(path,tmpPic):

result = api.detection.detect(url = path,mode = 'oneface')

ID = result['face'][0]['face_id']

points = api.detection.landmark(face_id=ID,type = '25p')

xy = getPoints(points)

print 'downloading the picture....'

urllib.urlretrieve(path,tmpPic) #为防止图片内容有变化，每次都下载一遍，调试可以不用

tmp = cv2.imread(tmpPic)

#测试

Img = drawPoints(tmp,xy)

cv2.imwrite('point.jpg',Img)

tmp = numpy.array(tmp)

h,w,c = tmp.shape

points = numpy.empty([25,2],dtype=numpy.int16)

n=0

for i,j in xy:

xp=float(i[0])*w/100.

yp=float(j[0])*h/100.

points[n][0]=int(xp)

points[n][1]=int(yp)

n+=1

return numpy.matrix([[i[0], i[1]] for i in points])

#return points

def transformation_from_points(points1, points2):

"""

Return an affine transformation [s * R | T] such that:

sum ||s*R*p1,i + T - p2,i||^2

is minimized.

"""

# Solve the procrustes problem by subtracting centroids, scaling by the

# standard deviation, and then using the SVD to calculate the rotation. See

# the following for more details:

# https://en.wikipedia.org/wiki/Orthogonal_Procrustes_problem

points1 = points1.astype(numpy.float64)

points2 = points2.astype(numpy.float64)

c1 = numpy.mean(points1, axis=0)

c2 = numpy.mean(points2, axis=0)

points1 -= c1

points2 -= c2

s1 = numpy.std(points1)

s2 = numpy.std(points2)

points1 /= s1

points2 /= s2

U, S, Vt = numpy.linalg.svd(points1.T * points2)

# The R we seek is in fact the transpose of the one given by U * Vt. This

# is because the above formulation assumes the matrix goes on the right

# (with row vectors) where as our solution requires the matrix to be on the

# left (with column vectors).

R = (U * Vt).T

return numpy.vstack([numpy.hstack(((s2 / s1) * R,

c2.T - (s2 / s1) * R * c1.T)),

numpy.matrix([0., 0., 1.])])

def warp_im(im, M, dshape):

output_im = numpy.zeros(dshape, dtype=im.dtype)

cv2.warpAffine(im,

M[:2],

(dshape[1], dshape[0]),

dst=output_im,

borderMode=cv2.BORDER_TRANSPARENT,

flags=cv2.WARP_INVERSE_MAP)

return output_im

if __name__ == '__main__':

api = API(API_KEY, API_SECRET)

path1='http://www.faceplusplus.com/static/img/demo/17.jpg'

path2='http://www.faceplusplus.com/static/img/demo/7.jpg'

#path2='http://cimg.163.com/auto/2004/8/28/200408281055448e023.jpg'

tmp1='./tmp1.jpg'

tmp2='./tmp2.jpg'

landmarks1=get_landmarks(path1,tmp1)

landmarks2=get_landmarks(path2,tmp2)

im1 = cv2.imread(tmp1,cv2.IMREAD_COLOR)

im2 = cv2.imread(tmp2,cv2.IMREAD_COLOR)

M = transformation_from_points(landmarks1[ALIGN_POINTS],

landmarks2[ALIGN_POINTS])

warped_im2 = warp_im(im2, M, im1.shape)

cv2.imwrite('wrap.jpg',warped_im2)

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