一、所需工具
**python版本:**3.5.4(64bit)
二、相关模块
- opencv_python模块
- sklearn模块
- numpy模块
- dlib模块
- 一些python自带的模块。
三、环境搭建
(1)安装相应版本的python并添加到环境变量中;
(2)pip安装相关模块中提到的模块。
例如:
若pip安装报错,请自行到:
http://www.lfd.uci.edu/~gohlke/pythonlibs/
下载pip安装报错模块的whl文件,并使用:
pip install whl文件路径+whl文件名安装。
例如:
(已在相关文件中提供了编译好的用于dlib库安装的whl文件——>因为这个库最不好装)
参考文献链接
【1】xxxph.d.的博客
http://www.learnopencv.com/computer-vision-for-predicting-facial-attractiveness/
【2】华南理工大学某实验室
http://www.hcii-lab.net/data/scut-fbp/en/introduce.html
四、主要思路
(1)模型训练
用了pca算法对特征进行了压缩降维;
然后用随机森林训练模型。
数据源于网络,据说数据“发源地”就是华南理工大学某实验室,因此我在参考文献上才加上了这个实验室的链接。
(2)提取人脸关键点
主要使用了dlib库。
使用官方提供的模型构建特征提取器。
(3)特征生成
完全参考了xxxph.d.的博客。
(4)颜值预测
利用之前的数据和模型进行颜值预测。
使用方式
有特殊疾病者请慎重尝试预测自己的颜值,本人不对颜值预测的结果和带来的所有负面影响负责!!!
言归正传。
环境搭建完成后,解压相关文件中的face_value.rar文件,cmd窗口切换到解压后的*.py文件所在目录。
例如:
打开test_img文件夹,将需要预测颜值的照片放入并重命名为test.jpg。
例如:
若嫌麻烦或者有其他需求,请自行修改:
getlandmarks.py文件中第13行。
最后依次运行:
train_model.py(想直接用我模型的请忽略此步)
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# 模型训练脚本
import numpy as np
from sklearn import decomposition
from sklearn.ensemble import randomforestregressor
from sklearn.externals import joblib
# 特征和对应的分数路径
features_path = './data/features_all.txt'
ratings_path = './data/ratings.txt'
# 载入数据
# 共500组数据
# 其中前480组数据作为训练集,后20组数据作为测试集
features = np.loadtxt(features_path, delimiter = ',' )
features_train = features[ 0 : - 20 ]
features_test = features[ - 20 : ]
ratings = np.loadtxt(ratings_path, delimiter = ',' )
ratings_train = ratings[ 0 : - 20 ]
ratings_test = ratings[ - 20 : ]
# 训练模型
# 这里用pca算法对特征进行了压缩和降维。
# 降维之后特征变成了20维,也就是说特征一共有500行,每行是一个人的特征向量,每个特征向量有20个元素。
# 用随机森林训练模型
pca = decomposition.pca(n_components = 20 )
pca.fit(features_train)
features_train = pca.transform(features_train)
features_test = pca.transform(features_test)
regr = randomforestregressor(n_estimators = 50 , max_depth = none, min_samples_split = 10 , random_state = 0 )
regr = regr.fit(features_train, ratings_train)
joblib.dump(regr, './model/face_rating.pkl' , compress = 1 )
# 训练完之后提示训练结束
print ( 'generate model successfully!' )
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getlandmarks.py
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# 人脸关键点提取脚本
import cv2
import dlib
import numpy
# 模型路径
predictor_path = './model/shape_predictor_68_face_landmarks.dat'
# 使用dlib自带的frontal_face_detector作为人脸提取器
detector = dlib.get_frontal_face_detector()
# 使用官方提供的模型构建特征提取器
predictor = dlib.shape_predictor(predictor_path)
face_img = cv2.imread( "test_img/test.jpg" )
# 使用detector进行人脸检测,rects为返回的结果
rects = detector(face_img, 1 )
# 如果检测到人脸
if len (rects) > = 1 :
print ( "{} faces detected" . format ( len (rects)))
else :
print ( 'no faces detected' )
exit()
with open ( './results/landmarks.txt' , 'w' ) as f:
f.truncate()
for faces in range ( len (rects)):
# 使用predictor进行人脸关键点识别
landmarks = numpy.matrix([[p.x, p.y] for p in predictor(face_img, rects[faces]).parts()])
face_img = face_img.copy()
# 使用enumerate函数遍历序列中的元素以及它们的下标
for idx, point in enumerate (landmarks):
pos = (point[ 0 , 0 ], point[ 0 , 1 ])
f.write( str (point[ 0 , 0 ]))
f.write( ',' )
f.write( str (point[ 0 , 1 ]))
f.write( ',' )
f.write( '\n' )
f.close()
# 成功后提示
print ( 'get landmarks successfully' )
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getfeatures.py
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# 特征生成脚本
# 具体原理请参见参考论文
import math
import numpy
import itertools
def facialratio(points):
x1 = points[ 0 ]
y1 = points[ 1 ]
x2 = points[ 2 ]
y2 = points[ 3 ]
x3 = points[ 4 ]
y3 = points[ 5 ]
x4 = points[ 6 ]
y4 = points[ 7 ]
dist1 = math.sqrt((x1 - x2) * * 2 + (y1 - y2) * * 2 )
dist2 = math.sqrt((x3 - x4) * * 2 + (y3 - y4) * * 2 )
ratio = dist1 / dist2
return ratio
def generatefeatures(pointindices1, pointindices2, pointindices3, pointindices4, alllandmarkcoordinates):
size = alllandmarkcoordinates.shape
if len (size) > 1 :
allfeatures = numpy.zeros((size[ 0 ], len (pointindices1)))
for x in range ( 0 , size[ 0 ]):
landmarkcoordinates = alllandmarkcoordinates[x, :]
ratios = []
for i in range ( 0 , len (pointindices1)):
x1 = landmarkcoordinates[ 2 * (pointindices1[i] - 1 )]
y1 = landmarkcoordinates[ 2 * pointindices1[i] - 1 ]
x2 = landmarkcoordinates[ 2 * (pointindices2[i] - 1 )]
y2 = landmarkcoordinates[ 2 * pointindices2[i] - 1 ]
x3 = landmarkcoordinates[ 2 * (pointindices3[i] - 1 )]
y3 = landmarkcoordinates[ 2 * pointindices3[i] - 1 ]
x4 = landmarkcoordinates[ 2 * (pointindices4[i] - 1 )]
y4 = landmarkcoordinates[ 2 * pointindices4[i] - 1 ]
points = [x1, y1, x2, y2, x3, y3, x4, y4]
ratios.append(facialratio(points))
allfeatures[x, :] = numpy.asarray(ratios)
else :
allfeatures = numpy.zeros(( 1 , len (pointindices1)))
landmarkcoordinates = alllandmarkcoordinates
ratios = []
for i in range ( 0 , len (pointindices1)):
x1 = landmarkcoordinates[ 2 * (pointindices1[i] - 1 )]
y1 = landmarkcoordinates[ 2 * pointindices1[i] - 1 ]
x2 = landmarkcoordinates[ 2 * (pointindices2[i] - 1 )]
y2 = landmarkcoordinates[ 2 * pointindices2[i] - 1 ]
x3 = landmarkcoordinates[ 2 * (pointindices3[i] - 1 )]
y3 = landmarkcoordinates[ 2 * pointindices3[i] - 1 ]
x4 = landmarkcoordinates[ 2 * (pointindices4[i] - 1 )]
y4 = landmarkcoordinates[ 2 * pointindices4[i] - 1 ]
points = [x1, y1, x2, y2, x3, y3, x4, y4]
ratios.append(facialratio(points))
allfeatures[ 0 , :] = numpy.asarray(ratios)
return allfeatures
def generateallfeatures(alllandmarkcoordinates):
a = [ 18 , 22 , 23 , 27 , 37 , 40 , 43 , 46 , 28 , 32 , 34 , 36 , 5 , 9 , 13 , 49 , 55 , 52 , 58 ]
combinations = itertools.combinations(a, 4 )
i = 0
pointindices1 = []
pointindices2 = []
pointindices3 = []
pointindices4 = []
for combination in combinations:
pointindices1.append(combination[ 0 ])
pointindices2.append(combination[ 1 ])
pointindices3.append(combination[ 2 ])
pointindices4.append(combination[ 3 ])
i = i + 1
pointindices1.append(combination[ 0 ])
pointindices2.append(combination[ 2 ])
pointindices3.append(combination[ 1 ])
pointindices4.append(combination[ 3 ])
i = i + 1
pointindices1.append(combination[ 0 ])
pointindices2.append(combination[ 3 ])
pointindices3.append(combination[ 1 ])
pointindices4.append(combination[ 2 ])
i = i + 1
return generatefeatures(pointindices1, pointindices2, pointindices3, pointindices4, alllandmarkcoordinates)
landmarks = numpy.loadtxt( "./results/landmarks.txt" , delimiter = ',' , usecols = range ( 136 ))
featuresall = generateallfeatures(landmarks)
numpy.savetxt( "./results/my_features.txt" , featuresall, delimiter = ',' , fmt = '%.04f' )
print ( "generate feature successfully!" )
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predict.py
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# 颜值预测脚本
from sklearn.externals import joblib
import numpy as np
from sklearn import decomposition
pre_model = joblib.load( './model/face_rating.pkl' )
features = np.loadtxt( './data/features_all.txt' , delimiter = ',' )
my_features = np.loadtxt( './results/my_features.txt' , delimiter = ',' )
pca = decomposition.pca(n_components = 20 )
pca.fit(features)
predictions = []
if len (my_features.shape) > 1 :
for i in range ( len (my_features)):
feature = my_features[i, :]
feature_transfer = pca.transform(feature.reshape( 1 , - 1 ))
predictions.append(pre_model.predict(feature_transfer))
print ( '照片中的人颜值得分依次为(满分为5分):' )
k = 1
for pre in predictions:
print ( '第%d个人:' % k, end = '')
print ( str (pre) + '分' )
k + = 1
else :
feature = my_features
feature_transfer = pca.transform(feature.reshape( 1 , - 1 ))
predictions.append(pre_model.predict(feature_transfer))
print ( '照片中的人颜值得分为(满分为5分):' )
k = 1
for pre in predictions:
print ( str (pre) + '分' )
k + = 1
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原文链接:https://blog.csdn.net/weixin_43649691/article/details/118002240