前言

利用python教程对照片中人脸进行颜值预测！

所需工具

Python版本：3.5.4(64bit)

相关模块：

opencv_python模块、sklearn模块、numpy模块、dlib模块以及一些Python自带的模块。

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

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)

# 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!')

getLandmarks.py

# 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')

getFeatures.py

# 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!")

Predict.py

# 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

文章到这里就结束了，感谢你的观看，下篇文章预测NBA比赛结果。

为了感谢读者们，我想把我最近收藏的一些编程干货分享给大家，回馈每一个读者，希望能帮到你们。