Python实现对照片中的人脸进行颜值预测

• 三、环境搭建
• 四、主要思路

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：

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-600。

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)

<code># 模型训练脚本import numpy as npfrom sklearn import decompositionfrom sklearn.ensemble import RandomForestRegressorfrom 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!')</code>

getLandmarks.py

<code># 人脸关键点提取脚本import cv2import dlibimport 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-600")# 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')</code>

getFeatures.py

<code># 特征生成脚本# 具体原理请参见参考论文import mathimport numpyimport itertoolsdef 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/dist2return ratiodef generateFeatures(pointIndices1, pointIndices2, pointIndices3, pointIndices4, allLandmarkCoordinates):size = allLandmarkCoordinates.shapeif 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 = allLandmarkCoordinatesratios = []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 allFeaturesdef 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 = 0pointIndices1 = []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+1pointIndices1.append(combination[0])pointIndices2.append(combination[2])pointIndices3.append(combination[1])pointIndices4.append(combination[3])i = i+1pointIndices1.append(combination[0])pointIndices2.append(combination[3])pointIndices3.append(combination[1])pointIndices4.append(combination[2])i = i+1return 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!")</code>

Predict.py

<code># 颜值预测脚本from sklearn.externals import joblibimport numpy as npfrom sklearn import decompositionpre_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))prin<b style="color:transparent">来源gao@dai!ma.com搞$代^码网</b>t('照片中的人颜值得分依次为(满分为5分)：')k = 1for pre in predictions:print('第%d个人：' % k, end='')print(str(pre)+'分')k += 1else:feature = my_featuresfeature_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 = 1for pre in predictions:print(str(pre)+'分')k += 1</code>

“>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-600。

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-600") # 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