Python实现对照片中的人脸进行颜值预测

时间:2021-07-16 20:36:26

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:

Python实现对照片中的人脸进行颜值预测

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)

Python实现对照片中的人脸进行颜值预测

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:

Python实现对照片中的人脸进行颜值预测

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:

Python实现对照片中的人脸进行颜值预测

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!')

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')

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!")

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