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 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!') |
getlandmarks.py
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# 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")# 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 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/dist2 return ratiodef 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 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 = 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 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)) 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 += 1else: 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
