自学教程：Python实现对照片中的人脸进行颜值预测

# 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!')

# 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')

# 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!")

# 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