├── web ├── model │ └── model.h5 ├── uploads │ ├── demo.jpeg │ ├── demo1.jpg │ ├── demo2.jpg │ ├── demo3.jpg │ ├── demo4.jpg │ ├── demo5.jpg │ ├── 6eae4214-033b-4d24-8889-2e02bbeb8b8b.jpg │ └── cropped_6eae4214-033b-4d24-8889-2e02bbeb8b8b.jpg ├── static │ ├── css │ │ └── themes │ │ │ └── default │ │ │ ├── .DS_Store │ │ │ └── assets │ │ │ ├── .DS_Store │ │ │ ├── fonts │ │ │ ├── icons.eot │ │ │ ├── icons.otf │ │ │ ├── icons.ttf │ │ │ ├── icons.woff │ │ │ └── icons.woff2 │ │ │ └── images │ │ │ └── flags.png │ └── js │ │ └── jquery.min.js ├── templates │ ├── error.html │ └── upload.html └── app.py ├── README.md ├── keras.sln ├── keras.pyproj └── train.py /web/model/model.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/UPCCV/Face-Attractiveness/HEAD/web/model/model.h5 -------------------------------------------------------------------------------- /web/uploads/demo.jpeg: 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-------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## Face Attriactiveness by keras 2 | 3 | 基于深度学习的颜值评价 4 | 5 | 本文基于[SCUT-FBP5500](https://github.com/HCIILAB/SCUT-FBP5500-Database-Release)数据库实现基于深度学习的颜值评价 6 | 7 | ## 下载数据 8 | 9 | SCUT-FBP5500是包含5500张人脸图片的数据集，其下载地址为[百度网盘](https://pan.baidu.com/s/1Ff2W2VLJ1ZbWSeV5JbF0Iw)访问密码: if7p ，Size = 172MB 10 | 11 | ![](https://raw.githubusercontent.com/HCIILAB/SCUT-FBP5500-Database-Release/master/SCUT-FBP5500.jpg) 12 | 13 | ## 训练 14 | 15 | [基于深度学习的颜值打分器](https://zhuanlan.zhihu.com/p/36138077)曾实现过一个基于ResNet50的网络进行打分，其存在以下两个问题： 16 | 17 | 1. 所有数据一次性读入内存，小于20G的机器无法训练 18 | 19 | 2. 选用模型太大，运行时需要的显存在8G以上，一般的机器也无法运行 20 | 21 | 针对以上两个问题，本文采用fit_generator，大大减少了训练所需的资源,模型也换成自己的小模型，取得了不错的效果. 22 | 23 | ## 部署 24 | 25 | cd web 26 | python app.py 27 | 28 | 打开浏览器,输入网址localhost:5000.选择一张图片上传，稍等片刻就会返回识别结果。 29 | 30 | ## 参考 31 | 32 | [基于深度学习的颜值打分器](https://zhuanlan.zhihu.com/p/36138077) -------------------------------------------------------------------------------- /keras.sln: -------------------------------------------------------------------------------- 1 | 2 | Microsoft Visual Studio Solution File, Format Version 12.00 3 | # Visual Studio 14 4 | VisualStudioVersion = 14.0.25420.1 5 | MinimumVisualStudioVersion = 10.0.40219.1 6 | Project("{888888A0-9F3D-457C-B088-3A5042F75D52}") = "keras", "keras.pyproj", "{66E55EA6-E9FE-41E2-9648-0894CADA2385}" 7 | EndProject 8 | Global 9 | GlobalSection(SolutionConfigurationPlatforms) = preSolution 10 | Debug|Any CPU = Debug|Any CPU 11 | Release|Any CPU = Release|Any CPU 12 | EndGlobalSection 13 | GlobalSection(ProjectConfigurationPlatforms) = postSolution 14 | {66E55EA6-E9FE-41E2-9648-0894CADA2385}.Debug|Any CPU.ActiveCfg = Debug|Any CPU 15 | {66E55EA6-E9FE-41E2-9648-0894CADA2385}.Release|Any CPU.ActiveCfg = Release|Any CPU 16 | EndGlobalSection 17 | GlobalSection(SolutionProperties) = preSolution 18 | HideSolutionNode = FALSE 19 | EndGlobalSection 20 | EndGlobal 21 | -------------------------------------------------------------------------------- /web/templates/error.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 颜值打分器 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 |
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46 | 47 | 48 | -------------------------------------------------------------------------------- /keras.pyproj: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Debug 5 | 2.0 6 | {66e55ea6-e9fe-41e2-9648-0894cada2385} 7 | . 8 | train.py 9 | 10 | 11 | . 12 | . 13 | Kaggle 14 | Kaggle 15 | {9a7a9026-48c1-4688-9d5d-e5699d47d074} 16 | 3.5 17 | 18 | 19 | true 20 | false 21 | 22 | 23 | true 24 | false 25 | 26 | 27 | 28 | 29 | 30 | 31 | Code 32 | 33 | 34 | Code 35 | 36 | 37 | 38 | 10.0 39 | $(MSBuildExtensionsPath32)\Microsoft\VisualStudio\v$(VisualStudioVersion)\Python Tools\Microsoft.PythonTools.targets 40 | 41 | 42 | 43 | 46 | 47 | 48 | 49 | 50 | 51 | -------------------------------------------------------------------------------- /web/app.py: -------------------------------------------------------------------------------- 1 | #coding: utf-8 2 | 3 | from uuid import uuid4 4 | 5 | from flask import Flask, render_template, request, send_from_directory 6 | from flask_uploads import UploadSet, configure_uploads, IMAGES 7 | 8 | from keras.models import load_model 9 | from keras.preprocessing.image import array_to_img, img_to_array, load_img 10 | from scipy.misc import imresize 11 | import numpy as np 12 | import cv2 13 | 14 | from gevent.pywsgi import WSGIServer 15 | 16 | app = Flask(__name__) 17 | 18 | photos = UploadSet('photos', IMAGES) 19 | 20 | app.config['UPLOADED_PHOTOS_DEST'] = 'uploads' 21 | configure_uploads(app, photos) 22 | 23 | haar_face_cascade = cv2.CascadeClassifier('data/haarcascade_frontalface_alt.xml') 24 | 25 | img_height, img_width, channels = 80, 80, 3 26 | model = load_model("model/model.h5") 27 | 28 | def detect_face(f_cascade, filepath, scaleFactor=1.1): 29 | img = load_img(filepath) 30 | img = imresize(img, size=(img_height, img_width)) 31 | gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 32 | faces = f_cascade.detectMultiScale(gray, scaleFactor=scaleFactor, minNeighbors=5) 33 | if len(faces) == 0: 34 | return np.zeros((1,1)) 35 | x, y, w, h = faces[0] 36 | cropped_image = img[y:y + h, x:x + w, :] 37 | resized_image = cv2.resize(cropped_image, (img_height, img_width)) 38 | filename = filepath.split('/')[-1] 39 | cv2.imwrite("uploads/cropped_{}".format(filename), resized_image) 40 | return resized_image 41 | 42 | def get_score(filepath): 43 | test_x = detect_face(haar_face_cascade, filepath) 44 | if not test_x.any(): 45 | return None 46 | test_x = test_x / 255. 47 | test_x = test_x.reshape((1,) + test_x.shape) 48 | predicted = model.predict(test_x) 49 | return round(predicted[0][0], 2) 50 | 51 | @app.route("/") 52 | def index(): 53 | return render_template("upload.html", image_name="demo.jpeg", score=4.04) 54 | 55 | @app.route('/upload', methods=['GET', 'POST']) 56 | def upload(): 57 | if request.method == 'POST' and 'photo' in request.files: 58 | filename = photos.save(request.files['photo'], name="{}.".format(str(uuid4()))) 59 | score = get_score("uploads/{}".format(filename)) 60 | if not score: 61 | return render_template("error.html") 62 | else: 63 | return render_template("upload.html", image_name="{}".format(filename), score=score) 64 | 65 | @app.route('/uploads/') 66 | def send_image(filename): 67 | return send_from_directory("uploads", filename) 68 | 69 | 70 | if __name__ == '__main__': 71 | WSGIServer(('0.0.0.0', 5000), app).serve_forever() 72 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | import os, keras,cv2,math 2 | import pandas as pd 3 | import numpy as np 4 | import matplotlib.pyplot as plt 5 | from keras.applications import ResNet50 6 | from keras.layers import Conv2D, MaxPooling2D 7 | from keras.layers import Activation, Dropout, Flatten, Dense 8 | from keras.preprocessing.image import img_to_array, load_img 9 | from sklearn.model_selection import train_test_split 10 | from keras.models import load_model 11 | from keras.models import Sequential 12 | 13 | img_width, img_height, channels = 80, 80, 3 14 | sample_dir='Images' 15 | 16 | class DataGenerator(keras.utils.Sequence): 17 | def __init__(self,labels,batch_size=8,shuffle=True): 18 | self.labels=labels 19 | self.indexes = np.arange(len(self.labels)) 20 | self.batch_size=batch_size 21 | self.shuffle = shuffle 22 | self.dir=sample_dir 23 | self.files=os.listdir(self.dir) 24 | 25 | def __len__(self): 26 | return math.ceil(len(self.labels) / float(self.batch_size)) 27 | 28 | def __getitem__(self, index): 29 | batch_indexs = self.indexes[index * self.batch_size:(index + 1) * self.batch_size] 30 | X, y = self.data_generation(batch_indexs) 31 | return X, y 32 | 33 | def on_epoch_end(self): 34 | if self.shuffle == True: 35 | np.random.shuffle(self.indexes) 36 | #print("Epoch end") 37 | 38 | def data_generation(self, batch_indexs): 39 | x = [] 40 | y = [] 41 | for bi in batch_indexs: 42 | file=self.files[bi] 43 | imgpath=self.dir+"/"+file 44 | #print(bi,imgpath) 45 | img = load_img(imgpath, target_size=(img_height, img_width)) 46 | img = img_to_array(img).reshape(img_height, img_width, channels) 47 | img=img.astype('float32') / 255. 48 | x.append(img) 49 | l=self.labels[file] 50 | y.append(l) 51 | return np.array(x), np.array(y) 52 | 53 | def convert_to_gt_txt(gtfile="All_Ratings.xlsx"): 54 | ratings = pd.read_excel(gtfile, sheet_name=None)["ALL"] 55 | filenames = ratings['Filename'] 56 | scores = ratings['Rating'] 57 | labels = {} 58 | files = set(filenames) 59 | for f in files: 60 | labels[f] = [] 61 | for f, s in zip(filenames, scores): 62 | labels[f].append(s) 63 | with open("gt.txt", "w")as fgt: 64 | for f in files: 65 | sum = 0 66 | for s in labels[f]: 67 | sum += s 68 | s = sum / len(labels[f]) 69 | print(f, s) 70 | fgt.write(f + " " + str(s) + "\n") 71 | return labels 72 | 73 | def load_gt_file(gtfile="gt.txt"): 74 | with open(gtfile) as f: 75 | labels = {} 76 | lines = f.readlines() 77 | for line in lines: 78 | items = line.split() 79 | filename = items[0] 80 | label = (float)(items[1]) 81 | labels[filename] = label 82 | return labels 83 | 84 | def get_model(): 85 | input_shape = (img_width, img_height, channels) 86 | #resnet = ResNet50(include_top=False, pooling='avg', input_shape=input_shape) 87 | #model = Sequential() 88 | #model.add(resnet) 89 | #model.add(Dense(1)) 90 | #model.layers[0].trainable = False 91 | 92 | model = Sequential() 93 | model.add(Conv2D(32, (3, 3), input_shape=input_shape)) 94 | model.add(Activation('relu')) 95 | model.add(MaxPooling2D(pool_size=(2, 2))) 96 | 97 | model.add(Conv2D(32, (3, 3))) 98 | model.add(Activation('relu')) 99 | model.add(MaxPooling2D(pool_size=(2, 2))) 100 | 101 | model.add(Conv2D(64, (3, 3))) 102 | model.add(Activation('relu')) 103 | model.add(MaxPooling2D(pool_size=(2, 2))) 104 | # model.summary() 105 | model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors 106 | model.add(Dense(64)) 107 | model.add(Activation('relu')) 108 | model.add(Dropout(0.5)) 109 | model.add(Dense(1)) 110 | model.compile(loss='mse', optimizer='adam') 111 | return model 112 | 113 | def plot_histrory(history): 114 | plt.rcParams['figure.figsize'] = (6, 6) 115 | 116 | loss = history.history['loss'] 117 | epochs = range(1, len(loss) + 1) 118 | 119 | plt.figure() 120 | plt.title('Training loss') 121 | plt.plot(epochs, loss, 'red', label='Training loss') 122 | plt.legend() 123 | plt.show() 124 | 125 | 126 | def train(): 127 | labels = load_gt_file() 128 | model = get_model() 129 | earlystop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=20, verbose=1, mode='auto') 130 | filepath = "{epoch:02d}-{val_loss:.2f}.h5" 131 | checkpoints = keras.callbacks.ModelCheckpoint(filepath, monitor='acc', verbose=1, save_best_only=True, mode='min') 132 | tensorboard = keras.callbacks.TensorBoard(log_dir='logs', histogram_freq=0, write_graph=True, write_images=False,embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None) 133 | callbacks = [earlystop, checkpoints, tensorboard] 134 | model.layers[0].trainable = True 135 | model.compile(loss='mse', optimizer='adam') 136 | train_generator=DataGenerator(labels) 137 | val_generator=DataGenerator(labels) 138 | #max_queue_size=1,workers=1,verbose=1, 139 | history = model.fit_generator(train_generator,validation_data=val_generator,validation_steps=5000/8,callbacks=callbacks,steps_per_epoch=None,epochs=10) 140 | plot_histrory(history) 141 | model.save("model.h5") 142 | 143 | def evaluate(): 144 | model = load_model("model.h5") 145 | labels = load_gt_file() 146 | val_generator = DataGenerator(labels) 147 | scores = model.evaluate_generator(val_generator) 148 | print(scores) 149 | #plt.scatter(y_train, model.predict(x_train)) 150 | #plt.plot(y_train, y_train, 'ro') 151 | #plt.show() 152 | 153 | def test(imgpath="Images/AF1.jpg"): 154 | model = load_model("model.h5") 155 | img = load_img(imgpath) 156 | x = img_to_array(img).reshape(img_height, img_width, channels) 157 | x = x.astype('float32') / 255. 158 | x=np.expand_dims(x,axis=0) 159 | l = model.predict(x) 160 | print(l[0]) 161 | 162 | def test_one_image(imgpath="Images/AF1.jpg"): 163 | model = load_model("model.h5") 164 | img=cv2.imread(imgpath) 165 | img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB) 166 | img=cv2.resize(img,(img_height,img_width)) 167 | img=img/255.0 168 | img = np.expand_dims(img, axis=0) 169 | l=model.predict(img) 170 | print(l[0]) 171 | 172 | def train_all(): 173 | labels = load_gt_file() 174 | nb_samples = len(os.listdir(sample_dir)) 175 | x_total = np.empty((nb_samples, img_width, img_height, channels), dtype=np.float32) 176 | y_total = np.empty((nb_samples, 1), dtype=np.float32) 177 | for i, fn in enumerate(os.listdir(sample_dir)): 178 | img = load_img('%s/%s' % (sample_dir, fn),target_size=(img_width,img_height)) 179 | x = img_to_array(img).reshape(img_height, img_width, channels) 180 | x = x.astype('float32') / 255. 181 | y = labels[fn] 182 | x_total[i] = x 183 | y_total[i] = y 184 | seed = 42 185 | x_train_all, x_test, y_train_all, y_test = train_test_split(x_total, y_total, test_size=0.2, random_state=seed) 186 | x_train, x_val, y_train, y_val = train_test_split(x_train_all, y_train_all, test_size=0.2, random_state=seed) 187 | model = get_model() 188 | filepath = "{epoch:02d}-{val_loss:.2f}.h5" 189 | checkpoint = keras.callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min') 190 | reduce_learning_rate = keras.callbacks.ReduceLROnPlateau(monitor='loss', 191 | factor=0.1,patience=2,cooldown=2,min_lr=0.00001,verbose=1) 192 | callback_list = [checkpoint, reduce_learning_rate] 193 | model.layers[0].trainable = True 194 | model.compile(loss='mse', optimizer='adam') 195 | history = model.fit(x=x_train,y=y_train,batch_size=8,epochs=10,validation_data=(x_val, y_val),callbacks=callback_list) 196 | #plot_histrory(history) 197 | model.save("model.h5") 198 | 199 | 200 | def plot_scatter(): 201 | model=load_model("model.h5") 202 | labels = load_gt_file() 203 | nb_samples = len(os.listdir(sample_dir)) 204 | x_total = np.empty((nb_samples, img_width, img_height, channels), dtype=np.float32) 205 | y_total = np.empty((nb_samples, 1), dtype=np.float32) 206 | for i, fn in enumerate(os.listdir(sample_dir)): 207 | img = load_img('%s/%s' % (sample_dir, fn), target_size=(img_width, img_height)) 208 | x = img_to_array(img).reshape(img_height, img_width, channels) 209 | x = x.astype('float32') / 255. 210 | y = labels[fn] 211 | x_total[i] = x 212 | y_total[i] = y 213 | plt.scatter(y_total, model.predict(x_total)) 214 | plt.plot(y_total, y_total, 'ro') 215 | plt.show() 216 | 217 | if __name__ == "__main__": 218 | # convert_to_gt_txt() 219 | #train_all() 220 | #train() 221 | #evaluate() 222 | plot_scatter() 223 | #test_one_image() 224 | #test() -------------------------------------------------------------------------------- /web/static/js/jquery.min.js: 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