#!/usr/bin/env python # -*- coding: utf-8 -*- __author__ = 'Seven' import random import numpy as np from sklearn.model_selection import train_test_split from keras.preprocessing.image import ImageDataGenerator from keras.models import Sequential from keras.layers import Dense, Activation, Flatten, Dropout from keras.layers import Conv2D, MaxPool2D from keras.optimizers import SGD from keras.utils import np_utils from keras.models import load_model from keras import backend as K from face_dataset import load_dataset, resize_image, IMAGE_SIZE import warnings import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' warnings.filterwarnings('ignore') class Dataset: def __init__(self, path_name): # 训练集 self.train_images = None self.train_labels = None # 验证集 # self.valid_images = None # self.valid_labels = None # 测试集 self.test_images = None self.test_labels = None # 数据加载路径 self.path_name = path_name # 当前库采用的维度顺序 self.input_shape = None def load(self, img_rows=IMAGE_SIZE, img_cols=IMAGE_SIZE, img_channels=3, nb_classes=2): # 加载数据集至内存 images, labels = load_dataset(self.path_name) train_images, test_images, train_labels, test_labels = train_test_split(images, labels, test_size=0.3, random_state=random.randint(0, 10)) if K.image_data_format() == 'channels_first': train_images = train_images.reshape(train_images.shape[0], img_channels, img_rows, img_cols) test_images = test_images.reshape(test_images.shape[0], img_channels, img_rows, img_cols) self.input_shape = (img_channels, img_rows, img_cols) else: train_images = train_images.reshape(train_images.shape[0], img_rows, img_cols, img_channels) test_images = test_images.reshape(test_images.shape[0], img_rows, img_cols, img_channels) self.input_shape = (img_rows, img_cols, img_channels) # 输出训练集、测试集的数量 print(train_images.shape[0], 'train samples') print(test_images.shape[0], 'test samples') # 我们的模型使用categorical_crossentropy作为损失函数，因此需要根据类别数量nb_classes将 # 类别标签进行one-hot编码使其向量化，在这里我们的类别只有两种，经过转化后标签数据变为二维 train_labels = np_utils.to_categorical(train_labels, nb_classes) test_labels = np_utils.to_categorical(test_labels, nb_classes) # 像素数据浮点化以便归一化 train_images = train_images.astype('float32') test_images = test_images.astype('float32') # 将其归一化,图像的各像素值归一化到0~1区间 train_images /= 255.0 test_images /= 255.0 self.train_images = train_images self.test_images = test_images self.train_labels = train_labels self.test_labels = test_labels # CNN网络模型类 class Model: def __init__(self): self.model = None # 建立模型 def build_model(self, dataset, nb_classes=2): # 构建一个空的网络模型，它是一个线性堆叠模型，各神经网络层会被顺序添加，专业名称为序贯模型或线性堆叠模型 self.model = Sequential() # 以下代码将顺序添加CNN网络需要的各层，一个add就是一个网络层 self.model.add(Conv2D(32, 3, 3, border_mode='same', input_shape=dataset.input_shape)) # 1 2维卷积层 self.model.add(Activation('relu')) # 2 激活函数层 self.model.add(Conv2D(32, 3, 3)) # 3 2维卷积层 self.model.add(Activation('relu')) # 4 激活函数层 self.model.add(MaxPool2D(pool_size=(2, 2))) # 5 池化层 self.model.add(Dropout(0.25)) # 6 Dropout层 self.model.add(Conv2D(64, 3, 3, border_mode='same')) # 7 2维卷积层 self.model.add(Activation('relu')) # 8 激活函数层 self.model.add(Conv2D(64, 3, 3)) # 9 2维卷积层 self.model.add(Activation('relu')) # 10 激活函数层 self.model.add(MaxPool2D(pool_size=(2, 2))) # 11 池化层 self.model.add(Dropout(0.25)) # 12 Dropout层 self.model.add(Flatten()) # 13 Flatten层 self.model.add(Dense(512)) # 14 Dense层,又被称作全连接层 self.model.add(Activation('relu')) # 15 激活函数层 self.model.add(Dropout(0.5)) # 16 Dropout层 self.model.add(Dense(nb_classes)) # 17 Dense层 self.model.add(Activation('softmax')) # 18 分类层，输出最终结果 # 输出模型概况 self.model.summary() # 训练模型 def train(self, dataset, batch_size=20, nb_epoch=100, data_augmentation=True): sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) # 采用SGD+momentum的优化器进行训练，首先生成一个优化器对象 self.model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) # 完成实际的模型配置工作 # 不使用数据提升，所谓的提升就是从我们提供的训练数据中利用旋转、翻转、加噪声等方法创造新的 # 训练数据，有意识的提升训练数据规模，增加模型训练量 if not data_augmentation: self.model.fit(dataset.train_images, dataset.train_labels, batch_size=batch_size, nb_epoch=nb_epoch, validation_data=(dataset.test_images, dataset.test_labels), shuffle=True) # 使用实时数据提升 else: # 定义数据生成器用于数据提升，其返回一个生成器对象datagen，datagen每被调用一 # 次其生成一组数据（顺序生成），节省内存，其实就是python的数据生成器 datagen = ImageDataGenerator( featurewise_center=False, # 是否使输入数据去中心化（均值为0）， samplewise_center=False, # 是否使输入数据的每个样本均值为0 featurewise_std_normalization=False, # 是否数据标准化（输入数据除以数据集的标准差） samplewise_std_normalization=False, # 是否将每个样本数据除以自身的标准差 zca_whitening=False, # 是否对输入数据施以ZCA白化 rotation_range=20, # 数据提升时图片随机转动的角度(范围为0～180) width_shift_range=0.2, # 数据提升时图片水平偏移的幅度（单位为图片宽度的占比，0~1之间的浮点数） height_shift_range=0.2, # 同上，只不过这里是垂直 horizontal_flip=True, # 是否进行随机水平翻转 vertical_flip=False) # 是否进行随机垂直翻转 # 计算整个训练样本集的数量以用于特征值归一化、ZCA白化等处理 datagen.fit(dataset.train_images) # 利用生成器开始训练模型 self.model.fit_generator(datagen.flow(dataset.train_images, dataset.train_labels, batch_size=batch_size), samples_per_epoch=dataset.train_images.shape[0], nb_epoch=nb_epoch, validation_data=(dataset.test_images, dataset.test_labels)) MODEL_PATH = 'D:/model/model.h5' def save_model(self, file_path=MODEL_PATH): self.model.save(file_path) def load_model(self, file_path=MODEL_PATH): self.model = load_model(file_path) def evaluate(self, dataset): score = self.model.evaluate(dataset.test_images, dataset.test_labels, verbose=1) # print("%s: %.2f%%" % (self.model.metrics_names[1], score[1] * 100)) print(f'{self.model.metrics_names[1]}:{score[1] * 100}%') # 识别人脸 def face_predict(self, image): # 依然是根据后端系统确定维度顺序 if K.image_dim_ordering() == 'th' and image.shape != (1, 3, IMAGE_SIZE, IMAGE_SIZE): image = resize_image(image) # 尺寸必须与训练集一致都应该是IMAGE_SIZE x IMAGE_SIZE image = image.reshape((1, 3, IMAGE_SIZE, IMAGE_SIZE)) # 与模型训练不同，这次只是针对1张图片进行预测 elif K.image_dim_ordering() == 'tf' and image.shape != (1, IMAGE_SIZE, IMAGE_SIZE, 3): image = resize_image(image) image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3)) # 浮点并归一化 image = image.astype('float32') image /= 255.0 # 给出输入属于各个类别的概率，我们是二值类别，则该函数会给出输入图像属于0和1的概率各为多少 result = self.model.predict_proba(image) print('result:', result) # 给出类别预测：0或者1 result = self.model.predict_classes(image) # 返回类别预测结果 return result[0] if __name__ == '__main__': dataset = Dataset('D:/data') dataset.load() #训练模型 model = Model() model.build_model(dataset) #测试训练函数 model.train(dataset) model.save_model(file_path='D:/model/model.h5') #评估模型 #model = Model() #model.load_model(file_path='D:/model/model.h5') #model.evaluate(dataset)