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face_train.py
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#-*- coding: utf-8 -*-
import random
import cv2
from sklearn.cross_validation import train_test_split
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.optimizers import SGD
from keras.utils import np_utils
from keras.models import load_model
from keras import backend as K
from load_face import load_train_orl,load_test_orl,load_train_pie,load_test_pie
from load_face_re import load_train_re,load_test_re
def resize_image(image, height = 128, width = 128):
top, bottom, left, right = (0, 0, 0, 0)
h, w, _ = image.shape
longest_edge = max(h, w)
if h < longest_edge:
dh = longest_edge - h
top = dh // 2
bottom = dh - top
elif w < longest_edge:
dw = longest_edge - w
left = dw // 2
right = dw - left
else:
pass
BLACK = [0, 0, 0]
#给图像增加边界，是图片长、宽等长，cv2.BORDER_CONSTANT指定边界颜色由value指定
constant = cv2.copyMakeBorder(image, top , bottom, left, right, cv2.BORDER_CONSTANT, value = BLACK)
#调整图像大小并返回
return cv2.resize(constant, (height, width))
class Dataset:
def __init__(self, path_name):
self.train_images = None
self.train_labels = None
self.valid_images = None
self.valid_labels = None
self.test_images = None
self.test_labels = None
self.path_name = path_name
self.input_shape = None
#加载数据集并按照交叉验证的原则划分数据集并进行相关预处理工作
def load(self, img_rows = 128, img_cols = 128,
img_channels = 3, nb_classes = 3):
images, labels = load_train_re(self.path_name)
train_images, valid_images, train_labels, valid_labels = train_test_split(images, labels, test_size = 0.2, random_state = random.randint(0, 100))
test_images,test_labels = load_test_re(self.path_name)
#当前的维度顺序如果为'th'，则输入图片数据时的顺序为：channels,rows,cols，否则:rows,cols,channels
#这部分代码就是根据keras库要求的维度顺序重组训练数据集
if K.image_dim_ordering() == 'th':
train_images = train_images.reshape(train_images.shape[0], img_rows, img_cols)
valid_images = valid_images.reshape(valid_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)
valid_images = valid_images.reshape(valid_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)
print(train_images.shape[0], 'train samples')
print(valid_images.shape[0], 'valid 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)
valid_labels = np_utils.to_categorical(valid_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')
valid_images = valid_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
valid_images /= 255
test_images /= 255
self.train_images = train_images
self.valid_images = valid_images
self.test_images = test_images
self.train_labels = train_labels
self.valid_labels = valid_labels
self.test_labels = test_labels
class Model:
def __init__(self):
self.model = None
def save_model(self, file_path):
self.model.save(file_path)
def load_model(self, file_path):
self.model = load_model(file_path)
def build_model(self, dataset, nb_classes = 3):
self.model = Sequential()
self.model.add(Conv2D(32, (3, 3),input_shape = dataset.input_shape,padding='same')) #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(MaxPooling2D(pool_size=(2, 2))) #5 池化层
self.model.add(Dropout(0.25)) #6 Dropout层
self.model.add(Conv2D(64, (3, 3), padding='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(MaxPooling2D(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()
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))
#训练模型
def train(self, dataset, batch_size = 50, nb_epoch = 40, data_augmentation = False):
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
if not data_augmentation:
self.model.fit(dataset.train_images,
dataset.train_labels,
batch_size = batch_size,
nb_epoch = nb_epoch,
validation_data = (dataset.valid_images, dataset.valid_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.valid_images, dataset.valid_labels))
#识别人脸
def face_predict(self, image):
if K.image_dim_ordering() == 'th' and image.shape != (1, 3, 128, 128):
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, 128, 128)) #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, 128, 128, 3):
image = resize_image(image)
image = image.reshape((1, 128, 128, 3))
image = image.astype('float32')
image /= 255
result = self.model.predict_proba(image)
print('result:', result[0])
# if result[0][0]<0.7 and result[0][1]<0.7:
# return -1;
result = self.model.predict_classes(image)
return result[0]
if __name__ == '__main__':
dataset = Dataset('pic')
dataset.load()
#train
# model = Model()
# model.build_model(dataset)
# model.train(dataset)
# model.save_model('face_model_re.h5')
#test
model = Model()
model.load_model(file_path = 'face_model_re.h5')
model.evaluate(dataset)