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import warnings
# 忽视警告
warnings.filterwarnings('ignore')
import os
import matplotlib
import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from tensorflow.keras.applications.imagenet_utils import preprocess_input
from tensorflow.keras import backend as K
from tensorflow.keras.optimizers import Adam
K.image_data_format() == 'channels_last'
from keras_py.utils import get_random_data
from keras_py.face_rec import mask_rec
from keras_py.face_rec import face_rec
from keras_py.mobileNet import MobileNet
from tensorflow.keras.preprocessing.image import ImageDataGenerator
# 数据集路径
basic_path = "./datasets/5f680a696ec9b83bb0037081-momodel/data/"
def letterbox_image(image, size): # 调整图片尺寸，返回经过调整的照片
new_image = cv.resize(image, size, interpolation=cv.INTER_AREA)
return new_image
read_img = cv.imread("test1.jpg")
print("调整前图片的尺寸:", read_img.shape)
read_img = letterbox_image(image=read_img, size=(50, 50))
print("调整前图片的尺寸:", read_img.shape)
def processing_data(data_path, height, width, batch_size=32, test_split=0.1): # 数据处理，batch_size默认大小为32
train_data = ImageDataGenerator(
# 对图片的每个像素值均乘上这个放缩因子，把像素值放缩到0和1之间有利于模型的收敛
rescale=1. / 255,
# 浮点数，剪切强度（逆时针方向的剪切变换角度）
shear_range=0.1,
# 随机缩放的幅度，若为浮点数，则相当于[lower,upper] = [1 - zoom_range, 1+zoom_range]
zoom_range=0.1,
# 浮点数，图片宽度的某个比例，数据提升时图片水平偏移的幅度
width_shift_range=0.1,
# 浮点数，图片高度的某个比例，数据提升时图片竖直偏移的幅度
height_shift_range=0.1,
# 布尔值，进行随机水平翻转
horizontal_flip=True,
# 布尔值，进行随机竖直翻转
vertical_flip=True,
# 在 0 和 1 之间浮动。用作验证集的训练数据的比例
validation_split=test_split
)
# 接下来生成测试集，可以参考训练集的写法
test_data = ImageDataGenerator(
rescale=1. / 255,
validation_split=test_split)
train_generator = train_data.flow_from_directory(
# 提供的路径下面需要有子目录
data_path,
# 整数元组 (height, width)，默认：(256, 256)。 所有的图像将被调整到的尺寸。
target_size=(height, width),
# 一批数据的大小
batch_size=batch_size,
# "categorical", "binary", "sparse", "input" 或 None 之一。
# 默认："categorical",返回one-hot 编码标签。
class_mode='categorical',
# 数据子集 ("training" 或 "validation")
subset='training',
seed=0)
test_generator = test_data.flow_from_directory(
data_path,
target_size=(height, width),
batch_size=batch_size,
class_mode='categorical',
subset='validation',
seed=0)
return train_generator, test_generator
# 数据路径
data_path = basic_path + 'image'
# 图像数据的行数和列数
height, width = 160, 160
# 获取训练数据和验证数据集
train_generator, test_generator = processing_data(data_path, height, width)
# 通过属性class_indices可获得文件夹名与类的序号的对应字典。
labels = train_generator.class_indices
print(labels)
# 转换为类的序号与文件夹名对应的字典
labels = dict((v, k) for k, v in labels.items())
print(labels)
pnet_path = "./datasets/5f680a696ec9b83bb0037081-momodel/data/keras_model_data/pnet.h5"
rnet_path = "./datasets/5f680a696ec9b83bb0037081-momodel/data/keras_model_data/rnet.h5"
onet_path = "./datasets/5f680a696ec9b83bb0037081-momodel/data/keras_model_data/onet.h5"
# 加载 MobileNet 的预训练模型权重
weights_path = basic_path + 'keras_model_data/mobilenet_1_0_224_tf_no_top.h5'
# 图像数据的行数和列数
height, width = 160, 160
model = MobileNet(input_shape=[height,width,3],classes=2)
model.load_weights(weights_path,by_name=True)
print('加载完成...')
def save_model(model, checkpoint_save_path, model_dir): # 保存模型
if os.path.exists(checkpoint_save_path):
print("模型加载中")
model.load_weights(checkpoint_save_path)
print("模型加载完毕")
checkpoint_period = 跟单网gendan5.comModelCheckpoint(
# 模型存储路径
model_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',
# 检测的指标
monitor='val_acc',
# ‘auto’，‘min’，‘max’中选择
mode='max',
# 是否只存储模型权重
save_weights_only=False,
# 是否只保存最优的模型
save_best_only=True,
# 检测的轮数是每隔2轮
period=2
)
return checkpoint_period
checkpoint_save_path = "./results/last_one88.h5"
model_dir = "./results/"
checkpoint_period = save_model(model, checkpoint_save_path, model_dir)
# 学习率下降的方式，acc三次不下降就下降学习率继续训练
reduce_lr = ReduceLROnPlateau(
monitor='accuracy', # 检测的指标
factor=0.5, # 当acc不下降时将学习率下调的比例
patience=3, # 检测轮数是每隔三轮
verbose=2 # 信息展示模式
)
early_stopping = EarlyStopping(
monitor='val_accuracy', # 检测的指标
min_delta=0.0001, # 增大或减小的阈值
patience=3, # 检测的轮数频率
verbose=1 # 信息展示的模式
)
# 一次的训练集大小
batch_size = 64
# 图片数据路径
data_path = basic_path + 'image'
# 图片处理
train_generator, test_generator = processing_data(data_path, height=160, width=160, batch_size=batch_size, test_split=0.1)
# 编译模型
model.compile(loss='binary_crossentropy', # 二分类损失函数
optimizer=Adam(lr=0.001), # 优化器
metrics=['accuracy']) # 优化目标
# 训练模型
history = model.fit(train_generator,
epochs=20, # epochs: 整数，数据的迭代总轮数。
# 一个epoch包含的步数,通常应该等于你的数据集的样本数量除以批量大小。
steps_per_epoch=637 // batch_size,
validation_data=test_generator,
validation_steps=70 // batch_size,
initial_epoch=0, # 整数。开始训练的轮次（有助于恢复之前的训练）。
callbacks=[checkpoint_period, reduce_lr])
# 保存模型
model.save_weights(model_dir + 'temp.h5')
plt.plot(history.history['loss'],label = 'train_loss')
plt.plot(history.history['val_loss'],'r',label = 'val_loss')
plt.legend()
plt.show()
plt.plot(history.history['accuracy'],label = 'acc')
plt.plot(history.history['val_accuracy'],'r',label = 'val_acc')
plt.legend()
plt.show()
