1 文件架构
-train.py # 训练
-predict.py # 预测
-IMG # 存放正负样本
--False.0.jpg # 负样本命名
--True.0.jpg # 正样本命名
--MODEL # 存放生成模型
重命名：可以使用os.rename(srcpath+name, dstpath+name)
dnn调用mobilenetv2时，mobilenetv2支持avg-pooling，或不池化，不支持max-pooling
2 模型的训练
# -*- coding: utf-8 -*-
"""
train.py
"""
from keras.applications.mobilenet_v2 import MobileNetV2
from keras.optimizers import Adam
from keras.utils import np_utils
from keras.utils.vis_utils import plot_model
from keras.engine.topology import Input
from keras.layers import *
from keras.models import Model
from keras.models import load_model
from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split
from tensorflow.python.framework.graph_util import convert_variables_to_constants
from tensorflow.python.framework import graph_io
from keras.callbacks import ModelCheckpoint
import cv2
import os
import numpy as np
import tensorflow as tf
def build_model():
# (1) model1
img_shape = (224, 224, 3)
# （a）直接使用mobilenet进行1000分类；（b）也可以使用mobilenet进行特征提取，保存特征传入下一层网络，重新设计全连接层
model1 = MobileNetV2(input_shape=img_shape,
include_top=False, # 是否包括顶层的全连接层。
weights='imagenet',
pooling='avg') # 'None', 'max', 'avg'
# MobileNetV2()函数没有提示参数，搜索keras文档查询：https://keras.io/zh/applications/#mobilenetv2
plot_model(model1, to_file="./MODEL/model1.png", show_shapes=True)
# 不知道在该输入shape下输出大小时，可以使用plot_model()查看
# (2) model2
xa = Input(shape=model1.output_shape[1:])
x = Dense(2, use_bias=True, activation='softmax', name='weighted-average')(xa)
model2 = Model(xa, x, name='m2')
plot_model(model2, to_file="./MODEL/model2.png", show_shapes=True)
# (3) model
img_a = Input(shape=img_shape)
xa = model1(img_a)
x = model2(xa)
model = Model(img_a, x, name="m")
plot_model(model, to_file="./MODEL/model.png", show_shapes=True)
# model.summary() # 输出模型各层的参数
return model
def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
# 将会话状态冻结为已删除的计算图,创建一个新的计算图,其中变量节点由在会话中获取其当前值的常量替换.
# session要冻结的TensorFlow会话,keep_var_names不应冻结的变量名列表,或者无冻结图中的所有变量
# output_names相关图输出的名称,clear_devices从图中删除设备以获得更好的可移植性
graph = session.graph
with graph.as_default():
freeze_var_names = list(
set(v.op.name for v in tf.compat.v1.global_variables()).difference(keep_var_names or []))
output_names = output_names or []
output_names += [v.op.name for v in tf.compat.v1.global_variables()]
input_graph_def = graph.as_graph_def()
# 从图中删除设备以获得更好的可移植性
if clear_devices:
for node in input_graph_def.node:
node.device = ""
# 用相同值的常量替换图中的所有变量
frozen_graph = convert_variables_to_constants(session, input_graph_def, output_names, freeze_var_names)
return frozen_graph
if __name__ == "__main__":
"""
(1) 图像预处理
"""
img_path = "./IMG"
X_train_list = []
Y_train_list = []
for path, dir_list, file_list in os.walk(img_path):
for file in file_list:
filename = os.path.join(path, file)
img = cv2.imread(filename)
img = cv2.resize(img, (224, 224))
X_train_list.append(img)
if filename.split("\\")[-1].split(".")[0] == "False":
Y_train_list.append(0)
elif filename.split("\\")[-1].split(".")[0] == "True":
Y_train_list.append(1)
else:
print("命名出错！")
X_train = np.array(X_train_list) / 255.
Y_train = np_utils.to_categorical(np.array(Y_train_list), 2)
# # 切分测试集
# (X_train, X_test, Y_train, Y_test) = train_test_split(X_train_list, Y_train_list, test_size=0.1,
# random_state=42) # random_state随机数种子
# # 对应训练model.fit_generator()中的validation_data
"""
(2) 模型搭建
"""
model = build_model()
"""
(3) 模型编译和训练
"""
# 编译：compile怎么用，keras文档直接搜索compile
model.compile(Adam(lr=0.001),
loss='binary_crossentropy',
metrics=['accuracy'])
# 图像增强, fit_generator会用到
aug = ImageDataGenerator(rotation_range=0,
zoom_range=0,
width_shift_range=0,
height_shift_range=0,
shear_range=0,
horizontal_flip=False,
fill_mode="nearest")
# 保存每个epoch训练后的模型
save_dir = "./MODEL/"
filepath = "model_{epoch:04d}-{accuracy:.4f}.hdf5"
checkpoint = ModelCheckpoint(os.path.join(save_dir, filepath), monitor='accuracy', verbose=0, save_best_only=False,
mode='max', save_freq='epoch')
# # （1）fit方法1
# nb_batch_size = 12
# H = model.fit_generator(aug.flow(X_train, Y_train, batch_size=nb_batch_size),
# steps_per_epoch=len(X_train)//nb_batch_size, # "//"表示相除之后向下 取整
# epochs=30)
# # （2）fit方法2, 加上测试集
# nb_batch_size = 12
# H = model.fit_generator(aug.flow(X_train, Y_train, batch_size=nb_batch_size),
# validation_data=(X_test, Y_test), steps_per_epoch=len(Y_train) // nb_batch_size,
# epochs=30, callbacks=checkpoint)
# （3）fit方法3, 不带数据增强
Trainning = model.fit(X_train, Y_train,
batch_size=12,
epochs=10,
verbose=1,
callbacks=checkpoint
# 默认为1，输出显示进度条，显示每个Epoch
# 2，输出不显示进度条，显示每个Epoch
# 0，输出什么都不显示
)
# # （4）断点续训
# hdf5_model_name = "model_0010-0.9825.hdf5" # 训练好的模型及权重
# model_loaded = load_model('./MODEL/'+hdf5_model_name) # 加载训练好的模型及权重
# weights = model_loaded.get_weights() # 获取训练好的模型的权重
# model.set_weights(weights) # 将该权重赋值给model，model是前面定义的模型
# Trainning = model.fit(X_train, Y_train,
# batch_size=12,
# epochs=10,
# verbose=1,
# callbacks=checkpoint
# # 默认为1，输出显示进度条，显示每个Epoch
# # 2，输出不显示进度条，显示每个Epoch
# # 0，输出什么都不显示
# )
"""
(4) 模型保存，保存hdf5--h5--json--pb文件；model1、model2同理也可以
"""
# 1 保存hdf5格式文件：模型+权重
model_hdf5_file = './MODEL/model.hdf5'
model.save(model_hdf5_file)
# 2 保存h5格式文件：权重
model_h5_file = './MODEL/model_weight.h5'
model.save_weights(model_h5_file)
# 3 保存json文件格式：模型
json_string = model.to_json()
with open('./MODEL/model.json', "w") as f:
f.write(json_string) # 将模型转为json文件后的字符串写入本地
# 4 保存pb文件：加载hdf5，转换成pb文件
# # 连续保存多个pb文件时，有时可能需要清空graph，获取当前图时。可用下面方式清空：
# from tensorflow.python.framework import ops # v1: tf.reset_default_graph()
# ops.reset_default_graph()
tf.compat.v1.disable_eager_execution()
# eager_execution见补充1
# TensorFlow 2.0中提供了tensorflow.compat.v1代码包来兼容原有1.x的代码，可以做到几乎不加修改的运行
tf.compat.v1.keras.backend.set_learning_phase(0) # 0训练模式，1预测模式，之所以这么区分，是因为某些层在预测和训练时不同
hdf5_model = load_model(model_hdf5_file) # hdf5文件的加载
# 获得当前图
sess = tf.compat.v1.keras.backend.get_session()
# 冻结图
frozen_graph = freeze_session(sess, output_names=[hdf5_model.output.op.name])
output_fld = './MODEL/'
graph_io.write_graph(frozen_graph, output_fld, 'model.pb', as_text=False)
print("*********pb保存完成**********")
print("end!")
# 补充1：
# 参考：https://blog.csdn.net/happyhorizion/article/details/103849408
3 模型的预测
"""
predict.py
"""
import train
# from train import build_model # 函数写在文件最前面，其他内容放在if __name__ == "__main__":里面
import pickle
import os
import time
import cv2
import numpy as np
import math
from keras.models import model_from_json
from keras import backend as K
from mtcnn.mtcnn import MTCNN
def init(path):
"""
模型加载方法1
"""
# # （1）json模型结构加载
# t1 = time.time()
# model_json = open(os.path.join(path, 'model.json')).read()
# model = model_from_json(model_json)
# t2 = time.time() # 睡眠函数time.sleep(1) 休眠一秒；t2-t1单位也是秒
# print(t2-t1) # 2.1138834953308105秒
#
# # （2）h5参数加载
# model.load_weights(os.path.join(path, 'model_weight.h5'))
# t3 = time.time()
# print(t3-t2) # 0.08873796463012695秒
"""
模型加载方法2
"""
model = train.build_model()
model.save(os.path.join(path, 'model.hdf5'))
t4 = time.time()
# print(t4-t3) # 0.1326446533203125秒
"""
pickle文件保存，可保存字典、数组
"""
# f1 = open(path + '/model/h2ws.pickle', 'wb')
# pickle.dump(dict1, f3) # dict1 -> array1
# f1.close()
# # pickle文件读取
# f2 = open(os.path.join(path, 'fknown.pickle'), 'rb')
# fknown = pickle.load(f2)
return model
def filter_(value, max_value, min_value):
if value > max_value:
return max_value
elif value < min_value:
return min_value
else:
return value
def predict(model, img):
img = cv2.resize(img, (box_size, box_size))
image = img / 255. # 预处理必须和训练一致
result = model.predict(np.expand_dims(image, 0))
label = np.argmax(result[0])
return label
if __name__ == "__main__":
path = "./MODEL/"
test_path = "./TEST/"
box_size = 224
model = init(path)
for path, dir_list, file_list in os.walk(test_path):
for file in file_list:
filename = os.path.join(path, file)
img = cv2.imread(filename)
label = predict(model, img)
if label == 0:
print(file + ": False")
elif label == 1:
print(file + ": True")
else:
print(file + "未检测到")