这篇博客主要是使用Keras框架微调Inception V3模型对卫星图片进行分类，并测试；
目录
1. 流程概述
微调Inception V3对卫星图片进行分类；整个流程可以大致分成四个步骤，如下：
- （1）Satellite数据集准备；
- （2）搭建Inception V3网络；
- （3）进行训练；
- （4）测试；
2. 准备数据集
2.1 Satellite数据集介绍
用于实验训练与测试的数据集来自于《21个项目玩转深度学习：基于Tensorflow的实践详解》第三章中提供的实验卫星图片数据集；
Satellite数据集目录结构如下：
# 其中共6类卫星图片，训练集总共4800张，每类800张；验证集共1200张，每类200张；
Satellite/
train/
glacier/
rock/
urban/
water/
wetland/
wood/
validation/
glacier/
rock/
urban/
water/
wetland/
wood/
3. Inception V3网络
待补充；
4. 训练
4.1 基于Keras微调Inception V3网络
from keras.application.incepiton_v3 import InceptionV3, preprocess_input
from keras.layers import GlobalAveragePooling2D, Dense
# 基础Inception_V3模型，不包含全连接层
base_model = InceptionV3(weights='imagenet', include_top=False)
# 增加新的输出层
x = base_model.output
x = GlobalAveragePooling2D()(x) # 添加Global average pooling层
x = Dense(1024, activation='relu')(x)
predictions = Dense(6, activation='softmax')(x)
4.2 Keras实时生成批量增强数据
# keras实时生成批量增强数据
train_datagen = ImageDataGenerator(
preprocessing_function=preprocess_input, # 将每一张图片归一化到[-1,1]；数据增强后执行；
rotation_range=30,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
)
val_datagen = ImageDataGenerator(
preprocessing_function=preprocess_input,
rotation_range=30,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
)
# 指定数据集路径并批量生成增强数据
train_generator = train_datagen.flow_from_directory(directory='satellite/data/train',
target_size=(299, 299),#Inception V3规定大小
batch_size=64)
val_generator = val_datagen.flow_from_directory(directory='satellite/data/validation',
target_size=(299,299),
batch_size=64)
4.3 配置transfer learning & finetune
from keras.optimizers import Adagrad
# transfer learning
def setup_to_transfer_learning(model,base_model):#base_model
for layer in base_model.layers:
layer.trainable = False
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) # 配置模型，为下一步训练
# finetune
def setup_to_fine_tune(model,base_model):
GAP_LAYER = 17 # max_pooling_2d_2
for layer in base_model.layers[:GAP_LAYER+1]:
layer.trainable = False
for layer in base_model.layers[GAP_LAYER+1:]:
layer.trainable = True
model.compile(optimizer=Adagrad(lr=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])
4.4 执行训练
# Step 1: transfer learning
setup_to_transfer_learning(model,base_model)
history_tl = model.fit_generator(generator=train_generator,
steps_per_epoch=75, # 800
epochs=10,
validation_data=val_generator,
validation_steps=64, # 12
class_weight='auto'
)
model.save('satellite/train_dir/satellite_iv3_tl.h5')
# Step 2: finetune
setup_to_fine_tune(model,base_model)
history_ft = model.fit_generator(generator=train_generator,
steps_per_epoch=75,
epochs=10,
validation_data=val_generator,
validation_steps=64,
class_weight='auto')
model.save('satellite/train_dir/satellite_iv3_ft.h5')
5. 测试
5.1 对单张图片进行测试
# *-coding: utf-8 -*
"""
使用h5模型文件对satellite进行测试
"""
# ================================================================
import tensorflow as tf
import numpy as np
from skimage import io
from keras.models import load_model
def normalize(array):
"""对给定数组进行归一化
Argument:
array: array
给定数组
Return:
array_norm: array
归一化后的数组
"""
array_flatten = array.flatten()
array_mean = np.mean(array_flatten)
mx = np.max(array_flatten)
mn = np.min(array_flatten)
array_norm = [(float(i) - array_mean) / (mx - mn) for i in array_flatten]
return np.reshape(array_norm, array.shape)
def img_preprocess(image_path):
"""根据图片路径，对图片进行相应预处理
Argument:
image_path: str
输入图片路径
Return:
image_data: array
预处理好的图像数组
"""
img_array = io.imread(image_path)
img_norm = normalize(img_array)
size = img_norm.shape
image_data = np.reshape(img_norm, (1, size[0], size[1], 3))
return image_data
def index_to_label(index):
"""将标签索引转换成可读的标签
Argument:
index: int
标签索引位置
Return:
human_label: str
人可读的标签
"""
labels = ["glacier", "rock", "urban", "water", "wetland", "wood"]
human_label = labels[index]
return human_label
def classifier_satellite_byh5(image_path, model_file_path):
"""对给定单张图片使用训练好的模型进行分类
Argument:
image_path: str
输入图片路径
model_file_path: str
训练好的h5模型文件名称
Return:
human_label: str
人可读的图片标签
"""
image_data = img_preprocess(image_path)
# 加载模型文件
model = load_model(model_file_path)
predictions = model.predict(image_data)
human_label = index_to_label(np.argmax(predictions))
return human_label
def classifier_satellite_byh5_hci(image_path):
"""用于对从交互界面传来的图片进行分类
Argument:
image_path: str
Return:
human_label: str
人可读的图片标签
"""
# 模型文件，如果有新的模型需要修改
model_file_path = "satellite/train_dir/models/satellite_iv3_ft.h5"
image_data = img_preprocess(image_path)
# 加载模型文件
model = load_model(model_file_path)
predictions = model.predict(image_data)
human_label = index_to_label(np.argmax(predictions))
return human_label
# 测试单张图片
if __name__ == "__main__":
image_path = "satellite/data/train/glacier/40965_91335_18.jpg"
model_file_path = "satellite/train_dir/models/satellite_iv3_ft.h5"
human_label = classifier_satellite_byh5(image_path, model_file_path)
print(human_label)
6. 可视化分类界面
6.1 交互界面设计
# encoding: utf-8
"""
交互界面：使用训练好的模型对卫星图片进行分类；
"""
from tkinter import *
import tkinter
import tkinter.filedialog
import os
import tkinter.messagebox
from PIL import Image, ImageTk
import test_satellite_bypb
# 窗口属性
root = tkinter.Tk()
root.title('Satellite图像分类')
root.geometry('800x600')
formatImg = ['jpg']
def resize(w, h, w_box, h_box, pil_image):
# 对一个pil_image对象进行缩放，让它在一个矩形框内，还能保持比例
f1 = 1.0*w_box/w # 1.0 forces float division in Python2
f2 = 1.0*h_box/h
factor = min([f1, f2])
width = int(w*factor)
height = int(h*factor)
return pil_image.resize((width, height), Image.ANTIALIAS)
def showImg():
img1 = entry_imgPath.get() # 获取图片路径地址
pil_image = Image.open(img1) # 打开图片
# 期望显示大小
w_box = 400
h_box = 400
# 获取原始图像的大小
w, h = pil_image.size
pil_image_resized = resize(w, h, w_box, h_box, pil_image)
# 把PIL图像对象转变为Tkinter的PhotoImage对象
tk_image = ImageTk.PhotoImage(pil_image_resized)
img = tkinter.Label(image=tk_image, width=w_box, height=h_box)
img.image = tk_image
img.place(x=50, y=150)
def choose_file():
text_showClass.delete(0.0, END) # 清空输出结果文本框，在再次选择图片文件之前清空上次结果；
selectFileName = tkinter.filedialog.askopenfilename(title='选择文件') # 选择文件
if selectFileName[-3:] not in formatImg:
tkinter.messagebox.askokcancel(title='出错', message='未选择图片或图片格式不正确') # 弹出错误窗口
return
else:
e.set(selectFileName) # 设置变量
showImg() # 显示图片
def ouputOfModel():
# 完成识别，显示类别
# 图片文件路径
text_showClass.delete(0.0, END) # 清空上次结果文本框
img_path = entry_imgPath.get() # 获取所选择的图片路径地址
# 判断是否存在改图片
if not os.path.exists(img_path):
tkinter.messagebox.askokcancel(title='出错', message='未选择图片文件或图片格式不正确')
else:
# 得到输出结果，以及相应概率
human_label = test_satellite_bypb.classifier_satellite_img(img_path)
# 通过训练的模型，计算得到相对应输出类别
# 清空文本框中的内容，写入识别出来的类别
text_showClass.config(state=NORMAL)
text_showClass.insert('insert', '%s
' % (human_label))
##################
# 窗口部件
##################
e = tkinter.StringVar() # 字符串变量
# label : 选择文件
label_selectImg = tkinter.Label(root, text='选择图片：')
label_selectImg.grid(row=0, column=0)
# Entry: 显示图片文件路径地址
entry_imgPath = tkinter.Entry(root, width=80, textvariable=e)
entry_imgPath.grid(row=0, column=1)
# Button: 选择图片文件
button_selectImg = tkinter.Button(root, text="选择", command=choose_file)
button_selectImg.grid(row=0, column=2)
# Button: 执行识别程序按钮
button_recogImg = tkinter.Button(root, text="开始识别", command=ouputOfModel)
button_recogImg.grid(row=0, column=3)
# Text: 显示结果类别文本框
text_showClass = tkinter.Text(root, width=20, height=1, font='18',)
text_showClass.grid(row=1, column=1)
text_showClass.config(state=DISABLED)
root.mainloop()
6.2 后台核心代码：模型加载并分类
# *-coding: utf-8 -*
"""
使用h5模型文件对satellite进行测试
"""
# ================================================================
import tensorflow as tf
import numpy as np
from skimage import io
from keras.models import load_model
def normalize(array):
"""对给定数组进行归一化
Argument:
array: array
给定数组
Return:
array_norm: array
归一化后的数组
"""
array_flatten = array.flatten()
array_mean = np.mean(array_flatten)
mx = np.max(array_flatten)
mn = np.min(array_flatten)
array_norm = [(float(i) - array_mean) / (mx - mn) for i in array_flatten]
return np.reshape(array_norm, array.shape)
def img_preprocess(image_path):
"""根据图片路径，对图片进行相应预处理
Argument:
image_path: str
输入图片路径
Return:
image_data: array
预处理好的图像数组
"""
img_array = io.imread(image_path)
img_norm = normalize(img_array)
size = img_norm.shape
image_data = np.reshape(img_norm, (1, size[0], size[1], 3))
return image_data
def index_to_label(index):
"""将标签索引转换成可读的标签
Argument:
index: int
标签索引位置
Return:
human_label: str
人可读的标签
"""
labels = ["glacier", "rock", "urban", "water", "wetland", "wood"]
human_label = labels[index]
return human_label
def classifier_satellite_byh5(image_path, model_file_path):
"""对给定单张图片使用训练好的模型进行分类
Argument:
image_path: str
输入图片路径
model_file_path: str
训练好的h5模型文件名称
Return:
human_label: str
人可读的图片标签
"""
image_data = img_preprocess(image_path)
# 加载模型文件
model = load_model(model_file_path)
predictions = model.predict(image_data)
human_label = index_to_label(np.argmax(predictions))
return human_label
def classifier_satellite_byh5_hci(image_path):
"""用于对从交互界面传来的图片进行分类
Argument:
image_path: str
Return:
human_label: str
人可读的图片标签
"""
# 模型文件，如果有新的模型需要修改
model_file_path = "satellite/train_dir/models/satellite_iv3_ft.h5"
image_data = img_preprocess(image_path)
# 加载模型文件
model = load_model(model_file_path)
predictions = model.predict(image_data)
human_label = index_to_label(np.argmax(predictions))
return human_label
# 测试单张图片
if __name__ == "__main__":
image_path = "satellite/data/train/glacier/40965_91335_18.jpg"
model_file_path = "satellite/train_dir/models/satellite_iv3_ft.h5"
human_label = classifier_satellite_byh5(image_path, model_file_path)
print(human_label)
6.3 交互界面效果
