微调Inception V3网络-对Satellite分类

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；

1. 流程概述
2. 准备数据集
- 2.1 Satellite数据集介绍
3. Inception V3网络
4. 训练
5. 测试
- 5.1 对单张图片进行测试
6. 可视化分类界面

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\n' % (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)

巴特西

微调Inception V3网络-对Satellite分类

1. 流程概述

2. 准备数据集

2.1 Satellite数据集介绍

3. Inception V3网络

4. 训练

4.1 基于Keras微调Inception V3网络

4.2 Keras实时生成批量增强数据

4.3 配置transfer learning & finetune

4.4 执行训练

5. 测试

5.1 对单张图片进行测试

6. 可视化分类界面

6.1 交互界面设计

6.2 后台核心代码：模型加载并分类

6.3 交互界面效果

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