图像自动标注Python代码

以下是一个基于深度学习的图像自动标注Python代码示例：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from PIL import Image
import os
import tensorflow as tf
from tensorflow.keras.applications.inception_v3 import InceptionV3, preprocess_input
from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img
from tensorflow.keras.layers import Input, Dense, Dropout, GlobalAveragePooling2D
from tensorflow.keras.models import Model, load_model
from tensorflow.keras.optimizers import Adam

# 加载InceptionV3模型
base_model = InceptionV3(include_top=False, weights='imagenet', input_tensor=Input(shape=(299, 299, 3)))

# 添加全局平均池化层和全连接层
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
x = Dropout(0.5)(x)
predictions = Dense(20, activation='softmax')(x)

# 构建模型
model = Model(inputs=base_model.input, outputs=predictions)

# 冻结InceptionV3模型的前249层
for layer in model.layers[:249]:
    layer.trainable = False

# 编译模型
model.compile(optimizer=Adam(lr=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])

# 定义图像数据生成器
train_datagen = ImageDataGenerator(preprocessing_function=preprocess_input,
                                   shear_range=0.2,
                                   zoom_range=0.2,
                                   horizontal_flip=True)
test_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)

# 加载数据
train_generator = train_datagen.flow_from_directory('train/',
                                                    target_size=(299, 299),
                                                    batch_size=32,
                                                    class_mode='categorical')
test_generator = test_datagen.flow_from_directory('test/',
                                                  target_size=(299, 299),
                                                  batch_size=32,
                                                  class_mode='categorical')

# 训练模型
history = model.fit_generator(train_generator,
                              steps_per_epoch=len(train_generator),
                              epochs=10,
                              validation_data=test_generator,
                              validation_steps=len(test_generator))

# 保存模型
model.save('image_captioning_model.h5')

说明：

- 该代码使用了InceptionV3模型作为基础模型，添加了全局平均池化层和全连接层构建了一个新的模型。
- 冻结了InceptionV3模型的前249层，只训练新模型的层。
- 定义了图像数据生成器，并加载了训练和测试数据。
- 训练模型，并保存了模型。