如何使用keras和tensorflow的ImageDataGenerator执行数据增强

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator

datagen = ImageDataGenerator(
    rotation_range=20,  # 随机旋转角度范围
    width_shift_range=0.2,  # 随机水平平移范围
    height_shift_range=0.2,  # 随机垂直平移范围
    shear_range=0.2,  # 随机错切变换范围
    zoom_range=0.2,  # 随机缩放范围
    horizontal_flip=True,  # 随机水平翻转
    fill_mode='nearest'  # 填充像素的策略
)

train_data = tf.keras.preprocessing.image.load_img('train.jpg')

train_array = tf.keras.preprocessing.image.img_to_array(train_data)
train_array = tf.expand_dims(train_array, 0)

augmented_images = datagen.flow(train_array, batch_size=1)

for i in range(5):
    augmented_image = augmented_images.next()
    # 处理增强后的图像数据，例如保存到本地文件或者用于模型训练