keras的图像数据过采样

import numpy as np
from keras.preprocessing.image import ImageDataGenerator

# 假设我们有一个不平衡的数据集
X_train = ...  # 训练图像数据
y_train = ...  # 训练标签

# 创建一个ImageDataGenerator实例，用于数据增强
datagen = ImageDataGenerator(
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest'
)

# 对少数类别进行过采样
for class_label in minority_classes:
    class_indices = np.where(y_train == class_label)[0]
    datagen.fit(X_train[class_indices])
    for i in range(len(class_indices)):
        X_train = np.append(X_train, datagen.random_transform(X_train[class_indices[i]]), axis=0)
        y_train = np.append(y_train, class_label)

# 现在X_train和y_train已经包含了过采样后的数据