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为训练分割进行增强，但图像总数约为 26,000+。这就是制作图像数组时面临问题的原因。

试过：

def get_data():
    X_data = []
    train_images = sorted(glob('../input/fg_image/images/*.jpg', recursive=True))
    size = 128, 128
    X_data = np.empty((len(train_images),128, 128, 3), dtype=np.float32)
    for i, image in enumerate(train_images):
        X_data[i] = np.asarray(Image.open(image).thumbnail(size))
    return X_data

X_train = get_data()

按照上述方法，我正在收集 X_train、Y_train。到这一步为止，一切正常。

但进一步应用以下增强方法时，整个笔记本都崩溃了。

def augmentation(X_data, Y_data, validation_split=0.2, batch_size=32, seed=42):
    X_train, X_test, Y_train, Y_test = train_test_split(X_data,
                                                        Y_data,
                                                        train_size=1-validation_split,
                                                        test_size=validation_split,
                                                        random_state=seed)
    

    data_gen_args = dict(rotation_range=45.,
                         width_shift_range=0.1,
                         height_shift_range=0.1)

    X_datagen = ImageDataGenerator(**data_gen_args)
    Y_datagen = ImageDataGenerator(**data_gen_args)
    X_datagen.fit(X_train, augment=True, seed=seed)
    Y_datagen.fit(Y_train, augment=True, seed=seed)
    X_train_augmented = X_datagen.flow(X_train, batch_size=batch_size, shuffle=True, seed=seed)
    Y_train_augmented = Y_datagen.flow(Y_train, batch_size=batch_size, shuffle=True, seed=seed)
    
    
    train_generator = zip(X_train_augmented, Y_train_augmented)
    
    return train_generator

train_generator = augmentation(X_train, Y_train)
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