在ImageDataGenerator中添加自定义预处理功能以实现高斯模糊

问题描述

我正在使用Image Data Generator扩展我的数据集.

I am using Image Data Generator to extend my dataset.

train_generator = train_datagen.flow_from_directory(
        path_to_train,
        target_size=(150, 150),
        batch_size=32,
        class_mode = "sparse",
        color_mode="grayscale")

然后

history = model.fit(
        train_generator,
        steps_per_epoch=20,
        epochs=100
        )

问题是我想对输入图像应用某些方法.我想使用高斯模糊等.但是，当我直接加载它并使用模型拟合时，如何应用它.

The problem is that I want to apply some methods to the input images.I want to use Gaussian bluring and etc.But how can I apply it when I directly load it and use model fit.

能帮我吗?谢谢您的建议.

Can you help me please? Thanks for you advices.

推荐答案

您可以通过 tfa.image.gaussian_filter2d :

You can do this with tfa.image.gaussian_filter2d:

@tf.function
tfa.image.gaussian_filter2d(
    image: tfa.types.TensorLike,
    filter_shape: Union[List[int], Tuple[int], int] = [3, 3],
    sigma: Union[List[float], Tuple[float], float] = 1.0,
    padding: str = 'REFLECT',
    constant_values: tfa.types.TensorLike = 0,
    name: Optional[str] = None
) -> tfa.types.TensorLike

您可以在 ImageDataGenerator 中传递此函数:

You can pass this function in ImageDataGenerator:

img_gen = ImageDataGenerator(
    proprocessing_function=tfa.image.gaussian_filter2d
)

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