在Tensorflow中的图像中应用转换模型（数据增强）

Question

我是与Python的Tensorflow的一些顺序模型中的新手。我有一个类似于下面的转换顺序模型。它随机适用于给定的图像输入某些具有随机参数的操作。

import tensorflow as tf
from tensorflow.keras import layers

data_transformation = tf.keras.Sequential(
    [
        layers.Lambda(lambda x: my_random_brightness(x, 1, 20)))
        layers.GaussianNoise(stddev=tf.random.uniform(shape=(), minval=0, maxval=1)),
        layers.experimental.preprocessing.RandomRotation(
            factor=0.01,
            fill_mode="reflect",
            interpolation="bilinear",
            seed=None,
            name=None,
            fill_value=0.0,
        ),
        layers.experimental.preprocessing.RandomZoom(
            height_factor=(0.1, 0.2),
            width_factor=(0.1, 0.2),
            fill_mode="reflect",
            interpolation="bilinear",
            seed=None,
            name=None,
            fill_value=0.0,
        ),
    ]
)

该模型中还有一个lambda函数，该函数在下面如下定义，

def my_random_brightness(
    image_to_be_transformed, brightness_factor_min, brightness_factor_max
):

    # build the brightness factor
    selected_brightness_factor = tf.random.uniform(
        (), minval=brightness_factor_min, maxval=brightness_factor_max
    )

    c0 = image_to_be_transformed[:, :, :, 0] + selected_brightness_factor
    c1 = image_to_be_transformed[:, :, :, 1] + selected_brightness_factor
    c2 = image_to_be_transformed[:, :, :, 2] + selected_brightness_factor

    image_to_be_transformed = tf.concat(
        [c0[..., tf.newaxis], image_to_be_transformed[:, :, :, 1:]], axis=-1
    )

    image_to_be_transformed = tf.concat(
        [
            image_to_be_transformed[:, :, :, 0][..., tf.newaxis],
            c1[..., tf.newaxis],
            image_to_be_transformed[:, :, :, 2][..., tf.newaxis],
        ],
        axis=-1,
    )

    image_to_be_transformed = tf.concat(
        [image_to_be_transformed[:, :, :, :2], c2[..., tf.newaxis]], axis=-1
    )

    return image_to_be_transformed

假设我想将这样的模型应用于仅包含一个图像的一批随机操作，我想查看并保存结果。那怎么做？是否有任何预测（）或flow（）像输出这样的结果的功能？

编辑：我尝试了result = data_transformation（image），我有以下错误：

tensorflow.python.framework.errors_impl.invalidargumenterror：index 使用输入DIM 3超出范围；输入只有3个DIM [OP：Stridslice]名称：sequential/lambda/strided_slice/

Answer 1

除了亮度处理层的正确性（上图）之外，还编码了一批图像而不是单个图像。这就是它给出预期错误的原因。在这种情况下，您应该在传递单个图像时添加批处理轴。它应该起作用。

result=data_transformation(image[None, ...])

另外，在自定义层实现中，尝试始终采用子分类方式。

class MyCustomBrightNess(layers.Layer):
    def __init__(self, pbrightness_factor_min, brightness_factor_max, **kwargs):
        super().__init__(**kwargs)
        self.brightness_factor_max = brightness_factor_max
        self.pbrightness_factor_min = pbrightness_factor_min
        
    def call(self, inputs):
         # build the brightness factor
      selected_brightness_factor = tf.random.uniform(
         (), minval=self.brightness_factor_min, maxval=self.brightness_factor_max
      )

      c0 = inputs[:, :, :, 0] + selected_brightness_factor
      c1 = inputs[:, :, :, 1] + selected_brightness_factor
      c2 = inputs[:, :, :, 2] + selected_brightness_factor

      inputs = tf.concat(
         [c0[..., tf.newaxis], inputs[:, :, :, 1:]], axis=-1
      )

      inputs = tf.concat(
         [
               inputs[:, :, :, 0][..., tf.newaxis],
               c1[..., tf.newaxis],
               inputs[:, :, :, 2][..., tf.newaxis],
         ],
         axis=-1,
      )

      inputs = tf.concat(
         [inputs[:, :, :, :2], c2[..., tf.newaxis]], axis=-1
      )

      return inputs
        
    def get_config(self):
        config = {
            'pbrightness_factor_min': self.pbrightness_factor_min,
            'brightness_factor_max': self.brightness_factor_max
        }
        base_config = super(MyCustomBrightNess, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))

---

关于此实施的正确性，我没有严格检查。我建议使用 randy_brightness a href =“ https://www.tensorflow.org/api_docs/python/tf/image/adjust_brightness” rel =“ nofollow noreferrer”> aption> aption_brightness 来自正式实施。或者，如果您正在使用Tensorflow2.9，请向新的 kerascv ，我们可以找到Randombrightness layers。