class_docstr: Sharpen images and alpha-blend the result with the original input images.
  
  **Supported dtypes**:
  
  See :class:`~imgaug.augmenters.convolutional.Convolve`.
  
  Parameters
  ----------
  alpha : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
      Blending factor of the sharpened image. At ``0.0``, only the original
      image is visible, at ``1.0`` only its sharpened version is visible.
  
          * If a number, exactly that value will always be used.
          * If a tuple ``(a, b)``, a random value will be sampled from the
            interval ``[a, b]`` per image.
          * If a list, a random value will be sampled from that list
            per image.
          * If a ``StochasticParameter``, a value will be sampled from that
            parameter per image.
  
  lightness : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
      Lightness/brightness of the sharped image.
      Sane values are somewhere in the interval ``[0.5, 2.0]``.
      The value ``0.0`` results in an edge map. Values higher than ``1.0``
      create bright images. Default value is ``1.0``.
  
          * If a number, exactly that value will always be used.
          * If a tuple ``(a, b)``, a random value will be sampled from the
            interval ``[a, b]`` per image.
          * If a list, a random value will be sampled from that list
            per image.
          * If a ``StochasticParameter``, a value will be sampled from that
            parameter per image.
  
  seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
      See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
  
  name : None or str, optional
      See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.
  
  random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
      Old name for parameter `seed`.
      Its usage will not yet cause a deprecation warning,
      but it is still recommended to use `seed` now.
      Outdated since 0.4.0.
  
  deterministic : bool, optional
      Deprecated since 0.4.0.
      See method ``to_deterministic()`` for an alternative and for
      details about what the "deterministic mode" actually does.
  
  Examples
  --------
  >>> import imgaug.augmenters as iaa
  >>> aug = iaa.Sharpen(alpha=(0.0, 1.0))
  
  Sharpens input images and blends the sharpened image with the input image
  using a random blending factor between ``0%`` and ``100%`` (uniformly
  sampled).
  
  >>> aug = iaa.Sharpen(alpha=(0.0, 1.0), lightness=(0.75, 2.0))
  
  Sharpens input images with a variable `lightness` sampled uniformly from
  the interval ``[0.75, 2.0]`` and with a fully random blending factor
  (as in the above example).
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code: def __init__(
      self,
      alpha=(0.0, 0.2),
      lightness=(0.8, 1.2),
      seed=None,
      name=None,
      random_state="deprecated",
      deterministic="deprecated",
  ):
      alpha_param = iap.handle_continuous_param(
          alpha,
          "alpha",
          value_range=(0, 1.0),
          tuple_to_uniform=True,
          list_to_choice=True,
      )
      lightness_param = iap.handle_continuous_param(
          lightness,
          "lightness",
          value_range=(0, None),
          tuple_to_uniform=True,
          list_to_choice=True,
      )
  
      matrix_gen = _SharpeningMatrixGenerator(
          alpha_param, lightness_param
      )
  
      super(Sharpen, self).__init__(
          matrix=matrix_gen,
          seed=seed,
          name=name,
          random_state=random_state,
          deterministic=deterministic,
      )
  
imports: from __future__ import print_function, division, absolute_import
  import itertools
  import numpy
  import cv2
  import six.moves
  import imgaug
  from .None import meta
  from ..None import parameters
  from ..None import dtypes
label_desc: Use image node_id 1 to create a new Sharpen object from inherited base classes: Convolve with example: obj = Sharpen(alpha, lightness, seed, name, random_state, deterministic)