class_docstr: Apply Histogram Eq. to L/V/L channels of images in HLS/HSV/Lab colorspaces.
  
  This augmenter is similar to ``imgaug.augmenters.contrast.CLAHE``.
  
  The augmenter transforms input images to a target colorspace (e.g.
  ``Lab``), extracts an intensity-related channel from the converted images
  (e.g. ``L`` for ``Lab``), applies Histogram Equalization to the channel
  and then converts the resulting image back to the original colorspace.
  
  Grayscale images (images without channel axis or with only one channel
  axis) are automatically handled, `from_colorspace` does not have to be
  adjusted for them. For images with four channels (e.g. RGBA), the fourth
  channel is ignored in the colorspace conversion (e.g. from an ``RGBA``
  image, only the ``RGB`` part is converted, normalized, converted back and
  concatenated with the input ``A`` channel). Images with unusual channel
  numbers (2, 5 or more than 5) are normalized channel-by-channel (same
  behaviour as ``AllChannelsHistogramEqualization``, though a warning will
  be raised).
  
  If you want to apply HistogramEqualization to each channel of the original
  input image's colorspace (without any colorspace conversion), use
  ``imgaug.augmenters.contrast.AllChannelsHistogramEqualization`` instead.
  
  **Supported dtypes**:
  
      * ``uint8``: yes; fully tested
      * ``uint16``: no (1)
      * ``uint32``: no (1)
      * ``uint64``: no (1)
      * ``int8``: no (1)
      * ``int16``: no (1)
      * ``int32``: no (1)
      * ``int64``: no (1)
      * ``float16``: no (1)
      * ``float32``: no (1)
      * ``float64``: no (1)
      * ``float128``: no (1)
      * ``bool``: no (1)
  
      - (1) This augmenter uses :class:`AllChannelsHistogramEqualization`,
            which only supports ``uint8``.
  
  Parameters
  ----------
  from_colorspace : {"RGB", "BGR", "HSV", "HLS", "Lab"}, optional
      Colorspace of the input images.
      If any input image has only one or zero channels, this setting will be
      ignored and it will be assumed that the input is grayscale.
      If a fourth channel is present in an input image, it will be removed
      before the colorspace conversion and later re-added.
      See also :func:`~imgaug.augmenters.color.change_colorspace_` for
      details.
  
  to_colorspace : {"Lab", "HLS", "HSV"}, optional
      Colorspace in which to perform Histogram Equalization. For ``Lab``,
      the equalization will only be applied to the first channel (``L``),
      for ``HLS`` to the second (``L``) and for ``HSV`` to the third (``V``).
      To apply histogram equalization to all channels of an input image
      (without colorspace conversion), see
      ``imgaug.augmenters.contrast.AllChannelsHistogramEqualization``.
  
  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.HistogramEqualization()
  
  Create an augmenter that converts images to ``HLS``/``HSV``/``Lab``
  colorspaces, extracts intensity-related channels (i.e. ``L``/``V``/``L``),
  applies histogram equalization to these channels and converts back to the
  input colorspace.
  
  >>> aug = iaa.Alpha((0.0, 1.0), iaa.HistogramEqualization())
  
  Same as in the previous example, but alpha blends the result, leading
  to various strengths of contrast normalization.
  
  >>> aug = iaa.HistogramEqualization(
  >>>     from_colorspace=iaa.CSPACE_BGR,
  >>>     to_colorspace=iaa.CSPACE_HSV)
  
  Same as in the first example, but the colorspace of input images has
  to be ``BGR`` (instead of default ``RGB``) and the histogram equalization
  is applied to the ``V`` channel in ``HSV`` colorspace.
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docstr: See :func:`~imgaug.augmenters.meta.Augmenter.get_parameters`.
docstr_tok: ["See",":","func",":","`","~imgaug.augmenters.meta.Augmenter.get_parameters","`","."]
code: def get_parameters(self):
      """See :func:`~imgaug.augmenters.meta.Augmenter.get_parameters`."""
      icb_applier = self.intensity_channel_based_applier
      return icb_applier.get_parameters()
  
imports: from __future__ import print_function, division, absolute_import
  import numpy
  import six.moves
  import skimage.exposure
  import cv2
  import imgaug
  from imgaug.imgaug import _normalize_cv2_input_arr_
  from .None import meta
  from .None import color
  from ..None import parameters
  from ..None import dtypes
  from ..augmentables.batches import _BatchInAugmentation
label_desc: Use image node_id 3 for calling the HistogramEqualization obj's underlying member method code with example usage: obj.get_parameters() and returns: icb_applier