class_docstr: RGB histogram equilisation. Unlike the multi-modality general
  histogram normalisation this is done conventionally, on a
  per-image basis. This layer requires OpenCV.
docstr: :param image: a 3-channel tensor assumed to be an image in floating-point
  RGB format (each channel in [0, 1])
  :return: the equilised image
code: def layer_op(self, image, mask=None):
      """
      :param image: a 3-channel tensor assumed to be an image in floating-point
      RGB format (each channel in [0, 1])
      :return: the equilised image
      """
  
      if isinstance(image, dict):
          image[self.image_name] = self._normalise_image(
              image[self.image_name]
          )
  
          return image, mask
      else:
          return self._normalise_image(image), mask
  
file_path: NiftyNet/niftynet/layer/rgb_histogram_equilisation.py
imports: from __future__ import absolute_import, print_function
  import numpy
  from niftynet.layer.base_layer import Layer
  from niftynet.utilities.util_import import require_module
label_desc: Use image node_id 3 for calling the RGBHistogramEquilisationLayer obj's underlying member method code with example usage: obj.layer_op(image, mask) and returns: image,  mask, self,  mask