class_docstr: Lighting noise, as in the paper
  `ImageNet Classification with Deep Convolutional Neural Networks
  <https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf>`_.
  The implementation follows `fb.resnet.torch
  <https://github.com/facebook/fb.resnet.torch/blob/master/datasets/transforms.lua#L184>`__.
class_docstr_tok: ["Lighting","noise",",","as","in","the","paper","`","ImageNet","Classification","with","Deep","Convolutional","Neural","Networks","<","https",":","\/\/papers.nips.cc\/paper\/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf",">","`","_",".","The","implementation","follows","`","fb.resnet.torch","<","https",":","\/\/github.com\/facebook\/fb.resnet.torch\/blob\/master\/datasets\/transforms.lua","#","L184",">","`","__","."]
docstr: Args:
      std (float): maximum standard deviation
      eigval: a vector of (3,). The eigenvalues of 3 channels.
      eigvec: a 3x3 matrix. Each column is one eigen vector.
      clip (bool): clip results to [0,255] even when data type is not uint8.
code: def __init__(self, std, eigval, eigvec, clip=True):
      """
      Args:
          std (float): maximum standard deviation
          eigval: a vector of (3,). The eigenvalues of 3 channels.
          eigvec: a 3x3 matrix. Each column is one eigen vector.
          clip (bool): clip results to [0,255] even when data type is not uint8.
      """
      super(Lighting, self).__init__()
      eigval = np.asarray(eigval, dtype="float32")
      eigvec = np.asarray(eigvec, dtype="float32")
      assert eigval.shape == (3,)
      assert eigvec.shape == (3, 3)
      self._init(locals())
  
imports: import numpy
  import cv2
  from ...utils.develop import log_deprecated
  from .base import PhotometricAugmentor
label_desc: Use image node_id 1 to create a new Lighting object from inherited base classes: PhotometricAugmentor with example: obj = Lighting(std, eigval, eigvec, clip)