class_docstr: Apply Color transformation to image. The bboxes, masks, and
  segmentations are not modified.
  
  Args:
      level (int | float): Should be in range [0,_MAX_LEVEL].
      prob (float): The probability for performing Color transformation.
class_docstr_tok: ["Apply","Color","transformation","to","image",".","The","bboxes",",","masks",",","and","segmentations","are","not","modified",".","Args",":","level","(","int","|","float",")",":","Should","be","in","range","[","0",",","_MAX_LEVEL","]",".","prob","(","float",")",":","The","probability","for","performing","Color","transformation","."]
docstr: Call function for Color transformation.
  
  Args:
      results (dict): Result dict from loading pipeline.
  
  Returns:
      dict: Colored results.
docstr_tok: ["Call","function","for","Color","transformation",".","Args",":","results","(","dict",")",":","Result","dict","from","loading","pipeline",".","Returns",":","dict",":","Colored","results","."]
code: def __call__(self, results):
      """Call function for Color transformation.
  
      Args:
          results (dict): Result dict from loading pipeline.
  
      Returns:
          dict: Colored results.
      """
      if np.random.rand() > self.prob:
          return results
      self._adjust_color_img(results, self.factor)
      return results
  
imports: import copy
  import cv2
  import mmcv
  import numpy
  from ..builder import PIPELINES
  from .compose import Compose
label_desc: Use image node_id 3 for calling the ColorTransform obj's underlying member method code with example usage: obj.__call__(results) and returns: results, results