old_contents: # Gamma Correction Function
  
  import os
  from skimage import exposure
  from plantcv.plantcv import params
  from plantcv.plantcv._debug import _debug
  
  
  def gamma_correct(img, gamma=1, gain=1):
      """Wrapper for scikit-image gamma correction function. Performs Gamma Correction on the input image. Also known as
      Power Law Transform. This function transforms the input image pixelwise according to the equation O = I**gamma
      after scaling each pixel to the range 0 to 1.
  
      Inputs:
      img     = input image (RGB or grayscale)
      gamma   = Non negative real number. Default value is 1.
      gain    = The constant multiplier. Default value is 1.
  
      :param img: ndarray
      :param gamma = float
      :param gain = float
      :return corrected_img: ndarray
      """
  
      corrected_img = exposure.adjust_gamma(image=img, gamma=gamma, gain=gain)
  
      _debug(visual=corrected_img,
             filename=os.path.join(params.debug_outdir, str(params.device) + '_gamma_corrected.png'))
  
      return corrected_img
  
new_contents: # Gamma Correction Function
  
  import os
  from skimage import exposure
  from plantcv.plantcv import params
  from plantcv.plantcv._debug import _debug
  
  
  def gamma_correct(img, gamma=1, gain=1):
      """
      Apply a gamma correction to the input image.
  
      Inputs:
      img     = input image (RGB or grayscale)
      gamma   = Non negative real number. Default value is 1.
      gain    = The constant multiplier. Default value is 1.
  
      :param img: numpy.ndarray
      :param gamma: float
      :param gain: float
      :return corrected_img: numpy.ndarray
      """
      corrected_img = exposure.adjust_gamma(image=img, gamma=gamma, gain=gain)
  
      _debug(visual=corrected_img,
             filename=os.path.join(params.debug_outdir, str(params.device) + '_gamma_corrected.png'))
  
      return corrected_img
  
subject: Revise docstring