new_contents: import numpy as np
  import scipy as sp
  
  
  def cLoG(I, Mask, SigmaMin=30*1.414, SigmaMax=50*1.414):
      """Constrainted Laplacian of Gaussian filter.
  
      Takes as input a grayscale nuclear image and binary mask of cell nuclei,
      and uses the distance transform of the nuclear mask to constrain the LoG
      filter response of the image for nuclear seeding. Returns a LoG filter
      image of type float. Local maxima are used for seeding cells.
  
      Parameters
      ----------
      I : array_like
          A hematoxylin intensity image obtained from ColorDeconvolution. Objects
          are assumed to be dark with a light background.
      Mask : array_like
          A binary image where nuclei pixels have value 1/True, and non-nuclear
          pixels have value 0/False.
      SigmaMin : float
          A scalar defining the minimum scaled nuclear radius. Radius is scaled
          by sqrt(2). Default value = 30 * 2 ** 0.5.
      SigmaMax : float
          A scalar defining the maximum scaled nuclear radius. Radius is scaled
          by sqrt(2). Default value = 50 * 2 ** 0.5.
  
      Returns
      -------
      Iout : array_like
          A color image of type unsigned char where boundary pixels take
          on the color defined by the RGB-triplet 'Color'.
  
      See Also
      --------
      MergeSeeds
  
      References
      ----------
      .. [1] Y. Al-Kofahi et al "Improved Automatic Detection and Segmentation
      of Cell Nuclei in Histopathology Images" in IEEE Transactions on Biomedical
      Engineering,vol.57,no.4,pp.847-52, 2010.
      """
  
      # convert intensity image type to float if needed
      if I.dtype == np.uint8:
          I = I.astype(np.float)
  
      # generate distance map
      Distance = sp.ndimage.morphology.distance_transform_edt(Mask)
  
      # initialize constraint
      Constraint = np.maximum(SigmaMin, np.minimum(SigmaMax, 2*Distance))
  
      # initialize log filter response array
      Iout = np.finfo(Distance.dtype).min * np.ones(Mask.shape)
  
      # LoG filter over scales
      Start = np.floor(SigmaMin)
      Stop = np.ceil(SigmaMax)
      Sigmas = np.linspace(Start, Stop, Stop-Start+1)
      for Sigma in Sigmas:
  
          # generate normalized filter response
          Response = Sigma ** 2 * \
              sp.ndimage.filters.gaussian_laplace(I, Sigma, mode='constant',
                                                  cval=0.0)
  
          # constrain response
          Map = Sigma < Constraint
          Response[~Map] = np.finfo(Distance.dtype).min
  
          # replace with maxima
          Iout = np.maximum(Iout, Response)
  
      # translate filtered image
  
      # replace min floats
      Iout[Iout == np.finfo(Distance.dtype).min] = 0
  
      return Iout
  
subject: Create cLoG.py