--- a/anisotropic_diffusion.py	Mon Jul 22 05:01:04 2019 -0400
+++ b/anisotropic_diffusion.py	Mon Nov 13 22:10:29 2023 +0000
@@ -1,7 +1,7 @@
 import argparse
 import sys
 import warnings
-import numpy as np
+
 import skimage.io
 import skimage.util
 from medpy.filter.smoothing import anisotropic_diffusion
@@ -12,17 +12,17 @@
 parser.add_argument('niter', type=int, help='Number of iterations', default=1)
 parser.add_argument('kappa', type=int, help='Conduction coefficient', default=50)
 parser.add_argument('gamma', type=float, help='Speed of diffusion', default=0.1)
-parser.add_argument('eqoption', type=int, choices=[1,2], help='Perona Malik diffusion equation', default=1)
+parser.add_argument('eqoption', type=int, choices=[1, 2], help='Perona Malik diffusion equation', default=1)
 args = parser.parse_args()
- 
+
 with warnings.catch_warnings():
-	warnings.simplefilter("ignore") #to ignore FutureWarning as well 
+    warnings.simplefilter("ignore")  # to ignore FutureWarning as well
 
-	img_in = skimage.io.imread(args.input_file.name, plugin='tifffile')
-	res = anisotropic_diffusion(img_in, niter=args.niter, kappa=args.kappa, gamma=args.gamma, option=args.eqoption)
-	res[res<-1]=-1
-	res[res>1]=1
+    img_in = skimage.io.imread(args.input_file.name, plugin='tifffile')
+    res = anisotropic_diffusion(img_in, niter=args.niter, kappa=args.kappa, gamma=args.gamma, option=args.eqoption)
+    res[res < -1] = -1
+    res[res > +1] = +1
 
-	res = skimage.util.img_as_uint(res) #Attention: precision loss
+    res = skimage.util.img_as_uint(res)  # Attention: precision loss
 
-	skimage.io.imsave(args.out_file.name, res, plugin='tifffile')
+    skimage.io.imsave(args.out_file.name, res, plugin='tifffile')