--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/anisotropic_diffusion.py	Sat Feb 09 14:30:00 2019 -0500
@@ -0,0 +1,28 @@
+import argparse
+import sys
+import warnings
+import numpy as np
+import skimage.io
+import skimage.util
+from medpy.filter.smoothing import anisotropic_diffusion
+
+parser = argparse.ArgumentParser()
+parser.add_argument('input_file', type=argparse.FileType('r'), default=sys.stdin, help='input file')
+parser.add_argument('out_file', type=argparse.FileType('w'), default=sys.stdin, help='out file (TIFF)')
+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)
+args = parser.parse_args()
+
+with warnings.catch_warnings():
+	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
+
+	res = skimage.util.img_as_uint(res) #Attention: precision loss
+
+	skimage.io.imsave(args.out_file.name, res, plugin='tifffile')