Mercurial > repos > imgteam > anisotropic_diffusion
view anisotropic_diffusion.py @ 1:17d3cfba9b5a draft
planemo upload for repository https://github.com/BMCV/galaxy-image-analysis/tools/anisotropic-diffusion/ commit b2acc1845a25828181597fe5b6982fe116a7796d
author | imgteam |
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date | Mon, 22 Jul 2019 05:01:04 -0400 |
parents | d13e26f576bc |
children | e6987afa0484 |
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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')