/usr/lib/python2.7/dist-packages/dipy/denoise/nlmeans.py is in python-dipy 0.10.1-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | from __future__ import division, print_function
import numpy as np
from dipy.denoise.denspeed import nlmeans_3d
def nlmeans(arr, sigma, mask=None, patch_radius=1, block_radius=5,
rician=True, num_threads=None):
""" Non-local means for denoising 3D and 4D images
Parameters
----------
arr : 3D or 4D ndarray
The array to be denoised
mask : 3D ndarray
sigma : float or 3D array
standard deviation of the noise estimated from the data
patch_radius : int
patch size is ``2 x patch_radius + 1``. Default is 1.
block_radius : int
block size is ``2 x block_radius + 1``. Default is 5.
rician : boolean
If True the noise is estimated as Rician, otherwise Gaussian noise
is assumed.
num_threads : int
Number of threads. If None (default) then all available threads
will be used (all CPU cores).
Returns
-------
denoised_arr : ndarray
the denoised ``arr`` which has the same shape as ``arr``.
"""
if arr.ndim == 3:
sigma = np.ones(arr.shape, dtype=np.float64) * sigma
return nlmeans_3d(arr, mask, sigma,
patch_radius, block_radius,
rician).astype(arr.dtype)
elif arr.ndim == 4:
denoised_arr = np.zeros_like(arr)
if isinstance(sigma, np.ndarray) and sigma.ndim == 3:
sigma = (np.ones(arr.shape, dtype=np.float64) *
sigma[..., np.newaxis])
else:
sigma = np.ones(arr.shape, dtype=np.float64) * sigma
for i in range(arr.shape[-1]):
denoised_arr[..., i] = nlmeans_3d(arr[..., i],
mask,
sigma[..., i],
patch_radius,
block_radius,
rician,
num_threads).astype(arr.dtype)
return denoised_arr
else:
raise ValueError("Only 3D or 4D array are supported!", arr.shape)
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