wassname/scikit-image
1
0
Fork 0
mirror of https://github.com/wassname/scikit-image.git synced 2026-08-07 11:28:14 +08:00
Code Issues Packages Projects Releases Wiki Activity

[ENH] Docstring cleaning

Browse Source
This commit is contained in:
emmanuelle
2015-01-29 22:40:46 +01:00
parent 112b0b4fcd
commit 009e761411
2 changed files with 84 additions and 62 deletions
Download Patch File Download Diff File Expand all files Collapse all files
skimage/restoration/_nl_means_denoising.pyx
+78 -56
View File
@@ -18,13 +18,13 @@ cdef inline float patch_distance_2d(DTYPE_t [:, :] p1,
Parameters
----------
p1 : 2-D array_like
first patch
First patch.
p2 : 2-D array_like
first patch
Second patch.
w : 2-D array_like
array of weigths for the different pixels of the patches
Array of weigths for the different pixels of the patches.
s : int
linear size of the patches
Linear size of the patches.
Returns
-------
@@ -35,7 +35,7 @@ cdef inline float patch_distance_2d(DTYPE_t [:, :] p1,
-----
The returned distance is given by
exp( -w * (p1 - p2)**2)
.. math:: \exp( -w (p1 - p2)^2)
"""
cdef int i, j
cdef int center = s / 2
@@ -52,7 +52,7 @@ cdef inline float patch_distance_2d(DTYPE_t [:, :] p1,
for j in range(s):
tmp_diff = p1[i, j] - p2[i, j]
distance += (w[i, j] * tmp_diff * tmp_diff)
distance = exp(- distance)
distance = exp(-distance)
return distance
@@ -66,13 +66,13 @@ cdef inline float patch_distance_2drgb(DTYPE_t [:, :, :] p1,
Parameters
----------
p1 : 3-D array_like
first patch, 2D image with last dimension corresponding to channels
First patch, 2D image with last dimension corresponding to channels.
p2 : 3-D array_like
first patch, 2D image with last dimension corresponding to channels
Second patch, 2D image with last dimension corresponding to channels.
w : 2-D array_like
array of weigths for the different pixels of the patches
Array of weights for the different pixels of the patches.
s : int
linear size of the patches
Linear size of the patches.
Returns
-------
@@ -83,7 +83,7 @@ cdef inline float patch_distance_2drgb(DTYPE_t [:, :, :] p1,
-----
The returned distance is given by
exp( -w * (p1 - p2)**2)
.. math:: \exp( -w (p1 - p2)^2)
"""
cdef int i, j
cdef int center = s / 2
@@ -98,7 +98,7 @@ cdef inline float patch_distance_2drgb(DTYPE_t [:, :, :] p1,
for color in range(3):
tmp_diff = p1[i, j, color] - p2[i, j, color]
distance += w[i, j] * tmp_diff * tmp_diff
distance = exp(- distance)
distance = exp(-distance)
return distance
@@ -112,13 +112,13 @@ cdef inline float patch_distance_3d(DTYPE_t [:, :, :] p1,
Parameters
----------
p1 : 3-D array_like
first patch
First patch.
p2 : 3-D array_like
first patch
Second patch.
w : 3-D array_like
array of weigths for the different pixels of the patches
Array of weights for the different pixels of the patches.
s : int
linear size of the patches
Linear size of the patches.
Returns
-------
@@ -129,7 +129,7 @@ cdef inline float patch_distance_3d(DTYPE_t [:, :, :] p1,
-----
The returned distance is given by
exp( -w * (p1 - p2)**2)
.. math:: \exp( -w (p1 - p2)^2)
"""
cdef int i, j, k
cdef float distance = 0
@@ -142,7 +142,7 @@ cdef inline float patch_distance_3d(DTYPE_t [:, :, :] p1,
for k in range(s):
tmp_diff = p1[i, j, k] - p2[i, j, k]
distance += w[i, j, k] * tmp_diff * tmp_diff
distance = exp(- distance)
distance = exp(-distance)
return distance
@@ -154,15 +154,20 @@ def _nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1):
Parameters
----------
image: ndarray
input RGB image to be denoised
s: int, optional
size of patches used for denoising
d: int, optional
maximal distance in pixels where to search patches used for denoising
h: float, optional
cut-off distance (in gray levels). The higher h, the more permissive
image : ndarray
Input RGB image to be denoised
s : int, optional
Size of patches used for denoising
d : int, optional
Maximal distance in pixels where to search patches used for denoising
h : float, optional
Cut-off distance (in gray levels). The higher h, the more permissive
one is in accepting patches.
Returns
-------
result : ndarray
Denoised image, of same shape as input image.
"""
if s % 2 == 0:
s += 1 # odd value for symmetric patch
@@ -182,8 +187,8 @@ def _nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1):
cdef float weight_sum, weight
xg_row, xg_col = np.mgrid[-offset:offset + 1, -offset:offset + 1]
cdef DTYPE_t [:, ::1] w = np.ascontiguousarray(np.exp(
- (xg_row ** 2 + xg_col ** 2) / (2 * A ** 2)).
astype(np.float32))
-(xg_row ** 2 + xg_col ** 2) / (2 * A ** 2)).
astype(np.float32))
cdef float distance
w = 1. / (n_ch * np.sum(w) * h ** 2) * w
# Coordinates of central pixel and patch bounds
@@ -237,14 +242,19 @@ def _nl_means_denoising_3d(image, int s=7,
Parameters
----------
image: ndarray
input data to be denoised
s: int, optional
size of patches used for denoising
d: int, optional
maximal distance in pixels where to search patches used for denoising
h: float, optional
cut-off distance (in gray levels)
image : ndarray
Input data to be denoised.
s : int, optional
Size of patches used for denoising.
d : int, optional
Maximal distance in pixels where to search patches used for denoising.
h : float, optional
Cut-off distance (in gray levels).
Returns
-------
result : ndarray
Denoised image, of same shape as input image.
"""
if s % 2 == 0:
s += 1 # odd value for symmetric patch
@@ -263,7 +273,7 @@ def _nl_means_denoising_3d(image, int s=7,
-offset: offset + 1,
-offset: offset + 1]
cdef DTYPE_t [:, :, ::1] w = np.ascontiguousarray(np.exp(
- (xg_pln ** 2 + xg_row ** 2 + xg_col ** 2) /
-(xg_pln ** 2 + xg_row ** 2 + xg_col ** 2) /
(2 * A ** 2)).astype(np.float32))
cdef float distance
cdef int x_pln, x_row, x_col, i, j, k
@@ -318,7 +328,8 @@ def _nl_means_denoising_3d(image, int s=7,
cdef inline float _integral_to_distance_2d(DTYPE_t [:, ::] integral,
int x_row, int x_col, int offset, float h2s2):
"""
See
References
----------
Jacques Froment. Parameter-Free Fast Pixelwise Non-Local Means
Denoising. Image Processing On Line, 2014, vol. 4, p. 300-326.
@@ -338,7 +349,8 @@ cdef inline float _integral_to_distance_3d(DTYPE_t [:, :, ::] integral,
int x_pln, int x_row, int x_col, int offset,
float s_cube_h_square):
"""
See
References
----------
Jacques Froment. Parameter-Free Fast Pixelwise Non-Local Means
Denoising. Image Processing On Line, 2014, vol. 4, p. 300-326.
@@ -367,15 +379,20 @@ def _fast_nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1):
Parameters
----------
image: ndarray
2-D input data to be denoised, grayscale or RGB
s: int, optional
size of patches used for denoising
d: int, optional
maximal distance in pixels where to search patches used for denoising
h: float, optional
cut-off distance (in gray levels). The higher h, the more permissive
image : ndarray
2-D input data to be denoised, grayscale or RGB.
s : int, optional
Size of patches used for denoising.
d : int, optional
Maximal distance in pixels where to search patches used for denoising.
h : float, optional
Cut-off distance (in gray levels). The higher h, the more permissive
one is in accepting patches.
Returns
-------
result : ndarray
Denoised image, of same shape as input image.
"""
if s % 2 == 0:
s += 1 # odd value for symmetric patch
@@ -434,7 +451,7 @@ def _fast_nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1):
# exp of large negative numbers is close to zero
if distance > DISTANCE_CUTOFF:
continue
weight = alpha * exp(- distance)
weight = alpha * exp(-distance)
weights[x_row, x_col] += weight
weights[x_row + t_row, x_col + t_col] += weight
for ch in range(n_ch):
@@ -461,15 +478,20 @@ def _fast_nl_means_denoising_3d(image, int s=5, int d=7, float h=0.1):
Parameters
----------
image: ndarray
3-D input data to be denoised
s: int, optional
size of patches used for denoising
d: int, optional
maximal distance in pixels where to search patches used for denoising
h: float, optional
image : ndarray
3-D input data to be denoised.
s : int, optional
Size of patches used for denoising.
d : int, optional
Maximal distance in pixels where to search patches used for denoising.
h : float, optional
cut-off distance (in gray levels). The higher h, the more permissive
one is in accepting patches.
Returns
-------
result : ndarray
Denoised image, of same shape as input image.
"""
if s % 2 == 0:
s += 1 # odd value for symmetric patch
@@ -546,7 +568,7 @@ def _fast_nl_means_denoising_3d(image, int s=5, int d=7, float h=0.1):
# exp of large negative numbers is close to zero
if distance > DISTANCE_CUTOFF:
continue
weight = alpha * exp(- distance)
weight = alpha * exp(-distance)
weights[x_pln, x_row, x_col] += weight
weights[x_pln + t_pln, x_row + t_row,
x_col + t_col] += weight
skimage/restoration/non_local_means.py
+6 -6
View File
@@ -12,14 +12,14 @@ def nl_means_denoising(image, patch_size=7, patch_distance=11, h=0.1,
Parameters
----------
image : 2D or 3D ndarray
input image to be denoised, which can be 2D or 3D, and grayscale
Input image to be denoised, which can be 2D or 3D, and grayscale
or RGB (for 2D images only, see ``multichannel`` parameter).
patch_size : int, optional
size of patches used for denoising
Size of patches used for denoising.
patch_distance : int, optional
maximal distance in pixels where to search patches used for denoising
Maximal distance in pixels where to search patches used for denoising.
h : float, optional
cut-off distance (in gray levels). The higher h, the more permissive
Cut-off distance (in gray levels). The higher h, the more permissive
one is in accepting patches. A higher h results in a smoother image,
at the expense of blurring features. For a Gaussian noise of standard
deviation sigma, a rule of thumb is to choose the value of h to be
@@ -28,7 +28,7 @@ def nl_means_denoising(image, patch_size=7, patch_distance=11, h=0.1,
Whether the last axis of the image is to be interpreted as multiple
channels or another spatial dimension. Set to ``False`` for 3-D images.
fast_mode : bool, optional
if True (default value), a fast version of the non-local means
If True (default value), a fast version of the non-local means
algorithm is used. If False, the original version of non-local means is
used. See the Notes section for more details about the algorithms.
@@ -36,7 +36,7 @@ def nl_means_denoising(image, patch_size=7, patch_distance=11, h=0.1,
-------
result : ndarray
denoised image, of same shape as `image`.
Denoised image, of same shape as `image`.
See Also
--------