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PEP 8: spaces and operators

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emmanuelle
2015-02-01 20:33:57 +01:00
parent 009e761411
commit 4dbf33b9ca
1 changed files with 51 additions and 51 deletions
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skimage/restoration/_nl_means_denoising.pyx
+51 -51
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@@ -202,27 +202,27 @@ def _nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1):
x_col_start = x_col - offset
x_col_end = x_col + offset + 1
# Coordinates of test pixel and patch bounds
for i in range(max(- d, offset - x_row),
for i in range(max(-d, offset - x_row),
min(d + 1, n_row + offset - x_row)):
x_row_start_i = x_row_start + i
x_row_end_i = x_row_end + i
for j in range(max(- d, offset - x_col),
for j in range(max(-d, offset - x_col),
min(d + 1, n_col + offset - x_col)):
x_col_start_j = x_col_start + j
x_col_end_j = x_col_end + j
if n_ch == 1:
weight = patch_distance_2d(
padded[x_row_start: x_row_end,
x_col_start: x_col_end, 0],
padded[x_row_start_i: x_row_end_i,
x_col_start_j: x_col_end_j, 0],
padded[x_row_start:x_row_end,
x_col_start:x_col_end, 0],
padded[x_row_start_i:x_row_end_i,
x_col_start_j:x_col_end_j, 0],
w, s)
else:
weight = patch_distance_2drgb(
padded[x_row_start: x_row_end,
x_col_start: x_col_end, :],
padded[x_row_start_i: x_row_end_i,
x_col_start_j: x_col_end_j, :],
padded[x_row_start:x_row_end,
x_col_start:x_col_end, :],
padded[x_row_start_i:x_row_end_i,
x_col_start_j:x_col_end_j, :],
w, s)
weight_sum += weight
for color in range(n_ch):
@@ -295,25 +295,25 @@ def _nl_means_denoising_3d(image, int s=7,
new_value = 0
weight_sum = 0
# Coordinates of test pixel and patch bounds
for i in range(max(- d, offset - x_pln),
for i in range(max(-d, offset - x_pln),
min(d + 1, n_pln + offset - x_pln)):
x_pln_start_i = x_pln_start + i
x_pln_end_i = x_pln_end + i
for j in range(max(- d, offset - x_row),
for j in range(max(-d, offset - x_row),
min(d + 1, n_row + offset - x_row)):
x_row_start_j = x_row_start + j
x_row_end_j = x_row_end + j
for k in range(max(- d, offset - x_col),
for k in range(max(-d, offset - x_col),
min(d + 1, n_col + offset - x_col)):
x_col_start_k = x_col_start + k
x_col_end_k = x_col_end + k
weight = patch_distance_3d(
padded[x_pln_start: x_pln_end,
x_row_start: x_row_end,
x_col_start: x_col_end],
padded[x_pln_start_i: x_pln_end_i,
x_row_start_j: x_row_end_j,
x_col_start_k: x_col_end_k],
padded[x_pln_start:x_pln_end,
x_row_start:x_row_end,
x_col_start:x_col_end],
padded[x_pln_start_i:x_pln_end_i,
x_row_start_j:x_row_end_j,
x_col_start_k:x_col_end_k],
w, s)
weight_sum += weight
new_value += weight * padded[x_pln + i,
@@ -323,6 +323,7 @@ def _nl_means_denoising_3d(image, int s=7,
#-------------- Accelerated algorithm of Froment 2015 ------------------
@cython.cdivision(True)
@cython.boundscheck(False)
cdef inline float _integral_to_distance_2d(DTYPE_t [:, ::] integral,
@@ -343,6 +344,7 @@ cdef inline float _integral_to_distance_2d(DTYPE_t [:, ::] integral,
distance /= h2s2
return distance
@cython.cdivision(True)
@cython.boundscheck(False)
cdef inline float _integral_to_distance_3d(DTYPE_t [:, :, ::] integral,
@@ -356,16 +358,15 @@ cdef inline float _integral_to_distance_3d(DTYPE_t [:, :, ::] integral,
Used in _fast_nl_means_denoising_3d
"""
cdef float distance
distance = (integral[x_pln + offset, x_row + offset, x_col + offset]
- integral[x_pln - offset, x_row - offset, x_col - offset]
+ integral[x_pln - offset, x_row - offset, x_col + offset]
+ integral[x_pln - offset, x_row + offset, x_col - offset]
+ integral[x_pln + offset, x_row - offset, x_col - offset]
- integral[x_pln - offset, x_row + offset, x_col + offset]
- integral[x_pln + offset, x_row - offset, x_col + offset]
- integral[x_pln + offset, x_row + offset, x_col - offset])
distance = (integral[x_pln + offset, x_row + offset, x_col + offset] -
integral[x_pln - offset, x_row - offset, x_col - offset] +
integral[x_pln - offset, x_row - offset, x_col + offset] +
integral[x_pln - offset, x_row + offset, x_col - offset] +
integral[x_pln + offset, x_row - offset, x_col - offset] -
integral[x_pln - offset, x_row + offset, x_col + offset] -
integral[x_pln + offset, x_row - offset, x_col + offset] -
integral[x_pln + offset, x_row + offset, x_col - offset])
distance /= s_cube_h_square
return distance
@@ -426,22 +427,22 @@ def _fast_nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1):
else:
alpha = 1.
integral = np.zeros_like(padded[..., 0], order='C')
for x_row in range(max(1, - t_row), min(n_row, n_row - t_row)):
for x_col in range(max(1, - t_col), min(n_col, n_col - t_col)):
for x_row in range(max(1, -t_row), min(n_row, n_row - t_row)):
for x_col in range(max(1, -t_col), min(n_col, n_col - t_col)):
if n_ch == 1:
distance = (padded[x_row, x_col, 0] -
padded[x_row + t_row, x_col + t_col, 0])**2
else:
distance = ((padded[x_row, x_col, 0] -
padded[x_row + t_row, x_col + t_col, 0])**2
+(padded[x_row, x_col, 1] -
padded[x_row + t_row, x_col + t_col, 1])**2
+(padded[x_row, x_col, 2] -
padded[x_row + t_row, x_col + t_col, 2])**2)
padded[x_row + t_row, x_col + t_col, 0])**2 +
(padded[x_row, x_col, 1] -
padded[x_row + t_row, x_col + t_col, 1])**2 +
(padded[x_row, x_col, 2] -
padded[x_row + t_row, x_col + t_col, 2])**2)
integral[x_row, x_col] = distance + \
integral[x_row - 1, x_col] + \
integral[x_row, x_col - 1] \
- integral[x_row - 1, x_col - 1]
integral[x_row, x_col - 1] - \
integral[x_row - 1, x_col - 1]
for x_row in range(max(offset, offset - t_row),
min(n_row - offset, n_row - offset - t_row)):
for x_col in range(max(offset, offset - t_col),
@@ -456,7 +457,7 @@ def _fast_nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1):
weights[x_row + t_row, x_col + t_col] += weight
for ch in range(n_ch):
result[x_row, x_col, ch] += weight * \
padded[x_row + t_row, x_col + t_col, ch]
padded[x_row + t_row, x_col + t_col, ch]
result[x_row + t_row, x_col + t_col, ch] += \
weight * padded[x_row, x_col, ch]
# Normalize pixel values using sum of weights of contributing patches
@@ -466,7 +467,7 @@ def _fast_nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1):
# No risk of division by zero, since the contribution
# of a null shift is strictly positive
result[x_row, x_col, channel] /= weights[x_row, x_col]
return result[pad_size: - pad_size, pad_size: - pad_size]
return result[pad_size:-pad_size, pad_size:-pad_size]
@cython.cdivision(True)
@@ -523,17 +524,17 @@ def _fast_nl_means_denoising_3d(image, int s=5, int d=7, float h=0.1):
# Outer loops on patch shifts
# With t2 >= 0, reference patch is always on the left of test patch
for t_pln in range(-d, d + 1):
x_pln_integral_min = max(1, - t_pln)
x_pln_integral_min = max(1, -t_pln)
x_pln_integral_max = min(n_pln, n_pln - t_pln)
x_pln_dist_min = max(offset, offset - t_pln)
x_pln_dist_max = min(n_pln - offset, n_pln - offset - t_pln)
for t_row in range(-d, d + 1):
x_row_integral_min = max(1, - t_row)
x_row_integral_min = max(1, -t_row)
x_row_integral_max = min(n_row, n_row - t_row)
x_row_dist_min = max(offset, offset - t_row)
x_row_dist_max = min(n_row - offset, n_row - offset - t_row)
for t_col in range(0, d + 1):
x_col_integral_min = max(1, - t_col)
x_col_integral_min = max(1, -t_col)
x_col_integral_max = min(n_col, n_col - t_col)
x_col_dist_min = max(offset, offset - t_col)
x_col_dist_max = min(n_col - offset, n_col - offset - t_col)
@@ -550,15 +551,15 @@ def _fast_nl_means_denoising_3d(image, int s=5, int d=7, float h=0.1):
x_col_integral_max):
integral[x_pln, x_row, x_col] = \
((padded[x_pln, x_row, x_col] -
padded[x_pln + t_pln, x_row + t_row,
padded[x_pln + t_pln, x_row + t_row,
x_col + t_col])**2 +
integral[x_pln - 1, x_row, x_col] +
integral[x_pln, x_row - 1, x_col] +
integral[x_pln, x_row, x_col - 1] +
integral[x_pln - 1, x_row - 1, x_col - 1]
- integral[x_pln - 1, x_row - 1, x_col]
- integral[x_pln, x_row - 1, x_col - 1]
- integral[x_pln - 1, x_row, x_col - 1])
integral[x_pln - 1, x_row - 1, x_col - 1] -
integral[x_pln - 1, x_row - 1, x_col] -
integral[x_pln, x_row - 1, x_col - 1] -
integral[x_pln - 1, x_row, x_col - 1])
for x_pln in range(x_pln_dist_min, x_pln_dist_max):
for x_row in range(x_row_dist_min, x_row_dist_max):
for x_col in range(x_col_dist_min, x_col_dist_max):
@@ -581,8 +582,7 @@ def _fast_nl_means_denoising_3d(image, int s=5, int d=7, float h=0.1):
for x_pln in range(offset, n_pln - offset):
for x_row in range(offset, n_row - offset):
for x_col in range(offset, n_col - offset):
# I think there is no risk of division by zero
# except in padded zone
# No risk of division by zero, since the contribution
# of a null shift is strictly positive
result[x_pln, x_row, x_col] /= weights[x_pln, x_row, x_col]
return result[pad_size: - pad_size, pad_size: - pad_size,
pad_size: -pad_size]
return result[pad_size:-pad_size, pad_size:-pad_size, pad_size:-pad_size]