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Modified variables' names.

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This commit is contained in:
emmanuelle
2015-02-01 22:43:18 +01:00
parent ee81e00c0e
commit 50907cd8e7
1 changed files with 73 additions and 74 deletions
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skimage/restoration/_nl_means_denoising.pyx
+73 -74
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@@ -174,9 +174,9 @@ def _nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1):
cdef int n_row, n_col, n_ch
n_row, n_col, n_ch = image.shape
cdef int offset = s / 2
cdef int x_row, x_col, i, j, color
cdef int x_row_start, x_row_end, x_col_start, x_col_end
cdef int x_row_start_i, x_row_end_i, x_col_start_j, x_col_end_j
cdef int row, col, i, j, color
cdef int row_start, row_end, col_start, col_end
cdef int row_start_i, row_end_i, col_start_j, col_end_j
cdef DTYPE_t [::1] new_values = np.zeros(n_ch).astype(np.float32)
cdef DTYPE_t [:, :, ::1] padded = np.ascontiguousarray(util.pad(image,
((offset, offset), (offset, offset), (0, 0)),
@@ -192,44 +192,44 @@ def _nl_means_denoising_2d(image, int s=7, int d=13, float h=0.1):
cdef float distance
w = 1. / (n_ch * np.sum(w) * h ** 2) * w
# Coordinates of central pixel and patch bounds
for x_row in range(offset, n_row + offset):
x_row_start = x_row - offset
x_row_end = x_row + offset + 1
for x_col in range(offset, n_col + offset):
for row in range(offset, n_row + offset):
row_start = row - offset
row_end = row + offset + 1
for col in range(offset, n_col + offset):
for color in range(n_ch):
new_values[color] = 0
weight_sum = 0
x_col_start = x_col - offset
x_col_end = x_col + offset + 1
col_start = col - offset
col_end = col + offset + 1
# Coordinates of test pixel and patch bounds
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),
min(d + 1, n_col + offset - x_col)):
x_col_start_j = x_col_start + j
x_col_end_j = x_col_end + j
for i in range(max(-d, offset - row),
min(d + 1, n_row + offset - row)):
row_start_i = row_start + i
row_end_i = row_end + i
for j in range(max(-d, offset - col),
min(d + 1, n_col + offset - col)):
col_start_j = col_start + j
col_end_j = 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[row_start:row_end,
col_start:col_end, 0],
padded[row_start_i:row_end_i,
col_start_j: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[row_start:row_end,
col_start:col_end, :],
padded[row_start_i:row_end_i,
col_start_j:col_end_j, :],
w, s)
weight_sum += weight
for color in range(n_ch):
new_values[color] += weight * padded[x_row + i,
x_col + j, color]
new_values[color] += weight * padded[row + i,
col + j, color]
for color in range(n_ch):
result[x_row, x_col, color] = new_values[color] / weight_sum
result[row, col, color] = new_values[color] / weight_sum
return result[offset:-offset, offset:-offset]
@@ -276,49 +276,48 @@ def _nl_means_denoising_3d(image, int s=7,
-(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
cdef int x_pln_start, x_pln_end, x_row_start, x_row_end, \
x_col_start, x_col_end
cdef int x_pln_start_i, x_pln_end_i, x_row_start_j, x_row_end_j, \
x_col_start_k, x_col_end_k
cdef int pln, row, col, i, j, k
cdef int pln_start, pln_end, row_start, row_end, col_start, col_end
cdef int pln_start_i, pln_end_i, row_start_j, row_end_j, \
col_start_k, col_end_k
w = 1. / (np.sum(w) * h ** 2) * w
# Coordinates of central pixel and patch bounds
for x_pln in range(offset, n_pln + offset):
x_pln_start = x_pln - offset
x_pln_end = x_pln + offset + 1
for x_row in range(offset, n_row + offset):
x_row_start = x_row - offset
x_row_end = x_row + offset + 1
for x_col in range(offset, n_col + offset):
x_col_start = x_col - offset
x_col_end = x_col + offset + 1
for pln in range(offset, n_pln + offset):
pln_start = pln - offset
pln_end = pln + offset + 1
for row in range(offset, n_row + offset):
row_start = row - offset
row_end = row + offset + 1
for col in range(offset, n_col + offset):
col_start = col - offset
col_end = col + offset + 1
new_value = 0
weight_sum = 0
# Coordinates of test pixel and patch bounds
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),
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),
min(d + 1, n_col + offset - x_col)):
x_col_start_k = x_col_start + k
x_col_end_k = x_col_end + k
for i in range(max(-d, offset - pln),
min(d + 1, n_pln + offset - pln)):
pln_start_i = pln_start + i
pln_end_i = pln_end + i
for j in range(max(-d, offset - row),
min(d + 1, n_row + offset - row)):
row_start_j = row_start + j
row_end_j = row_end + j
for k in range(max(-d, offset - col),
min(d + 1, n_col + offset - col)):
col_start_k = col_start + k
col_end_k = 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[pln_start:pln_end,
row_start:row_end,
col_start:col_end],
padded[pln_start_i:pln_end_i,
row_start_j:row_end_j,
col_start_k:col_end_k],
w, s)
weight_sum += weight
new_value += weight * padded[x_pln + i,
x_row + j, x_col + k]
result[x_pln, x_row, x_col] = new_value / weight_sum
new_value += weight * padded[pln + i,
row + j, col + k]
result[pln, row, col] = new_value / weight_sum
return result[offset:-offset, offset:-offset, offset:-offset]
#-------------- Accelerated algorithm of Froment 2015 ------------------
@@ -594,8 +593,8 @@ def _fast_nl_means_denoising_3d(image, int s=5, int d=7, float h=0.1):
cdef DTYPE_t [:, :, ::1] integral = np.zeros_like(padded)
cdef int n_pln, n_row, n_col, t_pln, t_row, t_col, \
pln, row, col
cdef int x_pln_dist_min, x_pln_dist_max, x_row_dist_min, x_row_dist_max, \
x_col_dist_min, x_col_dist_max
cdef int pln_dist_min, pln_dist_max, row_dist_min, row_dist_max, \
col_dist_min, col_dist_max
cdef float weight, distance
cdef float alpha
cdef float h_square = h ** 2.
@@ -608,14 +607,14 @@ 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_dist_min = max(offset, offset - t_pln)
x_pln_dist_max = min(n_pln - offset, n_pln - offset - t_pln)
pln_dist_min = max(offset, offset - t_pln)
pln_dist_max = min(n_pln - offset, n_pln - offset - t_pln)
for t_row in range(-d, d + 1):
x_row_dist_min = max(offset, offset - t_row)
x_row_dist_max = min(n_row - offset, n_row - offset - t_row)
row_dist_min = max(offset, offset - t_row)
row_dist_max = min(n_row - offset, n_row - offset - t_row)
for t_col in range(0, d + 1):
x_col_dist_min = max(offset, offset - t_col)
x_col_dist_max = min(n_col - offset, n_col - offset - t_col)
col_dist_min = max(offset, offset - t_col)
col_dist_max = min(n_col - offset, n_col - offset - t_col)
# alpha is to account for patches on the same column
# distance is computed twice in this case
if t_col == 0 and (t_pln is not 0 or t_row is not 0):
@@ -625,9 +624,9 @@ def _fast_nl_means_denoising_3d(image, int s=5, int d=7, float h=0.1):
integral = np.zeros_like(padded)
_integral_image_3d(padded, integral, t_pln, t_row, t_col,
n_pln, n_row, n_col)
for pln in range(x_pln_dist_min, x_pln_dist_max):
for row in range(x_row_dist_min, x_row_dist_max):
for col in range(x_col_dist_min, x_col_dist_max):
for pln in range(pln_dist_min, pln_dist_max):
for row in range(row_dist_min, row_dist_max):
for col in range(col_dist_min, col_dist_max):
distance = _integral_to_distance_3d(integral,
pln, row, col, offset, s_cube_h_square)
# exp of large negative numbers is close to zero