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apply PEP8 guidelines

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Johannes Schönberger
2012-08-20 22:46:57 +02:00
parent d9444e7612
commit 82868dd41b
1 changed files with 14 additions and 11 deletions
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skimage/feature/_texture.py
+14 -11
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@@ -182,11 +182,11 @@ def greycoprops(P, prop='contrast'):
# create weights for specified property
I, J = np.ogrid[0:num_level, 0:num_level]
if prop == 'contrast':
weights = (I - J)**2
weights = (I - J) ** 2
elif prop == 'dissimilarity':
weights = np.abs(I - J)
elif prop == 'homogeneity':
weights = 1. / (1. + (I - J)**2)
weights = 1. / (1. + (I - J) ** 2)
elif prop in ['ASM', 'energy', 'correlation']:
pass
else:
@@ -194,10 +194,10 @@ def greycoprops(P, prop='contrast'):
# compute property for each GLCM
if prop == 'energy':
asm = np.apply_over_axes(np.sum, (P**2), axes=(0, 1))[0, 0]
asm = np.apply_over_axes(np.sum, (P ** 2), axes=(0, 1))[0, 0]
results = np.sqrt(asm)
elif prop == 'ASM':
results = np.apply_over_axes(np.sum, (P**2), axes=(0, 1))[0, 0]
results = np.apply_over_axes(np.sum, (P ** 2), axes=(0, 1))[0, 0]
elif prop == 'correlation':
results = np.zeros((num_dist, num_angle), dtype=np.float64)
I = np.array(range(num_level)).reshape((num_level, 1, 1, 1))
@@ -205,9 +205,9 @@ def greycoprops(P, prop='contrast'):
diff_i = I - np.apply_over_axes(np.sum, (I * P), axes=(0, 1))[0, 0]
diff_j = J - np.apply_over_axes(np.sum, (J * P), axes=(0, 1))[0, 0]
std_i = np.sqrt(np.apply_over_axes(np.sum, (P * (diff_i)**2),
std_i = np.sqrt(np.apply_over_axes(np.sum, (P * (diff_i) ** 2),
axes=(0, 1))[0, 0])
std_j = np.sqrt(np.apply_over_axes(np.sum, (P * (diff_j)**2),
std_j = np.sqrt(np.apply_over_axes(np.sum, (P * (diff_j) ** 2),
axes=(0, 1))[0, 0])
cov = np.apply_over_axes(np.sum, (P * (diff_i * diff_j)),
axes=(0, 1))[0, 0]
@@ -226,6 +226,7 @@ def greycoprops(P, prop='contrast'):
return results
def bit_rotate_right(value, length):
"""Cyclic bit shift to the right.
@@ -239,9 +240,10 @@ def bit_rotate_right(value, length):
"""
return (value >> 1) | ((value & 1) << (length - 1))
def local_binary_pattern(image, P, R, method='default'):
"""Texture classification using gray scale and rotation invariant LBP (Local
Binary Patterns).
"""Texture classification using gray scale and rotation invariant LBP
(Local Binary Patterns).
Parameters
----------
@@ -301,8 +303,8 @@ def local_binary_pattern(image, P, R, method='default'):
#: signed / thresholded texture
signed = texture.copy()
signed[signed>=0] = 1
signed[signed<0] = 0
signed[signed >= 0] = 1
signed[signed < 0] = 0
if method in ('uniform', 'var'):
#: determine number of 0 - 1 changes
@@ -324,7 +326,8 @@ def local_binary_pattern(image, P, R, method='default'):
#: shift LBP P times to the right and get minimum value
rotation_chain[0] = lbp
for i in xrange(1, P):
rotation_chain[i] = bit_rotate_right(rotation_chain[i-1], P)
rotation_chain[i] = \
bit_rotate_right(rotation_chain[i - 1], P)
lbp = np.min(rotation_chain)
return lbp