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Converted logical ops to bitwise ops and made cosmetic changes wrt pep8 compliance

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This commit is contained in:
Dan Farmer
2011-04-04 22:48:40 -07:00
parent 1ce0936e3d
commit e60d38892e
1 changed files with 41 additions and 57 deletions
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scikits/image/filter/canny.py
+41 -57
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@@ -121,15 +121,15 @@ def canny(image, sigma, low_threshold, high_threshold, mask=None):
isobel = ndi.sobel(smoothed, axis=0)
abs_isobel = np.abs(isobel)
abs_jsobel = np.abs(jsobel)
magnitude = np.sqrt(isobel * isobel + jsobel * jsobel)
magnitude = np.hypot(isobel, jsobel)
#
# Make the eroded mask. Setting the border value to zero will wipe
# out the image edges for us.
#
s = generate_binary_structure(2,2)
s = generate_binary_structure(2, 2)
eroded_mask = binary_erosion(mask, s, border_value=0)
eroded_mask = np.logical_and(eroded_mask, magnitude > 0)
eroded_mask = eroded_mask & (magnitude > 0)
#
#--------- Find local maxima --------------
#
@@ -138,97 +138,81 @@ def canny(image, sigma, low_threshold, high_threshold, mask=None):
#
local_maxima = np.zeros(image.shape,bool)
#----- 0 to 45 degrees ------
pts_plus = np.logical_and(isobel >= 0,
np.logical_and(jsobel >= 0,
abs_isobel >= abs_jsobel))
pts_minus = np.logical_and(isobel <= 0,
np.logical_and(jsobel <= 0,
abs_isobel >= abs_jsobel))
pts = np.logical_or(pts_plus, pts_minus)
pts = np.logical_and(eroded_mask, pts)
pts_plus = (isobel >= 0) & (jsobel >= 0) & (abs_isobel >= abs_jsobel)
pts_minus = (isobel <= 0) & (jsobel <= 0) & (abs_isobel >= abs_jsobel)
pts = pts_plus | pts_minus
pts = eroded_mask & pts
# Get the magnitudes shifted left to make a matrix of the points to the
# right of pts. Similarly, shift left and down to get the points to the
# top right of pts.
c1 = magnitude[1:,:][pts[:-1,:]]
c2 = magnitude[1:,1:][pts[:-1,:-1]]
c1 = magnitude[1:, :][pts[:-1, :]]
c2 = magnitude[1:, 1:][pts[:-1, :-1]]
m = magnitude[pts]
w = abs_jsobel[pts] / abs_isobel[pts]
c_plus = c2 * w + c1 * (1 - w) <= m
c1 = magnitude[:-1,:][pts[1:,:]]
c2 = magnitude[:-1,:-1][pts[1:,1:]]
c1 = magnitude[:-1, :][pts[1:, :]]
c2 = magnitude[:-1, :-1][pts[1:, 1:]]
c_minus = c2 * w + c1 * (1 - w) <= m
local_maxima[pts] = np.logical_and(c_plus, c_minus)
local_maxima[pts] = c_plus & c_minus
#----- 45 to 90 degrees ------
# Mix diagonal and vertical
#
pts_plus = np.logical_and(isobel >= 0,
np.logical_and(jsobel >= 0,
abs_isobel <= abs_jsobel))
pts_minus = np.logical_and(isobel <= 0,
np.logical_and(jsobel <= 0,
abs_isobel <= abs_jsobel))
pts = np.logical_or(pts_plus, pts_minus)
pts = np.logical_and(eroded_mask, pts)
c1 = magnitude[:,1:][pts[:,:-1]]
c2 = magnitude[1:,1:][pts[:-1,:-1]]
pts_plus = (isobel >= 0) & (jsobel >= 0) & (abs_isobel <= abs_jsobel)
pts_minus = (isobel <= 0) & (jsobel <= 0) & (abs_isobel <= abs_jsobel)
pts = pts_plus | pts_minus
pts = eroded_mask & pts
c1 = magnitude[:, 1:][pts[:, :-1]]
c2 = magnitude[1:, 1:][pts[:-1, :-1]]
m = magnitude[pts]
w = abs_isobel[pts] / abs_jsobel[pts]
c_plus = c2 * w + c1 * (1 - w) <= m
c1 = magnitude[:,:-1][pts[:,1:]]
c2 = magnitude[:-1,:-1][pts[1:,1:]]
c1 = magnitude[:, :-1][pts[:, 1:]]
c2 = magnitude[:-1, :-1][pts[1:, 1:]]
c_minus = c2 * w + c1 * (1 - w) <= m
local_maxima[pts] = np.logical_and(c_plus, c_minus)
local_maxima[pts] = c_plus & c_minus
#----- 90 to 135 degrees ------
# Mix anti-diagonal and vertical
#
pts_plus = np.logical_and(isobel <= 0,
np.logical_and(jsobel >= 0,
abs_isobel <= abs_jsobel))
pts_minus = np.logical_and(isobel >= 0,
np.logical_and(jsobel <= 0,
abs_isobel <= abs_jsobel))
pts = np.logical_or(pts_plus, pts_minus)
pts = np.logical_and(eroded_mask, pts)
c1a = magnitude[:,1:][pts[:,:-1]]
c2a = magnitude[:-1,1:][pts[1:,:-1]]
pts_plus = (isobel <= 0) & (jsobel >= 0) & (abs_isobel <= abs_jsobel)
pts_minus = (isobel >= 0) & (jsobel <= 0) & (abs_isobel <= abs_jsobel)
pts = pts_plus | pts_minus
pts = eroded_mask & pts
c1a = magnitude[:, 1:][pts[:, :-1]]
c2a = magnitude[:-1, 1:][pts[1:, :-1]]
m = magnitude[pts]
w = abs_isobel[pts] / abs_jsobel[pts]
c_plus = c2a * w + c1a * (1.0 - w) <= m
c1 = magnitude[:,:-1][pts[:,1:]]
c2 = magnitude[1:,:-1][pts[:-1,1:]]
c1 = magnitude[:, :-1][pts[:, 1:]]
c2 = magnitude[1:, :-1][pts[:-1, 1:]]
c_minus = c2 * w + c1 * (1.0 - w) <= m
cc = np.logical_and(c_plus,c_minus)
local_maxima[pts] = np.logical_and(c_plus, c_minus)
local_maxima[pts] = c_plus & c_minus
#----- 135 to 180 degrees ------
# Mix anti-diagonal and anti-horizontal
#
pts_plus = np.logical_and(isobel <= 0,
np.logical_and(jsobel >= 0,
abs_isobel >= abs_jsobel))
pts_minus = np.logical_and(isobel >= 0,
np.logical_and(jsobel <= 0,
abs_isobel >= abs_jsobel))
pts = np.logical_or(pts_plus, pts_minus)
pts = np.logical_and(eroded_mask, pts)
c1 = magnitude[:-1,:][pts[1:,:]]
c2 = magnitude[:-1,1:][pts[1:,:-1]]
pts_plus = (isobel <= 0) & (jsobel >= 0) & (abs_isobel >= abs_jsobel)
pts_minus = (isobel >= 0) & (jsobel <= 0) & (abs_isobel >= abs_jsobel)
pts = pts_plus | pts_minus
pts = eroded_mask & pts
c1 = magnitude[:-1, :][pts[1:, :]]
c2 = magnitude[:-1, 1:][pts[1:, :-1]]
m = magnitude[pts]
w = abs_jsobel[pts] / abs_isobel[pts]
c_plus = c2 * w + c1 * (1 - w) <= m
c1 = magnitude[1:,:][pts[:-1,:]]
c1 = magnitude[1:, :][pts[:-1, :]]
c2 = magnitude[1:,:-1][pts[:-1,1:]]
c_minus = c2 * w + c1 * (1 - w) <= m
local_maxima[pts] = np.logical_and(c_plus, c_minus)
local_maxima[pts] = c_plus & c_minus
#
#---- Create two masks at the two thresholds.
#
high_mask = np.logical_and(local_maxima, magnitude >= high_threshold)
low_mask = np.logical_and(local_maxima, magnitude >= low_threshold)
high_mask = local_maxima & (magnitude >= high_threshold)
low_mask = local_maxima & (magnitude >= low_threshold)
#
# Segment the low-mask, then only keep low-segments that have
# some high_mask component in them
#
labels,count = label(low_mask, np.ndarray((3,3),bool))
labels,count = label(low_mask, np.ndarray((3, 3),bool))
if count == 0:
return low_mask