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pep8, removed unused import and variables

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This commit is contained in:
Andreas Mueller
2011-09-26 19:29:44 +02:00
parent fcf4c41bd6
commit 8e33b17b12
2 changed files with 33 additions and 32 deletions
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scikits/image/filter/canny.py
+17 -18
View File
@@ -14,7 +14,7 @@ Original author: Lee Kamentsky
import numpy as np
import scipy.ndimage as ndi
from scipy.ndimage import (gaussian_filter, convolve,
from scipy.ndimage import (gaussian_filter,
generate_binary_structure, binary_erosion, label)
@@ -25,10 +25,10 @@ def smooth_with_function_and_mask(image, function, mask):
----------
image : array
The image to smooth
function : callable
A function that takes an image and returns a smoothed image
mask : array
Mask with 1's for significant pixels, 0 for masked pixels
@@ -37,11 +37,11 @@ def smooth_with_function_and_mask(image, function, mask):
This function calculates the fractional contribution of masked pixels
by applying the function to the mask (which gets you the fraction of
the pixel data that's due to significant points). We then mask the image
and apply the function. The resulting values will be lower by the bleed-over
fraction, so you can recalibrate by dividing by the function on the mask
to recover the effect of smoothing from just the significant pixels.
and apply the function. The resulting values will be lower by the
bleed-over fraction, so you can recalibrate by dividing by the function
on the mask to recover the effect of smoothing from just the significant
pixels.
"""
not_mask = np.logical_not(mask)
bleed_over = function(mask.astype(float))
masked_image = np.zeros(image.shape, image.dtype)
masked_image[mask] = image[mask]
@@ -58,10 +58,10 @@ def canny(image, sigma=1., low_threshold=.1, high_threshold=.2, mask=None):
image : array_like, dtype=float
The greyscale input image to detect edges on; should be normalized to 0.0
to 1.0.
sigma : float
The standard deviation of the Gaussian filter
low_threshold : float
The lower bound for hysterisis thresholding (linking edges)
@@ -112,7 +112,7 @@ def canny(image, sigma=1., low_threshold=.1, high_threshold=.2, mask=None):
# because who knows what lies beyond the edge of the image?
#
if image.ndim!=2:
if image.ndim != 2:
raise TypeError("The input 'image' must be a two dimensional array.")
if mask is None:
@@ -138,7 +138,7 @@ def canny(image, sigma=1., low_threshold=.1, high_threshold=.2, mask=None):
# Assign each point to have a normal of 0-45 degrees, 45-90 degrees,
# 90-135 degrees and 135-180 degrees.
#
local_maxima = np.zeros(image.shape,bool)
local_maxima = np.zeros(image.shape, bool)
#----- 0 to 45 degrees ------
pts_plus = (isobel >= 0) & (jsobel >= 0) & (abs_isobel >= abs_jsobel)
pts_minus = (isobel <= 0) & (jsobel <= 0) & (abs_isobel >= abs_jsobel)
@@ -187,7 +187,6 @@ def canny(image, sigma=1., low_threshold=.1, high_threshold=.2, mask=None):
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] = c_plus & c_minus
#----- 135 to 180 degrees ------
# Mix anti-diagonal and anti-horizontal
@@ -202,7 +201,7 @@ def canny(image, sigma=1., low_threshold=.1, high_threshold=.2, mask=None):
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:]]
c2 = magnitude[1:, :-1][pts[:-1, 1:]]
c_minus = c2 * w + c1 * (1 - w) <= m
local_maxima[pts] = c_plus & c_minus
#
@@ -214,14 +213,14 @@ def canny(image, sigma=1., low_threshold=.1, high_threshold=.2, mask=None):
# 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
sums = (np.array(ndi.sum(high_mask,labels,
np.arange(count,dtype=np.int32) + 1),
sums = (np.array(ndi.sum(high_mask, labels,
np.arange(count, dtype=np.int32) + 1),
copy=False, ndmin=1))
good_label = np.zeros((count + 1,),bool)
good_label = np.zeros((count + 1,), bool)
good_label[1:] = sums > 0
output_mask = good_label[labels]
return output_mask
scikits/image/filter/tests/test_canny.py
+16 -14
View File
@@ -3,22 +3,23 @@ import numpy as np
from scipy.ndimage import binary_dilation, binary_erosion
import scikits.image.filter as F
class TestCanny(unittest.TestCase):
def test_00_00_zeros(self):
'''Test that the Canny filter finds no points for a blank field'''
result = F.canny(np.zeros((20,20)), 4, 0, 0, np.ones((20,20),bool))
result = F.canny(np.zeros((20, 20)), 4, 0, 0, np.ones((20, 20), bool))
self.assertFalse(np.any(result))
def test_00_01_zeros_mask(self):
'''Test that the Canny filter finds no points in a masked image'''
result = (F.canny(np.random.uniform(size=(20,20)), 4, 0, 0,
result = (F.canny(np.random.uniform(size=(20, 20)), 4, 0, 0,
np.zeros((20, 20), bool)))
self.assertFalse(np.any(result))
def test_01_01_circle(self):
'''Test that the Canny filter finds the outlines of a circle'''
i, j = np.mgrid[-200:200, -200:200].astype(float) / 200
c = np.abs(np.sqrt( i * i + j * j) - .5) < .02
c = np.abs(np.sqrt(i * i + j * j) - .5) < .02
result = F.canny(c.astype(float), 4, 0, 0, np.ones(c.shape, bool))
#
# erode and dilate the circle to get rings that should contain the
@@ -30,19 +31,20 @@ class TestCanny(unittest.TestCase):
self.assertTrue(np.all(cde[result]))
#
# The circle has a radius of 100. There are two rings here, one
# for the inside edge and one for the outside. So that's 100 * 2 * 2 * 3
# for those places where pi is still 3. The edge contains both pixels
# if there's a tie, so we bump the count a little.
#
# for the inside edge and one for the outside. So that's
# 100 * 2 * 2 * 3 for those places where pi is still 3.
# The edge contains both pixels if there's a tie, so we
# bump the count a little.
point_count = np.sum(result)
self.assertTrue(point_count > 1200)
self.assertTrue(point_count < 1600)
def test_01_02_circle_with_noise(self):
'''Test that the Canny filter finds the circle outlines in a noisy image'''
'''Test that the Canny filter finds the circle outlines
in a noisy image'''
np.random.seed(0)
i,j = np.mgrid[-200:200, -200:200].astype(float) / 200
c = np.abs(np.sqrt( i * i + j * j) - .5) < .02
i, j = np.mgrid[-200:200, -200:200].astype(float) / 200
c = np.abs(np.sqrt(i * i + j * j) - .5) < .02
cf = c.astype(float) * .5 + np.random.uniform(size=c.shape) * .5
result = F.canny(cf, 4, .1, .2, np.ones(c.shape, bool))
#
@@ -58,4 +60,4 @@ class TestCanny(unittest.TestCase):
self.assertTrue(point_count < 1600)
def test_image_shape(self):
self.assertRaises(TypeError,F.canny,np.zeros((20, 20, 20)), 4, 0, 0)
self.assertRaises(TypeError, F.canny, np.zeros((20, 20, 20)), 4, 0, 0)