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Added quantile_threshold option to canny edge detection

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This allows you to specify the high and low thresholds as quantiles of the edge magnitude
image, rather than as absolute edge magnitude values
This commit is contained in:
Robin Wilson
2015-08-28 22:30:42 +01:00
parent 13b2170dfd
commit b29ad8ef6d
2 changed files with 40 additions and 1 deletions
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skimage/feature/_canny.py
+13 -1
View File
@@ -50,7 +50,8 @@ def smooth_with_function_and_mask(image, function, mask):
return output_image
def canny(image, sigma=1., low_threshold=None, high_threshold=None, mask=None):
def canny(image, sigma=1., low_threshold=None, high_threshold=None, mask=None,
quantile_threshold=False):
"""Edge filter an image using the Canny algorithm.
Parameters
@@ -67,6 +68,9 @@ def canny(image, sigma=1., low_threshold=None, high_threshold=None, mask=None):
If None, high_threshold is set to 20% of dtype's max.
mask : array, dtype=bool, optional
Mask to limit the application of Canny to a certain area.
quantile_threshold : bool, optional
If True then treat low_threshold and high_threshold as quantiles of the
edge magnitude image, rather than absolute edge magnitude values.
Returns
-------
@@ -246,6 +250,14 @@ def canny(image, sigma=1., low_threshold=None, high_threshold=None, mask=None):
c2 = magnitude[1:, :-1][pts[:-1, 1:]]
c_minus = c2 * w + c1 * (1 - w) <= m
local_maxima[pts] = c_plus & c_minus
#
#---- If quantile_threshold is set then calculate the thresholds to use
#
if quantile_threshold:
high_threshold = np.percentile(magnitude, 100.0 * high_threshold)
low_threshold = np.percentile(magnitude, 100.0 * low_threshold)
#
#---- Create two masks at the two thresholds.
#
skimage/feature/tests/test_canny.py
+27
View File
@@ -1,7 +1,10 @@
import unittest
import numpy as np
from numpy.testing import assert_equal
from scipy.ndimage import binary_dilation, binary_erosion
import skimage.feature as F
from skimage import filters, data
from skimage import img_as_float
class TestCanny(unittest.TestCase):
@@ -66,3 +69,27 @@ class TestCanny(unittest.TestCase):
result1 = F.canny(np.zeros((20, 20)), 4, 0, 0, np.ones((20, 20), bool))
result2 = F.canny(np.zeros((20, 20)), 4, 0, 0)
self.assertTrue(np.all(result1 == result2))
def test_quantile_threshold():
image = img_as_float(data.camera()[::50,::50])
# Correct output produced manually with quantiles
# of 0.8 and 0.6 for high and low respectively
correct_output = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool)
result = F.canny(image, low_threshold=0.6, high_threshold=0.8, quantile_threshold=True)
assert_equal(result, correct_output)