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ENH Faster perimeter computation

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Measured a 30% speedup on a 640x640 image. Fundamentally, this makes
fewer passes over the image; so it should never be worse that the
previous implementation.
This commit is contained in:
Luis Pedro Coelho
2013-10-01 13:07:17 +02:00
parent 4ab166bba4
commit f81fbf5b25
1 changed files with 19 additions and 7 deletions
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skimage/measure/_regionprops.py
+19 -7
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@@ -513,28 +513,40 @@ def perimeter(image, neighbourhood=4):
a Perimeter Estimator. The Queen's University of Belfast.
http://www.cs.qub.ac.uk/~d.crookes/webpubs/papers/perimeter.doc
"""
from skimage.exposure import histogram
if neighbourhood == 4:
strel = STREL_4
else:
strel = STREL_8
image = image.astype(np.uint8)
eroded_image = ndimage.binary_erosion(image, strel, border_value=0)
border_image = image - eroded_image
# perimeter contribution: corresponding values in convolved image
perimeter_weights = {
perimeter_weights_matrix = {
1: (5, 7, 15, 17, 25, 27),
sqrt(2): (21, 33),
(1 + sqrt(2)) / 2: (13, 23)
}
# specifying the perimeter_weights array inline did not make a measurable
# difference in execution time (for a 640x640 image), but made the code
# harder to follow, so we build it everytime:
perimeter_weights = np.zeros(34, float)
for v,indices in perimeter_weights_matrix.items():
for i in indices: perimeter_weights[i] = v
perimeter_image = ndimage.convolve(border_image, np.array([[10, 2, 10],
[ 2, 1, 2],
[10, 2, 10]]),
mode='constant', cval=0)
total_perimeter = 0
for weight, values in perimeter_weights.items():
num_values = 0
for value in values:
num_values += np.sum(perimeter_image == value)
total_perimeter += num_values * weight
# You can also write
# return perimeter_weights[perimeter_image].sum()
# but that was measured as taking much longer than histogram + np.dot (5x
# as much time)
perimeter_histogram,_ = histogram(perimeter_image, nbins=50)
size = min(len(perimeter_histogram), len(perimeter_weights))
total_perimeter = np.dot(perimeter_histogram[:size], perimeter_weights[:size])
return total_perimeter