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fix >80 linelength

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Olivier Debeir
2015-09-02 16:11:10 +02:00
parent 35341bed13
commit f0d2015993
1 changed files with 18 additions and 13 deletions
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doc/examples/plot_entropy.py
+18 -13
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@@ -3,20 +3,24 @@
Entropy
=======
In information theory, information entropy is the log-base-2 of the number of possible outcomes
for a message.
In information theory, information entropy is the log-base-2 of the number of
possible outcomes for a message.
For an image, local entropy is related to the complexity contained in a given neighborhood, typically defined by a
structuring element. A large number of various gray levels has a higher entropy than an homogeneous neighborhood.
For an image, local entropy is related to the complexity contained in a given
neighborhood, typically defined by a structuring element. A large number of
various gray levels has a higher entropy than an homogeneous neighborhood.
Entropy filter can detect subtle variations of local gray level distribution, in the example, the
image is composed of two surfaces with two slightly different distributions.
Entropy filter can detect subtle variations of local gray level distribution,
in the example, the image is composed of two surfaces with two slightly
different distributions.
Image center has a random distribution in the range [-14,+14] centered on 128, while the borders has a
random distribution in the range [-15,+15] centered on 128.
Image center has a random distribution in the range [-14,+14] centered on 128,
while the borders has a random distribution in the range [-15,+15] centered
on 128.
We apply the local entropy measure using a circular structuring element of radius 10. As a result, one can
detect the central square. Radius should be big enough to efficiently sample the local gray level distribution.
We apply the local entropy measure using a circular structuring element of
radius 10. As a result, one can detect the central square. Radius should be big
enough to efficiently sample the local gray level distribution.
In the second example, the local entropy is used to detect image texture.
@@ -29,10 +33,11 @@ from skimage.util import img_as_ubyte
from skimage.filters.rank import entropy
from skimage.morphology import disk
noise_mask = 28*np.ones((128, 128), dtype=np.uint8)
noise_mask = 28 * np.ones((128, 128), dtype=np.uint8)
noise_mask[32:-32, 32:-32] = 30
noise = (noise_mask*np.random.random(noise_mask.shape)-.5*noise_mask).astype(np.uint8)
noise = (noise_mask * np.random.random(noise_mask.shape) - .5 *
noise_mask).astype(np.uint8)
img = noise + 128
radius = 10
@@ -52,7 +57,7 @@ plt.imshow(e)
plt.xlabel('image local entropy ($r=%d$)' % radius)
plt.colorbar()
#second example: texture detection
# second example: texture detection
image = img_as_ubyte(data.camera())