From 35341bed139a0d17afe545cd7df355230dda86a2 Mon Sep 17 00:00:00 2001
From: odebeir <odebeir@ulb.ac.be>
Date: Sat, 29 Aug 2015 13:27:16 +0200
Subject: [PATCH] merge entropy examples+add description

---
 doc/examples/plot_entropy.py  | 45 ++++++++++++++++++++++++++++++++---
 doc/examples/plot_entropy2.py | 38 -----------------------------
 2 files changed, 42 insertions(+), 41 deletions(-)
 delete mode 100644 doc/examples/plot_entropy2.py

diff --git a/doc/examples/plot_entropy.py b/doc/examples/plot_entropy.py
index f33f26ff..f140b116 100644
--- a/doc/examples/plot_entropy.py
+++ b/doc/examples/plot_entropy.py
@@ -3,17 +3,56 @@
 Entropy
 =======
 
-Image entropy is a quantity which is used to describe the amount of information
-coded in an image.
+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.
+
+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.
+
+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.
 
 """
 import matplotlib.pyplot as plt
+import numpy as np
 
 from skimage import data
+from skimage.util import img_as_ubyte
 from skimage.filters.rank import entropy
 from skimage.morphology import disk
-from skimage.util import img_as_ubyte
 
+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)
+img = noise + 128
+
+radius = 10
+e = entropy(img, disk(radius))
+
+plt.figure(figsize=[15, 5])
+plt.subplot(1, 3, 1)
+plt.imshow(noise_mask, cmap=plt.cm.gray)
+plt.xlabel('noise mask')
+plt.colorbar()
+plt.subplot(1, 3, 2)
+plt.imshow(img, cmap=plt.cm.gray)
+plt.xlabel('noised image')
+plt.colorbar()
+plt.subplot(1, 3, 3)
+plt.imshow(e)
+plt.xlabel('image local entropy ($r=%d$)' % radius)
+plt.colorbar()
+
+#second example: texture detection
 
 image = img_as_ubyte(data.camera())
 
diff --git a/doc/examples/plot_entropy2.py b/doc/examples/plot_entropy2.py
deleted file mode 100644
index 1c596e19..00000000
--- a/doc/examples/plot_entropy2.py
+++ /dev/null
@@ -1,38 +0,0 @@
-"""
-=======
-Entropy
-=======
-
-The following example applies the local entropy measure to a noised image with a variable noise.
-
-"""
-import matplotlib.pyplot as plt
-import numpy as np
-
-from skimage.filters.rank import entropy
-from skimage.morphology import disk
-
-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)
-img = noise + 128
-
-radius = 10
-e = entropy(img, disk(radius))
-
-plt.figure(figsize=[15, 5])
-plt.subplot(1, 3, 1)
-plt.imshow(noise_mask, cmap=plt.cm.gray)
-plt.xlabel('noise mask')
-plt.colorbar()
-plt.subplot(1, 3, 2)
-plt.imshow(img, cmap=plt.cm.gray)
-plt.xlabel('noised image')
-plt.colorbar()
-plt.subplot(1, 3, 3)
-plt.imshow(e)
-plt.xlabel('image local entropy ($r=%d$)' % radius)
-plt.colorbar()
-
-plt.show()