From c95ceba94151117e0fb185a72981acef0d4c69b2 Mon Sep 17 00:00:00 2001
From: dave griffiths <dave@fo.am>
Date: Mon, 23 Nov 2015 11:06:40 +0000
Subject: [PATCH] some entropy code

---
 entropy/entropy.py       | 24 ++++++++++++++++++++
 entropy/image-entropy.py | 47 ++++++++++++++++++++++++++++++++++++++++
 2 files changed, 71 insertions(+)
 create mode 100644 entropy/entropy.py
 create mode 100644 entropy/image-entropy.py

diff --git a/entropy/entropy.py b/entropy/entropy.py
new file mode 100644
index 0000000..40b2553
--- /dev/null
+++ b/entropy/entropy.py
@@ -0,0 +1,24 @@
+from __future__ import division
+import math
+
+## simple measurement of entropy
+
+def hist(source):
+    hist = {}; l = 0;
+    for e in source:
+        l += 1
+        if e not in hist:
+            hist[e] = 0
+        hist[e] += 1
+    return (l,hist)
+
+def entropy(hist,l):
+    elist = []
+    for v in hist.values():
+        c = v / l
+        elist.append(-c * math.log(c ,2))
+    return sum(elist)
+
+source = "222222222222222222122"
+(l,h) = hist(source);
+print 'Entropy:', entropy(h, l)
diff --git a/entropy/image-entropy.py b/entropy/image-entropy.py
new file mode 100644
index 0000000..168e139
--- /dev/null
+++ b/entropy/image-entropy.py
@@ -0,0 +1,47 @@
+from PIL import Image
+import numpy as np
+from matplotlib import pyplot as plt
+
+def entropy(signal):
+        '''
+        function returns entropy of a signal
+        signal must be a 1-D numpy array
+        '''
+        lensig=signal.size
+        symset=list(set(signal))
+        numsym=len(symset)
+        propab=[np.size(signal[signal==i])/(1.0*lensig) for i in symset]
+        ent=np.sum([p*np.log2(1.0/p) for p in propab])
+        return ent
+
+
+colorIm=Image.open('mo.JPG')
+greyIm=colorIm.convert('L')
+colorIm=np.array(colorIm)
+greyIm=np.array(greyIm)
+
+N=5
+S=greyIm.shape
+E=np.array(greyIm)
+for row in range(S[0]):
+        for col in range(S[1]):
+                Lx=np.max([0,col-N])
+                Ux=np.min([S[1],col+N])
+                Ly=np.max([0,row-N])
+                Uy=np.min([S[0],row+N])
+                region=greyIm[Ly:Uy,Lx:Ux].flatten()
+                E[row,col]=entropy(region)
+
+
+plt.subplot(1,3,1)
+plt.imshow(colorIm)
+
+plt.subplot(1,3,2)
+plt.imshow(greyIm, cmap=plt.cm.gray)
+
+plt.subplot(1,3,3)
+plt.imshow(E, cmap=plt.cm.jet)
+plt.xlabel('Entropy in 10x10 neighbourhood')
+plt.colorbar()
+
+plt.show()