DOC Better documentation of code

Author: luispedro

ch10/edginess.py

@@ -10,10 +10,12 @@
 
 
 defedginess_sobel(image):
-'''
- edgi = edginess_sobel(image)
+'''Measure the "edginess" of an image
+
+ image should be a 2d numpy array (an image)
+
+ Returns a floating point value which is higher the "edgier" the image is.
 
- Measure the "edginess" of an image
  '''
 edges=mh.sobel(image, just_filter=True)
 edges=edges.ravel()

ch10/figure10.py

@@ -8,6 +8,8 @@
 importnumpyasnp
 importmahotasasmh
 
+# This little script just builds an image with two examples, side-by-side:
+
 text=mh.imread("simple-dataset/text21.jpg")
 scene=mh.imread("simple-dataset/scene00.jpg")
 h, w, _=text.shape

ch10/figure13.py

@@ -9,12 +9,19 @@
 frommahotas.colorsimportrgb2grey
 importnumpyasnp
 
+# Adds a little salt-n-pepper noise to an image
+
 im=mh.imread('lenna.jpg')
 im=rgb2grey(im)
 
+# Salt & pepper arrays
 salt=np.random.random(im.shape) >.975
 pepper=np.random.random(im.shape) >.975
 
+# salt is 170 & pepper is 30
+# Some playing around showed that setting these to more extreme values looks
+# very artificial. These look nicer
+
 im=np.maximum(salt*170, mh.stretch(im))
 im=np.minimum(pepper*30+im* (~pepper), im)
 

ch10/lenna-ring.py

@@ -7,23 +7,35 @@
 
 importmahotasasmh
 importnumpyasnp
+
+# Read in the image
 im=mh.imread('lenna.jpg')
+
+# This breaks up the image into RGB channels
 r, g, b=im.transpose(2, 0, 1)
 h, w=r.shape
+
+# smooth the image per channel:
 r12=mh.gaussian_filter(r, 12.)
 g12=mh.gaussian_filter(g, 12.)
 b12=mh.gaussian_filter(b, 12.)
+
+# build back the RGB image
 im12=mh.as_rgb(r12, g12, b12)
 
 X, Y=np.mgrid[:h, :w]
 X=X-h/2.
 Y=Y-w/2.
 X/=X.max()
 Y/=Y.max()
+
+# Array C will have the highest values in the center, fading out to the edges:
+
 C=np.exp(-2.* (X**2+Y**2))
 C-=C.min()
 C/=C.ptp()
 C=C[:, :, None]
 
+# The final result is sharp in the centre and smooths out to the borders:
 ring=mh.stretch(im*C+ (1-C) *im12)
 mh.imsave('lenna-ring.jpg', ring)