DOC Better documentation

Author: luispedro

ch10/README.rst

@@ -0,0 +1,38 @@
+==========
+Chapter 10
+==========
+
+Support code for *Chapter 10: Pattern Recognition & Computer Vision*
+
+Data
+----
+
+This chapter relies on a publicly available dataset (which can be downloaded
+using the ``download.sh`` script inside the ``data/`` directory) as well the
+dataset that is packaged with the repository at ``../SimpleImageDataset/``.
+
+Running ``download.sh`` will retrieve the other dataset into a directory
+``AnimTransDistr/``.
+
+Scripts
+-------
+
+threshold.py
+    Threshold building image with both Otsu and Ridley-Calvard threshold
+lena-ring.py
+    Show Lena in a sharp ring around a soft focus image
+figure5_6.py
+    Computes figures 5 and 6 in the book
+figure9.py
+    Compute Sobel images
+figure10.py
+    Just paste two images next to each others
+figure13.py
+    Demonstrate salt&pepper effect
+edginess.py
+    Contains the ``edginess_sobel`` function from the book
+simple_classification.py
+    Classify SimpleImageDataset with texture features + sobel feature
+figure18.py
+    Classify ``AnimTransDistr`` with both texture and SURF features.
+

ch10/figure5_6.py

@@ -9,9 +9,16 @@
 import mahotas as mh
 image = mh.imread('../SimpleImageDataset/building05.jpg')
 image = mh.colors.rgb2gray(image)
-im8 = mh.gaussian_filter(image, 8)
+
+# Compute Gaussian filtered versions with increasing kernel widths
+im8  = mh.gaussian_filter(image,  8)
 im16 = mh.gaussian_filter(image, 16)
 im32 = mh.gaussian_filter(image, 32)
+
+# We now build a composite image with three panels:
+#
+# [ IM8 | | IM16 | | IM32 ]
+
 h, w = im8.shape
 canvas = np.ones((h, 3 * w + 256), np.uint8)
 canvas *= 255
@@ -20,6 +27,11 @@
 canvas[:, -w:] = im32
 mh.imsave('../1400OS_10_05+.jpg', canvas[:, ::2])
 
+# Threshold the image
+# We need to first stretch it to convert to an integer image
 im32 = mh.stretch(im32)
 ot32 = mh.otsu(im32)
-mh.imsave('../1400OS_10_06+.jpg', (im32 > ot32).astype(np.uint8) * 255)
+
+# Convert to 255 np.uint8 to match the other images
+im255 = 255 * (im32 > ot32).astype(np.uint8)
+mh.imsave('../1400OS_10_06+.jpg', im255)

ch10/figure9.py

@@ -9,14 +9,17 @@
 import mahotas as mh
 image = mh.imread('../SimpleImageDataset/building05.jpg')
 image = mh.colors.rgb2gray(image, dtype=np.uint8)
+
+# We need to downsample ``image`` so that the details are visibly pixelated.
+# This exaggerates the effect so that the result is clear
 image = image[::4, ::4]
 thresh = mh.sobel(image)
 filtered = mh.sobel(image, just_filter=True)
 
 thresh = mh.dilate(thresh, np.ones((7, 7)))
 filtered = mh.dilate(mh.stretch(filtered), np.ones((7, 7)))
 
-
+# Paste the thresholded and non-thresholded versions of the image side-by-side
 h, w = thresh.shape
 canvas = 255 * np.ones((h, w * 2 + 64), np.uint8)
 canvas[:, :w] = thresh * 255