ENH Add code for image neighbors

This is a new section in the second edition, but it makes sense to
demonstrate the possibilities of images features in some other task than
classification.

Author: luispedro

ch10/README.rst

@@ -17,23 +17,15 @@ Running ``download.sh`` will retrieve the other dataset into a directory
 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
 features.py
-    Contains the ``edginess_sobel`` function from the book as well as a simple
+    Contains the color histogram function from the book as well as a simple
     wrapper around ``mahotas.texture.haralick``
 simple_classification.py
     Classify SimpleImageDataset with texture features + color histogram features
-figure18.py
+large_classification.py
     Classify ``AnimTransDistr`` with both texture and SURF features.
+neighbors.py
+    Computes image neighbors as well as the neighbor figure from the book.
 

ch10/neighbors.py

@@ -0,0 +1,62 @@
+# This code is supporting material for the book
+# Building Machine Learning Systems with Python
+# by Willi Richert and Luis Pedro Coelho
+# published by PACKT Publishing
+
+import numpy as np
+import mahotas as mh
+from glob import glob
+from features import texture, color_histogram
+from matplotlib import pyplot as plt
+from sklearn.preprocessing import StandardScaler
+from scipy.spatial import distance
+
+basedir = '../SimpleImageDataset/'
+
+
+haralicks = []
+chists = []
+
+print('Computing features...')
+# Use glob to get all the images
+images = glob('{}/*.jpg'.format(basedir))
+# We sort the images to ensure that they are always processed in the same order
+# Otherwise, this would introduce some variation just based on the random
+# ordering that the filesystem uses
+images.sort()
+
+for fname in images:
+    imc = mh.imread(fname)
+    imc = imc[200:-200,200:-200]
+    haralicks.append(texture(mh.colors.rgb2grey(imc)))
+    chists.append(color_histogram(imc))
+
+haralicks = np.array(haralicks)
+chists = np.array(chists)
+features = np.hstack([chists, haralicks])
+
+print('Computing neighbors...')
+sc = StandardScaler()
+dists = distance.squareform(distance.pdist(features))
+
+print('Plotting...')
+fig, axes = plt.subplots(2, 9, figsize=(16,8))
+
+# Remove ticks from all subplots
+for ax in axes.flat:
+    ax.set_xticks([])
+    ax.set_yticks([])
+
+for ci,i in enumerate(range(0,90,10)):
+    left = images[i]
+    dists_left = dists[i]
+    right = dists_left.argsort()
+    right = right[1]
+    right = images[right]
+    left = mh.imread(left)
+    right = mh.imread(right)
+    axes[0, ci].imshow(left)
+    axes[1, ci].imshow(right)
+
+fig.tight_layout()
+fig.savefig('figure_neighbors.png', dpi=300)