RFCT Break out feature computation

Author: luispedro

ch10/README.rst

@@ -29,8 +29,9 @@ 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
+features.py
+    Contains the ``edginess_sobel`` function from the book as well as a simple
+    wrapper around ``mahotas.texture.haralick``
 simple_classification.py
     Classify SimpleImageDataset with texture features + sobel feature
 figure18.py

ch10/edginess.py ch10/features.pych10/edginess.py renamed to ch10/features.py

@@ -20,3 +20,20 @@ def edginess_sobel(image):
     edges = mh.sobel(image, just_filter=True)
     edges = edges.ravel()
     return np.sqrt(np.dot(edges, edges))
+
+def texture(im):
+    '''Compute features for an image
+
+    Parameters
+    ----------
+    im : ndarray
+
+    Returns
+    -------
+    fs : ndarray
+        1-D array of features
+    '''
+    im = im.astype(np.uint8)
+    return mh.features.haralick(im).mean(0)
+
+

ch10/simple_classification.py

@@ -10,27 +10,11 @@
 from sklearn.linear_model.logistic import LogisticRegression
 import numpy as np
 from glob import glob
-from edginess import edginess_sobel
+from features import texture, edginess_sobel
 
 basedir = '../SimpleImageDataset/'
 
 
-def features_for(im):
-    '''Compute features for an image
-
-    Parameters
-    ----------
-    im : str
-        filepath for image to process
-
-    Returns
-    -------
-    fs : ndarray
-        1-D array of features
-    '''
-    im = mh.imread(im, as_grey=True).astype(np.uint8)
-    return mh.features.haralick(im).mean(0)
-
 haralicks = []
 sobels = []
 labels = []
@@ -40,8 +24,9 @@ def features_for(im):
 # Use glob to get all the images
 images = glob('{}/*.jpg'.format(basedir))
 for fname in images:
-    haralicks.append(features_for(fname))
-    sobels.append(edginess_sobel(mh.imread(fname, as_grey=True)))
+    im = mh.imread(fname, as_grey=True)
+    haralicks.append(texture(im))
+    sobels.append(edginess_sobel(im))
 
     # Files are named like building00.jpg, scene23.jpg...
     labels.append(fname[:-len('xx.jpg')])