Building Machine Learning Systems with Python - Richert, Coelho

Computer Vision – Pattern Recognition
The just_filter=True argument is necessary, otherwise thresholding is performed
and you get an estimate of where the edges are. The following screenshot shows the
result of applying the filter (so that lighter areas are edgier) on the left and the result
of thresholding on the right:
Based on this operator, we may want to define a global feature as the overall
edginess of the result:
def edginess_sobel(image):
edges = mh.sobel(image, just_filter=True)
edges = edges.ravel()
return np.sqrt(np.dot(edges, edges))
In the last line, we used a trick to compute the root mean square—using the inner
product function np.dot is equivalent to writing np.sum(edges ** 2), but much
faster (we just need to make sure we unraveled the array first). Naturally, we
could have thought up many different ways to achieve similar results. Using the
thresholding operation and counting the fraction of pixels above threshold would
be another obvious example.
We can add this feature to the previous pipeline very easily:
features = []
for im in images:
image = mh.imread(im)
features.append(np.concatenate(
mh.features.haralick(im).mean(0),
# Build a 1-element list with our feature to match
expectations
# of np.concatenate
[edginess_sobel(im)],
))
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