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# 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

#

# It is made available under the MIT License

import mahotas as mh

import numpy as np

# Read in the image

im = mh.demos.load('lena')

# 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('lena-ring.jpg', ring)