# 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 numpy as np

import mahotas as mh

image = mh.imread('../SimpleImageDataset/building05.jpg')

image = mh.colors.rgb2gray(image)

# 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

canvas[:, :w] = im8

canvas[:, w + 128:2 * w + 128] = im16

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)

# Convert to 255 np.uint8 to match the other images

im255 = 255 * (im32 > ot32).astype(np.uint8)

mh.imsave('../1400OS_10_06+.jpg', im255)