# 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()

features = sc.fit_transform(features)

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[0] is the same as left[i], so pick the next closest element

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)