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from jug import TaskGenerator

from glob import glob

import mahotas as mh

@TaskGenerator

def compute_texture(im):

from features import texture

imc = mh.imread(im)

return texture(mh.colors.rgb2gray(imc))

@TaskGenerator

def chist_file(fname):

from features import chist

im = mh.imread(fname)

return chist(im)

import numpy as np

to_array = TaskGenerator(np.array)

hstack = TaskGenerator(np.hstack)

haralicks = []

chists = []

labels = []

# Change this variable to point to

# the location of the dataset is on disk

basedir = '../SimpleImageDataset/'

# Use glob to get all the images

images = glob('{}/*.jpg'.format(basedir))

for fname in sorted(images):

haralicks.append(compute_texture(fname))

chists.append(chist_file(fname))

# The class is encoded in the filename as xxxx00.jpg

labels.append(fname[:-len('00.jpg')])

haralicks = to_array(haralicks)

chists = to_array(chists)

labels = to_array(labels)

@TaskGenerator

def accuracy(features, labels):

from sklearn.linear_model import LogisticRegression

from sklearn.pipeline import Pipeline

from sklearn.preprocessing import StandardScaler

from sklearn import cross_validation

clf = Pipeline([('preproc', StandardScaler()),

('classifier', LogisticRegression())])

cv = cross_validation.LeaveOneOut(len(features))

scores = cross_validation.cross_val_score(

clf, features, labels, cv=cv)

return scores.mean()

scores_base = accuracy(haralicks, labels)

scores_chist = accuracy(chists, labels)

combined = hstack([chists, haralicks])

scores_combined = accuracy(combined, labels)

@TaskGenerator

def print_results(scores):

with open('results.image.txt', 'w') as output:

for k,v in scores:

output.write('Accuracy [{}]: {:.1%}\n'.format(

k, v.mean()))

print_results([

('base', scores_base),

('chists', scores_chist),

('combined' , scores_combined),

])

@TaskGenerator

def compute_lbp(fname):

from mahotas.features import lbp

imc = mh.imread(fname)

im = mh.colors.rgb2grey(imc)

return lbp(im, radius=8, points=6)

lbps = []

for fname in sorted(images):

# the rest of the loop as before

lbps.append(compute_lbp(fname))

lbps = to_array(lbps)

scores_lbps = accuracy(lbps, labels)

combined_all = hstack([chists, haralicks, lbps])

scores_combined_all = accuracy(combined_all, labels)

print_results([

('base', scores_base),

('chists', scores_chist),

('lbps', scores_lbps),

('combined' , scores_combined),

('combined_all' , scores_combined_all),

])