# 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
from glob import glob
from jug import TaskGenerator

# We need to use the `features` module from chapter 10.
from sys import path
path.append('../ch10')


# This is the jug-enabled version of the script ``figure18.py`` in Chapter 10

basedir = '../SimpleImageDataset/'

@TaskGenerator
def compute_texture(im):
    '''Compute features for an image

    Parameters
    ----------
    im : str
        filepath for image to process

    Returns
    -------
    fs : ndarray
        1-D array of features
    '''
    from features import texture
    imc = mh.imread(im)
    return texture(mh.colors.rgb2grey(imc))

@TaskGenerator
def chist(fname):
    from features import color_histogram
    im = mh.imread(fname)
    return color_histogram(im)

@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)


@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
    # We use logistic regression because it is very fast.
    # Feel free to experiment with other classifiers
    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()


@TaskGenerator
def print_results(scores):
    with open('results.image.txt', 'w') as output:
        for k,v in scores:
            output.write('Accuracy (LOO x-val) with Logistic Regression [{0}]: {1:.1%}\n'.format(
                k, v.mean()))


to_array = TaskGenerator(np.array)
hstack = TaskGenerator(np.hstack)

haralicks = []
chists = []
lbps = []
labels = []

# Use glob to get all the images
images = glob('{0}/*.jpg'.format(basedir))
for fname in sorted(images):
    haralicks.append(compute_texture(fname))
    chists.append(chist(fname))
    lbps.append(compute_lbp(fname))
    labels.append(fname[:-len('00.jpg')]) # The class is encoded in the filename as xxxx00.jpg

haralicks = to_array(haralicks)
chists = to_array(chists)
lbps = to_array(lbps)
labels = to_array(labels)

scores_base = accuracy(haralicks, labels)
scores_chist = accuracy(chists, labels)
scores_lbps = accuracy(lbps, labels)

combined = hstack([chists, haralicks])
scores_combined = accuracy(combined, 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),
        ])