from sklearn.metrics import euclidean_distances
from hmms.segment import find_letters, split_on, show_segments, find_words
from hmms.data.load import load_text_images
from hmms import vdhmms
from hmms.utils import normalise
from hmms.analyzer import LETTER_MAP
print LETTER_MAP['a']
# Because we know it... FIXME
h, w = 98, 22
voc = np.load('vocabulary.npy')
text = load_text_images()
image, _ = text.next()
image, _ = text.next()
segments = find_words(image)
el = split_on(image, segments, clean=True)
im = el[0]
# load all the probability matrices we need
transition = np.load('transition.npy')
first_letter = np.load('first_letter.npy')
last_letter = np.load('last_letter.npy')
emission = np.load('emission.npy')
occurances = np.load('occurances.npy')
# Adding this by hand...
bits = split_on(letter, segments)
letters_list[i].append(bits)
# Computes the average space taken per letter
ave_letter = []
for letter in range(26):
m = [j.shape[1] for l in letters_list[letter] for j in l]
d = [len(l) for l in letters_list[letter]]
ave_letter.append([
sum(m) / len(letters_list[letter]),
sum(d) / len(letters_list[letter])
])
# OK, now we have all the letters initialized
num = 0
texts = load_text_images()
for image, text in texts:
words = text.split()
segments = find_words(image)
bits = split_on(image, segments, clean=True)
# Just to check if we have segmented properly !
show_segments(image,
segments,
title=('./text_seg/%s' % text.replace(' ', '_')),
save=True)
if len(bits) != len(words):
print "problem with image %s" % text
continue
for im, word in zip(bits, words):
h, w = im.shape
seg = find_letters(im)
from sklearn.metrics import euclidean_distances
from hmms.segment import find_letters, split_on, show_segments, find_words
from hmms.data.load import load_text_images
from hmms import vdhmms
from hmms.utils import normalise
from hmms.analyzer import LETTER_MAP
print LETTER_MAP['a']
# Because we know it... FIXME
h, w = 98, 22
voc = np.load('vocabulary.npy')
text = load_text_images()
image, _ = text.next()
image, _ = text.next()
segments = find_words(image)
el = split_on(image, segments, clean=True)
im = el[0]
# load all the probability matrices we need
transition = np.load('transition.npy')
first_letter = np.load('first_letter.npy')
last_letter = np.load('last_letter.npy')
emission = np.load('emission.npy')
occurances = np.load('occurances.npy')
# Adding this by hand...
letters_list[i].append(bits)
# Computes the average space taken per letter
ave_letter = []
for letter in range(26):
m = [j.shape[1] for l in letters_list[letter]
for j in l]
d = [len(l) for l in letters_list[letter]]
ave_letter.append(
[sum(m) / len(letters_list[letter]),
sum(d) / len(letters_list[letter])])
# OK, now we have all the letters initialized
num = 0
texts = load_text_images()
for image, text in texts:
words = text.split()
segments = find_words(image)
bits = split_on(image, segments, clean=True)
# Just to check if we have segmented properly !
show_segments(image, segments,
title=('./text_seg/%s' % text.replace(' ', '_')),
save=True)
if len(bits) != len(words):
print "problem with image %s" % text
continue
for im, word in zip(bits, words):
h, w = im.shape
seg = find_letters(im)
el = split_on(im, seg)
