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...
occurances[3, 0] += 0.5

emission[np.isnan(emission)] = 0

#im = imread('./data/and.png').mean(axis=2)
segments = find_letters(im)
Exemple #2
0
database = []


def make_gen(bits):
    for bit in bits:
        yield bit


letters = load_letters()
letters_list = {}
for i in range(26):
    letters_list[i] = []

for i, letter in enumerate(letters):
    segments = find_letters(letter)
    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()
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...
occurances[3, 0] += 0.5

emission[np.isnan(emission)] = 0

#im = imread('./data/and.png').mean(axis=2)
segments = find_letters(im)
Exemple #4
0
from hmms.analyzer import LETTER_MAP

database = []

def make_gen(bits):
    for bit in bits:
        yield bit

letters = load_letters()
letters_list = {}
for i in range(26):
    letters_list[i] = []

for i, letter in enumerate(letters):
    segments = find_letters(letter)
    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