def load_characters(neighbours, blur_scale, verbose=0):
chars_file = 'characters_%s_%s.dat' % (blur_scale, neighbours)
if exists(chars_file):
print 'Loading characters...'
chars = fload(chars_file)
else:
print 'Going to generate character objects...'
chars = []
for char in sorted(listdir(IMAGES_FOLDER)):
count = 0
for image in sorted(listdir(IMAGES_FOLDER + char)):
image = GrayscaleImage(IMAGES_FOLDER + char + '/' + image)
norm = NormalizedCharacterImage(image, blur=blur_scale, \
height=NORMALIZED_HEIGHT)
character = Character(char, [], norm)
character.get_single_cell_feature_vector(neighbours)
chars.append(character)
count += 1
if verbose:
print 'Loaded character %s %d times' % (char, count)
if verbose:
print 'Saving characters...'
fdump(chars, chars_file)
return chars
def load_test_set(neighbours, blur_scale, verbose=0):
test_set_file = 'test_set_%s_%s.dat' % (blur_scale, neighbours)
if exists(test_set_file):
if verbose:
print 'Loading test set...'
test_set = fload(test_set_file)
if verbose:
print 'Test set:', [c.value for c in test_set]
else:
test_set = generate_sets(neighbours, blur_scale, verbose=verbose)[1]
return test_set
def load_learning_set(neighbours, blur_scale, verbose=0):
learning_set_file = 'learning_set_%s_%s.dat' % (blur_scale, neighbours)
if exists(learning_set_file):
if verbose:
print 'Loading learning set...'
learning_set = fload(learning_set_file)
if verbose:
print 'Learning set:', [c.value for c in learning_set]
else:
learning_set = generate_sets(neighbours, blur_scale, \
verbose=verbose)[0]
return learning_set