def lexicon(self):
'''
returns the closed lexicon (train + test)
'''
unique_word_strings = np.unique(self.label_encoder.classes_)
unique_word_embeddings = build_phoc_descriptor(words=unique_word_strings,
phoc_unigrams=self.unigrams,
unigram_levels=self.embedding_config['levels'])
class_ids = self.label_encoder.transform(unique_word_strings)
return unique_word_strings, unique_word_embeddings, class_ids
def get_qry_strings(self):
'''
returns query_strings, embeddings, ids for qbs word spotting
'''
# load predefined queries
qry_string_path = self.bot_root_dir + 'Botany_Test_QryStrings.lst'
qry_string_lines = LineListIO.read_list(qry_string_path)
qry_strings = [q.lower() for q in qry_string_lines]
qry_string_ids = self.label_encoder.transform(qry_strings)
qry_string_embeddings = build_phoc_descriptor(
words=qry_strings,
phoc_unigrams=self.unigrams,
unigram_levels=self.embedding_config['levels'])
return qry_strings, qry_string_embeddings, qry_string_ids
def get_qry_strings(self):
'''
returns query_strings, embeddings, ids for qbs word spotting
'''
qry_strings = np.unique([word.get_transcription() for word in self.test_list])
qry_string_embeddings = build_phoc_descriptor(words=qry_strings,
phoc_unigrams=self.unigrams,
unigram_levels=self.embedding_config['levels'])
# make sure all strings result in a valid phoc encoding (no zero strings)
non_zero_embeddings = np.where((np.sum(qry_string_embeddings, axis=1) > 0))
qry_strings = qry_strings[non_zero_embeddings]
qry_string_embeddings = qry_string_embeddings[non_zero_embeddings]
qry_string_ids = self.label_encoder.transform(qry_strings)
return qry_strings, qry_string_embeddings, qry_string_ids
def lexicon(self):
'''
returns the closed lexicon (train + test)
'''
unique_word_strings = np.unique(self.label_encoder.classes_)
unique_word_embeddings = build_phoc_descriptor(words=unique_word_strings,
phoc_unigrams=self.unigrams,
unigram_levels=self.embedding_config['levels'])
# make sure all strings result in a valid phoc encoding (no zero strings)
non_zero_embeddings = np.where((np.sum(unique_word_embeddings, axis=1) > 0))
unique_word_strings = unique_word_strings[non_zero_embeddings]
unique_word_embeddings = unique_word_embeddings[non_zero_embeddings]
class_ids = self.label_encoder.transform(unique_word_strings)
return unique_word_strings, unique_word_embeddings, class_ids
def get_qry_strings(self):
'''
returns query_strings, embeddings, ids for qbs word spotting
'''
qry_strings = np.unique([word.get_transcription() for word in self.test_list])
with open(os.path.join(self.iam_root_dir, 'swIAM.txt')) as sw_file:
stop_words = sw_file.read().split(',')
qry_strings = np.array([word for word in qry_strings if word not in stop_words])
qry_string_embeddings = build_phoc_descriptor(words=qry_strings,
phoc_unigrams=self.unigrams,
unigram_levels=self.embedding_config['levels'])
# make sure all strings result in a valid phoc encoding (no zero strings)
non_zero_embeddings = np.where((np.sum(qry_string_embeddings, axis=1) > 0))
qry_strings = qry_strings[non_zero_embeddings]
qry_string_embeddings = qry_string_embeddings[non_zero_embeddings]
qry_string_ids = self.label_encoder.transform(qry_strings)
return qry_strings, qry_string_embeddings, qry_string_ids
def __init__(self,
rimes_root_dir,
embedding_config={
'unigrams': 'all',
'type': 'phoc',
'levels': (1, 2, 4, 8)
},
ignore_diacrits=False,
fixed_image_size=None,
min_image_width_height=30):
'''
Constructor
@param rimes_root_dir: full path to the Rimes root dir
@param embedding_config: configuration of embedding (only phoc available)
-unigrams: alnum36 or all
-type: phoc
-levels: embedding levels
@param ignore_diacrits: diacrits are mapped to ascii chars
@param fixed_image_size: resize images to a fixed size
@param min_image_width_height: the minimum height or width a word image
has to have
'''
# sanity checks
if embedding_config['unigrams'] not in ['alnum36', 'all']:
raise ValueError('Unknown unigram definition')
if embedding_config['type'] not in ['phoc']:
raise ValueError('embedding must be phoc')
# class members
self.embedding_config = embedding_config
self.fixed_image_size = fixed_image_size
self.min_image_width_height = min_image_width_height
# load the dataset
self.train_list, self.test_list = DatasetLoader.load_rimes(
path=rimes_root_dir, ignore_diacrits=ignore_diacrits)
# compute string embeddings
# extract unigrams from train split
if embedding_config['unigrams'] == 'alnum36':
self.unigrams = [
chr(i) for i in np.hstack([
np.arange(ord('a'),
ord('z') + 1),
np.arange(ord('0'),
ord('9') + 1)
])
]
elif embedding_config['unigrams'] == 'all':
self.unigrams = get_unigrams_from_strings([
word.get_transcription()
for word in self.train_list + self.test_list
])
else:
raise ValueError('Unknown unigram type')
if embedding_config['type'] == 'phoc':
self.train_embeddings = build_phoc_descriptor(
words=[word.get_transcription() for word in self.train_list],
phoc_unigrams=self.unigrams,
unigram_levels=embedding_config['levels'])
self.test_embeddings = build_phoc_descriptor(
words=[word.get_transcription() for word in self.test_list],
phoc_unigrams=self.unigrams,
unigram_levels=embedding_config['levels'])
else:
raise ValueError('Unknown embedding type')
# compute a mapping from class string to class id
self.label_encoder = LabelEncoder()
self.label_encoder.fit([
word.get_transcription()
for word in self.train_list + self.test_list
])
def __init__(self,
gw_root_dir,
split_idx=1,
embedding_config={
'unigrams': 'alnum36',
'type': 'phoc',
'levels': (1, 2, 4, 8)
},
fixed_image_size=None,
min_image_width_height=30):
'''
Constructor
@param gw_root_dir: full path to the GW root dir
@param split_idx: the index of the CV split to be used
@param embedding_config: configuration of embedding (only phoc available)
-unigrams: alnum36 or all
-type: phoc
-levels: embedding levels
@param fixed_image_size: resize images to a fixed size
@param min_image_width_height: the minimum height or width a word image
has to have
'''
# sanity checks
if embedding_config['unigrams'] not in ['alnum36', 'all']:
raise ValueError('Unknown unigram definition')
if embedding_config['type'] not in ['phoc']:
raise ValueError('embedding must be phoc')
if split_idx is None:
raise ValueError('You need to choose a cv split')
# class members
self.embedding_config = embedding_config
self.fixed_image_size = fixed_image_size
self.min_image_width_height = min_image_width_height
# load split ids for cross validation
split_ids = np.load(os.path.join(gw_root_dir,
'almazan_cv_indices.npy'))
# load the dataset
word_list = DatasetLoader.load_gw(path=gw_root_dir)
self.train_list = [
word for word, split_id in zip(word_list, split_ids)
if split_id != split_idx
]
self.test_list = [
word for word, split_id in zip(word_list, split_ids)
if split_id == split_idx
]
# compute string embeddings
# extract unigrams from train split
if embedding_config['unigrams'] == 'alnum36':
self.unigrams = [
chr(i) for i in np.hstack([
np.arange(ord('a'),
ord('z') + 1),
np.arange(ord('0'),
ord('9') + 1)
])
]
elif embedding_config['unigrams'] == 'all':
self.unigrams = get_unigrams_from_strings([
word.get_transcription()
for word in self.train_list + self.test_list
])
else:
raise ValueError('Unknown unigram type')
if embedding_config['type'] == 'phoc':
self.train_embeddings = build_phoc_descriptor(
words=[word.get_transcription() for word in self.train_list],
phoc_unigrams=self.unigrams,
unigram_levels=embedding_config['levels'])
self.test_embeddings = build_phoc_descriptor(
words=[word.get_transcription() for word in self.test_list],
phoc_unigrams=self.unigrams,
unigram_levels=embedding_config['levels'])
else:
raise ValueError('Unknown embedding type')
# compute a mapping from class string to class id
self.label_encoder = LabelEncoder()
self.label_encoder.fit([
word.get_transcription()
for word in self.train_list + self.test_list
])
def __init__(self,
iam_root_dir,
embedding_config = {'unigrams': 'alnum36', 'type': 'phoc', 'levels': (1, 2, 4, 8)},
fixed_image_size=None,
min_image_width_height=30,
remove_punctuation=True):
'''
Constructor
@param iam_root_dir: full path to the GW root dir
@param embedding_config: configuration of embedding (only phoc available)
-unigrams: alnum36 or all
-type: phoc
-levels: embedding levels
@param fixed_image_size: resize images to a fixed size
@param min_image_width_height: the minimum height or width a word image
has to have
@param remove_punctuation: remove punctuation elements in train and test list
'''
# sanity checks
if embedding_config['unigrams'] not in ['alnum36', 'all']:
raise ValueError('Unknown unigram definition')
if embedding_config['type'] not in ['phoc']:
raise ValueError('embedding must be phoc')
# class members
self.embedding_config = embedding_config
self.fixed_image_size = fixed_image_size
self.min_image_width_height = min_image_width_height
self.iam_root_dir = iam_root_dir
# load the dataset
self.train_list, self.test_list = DatasetLoader.load_iam(path = iam_root_dir)
if remove_punctuation:
punctuation = string.punctuation
self.train_list = WordList([elem for elem in self.train_list if elem.get_transcription() not in punctuation])
self.test_list = WordList([elem for elem in self.test_list if elem.get_transcription() not in punctuation])
# compute string embeddings
# extract unigrams from train split
if embedding_config['unigrams'] == 'alnum36':
self.unigrams = [chr(i) for i in np.hstack([np.arange(ord('a'), ord('z') + 1),
np.arange(ord('0'), ord('9') + 1)])]
elif embedding_config['unigrams'] == 'all':
self.unigrams = get_unigrams_from_strings([word.get_transcription()
for word in self.train_list + self.test_list])
else:
raise ValueError('Unknown unigram type')
if embedding_config['type'] == 'phoc':
self.train_embeddings = build_phoc_descriptor(words = [word.get_transcription() for word in self.train_list],
phoc_unigrams=self.unigrams,
unigram_levels=embedding_config['levels'])
self.test_embeddings = build_phoc_descriptor(words = [word.get_transcription() for word in self.test_list],
phoc_unigrams=self.unigrams,
unigram_levels=embedding_config['levels'])
else:
raise ValueError('Unknown embedding type')
# compute a mapping from class string to class id
self.label_encoder = LabelEncoder()
self.label_encoder.fit([word.get_transcription() for word in self.train_list + self.test_list])