def __init__(self,
root,
voc_type,
height,
width,
num_samples,
is_aug,
mix_data=False):
super(TOTALTEXT, self).__init__()
self.root = root
self.voc_type = voc_type
self.height = height
self.width = width
self.num_samples = num_samples
self.sampling_num_per_side = global_args.sampling_num_per_side
self.fetcher = lambda path: cv2.imread(path)
self.nid_labels = []
self.max_len = 0
# for recognition
assert voc_type in [
'LOWERCASE', 'ALLCASES', 'ALLCASES_SYMBOLS', 'LOWERCASE_SYMBOLS'
]
self.EOS = 'EOS'
self.PADDING = 'PADDING'
self.UNKNOWN = 'UNKNOWN'
self.UNKNOWN_COUNT = 0
self.voc = get_vocabulary(voc_type,
EOS=self.EOS,
PADDING=self.PADDING,
UNKNOWN=self.UNKNOWN)
self.char2id = dict(zip(self.voc, range(len(self.voc))))
self.id2char = dict(zip(range(len(self.voc)), self.voc))
self.rec_num_classes = len(self.voc)
self.lowercase = (voc_type.find('LOWERCASE') >= 0)
self.ratio = 4
label_json_path = os.path.join(self.root, 'Label.json')
labels = json.load(open(label_json_path, 'r'))
for label in labels:
nid = os.path.join(self.root, label["img"]) # i.e. image path
word = label["word"]
poly_x = label["poly_x"]
poly_y = label["poly_y"]
self.nid_labels.append((nid, word, poly_x, poly_y))
if len(word) > self.max_len:
self.max_len = len(word)
# the last <eos> should be included.
self.max_len += 1
self.nSamples = len(self.nid_labels)
print(
'\n ===== ===== ===== =====\nread {} images from {}.\n ===== ===== ===== =====\n'
.format(self.nSamples, self.root))
def __init__(self,
root,
voc_type,
height,
width,
num_samples,
is_aug,
lexicon_type='0',
mix_data=False):
super(SVT, self).__init__()
self.root = root
self.voc_type = voc_type
self.height = height
self.width = width
self.num_samples = num_samples
self.lexicon_type = lexicon_type
self.sampling_num_per_side = global_args.sampling_num_per_side
lexicon_root_dir = './Source/datasets/lexicon'
assert self.lexicon_type in ['0', '50']
self.fetcher = lambda path: cv2.imread(path)
self.nid_labels = []
self.max_len = 0
# for recognition
assert voc_type in [
'LOWERCASE', 'ALLCASES', 'ALLCASES_SYMBOLS', 'LOWERCASE_SYMBOLS'
]
self.EOS = 'EOS'
self.PADDING = 'PADDING'
self.UNKNOWN = 'UNKNOWN'
self.UNKNOWN_COUNT = 0
self.voc = get_vocabulary(voc_type,
EOS=self.EOS,
PADDING=self.PADDING,
UNKNOWN=self.UNKNOWN)
self.char2id = dict(zip(self.voc, range(len(self.voc))))
self.id2char = dict(zip(range(len(self.voc)), self.voc))
self.rec_num_classes = len(self.voc)
self.lowercase = (voc_type.find('LOWERCASE') >= 0)
self.ratio = 4
# load lexicon.
self.lexicons50 = dict()
with open(os.path.join(lexicon_root_dir, 'svt.list'), 'r') as f:
lines = f.readlines()
for line in lines:
line = line.strip()
image_path = line.split(' ')[0].split('/')[1]
lexicon50 = line.split(' ')[3].split(',')
lexicon50 = [w.lower() for w in lexicon50]
self.lexicons50[image_path] = lexicon50
self.lexicons1k = dict()
for k, v in self.lexicons50.items():
self.lexicons1k[k] = []
self.lexiconsfull = dict()
for k, v in self.lexicons50.items():
self.lexiconsfull[k] = []
label_json_path = os.path.join(self.root, 'Label.json')
labels = json.load(open(label_json_path, 'r'))
for label in labels:
nid = os.path.join(self.root, label["img"]) # i.e. image path
word = label["word"]
if len(word) < 3 or (not word.isalnum()):
continue
self.nid_labels.append((nid, word))
if len(word) > self.max_len:
self.max_len = len(word)
# the last <eos> should be included.
self.max_len += 1
self.nSamples = len(self.nid_labels)
print(
'\n ===== ===== ===== =====\nread {} images from {}.\n ===== ===== ===== =====\n'
.format(self.nSamples, self.root))
