def __init__(self, root=data_dir(), model_epoch=MODEL_EPOCE):
self._model_dir = os.path.join(root, 'models')
self._model_epoch = model_epoch
self._assert_and_prepare_model_files(root)
self._alphabet, _ = read_charset(os.path.join(self._model_dir, 'label_cn.txt'))
self._hp = Hyperparams()
self._mods = {}
def process_baidu_innovation():
vocab, letter2id = read_charset(VOCAB_FP)
baidu_index_fp = (
'/Users/king/Documents/beiye-Ein/语料/text-detection/baidu-innovation/train.list'
)
out_index_fp = 'train_baidu_innovation.tsv'
image_folder = 'baidu-innovation/train_images'
data = read_index_file(baidu_index_fp, letter2id)
logger.info(f'{len(data)} legal examples are found')
with open(out_index_fp, 'w') as f:
for example in data:
fname, gt = example
gt = ' '.join(gt)
fp = image_folder + '/' + fname
f.write('\t'.join((fp, gt)) + '\n')
def __init__(self,
model_name='conv-lite-fc',
model_epoch=None,
cand_alphabet=None,
root=data_dir(),
gpus=0):
"""
:param model_name: 模型名称
:param model_epoch: 模型迭代次数
:param cand_alphabet: 待识别字符所在的候选集合。默认为 `None`，表示不限定识别字符范围
:param root: 模型文件所在的根目录。
Linux/Mac下默认值为 `~/.cnocr`，表示模型文件所处文件夹类似 `~/.cnocr/1.1.0/conv-lite-fc-0027`。
Windows下默认值为 ``。
"""
check_model_name(model_name)
self._model_name = model_name
self._model_file_prefix = '{}-{}'.format(self.MODEL_FILE_PREFIX,
model_name)
self._model_epoch = model_epoch or AVAILABLE_MODELS[model_name][0]
root = os.path.join(root, MODEL_VERSION)
self._model_dir = os.path.join(root, self._model_name)
self._assert_and_prepare_model_files()
self._alphabet, inv_alph_dict = read_charset(
os.path.join(self._model_dir, 'label_cn.txt'))
self._cand_alph_idx = None
if cand_alphabet is not None:
self._cand_alph_idx = [0] + [
inv_alph_dict[word] for word in cand_alphabet
]
self._cand_alph_idx.sort()
self._hp = Hyperparams()
self._hp._loss_type = None # infer mode
# DCMMC: gpu context for mxnet
if gpus > 0:
self.context = [mx.context.gpu(i) for i in range(gpus)]
else:
self.context = [mx.context.cpu()]
self._mod = self._get_module()
def __init__(
self,
model_name='densenet-lite-fc',
model_epoch=None,
cand_alphabet=None,
root=data_dir(),
context='cpu',
name=None,
):
"""
:param model_name: 模型名称
:param model_epoch: 模型迭代次数
:param cand_alphabet: 待识别字符所在的候选集合。默认为 `None`，表示不限定识别字符范围
:param root: 模型文件所在的根目录。
Linux/Mac下默认值为 `~/.cnocr`，表示模型文件所处文件夹类似 `~/.cnocr/1.1.0/conv-lite-fc-0027`。
Windows下默认值为 ``。
:param context: 'cpu', or 'gpu'。表明预测时是使用CPU还是GPU。默认为CPU。
:param name: 正在初始化的这个实例名称。如果需要同时初始化多个实例，需要为不同的实例指定不同的名称。
"""
check_model_name(model_name)
self._model_name = model_name
self._model_file_prefix = '{}-{}'.format(self.MODEL_FILE_PREFIX,
model_name)
self._model_epoch = model_epoch or AVAILABLE_MODELS[model_name][0]
root = os.path.join(root, MODEL_VERSION)
self._model_dir = os.path.join(root, self._model_name)
self._assert_and_prepare_model_files()
self._alphabet, self._inv_alph_dict = read_charset(
os.path.join(self._model_dir, 'label_cn.txt'))
self._cand_alph_idx = None
self.set_cand_alphabet(cand_alphabet)
self._hp = Hyperparams()
self._hp._loss_type = None # infer mode
# 传入''的话，也改成传入None
self._net_prefix = None if name == '' else name
self._mod = self._get_module(context)
def main():
charset_fp = os.path.join(data_dir(), 'models', 'label_cn.txt')
alphabet, inv_alph_dict = read_charset(charset_fp)
for idx in BAD_CHARS:
print('idx: {}, char: {}'.format(idx, alphabet[idx]))
def _gen_iters(hp,
train_fp_prefix,
val_fp_prefix,
use_train_image_aug,
dataset_fp,
charset_fp,
debug=False):
height, width = hp.img_height, hp.img_width
augs = None
if use_train_image_aug:
augs = mx.image.CreateAugmenter(
data_shape=(3, height, width),
resize=0,
rand_crop=False,
rand_resize=False,
rand_mirror=False,
mean=None,
std=None,
brightness=0.001,
contrast=0.001,
saturation=0.001,
hue=0.05,
pca_noise=0.1,
inter_method=2,
)
augs.append(FgBgFlipAug(p=0.2))
# train_iter = GrayImageIter(
# batch_size=hp.batch_size,
# data_shape=(3, height, width),
# label_width=hp.num_label,
# dtype='float32',
# shuffle=True,
# path_imgrec=str(train_fp_prefix) + ".rec",
# path_imgidx=str(train_fp_prefix) + ".idx",
# aug_list=augs,
# )
# val_iter = GrayImageIter(
# batch_size=hp.batch_size,
# data_shape=(3, height, width),
# label_width=hp.num_label,
# dtype='float32',
# path_imgrec=str(val_fp_prefix) + ".rec",
# path_imgidx=str(val_fp_prefix) + ".idx",
# )
_, token2id = read_charset(charset_fp)
assert all([len(c) == 1 for c in token2id.keys() if c])
train_iter = Hdf5ImgIter(batch_size=hp.batch_size,
data_shape=(3, height, width),
dataset_fp=dataset_fp,
label_width=hp.num_label,
classes_dict=token2id,
mode='train',
dtype='float32',
shuffle=True,
aug_list=augs,
debug=debug)
val_iter = Hdf5ImgIter(batch_size=hp.batch_size,
data_shape=(3, height, width),
dataset_fp=dataset_fp,
classes_dict=token2id,
mode='val',
label_width=hp.num_label,
dtype='float32',
debug=debug)
return train_iter, val_iter
def __init__(
self,
model_name: str = 'densenet_lite_136-fc',
*,
cand_alphabet: Optional[Union[Collection, str]] = None,
context: str = 'cpu', # ['cpu', 'gpu', 'cuda']
model_fp: Optional[str] = None,
root: Union[str, Path] = data_dir(),
**kwargs,
):
"""
识别模型初始化函数。
Args:
model_name (str): 模型名称。默认为 `densenet_lite_136-fc`
cand_alphabet (Optional[Union[Collection, str]]): 待识别字符所在的候选集合。默认为 `None`，表示不限定识别字符范围
context (str): 'cpu', or 'gpu'。表明预测时是使用CPU还是GPU。默认为 `cpu`
model_fp (Optional[str]): 如果不使用系统自带的模型，可以通过此参数直接指定所使用的模型文件（'.ckpt' 文件）
root (Union[str, Path]): 模型文件所在的根目录。
Linux/Mac下默认值为 `~/.cnocr`，表示模型文件所处文件夹类似 `~/.cnocr/2.1/densenet_lite_136-fc`。
Windows下默认值为 `C:/Users/<username>/AppData/Roaming/cnocr`。
**kwargs: 目前未被使用。
Examples:
使用默认参数：
>>> ocr = CnOcr()
使用指定模型：
>>> ocr = CnOcr(model_name='densenet_lite_136-fc')
识别时只考虑数字：
>>> ocr = CnOcr(model_name='densenet_lite_136-fc', cand_alphabet='0123456789')
"""
if 'name' in kwargs:
logger.warning(
'param `name` is useless and deprecated since version %s' %
MODEL_VERSION)
check_model_name(model_name)
check_context(context)
self._model_name = model_name
if context == 'gpu':
context = 'cuda'
self.context = context
self._model_file_prefix = '{}-{}'.format(self.MODEL_FILE_PREFIX,
model_name)
model_epoch = AVAILABLE_MODELS.get(model_name, [None])[0]
if model_epoch is not None:
self._model_file_prefix = '%s-epoch=%03d' % (
self._model_file_prefix,
model_epoch,
)
self._assert_and_prepare_model_files(model_fp, root)
self._vocab, self._letter2id = read_charset(VOCAB_FP)
self._candidates = None
self.set_cand_alphabet(cand_alphabet)
self._model = self._get_model(context)
