mmocr/dataset_zoo/iiit5k/textrecog.py

51 lines
1.8 KiB
Python

data_root = 'data/iiit5k'
cache_path = 'data/cache'
data_obtainer = dict(
type='NaiveDataObtainer',
cache_path=cache_path,
data_root=data_root,
files=[
dict(
url='http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/'
'IIIT5K-Word_V3.0.tar.gz',
save_name='IIIT5K.tar.gz',
md5='56781bc327d22066aa1c239ee788fd46',
split=['test', 'train'],
content=['image'],
mapping=[['IIIT5K/IIIT5K/test', 'textrecog_imgs/test'],
['IIIT5K/IIIT5K/train', 'textrecog_imgs/train']]),
dict(
url='https://download.openmmlab.com/mmocr/data/mixture/IIIT5K/'
'test_label.txt',
save_name='iiit5k_test.txt',
md5='82ecfa34a28d59284d1914dc906f5380',
split=['test'],
content=['annotation'],
mapping=[['iiit5k_test.txt', 'annotations/test.txt']]),
dict(
url='https://download.openmmlab.com/mmocr/data/mixture/IIIT5K/'
'train_label.txt',
save_name='iiit5k_train.txt',
md5='f4731ce1eadc259532c2834266e5126d',
split=['train'],
content=['annotation'],
mapping=[['iiit5k_train.txt', 'annotations/train.txt']]),
])
data_converter = dict(
type='TextRecogDataConverter',
splits=['train', 'test'],
data_root=data_root,
gatherer=dict(
type='mono_gather', train_ann='train.txt', test_ann='test.txt'),
parser=dict(
type='ICDARTxtTextRecogAnnParser',
encoding='utf-8',
separator=' ',
format='img text'),
dumper=dict(type='JsonDumper'),
delete=['annotations', 'IIIT5K'])
config_generator = dict(type='TextRecogConfigGenerator', data_root=data_root)