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data_root = 'data/naf'
cache_path = 'data/cache'
obtainer = dict(
type='NaiveDataObtainer',
cache_path=cache_path,
files=[
dict(
url='https://github.com/herobd/NAF_dataset/releases/'
'download/v1.0/labeled_images.tar.gz',
save_name='naf_image.tar.gz',
md5='6521cdc25c313a1f2928a16a77ad8f29',
content=['image'],
mapping=[['naf_image/labeled_images', 'temp_images/']]),
dict(
url='https://github.com/herobd/NAF_dataset/archive/'
'refs/heads/master.zip',
save_name='naf_anno.zip',
md5='abf5af6266cc527d772231751bc884b3',
content=['annotation'],
mapping=[
[
'naf_anno/NAF_dataset-master/groups/**/*.json',
'annotations/'
],
[
'naf_anno/NAF_dataset-master/train_valid_test_split.json',
'data_split.json'
]
]),
])
train_preparer = dict(
obtainer=obtainer,
gatherer=dict(type='NAFGatherer'),
parser=dict(type='NAFAnnParser', det=True),
packer=dict(type='TextDetPacker'),
dumper=dict(type='JsonDumper'),
)
test_preparer = train_preparer
val_preparer = train_preparer
delete = [
'temp_images', 'data_split.json', 'annotations', 'naf_anno', 'naf_image'
]
config_generator = dict(
type='TextDetConfigGenerator',
val_anns=[dict(ann_file='textdet_val.json', dataset_postfix='')])
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