mirror of
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81 lines
3.2 KiB
Python
81 lines
3.2 KiB
Python
# By default, this configuration prepares the ICDAR13 1015 version.
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# If you want to use the original 1095 version, please
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# Comment out the following lines:
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# Line #38-45; #62-69
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# and uncomment the following lines:
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# Line #24-27; #51-58; #73-80
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data_root = 'data/icdar2013'
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cache_path = 'data/cache'
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data_obtainer = dict(
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type='NaiveDataObtainer',
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cache_path=cache_path,
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data_root=data_root,
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files=[
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dict(
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url='https://rrc.cvc.uab.es/downloads/'
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'Challenge2_Training_Task3_Images_GT.zip',
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save_name='ic13_textrecog_train_img_gt.zip',
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md5='6f0dbc823645968030878df7543f40a4',
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split=['train'],
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content=['image'],
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mapping=[
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# [
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# 'ic13_textrecog_train_img_gt/gt.txt',
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# 'annotations/train.txt'
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# ],
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['ic13_textrecog_train_img_gt', 'textrecog_imgs/train']
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]),
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dict(
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url='https://rrc.cvc.uab.es/downloads/'
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'Challenge2_Test_Task3_Images.zip',
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save_name='ic13_textrecog_test_img.zip',
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md5='3206778eebb3a5c5cc15c249010bf77f',
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split=['test'],
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content=['image'],
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mapping=[['ic13_textrecog_test_img', 'textrecog_imgs/test']]),
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dict(
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url='https://download.openmmlab.com/mmocr/data/1.x/recog/'
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'icdar_2013/train_labels.json',
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save_name='ic13_train_labels.json',
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md5='008fcd0056e72c4cf3064fb4d1fce81b',
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split=['train'],
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content=['annotation'],
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mapping=[['ic13_train_labels.json', 'textrecog_train.json']]),
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# Note that we offer two versions of test set annotations as follows.
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# Please choose one of them to download and comment the other. By
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# default, we use the second one.
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# 1. The original official annotation, which contains 1095 test
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# samples.
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# dict(
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# url='https://rrc.cvc.uab.es/downloads/'
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# 'Challenge2_Test_Task3_GT.txt',
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# save_name='ic13_textrecog_test_gt.txt',
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# md5='2634060ed8fe6e7a4a9b8d68785835a1',
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# split=['test'],
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# content=['annotation'],
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# mapping=[['ic13_textrecog_test_gt.txt', 'annotations/test.txt']])
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# 2. The widely-used version for academic purpose, which filters out
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# words with non-alphanumeric characters. This version contains 1015
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# test samples.
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dict(
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url='https://download.openmmlab.com/mmocr/data/1.x/recog/'
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'icdar_2013/test_labels.json',
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save_name='ic13_test_labels.json',
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md5='2b78d3b29851402318bd4f69f41273b2',
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split=['test'],
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content=['annotation'],
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mapping=[['ic13_test_labels.json', 'textrecog_test.json']])
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])
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# Uncomment the data converter if you want to use the original 1095 version.
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# data_converter = dict(
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# type='TextRecogDataConverter',
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# splits=['train', 'test'],
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# data_root=data_root,
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# gatherer=dict(type='mono_gather', mapping="f'{split}.txt'"),
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# parser=dict(
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# type='ICDARTxtTextRecogAnnParser', separator=', ', format='img, text'),
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# dumper=dict(type='JsonDumper'))
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