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img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
gt_label_convertor = dict(
type='SegConvertor', dict_type='DICT36', with_unknown=True, lower=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomPaddingOCR',
max_ratio=[0.15, 0.2, 0.15, 0.2],
box_type='char_quads'),
dict(type='OpencvToPil'),
dict(
type='RandomRotateImageBox',
min_angle=-17,
max_angle=17,
box_type='char_quads'),
dict(type='PilToOpencv'),
dict(
type='ResizeOCR',
height=64,
min_width=64,
max_width=512,
keep_aspect_ratio=True),
dict(
type='OCRSegTargets',
label_convertor=gt_label_convertor,
box_type='char_quads'),
dict(type='RandomRotateTextDet', rotate_ratio=0.5, max_angle=15),
dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
dict(type='ToTensorOCR'),
dict(type='FancyPCA'),
dict(type='NormalizeOCR', **img_norm_cfg),
dict(
type='CustomFormatBundle',
keys=['gt_kernels'],
visualize=dict(flag=False, boundary_key=None),
call_super=False),
dict(
type='Collect',
keys=['img', 'gt_kernels'],
meta_keys=['filename', 'ori_shape', 'resize_shape'])
]
test_img_norm_cfg = dict(
mean=[x * 255 for x in img_norm_cfg['mean']],
std=[x * 255 for x in img_norm_cfg['std']])
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='ResizeOCR',
height=64,
min_width=64,
max_width=None,
keep_aspect_ratio=True),
dict(type='Normalize', **test_img_norm_cfg),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'filename', 'resize_shape', 'img_norm_cfg', 'ori_filename',
'img_shape', 'ori_shape'
])
]
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