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