Spaces:
Sleeping
Sleeping
File size: 3,920 Bytes
9bf4bd7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.dataset import BaseDataset
from mmocr.registry import DATASETS
@DATASETS.register_module()
class OCRDataset(BaseDataset):
r"""OCRDataset for text detection and text recognition.
The annotation format is shown as follows.
.. code-block:: none
{
"metainfo":
{
"dataset_type": "test_dataset",
"task_name": "test_task"
},
"data_list":
[
{
"img_path": "test_img.jpg",
"height": 604,
"width": 640,
"instances":
[
{
"bbox": [0, 0, 10, 20],
"bbox_label": 1,
"mask": [0,0,0,10,10,20,20,0],
"text": '123'
},
{
"bbox": [10, 10, 110, 120],
"bbox_label": 2,
"mask": [10,10],10,110,110,120,120,10]],
"extra_anns": '456'
}
]
},
]
}
Args:
ann_file (str): Annotation file path. Defaults to ''.
metainfo (dict, optional): Meta information for dataset, such as class
information. Defaults to None.
data_root (str, optional): The root directory for ``data_prefix`` and
``ann_file``. Defaults to None.
data_prefix (dict): Prefix for training data. Defaults to
dict(img_path='').
filter_cfg (dict, optional): Config for filter data. Defaults to None.
indices (int or Sequence[int], optional): Support using first few
data in annotation file to facilitate training/testing on a smaller
dataset. Defaults to None which means using all ``data_infos``.
serialize_data (bool, optional): Whether to hold memory using
serialized objects, when enabled, data loader workers can use
shared RAM from master process instead of making a copy. Defaults
to True.
pipeline (list, optional): Processing pipeline. Defaults to [].
test_mode (bool, optional): ``test_mode=True`` means in test phase.
Defaults to False.
lazy_init (bool, optional): Whether to load annotation during
instantiation. In some cases, such as visualization, only the meta
information of the dataset is needed, which is not necessary to
load annotation file. ``OCRdataset`` can skip load annotations to
save time by set ``lazy_init=False``. Defaults to False.
max_refetch (int, optional): If ``OCRdataset.prepare_data`` get a
None img. The maximum extra number of cycles to get a valid
image. Defaults to 1000.
Note:
OCRDataset collects meta information from `annotation file` (the
lowest priority), ``OCRDataset.METAINFO``(medium) and `metainfo
parameter` (highest) passed to constructors. The lower priority meta
information will be overwritten by higher one.
Examples:
Assume the annotation file is given above.
>>> class CustomDataset(OCRDataset):
>>> METAINFO: dict = dict(task_name='custom_task',
>>> dataset_type='custom_type')
>>> metainfo=dict(task_name='custom_task_name')
>>> custom_dataset = CustomDataset(
>>> 'path/to/ann_file',
>>> metainfo=metainfo)
>>> # meta information of annotation file will be overwritten by
>>> # `CustomDataset.METAINFO`. The merged meta information will
>>> # further be overwritten by argument `metainfo`.
>>> custom_dataset.metainfo
{'task_name': custom_task_name, dataset_type: custom_type}
"""
|