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RecogTextDataset

class mmocr.datasets.RecogTextDataset(ann_file='', backend_args=None, parser_cfg={'keys': ['filename', 'text'], 'type': 'LineJsonParser'}, metainfo=None, data_root='', data_prefix={'img_path': ''}, filter_cfg=None, indices=None, serialize_data=True, pipeline=[], test_mode=False, lazy_init=False, max_refetch=1000)[source]

RecogTextDataset for text recognition.

The annotation format can be both in jsonl and txt. If the annotation file is in jsonl format, it should be a list of dicts. If the annotation file is in txt format, it should be a list of lines.

The annotation formats are shown as follows. - txt format .. code-block:: none

test_img1.jpg OpenMMLab test_img2.jpg MMOCR

  • jsonl format

``{"filename": "test_img1.jpg", "text": "OpenMMLab"}``
``{"filename": "test_img2.jpg", "text": "MMOCR"}``
Parameters
  • ann_file (str) – Annotation file path. Defaults to ‘’.

  • backend_args (dict, optional) – Arguments to instantiate the prefix of uri corresponding backend. Defaults to None.

  • parse_cfg (dict, optional) – Config of parser for parsing annotations. Use LineJsonParser when the annotation file is in jsonl format with keys of filename and text. The keys in parse_cfg should be consistent with the keys in jsonl annotations. The first key in parse_cfg should be the key of the path in jsonl annotations. The second key in parse_cfg should be the key of the text in jsonl Use LineStrParser when the annotation file is in txt format. Defaults to dict(type='LineJsonParser', keys=['filename', 'text']).

  • metainfo (dict, optional) – Meta information for dataset, such as class information. Defaults to None.

  • data_root (str) – The root directory for data_prefix and ann_file. Defaults to ‘’.

  • 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. RecogTextDataset can skip load annotations to save time by set lazy_init=False. Defaults to False.

  • max_refetch (int, optional) – If RecogTextDataset.prepare_data get a None img. The maximum extra number of cycles to get a valid image. Defaults to 1000.

load_data_list()[source]

Load annotations from an annotation file named as self.ann_file

Returns

A list of annotation.

Return type

List[dict]

parse_data_info(raw_anno_info)[source]

Parse raw annotation to target format.

Parameters

raw_anno_info (str) – One raw data information loaded from ann_file.

Returns

Parsed annotation.

Return type

(dict)

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