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OCRDataset

class mmocr.datasets.OCRDataset(ann_file='', 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]

OCRDataset for text detection and text recognition.

The annotation format is shown as follows.

{
    "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'
          }
        ]
      },
    ]
}
Parameters
  • 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}

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