WildReceiptDataset¶

class mmocr.datasets.WildReceiptDataset(directed=False, ann_file='', metainfo=None, data_root='', data_prefix={'img_path': ''}, filter_cfg=None, indices=None, serialize_data=True, pipeline=Ellipsis, test_mode=False, lazy_init=False, max_refetch=1000)[source]¶

WildReceipt Dataset for key information extraction. There are two files to be loaded: metainfo and annotation. The metainfo file contains the mapping between classes and labels. The annotation file contains the all necessary information about the image, such as bounding boxes, texts, and labels etc.

The metainfo file is a text file with the following format:

Ignore
Store_name_value
Store_name_key

The annotation format is shown as follows.

{
    "file_name": "a.jpeg",
    "height": 348,
    "width": 348,
    "annotations": [
        {
            "box": [
                114.0,
                19.0,
                230.0,
                19.0,
                230.0,
                1.0,
                114.0,
                1.0
            ],
            "text": "CHOEUN",
            "label": 1
        },
        {
            "box": [
                97.0,
                35.0,
                236.0,
                35.0,
                236.0,
                19.0,
                97.0,
                19.0
            ],
            "text": "KOREANRESTAURANT",
            "label": 2
        }
    ]
}

Parameters

directed (bool) – Whether to use directed graph. Defaults to False.
ann_file (str) – Annotation file path. Defaults to ‘’.
metainfo (str or dict, optional) – Meta information for dataset, such as class information. If it’s a string, it will be treated as a path to the class file from which the class information will be loaded. Defaults to None.
data_root (str, optional) – The root directory for data_prefix and ann_file. Defaults to ‘’.
data_prefix (dict, optional) – 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. Basedataset can skip load annotations to save time by set lazy_init=False. Defaults to False.
max_refetch (int, optional) – If Basedataset.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 data list from annotation file.

Returns: A list of annotation dict.
Return type: List[dict]

parse_data_info(raw_data_info)[source]¶

Parse data info from raw data info.

Parameters

raw_data_info (dict) – Raw data info.

Returns

Parsed data info.

img_path (str): Path to the image.
img_shape (tuple(int, int)): Image shape in (H, W).
instances (list[dict]): A list of instances. - bbox (ndarray(dtype=np.float32)): Shape (4, ). Bounding box. - text (str): Annotation text. - edge_label (int): Edge label. - bbox_label (int): Bounding box label.

Return type

dict