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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict
import numpy as np
from mmocr.structures import TextRecogDataSample
from .base_mmocr_inferencer import BaseMMOCRInferencer
class TextRecInferencer(BaseMMOCRInferencer):
"""Text Recognition inferencer.
Args:
model (str, optional): Path to the config file or the model name
defined in metafile. For example, it could be
"crnn_mini-vgg_5e_mj" or
"configs/textrecog/crnn/crnn_mini-vgg_5e_mj.py".
If model is not specified, user must provide the
`weights` saved by MMEngine which contains the config string.
Defaults to None.
weights (str, optional): Path to the checkpoint. If it is not specified
and model is a model name of metafile, the weights will be loaded
from metafile. Defaults to None.
device (str, optional): Device to run inference. If None, the available
device will be automatically used. Defaults to None.
scope (str, optional): The scope of the model. Defaults to "mmocr".
"""
def pred2dict(self, data_sample: TextRecogDataSample) -> Dict:
"""Extract elements necessary to represent a prediction into a
dictionary. It's better to contain only basic data elements such as
strings and numbers in order to guarantee it's json-serializable.
Args:
data_sample (TextRecogDataSample): The data sample to be converted.
Returns:
dict: The output dictionary.
"""
result = {}
result['text'] = data_sample.pred_text.item
score = self._array2list(data_sample.pred_text.score)
result['scores'] = float(np.mean(score))
return result
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