Spaces:
Sleeping
Sleeping
| # Copyright (c) OpenMMLab. All rights reserved. | |
| import json | |
| from typing import List, Tuple | |
| import numpy as np | |
| from mmocr.registry import DATA_PARSERS | |
| from .base import BaseParser | |
| class NAFAnnParser(BaseParser): | |
| """NAF dataset parser. | |
| The original annotation format of this dataset is stored in json files, | |
| which has the following keys that will be used here: | |
| - 'textBBs': List of text bounding box objects | |
| - 'poly_points': list of [x,y] pairs, the box corners going | |
| top-left,top-right,bottom-right,bottom-left | |
| - 'id': id of the textBB, used to match with the text | |
| - 'transcriptions': Dict of transcription objects, use the 'id' key | |
| to match with the textBB. | |
| Some special characters are used in the transcription: | |
| "«text»" indicates that "text" had a strikethrough | |
| "¿" indicates the transcriber could not read a character | |
| "§" indicates the whole line or word was illegible | |
| "" (empty string) is if the field was blank | |
| Args: | |
| ignore (list(str)): The text of the ignored instances. Default: ['#']. | |
| det (bool): Whether to parse the detection annotation. Default: True. | |
| If False, the parser will consider special case in NAF dataset | |
| where the transcription is not available. | |
| """ | |
| def __init__(self, | |
| ignore: List[str] = ['#'], | |
| det: bool = True, | |
| **kwargs) -> None: | |
| self.ignore = ignore | |
| self.det = det | |
| super().__init__(**kwargs) | |
| def parse_file(self, img_path: str, ann_path: str) -> Tuple: | |
| """Convert single annotation.""" | |
| instances = list() | |
| for poly, text in self.loader(ann_path): | |
| instances.append( | |
| dict(poly=poly, text=text, ignore=text in self.ignore)) | |
| return img_path, instances | |
| def loader(self, file_path: str) -> str: | |
| """Load the annotation of the NAF dataset. | |
| Args: | |
| file_path (str): Path to the json file | |
| Retyrb: | |
| str: Complete annotation of the json file | |
| """ | |
| with open(file_path, 'r') as f: | |
| data = json.load(f) | |
| # 'textBBs' contains the printed texts of the table while 'fieldBBs' | |
| # contains the text filled by human. | |
| for box_type in ['textBBs', 'fieldBBs']: | |
| if not self.det: | |
| # 'textBBs' is only used for detection task. | |
| if box_type == 'textBBs': | |
| continue | |
| for anno in data[box_type]: | |
| # Skip blanks | |
| if self.det: | |
| if box_type == 'fieldBBs': | |
| if anno['type'] == 'blank': | |
| continue | |
| poly = np.array(anno['poly_points']).reshape( | |
| 1, 8)[0].tolist() | |
| # Since detection task only need poly, we can skip the | |
| # transcription part that can be empty. | |
| text = None | |
| else: | |
| # For tasks that need transcription, NAF dataset has | |
| # serval special cases: | |
| # 1. The transcription for the whole image is not | |
| # available. | |
| # 2. The transcription for the certain text is not | |
| # available. | |
| # 3. If the length of the transcription is 0, it should | |
| # be ignored. | |
| if 'transcriptions' not in data.keys(): | |
| break | |
| if anno['id'] not in data['transcriptions'].keys(): | |
| continue | |
| text = data['transcriptions'][anno['id']] | |
| text = text.strip( | |
| '\u202a') # Remove unicode control character | |
| text = text.replace('»', '').replace( | |
| '«', '') # Remove strikethrough flag | |
| if len(text) == 0: | |
| continue | |
| poly = np.array(anno['poly_points']).reshape( | |
| 1, 8)[0].tolist() | |
| yield poly, text | |