Spaces:
Running
Running
File size: 4,229 Bytes
9bf4bd7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 |
# 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
@DATA_PARSERS.register_module()
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
|