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# Copyright (c) OpenMMLab. All rights reserved.
from abc import abstractmethod
from typing import Dict, List, Tuple, Union
from mmocr.utils import track_parallel_progress_multi_args
class BaseParser:
"""Base class for parsing annotations.
Args:
split (str): The split of the dataset. It is usually set automatically
and users do not need to set it manually in config file in most
cases.
nproc (int): Number of processes to process the data. Defaults to 1.
It is usually set automatically and users do not need to set it
manually in config file in most cases.
"""
def __init__(self, split: str, nproc: int = 1) -> None:
self.nproc = nproc
self.split = split
def __call__(self, img_paths: Union[List[str], str],
ann_paths: Union[List[str], str]) -> List[Tuple]:
"""Parse annotations.
Args:
img_paths (str or list[str]): the list of image paths or the
directory of the images.
ann_paths (str or list[str]): the list of annotation paths or the
path of the annotation file which contains all the annotations.
Returns:
List: A list of a tuple of (image_path, instances)
"""
samples = self.parse_files(img_paths, ann_paths)
return samples
def parse_files(self, img_paths: Union[List[str], str],
ann_paths: Union[List[str], str]) -> List[Tuple]:
"""Convert annotations to MMOCR format.
Args:
img_paths (str or list[str]): the list of image paths or the
directory of the images.
ann_paths (str or list[str]): the list of annotation paths or the
path of the annotation file which contains all the annotations.
Returns:
List[Tuple]: A list of a tuple of (image_path, instances).
- img_path (str): The path of image file, which can be read
directly by opencv.
- instance: instance is a list of dict containing parsed
annotations, which should contain the following keys:
- 'poly' or 'box' (textdet or textspotting)
- 'text' (textspotting or textrecog)
- 'ignore' (all task)
"""
samples = track_parallel_progress_multi_args(
self.parse_file, (img_paths, ann_paths), nproc=self.nproc)
return samples
@abstractmethod
def parse_file(self, img_path: str, ann_path: str) -> Tuple:
"""Convert annotation for a single image.
Args:
img_path (str): The path of image.
ann_path (str): The path of annotation.
Returns:
Tuple: A tuple of (img_path, instance).
- img_path (str): The path of image file, which can be read
directly by opencv.
- instance: instance is a list of dict containing parsed
annotations, which should contain the following keys:
- 'poly' or 'box' (textdet or textspotting)
- 'text' (textspotting or textrecog)
- 'ignore' (all task)
Examples:
An example of returned values:
>>> ('imgs/train/xxx.jpg',
>>> dict(
>>> poly=[[[0, 1], [1, 1], [1, 0], [0, 0]]],
>>> text='hello',
>>> ignore=False)
>>> )
"""
raise NotImplementedError
def loader(self,
file_path: str,
separator: str = ',',
format: str = 'x1,y1,x2,y2,x3,y3,x4,y4,trans',
encoding='utf-8') -> Union[Dict, str]:
"""A basic loader designed for .txt format annotation. It greedily
extracts information separated by separators.
Args:
file_path (str): Path to the txt file.
separator (str, optional): Separator of data. Defaults to ','.
format (str, optional): Annotation format.
Defaults to 'x1,y1,x2,y2,x3,y3,x4,y4,trans'.
encoding (str, optional): Encoding format. Defaults to 'utf-8'.
Yields:
Iterator[Union[Dict, str]]: Original text line or a dict containing
the information of the text line.
"""
keys = format.split(separator)
with open(file_path, 'r', encoding=encoding) as f:
for line in f.readlines():
line = line.strip()
values = line.split(separator)
values = values[:len(keys) -
1] + [separator.join(values[len(keys) - 1:])]
if line:
yield dict(zip(keys, values))
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