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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 | |
| 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)) | |