mirror of https://github.com/open-mmlab/mmocr.git
[Dataset Preparer] Add SCUT-CTW1500 (#1677)
* update metafile and download * update parser * updata ctw1500 to new dataprepare design * add lexicon into ctw1500 textspotting * fix --------- Co-authored-by: liukuikun <641417025@qq.com> Co-authored-by: gaotongxiao <gaotongxiao@gmail.com>pull/1773/head
Qing Jiang
GitHub
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Name: 'CTW1500'
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Paper:
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Title: Curved scene text detection via transverse and longitudinal sequence connection
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URL: https://www.sciencedirect.com/science/article/pii/S0031320319300664
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Venue: PR
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Year: '2019'
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BibTeX: '@article{liu2019curved,
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title={Curved scene text detection via transverse and longitudinal sequence connection},
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author={Liu, Yuliang and Jin, Lianwen and Zhang, Shuaitao and Luo, Canjie and Zhang, Sheng},
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journal={Pattern Recognition},
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volume={90},
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pages={337--345},
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year={2019},
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publisher={Elsevier}
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}'
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Data:
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Website: https://github.com/Yuliang-Liu/Curve-Text-Detector
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Language:
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- English
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Scene:
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- Scene
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Granularity:
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- Word
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- Line
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Tasks:
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- textrecog
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- textdet
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- textspotting
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License:
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Type: N/A
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Link: N/A
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Format: .xml
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data_root = 'data/ctw1500'
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cache_path = 'data/cache'
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train_preparer = dict(
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obtainer=dict(
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type='NaiveDataObtainer',
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cache_path=cache_path,
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files=[
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dict(
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url='https://universityofadelaide.box.com/shared/static/'
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'py5uwlfyyytbb2pxzq9czvu6fuqbjdh8.zip',
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save_name='ctw1500_train_images.zip',
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md5='f1453464b764343040644464d5c0c4fa',
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split=['train'],
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content=['image'],
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mapping=[[
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'ctw1500_train_images/train_images', 'textdet_imgs/train'
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]]),
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dict(
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url='https://universityofadelaide.box.com/shared/static/'
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'jikuazluzyj4lq6umzei7m2ppmt3afyw.zip',
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save_name='ctw1500_train_labels.zip',
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md5='d9ba721b25be95c2d78aeb54f812a5b1',
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split=['train'],
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content=['annotation'],
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mapping=[[
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'ctw1500_train_labels/ctw1500_train_labels/',
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'annotations/train'
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]])
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]),
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gatherer=dict(
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type='PairGatherer',
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img_suffixes=['.jpg', '.JPG'],
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rule=[r'(\d{4}).jpg', r'\1.xml']),
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parser=dict(type='CTW1500AnnParser'),
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packer=dict(type='TextDetPacker'),
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dumper=dict(type='JsonDumper'),
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)
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test_preparer = dict(
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obtainer=dict(
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type='NaiveDataObtainer',
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cache_path=cache_path,
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files=[
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dict(
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url='https://universityofadelaide.box.com/shared/static/'
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't4w48ofnqkdw7jyc4t11nsukoeqk9c3d.zip',
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save_name='ctw1500_test_images.zip',
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md5='79103fd77dfdd2c70ae6feb3a2fb4530',
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split=['test'],
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content=['image'],
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mapping=[[
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'ctw1500_test_images/test_images', 'textdet_imgs/test'
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]]),
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dict(
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url='https://cloudstor.aarnet.edu.au/plus/s/uoeFl0pCN9BOCN5/'
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'download',
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save_name='ctw1500_test_labels.zip',
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md5='7f650933a30cf1bcdbb7874e4962a52b',
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split=['test'],
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content=['annotation'],
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mapping=[['ctw1500_test_labels', 'annotations/test']])
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]),
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gatherer=dict(
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type='PairGatherer',
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img_suffixes=['.jpg', '.JPG'],
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rule=[r'(\d{4}).jpg', r'000\1.txt']),
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parser=dict(type='CTW1500AnnParser'),
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packer=dict(type='TextDetPacker'),
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dumper=dict(type='JsonDumper'),
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)
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delete = [
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'ctw1500_train_images', 'ctw1500_test_images', 'annotations',
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'ctw1500_train_labels', 'ctw1500_test_labels'
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]
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config_generator = dict(type='TextDetConfigGenerator')
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_base_ = ['textdet.py']
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_base_.train_preparer.gatherer.img_dir = 'textdet_imgs/train'
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_base_.test_preparer.gatherer.img_dir = 'textdet_imgs/test'
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_base_.train_preparer.packer.type = 'TextRecogCropPacker'
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_base_.test_preparer.packer.type = 'TextRecogCropPacker'
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config_generator = dict(type='TextRecogConfigGenerator')
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_base_ = ['textdet.py']
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_base_.train_preparer.gatherer.img_dir = 'textdet_imgs/train'
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_base_.test_preparer.gatherer.img_dir = 'textdet_imgs/test'
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_base_.train_preparer.packer.type = 'TextSpottingPacker'
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_base_.test_preparer.packer.type = 'TextSpottingPacker'
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_base_.test_preparer.obtainer.files.append(
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dict(
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url='https://download.openmmlab.com/mmocr/data/1.x/textspotting/'
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'ctw1500/lexicons.zip',
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save_name='ctw1500_lexicons.zip',
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md5='168150ca45da161917bf35a20e45b8d6',
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content=['lexicons'],
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mapping=[['ctw1500_lexicons/lexicons', 'lexicons']]))
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_base_.delete.append('ctw1500_lexicons')
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config_generator = dict(type='TextSpottingConfigGenerator')
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@ -1,6 +1,7 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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from .base import BaseParser
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from .coco_parser import COCOTextDetAnnParser
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from .ctw1500_parser import CTW1500AnnParser
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from .funsd_parser import FUNSDTextDetAnnParser
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from .icdar_txt_parser import (ICDARTxtTextDetAnnParser,
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ICDARTxtTextRecogAnnParser)
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@ -14,5 +15,5 @@ __all__ = [
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'BaseParser', 'ICDARTxtTextDetAnnParser', 'ICDARTxtTextRecogAnnParser',
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'TotaltextTextDetAnnParser', 'WildreceiptKIEAnnParser',
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'COCOTextDetAnnParser', 'SVTTextDetAnnParser', 'FUNSDTextDetAnnParser',
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'SROIETextDetAnnParser', 'NAFAnnParser'
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'SROIETextDetAnnParser', 'NAFAnnParser', 'CTW1500AnnParser'
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]
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# Copyright (c) OpenMMLab. All rights reserved.
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import xml.etree.ElementTree as ET
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from typing import List, Tuple
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import numpy as np
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from mmocr.datasets.preparers.data_preparer import DATA_PARSERS
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from mmocr.datasets.preparers.parsers.base import BaseParser
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from mmocr.utils import list_from_file
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@DATA_PARSERS.register_module()
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class CTW1500AnnParser(BaseParser):
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"""SCUT-CTW1500 dataset parser.
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Args:
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ignore (str): The text of the ignored instances. Defaults to
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'###'.
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"""
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def __init__(self, ignore: str = '###', **kwargs) -> None:
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self.ignore = ignore
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super().__init__(**kwargs)
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def parse_file(self, img_path: str, ann_path: str) -> Tuple:
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"""Convert annotation for a single image.
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Args:
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img_path (str): The path of image.
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ann_path (str): The path of annotation.
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Returns:
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Tuple: A tuple of (img_path, instance).
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- img_path (str): The path of image file, which can be read
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directly by opencv.
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- instance: instance is a list of dict containing parsed
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annotations, which should contain the following keys:
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- 'poly' or 'box' (textdet or textspotting)
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- 'text' (textspotting or textrecog)
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- 'ignore' (all task)
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Examples:
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An example of returned values:
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>>> ('imgs/train/xxx.jpg',
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>>> dict(
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>>> poly=[[[0, 1], [1, 1], [1, 0], [0, 0]]],
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>>> text='hello',
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>>> ignore=False)
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>>> )
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"""
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if self.split == 'train':
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instances = self.load_xml_info(ann_path)
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elif self.split == 'test':
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instances = self.load_txt_info(ann_path)
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return img_path, instances
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def load_txt_info(self, anno_dir: str) -> List:
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"""Load the annotation of the SCUT-CTW dataset (test split).
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Args:
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anno_dir (str): Path to the annotation file.
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Returns:
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list[Dict]: List of instances.
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"""
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instances = list()
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for line in list_from_file(anno_dir):
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# each line has one ploygen (n vetices), and one text.
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# e.g., 695,885,866,888,867,1146,696,1143,####Latin 9
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line = line.strip()
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strs = line.split(',')
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assert strs[28][0] == '#'
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xy = [int(x) for x in strs[0:28]]
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assert len(xy) == 28
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poly = np.array(xy).reshape(-1).tolist()
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text = strs[28][4:]
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instances.append(
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dict(poly=poly, text=text, ignore=text == self.ignore))
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return instances
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def load_xml_info(self, anno_dir: str) -> List:
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"""Load the annotation of the SCUT-CTW dataset (train split).
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Args:
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anno_dir (str): Path to the annotation file.
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Returns:
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list[Dict]: List of instances.
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"""
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obj = ET.parse(anno_dir)
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instances = list()
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for image in obj.getroot(): # image
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for box in image: # image
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text = box[0].text
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segs = box[1].text
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pts = segs.strip().split(',')
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pts = [int(x) for x in pts]
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assert len(pts) == 28
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poly = np.array(pts).reshape(-1).tolist()
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instances.append(dict(poly=poly, text=text, ignore=0))
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return instances
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# Copyright (c) OpenMMLab. All rights reserved.
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import os.path as osp
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import tempfile
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import unittest
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from mmocr.datasets.preparers.parsers import CTW1500AnnParser
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from mmocr.utils import list_to_file
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class TestCTW1500AnnParser(unittest.TestCase):
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def setUp(self) -> None:
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self.root = tempfile.TemporaryDirectory()
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def _create_dummy_ctw1500_det(self):
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fake_train_anno = [
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'<Annotations>',
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' <image file="0200.jpg">',
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' <box height="197" left="131" top="49" width="399">',
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' <label>OLATHE</label>',
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' <segs>131,58,208,49,279,56,346,76,412,101,473,141,530,192,510,246,458,210,405,175,350,151,291,137,228,133,165,134</segs>', # noqa: E501
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' <pts x="183" y="95" />',
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' <pts x="251" y="89" />',
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' <pts x="322" y="107" />',
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' <pts x="383" y="124" />',
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' <pts x="441" y="161" />',
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' <pts x="493" y="201" />',
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' </box>',
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' </image>',
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'</Annotations>',
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]
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train_ann_file = osp.join(self.root.name, 'ctw1500_train.xml')
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list_to_file(train_ann_file, fake_train_anno)
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fake_test_anno = [
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'48,84,61,79,75,73,88,68,102,74,116,79,130,84,135,73,119,67,104,60,89,56,74,61,59,67,45,73,#######', # noqa: E501
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'51,137,58,137,66,137,74,137,82,137,90,137,98,137,98,119,90,119,82,119,74,119,66,119,58,119,50,119,####E-313', # noqa: E501
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'41,155,49,155,57,155,65,155,73,155,81,155,89,155,87,136,79,136,71,136,64,136,56,136,48,136,41,137,#######', # noqa: E501
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'41,193,57,193,74,194,90,194,107,195,123,195,140,196,146,168,128,167,110,167,92,167,74,166,56,166,39,166,####F.D.N.Y.', # noqa: E501
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]
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test_ann_file = osp.join(self.root.name, 'ctw1500_test.txt')
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list_to_file(test_ann_file, fake_test_anno)
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return (osp.join(self.root.name,
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'ctw1500.jpg'), train_ann_file, test_ann_file)
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def test_textdet_parsers(self):
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parser = CTW1500AnnParser(split='train')
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img_path, train_file, test_file = self._create_dummy_ctw1500_det()
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img_path, instances = parser.parse_file(img_path, train_file)
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self.assertEqual(img_path, osp.join(self.root.name, 'ctw1500.jpg'))
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self.assertEqual(len(instances), 1)
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self.assertEqual(instances[0]['text'], 'OLATHE')
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self.assertEqual(instances[0]['poly'], [
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131, 58, 208, 49, 279, 56, 346, 76, 412, 101, 473, 141, 530, 192,
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510, 246, 458, 210, 405, 175, 350, 151, 291, 137, 228, 133, 165,
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134
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])
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self.assertEqual(instances[0]['ignore'], False)
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parser = CTW1500AnnParser(split='test')
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img_path, instances = parser.parse_file(img_path, test_file)
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self.assertEqual(img_path, osp.join(self.root.name, 'ctw1500.jpg'))
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self.assertEqual(len(instances), 4)
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self.assertEqual(instances[0]['ignore'], True)
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self.assertEqual(instances[1]['text'], 'E-313')
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self.assertEqual(instances[3]['poly'], [
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41, 193, 57, 193, 74, 194, 90, 194, 107, 195, 123, 195, 140, 196,
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146, 168, 128, 167, 110, 167, 92, 167, 74, 166, 56, 166, 39, 166
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])
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def tearDown(self) -> None:
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self.root.cleanup()
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