mirror of https://github.com/open-mmlab/mmocr.git
67 lines
2.3 KiB
Python
67 lines
2.3 KiB
Python
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#!/usr/bin/env python
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import os
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import os.path as osp
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import yaml
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dataset_zoo_path = '../../dataset_zoo'
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datasets = os.listdir(dataset_zoo_path)
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datasets.sort()
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table = '# Overview\n'
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table += '## Supported Datasets\n'
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table += '| Dataset Name | Text Detection | Text Recognition | Text Spotting | KIE |\n' \
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'|--------------|----------------|------------------|---------------|-----|\n' # noqa: E501
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details = '## Dataset Details\n'
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for dataset in datasets:
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meta = yaml.safe_load(
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open(osp.join(dataset_zoo_path, dataset, 'metafile.yml')))
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dataset_name = meta['Name']
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paper = meta['Paper']
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data = meta['Data']
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table += '| [{}](#{}) | {} | {} | {} | {} |\n'.format(
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dataset,
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dataset_name.lower().replace(' ', '-'),
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'✓' if 'textdet' in data['Tasks'] else '',
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'✓' if 'textrecog' in data['Tasks'] else '',
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'✓' if 'textspotting' in data['Tasks'] else '',
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'✓' if 'kie' in data['Tasks'] else '',
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)
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details += '### {}\n'.format(dataset_name)
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details += "> \"{}\", *{}*, {}.\n\n".format(paper['Title'], paper['Venue'],
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paper['Year'])
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# Basic Info
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details += 'A. Basic Info\n'
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details += ' - Official Website: [{}]({})\n'.format(
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dataset, data['Website'])
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details += ' - Year: {}\n'.format(paper['Year'])
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details += ' - Language: {}\n'.format(data['Language'])
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details += ' - Scene: {}\n'.format(data['Scene'])
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details += ' - Annotation Granularity: {}\n'.format(data['Granularity'])
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details += ' - Supported Tasks: {}\n'.format(data['Tasks'])
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details += ' - License: [{}]({})\n'.format(data['License']['Type'],
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data['License']['Link'])
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# Format
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details += '<details> <summary>B. Annotation Format</summary>\n\n</br>'
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sample_path = osp.join(dataset_zoo_path, dataset, 'sample_anno.md')
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if osp.exists(sample_path):
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with open(sample_path, 'r') as f:
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samples = f.readlines()
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samples = ''.join(samples)
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details += samples
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details += '</details>\n\n</br>'
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# Reference
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details += 'C. Reference\n'
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details += '```bibtex\n{}\n```\n'.format(paper['BibTeX'])
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datasetzoo = table + details
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with open('user_guides/data_prepare/datasetzoo.md', 'w') as f:
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f.write(datasetzoo)
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