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extract TSV tools from neath
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assets/example.xml
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assets/example.xml
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keypress.js
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keypress.js
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# TSV - Processing Tools
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## Installation:
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Setup virtual environment:
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```
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virtualenv --python=python3.6 venv
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```
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Activate virtual environment:
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```
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source venv/bin/activate
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```
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Upgrade pip:
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```
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pip install -U pip
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```
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Install package together with its dependencies in development mode:
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```
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pip install -e ./
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```
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## PAGE-XML to TSV Transformation:
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Create a TSV file from OCR in PAGE-XML format (with word segmentation):
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```
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page2tsv PAGE1.xml PAGE.tsv --image-url=http://link-to-corresponding-image-1
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```
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In order to create a TSV file for multiple PAGE XML files just perform successive calls
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of the tool using the same TSV file:
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```
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page2tsv PAGE1.xml PAGE.tsv --image-url=http://link-to-corresponding-image-1
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page2tsv PAGE2.xml PAGE.tsv --image-url=http://link-to-corresponding-image-2
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page2tsv PAGE3.xml PAGE.tsv --image-url=http://link-to-corresponding-image-3
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page2tsv PAGE4.xml PAGE.tsv --image-url=http://link-to-corresponding-image-4
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page2tsv PAGE5.xml PAGE.tsv --image-url=http://link-to-corresponding-image-5
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...
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...
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...
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```
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For instance, for the file assets/example.xml:
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```
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page2tsv example.xml example.tsv --image-url=http://content.staatsbibliothek-berlin.de/zefys/SNP27646518-18800101-0-3-0-0/left,top,width,height/full/0/default.jpg
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```
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---
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## Processing of already existing TSV files:
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Create a URL-annotated TSV file from an existing TSV file:
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```
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annotate-tsv enp_DE.tsv enp_DE-annotated.tsv
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```
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tools/cli.py
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tools/cli.py
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import re
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import click
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import pandas as pd
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from io import StringIO
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import os
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import xml.etree.ElementTree as ET
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import requests
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import unicodedata
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import json
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@click.command()
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@click.argument('tsv-file', type=click.Path(exists=True), required=True, nargs=1)
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@click.argument('url-file', type=click.Path(exists=False), required=True, nargs=1)
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def extract_document_links(tsv_file, url_file):
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parts = extract_doc_links(tsv_file)
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urls = [part['url'] for part in parts]
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urls = pd.DataFrame(urls, columns=['url'])
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urls.to_csv(url_file, sep="\t", quoting=3, index=False)
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@click.command()
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@click.argument('tsv-file', type=click.Path(exists=True), required=True, nargs=1)
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@click.argument('annotated-tsv-file', type=click.Path(exists=False), required=True, nargs=1)
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def annotate_tsv(tsv_file, annotated_tsv_file):
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parts = extract_doc_links(tsv_file)
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annotated_parts = []
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for part in parts:
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part_data = StringIO(part['header'] + part['text'])
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df = pd.read_csv(part_data, sep="\t", comment='#', quoting=3)
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df['url_id'] = len(annotated_parts)
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annotated_parts.append(df)
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df = pd.concat(annotated_parts)
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df.to_csv(annotated_tsv_file, sep="\t", quoting=3, index=False)
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def extract_doc_links(tsv_file):
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parts = []
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header = None
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with open(tsv_file, 'r') as f:
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text = []
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url = None
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for line in f:
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if header is None:
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header = "\t".join(line.split()) + '\n'
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continue
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urls = [url for url in
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re.findall(r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', line)]
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if len(urls) > 0:
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if url is not None:
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parts.append({"url": url, 'header': header, 'text': "".join(text)})
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text = []
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url = urls[-1]
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else:
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if url is None:
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continue
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line = '\t'.join(line.split())
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if line.count('\t') == 2:
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line = "\t" + line
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if line.count('\t') >= 3:
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text.append(line + '\n')
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continue
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if line.startswith('#'):
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continue
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if len(line) == 0:
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continue
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print('Line error: |', line, '|Number of Tabs: ', line.count('\t'))
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if url is not None:
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parts.append({"url": url, 'header': header, 'text': "".join(text)})
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return parts
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def ner(tsv, ner_rest_endpoint):
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resp = requests.post(url=ner_rest_endpoint, json={'text': " ".join(tsv.TOKEN.tolist())})
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def iterate_ner_results(result_sentences):
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for sen in result_sentences:
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for token in sen:
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yield unicodedata.normalize('NFC', token['word']), token['prediction'], False
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yield '', '', True
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result_sequence = iterate_ner_results(json.loads(resp.content))
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tsv_result = []
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for idx, row in tsv.iterrows():
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row_token = unicodedata.normalize('NFC', row.TOKEN.replace(' ', ''))
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ner_token_concat = ''
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while row_token != ner_token_concat:
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ner_token, ner_tag, sentence_break = next(result_sequence)
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ner_token_concat += ner_token
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assert len(row_token) >= len(ner_token_concat)
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if sentence_break:
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tsv_result.append((0, '', 'O', 'O', '-', row.url_id, row.left, row.right, row.top, row.bottom))
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else:
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tsv_result.append((0, ner_token, ner_tag, 'O', '-', row.url_id, row.left, row.right, row.top,
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row.bottom))
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return pd.DataFrame(tsv_result, columns=['No.', 'TOKEN', 'NE-TAG', 'NE-EMB', 'GND-ID', 'url_id',
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'left', 'right', 'top', 'bottom'])
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@click.command()
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@click.argument('page-xml-file', type=click.Path(exists=True), required=True, nargs=1)
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@click.argument('tsv-out-file', type=click.Path(), required=True, nargs=1)
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@click.option('--image-url', type=str, default='http://empty')
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@click.option('--ner-rest-endpoint', type=str, default=None,
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help="REST endpoint of sbb_ner service. See https://github.com/qurator-spk/sbb_ner for details.")
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@click.option('--noproxy', type=bool, is_flag=True, help='disable proxy. default: enabled.')
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def page2tsv(page_xml_file, tsv_out_file, image_url, ner_rest_endpoint, noproxy):
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if noproxy:
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os.environ['no_proxy'] = '*'
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tree = ET.parse(page_xml_file)
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xmlns = tree.getroot().tag.split('}')[0].strip('{')
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urls = []
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if os.path.exists(tsv_out_file):
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parts = extract_doc_links(tsv_out_file)
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urls = [part['url'] for part in parts]
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else:
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pd.DataFrame([], columns=['No.', 'TOKEN', 'NE-TAG', 'NE-EMB', 'GND-ID', 'url_id', 'left', 'right', 'top',
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'bottom']). to_csv(tsv_out_file, sep="\t", quoting=3, index=False)
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tsv = []
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for words in tree.findall('.//{%s}Word' % xmlns):
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for word in words.findall('.//{%s}Unicode' % xmlns):
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text = word.text
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for coords in words.findall('.//{%s}Coords' % xmlns):
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# transform the OCR coordinates by 0.5685 to derive the correct coords for the web presentation image
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points = [int(0.5685 * float(pos)) for p in coords.attrib['points'].split(' ') for pos in p.split(',')]
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x_points = [points[i] for i in range(0, len(points), 2)]
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y_points = [points[i] for i in range(1, len(points), 2)]
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left = min(x_points)
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right = max(x_points)
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top = min(y_points)
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bottom = max(y_points)
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tsv.append((0, text, 'O', 'O', '-', len(urls), left, right, top, bottom))
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with open(tsv_out_file, 'a') as f:
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f.write('# ' + image_url + '\n')
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tsv = pd.DataFrame(tsv, columns=['No.', 'TOKEN', 'NE-TAG', 'NE-EMB', 'GND-ID',
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'url_id', 'left', 'right', 'top', 'bottom'])
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if ner_rest_endpoint is not None:
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tsv = ner(tsv, ner_rest_endpoint)
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tsv.to_csv(tsv_out_file, sep="\t", quoting=3, index=False, mode='a', header=False)
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numpy
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pandas
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click
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requests
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from io import open
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from setuptools import find_packages, setup
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with open('requirements.txt') as fp:
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install_requires = fp.read()
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setup(
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name="neath",
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version="0.0.1",
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author="",
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author_email="qurator@sbb.spk-berlin.de",
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description="neath",
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long_description=open("README.md", "r", encoding='utf-8').read(),
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long_description_content_type="text/markdown",
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keywords='qurator',
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license='Apache License 2.0',
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url="https://github.com/qurator-spk/neath",
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packages=find_packages(exclude=["*.tests", "*.tests.*",
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"tests.*", "tests"]),
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install_requires=install_requires,
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entry_points={
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'console_scripts': [
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"extract-doc-links=cli:extract_document_links",
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"annotate-tsv=cli:annotate_tsv",
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"page2tsv=cli:page2tsv"
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]
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},
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python_requires='>=3.6.0',
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tests_require=['pytest'],
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classifiers=[
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'Intended Audience :: Science/Research',
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'License :: OSI Approved :: Apache Software License',
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'Programming Language :: Python :: 3',
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'Topic :: Scientific/Engineering :: Artificial Intelligence',
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],
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)
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