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page2tsv/cli.py

357 lines
12 KiB
Python

import re
import click
import pandas as pd
from io import StringIO
import os
import xml.etree.ElementTree as ET
import requests
import unicodedata
import json
@click.command()
@click.argument('tsv-file', type=click.Path(exists=True), required=True, nargs=1)
@click.argument('url-file', type=click.Path(exists=False), required=True, nargs=1)
def extract_document_links(tsv_file, url_file):
parts = extract_doc_links(tsv_file)
urls = [part['url'] for part in parts]
urls = pd.DataFrame(urls, columns=['url'])
urls.to_csv(url_file, sep="\t", quoting=3, index=False)
@click.command()
@click.argument('tsv-file', type=click.Path(exists=True), required=True, nargs=1)
@click.argument('annotated-tsv-file', type=click.Path(exists=False), required=True, nargs=1)
def annotate_tsv(tsv_file, annotated_tsv_file):
parts = extract_doc_links(tsv_file)
annotated_parts = []
for part in parts:
part_data = StringIO(part['header'] + part['text'])
df = pd.read_csv(part_data, sep="\t", comment='#', quoting=3)
df['url_id'] = len(annotated_parts)
annotated_parts.append(df)
df = pd.concat(annotated_parts)
df.to_csv(annotated_tsv_file, sep="\t", quoting=3, index=False)
def extract_doc_links(tsv_file):
parts = []
header = None
with open(tsv_file, 'r') as f:
text = []
url = None
for line in f:
if header is None:
header = "\t".join(line.split()) + '\n'
continue
urls = [url for url in
re.findall(r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', line)]
if len(urls) > 0:
if url is not None:
parts.append({"url": url, 'header': header, 'text': "".join(text)})
text = []
url = urls[-1]
else:
if url is None:
continue
line = '\t'.join(line.split())
if line.count('\t') == 2:
line = "\t" + line
if line.count('\t') >= 3:
text.append(line + '\n')
continue
if line.startswith('#'):
continue
if len(line) == 0:
continue
print('Line error: |', line, '|Number of Tabs: ', line.count('\t'))
if url is not None:
parts.append({"url": url, 'header': header, 'text': "".join(text)})
return parts
def ner(tsv, ner_rest_endpoint):
resp = requests.post(url=ner_rest_endpoint, json={'text': " ".join(tsv.TOKEN.astype(str).tolist())})
resp.raise_for_status()
def iterate_ner_results(result_sentences):
for sen in result_sentences:
for token in sen:
yield unicodedata.normalize('NFC', token['word']), token['prediction'], False
yield '', '', True
ner_result = json.loads(resp.content)
result_sequence = iterate_ner_results(ner_result)
tsv_result = []
for idx, row in tsv.iterrows():
row_token = unicodedata.normalize('NFC', str(row.TOKEN).replace(' ', ''))
ner_token_concat = ''
while row_token != ner_token_concat:
ner_token, ner_tag, sentence_break = next(result_sequence)
ner_token_concat += ner_token
assert len(row_token) >= len(ner_token_concat)
if sentence_break:
tsv_result.append((0, '', 'O', 'O', '-', row.url_id, row.left, row.right, row.top, row.bottom))
else:
tsv_result.append((0, ner_token, ner_tag, 'O', '-', row.url_id, row.left, row.right, row.top,
row.bottom))
return pd.DataFrame(tsv_result, columns=['No.', 'TOKEN', 'NE-TAG', 'NE-EMB', 'ID', 'url_id',
'left', 'right', 'top', 'bottom']), ner_result
def ned(tsv, ner_result, ned_rest_endpoint, json_file=None, threshold=None):
if os.path.exists(json_file):
print('Loading {}'.format(json_file))
with open(json_file, "r") as fp:
ned_result = json.load(fp)
else:
resp = requests.post(url=ned_rest_endpoint + '/parse', json=ner_result)
resp.raise_for_status()
ner_parsed = json.loads(resp.content)
ned_rest_endpoint = ned_rest_endpoint + '/ned?return_full=' + str(json_file is not None).lower()
resp = requests.post(url=ned_rest_endpoint, json=ner_parsed, timeout=3600000)
resp.raise_for_status()
ned_result = json.loads(resp.content)
rids = []
entity = ""
entity_type = None
def check_entity(tag):
nonlocal entity, entity_type, rids
if (entity != "") and ((tag == 'O') or tag.startswith('B-') or (tag[2:] != entity_type)):
eid = entity + "-" + entity_type
if eid in ned_result:
if 'ranking' in ned_result[eid]:
ranking = ned_result[eid]['ranking']
#tsv.loc[rids, 'ID'] = ranking[0][1]['wikidata'] if threshold is None or ranking[0][1]['proba_1'] >= threshold else ''
tmp = "|".join([ranking[i][1]['wikidata'] for i in range(len(ranking)) if threshold is None or ranking[i][1]['proba_1'] >= threshold])
tsv.loc[rids, 'ID'] = tmp if len(tmp) > 0 else '-'
rids = []
entity = ""
entity_type = None
for rid, row in tsv.iterrows():
check_entity(row['NE-TAG'])
if row['NE-TAG'] != 'O':
entity_type = row['NE-TAG'][2:]
entity += " " if entity != "" else ""
entity += row['TOKEN']
rids.append(rid)
check_entity('O')
return tsv, ned_result
@click.command()
@click.argument('page-xml-file', type=click.Path(exists=True), required=True, nargs=1)
@click.argument('tsv-out-file', type=click.Path(), required=True, nargs=1)
@click.option('--image-url', type=str, default='http://empty')
@click.option('--ner-rest-endpoint', type=str, default=None,
help="REST endpoint of sbb_ner service. See https://github.com/qurator-spk/sbb_ner for details.")
@click.option('--ned-rest-endpoint', type=str, default=None,
help="REST endpoint of sbb_ned service. See https://github.com/qurator-spk/sbb_ned for details.")
@click.option('--noproxy', type=bool, is_flag=True, help='disable proxy. default: enabled.')
@click.option('--scale-factor', type=float, default=0.5685, help='default: 0.5685')
@click.option('--ned-threshold', type=float, default=None)
def page2tsv(page_xml_file, tsv_out_file, image_url, ner_rest_endpoint, ned_rest_endpoint, noproxy, scale_factor,
ned_threshold):
out_columns = ['No.', 'TOKEN', 'NE-TAG', 'NE-EMB', 'ID', 'url_id', 'left', 'right', 'top', 'bottom']
if noproxy:
os.environ['no_proxy'] = '*'
tree = ET.parse(page_xml_file)
xmlns = tree.getroot().tag.split('}')[0].strip('{')
urls = []
if os.path.exists(tsv_out_file):
parts = extract_doc_links(tsv_out_file)
urls = [part['url'] for part in parts]
else:
pd.DataFrame([], columns=out_columns). to_csv(tsv_out_file, sep="\t", quoting=3, index=False)
tsv = []
line_number = 0
rgn_number = 0
for region in tree.findall('.//{%s}TextRegion' % xmlns):
rgn_number += 1
for text_line in region.findall('.//{%s}TextLine' % xmlns):
line_number += 1
for words in text_line.findall('./{%s}Word' % xmlns):
for word in words.findall('./{%s}TextEquiv/{%s}Unicode' % (xmlns, xmlns)):
text = word.text
for coords in words.findall('./{%s}Coords' % xmlns):
# transform OCR coordinates using `scale_factor` to derive
# correct coordinates for the web presentation image
points = [int(scale_factor * float(pos))
for p in coords.attrib['points'].split(' ') for pos in p.split(',')]
x_points = [points[i] for i in range(0, len(points), 2)]
y_points = [points[i] for i in range(1, len(points), 2)]
left = min(x_points)
right = max(x_points)
top = min(y_points)
bottom = max(y_points)
tsv.append((rgn_number, line_number, left + (right-left)/2.0,
0, text, 'O', 'O', '-', len(urls), left, right, top, bottom))
with open(tsv_out_file, 'a') as f:
f.write('# ' + image_url + '\n')
tsv = pd.DataFrame(tsv, columns=['rid', 'line', 'hcenter'] + out_columns)
vlinecenter = pd.DataFrame(tsv[['line', 'top']].groupby('line', sort=False).mean().top +
(tsv[['line', 'bottom']].groupby('line', sort=False).mean().bottom -
tsv[['line', 'top']].groupby('line', sort=False).mean().top) / 2,
columns=['vlinecenter'])
tsv = tsv.merge(vlinecenter, left_on='line', right_index=True)
regions = [region.sort_values(['vlinecenter', 'hcenter']) for rid, region in tsv.groupby('rid', sort=False)]
tsv = pd.concat(regions)
tsv = tsv[out_columns].reset_index(drop=True)
try:
if ner_rest_endpoint is not None:
tsv, ner_result = ner(tsv, ner_rest_endpoint)
if ned_rest_endpoint is not None:
tsv, _ = ned(tsv, ner_result, ned_rest_endpoint, threshold=ned_threshold)
tsv.to_csv(tsv_out_file, sep="\t", quoting=3, index=False, mode='a', header=False)
except requests.HTTPError as e:
print(e)
@click.command()
@click.argument('tsv-file', type=click.Path(exists=True), required=True, nargs=1)
@click.argument('tsv-out-file', type=click.Path(), required=True, nargs=1)
@click.option('--ner-rest-endpoint', type=str, default=None,
help="REST endpoint of sbb_ner service. See https://github.com/qurator-spk/sbb_ner for details.")
@click.option('--ned-rest-endpoint', type=str, default=None,
help="REST endpoint of sbb_ned service. See https://github.com/qurator-spk/sbb_ned for details.")
@click.option('--ned-json-file', type=str, default=None)
@click.option('--noproxy', type=bool, is_flag=True, help='disable proxy. default: proxy is enabled.')
@click.option('--ned-threshold', type=float, default=None)
def find_entities(tsv_file, tsv_out_file, ner_rest_endpoint, ned_rest_endpoint, ned_json_file, noproxy, ned_threshold):
if noproxy:
os.environ['no_proxy'] = '*'
tsv = pd.read_csv(tsv_file, sep='\t', comment='#', quoting=3)
try:
if ner_rest_endpoint is not None:
tsv, ner_result = ner(tsv, ner_rest_endpoint)
elif os.path.exists(tsv_file):
print('Using NER information that is already contained in file: {}'.format(tsv_file))
tmp = tsv.copy()
tmp['sen'] = (tmp['No.'] == 0).cumsum()
ner_result = [[{'word': row.TOKEN, 'prediction': row['NE-TAG']} for _, row in sen.iterrows()]
for _, sen in tmp.groupby('sen')]
else:
raise RuntimeError("Either NER rest endpoint or NER-TAG information within tsv_file required.")
if ned_rest_endpoint is not None:
tsv, ned_result = ned(tsv, ner_result, ned_rest_endpoint, json_file=ned_json_file, threshold=ned_threshold)
if ned_json_file is not None and not os.path.exists(ned_json_file):
with open(ned_json_file, "w") as fp_json:
json.dump(ned_result, fp_json, indent=2, separators=(',', ': '))
print('Writing to {}...'.format(tsv_out_file))
tsv.to_csv(tsv_out_file, sep="\t", quoting=3, index=False)
except requests.HTTPError as e:
print(e)