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.tolist())}) 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 result_sequence = iterate_ner_results(json.loads(resp.content)) tsv_result = [] for idx, row in tsv.iterrows(): row_token = unicodedata.normalize('NFC', 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', 'GND-ID', 'url_id', 'left', 'right', 'top', 'bottom']) @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('--noproxy', type=bool, is_flag=True, help='disable proxy. default: enabled.') def page2tsv(page_xml_file, tsv_out_file, image_url, ner_rest_endpoint, noproxy): 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=['No.', 'TOKEN', 'NE-TAG', 'NE-EMB', 'GND-ID', 'url_id', 'left', 'right', 'top', 'bottom']). to_csv(tsv_out_file, sep="\t", quoting=3, index=False) tsv = [] for words in tree.findall('.//{%s}Word' % xmlns): for word in words.findall('.//{%s}Unicode' % xmlns): text = word.text for coords in words.findall('.//{%s}Coords' % xmlns): # transform the OCR coordinates by 0.5685 to derive the correct coords for the web presentation image points = [int(0.5685 * 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((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=['No.', 'TOKEN', 'NE-TAG', 'NE-EMB', 'GND-ID', 'url_id', 'left', 'right', 'top', 'bottom']) if ner_rest_endpoint is not None: tsv = ner(tsv, ner_rest_endpoint) tsv.to_csv(tsv_out_file, sep="\t", quoting=3, index=False, mode='a', header=False)