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257 lines
11 KiB
Python
257 lines
11 KiB
Python
import glob
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import re
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import os
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from io import StringIO
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from pathlib import Path
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import numpy as np
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import click
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import pandas as pd
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import requests
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from lxml import etree as ET
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from ocrd_models.ocrd_page import parse
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from ocrd_utils import bbox_from_points
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from qurator.utils.tsv import read_tsv, write_tsv, extract_doc_links
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from .ocr import get_conf_color
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from qurator.utils.ner import ner
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from qurator.utils.ned import ned
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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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@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('--purpose', type=click.Choice(['NERD', 'OCR'], case_sensitive=False), default="NERD",
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help="Purpose of output tsv file. "
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"\n\nNERD: NER/NED application/ground-truth creation. "
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"\n\nOCR: OCR application/ground-truth creation. "
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"\n\ndefault: NERD.")
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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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"Only applicable in case of NERD.")
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@click.option('--ned-rest-endpoint', type=str, default=None,
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help="REST endpoint of sbb_ned service. See https://github.com/qurator-spk/sbb_ned for details. "
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"Only applicable in case of NERD.")
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@click.option('--noproxy', type=bool, is_flag=True, help='disable proxy. default: enabled.')
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@click.option('--scale-factor', type=float, default=1.0, help='default: 1.0')
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@click.option('--ned-threshold', type=float, default=None)
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@click.option('--min-confidence', type=float, default=None)
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@click.option('--max-confidence', type=float, default=None)
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@click.option('--ned-priority', type=int, default=1)
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def page2tsv(page_xml_file, tsv_out_file, purpose, image_url, ner_rest_endpoint, ned_rest_endpoint,
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noproxy, scale_factor, ned_threshold, min_confidence, max_confidence, ned_priority):
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if purpose == "NERD":
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out_columns = ['No.', 'TOKEN', 'NE-TAG', 'NE-EMB', 'ID', 'url_id', 'left', 'right', 'top', 'bottom', 'conf']
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elif purpose == "OCR":
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out_columns = ['TEXT', 'url_id', 'left', 'right', 'top', 'bottom', 'conf', 'line_id']
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if min_confidence is not None and max_confidence is not None:
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out_columns += ['ocrconf']
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else:
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raise RuntimeError("Unknown purpose.")
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if noproxy:
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os.environ['no_proxy'] = '*'
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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=out_columns).to_csv(tsv_out_file, sep="\t", quoting=3, index=False)
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pcgts = parse(page_xml_file)
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tsv = []
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line_info = []
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for region_idx, region in enumerate(pcgts.get_Page().get_AllRegions(classes=['Text'], order='reading-order')):
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for text_line in region.get_TextLine():
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left, top, right, bottom = [int(scale_factor * x) for x in bbox_from_points(text_line.get_Coords().points)]
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if min_confidence is not None and max_confidence is not None:
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conf = np.max([textequiv.conf for textequiv in text_line.get_TextEquiv()])
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else:
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conf = np.nan
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line_info.append((len(urls), left, right, top, bottom, conf, text_line.id))
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words = [word for word in text_line.get_Word()]
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if len(words) <= 0:
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for text_equiv in text_line.get_TextEquiv():
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# transform OCR coordinates using `scale_factor` to derive
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# correct coordinates for the web presentation image
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left, top, right, bottom = [int(scale_factor * x) for x in
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bbox_from_points(text_line.get_Coords().points)]
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tsv.append((region_idx, len(line_info) - 1, left + (right - left) / 2.0,
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text_equiv.get_Unicode(), len(urls), left, right, top, bottom, text_line.id))
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else:
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for word in words:
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for text_equiv in word.get_TextEquiv():
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# transform OCR coordinates using `scale_factor` to derive
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# correct coordinates for the web presentation image
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left, top, right, bottom = [int(scale_factor * x) for x in bbox_from_points(word.get_Coords().points)]
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tsv.append((region_idx, len(line_info) - 1, left + (right - left) / 2.0,
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text_equiv.get_Unicode(), len(urls), left, right, top, bottom, text_line.id))
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line_info = pd.DataFrame(line_info, columns=['url_id', 'left', 'right', 'top', 'bottom', 'conf', 'line_id'])
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if min_confidence is not None and max_confidence is not None:
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line_info['ocrconf'] = line_info.conf.map(lambda x: get_conf_color(x, min_confidence, max_confidence))
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tsv = pd.DataFrame(tsv, columns=['rid', 'line', 'hcenter'] +
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['TEXT', 'url_id', 'left', 'right', 'top', 'bottom', 'line_id'])
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if len(tsv) == 0:
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return
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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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vlinecenter = pd.DataFrame(tsv[['line', 'top']].groupby('line', sort=False).mean().top +
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(tsv[['line', 'bottom']].groupby('line', sort=False).mean().bottom -
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tsv[['line', 'top']].groupby('line', sort=False).mean().top) / 2,
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columns=['vlinecenter'])
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tsv = tsv.merge(vlinecenter, left_on='line', right_index=True)
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regions = [region.sort_values(['vlinecenter', 'hcenter']) for rid, region in tsv.groupby('rid', sort=False)]
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tsv = pd.concat(regions)
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if purpose == 'NERD':
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tsv['No.'] = 0
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tsv['NE-TAG'] = 'O'
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tsv['NE-EMB'] = 'O'
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tsv['ID'] = '-'
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tsv['conf'] = '-'
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tsv = tsv.rename(columns={'TEXT': 'TOKEN'})
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elif purpose == 'OCR':
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tsv = pd.DataFrame([(line, " ".join(part.TEXT.to_list())) for line, part in tsv.groupby('line')],
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columns=['line', 'TEXT'])
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tsv = tsv.merge(line_info, left_on='line', right_index=True)
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tsv = tsv[out_columns].reset_index(drop=True)
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try:
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if purpose == 'NERD' and ner_rest_endpoint is not None:
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tsv, ner_result = ner(tsv, ner_rest_endpoint)
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if ned_rest_endpoint is not None:
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tsv, _ = ned(tsv, ner_result, ned_rest_endpoint, threshold=ned_threshold, priority=ned_priority)
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tsv.to_csv(tsv_out_file, sep="\t", quoting=3, index=False, mode='a', header=False)
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except requests.HTTPError as e:
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print(e)
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@click.command()
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@click.option('--output-filename', '-o', help="Output filename. "
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"If omitted, PAGE-XML filename with .corrected.xml extension")
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@click.option('--keep-words', '-k', is_flag=True, help="Keep (out-of-date) Words of TextLines")
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@click.argument('page-file')
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@click.argument('tsv-file')
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def tsv2page(output_filename, keep_words, page_file, tsv_file):
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if not output_filename:
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output_filename = Path(page_file).stem + '.corrected.xml'
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ns = {'pc': 'http://schema.primaresearch.org/PAGE/gts/pagecontent/2019-07-15'}
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tsv = pd.read_csv(tsv_file, sep='\t', comment='#', quoting=3)
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tree = ET.parse(page_file)
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for _, row in tsv.iterrows():
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el_textline = tree.find(f'//pc:TextLine[@id="{row.line_id}"]', namespaces=ns)
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el_textline.find('pc:TextEquiv/pc:Unicode', namespaces=ns).text = row.TEXT
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if not keep_words:
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for el_word in el_textline.findall('pc:Word', namespaces=ns):
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el_textline.remove(el_word)
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with open(output_filename, 'w', encoding='utf-8') as f:
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f.write(ET.tostring(tree, pretty_print=True).decode('utf-8'))
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@click.command()
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@click.option('--xls-file', type=click.Path(exists=True), default=None,
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help="Read parameters from xls-file. Expected columns: Filename, iiif_url, scale_factor.")
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@click.option('--directory', type=click.Path(exists=True), default=None,
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help="Search directory for PPN**/*.xml files. Extract PPN and file number into image-url.")
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@click.option('--purpose', type=click.Choice(['NERD', 'OCR'], case_sensitive=False), default="NERD",
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help="Purpose of output tsv file. "
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"\n\nNERD: NER/NED application/ground-truth creation. "
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"\n\nOCR: OCR application/ground-truth creation. "
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"\n\ndefault: NERD.")
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def make_page2tsv_commands(xls_file, directory, purpose):
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if xls_file is not None:
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if xls_file.endswith(".xls"):
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df = pd.read_excel(xls_file)
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else:
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df = pd.read_excel(xls_file, engine='openpyxl')
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df = df.dropna(how='all')
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for _, row in df.iterrows():
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print('page2tsv $(OPTIONS) {}.xml {}.tsv --image-url={} --scale-factor={} --purpose={}'.
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format(row.Filename, row.Filename, row.iiif_url.replace('/full/full', '/left,top,width,height/full'),
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row.scale_factor, purpose))
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elif directory is not None:
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for file in glob.glob('{}/**/*.xml'.format(directory), recursive=True):
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ma = re.match('(.*/(PPN[0-9X]+)/.*?([0-9]+).*?).xml', file)
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if ma:
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print('page2tsv {} {}.tsv '
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'--image-url=https://content.staatsbibliothek-berlin.de/dc/'
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'{}-{:08d}/left,top,width,height/full/0/default.jpg --scale-factor=1.0 --purpose={}'.
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format(file, ma.group(1), ma.group(2), int(ma.group(3)), purpose))
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