Source code for fonduer.utils.data_model_utils.visual

###########################
# Visual modality utilities
###########################

from builtins import range
from collections import defaultdict
from functools import lru_cache
from typing import DefaultDict, Iterator, List, Set, Union

from fonduer.candidates.mentions import Ngrams
from fonduer.candidates.models import Candidate, Mention
from fonduer.candidates.models.span_mention import TemporarySpanMention
from fonduer.parser.models import Document, Sentence
from fonduer.utils.data_model_utils.utils import _to_span, _to_spans
from fonduer.utils.utils import tokens_to_ngrams
from fonduer.utils.utils_visual import (
    bbox_from_sentence,
    bbox_from_span,
    bbox_horz_aligned,
    bbox_vert_aligned,
    bbox_vert_aligned_center,
    bbox_vert_aligned_left,
    bbox_vert_aligned_right,
)


[docs]@lru_cache(maxsize=1024) def get_page(mention: Union[Candidate, Mention, TemporarySpanMention]) -> int: """Return the page number of the given mention. If a candidate is passed in, this returns the page of its first Mention. :param mention: The Mention to get the page number of. :rtype: integer """ span = _to_span(mention) return span.get_attrib_tokens("page")[0]
[docs]@lru_cache(maxsize=1024) def is_horz_aligned(c: Candidate) -> bool: """Return True if all the components of c are horizontally aligned. Horizontal alignment means that the bounding boxes of each Mention of c shares a similar y-axis value in the visual rendering of the document. :param c: The candidate to evaluate :rtype: boolean """ return all( [ _to_span(c[i]).sentence.is_visual() and bbox_horz_aligned( bbox_from_span(_to_span(c[i])), bbox_from_span(_to_span(c[0])) ) for i in range(len(c)) ] )
[docs]@lru_cache(maxsize=1024) def is_vert_aligned(c: Candidate) -> bool: """Return true if all the components of c are vertically aligned. Vertical alignment means that the bounding boxes of each Mention of c shares a similar x-axis value in the visual rendering of the document. :param c: The candidate to evaluate :rtype: boolean """ return all( [ _to_span(c[i]).sentence.is_visual() and bbox_vert_aligned( bbox_from_span(_to_span(c[i])), bbox_from_span(_to_span(c[0])) ) for i in range(len(c)) ] )
[docs]@lru_cache(maxsize=1024) def is_vert_aligned_left(c: Candidate) -> bool: """Return true if all components are vertically aligned on their left border. Vertical alignment means that the bounding boxes of each Mention of c shares a similar x-axis value in the visual rendering of the document. In this function the similarity of the x-axis value is based on the left border of their bounding boxes. :param c: The candidate to evaluate :rtype: boolean """ return all( [ _to_span(c[i]).sentence.is_visual() and bbox_vert_aligned_left( bbox_from_span(_to_span(c[i])), bbox_from_span(_to_span(c[0])) ) for i in range(len(c)) ] )
[docs]@lru_cache(maxsize=1024) def is_vert_aligned_right(c: Candidate) -> bool: """Return true if all components vertically aligned on their right border. Vertical alignment means that the bounding boxes of each Mention of c shares a similar x-axis value in the visual rendering of the document. In this function the similarity of the x-axis value is based on the right border of their bounding boxes. :param c: The candidate to evaluate :rtype: boolean """ return all( [ _to_span(c[i]).sentence.is_visual() and bbox_vert_aligned_right( bbox_from_span(_to_span(c[i])), bbox_from_span(_to_span(c[0])) ) for i in range(len(c)) ] )
[docs]@lru_cache(maxsize=1024) def is_vert_aligned_center(c: Candidate) -> bool: """Return true if all the components are vertically aligned on their center. Vertical alignment means that the bounding boxes of each Mention of c shares a similar x-axis value in the visual rendering of the document. In this function the similarity of the x-axis value is based on the center of their bounding boxes. :param c: The candidate to evaluate :rtype: boolean """ return all( [ _to_span(c[i]).sentence.is_visual() and bbox_vert_aligned_center( bbox_from_span(_to_span(c[i])), bbox_from_span(_to_span(c[0])) ) for i in range(len(c)) ] )
[docs]@lru_cache(maxsize=1024) def same_page(c: Candidate) -> bool: """Return true if all the components of c are on the same page of the document. Page numbers are based on the PDF rendering of the document. If a PDF file is provided, it is used. Otherwise, if only a HTML/XML document is provided, a PDF is created and then used to determine the page number of a Mention. :param c: The candidate to evaluate :rtype: boolean """ return all( [ _to_span(c[i]).sentence.is_visual() and bbox_from_span(_to_span(c[i])).page == bbox_from_span(_to_span(c[0])).page for i in range(len(c)) ] )
[docs]def get_horz_ngrams( mention: Union[Candidate, Mention, TemporarySpanMention], attrib: str = "words", n_min: int = 1, n_max: int = 1, lower: bool = True, from_sentence: bool = True, ) -> Iterator[str]: """Return all ngrams which are visually horizontally aligned with the Mention. Note that if a candidate is passed in, all of its Mentions will be searched. :param mention: The Mention to evaluate :param attrib: The token attribute type (e.g. words, lemmas, poses) :param n_min: The minimum n of the ngrams that should be returned :param n_max: The maximum n of the ngrams that should be returned :param lower: If True, all ngrams will be returned in lower case :param from_sentence: If True, returns ngrams from any horizontally aligned Sentences, rather than just horizontally aligned ngrams themselves. :rtype: a *generator* of ngrams """ spans = _to_spans(mention) for span in spans: for ngram in _get_direction_ngrams( "horz", span, attrib, n_min, n_max, lower, from_sentence ): yield ngram
[docs]def get_vert_ngrams( mention: Union[Candidate, Mention, TemporarySpanMention], attrib: str = "words", n_min: int = 1, n_max: int = 1, lower: bool = True, from_sentence: bool = True, ) -> Iterator[str]: """Return all ngrams which are visually vertivally aligned with the Mention. Note that if a candidate is passed in, all of its Mentions will be searched. :param mention: The Mention to evaluate :param attrib: The token attribute type (e.g. words, lemmas, poses) :param n_min: The minimum n of the ngrams that should be returned :param n_max: The maximum n of the ngrams that should be returned :param lower: If True, all ngrams will be returned in lower case :param from_sentence: If True, returns ngrams from any horizontally aligned Sentences, rather than just horizontally aligned ngrams themselves. :rtype: a *generator* of ngrams """ spans = _to_spans(mention) for span in spans: for ngram in _get_direction_ngrams( "vert", span, attrib, n_min, n_max, lower, from_sentence ): yield ngram
def _get_direction_ngrams( direction: str, c: Union[Candidate, Mention, TemporarySpanMention], attrib: str, n_min: int, n_max: int, lower: bool, from_sentence: bool, ) -> Iterator[str]: # TODO: this currently looks only in current table; # precompute over the whole document/page instead bbox_direction_aligned = ( bbox_vert_aligned if direction == "vert" else bbox_horz_aligned ) ngrams_space = Ngrams(n_max=n_max, split_tokens=[]) f = (lambda w: w.lower()) if lower else (lambda w: w) spans = _to_spans(c) for span in spans: if not span.sentence.is_tabular() or not span.sentence.is_visual(): continue for sentence in span.sentence.table.sentences: if from_sentence: if ( bbox_direction_aligned( bbox_from_sentence(sentence), bbox_from_span(span) ) and sentence is not span.sentence ): for ngram in tokens_to_ngrams( getattr(sentence, attrib), n_min=n_min, n_max=n_max, lower=lower ): yield ngram else: for ts in ngrams_space.apply(sentence): if bbox_direction_aligned( bbox_from_span(ts), bbox_from_span(span) ) and not ( sentence == span.sentence and ts.get_span() in span.get_span() ): yield f(ts.get_span())
[docs]def get_vert_ngrams_left(c): # type: ignore """Not implemented.""" # TODO return
[docs]def get_vert_ngrams_right(c): # type: ignore """Not implemented.""" # TODO return
[docs]def get_vert_ngrams_center(c): # type: ignore """Not implemented.""" # TODO return
[docs]def get_visual_header_ngrams(c, axis=None): # type: ignore """Not implemented.""" # TODO return
[docs]def get_visual_distance(c, axis=None): # type: ignore """Not implemented.""" # TODO return
# Default dimensions for 8.5" x 11" DEFAULT_WIDTH = 612 DEFAULT_HEIGHT = 792
[docs]def get_page_vert_percentile( mention: Union[Candidate, Mention, TemporarySpanMention], page_width: int = DEFAULT_WIDTH, page_height: int = DEFAULT_HEIGHT, ) -> float: """Return which percentile from the TOP in the page the Mention is located in. Percentile is calculated where the top of the page is 0.0, and the bottom of the page is 1.0. For example, a Mention in at the top 1/4 of the page will have a percentile of 0.25. Page width and height are based on pt values:: Letter 612x792 Tabloid 792x1224 Ledger 1224x792 Legal 612x1008 Statement 396x612 Executive 540x720 A0 2384x3371 A1 1685x2384 A2 1190x1684 A3 842x1190 A4 595x842 A4Small 595x842 A5 420x595 B4 729x1032 B5 516x729 Folio 612x936 Quarto 610x780 10x14 720x1008 and should match the source documents. Letter size is used by default. Note that if a candidate is passed in, only the vertical percentil of its first Mention is returned. :param mention: The Mention to evaluate :param page_width: The width of the page. Default to Letter paper width. :param page_height: The heigh of the page. Default to Letter paper height. :rtype: float in [0.0, 1.0] """ span = _to_span(mention) return bbox_from_span(span).top / page_height
[docs]def get_page_horz_percentile( mention: Union[Candidate, Mention, TemporarySpanMention], page_width: int = DEFAULT_WIDTH, page_height: int = DEFAULT_HEIGHT, ) -> float: """Return which percentile from the LEFT in the page the Mention is located in. Percentile is calculated where the left of the page is 0.0, and the right of the page is 1.0. Page width and height are based on pt values:: Letter 612x792 Tabloid 792x1224 Ledger 1224x792 Legal 612x1008 Statement 396x612 Executive 540x720 A0 2384x3371 A1 1685x2384 A2 1190x1684 A3 842x1190 A4 595x842 A4Small 595x842 A5 420x595 B4 729x1032 B5 516x729 Folio 612x936 Quarto 610x780 10x14 720x1008 and should match the source documents. Letter size is used by default. Note that if a candidate is passed in, only the vertical percentile of its first Mention is returned. :param c: The Mention to evaluate :param page_width: The width of the page. Default to Letter paper width. :param page_height: The heigh of the page. Default to Letter paper height. :rtype: float in [0.0, 1.0] """ span = _to_span(mention) return bbox_from_span(span).left / page_width
def _assign_alignment_features(sentences_by_key: defaultdict, align_type: str) -> None: for key, sentences in sentences_by_key.items(): if len(sentences) == 1: continue context_lemmas: Set[str] = set() for p in sentences: p._aligned_lemmas.update(context_lemmas) # update lemma context for upcoming sentences in the group if len(p.lemmas) < 7: new_lemmas = [lemma.lower() for lemma in p.lemmas if lemma.isalpha()] context_lemmas.update(new_lemmas) context_lemmas.update(align_type + lemma for lemma in new_lemmas) @lru_cache(maxsize=2) def _preprocess_visual_features(doc: Document) -> None: if hasattr(doc, "_visual_features"): return # cache flag doc._visual_features = True sentence_by_page: DefaultDict[str, List[Sentence]] = defaultdict(list) for sentence in doc.sentences: sentence_by_page[sentence.page[0]].append(sentence) sentence._aligned_lemmas = set() for page, sentences in sentence_by_page.items(): # process per page alignments yc_aligned: DefaultDict[int, List[Sentence]] = defaultdict(list) x0_aligned: DefaultDict[int, List[Sentence]] = defaultdict(list) xc_aligned: DefaultDict[int, List[Sentence]] = defaultdict(list) x1_aligned: DefaultDict[int, List[Sentence]] = defaultdict(list) for sentence in sentences: sentence.bbox = bbox_from_sentence(sentence) sentence.yc = (sentence.bbox.top + sentence.bbox.bottom) / 2 sentence.x0 = sentence.bbox.left sentence.x1 = sentence.bbox.right sentence.xc = (sentence.x0 + sentence.x1) / 2 # index current sentence by different alignment keys yc_aligned[sentence.yc].append(sentence) x0_aligned[sentence.x0].append(sentence) x1_aligned[sentence.x1].append(sentence) xc_aligned[sentence.xc].append(sentence) for l in yc_aligned.values(): l.sort(key=lambda p: p.xc) for l in x0_aligned.values(): l.sort(key=lambda p: p.yc) for l in x1_aligned.values(): l.sort(key=lambda p: p.yc) for l in xc_aligned.values(): l.sort(key=lambda p: p.yc) _assign_alignment_features(yc_aligned, "Y_") _assign_alignment_features(x0_aligned, "LEFT_") _assign_alignment_features(x1_aligned, "RIGHT_") _assign_alignment_features(xc_aligned, "CENTER_")
[docs]def get_visual_aligned_lemmas( mention: Union[Candidate, Mention, TemporarySpanMention] ) -> Iterator[str]: """Return a generator of the lemmas aligned visually with the Mention. Note that if a candidate is passed in, all of its Mentions will be searched. :param mention: The Mention to evaluate. :rtype: a *generator* of lemmas """ spans = _to_spans(mention) for span in spans: sentence = span.sentence doc = sentence.document # cache features for the entire document _preprocess_visual_features(doc) for aligned_lemma in sentence._aligned_lemmas: yield aligned_lemma
[docs]def get_aligned_lemmas( mention: Union[Candidate, Mention, TemporarySpanMention] ) -> Set[str]: """Return a set of the lemmas aligned visually with the Mention. Note that if a candidate is passed in, all of its Mentions will be searched. :param mention: The Mention to evaluate. :rtype: a set of lemmas """ return set(get_visual_aligned_lemmas(mention))