74 lines
2.3 KiB
Python
74 lines
2.3 KiB
Python
from pathlib import Path
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import Levenshtein
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import fitz_new as fitz
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import papis.logging
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import papis.config
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from papis_extract.annotation_data import Annotation
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COLOR_MAPPING = {}
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logger = papis.logging.get_logger(__name__)
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def start(filename: Path) -> list[Annotation]:
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"""Extract annotations from a file.
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Returns all readable annotations contained in the file
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passed in. Only returns Highlight or Text annotations.
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"""
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annotations = []
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with fitz.Document(filename) as doc:
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for page in doc:
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for annot in page.annots():
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quote, note = _retrieve_annotation_content(page, annot)
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a = Annotation(
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file=str(filename),
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text=quote,
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content=note,
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colors=annot.colors,
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type=annot.type[1],
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page=(page.number or 0) + 1,
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)
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a.tag = _tag_from_colorname(a.colorname)
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annotations.append(a)
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logger.debug(
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f"Found {len(annotations)} "
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f"{'annotation' if len(annotations) == 1 else 'annotations'} for {filename}."
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)
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return annotations
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def _tag_from_colorname(colorname):
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return COLOR_MAPPING.get(colorname, "")
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def _retrieve_annotation_content(page, annotation):
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"""Gets the text content of an annotation.
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Returns the actual content of an annotation. Sometimes
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that is only the written words, sometimes that is only
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annotation notes, sometimes it is both. Runs a similarity
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comparison between strings to find out whether they
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should both be included or are the same, using
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Levenshtein distance.
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"""
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content = annotation.info["content"].replace("\n", " ")
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written = page.get_textbox(annotation.rect).replace("\n", " ")
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# highlight with selection in note
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minimum_similarity = (
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papis.config.getfloat("minimum_similarity_content", "plugins.extract") or 1.0
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)
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if Levenshtein.ratio(content, written) > minimum_similarity:
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return (content, "")
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# an independent note, not a highlight
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elif content and not written:
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return ("", content)
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# both a highlight and a note
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elif content:
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return (written, content)
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# highlight with selection not in note
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return (written, "")
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