feat: Make Page.parsed_page the only source of truth for text cells, add OCR cells to it (#1745)
* Keep page.parsed_page.textline_cells and page.cells in sync, including OCR Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Make page.parsed_page the only source of truth for text cells Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Small fix Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Correctly compute PDF boxes from pymupdf Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Use different OCR engine order Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Add type hints and fix mypy Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * One more test fix Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Remove with pypdfium2_lock from caller sites Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Fix typing Signed-off-by: Christoph Auer <cau@zurich.ibm.com> --------- Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
This commit is contained in:
Christoph Auer
GitHub
parent
0432a31b2f
commit
7d3302cb48
@ -7,12 +7,17 @@ from typing import List, Optional, Union
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import pypdfium2 as pdfium
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from docling_core.types.doc import BoundingBox, CoordOrigin, Size
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from docling_core.types.doc.page import BoundingRectangle, SegmentedPdfPage, TextCell
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from docling_core.types.doc.page import (
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BoundingRectangle,
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SegmentedPdfPage,
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TextCell,
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)
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from docling_parse.pdf_parsers import pdf_parser_v1
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from PIL import Image, ImageDraw
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from pypdfium2 import PdfPage
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from docling.backend.pdf_backend import PdfDocumentBackend, PdfPageBackend
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from docling.backend.pypdfium2_backend import get_pdf_page_geometry
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from docling.datamodel.document import InputDocument
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_log = logging.getLogger(__name__)
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@ -36,6 +41,51 @@ class DoclingParsePageBackend(PdfPageBackend):
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def is_valid(self) -> bool:
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return self.valid
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def _compute_text_cells(self) -> List[TextCell]:
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"""Compute text cells from docling-parse data."""
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cells: List[TextCell] = []
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cell_counter = 0
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if not self.valid:
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return cells
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page_size = self.get_size()
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parser_width = self._dpage["width"]
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parser_height = self._dpage["height"]
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for i in range(len(self._dpage["cells"])):
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rect = self._dpage["cells"][i]["box"]["device"]
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x0, y0, x1, y1 = rect
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if x1 < x0:
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x0, x1 = x1, x0
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if y1 < y0:
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y0, y1 = y1, y0
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text_piece = self._dpage["cells"][i]["content"]["rnormalized"]
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cells.append(
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TextCell(
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index=cell_counter,
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text=text_piece,
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orig=text_piece,
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from_ocr=False,
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rect=BoundingRectangle.from_bounding_box(
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BoundingBox(
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l=x0 * page_size.width / parser_width,
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b=y0 * page_size.height / parser_height,
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r=x1 * page_size.width / parser_width,
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t=y1 * page_size.height / parser_height,
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coord_origin=CoordOrigin.BOTTOMLEFT,
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)
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).to_top_left_origin(page_size.height),
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)
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)
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cell_counter += 1
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return cells
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def get_text_in_rect(self, bbox: BoundingBox) -> str:
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if not self.valid:
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return ""
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@ -70,75 +120,27 @@ class DoclingParsePageBackend(PdfPageBackend):
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return text_piece
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def get_segmented_page(self) -> Optional[SegmentedPdfPage]:
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return None
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if not self.valid:
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return None
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text_cells = self._compute_text_cells()
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# Get the PDF page geometry from pypdfium2
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dimension = get_pdf_page_geometry(self._ppage)
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# Create SegmentedPdfPage
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return SegmentedPdfPage(
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dimension=dimension,
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textline_cells=text_cells,
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char_cells=[],
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word_cells=[],
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has_lines=len(text_cells) > 0,
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has_words=False,
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has_chars=False,
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)
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def get_text_cells(self) -> Iterable[TextCell]:
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cells: List[TextCell] = []
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cell_counter = 0
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if not self.valid:
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return cells
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page_size = self.get_size()
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parser_width = self._dpage["width"]
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parser_height = self._dpage["height"]
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for i in range(len(self._dpage["cells"])):
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rect = self._dpage["cells"][i]["box"]["device"]
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x0, y0, x1, y1 = rect
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if x1 < x0:
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x0, x1 = x1, x0
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if y1 < y0:
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y0, y1 = y1, y0
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text_piece = self._dpage["cells"][i]["content"]["rnormalized"]
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cells.append(
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TextCell(
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index=cell_counter,
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text=text_piece,
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orig=text_piece,
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from_ocr=False,
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rect=BoundingRectangle.from_bounding_box(
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BoundingBox(
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# l=x0, b=y0, r=x1, t=y1,
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l=x0 * page_size.width / parser_width,
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b=y0 * page_size.height / parser_height,
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r=x1 * page_size.width / parser_width,
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t=y1 * page_size.height / parser_height,
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coord_origin=CoordOrigin.BOTTOMLEFT,
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)
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).to_top_left_origin(page_size.height),
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)
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)
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cell_counter += 1
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def draw_clusters_and_cells():
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image = (
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self.get_page_image()
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) # make new image to avoid drawing on the saved ones
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draw = ImageDraw.Draw(image)
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for c in cells:
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x0, y0, x1, y1 = c.rect.to_bounding_box().as_tuple()
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cell_color = (
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random.randint(30, 140),
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random.randint(30, 140),
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random.randint(30, 140),
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)
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draw.rectangle([(x0, y0), (x1, y1)], outline=cell_color)
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image.show()
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# before merge:
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# draw_clusters_and_cells()
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# cells = merge_horizontal_cells(cells)
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# after merge:
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# draw_clusters_and_cells()
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return cells
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return self._compute_text_cells()
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def get_bitmap_rects(self, scale: float = 1) -> Iterable[BoundingBox]:
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AREA_THRESHOLD = 0 # 32 * 32
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@ -7,12 +7,19 @@ from typing import TYPE_CHECKING, List, Optional, Union
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import pypdfium2 as pdfium
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from docling_core.types.doc import BoundingBox, CoordOrigin
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from docling_core.types.doc.page import BoundingRectangle, SegmentedPdfPage, TextCell
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from docling_core.types.doc.page import (
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BoundingRectangle,
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PdfPageBoundaryType,
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PdfPageGeometry,
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SegmentedPdfPage,
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TextCell,
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)
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from docling_parse.pdf_parsers import pdf_parser_v2
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from PIL import Image, ImageDraw
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from pypdfium2 import PdfPage
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from docling.backend.pdf_backend import PdfDocumentBackend, PdfPageBackend
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from docling.backend.pypdfium2_backend import get_pdf_page_geometry
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from docling.datamodel.base_models import Size
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from docling.utils.locks import pypdfium2_lock
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@ -40,6 +47,55 @@ class DoclingParseV2PageBackend(PdfPageBackend):
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def is_valid(self) -> bool:
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return self.valid
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def _compute_text_cells(self) -> List[TextCell]:
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"""Compute text cells from docling-parse v2 data."""
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cells: List[TextCell] = []
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cell_counter = 0
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if not self.valid:
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return cells
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page_size = self.get_size()
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parser_width = self._dpage["sanitized"]["dimension"]["width"]
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parser_height = self._dpage["sanitized"]["dimension"]["height"]
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cells_data = self._dpage["sanitized"]["cells"]["data"]
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cells_header = self._dpage["sanitized"]["cells"]["header"]
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for i, cell_data in enumerate(cells_data):
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x0 = cell_data[cells_header.index("x0")]
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y0 = cell_data[cells_header.index("y0")]
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x1 = cell_data[cells_header.index("x1")]
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y1 = cell_data[cells_header.index("y1")]
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if x1 < x0:
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x0, x1 = x1, x0
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if y1 < y0:
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y0, y1 = y1, y0
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text_piece = cell_data[cells_header.index("text")]
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cells.append(
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TextCell(
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index=cell_counter,
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text=text_piece,
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orig=text_piece,
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from_ocr=False,
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rect=BoundingRectangle.from_bounding_box(
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BoundingBox(
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l=x0 * page_size.width / parser_width,
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b=y0 * page_size.height / parser_height,
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r=x1 * page_size.width / parser_width,
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t=y1 * page_size.height / parser_height,
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coord_origin=CoordOrigin.BOTTOMLEFT,
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)
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).to_top_left_origin(page_size.height),
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)
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)
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cell_counter += 1
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return cells
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def get_text_in_rect(self, bbox: BoundingBox) -> str:
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if not self.valid:
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return ""
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@ -81,73 +137,27 @@ class DoclingParseV2PageBackend(PdfPageBackend):
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return text_piece
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def get_segmented_page(self) -> Optional[SegmentedPdfPage]:
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return None
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if not self.valid:
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return None
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text_cells = self._compute_text_cells()
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# Get the PDF page geometry from pypdfium2
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dimension = get_pdf_page_geometry(self._ppage)
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# Create SegmentedPdfPage
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return SegmentedPdfPage(
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dimension=dimension,
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textline_cells=text_cells,
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char_cells=[],
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word_cells=[],
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has_textlines=len(text_cells) > 0,
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has_words=False,
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has_chars=False,
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)
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def get_text_cells(self) -> Iterable[TextCell]:
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cells: List[TextCell] = []
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cell_counter = 0
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if not self.valid:
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return cells
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page_size = self.get_size()
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parser_width = self._dpage["sanitized"]["dimension"]["width"]
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parser_height = self._dpage["sanitized"]["dimension"]["height"]
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cells_data = self._dpage["sanitized"]["cells"]["data"]
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cells_header = self._dpage["sanitized"]["cells"]["header"]
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for i, cell_data in enumerate(cells_data):
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x0 = cell_data[cells_header.index("x0")]
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y0 = cell_data[cells_header.index("y0")]
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x1 = cell_data[cells_header.index("x1")]
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y1 = cell_data[cells_header.index("y1")]
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if x1 < x0:
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x0, x1 = x1, x0
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if y1 < y0:
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y0, y1 = y1, y0
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text_piece = cell_data[cells_header.index("text")]
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cells.append(
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TextCell(
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index=cell_counter,
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text=text_piece,
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orig=text_piece,
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from_ocr=False,
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rect=BoundingRectangle.from_bounding_box(
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BoundingBox(
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# l=x0, b=y0, r=x1, t=y1,
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l=x0 * page_size.width / parser_width,
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b=y0 * page_size.height / parser_height,
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r=x1 * page_size.width / parser_width,
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t=y1 * page_size.height / parser_height,
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coord_origin=CoordOrigin.BOTTOMLEFT,
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)
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).to_top_left_origin(page_size.height),
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)
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)
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cell_counter += 1
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def draw_clusters_and_cells():
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image = (
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self.get_page_image()
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) # make new image to avoid drawing on the saved ones
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draw = ImageDraw.Draw(image)
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for c in cells:
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x0, y0, x1, y1 = c.bbox.as_tuple()
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cell_color = (
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random.randint(30, 140),
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random.randint(30, 140),
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random.randint(30, 140),
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)
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draw.rectangle([(x0, y0), (x1, y1)], outline=cell_color)
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image.show()
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# draw_clusters_and_cells()
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return cells
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return self._compute_text_cells()
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def get_bitmap_rects(self, scale: float = 1) -> Iterable[BoundingBox]:
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AREA_THRESHOLD = 0 # 32 * 32
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@ -59,20 +59,6 @@ class DoclingParseV4PageBackend(PdfPageBackend):
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return self._dpage
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def get_text_cells(self) -> Iterable[TextCell]:
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page_size = self.get_size()
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[tc.to_top_left_origin(page_size.height) for tc in self._dpage.textline_cells]
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# for cell in self._dpage.textline_cells:
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# rect = cell.rect
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#
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# assert (
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# rect.to_bounding_box().l <= rect.to_bounding_box().r
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# ), f"left is > right on bounding box {rect.to_bounding_box()} of rect {rect}"
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# assert (
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# rect.to_bounding_box().t <= rect.to_bounding_box().b
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# ), f"top is > bottom on bounding box {rect.to_bounding_box()} of rect {rect}"
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return self._dpage.textline_cells
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def get_bitmap_rects(self, scale: float = 1) -> Iterable[BoundingBox]:
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@ -171,12 +157,28 @@ class DoclingParseV4DocumentBackend(PdfDocumentBackend):
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self, page_no: int, create_words: bool = True, create_textlines: bool = True
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) -> DoclingParseV4PageBackend:
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with pypdfium2_lock:
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seg_page = self.dp_doc.get_page(
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page_no + 1,
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create_words=create_words,
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create_textlines=create_textlines,
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)
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# In Docling, all TextCell instances are expected with top-left origin.
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[
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tc.to_top_left_origin(seg_page.dimension.height)
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for tc in seg_page.textline_cells
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]
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[
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tc.to_top_left_origin(seg_page.dimension.height)
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for tc in seg_page.char_cells
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]
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[
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tc.to_top_left_origin(seg_page.dimension.height)
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for tc in seg_page.word_cells
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]
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return DoclingParseV4PageBackend(
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self.dp_doc.get_page(
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page_no + 1,
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create_words=create_words,
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create_textlines=create_textlines,
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),
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seg_page,
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self._pdoc[page_no],
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)
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@ -8,7 +8,13 @@ from typing import TYPE_CHECKING, List, Optional, Union
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import pypdfium2 as pdfium
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import pypdfium2.raw as pdfium_c
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from docling_core.types.doc import BoundingBox, CoordOrigin, Size
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from docling_core.types.doc.page import BoundingRectangle, SegmentedPdfPage, TextCell
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from docling_core.types.doc.page import (
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BoundingRectangle,
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PdfPageBoundaryType,
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PdfPageGeometry,
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SegmentedPdfPage,
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TextCell,
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)
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from PIL import Image, ImageDraw
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from pypdfium2 import PdfTextPage
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from pypdfium2._helpers.misc import PdfiumError
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@ -16,6 +22,76 @@ from pypdfium2._helpers.misc import PdfiumError
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from docling.backend.pdf_backend import PdfDocumentBackend, PdfPageBackend
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from docling.utils.locks import pypdfium2_lock
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def get_pdf_page_geometry(
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ppage: pdfium.PdfPage,
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angle: float = 0.0,
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boundary_type: PdfPageBoundaryType = PdfPageBoundaryType.CROP_BOX,
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) -> PdfPageGeometry:
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"""
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Create PdfPageGeometry from a pypdfium2 PdfPage object.
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Args:
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ppage: pypdfium2 PdfPage object
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angle: Page rotation angle in degrees (default: 0.0)
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boundary_type: The boundary type for the page (default: CROP_BOX)
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Returns:
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PdfPageGeometry with all the different bounding boxes properly set
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"""
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with pypdfium2_lock:
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# Get the main bounding box (intersection of crop_box and media_box)
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bbox_tuple = ppage.get_bbox()
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bbox = BoundingBox.from_tuple(bbox_tuple, CoordOrigin.BOTTOMLEFT)
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# Get all the different page boxes from pypdfium2
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media_box_tuple = ppage.get_mediabox()
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crop_box_tuple = ppage.get_cropbox()
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art_box_tuple = ppage.get_artbox()
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bleed_box_tuple = ppage.get_bleedbox()
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trim_box_tuple = ppage.get_trimbox()
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# Convert to BoundingBox objects using existing from_tuple method
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# pypdfium2 returns (x0, y0, x1, y1) in PDF coordinate system (bottom-left origin)
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# Use bbox as fallback when specific box types are not defined
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media_bbox = (
|
||||
BoundingBox.from_tuple(media_box_tuple, CoordOrigin.BOTTOMLEFT)
|
||||
if media_box_tuple
|
||||
else bbox
|
||||
)
|
||||
crop_bbox = (
|
||||
BoundingBox.from_tuple(crop_box_tuple, CoordOrigin.BOTTOMLEFT)
|
||||
if crop_box_tuple
|
||||
else bbox
|
||||
)
|
||||
art_bbox = (
|
||||
BoundingBox.from_tuple(art_box_tuple, CoordOrigin.BOTTOMLEFT)
|
||||
if art_box_tuple
|
||||
else bbox
|
||||
)
|
||||
bleed_bbox = (
|
||||
BoundingBox.from_tuple(bleed_box_tuple, CoordOrigin.BOTTOMLEFT)
|
||||
if bleed_box_tuple
|
||||
else bbox
|
||||
)
|
||||
trim_bbox = (
|
||||
BoundingBox.from_tuple(trim_box_tuple, CoordOrigin.BOTTOMLEFT)
|
||||
if trim_box_tuple
|
||||
else bbox
|
||||
)
|
||||
|
||||
return PdfPageGeometry(
|
||||
angle=angle,
|
||||
rect=BoundingRectangle.from_bounding_box(bbox),
|
||||
boundary_type=boundary_type,
|
||||
art_bbox=art_bbox,
|
||||
bleed_bbox=bleed_bbox,
|
||||
crop_bbox=crop_bbox,
|
||||
media_bbox=media_bbox,
|
||||
trim_bbox=trim_bbox,
|
||||
)
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from docling.datamodel.document import InputDocument
|
||||
|
||||
@ -41,38 +117,8 @@ class PyPdfiumPageBackend(PdfPageBackend):
|
||||
def is_valid(self) -> bool:
|
||||
return self.valid
|
||||
|
||||
def get_bitmap_rects(self, scale: float = 1) -> Iterable[BoundingBox]:
|
||||
AREA_THRESHOLD = 0 # 32 * 32
|
||||
page_size = self.get_size()
|
||||
with pypdfium2_lock:
|
||||
for obj in self._ppage.get_objects(filter=[pdfium_c.FPDF_PAGEOBJ_IMAGE]):
|
||||
pos = obj.get_pos()
|
||||
cropbox = BoundingBox.from_tuple(
|
||||
pos, origin=CoordOrigin.BOTTOMLEFT
|
||||
).to_top_left_origin(page_height=page_size.height)
|
||||
|
||||
if cropbox.area() > AREA_THRESHOLD:
|
||||
cropbox = cropbox.scaled(scale=scale)
|
||||
|
||||
yield cropbox
|
||||
|
||||
def get_text_in_rect(self, bbox: BoundingBox) -> str:
|
||||
with pypdfium2_lock:
|
||||
if not self.text_page:
|
||||
self.text_page = self._ppage.get_textpage()
|
||||
|
||||
if bbox.coord_origin != CoordOrigin.BOTTOMLEFT:
|
||||
bbox = bbox.to_bottom_left_origin(self.get_size().height)
|
||||
|
||||
with pypdfium2_lock:
|
||||
text_piece = self.text_page.get_text_bounded(*bbox.as_tuple())
|
||||
|
||||
return text_piece
|
||||
|
||||
def get_segmented_page(self) -> Optional[SegmentedPdfPage]:
|
||||
return None
|
||||
|
||||
def get_text_cells(self) -> Iterable[TextCell]:
|
||||
def _compute_text_cells(self) -> List[TextCell]:
|
||||
"""Compute text cells from pypdfium."""
|
||||
with pypdfium2_lock:
|
||||
if not self.text_page:
|
||||
self.text_page = self._ppage.get_textpage()
|
||||
@ -203,30 +249,58 @@ class PyPdfiumPageBackend(PdfPageBackend):
|
||||
|
||||
return merged_cells
|
||||
|
||||
def draw_clusters_and_cells():
|
||||
image = (
|
||||
self.get_page_image()
|
||||
) # make new image to avoid drawing on the saved ones
|
||||
draw = ImageDraw.Draw(image)
|
||||
for c in cells:
|
||||
x0, y0, x1, y1 = c.rect.to_bounding_box().as_tuple()
|
||||
cell_color = (
|
||||
random.randint(30, 140),
|
||||
random.randint(30, 140),
|
||||
random.randint(30, 140),
|
||||
)
|
||||
draw.rectangle([(x0, y0), (x1, y1)], outline=cell_color)
|
||||
image.show()
|
||||
return merge_horizontal_cells(cells)
|
||||
|
||||
# before merge:
|
||||
# draw_clusters_and_cells()
|
||||
def get_bitmap_rects(self, scale: float = 1) -> Iterable[BoundingBox]:
|
||||
AREA_THRESHOLD = 0 # 32 * 32
|
||||
page_size = self.get_size()
|
||||
with pypdfium2_lock:
|
||||
for obj in self._ppage.get_objects(filter=[pdfium_c.FPDF_PAGEOBJ_IMAGE]):
|
||||
pos = obj.get_pos()
|
||||
cropbox = BoundingBox.from_tuple(
|
||||
pos, origin=CoordOrigin.BOTTOMLEFT
|
||||
).to_top_left_origin(page_height=page_size.height)
|
||||
|
||||
cells = merge_horizontal_cells(cells)
|
||||
if cropbox.area() > AREA_THRESHOLD:
|
||||
cropbox = cropbox.scaled(scale=scale)
|
||||
|
||||
# after merge:
|
||||
# draw_clusters_and_cells()
|
||||
yield cropbox
|
||||
|
||||
return cells
|
||||
def get_text_in_rect(self, bbox: BoundingBox) -> str:
|
||||
with pypdfium2_lock:
|
||||
if not self.text_page:
|
||||
self.text_page = self._ppage.get_textpage()
|
||||
|
||||
if bbox.coord_origin != CoordOrigin.BOTTOMLEFT:
|
||||
bbox = bbox.to_bottom_left_origin(self.get_size().height)
|
||||
|
||||
with pypdfium2_lock:
|
||||
text_piece = self.text_page.get_text_bounded(*bbox.as_tuple())
|
||||
|
||||
return text_piece
|
||||
|
||||
def get_segmented_page(self) -> Optional[SegmentedPdfPage]:
|
||||
if not self.valid:
|
||||
return None
|
||||
|
||||
text_cells = self._compute_text_cells()
|
||||
|
||||
# Get the PDF page geometry from pypdfium2
|
||||
dimension = get_pdf_page_geometry(self._ppage)
|
||||
|
||||
# Create SegmentedPdfPage
|
||||
return SegmentedPdfPage(
|
||||
dimension=dimension,
|
||||
textline_cells=text_cells,
|
||||
char_cells=[],
|
||||
word_cells=[],
|
||||
has_textlines=len(text_cells) > 0,
|
||||
has_words=False,
|
||||
has_chars=False,
|
||||
)
|
||||
|
||||
def get_text_cells(self) -> Iterable[TextCell]:
|
||||
return self._compute_text_cells()
|
||||
|
||||
def get_page_image(
|
||||
self, scale: float = 1, cropbox: Optional[BoundingBox] = None
|
||||
|
||||
@ -232,7 +232,6 @@ class Page(BaseModel):
|
||||
page_no: int
|
||||
# page_hash: Optional[str] = None
|
||||
size: Optional[Size] = None
|
||||
cells: List[TextCell] = []
|
||||
parsed_page: Optional[SegmentedPdfPage] = None
|
||||
predictions: PagePredictions = PagePredictions()
|
||||
assembled: Optional[AssembledUnit] = None
|
||||
@ -245,6 +244,14 @@ class Page(BaseModel):
|
||||
float, Image
|
||||
] = {} # Cache of images in different scales. By default it is cleared during assembling.
|
||||
|
||||
@property
|
||||
def cells(self) -> List[TextCell]:
|
||||
"""Return text cells as a read-only view of parsed_page.textline_cells."""
|
||||
if self.parsed_page is not None:
|
||||
return self.parsed_page.textline_cells
|
||||
else:
|
||||
return []
|
||||
|
||||
def get_image(
|
||||
self, scale: float = 1.0, cropbox: Optional[BoundingBox] = None
|
||||
) -> Optional[Image]:
|
||||
|
||||
@ -292,7 +292,9 @@ class PdfPipelineOptions(PaginatedPipelineOptions):
|
||||
),
|
||||
)
|
||||
|
||||
generate_parsed_pages: bool = False
|
||||
generate_parsed_pages: Literal[True] = (
|
||||
True # Always True since parsed_page is now mandatory
|
||||
)
|
||||
|
||||
|
||||
class PdfPipeline(str, Enum):
|
||||
|
||||
@ -7,6 +7,7 @@ from typing import List, Optional, Type
|
||||
|
||||
import numpy as np
|
||||
from docling_core.types.doc import BoundingBox, CoordOrigin
|
||||
from docling_core.types.doc.page import TextCell
|
||||
from PIL import Image, ImageDraw
|
||||
from rtree import index
|
||||
from scipy.ndimage import binary_dilation, find_objects, label
|
||||
@ -107,7 +108,9 @@ class BaseOcrModel(BasePageModel, BaseModelWithOptions):
|
||||
return []
|
||||
|
||||
# Filters OCR cells by dropping any OCR cell that intersects with an existing programmatic cell.
|
||||
def _filter_ocr_cells(self, ocr_cells, programmatic_cells):
|
||||
def _filter_ocr_cells(
|
||||
self, ocr_cells: List[TextCell], programmatic_cells: List[TextCell]
|
||||
) -> List[TextCell]:
|
||||
# Create R-tree index for programmatic cells
|
||||
p = index.Property()
|
||||
p.dimension = 2
|
||||
@ -130,19 +133,38 @@ class BaseOcrModel(BasePageModel, BaseModelWithOptions):
|
||||
]
|
||||
return filtered_ocr_cells
|
||||
|
||||
def post_process_cells(self, ocr_cells, programmatic_cells):
|
||||
def post_process_cells(self, ocr_cells: List[TextCell], page: Page) -> None:
|
||||
r"""
|
||||
Post-process the ocr and programmatic cells and return the final list of of cells
|
||||
Post-process the OCR cells and update the page object.
|
||||
Updates parsed_page.textline_cells directly since page.cells is now read-only.
|
||||
"""
|
||||
if self.options.force_full_page_ocr:
|
||||
# If a full page OCR is forced, use only the OCR cells
|
||||
cells = ocr_cells
|
||||
return cells
|
||||
# Get existing cells from the read-only property
|
||||
existing_cells = page.cells
|
||||
|
||||
## Remove OCR cells which overlap with programmatic cells.
|
||||
filtered_ocr_cells = self._filter_ocr_cells(ocr_cells, programmatic_cells)
|
||||
programmatic_cells.extend(filtered_ocr_cells)
|
||||
return programmatic_cells
|
||||
# Combine existing and OCR cells with overlap filtering
|
||||
final_cells = self._combine_cells(existing_cells, ocr_cells)
|
||||
|
||||
assert page.parsed_page is not None
|
||||
|
||||
# Update parsed_page.textline_cells directly
|
||||
page.parsed_page.textline_cells = final_cells
|
||||
page.parsed_page.has_lines = len(final_cells) > 0
|
||||
|
||||
def _combine_cells(
|
||||
self, existing_cells: List[TextCell], ocr_cells: List[TextCell]
|
||||
) -> List[TextCell]:
|
||||
"""Combine existing and OCR cells with filtering and re-indexing."""
|
||||
if self.options.force_full_page_ocr:
|
||||
combined = ocr_cells
|
||||
else:
|
||||
filtered_ocr_cells = self._filter_ocr_cells(ocr_cells, existing_cells)
|
||||
combined = list(existing_cells) + filtered_ocr_cells
|
||||
|
||||
# Re-index in-place
|
||||
for i, cell in enumerate(combined):
|
||||
cell.index = i
|
||||
|
||||
return combined
|
||||
|
||||
def draw_ocr_rects_and_cells(self, conv_res, page, ocr_rects, show: bool = False):
|
||||
image = copy.deepcopy(page.image)
|
||||
|
||||
@ -177,7 +177,7 @@ class EasyOcrModel(BaseOcrModel):
|
||||
all_ocr_cells.extend(cells)
|
||||
|
||||
# Post-process the cells
|
||||
page.cells = self.post_process_cells(all_ocr_cells, page.cells)
|
||||
self.post_process_cells(all_ocr_cells, page)
|
||||
|
||||
# DEBUG code:
|
||||
if settings.debug.visualize_ocr:
|
||||
|
||||
@ -176,9 +176,9 @@ class LayoutModel(BasePageModel):
|
||||
# Apply postprocessing
|
||||
|
||||
processed_clusters, processed_cells = LayoutPostprocessor(
|
||||
page.cells, clusters, page.size
|
||||
page, clusters
|
||||
).postprocess()
|
||||
# processed_clusters, processed_cells = clusters, page.cells
|
||||
# Note: LayoutPostprocessor updates page.cells and page.parsed_page internally
|
||||
|
||||
with warnings.catch_warnings():
|
||||
warnings.filterwarnings(
|
||||
@ -198,7 +198,6 @@ class LayoutModel(BasePageModel):
|
||||
)
|
||||
)
|
||||
|
||||
page.cells = processed_cells
|
||||
page.predictions.layout = LayoutPrediction(
|
||||
clusters=processed_clusters
|
||||
)
|
||||
|
||||
@ -132,7 +132,7 @@ class OcrMacModel(BaseOcrModel):
|
||||
all_ocr_cells.extend(cells)
|
||||
|
||||
# Post-process the cells
|
||||
page.cells = self.post_process_cells(all_ocr_cells, page.cells)
|
||||
self.post_process_cells(all_ocr_cells, page)
|
||||
|
||||
# DEBUG code:
|
||||
if settings.debug.visualize_ocr:
|
||||
|
||||
@ -2,7 +2,7 @@ import re
|
||||
import warnings
|
||||
from collections.abc import Iterable
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
from typing import Literal, Optional
|
||||
|
||||
import numpy as np
|
||||
from PIL import ImageDraw
|
||||
@ -17,7 +17,6 @@ from docling.utils.profiling import TimeRecorder
|
||||
|
||||
class PagePreprocessingOptions(BaseModel):
|
||||
images_scale: Optional[float]
|
||||
create_parsed_page: bool
|
||||
|
||||
|
||||
class PagePreprocessingModel(BasePageModel):
|
||||
@ -66,10 +65,8 @@ class PagePreprocessingModel(BasePageModel):
|
||||
def _parse_page_cells(self, conv_res: ConversionResult, page: Page) -> Page:
|
||||
assert page._backend is not None
|
||||
|
||||
page.cells = list(page._backend.get_text_cells())
|
||||
|
||||
if self.options.create_parsed_page:
|
||||
page.parsed_page = page._backend.get_segmented_page()
|
||||
page.parsed_page = page._backend.get_segmented_page()
|
||||
assert page.parsed_page is not None
|
||||
|
||||
# Rate the text quality from the PDF parser, and aggregate on page
|
||||
text_scores = []
|
||||
|
||||
@ -134,7 +134,7 @@ class RapidOcrModel(BaseOcrModel):
|
||||
all_ocr_cells.extend(cells)
|
||||
|
||||
# Post-process the cells
|
||||
page.cells = self.post_process_cells(all_ocr_cells, page.cells)
|
||||
self.post_process_cells(all_ocr_cells, page)
|
||||
|
||||
# DEBUG code:
|
||||
if settings.debug.visualize_ocr:
|
||||
|
||||
@ -306,7 +306,7 @@ class TesseractOcrCliModel(BaseOcrModel):
|
||||
all_ocr_cells.append(cell)
|
||||
|
||||
# Post-process the cells
|
||||
page.cells = self.post_process_cells(all_ocr_cells, page.cells)
|
||||
self.post_process_cells(all_ocr_cells, page)
|
||||
|
||||
# DEBUG code:
|
||||
if settings.debug.visualize_ocr:
|
||||
|
||||
@ -235,7 +235,7 @@ class TesseractOcrModel(BaseOcrModel):
|
||||
all_ocr_cells.extend(cells)
|
||||
|
||||
# Post-process the cells
|
||||
page.cells = self.post_process_cells(all_ocr_cells, page.cells)
|
||||
self.post_process_cells(all_ocr_cells, page)
|
||||
|
||||
# DEBUG code:
|
||||
if settings.debug.visualize_ocr:
|
||||
|
||||
@ -72,7 +72,6 @@ class StandardPdfPipeline(PaginatedPipeline):
|
||||
PagePreprocessingModel(
|
||||
options=PagePreprocessingOptions(
|
||||
images_scale=pipeline_options.images_scale,
|
||||
create_parsed_page=pipeline_options.generate_parsed_pages,
|
||||
)
|
||||
),
|
||||
# OCR
|
||||
|
||||
@ -8,7 +8,7 @@ from docling_core.types.doc import DocItemLabel, Size
|
||||
from docling_core.types.doc.page import TextCell
|
||||
from rtree import index
|
||||
|
||||
from docling.datamodel.base_models import BoundingBox, Cluster
|
||||
from docling.datamodel.base_models import BoundingBox, Cluster, Page
|
||||
|
||||
_log = logging.getLogger(__name__)
|
||||
|
||||
@ -194,11 +194,11 @@ class LayoutPostprocessor:
|
||||
DocItemLabel.TITLE: DocItemLabel.SECTION_HEADER,
|
||||
}
|
||||
|
||||
def __init__(self, cells: List[TextCell], clusters: List[Cluster], page_size: Size):
|
||||
"""Initialize processor with cells and clusters."""
|
||||
"""Initialize processor with cells and spatial indices."""
|
||||
self.cells = cells
|
||||
self.page_size = page_size
|
||||
def __init__(self, page: Page, clusters: List[Cluster]) -> None:
|
||||
"""Initialize processor with page and clusters."""
|
||||
self.cells = page.cells
|
||||
self.page = page
|
||||
self.page_size = page.size
|
||||
self.all_clusters = clusters
|
||||
self.regular_clusters = [
|
||||
c for c in clusters if c.label not in self.SPECIAL_TYPES
|
||||
@ -240,6 +240,10 @@ class LayoutPostprocessor:
|
||||
for child in cluster.children:
|
||||
child.cells = self._sort_cells(child.cells)
|
||||
|
||||
assert self.page.parsed_page is not None
|
||||
self.page.parsed_page.textline_cells = self.cells
|
||||
self.page.parsed_page.has_lines = len(self.cells) > 0
|
||||
|
||||
return final_clusters, self.cells
|
||||
|
||||
def _process_regular_clusters(self) -> List[Cluster]:
|
||||
@ -301,6 +305,7 @@ class LayoutPostprocessor:
|
||||
special_clusters = self._handle_cross_type_overlaps(special_clusters)
|
||||
|
||||
# Calculate page area from known page size
|
||||
assert self.page_size is not None
|
||||
page_area = self.page_size.width * self.page_size.height
|
||||
if page_area > 0:
|
||||
# Filter out full-page pictures
|
||||
|
||||
101474
tests/data/groundtruth/docling_v1/2203.01017v2.pages.json
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tests/data/groundtruth/docling_v1/multi_page.pages.json
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tests/data/groundtruth/docling_v2/multi_page.pages.json
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"r_y2": 89.1266754140729,
|
||||
"r_x3": 100.64168123325977,
|
||||
"r_y3": 89.1266754140729,
|
||||
"coord_origin": "TOPLEFT"
|
||||
},
|
||||
"text": "JSON and Markdown in an easy self contained",
|
||||
"orig": "JSON and Markdown in an easy self contained",
|
||||
"text_direction": "left_to_right",
|
||||
"confidence": 1.0,
|
||||
"from_ocr": true
|
||||
},
|
||||
"text": "JSON and Markdown in an easy self contained",
|
||||
"orig": "JSON and Markdown in an easy self contained",
|
||||
"text_direction": "left_to_right",
|
||||
"confidence": 1.0,
|
||||
"from_ocr": true
|
||||
},
|
||||
{
|
||||
"index": 2,
|
||||
"rgba": {
|
||||
"r": 0,
|
||||
"g": 0,
|
||||
"b": 0,
|
||||
"a": 255
|
||||
},
|
||||
"rect": {
|
||||
"r_x0": 131.21306574279092,
|
||||
"r_y0": 521.0762158417759,
|
||||
"r_x1": 152.19606490864376,
|
||||
"r_y1": 521.0762158417759,
|
||||
"r_x2": 152.19606490864376,
|
||||
"r_y2": 441.0071698212682,
|
||||
"r_x3": 131.21306574279092,
|
||||
"r_y3": 441.0071698212682,
|
||||
"coord_origin": "TOPLEFT"
|
||||
},
|
||||
"text": "package",
|
||||
"orig": "package",
|
||||
"text_direction": "left_to_right",
|
||||
"confidence": 1.0,
|
||||
"from_ocr": true
|
||||
}
|
||||
],
|
||||
"parsed_page": null,
|
||||
{
|
||||
"index": 2,
|
||||
"rgba": {
|
||||
"r": 0,
|
||||
"g": 0,
|
||||
"b": 0,
|
||||
"a": 255
|
||||
},
|
||||
"rect": {
|
||||
"r_x0": 131.21306574279092,
|
||||
"r_y0": 521.0762158417759,
|
||||
"r_x1": 152.19606490864376,
|
||||
"r_y1": 521.0762158417759,
|
||||
"r_x2": 152.19606490864376,
|
||||
"r_y2": 441.0071698212682,
|
||||
"r_x3": 131.21306574279092,
|
||||
"r_y3": 441.0071698212682,
|
||||
"coord_origin": "TOPLEFT"
|
||||
},
|
||||
"text": "package",
|
||||
"orig": "package",
|
||||
"text_direction": "left_to_right",
|
||||
"confidence": 1.0,
|
||||
"from_ocr": true
|
||||
}
|
||||
],
|
||||
"has_chars": false,
|
||||
"has_words": false,
|
||||
"has_lines": true,
|
||||
"image": null,
|
||||
"lines": []
|
||||
},
|
||||
"predictions": {
|
||||
"layout": {
|
||||
"clusters": [
|
||||
|
||||
@ -57,14 +57,14 @@ def test_e2e_conversions():
|
||||
pdf_paths = get_pdf_paths()
|
||||
|
||||
engines: List[Tuple[OcrOptions, bool]] = [
|
||||
(EasyOcrOptions(), False),
|
||||
(TesseractOcrOptions(), True),
|
||||
(TesseractCliOcrOptions(), True),
|
||||
(EasyOcrOptions(force_full_page_ocr=True), False),
|
||||
(EasyOcrOptions(), False),
|
||||
(TesseractOcrOptions(force_full_page_ocr=True), True),
|
||||
(TesseractOcrOptions(force_full_page_ocr=True, lang=["auto"]), True),
|
||||
(TesseractCliOcrOptions(force_full_page_ocr=True), True),
|
||||
(TesseractCliOcrOptions(force_full_page_ocr=True, lang=["auto"]), True),
|
||||
(EasyOcrOptions(force_full_page_ocr=True), False),
|
||||
]
|
||||
|
||||
# rapidocr is only available for Python >=3.6,<3.13
|
||||
|
||||
Loading…
Reference in New Issue
Block a user