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import copy
import logging
from abc import abstractmethod
from pathlib import Path
from typing import Iterable, List
import numpy as np
from docling_core.types.doc import BoundingBox, CoordOrigin
from PIL import Image, ImageDraw
from rtree import index
from scipy.ndimage import binary_dilation, find_objects, label
from docling.datamodel.base_models import Cell, OcrCell, Page
from docling.datamodel.document import ConversionResult
from docling.datamodel.pipeline_options import OcrOptions
from docling.datamodel.settings import settings
from docling.models.base_model import BasePageModel
_log = logging.getLogger(__name__)
class BaseOcrModel(BasePageModel):
def __init__(self, enabled: bool, options: OcrOptions):
self.enabled = enabled
self.options = options
# Computes the optimum amount and coordinates of rectangles to OCR on a given page
def get_ocr_rects(self, page: Page) -> List[BoundingBox]:
BITMAP_COVERAGE_TRESHOLD = 0.75
assert page.size is not None
def find_ocr_rects(size, bitmap_rects):
image = Image.new(
"1", (round(size.width), round(size.height))
) # '1' mode is binary
# Draw all bitmap rects into a binary image
draw = ImageDraw.Draw(image)
for rect in bitmap_rects:
x0, y0, x1, y1 = rect.as_tuple()
x0, y0, x1, y1 = round(x0), round(y0), round(x1), round(y1)
draw.rectangle([(x0, y0), (x1, y1)], fill=1)
np_image = np.array(image)
# Dilate the image by 10 pixels to merge nearby bitmap rectangles
structure = np.ones(
(20, 20)
) # Create a 20x20 structure element (10 pixels in all directions)
np_image = binary_dilation(np_image > 0, structure=structure)
# Find the connected components
labeled_image, num_features = label(
np_image > 0
) # Label black (0 value) regions
# Find enclosing bounding boxes for each connected component.
slices = find_objects(labeled_image)
bounding_boxes = [
BoundingBox(
l=slc[1].start,
t=slc[0].start,
r=slc[1].stop - 1,
b=slc[0].stop - 1,
coord_origin=CoordOrigin.TOPLEFT,
)
for slc in slices
]
# Compute area fraction on page covered by bitmaps
area_frac = np.sum(np_image > 0) / (size.width * size.height)
return (area_frac, bounding_boxes) # fraction covered # boxes
if page._backend is not None:
bitmap_rects = page._backend.get_bitmap_rects()
else:
bitmap_rects = []
coverage, ocr_rects = find_ocr_rects(page.size, bitmap_rects)
# return full-page rectangle if page is dominantly covered with bitmaps
if self.options.force_full_page_ocr or coverage > max(
BITMAP_COVERAGE_TRESHOLD, self.options.bitmap_area_threshold
):
return [
BoundingBox(
l=0,
t=0,
r=page.size.width,
b=page.size.height,
coord_origin=CoordOrigin.TOPLEFT,
)
]
# return individual rectangles if the bitmap coverage is above the threshold
elif coverage > self.options.bitmap_area_threshold:
return ocr_rects
else: # overall coverage of bitmaps is too low, drop all bitmap rectangles.
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):
# Create R-tree index for programmatic cells
p = index.Property()
p.dimension = 2
idx = index.Index(properties=p)
for i, cell in enumerate(programmatic_cells):
idx.insert(i, cell.bbox.as_tuple())
def is_overlapping_with_existing_cells(ocr_cell):
# Query the R-tree to get overlapping rectangles
possible_matches_index = list(idx.intersection(ocr_cell.bbox.as_tuple()))
return (
len(possible_matches_index) > 0
) # this is a weak criterion but it works.
filtered_ocr_cells = [
rect for rect in ocr_cells if not is_overlapping_with_existing_cells(rect)
]
return filtered_ocr_cells
def post_process_cells(self, ocr_cells, programmatic_cells):
r"""
Post-process the ocr and programmatic cells and return the final list of of cells
"""
if self.options.force_full_page_ocr:
# If a full page OCR is forced, use only the OCR cells
cells = [
Cell(id=c_ocr.id, text=c_ocr.text, bbox=c_ocr.bbox)
for c_ocr in ocr_cells
]
return 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
def draw_ocr_rects_and_cells(self, conv_res, page, ocr_rects, show: bool = False):
image = copy.deepcopy(page.image)
scale_x = image.width / page.size.width
scale_y = image.height / page.size.height
draw = ImageDraw.Draw(image, "RGBA")
# Draw OCR rectangles as yellow filled rect
for rect in ocr_rects:
x0, y0, x1, y1 = rect.as_tuple()
y0 *= scale_x
y1 *= scale_y
x0 *= scale_x
x1 *= scale_x
shade_color = (255, 255, 0, 40) # transparent yellow
draw.rectangle([(x0, y0), (x1, y1)], fill=shade_color, outline=None)
# Draw OCR and programmatic cells
for tc in page.cells:
x0, y0, x1, y1 = tc.bbox.as_tuple()
y0 *= scale_x
y1 *= scale_y
x0 *= scale_x
x1 *= scale_x
if y1 <= y0:
y1, y0 = y0, y1
color = "gray"
if isinstance(tc, OcrCell):
color = "magenta"
draw.rectangle([(x0, y0), (x1, y1)], outline=color)
if show:
image.show()
else:
out_path: Path = (
Path(settings.debug.debug_output_path)
/ f"debug_{conv_res.input.file.stem}"
)
out_path.mkdir(parents=True, exist_ok=True)
out_file = out_path / f"ocr_page_{page.page_no:05}.png"
image.save(str(out_file), format="png")
@abstractmethod
def __call__(
self, conv_res: ConversionResult, page_batch: Iterable[Page]
) -> Iterable[Page]:
pass
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