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import copy
import warnings
from pathlib import Path
from typing import Iterable, Optional, Union
import numpy
from docling_core.types.doc import BoundingBox, DocItemLabel, TableCell
from docling_ibm_models.tableformer.data_management.tf_predictor import TFPredictor
from PIL import ImageDraw
from docling.datamodel.base_models import Page, Table, TableStructurePrediction
from docling.datamodel.document import ConversionResult
from docling.datamodel.pipeline_options import (
AcceleratorDevice,
AcceleratorOptions,
TableFormerMode,
TableStructureOptions,
)
from docling.datamodel.settings import settings
from docling.models.base_model import BasePageModel
from docling.utils.accelerator_utils import decide_device
from docling.utils.profiling import TimeRecorder
class TableStructureModel(BasePageModel):
_model_repo_folder = "ds4sd--docling-models"
_model_path = "model_artifacts/tableformer"
def __init__(
self,
enabled: bool,
artifacts_path: Optional[Path],
options: TableStructureOptions,
accelerator_options: AcceleratorOptions,
):
self.options = options
self.do_cell_matching = self.options.do_cell_matching
self.mode = self.options.mode
self.enabled = enabled
if self.enabled:
if artifacts_path is None:
artifacts_path = self.download_models() / self._model_path
else:
# will become the default in the future
if (artifacts_path / self._model_repo_folder).exists():
artifacts_path = (
artifacts_path / self._model_repo_folder / self._model_path
)
elif (artifacts_path / self._model_path).exists():
warnings.warn(
"The usage of artifacts_path containing directly "
f"{self._model_path} is deprecated. Please point "
"the artifacts_path to the parent containing "
f"the {self._model_repo_folder} folder.",
DeprecationWarning,
stacklevel=3,
)
artifacts_path = artifacts_path / self._model_path
if self.mode == TableFormerMode.ACCURATE:
artifacts_path = artifacts_path / "accurate"
else:
artifacts_path = artifacts_path / "fast"
# Third Party
import docling_ibm_models.tableformer.common as c
device = decide_device(accelerator_options.device)
# Disable MPS here, until we know why it makes things slower.
if device == AcceleratorDevice.MPS.value:
device = AcceleratorDevice.CPU.value
self.tm_config = c.read_config(f"{artifacts_path}/tm_config.json")
self.tm_config["model"]["save_dir"] = artifacts_path
self.tm_model_type = self.tm_config["model"]["type"]
self.tf_predictor = TFPredictor(
self.tm_config, device, accelerator_options.num_threads
)
self.scale = 2.0 # Scale up table input images to 144 dpi
@staticmethod
def download_models(
local_dir: Optional[Path] = None, force: bool = False, progress: bool = False
) -> Path:
from huggingface_hub import snapshot_download
from huggingface_hub.utils import disable_progress_bars
if not progress:
disable_progress_bars()
download_path = snapshot_download(
repo_id="ds4sd/docling-models",
force_download=force,
local_dir=local_dir,
revision="v2.1.0",
)
return Path(download_path)
def draw_table_and_cells(
self,
conv_res: ConversionResult,
page: Page,
tbl_list: Iterable[Table],
show: bool = False,
):
assert page._backend is not None
assert page.size is not None
image = (
page._backend.get_page_image()
) # make new image to avoid drawing on the saved ones
scale_x = image.width / page.size.width
scale_y = image.height / page.size.height
draw = ImageDraw.Draw(image)
for table_element in tbl_list:
x0, y0, x1, y1 = table_element.cluster.bbox.as_tuple()
y0 *= scale_x
y1 *= scale_y
x0 *= scale_x
x1 *= scale_x
draw.rectangle([(x0, y0), (x1, y1)], outline="red")
for cell in table_element.cluster.cells:
x0, y0, x1, y1 = cell.bbox.as_tuple()
x0 *= scale_x
x1 *= scale_x
y0 *= scale_x
y1 *= scale_y
draw.rectangle([(x0, y0), (x1, y1)], outline="green")
for tc in table_element.table_cells:
if tc.bbox is not None:
x0, y0, x1, y1 = tc.bbox.as_tuple()
x0 *= scale_x
x1 *= scale_x
y0 *= scale_x
y1 *= scale_y
if tc.column_header:
width = 3
else:
width = 1
draw.rectangle([(x0, y0), (x1, y1)], outline="blue", width=width)
draw.text(
(x0 + 3, y0 + 3),
text=f"{tc.start_row_offset_idx}, {tc.start_col_offset_idx}",
fill="black",
)
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"table_struct_page_{page.page_no:05}.png"
image.save(str(out_file), format="png")
def __call__(
self, conv_res: ConversionResult, page_batch: Iterable[Page]
) -> Iterable[Page]:
if not self.enabled:
yield from page_batch
return
for page in page_batch:
assert page._backend is not None
if not page._backend.is_valid():
yield page
else:
with TimeRecorder(conv_res, "table_structure"):
assert page.predictions.layout is not None
assert page.size is not None
page.predictions.tablestructure = (
TableStructurePrediction()
) # dummy
in_tables = [
(
cluster,
[
round(cluster.bbox.l) * self.scale,
round(cluster.bbox.t) * self.scale,
round(cluster.bbox.r) * self.scale,
round(cluster.bbox.b) * self.scale,
],
)
for cluster in page.predictions.layout.clusters
if cluster.label
in [DocItemLabel.TABLE, DocItemLabel.DOCUMENT_INDEX]
]
if not len(in_tables):
yield page
continue
page_input = {
"width": page.size.width * self.scale,
"height": page.size.height * self.scale,
"image": numpy.asarray(page.get_image(scale=self.scale)),
}
table_clusters, table_bboxes = zip(*in_tables)
if len(table_bboxes):
for table_cluster, tbl_box in in_tables:
tokens = []
for c in table_cluster.cells:
# Only allow non empty stings (spaces) into the cells of a table
if len(c.text.strip()) > 0:
new_cell = copy.deepcopy(c)
new_cell.bbox = new_cell.bbox.scaled(
scale=self.scale
)
tokens.append(new_cell.model_dump())
page_input["tokens"] = tokens
tf_output = self.tf_predictor.multi_table_predict(
page_input, [tbl_box], do_matching=self.do_cell_matching
)
table_out = tf_output[0]
table_cells = []
for element in table_out["tf_responses"]:
if not self.do_cell_matching:
the_bbox = BoundingBox.model_validate(
element["bbox"]
).scaled(1 / self.scale)
text_piece = page._backend.get_text_in_rect(
the_bbox
)
element["bbox"]["token"] = text_piece
tc = TableCell.model_validate(element)
if self.do_cell_matching and tc.bbox is not None:
tc.bbox = tc.bbox.scaled(1 / self.scale)
table_cells.append(tc)
assert "predict_details" in table_out
# Retrieving cols/rows, after post processing:
num_rows = table_out["predict_details"].get("num_rows", 0)
num_cols = table_out["predict_details"].get("num_cols", 0)
otsl_seq = (
table_out["predict_details"]
.get("prediction", {})
.get("rs_seq", [])
)
tbl = Table(
otsl_seq=otsl_seq,
table_cells=table_cells,
num_rows=num_rows,
num_cols=num_cols,
id=table_cluster.id,
page_no=page.page_no,
cluster=table_cluster,
label=table_cluster.label,
)
page.predictions.tablestructure.table_map[
table_cluster.id
] = tbl
# For debugging purposes:
if settings.debug.visualize_tables:
self.draw_table_and_cells(
conv_res,
page,
page.predictions.tablestructure.table_map.values(),
)
yield page
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