e94d317c02
* Introduce adaptive OCR Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Factor out BaseOcrModel, add docling-parse backend tests, fixes * Make easyocr default dep Signed-off-by: Christoph Auer <cau@zurich.ibm.com> --------- Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
145 lines
5.3 KiB
Python
145 lines
5.3 KiB
Python
import copy
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from typing import Iterable, List
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import numpy
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from docling_ibm_models.tableformer.data_management.tf_predictor import TFPredictor
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from PIL import ImageDraw
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from docling.datamodel.base_models import (
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BoundingBox,
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Page,
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TableCell,
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TableElement,
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TableStructurePrediction,
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)
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class TableStructureModel:
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def __init__(self, config):
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self.config = config
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self.do_cell_matching = config["do_cell_matching"]
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self.enabled = config["enabled"]
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if self.enabled:
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artifacts_path = config["artifacts_path"]
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# Third Party
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import docling_ibm_models.tableformer.common as c
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self.tm_config = c.read_config(f"{artifacts_path}/tm_config.json")
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self.tm_config["model"]["save_dir"] = artifacts_path
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self.tm_model_type = self.tm_config["model"]["type"]
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self.tf_predictor = TFPredictor(self.tm_config)
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self.scale = 2.0 # Scale up table input images to 144 dpi
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def draw_table_and_cells(self, page: Page, tbl_list: List[TableElement]):
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image = (
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page._backend.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 table_element in tbl_list:
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x0, y0, x1, y1 = table_element.cluster.bbox.as_tuple()
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draw.rectangle([(x0, y0), (x1, y1)], outline="red")
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for tc in table_element.table_cells:
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x0, y0, x1, y1 = tc.bbox.as_tuple()
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draw.rectangle([(x0, y0), (x1, y1)], outline="blue")
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image.show()
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def __call__(self, page_batch: Iterable[Page]) -> Iterable[Page]:
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if not self.enabled:
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yield from page_batch
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return
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for page in page_batch:
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page.predictions.tablestructure = TableStructurePrediction() # dummy
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in_tables = [
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(
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cluster,
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[
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round(cluster.bbox.l) * self.scale,
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round(cluster.bbox.t) * self.scale,
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round(cluster.bbox.r) * self.scale,
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round(cluster.bbox.b) * self.scale,
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],
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)
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for cluster in page.predictions.layout.clusters
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if cluster.label == "Table"
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]
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if not len(in_tables):
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yield page
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continue
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tokens = []
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for c in page.cells:
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for cluster, _ in in_tables:
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if c.bbox.area() > 0:
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if (
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c.bbox.intersection_area_with(cluster.bbox) / c.bbox.area()
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> 0.2
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):
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# Only allow non empty stings (spaces) into the cells of a table
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if len(c.text.strip()) > 0:
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new_cell = copy.deepcopy(c)
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new_cell.bbox = new_cell.bbox.scaled(scale=self.scale)
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tokens.append(new_cell.model_dump())
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page_input = {
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"tokens": tokens,
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"width": page.size.width * self.scale,
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"height": page.size.height * self.scale,
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}
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page_input["image"] = numpy.asarray(page.get_image(scale=self.scale))
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table_clusters, table_bboxes = zip(*in_tables)
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if len(table_bboxes):
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tf_output = self.tf_predictor.multi_table_predict(
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page_input, table_bboxes, do_matching=self.do_cell_matching
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)
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for table_cluster, table_out in zip(table_clusters, tf_output):
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table_cells = []
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for element in table_out["tf_responses"]:
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if not self.do_cell_matching:
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the_bbox = BoundingBox.model_validate(
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element["bbox"]
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).scaled(1 / self.scale)
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text_piece = page._backend.get_text_in_rect(the_bbox)
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element["bbox"]["token"] = text_piece
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tc = TableCell.model_validate(element)
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if self.do_cell_matching:
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tc.bbox = tc.bbox.scaled(1 / self.scale)
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table_cells.append(tc)
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# Retrieving cols/rows, after post processing:
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num_rows = table_out["predict_details"]["num_rows"]
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num_cols = table_out["predict_details"]["num_cols"]
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otsl_seq = table_out["predict_details"]["prediction"]["rs_seq"]
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tbl = TableElement(
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otsl_seq=otsl_seq,
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table_cells=table_cells,
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num_rows=num_rows,
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num_cols=num_cols,
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id=table_cluster.id,
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page_no=page.page_no,
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cluster=table_cluster,
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label="Table",
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)
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page.predictions.tablestructure.table_map[table_cluster.id] = tbl
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# For debugging purposes:
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# self.draw_table_and_cells(page, page.predictions.tablestructure.table_map.values())
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yield page
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