diff --git a/docling/datamodel/base_models.py b/docling/datamodel/base_models.py index d6695e79..71238b8d 100644 --- a/docling/datamodel/base_models.py +++ b/docling/datamodel/base_models.py @@ -71,6 +71,15 @@ def scaled(self, scale: float) -> "BoundingBox": return out_bbox + def normalized(self, page_size: PageSize) -> "BoundingBox": + out_bbox = copy.deepcopy(self) + out_bbox.l /= page_size.width + out_bbox.r /= page_size.width + out_bbox.t /= page_size.height + out_bbox.b /= page_size.height + + return out_bbox + def as_tuple(self): if self.coord_origin == CoordOrigin.TOPLEFT: return (self.l, self.t, self.r, self.b) diff --git a/docling/utils/export.py b/docling/utils/export.py new file mode 100644 index 00000000..31b17ea8 --- /dev/null +++ b/docling/utils/export.py @@ -0,0 +1,193 @@ +import logging +from typing import Any, Dict, Iterable, List, Tuple + +from docling_core.types.doc.base import BaseCell, Ref, Table, TableCell + +from docling.datamodel.base_models import BoundingBox, CoordOrigin, OcrCell +from docling.datamodel.document import ConvertedDocument, Page + +_log = logging.getLogger(__name__) + + +def _export_table_to_html(table: Table): + + # TODO: this is flagged as internal, because we will move it + # to the docling-core package. + + def _get_tablecell_span(cell: TableCell, ix): + span = set([s[ix] for s in cell.spans]) + if len(span) == 0: + return 1, None, None + return len(span), min(span), max(span) + + body = "" + nrows = table.num_rows + ncols = table.num_cols + + for i in range(nrows): + body += "" + for j in range(ncols): + cell: TableCell = table.data[i][j] + + rowspan, rowstart, rowend = _get_tablecell_span(cell, 0) + colspan, colstart, colend = _get_tablecell_span(cell, 1) + + if rowstart is not None and rowstart != i: + continue + if colstart is not None and colstart != j: + continue + + if rowstart is None: + rowstart = i + if colstart is None: + colstart = j + + content = cell.text.strip() + label = cell.obj_type + label_class = "body" + celltag = "td" + if label in ["row_header", "row_multi_header", "row_title"]: + label_class = "header" + elif label in ["col_header", "col_multi_header"]: + label_class = "header" + celltag = "th" + + opening_tag = f"{celltag}" + if rowspan > 1: + opening_tag += f' rowspan="{rowspan}"' + if colspan > 1: + opening_tag += f' colspan="{colspan}"' + + body += f"<{opening_tag}>{content}" + body += "" + body = f"{body}
" + + return body + + +def generate_multimodal_pages( + doc_result: ConvertedDocument, +) -> Iterable[Tuple[str, str, List[Dict[str, Any]], List[Dict[str, Any]], Page]]: + + label_to_doclaynet = { + "title": "title", + "table-of-contents": "document_index", + "subtitle-level-1": "section_header", + "checkbox-selected": "checkbox_selected", + "checkbox-unselected": "checkbox_unselected", + "caption": "caption", + "page-header": "page_header", + "page-footer": "page_footer", + "footnote": "footnote", + "table": "table", + "formula": "formula", + "list-item": "list_item", + "code": "code", + "figure": "picture", + "picture": "picture", + "reference": "text", + "paragraph": "text", + "text": "text", + } + + content_text = "" + page_no = 0 + start_ix = 0 + end_ix = 0 + doc_items = [] + + doc = doc_result.output + + def _process_page_segments(doc_items: list[Tuple[int, BaseCell]], page: Page): + segments = [] + + for ix, item in doc_items: + item_type = item.obj_type + label = label_to_doclaynet.get(item_type, None) + + if label is None: + continue + + bbox = BoundingBox.from_tuple( + item.prov[0].bbox, origin=CoordOrigin.BOTTOMLEFT + ) + new_bbox = bbox.to_top_left_origin(page_height=page.size.height).normalized( + page_size=page.size + ) + + new_segment = { + "index_in_doc": ix, + "label": label, + "text": item.text if item.text is not None else "", + "bbox": new_bbox.as_tuple(), + "data": [], + } + + if isinstance(item, Table): + table_html = _export_table_to_html(item) + new_segment["data"].append( + { + "html_seq": table_html, + "otsl_seq": "", + } + ) + + segments.append(new_segment) + + return segments + + def _process_page_cells(page: Page): + cells = [] + for cell in page.cells: + new_bbox = cell.bbox.to_top_left_origin( + page_height=page.size.height + ).normalized(page_size=page.size) + is_ocr = isinstance(cell, OcrCell) + ocr_confidence = cell.confidence if is_ocr else 1.0 + cells.append( + { + "text": cell.text, + "bbox": new_bbox.as_tuple(), + "ocr": is_ocr, + "ocr_confidence": ocr_confidence, + } + ) + return cells + + def _process_page(): + page_ix = page_no - 1 + page = doc_result.pages[page_ix] + + page_cells = _process_page_cells(page=page) + page_segments = _process_page_segments(doc_items=doc_items, page=page) + content_md = doc.export_to_markdown( + main_text_start=start_ix, main_text_stop=end_ix + ) + + return content_text, content_md, page_cells, page_segments, page + + for ix, orig_item in enumerate(doc.main_text): + + item = doc._resolve_ref(orig_item) if isinstance(orig_item, Ref) else orig_item + if item is None or item.prov is None or len(item.prov) == 0: + _log.debug(f"Skipping item {orig_item}") + continue + + item_page = item.prov[0].page + + # Page is complete + if page_no > 0 and item_page > page_no: + yield _process_page() + + start_ix = ix + doc_items = [] + content_text = "" + + page_no = item_page + end_ix = ix + doc_items.append((ix, item)) + if item.text is not None and item.text != "": + content_text += item.text + " " + + if len(doc_items) > 0: + yield _process_page() diff --git a/examples/export_multimodal.py b/examples/export_multimodal.py new file mode 100644 index 00000000..cc0b9fab --- /dev/null +++ b/examples/export_multimodal.py @@ -0,0 +1,114 @@ +import datetime +import logging +import time +from pathlib import Path + +import pandas as pd + +from docling.datamodel.base_models import AssembleOptions, ConversionStatus +from docling.datamodel.document import DocumentConversionInput +from docling.document_converter import DocumentConverter +from docling.utils.export import generate_multimodal_pages + +_log = logging.getLogger(__name__) + +IMAGE_RESOLUTION_SCALE = 2.0 + + +def main(): + logging.basicConfig(level=logging.INFO) + + input_doc_paths = [ + Path("./tests/data/2206.01062.pdf"), + ] + output_dir = Path("./scratch") + + input_files = DocumentConversionInput.from_paths(input_doc_paths) + + # Important: For operating with page images, we must keep them, otherwise the DocumentConverter + # will destroy them for cleaning up memory. + # This is done by setting AssembleOptions.images_scale, which also defines the scale of images. + # scale=1 correspond of a standard 72 DPI image + assemble_options = AssembleOptions() + assemble_options.images_scale = IMAGE_RESOLUTION_SCALE + + doc_converter = DocumentConverter(assemble_options=assemble_options) + + start_time = time.time() + + converted_docs = doc_converter.convert(input_files) + + success_count = 0 + failure_count = 0 + output_dir.mkdir(parents=True, exist_ok=True) + for doc in converted_docs: + if doc.status != ConversionStatus.SUCCESS: + _log.info(f"Document {doc.input.file} failed to convert.") + failure_count += 1 + continue + + rows = [] + for ( + content_text, + content_md, + page_cells, + page_segments, + page, + ) in generate_multimodal_pages(doc): + + dpi = page._default_image_scale * 72 + + rows.append( + { + "document": doc.input.file.name, + "hash": doc.input.document_hash, + "page_hash": page.page_hash, + "image": { + "width": page.image.width, + "height": page.image.height, + "bytes": page.image.tobytes(), + }, + "cells": page_cells, + "contents": content_text, + "contents_md": content_md, + "segments": page_segments, + "extra": { + "page_num": page.page_no + 1, + "width_in_points": page.size.width, + "height_in_points": page.size.height, + "dpi": dpi, + }, + } + ) + success_count += 1 + + # Generate one parquet from all documents + df = pd.json_normalize(rows) + now = datetime.datetime.now() + output_filename = output_dir / f"multimodal_{now:%Y-%m-%d_%H%M%S}.parquet" + df.to_parquet(output_filename) + + end_time = time.time() - start_time + + _log.info(f"All documents were converted in {end_time:.2f} seconds.") + + if failure_count > 0: + raise RuntimeError( + f"The example failed converting {failure_count} on {len(input_doc_paths)}." + ) + + # This block demonstrates how the file can be opened with the HF datasets library + # from datasets import 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