diff --git a/docling_eval/campaign_tools/cvat_create_annotation_tasks_from_folders.py b/docling_eval/campaign_tools/cvat_create_annotation_tasks_from_folders.py index 8ee8b755..faa937c9 100644 --- a/docling_eval/campaign_tools/cvat_create_annotation_tasks_from_folders.py +++ b/docling_eval/campaign_tools/cvat_create_annotation_tasks_from_folders.py @@ -1,5 +1,5 @@ """ -CLI utility to batch-create datasets for annotation workflows from directores containing plain files. +CLI utility to batch-create datasets for annotation workflows from directories containing plain files. Given a root input directory containing subdirectories (each with plain files: PDF, image, etc.), this tool creates, for each subdirectory: @@ -7,16 +7,22 @@ - eval_dataset: weak annotation dataset using Docling predictions - cvat_dataset_preannotated: CVAT-ready input structure for annotation -This is useful for preparing large-scale annotation tasks for CVAT or similar tools. +If a subdirectory contains more than max_files_per_chunk files (default: 1000), it will be +automatically split into multiple chunks with separate output directories named: + - {subdir_name}_chunk_001, {subdir_name}_chunk_002, etc. + +This is useful for preparing large-scale annotation tasks for CVAT or similar tools while +avoiding "too many files open" errors. Usage: - uv run python docling_eval/campaign_tools/cvat_create_annotation_tasks_from_folders.py batch-prepare --input-directory --output-directory [--sliding-window ] [--use-predictions/--no-use-predictions] + uv run python docling_eval/campaign_tools/cvat_create_annotation_tasks_from_folders.py batch-prepare --input-directory --output-directory [--sliding-window ] [--use-predictions/--no-use-predictions] [--max-files-per-chunk ] Arguments: input_directory: Root directory containing subdirectories with files to process - output_directory: Where to store the generated datasets (one subdir per input subdir) + output_directory: Where to store the generated datasets (one subdir per input subdir, with chunk suffixes if needed) sliding_window: Number of pages per CVAT task (default: 1) use_predictions: Whether to create prediction dataset and use predictions in CVAT (default: True) + max_files_per_chunk: Maximum number of files to process per chunk (default: 1000) """ from pathlib import Path @@ -35,16 +41,19 @@ def process_subdirectories( output_directory: Path, sliding_window: int = 1, use_predictions: bool = True, + max_files_per_chunk: int = 1000, ) -> None: """ For each subdirectory in input_directory, create gt_dataset, eval_dataset, and cvat_dataset_preannotated - in the corresponding output_directory. + in the corresponding output_directory. If a subdirectory contains more than max_files_per_chunk files, + it will be automatically split into multiple chunks with separate output directories. Args: input_directory: Root directory with subdirectories to process output_directory: Where to store generated datasets sliding_window: Number of pages per CVAT task (default: 1) use_predictions: Whether to create prediction dataset and use predictions in CVAT + max_files_per_chunk: Maximum number of files to process per chunk (default: 1000) """ input_directory = input_directory.expanduser().resolve() output_directory = output_directory.expanduser().resolve() @@ -57,62 +66,111 @@ def process_subdirectories( for subdir in subdirs: subdir_name = subdir.name - odir = output_directory / subdir_name - odir.mkdir(parents=True, exist_ok=True) + + # Count total files in subdirectory + total_files = 0 + for ext in ["pdf", "tif", "tiff", "jpg", "jpeg", "png", "bmp", "gif", "json"]: + total_files += len(list(subdir.glob(f"*.{ext}"))) + total_files += len(list(subdir.glob(f"*.{ext.upper()}"))) + typer.echo(f"\nProcessing: {subdir_name}") + typer.echo(f" Total files found: {total_files}") + + # Calculate number of chunks needed + num_chunks = (total_files + max_files_per_chunk - 1) // max_files_per_chunk + typer.echo( + f" Will create {num_chunks} chunk(s) of max {max_files_per_chunk} files each" + ) + + # Process each chunk + for chunk_idx in range(num_chunks): + begin_index = chunk_idx * max_files_per_chunk + end_index = min((chunk_idx + 1) * max_files_per_chunk, total_files) + files_in_chunk = end_index - begin_index + + # Create chunk-specific output directory + if num_chunks == 1: + # Single chunk, use original directory name + chunk_output_dir = output_directory / subdir_name + else: + # Multiple chunks, append chunk number + chunk_output_dir = ( + output_directory / f"{subdir_name}_chunk_{chunk_idx + 1:03d}" + ) - gt_dir = odir / "gt_dataset" - eval_dir = odir / "eval_dataset" - cvat_dir = odir / "cvat_dataset_preannotated" - - if not gt_dir.exists(): - typer.echo(f" Creating GT dataset...") - create_gt( - benchmark=BenchMarkNames.PLAIN_FILES, - dataset_source=subdir, - output_dir=odir, - do_visualization=False, + chunk_output_dir.mkdir(parents=True, exist_ok=True) + + typer.echo( + f" Processing chunk {chunk_idx + 1}/{num_chunks}: files {begin_index} to {end_index-1} ({files_in_chunk} files)" ) - else: - typer.echo(f" GT dataset already exists, skipping.") + typer.echo(f" Output directory: {chunk_output_dir}") - if use_predictions: - if not eval_dir.exists(): - typer.echo(f" Creating prediction dataset (Docling)...") - create_eval( + gt_dir = chunk_output_dir / "gt_dataset" + eval_dir = chunk_output_dir / "eval_dataset" + cvat_dir = chunk_output_dir / "cvat_dataset_preannotated" + + if not gt_dir.exists(): + typer.echo(f" Creating GT dataset...") + create_gt( benchmark=BenchMarkNames.PLAIN_FILES, - output_dir=odir, - prediction_provider=PredictionProviderType.DOCLING, - do_visualization=True, - image_scale_factor=2.0, - do_table_structure=False, + dataset_source=subdir, + output_dir=chunk_output_dir, + do_visualization=False, + begin_index=begin_index, + end_index=end_index, ) else: - typer.echo(f" Prediction dataset already exists, skipping.") - else: - typer.echo( - f" Skipping prediction dataset creation (use_predictions=False)." - ) + typer.echo(f" GT dataset already exists, skipping.") + + if use_predictions: + if not eval_dir.exists(): + typer.echo(f" Creating prediction dataset (Docling)...") + create_eval( + benchmark=BenchMarkNames.PLAIN_FILES, + output_dir=chunk_output_dir, + prediction_provider=PredictionProviderType.DOCLING, + do_visualization=True, + image_scale_factor=2.0, + do_table_structure=False, + begin_index=begin_index, + end_index=end_index, + ) + else: + typer.echo(f" Prediction dataset already exists, skipping.") + else: + typer.echo( + f" Skipping prediction dataset creation (use_predictions=False)." + ) - if not cvat_dir.exists(): - typer.echo(f" Creating CVAT pre-annotated dataset...") - # Use gt_dir when no predictions, eval_dir when using predictions - source_dir = (eval_dir / "test") if use_predictions else (gt_dir / "test") - create_cvat( - gt_dir=source_dir, - output_dir=cvat_dir, - bucket_size=100, - use_predictions=use_predictions, - sliding_window=sliding_window, + if not cvat_dir.exists(): + typer.echo(f" Creating CVAT pre-annotated dataset...") + # Use gt_dir when no predictions, eval_dir when using predictions + source_dir = ( + (eval_dir / "test") if use_predictions else (gt_dir / "test") + ) + create_cvat( + gt_dir=source_dir, + output_dir=cvat_dir, + bucket_size=100, + use_predictions=use_predictions, + sliding_window=sliding_window, + ) + else: + typer.echo(f" CVAT dataset already exists, skipping.") + + assert ( + gt_dir.exists() + ), f"gt_dataset not created for {subdir_name} chunk {chunk_idx + 1}" + assert ( + cvat_dir.exists() + ), f"cvat_dataset_preannotated not created for {subdir_name} chunk {chunk_idx + 1}" + typer.echo( + f" Successfully created all datasets for chunk {chunk_idx + 1}" ) - else: - typer.echo(f" CVAT dataset already exists, skipping.") - assert gt_dir.exists(), f"gt_dataset not created for {subdir_name}" - assert ( - cvat_dir.exists() - ), f"cvat_dataset_preannotated not created for {subdir_name}" - typer.echo(f" Successfully created all datasets for {subdir_name}") + typer.echo( + f" Successfully processed all {num_chunks} chunk(s) for {subdir_name}" + ) @app.command() @@ -129,12 +187,19 @@ def batch_prepare( use_predictions: bool = typer.Option( True, help="Whether to create prediction dataset and use predictions in CVAT" ), + max_files_per_chunk: int = typer.Option( + 1000, help="Maximum number of files to process per chunk (default: 1000)" + ), ) -> None: """ Batch-create Docling evaluation datasets for all subdirectories in input_directory. """ process_subdirectories( - input_directory, output_directory, sliding_window, use_predictions + input_directory, + output_directory, + sliding_window, + use_predictions, + max_files_per_chunk, ) typer.echo("\nAll benchmarks created successfully!") diff --git a/docling_eval/cli/cvat_to_docling_cli.py b/docling_eval/cli/cvat_to_docling_cli.py index 21fb2ed8..36c6702b 100644 --- a/docling_eval/cli/cvat_to_docling_cli.py +++ b/docling_eval/cli/cvat_to_docling_cli.py @@ -187,7 +187,7 @@ def main(): description="Convert CVAT annotations to DoclingDocuments in batch." ) parser.add_argument( - "input_path", + "--input_path", type=str, help="Path to input directory or XML file", ) diff --git a/docling_eval/cli/main.py b/docling_eval/cli/main.py index 76c62a4c..50b3bb53 100644 --- a/docling_eval/cli/main.py +++ b/docling_eval/cli/main.py @@ -27,6 +27,10 @@ PdfPipelineOptions, VlmPipelineOptions, ) +from docling.datamodel.vlm_model_specs import ( + GRANITEDOCLING_MLX, + GRANITEDOCLING_TRANSFORMERS, +) from docling.datamodel.vlm_model_specs import ( SMOLDOCLING_MLX as smoldocling_vlm_mlx_conversion_options, ) @@ -475,17 +479,49 @@ def get_prediction_provider( pipeline_cls=VlmPipeline, pipeline_options=pipeline_options ) - format_options: Dict[InputFormat, FormatOption] = { - InputFormat.PDF: pdf_format_option, - InputFormat.IMAGE: pdf_format_option, - } - return DoclingPredictionProvider( - format_options=format_options, + format_options={ + InputFormat.PDF: pdf_format_option, + InputFormat.IMAGE: pdf_format_option, + }, do_visualization=do_visualization, ignore_missing_predictions=True, ) + elif provider_type == PredictionProviderType.GRANITEDOCLING: + pipeline_options = VlmPipelineOptions() + + pipeline_options.images_scale = image_scale_factor or 2.0 + pipeline_options.generate_page_images = True + pipeline_options.generate_picture_images = True + + pipeline_options.vlm_options = GRANITEDOCLING_TRANSFORMERS + if artifacts_path is not None: + pipeline_options.artifacts_path = artifacts_path + + if sys.platform == "darwin": + try: + import mlx_vlm # type: ignore + + pipeline_options.vlm_options = GRANITEDOCLING_MLX + + except ImportError: + _log.warning( + "To run SmolDocling faster, please install mlx-vlm:\n" + "pip install mlx-vlm" + ) + + pdf_format_option = PdfFormatOption( + pipeline_cls=VlmPipeline, pipeline_options=pipeline_options + ) + return DoclingPredictionProvider( + format_options={ + InputFormat.PDF: pdf_format_option, + InputFormat.IMAGE: pdf_format_option, + }, + do_visualization=do_visualization, + ignore_missing_predictions=True, + ) elif provider_type == PredictionProviderType.TABLEFORMER: return TableFormerPredictionProvider( do_visualization=do_visualization, diff --git a/docling_eval/cvat_tools/cvat_to_docling.py b/docling_eval/cvat_tools/cvat_to_docling.py index 1ad15649..d4a078d5 100644 --- a/docling_eval/cvat_tools/cvat_to_docling.py +++ b/docling_eval/cvat_tools/cvat_to_docling.py @@ -7,9 +7,12 @@ """ import logging +from dataclasses import dataclass +from enum import Enum from pathlib import Path from typing import Dict, List, Optional, Set, Tuple +from docling_core.types.doc import RichTableCell, TableCell from docling_core.types.doc.base import BoundingBox, CoordOrigin from docling_core.types.doc.document import ( ContentLayer, @@ -43,7 +46,7 @@ from PIL import Image as PILImage from docling_eval.cvat_tools.document import DocumentStructure -from docling_eval.cvat_tools.models import CVATElement +from docling_eval.cvat_tools.models import CVATElement, TableStructLabel from docling_eval.cvat_tools.parser import MissingImageInCVATXML from docling_eval.cvat_tools.tree import ( TreeNode, @@ -57,6 +60,293 @@ _logger = logging.getLogger(__name__) +class SemClass(str, Enum): + COL_HEADER = "col_header" + ROW_HEADER = "row_header" + ROW_SECTION = "row_section" + BODY = "body" + + +SEM_TO_TABLE_LABEL: dict[SemClass, TableStructLabel] = { + SemClass.COL_HEADER: TableStructLabel.COL_HEADER, + SemClass.ROW_HEADER: TableStructLabel.ROW_HEADER, + SemClass.ROW_SECTION: TableStructLabel.ROW_SECTION, + SemClass.BODY: TableStructLabel.BODY, +} + +DEFAULT_TABLE_PAIR_IOU: float = 0.20 +DEFAULT_CONTAINMENT_THRESH: float = 0.50 +DEFAULT_SEM_MATCH_IOU: float = 0.30 + + +def inter_area(a: BoundingBox, b: BoundingBox) -> float: + return a.intersection_area_with(b) + + +def inside_with_tolerance( + child: BoundingBox, parent: BoundingBox, thresh: float +) -> bool: + a = child.area() + if a <= 0.0: + return False + return (inter_area(child, parent) / a) >= thresh + + +@dataclass(frozen=True) +class Cell: + start_row: int + end_row: int + start_column: int + end_column: int + row_span_length: int + column_span_length: int + bbox: BoundingBox # cell bounding box + column_header: bool = False + row_header: bool = False + row_section: bool = False + fillable_cell: bool = False + + +# === CVAT ELEMENTS BBOX CHECKS === +# Compute IOU between two bboxes +def compute_iou(a: "BoundingBox", b: "BoundingBox") -> float: + """Intersection over Union with another box.""" + inter_w = max(0.0, min(a.r, b.r) - max(a.l, b.l)) + inter_h = max(0.0, min(a.b, b.b) - max(a.t, b.t)) + inter = inter_w * inter_h + if inter == 0.0: + return 0.0 + union = a.area() + b.area() - inter + return inter / union if union > 0.0 else 0.0 + + +# Deduplicate list of CANVA elements based on their bbox IOU +def _dedupe_by_bboxes( + elements: list[CVATElement], + iou_threshold: float = 0.9, +) -> List[CVATElement]: + """ + Return a cleaned list of elements where no two boxes have IoU >= iou_threshold. + If duplicates exist, keep just one. + """ + if not elements: + return [] + + kept: List[CVATElement] = [] + for elem in elements: + # keep only if it's not too similar to any already kept box + if all(compute_iou(elem.bbox, k.bbox) < iou_threshold for k in kept): + kept.append(elem) + return kept + + +def is_bbox_within( + bbox_a: BoundingBox, bbox_b: BoundingBox, threshold: float = 0.5 +) -> bool: + """ + Returns True if at least `threshold` proportion of B is inside A. + """ + + # Intersection coordinates + inter_left = max(bbox_a.l, bbox_b.l) + inter_top = max(bbox_a.t, bbox_b.t) + inter_right = min(bbox_a.r, bbox_b.r) + inter_bottom = min(bbox_a.b, bbox_b.b) + + # Compute intersection area + inter_width = max(0.0, inter_right - inter_left) + inter_height = max(0.0, inter_bottom - inter_top) + inter_area = inter_width * inter_height + + # Compute ratio of B’s area inside A + b_area = bbox_b.area() + if b_area == 0: + return False # degenerate box + + inside_ratio = inter_area / b_area + return inside_ratio >= threshold + + +# COMPUTE UNIQUE TABLE CELLS +def intersect_bboxes(a: BoundingBox, b: BoundingBox) -> Optional[BoundingBox]: + l1 = max(a.l, b.l) + t1 = max(a.t, b.t) + r1 = min(a.r, b.r) + b1 = min(a.b, b.b) + if r1 <= l1 or b1 <= t1: + return None + return BoundingBox(l=l1, t=t1, r=r1, b=b1, coord_origin=a.coord_origin) + + +def compute_cells( + rows: List[CVATElement], + columns: List[CVATElement], + merges: List[CVATElement], + col_headers: List[CVATElement], + row_headers: List[CVATElement], + row_sections: List[CVATElement], + fillable_cells: List[CVATElement], + row_overlap_threshold: float = 0.5, # how much of a row a merge must cover vertically + col_overlap_threshold: float = 0.5, # how much of a column a merge must cover horizontally +) -> List[Cell]: + """ + rows, columns, merges are lists of BoundingBox(l,t,r,b). + Returns 0-based indexed Cells. Merged cells are aligned to grid boundaries. + """ + rows.sort(key=lambda r: (r.bbox.t + r.bbox.b) / 2.0) + columns.sort(key=lambda c: (c.bbox.l + c.bbox.r) / 2.0) + + n_rows, n_cols = len(rows), len(columns) + + def span_from_merge( + m: BoundingBox, lines: List[CVATElement], axis: str, frac_threshold: float + ) -> Optional[Tuple[int, int]]: + """ + Map a merge bbox to an inclusive index span over rows or columns. + axis='row' uses vertical overlap vs row height; axis='col' uses horizontal overlap vs col width. + If nothing meets threshold, pick the single best-overlapping line if overlap>0; else return None. + """ + idxs = [] + best_i, best_len = None, 0.0 + for i, elem in enumerate(lines): + inter = intersect_bboxes(m, elem.bbox) + if not inter: + continue + if axis == "row": + overlap_len = inter.height + base = max(1e-9, elem.bbox.height) + else: + overlap_len = inter.width + base = max(1e-9, elem.bbox.width) + + frac = overlap_len / base + if frac >= frac_threshold: + idxs.append(i) + + if overlap_len > best_len: + best_len, best_i = overlap_len, i + + if idxs: + return min(idxs), max(idxs) + if best_i is not None and best_len > 0.0: + return best_i, best_i + return None + + cells: List[Cell] = [] + covered: Set[Tuple[int, int]] = set() + seen_merge_rects: Set[Tuple[int, int, int, int]] = set() + + # 1) Add merged cells first (and mark their covered simple cells) + for m in merges: + rspan = span_from_merge( + m.bbox, rows, axis="row", frac_threshold=row_overlap_threshold + ) + cspan = span_from_merge( + m.bbox, columns, axis="col", frac_threshold=col_overlap_threshold + ) + if rspan is None or cspan is None: + # Can't confidently map this merge to grid -> skip it + continue + + sr, er = rspan + sc, ec = cspan + rect_key = (sr, er, sc, ec) + if rect_key in seen_merge_rects: + continue + seen_merge_rects.add(rect_key) + + # Grid-aligned bbox for the merged cell + grid_bbox = BoundingBox( + l=columns[sc].bbox.l, + t=rows[sr].bbox.t, + r=columns[ec].bbox.r, + b=rows[er].bbox.b, + ) + c_column_header, c_row_header, c_row_section, c_fillable = ( + process_table_headers( + grid_bbox, col_headers, row_headers, row_sections, fillable_cells + ) + ) + + cells.append( + Cell( + start_row=sr, + end_row=er, + start_column=sc, + end_column=ec, + row_span_length=er - sr + 1, + column_span_length=ec - sc + 1, + bbox=grid_bbox, + column_header=c_column_header, + row_header=c_row_header, + row_section=c_row_section, + fillable_cell=c_fillable, + ) + ) + + for ri in range(sr, er + 1): + for ci in range(sc, ec + 1): + covered.add((ri, ci)) + + # 2) Add simple (1x1) cells where not covered by merges + for ri, row in enumerate(rows): + for ci, col in enumerate(columns): + if (ri, ci) in covered: + continue + inter = intersect_bboxes(row.bbox, col.bbox) + if not inter: + # In degenerate cases (big gaps), there might be no intersection; skip. + continue + c_column_header, c_row_header, c_row_section, c_fillable = ( + process_table_headers( + inter, col_headers, row_headers, row_sections, fillable_cells + ) + ) + cells.append( + Cell( + start_row=ri, + end_row=ri, + start_column=ci, + end_column=ci, + row_span_length=1, + column_span_length=1, + bbox=inter, + column_header=c_column_header, + row_header=c_row_header, + row_section=c_row_section, + fillable_cell=c_fillable, + ) + ) + return cells + + +def process_table_headers( + bbox: BoundingBox, + col_headers: List[CVATElement], + row_headers: List[CVATElement], + row_sections: List[CVATElement], + fillable_cells: List[CVATElement], +) -> Tuple[bool, bool, bool, bool]: + c_column_header = False + c_row_header = False + c_row_section = False + c_fillable = False + + for col_header in col_headers: + if is_bbox_within(col_header.bbox, bbox): + c_column_header = True + for row_header in row_headers: + if is_bbox_within(row_header.bbox, bbox): + c_row_header = True + for row_section in row_sections: + if is_bbox_within(row_section.bbox, bbox): + c_row_section = True + for fillable_cell in fillable_cells: + if is_bbox_within(fillable_cell.bbox, bbox): + c_fillable = True + return c_column_header, c_row_header, c_row_section, c_fillable + + class ListHierarchyManager: """Manages list hierarchy creation and tracking. @@ -184,7 +474,25 @@ def _find_group_id_for_element( class CVATToDoclingConverter: - """Converts CVAT DocumentStructure to DoclingDocument.""" + """Converts CVAT DocumentStructure to DoclingDocument. + + Caption and Footnote Support: + ---------------------------- + The following containers (FloatingItem subclasses) can have captions and footnotes: + - TableItem (DocItemLabel.TABLE, DocItemLabel.DOCUMENT_INDEX) + - PictureItem (DocItemLabel.PICTURE, DocItemLabel.HANDWRITTEN_TEXT) + - CodeItem (DocItemLabel.CODE) + - FormItem (DocItemLabel.FORM) + - KeyValueItem (DocItemLabel.KEY_VALUE_REGION) + + Caption/footnote relationships support both directions: + - Forward: container -> caption/footnote (standard case) + - Backward: caption/footnote -> container (handles annotation errors) + + The converter is resilient to caption elements being labeled as TEXT instead + of CAPTION in the CVAT annotations - it will create the proper DocItemLabel.CAPTION + items regardless of the original label. + """ def __init__( self, @@ -227,6 +535,9 @@ def __init__( self.page_widths = {} cumulative_width = 0.0 + # To track tables, structures and rich cell elements + self.tabular_data: List[object] = [] + # Use CVAT image info dimensions if available, otherwise calculate from pages if self.doc_structure.image_info: # For multi-page documents, CVAT concatenates pages horizontally @@ -337,6 +648,9 @@ def convert(self) -> DoclingDocument: # Process elements in reading order, building list hierarchy on-demand self._process_elements_in_order(global_order) + # Process table data + self._process_table_data() + # Process captions and footnotes self._process_captions_and_footnotes() @@ -405,6 +719,7 @@ def _get_group_for_element( Returns: Tuple of (path_id, element_ids) if element is in a group, None otherwise """ + for path_id, element_ids in self.doc_structure.path_mappings.group.items(): if element_id in element_ids and len(element_ids) >= 2: return (path_id, element_ids) @@ -464,7 +779,10 @@ def _find_group_parent(self, element: CVATElement) -> Optional[NodeItem]: return None def _process_list_item_with_hierarchy( - self, element: CVATElement, parent_item: Optional[NodeItem] + self, + element: CVATElement, + global_order: List[int], + current_position: Optional[int], ) -> Optional[ListItem]: """Process a list item with proper hierarchy based on level.""" level = element.level or 1 @@ -499,6 +817,19 @@ def _process_list_item_with_hierarchy( # Update level stack self.list_manager.update_level_stack(level, list_item) + # Process logical child text elements immediately if position is available + if current_position is not None: + child_elements = self._find_logical_children_for_list_item( + element, global_order, current_position + ) + for child_element in child_elements: + # Create child item with list_item as parent + child_item = self._create_single_item(child_element, list_item) + if child_item: + # Mark as processed to prevent duplicate processing + self.processed_elements.add(child_element.id) + self.element_to_item[child_element.id] = child_item + return list_item def _find_group_id_for_element(self, element: CVATElement) -> Optional[int]: @@ -511,10 +842,74 @@ def _find_group_id_for_element(self, element: CVATElement) -> Optional[int]: return path_id return None + def _find_logical_children_for_list_item( + self, list_element: CVATElement, global_order: List[int], current_pos: int + ) -> List[CVATElement]: + """Find elements that should be children of this list item. + + Looks ahead in reading order to find elements between this list item + and the next list item, stopping at group/structural boundaries. + + Args: + list_element: The list item element to find children for + global_order: The global reading order + current_pos: Current position in the global_order + + Returns: + List of elements that should be children of this list item + """ + group_id = self._find_group_id_for_element(list_element) + list_content_layer = list_element.content_layer + + children = [] + # Look ahead in reading order + for i in range(current_pos + 1, len(global_order)): + next_element_id = global_order[i] + next_element = self.doc_structure.get_element_by_id(next_element_id) + + if not next_element: + continue + + # Stop if we hit another list item (any list item, any group) + if next_element.label == DocItemLabel.LIST_ITEM: + break + + # Stop if we hit structural elements that indicate new sections + if next_element.label in [ + DocItemLabel.SECTION_HEADER, + DocItemLabel.TITLE, + DocItemLabel.TABLE, + DocItemLabel.PICTURE, + DocItemLabel.FORM, + ]: + break + + # Collect any elements (except list items) on the same content layer + if next_element.content_layer == list_content_layer: + + next_group_id = self._find_group_id_for_element(next_element) + + # If we have a group, only include elements from same group or no group + if group_id is not None: + if next_group_id is None or next_group_id == group_id: + children.append(next_element) + else: + # Different group means we've moved to a new section + break + else: + # If list item has no group, only include elements with no group + if next_group_id is None: + children.append(next_element) + else: + # Element has a group but list doesn't - stop here + break + + return children + def _process_elements_in_order(self, global_order: List[int]): """Process elements in reading order.""" # Process elements in global reading order - for element_id in global_order: + for i, element_id in enumerate(global_order): # Skip if already processed if element_id in self.processed_elements: continue @@ -522,7 +917,118 @@ def _process_elements_in_order(self, global_order: List[int]): # Find the node containing this element node = find_node_by_element_id(self.doc_structure.tree_roots, element_id) if node: - self._process_node(node, parent_item=None, global_order=global_order) + self._process_node( + node, + None, + parent_item=None, + global_order=global_order, + current_position=i, + ) + + def _process_table_data(self): + # After all CVAT elements have been processed, + # go over tables and populate them with cells + # This includes rich cell elements + + for tind, table_item in enumerate(self.doc.tables): + # table_item.children - would be rich elements for each table cell + table_cell_data = self.tabular_data[tind]["computed_table_cells"] + page_no = self.tabular_data[tind]["page_no"] + page_height = self.doc.pages[page_no].size.height + + table_item.children = [] + all_items = [] + for item, _ in self.doc.iterate_items(page_no=page_no): + all_items.append(item.get_ref()) + + for c in table_cell_data: + # Get page number from bbox position + # Get text to populate TableData + cell_text = self._extract_text_from_bbox(c.bbox, page_no) + + # Define if cell is Rich + rich_cell = False + provs_in_cell = [] + + # FIND RICH ELEMENTS REFs HERE, MAKE A GROUP IF MANY + for item_ref in all_items: + item = item_ref.resolve(self.doc) + + # Skip the table itself - it should never be processed as cell content + if item == table_item: + continue + + if isinstance(item, DocItem): + for prov in item.prov: + prov_bbox_tl = prov.bbox.to_top_left_origin(page_height) + + if is_bbox_within(c.bbox, prov_bbox_tl): + # At least one child is inside the cell! + rich_cell = True + item_parent = ( + item.parent.resolve(self.doc) + if item.parent + else None + ) + # Only remove from parent if parent is not the table itself + if ( + item_parent + and item_parent != table_item + and item.get_ref() in item_parent.children + ): + item_parent.children.remove(item.get_ref()) + item.parent = table_item.get_ref() + provs_in_cell.append(item.get_ref()) + if rich_cell: + # Get Ref + ref_for_rich_cell = provs_in_cell[0] + + if len(provs_in_cell) > 1: + group_element = self.doc.add_group( + label=GroupLabel.UNSPECIFIED, + name="rich_cell_group_{}_{}_{}".format( + tind, c.start_column, c.start_row + ), + parent=table_item, + ) + for pr in provs_in_cell: + group_element.children.append(pr) + pr_item = pr.resolve(self.doc) + pr_item.parent = group_element.get_ref() + ref_for_rich_cell = group_element.get_ref() + + cell = RichTableCell( + # bbox=c.bbox, + text=cell_text, + row_span=c.row_span_length, + col_span=c.column_span_length, + start_row_offset_idx=c.start_row, + end_row_offset_idx=c.start_row + c.row_span_length, + start_col_offset_idx=c.start_column, + end_col_offset_idx=c.start_column + c.column_span_length, + column_header=c.column_header, + row_header=c.row_header, + row_section=c.row_section, + fillable=c.fillable_cell, + ref=ref_for_rich_cell, # points to an artificial group around children, or to child directly + ) + self.doc.add_table_cell(table_item=table_item, cell=cell) + else: + cell = TableCell( + bbox=c.bbox, + text=cell_text, + row_span=c.row_span_length, + col_span=c.column_span_length, + start_row_offset_idx=c.start_row, + end_row_offset_idx=c.start_row + c.row_span_length, + start_col_offset_idx=c.start_column, + end_col_offset_idx=c.start_column + c.column_span_length, + column_header=c.column_header, + row_header=c.row_header, + fillable=c.fillable_cell, + row_section=c.row_section, + ) + self.doc.add_table_cell(table_item=table_item, cell=cell) def _find_parent_node(self, node: TreeNode) -> Optional[TreeNode]: """Find the parent node of a given node in the tree.""" @@ -545,7 +1051,12 @@ def search_parent(current: TreeNode, target: TreeNode) -> Optional[TreeNode]: return None def _process_node( - self, node: TreeNode, parent_item: Optional[NodeItem], global_order: List[int] + self, + node: TreeNode, + parentTreeNode: Optional[TreeNode], + parent_item: Optional[NodeItem], + global_order: List[int], + current_position: Optional[int] = None, ): """Process a tree node and its children.""" element = node.element @@ -560,7 +1071,9 @@ def _process_node( # Handle list items with hierarchy if element.label == DocItemLabel.LIST_ITEM: - list_item = self._process_list_item_with_hierarchy(element, parent_item) + list_item = self._process_list_item_with_hierarchy( + element, global_order, current_position + ) if list_item: self.element_to_item[element.id] = list_item self.processed_elements.add(element.id) @@ -617,24 +1130,26 @@ def _process_node( ) for child in sorted_children: - self._process_node(child, item, global_order) + self._process_node( + child, node, item, global_order, current_position=None + ) def _is_caption_or_footnote_target(self, element_id: int) -> bool: """Check if element is a target of caption/footnote relationship.""" # Check captions for path_id, ( container_id, - target_id, + caption_id, ) in self.doc_structure.path_mappings.to_caption.items(): - if target_id == element_id: + if caption_id == element_id: return True # Check footnotes for path_id, ( container_id, - target_id, + footnote_id, ) in self.doc_structure.path_mappings.to_footnote.items(): - if target_id == element_id: + if footnote_id == element_id: return True return False @@ -692,6 +1207,9 @@ def _create_merged_item( primary_element, parent, ) + else: + # Return None if label is not a DocItemLabel + return None # Add additional provenances if item and len(all_provs) > 1: @@ -774,8 +1292,129 @@ def _create_item_by_label( ) elif doc_label in [DocItemLabel.TABLE, DocItemLabel.DOCUMENT_INDEX]: - # Create empty table data - table_data = TableData(num_rows=0, num_cols=0, table_cells=[]) + # TODO: INSERT TABLE DATA PREP HERE + pool_rows = self.doc_structure.get_elements_by_label( + TableStructLabel.TABLE_ROW + ) + pool_cols = self.doc_structure.get_elements_by_label( + TableStructLabel.TABLE_COLUMN + ) + pool_merges = self.doc_structure.get_elements_by_label( + TableStructLabel.TABLE_MERGED_CELL + ) + + pool_col_headers = self.doc_structure.get_elements_by_label( + TableStructLabel.COL_HEADER + ) + pool_row_headers = self.doc_structure.get_elements_by_label( + TableStructLabel.ROW_HEADER + ) + pool_row_sections = self.doc_structure.get_elements_by_label( + TableStructLabel.ROW_SECTION + ) + + pool_fillable_cells = self.doc_structure.get_elements_by_label( + TableStructLabel.TABLE_FILLABLE_CELLS + ) + + tb = element.bbox + + pool_rows.extend( + pool_row_sections + ) # use row sections to compensate for missing rows + # pool_rows.extend(pool_col_headers) # use column headers to compensate for missing rows + + rows = _dedupe_by_bboxes( + [ + e + for e in pool_rows + if inside_with_tolerance(e.bbox, tb, DEFAULT_CONTAINMENT_THRESH) + ] + ) + cols = _dedupe_by_bboxes( + [ + e + for e in pool_cols + if inside_with_tolerance(e.bbox, tb, DEFAULT_CONTAINMENT_THRESH) + ] + ) + merges = _dedupe_by_bboxes( + [ + e + for e in pool_merges + if inside_with_tolerance(e.bbox, tb, DEFAULT_CONTAINMENT_THRESH) + ] + ) + + col_headers = _dedupe_by_bboxes( + [ + e + for e in pool_col_headers + if inside_with_tolerance(e.bbox, tb, DEFAULT_CONTAINMENT_THRESH) + ] + ) + row_headers = _dedupe_by_bboxes( + [ + e + for e in pool_row_headers + if inside_with_tolerance(e.bbox, tb, DEFAULT_CONTAINMENT_THRESH) + ] + ) + row_sections = _dedupe_by_bboxes( + [ + e + for e in pool_row_sections + if inside_with_tolerance(e.bbox, tb, DEFAULT_CONTAINMENT_THRESH) + ] + ) + fillable_cells = _dedupe_by_bboxes( + [ + e + for e in pool_fillable_cells + if inside_with_tolerance(e.bbox, tb, DEFAULT_CONTAINMENT_THRESH) + ] + ) + + # Compute table cells from CVAT elements: rows, cols, merges + computed_table_cells = compute_cells( + rows, + cols, + merges, + col_headers, + row_headers, + row_sections, + fillable_cells, + ) + + # If no table structure found, create single fake cell for content + if not rows or not cols: + computed_table_cells = [ + Cell( + start_row=0, + end_row=0, + start_column=0, + end_column=0, + row_span_length=1, + column_span_length=1, + bbox=tb, + column_header=False, + row_header=False, + row_section=False, + fillable_cell=False, + ) + ] + table_data = TableData(num_rows=1, num_cols=1) + else: + table_data = TableData(num_rows=len(rows), num_cols=len(cols)) + # Store pre-computed table structure + # to be used for re-construction of actual table cells in _process_table_data + self.tabular_data.append( + { + "computed_table_cells": computed_table_cells, + "bbox": tb, + "page_no": self._get_page_number_from_bbox(tb), + } + ) return self.doc.add_table( data=table_data, prov=prov, @@ -784,7 +1423,7 @@ def _create_item_by_label( content_layer=content_layer, ) - elif doc_label == DocItemLabel.PICTURE: + elif doc_label in [DocItemLabel.PICTURE, DocItemLabel.HANDWRITTEN_TEXT]: pic_item = self.doc.add_picture( prov=prov, parent=parent, content_layer=content_layer ) @@ -826,9 +1465,9 @@ def _create_item_by_label( elif doc_label == DocItemLabel.GRADING_SCALE: _logger.warning(f"Untreatable label: {doc_label}, ignoring.") return None - elif doc_label == DocItemLabel.HANDWRITTEN_TEXT: - _logger.warning(f"Untreatable label: {doc_label}, ignoring.") - return None + # elif doc_label == DocItemLabel.HANDWRITTEN_TEXT: + # _logger.warning(f"Untreatable label: {doc_label}, ignoring.") + # return None else: return self.doc.add_text( label=doc_label, @@ -930,28 +1569,41 @@ def _make_cell(element_id: int, label: GraphCellLabel) -> GraphCell: def _add_caption_or_footnote( self, container_id: int, target_id: int, is_caption: bool ): - """Add caption or footnote to a container item.""" - # Get container item - container_item = self.element_to_item.get(container_id) - if not container_item: - _logger.warning(f"Container {container_id} not found.") + """Add caption or footnote to a container item. + + Handles both forward and backward relationships. Tolerates caption/footnote + elements labeled as TEXT - always creates items with correct CAPTION/FOOTNOTE + labels regardless of original label. + """ + # Try forward direction first (container -> caption) + if self._try_caption_direction(container_id, target_id, is_caption): return - # Check if container supports captions/footnotes - if not isinstance(container_item, FloatingItem): - _logger.warning( - f"Container {container_id} does not support {'captions' if is_caption else 'footnotes'}" - ) + # Try backward direction (caption -> container) + if self._try_caption_direction(target_id, container_id, is_caption): return + _logger.warning( + f"Could not establish {'caption' if is_caption else 'footnote'} relationship between {container_id} and {target_id}" + ) + + def _try_caption_direction( + self, container_id: int, target_id: int, is_caption: bool + ) -> bool: + """Try to add caption/footnote in a specific direction. Returns True if successful.""" + # Get container item + container_item = self.element_to_item.get(container_id) + if not container_item or not isinstance(container_item, FloatingItem): + return False + # Get target element target_element = self.doc_structure.get_element_by_id(target_id) if not target_element: - return + return False page_no, text, provenance = self._process_element_bbox(target_element) - # Create caption/footnote item + # Create caption/footnote item - always use correct label regardless of original element label label = DocItemLabel.CAPTION if is_caption else DocItemLabel.FOOTNOTE item = self.doc.add_text( label=label, @@ -970,6 +1622,9 @@ def _add_caption_or_footnote( self.element_to_item[target_id] = item self.processed_elements.add(target_id) + return True + + return False def _extract_text_from_bbox(self, bbox: BoundingBox, page_no: int) -> str: """Extract text from bounding box using SegmentedPage text cells.""" @@ -1017,7 +1672,11 @@ def _extract_text_from_bbox(self, bbox: BoundingBox, page_no: int) -> str: seg_page.dimension.height ) - if search_bbox.intersection_over_union(cell_bbox) > 0.1: + # IOU is not good for obtaining text cells, if text cell is much smaller than + # tagret bbox, even if it's fully inside - IOU will be very small + # if search_bbox.intersection_over_union(cell_bbox) > 0.001: + # cells.append(cell) + if is_bbox_within(search_bbox, cell_bbox): cells.append(cell) if cells: diff --git a/docling_eval/cvat_tools/document.py b/docling_eval/cvat_tools/document.py index f415d7c3..cfc1acdd 100644 --- a/docling_eval/cvat_tools/document.py +++ b/docling_eval/cvat_tools/document.py @@ -76,6 +76,9 @@ def from_cvat_xml( image_info=image_info, ) + def get_elements_by_label(self, label: object) -> list[CVATElement]: + return [e for e in self.elements if e.label == label] + def get_element_by_id(self, element_id: int) -> Optional[CVATElement]: """Get an element by its ID.""" return next((el for el in self.elements if el.id == element_id), None) diff --git a/docling_eval/cvat_tools/models.py b/docling_eval/cvat_tools/models.py index cce71861..f9391bd1 100644 --- a/docling_eval/cvat_tools/models.py +++ b/docling_eval/cvat_tools/models.py @@ -21,7 +21,8 @@ class TableStructLabel(str, Enum): TABLE_MERGED_CELL = "table_merged_cell" COL_HEADER = "col_header" ROW_HEADER = "row_header" - ROW_SECTION = "row_section" + ROW_SECTION = "table_row_section" + TABLE_FILLABLE_CELLS = "fillable_cells" BODY = "body" diff --git a/docling_eval/cvat_tools/parser.py b/docling_eval/cvat_tools/parser.py index 413ccf22..4c600e86 100644 --- a/docling_eval/cvat_tools/parser.py +++ b/docling_eval/cvat_tools/parser.py @@ -100,14 +100,19 @@ def _parse_image_element( try: label_obj = DocItemLabel(label_str) except ValueError: + # Handle common CVAT label variations (e.g., "document Index" -> "document_index") + normalized_label = label_str.lower().replace(" ", "_") try: - label_obj = GraphCellLabel(label_str) # type: ignore[assignment] + label_obj = DocItemLabel(normalized_label) except ValueError: try: - label_obj = TableStructLabel(label_str) # type: ignore[assignment] + label_obj = GraphCellLabel(label_str) # type: ignore[assignment] except ValueError: - logger.debug(f"Skipping invalid label: {label_str}") - continue + try: + label_obj = TableStructLabel(label_str) # type: ignore[assignment] + except ValueError: + logger.debug(f"Skipping invalid label: {label_str}") + continue xtl = float(box.attrib["xtl"]) ytl = float(box.attrib["ytl"]) diff --git a/docling_eval/cvat_tools/path_mappings.py b/docling_eval/cvat_tools/path_mappings.py index a98c019f..200713e6 100644 --- a/docling_eval/cvat_tools/path_mappings.py +++ b/docling_eval/cvat_tools/path_mappings.py @@ -4,6 +4,7 @@ to elements and validating their relationships. """ +import logging from dataclasses import dataclass from typing import Dict, List, Optional, Set, Tuple @@ -17,6 +18,8 @@ is_footnote_element, ) +logger = logging.getLogger(__name__) + @dataclass class PathMappings: @@ -74,14 +77,53 @@ def map_path_points_to_elements( group[path.id] = touched_elements elif path.label == "to_caption" and len(touched_elements) == 2: # First element should be container, second should be caption - to_caption[path.id] = (touched_elements[0], touched_elements[1]) + container_id, caption_id = touched_elements[0], touched_elements[1] + + # Get elements to check their types + container_el = next((el for el in elements if el.id == container_id), None) + caption_el = next((el for el in elements if el.id == caption_id), None) + + # Check if the relationship is backwards and auto-correct with warning + if ( + container_el + and caption_el + and is_caption_element(container_el) + and is_container_element(caption_el) + ): + logger.warning( + f"Caption path {path.id}: Backwards annotation detected, auto-correcting" + ) + container_id, caption_id = caption_id, container_id + + to_caption[path.id] = (container_id, caption_id) elif path.label == "to_footnote" and len(touched_elements) == 2: # First element should be container, second should be footnote - to_footnote[path.id] = (touched_elements[0], touched_elements[1]) + container_id, footnote_id = touched_elements[0], touched_elements[1] + + # Get elements to check their types + container_el = next((el for el in elements if el.id == container_id), None) + footnote_el = next((el for el in elements if el.id == footnote_id), None) + + # Check if the relationship is backwards and auto-correct with warning + if ( + container_el + and footnote_el + and is_footnote_element(container_el) + and is_container_element(footnote_el) + ): + logger.warning( + f"Footnote path {path.id}: Backwards annotation detected, auto-correcting" + ) + container_id, footnote_id = footnote_id, container_id + + to_footnote[path.id] = (container_id, footnote_id) elif path.label == "to_value" and len(touched_elements) == 2: # First element should be key, second should be value to_value[path.id] = (touched_elements[0], touched_elements[1]) + # Resolve reading order conflicts before returning + reading_order = _resolve_reading_order_conflicts(reading_order, paths, elements) + return PathMappings( reading_order=reading_order, merge=merge, @@ -92,6 +134,89 @@ def map_path_points_to_elements( ) +def _find_container_for_conflicted_path( + path: CVATAnnotationPath, lost_elements: List[int], elements: List[CVATElement] +) -> Optional[CVATElement]: + """Find container element for reading order path using existing spatial utilities.""" + from .tree import contains + from .utils import is_container_element + + # Find containers that contain any of the lost elements, but path isn't fully inside + container_candidates = [] + for element in elements: + if is_container_element(element): + # Skip if path is fully contained (violates constraint) + if all( + element.bbox.l <= x <= element.bbox.r + and element.bbox.t <= y <= element.bbox.b + for x, y in path.points + ): + continue + + # Check if container holds any lost elements + for lost_id in lost_elements: + lost_el = next((el for el in elements if el.id == lost_id), None) + if lost_el and contains(element, lost_el): + container_candidates.append(element) + break + + return ( + min(container_candidates, key=lambda e: e.bbox.area()) + if container_candidates + else None + ) + + +def _resolve_reading_order_conflicts( + reading_order: Dict[int, List[int]], + paths: List[CVATAnnotationPath], + elements: List[CVATElement], +) -> Dict[int, List[int]]: + """Resolve conflicts where elements appear in multiple reading order paths.""" + # Build path mappings (reuse existing pattern from associate_paths_to_containers) + path_levels = { + p.id: p.level or 1 for p in paths if p.label.startswith("reading_order") + } + path_by_id = {p.id: p for p in paths if p.label.startswith("reading_order")} + + # Find element conflicts + element_to_paths: Dict[int, List[Tuple[int, int]]] = {} + for path_id, element_ids in reading_order.items(): + level = path_levels.get(path_id, 1) + for element_id in element_ids: + element_to_paths.setdefault(element_id, []).append((path_id, level)) + + conflicts = { + eid: paths for eid, paths in element_to_paths.items() if len(paths) > 1 + } + if conflicts: + logger.info(f"Resolving {len(conflicts)} reading order conflicts") + + # Resolve conflicts: assign to deepest level, find containers for emptied paths + emptied_paths: Dict[int, List[int]] = {} + for element_id, path_level_pairs in conflicts.items(): + keep_path_id = max(path_level_pairs, key=lambda x: x[1])[0] + for path_id, _ in path_level_pairs: + if path_id != keep_path_id: + reading_order[path_id].remove(element_id) + emptied_paths.setdefault(path_id, []).append(element_id) + + # Add containers for paths that lost elements + for path_id, lost_elements in emptied_paths.items(): + path = path_by_id.get(path_id) + if path: + container = _find_container_for_conflicted_path( + path, lost_elements, elements + ) + if container and container.id not in reading_order[path_id]: + reading_order[path_id].append(container.id) + logger.info( + f"Added container element {container.id} ({container.label}) to reading order path {path_id}" + ) + + return reading_order + + def associate_paths_to_containers( mappings: PathMappings, tree_roots: List[TreeNode], diff --git a/docling_eval/cvat_tools/tree.py b/docling_eval/cvat_tools/tree.py index bb9dba3e..698cd34f 100644 --- a/docling_eval/cvat_tools/tree.py +++ b/docling_eval/cvat_tools/tree.py @@ -33,7 +33,14 @@ def get_descendant_ids(self) -> Set[int]: def contains(parent: CVATElement, child: CVATElement, iou_thresh: float = 0.7) -> bool: - """Check if parent element contains child element based on IOU threshold.""" + """Check if parent element contains child element based on IOU threshold. + + Only larger elements can contain smaller ones to prevent circular dependencies. + """ + # Only larger elements can contain smaller ones + if parent.bbox.area() <= child.bbox.area(): + return False + intersection = parent.bbox.intersection_area_with(child.bbox) return intersection / (child.bbox.area() + 1e-6) > iou_thresh diff --git a/docling_eval/cvat_tools/utils.py b/docling_eval/cvat_tools/utils.py index 42491921..9923adac 100644 --- a/docling_eval/cvat_tools/utils.py +++ b/docling_eval/cvat_tools/utils.py @@ -52,6 +52,9 @@ def get_deepest_element_at_point( ) -> Optional[CVATElement]: """Get the deepest (smallest area) element containing the given point. + Filters out TableStructLabel elements as they are only structural annotations + for table reconstruction, not content elements for reading order. + Args: point: (x, y) coordinates of the point elements: List of elements to search @@ -60,8 +63,16 @@ def get_deepest_element_at_point( Returns: The deepest element containing the point, or None if no element found """ + from .models import TableStructLabel + candidates = find_elements_containing_point(point, elements, proximity_thresh) - return candidates[0] if candidates else None + + # Filter out TableStructLabel elements (they are only structural annotations) + filtered_candidates = [ + el for el in candidates if not isinstance(el.label, TableStructLabel) + ] + + return filtered_candidates[0] if filtered_candidates else None def validate_element_types( @@ -118,7 +129,7 @@ def is_caption_element(element: CVATElement) -> bool: Returns: True if the element is a caption """ - return element.label == "caption" + return element.label in ["caption", "text"] def is_footnote_element(element: CVATElement) -> bool: diff --git a/docling_eval/datamodels/types.py b/docling_eval/datamodels/types.py index 437d308a..65d55b21 100644 --- a/docling_eval/datamodels/types.py +++ b/docling_eval/datamodels/types.py @@ -133,6 +133,7 @@ class PredictionProviderType(str, Enum): TABLEFORMER = "TableFormer" FILE = "File" SMOLDOCLING = "SmolDocling" + GRANITEDOCLING = "GraniteDocling" AWS = "AWS" AZURE = "Azure" GOOGLE = "Google" diff --git a/docling_eval/dataset_builders/dataset_builder.py b/docling_eval/dataset_builders/dataset_builder.py index ae0a10e1..c276d4ac 100644 --- a/docling_eval/dataset_builders/dataset_builder.py +++ b/docling_eval/dataset_builders/dataset_builder.py @@ -165,6 +165,7 @@ def retrieve_input_dataset(self) -> Path: if not self.dataset_local_path: path_str = snapshot_download( repo_id=self.dataset_source.repo_id, + revision=self.dataset_source.revision, repo_type="dataset", token=self.dataset_source.hf_token, ) @@ -173,6 +174,7 @@ def retrieve_input_dataset(self) -> Path: else: path_str = snapshot_download( repo_id=self.dataset_source.repo_id, + revision=self.dataset_source.revision, repo_type="dataset", token=self.dataset_source.hf_token, local_dir=self.dataset_local_path, diff --git a/docling_eval/dataset_builders/omnidocbench_builder.py b/docling_eval/dataset_builders/omnidocbench_builder.py index 29f4fb0d..68d0d9a7 100644 --- a/docling_eval/dataset_builders/omnidocbench_builder.py +++ b/docling_eval/dataset_builders/omnidocbench_builder.py @@ -108,7 +108,9 @@ def __init__( """ super().__init__( name="OmniDocBench: end-to-end", - dataset_source=HFSource(repo_id="opendatalab/OmniDocBench"), + dataset_source=HFSource( + repo_id="opendatalab/OmniDocBench", revision="v1_0" + ), target=target, split=split, begin_index=begin_index, diff --git a/docling_eval/prediction_providers/base_prediction_provider.py b/docling_eval/prediction_providers/base_prediction_provider.py index c7b595a9..9366ad60 100644 --- a/docling_eval/prediction_providers/base_prediction_provider.py +++ b/docling_eval/prediction_providers/base_prediction_provider.py @@ -262,18 +262,136 @@ def add_prediction(self, record: DatasetRecord) -> DatasetRecordWithPrediction: # Copy the original input data to avoid modifying it input_data = copy.deepcopy(record.original) - # Convert Path to DocumentStream if needed - if not isinstance(input_data, DocumentStream): - if isinstance(input_data, Path): - input_data = DocumentStream( - name=input_data.name, stream=BytesIO(input_data.open("rb").read()) - ) + # Special handling for JSON DoclingDocument records with page images + if record.mime_type == "application/json": + input_data, new_mime_type = self._handle_json_docling_document(record) + record.mime_type = new_mime_type + else: + # Standard processing for other mime types + # Convert Path to DocumentStream if needed + if not isinstance(input_data, DocumentStream): + if isinstance(input_data, Path): + input_data = DocumentStream( + name=input_data.name, + stream=BytesIO(input_data.open("rb").read()), + ) record.original = input_data pred_record = self.predict(record) return pred_record + def _handle_json_docling_document( + self, record: DatasetRecord + ) -> tuple[DocumentStream, str]: + """ + Handle records where the original is a JSON DoclingDocument. + + Validates DoclingDocument and converts ground_truth_page_images to document stream. + Returns the DocumentStream and appropriate mime_type. + """ + # Validate DoclingDocument structure + try: + if not isinstance(record.original, DocumentStream): + raise ValueError("Original is not a DocumentStream") + + content = record.original.stream.read().decode("utf-8") + record.original.stream.seek(0) + + import json + + doc_data = json.loads(content) + + if not ( + isinstance(doc_data, dict) + and "schema_name" in doc_data + and "version" in doc_data + and "body" in doc_data + ): + raise ValueError("Invalid DoclingDocument structure") + + except (UnicodeDecodeError, json.JSONDecodeError, KeyError, ValueError) as e: + raise ValueError( + f"Record {record.doc_id} has mime_type 'application/json' but " + f"original is not a valid DoclingDocument: {e}" + ) + + # Ensure we have page images + if not record.ground_truth_page_images: + raise ValueError( + f"Record {record.doc_id} has mime_type 'application/json' but " + "no ground_truth_page_images available for processing" + ) + + # Log the conversion + _log.info( + f"Converting JSON DoclingDocument to document stream using " + f"{len(record.ground_truth_page_images)} page image(s) for record {record.doc_id}" + ) + + # Convert page images to document stream (unified path for single/multi-page) + images = record.ground_truth_page_images + + if not images: + raise ValueError("No page images available for processing") + + # Unified approach: Always create PDF (works for both single and multi-page) + pdf_buffer = BytesIO() + + import io + + from reportlab.lib.utils import ImageReader + from reportlab.pdfgen import canvas + + # Convert images to RGB if needed + rgb_images = [] + for img in images: + if img.mode != "RGB": + rgb_images.append(img.convert("RGB")) + else: + rgb_images.append(img) + + # Create PDF using reportlab (lossless embedding) + # Adapted from user's pil_rgbs_to_lossless_pdf function + c = None + + for img in rgb_images: + w_px, h_px = img.size + # Hard coded scale for 72 DPI (1 pixel = 1 point) + page_w_pt = w_px * 1.0 + page_h_pt = h_px * 1.0 + + if c is None: + c = canvas.Canvas(pdf_buffer, pagesize=(page_w_pt, page_h_pt)) + else: + c.setPageSize((page_w_pt, page_h_pt)) + + # Convert image to PNG in memory buffer for lossless embedding + buf = io.BytesIO() + img.save(buf, format="PNG") + buf.seek(0) + + # Draw image at full size (lossless) + c.drawImage( + ImageReader(buf), + 0, + 0, + width=page_w_pt, + height=page_h_pt, + preserveAspectRatio=False, + ) + c.showPage() + + if c is not None: + c.save() + return ( + DocumentStream(name="document.pdf", stream=pdf_buffer), + "application/pdf", + ) + + # This should never be reached due to the structure above, but mypy requires it + raise RuntimeError("Unexpected error in PDF creation") + def get_effective_indices( self, total_items: int, begin_index: int, end_index: int ) -> Tuple[int, int]: diff --git a/docling_eval/visualisation/visualisations.py b/docling_eval/visualisation/visualisations.py index 8ef2f8f4..0686b9f8 100644 --- a/docling_eval/visualisation/visualisations.py +++ b/docling_eval/visualisation/visualisations.py @@ -149,7 +149,11 @@ def _get_document_visualization_data( doc_img_b64 = from_pil_to_base64(get_missing_pageimg()) try: - doc_page = doc.export_to_html(image_mode=ImageRefMode.EMBEDDED, page_no=page_no) + doc_page = doc.export_to_html( + image_mode=ImageRefMode.EMBEDDED, + page_no=page_no, + included_content_layers={ContentLayer.BODY, ContentLayer.FURNITURE}, + ) except ValueError as e: logging.error( f"Could not export page {page_no} to HTML due to a ValueError: {e}" diff --git a/pyproject.toml b/pyproject.toml index dd8c156f..1e091053 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,7 +26,7 @@ classifiers = [ requires-python = '>=3.10,<4.0' dependencies = [ 'docling[vlm] (>=2.42.0,<3.0.0)', - "docling-core>=2.43.1", + 'docling-core (>=2.48.0,<3.0.0)', 'pydantic (>=2.0.0,<3.0.0)', 'lxml (>=5.3.0,<6.0.0)', 'datasets (>=3.2.0,<4.0.0)', @@ -48,12 +48,12 @@ dependencies = [ 'pypdf2 (>=3.0.1,<4.0.0)', "xmltodict (>=0.14.2,<0.15.0)", 'edit-distance (>=1.0.6,<2.0.0)', - 'mlx-vlm<0.2; sys_platform == "darwin" and platform_machine == "arm64"', "pyarrow>=20.0.0", "typer>=0.16.0", "scipy>=1.15.3", "scipy-stubs>=1.15.3.0", "editdistance>=0.8.1", + "reportlab>=4.4.3" ] [project.urls] @@ -106,6 +106,9 @@ dev = [ [tool.uv] package = true +[tool.uv.sources] +docling = { git = "https://github.com/docling-project/docling.git", rev = "dev/add-granite-docling-extension" } + [tool.setuptools.packages.find] include = ["docling_eval*"] @@ -125,6 +128,7 @@ pretty = true no_implicit_optional = true plugins = "pydantic.mypy" python_version = "3.10" +exclude = '^\\.venv/' [[tool.mypy.overrides]] module = [ @@ -151,8 +155,11 @@ module = [ "ocrmac.*", "editdistance.*", "xlsxwriter.*", + "reportlab.*", + "mlx.*", ] ignore_missing_imports = true +follow_imports = "skip" [tool.flake8] max-line-length = 88 diff --git a/tests/data/cvat_pdfs_dataset_e2e/case_01_annotations.xml b/tests/data/cvat_pdfs_dataset_e2e/case_01_annotations.xml index 9e37cd74..7771e7d9 100644 --- a/tests/data/cvat_pdfs_dataset_e2e/case_01_annotations.xml +++ b/tests/data/cvat_pdfs_dataset_e2e/case_01_annotations.xml @@ -274,7 +274,7 @@ 2025-05-06 11:39:39.138521+00:00 - + @@ -379,7 +379,7 @@ - + @@ -468,7 +468,7 @@ - + @@ -620,7 +620,7 @@ 1 - + @@ -678,7 +678,7 @@ - + @@ -761,7 +761,7 @@ - + @@ -880,7 +880,7 @@ - + @@ -973,7 +973,7 @@ - + @@ -1090,7 +1090,7 @@ - + @@ -1205,7 +1205,7 @@ - + diff --git a/tests/data/cvat_pdfs_dataset_e2e/case_02_annotations.xml b/tests/data/cvat_pdfs_dataset_e2e/case_02_annotations.xml index d3972f05..4dd97fde 100644 --- a/tests/data/cvat_pdfs_dataset_e2e/case_02_annotations.xml +++ b/tests/data/cvat_pdfs_dataset_e2e/case_02_annotations.xml @@ -274,7 +274,7 @@ 2025-05-06 16:59:34.713734+00:00 - + 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