commit e2d996753bac9c7706f3fd4c877e1d83845df805 Author: Christoph Auer Date: Mon Jul 15 09:42:42 2024 +0200 Initial commit diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..bb3e254 --- /dev/null +++ b/.gitignore @@ -0,0 +1,442 @@ +model_artifacts/ +scratch/ +ds_convert_models/ + +# Created by https://www.toptal.com/developers/gitignore/api/python,macos,virtualenv,pycharm,visualstudiocode,emacs,vim,jupyternotebooks +# Edit at https://www.toptal.com/developers/gitignore?templates=python,macos,virtualenv,pycharm,visualstudiocode,emacs,vim,jupyternotebooks + +### Emacs ### +# -*- mode: gitignore; -*- +*~ +\#*\# +/.emacs.desktop +/.emacs.desktop.lock +*.elc +auto-save-list +tramp +.\#* + +# Org-mode +.org-id-locations +*_archive + +# flymake-mode +*_flymake.* + +# eshell files +/eshell/history +/eshell/lastdir + +# elpa packages +/elpa/ + +# reftex files +*.rel + +# AUCTeX auto folder +/auto/ + +# cask packages +.cask/ +dist/ + +# Flycheck +flycheck_*.el + +# server auth directory +/server/ + +# projectiles files +.projectile + +# directory configuration +.dir-locals.el + +# network security +/network-security.data + + +### JupyterNotebooks ### +# gitignore template for Jupyter Notebooks +# website: http://jupyter.org/ + +.ipynb_checkpoints +*/.ipynb_checkpoints/* + +# IPython +profile_default/ +ipython_config.py + +# Remove previous ipynb_checkpoints +# git rm -r .ipynb_checkpoints/ + +### macOS ### +# General +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon + + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + +### macOS Patch ### +# iCloud generated files +*.icloud + +### PyCharm ### +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff +.idea/**/workspace.xml +.idea/**/tasks.xml +.idea/**/usage.statistics.xml +.idea/**/dictionaries +.idea/**/shelf + +# AWS User-specific +.idea/**/aws.xml + +# Generated files +.idea/**/contentModel.xml + +# Sensitive or high-churn files +.idea/**/dataSources/ +.idea/**/dataSources.ids +.idea/**/dataSources.local.xml +.idea/**/sqlDataSources.xml +.idea/**/dynamic.xml +.idea/**/uiDesigner.xml +.idea/**/dbnavigator.xml + +# Gradle +.idea/**/gradle.xml +.idea/**/libraries + +# Gradle and Maven with auto-import +# When using Gradle or Maven with auto-import, you should exclude module files, +# since they will be recreated, and may cause churn. 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For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +.idea/ + +### Python Patch ### +# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration +poetry.toml + +# ruff +.ruff_cache/ + +### Vim ### +# Swap +[._]*.s[a-v][a-z] +!*.svg # comment out if you don't need vector files +[._]*.sw[a-p] +[._]s[a-rt-v][a-z] +[._]ss[a-gi-z] +[._]sw[a-p] + +# Session +Session.vim +Sessionx.vim + +# Temporary +.netrwhist +# Auto-generated tag files +tags +# Persistent undo +[._]*.un~ + + +### Visual Studio Code ### +.vscode/ + +### VirtualEnv ### +# Virtualenv +# http://iamzed.com/2009/05/07/a-primer-on-virtualenv/ +[Bb]in +[Ii]nclude +[Ll]ib +[Ll]ib64 +[Ll]ocal +[Ss]cripts +pyvenv.cfg +pip-selfcheck.json + +### VisualStudioCode ### +.vscode/* +!.vscode/settings.json +!.vscode/tasks.json +!.vscode/launch.json +!.vscode/extensions.json +!.vscode/*.code-snippets + +# Local History for Visual Studio Code +.history/ + +# Built Visual Studio Code Extensions +*.vsix + +### VisualStudioCode Patch ### +# Ignore all local history of files +.history +.ionide + + +# Docs +# docs/**/*.png +# docs/**/*.svg diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..db2213d --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,34 @@ +fail_fast: true +repos: + - repo: local + hooks: + - id: system + name: Black + entry: poetry run black docling examples + pass_filenames: false + language: system + files: '\.py$' + - repo: local + hooks: + - id: system + name: isort + entry: poetry run isort docling examples + pass_filenames: false + language: system + files: '\.py$' +# - repo: local +# hooks: +# - id: system +# name: flake8 +# entry: poetry run flake8 docling +# pass_filenames: false +# language: system +# files: '\.py$' +# - repo: local +# hooks: +# - id: system +# name: MyPy +# entry: poetry run mypy docling +# pass_filenames: false +# language: system +# files: '\.py$' diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..c7d5ea1 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,129 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, religion, or sexual identity +and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the + overall community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or + advances of any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email + address, without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement using +[deepsearch-core@zurich.ibm.com](mailto:deepsearch-core@zurich.ibm.com). + +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series +of actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or +permanent ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within +the community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.0, available at +[https://www.contributor-covenant.org/version/2/0/code_of_conduct.html](https://www.contributor-covenant.org/version/2/0/code_of_conduct.html). + +Community Impact Guidelines were inspired by [Mozilla's code of conduct +enforcement ladder](https://github.com/mozilla/diversity). + +Homepage: [https://www.contributor-covenant.org](https://www.contributor-covenant.org) + +For answers to common questions about this code of conduct, see the FAQ at +[https://www.contributor-covenant.org/faq](https://www.contributor-covenant.org/faq). Translations are available at +[https://www.contributor-covenant.org/translations](https://www.contributor-covenant.org/translations). \ No newline at end of file diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..7b40242 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,184 @@ +## Contributing In General +Our project welcomes external contributions. If you have an itch, please feel +free to scratch it. + +To contribute code or documentation, please submit a [pull request](https://github.com/DS4SD/docling/pulls). + +A good way to familiarize yourself with the codebase and contribution process is +to look for and tackle low-hanging fruit in the [issue tracker](https://github.com/DS4SD/docling/issues). +Before embarking on a more ambitious contribution, please quickly [get in touch](#communication) with us. + +For general questions or support requests, please refer to the [discussion section](https://github.com/DS4SD/docling/discussions). + +**Note: We appreciate your effort, and want to avoid a situation where a contribution +requires extensive rework (by you or by us), sits in backlog for a long time, or +cannot be accepted at all!** + +### Proposing new features + +If you would like to implement a new feature, please [raise an issue](https://github.com/DS4SD/docling/issues) +before sending a pull request so the feature can be discussed. This is to avoid +you wasting your valuable time working on a feature that the project developers +are not interested in accepting into the code base. + +### Fixing bugs + +If you would like to fix a bug, please [raise an issue](https://github.com/DS4SD/docling/issues) before sending a +pull request so it can be tracked. + +### Merge approval + +The project maintainers use LGTM (Looks Good To Me) in comments on the code +review to indicate acceptance. A change requires LGTMs from two of the +maintainers of each component affected. + +For a list of the maintainers, see the [MAINTAINERS.md](MAINTAINERS.md) page. + + +## Legal + +Each source file must include a license header for the MIT +Software. Using the SPDX format is the simplest approach. +e.g. + +``` +/* +Copyright IBM Inc. All rights reserved. + +SPDX-License-Identifier: MIT +*/ +``` + +We have tried to make it as easy as possible to make contributions. This +applies to how we handle the legal aspects of contribution. We use the +same approach - the [Developer's Certificate of Origin 1.1 (DCO)](https://github.com/hyperledger/fabric/blob/master/docs/source/DCO1.1.txt) - that the Linuxยฎ Kernel [community](https://elinux.org/Developer_Certificate_Of_Origin) +uses to manage code contributions. + +We simply ask that when submitting a patch for review, the developer +must include a sign-off statement in the commit message. + +Here is an example Signed-off-by line, which indicates that the +submitter accepts the DCO: + +``` +Signed-off-by: John Doe +``` + +You can include this automatically when you commit a change to your +local git repository using the following command: + +``` +git commit -s +``` + + +## Communication + +Please feel free to connect with us using the [discussion section](https://github.com/DS4SD/docling/discussions). + + + +## Developing + +### Usage of Poetry + +We use Poetry to manage dependencies. + + +#### Install + +To install, see the documentation here: https://python-poetry.org/docs/master/#installing-with-the-official-installer + +1. Install the Poetry globally in your machine + ```bash + curl -sSL https://install.python-poetry.org | python3 - + ``` + The installation script will print the installation bin folder `POETRY_BIN` which you need in the next steps. + +2. Make sure Poetry is in your `$PATH` + - for `zsh` + ```sh + echo 'export PATH="POETRY_BIN:$PATH"' >> ~/.zshrc + ``` + - for `bash` + ```sh + echo 'export PATH="POETRY_BIN:$PATH"' >> ~/.bashrc + ``` + +3. The official guidelines linked above include useful details on the configuration of autocomplete for most shell environments, e.g. Bash and Zsh. + + +#### Create a Virtual Environment and Install Dependencies + +To activate the Virtual Environment, run: + +```bash +poetry shell +``` + +To spawn a shell with the Virtual Environment activated. If the Virtual Environment doesn't exist, Poetry will create one for you. Then, to install dependencies, run: + +```bash +poetry install +``` + +**(Advanced) Use a Specific Python Version** + +If for whatever reason you need to work in a specific (older) version of Python, run: + +```bash +poetry env use $(which python3.8) +``` + +This creates a Virtual Environment with Python 3.8. For other versions, replace `$(which python3.8)` by the path to the interpreter (e.g., `/usr/bin/python3.8`) or use `$(which pythonX.Y)`. + + +#### Add a new dependency + +```bash +poetry add NAME +``` + +## Coding style guidelines + +We use the following tools to enforce code style: + +- iSort, to sort imports +- Black, to format code + + +We run a series of checks on the code base on every commit, using `pre-commit`. To install the hooks, run: + +```bash +pre-commit install +``` + +To run the checks on-demand, run: + +``` +pre-commit run --all-files +``` + +Note: Checks like `Black` and `isort` will "fail" if they modify files. This is because `pre-commit` doesn't like to see files modified by their Hooks. In these cases, `git add` the modified files and `git commit` again. + + + +## Documentation + +We use [MkDocs](https://www.mkdocs.org/) to write documentation. + +To run the documentation server, do: + +```bash +mkdocs serve +``` + +The server will be available on [http://localhost:8000](http://localhost:8000). + +### Pushing Documentation to GitHub pages + +Run the following: + +```bash +mkdocs gh-deploy +``` \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..bef7be5 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,23 @@ +FROM python:3.11-slim-bookworm + +ENV GIT_SSH_COMMAND="ssh -o StrictHostKeyChecking=no" + +RUN apt-get update \ + && apt-get install -y libgl1 libglib2.0-0 curl wget git \ + && apt-get clean + +RUN --mount=type=ssh \ + pip install --no-cache-dir https://github.com/DS4SD/docling.git + +ENV HF_HOME=/tmp/ +ENV TORCH_HOME=/tmp/ + +COPY examples/minimal.py /root/minimal.py + +RUN python -c 'from deepsearch_glm.utils.load_pretrained_models import load_pretrained_nlp_models; load_pretrained_nlp_models(verbose=True);' +RUN python -c 'from docling.document_converter import DocumentConverter; artifacts_path = DocumentConverter.download_models_hf(force=True);' +RUN wget "https://www.ibm.com/docs/en/SSQRB8/com.ibm.spectrum.si.pdfs/IBM_Storage_Insights_Fact_Sheet.pdf" -O /root/factsheet.pdf + +# On container shell: +# > cd /root/ +# > python minimal.py diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..8aa2645 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) [year] [fullname] + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/MAINTAINERS.md b/MAINTAINERS.md new file mode 100644 index 0000000..65a2b69 --- /dev/null +++ b/MAINTAINERS.md @@ -0,0 +1,10 @@ +# MAINTAINERS + +- Christoph Auer - [@cau-git](https://github.com/cau-git) +- Michele Dolfi - [@dolfim-ibm](https://github.com/dolfim-ibm) +- Maxim Lysak - [@maxmnemonic](https://github.com/maxmnemonic) +- Nikos Livathinos - [@nikos-livathinos](https://github.com/nikos-livathinos) +- Ahmed Nassar [@nassarofficial](https://github.com/nassarofficial) +- Peter Staar - [@PeterStaar-IBM](https://github.com/PeterStaar-IBM) + +Maintainers can be contacted at [deepsearch-core@zurich.ibm.com](mailto:deepsearch-core@zurich.ibm.com). \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..c9e0e20 --- /dev/null +++ b/README.md @@ -0,0 +1,99 @@ +

+ Docling +

+ +# Docling + +Dockling bundles PDF document conversion to JSON and Markdown in an easy, self-contained package. + +## Features +* โšก Converts any PDF document to JSON or Markdown format, stable and lightning fast +* ๐Ÿ“‘ Understands detailed page layout, reading order and recovers table structures +* ๐Ÿ“ Extracts metadata from the document, such as title, authors, references and language +* ๐Ÿ” Optionally applies OCR (use with scanned PDFs) + +## Setup + +You need Python 3.11 and poetry. Install poetry from [here](https://python-poetry.org/docs/#installing-with-the-official-installer). + +Once you have `poetry` installed, create an environment and install the package: + +```bash +poetry env use $(which python3.11) +poetry shell +poetry install +``` + +**Notes**: +* Works on macOS and Linux environments. Windows platforms are currently not tested. + + +## Usage + +For basic usage, see the [convert.py](examples/convert.py) example module. Run with: + +``` +python examples/convert.py +``` +The output of the above command will be written to `./scratch`. + +### Enable or disable pipeline features + +You can control if table structure recognition or OCR should be performed by arguments passed to `DocumentConverter` +```python +doc_converter = DocumentConverter( + artifacts_path=artifacts_path, + pipeline_options=PipelineOptions(do_table_structure=False, # Controls if table structure is recovered. + do_ocr=True), # Controls if OCR is applied (ignores programmatic content) +) +``` + +### Impose limits on the document size + +You can limit the file size and number of pages which should be allowed to process per document. +```python +paths = [Path("./test/data/2206.01062.pdf")] + +input = DocumentConversionInput.from_paths( + paths, limits=DocumentLimits(max_num_pages=100, max_file_size=20971520) +) +``` + +### Convert from binary PDF streams + +You can convert PDFs from a binary stream instead of from the filesystem as follows: +```python +buf = BytesIO(your_binary_stream) +docs = [DocumentStream(filename="my_doc.pdf", stream=buf)] +input = DocumentConversionInput.from_streams(docs) +converted_docs = doc_converter.convert(input) +``` +### Limit resource usage + +You can limit the CPU threads used by `docling` by setting the environment variable `OMP_NUM_THREADS` accordingly. The default setting is using 4 CPU threads. + + +## Contributing + +Please read [Contributing to Docling](./CONTRIBUTING.md) for details. + + +## References + +If you use `Docling` in your projects, please consider citing the following: + +```bib +@software{Docling, +author = {Deep Search Team}, +month = {7}, +title = {{Docling}}, +url = {https://github.com/DS4SD/docling}, +version = {main}, +year = {2024} +} +``` + +## License + +The `Docling` codebase is under MIT license. +For individual model usage, please refer to the model licenses found in the original packages. diff --git a/docling/__init__.py b/docling/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/docling/backend/__init__.py b/docling/backend/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/docling/backend/abstract_backend.py b/docling/backend/abstract_backend.py new file mode 100644 index 0000000..be4c4a5 --- /dev/null +++ b/docling/backend/abstract_backend.py @@ -0,0 +1,55 @@ +from abc import ABC, abstractmethod +from io import BytesIO +from pathlib import Path +from typing import Any, Iterable, Optional, Union + +from PIL import Image + + +class PdfPageBackend(ABC): + def __init__(self, page_obj: Any) -> object: + pass + + @abstractmethod + def get_text_in_rect(self, bbox: "BoundingBox") -> str: + pass + + @abstractmethod + def get_text_cells(self) -> Iterable["Cell"]: + pass + + @abstractmethod + def get_page_image( + self, scale: int = 1, cropbox: Optional["BoundingBox"] = None + ) -> Image.Image: + pass + + @abstractmethod + def get_size(self) -> "PageSize": + pass + + @abstractmethod + def unload(self): + pass + + +class PdfDocumentBackend(ABC): + @abstractmethod + def __init__(self, path_or_stream: Iterable[Union[BytesIO, Path]]): + pass + + @abstractmethod + def load_page(self, page_no: int) -> PdfPageBackend: + pass + + @abstractmethod + def page_count(self) -> int: + pass + + @abstractmethod + def is_valid(self) -> bool: + pass + + @abstractmethod + def unload(self): + pass diff --git a/docling/backend/pypdfium2_backend.py b/docling/backend/pypdfium2_backend.py new file mode 100644 index 0000000..198ec51 --- /dev/null +++ b/docling/backend/pypdfium2_backend.py @@ -0,0 +1,223 @@ +import random +from io import BytesIO +from pathlib import Path +from typing import Iterable, List, Optional, Union + +import pypdfium2 as pdfium +from PIL import Image, ImageDraw +from pypdfium2 import PdfPage + +from docling.backend.abstract_backend import PdfDocumentBackend, PdfPageBackend +from docling.datamodel.base_models import BoundingBox, Cell, CoordOrigin, PageSize + + +class PyPdfiumPageBackend(PdfPageBackend): + def __init__(self, page_obj: PdfPage): + super().__init__(page_obj) + self._ppage = page_obj + self.text_page = None + + def get_text_in_rect(self, bbox: BoundingBox) -> str: + if not self.text_page: + self.text_page = self._ppage.get_textpage() + + if bbox.coord_origin != CoordOrigin.BOTTOMLEFT: + bbox = bbox.to_bottom_left_origin(self.get_size().height) + + text_piece = self.text_page.get_text_bounded(*bbox.as_tuple()) + + return text_piece + + def get_text_cells(self) -> Iterable[Cell]: + if not self.text_page: + self.text_page = self._ppage.get_textpage() + + cells = [] + cell_counter = 0 + + page_size = self.get_size() + + for i in range(self.text_page.count_rects()): + rect = self.text_page.get_rect(i) + text_piece = self.text_page.get_text_bounded(*rect) + x0, y0, x1, y1 = rect + cells.append( + Cell( + id=cell_counter, + text=text_piece, + bbox=BoundingBox( + l=x0, b=y0, r=x1, t=y1, coord_origin=CoordOrigin.BOTTOMLEFT + ).to_top_left_origin(page_size.height), + ) + ) + cell_counter += 1 + + # PyPdfium2 produces very fragmented cells, with sub-word level boundaries, in many PDFs. + # The cell merging code below is to clean this up. + def merge_horizontal_cells( + cells: List[Cell], + horizontal_threshold_factor: float = 1.0, + vertical_threshold_factor: float = 0.5, + ) -> List[Cell]: + if not cells: + return [] + + def group_rows(cells: List[Cell]) -> List[List[Cell]]: + rows = [] + current_row = [cells[0]] + row_top = cells[0].bbox.t + row_bottom = cells[0].bbox.b + row_height = cells[0].bbox.height + + for cell in cells[1:]: + vertical_threshold = row_height * vertical_threshold_factor + if ( + abs(cell.bbox.t - row_top) <= vertical_threshold + and abs(cell.bbox.b - row_bottom) <= vertical_threshold + ): + current_row.append(cell) + row_top = min(row_top, cell.bbox.t) + row_bottom = max(row_bottom, cell.bbox.b) + row_height = row_bottom - row_top + else: + rows.append(current_row) + current_row = [cell] + row_top = cell.bbox.t + row_bottom = cell.bbox.b + row_height = cell.bbox.height + + if current_row: + rows.append(current_row) + + return rows + + def merge_row(row: List[Cell]) -> List[Cell]: + merged = [] + current_group = [row[0]] + + for cell in row[1:]: + prev_cell = current_group[-1] + avg_height = (prev_cell.bbox.height + cell.bbox.height) / 2 + if ( + cell.bbox.l - prev_cell.bbox.r + <= avg_height * horizontal_threshold_factor + ): + current_group.append(cell) + else: + merged.append(merge_group(current_group)) + current_group = [cell] + + if current_group: + merged.append(merge_group(current_group)) + + return merged + + def merge_group(group: List[Cell]) -> Cell: + if len(group) == 1: + return group[0] + + merged_text = "".join(cell.text for cell in group) + merged_bbox = BoundingBox( + l=min(cell.bbox.l for cell in group), + t=min(cell.bbox.t for cell in group), + r=max(cell.bbox.r for cell in group), + b=max(cell.bbox.b for cell in group), + ) + return Cell(id=group[0].id, text=merged_text, bbox=merged_bbox) + + rows = group_rows(cells) + merged_cells = [cell for row in rows for cell in merge_row(row)] + + for i, cell in enumerate(merged_cells, 1): + cell.id = i + + return merged_cells + + def draw_clusters_and_cells(): + image = self.get_page_image() + draw = ImageDraw.Draw(image) + for c in cells: + x0, y0, x1, y1 = c.bbox.as_tuple() + cell_color = ( + random.randint(30, 140), + random.randint(30, 140), + random.randint(30, 140), + ) + draw.rectangle([(x0, y0), (x1, y1)], outline=cell_color) + image.show() + + # before merge: + # draw_clusters_and_cells() + + cells = merge_horizontal_cells(cells) + + # after merge: + # draw_clusters_and_cells() + + return cells + + def get_page_image( + self, scale: int = 1, cropbox: Optional[BoundingBox] = None + ) -> Image.Image: + + page_size = self.get_size() + + if not cropbox: + cropbox = BoundingBox( + l=0, + r=page_size.width, + t=0, + b=page_size.height, + coord_origin=CoordOrigin.TOPLEFT, + ) + padbox = BoundingBox( + l=0, r=0, t=0, b=0, coord_origin=CoordOrigin.BOTTOMLEFT + ) + else: + padbox = cropbox.to_bottom_left_origin(page_size.height) + padbox.r = page_size.width - padbox.r + padbox.t = page_size.height - padbox.t + + image = ( + self._ppage.render( + scale=scale * 1.5, + rotation=0, # no additional rotation + crop=padbox.as_tuple(), + ) + .to_pil() + .resize(size=(round(cropbox.width * scale), round(cropbox.height * scale))) + ) # We resize the image from 1.5x the given scale to make it sharper. + + return image + + def get_size(self) -> PageSize: + return PageSize(width=self._ppage.get_width(), height=self._ppage.get_height()) + + def unload(self): + self._ppage = None + self.text_page = None + + +class PyPdfiumDocumentBackend(PdfDocumentBackend): + def __init__(self, path_or_stream: Iterable[Union[BytesIO, Path]]): + super().__init__(path_or_stream) + + if isinstance(path_or_stream, Path): + self._pdoc = pdfium.PdfDocument(path_or_stream) + elif isinstance(path_or_stream, BytesIO): + self._pdoc = pdfium.PdfDocument( + path_or_stream + ) # TODO Fix me, won't accept bytes. + + def page_count(self) -> int: + return len(self._pdoc) + + def load_page(self, page_no: int) -> PdfPage: + return PyPdfiumPageBackend(self._pdoc[page_no]) + + def is_valid(self) -> bool: + return self.page_count() > 0 + + def unload(self): + self._pdoc.close() + self._pdoc = None diff --git a/docling/datamodel/__init__.py b/docling/datamodel/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/docling/datamodel/base_models.py b/docling/datamodel/base_models.py new file mode 100644 index 0000000..dd9795a --- /dev/null +++ b/docling/datamodel/base_models.py @@ -0,0 +1,247 @@ +from enum import Enum, auto +from io import BytesIO +from typing import Any, Dict, List, Optional, Tuple, Union + +from PIL.Image import Image +from pydantic import BaseModel, ConfigDict, model_validator + +from docling.backend.abstract_backend import PdfPageBackend + + +class ConversionStatus(str, Enum): + PENDING = auto() + STARTED = auto() + FAILURE = auto() + SUCCESS = auto() + SUCCESS_WITH_ERRORS = auto() + + +class DocInputType(str, Enum): + PATH = auto() + STREAM = auto() + + +class CoordOrigin(str, Enum): + TOPLEFT = auto() + BOTTOMLEFT = auto() + + +class PageSize(BaseModel): + width: float = 0.0 + height: float = 0.0 + + +class BoundingBox(BaseModel): + l: float # left + t: float # top + r: float # right + b: float # bottom + + coord_origin: CoordOrigin = CoordOrigin.TOPLEFT + + @property + def width(self): + return self.r - self.l + + @property + def height(self): + return abs(self.t - self.b) + + def as_tuple(self): + if self.coord_origin == CoordOrigin.TOPLEFT: + return (self.l, self.t, self.r, self.b) + elif self.coord_origin == CoordOrigin.BOTTOMLEFT: + return (self.l, self.b, self.r, self.t) + + @classmethod + def from_tuple(cls, coord: Tuple[float], origin: CoordOrigin): + if origin == CoordOrigin.TOPLEFT: + return BoundingBox( + l=coord[0], t=coord[1], r=coord[2], b=coord[3], coord_origin=origin + ) + elif origin == CoordOrigin.BOTTOMLEFT: + return BoundingBox( + l=coord[0], b=coord[1], r=coord[2], t=coord[3], coord_origin=origin + ) + + def area(self) -> float: + return (self.r - self.l) * (self.b - self.t) + + def intersection_area_with(self, other: "BoundingBox") -> float: + # Calculate intersection coordinates + left = max(self.l, other.l) + top = max(self.t, other.t) + right = min(self.r, other.r) + bottom = min(self.b, other.b) + + # Calculate intersection dimensions + width = right - left + height = bottom - top + + # If the bounding boxes do not overlap, width or height will be negative + if width <= 0 or height <= 0: + return 0.0 + + return width * height + + def to_bottom_left_origin(self, page_height) -> "BoundingBox": + if self.coord_origin == CoordOrigin.BOTTOMLEFT: + return self + elif self.coord_origin == CoordOrigin.TOPLEFT: + return BoundingBox( + l=self.l, + r=self.r, + t=page_height - self.t, + b=page_height - self.b, + coord_origin=CoordOrigin.BOTTOMLEFT, + ) + + def to_top_left_origin(self, page_height): + if self.coord_origin == CoordOrigin.TOPLEFT: + return self + elif self.coord_origin == CoordOrigin.BOTTOMLEFT: + return BoundingBox( + l=self.l, + r=self.r, + t=page_height - self.t, # self.b + b=page_height - self.b, # self.t + coord_origin=CoordOrigin.TOPLEFT, + ) + + +class Cell(BaseModel): + id: int + text: str + bbox: BoundingBox + + +class OcrCell(Cell): + confidence: float + + +class Cluster(BaseModel): + id: int + label: str + bbox: BoundingBox + confidence: float = 1.0 + cells: List[Cell] = [] + + +class BasePageElement(BaseModel): + label: str + id: int + page_no: int + cluster: Cluster + text: Optional[str] = None + + +class LayoutPrediction(BaseModel): + clusters: List[Cluster] = [] + + +class TableCell(BaseModel): + bbox: BoundingBox + row_span: int + col_span: int + start_row_offset_idx: int + end_row_offset_idx: int + start_col_offset_idx: int + end_col_offset_idx: int + text: str + column_header: bool = False + row_header: bool = False + row_section: bool = False + + @model_validator(mode="before") + @classmethod + def from_dict_format(cls, data: Any) -> Any: + if isinstance(data, Dict): + text = data["bbox"].get("token", "") + if not len(text): + text_cells = data.pop("text_cell_bboxes", None) + if text_cells: + for el in text_cells: + text += el["token"] + " " + + text = text.strip() + data["text"] = text + + return data + + +class TableElement(BasePageElement): + otsl_seq: List[str] + num_rows: int = 0 + num_cols: int = 0 + table_cells: List[TableCell] + + +class TableStructurePrediction(BaseModel): + table_map: Dict[int, TableElement] = {} + + +class TextElement(BasePageElement): + ... + + +class FigureData(BaseModel): + pass + + +class FigureElement(BasePageElement): + data: Optional[FigureData] = None + provenance: Optional[str] = None + predicted_class: Optional[str] = None + confidence: Optional[float] = None + + +class FigureClassificationPrediction(BaseModel): + figure_count: int = 0 + figure_map: Dict[int, FigureElement] = {} + + +class EquationPrediction(BaseModel): + equation_count: int = 0 + equation_map: Dict[int, TextElement] = {} + + +class PagePredictions(BaseModel): + layout: LayoutPrediction = None + tablestructure: TableStructurePrediction = None + figures_classification: FigureClassificationPrediction = None + equations_prediction: EquationPrediction = None + + +PageElement = Union[TextElement, TableElement, FigureElement] + + +class AssembledUnit(BaseModel): + elements: List[PageElement] + body: List[PageElement] + headers: List[PageElement] + + +class Page(BaseModel): + model_config = ConfigDict(arbitrary_types_allowed=True) + + page_no: int + page_hash: str = None + size: PageSize = None + image: Image = None + cells: List[Cell] = None + predictions: PagePredictions = PagePredictions() + assembled: AssembledUnit = None + + _backend: PdfPageBackend = None # Internal PDF backend + + +class DocumentStream(BaseModel): + model_config = ConfigDict(arbitrary_types_allowed=True) + + filename: str + stream: BytesIO + + +class PipelineOptions(BaseModel): + do_table_structure: bool = True + do_ocr: bool = False diff --git a/docling/datamodel/document.py b/docling/datamodel/document.py new file mode 100644 index 0000000..cc12d7d --- /dev/null +++ b/docling/datamodel/document.py @@ -0,0 +1,351 @@ +import logging +from io import BytesIO +from pathlib import Path, PurePath +from typing import ClassVar, Dict, Iterable, List, Optional, Type, Union + +from deepsearch.documents.core.export import export_to_markdown +from docling_core.types import BaseCell, BaseText +from docling_core.types import BoundingBox as DsBoundingBox +from docling_core.types import Document as DsDocument +from docling_core.types import DocumentDescription as DsDocumentDescription +from docling_core.types import FileInfoObject as DsFileInfoObject +from docling_core.types import PageDimensions, PageReference, Prov, Ref +from docling_core.types import Table as DsSchemaTable +from docling_core.types import TableCell +from pydantic import BaseModel + +from docling.backend.abstract_backend import PdfDocumentBackend +from docling.backend.pypdfium2_backend import PyPdfiumDocumentBackend +from docling.datamodel.base_models import ( + AssembledUnit, + ConversionStatus, + DocumentStream, + FigureElement, + Page, + TableElement, + TextElement, +) +from docling.datamodel.settings import DocumentLimits +from docling.utils.utils import create_file_hash + +_log = logging.getLogger(__name__) + +layout_label_to_ds_type = { + "Title": "title", + "Document Index": "table-of-path_or_stream", + "Section-header": "subtitle-level-1", + "Checkbox-Selected": "checkbox-selected", + "Checkbox-Unselected": "checkbox-unselected", + "Caption": "caption", + "Page-header": "page-header", + "Page-footer": "page-footer", + "Footnote": "footnote", + "Table": "table", + "Formula": "equation", + "List-item": "paragraph", + "Code": "paragraph", + "Picture": "figure", + "Text": "paragraph", +} + + +class InputDocument(BaseModel): + file: PurePath = None + document_hash: Optional[str] = None + valid: bool = False + limits: DocumentLimits = DocumentLimits() + + filesize: Optional[int] = None + page_count: Optional[int] = None + + _backend: PdfDocumentBackend = None # Internal PDF backend used + + def __init__( + self, + path_or_stream: Union[BytesIO, Path], + filename: Optional[str] = None, + limits: Optional[DocumentLimits] = None, + pdf_backend=PyPdfiumDocumentBackend, + ): + super().__init__() + + self.limits = limits or DocumentLimits() + + try: + if isinstance(path_or_stream, Path): + self.file = path_or_stream + self.filesize = path_or_stream.stat().st_size + if self.filesize > self.limits.max_file_size: + self.valid = False + else: + self.document_hash = create_file_hash(path_or_stream) + self._backend = pdf_backend(path_or_stream=path_or_stream) + + elif isinstance(path_or_stream, BytesIO): + self.file = PurePath(filename) + self.filesize = path_or_stream.getbuffer().nbytes + + if self.filesize > self.limits.max_file_size: + self.valid = False + else: + self.document_hash = create_file_hash(path_or_stream) + self._backend = pdf_backend(path_or_stream=path_or_stream) + + if self.document_hash and self._backend.page_count() > 0: + self.page_count = self._backend.page_count() + + if self.page_count <= self.limits.max_num_pages: + self.valid = True + + except (FileNotFoundError, OSError) as e: + _log.exception( + f"File {self.file.name} not found or cannot be opened.", exc_info=e + ) + # raise + except RuntimeError as e: + _log.exception( + f"An unexpected error occurred while opening the document {self.file.name}", + exc_info=e, + ) + # raise + + +class ConvertedDocument(BaseModel): + input: InputDocument + + status: ConversionStatus = ConversionStatus.PENDING # failure, success + errors: List[Dict] = [] # structure to keep errors + + pages: List[Page] = [] + assembled: AssembledUnit = None + + output: DsDocument = None + + def to_ds_document(self) -> DsDocument: + title = "" + desc = DsDocumentDescription(logs=[]) + + page_hashes = [ + PageReference(hash=p.page_hash, page=p.page_no, model="default") + for p in self.pages + ] + + file_info = DsFileInfoObject( + filename=self.input.file.name, + document_hash=self.input.document_hash, + num_pages=self.input.page_count, + page_hashes=page_hashes, + ) + + main_text = [] + tables = [] + figures = [] + + page_no_to_page = {p.page_no: p for p in self.pages} + + for element in self.assembled.elements: + # Convert bboxes to lower-left origin. + target_bbox = DsBoundingBox( + element.cluster.bbox.to_bottom_left_origin( + page_no_to_page[element.page_no].size.height + ).as_tuple() + ) + + if isinstance(element, TextElement): + main_text.append( + BaseText( + text=element.text, + obj_type=layout_label_to_ds_type.get(element.label), + name=element.label, + prov=[ + Prov( + bbox=target_bbox, + page=element.page_no, + span=[0, len(element.text)], + ) + ], + ) + ) + elif isinstance(element, TableElement): + index = len(tables) + ref_str = f"#/tables/{index}" + main_text.append( + Ref( + name=element.label, + obj_type=layout_label_to_ds_type.get(element.label), + ref=ref_str, + ), + ) + + # Initialise empty table data grid (only empty cells) + table_data = [ + [ + TableCell( + text="", + # bbox=[0,0,0,0], + spans=[[i, j]], + obj_type="body", + ) + for j in range(element.num_cols) + ] + for i in range(element.num_rows) + ] + + # Overwrite cells in table data for which there is actual cell content. + for cell in element.table_cells: + for i in range( + min(cell.start_row_offset_idx, element.num_rows), + min(cell.end_row_offset_idx, element.num_rows), + ): + for j in range( + min(cell.start_col_offset_idx, element.num_cols), + min(cell.end_col_offset_idx, element.num_cols), + ): + celltype = "body" + if cell.column_header: + celltype = "col_header" + elif cell.row_header: + celltype = "row_header" + + def make_spans(cell): + for rspan in range( + min(cell.start_row_offset_idx, element.num_rows), + min(cell.end_row_offset_idx, element.num_rows), + ): + for cspan in range( + min( + cell.start_col_offset_idx, element.num_cols + ), + min(cell.end_col_offset_idx, element.num_cols), + ): + yield [rspan, cspan] + + spans = list(make_spans(cell)) + table_data[i][j] = TableCell( + text=cell.text, + bbox=cell.bbox.to_bottom_left_origin( + page_no_to_page[element.page_no].size.height + ).as_tuple(), + # col=j, + # row=i, + spans=spans, + obj_type=celltype, + # col_span=[cell.start_col_offset_idx, cell.end_col_offset_idx], + # row_span=[cell.start_row_offset_idx, cell.end_row_offset_idx] + ) + + tables.append( + DsSchemaTable( + num_cols=element.num_cols, + num_rows=element.num_rows, + obj_type=layout_label_to_ds_type.get(element.label), + data=table_data, + prov=[ + Prov( + bbox=target_bbox, + page=element.page_no, + span=[0, 0], + ) + ], + ) + ) + + elif isinstance(element, FigureElement): + index = len(figures) + ref_str = f"#/figures/{index}" + main_text.append( + Ref( + name=element.label, + obj_type=layout_label_to_ds_type.get(element.label), + ref=ref_str, + ), + ) + figures.append( + BaseCell( + prov=[ + Prov( + bbox=target_bbox, + page=element.page_no, + span=[0, 0], + ) + ], + obj_type=layout_label_to_ds_type.get(element.label), + # data=[[]], + ) + ) + + page_dimensions = [ + PageDimensions(page=p.page_no, height=p.size.height, width=p.size.width) + for p in self.pages + ] + + ds_doc = DsDocument( + name=title, + description=desc, + file_info=file_info, + main_text=main_text, + tables=tables, + figures=figures, + page_dimensions=page_dimensions, + ) + + return ds_doc + + def render_as_dict(self): + if self.output: + return self.output.model_dump(by_alias=True, exclude_none=True) + else: + return {} + + def render_as_markdown(self): + if self.output: + return export_to_markdown( + self.output.model_dump(by_alias=True, exclude_none=True) + ) + else: + return "" + + +class DocumentConversionInput(BaseModel): + + _path_or_stream_iterator: Iterable[Union[Path, DocumentStream]] = None + limits: Optional[DocumentLimits] = DocumentLimits() + + DEFAULT_BACKEND: ClassVar = PyPdfiumDocumentBackend + + def docs( + self, pdf_backend: Optional[Type[PdfDocumentBackend]] = None + ) -> Iterable[InputDocument]: + + pdf_backend = pdf_backend or DocumentConversionInput.DEFAULT_BACKEND + + for obj in self._path_or_stream_iterator: + if isinstance(obj, Path): + yield InputDocument( + path_or_stream=obj, limits=self.limits, pdf_backend=pdf_backend + ) + elif isinstance(obj, DocumentStream): + yield InputDocument( + path_or_stream=obj.stream, + filename=obj.filename, + limits=self.limits, + pdf_backend=pdf_backend, + ) + + @classmethod + def from_paths(cls, paths: Iterable[Path], limits: Optional[DocumentLimits] = None): + paths = [Path(p) for p in paths] + + doc_input = cls(limits=limits) + doc_input._path_or_stream_iterator = paths + + return doc_input + + @classmethod + def from_streams( + cls, streams: Iterable[DocumentStream], limits: Optional[DocumentLimits] = None + ): + doc_input = cls(limits=limits) + doc_input._path_or_stream_iterator = streams + + return doc_input diff --git a/docling/datamodel/settings.py b/docling/datamodel/settings.py new file mode 100644 index 0000000..5ca6e5a --- /dev/null +++ b/docling/datamodel/settings.py @@ -0,0 +1,32 @@ +import sys + +from pydantic import BaseModel +from pydantic_settings import BaseSettings + + +class DocumentLimits(BaseModel): + max_num_pages: int = sys.maxsize + max_file_size: int = sys.maxsize + + +class BatchConcurrencySettings(BaseModel): + doc_batch_size: int = 2 + doc_batch_concurrency: int = 2 + page_batch_size: int = 4 + page_batch_concurrency: int = 2 + + # doc_batch_size: int = 1 + # doc_batch_concurrency: int = 1 + # page_batch_size: int = 1 + # page_batch_concurrency: int = 1 + + # model_concurrency: int = 2 + + # To force models into single core: export OMP_NUM_THREADS=1 + + +class AppSettings(BaseSettings): + perf: BatchConcurrencySettings + + +settings = AppSettings(perf=BatchConcurrencySettings()) diff --git a/docling/document_converter.py b/docling/document_converter.py new file mode 100644 index 0000000..cf24c9a --- /dev/null +++ b/docling/document_converter.py @@ -0,0 +1,207 @@ +import functools +import logging +import time +import traceback +from pathlib import Path +from typing import Iterable, Optional, Type, Union + +from PIL import ImageDraw + +from docling.backend.abstract_backend import PdfDocumentBackend +from docling.datamodel.base_models import ( + AssembledUnit, + ConversionStatus, + Page, + PipelineOptions, +) +from docling.datamodel.document import ( + ConvertedDocument, + DocumentConversionInput, + InputDocument, +) +from docling.datamodel.settings import settings +from docling.models.ds_glm_model import GlmModel +from docling.models.page_assemble_model import PageAssembleModel +from docling.pipeline.base_model_pipeline import BaseModelPipeline +from docling.pipeline.standard_model_pipeline import StandardModelPipeline +from docling.utils.utils import chunkify, create_hash + +_log = logging.getLogger(__name__) + + +class DocumentConverter: + _layout_model_path = "model_artifacts/layout/beehive_v0.0.5" + _table_model_path = "model_artifacts/tableformer" + + def __init__( + self, + artifacts_path: Optional[Union[Path, str]] = None, + pipeline_options: PipelineOptions = PipelineOptions(), + pdf_backend: Type[PdfDocumentBackend] = DocumentConversionInput.DEFAULT_BACKEND, + pipeline_cls: Type[BaseModelPipeline] = StandardModelPipeline, + ): + if not artifacts_path: + artifacts_path = self.download_models_hf() + + artifacts_path = Path(artifacts_path) + + self.model_pipeline = pipeline_cls( + artifacts_path=artifacts_path, pipeline_options=pipeline_options + ) + + self.page_assemble_model = PageAssembleModel(config={}) + self.glm_model = GlmModel(config={}) + self.pdf_backend = pdf_backend + + @staticmethod + def download_models_hf( + local_dir: Optional[Path] = None, force: bool = False + ) -> Path: + from huggingface_hub import snapshot_download + + download_path = snapshot_download( + repo_id="ds4sd/docling-models", force_download=force, local_dir=local_dir + ) + + return Path(download_path) + + def convert(self, input: DocumentConversionInput) -> Iterable[ConvertedDocument]: + + for input_batch in chunkify( + input.docs(pdf_backend=self.pdf_backend), settings.perf.doc_batch_size + ): + _log.info(f"Going to convert document batch...") + # parallel processing only within input_batch + # with ThreadPoolExecutor( + # max_workers=settings.perf.doc_batch_concurrency + # ) as pool: + # yield from pool.map(self.process_document, input_batch) + + # Note: Pdfium backend is not thread-safe, thread pool usage was disabled. + yield from map(self.process_document, input_batch) + + def process_document(self, in_doc: InputDocument) -> ConvertedDocument: + start_doc_time = time.time() + converted_doc = ConvertedDocument(input=in_doc) + + if not in_doc.valid: + converted_doc.status = ConversionStatus.FAILURE + return converted_doc + + for i in range(0, in_doc.page_count): + converted_doc.pages.append(Page(page_no=i)) + + all_assembled_pages = [] + + try: + # Iterate batches of pages (page_batch_size) in the doc + for page_batch in chunkify( + converted_doc.pages, settings.perf.page_batch_size + ): + + start_pb_time = time.time() + # Pipeline + + # 1. Initialise the page resources + init_pages = map( + functools.partial(self.initialize_page, in_doc), page_batch + ) + + # 2. Populate page image + pages_with_images = map( + functools.partial(self.populate_page_images, in_doc), init_pages + ) + + # 3. Populate programmatic page cells + pages_with_cells = map( + functools.partial(self.parse_page_cells, in_doc), + pages_with_images, + ) + + pipeline_pages = self.model_pipeline.apply(pages_with_cells) + + # 7. Assemble page elements (per page) + assembled_pages = self.page_assemble_model(pipeline_pages) + + # exhaust assembled_pages + for assembled_page in assembled_pages: + # Free up mem resources before moving on with next batch + assembled_page.image = ( + None # Comment this if you want to visualize page images + ) + assembled_page._backend.unload() + + all_assembled_pages.append(assembled_page) + + end_pb_time = time.time() - start_pb_time + _log.info(f"Finished converting page batch time={end_pb_time:.3f}") + + # Free up mem resources of PDF backend + in_doc._backend.unload() + + converted_doc.pages = all_assembled_pages + self.assemble_doc(converted_doc) + + converted_doc.status = ConversionStatus.SUCCESS + + except Exception as e: + converted_doc.status = ConversionStatus.FAILURE + trace = "\n".join(traceback.format_exception(e)) + _log.info(f"Encountered an error during conversion: {trace}") + + end_doc_time = time.time() - start_doc_time + _log.info( + f"Finished converting document time-pages={end_doc_time:.2f}/{in_doc.page_count}" + ) + + return converted_doc + + # Initialise and load resources for a page, before downstream steps (populate images, cells, ...) + def initialize_page(self, doc: InputDocument, page: Page) -> Page: + page._backend = doc._backend.load_page(page.page_no) + page.size = page._backend.get_size() + page.page_hash = create_hash(doc.document_hash + ":" + str(page.page_no)) + + return page + + # Generate the page image and store it in the page object + def populate_page_images(self, doc: InputDocument, page: Page) -> Page: + page.image = page._backend.get_page_image() + + return page + + # Extract and populate the page cells and store it in the page object + def parse_page_cells(self, doc: InputDocument, page: Page) -> Page: + page.cells = page._backend.get_text_cells() + + # DEBUG code: + def draw_text_boxes(image, cells): + draw = ImageDraw.Draw(image) + for c in cells: + x0, y0, x1, y1 = c.bbox.as_tuple() + draw.rectangle([(x0, y0), (x1, y1)], outline="red") + image.show() + + # draw_text_boxes(page.image, cells) + + return page + + def assemble_doc(self, converted_doc: ConvertedDocument): + all_elements = [] + all_headers = [] + all_body = [] + + for p in converted_doc.pages: + + for el in p.assembled.body: + all_body.append(el) + for el in p.assembled.headers: + all_headers.append(el) + for el in p.assembled.elements: + all_elements.append(el) + + converted_doc.assembled = AssembledUnit( + elements=all_elements, headers=all_headers, body=all_body + ) + + converted_doc.output = self.glm_model(converted_doc) diff --git a/docling/models/__init__.py b/docling/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/docling/models/ds_glm_model.py b/docling/models/ds_glm_model.py new file mode 100644 index 0000000..158961f --- /dev/null +++ b/docling/models/ds_glm_model.py @@ -0,0 +1,82 @@ +import copy +import random + +from deepsearch_glm.nlp_utils import init_nlp_model +from deepsearch_glm.utils.ds_utils import to_legacy_document_format +from deepsearch_glm.utils.load_pretrained_models import load_pretrained_nlp_models +from docling_core.types import BaseText +from docling_core.types import Document as DsDocument +from docling_core.types import Ref +from PIL import ImageDraw + +from docling.datamodel.base_models import BoundingBox, Cluster, CoordOrigin +from docling.datamodel.document import ConvertedDocument + + +class GlmModel: + def __init__(self, config): + self.config = config + load_pretrained_nlp_models() + model = init_nlp_model(model_names="language;term;reference") + self.model = model + + def __call__(self, document: ConvertedDocument) -> DsDocument: + ds_doc = document.to_ds_document() + ds_doc_dict = ds_doc.model_dump(by_alias=True) + + glm_doc = self.model.apply_on_doc(ds_doc_dict) + ds_doc_dict = to_legacy_document_format( + glm_doc, ds_doc_dict, update_name_label=True + ) + + exported_doc = DsDocument.model_validate(ds_doc_dict) + + # DEBUG code: + def draw_clusters_and_cells(ds_document, page_no): + clusters_to_draw = [] + image = copy.deepcopy(document.pages[page_no].image) + for ix, elem in enumerate(ds_document.main_text): + if isinstance(elem, BaseText): + prov = elem.prov[0] + elif isinstance(elem, Ref): + _, arr, index = elem.ref.split("/") + index = int(index) + if arr == "tables": + prov = ds_document.tables[index].prov[0] + elif arr == "figures": + prov = ds_document.figures[index].prov[0] + else: + prov = None + + if prov and prov.page == page_no: + clusters_to_draw.append( + Cluster( + id=ix, + label=elem.name, + bbox=BoundingBox.from_tuple( + coord=prov.bbox, + origin=CoordOrigin.BOTTOMLEFT, + ).to_top_left_origin(document.pages[page_no].size.height), + ) + ) + + draw = ImageDraw.Draw(image) + for c in clusters_to_draw: + x0, y0, x1, y1 = c.bbox.as_tuple() + draw.rectangle([(x0, y0), (x1, y1)], outline="red") + draw.text((x0 + 2, y0 + 2), f"{c.id}:{c.label}", fill=(255, 0, 0, 255)) + + cell_color = ( + random.randint(30, 140), + random.randint(30, 140), + random.randint(30, 140), + ) + for tc in c.cells: # [:1]: + x0, y0, x1, y1 = tc.bbox.as_tuple() + draw.rectangle([(x0, y0), (x1, y1)], outline=cell_color) + image.show() + + # draw_clusters_and_cells(ds_doc, 0) + # draw_clusters_and_cells(exported_doc, 0) + + return exported_doc diff --git a/docling/models/easyocr_model.py b/docling/models/easyocr_model.py new file mode 100644 index 0000000..d5bca50 --- /dev/null +++ b/docling/models/easyocr_model.py @@ -0,0 +1,77 @@ +import copy +import logging +import random +from typing import Iterable + +import numpy +from PIL import ImageDraw + +from docling.datamodel.base_models import BoundingBox, CoordOrigin, OcrCell, Page + +_log = logging.getLogger(__name__) + + +class EasyOcrModel: + def __init__(self, config): + self.config = config + self.enabled = config["enabled"] + self.scale = 3 # multiplier for 72 dpi == 216 dpi. + + if self.enabled: + import easyocr + + self.reader = easyocr.Reader(config["lang"]) + + def __call__(self, page_batch: Iterable[Page]) -> Iterable[Page]: + + if not self.enabled: + yield from page_batch + return + + for page in page_batch: + # rects = page._fpage. + high_res_image = page._backend.get_page_image(scale=self.scale) + im = numpy.array(high_res_image) + result = self.reader.readtext(im) + + del high_res_image + del im + + cells = [ + OcrCell( + id=ix, + text=line[1], + confidence=line[2], + bbox=BoundingBox.from_tuple( + coord=( + line[0][0][0] / self.scale, + line[0][0][1] / self.scale, + line[0][2][0] / self.scale, + line[0][2][1] / self.scale, + ), + origin=CoordOrigin.TOPLEFT, + ), + ) + for ix, line in enumerate(result) + ] + + page.cells = cells # For now, just overwrites all digital cells. + + # DEBUG code: + def draw_clusters_and_cells(): + image = copy.deepcopy(page.image) + draw = ImageDraw.Draw(image) + + cell_color = ( + random.randint(30, 140), + random.randint(30, 140), + random.randint(30, 140), + ) + for tc in cells: + x0, y0, x1, y1 = tc.bbox.as_tuple() + draw.rectangle([(x0, y0), (x1, y1)], outline=cell_color) + image.show() + + # draw_clusters_and_cells() + + yield page diff --git a/docling/models/layout_model.py b/docling/models/layout_model.py new file mode 100644 index 0000000..1b1f224 --- /dev/null +++ b/docling/models/layout_model.py @@ -0,0 +1,318 @@ +import copy +import logging +import random +import time +from typing import Iterable, List + +from docling_ibm_models.layoutmodel.layout_predictor import LayoutPredictor +from PIL import ImageDraw + +from docling.datamodel.base_models import ( + BoundingBox, + Cell, + Cluster, + CoordOrigin, + LayoutPrediction, + Page, +) +from docling.utils import layout_utils as lu + +_log = logging.getLogger(__name__) + + +class LayoutModel: + + TEXT_ELEM_LABELS = [ + "Text", + "Footnote", + "Caption", + "Checkbox-Unselected", + "Checkbox-Selected", + "Section-header", + "Page-header", + "Page-footer", + "Code", + "List-item", + # "Formula", + ] + PAGE_HEADER_LABELS = ["Page-header", "Page-footer"] + + TABLE_LABEL = "Table" + FIGURE_LABEL = "Picture" + FORMULA_LABEL = "Formula" + + def __init__(self, config): + self.config = config + self.layout_predictor = LayoutPredictor( + config["artifacts_path"] + ) # TODO temporary + + def postprocess(self, clusters: List[Cluster], cells: List[Cell], page_height): + MIN_INTERSECTION = 0.2 + CLASS_THRESHOLDS = { + "Caption": 0.35, + "Footnote": 0.35, + "Formula": 0.35, + "List-item": 0.35, + "Page-footer": 0.35, + "Page-header": 0.35, + "Picture": 0.2, # low threshold adjust to capture chemical structures for examples. + "Section-header": 0.45, + "Table": 0.35, + "Text": 0.45, + "Title": 0.45, + "Document Index": 0.45, + "Code": 0.45, + "Checkbox-Selected": 0.45, + "Checkbox-Unselected": 0.45, + "Form": 0.45, + "Key-Value Region": 0.45, + } + + _log.debug("================= Start postprocess function ====================") + start_time = time.time() + # Apply Confidence Threshold to cluster predictions + # confidence = self.conf_threshold + clusters_out = [] + + for cluster in clusters: + confidence = CLASS_THRESHOLDS[cluster.label] + if cluster.confidence >= confidence: + # annotation["created_by"] = "high_conf_pred" + clusters_out.append(cluster) + + # map to dictionary clusters and cells, with bottom left origin + clusters = [ + { + "id": c.id, + "bbox": list( + c.bbox.to_bottom_left_origin(page_height).as_tuple() + ), # TODO + "confidence": c.confidence, + "cell_ids": [], + "type": c.label, + } + for c in clusters + ] + + clusters_out = [ + { + "id": c.id, + "bbox": list( + c.bbox.to_bottom_left_origin(page_height).as_tuple() + ), # TODO + "confidence": c.confidence, + "created_by": "high_conf_pred", + "cell_ids": [], + "type": c.label, + } + for c in clusters_out + ] + + raw_cells = [ + { + "id": c.id, + "bbox": list( + c.bbox.to_bottom_left_origin(page_height).as_tuple() + ), # TODO + "text": c.text, + } + for c in cells + ] + cell_count = len(raw_cells) + + _log.debug("---- 0. Treat cluster overlaps ------") + clusters_out = lu.remove_cluster_duplicates_by_conf(clusters_out, 0.8) + + _log.debug( + "---- 1. Initially assign cells to clusters based on minimum intersection ------" + ) + ## Check for cells included in or touched by clusters: + clusters_out = lu.assigning_cell_ids_to_clusters( + clusters_out, raw_cells, MIN_INTERSECTION + ) + + _log.debug("---- 2. Assign Orphans with Low Confidence Detections") + # Creates a map of cell_id->cluster_id + ( + clusters_around_cells, + orphan_cell_indices, + ambiguous_cell_indices, + ) = lu.cell_id_state_map(clusters_out, cell_count) + + # Assign orphan cells with lower confidence predictions + clusters_out, orphan_cell_indices = lu.assign_orphans_with_low_conf_pred( + clusters_out, clusters, raw_cells, orphan_cell_indices + ) + + # Refresh the cell_ids assignment, after creating new clusters using low conf predictions + clusters_out = lu.assigning_cell_ids_to_clusters( + clusters_out, raw_cells, MIN_INTERSECTION + ) + + _log.debug("---- 3. Settle Ambigous Cells") + # Creates an update map after assignment of cell_id->cluster_id + ( + clusters_around_cells, + orphan_cell_indices, + ambiguous_cell_indices, + ) = lu.cell_id_state_map(clusters_out, cell_count) + + # Settle pdf cells that belong to multiple clusters + clusters_out, ambiguous_cell_indices = lu.remove_ambigous_pdf_cell_by_conf( + clusters_out, raw_cells, ambiguous_cell_indices + ) + + _log.debug("---- 4. Set Orphans as Text") + ( + clusters_around_cells, + orphan_cell_indices, + ambiguous_cell_indices, + ) = lu.cell_id_state_map(clusters_out, cell_count) + + clusters_out, orphan_cell_indices = lu.set_orphan_as_text( + clusters_out, clusters, raw_cells, orphan_cell_indices + ) + + _log.debug("---- 5. Merge Cells & and adapt the bounding boxes") + # Merge cells orphan cells + clusters_out = lu.merge_cells(clusters_out) + + # Clean up clusters that remain from merged and unreasonable clusters + clusters_out = lu.clean_up_clusters( + clusters_out, + raw_cells, + merge_cells=True, + img_table=True, + one_cell_table=True, + ) + + new_clusters = lu.adapt_bboxes(raw_cells, clusters_out, orphan_cell_indices) + clusters_out = new_clusters + + ## We first rebuild where every cell is now: + ## Now we write into a prediction cells list, not into the raw cells list. + ## As we don't need previous labels, we best overwrite any old list, because that might + ## have been sorted differently. + ( + clusters_around_cells, + orphan_cell_indices, + ambiguous_cell_indices, + ) = lu.cell_id_state_map(clusters_out, cell_count) + + target_cells = [] + for ix, cell in enumerate(raw_cells): + new_cell = { + "id": ix, + "rawcell_id": ix, + "label": "None", + "bbox": cell["bbox"], + "text": cell["text"], + } + for cluster_index in clusters_around_cells[ + ix + ]: # By previous analysis, this is always 1 cluster. + new_cell["label"] = clusters_out[cluster_index]["type"] + target_cells.append(new_cell) + # _log.debug("New label of cell " + str(ix) + " is " + str(new_cell["label"])) + cells_out = target_cells + + ## ------------------------------- + ## Sort clusters into reasonable reading order, and sort the cells inside each cluster + _log.debug("---- 5. Sort clusters in reading order ------") + sorted_clusters = lu.produce_reading_order( + clusters_out, "raw_cell_ids", "raw_cell_ids", True + ) + clusters_out = sorted_clusters + + # end_time = timer() + _log.debug("---- End of postprocessing function ------") + end_time = time.time() - start_time + _log.debug(f"Finished post processing in seconds={end_time:.3f}") + + cells_out = [ + Cell( + id=c["id"], + bbox=BoundingBox.from_tuple( + coord=c["bbox"], origin=CoordOrigin.BOTTOMLEFT + ).to_top_left_origin(page_height), + text=c["text"], + ) + for c in cells_out + ] + clusters_out_new = [] + for c in clusters_out: + cluster_cells = [ccell for ccell in cells_out if ccell.id in c["cell_ids"]] + c_new = Cluster( + id=c["id"], + bbox=BoundingBox.from_tuple( + coord=c["bbox"], origin=CoordOrigin.BOTTOMLEFT + ).to_top_left_origin(page_height), + confidence=c["confidence"], + label=c["type"], + cells=cluster_cells, + ) + clusters_out_new.append(c_new) + + return clusters_out_new, cells_out + + def __call__(self, page_batch: Iterable[Page]) -> Iterable[Page]: + for page in page_batch: + clusters = [] + for ix, pred_item in enumerate(self.layout_predictor.predict(page.image)): + cluster = Cluster( + id=ix, + label=pred_item["label"], + confidence=pred_item["confidence"], + bbox=BoundingBox.model_validate(pred_item), + cells=[], + ) + clusters.append(cluster) + + # Map cells to clusters + # TODO: Remove, postprocess should take care of it anyway. + for cell in page.cells: + for cluster in clusters: + if not cell.bbox.area() > 0: + overlap_frac = 0.0 + else: + overlap_frac = ( + cell.bbox.intersection_area_with(cluster.bbox) + / cell.bbox.area() + ) + + if overlap_frac > 0.5: + cluster.cells.append(cell) + + # Pre-sort clusters + # clusters = self.sort_clusters_by_cell_order(clusters) + + # DEBUG code: + def draw_clusters_and_cells(): + image = copy.deepcopy(page.image) + draw = ImageDraw.Draw(image) + for c in clusters: + x0, y0, x1, y1 = c.bbox.as_tuple() + draw.rectangle([(x0, y0), (x1, y1)], outline="green") + + cell_color = ( + random.randint(30, 140), + random.randint(30, 140), + random.randint(30, 140), + ) + for tc in c.cells: # [:1]: + x0, y0, x1, y1 = tc.bbox.as_tuple() + draw.rectangle([(x0, y0), (x1, y1)], outline=cell_color) + image.show() + + # draw_clusters_and_cells() + + clusters, page.cells = self.postprocess( + clusters, page.cells, page.size.height + ) + + # draw_clusters_and_cells() + + page.predictions.layout = LayoutPrediction(clusters=clusters) + + yield page diff --git a/docling/models/page_assemble_model.py b/docling/models/page_assemble_model.py new file mode 100644 index 0000000..4ed0832 --- /dev/null +++ b/docling/models/page_assemble_model.py @@ -0,0 +1,160 @@ +import logging +import re +from typing import Iterable, List + +from docling.datamodel.base_models import ( + AssembledUnit, + FigureElement, + Page, + PageElement, + TableElement, + TextElement, +) +from docling.models.layout_model import LayoutModel + +_log = logging.getLogger(__name__) + + +class PageAssembleModel: + def __init__(self, config): + self.config = config + + # self.line_wrap_pattern = re.compile(r'(?<=[^\W_])- \n(?=\w)') + + # def sanitize_text_poor(self, lines): + # text = '\n'.join(lines) + # + # # treat line wraps. + # sanitized_text = self.line_wrap_pattern.sub('', text) + # + # sanitized_text = sanitized_text.replace('\n', ' ') + # + # return sanitized_text + + def sanitize_text(self, lines): + if len(lines) <= 1: + return " ".join(lines) + + for ix, line in enumerate(lines[1:]): + prev_line = lines[ix] + + if prev_line.endswith("-"): + prev_words = re.findall(r"\b[\w]+\b", prev_line) + line_words = re.findall(r"\b[\w]+\b", line) + + if ( + len(prev_words) + and len(line_words) + and prev_words[-1].isalnum() + and line_words[0].isalnum() + ): + lines[ix] = prev_line[:-1] + else: + lines[ix] += " " + + sanitized_text = "".join(lines) + + return sanitized_text.strip() # Strip any leading or trailing whitespace + + def __call__(self, page_batch: Iterable[Page]) -> Iterable[Page]: + for page in page_batch: + # assembles some JSON output page by page. + + elements: List[PageElement] = [] + headers: List[PageElement] = [] + body: List[PageElement] = [] + + for cluster in page.predictions.layout.clusters: + # _log.info("Cluster label seen:", cluster.label) + if cluster.label in LayoutModel.TEXT_ELEM_LABELS: + + textlines = [ + cell.text.replace("\x02", "-").strip() + for cell in cluster.cells + if len(cell.text.strip()) > 0 + ] + text = self.sanitize_text(textlines) + text_el = TextElement( + label=cluster.label, + id=cluster.id, + text=text, + page_no=page.page_no, + cluster=cluster, + ) + elements.append(text_el) + + if cluster.label in LayoutModel.PAGE_HEADER_LABELS: + headers.append(text_el) + else: + body.append(text_el) + elif cluster.label == LayoutModel.TABLE_LABEL: + tbl = None + if page.predictions.tablestructure: + tbl = page.predictions.tablestructure.table_map.get( + cluster.id, None + ) + if ( + not tbl + ): # fallback: add table without structure, if it isn't present + tbl = TableElement( + label=cluster.label, + id=cluster.id, + text="", + otsl_seq=[], + table_cells=[], + cluster=cluster, + page_no=page.page_no, + ) + + elements.append(tbl) + body.append(tbl) + elif cluster.label == LayoutModel.FIGURE_LABEL: + fig = None + if page.predictions.figures_classification: + fig = page.predictions.figures_classification.figure_map.get( + cluster.id, None + ) + if ( + not fig + ): # fallback: add figure without classification, if it isn't present + fig = FigureElement( + label=cluster.label, + id=cluster.id, + text="", + data=None, + cluster=cluster, + page_no=page.page_no, + ) + elements.append(fig) + body.append(fig) + elif cluster.label == LayoutModel.FORMULA_LABEL: + equation = None + if page.predictions.equations_prediction: + equation = ( + page.predictions.equations_prediction.equation_map.get( + cluster.id, None + ) + ) + if not equation: # fallback: add empty formula, if it isn't present + text = self.sanitize_text( + [ + cell.text.replace("\x02", "-").strip() + for cell in cluster.cells + if len(cell.text.strip()) > 0 + ] + ) + equation = TextElement( + label=cluster.label, + id=cluster.id, + cluster=cluster, + page_no=page.page_no, + text=text, + ) + elements.append(equation) + body.append(equation) + + page.assembled = AssembledUnit( + elements=elements, headers=headers, body=body + ) + + yield page diff --git a/docling/models/table_structure_model.py b/docling/models/table_structure_model.py new file mode 100644 index 0000000..8ee4bda --- /dev/null +++ b/docling/models/table_structure_model.py @@ -0,0 +1,114 @@ +from typing import Iterable + +import numpy +from docling_ibm_models.tableformer.data_management.tf_predictor import TFPredictor + +from docling.datamodel.base_models import ( + BoundingBox, + Page, + TableCell, + TableElement, + TableStructurePrediction, +) + + +class TableStructureModel: + def __init__(self, config): + self.config = config + self.do_cell_matching = config["do_cell_matching"] + + self.enabled = config["enabled"] + if self.enabled: + artifacts_path = config["artifacts_path"] + # Third Party + import docling_ibm_models.tableformer.common as c + + 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) + + def __call__(self, page_batch: Iterable[Page]) -> Iterable[Page]: + + if not self.enabled: + yield from page_batch + return + + for page in page_batch: + page.predictions.tablestructure = TableStructurePrediction() # dummy + + in_tables = [ + ( + cluster, + [ + round(cluster.bbox.l), + round(cluster.bbox.t), + round(cluster.bbox.r), + round(cluster.bbox.b), + ], + ) + for cluster in page.predictions.layout.clusters + if cluster.label == "Table" + ] + if not len(in_tables): + yield page + continue + + tokens = [] + for c in page.cells: + for cluster, _ in in_tables: + if c.bbox.area() > 0: + if ( + c.bbox.intersection_area_with(cluster.bbox) / c.bbox.area() + > 0.2 + ): + # Only allow non empty stings (spaces) into the cells of a table + if len(c.text.strip()) > 0: + tokens.append(c.model_dump()) + + iocr_page = { + "image": numpy.asarray(page.image), + "tokens": tokens, + "width": page.size.width, + "height": page.size.height, + } + + table_clusters, table_bboxes = zip(*in_tables) + + if len(table_bboxes): + tf_output = self.tf_predictor.multi_table_predict( + iocr_page, table_bboxes, do_matching=self.do_cell_matching + ) + + for table_cluster, table_out in zip(table_clusters, tf_output): + table_cells = [] + for element in table_out["tf_responses"]: + + if not self.do_cell_matching: + the_bbox = BoundingBox.model_validate(element["bbox"]) + text_piece = page._backend.get_text_in_rect(the_bbox) + element["bbox"]["token"] = text_piece + + tc = TableCell.model_validate(element) + table_cells.append(tc) + + # Retrieving cols/rows, after post processing: + num_rows = table_out["predict_details"]["num_rows"] + num_cols = table_out["predict_details"]["num_cols"] + otsl_seq = table_out["predict_details"]["prediction"]["rs_seq"] + + tbl = TableElement( + 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", + ) + + page.predictions.tablestructure.table_map[table_cluster.id] = tbl + + yield page diff --git a/docling/pipeline/__init__.py b/docling/pipeline/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/docling/pipeline/base_model_pipeline.py b/docling/pipeline/base_model_pipeline.py new file mode 100644 index 0000000..0230a2d --- /dev/null +++ b/docling/pipeline/base_model_pipeline.py @@ -0,0 +1,18 @@ +from abc import abstractmethod +from pathlib import Path +from typing import Iterable + +from docling.datamodel.base_models import Page, PipelineOptions + + +class BaseModelPipeline: + def __init__(self, artifacts_path: Path, pipeline_options: PipelineOptions): + self.model_pipe = [] + self.artifacts_path = artifacts_path + self.pipeline_options = pipeline_options + + def apply(self, page_batch: Iterable[Page]) -> Iterable[Page]: + for model in self.model_pipe: + page_batch = model(page_batch) + + yield from page_batch diff --git a/docling/pipeline/standard_model_pipeline.py b/docling/pipeline/standard_model_pipeline.py new file mode 100644 index 0000000..07c0113 --- /dev/null +++ b/docling/pipeline/standard_model_pipeline.py @@ -0,0 +1,40 @@ +from pathlib import Path +from typing import Iterable + +from docling.datamodel.base_models import Page, PipelineOptions +from docling.models.easyocr_model import EasyOcrModel +from docling.models.layout_model import LayoutModel +from docling.models.page_assemble_model import PageAssembleModel +from docling.models.table_structure_model import TableStructureModel +from docling.pipeline.base_model_pipeline import BaseModelPipeline + + +class StandardModelPipeline(BaseModelPipeline): + _layout_model_path = "model_artifacts/layout/beehive_v0.0.5" + _table_model_path = "model_artifacts/tableformer" + + def __init__(self, artifacts_path: Path, pipeline_options: PipelineOptions): + super().__init__(artifacts_path, pipeline_options) + + self.model_pipe = [ + EasyOcrModel( + config={ + "lang": ["fr", "de", "es", "en"], + "enabled": pipeline_options.do_ocr, + } + ), + LayoutModel( + config={ + "artifacts_path": artifacts_path + / StandardModelPipeline._layout_model_path + } + ), + TableStructureModel( + config={ + "artifacts_path": artifacts_path + / StandardModelPipeline._table_model_path, + "enabled": pipeline_options.do_table_structure, + "do_cell_matching": False, + } + ), + ] diff --git a/docling/utils/__init__.py b/docling/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/docling/utils/layout_utils.py b/docling/utils/layout_utils.py new file mode 100644 index 0000000..8b7a6b6 --- /dev/null +++ b/docling/utils/layout_utils.py @@ -0,0 +1,806 @@ +import copy +import logging + +import networkx as nx + +logger = logging.getLogger("layout_utils") + + +## ------------------------------- +## Geometric helper functions +## The coordinates grow left to right, and bottom to top. +## The bounding box list elements 0 to 3 are x_left, y_bottom, x_right, y_top. + + +def area(bbox): + return (bbox[2] - bbox[0]) * (bbox[3] - bbox[1]) + + +def contains(bbox_i, bbox_j): + ## Returns True if bbox_i contains bbox_j, else False + return ( + bbox_i[0] <= bbox_j[0] + and bbox_i[1] <= bbox_j[1] + and bbox_i[2] >= bbox_j[2] + and bbox_i[3] >= bbox_j[3] + ) + + +def is_intersecting(bbox_i, bbox_j): + return not ( + bbox_i[2] < bbox_j[0] + or bbox_i[0] > bbox_j[2] + or bbox_i[3] < bbox_j[1] + or bbox_i[1] > bbox_j[3] + ) + + +def bb_iou(boxA, boxB): + # determine the (x, y)-coordinates of the intersection rectangle + xA = max(boxA[0], boxB[0]) + yA = max(boxA[1], boxB[1]) + xB = min(boxA[2], boxB[2]) + yB = min(boxA[3], boxB[3]) + # compute the area of intersection rectangle + interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1) + # compute the area of both the prediction and ground-truth + # rectangles + boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1) + boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1) + # compute the intersection over union by taking the intersection + # area and dividing it by the sum of prediction + ground-truth + # areas - the interesection area + iou = interArea / float(boxAArea + boxBArea - interArea) + # return the intersection over union value + return iou + + +def compute_intersection(bbox_i, bbox_j): + ## Returns the size of the intersection area of the two boxes + if not is_intersecting(bbox_i, bbox_j): + return 0 + ## Determine the (x, y)-coordinates of the intersection rectangle: + xA = max(bbox_i[0], bbox_j[0]) + yA = max(bbox_i[1], bbox_j[1]) + xB = min(bbox_i[2], bbox_j[2]) + yB = min(bbox_i[3], bbox_j[3]) + ## Compute the area of intersection rectangle: + interArea = (xB - xA) * (yB - yA) + if interArea < 0: + logger.debug("Warning: Negative intersection detected!") + return 0 + return interArea + + +def surrounding(bbox_i, bbox_j): + ## Computes minimal box that contains both input boxes + sbox = [] + sbox.append(min(bbox_i[0], bbox_j[0])) + sbox.append(min(bbox_i[1], bbox_j[1])) + sbox.append(max(bbox_i[2], bbox_j[2])) + sbox.append(max(bbox_i[3], bbox_j[3])) + return sbox + + +def surrounding_list(bbox_list): + ## Computes minimal box that contains all boxes in the input list + ## The list should be non-empty, but just in case it's not: + if len(bbox_list) == 0: + sbox = [0, 0, 0, 0] + else: + sbox = [] + sbox.append(min([bbox[0] for bbox in bbox_list])) + sbox.append(min([bbox[1] for bbox in bbox_list])) + sbox.append(max([bbox[2] for bbox in bbox_list])) + sbox.append(max([bbox[3] for bbox in bbox_list])) + return sbox + + +def vertical_overlap(bboxA, bboxB): + ## bbox[1] is the lower bound, bbox[3] the upper bound (larger number) + if bboxB[3] < bboxA[1]: ## B below A + return False + elif bboxA[3] < bboxB[1]: ## A below B + return False + else: + return True + + +def vertical_overlap_fraction(bboxA, bboxB): + ## Returns the vertical overlap as fraction of the lower bbox height. + ## bbox[1] is the lower bound, bbox[3] the upper bound (larger number) + ## Height 0 is permitted in the input. + heightA = bboxA[3] - bboxA[1] + heightB = bboxB[3] - bboxB[1] + min_height = min(heightA, heightB) + if bboxA[3] >= bboxB[3]: ## A starts higher or equal + if ( + bboxA[1] <= bboxB[1] + ): ## B is completely in A; this can include height of B = 0: + fraction = 1 + else: + overlap = max(bboxB[3] - bboxA[1], 0) + fraction = overlap / max(min_height, 0.001) + else: + if ( + bboxB[1] <= bboxA[1] + ): ## A is completely in B; this can include height of A = 0: + fraction = 1 + else: + overlap = max(bboxA[3] - bboxB[1], 0) + fraction = overlap / max(min_height, 0.001) + return fraction + + +## ------------------------------- +## Cluster-and-cell relations + + +def compute_enclosed_cells( + cluster_bbox, raw_cells, min_cell_intersection_with_cluster=0.2 +): + cells_in_cluster = [] + cells_in_cluster_int = [] + for ix, cell in enumerate(raw_cells): + cell_bbox = cell["bbox"] + intersection = compute_intersection(cell_bbox, cluster_bbox) + frac_area = area(cell_bbox) * min_cell_intersection_with_cluster + + if ( + intersection > frac_area and frac_area > 0 + ): # intersect > certain fraction of cell + cells_in_cluster.append(ix) + cells_in_cluster_int.append(intersection) + elif contains( + cluster_bbox, + [cell_bbox[0] + 3, cell_bbox[1] + 3, cell_bbox[2] - 3, cell_bbox[3] - 3], + ): + cells_in_cluster.append(ix) + return cells_in_cluster, cells_in_cluster_int + + +def find_clusters_around_cells(cell_count, clusters): + ## Per raw cell, find to which clusters it belongs. + ## Return list of these indices in the raw-cell order. + clusters_around_cells = [[] for _ in range(cell_count)] + for cl_ix, cluster in enumerate(clusters): + for ix in cluster["cell_ids"]: + clusters_around_cells[ix].append(cl_ix) + return clusters_around_cells + + +def find_cell_index(raw_ix, cell_array): + ## "raw_ix" is a rawcell_id. + ## "cell_array" has the structure of an (annotation) cells array. + ## Returns index of cell in cell_array that has this rawcell_id. + for ix, cell in enumerate(cell_array): + if cell["rawcell_id"] == raw_ix: + return ix + + +def find_cell_indices(cluster, cell_array): + ## "cluster" must have the structure as in a clusters array in a prediction, + ## "cell_array" that of a cells array. + ## Returns list of indices of cells in cell_array that have the rawcell_ids as in the cluster, + ## in the order of the rawcell_ids. + result = [] + for raw_ix in sorted(cluster["cell_ids"]): + ## Find the cell with this rawcell_id (if any) + for ix, cell in enumerate(cell_array): + if cell["rawcell_id"] == raw_ix: + result.append(ix) + return result + + +def find_first_cell_index(cluster, cell_array): + ## "cluster" must be a dict with key "cell_ids"; it can also be a line. + ## "cell_array" has the structure of a cells array in an annotation. + ## Returns index of cell in cell_array that has the lowest rawcell_id from the cluster. + result = [] ## We keep it a list as it can be empty (picture without text cells) + if len(cluster["cell_ids"]) == 0: + return result + raw_ix = min(cluster["cell_ids"]) + ## Find the cell with this rawcell_id (if any) + for ix, cell in enumerate(cell_array): + if cell["rawcell_id"] == raw_ix: + result.append(ix) + break ## One is enough; should be only one anyway. + if result == []: + logger.debug( + " Warning: Raw cell " + str(raw_ix) + " not found in annotation cells" + ) + return result + + +## ------------------------------- +## Cluster labels and text + + +def relabel_cluster(cluster, cl_ix, new_label, target_pred): + ## "cluster" must have the structure as in a clusters array in a prediction, + ## "cl_ix" is its index in target_pred, + ## "new_label" is the intended new label, + ## "target_pred" is the entire current target prediction. + ## Sets label on the cluster itself, and on the cells in the target_pred. + ## Returns new_label so that also the cl_label variable in the main code is easily set. + target_pred["clusters"][cl_ix]["type"] = new_label + cluster_target_cells = find_cell_indices(cluster, target_pred["cells"]) + for ix in cluster_target_cells: + target_pred["cells"][ix]["label"] = new_label + return new_label + + +def find_cluster_text(cluster, raw_cells): + ## "cluster" must be a dict with "cell_ids"; it can also be a line. + ## "raw_cells" must have the format of item["raw"]["cells"] + ## Returns the text of the cluster, with blanks between the cell contents + ## (which seem to be words or phrases without starting or trailing blanks). + ## Note that in formulas, this may give a lot more blanks than originally + cluster_text = "" + for raw_ix in sorted(cluster["cell_ids"]): + cluster_text = cluster_text + raw_cells[raw_ix]["text"] + " " + return cluster_text.rstrip() + + +def find_cluster_text_without_blanks(cluster, raw_cells): + ## "cluster" must be a dict with "cell_ids"; it can also be a line. + ## "raw_cells" must have the format of item["raw"]["cells"] + ## Returns the text of the cluster, without blanks between the cell contents + ## Interesting in formula analysis. + cluster_text = "" + for raw_ix in sorted(cluster["cell_ids"]): + cluster_text = cluster_text + raw_cells[raw_ix]["text"] + return cluster_text.rstrip() + + +## ------------------------------- +## Clusters and lines +## (Most line-oriented functions are only needed in TextAnalysisGivenClusters, +## but this one also in FormulaAnalysis) + + +def build_cluster_from_lines(lines, label, id): + ## Lines must be a non-empty list of dicts (lines) with elements "cell_ids" and "bbox" + ## (There is no condition that they are really geometrically lines) + ## A cluster in standard format is returned with given label and id + local_lines = copy.deepcopy( + lines + ) ## without this, it changes "lines" also outside this function + first_line = local_lines.pop(0) + cluster = { + "id": id, + "type": label, + "cell_ids": first_line["cell_ids"], + "bbox": first_line["bbox"], + "confidence": 0, + "created_by": "merged_cells", + } + confidence = 0 + counter = 0 + for line in local_lines: + new_cell_ids = cluster["cell_ids"] + line["cell_ids"] + cluster["cell_ids"] = new_cell_ids + cluster["bbox"] = surrounding(cluster["bbox"], line["bbox"]) + counter += 1 + confidence += line["confidence"] + confidence = confidence / counter + cluster["confidence"] = confidence + return cluster + + +## ------------------------------- +## Reading order + + +def produce_reading_order(clusters, cluster_sort_type, cell_sort_type, sort_ids): + ## In: + ## Clusters: list as in predictions. + ## cluster_sort_type: string, currently only "raw_cells". + ## cell_sort_type: string, currently only "raw_cells". + ## sort_ids: Boolean, whether the cluster ids should be adapted to their new position + ## Out: Another clusters list, sorted according to the type. + + logger.debug("---- Start cluster sorting ------") + + if cell_sort_type == "raw_cell_ids": + for cl in clusters: + sorted_cell_ids = sorted(cl["cell_ids"]) + cl["cell_ids"] = sorted_cell_ids + else: + logger.debug( + "Unknown cell_sort_type `" + + cell_sort_type + + "`, no cell sorting will happen." + ) + + if cluster_sort_type == "raw_cell_ids": + clusters_with_cells = [cl for cl in clusters if cl["cell_ids"] != []] + clusters_without_cells = [cl for cl in clusters if cl["cell_ids"] == []] + logger.debug( + "Clusters with cells: " + str([cl["id"] for cl in clusters_with_cells]) + ) + logger.debug( + " Their first cell ids: " + + str([cl["cell_ids"][0] for cl in clusters_with_cells]) + ) + logger.debug( + "Clusters without cells: " + + str([cl["id"] for cl in clusters_without_cells]) + ) + clusters_with_cells_sorted = sorted( + clusters_with_cells, key=lambda cluster: cluster["cell_ids"][0] + ) + logger.debug( + " First cell ids after sorting: " + + str([cl["cell_ids"][0] for cl in clusters_with_cells_sorted]) + ) + sorted_clusters = clusters_with_cells_sorted + clusters_without_cells + else: + logger.debug( + "Unknown cluster_sort_type: `" + + cluster_sort_type + + "`, no cluster sorting will happen." + ) + + if sort_ids: + for i, cl in enumerate(sorted_clusters): + cl["id"] = i + return sorted_clusters + + +## ------------------------------- +## Line Splitting + + +def sort_cells_horizontal(line_cell_ids, raw_cells): + ## "line_cells" should be a non-empty list of (raw) cell_ids + ## "raw_cells" has the structure of item["raw"]["cells"]. + ## Sorts the cells in the line by x0 (left start). + new_line_cell_ids = sorted( + line_cell_ids, key=lambda cell_id: raw_cells[cell_id]["bbox"][0] + ) + return new_line_cell_ids + + +def adapt_bboxes(raw_cells, clusters, orphan_cell_indices): + new_clusters = [] + for ix, cluster in enumerate(clusters): + new_cluster = copy.deepcopy(cluster) + logger.debug( + "Treating cluster " + str(ix) + ", type " + str(new_cluster["type"]) + ) + logger.debug(" with cells: " + str(new_cluster["cell_ids"])) + if len(cluster["cell_ids"]) == 0 and cluster["type"] != "Picture": + logger.debug(" Empty non-picture, removed") + continue ## Skip this former cluster, now without cells. + new_bbox = adapt_bbox(raw_cells, new_cluster, orphan_cell_indices) + new_cluster["bbox"] = new_bbox + new_clusters.append(new_cluster) + return new_clusters + + +def adapt_bbox(raw_cells, cluster, orphan_cell_indices): + if not (cluster["type"] in ["Table", "Picture"]): + ## A text-like cluster. The bbox only needs to be around the text cells: + logger.debug(" Initial bbox: " + str(cluster["bbox"])) + new_bbox = surrounding_list( + [raw_cells[cid]["bbox"] for cid in cluster["cell_ids"]] + ) + logger.debug(" New bounding box:" + str(new_bbox)) + if cluster["type"] == "Picture": + ## We only make the bbox completely comprise included text cells: + logger.debug(" Picture") + if len(cluster["cell_ids"]) != 0: + min_bbox = surrounding_list( + [raw_cells[cid]["bbox"] for cid in cluster["cell_ids"]] + ) + logger.debug(" Minimum bbox: " + str(min_bbox)) + logger.debug(" Initial bbox: " + str(cluster["bbox"])) + new_bbox = surrounding(min_bbox, cluster["bbox"]) + logger.debug(" New bbox (initial and text cells): " + str(new_bbox)) + else: + logger.debug(" without text cells, no change.") + new_bbox = cluster["bbox"] + else: ## A table + ## At least we have to keep the included text cells, and we make the bbox completely comprise them + min_bbox = surrounding_list( + [raw_cells[cid]["bbox"] for cid in cluster["cell_ids"]] + ) + logger.debug(" Minimum bbox: " + str(min_bbox)) + logger.debug(" Initial bbox: " + str(cluster["bbox"])) + new_bbox = surrounding(min_bbox, cluster["bbox"]) + logger.debug(" Possibly increased bbox: " + str(new_bbox)) + + ## Now we look which non-belonging cells are covered. + ## (To decrease dependencies, we don't make use of which cells we actually removed.) + ## We don't worry about orphan cells, those could still be added to the table. + enclosed_cells = compute_enclosed_cells( + new_bbox, raw_cells, min_cell_intersection_with_cluster=0.3 + )[0] + additional_cells = set(enclosed_cells) - set(cluster["cell_ids"]) + logger.debug( + " Additional cells enclosed by Table bbox: " + str(additional_cells) + ) + spurious_cells = additional_cells - set(orphan_cell_indices) + logger.debug( + " Spurious cells enclosed by Table bbox (additional minus orphans): " + + str(spurious_cells) + ) + if len(spurious_cells) == 0: + return new_bbox + + ## Else we want to keep as much as possible, e.g., grid lines, but not the spurious cells if we can. + ## We initialize possible cuts with the current bbox. + left_cut = new_bbox[0] + right_cut = new_bbox[2] + upper_cut = new_bbox[3] + lower_cut = new_bbox[1] + + for cell_ix in spurious_cells: + cell = raw_cells[cell_ix] + # logger.debug(" Spurious cell bbox: " + str(cell["bbox"])) + is_left = cell["bbox"][2] < min_bbox[0] + is_right = cell["bbox"][0] > min_bbox[2] + is_above = cell["bbox"][1] > min_bbox[3] + is_below = cell["bbox"][3] < min_bbox[1] + # logger.debug(" Left, right, above, below? " + str([is_left, is_right, is_above, is_below])) + + if is_left: + if cell["bbox"][2] > left_cut: + ## We move the left cut to exclude this cell: + left_cut = cell["bbox"][2] + if is_right: + if cell["bbox"][0] < right_cut: + ## We move the right cut to exclude this cell: + right_cut = cell["bbox"][0] + if is_above: + if cell["bbox"][1] < upper_cut: + ## We move the upper cut to exclude this cell: + upper_cut = cell["bbox"][1] + if is_below: + if cell["bbox"][3] > lower_cut: + ## We move the left cut to exclude this cell: + lower_cut = cell["bbox"][3] + # logger.debug(" Current bbox: " + str([left_cut, lower_cut, right_cut, upper_cut])) + + new_bbox = [left_cut, lower_cut, right_cut, upper_cut] + + logger.debug(" Final bbox: " + str(new_bbox)) + return new_bbox + + +def remove_cluster_duplicates_by_conf(cluster_predictions, threshold=0.5): + DuplicateDeletedClusterIDs = [] + for cluster_1 in cluster_predictions: + for cluster_2 in cluster_predictions: + if cluster_1["id"] != cluster_2["id"]: + if_conf = False + if cluster_1["confidence"] > cluster_2["confidence"]: + if_conf = True + if if_conf == True: + if bb_iou(cluster_1["bbox"], cluster_2["bbox"]) > threshold: + DuplicateDeletedClusterIDs.append(cluster_2["id"]) + elif contains( + cluster_1["bbox"], + [ + cluster_2["bbox"][0] + 3, + cluster_2["bbox"][1] + 3, + cluster_2["bbox"][2] - 3, + cluster_2["bbox"][3] - 3, + ], + ): + DuplicateDeletedClusterIDs.append(cluster_2["id"]) + + DuplicateDeletedClusterIDs = list(set(DuplicateDeletedClusterIDs)) + + for cl_id in DuplicateDeletedClusterIDs: + for cluster in cluster_predictions: + if cl_id == cluster["id"]: + cluster_predictions.remove(cluster) + return cluster_predictions + + +# Assign orphan cells by a low confidence prediction that is below the assigned confidence +def assign_orphans_with_low_conf_pred( + cluster_predictions, cluster_predictions_low, raw_cells, orphan_cell_indices +): + for orph_id in orphan_cell_indices: + cluster_chosen = {} + iou_thresh = 0.05 + confidence = 0.05 + + # Loop over all predictions, and find the one with the highest IOU, and confidence + for cluster in cluster_predictions_low: + calc_iou = bb_iou(cluster["bbox"], raw_cells[orph_id]["bbox"]) + cluster_area = (cluster["bbox"][3] - cluster["bbox"][1]) * ( + cluster["bbox"][2] - cluster["bbox"][0] + ) + cell_area = ( + raw_cells[orph_id]["bbox"][3] - raw_cells[orph_id]["bbox"][1] + ) * (raw_cells[orph_id]["bbox"][2] - raw_cells[orph_id]["bbox"][0]) + + if ( + (iou_thresh < calc_iou) + and (cluster["confidence"] > confidence) + and (cell_area * 3 > cluster_area) + ): + cluster_chosen = cluster + iou_thresh = calc_iou + confidence = cluster["confidence"] + # If a candidate is found, assign to it the PDF cell ids, and tag that it was created by this function for tracking + if iou_thresh != 0.05 and confidence != 0.05: + cluster_chosen["cell_ids"].append(orph_id) + cluster_chosen["created_by"] = "orph_low_conf" + cluster_predictions.append(cluster_chosen) + orphan_cell_indices.remove(orph_id) + return cluster_predictions, orphan_cell_indices + + +def remove_ambigous_pdf_cell_by_conf(cluster_predictions, raw_cells, amb_cell_idxs): + for amb_cell_id in amb_cell_idxs: + highest_conf = 0 + highest_bbox_iou = 0 + cluster_chosen = None + problamatic_clusters = [] + + # Find clusters in question + for cluster in cluster_predictions: + + if amb_cell_id in cluster["cell_ids"]: + problamatic_clusters.append(amb_cell_id) + + # If the cell_id is in a cluster of high conf, and highest iou score, and smaller in area + bbox_iou_val = bb_iou(cluster["bbox"], raw_cells[amb_cell_id]["bbox"]) + + if ( + cluster["confidence"] > highest_conf + and bbox_iou_val > highest_bbox_iou + ): + cluster_chosen = cluster + highest_conf = cluster["confidence"] + highest_bbox_iou = bbox_iou_val + if cluster["id"] in problamatic_clusters: + problamatic_clusters.remove(cluster["id"]) + + # now remove the assigning of cell id from lower confidence, and threshold + for cluster in cluster_predictions: + for prob_amb_id in problamatic_clusters: + if prob_amb_id in cluster["cell_ids"]: + cluster["cell_ids"].remove(prob_amb_id) + amb_cell_idxs.remove(amb_cell_id) + + return cluster_predictions, amb_cell_idxs + + +def ranges(nums): + # Find if consecutive numbers exist within pdf cells + # Used to remove line numbers for review manuscripts + nums = sorted(set(nums)) + gaps = [[s, e] for s, e in zip(nums, nums[1:]) if s + 1 < e] + edges = iter(nums[:1] + sum(gaps, []) + nums[-1:]) + return list(zip(edges, edges)) + + +def set_orphan_as_text( + cluster_predictions, cluster_predictions_low, raw_cells, orphan_cell_indices +): + max_id = -1 + figures = [] + for cluster in cluster_predictions: + if cluster["type"] == "Picture": + figures.append(cluster) + + if cluster["id"] > max_id: + max_id = cluster["id"] + max_id += 1 + + lines_detector = False + content_of_orphans = [] + for orph_id in orphan_cell_indices: + orph_cell = raw_cells[orph_id] + content_of_orphans.append(raw_cells[orph_id]["text"]) + + fil_content_of_orphans = [] + for cell_content in content_of_orphans: + if cell_content.isnumeric(): + try: + num = int(cell_content) + fil_content_of_orphans.append(num) + except ValueError: # ignore the cell + pass + + # line_orphans = [] + # Check if there are more than 2 pdf orphan cells, if there are more than 2, + # then check between the orphan cells if they are numeric + # and if they are a consecutive series of numbers (using ranges function) to decide + + if len(fil_content_of_orphans) > 2: + out_ranges = ranges(fil_content_of_orphans) + if len(out_ranges) > 1: + cnt_range = 0 + for ranges_ in out_ranges: + if ranges_[0] != ranges_[1]: + # If there are more than 75 (half the total line number of a review manuscript page) + # decide that there are line numbers on page to be ignored. + if len(list(range(ranges_[0], ranges_[1]))) > 75: + lines_detector = True + # line_orphans = line_orphans + list(range(ranges_[0], ranges_[1])) + + for orph_id in orphan_cell_indices: + orph_cell = raw_cells[orph_id] + if bool(orph_cell["text"] and not orph_cell["text"].isspace()): + fig_flag = False + # Do not assign orphan cells if they are inside a figure + for fig in figures: + if contains(fig["bbox"], orph_cell["bbox"]): + fig_flag = True + + # if fig_flag == False and raw_cells[orph_id]["text"] not in line_orphans: + if fig_flag == False and lines_detector == False: + # get class from low confidence detections if not set as text: + class_type = "Text" + + for cluster in cluster_predictions_low: + intersection = compute_intersection( + orph_cell["bbox"], cluster["bbox"] + ) + class_type = "Text" + if ( + cluster["confidence"] > 0.1 + and bb_iou(cluster["bbox"], orph_cell["bbox"]) > 0.4 + ): + class_type = cluster["type"] + elif contains( + cluster["bbox"], + [ + orph_cell["bbox"][0] + 3, + orph_cell["bbox"][1] + 3, + orph_cell["bbox"][2] - 3, + orph_cell["bbox"][3] - 3, + ], + ): + class_type = cluster["type"] + elif intersection > area(orph_cell["bbox"]) * 0.2: + class_type = cluster["type"] + + new_cluster = { + "id": max_id, + "bbox": orph_cell["bbox"], + "type": class_type, + "cell_ids": [orph_id], + "confidence": -1, + "created_by": "orphan_default", + } + max_id += 1 + cluster_predictions.append(new_cluster) + return cluster_predictions, orphan_cell_indices + + +def merge_cells(cluster_predictions): + # Using graph component creates clusters if orphan cells are touching or too close. + G = nx.Graph() + for cluster in cluster_predictions: + if cluster["created_by"] == "orphan_default": + G.add_node(cluster["id"]) + + for cluster_1 in cluster_predictions: + for cluster_2 in cluster_predictions: + if ( + cluster_1["id"] != cluster_2["id"] + and cluster_2["created_by"] == "orphan_default" + and cluster_1["created_by"] == "orphan_default" + ): + cl1 = copy.deepcopy(cluster_1["bbox"]) + cl2 = copy.deepcopy(cluster_2["bbox"]) + cl1[0] = cl1[0] - 2 + cl1[1] = cl1[1] - 2 + cl1[2] = cl1[2] + 2 + cl1[3] = cl1[3] + 2 + cl2[0] = cl2[0] - 2 + cl2[1] = cl2[1] - 2 + cl2[2] = cl2[2] + 2 + cl2[3] = cl2[3] + 2 + if is_intersecting(cl1, cl2): + G.add_edge(cluster_1["id"], cluster_2["id"]) + + component = sorted(map(sorted, nx.k_edge_components(G, k=1))) + max_id = -1 + for cluster_1 in cluster_predictions: + if cluster_1["id"] > max_id: + max_id = cluster_1["id"] + + for nodes in component: + if len(nodes) > 1: + max_id += 1 + lines = [] + for node in nodes: + for cluster in cluster_predictions: + if cluster["id"] == node: + lines.append(cluster) + cluster_predictions.remove(cluster) + new_merged_cluster = build_cluster_from_lines(lines, "Text", max_id) + cluster_predictions.append(new_merged_cluster) + return cluster_predictions + + +def clean_up_clusters( + cluster_predictions, + raw_cells, + merge_cells=False, + img_table=False, + one_cell_table=False, +): + DuplicateDeletedClusterIDs = [] + + for cluster_1 in cluster_predictions: + for cluster_2 in cluster_predictions: + if cluster_1["id"] != cluster_2["id"]: + # remove any artifcats created by merging clusters + if merge_cells == True: + if contains( + cluster_1["bbox"], + [ + cluster_2["bbox"][0] + 3, + cluster_2["bbox"][1] + 3, + cluster_2["bbox"][2] - 3, + cluster_2["bbox"][3] - 3, + ], + ): + cluster_1["cell_ids"] = ( + cluster_1["cell_ids"] + cluster_2["cell_ids"] + ) + DuplicateDeletedClusterIDs.append(cluster_2["id"]) + # remove clusters that might appear inside tables, or images (such as pdf cells in graphs) + elif img_table == True: + if ( + cluster_1["type"] == "Text" + and cluster_2["type"] == "Picture" + or cluster_2["type"] == "Table" + ): + if bb_iou(cluster_1["bbox"], cluster_2["bbox"]) > 0.5: + DuplicateDeletedClusterIDs.append(cluster_1["id"]) + elif contains( + [ + cluster_2["bbox"][0] - 3, + cluster_2["bbox"][1] - 3, + cluster_2["bbox"][2] + 3, + cluster_2["bbox"][3] + 3, + ], + cluster_1["bbox"], + ): + DuplicateDeletedClusterIDs.append(cluster_1["id"]) + # remove tables that have one pdf cell + if one_cell_table == True: + if cluster_1["type"] == "Table" and len(cluster_1["cell_ids"]) < 2: + DuplicateDeletedClusterIDs.append(cluster_1["id"]) + + DuplicateDeletedClusterIDs = list(set(DuplicateDeletedClusterIDs)) + + for cl_id in DuplicateDeletedClusterIDs: + for cluster in cluster_predictions: + if cl_id == cluster["id"]: + cluster_predictions.remove(cluster) + return cluster_predictions + + +def assigning_cell_ids_to_clusters(clusters, raw_cells, threshold): + for cluster in clusters: + cells_in_cluster, _ = compute_enclosed_cells( + cluster["bbox"], raw_cells, min_cell_intersection_with_cluster=threshold + ) + cluster["cell_ids"] = cells_in_cluster + ## These cell_ids are ids of the raw cells. + ## They are often, but not always, the same as the "id" or the index of the "cells" list in a prediction. + return clusters + + +# Creates a map of cell_id->cluster_id +def cell_id_state_map(clusters, cell_count): + clusters_around_cells = find_clusters_around_cells(cell_count, clusters) + orphan_cell_indices = [ + ix for ix in range(cell_count) if len(clusters_around_cells[ix]) == 0 + ] # which cells are assigned no cluster? + ambiguous_cell_indices = [ + ix for ix in range(cell_count) if len(clusters_around_cells[ix]) > 1 + ] # which cells are assigned > 1 clusters? + return clusters_around_cells, orphan_cell_indices, ambiguous_cell_indices diff --git a/docling/utils/utils.py b/docling/utils/utils.py new file mode 100644 index 0000000..24b6942 --- /dev/null +++ b/docling/utils/utils.py @@ -0,0 +1,41 @@ +import hashlib +from io import BytesIO +from itertools import islice +from pathlib import Path +from typing import List, Union + + +def chunkify(iterator, chunk_size): + """Yield successive chunks of chunk_size from the iterable.""" + if isinstance(iterator, List): + iterator = iter(iterator) + for first in iterator: # Take the first element from the iterator + yield [first] + list(islice(iterator, chunk_size - 1)) + + +def create_file_hash(path_or_stream: Union[BytesIO, Path]) -> str: + """Create a stable page_hash of the path_or_stream of a file""" + + block_size = 65536 + hasher = hashlib.sha256() + + def _hash_buf(binary_stream): + buf = binary_stream.read(block_size) # read and page_hash in chunks + while len(buf) > 0: + hasher.update(buf) + buf = binary_stream.read(block_size) + + if isinstance(path_or_stream, Path): + with path_or_stream.open("rb") as afile: + _hash_buf(afile) + elif isinstance(path_or_stream, BytesIO): + _hash_buf(path_or_stream) + + return hasher.hexdigest() + + +def create_hash(string: str): + hasher = hashlib.sha256() + hasher.update(string.encode("utf-8")) + + return hasher.hexdigest() diff --git a/examples/convert.py b/examples/convert.py new file mode 100644 index 0000000..89b3726 --- /dev/null +++ b/examples/convert.py @@ -0,0 +1,73 @@ +import json +import logging +import time +from pathlib import Path +from typing import Iterable + +from docling.backend.pypdfium2_backend import PyPdfiumDocumentBackend +from docling.datamodel.base_models import ConversionStatus, PipelineOptions +from docling.datamodel.document import ConvertedDocument, DocumentConversionInput +from docling.document_converter import DocumentConverter + +_log = logging.getLogger(__name__) + + +def export_documents( + converted_docs: Iterable[ConvertedDocument], + output_dir: Path, +): + output_dir.mkdir(parents=True, exist_ok=True) + + success_count = 0 + failure_count = 0 + + for doc in converted_docs: + if doc.status == ConversionStatus.SUCCESS: + success_count += 1 + doc_filename = doc.input.file.stem + + # Export Deep Search document JSON format: + with (output_dir / f"{doc_filename}.json").open("w") as fp: + fp.write(json.dumps(doc.render_as_dict())) + + # Export Markdown format: + with (output_dir / f"{doc_filename}.md").open("w") as fp: + fp.write(doc.render_as_markdown()) + else: + _log.info(f"Document {doc.input.file} failed to convert.") + failure_count += 1 + + _log.info( + f"Processed {success_count + failure_count} docs, of which {failure_count} failed" + ) + + +def main(): + logging.basicConfig(level=logging.INFO) + + input_doc_paths = [ + # Path("/Users/cau/Downloads/Issue-36122.pdf"), + # Path("/Users/cau/Downloads/IBM_Storage_Insights_Fact_Sheet.pdf"), + Path("./test/data/2206.01062.pdf"), + Path("./test/data/2203.01017v2.pdf"), + Path("./test/data/2305.03393v1.pdf"), + ] + + artifacts_path = DocumentConverter.download_models_hf() + + doc_converter = DocumentConverter(artifacts_path=artifacts_path) + + input = DocumentConversionInput.from_paths(input_doc_paths) + + start_time = time.time() + + converted_docs = doc_converter.convert(input) + export_documents(converted_docs, output_dir=Path("./scratch")) + + end_time = time.time() - start_time + + _log.info(f"All documents were converted in {end_time:.2f} seconds.") + + +if __name__ == "__main__": + main() diff --git a/examples/minimal.py b/examples/minimal.py new file mode 100644 index 0000000..3f77910 --- /dev/null +++ b/examples/minimal.py @@ -0,0 +1,11 @@ +from docling.datamodel.document import DocumentConversionInput +from docling.document_converter import DocumentConverter + +artifacts_path = DocumentConverter.download_models_hf() +doc_converter = DocumentConverter(artifacts_path=artifacts_path) + +input = DocumentConversionInput.from_paths(["factsheet.pdf"]) +converted_docs = doc_converter.convert(input) + +for d in converted_docs: + print(d.render_as_dict()) diff --git a/logo.png b/logo.png new file mode 100644 index 0000000..07964a5 Binary files /dev/null and b/logo.png differ diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..25ce193 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,4865 @@ +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. + +[[package]] +name = "annotated-types" +version = "0.7.0" +description = "Reusable constraint types to use with typing.Annotated" 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+license = "MIT" +readme = "README.md" +keywords= ["docling", "convert", "document", "pdf", "layout model", "segmentation", "table structure", "table former"] + classifiers = [ + "License :: OSI Approved :: MIT License", + "Operating System :: MacOS :: MacOS X", + "Operating System :: POSIX :: Linux", + "Development Status :: 5 - Production/Stable", + "Intended Audience :: Developers", + "Intended Audience :: Science/Research", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Programming Language :: Python :: 3" + ] +packages = [{include = "docling"}] + +[tool.poetry.dependencies] +python = "^3.11" +pydantic = "^2.0.0" +docling-core = "^0.2.0" +docling-ibm-models = "^0.2.0" +deepsearch-glm = ">=0.18.4,<1" +deepsearch-toolkit = ">=0.47.0,<1" +filetype = "^1.2.0" +pypdfium2 = "^4.30.0" +pydantic-settings = "^2.3.0" +huggingface_hub = ">=0.23,<1" + +[tool.poetry.group.ocr.dependencies] +easyocr = "^1.7" + +[tool.poetry.group.dev.dependencies] +black = {extras = ["jupyter"], version = "^24.4.2"} +pytest = "^7.2.2" +pre-commit = "^3.7.1" +mypy = "^1.10.1" +isort = "^5.10.1" +python-semantic-release = "^7.32.2" +flake8 = "^6.0.0" +pyproject-flake8 = "^6.0.0" +pytest-xdist = "^3.3.1" +types-requests = "^2.31.0.2" +flake8-pyproject = "^1.2.3" +pylint = "^2.17.5" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.black] +line-length = 88 +target-version = ["py311"] +include = '\.pyi?$' + +[tool.isort] +profile = "black" +line_length = 88 +py_version=311 + +[tool.mypy] +pretty = true +# strict = true +no_implicit_optional = true +python_version = "3.11" + +[tool.flake8] +max-line-length = 88 +extend-ignore = ["E203", "E501"] diff --git a/test/data/2203.01017v2.pdf b/test/data/2203.01017v2.pdf new file mode 100644 index 0000000..f35eecd Binary files /dev/null and b/test/data/2203.01017v2.pdf differ diff --git a/test/data/2206.01062.pdf b/test/data/2206.01062.pdf new file mode 100644 index 0000000..3d499d6 Binary files /dev/null and b/test/data/2206.01062.pdf differ diff --git a/test/data/2305.03393v1.pdf b/test/data/2305.03393v1.pdf new file mode 100644 index 0000000..9392daf Binary files /dev/null and b/test/data/2305.03393v1.pdf differ diff --git a/test/test_backend_pdfium.py b/test/test_backend_pdfium.py new file mode 100644 index 0000000..2ba239c --- /dev/null +++ b/test/test_backend_pdfium.py @@ -0,0 +1,33 @@ +from pathlib import Path + +import pytest + +from docling.backend.pypdfium2_backend import PyPdfiumDocumentBackend, PyPdfiumPageBackend +from docling.datamodel.base_models import BoundingBox + + +@pytest.fixture +def test_doc_path(): + return Path("./data/2206.01062.pdf") + +def test_get_text_from_rect(test_doc_path): + doc_backend = PyPdfiumDocumentBackend(test_doc_path) + page_backend: PyPdfiumPageBackend = doc_backend.load_page(0) + + # Get the title text of the DocLayNet paper + textpiece = page_backend.get_text_in_rect(bbox=BoundingBox(l=102,t=77,r=511,b=124)) + ref = "DocLayNet: A Large Human-Annotated Dataset for\r\nDocument-Layout Analysis" + + assert textpiece.strip() == ref + +def test_crop_page_image(test_doc_path): + doc_backend = PyPdfiumDocumentBackend(test_doc_path) + page_backend: PyPdfiumPageBackend = doc_backend.load_page(0) + + # Crop out "Figure 1" from the DocLayNet paper + im = page_backend.get_page_image(scale=2, cropbox=BoundingBox(l=317,t=246,r=574,b=527)) + # im.show() + +def test_num_pages(test_doc_path): + doc_backend = PyPdfiumDocumentBackend(test_doc_path) + doc_backend.page_count() == 9