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# Contributing to Layout Parser
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🎉👍 Thank you for reading this and plan to contribute! We hope you can join us and work on
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this exciting project that can transform document image analysis pipelines with the full
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power of Deep Learning.
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🙌 Thank you for reading this and plan to contribute! We hope you can join us and work on this exciting project that can transform document image analysis pipelines with the full power of Deep Learning.
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All kinds of contributions are welcome, including but not limited to:
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## Planned features
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We are planning to improve different aspects of Layout Parser, any feedbacks and contributions are welcomed!
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We are planning to improve different aspects of Layout Parser, any feedbacks and contributions would be great!
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### Layout Modeling
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(Pre-trained) layout models are one of the most components in Layout Parser, and we are planning to broadening the support for layout models:
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(Pre-trained) layout models are one of the most important components in Layout Parser, and we are planning to broaden the support for layout models:
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- Support frameworks other than Detectron2, e.g., [MMOCR](https://github.com/open-mmlab/mmocr). It may leads to easier installation and support for more application scenarios like recipt or invoice detection.
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- Support framework other than Detectron2, e.g., [MMOCR](https://github.com/open-mmlab/mmocr). It may lead to easier installation and support for more application scenarios like receipt or invoice detection.
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- Support segmentation-based models, e.g., [dhSegment](https://github.com/dhlab-epfl/dhSegment)
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- Better customized training of layout detection models, see [layout-model-training](https://github.com/Layout-Parser/layout-model-training)
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- Reproducing novel layout models in the current framework, e.g., [CascadeTabNet](https://github.com/DevashishPrasad/CascadeTabNet)
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We are also working on the Layout Parser platforms that can support users' sharing their own models. Please check more details in [community-platform](https://github.com/Layout-Parser/community-platform).
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We are also working on the Layout Parser platform that can support users' sharing their own models. Please check [community-platform](https://github.com/Layout-Parser/community-platform) for more detail.
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### Advanced Layout Pipeline
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- Support defining `Pipeline` that specifies an end-to-end layout processing pipeline for complex documents.
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- Support defining `Pipeline` that specifies an end-to-end layout processing pipeline for complex documents
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### Command Line Tool and Layout Detection Service
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Layout Parser can be easily turned into a command line tool or service to process documents in bulk.
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Layout Parser can be easily turned into a command line tool or service to process documents in bulk
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- Build a command line tool based on `Click` that supports commands like `layoutparser process --path <path/to/document/folders>`
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- Build a RESTful Layout Parser service based on tools like `FastAPI` with similar supports as the ccommand line tool.
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- Performance improvements for these service
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- Build a RESTful Layout Parser service based on tools like `FastAPI` with similar supports as the command line tool
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- Performance improvements for such services
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### Easy Installation and Deployment
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## How to Contribute?
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This how-to-guide is abridged from the [MMOCR repo](https://github.com/open-mmlab/mmocr/blob/main/.github/CONTRIBUTING.md)
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This how-to-guide is abridged from the [MMOCR Repository](https://github.com/open-mmlab/mmocr/blob/main/.github/CONTRIBUTING.md).
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### Main Steps
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1. Fork and pull the latest Layout Parser Repo
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1. Fork and pull the latest Layout Parser Repository
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2. Checkout a new branch (do not use main branch for PRs)
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3. Commit your changes
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4. Create a PR
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**Notes**:
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1. If you plan to add some new features that involve large changes, please open an issue for discussion first.
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2. If you are the author of some papers and would like to include your method to Layout Parser, please let us know (open an issue or contact the maintainers). We will much appreciate your contribution.
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3. For new features and new modules, unit tests are required to improve the robustness.
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4. You might want to run `pip install -r dev-requirements.txt` to install the dev-dependencies.
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1. If you plan to add some new features that involve big changes, please open an issue to discuss with us first
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2. If you are the author of some papers and would like to include your method into Layout Parser, please let us know (open an issue or contact the maintainers). Your contribution would be much appreciated.
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3. For new features and new modules, unit tests are required to improve the code robustness
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4. You might want to run `pip install -r dev-requirements.txt` to install the dev-dependencies.
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### Code Style
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1. We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style.
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2. We use the following tools for linting and formatting:
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- pylint: linter
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- black: formatter
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3. We suggest adding [type hints](https://docs.python.org/3/library/typing.html) for all APIs.
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3. We suggest adding [type hints](https://docs.python.org/3/library/typing.html) for all APIs.
* For PubLayNet models, we suggest using `mask_rcnn_X_101_32x8d_FPN_3x` model as it's trained on the whole training set, while others are only trained on the validation set (the size is only around 1/50). You could expect a 15% AP improvement using the `mask_rcnn_X_101_32x8d_FPN_3x` model.
Copy file name to clipboardExpand all lines: installation.md
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## Install Python
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Layout Parser is a Python package that requires Python >= 3.6. If you do not have Python
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installed on your computer, you might want to turn to [the official instruction](https://www.python.org/downloads/)
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and download and install the appropriate version of Python.
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Layout Parser is a Python package that requires Python >= 3.6. If you do not have Python installed on your computer, you might want to turn to [the official instruction](https://www.python.org/downloads/) to download and install the appropriate version of Python.
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## Install the Layout Parser main library
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pip3 install -U layoutparser
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```
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## [Optional] Install Detectron2 and modeling utils
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## [Optional] Install Detectron2 for Using Layout Models
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### For Mac OS and Linux users
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### For Mac OS and Linux Users
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If you would like to use deep learning model detection, you need also install Detectron2
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on your computer. This could be done by running the following command:
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If you would like to use deep learning models for layout detection, you also need to install Detectron2 on your computer. This could be done by running the following command:
This might take sometime as the command will *compile* the library. You might also install a Detectron2 version with
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GPU supports or encounter some issues during the installation process. Please refer to the official Detectron2
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This might take some time as the command will *compile* the library. You might also want to install a Detectron2 version
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with GPU support or encounter some issues during the installation process. Please refer to the official Detectron2
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[installation instruction](https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md) for detailed
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information.
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### For Windows users
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However, as reported by many users, the installation of Detectron2 is very tricky on Windows platforms.
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In our extensive tests, we find that it is nearly impossible to provide a one-line installation command for Windows users.
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As a workaround solution, for now we conclude the possible challenges for installing Detectron2 on Windows, and attach helpful
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resources for solving them.
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We are also investigating other possibilities for avoiding Detectron2 and enabling the pre-trained models. If you have any
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suggestions or ideas for fixing this issue, please feel free to [submit an issue](https://github.com/Layout-Parser/layout-parser/issues) in our repo.
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As reported by many users, the installation of Detectron2 can be rather tricky on Windows platforms. In our extensive tests, we find that it is nearly impossible to provide a one-line installation command for Windows users. As a workaround solution, for now we list the possible challenges for installing Detectron2 on Windows, and attach helpful resources for solving them. We are also investigating other possibilities to avoid installing Detectron2 to use pre-trained models. If you have any suggestions or ideas, please feel free to [submit an issue](https://github.com/Layout-Parser/layout-parser/issues) in our repo.
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1. Challenges for installing `pycocotools`
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- You can find detailed instructions on [this post](https://changhsinlee.com/pycocotools/) from Chang Hsin Lee.
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- Another solution is try to install `pycocotools-windows`, see https://github.com/cocodataset/cocoapi/issues/415.
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2. Challenges for installing the `Detectron2`library itself
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2. Challenges for installing `Detectron2`
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-[@ivanpp](https://github.com/ivanpp) curates a detailed description for installing `Detectron2` on Windows: [Detectron2 walkthrough (Windows)](https://ivanpp.cc/detectron2-walkthrough-windows/#step3installdetectron2)
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-`Detectron2`claims it won't provide official support for Windows (see [1](https://github.com/facebookresearch/detectron2/issues/9#issuecomment-540974288) and [2](https://detectron2.readthedocs.io/en/latest/tutorials/install.html)), but they claim that Detectron2 is continuously built on windows with CircleCI (see [3](https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md#common-installation-issues)). Hopefully this situation will be improved in the future.
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-`Detectron2`maintainers claim that they won't provide official support for Windows (see [1](https://github.com/facebookresearch/detectron2/issues/9#issuecomment-540974288) and [2](https://detectron2.readthedocs.io/en/latest/tutorials/install.html)), but Detectron2 is continuously built on windows with CircleCI (see [3](https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md#common-installation-issues)). Hopefully this situation will be improved in the future.
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## [Optional] Install OCR utils
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Layout Parser also comes with supports for OCR functions. In order to use them, you need
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to install the OCR utils via:
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Layout Parser also comes with supports for OCR functions. In order to use them, you need to install the OCR utils via:
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```bash
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pip3 install -U layoutparser[ocr]
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## Known issues
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<details><summary>Error: instantiating lp.GCVAgent.with_credential returns module 'google.cloud.vision' has no attribute 'types'. </summary>
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<details><summary>Error: instantiating `lp.GCVAgent.with_credential` returns module 'google.cloud.vision' has no attribute 'types'. </summary>
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<p>
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In this case, you have a newer version of the google-cloud-vision. Please consider downgrading the API using:
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