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+/layoutparser-0.3.4.tar.gz
diff --git a/python-layoutparser.spec b/python-layoutparser.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-layoutparser
+Version:	0.3.4
+Release:	1
+Summary:	A unified toolkit for Deep Learning Based Document Image Analysis
+License:	Apache-2.0
+URL:		https://github.com/Layout-Parser/layout-parser
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/95/16/3ff7629fd684047ad9779394aadc7b612c5ae91e41a27f1bad1469e23f05/layoutparser-0.3.4.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-opencv-python
+Requires:	python3-scipy
+Requires:	python3-pandas
+Requires:	python3-pillow
+Requires:	python3-pyyaml
+Requires:	python3-iopath
+Requires:	python3-pdfplumber
+Requires:	python3-pdf2image
+Requires:	python3-torch
+Requires:	python3-torchvision
+Requires:	python3-effdet
+Requires:	python3-google-cloud-vision
+Requires:	python3-torch
+Requires:	python3-torchvision
+Requires:	python3-effdet
+Requires:	python3-google-cloud-vision
+Requires:	python3-pytesseract
+Requires:	python3-paddlepaddle
+Requires:	python3-pytesseract
+
+%description
+## What is LayoutParser
+![Example Usage](https://github.com/Layout-Parser/layout-parser/raw/main/.github/example.png)
+LayoutParser aims to provide a wide range of tools that aims to streamline Document Image Analysis (DIA) tasks. Please check the LayoutParser [demo video](https://youtu.be/8yA5xB4Dg8c) (1 min) or [full talk](https://www.youtube.com/watch?v=YG0qepPgyGY) (15 min) for details. And here are some key features:
+- LayoutParser provides a rich repository of deep learning models for layout detection as well as a set of unified APIs for using them. For example, 
+  <details>
+  <summary>Perform DL layout detection in 4 lines of code</summary>
+  ```python
+  import layoutparser as lp
+  model = lp.AutoLayoutModel('lp://EfficientDete/PubLayNet')
+  # image = Image.open("path/to/image")
+  layout = model.detect(image) 
+  ```
+  </details>
+- LayoutParser comes with a set of layout data structures with carefully designed APIs that are optimized for document image analysis tasks. For example, 
+  <details>
+  <summary>Selecting layout/textual elements in the left column of a page</summary>
+  ```python
+  image_width = image.size[0]
+  left_column = lp.Interval(0, image_width/2, axis='x')
+  layout.filter_by(left_column, center=True) # select objects in the left column 
+  ```
+  </details>
+  <details>
+  <summary>Performing OCR for each detected Layout Region</summary>
+  ```python
+  ocr_agent = lp.TesseractAgent()
+  for layout_region in layout: 
+      image_segment = layout_region.crop(image)
+      text = ocr_agent.detect(image_segment)
+  ```
+  </details>  
+  <details>
+  <summary>Flexible APIs for visualizing the detected layouts</summary>
+  ```python
+  lp.draw_box(image, layout, box_width=1, show_element_id=True, box_alpha=0.25)
+  ```
+  </details>  
+  </details>  
+  <details>
+  <summary>Loading layout data stored in json, csv, and even PDFs</summary>
+  ```python 
+  layout = lp.load_json("path/to/json")
+  layout = lp.load_csv("path/to/csv")
+  pdf_layout = lp.load_pdf("path/to/pdf")
+  ```
+  </details>
+- LayoutParser is also a open platform that enables the sharing of layout detection models and DIA pipelines among the community. 
+  <details>
+  <summary><a href="https://layout-parser.github.io/platform/">Check</a> the LayoutParser open platform</summary>
+  </details>
+  <details>
+  <summary><a href="https://github.com/Layout-Parser/platform">Submit</a> your models/pipelines to LayoutParser</summary>
+  </details>
+## Installation 
+After several major updates, layoutparser provides various functionalities and deep learning models from different backends. But it still easy to install layoutparser, and we designed the installation method in a way such that you can choose to install only the needed dependencies for your project:
+```bash
+pip install layoutparser # Install the base layoutparser library with  
+pip install "layoutparser[layoutmodels]" # Install DL layout model toolkit 
+pip install "layoutparser[ocr]" # Install OCR toolkit
+```
+Extra steps are needed if you want to use Detectron2-based models. Please check [installation.md](installation.md) for additional details on layoutparser installation. 
+## Examples 
+We provide a series of examples for to help you start using the layout parser library: 
+1. [Table OCR and Results Parsing](https://github.com/Layout-Parser/layout-parser/blob/main/examples/OCR%20Tables%20and%20Parse%20the%20Output.ipynb): `layoutparser` can be used for conveniently OCR documents and convert the output in to structured data. 
+2. [Deep Layout Parsing Example](https://github.com/Layout-Parser/layout-parser/blob/main/examples/Deep%20Layout%20Parsing.ipynb): With the help of Deep Learning, `layoutparser` supports the analysis very complex documents and processing of the hierarchical structure in the layouts. 
+## Contributing
+We encourage you to contribute to Layout Parser! Please check out the [Contributing guidelines](.github/CONTRIBUTING.md) for guidelines about how to proceed. Join us!
+## Citing `layoutparser`
+If you find `layoutparser` helpful to your work, please consider citing our tool and [paper](https://arxiv.org/pdf/2103.15348.pdf) using the following BibTeX entry.
+```
+@article{shen2021layoutparser,
+  title={LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis},
+  author={Shen, Zejiang and Zhang, Ruochen and Dell, Melissa and Lee, Benjamin Charles Germain and Carlson, Jacob and Li, Weining},
+  journal={arXiv preprint arXiv:2103.15348},
+  year={2021}
+}
+```
+
+%package -n python3-layoutparser
+Summary:	A unified toolkit for Deep Learning Based Document Image Analysis
+Provides:	python-layoutparser
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-layoutparser
+## What is LayoutParser
+![Example Usage](https://github.com/Layout-Parser/layout-parser/raw/main/.github/example.png)
+LayoutParser aims to provide a wide range of tools that aims to streamline Document Image Analysis (DIA) tasks. Please check the LayoutParser [demo video](https://youtu.be/8yA5xB4Dg8c) (1 min) or [full talk](https://www.youtube.com/watch?v=YG0qepPgyGY) (15 min) for details. And here are some key features:
+- LayoutParser provides a rich repository of deep learning models for layout detection as well as a set of unified APIs for using them. For example, 
+  <details>
+  <summary>Perform DL layout detection in 4 lines of code</summary>
+  ```python
+  import layoutparser as lp
+  model = lp.AutoLayoutModel('lp://EfficientDete/PubLayNet')
+  # image = Image.open("path/to/image")
+  layout = model.detect(image) 
+  ```
+  </details>
+- LayoutParser comes with a set of layout data structures with carefully designed APIs that are optimized for document image analysis tasks. For example, 
+  <details>
+  <summary>Selecting layout/textual elements in the left column of a page</summary>
+  ```python
+  image_width = image.size[0]
+  left_column = lp.Interval(0, image_width/2, axis='x')
+  layout.filter_by(left_column, center=True) # select objects in the left column 
+  ```
+  </details>
+  <details>
+  <summary>Performing OCR for each detected Layout Region</summary>
+  ```python
+  ocr_agent = lp.TesseractAgent()
+  for layout_region in layout: 
+      image_segment = layout_region.crop(image)
+      text = ocr_agent.detect(image_segment)
+  ```
+  </details>  
+  <details>
+  <summary>Flexible APIs for visualizing the detected layouts</summary>
+  ```python
+  lp.draw_box(image, layout, box_width=1, show_element_id=True, box_alpha=0.25)
+  ```
+  </details>  
+  </details>  
+  <details>
+  <summary>Loading layout data stored in json, csv, and even PDFs</summary>
+  ```python 
+  layout = lp.load_json("path/to/json")
+  layout = lp.load_csv("path/to/csv")
+  pdf_layout = lp.load_pdf("path/to/pdf")
+  ```
+  </details>
+- LayoutParser is also a open platform that enables the sharing of layout detection models and DIA pipelines among the community. 
+  <details>
+  <summary><a href="https://layout-parser.github.io/platform/">Check</a> the LayoutParser open platform</summary>
+  </details>
+  <details>
+  <summary><a href="https://github.com/Layout-Parser/platform">Submit</a> your models/pipelines to LayoutParser</summary>
+  </details>
+## Installation 
+After several major updates, layoutparser provides various functionalities and deep learning models from different backends. But it still easy to install layoutparser, and we designed the installation method in a way such that you can choose to install only the needed dependencies for your project:
+```bash
+pip install layoutparser # Install the base layoutparser library with  
+pip install "layoutparser[layoutmodels]" # Install DL layout model toolkit 
+pip install "layoutparser[ocr]" # Install OCR toolkit
+```
+Extra steps are needed if you want to use Detectron2-based models. Please check [installation.md](installation.md) for additional details on layoutparser installation. 
+## Examples 
+We provide a series of examples for to help you start using the layout parser library: 
+1. [Table OCR and Results Parsing](https://github.com/Layout-Parser/layout-parser/blob/main/examples/OCR%20Tables%20and%20Parse%20the%20Output.ipynb): `layoutparser` can be used for conveniently OCR documents and convert the output in to structured data. 
+2. [Deep Layout Parsing Example](https://github.com/Layout-Parser/layout-parser/blob/main/examples/Deep%20Layout%20Parsing.ipynb): With the help of Deep Learning, `layoutparser` supports the analysis very complex documents and processing of the hierarchical structure in the layouts. 
+## Contributing
+We encourage you to contribute to Layout Parser! Please check out the [Contributing guidelines](.github/CONTRIBUTING.md) for guidelines about how to proceed. Join us!
+## Citing `layoutparser`
+If you find `layoutparser` helpful to your work, please consider citing our tool and [paper](https://arxiv.org/pdf/2103.15348.pdf) using the following BibTeX entry.
+```
+@article{shen2021layoutparser,
+  title={LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis},
+  author={Shen, Zejiang and Zhang, Ruochen and Dell, Melissa and Lee, Benjamin Charles Germain and Carlson, Jacob and Li, Weining},
+  journal={arXiv preprint arXiv:2103.15348},
+  year={2021}
+}
+```
+
+%package help
+Summary:	Development documents and examples for layoutparser
+Provides:	python3-layoutparser-doc
+%description help
+## What is LayoutParser
+![Example Usage](https://github.com/Layout-Parser/layout-parser/raw/main/.github/example.png)
+LayoutParser aims to provide a wide range of tools that aims to streamline Document Image Analysis (DIA) tasks. Please check the LayoutParser [demo video](https://youtu.be/8yA5xB4Dg8c) (1 min) or [full talk](https://www.youtube.com/watch?v=YG0qepPgyGY) (15 min) for details. And here are some key features:
+- LayoutParser provides a rich repository of deep learning models for layout detection as well as a set of unified APIs for using them. For example, 
+  <details>
+  <summary>Perform DL layout detection in 4 lines of code</summary>
+  ```python
+  import layoutparser as lp
+  model = lp.AutoLayoutModel('lp://EfficientDete/PubLayNet')
+  # image = Image.open("path/to/image")
+  layout = model.detect(image) 
+  ```
+  </details>
+- LayoutParser comes with a set of layout data structures with carefully designed APIs that are optimized for document image analysis tasks. For example, 
+  <details>
+  <summary>Selecting layout/textual elements in the left column of a page</summary>
+  ```python
+  image_width = image.size[0]
+  left_column = lp.Interval(0, image_width/2, axis='x')
+  layout.filter_by(left_column, center=True) # select objects in the left column 
+  ```
+  </details>
+  <details>
+  <summary>Performing OCR for each detected Layout Region</summary>
+  ```python
+  ocr_agent = lp.TesseractAgent()
+  for layout_region in layout: 
+      image_segment = layout_region.crop(image)
+      text = ocr_agent.detect(image_segment)
+  ```
+  </details>  
+  <details>
+  <summary>Flexible APIs for visualizing the detected layouts</summary>
+  ```python
+  lp.draw_box(image, layout, box_width=1, show_element_id=True, box_alpha=0.25)
+  ```
+  </details>  
+  </details>  
+  <details>
+  <summary>Loading layout data stored in json, csv, and even PDFs</summary>
+  ```python 
+  layout = lp.load_json("path/to/json")
+  layout = lp.load_csv("path/to/csv")
+  pdf_layout = lp.load_pdf("path/to/pdf")
+  ```
+  </details>
+- LayoutParser is also a open platform that enables the sharing of layout detection models and DIA pipelines among the community. 
+  <details>
+  <summary><a href="https://layout-parser.github.io/platform/">Check</a> the LayoutParser open platform</summary>
+  </details>
+  <details>
+  <summary><a href="https://github.com/Layout-Parser/platform">Submit</a> your models/pipelines to LayoutParser</summary>
+  </details>
+## Installation 
+After several major updates, layoutparser provides various functionalities and deep learning models from different backends. But it still easy to install layoutparser, and we designed the installation method in a way such that you can choose to install only the needed dependencies for your project:
+```bash
+pip install layoutparser # Install the base layoutparser library with  
+pip install "layoutparser[layoutmodels]" # Install DL layout model toolkit 
+pip install "layoutparser[ocr]" # Install OCR toolkit
+```
+Extra steps are needed if you want to use Detectron2-based models. Please check [installation.md](installation.md) for additional details on layoutparser installation. 
+## Examples 
+We provide a series of examples for to help you start using the layout parser library: 
+1. [Table OCR and Results Parsing](https://github.com/Layout-Parser/layout-parser/blob/main/examples/OCR%20Tables%20and%20Parse%20the%20Output.ipynb): `layoutparser` can be used for conveniently OCR documents and convert the output in to structured data. 
+2. [Deep Layout Parsing Example](https://github.com/Layout-Parser/layout-parser/blob/main/examples/Deep%20Layout%20Parsing.ipynb): With the help of Deep Learning, `layoutparser` supports the analysis very complex documents and processing of the hierarchical structure in the layouts. 
+## Contributing
+We encourage you to contribute to Layout Parser! Please check out the [Contributing guidelines](.github/CONTRIBUTING.md) for guidelines about how to proceed. Join us!
+## Citing `layoutparser`
+If you find `layoutparser` helpful to your work, please consider citing our tool and [paper](https://arxiv.org/pdf/2103.15348.pdf) using the following BibTeX entry.
+```
+@article{shen2021layoutparser,
+  title={LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis},
+  author={Shen, Zejiang and Zhang, Ruochen and Dell, Melissa and Lee, Benjamin Charles Germain and Carlson, Jacob and Li, Weining},
+  journal={arXiv preprint arXiv:2103.15348},
+  year={2021}
+}
+```
+
+%prep
+%autosetup -n layoutparser-0.3.4
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-layoutparser -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.4-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..67aa8f9
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+c020dd8d304da93b56ade8be594bdcf6  layoutparser-0.3.4.tar.gz