%global _empty_manifest_terminate_build 0 Name: python-rembg Version: 2.0.35 Release: 1 Summary: Remove image background License: MIT License URL: https://github.com/danielgatis/rembg Source0: https://mirrors.nju.edu.cn/pypi/web/packages/a7/8c/0ac2711dfd4440225963395d00d4768528cc9c4572539dd4a51df067d159/rembg-2.0.35.tar.gz BuildArch: noarch Requires: python3-aiohttp Requires: python3-asyncer Requires: python3-click Requires: python3-fastapi Requires: python3-filetype Requires: python3-imagehash Requires: python3-numpy Requires: python3-onnxruntime Requires: python3-opencv-python-headless Requires: python3-pillow Requires: python3-pooch Requires: python3-pymatting Requires: python3-multipart Requires: python3-scikit-image Requires: python3-scipy Requires: python3-tqdm Requires: python3-uvicorn Requires: python3-watchdog Requires: python3-onnxruntime-gpu %description # Rembg [![Downloads](https://pepy.tech/badge/rembg)](https://pepy.tech/project/rembg) [![Downloads](https://pepy.tech/badge/rembg/month)](https://pepy.tech/project/rembg) [![Downloads](https://pepy.tech/badge/rembg/week)](https://pepy.tech/project/rembg) [![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://img.shields.io/badge/License-MIT-blue.svg) [![Hugging Face Spaces](https://img.shields.io/badge/🤗%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/KenjieDec/RemBG) [![Streamlit App](https://img.shields.io/badge/🎈%20Streamlit%20Community-Cloud-blue)](https://bgremoval.streamlit.app/) Rembg is a tool to remove images background.

**If this project has helped you, please consider making a [donation](https://www.buymeacoffee.com/danielgatis).** ## Sponsor
Unsplash PhotoRoom Remove Background API
https://photoroom.com/api

Fast and accurate background remover API
## Requirements ``` python: >3.7, <3.11 ``` ## Installation CPU support: ```bash pip install rembg ``` GPU support: First of all, you need to check if your system supports the `onnxruntime-gpu`. Go to https://onnxruntime.ai and check the installation matrix.

If yes, just run: ```bash pip install rembg[gpu] ``` ## Usage as a cli After the installation step you can use rembg just typing `rembg` in your terminal window. The `rembg` command has 3 subcommands, one for each input type: - `i` for files - `p` for folders - `s` for http server You can get help about the main command using: ``` rembg --help ``` As well, about all the subcommands using: ``` rembg --help ``` ### rembg `i` Used when input and output are files. Remove the background from a remote image ``` curl -s http://input.png | rembg i > output.png ``` Remove the background from a local file ``` rembg i path/to/input.png path/to/output.png ``` Remove the background specifying a model ``` rembg i -m u2netp path/to/input.png path/to/output.png ``` Remove the background returning only the mask ``` rembg i -om path/to/input.png path/to/output.png ``` Remove the background applying an alpha matting ``` rembg i -a path/to/input.png path/to/output.png ``` Passing extras parameters ``` rembg i -m sam -x '{"input_labels": [1], "input_points": [[100,100]]}' path/to/input.png path/to/output.png ``` ### rembg `p` Used when input and output are folders. Remove the background from all images in a folder ``` rembg p path/to/input path/to/output ``` Same as before, but watching for new/changed files to process ``` rembg p -w path/to/input path/to/output ``` ### rembg `s` Used to start http server. To see the complete endpoints documentation, go to: `http://localhost:5000/docs`. Remove the background from an image url ``` curl -s "http://localhost:5000/?url=http://input.png" -o output.png ``` Remove the background from an uploaded image ``` curl -s -F file=@/path/to/input.jpg "http://localhost:5000" -o output.png ``` ## Usage as a library Input and output as bytes ```python from rembg import remove input_path = 'input.png' output_path = 'output.png' with open(input_path, 'rb') as i: with open(output_path, 'wb') as o: input = i.read() output = remove(input) o.write(output) ``` Input and output as a PIL image ```python from rembg import remove from PIL import Image input_path = 'input.png' output_path = 'output.png' input = Image.open(input_path) output = remove(input) output.save(output_path) ``` Input and output as a numpy array ```python from rembg import remove import cv2 input_path = 'input.png' output_path = 'output.png' input = cv2.imread(input_path) output = remove(input) cv2.imwrite(output_path, output) ``` How to iterate over files in a performatic way ```python from pathlib import Path from rembg import remove, new_session session = new_session() for file in Path('path/to/folder').glob('*.png'): input_path = str(file) output_path = str(file.parent / (file.stem + ".out.png")) with open(input_path, 'rb') as i: with open(output_path, 'wb') as o: input = i.read() output = remove(input, session=session) o.write(output) ``` To see a full list of examples on how to use rembg, go to the [examples](USAGE.md) page. ## Usage as a docker Just replace the `rembg` command for `docker run danielgatis/rembg`. Try this: ``` docker run danielgatis/rembg i path/to/input.png path/to/output.png ``` ## Models All models are downloaded and saved in the user home folder in the `.u2net` directory. The available models are: - u2net ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2net.onnx), [source](https://github.com/xuebinqin/U-2-Net)): A pre-trained model for general use cases. - u2netp ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2netp.onnx), [source](https://github.com/xuebinqin/U-2-Net)): A lightweight version of u2net model. - u2net_human_seg ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2net_human_seg.onnx), [source](https://github.com/xuebinqin/U-2-Net)): A pre-trained model for human segmentation. - u2net_cloth_seg ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2net_cloth_seg.onnx), [source](https://github.com/levindabhi/cloth-segmentation)): A pre-trained model for Cloths Parsing from human portrait. Here clothes are parsed into 3 category: Upper body, Lower body and Full body. - silueta ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/silueta.onnx), [source](https://github.com/xuebinqin/U-2-Net/issues/295)): Same as u2net but the size is reduced to 43Mb. - isnet-general-use ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/isnet-general-use.onnx), [source](https://github.com/xuebinqin/DIS)): A new pre-trained model for general use cases. - sam ([download encoder](https://github.com/danielgatis/rembg/releases/download/v0.0.0/vit_b-encoder-quant.onnx), [download decoder](https://github.com/danielgatis/rembg/releases/download/v0.0.0/vit_b-decoder-quant.onnx), [source](https://github.com/facebookresearch/segment-anything)): A pre-trained model for any use cases. ### Some differences between the models result
original u2net u2netp u2net_human_seg u2net_cloth_seg silueta isnet-general-use sam
### How to train your own model If You need more fine tunned models try this: https://github.com/danielgatis/rembg/issues/193#issuecomment-1055534289 ## Some video tutorials - https://www.youtube.com/watch?v=3xqwpXjxyMQ - https://www.youtube.com/watch?v=dFKRGXdkGJU - https://www.youtube.com/watch?v=Ai-BS_T7yjE - https://www.youtube.com/watch?v=dFKRGXdkGJU - https://www.youtube.com/watch?v=D7W-C0urVcQ ## References - https://arxiv.org/pdf/2005.09007.pdf - https://github.com/NathanUA/U-2-Net - https://github.com/pymatting/pymatting ## Buy me a coffee Liked some of my work? Buy me a coffee (or more likely a beer) Buy Me A Coffee ## License Copyright (c) 2020-present [Daniel Gatis](https://github.com/danielgatis) Licensed under [MIT License](./LICENSE.txt) %package -n python3-rembg Summary: Remove image background Provides: python-rembg BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-rembg # Rembg [![Downloads](https://pepy.tech/badge/rembg)](https://pepy.tech/project/rembg) [![Downloads](https://pepy.tech/badge/rembg/month)](https://pepy.tech/project/rembg) [![Downloads](https://pepy.tech/badge/rembg/week)](https://pepy.tech/project/rembg) [![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://img.shields.io/badge/License-MIT-blue.svg) [![Hugging Face Spaces](https://img.shields.io/badge/🤗%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/KenjieDec/RemBG) [![Streamlit App](https://img.shields.io/badge/🎈%20Streamlit%20Community-Cloud-blue)](https://bgremoval.streamlit.app/) Rembg is a tool to remove images background.

**If this project has helped you, please consider making a [donation](https://www.buymeacoffee.com/danielgatis).** ## Sponsor
Unsplash PhotoRoom Remove Background API
https://photoroom.com/api

Fast and accurate background remover API
## Requirements ``` python: >3.7, <3.11 ``` ## Installation CPU support: ```bash pip install rembg ``` GPU support: First of all, you need to check if your system supports the `onnxruntime-gpu`. Go to https://onnxruntime.ai and check the installation matrix.

If yes, just run: ```bash pip install rembg[gpu] ``` ## Usage as a cli After the installation step you can use rembg just typing `rembg` in your terminal window. The `rembg` command has 3 subcommands, one for each input type: - `i` for files - `p` for folders - `s` for http server You can get help about the main command using: ``` rembg --help ``` As well, about all the subcommands using: ``` rembg --help ``` ### rembg `i` Used when input and output are files. Remove the background from a remote image ``` curl -s http://input.png | rembg i > output.png ``` Remove the background from a local file ``` rembg i path/to/input.png path/to/output.png ``` Remove the background specifying a model ``` rembg i -m u2netp path/to/input.png path/to/output.png ``` Remove the background returning only the mask ``` rembg i -om path/to/input.png path/to/output.png ``` Remove the background applying an alpha matting ``` rembg i -a path/to/input.png path/to/output.png ``` Passing extras parameters ``` rembg i -m sam -x '{"input_labels": [1], "input_points": [[100,100]]}' path/to/input.png path/to/output.png ``` ### rembg `p` Used when input and output are folders. Remove the background from all images in a folder ``` rembg p path/to/input path/to/output ``` Same as before, but watching for new/changed files to process ``` rembg p -w path/to/input path/to/output ``` ### rembg `s` Used to start http server. To see the complete endpoints documentation, go to: `http://localhost:5000/docs`. Remove the background from an image url ``` curl -s "http://localhost:5000/?url=http://input.png" -o output.png ``` Remove the background from an uploaded image ``` curl -s -F file=@/path/to/input.jpg "http://localhost:5000" -o output.png ``` ## Usage as a library Input and output as bytes ```python from rembg import remove input_path = 'input.png' output_path = 'output.png' with open(input_path, 'rb') as i: with open(output_path, 'wb') as o: input = i.read() output = remove(input) o.write(output) ``` Input and output as a PIL image ```python from rembg import remove from PIL import Image input_path = 'input.png' output_path = 'output.png' input = Image.open(input_path) output = remove(input) output.save(output_path) ``` Input and output as a numpy array ```python from rembg import remove import cv2 input_path = 'input.png' output_path = 'output.png' input = cv2.imread(input_path) output = remove(input) cv2.imwrite(output_path, output) ``` How to iterate over files in a performatic way ```python from pathlib import Path from rembg import remove, new_session session = new_session() for file in Path('path/to/folder').glob('*.png'): input_path = str(file) output_path = str(file.parent / (file.stem + ".out.png")) with open(input_path, 'rb') as i: with open(output_path, 'wb') as o: input = i.read() output = remove(input, session=session) o.write(output) ``` To see a full list of examples on how to use rembg, go to the [examples](USAGE.md) page. ## Usage as a docker Just replace the `rembg` command for `docker run danielgatis/rembg`. Try this: ``` docker run danielgatis/rembg i path/to/input.png path/to/output.png ``` ## Models All models are downloaded and saved in the user home folder in the `.u2net` directory. The available models are: - u2net ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2net.onnx), [source](https://github.com/xuebinqin/U-2-Net)): A pre-trained model for general use cases. - u2netp ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2netp.onnx), [source](https://github.com/xuebinqin/U-2-Net)): A lightweight version of u2net model. - u2net_human_seg ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2net_human_seg.onnx), [source](https://github.com/xuebinqin/U-2-Net)): A pre-trained model for human segmentation. - u2net_cloth_seg ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2net_cloth_seg.onnx), [source](https://github.com/levindabhi/cloth-segmentation)): A pre-trained model for Cloths Parsing from human portrait. Here clothes are parsed into 3 category: Upper body, Lower body and Full body. - silueta ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/silueta.onnx), [source](https://github.com/xuebinqin/U-2-Net/issues/295)): Same as u2net but the size is reduced to 43Mb. - isnet-general-use ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/isnet-general-use.onnx), [source](https://github.com/xuebinqin/DIS)): A new pre-trained model for general use cases. - sam ([download encoder](https://github.com/danielgatis/rembg/releases/download/v0.0.0/vit_b-encoder-quant.onnx), [download decoder](https://github.com/danielgatis/rembg/releases/download/v0.0.0/vit_b-decoder-quant.onnx), [source](https://github.com/facebookresearch/segment-anything)): A pre-trained model for any use cases. ### Some differences between the models result
original u2net u2netp u2net_human_seg u2net_cloth_seg silueta isnet-general-use sam
### How to train your own model If You need more fine tunned models try this: https://github.com/danielgatis/rembg/issues/193#issuecomment-1055534289 ## Some video tutorials - https://www.youtube.com/watch?v=3xqwpXjxyMQ - https://www.youtube.com/watch?v=dFKRGXdkGJU - https://www.youtube.com/watch?v=Ai-BS_T7yjE - https://www.youtube.com/watch?v=dFKRGXdkGJU - https://www.youtube.com/watch?v=D7W-C0urVcQ ## References - https://arxiv.org/pdf/2005.09007.pdf - https://github.com/NathanUA/U-2-Net - https://github.com/pymatting/pymatting ## Buy me a coffee Liked some of my work? Buy me a coffee (or more likely a beer) Buy Me A Coffee ## License Copyright (c) 2020-present [Daniel Gatis](https://github.com/danielgatis) Licensed under [MIT License](./LICENSE.txt) %package help Summary: Development documents and examples for rembg Provides: python3-rembg-doc %description help # Rembg [![Downloads](https://pepy.tech/badge/rembg)](https://pepy.tech/project/rembg) [![Downloads](https://pepy.tech/badge/rembg/month)](https://pepy.tech/project/rembg) [![Downloads](https://pepy.tech/badge/rembg/week)](https://pepy.tech/project/rembg) [![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://img.shields.io/badge/License-MIT-blue.svg) [![Hugging Face Spaces](https://img.shields.io/badge/🤗%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/KenjieDec/RemBG) [![Streamlit App](https://img.shields.io/badge/🎈%20Streamlit%20Community-Cloud-blue)](https://bgremoval.streamlit.app/) Rembg is a tool to remove images background.

**If this project has helped you, please consider making a [donation](https://www.buymeacoffee.com/danielgatis).** ## Sponsor
Unsplash PhotoRoom Remove Background API
https://photoroom.com/api

Fast and accurate background remover API
## Requirements ``` python: >3.7, <3.11 ``` ## Installation CPU support: ```bash pip install rembg ``` GPU support: First of all, you need to check if your system supports the `onnxruntime-gpu`. Go to https://onnxruntime.ai and check the installation matrix.

If yes, just run: ```bash pip install rembg[gpu] ``` ## Usage as a cli After the installation step you can use rembg just typing `rembg` in your terminal window. The `rembg` command has 3 subcommands, one for each input type: - `i` for files - `p` for folders - `s` for http server You can get help about the main command using: ``` rembg --help ``` As well, about all the subcommands using: ``` rembg --help ``` ### rembg `i` Used when input and output are files. Remove the background from a remote image ``` curl -s http://input.png | rembg i > output.png ``` Remove the background from a local file ``` rembg i path/to/input.png path/to/output.png ``` Remove the background specifying a model ``` rembg i -m u2netp path/to/input.png path/to/output.png ``` Remove the background returning only the mask ``` rembg i -om path/to/input.png path/to/output.png ``` Remove the background applying an alpha matting ``` rembg i -a path/to/input.png path/to/output.png ``` Passing extras parameters ``` rembg i -m sam -x '{"input_labels": [1], "input_points": [[100,100]]}' path/to/input.png path/to/output.png ``` ### rembg `p` Used when input and output are folders. Remove the background from all images in a folder ``` rembg p path/to/input path/to/output ``` Same as before, but watching for new/changed files to process ``` rembg p -w path/to/input path/to/output ``` ### rembg `s` Used to start http server. To see the complete endpoints documentation, go to: `http://localhost:5000/docs`. Remove the background from an image url ``` curl -s "http://localhost:5000/?url=http://input.png" -o output.png ``` Remove the background from an uploaded image ``` curl -s -F file=@/path/to/input.jpg "http://localhost:5000" -o output.png ``` ## Usage as a library Input and output as bytes ```python from rembg import remove input_path = 'input.png' output_path = 'output.png' with open(input_path, 'rb') as i: with open(output_path, 'wb') as o: input = i.read() output = remove(input) o.write(output) ``` Input and output as a PIL image ```python from rembg import remove from PIL import Image input_path = 'input.png' output_path = 'output.png' input = Image.open(input_path) output = remove(input) output.save(output_path) ``` Input and output as a numpy array ```python from rembg import remove import cv2 input_path = 'input.png' output_path = 'output.png' input = cv2.imread(input_path) output = remove(input) cv2.imwrite(output_path, output) ``` How to iterate over files in a performatic way ```python from pathlib import Path from rembg import remove, new_session session = new_session() for file in Path('path/to/folder').glob('*.png'): input_path = str(file) output_path = str(file.parent / (file.stem + ".out.png")) with open(input_path, 'rb') as i: with open(output_path, 'wb') as o: input = i.read() output = remove(input, session=session) o.write(output) ``` To see a full list of examples on how to use rembg, go to the [examples](USAGE.md) page. ## Usage as a docker Just replace the `rembg` command for `docker run danielgatis/rembg`. Try this: ``` docker run danielgatis/rembg i path/to/input.png path/to/output.png ``` ## Models All models are downloaded and saved in the user home folder in the `.u2net` directory. The available models are: - u2net ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2net.onnx), [source](https://github.com/xuebinqin/U-2-Net)): A pre-trained model for general use cases. - u2netp ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2netp.onnx), [source](https://github.com/xuebinqin/U-2-Net)): A lightweight version of u2net model. - u2net_human_seg ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2net_human_seg.onnx), [source](https://github.com/xuebinqin/U-2-Net)): A pre-trained model for human segmentation. - u2net_cloth_seg ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2net_cloth_seg.onnx), [source](https://github.com/levindabhi/cloth-segmentation)): A pre-trained model for Cloths Parsing from human portrait. Here clothes are parsed into 3 category: Upper body, Lower body and Full body. - silueta ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/silueta.onnx), [source](https://github.com/xuebinqin/U-2-Net/issues/295)): Same as u2net but the size is reduced to 43Mb. - isnet-general-use ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/isnet-general-use.onnx), [source](https://github.com/xuebinqin/DIS)): A new pre-trained model for general use cases. - sam ([download encoder](https://github.com/danielgatis/rembg/releases/download/v0.0.0/vit_b-encoder-quant.onnx), [download decoder](https://github.com/danielgatis/rembg/releases/download/v0.0.0/vit_b-decoder-quant.onnx), [source](https://github.com/facebookresearch/segment-anything)): A pre-trained model for any use cases. ### Some differences between the models result
original u2net u2netp u2net_human_seg u2net_cloth_seg silueta isnet-general-use sam
### How to train your own model If You need more fine tunned models try this: https://github.com/danielgatis/rembg/issues/193#issuecomment-1055534289 ## Some video tutorials - https://www.youtube.com/watch?v=3xqwpXjxyMQ - https://www.youtube.com/watch?v=dFKRGXdkGJU - https://www.youtube.com/watch?v=Ai-BS_T7yjE - https://www.youtube.com/watch?v=dFKRGXdkGJU - https://www.youtube.com/watch?v=D7W-C0urVcQ ## References - https://arxiv.org/pdf/2005.09007.pdf - https://github.com/NathanUA/U-2-Net - https://github.com/pymatting/pymatting ## Buy me a coffee Liked some of my work? Buy me a coffee (or more likely a beer) Buy Me A Coffee ## License Copyright (c) 2020-present [Daniel Gatis](https://github.com/danielgatis) Licensed under [MIT License](./LICENSE.txt) %prep %autosetup -n rembg-2.0.35 %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-rembg -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 2.0.35-1 - Package Spec generated