Rembg

Rembg is a tool to remove images background.

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Requirements

Installation

CPU support:

GPU support:

First of all, you need to check if your system supports the

.

Go to https://onnxruntime.ai and check the installation matrix.

If yes, just run:

Usage as a cli

After the installation step you can use rembg just typing

in your terminal window.

The

command has 4 subcommands, one for each input type:

• i
  for files
• p
  for folders
• s
  for http server
• b
  for RGB24 pixel binary stream

You can get help about the main command using:

As well, about all the subcommands using:

rembg

i

Used when input and output are files.

Remove the background from a remote image

Remove the background from a local file

Remove the background specifying a model

Remove the background returning only the mask

Remove the background applying an alpha matting

Passing extras parameters

rembg

p

Used when input and output are folders.

Remove the background from all images in a folder

Same as before, but watching for new/changed files to process

rembg

s

Used to start http server.

To see the complete endpoints documentation, go to:

.

Remove the background from an image url

Remove the background from an uploaded image

rembg

b

Process a sequence of RGB24 images from stdin. This is intended to be used with another program, such as FFMPEG, that outputs RGB24 pixel data to stdout, which is piped into the stdin of this program, although nothing prevents you from manually typing in images at stdin.

Arguments:

• image_width : width of input image(s)
• image_height : height of input image(s)
• output_specifier: printf-style specifier for output filenames, for example if
  output-%03u.png
  , then output files will be named
  output-000.png
  ,
  output-001.png
  ,
  output-002.png
  , etc. Output files will be saved in PNG format regardless of the extension specified. You can omit it to write results to stdout.

Example usage with FFMPEG:

The width and height values must match the dimension of output images from FFMPEG. Note for FFMPEG, the "

" part is required for the whole thing to work.

Usage as a library

Input and output as bytes

Input and output as a PIL image

Input and output as a numpy array

Force output as bytes

How to iterate over files in a performatic way

To see a full list of examples on how to use rembg, go to the examples page.

Usage as a docker

Just replace the

command for

docker run danielgatis/rembg

.

Try this:

Models

All models are downloaded and saved in the user home folder in the

directory.

The available models are:

• u2net (download, source): A pre-trained model for general use cases.
• u2netp (download, source): A lightweight version of u2net model.
• u2net_human_seg (download, source): A pre-trained model for human segmentation.
• u2net_cloth_seg (download, source): 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, source): Same as u2net but the size is reduced to 43Mb.
• isnet-general-use (download, source): A new pre-trained model for general use cases.
• isnet-anime (download, source): A high-accuracy segmentation for anime character.
• sam (download encoder, download decoder, source): A pre-trained model for any use cases.
• birefnet-general (download, source): A pre-trained model for general use cases.
• birefnet-general-lite (download, source): A light pre-trained model for general use cases.
• birefnet-portrait (download, source): A pre-trained model for human portraits.
• birefnet-dis (download, source): A pre-trained model for dichotomous image segmentation (DIS).
• birefnet-hrsod (download, source): A pre-trained model for high-resolution salient object detection (HRSOD).
• birefnet-cod (download, source): A pre-trained model for concealed object detection (COD).
• birefnet-massive (download, source): A pre-trained model with massive dataset.

How to train your own model

If You need more fine tuned 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=D7W-C0urVcQ

References

• https://arxiv.org/pdf/2005.09007.pdf
• https://github.com/NathanUA/U-2-Net
• https://github.com/pymatting/pymatting

FAQ

When will this library provide support for Python version 3.xx?

This library directly depends on the onnxruntime library. Therefore, we can only update the Python version when onnxruntime provides support for that specific version.

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License

Copyright (c) 2020-present Daniel Gatis

Licensed under MIT License