rembg 2.0.35

Remove image background

Rembg

Rembg is a tool to remove images background.

If this project has helped you, please consider making a donation.

Sponsor

PhotoRoom Remove Background API https://photoroom.com/api Fast and accurate background remover API

Requirements

python: >3.7, <3.11

Installation

CPU support:

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:

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 <COMMAND> --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

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

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

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

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 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, 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.
• sam (download encoder, download decoder, source): 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

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License

Copyright (c) 2020-present Daniel Gatis

Licensed under MIT License