Rembg is a tool to remove images background.

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

python: >3.7, <3.13

CPU support:

pip install rembg # for library
pip install "rembg[cli]" # for library + cli

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]" # for library
pip install "rembg[gpu,cli]" # for library + cli

After the installation step you can use rembg just typing rembg in your terminal window.

The rembg 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:

rembg --help

As well, about all the subcommands using:

rembg <COMMAND> --help

Used when input and output are files.

Remove the background from a remote image

curl -s https://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

SAM example

rembg i -m sam -x '{ "sam_prompt": [{"type": "point", "data": [724, 740], "label": 1}] }' examples/plants-1.jpg examples/plants-1.out.png

Custom model example

rembg i -m u2net_custom -x '{"model_path": "~/.u2net/u2net.onnx"}' path/to/input.png path/to/output.png

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

Used to start http server.

rembg s --host 0.0.0.0 --port 7000 --log_level info

To see the complete endpoints documentation, go to: https://localhost:7000/api.

Remove the background from an image url

curl -s "https://localhost:7000/api/remove?url=https://input.png" -o output.png

Remove the background from an uploaded image

curl -s -F file=@/path/to/input.jpg "https://localhost:7000/api/remove"  -o output.png

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.

rembg b image_width image_height -o output_specifier

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:

ffmpeg -i input.mp4 -ss 10 -an -f rawvideo -pix_fmt rgb24 pipe:1 | rembg b 1280 720 -o folder/output-%03u.png

The width and height values must match the dimension of output images from FFMPEG. Note for FFMPEG, the "-an -f rawvideo -pix_fmt rgb24 pipe:1" part is required for the whole thing to work.

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)

Force 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, force_return_bytes=True)
        o.write(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.

Just replace the rembg command for docker run danielgatis/rembg.

Try this:

docker run -v path/to/input:/rembg danielgatis/rembg i input.png path/to/output/output.png

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.
• 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.

If You need more fine tuned models try this: #193 (comment)

• 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

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

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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Copyright (c) 2020-present Daniel Gatis

Licensed under MIT License