removebg-infusiblecoder 0.0.15

Remove image background

removebg_infusiblecoder

removebg_infusiblecoder is a tool to remove images background.

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Requirements

python: >3.7, <3.11

Installation

CPU support:

pip install removebg-infusiblecoder # for library
pip install removebg-infusiblecoder[cli] # for library + cli

GPU support:

pip install removebg-infusiblecoder[gpu] # for library
pip install removebg-infusiblecoder[gpu,cli] # for library + cli

Usage as a cli

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

The removebg_infusiblecoder 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:

removebg_infusiblecoder --help

As well, about all the subcommands using:

removebg_infusiblecoder <COMMAND> --help

removebg_infusiblecoder i

Used when input and output are files.

Remove the background from a remote image

curl -s http://input.png | removebg_infusiblecoder i > output.png

Remove the background from a local file

removebg_infusiblecoder i path/to/input.png path/to/output.png

Remove the background specifying a model

removebg_infusiblecoder -m u2netp i path/to/input.png path/to/output.png

Remove the background returning only the mask

removebg_infusiblecoder -om i path/to/input.png path/to/output.png

Remove the background applying an alpha matting

removebg_infusiblecoder i -a path/to/input.png path/to/output.png

Passing extras parameters

SAM example

removebg_infusiblecoder 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

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

removebg_infusiblecoder p

Used when input and output are folders.

Remove the background from all images in a folder

removebg_infusiblecoder p path/to/input path/to/output

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

removebg_infusiblecoder p -w path/to/input path/to/output

removebg_infusiblecoder s

Used to start http server.

removebg_infusiblecoder s --host 0.0.0.0 --port 5000 --log_level info

To see the complete endpoints documentation, go to: http://localhost:5000/api.

Remove the background from an image url

curl -s "http://localhost:5000/api/remove?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/api/remove"  -o output.png

removebg_infusiblecoder 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.

removebg_infusiblecoder 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 | removebg_infusiblecoder 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.

Usage as a library

Input and output as bytes

from removebg_infusiblecoder 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 removebg_infusiblecoder 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 removebg_infusiblecoder 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 removebg_infusiblecoder 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)

Usage as a docker

All builds can be found from this link https://hub.docker.com/r/syedusama5556/removebg_infusiblecoder/tags

Just replace the removebg_infusiblecoder command for docker run syedusama5556/removebg_infusiblecoder.

Try this:

docker run syedusama5556/removebg_infusiblecoder 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:

• modnet (download, source): A pre-trained model for general use cases modnet.

• isnet-general-use (download, source): A pre-trained model for general use cases isnet-general-use.

• 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.

How to train your own model

If You need more fine tunned models try this: https://github.com/syedusama5556/removebg_infusiblecoder/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) 2024-present Syed Usama Ahmad

Licensed under MIT License