Удаление фона изображения python

Rembg

First of all, you need to check if your system supports the onnxruntime-gpu .

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

pip install rembg pip install rembg 

Usage as a 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:

As well, about all the subcommands using:

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 '' path/to/input.png path/to/output.png 

rembg i -m u2net_custom -x '' 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/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 

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.

rembg b image_width image_height -o output_specifier 

• 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.

Usage as a library

Input and output as bytes

Input and output as a PIL image

Input and output as a numpy array

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 rembg command for docker run danielgatis/rembg .

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.

• 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

Some video tutorials

References

Buy me a coffee

Liked some of my work? Buy me a coffee (or more likely a beer)

License

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Rembg is a tool to remove images background

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danielgatis/rembg

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README.md

Rembg is a tool to remove images background.

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

Fast and accurate background remover API

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

First of all, you need to check if your system supports the onnxruntime-gpu .

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

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:

As well, about all the subcommands using:

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 '' path/to/input.png path/to/output.png 

rembg i -m u2net_custom -x '' 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.

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 

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 

• 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)

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 .

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

All models are downloaded and saved in the user home folder in the .u2net directory.

• 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 tuned models try this: #193 (comment)

Liked some of my work? Buy me a coffee (or more likely a beer)

Источник