/Image_matting

一键批量抠图。两种方法。一.使用remove.bg网站账号免费api额度，二.使用rembg和onnxruntime训练模型本地抠图

Primary LanguagePython

使用说明：

任选其一
main(rebg_type="web") # 更精准，低清晰度，需要网络
main(rebg_type="onnx") # 更快速，更清晰，需要性能

在线apikey，remove.bg网站。临时邮箱 ,可用域名：@bestdefinitions.com

思路来源：https://github.com/huanghaozi/AutoMatting (在此基础上，批量获取，自动切换apikey解决频繁，错误，额度等问题，自动换背景图，规范导入导出文件名，不足之处就是没有大佬的GUI)

  • 必要依赖：pip install -r requiremens.txt

  • 可选依赖：

    安装cv2，pip install opencv-contrib-python==4.5.5.64。适用于:python3.7 python3.8 python3.9 解释器

    安装Pillow，pip install Pillow==9.3.0。适配rembg==2.0.30

以下内容为rembg库官方文档，仅用于存档。

Rembg

Downloads Downloads Downloads License Hugging Face Spaces Streamlit App

Rembg is a tool to remove images background.

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

Requirements

python: >3.7, <3.11

Installation

CPU support:

pip install rembg

GPU support:

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

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)

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.

How to train your own model

If You need more fine tunned models try this: danielgatis/rembg#193 (comment)

Some video tutorials

References

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License

Copyright (c) 2020-present Daniel Gatis

Licensed under MIT License