Remove image background
Reason this release was yanked:
*.onnx files are not included
Project description
Rembg (AWS Lambda)
This is a stripped-down fork of
danielgatis/rembg
designed for AWS Lambda environments.
rembg-aws-lambda
is a tool to remove images background.
Requirements
python: >3.7, <3.11
Installation
CPU support:
pip install rembg-aws-lambda
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-aws-lambda[gpu]
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: 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
License
Copyright (c) 2020-present Daniel Gatis
Licensed under MIT License
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