This repository only contains the blurring algorithms and API.
It is based on YOLOv8 for object detection (faces and license plates) using a custom trained model.
Blurring is done on original JPEG pictures by manipulating low-level MCU in the JPEG raw data, to keep all other parts of the original image unchanged (no decompression/recompression). This also saves CPU usage.
These dependencies are needed for lossless JPEG transformations :
- turbojpeg library and headers
- exiftran
You can install them through your package manager, for example in Ubuntu:
sudo apt install libturbojpeg0-dev libjpeg-turbo-progs exiftranBasic dependencies may also need:
sudo apt install git python-is-python3 python3-pipRunning on a GPU will requires NVidia drivers and Cuda.
You can download code from this repository with git clone:
git clone https://github.com/cquest/sgblur.git
cd sgblur/We use Pip to handle Python dependencies. You can create a virtual environment first:
python -m venv env
source ./env/bin/activateInstall python dependencies for the API:
pip install -r requirements-api.txtThe Web API can be launched with the following command:
uvicorn src.api:app --reloadIt is then accessible on localhost:8000.
A single picture can be blurred using the following HTTP call (here made using curl):
# Considering your picture is called original.jpg
curl -X 'POST' \
'http://127.0.0.1:8000/blur/' \
-F 'picture=@original.jpg' \
--output blurred.jpgExemple using httpie :
http --form POST http://127.0.0.1:8000/blur/ picture@original.jpg --download --output blurred.jpgA demo API is running on https://api.cquest.org/blur/
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
You might want to read more about available blur algorithms.
Copyright (c) GeoVisio/panoramax team 2022-2023, released under MIT license.