A Python colors clustering application built with Qt.
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The goal of this project is to build a application to cluster pictures colors by applying K-means and DBScan algortihms.
In the course of data mining, we have seen the use of machine learning, and in particular clustering, with the use of k-Means and DBScan. In order to apply the concepts seen in the course and to have a practical point of view in In order to apply the concepts seen in the course and to have a practical point of view in the course, you are asked to implement a version of K-means and DBScan in the language of your choice.
This project is based on Python. No framework is needed, but you must install the PySide6 package. We strongly suggest your to create a virtual environment to create the app environment. You can also use the Dockerfile to use with Docker.
Before clonning the repository, you should create a new virtual environment (using venv for exemple).
- Clone the project
git clone https://github.com/killian-mahe/agent-aspirateur.git
- Install virtual environment
cd colors-clustering && python3 -m venv env
- Activate the new environment
source env/bin/activate - Install required packages
pip3 install -r requirements.txt
- Start the project
python colors_clustering
See the open issues for a list of proposed features (and known issues).
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE for more information.
Killian Mahé - @killian-mahe - killianmahe.pro@gmail.com
Project Link: https://github.com/killian-mahe/agent-aspirateur