A PoC of a tool designed for benchmarking parallelizable image processing algorithms using Python and the multiprocessing module.
It has no real practical use because Python does not offer optimal performance for image processing. It was simply faster to implement in Python than in C++.
Table of Contents
MultiProcess Image Processing is a tool designed for benchmarking parallelizable image processing algorithms using Python and the multiprocessing module. This tool enables users to assess the performance and efficiency of different image processing techniques when executed in parallel on multiple cores.
The tool provides a user-friendly interface that allows users to input their image, select a processing algorithm and specify the desired number of processes to be utilized. It then automatically divides the image data into manageable chunks and assigns them to separate processes for parallel execution.
The tool is written in Python and uses the multiprocessing module to parallelize the execution of the image processing algorithms. It also uses the Matplotlib library to display the results and the CustomTkinter library to create the user interface.
This tool was developed as part of a final project for the course "Multimedia" at the University of Study of Catania, Department of Mathematics and Computer Science.
It is compatible with Windows, Linux and MacOS.
This tool requires Python 3.9 or higher to run. You can download the latest version of Python here.
First of all, clone the repository anywhere on your pc:
git clone https://github.com/LightDestory/MultiProcessImageProcessingBench
Now, to install this tool you need to install the dependencies:
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Install the requirements using
pip(create avenvif you want):`pip install -r requirements.txt`
You can run the tool by executing the main.py file:
python main.py
The content of this repository is distributed under the GNU GPL-3.0 License. See LICENSE for more information.