Image_Processing_Tkinter_For_Beginner

Workshop: Image Processing

Learning Outcomes:

Upon successful completion of this workshop, you will have demonstrated the abilities to:

• Understand the knowledge of point operators, linear filters, nonlinear filters in image processing.
• Write a demo program: Color balance, Histogram equalization, Mean filter, Median filter, Gaussian Smoothing.

Introduction

Project Goal

In this exercise, students are asked to write a simple image processing program that has the following basic functions: performing color balance, calculating histogram- performing histogram equalization. Then apply filters like median filter, mean filter, and Gaussian smoothing. Details of the functions are described below:

• Function 1: Color balance, to perform this function, the user needs to enter the necessary parameters to perform color balance. (can use the slider to represent it visually)
• Function 2: Show histogram and enter the necessary information to perform histogram equalization.
• Function 3: Implement the median filter to remove noise in the image(salt and pepper noise)
• Function 4: Implement the Mean filter to remove noise in image (salt and pepper noise)
• Function 5: Implement Gaussian smoothing to perform image smoothing.

Educational Purpose

I want to let you know that this project is intended primarily for educational purposes. While it demonstrates the potential of facial recognition systems for attendance tracking, it is a work in progress and has not undergone extensive testing. As such, it may contain errors, bugs, or limitations.

Issue Reporting

We encourage users to participate in the improvement of this project actively. If you encounter any issues, or errors, or have suggestions for enhancements, please don't hesitate to create an issue report in the repository. Your feedback is valuable in helping us refine and enhance the project.

You can explore the project's code, documentation, and accompanying materials to gain insights into the implementation of real-time facial recognition systems. By contributing to this project or adapting it for your own educational purposes, you can further advance your understanding of computer vision and machine learning.

Please refer to the sections below for instructions on installation, usage, and contributing to the project.

Keep in mind that this project is a work in progress, and your understanding and patience are greatly appreciated as we continue to develop and refine it.

Happy exploring and learning!

Installation

Clone repoitory

git clone https://github.com/wise-warlock/Image_Processing_Tkinter_For_Beginner.git

Intall necessary libraries

pip install -r requirements.txt

Usage

To use this project, follow these steps:

1. Run the application:

python Main.py

2. Click the "load image" button to upload a new image

Contribution

We welcome contributions from the community. To contribute to this project, follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them.
4. Submit a pull request.

Lisence

This project is licensed under the MIT License - see the MIT License file for details.