Introduction-to-image-processing

Image Processing Basics - IUTCVC

Introduction

Welcome to the Introduction to image processing repository! This repository contains a Jupyter Notebook that introduces fundamental concepts of image processing. The notebook is designed to help students get started with essential techniques and methods used in computer vision.

This project was created for the IUTCVC (Isfahan University of Technology Computer Vision Cup) to provide a solid foundation for participants.

Contents

The Jupyter Notebook covers the following topics:

• Pixel Concept - Understanding the basic unit of digital images
• Flip and Rotation - Transforming images by flipping and rotating
• Color Channels - Extracting and manipulating RGB channels
• Grayscale Images - Converting images to grayscale
• Negative Images - Creating negative versions of images
• Image Stitching - Merging multiple images into one
• Image Resizing - Scaling images up or down
• Adding Images and Watermarking - Overlaying images and embedding watermarks
• PSNR (Peak Signal-to-Noise Ratio) - Evaluating image quality
• Image Formats - Understanding different file formats and their characteristics

Folder Structure

├── Images/        # Contains images used in the Jupyter Notebook
├── Introduction_to_image_processing_Ali_Shakeri.ipynb  # Main Jupyter Notebook with explanations and code
├── README.md      # Documentation file (this file)

Requirements

To run the Jupyter Notebook, you need the following dependencies:

pip install numpy matplotlib opencv-python

Additionally, ensure that you have Jupyter installed:

pip install jupyter

Usage

1. Clone this repository:

   git clone https://github.com/ALISHA8484/Introduction-to-image-processing.git

2. Navigate into the project directory:

   cd Introduction-to-image-processing

3. Launch Jupyter Notebook:

   jupyter notebook

4. Open Introduction_to_image_processing_Ali_Shakeri and start exploring!

Contributions

Contributions are welcome! If you find any issues or have suggestions for improvement, feel free to open an issue or submit a pull request.

License

This project is released under the MIT License. Feel free to use and modify it for educational purposes.

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