Basic CNN Image Processing

This repository contains a Jupyter notebook that demonstrates basic image processing and custom convolution operations, a key component in Convolutional Neural Networks (CNNs). This project is ideal for beginners exploring the fundamentals of CNNs and how they process image data.

🌟 Features

• 📷 Image Loading & Preprocessing: Learn how to load, resize, and convert images for further processing.
• 🧠 Custom Convolution Implementation: Explore a step-by-step guide on implementing the convolution operation from scratch.
• 🔍 Image Visualization: Visualize images before and after applying filters to understand how feature extraction works.

📂 Project Structure

.
├── Basic_CNN.ipynb        # Main notebook with code and explanations
└── README.md              # Project documentation

🚀 Getting Started

1. Clone the repository:

   git clone https://github.com/your-username/Basic-CNN-Image-Processing.git
   cd Basic-CNN-Image-Processing

2. Install the required libraries:

   The code relies on popular Python libraries like NumPy, OpenCV, and Matplotlib. Make sure they are installed:

   pip install numpy opencv-python matplotlib

3. Run the notebook:

   Open the Jupyter notebook to explore the code and visualize the output:

   jupyter notebook Basic_CNN.ipynb

📊 Example Output

Here’s an example of the input image and the result after applying custom convolution:

Input Image	Grayscale Conversion

💡 Concepts Covered

• Convolution operations in image processing
• Understanding how CNNs extract features
• Visualizing the effects of different filters

🛠️ Technologies Used

• Python
• NumPy
• OpenCV
• Matplotlib

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page for open topics.