OpenCV Image Processing Tutorial 🖼️

A comprehensive Jupyter notebook tutorial demonstrating fundamental image processing techniques using OpenCV and Python. This project covers essential computer vision operations including color space conversions, blurring techniques, edge detection, and thresholding.

📋 Table of Contents

• Features
• Prerequisites
• Installation
• Usage
• Project Structure
• Techniques Covered
• Sample Images
• Contributing
• License

✨ Features

• Color Space Conversions: RGB, BGR, Grayscale, and HSV transformations
• Image Blurring: Average, Gaussian, and Median blur techniques
• Edge Detection: Sobel and Canny edge detection algorithms
• Thresholding: Binary, adaptive, and Otsu thresholding methods
• Interactive Visualizations: Side-by-side comparisons with matplotlib
• Well-documented Code: Step-by-step explanations for beginners

🔧 Prerequisites

• Python 3.7 or higher
• Basic understanding of Python programming
• Familiarity with NumPy arrays (helpful but not required)

🚀 Installation

1. Clone the Repository

git clone https://github.com/rithin-rajpoot/opencv-image-processing.git
cd opencv-image-processing

2. Create a Virtual Environment

On Windows:

python -m venv venv
venv\Scripts\activate

On macOS/Linux:

python3 -m venv venv
source venv/bin/activate

3. Install Required Dependencies

pip install opencv-python matplotlib numpy jupyter

4. Launch Jupyter Notebook

jupyter notebook

Navigate to the notebooks/ directory and open image_processing.ipynb.

💻 Usage

1. Start the Jupyter Notebook: Open notebooks/image_processing.ipynb
2. Run Cells Sequentially: Execute each cell to see the image processing techniques in action
3. Experiment: Try different parameters or use your own images from the images/ folder
4. Learn: Follow the detailed explanations and comments in each section

Quick Start Example

import cv2
from matplotlib import pyplot as plt

# Load an image
img = cv2.imread('images/jeep.jpg')

# Convert BGR to RGB for correct matplotlib display
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# Convert to grayscale
gray = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2GRAY)

# Display results
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.imshow(img_rgb)
plt.title('Original Image')
plt.axis('off')

plt.subplot(1, 2, 2)
plt.imshow(gray, cmap='gray')
plt.title('Grayscale Image')
plt.axis('off')
plt.show()

📁 Project Structure

opencv-image-processing/
│
├── images/                     # Sample images for processing
│   ├── buildings.png
│   ├── camera.jpeg
│   ├── jeep.jpg
│   └── Stone- (69).jpg
│
├── notebooks/                  # Jupyter notebooks
│   └── image_processing.ipynb  # Main tutorial notebook
│
├── venv/                       # Virtual environment (auto-generated)
│
├── .gitignore                  # Git ignore file
└── README.md                   # This file

🎯 Techniques Covered

1. Image Loading and Color Space Conversion

• Loading images with OpenCV
• BGR ↔ RGB conversion
• RGB → Grayscale conversion
• RGB → HSV conversion

2. Image Blurring/Smoothing

• Average Blur: Simple neighborhood averaging
• Gaussian Blur: Weighted averaging with Gaussian kernel
• Median Blur: Noise reduction while preserving edges

3. Edge Detection

• Sobel Edge Detection: Gradient-based edge detection (X and Y directions)
• Canny Edge Detection: Multi-stage edge detection algorithm

4. Thresholding

• Binary Thresholding: Simple black and white conversion
• Adaptive Thresholding: Local threshold adaptation
• Otsu's Thresholding: Automatic threshold selection

🖼️ Sample Images

The project includes several sample images in the images/ folder:

• jeep.jpg - Vehicle image for general processing
• buildings.png - Architecture for edge detection
• camera.jpeg - Object detection practice
• Stone- (69).jpg - Texture analysis

Feel free to add your own images to experiment with different techniques!

🔍 Learning Path

1. Beginners: Start with color space conversions and basic blurring
2. Intermediate: Move to edge detection and understand different algorithms
3. Advanced: Experiment with thresholding and combine multiple techniques

🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Ideas for Contributions:

• Add more image processing techniques
• Include more sample images
• Improve documentation
• Add performance comparisons
• Create additional tutorial notebooks

📚 Additional Resources

• OpenCV Documentation
• OpenCV Python Tutorials
• Computer Vision Basics

📄 License

This project is open source and available under the MIT License.

👤 Author

Rithin Rajpoot

• GitHub: @rithin-rajpoot

🙏 Acknowledgments

• OpenCV community for the excellent computer vision library
• Matplotlib for powerful visualization capabilities
• Sample images from various sources for educational purposes

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Happy learning! 🚀