Computer Vision Mastery 🎓

A comprehensive, interactive course on Computer Vision built with React. This course covers everything from fundamental concepts to state-of-the-art deep learning techniques, designed for aspiring researchers and practitioners in Machine Learning, Deep Learning, and Computer Vision.

✨ Features

• 📚 Deep Theory: Rigorous mathematical foundations and theoretical concepts
• 📊 Interactive Visualizations: Real-time visualizations to understand complex algorithms
• 💻 Python Code Examples: Practical implementations with OpenCV, NumPy, and deep learning frameworks
• 🧩 Exercises & Quizzes: Test your knowledge and reinforce learning
• 🎨 Beautiful UI: Modern, responsive design built with React and Tailwind CSS
• 🔗 Curated Resources: Links to papers, courses, and additional learning materials

📖 Course Modules

Module 1: Foundations of Computer Vision

• Image formation and camera models
• Pinhole camera and perspective projection
• Color spaces (RGB, HSV, LAB)
• Geometric transformations
• Image histograms and enhancement

Module 2: Image Processing

• Convolution and filtering (Gaussian, median, etc.)
• Edge detection (Sobel, Canny)
• Morphological operations
• Frequency domain analysis (Fourier Transform)
• Interactive filter visualizations

Module 3: Feature Detection & Matching (Coming Soon)

• Harris corner detection
• SIFT and SURF descriptors
• Feature matching algorithms
• RANSAC and robust estimation
• Image alignment and panoramas

Module 4: Deep Learning for Computer Vision (Coming Soon)

• Convolutional Neural Networks (CNNs)
• Classic architectures (LeNet, AlexNet, VGG, ResNet)
• Training techniques and optimization
• Transfer learning
• Data augmentation

Module 5: Object Detection & Recognition (Coming Soon)

• Two-stage detectors (R-CNN family)
• Single-shot detectors (YOLO, SSD)
• Modern architectures (EfficientDet, DETR)
• Evaluation metrics (mAP, IoU)

Module 6: Image Segmentation (Coming Soon)

• Semantic segmentation (FCN, U-Net)
• Instance segmentation (Mask R-CNN)
• Panoptic segmentation
• Medical image segmentation

Module 7: 3D Vision & Geometry (Coming Soon)

• Epipolar geometry
• Stereo vision and depth estimation
• Structure from Motion (SfM)
• 3D reconstruction
• SLAM fundamentals

Module 8: Motion & Tracking (Coming Soon)

• Optical flow (Lucas-Kanade, Farneback)
• Object tracking algorithms
• Multi-object tracking
• Video analysis and action recognition

Module 9: Advanced Topics (Coming Soon)

• Vision Transformers (ViT, DETR, Swin)
• Generative models for vision (GANs, Diffusion Models)
• Self-supervised learning
• Neural Radiance Fields (NeRF)
• Latest research trends

🚀 Getting Started

Prerequisites

• Node.js (version 16 or higher)
• npm or yarn package manager

Installation

1. Clone the repository

   git clone https://github.com/expigo/computer-vision.git
   cd computer-vision
   

2. Install dependencies

   npm install
   

3. Start the development server

   npm run dev
   

4. Open your browser Navigate to http://localhost:3000

Build for Production

npm run build
npm run preview

🛠️ Tech Stack

• Frontend Framework: React 18.3
• Build Tool: Vite
• Styling: Tailwind CSS
• Routing: React Router v6
• Animations: Framer Motion
• Code Highlighting: React Syntax Highlighter
• Math Rendering: KaTeX
• Icons: Lucide React
• Charts: Recharts

📚 Python Environment Setup

For running the code examples in this course:

# Create a virtual environment
python -m venv cv_env
source cv_env/bin/activate  # On Windows: cv_env\Scripts\activate

# Install required packages
pip install opencv-python numpy scipy matplotlib
pip install torch torchvision  # For deep learning modules
pip install scikit-learn scikit-image
pip install jupyter notebook

🎯 Learning Path

For Beginners

1. Start with Module 1 (Foundations)
2. Work through Module 2 (Image Processing)
3. Practice with provided exercises
4. Implement code examples from scratch

For Intermediate Learners

1. Review Modules 1-2 quickly
2. Focus on Modules 3-4 (Features & Deep Learning)
3. Implement projects combining multiple concepts
4. Explore additional resources

For Advanced Learners

1. Skim fundamentals in Modules 1-4
2. Deep dive into Modules 5-9 (Advanced topics)
3. Read referenced research papers
4. Contribute to open-source CV projects

📝 Project Structure

computer-vision/
├── src/
│   ├── components/
│   │   ├── layout/         # Navbar, Footer, Layout
│   │   ├── code/           # Code blocks with syntax highlighting
│   │   ├── quiz/           # Interactive quiz components
│   │   └── visualizations/ # Interactive CV visualizations
│   ├── pages/
│   │   ├── Home.jsx        # Landing page
│   │   └── modules/        # Course modules
│   ├── utils/              # Utility functions
│   ├── hooks/              # Custom React hooks
│   ├── data/               # Course content data
│   ├── App.jsx             # Main app component
│   └── main.jsx            # Entry point
├── public/                 # Static assets
├── notebooks/              # Jupyter notebooks
├── exercises/              # Exercise solutions
└── README.md

🤝 Contributing

Contributions are welcome! This is an educational project aimed at helping people learn Computer Vision.

How to Contribute

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

Areas for Contribution

• Adding more interactive visualizations
• Creating additional exercises and quizzes
• Improving code examples
• Writing Jupyter notebook tutorials
• Fixing typos or improving explanations
• Adding support for additional languages

📚 Recommended Books

• Computer Vision: Algorithms and Applications by Richard Szeliski
• Deep Learning by Goodfellow, Bengio, and Courville
• Multiple View Geometry in Computer Vision by Hartley and Zisserman
• Programming Computer Vision with Python by Jan Erik Solem

🎓 Recommended Courses

• Stanford CS231n: CNNs for Visual Recognition
• Coursera: Deep Learning Specialization
• First Principles of Computer Vision
• PyImageSearch Courses

📄 License

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

🙏 Acknowledgments

• OpenCV community for excellent documentation
• Stanford CS231n for inspiration
• All the researchers and educators in the Computer Vision community
• Contributors to open-source CV libraries and frameworks

📧 Contact

For questions, suggestions, or feedback:

• Open an issue on GitHub
• Star the repository if you find it helpful!

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Made with ❤️ for the Computer Vision community

Happy Learning! 🚀