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https://github.com/iv4n-ga6l/computervision_foundations

A repo providing a structured path, blending theoretical knowledge with practical experience through projects and exercises for computer vision
https://github.com/iv4n-ga6l/computervision_foundations

artificial-intelligence computer-vision jupyter-notebook opencv projects python roadmap vision-language

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A repo providing a structured path, blending theoretical knowledge with practical experience through projects and exercises for computer vision

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README 

          
# Computer Vision Foundations



![Computer Vision](https://img.shields.io/badge/computer_vision-cyan) ![Roadmap](https://img.shields.io/badge/roadmap-8A2BE2)






This is a comprehensive roadmap to build strong foundation in programming, mathematics, deep learning, and hands-on experience with real-world projects to become a successful Computer Vision Engineer.



## Base Structure



- Phase 1: Prerequisites

  * [Programming Skills](phase%201/Programming%20Skills/README.md)

  * [Mathematics for Computer Vision](phase%201/Mathematics%20for%20Computer%20Vision/README.md)

- Phase 2: Core Concepts of Computer Vision

  * [Basic Image Processing](phase%202/Basic%20Image%20Processing/README.md)

  * [Feature Detection and Matching](phase%202/Feature%20Detection%20and%20Matching/README.md)

  * [Object Detection and Tracking](phase%202/Object%20Detection%20and%20Tracking/README.md)

- Phase 3: Machine Learning in Computer Vision

  * [Classical Machine Learning](phase%203/Classical%20Machine%20Learning/README.md)

  * [Deep Learning Fundamentals](phase%203/Deep%20Learning%20Fundamentals/README.md)

- Phase 4: Advanced Deep Learning for Computer Vision

  * [Convolutional Neural Networks (CNNs)](phase%204/Convolutional%20Neural%20Networks/README.md)

  * [Object Detection and Segmentation](phase%204/Object%20Detection%20and%20Segmentation/README.md)

  * [Image Generation and GANs](phase%204/Image%20Generation%20and%20GANs/README.md)

- Phase 5: Specialized Topics

  * [3D Computer Vision](phase%205/3D%20Computer%20Vision/README.md)

  * [Edge Computing and Deployment](phase%205/Edge%20Computing%20and%20Deployment/README.md)

  * [Research and Advanced Topics](phase%205/Research%20and%20Advanced%20Topics/README.md)



## 📋 Complete Roadmap Structure



This comprehensive roadmap contains **25+ hands-on projects** across 5 phases, designed to take you from beginner to advanced computer vision engineer.



### Phase 1: Prerequisites (6 Projects)

**Programming Skills**

- Project 1: Python fundamentals and OpenCV basics

- Project 2: NumPy for image processing

- Project 3: Data structures and algorithms for CV

- Project 4: Version control and project management

- Project 5: Python optimization and profiling



**Mathematics for Computer Vision**

- Project 1: Linear algebra operations

- Project 2: Probability and statistics

- Project 3: Calculus and optimization

- Project 4: Geometric transformations



### Phase 2: Core Computer Vision (15 Projects)

**Basic Image Processing**

- Project 1: Edge detection (Sobel vs Canny)

- Project 2: Image filtering and enhancement

- Project 3: Morphological operations

- Project 4: Histogram analysis

- Project 5: Video processing basics



**Feature Detection and Matching**

- Project 1: Corner detection (Harris, FAST)

- Project 2: Feature descriptors (SIFT, ORB)

- Project 3: Feature matching and homography

- Project 4: Template matching



**Object Detection and Tracking**

- Project 1: Background subtraction

- Project 2: Optical flow

- Project 3: Template matching

- Project 4: Multi-object tracking

- Project 5: Motion analysis



### Phase 3: Machine Learning in Computer Vision (9 Projects)

**Classical Machine Learning**

- Project 1: k-NN digit recognition (MNIST)

- Project 2: SVM image classification with HOG features

- Project 3: PCA face recognition (Eigenfaces)

- Project 4: K-Means image segmentation

- Project 5: Random Forest texture classification



**Deep Learning Fundamentals**

- Project 1: Neural network from scratch

- Project 2: Image classification with MLP

- Project 3: Optimization algorithms comparison

- Project 4: Regularization techniques



### Phase 4: Advanced Deep Learning (15 Projects)

**Convolutional Neural Networks**

- Project 1: CNN from scratch

- Project 2: Image classification with CNNs

- Project 3: Transfer learning

- Project 4: CNN architectures comparison

- Project 5: Custom dataset classification



**Object Detection and Segmentation**

- Project 1: YOLO object detection

- Project 2: Semantic segmentation with U-Net

- Project 3: Instance segmentation

- Project 4: Custom object detector

- Project 5: Video object tracking



**Image Generation and GANs**

- Project 1: Basic GAN implementation

- Project 2: DCGAN for image generation

- Project 3: Conditional GAN

- Project 4: Style transfer

- Project 5: VAE for image generation



### Phase 5: Specialized Topics (15 Projects)

**3D Computer Vision**

- Project 1: Stereo vision and depth estimation

- Project 2: 3D reconstruction from multiple views

- Project 3: Point cloud processing

- Project 4: 3D object pose estimation

- Project 5: SLAM implementation



**Edge Computing and Deployment**

- Project 1: Model optimization and quantization

- Project 2: Mobile app with computer vision

- Project 3: Edge device deployment

- Project 4: Real-time video processing

- Project 5: Production pipeline



**Research and Advanced Topics**

- Project 1: Vision Transformer implementation

- Project 2: Self-supervised learning

- Project 3: Few-shot learning

- Project 4: Multimodal vision-language model

- Project 5: Independent research project



## 🛠️ Technologies and Tools Used



### Programming Languages

- **Python**: Primary language for all projects

- **C++**: Performance-critical implementations

- **JavaScript**: Web-based demos and visualizations



### Libraries and Frameworks

- **OpenCV**: Computer vision operations

- **NumPy/SciPy**: Mathematical computations

- **Scikit-learn**: Machine learning algorithms

- **TensorFlow/PyTorch**: Deep learning frameworks

- **Matplotlib/Seaborn**: Data visualization



### Specialized Tools

- **CUDA**: GPU acceleration

- **TensorRT**: Model optimization

- **ONNX**: Model interoperability

- **Docker**: Containerization

- **Git**: Version control



## 📈 Learning Progression



### Beginner Level (Phases 1-2)

- Strong programming foundation

- Core computer vision concepts

- Traditional image processing techniques

- Feature extraction and matching



### Intermediate Level (Phase 3)

- Machine learning for computer vision

- Classical ML algorithms

- Introduction to neural networks

- Deep learning fundamentals



### Advanced Level (Phases 4-5)

- Modern deep learning architectures

- State-of-the-art techniques

- Production deployment

- Research-level topics



## 🎯 Career Preparation



### Job Roles Targeted

- **Computer Vision Engineer**

- **Machine Learning Engineer**

- **Research Scientist**

- **AI/ML Consultant**

- **Technical Lead**



### Skills Developed

- End-to-end project development

- Research and implementation abilities

- Production deployment experience

- Problem-solving and debugging

- Code optimization and efficiency



## 📚 Additional Resources



### Books

- "Computer Vision: Algorithms and Applications" by Richard Szeliski

- "Pattern Recognition and Machine Learning" by Christopher Bishop

- "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville



### Online Courses

- CS231n: Convolutional Neural Networks (Stanford)

- CS234: Reinforcement Learning (Stanford)

- Deep Learning Specialization (Coursera)



### Competitions and Challenges

- ImageNet Challenge

- COCO Detection Challenge

- Kaggle Computer Vision Competitions

- Papers With Code Leaderboards



## 🚀 Getting Started



1. **Prerequisites**: Ensure Python 3.8+ is installed

2. **Environment Setup**: Create virtual environment for each project

3. **Dependencies**: Install requirements.txt for each project

4. **Data**: Download required datasets (instructions in each project)

5. **Execution**: Follow project-specific README files



## 📝 Project Structure



Each project follows a consistent structure:

```

Project X/

├── README.md           # Detailed project description

├── main.py            # Main implementation file

├── requirements.txt   # Python dependencies

├── data/             # Dataset directory (if applicable)

├── models/           # Saved models (if applicable)

├── results/          # Output results and visualizations

└── utils/            # Helper functions and utilities

```



## 🤝 Contributing



This is an educational project designed to be:

- **Hands-on**: Every concept includes practical implementation

- **Progressive**: Builds complexity gradually

- **Comprehensive**: Covers breadth and depth of computer vision

- **Current**: Includes latest techniques and best practices



## 📞 Support and Community



- **Issues**: Report bugs or suggest improvements via GitHub issues

- **Updates**: Follow for regular updates and new project additions