A centralized collection of Python-based algorithms and implementations for Computer Vision (CV), complete with ready-to-run notebooks, resources, and technical references.
Welcome to Computer Vision (CV)! This repository provides a structured toolkit of foundational and advanced computer vision techniques implemented in Python. Designed for students, researchers, and developers, the project offers clean, reproducible, and easily executable environments to explore Image Processing and ML Models.
| Pillar | Description |
|---|---|
| 🎯 Practical Learning | Direct access to hands-on code for core concepts. |
| ⚡ Zero Setup | Fully configured to run instantly via Google Colab. |
| 📈 From Basics to Advanced | Covers everything from pixel manipulation to HOG and SVM detection. |
| 📚 Well-Documented | Includes references and explanations for each implemented algorithm. |
All algorithms were developed and tested using Google Colab notebooks (.ipynb), ensuring accessibility without heavy local dependencies.
- Core Language: Python
- Environment: Google Colab
- Key Libraries: OpenCV, NumPy, Matplotlib, scikit-image, TensorFlow / PyTorch
- Browse through the
Algorithms/directory. - Click on any notebook (
.ipynb) of your choice. - Open it directly in Google Colab to execute, tweak, and experiment with the code in real time.
Contributions, suggestions, and improvements are always welcome! If you want to add a new algorithm or optimize an existing one:
- Fork the Project.
- Create your Feature Branch (
git checkout -b feature/AmazingAlgorithm). - Commit your Changes (
git commit -m 'Add some AmazingAlgorithm'). - Push to the Branch (
git push origin feature/AmazingAlgorithm). - Open a Pull Request.
Distributed under the MIT License. See LICENSE for more information.