This repository serves as a centralized collection of Python-based algorithms and implementations for Computational Vision (CV). In addition to the codes, links and references to others resources.
The primary objective of this repository is to:
- Consolidate and showcase foundational and advanced CV techniques implemented in Python.
- Provide a clear, executable, and reproducible environment for learning and experimentation.
- Offer direct access to code, external resources, and references related to each algorithm.
All algorithms were developed and tested using Google Colab notebooks. This ensures that the code is easily accessible, runnable, and requires minimal setup for users.
- Python: The core programming language.
- Libraries: Common CV and Data Science libraries, including OpenCV, NumPy, Matplotlib, scikit-image, TensorFlow/PyTorch, etc.
- Google Colab: The primary execution environment (IPYNB notebooks).
This repository is organized to cover a wide range of CV topics. Key areas you will find include:
- Image Processing Fundamentals:
- Filters (e.g., Gaussian, Median, Sobel)
- Color Space Manipulation
- Thresholding and Segmentation
- Feature Detection and Extraction:
- Edge Detection (e.g., Canny, Hough Transform)
- Keypoint Descriptors (e.g., SIFT, ORB)
- Object Detection and Recognition:
- Implementations of classic or simple Machine Learning models for CV (if applicable).
- Other Resources:
- A dedicated section/file for links, academic papers, and external tutorials that inspired or relate to the implementations.
- Awesome Computer Vision Repository: https://github.com/jbhuang0604/awesome-computer-vision