This repository provides comprehensive course materials for learning computer vision and deep learning using Python and OpenCV. It targets students and developers seeking practical, hands-on experience with image and video processing techniques, offering a structured curriculum from fundamental operations to advanced recognition tasks.

How It Works

The project is organized into modules, each covering a specific area of computer vision. It utilizes Jupyter notebooks (.ipynb files) to present theoretical concepts alongside practical code examples and exercises. Core libraries include OpenCV for image manipulation and processing, with mentions of deep learning frameworks like TensorFlow for advanced topics.

Quick Start & Requirements

• Installation: Clone the repository and set up a Python environment with necessary libraries.
• Prerequisites: Python, OpenCV, and potentially deep learning libraries (TensorFlow). Jupyter Notebooks are the primary execution environment.
• Resources: Requires a standard development machine capable of running Python and Jupyter. Specific modules might benefit from GPU acceleration for deep learning components.
• Documentation: The repository structure itself serves as documentation, with notebooks detailing each topic.

Highlighted Details

• Covers fundamental OpenCV operations: image I/O, color spaces, morphological transformations, filtering, and video handling.
• Includes advanced modules on geometric transforms, feature detection (SIFT, ORB), image segmentation (GrabCut), and object detection (Haar cascades, HOG).
• Explores computational photography techniques like HDR, seamless cloning, and image inpainting.
• Introduces AI concepts with Persian and English digit classification examples using KNN and OpenCV.

Maintenance & Community

Information regarding maintenance, contributors, or community channels is not available in the provided text.

Licensing & Compatibility

The license type and compatibility notes are not specified in the provided text.

Limitations & Caveats

The repository primarily consists of course materials and example notebooks; it is not presented as a production-ready library or framework. Advanced deep learning components may require significant computational resources. Information on licensing, support, and project maintenance is absent.