This repository provides code and notes for a 4-hour course on OpenCV with Python, aimed at individuals looking to learn computer vision fundamentals and applications. It covers installation, basic image manipulation, advanced processing techniques, and culminates in face detection/recognition and a deep learning capstone project.

How It Works

The course utilizes Python and the OpenCV library, augmented by the caer library for high-performance vision tasks and simplified prototyping. It follows a structured curriculum, progressing from fundamental image reading and manipulation to more complex concepts like color space switching, edge detection, and machine learning models for face recognition and image classification.

Quick Start & Requirements

• Primary install: pip install caer
• Prerequisites: Python, OpenCV, caer library.
• Resources: No specific hardware requirements mentioned beyond standard Python execution environments.
• Links: freeCodeCamp.org (course platform)

Highlighted Details

• Comprehensive 4-hour curriculum covering installation to deep learning capstone.
• Utilizes caer library for high-performance vision research and prototyping.
• Includes practical examples for face detection and recognition.
• Capstone project involves building a deep computer vision model for character classification.

Maintenance & Community

• The project is maintained by jasmcaus, who is active on Twitter for course updates.
• A note indicates caer.train_val_split() is deprecated, recommending sklearn.model_selection.train_test_split().

Licensing & Compatibility

• The repository itself does not explicitly state a license. The included code is likely intended for educational purposes.

Limitations & Caveats

The course content is based on specific versions of libraries, and the deprecation notice for caer.train_val_split() suggests potential for breaking changes or the need for manual updates when using newer library versions.