This repository provides a comprehensive collection of code examples and tutorials for computer vision and deep learning, primarily using C++ and Python. It targets engineers, researchers, and hobbyists looking to learn and implement various AI and CV techniques, offering practical code for blog posts and articles.

How It Works

The repository is structured around blog posts and articles from LearnOpenCV.com, with each entry typically linking to corresponding code. It covers a vast range of topics, from fundamental OpenCV operations like image manipulation and feature detection to advanced deep learning models for object detection, segmentation, and generative AI. The approach is hands-on, providing runnable code to illustrate concepts and enable practical application.

Quick Start & Requirements

• Installation: Primarily involves cloning the repository and installing Python dependencies via pip install -r requirements.txt for Python examples, or compiling C++ code using CMake.
• Prerequisites: Python 3.x, OpenCV (Python/C++), and various deep learning frameworks (TensorFlow, PyTorch, Keras) are common. Specific articles may require CUDA, specific Python versions, or datasets.
• Resources: Setup time varies by article; some require significant downloads or GPU acceleration.
• Links: LearnOpenCV.com

Highlighted Details

• Extensive coverage of YOLO variants (v1 to v12, NAS) with fine-tuning examples.
• In-depth tutorials on 3D computer vision, SLAM, and Gaussian Splatting.
• Practical implementations of multimodal AI, VLM, and robotic control.
• Numerous guides on deploying and optimizing models for edge devices.
• Explanations and code for cutting-edge research papers in AI and CV.

Maintenance & Community

The repository is actively maintained by Satya Mallick and the LearnOpenCV.com team. Community engagement is fostered through the blog and associated resources.

Licensing & Compatibility

The code is generally provided under a permissive license (e.g., MIT, Apache 2.0) allowing for commercial use and integration into closed-source projects, though specific dependencies might have their own licenses.

Limitations & Caveats

The sheer volume of content means some older articles or code might not reflect the absolute latest best practices or library versions. Some examples may require specific hardware (e.g., GPUs) or complex environment setups.