OpenVeda Playbook: OpenCV

Your guide to contributing to the world's most popular library for computer vision and AI.

1. The "Why": Mission & Impact

• The Mission: OpenCV (Open Source Computer Vision Library) is a library of programming functions mainly aimed at real-time computer vision. Its goal is to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in commercial products.
• Your Impact: Your code will be part of the foundational library used in robotics, medical imaging, self-driving cars, and countless AI/ML applications.
• Why it's a Career Supercharger: "OpenCV Contributor" is an elite status for anyone pursuing a career in AI/ML or Computer Vision. It signals a deep understanding of performance-critical code, algorithms, and a challenging technical domain.

2. The "What": Tech Stack

• Core Library: C++. The heart of OpenCV is written in highly optimized, cross-platform C++.
• Primary Wrapper: Python. The most popular way to use OpenCV is through its Python bindings.
• Other Wrappers: Java, MATLAB.
• Key Tools: GitHub for everything.

3. The "How": Your Onboarding Journey

3.1: Join the Community

• Primary Channel (Forum): Most technical discussions happen on their Discourse forum.
  • Link: OpenCV Discourse Forum

3.2: The Setup Guide

• Official Guide: Compiling OpenCV from source can be complex, as it involves many dependencies.
  • Link: Introduction to OpenCV Development
• OpenVeda Pro-Tip: Follow a platform-specific guide (e.g., "Install OpenCV from source on Ubuntu/macOS"). The process is well-documented online. Successfully compiling it is your first major hurdle.

3.3: The Contribution Workflow

• Standard GitHub PR process.
• Key Point: Performance is critical. Any change you make will be scrutinized for its impact on performance. Writing benchmarks and tests is highly encouraged.

4. GSoC History & Focus Areas

• Historical Focus: OpenCV is a GSoC staple. Projects are always deeply technical, focusing on implementing new computer vision algorithms, optimizing existing functions for different hardware (like GPUs), and improving the deep learning module (DNN).
• What Mentors Look For: Strong C++ and Python skills, and a solid mathematical foundation. They want to see that you understand the algorithms you are working on. A link to a personal project where you used OpenCV is a huge plus.

5. Key Repositories to Know

• Main Library: github.com/opencv/opencv
• Extra Modules: github.com/opencv/opencv_contrib

6. Find Your First Task Right Now

• The Golden Link: The "good first issue" label is the designated starting point.
  • Link: "good first issue" tag on GitHub

7. The Unwritten Rules (Mentor Insights)

• Algorithms First, Code Second: Before you write a line of code, make sure you understand the computer vision algorithm you are implementing or fixing. Read the relevant academic paper if one is linked.
• Reproduce the Bug: If you're fixing a bug, your first step should always be to create a minimal, reproducible example that demonstrates the bug.
• Benchmarks are King: If you are proposing a performance improvement, you MUST provide benchmarks that prove your version is faster.