Detect-Hand-Poses-Using-OpenCV-and-MediaPipe

In this project, I developed a concise yet effective solution for hand detection and tracking using MediaPipe. The focus was on creating a fast, real-time system that recognizes and tracks hand poses with minimal code complexity.

🔧 Tools and Libraries Used

• Python 3: The primary programming language for scripting and project development, offering a vast ecosystem of libraries.
• NumPy: Utilized for efficient handling and manipulation of numerical data arrays, making operations on image frames seamless.
• OpenCV: A versatile open-source library for real-time computer vision tasks. It enables smooth video frame capture and processing, a critical aspect of this project.
• MediaPipe: A robust framework by Google for building multimodal machine learning pipelines. In this project, it powers the real-time hand tracking and landmark detection, providing accurate and reliable results.

📋 Project Workflow

1. Import Required Libraries Begin by importing Python libraries such as NumPy, OpenCV, and MediaPipe for video processing and hand tracking.

2. Make Real-Time Detections Capture video feed in real-time, process frames, and use MediaPipe to detect hand landmarks dynamically.

3. Apply MediaPipe Hand Pose Leverage MediaPipe's hand pose detection module to identify key points on the hands, enabling tracking of hand gestures and movements.

✨ Let’s Collaborate!

I’m always open to improving my projects and learning from others. 😊 Do you have ideas for enhancing this hand detection system? Or perhaps a cool application for it? Drop your thoughts and suggestions in the comments below—I’d love to hear them!