P7. Finger Counter Using OpenCV and Mediapipe
#OpenCV #ComputerVision #AI #MachineLearning
This project utilizes Python 3, OpenCV, and MediaPipe to build a real-time finger counting system that detects and tracks hand movements, analyzes finger positions, and accurately counts the number of extended fingers. The system highlights the power of computer vision and pre-trained AI models in creating gesture-based applications.
🔧 Tools and Libraries Used
• Python 3: Python serves as the programming language for this project due to its simplicity, versatility, and wide range of libraries available for machine learning, computer vision, and real-time processing.
• OpenCV (Open Source Computer Vision Library): OpenCV is a powerful library used for capturing, processing, and displaying video streams.
• MediaPipe: MediaPipe is a framework developed by Google for building multimodal machine learning pipelines.
📋 Project Workflow
The finger counting system follows a structured workflow to process the video stream, detect hand landmarks, analyze finger positions, and display the count in real-time. Here is a step-by-step breakdown:
1. Video Capture
2. Hand Detection and Tracking
3. Landmark Analysis for Finger Counting
4. Display the Results
5. Real-Time Visualization
✨ Let’s Collaborate!
I’m passionate about improving my projects and learning from the community! 😊
Have suggestions for enhancing this Finger Counter detection system? Or do you have ideas for creative use cases? Let’s connect! Share your thoughts and feedback in the comments below—I’d love to explore new possibilities together!
🔗 Explore the project here: https://github.com/iamramzan/P8.-Fing...
