This repository contains code for a real-time hand gesture recognition system using MediaPipe and OpenCV. The project enables users to control music playback by detecting hand gestures captured through a webcam.

This repository contains a Python-based hand gesture recognition system that allows users to control applications using simple hand gestures. The system leverages the MediaPipe framework for real-time hand tracking and finger counting. Users can interact with their computers hands-free, making it ideal for presentations, media playback, and more.

controlling LEDs using fingers with the help of arduino

The Hand Tracking Module is easy to be integrated within any project. It is based on Python 3.9 and 3.8 and supports python 3.9 and above. The module uses extensive libraries such as newly launched OpenCV 4.6 for best results and Mediapipe 0.8 to track hand movements and points more specifically. The applications which include volume control, gesture control and mouse pointer control uses libraries Pycaw, autopy, etc.

Program berbasis web pengenalan gerakan jari secara real-time yang dibuat menggunakan Streamlit dan OpenCV. Mendeteksi gerakan tangan lalu menghitung jumlah jari, dan memberikan umpan balik audio secara langsung.

This Python script utilizes the OpenCV library to perform real-time hand gesture recognition using a webcam. It employs a pre-trained hand detection model from the HandTrackingModule to detect and track landmarks on the hand.

This program is used to count the number of fingers open of your hand using openCV

This is a computer vision-based raised finger counter program that utilizes the MediaPipe library to identify hand landmarks and extract relevant information to count the number of raised fingers in a live webcam feed.

This program is used to count the number of fingers open in your hand using openCV

Starter Modules of Computer Vision ✨

Game that can be played with the help of a finger counter and hand tracking

An AI-powered Virtual Gym Trainer 🏋️ using OpenCV and MediaPipe that counts your exercise repetitions (e.g., bicep curls) by analyzing your form. It provides feedback by counting full reps when the full angle is reached and partial reps (e.g., 0.5) for halfway movements.

Machine Learning based program to trace your Hands and recognize your Fingers. Uses PC/Laptop Webcam for live detection.

A simple yet powerful AI-powered tool that counts how many fingers you're holding up using your webcam in real time! Built with Python, OpenCV, and MediaPipe, this project is a great starting point for learning gesture recognition.