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This project integrates MediaPipe Solutions with Node.js and Express for real-time computer vision tasks. It showcases examples of image segmentation, hand and face detection, and pose detection, with a combined example for all three types of landmark detection.
This project showcases the use of an ESP32-CAM with micro-ROS and an OLED screen to capture an image of a hand. The captured image is encapsulated in a custom micro-ROS message and sent to Huawei Cloud, where a Docker container running ROS2 Humble analyzes it using OpenCV and MediaPipe to identify whether the hand is showing rock, paper, or scissor
HandSync is an innovative application that enables precise computer mouse control using hand gestures. Leveraging computer vision and machine learning, HandSync translates hand movements into cursor actions, providing a seamless and intuitive interaction experience.
This project uses OpenCV and MediaPipe to track hand movements and detect a "crossed hands" gesture. When identified, it triggers an automatic system shutdown. The program operates in real-time via a webcam and effectively performs actions based on hand positions.
This is a program that recognizes hand signs and finger gestures with a simple MLP using the detected key points. MediaPipe is used to estimate the hand poses..
Controlling a Bluetooth speaker by gesture recognition enables users to adjust volume, skip tracks, and perform other functions through hand movements, eliminating the need for physical interaction.
Juego de piedra, papel o tijera implementado en Python usando OpenCV y MediaPipe para la detección de manos. Juega contra la computadora realizando gestos con tu mano frente a la cámara.