This is a Computer vision package that makes its easy to run Image processing and AI functions. At the core it uses OpenCV and Mediapipe libraries.

Create and control 3D shapes using hand gestures in real-time. Built with mediapipe computer vision and threejs

Control 3D models using hand gestures and voice commands in real-time. Threejs / mediapipe computer vision

This is Eyes :eye: :eye: Tracking Project, here we will use computer vision techniques to extracting the eyes, Mediapipe python modules will provide the face landmarks,

Hand-controlled arpeggiator, drum machine, and audio reactive visualizer. Built with mediapipe computer vision, threejs, tonejs

AirCanvas is a computer vision project that lets you draw using hand gestures. Built with OpenCV and MediaPipe, it tracks your hand movements to simulate basic painting—like MS Paint, but without a mouse.

Python project using OpenCV and Mediapipe to create a Dr. Strange filter. It detects hand gestures, calculates palm midpoint and openness, then overlays a mask if the hand is open. A concise demonstration of computer vision concepts like perspective and image rotation.

application uses computer vision and machine learning to analyze mental health based on facial expressions. The app includes login system, and real-time mental health analysis through facial landmarks, using OpenCV, Mediapipe, and PyQt5. This Application Delegation Paper Competition for

An interactive 3D particle visualization controlled through hand gestures and voice. Built with threejs, mediapipe computer vision, web speech API

In our continuing deep dive into practical real-time computer vision, we’ll show you how to code a hands-free webcam-based controller for your computer mouse using the OpenCV library on Python. This will allow you to control your computer without any physical peripheral required—Iron Man style! In this session, we’ll first obtain our live camera feed using OpenCV and then estimate hand poses using MediaPipe Hands, an open-source framework that employs machine learning to infer 3D landmarks of the hand from single frames in real-time without any fancy hardware acceleration, working even on mobile phones. Following this, we’ll set up our simulated mouse movement in response to the poses using the AutoPy automation module. Prerequisites: ✅ Python (latest release: https://www.python.org/downloads/release/python-395/) ✅ PyCharm (https://www.jetbrains.com/pycharm/download/) or any other Python code editor ✅ pip install: OpenCV (https://pypi.org/project/opencv-python/), MediaPipe, AutoPy ----------------------------------------- To learn more about The Assembly’s workshops, visit our website, social media or email us at workshops@theassembly.ae Our website: http://theassembly.ae Instagram: http://instagram.com/makesmartthings Facebook: http://fb.com/makesmartthings Twitter: http://twitter.com/makesmartthings

The fundamental Computer Vision and Machine Learning Game that uses the Mediapipe library to track the hand gestures of the player.

a simple computer vision project that tracks bicep curls, using mediapipe and open cv

A real-time fall detection system using computer vision with YOLOv12 for person detection and MediaPipe for pose estimation. The system features an intuitive dashboard for monitoring and analyzing falls.

A real-time American Sign Language (ASL) detection system using computer vision and deep learning. This project uses a combination of OpenCV, MediaPipe, and TensorFlow to detect and classify ASL hand signs from camera input. The system can recognize a wide range of ASL characters, and can be used to facilitate communication for sign language users.

Sign.AI is a Sign Language Recognition application built on top of MediaPipe and uses Computer Vision to Recognize Hand Signs from a users video capture device. SignAI is accurate and also has the capability to autocorrect words and phrase sentences using GingerIt. Sign.AI also provides Hindi Translation for predictions using Argostranslate.

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 uses OpenCV and MediaPipe to detect and classify hand gestures in real-time. The system utilizes computer vision and machine learning-based hand tracking to analyze hand landmarks and classify gestures accurately. Ideal for applications in human-computer interaction, gesture-based control, and sign language interpretation.

Toolbar application that leverages OpenCV's powerful computer vision capabilities and MediaPipe's pose detection to create an intelligent posture monitoring system. Running discreetly in your system tray, it provides real-time feedback and gentle reminders when your posture needs attention.

Eye Control Pointer is a computer vision application that tracks eye movements to control the mouse cursor. Using OpenCV, MediaPipe, and PyAutoGUI, it enables hands-free interaction by moving the cursor with eye motion and performing clicks by blinking, enhancing accessibility and usability.

Posture correction using computer vision and Mediapipe library enables the detection and correction of poor posture in images and live videos, promoting a healthier lifestyle in the digital era.

A small project I did while learning about Computer Vision. It enables hand gestures to control functions like light bulbs, computer speakers, mice, and fans using Python and Mediapipe. 🖐️💡🔊🖱️🌬️

FingerDJ is a computer vision-based interactive music player application built using Python, OpenCV, MediaPipe, and Pygame. It allows users to control basic DJ functions such as play, pause, skip to the next track, or return to the previous track using simple hand gestures in front of a webcam — no keyboard or mouse needed!

Computer Vision GUI game where I integrated Hand Gesture Tracking using MediaPipe and OpenCV to save Rick C-137 from Evil Morty.

Computer Vision; Deep LSTM Neural Network implemented with TensorFlow, OpenCV, Keres. LSTM NN was built on MediaPipe's pose & hands models for body detection

A smart, hands-free mouse controller powered by AI. This project leverages computer vision and machine learning libraries such as OpenCV and Mediapipe to enable gesture-based mouse control. Ideal for accessibility and innovative user interaction.

Squat Analyser is a GUI-based real-time computer vision application that uses MediaPipe and OpenCV to assess squat form. It can analyze squats using either a webcam or a video file, providing feedback on common squat issues such as excessive spine flexion, heels lifting off the ground, and knee positioning.

This is Real Time Hand Tracking project of Computer Vision using MediaPipe.

This repository features projects that track physical activities using computer vision, integrating OpenCV with MediaPipe's Pose Estimation module and YOLOv8 for object detection to count repetitions or actions from video inputs.

A COMPUTER VISION PROJECT USING GOOGLE MEDIAPIPE

Computer Vision app to prevent distraction using MediaPipe