🕺Full body detection and tracking

✌️Using this you can control your PC/Laptop volume by Hand Gestures created with Python.

Revolutionizing motion capture with real-time AI-powered animation workflows! Akira-app combines Google MediaPipe's cutting-edge pose estimation with Electron's cross-platform capabilities to deliver professional-grade 3D animation tools for creators across industries - from MMD enthusiasts to game developers.

EVERYTHING YOU NEED FOR DATA SCIENCE.

A Hand Gesture Volume Control application made using OpenCV & MediaPipe

Hands Detection Using Artificial Intellience With the help of Media Pipe.

Selfie Segmentation Using Artificial Intelligence With the Help of Media Pipe.

[MIPR2024] Official repository for "Device-Agnostic Remote Range-of-motion Assessment using Data Abstraction"

Face blur effect coded in python for desktop application.

Use computer vision to control your phone click & scroll without touching the screen (Kotlin, Jetpack Compose, CameraX, and MediaPipe).

Python Module to detect and track hands in real-time using a webcam feed.

A curated list of face shape detectors, facial symmetry analyzers, golden ratio calculators, and resources for understanding facial proportions. Tools, research, hairstyle guides, glasses fitting, and developer resources for face analysis.

Control the volume with hand gesture, written in python.

This repository is home to an exploration in the field of Facial Recognition using Convolutional Neural Networks. It is based on the performance comparison between different models such as ResNet50, MobileNetV3, InceptionV3, EfficientNet, and VGG16. The models were trained using two types of losses - Triplet Loss and Categorical Cross Entropy.

This is a submission for the DevFest 2021 Algiers Google's hackathon. (3rd place)

Getting hand landmarks data by using Mediapipe, which feed into deep learning model and put it on real-time.

Project MEDIBLES brings you the platform where most of the post discharge care is being taken care of.

A simplified home automation prototype that uses real-time hand gesture recognition to control electrical appliances like lights and fans. This project is built using ESP32, Python, OpenCV, and MediaPipe for gesture detection, and a simple web server to trigger appliance control via HTTP.

Transfer files through the air with just a gesture. Push. Pull. Done.