- Fork the repo
- clone it on your system
- create a new branch (git checkout -b assignment_3)
- make the required changes in the python file (main.py)
- push your changes on github
- make a pull request
Navigate to the folder and run the following command to install the required python libraries
pip install -r requirements.txt
Design and implement a computer vision system using the MediaPipe library to detect and classify whether a hand in a video frame is left or right. This assignment aims to leverage the precise hand landmarks provided by MediaPipe to develop an accurate and real-time solution for distinguishing between left and right hands in various hand poses and orientations.
MediaPipe is an open-source Python library developed by Google that provides a comprehensive suite of tools for building applications that process and analyze multimedia data, such as images and videos. It offers a wide range of pre-built machine learning models and pipelines for tasks like facial recognition, hand tracking, pose estimation, object detection, and more. MediaPipe simplifies the development of computer vision, making it accessible to developers with various levels of expertise. It is often used in applications related to augmented reality, gesture recognition, and real-time tracking, among others.
In MediaPipe, hand landmarks refer to the precise points or landmarks detected on a human hand in an image or video. The library's Hand module provides a machine learning model that can estimate the 21 key landmarks on a hand, including the tips of each finger, the base of the palm, and various points on the fingers and hand. These landmarks can be used for various applications, such as hand tracking, gesture recognition, sign language interpretation, and virtual reality interactions. MediaPipe's hand landmarks model makes it easier for developers to create applications that can understand and respond to hand movements and gestures in real-time.
