🤔 Overview Human pose estimation from video plays a critical role in various applications such as quantifying physical exercises, sign language recognition, and full-body gesture control. For example, it can form the basis for yoga, dance, and fitness applications. It can also enable the overlay of digital content and information on top of the physical world in augmented reality. MediaPipe Pose is a ML solution for high-fidelity body pose tracking, inferring 33 3D landmarks and background segmentation mask on the whole body from RGB video frames utilizing our BlazePose research that also powers the ML Kit Pose Detection API. Current state-of-the-art approaches rely primarily on powerful desktop environments for inference, whereas our method achieves real-time performance on most modern mobile phones, desktops/laptops, in python and even on the web.
Firstly I have created a Module "PoseModule.py", this file contains the code which detects our pose, in this file I have created functions for specific tasks in a "class poseDetector()". Short description of functions are -
findPose() - This function detect Pose and show landmarks of your hand and it return image in RGB. getPosition() - This function finds the position of particular landmark of your Pose and it returns a list containing id_of_that_landmark, x_position_of_that_landmark, y_position_of_that_landmark.
Full name : Abbas Ataei
Learning : Python
Gmail : AbbasAtaei.firstname.lastname@example.org
Telegram : https://t.me/Abbasataei_py
More : https://bioly.io/AbbasAtaei
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This project is an open-source project which I made due to sharing my experience around the Python programming language.