A real time Biceps curl counter using Streamlit and the Mediapipe's Pose Estimation Model along with OpenCV.

As a beginner, we frequently deal with inappropriate exercise postures, which can result in little benefit or, in the worst-case scenario, undesired muscle injury and strain. The goal of the counter is to alert the user of insufficient range of motion in real time so that he or she may rectify it without the assistance of a human exercising partner.

A suitable Python 3.x environment with a recent version of Mediapipe is required. Development and testing was done with Conda Python 3.6.8 on Windows. All prerequisitites can be found in the requirements.txt file. It is adviced that you install them in a virtual environment.

• Installation in Virtual Environment

  It is recommended that you use virtualenv to maintain a clean Python 3 environment. Create a virtualenv and install the requirements:

  $ conda create -n yourenvname python=x.x anaconda
  
  # To activate it again
  $ source activate yourenvname
            			
  # Install the requirements
  (yourenvname) $ pip3 install -r requirements.txt 

• System Wide Installation

  pip3 install -r requirements.txt

  Note: This might change versions of exisiting python packages and hence is not recommended.

Run the app in conda environment with:

streamlit run webapp.py

1. Guide to Human Pose Estimation with Deep Learning(Nanonets)
2. Mediapipe Pose Classification(Google's Github)
3. Real-time Human Pose Estimation in the Browser(TF Blog)
4. MediaPipePoseEstimation