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ML Installation

Overview

This document provides the details on how to install and set up a development environment for the Python-based/ML component of the Danceology project. All of the following documentation will assume a certain amount of familiarity with Python and Git/GitHub technologies.

Unity Project

https://github.com/ETCDanceology/Danceology-ML

Tech Requirements

Python

The Danceology project used Python 3.10 for development, along with pip to install all packages and dependencies.

Installation Steps

  1. Clone the Danceology ML repository locally
  2. (Optional) Set up a Python virtual environment
  3. Install all dependencies using
% pip install -r requirements.txt

Running ML Model on Input Video

All scripts and logic related to ML-based video processing are within the video_processing folder.

  1. cd into the video_processing directory
  2. Run the following command
% python main.py -i [path_to_input_video] -o [path_to_output_json]

The output json file from this can be directly used for data cleaning below.

Data Cleaning

All data cleaning scripts and logic are within the data_cleaning folder. The main.py file contains all the main cleaning functions; the util.py file contains all utility functions that are used during the data cleaning process.

You can customize how data cleaning is done by modifying different parameters within the main.py file.

  1. cd into the data_cleaning directory
  2. Run the following command
% python main.py -i [path_to_input_json] -o [path_to_output_json]

The output file from data cleaning can be used within the Unity project.