Predicting players position in demo (and possibly in game) with RNN and LSTM

This repo predicts the position of the players one side at a time.

Huge shoutout to awpy for providing a way to parse CS:GO demos.

Note: The requirement file contains other redundant modules for this repo. You can still use it but I recommend install the requirements in a python virtual environment.

The actual required modules are:

• awpy
• numpy
• keras
• matplotlib
• PIL

• Experiment with more timesteps and more training data
• Change hardcoded image length and width.
• Automate the entire process into a script

Download the script, download a demo that you wish to analyze, and download the layout image of the map in the demo.

For the script, you need to pass in these arguments:

• --layout: path to map layout image
• --demo: path to demo file
• --side: side of players to predict positions, values: (ct or t)
• --model: model type, values: (rnn or ltsm)
• --output: path of output predicted png files
• --debug: optional, no effect yet.