📝 Overview

This tool captures screenshots of Valorant matches, analyzes them in real-time, and predicts match outcomes based on extracted features. It includes live visualizations of win rates and detailed feature tables for analysis.

However, as the live processing is only consistent 90% of the time, this tool also offers an option to predict based on a screenshot instead of capturing screenshots and predicting live.

💪 Training

This model is only trained on the following features extracted from the image

• Number of players alive
• Health of each alive players
• Number of ability points
• Number of ults
• Time since spike is planted (0 if spike is not planted)

Through the training process an accuracy of 95% can be achieved

🔍 Features

• Real-time Analysis: Screenshots of the selected Valorant game window are captured and processed at regular intervals.
• Feature Extraction: Extracts various game features such as number of players alive, ability counts, health stats, and presence of game elements like spikes.
• Machine Learning Prediction: Uses a trained machine learning model (Random Forest) to predict match outcomes based on extracted features.
• Live Visualization: Displays live updates of win rates over time and detailed feature tables using Matplotlib.

🛠 Installation

1. Clone the Repository:
   
2. Install the dependencies pip install -r requirements.txt

🚀 Usage

• Using Live Version

  • Run the script python3 use_live_forest.py
  • Select Valorant Window:
    
    • A dialog will prompt you to select the Valorant game window from a list of currently open windows.
  • Monitor Live Updates:
    
    • The application will start capturing and analyzing screenshots in real-time.
    • Win rates will be plotted dynamically, and feature tables will be updated continuously.
  • Interpret Results:
    
    • View the live plot to monitor win rate trends.
    • Check the feature tables to understand the extracted data from each screenshot.

• Using Single Image Version

  • Change the image path at the bottom of the script (use_random_forest.py)
  • Run the script python3 use_random_forest.py

🐛 Known Issues

• Occasional delays or inaccuracies may occur in feature extraction depending on game window visibility and performance.
• Slight inconsistencies in extraction of features when taking screenshots and predicting lvie

🔮 Future Enhancements

• Gather more data to train the model (currently trained with 7000 images)
• Add in additional features (e.g. agents played, guns used, economy)
• Improve feature extraction to increase reliability
• Enhance GUI for better user interaction and feedback.

🤝 Contributing

Contributions are welcome! If interested, please fork the repository and submit a pull request with your improvements. I can provide the dataset for training.