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This project employs computer vision techniques to analyse 8-ball game footage and provide accurate shot predictions to enhance performance.

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8 Ball Pool Analysis

This is an Open Source project that analyses game footage from Miniclip's 8 ball pool.

It determines paths, which show the user a possibility of how the balls could be potted.

animated.gif

File Structure

  • Report/ - Contains the report for this project.
  • Slideshow/ - Contains the slideshow for this project.
  • Other files - Contains the source code for this project.

Installation

This tool uses standard image processing techniques through OpenCV, together with some vector algebra and graph logic.

The following packages are required to run this tool, OS (MacOS, Linux):

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Usage

This tool is run via the command-line, and contains various fine-tuning options, depending on the video input.

The current default values for ball and hole sizes were determined after rigorous testing, on a video from a 1080p display, with zoom and scaling set to 100%. As a result, videos which have been captured on displays with a different resolution, zoom and scaling might need further tweaking to obtain adequate results.

usage: start.py [-br N] [-hr N] [-bd N] [-tb type] [-ip file] [-op file] [-sf N] [-show] [-save] [-h]

This project analyses in game footage that indicates the optimal shot predictions using computer vision.

optional arguments:
  -br N, --ball_radius N         Radius of the pool balls (dependent on resolution, zooming and scaling).
  -hr N, --hole_radius N         Radius of the table holes (dependent on resolution, zooming and scaling).
  -bd N, --border_distance N     Distance from the centre of the holes to the outermost edge of the table.
  -tb type, --target_balls type  Choose ball type for path calculation.
  -ip file, --input_video file   File path containing the game footage to be analysed (*.MP4).
  -op file, --output_video file  File path for the output video (*.MP4).
  -sf N, --skip_frame N          Process a frame every N frame when analysing the video.
  -show, --show_video            Show the video while processing is being done.
  -save, --save_video            Save the video after the processing has finished.
  -h, --help                     Show this help message and exit.

About

This project employs computer vision techniques to analyse 8-ball game footage and provide accurate shot predictions to enhance performance.

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  • Python 99.8%
  • Shell 0.2%