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.
This tool uses standard image processing techniques through OpenCV, together with some vector algebra and graph logic.
pip install opencv-python
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.
