Billiards coach

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Scope

Draw for show, follow for dough!!! Use Computer vision and AI to create the ultimate billiards coach.

Slack Channel

Join our slack channel to follow the projects progress. #billiardscoach

If you had something to add, please comment in the slack channel.

https://app.slack.com/client/T013G365GF5/C014A4VNDQE

Application

Github

This is our Billiards coach github repository.

https://github.com/brisbaneroboticsclub/billiardscoach

Research

Thank you Dr Dave from Billiards University for your suggestion to look at these resources…

https://billiards.colostate.edu/videos/miscellaneous/

First Challenge

A lot of billiards games are now live streamed. The most common camera angle is from the foot of the table as shown below.

The first challenge is to convert this video/image into a two dimensional layout.

Keep in mind, every table has different dimensions.

The different table characteristics are shown in the wikipedia link below.

https://en.wikipedia.org/wiki/Billiard_table

Homography may be the best solution to convert a 3D image to 2D.

https://docs.opencv.org/master/d9/dab/tutorial_homography.html

https://docs.opencv.org/master/d1/de0/tutorial_py_feature_homography.html

https://www.learnopencv.com/homography-examples-using-opencv-python-c/

http://howto.goplaypool.com/dr-dave-drills-games.html

The first challenge is to convert the 3D image to data where each ball has an (x,y) co-ordinate.

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