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Collect Tetris Game Stats

Stats to Collect:

  1. Number of single/double/triple/Tetris clears
  2. Transition score
  3. Is the player a press-down player? (Yes/No)
  4. Number of times a J dependency was created and J was not the next piece
  5. Number of times a L dependency was created and L was not the next piece
  6. Number of times an I dependency was created and I was not the next piece
  7. Number of times a last-second micro-adjustment was made to accommodate the next piece
  8. Number of gaps formed in stack
  9. How many times the well was capped
  10. Average duration (piece-turns)that a well is left capped
  11. Number of overhangs created
  12. Number of tucks & spins (and at what level)
  13. Average stack height (broken down by individual columns)
  14. Number of "wasted" I pieces (need to think about this one more...)
  15. Average number of skims before Tetris'ing or capping the well?
  16. Number of times (and maybe even broken down by piece), "stack optionality" was needlessly reduced. (Need to take into account the next piece too.)
  17. Stack health-- a single number (possible?) that indicates the stack integrity & accommodability
  18. Piece distribution.
  19. Tetris rate (of lines cleared, how many were Tetrises?)
  20. Burned lines (lines cleared no Tetrises)
  21. Drought Counter-- piece durations without an I-piece.

Perform OCR to read Score, Level, Lines Cleared.

Perform OCR to read the following:

  • Score
  • Line
  • Level

Even if the "No-Code Revolution" is upon us and amazing tools now exist for laypeople everywhere to be more productive than ever before, some tasks are always simply going to need more specific elbow grease and technical know-how.

Low-level extraction of Tetris game data scraped from recorded YouTube videos is one such challenge. ๐Ÿ™‚

If you're looking for a task to get your hands dirty with Python and OpenCV, this is the job for you!

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