Steven Rosenthal

Would it be possible to post your images? Especially ones that fail. Also, can you give the parameters you used to generate the database, and also the parameters used when...

In the non-solving image, your brightest stars are bloomed to the point that solve_from_image() by default rejects them. Try passing max_size=200 to solve_from_image() (default is 100). Caveat-- I tried this...

I apologize-- the parameter is indeed max_area. +1 to the idea of downsampling, that will probably also eliminate the need to adjust the max_area setting.

Interesting. I'll investigate with Tetra3, not my fork.

Glad you got it sorted. The fact that increasing pattern_checking_stars solved the problem is consistent with my fork of Tetra3 working on the problem image, as one of the fork's...

@LonelySpaceman, may I have permission to include your four images into my Tetra3 fork's collection of test images? Please let me know what attribution, if any, you would like me...

I've confirmed Tetra3's behavior and have an explanation of why increasing the pattern_checking_stars value (from default 8) helps. When building the pattern database, Tetra3 employs a strategy to ensure a...

Iain, I have a (temporary) fork of Tetra3 that, among other things, improves how patterns are selected during database construction. As you have found, Tetra3's current database construction approach misses...

Hi Iain, I ran exercise_tetra3.py against the cedar-solve fork of Tetra3 on my Rpi4. Results: pattern_catalog length: 1560560 ... fail_count = 0 max solve time 319.1519810061436 min solve time 23.127488006139174...

I've made some further improvements on cedar-solve. Results: pattern_catalog length: 1424136 ... fail_count = 0 max solve time 77.0800780155696 min solve time 24.100307025946677 mean solve time 36.910300016851394 Number of T_solve...