2026-08-31T03:04:00 - INFO: Slick 0.0.8a13

🟢 Starting Calibration Pipeline

=== Processing MDC026_W024_2026-08-25 ===
2026-08-31T03:04:00 - INFO: Camera ID: 11481106
2026-08-31T03:04:00 - INFO: Wand ID: 
2026-08-31T03:04:00 - INFO: Recorded At: 2026-08-25T12:16:13Z

-------------------------------------------------------------------
                        Step 1: Digitization                       
-------------------------------------------------------------------

[🔄] Full PTD mode: Processing ALL images then creating calibration subset
[📸] Digitizing ALL images...
[📸] Full dataset mode: Processing ALL images (no debug visualizations)
[INFO] Found 2 las0.zip files to process

Processing Rigs:   0%|          | 0/2 [00:00<?, ?it/s][INFO] Filtered 631 stationary frames, 671 remain


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                                                                           [PASS2] Frame 0000310: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000311: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000312: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000314: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000315: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000316: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000317: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000318: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000341: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000342: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000343: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000345: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000347: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000350: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000351: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000353: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000354: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000355: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000356: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000357: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000359: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000360: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000361: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000362: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000366: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000367: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000368: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000372: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000373: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000374: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000375: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000376: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000377: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000454: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000455: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000456: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000457: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000458: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000461: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000462: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000463: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000465: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000466: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000468: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000469: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000472: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000488: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000489: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000490: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000491: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000501: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000502: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000503: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000504: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000505: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000506: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000507: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000508: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000509: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000510: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000513: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000514: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000515: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000516: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000517: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000518: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000519: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000521: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000523: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000581: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000587: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000588: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000589: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000590: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000592: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000593: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000594: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000595: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000596: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000597: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000598: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000599: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000600: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000604: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000605: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000606: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000607: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000608: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000609: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000610: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000611: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000612: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000613: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000621: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000622: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000623: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000624: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000632: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000633: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000634: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000635: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000636: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000637: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000644: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000645: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000647: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000648: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000649: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000650: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000651: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000658: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000659: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000660: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000663: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001021: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001022: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001023: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001024: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001036: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001037: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001038: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001039: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001040: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001041: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001047: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001048: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001049: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001050: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001052: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001053: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001054: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001055: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001056: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001057: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001058: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001059: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001060: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001061: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001062: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001125: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001126: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001127: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001128: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001129: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001130: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001133: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001134: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001135: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001136: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001140: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001141: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001142: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001143: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001144: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001145: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001146: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001147: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001148: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001149: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001150: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001151: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001154: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001155: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001156: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001157: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001158: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001159: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001163: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001164: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001165: fixed corners {2, 3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001170: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001171: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001172: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001186: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001187: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001188: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001191: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001197: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001200: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001201: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001203: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001284: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001285: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001286: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001287: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001288: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001289: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001290: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001293: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001298: fixed corners {0, 2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001302: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001303: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001304: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001305: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001306: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001307: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001308: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001311: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001312: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001314: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001315: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001316: fixed corners {2, 3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001318: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001319: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000312: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000313: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000315: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000317: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000351: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000352: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000353: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000357: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000358: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000360: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000361: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000375: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000376: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000456: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000457: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000463: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000464: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000467: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000468: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000490: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000503: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000504: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000508: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000509: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000515: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000517: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000518: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000519: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000521: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000523: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000581: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000589: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000594: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000595: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000596: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000598: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000599: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000600: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000606: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000608: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000612: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000623: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000634: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000636: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000647: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000650: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000660: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001023: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001040: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001049: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001058: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001059: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001128: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001129: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001135: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001143: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001147: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001149: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001150: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001156: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001157: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001158: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001167: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001170: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001171: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001188: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001201: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001286: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001288: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001289: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001290: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001299: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0001301: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001305: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001308: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001309: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001310: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001311: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001316: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001317: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001318: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000348: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000349: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000372: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000373: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000467: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000517: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000518: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000519: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000521: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000523: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000581: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000592: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000593: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000594: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000598: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000599: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000600: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001167: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001170: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001287: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001288: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001289: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0001298: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0001299: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Summary: 129 frames fixed, 120 untrusted, 671 total


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→ 1000 Phase 3: finalize:  64%|██████▍   | 430/671 [00:08<00:04, 48.27it/s]

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→ 1000 Phase 3: finalize:  72%|███████▏  | 485/671 [00:09<00:03, 50.47it/s]

→ 1000 Phase 3: finalize:  73%|███████▎  | 491/671 [00:09<00:03, 50.92it/s]

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→ 1000 Phase 3: finalize:  76%|███████▌  | 509/671 [00:09<00:03, 51.80it/s]

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→ 1000 Phase 3: finalize:  78%|███████▊  | 521/671 [00:10<00:02, 52.23it/s]

→ 1000 Phase 3: finalize:  79%|███████▊  | 527/671 [00:10<00:02, 52.67it/s]

→ 1000 Phase 3: finalize:  79%|███████▉  | 533/671 [00:10<00:02, 53.15it/s]

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→ 1000 Phase 3: finalize:  84%|████████▍ | 563/671 [00:10<00:02, 54.00it/s]

→ 1000 Phase 3: finalize:  85%|████████▍ | 569/671 [00:11<00:01, 54.22it/s]

→ 1000 Phase 3: finalize:  86%|████████▌ | 575/671 [00:11<00:01, 54.36it/s]

→ 1000 Phase 3: finalize:  87%|████████▋ | 581/671 [00:11<00:01, 54.21it/s]

→ 1000 Phase 3: finalize:  87%|████████▋ | 587/671 [00:11<00:01, 53.96it/s]

→ 1000 Phase 3: finalize:  88%|████████▊ | 593/671 [00:11<00:01, 53.78it/s]

→ 1000 Phase 3: finalize:  89%|████████▉ | 599/671 [00:11<00:01, 53.89it/s]

→ 1000 Phase 3: finalize:  90%|█████████ | 605/671 [00:11<00:01, 53.95it/s]

→ 1000 Phase 3: finalize:  91%|█████████ | 611/671 [00:11<00:01, 53.87it/s]

→ 1000 Phase 3: finalize:  92%|█████████▏| 617/671 [00:11<00:01, 53.78it/s]

→ 1000 Phase 3: finalize:  93%|█████████▎| 623/671 [00:12<00:00, 53.68it/s]

→ 1000 Phase 3: finalize:  94%|█████████▎| 629/671 [00:12<00:00, 53.31it/s]

→ 1000 Phase 3: finalize:  95%|█████████▍| 635/671 [00:12<00:00, 53.21it/s]

→ 1000 Phase 3: finalize:  96%|█████████▌| 641/671 [00:12<00:00, 53.18it/s]

→ 1000 Phase 3: finalize:  96%|█████████▋| 647/671 [00:12<00:00, 52.97it/s]

→ 1000 Phase 3: finalize:  97%|█████████▋| 653/671 [00:12<00:00, 52.80it/s]

→ 1000 Phase 3: finalize:  98%|█████████▊| 659/671 [00:12<00:00, 52.61it/s]

→ 1000 Phase 3: finalize:  99%|█████████▉| 665/671 [00:12<00:00, 52.46it/s]

→ 1000 Phase 3: finalize: 100%|██████████| 671/671 [00:12<00:00, 52.28it/s]

                                                                           
[TIMING] 1000 — 671 frames
  Phase 1  radial detect   (parallel)  :  11.24s  (16.7 ms/frame)
  Phase 2a corner detect   (parallel)  :   5.02s  (7.5 ms/frame)
  Phase 2b corner fix      (sequential):   4.94s
  Phase 3  finalize        (sequential):  12.94s  (19.3 ms/frame)
    decode + undistort                 :   6.49s  (9.7 ms/frame)
    corner apply + refinement          :   3.14s  (4.7 ms/frame)
    segment                            :   2.69s  (4.0 ms/frame)
    smooth                             :   0.39s  (0.6 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  34.14s


Processing Rigs:  50%|█████     | 1/2 [00:38<00:38, 38.51s/it][INFO] Filtered 668 stationary frames, 609 remain


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→ 500 (609 images) Phase 1:  99%|█████████▉| 604/609 [00:10<00:00, 78.05it/s]

                                                                             

→ 500 Phase 2: corners:   0%|          | 0/609 [00:00<?, ?it/s]

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→ 500 Phase 2: corners:  37%|███▋      | 226/609 [00:02<00:02, 137.29it/s]

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→ 500 Phase 2: corners:  48%|████▊     | 295/609 [00:02<00:01, 162.05it/s]

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→ 500 Phase 2: corners:  54%|█████▍    | 329/609 [00:02<00:01, 160.79it/s]

→ 500 Phase 2: corners:  57%|█████▋    | 346/609 [00:03<00:01, 156.44it/s]

→ 500 Phase 2: corners:  59%|█████▉    | 362/609 [00:03<00:01, 143.68it/s]

→ 500 Phase 2: corners:  62%|██████▏   | 378/609 [00:03<00:01, 141.93it/s]

→ 500 Phase 2: corners:  65%|██████▌   | 397/609 [00:03<00:01, 152.39it/s]

→ 500 Phase 2: corners:  68%|██████▊   | 413/609 [00:03<00:01, 143.44it/s]

→ 500 Phase 2: corners:  70%|███████   | 428/609 [00:03<00:01, 139.14it/s]

→ 500 Phase 2: corners:  73%|███████▎  | 443/609 [00:03<00:01, 134.45it/s]

→ 500 Phase 2: corners:  76%|███████▌  | 463/609 [00:03<00:00, 151.18it/s]

→ 500 Phase 2: corners:  79%|███████▉  | 480/609 [00:03<00:00, 151.67it/s]

→ 500 Phase 2: corners:  82%|████████▏ | 498/609 [00:04<00:00, 158.38it/s]

→ 500 Phase 2: corners:  85%|████████▍ | 516/609 [00:04<00:00, 164.22it/s]

→ 500 Phase 2: corners:  88%|████████▊ | 533/609 [00:04<00:00, 160.51it/s]

→ 500 Phase 2: corners:  90%|█████████ | 550/609 [00:04<00:00, 158.38it/s]

→ 500 Phase 2: corners:  93%|█████████▎| 566/609 [00:04<00:00, 158.63it/s]

→ 500 Phase 2: corners:  99%|█████████▉| 603/609 [00:04<00:00, 219.09it/s]

                                                                          [PASS2] Frame 0000341: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000342: fixed corners {2} (was_fixed=False, trusted=False)
[PASS2] Frame 0000343: fixed corners {2} (was_fixed=True, trusted=False)
[PASS2] Frame 0000344: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000437: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000438: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0000439: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0000440: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000963: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001185: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001186: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001189: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001192: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001257: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001258: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001259: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001260: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001275: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001276: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001277: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001278: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001290: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001291: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001292: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001293: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001294: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001295: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001300: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001301: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001302: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001303: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001306: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001307: fixed corners {3} (was_fixed=False, trusted=False)
[PASS2] Frame 0001308: fixed corners {3} (was_fixed=True, trusted=False)
[PASS2] Frame 0001309: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0000343: fixed corners {2} (was_fixed=True, trusted=True)
[PASS2] Frame 0000439: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001189: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001259: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001292: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001293: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001294: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001302: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Frame 0001308: fixed corners {3} (was_fixed=True, trusted=True)
[PASS2] Summary: 19 frames fixed, 17 untrusted, 609 total


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→ 500 Phase 3: finalize:  99%|█████████▉| 602/609 [00:11<00:00, 51.95it/s]

→ 500 Phase 3: finalize: 100%|█████████▉| 608/609 [00:11<00:00, 51.95it/s]

                                                                          
[TIMING] 500 — 609 frames
  Phase 1  radial detect   (parallel)  :  10.41s  (17.1 ms/frame)
  Phase 2a corner detect   (parallel)  :   4.72s  (7.8 ms/frame)
  Phase 2b corner fix      (sequential):   0.97s
  Phase 3  finalize        (sequential):  11.68s  (19.2 ms/frame)
    decode + undistort                 :   5.87s  (9.6 ms/frame)
    corner apply + refinement          :   2.65s  (4.4 ms/frame)
    segment                            :   2.48s  (4.1 ms/frame)
    smooth                             :   0.47s  (0.8 ms/frame)
    distort back                       :   0.00s  (0.0 ms/frame)
  Total                                :  27.78s


Processing Rigs: 100%|██████████| 2/2 [01:10<00:00, 34.47s/it]
Processing Rigs: 100%|██████████| 2/2 [01:10<00:00, 35.07s/it]
[💾] Saved RigFrameDataset to /working/MDC026_W024_2026-08-25/calibration/debug/1_digitized_full/rig_frames_full.json
[✅] Saved full dataset: 1280 frames from 2 rigs to /working/MDC026_W024_2026-08-25/calibration/debug/1_digitized_full/rig_frames_full.json
[💾] Saved RigFrameDataset to /working/MDC026_W024_2026-08-25/calibration/debug/1_digitized_full/rig_frames_full.json
[📋] Creating calibration subset (100 frames/rig) with visualizations...
[📋] 1000: 100 frames selected from 671
[📋] 500: 100 frames selected from 609
[💾] Saved RigFrameDataset to /working/MDC026_W024_2026-08-25/calibration/debug/1_digitized/rig_frames.json
[INFO] Reloading dataset from disk to ensure clean state for multiprocessing...
[📂] Loaded RigFrameDataset from /working/MDC026_W024_2026-08-25/calibration/debug/1_digitized/rig_frames.json

-------------------------------------------------------------------
                      Step 2: Casing Detection                     
-------------------------------------------------------------------

-> Calculating the casing center ...
[INFO] Using 40 total sampled bright frames

Computing max image from bright frames:   0%|          | 0/40 [00:00<?, ?it/s]
Computing max image from bright frames:   2%|▎         | 1/40 [00:04<02:56,  4.54s/it]
Computing max image from bright frames:   5%|▌         | 2/40 [00:04<01:18,  2.08s/it]
Computing max image from bright frames:   8%|▊         | 3/40 [00:05<00:46,  1.25s/it]
Computing max image from bright frames:  10%|█         | 4/40 [00:05<00:30,  1.18it/s]
Computing max image from bright frames:  12%|█▎        | 5/40 [00:05<00:22,  1.56it/s]
Computing max image from bright frames:  18%|█▊        | 7/40 [00:06<00:13,  2.43it/s]
Computing max image from bright frames:  20%|██        | 8/40 [00:06<00:11,  2.69it/s]
Computing max image from bright frames:  22%|██▎       | 9/40 [00:06<00:10,  2.92it/s]
Computing max image from bright frames:  28%|██▊       | 11/40 [00:06<00:06,  4.20it/s]
Computing max image from bright frames:  30%|███       | 12/40 [00:07<00:07,  3.60it/s]
Computing max image from bright frames:  32%|███▎      | 13/40 [00:07<00:06,  3.87it/s]
Computing max image from bright frames:  38%|███▊      | 15/40 [00:07<00:05,  4.97it/s]
Computing max image from bright frames:  42%|████▎     | 17/40 [00:07<00:03,  6.65it/s]
Computing max image from bright frames:  50%|█████     | 20/40 [00:07<00:02,  9.63it/s]
Computing max image from bright frames:  55%|█████▌    | 22/40 [00:08<00:02,  8.94it/s]
Computing max image from bright frames:  60%|██████    | 24/40 [00:08<00:01,  9.19it/s]
Computing max image from bright frames:  65%|██████▌   | 26/40 [00:08<00:01, 10.09it/s]
Computing max image from bright frames:  70%|███████   | 28/40 [00:08<00:01, 10.26it/s]
Computing max image from bright frames:  75%|███████▌  | 30/40 [00:08<00:00, 11.24it/s]
Computing max image from bright frames:  80%|████████  | 32/40 [00:09<00:00, 10.16it/s]
Computing max image from bright frames:  85%|████████▌ | 34/40 [00:09<00:01,  5.97it/s]
Computing max image from bright frames:  88%|████████▊ | 35/40 [00:09<00:00,  6.23it/s]
Computing max image from bright frames:  90%|█████████ | 36/40 [00:10<00:00,  6.28it/s]
Computing max image from bright frames:  95%|█████████▌| 38/40 [00:10<00:00,  7.89it/s]
Computing max image from bright frames:  98%|█████████▊| 39/40 [00:10<00:00,  5.34it/s]
                                                                                       
[✅] Casing detection complete: cx=998.0, cy=760.0

-------------------------------------------------------------------
                        Step 3: Calibration                        
-------------------------------------------------------------------


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Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   0%|          | 0/100 [00:00<?, ?it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   1%|          | 1/100 [00:00<00:27,  3.60it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   1%|          | 1/100 [00:00<00:27,  3.60it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   2%|▏         | 2/100 [00:00<00:27,  3.60it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   3%|▎         | 3/100 [00:00<00:26,  3.60it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   4%|▍         | 4/100 [00:00<00:07, 12.39it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   4%|▍         | 4/100 [00:00<00:07, 12.39it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   5%|▌         | 5/100 [00:00<00:07, 12.39it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   6%|▌         | 6/100 [00:00<00:07, 12.39it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   7%|▋         | 7/100 [00:00<00:07, 12.39it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   8%|▊         | 8/100 [00:00<00:07, 12.39it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 307.071} (0.000):   9%|▉         | 9/100 [00:00<00:07, 12.39it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  10%|█         | 10/100 [00:00<00:07, 12.39it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  11%|█         | 11/100 [00:00<00:07, 12.39it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  12%|█▏        | 12/100 [00:00<00:07, 12.39it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  13%|█▎        | 13/100 [00:00<00:02, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  13%|█▎        | 13/100 [00:00<00:02, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  14%|█▍        | 14/100 [00:00<00:02, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  15%|█▌        | 15/100 [00:00<00:02, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  16%|█▌        | 16/100 [00:00<00:02, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 307.071} (0.000):  17%|█▋        | 17/100 [00:00<00:02, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.032 | Best: {'fx': 519.192} (0.032):  18%|█▊        | 18/100 [00:00<00:02, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 519.192} (0.032):  19%|█▉        | 19/100 [00:00<00:02, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.084 | Best: {'fx': 533.333} (0.084):  20%|██        | 20/100 [00:00<00:02, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.057 | Best: {'fx': 533.333} (0.084):  21%|██        | 21/100 [00:00<00:02, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.084):  22%|██▏       | 22/100 [00:00<00:02, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.084):  23%|██▎       | 23/100 [00:00<00:02, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.084):  24%|██▍       | 24/100 [00:00<00:02, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.049 | Best: {'fx': 533.333} (0.084):  25%|██▌       | 25/100 [00:00<00:02, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.084):  26%|██▌       | 26/100 [00:00<00:02, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.084):  27%|██▋       | 27/100 [00:00<00:02, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.033 | Best: {'fx': 533.333} (0.084):  28%|██▊       | 28/100 [00:00<00:02, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.026 | Best: {'fx': 533.333} (0.084):  29%|██▉       | 29/100 [00:00<00:01, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.084):  30%|███       | 30/100 [00:00<00:01, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.017 | Best: {'fx': 533.333} (0.084):  31%|███       | 31/100 [00:00<00:01, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.019 | Best: {'fx': 533.333} (0.084):  32%|███▏      | 32/100 [00:00<00:01, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.084):  33%|███▎      | 33/100 [00:00<00:01, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.016 | Best: {'fx': 533.333} (0.084):  34%|███▍      | 34/100 [00:00<00:01, 35.90it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.016 | Best: {'fx': 533.333} (0.084):  35%|███▌      | 35/100 [00:00<00:00, 90.55it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.021 | Best: {'fx': 533.333} (0.084):  35%|███▌      | 35/100 [00:00<00:00, 90.55it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.015 | Best: {'fx': 533.333} (0.084):  36%|███▌      | 36/100 [00:00<00:00, 90.55it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.000 | Best: {'fx': 533.333} (0.084):  37%|███▋      | 37/100 [00:00<00:00, 90.55it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.013 | Best: {'fx': 533.333} (0.084):  38%|███▊      | 38/100 [00:00<00:00, 90.55it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.014 | Best: {'fx': 533.333} (0.084):  39%|███▉      | 39/100 [00:00<00:00, 90.55it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.013 | Best: {'fx': 533.333} (0.084):  40%|████      | 40/100 [00:00<00:00, 90.55it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.001 | Best: {'fx': 533.333} (0.084):  41%|████      | 41/100 [00:00<00:00, 90.55it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.012 | Best: {'fx': 533.333} (0.084):  42%|████▏     | 42/100 [00:00<00:00, 90.55it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.012 | Best: {'fx': 533.333} (0.084):  43%|████▎     | 43/100 [00:00<00:00, 90.55it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.012 | Best: {'fx': 533.333} (0.084):  44%|████▍     | 44/100 [00:00<00:00, 90.55it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.020 | Best: {'fx': 533.333} (0.084):  45%|████▌     | 45/100 [00:00<00:00, 90.55it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.084):  46%|████▌     | 46/100 [00:00<00:00, 90.55it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.005 | Best: {'fx': 533.333} (0.084):  47%|████▋     | 47/100 [00:00<00:00, 90.55it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.084):  48%|████▊     | 48/100 [00:00<00:00, 90.55it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.084):  49%|████▉     | 49/100 [00:00<00:00, 90.55it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.011 | Best: {'fx': 533.333} (0.084):  50%|█████     | 50/100 [00:00<00:00, 90.55it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.008 | Best: {'fx': 533.333} (0.084):  51%|█████     | 51/100 [00:00<00:00, 90.55it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.084):  52%|█████▏    | 52/100 [00:00<00:00, 90.55it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.084):  53%|█████▎    | 53/100 [00:00<00:00, 111.78it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.084):  53%|█████▎    | 53/100 [00:00<00:00, 111.78it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.084):  54%|█████▍    | 54/100 [00:00<00:00, 111.78it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.084):  55%|█████▌    | 55/100 [00:00<00:00, 111.78it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.084):  56%|█████▌    | 56/100 [00:00<00:00, 111.78it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.084):  57%|█████▋    | 57/100 [00:00<00:00, 111.78it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  58%|█████▊    | 58/100 [00:00<00:00, 111.78it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.084):  59%|█████▉    | 59/100 [00:00<00:00, 111.78it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.084):  60%|██████    | 60/100 [00:00<00:00, 111.78it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.010 | Best: {'fx': 533.333} (0.084):  61%|██████    | 61/100 [00:00<00:00, 111.78it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  62%|██████▏   | 62/100 [00:00<00:00, 111.78it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  63%|██████▎   | 63/100 [00:00<00:00, 111.78it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  64%|██████▍   | 64/100 [00:00<00:00, 111.78it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  65%|██████▌   | 65/100 [00:00<00:00, 111.78it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  66%|██████▌   | 66/100 [00:00<00:00, 111.78it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  67%|██████▋   | 67/100 [00:00<00:00, 117.87it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  67%|██████▋   | 67/100 [00:00<00:00, 117.87it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  68%|██████▊   | 68/100 [00:00<00:00, 117.87it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  69%|██████▉   | 69/100 [00:00<00:00, 117.87it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  70%|███████   | 70/100 [00:00<00:00, 117.87it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  71%|███████   | 71/100 [00:00<00:00, 117.87it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  72%|███████▏  | 72/100 [00:00<00:00, 117.87it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  73%|███████▎  | 73/100 [00:00<00:00, 117.87it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  74%|███████▍  | 74/100 [00:00<00:00, 117.87it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  75%|███████▌  | 75/100 [00:00<00:00, 117.87it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  76%|███████▌  | 76/100 [00:00<00:00, 117.87it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  77%|███████▋  | 77/100 [00:00<00:00, 117.87it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  78%|███████▊  | 78/100 [00:00<00:00, 117.87it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  79%|███████▉  | 79/100 [00:00<00:00, 117.87it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  80%|████████  | 80/100 [00:00<00:00, 120.59it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  80%|████████  | 80/100 [00:00<00:00, 120.59it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  81%|████████  | 81/100 [00:00<00:00, 120.59it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  82%|████████▏ | 82/100 [00:00<00:00, 120.59it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  83%|████████▎ | 83/100 [00:00<00:00, 120.59it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  84%|████████▍ | 84/100 [00:00<00:00, 120.59it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  85%|████████▌ | 85/100 [00:00<00:00, 120.59it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  86%|████████▌ | 86/100 [00:01<00:00, 120.59it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  87%|████████▋ | 87/100 [00:01<00:00, 120.59it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  88%|████████▊ | 88/100 [00:01<00:00, 120.59it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  89%|████████▉ | 89/100 [00:01<00:00, 120.59it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  90%|█████████ | 90/100 [00:01<00:00, 120.59it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  91%|█████████ | 91/100 [00:01<00:00, 120.59it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  92%|█████████▏| 92/100 [00:01<00:00, 120.59it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  93%|█████████▎| 93/100 [00:01<00:00, 113.94it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  93%|█████████▎| 93/100 [00:01<00:00, 113.94it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  94%|█████████▍| 94/100 [00:01<00:00, 113.94it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  95%|█████████▌| 95/100 [00:01<00:00, 113.94it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  96%|█████████▌| 96/100 [00:01<00:00, 113.94it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  97%|█████████▋| 97/100 [00:01<00:00, 113.94it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  98%|█████████▊| 98/100 [00:01<00:00, 113.94it/s]
Brute | Start: {'fx': 500.0} (0.005) | Current: 0.009 | Best: {'fx': 533.333} (0.084):  99%|█████████▉| 99/100 [00:01<00:00, 113.94it/s]
                                                                                                                                        

  0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.024 | Best: {'fx': 513.435} (0.024):   0%|          | 0/50 [00:00<?, ?it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.024 | Best: {'fx': 513.435} (0.024):   2%|▏         | 1/50 [00:00<00:06,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.013 | Best: {'fx': 513.435} (0.024):   2%|▏         | 1/50 [00:00<00:06,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.013 | Best: {'fx': 513.435} (0.024):   4%|▍         | 2/50 [00:00<00:06,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.025 | Best: {'fx': 514.456} (0.025):   6%|▌         | 3/50 [00:00<00:06,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.014 | Best: {'fx': 514.456} (0.025):   8%|▊         | 4/50 [00:00<00:05,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.018 | Best: {'fx': 514.456} (0.025):  10%|█         | 5/50 [00:00<00:05,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.021 | Best: {'fx': 514.456} (0.025):  12%|█▏        | 6/50 [00:00<00:05,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.026 | Best: {'fx': 515.476} (0.026):  14%|█▍        | 7/50 [00:00<00:05,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.028 | Best: {'fx': 516.497} (0.028):  16%|█▌        | 8/50 [00:00<00:05,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.030 | Best: {'fx': 517.517} (0.030):  18%|█▊        | 9/50 [00:00<00:05,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.032 | Best: {'fx': 518.537} (0.032):  20%|██        | 10/50 [00:00<00:05,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.039 | Best: {'fx': 521.599} (0.039):  22%|██▏       | 11/50 [00:00<00:04,  7.82it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.039 | Best: {'fx': 521.599} (0.039):  24%|██▍       | 12/50 [00:00<00:00, 59.15it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.033 | Best: {'fx': 521.599} (0.039):  24%|██▍       | 12/50 [00:00<00:00, 59.15it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.035 | Best: {'fx': 521.599} (0.039):  26%|██▌       | 13/50 [00:00<00:00, 59.15it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.059 | Best: {'fx': 526.701} (0.059):  28%|██▊       | 14/50 [00:00<00:00, 59.15it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.041 | Best: {'fx': 526.701} (0.059):  30%|███       | 15/50 [00:00<00:00, 59.15it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.045 | Best: {'fx': 526.701} (0.059):  32%|███▏      | 16/50 [00:00<00:00, 59.15it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.049 | Best: {'fx': 526.701} (0.059):  34%|███▍      | 17/50 [00:00<00:00, 59.15it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.054 | Best: {'fx': 526.701} (0.059):  36%|███▌      | 18/50 [00:00<00:00, 59.15it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.068 | Best: {'fx': 527.721} (0.068):  38%|███▊      | 19/50 [00:00<00:00, 59.15it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.073 | Best: {'fx': 528.741} (0.073):  40%|████      | 20/50 [00:00<00:00, 59.15it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.080 | Best: {'fx': 530.782} (0.080):  42%|████▏     | 21/50 [00:00<00:00, 59.15it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.080 | Best: {'fx': 530.782} (0.080):  44%|████▍     | 22/50 [00:00<00:00, 70.52it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.075 | Best: {'fx': 530.782} (0.080):  44%|████▍     | 22/50 [00:00<00:00, 70.52it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.090 | Best: {'fx': 531.803} (0.090):  46%|████▌     | 23/50 [00:00<00:00, 70.52it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.082 | Best: {'fx': 531.803} (0.090):  48%|████▊     | 24/50 [00:00<00:00, 70.52it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.086 | Best: {'fx': 531.803} (0.090):  50%|█████     | 25/50 [00:00<00:00, 70.52it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.080 | Best: {'fx': 531.803} (0.090):  52%|█████▏    | 26/50 [00:00<00:00, 70.52it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.071 | Best: {'fx': 531.803} (0.090):  54%|█████▍    | 27/50 [00:00<00:00, 70.52it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.056 | Best: {'fx': 531.803} (0.090):  56%|█████▌    | 28/50 [00:00<00:00, 70.52it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.051 | Best: {'fx': 531.803} (0.090):  58%|█████▊    | 29/50 [00:00<00:00, 70.52it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.062 | Best: {'fx': 531.803} (0.090):  60%|██████    | 30/50 [00:00<00:00, 70.52it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.047 | Best: {'fx': 531.803} (0.090):  62%|██████▏   | 31/50 [00:00<00:00, 70.52it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.055 | Best: {'fx': 531.803} (0.090):  64%|██████▍   | 32/50 [00:00<00:00, 70.52it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.043 | Best: {'fx': 531.803} (0.090):  66%|██████▌   | 33/50 [00:00<00:00, 70.52it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.039 | Best: {'fx': 531.803} (0.090):  68%|██████▊   | 34/50 [00:00<00:00, 70.52it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.036 | Best: {'fx': 531.803} (0.090):  70%|███████   | 35/50 [00:00<00:00, 70.52it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.036 | Best: {'fx': 531.803} (0.090):  72%|███████▏  | 36/50 [00:00<00:00, 94.77it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.040 | Best: {'fx': 531.803} (0.090):  72%|███████▏  | 36/50 [00:00<00:00, 94.77it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.034 | Best: {'fx': 531.803} (0.090):  74%|███████▍  | 37/50 [00:00<00:00, 94.77it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.033 | Best: {'fx': 531.803} (0.090):  76%|███████▌  | 38/50 [00:00<00:00, 94.77it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.032 | Best: {'fx': 531.803} (0.090):  78%|███████▊  | 39/50 [00:00<00:00, 94.77it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.027 | Best: {'fx': 531.803} (0.090):  80%|████████  | 40/50 [00:00<00:00, 94.77it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.031 | Best: {'fx': 531.803} (0.090):  82%|████████▏ | 41/50 [00:00<00:00, 94.77it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.029 | Best: {'fx': 531.803} (0.090):  84%|████████▍ | 42/50 [00:00<00:00, 94.77it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.028 | Best: {'fx': 531.803} (0.090):  86%|████████▌ | 43/50 [00:00<00:00, 94.77it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.026 | Best: {'fx': 531.803} (0.090):  88%|████████▊ | 44/50 [00:00<00:00, 94.77it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.029 | Best: {'fx': 531.803} (0.090):  90%|█████████ | 45/50 [00:00<00:00, 94.77it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.024 | Best: {'fx': 531.803} (0.090):  92%|█████████▏| 46/50 [00:00<00:00, 94.77it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.024 | Best: {'fx': 531.803} (0.090):  94%|█████████▍| 47/50 [00:00<00:00, 94.62it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.025 | Best: {'fx': 531.803} (0.090):  94%|█████████▍| 47/50 [00:00<00:00, 94.62it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.025 | Best: {'fx': 531.803} (0.090):  96%|█████████▌| 48/50 [00:00<00:00, 94.62it/s]
Brute | Start: {'fx': 533.333} (0.084) | Current: 0.024 | Best: {'fx': 531.803} (0.090):  98%|█████████▊| 49/50 [00:00<00:00, 94.62it/s]
                                                                                                                                        
[DEBUG] Parameters after Step 5 (before square optimization):
    fx: 531.8027210884354
    px: 998.0
    py: 760.0
    k1: 0.0
    k2: 0.0
    k3: 0.0
    k4: 0.0
    p1: 0.0
    p2: 0.0
    p3: 0.0
    p4: 0.0
    scale: 3.0
    rotation: 0.0


[🔲] Square Reference Optimization
    Including tangential distortion parameters (p1, p2, p3, p4) in joint optimization
  Selecting up to 20 frames per rig (excluded 0 incomplete frames): 20 frames will be used for optimization
  Rigs: 1000(1000mm):20/20
  Total: 20/20 frames used for optimization

Initial score: 12.4545: 0iter [00:00, ?iter/s][DEBUG] objective(x0) = 12.4545, picklable_objective(x0) = 12.4820

Best score: 12.4820: : 0iter [00:01, ?iter/s] 
Best score: 12.4820: : 1iter [00:01,  1.85s/iter]
Best score: 12.4820: : 1iter [00:01,  1.85s/iter]
Best score: 12.4820: : 2iter [00:02,  1.85s/iter]
Best score: 12.4820: : 3iter [00:02,  1.81iter/s]
Best score: 12.4820: : 3iter [00:02,  1.81iter/s]
Best score: 12.4820: : 4iter [00:02,  1.81iter/s]
Best score: 12.4820: : 5iter [00:02,  1.81iter/s]
Best score: 12.4820: : 6iter [00:02,  1.81iter/s]
Best score: 12.4820: : 7iter [00:02,  5.08iter/s]
Best score: 12.4820: : 7iter [00:02,  5.08iter/s]
Best score: 12.4820: : 8iter [00:02,  5.08iter/s]
Best score: 12.4820: : 9iter [00:02,  5.08iter/s]
Best score: 12.4820: : 10iter [00:02,  5.08iter/s]
Best score: 12.4820: : 11iter [00:02,  8.63iter/s]
Best score: 12.4820: : 11iter [00:02,  8.63iter/s]
Best score: 12.4820: : 12iter [00:02,  8.63iter/s]
Best score: 12.4820: : 13iter [00:02,  8.63iter/s]
Best score: 12.4820: : 14iter [00:02, 11.27iter/s]
Best score: 12.4820: : 14iter [00:02, 11.27iter/s]
Best score: 12.4820: : 15iter [00:02, 11.27iter/s]
Best score: 12.4820: : 16iter [00:02, 11.27iter/s]
Best score: 12.4820: : 17iter [00:02, 11.27iter/s]
Best score: 12.4820: : 18iter [00:02, 15.00iter/s]
Best score: 12.4820: : 18iter [00:02, 15.00iter/s]
Best score: 12.4820: : 19iter [00:02, 15.00iter/s]
Best score: 12.4820: : 20iter [00:02, 15.00iter/s]
Best score: 12.4820: : 21iter [00:02, 15.00iter/s]
Best score: 12.4820: : 22iter [00:02, 18.77iter/s]
Best score: 12.4820: : 22iter [00:02, 18.77iter/s]
Best score: 12.4820: : 23iter [00:02, 18.77iter/s]
Best score: 12.4820: : 24iter [00:02, 18.77iter/s]
Best score: 12.4820: : 25iter [00:02, 20.79iter/s]
Best score: 12.4820: : 25iter [00:02, 20.79iter/s]
Best score: 12.4820: : 26iter [00:02, 20.79iter/s]
Best score: 12.4820: : 27iter [00:02, 20.79iter/s]
Best score: 12.4820: : 28iter [00:02, 20.79iter/s]
Best score: 12.4820: : 29iter [00:02, 23.78iter/s]
Best score: 12.4820: : 29iter [00:02, 23.78iter/s]
Best score: 12.4820: : 30iter [00:02, 23.78iter/s]
Best score: 12.4820: : 31iter [00:02, 23.78iter/s]
Best score: 12.4820: : 32iter [00:02, 23.78iter/s]
Best score: 12.4820: : 33iter [00:02, 26.34iter/s]
Best score: 12.4820: : 33iter [00:03, 26.34iter/s]
Best score: 12.4820: : 34iter [00:03, 26.34iter/s]
Best score: 12.4820: : 35iter [00:03, 26.34iter/s]
Best score: 12.4820: : 36iter [00:03, 26.34iter/s]
Best score: 12.4820: : 37iter [00:03, 27.07iter/s]
Best score: 12.4820: : 37iter [00:03, 27.07iter/s]
Best score: 12.4820: : 38iter [00:03, 27.07iter/s]
Best score: 12.4820: : 39iter [00:03, 27.07iter/s]
Best score: 12.4820: : 40iter [00:03, 27.07iter/s]
Best score: 12.4820: : 41iter [00:03, 28.12iter/s]
Best score: 12.4820: : 41iter [00:03, 28.12iter/s]
Best score: 12.4820: : 42iter [00:03, 28.12iter/s]
Best score: 12.4820: : 43iter [00:03, 28.12iter/s]
Best score: 12.4820: : 44iter [00:03, 28.12iter/s]
Best score: 12.4820: : 45iter [00:03, 29.30iter/s]
Best score: 12.4820: : 45iter [00:03, 29.30iter/s]
Best score: 12.4820: : 46iter [00:03, 29.30iter/s]
Best score: 12.4820: : 47iter [00:03, 29.30iter/s]
Best score: 12.4820: : 48iter [00:03, 29.30iter/s]
Best score: 12.4820: : 49iter [00:03, 30.03iter/s]
Best score: 12.4820: : 49iter [00:03, 30.03iter/s]
[ITER 50] Best: 12.4820
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 998.0000
  py: 760.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000

Best score: 12.4820: : 50iter [00:03, 30.03iter/s]
Best score: 12.4820: : 51iter [00:03, 30.03iter/s]
Best score: 12.4820: : 52iter [00:03, 30.03iter/s]
Best score: 12.4820: : 53iter [00:03, 30.74iter/s]
Best score: 12.4820: : 53iter [00:03, 30.74iter/s]
Best score: 12.4820: : 54iter [00:03, 30.74iter/s]
Best score: 12.4820: : 55iter [00:03, 30.74iter/s]
Best score: 12.4820: : 56iter [00:03, 30.74iter/s]
Best score: 12.4820: : 57iter [00:03, 30.73iter/s]
Best score: 12.4820: : 57iter [00:03, 30.73iter/s]
Best score: 12.4820: : 58iter [00:03, 30.73iter/s]
Best score: 12.4820: : 59iter [00:03, 30.73iter/s]
Best score: 12.4820: : 60iter [00:03, 30.73iter/s]
Best score: 12.4820: : 61iter [00:03, 30.89iter/s]
Best score: 12.4820: : 61iter [00:03, 30.89iter/s]
Best score: 12.4820: : 62iter [00:03, 30.89iter/s]
Best score: 12.4820: : 63iter [00:03, 30.89iter/s]
Best score: 12.4820: : 64iter [00:04, 30.89iter/s]
Best score: 12.4820: : 65iter [00:04, 30.97iter/s]
Best score: 12.4820: : 65iter [00:04, 30.97iter/s]
Best score: 12.4820: : 66iter [00:04, 30.97iter/s]
Best score: 12.4820: : 67iter [00:04, 30.97iter/s]
Best score: 12.4820: : 68iter [00:04, 30.97iter/s]
Best score: 12.4820: : 69iter [00:04, 31.45iter/s]
Best score: 12.4820: : 69iter [00:04, 31.45iter/s]
Best score: 12.4820: : 70iter [00:04, 31.45iter/s]
Best score: 12.4820: : 71iter [00:04, 31.45iter/s]
Best score: 12.4820: : 72iter [00:04, 31.45iter/s]
Best score: 12.4820: : 73iter [00:04, 31.35iter/s]
Best score: 12.4820: : 73iter [00:04, 31.35iter/s]
Best score: 12.4820: : 74iter [00:04, 31.35iter/s]
Best score: 12.4820: : 75iter [00:04, 31.35iter/s]
Best score: 12.4820: : 76iter [00:04, 31.35iter/s]
Best score: 12.4820: : 77iter [00:04, 30.70iter/s]
Best score: 12.4820: : 77iter [00:04, 30.70iter/s]
Best score: 12.4820: : 78iter [00:04, 30.70iter/s]
Best score: 12.4820: : 79iter [00:04, 30.70iter/s]
Best score: 12.4820: : 80iter [00:04, 30.70iter/s]
Best score: 12.4820: : 81iter [00:04, 30.74iter/s]
Best score: 12.4820: : 81iter [00:04, 30.74iter/s]
Best score: 12.4820: : 82iter [00:04, 30.74iter/s]
Best score: 12.4820: : 83iter [00:04, 30.74iter/s]
Best score: 12.4820: : 84iter [00:04, 30.74iter/s]
Best score: 12.4820: : 85iter [00:04, 29.54iter/s]
Best score: 12.4820: : 85iter [00:04, 29.54iter/s]
Best score: 12.4820: : 86iter [00:04, 29.54iter/s]
Best score: 12.4820: : 87iter [00:04, 29.54iter/s]
Best score: 12.4820: : 88iter [00:04, 29.54iter/s]
Best score: 12.4820: : 89iter [00:04, 29.84iter/s]
Best score: 12.4820: : 89iter [00:04, 29.84iter/s]
Best score: 12.4820: : 90iter [00:04, 29.84iter/s]
Best score: 12.4820: : 91iter [00:04, 29.84iter/s]
Best score: 12.4820: : 92iter [00:04, 29.62iter/s]
Best score: 12.4820: : 92iter [00:04, 29.62iter/s]
Best score: 12.4820: : 93iter [00:04, 29.62iter/s]
Best score: 12.4820: : 94iter [00:05, 29.62iter/s]
Best score: 12.4820: : 95iter [00:05, 29.35iter/s]
Best score: 12.4820: : 95iter [00:05, 29.35iter/s]
Best score: 12.4820: : 96iter [00:05, 29.35iter/s]
Best score: 12.4820: : 97iter [00:05, 29.35iter/s]
Best score: 12.4820: : 98iter [00:05, 29.36iter/s]
Best score: 12.4820: : 98iter [00:05, 29.36iter/s]
[ITER 100] Best: 12.4820
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 998.0000
  py: 760.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000

Best score: 12.4820: : 99iter [00:05, 29.36iter/s]
Best score: 12.4820: : 100iter [00:05, 29.36iter/s]
Best score: 12.4820: : 101iter [00:05, 26.35iter/s]
Best score: 12.4820: : 101iter [00:05, 26.35iter/s]
Best score: 12.4820: : 102iter [00:05, 26.35iter/s]
Best score: 12.4820: : 103iter [00:05, 26.35iter/s]
Best score: 12.4820: : 104iter [00:05, 26.35iter/s]
Best score: 12.4820: : 105iter [00:05, 27.97iter/s]
Best score: 12.4820: : 105iter [00:05, 27.97iter/s]
Best score: 12.4820: : 106iter [00:05, 27.97iter/s]
Best score: 12.4820: : 107iter [00:05, 27.97iter/s]
Best score: 12.4820: : 108iter [00:05, 27.97iter/s]
Best score: 12.4820: : 109iter [00:05, 28.76iter/s]
Best score: 12.4820: : 109iter [00:05, 28.76iter/s]
Best score: 12.4820: : 110iter [00:05, 28.76iter/s]
Best score: 12.4820: : 111iter [00:05, 28.76iter/s]
Best score: 12.4820: : 112iter [00:05, 28.93iter/s]
Best score: 12.4820: : 112iter [00:05, 28.93iter/s]
Best score: 12.4820: : 113iter [00:05, 28.93iter/s]
Best score: 12.4820: : 114iter [00:05, 28.93iter/s]
Best score: 12.4820: : 115iter [00:05, 28.93iter/s]
Best score: 12.4820: : 116iter [00:05, 29.50iter/s]
Best score: 12.4820: : 116iter [00:05, 29.50iter/s]
Best score: 12.4820: : 117iter [00:05, 29.50iter/s]
Best score: 12.4820: : 118iter [00:05, 29.50iter/s]
Best score: 12.4820: : 119iter [00:05, 29.50iter/s]
Best score: 12.4820: : 120iter [00:05, 30.39iter/s]
Best score: 12.4820: : 120iter [00:05, 30.39iter/s]
Best score: 12.4820: : 121iter [00:05, 30.39iter/s]
Best score: 12.4820: : 122iter [00:05, 30.39iter/s]
Best score: 12.4820: : 123iter [00:06, 30.39iter/s]
Best score: 12.4820: : 124iter [00:06, 31.31iter/s]
Best score: 12.4820: : 124iter [00:06, 31.31iter/s]
Best score: 12.4820: : 125iter [00:06, 31.31iter/s]
Best score: 12.4820: : 126iter [00:06, 31.31iter/s]
Best score: 12.4820: : 127iter [00:06, 31.31iter/s]
Best score: 12.4820: : 128iter [00:06, 30.43iter/s]
Best score: 12.4820: : 128iter [00:06, 30.43iter/s]
Best score: 12.4820: : 129iter [00:06, 30.43iter/s]
Best score: 12.4820: : 130iter [00:06, 30.43iter/s]
Best score: 12.4820: : 131iter [00:06, 30.43iter/s]
Best score: 12.4820: : 132iter [00:06, 30.70iter/s]
Best score: 12.4820: : 132iter [00:06, 30.70iter/s]
Best score: 12.4820: : 133iter [00:06, 30.70iter/s]
Best score: 12.4820: : 134iter [00:06, 30.70iter/s]
Best score: 12.4820: : 135iter [00:06, 30.70iter/s]
Best score: 12.4820: : 136iter [00:06, 30.24iter/s]
Best score: 12.4820: : 136iter [00:06, 30.24iter/s]
Best score: 12.4820: : 137iter [00:06, 30.24iter/s]
Best score: 12.4820: : 138iter [00:06, 30.24iter/s]
Best score: 12.4820: : 139iter [00:06, 30.24iter/s]
Best score: 12.4820: : 140iter [00:06, 29.46iter/s]
Best score: 12.4820: : 140iter [00:06, 29.46iter/s]
Best score: 12.4820: : 141iter [00:06, 29.46iter/s]
Best score: 12.4820: : 142iter [00:06, 29.46iter/s]
Best score: 12.4820: : 143iter [00:06, 29.46iter/s]
Best score: 12.4820: : 144iter [00:06, 30.00iter/s]
Best score: 12.4820: : 144iter [00:06, 30.00iter/s]
Best score: 12.4820: : 145iter [00:06, 30.00iter/s]
Best score: 12.4820: : 146iter [00:06, 30.00iter/s]
Best score: 12.4820: : 147iter [00:06, 30.00iter/s]
Best score: 12.4820: : 148iter [00:06, 30.41iter/s]
Best score: 12.4820: : 148iter [00:06, 30.41iter/s]
[ITER 150] Best: 12.4820
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 998.0000
  py: 760.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000

Best score: 12.4820: : 149iter [00:06, 30.41iter/s]
Best score: 12.4820: : 150iter [00:06, 30.41iter/s]
Best score: 12.4820: : 151iter [00:07, 30.41iter/s]
Best score: 12.4820: : 152iter [00:07, 25.71iter/s]
Best score: 12.4820: : 152iter [00:07, 25.71iter/s]
Best score: 12.4820: : 153iter [00:07, 25.71iter/s]
Best score: 12.4820: : 154iter [00:07, 25.71iter/s]
Best score: 12.4820: : 155iter [00:07, 25.71iter/s]
Best score: 12.4820: : 156iter [00:07, 27.77iter/s]
Best score: 12.4820: : 156iter [00:07, 27.77iter/s]
Best score: 12.4820: : 157iter [00:07, 27.77iter/s]
Best score: 12.4820: : 158iter [00:07, 27.77iter/s]
Best score: 12.4820: : 159iter [00:07, 27.77iter/s]
Best score: 12.4820: : 160iter [00:07, 29.07iter/s]
Best score: 12.4820: : 160iter [00:07, 29.07iter/s]
Best score: 12.4820: : 161iter [00:07, 29.07iter/s]
Best score: 12.4820: : 162iter [00:07, 29.07iter/s]
Best score: 12.4820: : 163iter [00:07, 29.07iter/s]
Best score: 12.4820: : 164iter [00:07, 29.93iter/s]
Best score: 12.4820: : 164iter [00:07, 29.93iter/s]
Best score: 12.4820: : 165iter [00:07, 29.93iter/s]
Best score: 12.4820: : 166iter [00:07, 29.93iter/s]
Best score: 12.4820: : 167iter [00:07, 29.93iter/s]
Best score: 12.4820: : 168iter [00:07, 30.65iter/s]
Best score: 12.4820: : 168iter [00:07, 30.65iter/s]
Best score: 12.4820: : 169iter [00:07, 30.65iter/s]
Best score: 12.4820: : 170iter [00:07, 30.65iter/s]
Best score: 12.4820: : 171iter [00:07, 30.65iter/s]
Best score: 12.4820: : 172iter [00:07, 30.88iter/s]
Best score: 12.4820: : 172iter [00:07, 30.88iter/s]
Best score: 12.4820: : 173iter [00:07, 30.88iter/s]
Best score: 12.4820: : 174iter [00:07, 30.88iter/s]
Best score: 12.4820: : 175iter [00:07, 30.88iter/s]
Best score: 12.4820: : 176iter [00:07, 31.00iter/s]
Best score: 12.4820: : 176iter [00:07, 31.00iter/s]
Best score: 12.4820: : 177iter [00:07, 31.00iter/s]
Best score: 12.4820: : 178iter [00:07, 31.00iter/s]
Best score: 12.4820: : 179iter [00:07, 31.00iter/s]
Best score: 12.4820: : 180iter [00:07, 30.62iter/s]
Best score: 12.4820: : 180iter [00:07, 30.62iter/s]
Best score: 12.4820: : 181iter [00:07, 30.62iter/s]
Best score: 12.4820: : 182iter [00:07, 30.62iter/s]
Best score: 12.4820: : 183iter [00:08, 30.62iter/s]
Best score: 12.4820: : 184iter [00:08, 30.97iter/s]
Best score: 12.4820: : 184iter [00:08, 30.97iter/s]
Best score: 12.4820: : 185iter [00:08, 30.97iter/s]
Best score: 12.4820: : 186iter [00:08, 30.97iter/s]
Best score: 12.4820: : 187iter [00:08, 30.97iter/s]
Best score: 12.4820: : 188iter [00:08, 31.11iter/s]
Best score: 12.4820: : 188iter [00:08, 31.11iter/s]
Best score: 12.4820: : 189iter [00:08, 31.11iter/s]
Best score: 12.4820: : 190iter [00:08, 31.11iter/s]
Best score: 12.4820: : 191iter [00:08, 31.11iter/s]
Best score: 12.4820: : 192iter [00:08, 32.24iter/s]
Best score: 12.4820: : 192iter [00:08, 32.24iter/s]
Best score: 12.4820: : 193iter [00:08, 32.24iter/s]
Best score: 12.4820: : 194iter [00:08, 32.24iter/s]
Best score: 12.4820: : 195iter [00:08, 32.24iter/s]
Best score: 12.4820: : 196iter [00:08, 32.55iter/s]
Best score: 12.4820: : 196iter [00:08, 32.55iter/s]
Best score: 12.4820: : 197iter [00:08, 32.55iter/s]
Best score: 12.4820: : 198iter [00:08, 32.55iter/s]
[ITER 200] Best: 12.4820
[PARAMS] Current best parameters:
  fx: 531.8027
  fy: 531.8027
  px: 998.0000
  py: 760.0000
  k1: 0.00000000
  k2: -0.00000000
  k3: 0.00000000
  k4: 0.00000000
  p1: 0.00000000
  p2: 0.00000000
  p3: 0.00000000
  p4: 0.00000000
  s1: 0.0000
  s2: 0.0000
  s3: 0.0000
  s4: 0.0000
  tilt_x: 0.0000
  tilt_y: 0.0000
  scale: 3.0000
  rotation: 0.0000

Best score: 12.4820: : 199iter [00:08, 32.55iter/s]
Best score: 12.4820: : 200iter [00:08, 31.00iter/s]
Best score: 12.4820: : 200iter [00:08, 31.00iter/s]
Best score: 12.4820: : 201iter [00:08, 31.00iter/s]
Best score: 12.4820: : 202iter [00:08, 31.00iter/s]
Best score: 12.4820: : 203iter [00:08, 31.00iter/s]
Best score: 12.4820: : 204iter [00:08, 26.25iter/s]
Best score: 12.4820: : 204iter [00:08, 26.25iter/s]
Best score: 12.4820: : 205iter [00:08, 26.25iter/s]
Best score: 12.4820: : 206iter [00:08, 26.25iter/s]
Best score: 12.4820: : 207iter [00:08, 26.88iter/s]
Best score: 12.4820: : 207iter [00:08, 26.88iter/s]
Best score: 12.4820: : 208iter [00:08, 26.88iter/s]
Best score: 12.4820: : 209iter [00:08, 26.88iter/s]
Best score: 12.4820: : 210iter [00:08, 26.88iter/s]
Best score: 12.4820: : 211iter [00:08, 27.76iter/s]
Best score: 12.4820: : 211iter [00:09, 27.76iter/s]
Best score: 12.4820: : 212iter [00:09, 27.76iter/s]
Best score: 12.4820: : 213iter [00:09, 27.76iter/s]
Best score: 12.4820: : 214iter [00:09, 27.83iter/s]
Best score: 12.4820: : 214iter [00:09, 27.83iter/s]
Best score: 12.4820: : 215iter [00:09, 27.83iter/s]
Best score: 12.4820: : 216iter [00:09, 27.83iter/s]
Best score: 12.4820: : 217iter [00:09, 28.18iter/s]
Best score: 12.4820: : 217iter [00:09, 28.18iter/s]
Best score: 12.4820: : 218iter [00:09, 28.18iter/s]
Best score: 12.4820: : 219iter [00:09, 28.18iter/s]
Best score: 12.4820: : 220iter [00:09, 28.18iter/s]
Best score: 12.4820: : 221iter [00:09, 29.17iter/s]
Best score: 12.4820: : 221iter [00:09, 29.17iter/s]
Best score: 12.4820: : 222iter [00:09, 29.17iter/s]
Best score: 12.4820: : 223iter [00:09, 29.17iter/s]
Best score: 12.4820: : 224iter [00:09, 29.17iter/s]
Best score: 12.4820: : 225iter [00:09, 31.10iter/s]
Best score: 12.4820: : 225iter [00:09, 31.10iter/s]
Best score: 12.4820: : 226iter [00:09, 31.10iter/s]
Best score: 12.4820: : 227iter [00:09, 31.10iter/s]
Best score: 12.4820: : 228iter [00:09, 31.10iter/s]
Best score: 12.4820: : 229iter [00:09, 32.26iter/s]
Best score: 12.4820: : 229iter [00:09, 32.26iter/s]
Best score: 12.4820: : 230iter [00:09, 32.26iter/s]
Best score: 12.4820: : 231iter [00:09, 32.26iter/s]
Best score: 12.4820: : 232iter [00:09, 32.26iter/s]
Best score: 12.4820: : 233iter [00:09, 32.42iter/s]
Best score: 12.4820: : 233iter [00:09, 32.42iter/s]
Best score: 12.4820: : 234iter [00:09, 32.42iter/s]
Best score: 12.4820: : 235iter [00:09, 32.42iter/s]
Best score: 12.4820: : 236iter [00:09, 32.42iter/s]
Best score: 12.4820: : 237iter [00:09, 32.94iter/s]
Best score: 12.4820: : 237iter [00:09, 32.94iter/s]
Best score: 12.4820: : 238iter [00:09, 32.94iter/s]
Best score: 12.4820: : 239iter [00:09, 32.94iter/s]
Best score: 12.4820: : 240iter [00:09, 32.94iter/s]
Best score: 12.4820: : 241iter [00:09, 32.74iter/s]
Best score: 12.4820: : 241iter [00:09, 32.74iter/s]
Best score: 12.4820: : 242iter [00:09, 32.74iter/s]
Best score: 12.4820: : 243iter [00:09, 32.74iter/s]
Best score: 12.4820: : 244iter [00:10, 32.74iter/s]
Best score: 12.4820: : 245iter [00:10, 34.09iter/s]
Best score: 12.4820: : 245iter [00:10, 34.09iter/s]
Best score: 12.4820: : 246iter [00:10, 34.09iter/s]
Best score: 12.4820: : 247iter [00:10, 34.09iter/s]
Best score: 12.4820: : 248iter [00:10, 34.09iter/s]
Best score: 12.4820: : 249iter [00:10, 35.09iter/s]
[ITER 250] Best: 10.7010
[PARAMS] Current best parameters:
  fx: 532.8985
  fy: 532.9857
  px: 1002.0183
  py: 756.2571
  k1: -0.04348180
  k2: 0.03982051
  k3: -0.00011325
  k4: -0.00569554
  p1: -0.00053133
  p2: 0.02266260
  p3: -0.01396273
  p4: 0.00075219
  s1: -0.0080
  s2: 0.0031
  s3: -0.0217
  s4: 0.0096
  tilt_x: -0.0124
  tilt_y: 0.0326
  scale: 3.0532
  rotation: -0.4752

Best score: 10.7010: : 249iter [00:10, 35.09iter/s]
Best score: 10.7010: : 250iter [00:10, 35.09iter/s]
Best score: 10.7010: : 251iter [00:10, 35.09iter/s]
Best score: 10.7010: : 252iter [00:10, 35.09iter/s]
Best score: 10.7010: : 253iter [00:10, 28.48iter/s]
Best score: 10.7010: : 253iter [00:10, 28.48iter/s]
Best score: 10.7010: : 254iter [00:10, 28.48iter/s]
Best score: 10.7010: : 255iter [00:10, 28.48iter/s]
Best score: 10.7010: : 256iter [00:10, 28.48iter/s]
Best score: 10.7010: : 257iter [00:10, 30.13iter/s]
Best score: 10.7010: : 257iter [00:10, 30.13iter/s]
Best score: 10.7010: : 258iter [00:10, 30.13iter/s]
Best score: 10.7010: : 259iter [00:10, 30.13iter/s]
Best score: 10.7010: : 260iter [00:10, 30.13iter/s]
Best score: 10.7010: : 261iter [00:10, 31.17iter/s]
Best score: 10.7010: : 261iter [00:10, 31.17iter/s]
Best score: 10.7010: : 262iter [00:10, 31.17iter/s]
Best score: 10.7010: : 263iter [00:10, 31.17iter/s]
Best score: 10.7010: : 264iter [00:10, 31.17iter/s]
Best score: 10.7010: : 265iter [00:10, 31.03iter/s]
Best score: 10.7010: : 265iter [00:10, 31.03iter/s]
Best score: 10.7010: : 266iter [00:10, 31.03iter/s]
Best score: 10.7010: : 267iter [00:10, 31.03iter/s]
Best score: 10.7010: : 268iter [00:10, 31.03iter/s]
Best score: 10.7010: : 269iter [00:10, 30.68iter/s]
Best score: 10.7010: : 269iter [00:10, 30.68iter/s]
Best score: 10.7010: : 270iter [00:10, 30.68iter/s]
Best score: 10.7010: : 271iter [00:10, 30.68iter/s]
Best score: 10.7010: : 272iter [00:10, 30.68iter/s]
Best score: 10.7010: : 273iter [00:10, 30.32iter/s]
Best score: 10.7010: : 273iter [00:10, 30.32iter/s]
Best score: 10.7010: : 274iter [00:11, 30.32iter/s]
Best score: 10.7010: : 275iter [00:11, 30.32iter/s]
Best score: 10.7010: : 276iter [00:11, 30.32iter/s]
Best score: 10.7010: : 277iter [00:11, 30.48iter/s]
Best score: 10.7010: : 277iter [00:11, 30.48iter/s]
Best score: 10.7010: : 278iter [00:11, 30.48iter/s]
Best score: 5.8863: : 279iter [00:11, 30.48iter/s] 
Best score: 5.8863: : 280iter [00:11, 30.48iter/s]
Best score: 5.8863: : 281iter [00:11, 30.05iter/s]
Best score: 5.8863: : 281iter [00:11, 30.05iter/s]
Best score: 5.8863: : 282iter [00:11, 30.05iter/s]
Best score: 5.8863: : 283iter [00:11, 30.05iter/s]
Best score: 5.8863: : 284iter [00:11, 30.05iter/s]
Best score: 5.8863: : 285iter [00:11, 29.85iter/s]
Best score: 5.8863: : 285iter [00:11, 29.85iter/s]
Best score: 5.8863: : 286iter [00:11, 29.85iter/s]
Best score: 5.8863: : 287iter [00:11, 29.85iter/s]
Best score: 5.8863: : 288iter [00:11, 29.85iter/s]
Best score: 5.8863: : 289iter [00:11, 29.86iter/s]
Best score: 4.2505: : 289iter [00:11, 29.86iter/s]
Best score: 4.2505: : 290iter [00:11, 29.86iter/s]
Best score: 4.2505: : 291iter [00:11, 29.86iter/s]
Best score: 4.2505: : 292iter [00:11, 29.86iter/s]
Best score: 4.2505: : 293iter [00:11, 29.73iter/s]
Best score: 4.2505: : 293iter [00:11, 29.73iter/s]
Best score: 4.2505: : 294iter [00:11, 29.73iter/s]
Best score: 4.2505: : 295iter [00:11, 29.73iter/s]
Best score: 4.2505: : 296iter [00:11, 29.16iter/s]
Best score: 4.2505: : 296iter [00:11, 29.16iter/s]
Best score: 4.2505: : 297iter [00:11, 29.16iter/s]
Best score: 4.2505: : 298iter [00:11, 29.16iter/s]
Best score: 4.2505: : 299iter [00:11, 29.14iter/s]
[ITER 300] Best: 4.0579
[PARAMS] Current best parameters:
  fx: 537.1775
  fy: 536.6236
  px: 993.6484
  py: 754.2722
  k1: 0.03449428
  k2: -0.02056898
  k3: -0.00239164
  k4: 0.00126136
  p1: -0.00017124
  p2: 0.00946082
  p3: -0.01962875
  p4: -0.00021125
  s1: 0.0060
  s2: -0.0034
  s3: -0.0098
  s4: 0.0113
  tilt_x: -0.0295
  tilt_y: 0.0421
  scale: 2.8860
  rotation: -0.5847

Best score: 4.0579: : 299iter [00:11, 29.14iter/s]
Best score: 4.0579: : 300iter [00:11, 29.14iter/s]
Best score: 4.0579: : 301iter [00:12, 29.14iter/s]
Best score: 4.0579: : 302iter [00:12, 24.25iter/s]
Best score: 4.0579: : 302iter [00:12, 24.25iter/s]
Best score: 4.0579: : 303iter [00:12, 24.25iter/s]
Best score: 4.0579: : 304iter [00:12, 24.25iter/s]
Best score: 4.0579: : 305iter [00:12, 24.25iter/s]
Best score: 4.0579: : 306iter [00:12, 25.86iter/s]
Best score: 4.0579: : 306iter [00:12, 25.86iter/s]
Best score: 4.0579: : 307iter [00:12, 25.86iter/s]
Best score: 4.0579: : 308iter [00:12, 25.86iter/s]
Best score: 4.0579: : 309iter [00:12, 26.84iter/s]
Best score: 3.7071: : 309iter [00:12, 26.84iter/s]
Best score: 3.7071: : 310iter [00:12, 26.84iter/s]
Best score: 3.7071: : 311iter [00:12, 26.84iter/s]
Best score: 3.7071: : 312iter [00:12, 26.84iter/s]
Best score: 3.7071: : 313iter [00:12, 27.91iter/s]
Best score: 3.7071: : 313iter [00:12, 27.91iter/s]
Best score: 3.7071: : 314iter [00:12, 27.91iter/s]
Best score: 3.7071: : 315iter [00:12, 27.91iter/s]
Best score: 3.7071: : 316iter [00:12, 27.91iter/s]
Best score: 3.7071: : 317iter [00:12, 29.11iter/s]
Best score: 3.7071: : 317iter [00:12, 29.11iter/s]
Best score: 3.7071: : 318iter [00:12, 29.11iter/s]
Best score: 2.1685: : 319iter [00:12, 29.11iter/s]
Best score: 2.1685: : 320iter [00:12, 29.28iter/s]
Best score: 2.1685: : 320iter [00:12, 29.28iter/s]
Best score: 2.1685: : 321iter [00:12, 29.28iter/s]
Best score: 2.1685: : 322iter [00:12, 29.28iter/s]
Best score: 2.1685: : 323iter [00:12, 28.47iter/s]
Best score: 2.1685: : 323iter [00:12, 28.47iter/s]
Best score: 2.1685: : 324iter [00:12, 28.47iter/s]
Best score: 2.1685: : 325iter [00:12, 28.47iter/s]
Best score: 2.1685: : 326iter [00:12, 28.47iter/s]
Best score: 2.1685: : 327iter [00:12, 30.55iter/s]
Best score: 2.1685: : 327iter [00:12, 30.55iter/s]
Best score: 2.1685: : 328iter [00:12, 30.55iter/s]
Best score: 2.0891: : 329iter [00:12, 30.55iter/s]
Best score: 2.0891: : 330iter [00:12, 30.55iter/s]
Best score: 2.0891: : 331iter [00:12, 31.91iter/s]
Best score: 2.0891: : 331iter [00:12, 31.91iter/s]
Best score: 2.0891: : 332iter [00:13, 31.91iter/s]
Best score: 2.0891: : 333iter [00:13, 31.91iter/s]
Best score: 2.0891: : 334iter [00:13, 31.91iter/s]
Best score: 2.0891: : 335iter [00:13, 33.00iter/s]
Best score: 2.0891: : 335iter [00:13, 33.00iter/s]
Best score: 2.0891: : 336iter [00:13, 33.00iter/s]
Best score: 2.0891: : 337iter [00:13, 33.00iter/s]
Best score: 2.0891: : 338iter [00:13, 33.00iter/s]
Best score: 2.0891: : 339iter [00:13, 33.16iter/s]
Best score: 1.9299: : 339iter [00:13, 33.16iter/s]
Best score: 1.9299: : 340iter [00:13, 33.16iter/s]
Best score: 1.9299: : 341iter [00:13, 33.16iter/s]
Best score: 1.9299: : 342iter [00:13, 33.16iter/s]
Best score: 1.9299: : 343iter [00:13, 31.94iter/s]
Best score: 1.9299: : 343iter [00:13, 31.94iter/s]
Best score: 1.9299: : 344iter [00:13, 31.94iter/s]
Best score: 1.9299: : 345iter [00:13, 31.94iter/s]
Best score: 1.9299: : 346iter [00:13, 31.94iter/s]
Best score: 1.9299: : 347iter [00:13, 31.84iter/s]
Best score: 1.9299: : 347iter [00:13, 31.84iter/s]
Best score: 1.9299: : 348iter [00:13, 31.84iter/s]
[ITER 350] Best: 1.8666
[PARAMS] Current best parameters:
  fx: 539.7547
  fy: 539.3462
  px: 987.0376
  py: 756.3220
  k1: 0.02917235
  k2: -0.03912213
  k3: 0.01887039
  k4: -0.00450978
  p1: 0.00772415
  p2: 0.00262377
  p3: -0.00925774
  p4: -0.00932597
  s1: 0.0075
  s2: 0.0035
  s3: -0.0111
  s4: 0.0136
  tilt_x: -0.0435
  tilt_y: 0.0655
  scale: 2.9262
  rotation: -0.7167

Best score: 1.8666: : 349iter [00:13, 31.84iter/s]
Best score: 1.8666: : 350iter [00:13, 31.84iter/s]
Best score: 1.8666: : 351iter [00:13, 27.08iter/s]
Best score: 1.8666: : 351iter [00:13, 27.08iter/s]
Best score: 1.8666: : 352iter [00:13, 27.08iter/s]
Best score: 1.8666: : 353iter [00:13, 27.08iter/s]
Best score: 1.8666: : 354iter [00:13, 27.08iter/s]
Best score: 1.8666: : 355iter [00:13, 28.53iter/s]
Best score: 1.8666: : 355iter [00:13, 28.53iter/s]
Best score: 1.8666: : 356iter [00:13, 28.53iter/s]
Best score: 1.8666: : 357iter [00:13, 28.53iter/s]
Best score: 1.8666: : 358iter [00:13, 28.53iter/s]
Best score: 1.8666: : 359iter [00:13, 29.46iter/s]
Best score: 1.5893: : 359iter [00:13, 29.46iter/s]
Best score: 1.5893: : 360iter [00:13, 29.46iter/s]
Best score: 1.5893: : 361iter [00:13, 29.46iter/s]
Best score: 1.5893: : 362iter [00:14, 29.46iter/s]
Best score: 1.5893: : 363iter [00:14, 23.82iter/s]
Best score: 1.5893: : 363iter [00:14, 23.82iter/s]
Best score: 1.5893: : 364iter [00:14, 23.82iter/s]
Best score: 1.5893: : 365iter [00:14, 23.82iter/s]
Best score: 1.5893: : 366iter [00:14, 23.82iter/s]
Best score: 1.5893: : 367iter [00:14, 26.23iter/s]
Best score: 1.5893: : 367iter [00:14, 26.23iter/s]
Best score: 1.5893: : 368iter [00:14, 26.23iter/s]
Best score: 1.4298: : 369iter [00:14, 26.23iter/s]
Best score: 1.4298: : 370iter [00:14, 26.23iter/s]
Best score: 1.4298: : 371iter [00:14, 28.20iter/s]
Best score: 1.4298: : 371iter [00:14, 28.20iter/s]
Best score: 1.4298: : 372iter [00:14, 28.20iter/s]
Best score: 1.4298: : 373iter [00:14, 28.20iter/s]
Best score: 1.4298: : 374iter [00:14, 28.20iter/s]
Best score: 1.4298: : 375iter [00:14, 28.86iter/s]
Best score: 1.4298: : 375iter [00:14, 28.86iter/s]
Best score: 1.4298: : 376iter [00:14, 28.86iter/s]
Best score: 1.4298: : 377iter [00:14, 28.86iter/s]
Best score: 1.4298: : 378iter [00:14, 28.86iter/s]
Best score: 1.4298: : 379iter [00:14, 30.68iter/s]
Best score: 0.8895: : 379iter [00:14, 30.68iter/s]
Best score: 0.8895: : 380iter [00:14, 30.68iter/s]
Best score: 0.8895: : 381iter [00:14, 30.68iter/s]
Best score: 0.8895: : 382iter [00:14, 30.68iter/s]
Best score: 0.8895: : 383iter [00:14, 31.94iter/s]
Best score: 0.8895: : 383iter [00:14, 31.94iter/s]
Best score: 0.8895: : 384iter [00:14, 31.94iter/s]
Best score: 0.8895: : 385iter [00:14, 31.94iter/s]
Best score: 0.8895: : 386iter [00:14, 31.94iter/s]
Best score: 0.8895: : 387iter [00:14, 32.89iter/s]
Best score: 0.8895: : 387iter [00:14, 32.89iter/s]
Best score: 0.8895: : 388iter [00:14, 32.89iter/s]
Best score: 0.7773: : 389iter [00:14, 32.89iter/s]
Best score: 0.7773: : 390iter [00:14, 32.89iter/s]
Best score: 0.7773: : 391iter [00:14, 32.54iter/s]
Best score: 0.7773: : 391iter [00:14, 32.54iter/s]
Best score: 0.7773: : 392iter [00:15, 32.54iter/s]
Best score: 0.7773: : 393iter [00:15, 32.54iter/s]
Best score: 0.7773: : 394iter [00:15, 32.54iter/s]
Best score: 0.7773: : 395iter [00:15, 32.50iter/s]
Best score: 0.7773: : 395iter [00:15, 32.50iter/s]
Best score: 0.7773: : 396iter [00:15, 32.50iter/s]
Best score: 0.7773: : 397iter [00:15, 32.50iter/s]
Best score: 0.7773: : 398iter [00:15, 32.50iter/s]
Best score: 0.7773: : 399iter [00:15, 33.67iter/s]
[ITER 400] Best: 0.7773
Best score: 0.7773: : 399iter [00:15, 33.67iter/s]
[PARAMS] Current best parameters:
  fx: 540.3612
  fy: 539.4737
  px: 995.6356
  py: 759.5037
  k1: 0.00920610
  k2: -0.01435737
  k3: 0.00782968
  k4: -0.00271421
  p1: -0.00238300
  p2: 0.00048682
  p3: 0.00234047
  p4: 0.00071247
  s1: -0.0101
  s2: 0.0020
  s3: -0.0025
  s4: 0.0010
  tilt_x: -0.0058
  tilt_y: 0.0200
  scale: 2.9397
  rotation: -0.5561

Best score: 0.7773: : 400iter [00:15, 33.67iter/s]
Best score: 0.7773: : 401iter [00:15, 33.67iter/s]
Best score: 0.7773: : 402iter [00:15, 33.67iter/s]
Best score: 0.7773: : 403iter [00:15, 28.24iter/s]
Best score: 0.7773: : 403iter [00:15, 28.24iter/s]
Best score: 0.7773: : 404iter [00:15, 28.24iter/s]
Best score: 0.7773: : 405iter [00:15, 28.24iter/s]
Best score: 0.7773: : 406iter [00:15, 28.24iter/s]
Best score: 0.7773: : 407iter [00:15, 28.77iter/s]
Best score: 0.7773: : 407iter [00:15, 28.77iter/s]
Best score: 0.7773: : 408iter [00:15, 28.77iter/s]
Best score: 0.7073: : 409iter [00:15, 28.77iter/s]
Best score: 0.7073: : 410iter [00:15, 28.77iter/s]
Best score: 0.7073: : 411iter [00:15, 30.09iter/s]
Best score: 0.7073: : 411iter [00:15, 30.09iter/s]
Best score: 0.7073: : 412iter [00:15, 30.09iter/s]
Best score: 0.7073: : 413iter [00:15, 30.09iter/s]
Best score: 0.7073: : 414iter [00:15, 30.09iter/s]
Best score: 0.7073: : 415iter [00:15, 30.96iter/s]
Best score: 0.7073: : 415iter [00:15, 30.96iter/s]
Best score: 0.7073: : 416iter [00:15, 30.96iter/s]
Best score: 0.7073: : 417iter [00:15, 30.96iter/s]
Best score: 0.7073: : 418iter [00:15, 30.96iter/s]
Best score: 0.7073: : 419iter [00:15, 31.60iter/s]
Best score: 0.5948: : 419iter [00:15, 31.60iter/s]
Best score: 0.5948: : 420iter [00:15, 31.60iter/s]
Best score: 0.5948: : 421iter [00:15, 31.60iter/s]
Best score: 0.5948: : 422iter [00:16, 31.60iter/s]
Best score: 0.5948: : 423iter [00:16, 31.52iter/s]
Best score: 0.5948: : 423iter [00:16, 31.52iter/s]
Best score: 0.5948: : 424iter [00:16, 31.52iter/s]
Best score: 0.5948: : 425iter [00:16, 31.52iter/s]
Best score: 0.5948: : 426iter [00:16, 31.52iter/s]
Best score: 0.5948: : 427iter [00:16, 31.82iter/s]
Best score: 0.5948: : 427iter [00:16, 31.82iter/s]
Best score: 0.5948: : 428iter [00:16, 31.82iter/s]
Best score: 0.5948: : 429iter [00:16, 31.82iter/s]
Best score: 0.5948: : 430iter [00:16, 31.82iter/s]
Best score: 0.5948: : 431iter [00:16, 31.74iter/s]
Best score: 0.5948: : 431iter [00:16, 31.74iter/s]
Best score: 0.5948: : 432iter [00:16, 31.74iter/s]
Best score: 0.5948: : 433iter [00:16, 31.74iter/s]
Best score: 0.5948: : 434iter [00:16, 31.74iter/s]
Best score: 0.5948: : 435iter [00:16, 32.26iter/s]
Best score: 0.5948: : 435iter [00:16, 32.26iter/s]
Best score: 0.5948: : 436iter [00:16, 32.26iter/s]
Best score: 0.5948: : 437iter [00:16, 32.26iter/s]
Best score: 0.5948: : 438iter [00:16, 32.26iter/s]
Best score: 0.5948: : 439iter [00:16, 32.65iter/s]
Best score: 0.4924: : 439iter [00:16, 32.65iter/s]
Best score: 0.4924: : 440iter [00:16, 32.65iter/s]
Best score: 0.4924: : 441iter [00:16, 32.65iter/s]
Best score: 0.4924: : 442iter [00:16, 32.65iter/s]
Best score: 0.4924: : 443iter [00:16, 33.29iter/s]
Best score: 0.4924: : 443iter [00:16, 33.29iter/s]
Best score: 0.4924: : 444iter [00:16, 33.29iter/s]
Best score: 0.4924: : 445iter [00:16, 33.29iter/s]
Best score: 0.4924: : 446iter [00:16, 33.29iter/s]
Best score: 0.4924: : 447iter [00:16, 33.24iter/s]
Best score: 0.4924: : 447iter [00:16, 33.24iter/s]
Best score: 0.4924: : 448iter [00:16, 33.24iter/s]
[ITER 450] Best: 0.4924
[PARAMS] Current best parameters:
  fx: 538.0547
  fy: 537.2502
  px: 996.3835
  py: 759.8826
  k1: 0.01870961
  k2: -0.02020711
  k3: 0.00597714
  k4: -0.00118053
  p1: -0.00316297
  p2: 0.00398960
  p3: 0.00485701
  p4: -0.00003725
  s1: -0.0120
  s2: 0.0000
  s3: 0.0000
  s4: -0.0001
  tilt_x: -0.0027
  tilt_y: 0.0223
  scale: 2.9429
  rotation: -0.6106

Best score: 0.4924: : 449iter [00:16, 33.24iter/s]
Best score: 0.4924: : 450iter [00:16, 33.24iter/s]
Best score: 0.4924: : 451iter [00:16, 28.01iter/s]
Best score: 0.4924: : 451iter [00:16, 28.01iter/s]
Best score: 0.4924: : 452iter [00:16, 28.01iter/s]
Best score: 0.4924: : 453iter [00:17, 28.01iter/s]
Best score: 0.4924: : 454iter [00:17, 28.01iter/s]
Best score: 0.4924: : 455iter [00:17, 30.09iter/s]
Best score: 0.4924: : 455iter [00:17, 30.09iter/s]
Best score: 0.4924: : 456iter [00:17, 30.09iter/s]
Best score: 0.4924: : 457iter [00:17, 30.09iter/s]
Best score: 0.4924: : 458iter [00:17, 30.09iter/s]
Best score: 0.4924: : 459iter [00:17, 30.94iter/s]
Best score: 0.4706: : 459iter [00:17, 30.94iter/s]
Best score: 0.4706: : 460iter [00:17, 30.94iter/s]
Best score: 0.4706: : 461iter [00:17, 30.94iter/s]
Best score: 0.4706: : 462iter [00:17, 30.94iter/s]
Best score: 0.4706: : 463iter [00:17, 30.65iter/s]
Best score: 0.4706: : 463iter [00:17, 30.65iter/s]
Best score: 0.4706: : 464iter [00:17, 30.65iter/s]
Best score: 0.4706: : 465iter [00:17, 30.65iter/s]
Best score: 0.4706: : 466iter [00:17, 30.65iter/s]
Best score: 0.4706: : 467iter [00:17, 30.81iter/s]
Best score: 0.4706: : 467iter [00:17, 30.81iter/s]
Best score: 0.4706: : 468iter [00:17, 30.81iter/s]
Best score: 0.4381: : 469iter [00:17, 30.81iter/s]
Best score: 0.4381: : 470iter [00:17, 30.81iter/s]
Best score: 0.4381: : 471iter [00:17, 31.87iter/s]
Best score: 0.4381: : 471iter [00:17, 31.87iter/s]
Best score: 0.4381: : 472iter [00:17, 31.87iter/s]
Best score: 0.4381: : 473iter [00:17, 31.87iter/s]
Best score: 0.4381: : 474iter [00:17, 31.87iter/s]
Best score: 0.4381: : 475iter [00:17, 32.68iter/s]
Best score: 0.4381: : 475iter [00:17, 32.68iter/s]
Best score: 0.4381: : 476iter [00:17, 32.68iter/s]
Best score: 0.4381: : 477iter [00:17, 32.68iter/s]
Best score: 0.4381: : 478iter [00:17, 32.68iter/s]
Best score: 0.4381: : 479iter [00:17, 31.96iter/s]
Best score: 0.4381: : 479iter [00:17, 31.96iter/s]
Best score: 0.4381: : 480iter [00:17, 31.96iter/s]
Best score: 0.4381: : 481iter [00:17, 31.96iter/s]
Best score: 0.4381: : 482iter [00:17, 31.96iter/s]
Best score: 0.4381: : 483iter [00:17, 31.46iter/s]
Best score: 0.4381: : 483iter [00:17, 31.46iter/s]
Best score: 0.4381: : 484iter [00:17, 31.46iter/s]
Best score: 0.4381: : 485iter [00:17, 31.46iter/s]
Best score: 0.4381: : 486iter [00:18, 31.46iter/s]
Best score: 0.4381: : 487iter [00:18, 32.45iter/s]
Best score: 0.4381: : 487iter [00:18, 32.45iter/s]
Best score: 0.4381: : 488iter [00:18, 32.45iter/s]
Best score: 0.3937: : 489iter [00:18, 32.45iter/s]
Best score: 0.3937: : 490iter [00:18, 32.45iter/s]
Best score: 0.3937: : 491iter [00:18, 33.27iter/s]
Best score: 0.3937: : 491iter [00:18, 33.27iter/s]
Best score: 0.3937: : 492iter [00:18, 33.27iter/s]
Best score: 0.3937: : 493iter [00:18, 33.27iter/s]
Best score: 0.3937: : 494iter [00:18, 33.27iter/s]
Best score: 0.3937: : 495iter [00:18, 32.52iter/s]
Best score: 0.3937: : 495iter [00:18, 32.52iter/s]
Best score: 0.3937: : 496iter [00:18, 32.52iter/s]
Best score: 0.3937: : 497iter [00:18, 32.52iter/s]
Best score: 0.3937: : 498iter [00:18, 32.52iter/s]
Best score: 0.3937: : 499iter [00:18, 32.36iter/s]
[ITER 500] Best: 0.3937
[PARAMS] Current best parameters:
  fx: 534.4048
  fy: 533.8264
  px: 997.2628
  py: 759.4478
  k1: 0.02453597
  k2: -0.02393981
  k3: 0.00627049
  k4: -0.00071106
  p1: -0.00191377
  p2: 0.00568892
  p3: 0.00477122
  p4: -0.00106048
  s1: -0.0098
  s2: -0.0005
  s3: 0.0023
  s4: -0.0010
  tilt_x: -0.0081
  tilt_y: 0.0211
  scale: 2.9523
  rotation: -0.6342

Best score: 0.3937: : 499iter [00:18, 32.36iter/s]
Best score: 0.3937: : 500iter [00:18, 32.36iter/s]
Best score: 0.3937: : 501iter [00:18, 32.36iter/s]
Best score: 0.3937: : 502iter [00:18, 32.36iter/s]
Best score: 0.3937: : 503iter [00:18, 27.26iter/s]
Best score: 0.3937: : 503iter [00:18, 27.26iter/s]
Best score: 0.3937: : 504iter [00:18, 27.26iter/s]
Best score: 0.3937: : 505iter [00:18, 27.26iter/s]
Best score: 0.3937: : 506iter [00:18, 27.26iter/s]
Best score: 0.3937: : 507iter [00:18, 28.60iter/s]
Best score: 0.3937: : 507iter [00:18, 28.60iter/s]
Best score: 0.3937: : 508iter [00:18, 28.60iter/s]
Best score: 0.3622: : 509iter [00:18, 28.60iter/s]
Best score: 0.3622: : 510iter [00:18, 28.60iter/s]
Best score: 0.3622: : 511iter [00:18, 29.76iter/s]
Best score: 0.3622: : 511iter [00:18, 29.76iter/s]
Best score: 0.3622: : 512iter [00:18, 29.76iter/s]
Best score: 0.3622: : 513iter [00:18, 29.76iter/s]
Best score: 0.3622: : 514iter [00:18, 29.76iter/s]
Best score: 0.3622: : 515iter [00:18, 31.30iter/s]
Best score: 0.3622: : 515iter [00:18, 31.30iter/s]
Best score: 0.3622: : 516iter [00:19, 31.30iter/s]
Best score: 0.3622: : 517iter [00:19, 31.30iter/s]
Best score: 0.3622: : 518iter [00:19, 31.30iter/s]
Best score: 0.3622: : 519iter [00:19, 32.60iter/s]
Best score: 0.3527: : 519iter [00:19, 32.60iter/s]
Best score: 0.3527: : 520iter [00:19, 32.60iter/s]
Best score: 0.3527: : 521iter [00:19, 32.60iter/s]
Best score: 0.3527: : 522iter [00:19, 32.60iter/s]
Best score: 0.3527: : 523iter [00:19, 32.78iter/s]
Best score: 0.3527: : 523iter [00:19, 32.78iter/s]
Best score: 0.3527: : 524iter [00:19, 32.78iter/s]
Best score: 0.3527: : 525iter [00:19, 32.78iter/s]
Best score: 0.3527: : 526iter [00:19, 32.78iter/s]
Best score: 0.3527: : 527iter [00:19, 32.81iter/s]
Best score: 0.3527: : 527iter [00:19, 32.81iter/s]
Best score: 0.3527: : 528iter [00:19, 32.81iter/s]
Best score: 0.3527: : 529iter [00:19, 32.81iter/s]
Best score: 0.3527: : 530iter [00:19, 32.81iter/s]
Best score: 0.3527: : 531iter [00:19, 32.00iter/s]
Best score: 0.3527: : 531iter [00:19, 32.00iter/s]
Best score: 0.3527: : 532iter [00:19, 32.00iter/s]
Best score: 0.3527: : 533iter [00:19, 32.00iter/s]
Best score: 0.3527: : 534iter [00:19, 32.00iter/s]
Best score: 0.3527: : 535iter [00:19, 30.95iter/s]
Best score: 0.3527: : 535iter [00:19, 30.95iter/s]
Best score: 0.3527: : 536iter [00:19, 30.95iter/s]
Best score: 0.3527: : 537iter [00:19, 30.95iter/s]
Best score: 0.3527: : 538iter [00:19, 30.95iter/s]
Best score: 0.3527: : 539iter [00:19, 31.43iter/s]
Best score: 0.3527: : 539iter [00:19, 31.43iter/s]
Best score: 0.3527: : 540iter [00:19, 31.43iter/s]
Best score: 0.3527: : 541iter [00:19, 31.43iter/s]
Best score: 0.3527: : 542iter [00:19, 31.43iter/s]
Best score: 0.3527: : 543iter [00:19, 32.04iter/s]
Best score: 0.3527: : 543iter [00:19, 32.04iter/s]
Best score: 0.3527: : 544iter [00:19, 32.04iter/s]
Best score: 0.3527: : 545iter [00:19, 32.04iter/s]
Best score: 0.3527: : 546iter [00:19, 32.04iter/s]
Best score: 0.3527: : 547iter [00:19, 31.84iter/s]
Best score: 0.3527: : 547iter [00:19, 31.84iter/s]
Best score: 0.3527: : 548iter [00:20, 31.84iter/s]
[ITER 550] Best: 0.3297
[PARAMS] Current best parameters:
  fx: 537.6307
  fy: 536.9293
  px: 996.6252
  py: 758.7410
  k1: 0.01726047
  k2: -0.02359464
  k3: 0.00940022
  k4: -0.00182937
  p1: -0.00496793
  p2: 0.00328408
  p3: 0.00380923
  p4: -0.00172034
  s1: -0.0100
  s2: 0.0021
  s3: 0.0017
  s4: 0.0013
  tilt_x: -0.0049
  tilt_y: 0.0200
  scale: 2.9628
  rotation: -0.6078

Best score: 0.3297: : 549iter [00:20, 31.84iter/s]
[Periodic outlier @ iter 450] Removing 1000 frame 2 (score=1.40, threshold=1.10)
  Remaining frames: 19

Best score: 0.3297: : 550iter [00:20, 31.84iter/s]
Best score: 0.3297: : 551iter [00:20, 25.10iter/s]
Best score: 0.3297: : 551iter [00:20, 25.10iter/s]
Best score: 0.3297: : 552iter [00:20, 25.10iter/s]
Best score: 0.3297: : 553iter [00:20, 25.10iter/s]
Best score: 0.3297: : 554iter [00:20, 25.10iter/s]
Best score: 0.3297: : 555iter [00:20, 27.89iter/s]
Best score: 0.3297: : 555iter [00:20, 27.89iter/s]
Best score: 0.3297: : 556iter [00:20, 27.89iter/s]
Best score: 0.3297: : 557iter [00:20, 27.89iter/s]
Best score: 0.3297: : 558iter [00:20, 27.89iter/s]
Best score: 0.3297: : 559iter [00:20, 29.28iter/s]
Best score: 0.2736: : 559iter [00:20, 29.28iter/s]
Best score: 0.2736: : 560iter [00:20, 29.28iter/s]
Best score: 0.2736: : 561iter [00:20, 29.28iter/s]
Best score: 0.2736: : 562iter [00:20, 29.28iter/s]
Best score: 0.2736: : 563iter [00:20, 30.38iter/s]
Best score: 0.2736: : 563iter [00:20, 30.38iter/s]
Best score: 0.2736: : 564iter [00:20, 30.38iter/s]
Best score: 0.2736: : 565iter [00:20, 30.38iter/s]
Best score: 0.2736: : 566iter [00:20, 30.38iter/s]
Best score: 0.2736: : 567iter [00:20, 31.85iter/s]
Best score: 0.2736: : 567iter [00:20, 31.85iter/s]
Best score: 0.2736: : 568iter [00:20, 31.85iter/s]
Best score: 0.2736: : 569iter [00:20, 31.85iter/s]
Best score: 0.2736: : 570iter [00:20, 31.85iter/s]
Best score: 0.2736: : 571iter [00:20, 32.60iter/s]
Best score: 0.2736: : 571iter [00:20, 32.60iter/s]
Best score: 0.2736: : 572iter [00:20, 32.60iter/s]
Best score: 0.2736: : 573iter [00:20, 32.60iter/s]
Best score: 0.2736: : 574iter [00:20, 32.60iter/s]
Best score: 0.2736: : 575iter [00:20, 33.72iter/s]
Best score: 0.2736: : 575iter [00:20, 33.72iter/s]
Best score: 0.2736: : 576iter [00:20, 33.72iter/s]
Best score: 0.2736: : 577iter [00:20, 33.72iter/s]
Best score: 0.2736: : 578iter [00:20, 33.72iter/s]
Best score: 0.2736: : 579iter [00:20, 34.48iter/s]
Best score: 0.2736: : 579iter [00:21, 34.48iter/s]
Best score: 0.2736: : 580iter [00:21, 34.48iter/s]
Best score: 0.2736: : 581iter [00:21, 34.48iter/s]
Best score: 0.2736: : 582iter [00:21, 34.48iter/s]
Best score: 0.2736: : 583iter [00:21, 33.82iter/s]
Best score: 0.2736: : 583iter [00:21, 33.82iter/s]
Best score: 0.2736: : 584iter [00:21, 33.82iter/s]
Best score: 0.2736: : 585iter [00:21, 33.82iter/s]
Best score: 0.2736: : 586iter [00:21, 33.82iter/s]
Best score: 0.2736: : 587iter [00:21, 32.54iter/s]
Best score: 0.2736: : 587iter [00:21, 32.54iter/s]
Best score: 0.2736: : 588iter [00:21, 32.54iter/s]
Best score: 0.2736: : 589iter [00:21, 32.54iter/s]
Best score: 0.2736: : 590iter [00:21, 32.54iter/s]
Best score: 0.2736: : 591iter [00:21, 33.46iter/s]
Best score: 0.2736: : 591iter [00:21, 33.46iter/s]
Best score: 0.2736: : 592iter [00:21, 33.46iter/s]
Best score: 0.2736: : 593iter [00:21, 33.46iter/s]
Best score: 0.2736: : 594iter [00:21, 33.46iter/s]
Best score: 0.2736: : 595iter [00:21, 33.64iter/s]
Best score: 0.2736: : 595iter [00:21, 33.64iter/s]
Best score: 0.2736: : 596iter [00:21, 33.64iter/s]
Best score: 0.2736: : 597iter [00:21, 33.64iter/s]
Best score: 0.2736: : 598iter [00:21, 33.64iter/s]
Best score: 0.2736: : 599iter [00:21, 33.12iter/s]
[ITER 600] Best: 0.2736
[PARAMS] Current best parameters:
  fx: 537.6307
  fy: 536.9293
  px: 996.6252
  py: 758.7410
  k1: 0.01726047
  k2: -0.02359464
  k3: 0.00940022
  k4: -0.00182937
  p1: -0.00496793
  p2: 0.00328408
  p3: 0.00380923
  p4: -0.00172034
  s1: -0.0100
  s2: 0.0021
  s3: 0.0017
  s4: 0.0013
  tilt_x: -0.0049
  tilt_y: 0.0200
  scale: 2.9628
  rotation: -0.6078

Best score: 0.2736: : 599iter [00:21, 33.12iter/s]
Best score: 0.2736: : 600iter [00:21, 33.12iter/s]
Best score: 0.2736: : 601iter [00:21, 33.12iter/s]
Best score: 0.2736: : 602iter [00:21, 33.12iter/s]
Best score: 0.2736: : 603iter [00:21, 27.81iter/s]
Best score: 0.2736: : 603iter [00:21, 27.81iter/s]
Best score: 0.2736: : 604iter [00:21, 27.81iter/s]
Best score: 0.2736: : 605iter [00:21, 27.81iter/s]
Best score: 0.2736: : 606iter [00:21, 28.28iter/s]
Best score: 0.2736: : 606iter [00:21, 28.28iter/s]
Best score: 0.2736: : 607iter [00:21, 28.28iter/s]
Best score: 0.2736: : 608iter [00:21, 28.28iter/s]
Best score: 0.2736: : 609iter [00:22, 28.28iter/s]
Best score: 0.2736: : 610iter [00:22, 29.07iter/s]
Best score: 0.2736: : 610iter [00:22, 29.07iter/s]
Best score: 0.2736: : 611iter [00:22, 29.07iter/s]
Best score: 0.2736: : 612iter [00:22, 29.07iter/s]
Best score: 0.2736: : 613iter [00:22, 28.94iter/s]
Best score: 0.2736: : 613iter [00:22, 28.94iter/s]
Best score: 0.2736: : 614iter [00:22, 28.94iter/s]
Best score: 0.2736: : 615iter [00:22, 28.94iter/s]
Best score: 0.2736: : 616iter [00:22, 28.94iter/s]
Best score: 0.2736: : 617iter [00:22, 29.48iter/s]
Best score: 0.2736: : 617iter [00:22, 29.48iter/s]
Best score: 0.2736: : 618iter [00:22, 29.48iter/s]
Best score: 0.2736: : 619iter [00:22, 29.48iter/s]
Best score: 0.2736: : 620iter [00:22, 28.82iter/s]
Best score: 0.2736: : 620iter [00:22, 28.82iter/s]
Best score: 0.2736: : 621iter [00:22, 28.82iter/s]
Best score: 0.2736: : 622iter [00:22, 28.82iter/s]
Best score: 0.2736: : 623iter [00:22, 28.82iter/s]
Best score: 0.2736: : 624iter [00:22, 29.37iter/s]
Best score: 0.2736: : 624iter [00:22, 29.37iter/s]
Best score: 0.2736: : 625iter [00:22, 29.37iter/s]
Best score: 0.2736: : 626iter [00:22, 29.37iter/s]
Best score: 0.2736: : 627iter [00:22, 29.37iter/s]
Best score: 0.2736: : 628iter [00:22, 29.92iter/s]
Best score: 0.2736: : 628iter [00:22, 29.92iter/s]
Best score: 0.2736: : 629iter [00:22, 29.92iter/s]
Best score: 0.2736: : 630iter [00:22, 29.92iter/s]
Best score: 0.2736: : 631iter [00:22, 29.15iter/s]
Best score: 0.2736: : 631iter [00:22, 29.15iter/s]
Best score: 0.2736: : 632iter [00:22, 29.15iter/s]
Best score: 0.2736: : 633iter [00:22, 29.15iter/s]
Best score: 0.2736: : 634iter [00:22, 29.15iter/s]
Best score: 0.2736: : 635iter [00:22, 29.70iter/s]
Best score: 0.2736: : 635iter [00:22, 29.70iter/s]
Best score: 0.2736: : 636iter [00:22, 29.70iter/s]
Best score: 0.2736: : 637iter [00:22, 29.70iter/s]
Best score: 0.2736: : 638iter [00:22, 29.33iter/s]
Best score: 0.2736: : 638iter [00:22, 29.33iter/s]
Best score: 0.2736: : 639iter [00:23, 29.33iter/s]
Best score: 0.2736: : 640iter [00:23, 29.33iter/s]
Best score: 0.2736: : 641iter [00:23, 29.33iter/s]
Best score: 0.2736: : 642iter [00:23, 29.94iter/s]
Best score: 0.2736: : 642iter [00:23, 29.94iter/s]
Best score: 0.2736: : 643iter [00:23, 29.94iter/s]
Best score: 0.2736: : 644iter [00:23, 29.94iter/s]
Best score: 0.2736: : 645iter [00:23, 29.94iter/s]
Best score: 0.2736: : 646iter [00:23, 30.79iter/s]
Best score: 0.2736: : 646iter [00:23, 30.79iter/s]
Best score: 0.2736: : 647iter [00:23, 30.79iter/s]
Best score: 0.2736: : 648iter [00:23, 30.79iter/s]
[ITER 650] Best: 0.2736
Best score: 0.2736: : 649iter [00:23, 30.79iter/s]
Best score: 0.2736: : 650iter [00:23, 30.51iter/s]
[PARAMS] Current best parameters:
  fx: 537.6307
  fy: 536.9293
  px: 996.6252
  py: 758.7410
  k1: 0.01726047
  k2: -0.02359464
  k3: 0.00940022
  k4: -0.00182937
  p1: -0.00496793
  p2: 0.00328408
  p3: 0.00380923
  p4: -0.00172034
  s1: -0.0100
  s2: 0.0021
  s3: 0.0017
  s4: 0.0013
  tilt_x: -0.0049
  tilt_y: 0.0200
  scale: 2.9628
  rotation: -0.6078

Best score: 0.2736: : 650iter [00:23, 30.51iter/s]
Best score: 0.2736: : 651iter [00:23, 30.51iter/s]
Best score: 0.2736: : 652iter [00:23, 30.51iter/s]
Best score: 0.2736: : 653iter [00:23, 30.51iter/s]
Best score: 0.2736: : 654iter [00:23, 25.39iter/s]
Best score: 0.2736: : 654iter [00:23, 25.39iter/s]
Best score: 0.2736: : 655iter [00:23, 25.39iter/s]
Best score: 0.2736: : 656iter [00:23, 25.39iter/s]
Best score: 0.2736: : 657iter [00:23, 26.26iter/s]
Best score: 0.2736: : 657iter [00:23, 26.26iter/s]
Best score: 0.2736: : 658iter [00:23, 26.26iter/s]
Best score: 0.2736: : 659iter [00:23, 26.26iter/s]
Best score: 0.2736: : 660iter [00:23, 26.26iter/s]
Best score: 0.2736: : 661iter [00:23, 27.56iter/s]
Best score: 0.2736: : 661iter [00:23, 27.56iter/s]
Best score: 0.2736: : 662iter [00:23, 27.56iter/s]
Best score: 0.2736: : 663iter [00:23, 27.56iter/s]
Best score: 0.2736: : 664iter [00:23, 27.56iter/s]
Best score: 0.2736: : 665iter [00:23, 28.82iter/s]
Best score: 0.2736: : 665iter [00:23, 28.82iter/s]
Best score: 0.2736: : 666iter [00:23, 28.82iter/s]
Best score: 0.2736: : 667iter [00:24, 28.82iter/s]
Best score: 0.2736: : 668iter [00:24, 28.82iter/s]
Best score: 0.2736: : 669iter [00:24, 30.18iter/s]
Best score: 0.2736: : 669iter [00:24, 30.18iter/s]
Best score: 0.2736: : 670iter [00:24, 30.18iter/s]
Best score: 0.2736: : 671iter [00:24, 30.18iter/s]
Best score: 0.2736: : 672iter [00:24, 30.18iter/s]
Best score: 0.2736: : 673iter [00:24, 23.70iter/s]
Best score: 0.2736: : 673iter [00:24, 23.70iter/s]
Best score: 0.2736: : 674iter [00:24, 23.70iter/s]
Best score: 0.2736: : 675iter [00:24, 23.70iter/s]
Best score: 0.2736: : 676iter [00:24, 24.38iter/s]
Best score: 0.2736: : 676iter [00:24, 24.38iter/s]
Best score: 0.2736: : 677iter [00:24, 24.38iter/s]
Best score: 0.2736: : 678iter [00:24, 24.38iter/s]
Best score: 0.2736: : 679iter [00:24, 24.38iter/s]
Best score: 0.2736: : 680iter [00:24, 26.25iter/s]
Best score: 0.2736: : 680iter [00:24, 26.25iter/s]
Best score: 0.2736: : 681iter [00:24, 26.25iter/s]
Best score: 0.2736: : 682iter [00:24, 26.25iter/s]
Best score: 0.2736: : 683iter [00:24, 26.25iter/s]
Best score: 0.2736: : 684iter [00:24, 28.28iter/s]
Best score: 0.2736: : 684iter [00:24, 28.28iter/s]
Best score: 0.2736: : 685iter [00:24, 28.28iter/s]
Best score: 0.2736: : 686iter [00:24, 28.28iter/s]
Best score: 0.2736: : 687iter [00:24, 28.28iter/s]
Best score: 0.2736: : 688iter [00:24, 29.85iter/s]
Best score: 0.2736: : 688iter [00:24, 29.85iter/s]
Best score: 0.2736: : 689iter [00:24, 29.85iter/s]
Best score: 0.2736: : 690iter [00:24, 29.85iter/s]
Best score: 0.2736: : 691iter [00:24, 29.85iter/s]
Best score: 0.2736: : 692iter [00:24, 31.05iter/s]
Best score: 0.2736: : 692iter [00:24, 31.05iter/s]
Best score: 0.2736: : 693iter [00:24, 31.05iter/s]
Best score: 0.2736: : 694iter [00:24, 31.05iter/s]
Best score: 0.2736: : 695iter [00:25, 31.05iter/s]
Best score: 0.2736: : 696iter [00:25, 31.78iter/s]
Best score: 0.2736: : 696iter [00:25, 31.78iter/s]
Best score: 0.2736: : 697iter [00:25, 31.78iter/s]
Best score: 0.2736: : 698iter [00:25, 31.78iter/s]
[ITER 700] Best: 0.2736
[PARAMS] Current best parameters:
  fx: 537.6307
  fy: 536.9293
  px: 996.6252
  py: 758.7410
  k1: 0.01726047
  k2: -0.02359464
  k3: 0.00940022
  k4: -0.00182937
  p1: -0.00496793
  p2: 0.00328408
  p3: 0.00380923
  p4: -0.00172034
  s1: -0.0100
  s2: 0.0021
  s3: 0.0017
  s4: 0.0013
  tilt_x: -0.0049
  tilt_y: 0.0200
  scale: 2.9628
  rotation: -0.6078

Best score: 0.2736: : 699iter [00:25, 31.78iter/s]
Best score: 0.2736: : 700iter [00:25, 32.26iter/s]
Best score: 0.2736: : 700iter [00:25, 32.26iter/s]
Best score: 0.2736: : 701iter [00:25, 32.26iter/s]
Best score: 0.2736: : 702iter [00:25, 32.26iter/s]
Best score: 0.2736: : 703iter [00:25, 32.26iter/s]
Best score: 0.2736: : 704iter [00:25, 27.04iter/s]
Best score: 0.2736: : 704iter [00:25, 27.04iter/s]
Best score: 0.2736: : 705iter [00:25, 27.04iter/s]
Best score: 0.2736: : 706iter [00:25, 27.04iter/s]
Best score: 0.2736: : 707iter [00:25, 27.04iter/s]
Best score: 0.2736: : 708iter [00:25, 28.39iter/s]
Best score: 0.2736: : 708iter [00:25, 28.39iter/s]
Best score: 0.2736: : 709iter [00:25, 28.39iter/s]
Best score: 0.2736: : 710iter [00:25, 28.39iter/s]
Best score: 0.2736: : 711iter [00:25, 28.39iter/s]
Best score: 0.2736: : 712iter [00:25, 29.53iter/s]
Best score: 0.2736: : 712iter [00:25, 29.53iter/s]
Best score: 0.2736: : 713iter [00:25, 29.53iter/s]
Best score: 0.2736: : 714iter [00:25, 29.53iter/s]
Best score: 0.2736: : 715iter [00:25, 29.53iter/s]
Best score: 0.2736: : 716iter [00:25, 30.55iter/s]
Best score: 0.2736: : 716iter [00:25, 30.55iter/s]
Best score: 0.2736: : 717iter [00:25, 30.55iter/s]
Best score: 0.2736: : 718iter [00:25, 30.55iter/s]
Best score: 0.2736: : 719iter [00:25, 30.55iter/s]
Best score: 0.2736: : 720iter [00:25, 31.42iter/s]
Best score: 0.2736: : 720iter [00:25, 31.42iter/s]
Best score: 0.2736: : 721iter [00:25, 31.42iter/s]
Best score: 0.2736: : 722iter [00:25, 31.42iter/s]
Best score: 0.2736: : 723iter [00:25, 31.42iter/s]
Best score: 0.2736: : 724iter [00:25, 33.15iter/s]
Best score: 0.2736: : 724iter [00:25, 33.15iter/s]
Best score: 0.2736: : 725iter [00:25, 33.15iter/s]
Best score: 0.2736: : 726iter [00:25, 33.15iter/s]
Best score: 0.2736: : 727iter [00:26, 33.15iter/s]
Best score: 0.2736: : 728iter [00:26, 34.38iter/s]
Best score: 0.2736: : 728iter [00:26, 34.38iter/s]
Best score: 0.2736: : 729iter [00:26, 34.38iter/s]
Best score: 0.2736: : 730iter [00:26, 34.38iter/s]
Best score: 0.2736: : 731iter [00:26, 34.38iter/s]
Best score: 0.2736: : 732iter [00:26, 33.84iter/s]
Best score: 0.2736: : 732iter [00:26, 33.84iter/s]
Best score: 0.2736: : 733iter [00:26, 33.84iter/s]
Best score: 0.2736: : 734iter [00:26, 33.84iter/s]
Best score: 0.2736: : 735iter [00:26, 33.84iter/s]
Best score: 0.2736: : 736iter [00:26, 33.36iter/s]
Best score: 0.2736: : 736iter [00:26, 33.36iter/s]
Best score: 0.2736: : 737iter [00:26, 33.36iter/s]
Best score: 0.2736: : 738iter [00:26, 33.36iter/s]
Best score: 0.2736: : 739iter [00:26, 33.36iter/s]
Best score: 0.2736: : 740iter [00:26, 33.50iter/s]
Best score: 0.2736: : 740iter [00:26, 33.50iter/s]
Best score: 0.2736: : 741iter [00:26, 33.50iter/s]
Best score: 0.2736: : 742iter [00:26, 33.50iter/s]
Best score: 0.2736: : 743iter [00:26, 33.50iter/s]
Best score: 0.2736: : 744iter [00:26, 33.64iter/s]
Best score: 0.2736: : 744iter [00:26, 33.64iter/s]
Best score: 0.2736: : 745iter [00:26, 33.64iter/s]
Best score: 0.2736: : 746iter [00:26, 33.64iter/s]
Best score: 0.2736: : 747iter [00:26, 33.64iter/s]
Best score: 0.2736: : 748iter [00:26, 33.60iter/s]
Best score: 0.2736: : 748iter [00:26, 33.60iter/s]
[ITER 750] Best: 0.2736
[PARAMS] Current best parameters:
  fx: 537.6307
  fy: 536.9293
  px: 996.6252
  py: 758.7410
  k1: 0.01726047
  k2: -0.02359464
  k3: 0.00940022
  k4: -0.00182937
  p1: -0.00496793
  p2: 0.00328408
  p3: 0.00380923
  p4: -0.00172034
  s1: -0.0100
  s2: 0.0021
  s3: 0.0017
  s4: 0.0013
  tilt_x: -0.0049
  tilt_y: 0.0200
  scale: 2.9628
  rotation: -0.6078

Best score: 0.2736: : 749iter [00:26, 33.60iter/s]
Best score: 0.2736: : 750iter [00:26, 33.60iter/s]
Best score: 0.2736: : 751iter [00:26, 33.60iter/s]
Best score: 0.2736: : 752iter [00:26, 28.33iter/s]
Best score: 0.2736: : 752iter [00:26, 28.33iter/s]
Best score: 0.2736: : 753iter [00:26, 28.33iter/s]
Best score: 0.2736: : 754iter [00:26, 28.33iter/s]
Best score: 0.2736: : 755iter [00:26, 28.33iter/s]
Best score: 0.2736: : 756iter [00:26, 30.40iter/s]
Best score: 0.2736: : 756iter [00:26, 30.40iter/s]
Best score: 0.2736: : 757iter [00:26, 30.40iter/s]
Best score: 0.2736: : 758iter [00:27, 30.40iter/s]
Best score: 0.2736: : 759iter [00:27, 30.40iter/s]
Best score: 0.2736: : 760iter [00:27, 30.83iter/s]
Best score: 0.2736: : 760iter [00:27, 30.83iter/s]
Best score: 0.2736: : 761iter [00:27, 30.83iter/s]
Best score: 0.2736: : 762iter [00:27, 30.83iter/s]
Best score: 0.2736: : 763iter [00:27, 30.83iter/s]
Best score: 0.2736: : 764iter [00:27, 31.27iter/s]
Best score: 0.2736: : 764iter [00:27, 31.27iter/s]
Best score: 0.2736: : 765iter [00:27, 31.27iter/s]
Best score: 0.2736: : 766iter [00:27, 31.27iter/s]
Best score: 0.2736: : 767iter [00:27, 31.27iter/s]
Best score: 0.2736: : 768iter [00:27, 31.44iter/s]
Best score: 0.2736: : 768iter [00:27, 31.44iter/s]
Best score: 0.2736: : 769iter [00:27, 31.44iter/s]
Best score: 0.2736: : 770iter [00:27, 31.44iter/s]
Best score: 0.2736: : 771iter [00:27, 31.44iter/s]
Best score: 0.2736: : 772iter [00:27, 32.19iter/s]
Best score: 0.2736: : 772iter [00:27, 32.19iter/s]
Best score: 0.2736: : 773iter [00:27, 32.19iter/s]
Best score: 0.2736: : 774iter [00:27, 32.19iter/s]
Best score: 0.2736: : 775iter [00:27, 32.19iter/s]
Best score: 0.2736: : 776iter [00:27, 32.64iter/s]
Best score: 0.2736: : 776iter [00:27, 32.64iter/s]
Best score: 0.2736: : 777iter [00:27, 32.64iter/s]
Best score: 0.2736: : 778iter [00:27, 32.64iter/s]
Best score: 0.2736: : 779iter [00:27, 32.64iter/s]
Best score: 0.2736: : 780iter [00:27, 34.15iter/s]
Best score: 0.2736: : 780iter [00:27, 34.15iter/s]
Best score: 0.2736: : 781iter [00:27, 34.15iter/s]
Best score: 0.2736: : 782iter [00:27, 34.15iter/s]
Best score: 0.2736: : 783iter [00:27, 34.15iter/s]
Best score: 0.2736: : 784iter [00:27, 34.08iter/s]
Best score: 0.2736: : 784iter [00:27, 34.08iter/s]
Best score: 0.2736: : 785iter [00:27, 34.08iter/s]
Best score: 0.2736: : 786iter [00:27, 34.08iter/s]
Best score: 0.2736: : 787iter [00:27, 34.08iter/s]
Best score: 0.2736: : 788iter [00:27, 33.93iter/s]
Best score: 0.2736: : 788iter [00:27, 33.93iter/s]
Best score: 0.2736: : 789iter [00:27, 33.93iter/s]
Best score: 0.2736: : 790iter [00:27, 33.93iter/s]
Best score: 0.2736: : 791iter [00:28, 33.93iter/s]
Best score: 0.2736: : 792iter [00:28, 32.99iter/s]
Best score: 0.2736: : 792iter [00:28, 32.99iter/s]
Best score: 0.2736: : 793iter [00:28, 32.99iter/s]
Best score: 0.2736: : 794iter [00:28, 32.99iter/s]
Best score: 0.2736: : 795iter [00:28, 32.99iter/s]
Best score: 0.2736: : 796iter [00:28, 32.90iter/s]
Best score: 0.2736: : 796iter [00:28, 32.90iter/s]
Best score: 0.2736: : 797iter [00:28, 32.90iter/s]
Best score: 0.2736: : 798iter [00:28, 32.90iter/s]
[ITER 800] Best: 0.2736
[PARAMS] Current best parameters:
  fx: 537.6307
  fy: 536.9293
  px: 996.6252
  py: 758.7410
  k1: 0.01726047
  k2: -0.02359464
  k3: 0.00940022
  k4: -0.00182937
  p1: -0.00496793
  p2: 0.00328408
  p3: 0.00380923
  p4: -0.00172034
  s1: -0.0100
  s2: 0.0021
  s3: 0.0017
  s4: 0.0013
  tilt_x: -0.0049
  tilt_y: 0.0200
  scale: 2.9628
  rotation: -0.6078

Best score: 0.2736: : 799iter [00:28, 32.90iter/s]
Best score: 0.2736: : 800iter [00:28, 33.57iter/s]
Best score: 0.2736: : 800iter [00:28, 33.57iter/s]
Best score: 0.2736: : 801iter [00:28, 33.57iter/s]
Best score: 0.2736: : 802iter [00:28, 33.57iter/s]
Best score: 0.2736: : 803iter [00:28, 33.57iter/s]
Best score: 0.2736: : 804iter [00:28, 28.17iter/s]
Best score: 0.2736: : 804iter [00:28, 28.17iter/s]
Best score: 0.2736: : 805iter [00:28, 28.17iter/s]
Best score: 0.2736: : 806iter [00:28, 28.17iter/s]
Best score: 0.2736: : 807iter [00:28, 28.17iter/s]
Best score: 0.2736: : 808iter [00:28, 30.34iter/s]
Best score: 0.2736: : 808iter [00:28, 30.34iter/s]
Best score: 0.2736: : 809iter [00:28, 30.34iter/s]
Best score: 0.2736: : 810iter [00:28, 30.34iter/s]
Best score: 0.2736: : 811iter [00:28, 30.34iter/s]
Best score: 0.2736: : 812iter [00:28, 31.59iter/s]
Best score: 0.2736: : 812iter [00:28, 31.59iter/s]
Best score: 0.2736: : 813iter [00:28, 31.59iter/s]
Best score: 0.2736: : 814iter [00:28, 31.59iter/s]
Best score: 0.2736: : 815iter [00:28, 31.59iter/s]
Best score: 0.2736: : 816iter [00:28, 32.70iter/s]
Best score: 0.2736: : 816iter [00:28, 32.70iter/s]
Best score: 0.2736: : 817iter [00:28, 32.70iter/s]
Best score: 0.2736: : 818iter [00:28, 32.70iter/s]
Best score: 0.2736: : 819iter [00:28, 32.70iter/s]
Best score: 0.2736: : 820iter [00:28, 32.56iter/s]
Best score: 0.2736: : 820iter [00:28, 32.56iter/s]
Best score: 0.2736: : 821iter [00:28, 32.56iter/s]
Best score: 0.2736: : 822iter [00:28, 32.56iter/s]
Best score: 0.2736: : 823iter [00:29, 32.56iter/s]
Best score: 0.2736: : 824iter [00:29, 32.84iter/s]
Best score: 0.2736: : 824iter [00:29, 32.84iter/s]
Best score: 0.2736: : 825iter [00:29, 32.84iter/s]
Best score: 0.2736: : 826iter [00:29, 32.84iter/s]
Best score: 0.2736: : 827iter [00:29, 32.84iter/s]
Best score: 0.2736: : 828iter [00:29, 32.42iter/s]
Best score: 0.2736: : 828iter [00:29, 32.42iter/s]
Best score: 0.2736: : 829iter [00:29, 32.42iter/s]
Best score: 0.2736: : 830iter [00:29, 32.42iter/s]
Best score: 0.2736: : 831iter [00:29, 32.42iter/s]
Best score: 0.2736: : 832iter [00:29, 32.07iter/s]
Best score: 0.2736: : 832iter [00:29, 32.07iter/s]
Best score: 0.2736: : 833iter [00:29, 32.07iter/s]
Best score: 0.2736: : 834iter [00:29, 32.07iter/s]
Best score: 0.2736: : 835iter [00:29, 32.07iter/s]
Best score: 0.2736: : 836iter [00:29, 32.30iter/s]
Best score: 0.2736: : 836iter [00:29, 32.30iter/s]
Best score: 0.2736: : 837iter [00:29, 32.30iter/s]
Best score: 0.2736: : 838iter [00:29, 32.30iter/s]
Best score: 0.2736: : 839iter [00:29, 32.30iter/s]
Best score: 0.2736: : 840iter [00:29, 32.94iter/s]
Best score: 0.2736: : 840iter [00:29, 32.94iter/s]
Best score: 0.2736: : 841iter [00:29, 32.94iter/s]
Best score: 0.2736: : 842iter [00:29, 32.94iter/s]
Best score: 0.2736: : 843iter [00:29, 32.94iter/s]
Best score: 0.2736: : 844iter [00:29, 33.72iter/s]
Best score: 0.2736: : 844iter [00:29, 33.72iter/s]
Best score: 0.2736: : 845iter [00:29, 33.72iter/s]
Best score: 0.2736: : 846iter [00:29, 33.72iter/s]
Best score: 0.2736: : 847iter [00:29, 33.72iter/s]
Best score: 0.2736: : 848iter [00:29, 34.38iter/s]
Best score: 0.2736: : 848iter [00:29, 34.38iter/s]
[ITER 850] Best: 0.2736
[PARAMS] Current best parameters:
  fx: 537.6307
  fy: 536.9293
  px: 996.6252
  py: 758.7410
  k1: 0.01726047
  k2: -0.02359464
  k3: 0.00940022
  k4: -0.00182937
  p1: -0.00496793
  p2: 0.00328408
  p3: 0.00380923
  p4: -0.00172034
  s1: -0.0100
  s2: 0.0021
  s3: 0.0017
  s4: 0.0013
  tilt_x: -0.0049
  tilt_y: 0.0200
  scale: 2.9628
  rotation: -0.6078

Best score: 0.2736: : 849iter [00:29, 34.38iter/s]
Best score: 0.2736: : 850iter [00:29, 34.38iter/s]
Best score: 0.2736: : 851iter [00:29, 34.38iter/s]
Best score: 0.2736: : 852iter [00:29, 28.23iter/s]
Best score: 0.2736: : 852iter [00:29, 28.23iter/s]
Best score: 0.2736: : 853iter [00:29, 28.23iter/s]
Best score: 0.2736: : 854iter [00:30, 28.23iter/s]
Best score: 0.2736: : 855iter [00:30, 28.23iter/s]
Best score: 0.2736: : 856iter [00:30, 30.48iter/s]
Best score: 0.2736: : 856iter [00:30, 30.48iter/s]
Best score: 0.2736: : 857iter [00:30, 30.48iter/s]
Best score: 0.2736: : 858iter [00:30, 30.48iter/s]
Best score: 0.2736: : 859iter [00:30, 30.48iter/s]
Best score: 0.2736: : 860iter [00:30, 31.64iter/s]
Best score: 0.2736: : 860iter [00:30, 31.64iter/s]
Best score: 0.2736: : 861iter [00:30, 31.64iter/s]
Best score: 0.2736: : 862iter [00:30, 31.64iter/s]
Best score: 0.2736: : 863iter [00:30, 31.64iter/s]
Best score: 0.2736: : 864iter [00:30, 33.07iter/s]
Best score: 0.2736: : 864iter [00:30, 33.07iter/s]
Best score: 0.2736: : 865iter [00:30, 33.07iter/s]
Best score: 0.2736: : 866iter [00:30, 33.07iter/s]
Best score: 0.2736: : 867iter [00:30, 33.07iter/s]
Best score: 0.2736: : 868iter [00:30, 33.99iter/s]
Best score: 0.2736: : 868iter [00:30, 33.99iter/s]
Best score: 0.2736: : 869iter [00:30, 33.99iter/s]
Best score: 0.2736: : 870iter [00:30, 33.99iter/s]
Best score: 0.2736: : 871iter [00:30, 33.99iter/s]
Best score: 0.2736: : 872iter [00:30, 33.62iter/s]
Best score: 0.2736: : 872iter [00:30, 33.62iter/s]
Best score: 0.2736: : 873iter [00:30, 33.62iter/s]
Best score: 0.2736: : 874iter [00:30, 33.62iter/s]
Best score: 0.2736: : 875iter [00:30, 33.62iter/s]
Best score: 0.2736: : 876iter [00:30, 34.79iter/s]
Best score: 0.2736: : 876iter [00:30, 34.79iter/s]
Best score: 0.2736: : 877iter [00:30, 34.79iter/s]
Best score: 0.2736: : 878iter [00:30, 34.79iter/s]
Best score: 0.2736: : 879iter [00:30, 34.79iter/s]
Best score: 0.2736: : 880iter [00:30, 35.88iter/s]
Best score: 0.2736: : 880iter [00:30, 35.88iter/s]
Best score: 0.2736: : 881iter [00:30, 35.88iter/s]
Best score: 0.2736: : 882iter [00:30, 35.88iter/s]
Best score: 0.2736: : 883iter [00:30, 35.88iter/s]
Best score: 0.2736: : 884iter [00:30, 35.82iter/s]
Best score: 0.2736: : 884iter [00:30, 35.82iter/s]
Best score: 0.2736: : 885iter [00:30, 35.82iter/s]
Best score: 0.2736: : 886iter [00:30, 35.82iter/s]
Best score: 0.2736: : 887iter [00:30, 35.82iter/s]
Best score: 0.2736: : 888iter [00:30, 35.41iter/s]
Best score: 0.2736: : 888iter [00:30, 35.41iter/s]
Best score: 0.2719: : 889iter [00:31, 35.41iter/s]
Best score: 0.2719: : 890iter [00:31, 35.41iter/s]
Best score: 0.2719: : 891iter [00:31, 35.41iter/s]
Best score: 0.2719: : 892iter [00:31, 33.94iter/s]
Best score: 0.2719: : 892iter [00:31, 33.94iter/s]
Best score: 0.2719: : 893iter [00:31, 33.94iter/s]
Best score: 0.2719: : 894iter [00:31, 33.94iter/s]
Best score: 0.2719: : 895iter [00:31, 33.94iter/s]
Best score: 0.2719: : 896iter [00:31, 34.53iter/s]
Best score: 0.2719: : 896iter [00:31, 34.53iter/s]
Best score: 0.2719: : 897iter [00:31, 34.53iter/s]
Best score: 0.2719: : 898iter [00:31, 34.53iter/s]
[ITER 900] Best: 0.2705
[PARAMS] Current best parameters:
  fx: 538.5699
  fy: 537.9037
  px: 996.4807
  py: 760.4305
  k1: 0.01326865
  k2: -0.02330646
  k3: 0.01184557
  k4: -0.00277283
  p1: -0.00732566
  p2: 0.00431172
  p3: 0.00870560
  p4: -0.00123721
  s1: -0.0156
  s2: 0.0010
  s3: 0.0013
  s4: 0.0005
  tilt_x: 0.0082
  tilt_y: 0.0219
  scale: 2.9690
  rotation: -0.5565

Best score: 0.2705: : 899iter [00:31, 34.53iter/s]
Best score: 0.2705: : 900iter [00:31, 34.46iter/s]
Best score: 0.2705: : 900iter [00:31, 34.46iter/s]
Best score: 0.2705: : 901iter [00:31, 34.46iter/s]
Best score: 0.2705: : 902iter [00:31, 34.46iter/s]
Best score: 0.2705: : 903iter [00:31, 34.46iter/s]
Best score: 0.2705: : 904iter [00:31, 29.22iter/s]
Best score: 0.2705: : 904iter [00:31, 29.22iter/s]
Best score: 0.2705: : 905iter [00:31, 29.22iter/s]
Best score: 0.2705: : 906iter [00:31, 29.22iter/s]
Best score: 0.2705: : 907iter [00:31, 29.22iter/s]
Best score: 0.2705: : 908iter [00:31, 31.31iter/s]
Best score: 0.2705: : 908iter [00:31, 31.31iter/s]
Best score: 0.2531: : 909iter [00:31, 31.31iter/s]
Best score: 0.2531: : 910iter [00:31, 31.31iter/s]
Best score: 0.2531: : 911iter [00:31, 31.31iter/s]
Best score: 0.2531: : 912iter [00:31, 32.19iter/s]
Best score: 0.2531: : 912iter [00:31, 32.19iter/s]
Best score: 0.2531: : 913iter [00:31, 32.19iter/s]
Best score: 0.2531: : 914iter [00:31, 32.19iter/s]
Best score: 0.2531: : 915iter [00:31, 32.19iter/s]
Best score: 0.2531: : 916iter [00:31, 33.24iter/s]
Best score: 0.2531: : 916iter [00:31, 33.24iter/s]
Best score: 0.2531: : 917iter [00:31, 33.24iter/s]
Best score: 0.2531: : 918iter [00:31, 33.24iter/s]
Best score: 0.2531: : 919iter [00:31, 33.24iter/s]
Best score: 0.2531: : 920iter [00:31, 34.14iter/s]
Best score: 0.2531: : 920iter [00:31, 34.14iter/s]
Best score: 0.2531: : 921iter [00:31, 34.14iter/s]
Best score: 0.2531: : 922iter [00:32, 34.14iter/s]
Best score: 0.2531: : 923iter [00:32, 34.14iter/s]
Best score: 0.2531: : 924iter [00:32, 34.43iter/s]
Best score: 0.2531: : 924iter [00:32, 34.43iter/s]
Best score: 0.2531: : 925iter [00:32, 34.43iter/s]
Best score: 0.2531: : 926iter [00:32, 34.43iter/s]
Best score: 0.2531: : 927iter [00:32, 34.43iter/s]
Best score: 0.2531: : 928iter [00:32, 34.41iter/s]
Best score: 0.2531: : 928iter [00:32, 34.41iter/s]
Best score: 0.2531: : 929iter [00:32, 34.41iter/s]
Best score: 0.2531: : 930iter [00:32, 34.41iter/s]
Best score: 0.2531: : 931iter [00:32, 34.41iter/s]
Best score: 0.2531: : 932iter [00:32, 34.00iter/s]
Best score: 0.2531: : 932iter [00:32, 34.00iter/s]
Best score: 0.2531: : 933iter [00:32, 34.00iter/s]
Best score: 0.2531: : 934iter [00:32, 34.00iter/s]
Best score: 0.2531: : 935iter [00:32, 34.00iter/s]
Best score: 0.2531: : 936iter [00:32, 33.83iter/s]
Best score: 0.2531: : 936iter [00:32, 33.83iter/s]
Best score: 0.2531: : 937iter [00:32, 33.83iter/s]
Best score: 0.2531: : 938iter [00:32, 33.83iter/s]
Best score: 0.2423: : 939iter [00:32, 33.83iter/s]
Best score: 0.2423: : 940iter [00:32, 33.25iter/s]
Best score: 0.2423: : 940iter [00:32, 33.25iter/s]
Best score: 0.2423: : 941iter [00:32, 33.25iter/s]
Best score: 0.2423: : 942iter [00:32, 33.25iter/s]
Best score: 0.2423: : 943iter [00:32, 33.25iter/s]
Best score: 0.2423: : 944iter [00:32, 32.84iter/s]
Best score: 0.2423: : 944iter [00:32, 32.84iter/s]
Best score: 0.2423: : 945iter [00:32, 32.84iter/s]
Best score: 0.2423: : 946iter [00:32, 32.84iter/s]
Best score: 0.2423: : 947iter [00:32, 32.84iter/s]
Best score: 0.2423: : 948iter [00:32, 33.65iter/s]
Best score: 0.2423: : 948iter [00:32, 33.65iter/s]
[ITER 950] Best: 0.2422
Best score: 0.2422: : 949iter [00:32, 33.65iter/s]
[PARAMS] Current best parameters:
  fx: 536.8203
  fy: 536.0453
  px: 997.3548
  py: 758.1738
  k1: 0.01745730
  k2: -0.02423254
  k3: 0.01051463
  k4: -0.00218018
  p1: -0.00771139
  p2: 0.00369238
  p3: 0.00516662
  p4: -0.00065709
  s1: -0.0099
  s2: 0.0002
  s3: 0.0029
  s4: -0.0002
  tilt_x: -0.0015
  tilt_y: 0.0165
  scale: 2.9657
  rotation: -0.5858

Best score: 0.2422: : 950iter [00:32, 33.65iter/s]
Best score: 0.2422: : 951iter [00:32, 33.65iter/s]
Best score: 0.2422: : 952iter [00:32, 28.45iter/s]
Best score: 0.2422: : 952iter [00:32, 28.45iter/s]
Best score: 0.2422: : 953iter [00:32, 28.45iter/s]
Best score: 0.2422: : 954iter [00:33, 28.45iter/s]
Best score: 0.2422: : 955iter [00:33, 28.45iter/s]
Best score: 0.2422: : 956iter [00:33, 31.02iter/s]
Best score: 0.2422: : 956iter [00:33, 31.02iter/s]
Best score: 0.2422: : 957iter [00:33, 31.02iter/s]
Best score: 0.2422: : 958iter [00:33, 31.02iter/s]
Best score: 0.2422: : 959iter [00:33, 31.02iter/s]
Best score: 0.2422: : 960iter [00:33, 32.33iter/s]
Best score: 0.2422: : 960iter [00:33, 32.33iter/s]
Best score: 0.2422: : 961iter [00:33, 32.33iter/s]
Best score: 0.2422: : 962iter [00:33, 32.33iter/s]
Best score: 0.2422: : 963iter [00:33, 32.33iter/s]
Best score: 0.2422: : 964iter [00:33, 33.57iter/s]
Best score: 0.2422: : 964iter [00:33, 33.57iter/s]
Best score: 0.2422: : 965iter [00:33, 33.57iter/s]
Best score: 0.2422: : 966iter [00:33, 33.57iter/s]
Best score: 0.2422: : 967iter [00:33, 33.57iter/s]
Best score: 0.2422: : 968iter [00:33, 33.32iter/s]
Best score: 0.2422: : 968iter [00:33, 33.32iter/s]
Best score: 0.2382: : 969iter [00:33, 33.32iter/s]
Best score: 0.2382: : 970iter [00:33, 33.32iter/s]
Best score: 0.2382: : 971iter [00:33, 33.32iter/s]
Best score: 0.2382: : 972iter [00:33, 34.15iter/s]
Best score: 0.2382: : 972iter [00:33, 34.15iter/s]
Best score: 0.2382: : 973iter [00:33, 34.15iter/s]
Best score: 0.2382: : 974iter [00:33, 34.15iter/s]
Best score: 0.2382: : 975iter [00:33, 34.15iter/s]
Best score: 0.2382: : 976iter [00:33, 34.82iter/s]
Best score: 0.2382: : 976iter [00:33, 34.82iter/s]
Best score: 0.2382: : 977iter [00:33, 34.82iter/s]
Best score: 0.2382: : 978iter [00:33, 34.82iter/s]
Best score: 0.2364: : 979iter [00:33, 34.82iter/s]
Best score: 0.2364: : 980iter [00:33, 34.19iter/s]
Best score: 0.2364: : 980iter [00:33, 34.19iter/s]
Best score: 0.2364: : 981iter [00:33, 34.19iter/s]
Best score: 0.2364: : 982iter [00:33, 34.19iter/s]
Best score: 0.2364: : 983iter [00:33, 34.19iter/s]
Best score: 0.2364: : 984iter [00:33, 26.96iter/s]
Best score: 0.2364: : 984iter [00:33, 26.96iter/s]
Best score: 0.2364: : 985iter [00:34, 26.96iter/s]
Best score: 0.2364: : 986iter [00:34, 26.96iter/s]
Best score: 0.2364: : 987iter [00:34, 26.96iter/s]
Best score: 0.2364: : 988iter [00:34, 28.17iter/s]
Best score: 0.2364: : 988iter [00:34, 28.17iter/s]
Best score: 0.2364: : 989iter [00:34, 28.17iter/s]
Best score: 0.2364: : 990iter [00:34, 28.17iter/s]
Best score: 0.2364: : 991iter [00:34, 28.17iter/s]
Best score: 0.2364: : 992iter [00:34, 29.19iter/s]
Best score: 0.2364: : 992iter [00:34, 29.19iter/s]
Best score: 0.2364: : 993iter [00:34, 29.19iter/s]
Best score: 0.2364: : 994iter [00:34, 29.19iter/s]
Best score: 0.2364: : 995iter [00:34, 29.19iter/s]
Best score: 0.2364: : 996iter [00:34, 30.22iter/s]
Best score: 0.2364: : 996iter [00:34, 30.22iter/s]
Best score: 0.2364: : 997iter [00:34, 30.22iter/s]
Best score: 0.2364: : 998iter [00:34, 30.22iter/s]
[ITER 1000] Best: 0.2316
[PARAMS] Current best parameters:
  fx: 536.0455
  fy: 535.3703
  px: 999.1161
  py: 759.0110
  k1: 0.02425328
  k2: -0.03299212
  k3: 0.01528055
  k4: -0.00314919
  p1: -0.00923859
  p2: 0.00524546
  p3: 0.00691375
  p4: -0.00026163
  s1: -0.0108
  s2: -0.0006
  s3: 0.0030
  s4: -0.0003
  tilt_x: 0.0057
  tilt_y: 0.0109
  scale: 2.9577
  rotation: -0.5963

Best score: 0.2316: : 999iter [00:34, 30.22iter/s]
Best score: 0.2316: : 1000iter [00:34, 31.00iter/s]
Best score: 0.2316: : 1000iter [00:34, 31.00iter/s]
Best score: 0.2316: : 1001iter [00:34, 31.00iter/s]
Best score: 0.2316: : 1002iter [00:34, 31.00iter/s]
Best score: 0.2316: : 1003iter [00:34, 31.00iter/s]
Best score: 0.2316: : 1004iter [00:34, 26.99iter/s]
Best score: 0.2316: : 1004iter [00:34, 26.99iter/s]
Best score: 0.2316: : 1005iter [00:34, 26.99iter/s]
Best score: 0.2316: : 1006iter [00:34, 26.99iter/s]
Best score: 0.2316: : 1007iter [00:34, 27.40iter/s]
Best score: 0.2316: : 1007iter [00:34, 27.40iter/s]
Best score: 0.2316: : 1008iter [00:34, 27.40iter/s]
Best score: 0.2293: : 1009iter [00:34, 27.40iter/s]
Best score: 0.2293: : 1010iter [00:34, 27.40iter/s]
Best score: 0.2293: : 1011iter [00:34, 28.65iter/s]
Best score: 0.2293: : 1011iter [00:34, 28.65iter/s]
Best score: 0.2293: : 1012iter [00:34, 28.65iter/s]
Best score: 0.2293: : 1013iter [00:34, 28.65iter/s]
Best score: 0.2293: : 1014iter [00:34, 28.65iter/s]
Best score: 0.2293: : 1015iter [00:34, 30.44iter/s]
Best score: 0.2293: : 1015iter [00:35, 30.44iter/s]
Best score: 0.2293: : 1016iter [00:35, 30.44iter/s]
Best score: 0.2293: : 1017iter [00:35, 30.44iter/s]
Best score: 0.2293: : 1018iter [00:35, 30.44iter/s]
Best score: 0.2293: : 1019iter [00:35, 31.07iter/s]
Best score: 0.2261: : 1019iter [00:35, 31.07iter/s]
Best score: 0.2261: : 1020iter [00:35, 31.07iter/s]
Best score: 0.2261: : 1021iter [00:35, 31.07iter/s]
Best score: 0.2261: : 1022iter [00:35, 31.07iter/s]
Best score: 0.2261: : 1023iter [00:35, 31.37iter/s]
Best score: 0.2261: : 1023iter [00:35, 31.37iter/s]
Best score: 0.2261: : 1024iter [00:35, 31.37iter/s]
Best score: 0.2261: : 1025iter [00:35, 31.37iter/s]
Best score: 0.2261: : 1026iter [00:35, 31.37iter/s]
Best score: 0.2261: : 1027iter [00:35, 32.40iter/s]
Best score: 0.2261: : 1027iter [00:35, 32.40iter/s]
Best score: 0.2261: : 1028iter [00:35, 32.40iter/s]
Best score: 0.2261: : 1029iter [00:35, 32.40iter/s]
Best score: 0.2261: : 1030iter [00:35, 32.40iter/s]
Best score: 0.2261: : 1031iter [00:35, 32.95iter/s]
Best score: 0.2261: : 1031iter [00:35, 32.95iter/s]
Best score: 0.2261: : 1032iter [00:35, 32.95iter/s]
Best score: 0.2261: : 1033iter [00:35, 32.95iter/s]
Best score: 0.2261: : 1034iter [00:35, 32.95iter/s]
Best score: 0.2261: : 1035iter [00:35, 32.24iter/s]
Best score: 0.2261: : 1035iter [00:35, 32.24iter/s]
Best score: 0.2261: : 1036iter [00:35, 32.24iter/s]
Best score: 0.2261: : 1037iter [00:35, 32.24iter/s]
Best score: 0.2261: : 1038iter [00:35, 32.24iter/s]
Best score: 0.2261: : 1039iter [00:35, 31.72iter/s]
Best score: 0.2261: : 1039iter [00:35, 31.72iter/s]
Best score: 0.2261: : 1040iter [00:35, 31.72iter/s]
Best score: 0.2261: : 1041iter [00:35, 31.72iter/s]
Best score: 0.2261: : 1042iter [00:35, 31.72iter/s]
Best score: 0.2261: : 1043iter [00:35, 31.78iter/s]
Best score: 0.2261: : 1043iter [00:35, 31.78iter/s]
Best score: 0.2261: : 1044iter [00:35, 31.78iter/s]
Best score: 0.2261: : 1045iter [00:35, 31.78iter/s]
Best score: 0.2261: : 1046iter [00:35, 31.78iter/s]
Best score: 0.2261: : 1047iter [00:35, 31.16iter/s]
Best score: 0.2261: : 1047iter [00:36, 31.16iter/s]
Best score: 0.2261: : 1048iter [00:36, 31.16iter/s]
[ITER 1050] Best: 0.2253
[PARAMS] Current best parameters:
  fx: 536.0327
  fy: 535.3489
  px: 1000.1935
  py: 758.4358
  k1: 0.02434132
  k2: -0.03279130
  k3: 0.01499357
  k4: -0.00306964
  p1: -0.00940972
  p2: 0.00541995
  p3: 0.00705646
  p4: -0.00054539
  s1: -0.0108
  s2: 0.0001
  s3: 0.0041
  s4: -0.0008
  tilt_x: 0.0031
  tilt_y: 0.0059
  scale: 2.9575
  rotation: -0.6021

Best score: 0.2253: : 1049iter [00:36, 31.16iter/s]
Best score: 0.2253: : 1050iter [00:36, 31.16iter/s]
Best score: 0.2253: : 1051iter [00:36, 26.94iter/s]
Best score: 0.2253: : 1051iter [00:36, 26.94iter/s]
Best score: 0.2253: : 1052iter [00:36, 26.94iter/s]
Best score: 0.2253: : 1053iter [00:36, 26.94iter/s]
Best score: 0.2253: : 1054iter [00:36, 26.94iter/s]
Best score: 0.2253: : 1055iter [00:36, 28.83iter/s]
Best score: 0.2253: : 1055iter [00:36, 28.83iter/s]
Best score: 0.2253: : 1056iter [00:36, 28.83iter/s]
Best score: 0.2253: : 1057iter [00:36, 28.83iter/s]
Best score: 0.2253: : 1058iter [00:36, 28.83iter/s]
Best score: 0.2253: : 1059iter [00:36, 30.96iter/s]
Best score: 0.2253: : 1059iter [00:36, 30.96iter/s]
Best score: 0.2253: : 1060iter [00:36, 30.96iter/s]
Best score: 0.2253: : 1061iter [00:36, 30.96iter/s]
Best score: 0.2253: : 1062iter [00:36, 30.96iter/s]
Best score: 0.2253: : 1063iter [00:36, 32.11iter/s]
Best score: 0.2253: : 1063iter [00:36, 32.11iter/s]
Best score: 0.2253: : 1064iter [00:36, 32.11iter/s]
Best score: 0.2253: : 1065iter [00:36, 32.11iter/s]
Best score: 0.2253: : 1066iter [00:36, 32.11iter/s]
Best score: 0.2253: : 1067iter [00:36, 32.69iter/s]
Best score: 0.2253: : 1067iter [00:36, 32.69iter/s]
Best score: 0.2253: : 1068iter [00:36, 32.69iter/s]
Best score: 0.2253: : 1069iter [00:36, 32.69iter/s]
Best score: 0.2253: : 1070iter [00:36, 32.69iter/s]
Best score: 0.2253: : 1071iter [00:36, 32.97iter/s]
Best score: 0.2253: : 1071iter [00:36, 32.97iter/s]
Best score: 0.2253: : 1072iter [00:36, 32.97iter/s]
Best score: 0.2253: : 1073iter [00:36, 32.97iter/s]
Best score: 0.2253: : 1074iter [00:36, 32.97iter/s]
Best score: 0.2253: : 1075iter [00:36, 33.61iter/s]
Best score: 0.2253: : 1075iter [00:36, 33.61iter/s]
Best score: 0.2253: : 1076iter [00:36, 33.61iter/s]
Best score: 0.2253: : 1077iter [00:36, 33.61iter/s]
Best score: 0.2253: : 1078iter [00:36, 33.61iter/s]
Best score: 0.2253: : 1079iter [00:36, 33.55iter/s]
Best score: 0.2229: : 1079iter [00:37, 33.55iter/s]
Best score: 0.2229: : 1080iter [00:37, 33.55iter/s]
Best score: 0.2229: : 1081iter [00:37, 33.55iter/s]
Best score: 0.2229: : 1082iter [00:37, 33.55iter/s]
Best score: 0.2229: : 1083iter [00:37, 34.08iter/s]
Best score: 0.2229: : 1083iter [00:37, 34.08iter/s]
Best score: 0.2229: : 1084iter [00:37, 34.08iter/s]
Best score: 0.2229: : 1085iter [00:37, 34.08iter/s]
Best score: 0.2229: : 1086iter [00:37, 34.08iter/s]
Best score: 0.2229: : 1087iter [00:37, 34.26iter/s]
Best score: 0.2229: : 1087iter [00:37, 34.26iter/s]
Best score: 0.2229: : 1088iter [00:37, 34.26iter/s]
Best score: 0.2227: : 1089iter [00:37, 34.26iter/s]
Best score: 0.2227: : 1090iter [00:37, 34.26iter/s]
Best score: 0.2227: : 1091iter [00:37, 33.98iter/s]
Best score: 0.2227: : 1091iter [00:37, 33.98iter/s]
Best score: 0.2227: : 1092iter [00:37, 33.98iter/s]
Best score: 0.2227: : 1093iter [00:37, 33.98iter/s]
Best score: 0.2227: : 1094iter [00:37, 33.98iter/s]
Best score: 0.2227: : 1095iter [00:37, 33.98iter/s]
Best score: 0.2227: : 1095iter [00:37, 33.98iter/s]
Best score: 0.2227: : 1096iter [00:37, 33.98iter/s]
Best score: 0.2227: : 1097iter [00:37, 33.98iter/s]
Best score: 0.2227: : 1098iter [00:37, 33.98iter/s]
Best score: 0.2227: : 1099iter [00:37, 32.66iter/s]
[ITER 1100] Best: 0.2220
[PARAMS] Current best parameters:
  fx: 535.2681
  fy: 534.6326
  px: 999.5337
  py: 758.9524
  k1: 0.01839996
  k2: -0.02420391
  k3: 0.01103256
  k4: -0.00243956
  p1: -0.00954984
  p2: 0.00646165
  p3: 0.00636931
  p4: -0.00002920
  s1: -0.0121
  s2: -0.0002
  s3: 0.0057
  s4: -0.0015
  tilt_x: 0.0059
  tilt_y: 0.0113
  scale: 2.9671
  rotation: -0.6046

Best score: 0.2220: : 1099iter [00:37, 32.66iter/s]
Best score: 0.2220: : 1100iter [00:37, 29.18iter/s]

[📈] Running L-BFGS-B polish after 1100 DE iterations...
  Polish did not improve score: 0.222036

[⭕] Ring refinement pass (3500mm diameter)...
  Points: 24,623 → 24,623 within 3500mm ring
  Ring polish improved score: 0.222036 -> 0.221588
  Success: True
  Tangential distortion: p1=-0.009550, p2=0.006462, p3=0.006369, p4=-0.000029
  ✓ Tangential correction is significant for corner improvement
[✅] Square reference optimization complete. Score: 0.2216
[✅] Parameters copied to: /working/MDC026_W024_2026-08-25/calibration/params.json
[📌] Selecting best parameters...
[✅] Saved final refined parameters.


-------------------------------------------------------------------
                        Step 4: Undistortion                       
-------------------------------------------------------------------


[DEBUG] Parameters being applied to PTD conversion:
    fx: 535.2681059532212
    fy: 534.6325645006267
    px: 999.5336867900456
    py: 758.9524416609275
    k1: 0.018399956747346378
    k2: -0.024203908292618134
    k3: 0.011032564163542079
    k4: -0.002439564652842974
    p1: -0.009549837665650741
    p2: 0.0064616506359816755
    p3: 0.006369308878177194
    p4: -2.9203107093050964e-05
    scale: 2.967103809333315
    rotation: -0.60458491018474
    mmpp: 1.274648914317554

[🔄] Converting 1280 frames to PTD, creating debug images for 200 calibration frames...

📁 Rigs:   0%|          | 0/2 [00:00<?, ?rig/s]

   🔄 1000:   0%|          | 0/671 [00:00<?, ?frame/s]

   🔄 1000:   3%|▎         | 18/671 [00:00<00:03, 174.74frame/s]

   🔄 1000:   5%|▌         | 36/671 [00:00<00:03, 173.00frame/s]

   🔄 1000:   8%|▊         | 54/671 [00:00<00:03, 172.32frame/s]

   🔄 1000:  11%|█         | 72/671 [00:00<00:03, 173.28frame/s]

   🔄 1000:  13%|█▎        | 90/671 [00:00<00:03, 173.56frame/s]

   🔄 1000:  16%|█▌        | 108/671 [00:00<00:03, 174.58frame/s]

   🔄 1000:  19%|█▉        | 126/671 [00:00<00:03, 174.83frame/s]

   🔄 1000:  21%|██▏       | 144/671 [00:00<00:03, 175.11frame/s]

   🔄 1000:  24%|██▍       | 162/671 [00:00<00:02, 175.10frame/s]

   🔄 1000:  27%|██▋       | 180/671 [00:01<00:02, 175.07frame/s]

   🔄 1000:  30%|██▉       | 198/671 [00:01<00:02, 175.21frame/s]

   🔄 1000:  32%|███▏      | 216/671 [00:01<00:02, 175.25frame/s]

   🔄 1000:  35%|███▍      | 234/671 [00:01<00:02, 175.35frame/s]

   🔄 1000:  38%|███▊      | 252/671 [00:01<00:02, 174.99frame/s]

   🔄 1000:  40%|████      | 270/671 [00:01<00:02, 174.33frame/s]

   🔄 1000:  43%|████▎     | 288/671 [00:01<00:02, 173.81frame/s]

   🔄 1000:  46%|████▌     | 306/671 [00:01<00:02, 173.19frame/s]

   🔄 1000:  48%|████▊     | 324/671 [00:01<00:02, 172.81frame/s]

   🔄 1000:  51%|█████     | 342/671 [00:01<00:01, 171.31frame/s]

   🔄 1000:  54%|█████▎    | 360/671 [00:02<00:01, 171.41frame/s]

   🔄 1000:  56%|█████▋    | 378/671 [00:02<00:01, 171.40frame/s]

   🔄 1000:  59%|█████▉    | 396/671 [00:02<00:01, 171.96frame/s]

   🔄 1000:  62%|██████▏   | 414/671 [00:02<00:01, 172.15frame/s]

   🔄 1000:  64%|██████▍   | 432/671 [00:02<00:01, 171.37frame/s]

   🔄 1000:  67%|██████▋   | 450/671 [00:02<00:01, 172.53frame/s]

   🔄 1000:  70%|██████▉   | 468/671 [00:02<00:01, 173.48frame/s]

   🔄 1000:  72%|███████▏  | 486/671 [00:02<00:01, 173.27frame/s]

   🔄 1000:  75%|███████▌  | 504/671 [00:02<00:00, 174.88frame/s]

   🔄 1000:  78%|███████▊  | 522/671 [00:03<00:00, 176.21frame/s]

   🔄 1000:  80%|████████  | 540/671 [00:03<00:00, 177.19frame/s]

   🔄 1000:  83%|████████▎ | 558/671 [00:03<00:00, 177.74frame/s]

   🔄 1000:  86%|████████▌ | 576/671 [00:03<00:00, 178.01frame/s]

   🔄 1000:  89%|████████▊ | 594/671 [00:03<00:00, 178.41frame/s]

   🔄 1000:  91%|█████████ | 612/671 [00:03<00:00, 177.69frame/s]

   🔄 1000:  94%|█████████▍| 630/671 [00:03<00:00, 177.99frame/s]

   🔄 1000:  97%|█████████▋| 648/671 [00:03<00:00, 176.76frame/s]

   🔄 1000:  99%|█████████▉| 666/671 [00:03<00:00, 176.94frame/s]

                                                                 
📁 Rigs:  50%|█████     | 1/2 [00:07<00:07,  7.04s/rig]

   🔄 500:   0%|          | 0/609 [00:00<?, ?frame/s]

   🔄 500:   3%|▎         | 18/609 [00:00<00:03, 178.65frame/s]

   🔄 500:   6%|▌         | 36/609 [00:00<00:03, 179.04frame/s]

   🔄 500:   9%|▉         | 55/609 [00:00<00:03, 179.50frame/s]

   🔄 500:  12%|█▏        | 74/609 [00:00<00:02, 179.86frame/s]

   🔄 500:  15%|█▌        | 93/609 [00:00<00:02, 179.96frame/s]

   🔄 500:  18%|█▊        | 111/609 [00:00<00:02, 179.65frame/s]

   🔄 500:  21%|██        | 129/609 [00:00<00:02, 179.70frame/s]

   🔄 500:  24%|██▍       | 147/609 [00:00<00:02, 179.77frame/s]

   🔄 500:  27%|██▋       | 165/609 [00:00<00:02, 179.39frame/s]

   🔄 500:  30%|███       | 183/609 [00:01<00:02, 178.79frame/s]

   🔄 500:  33%|███▎      | 201/609 [00:01<00:02, 177.97frame/s]

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   🔄 500:  39%|███▉      | 237/609 [00:01<00:02, 176.91frame/s]

   🔄 500:  42%|████▏     | 255/609 [00:01<00:02, 175.20frame/s]

   🔄 500:  45%|████▍     | 274/609 [00:01<00:01, 177.82frame/s]

   🔄 500:  48%|████▊     | 293/609 [00:01<00:01, 178.97frame/s]

   🔄 500:  51%|█████     | 312/609 [00:01<00:01, 179.93frame/s]

   🔄 500:  54%|█████▍    | 331/609 [00:01<00:01, 180.62frame/s]

   🔄 500:  57%|█████▋    | 350/609 [00:01<00:01, 181.60frame/s]

   🔄 500:  61%|██████    | 369/609 [00:02<00:01, 181.07frame/s]

   🔄 500:  64%|██████▎   | 388/609 [00:02<00:01, 179.72frame/s]

   🔄 500:  67%|██████▋   | 406/609 [00:02<00:01, 178.56frame/s]

   🔄 500:  70%|██████▉   | 424/609 [00:02<00:01, 177.87frame/s]

   🔄 500:  73%|███████▎  | 442/609 [00:02<00:00, 177.50frame/s]

   🔄 500:  76%|███████▌  | 460/609 [00:02<00:00, 176.98frame/s]

   🔄 500:  78%|███████▊  | 478/609 [00:02<00:00, 177.09frame/s]

   🔄 500:  81%|████████▏ | 496/609 [00:02<00:00, 177.48frame/s]

   🔄 500:  84%|████████▍ | 514/609 [00:02<00:00, 177.46frame/s]

   🔄 500:  87%|████████▋ | 532/609 [00:02<00:00, 177.70frame/s]

   🔄 500:  90%|█████████ | 550/609 [00:03<00:00, 177.67frame/s]

   🔄 500:  93%|█████████▎| 568/609 [00:03<00:00, 177.45frame/s]

   🔄 500:  96%|█████████▌| 586/609 [00:03<00:00, 177.02frame/s]

   🔄 500:  99%|█████████▉| 604/609 [00:03<00:00, 176.86frame/s]

                                                                
📁 Rigs: 100%|██████████| 2/2 [00:13<00:00,  6.45s/rig]
📁 Rigs: 100%|██████████| 2/2 [00:13<00:00,  6.54s/rig]
[✅] Saved PTDs: /working/MDC026_W024_2026-08-25/calibration/debug/4_undistorted/output/ptd

📁 Rigs:   0%|          | 0/2 [00:00<?, ?rig/s]

   🔄 1000:   0%|          | 0/100 [00:00<?, ?frame/s]

   🔄 1000:  18%|█▊        | 18/100 [00:00<00:00, 175.21frame/s]

   🔄 1000:  36%|███▌      | 36/100 [00:00<00:00, 175.89frame/s]

   🔄 1000:  54%|█████▍    | 54/100 [00:00<00:00, 174.52frame/s]

   🔄 1000:  72%|███████▏  | 72/100 [00:00<00:00, 173.98frame/s]

   🔄 1000:  90%|█████████ | 90/100 [00:00<00:00, 174.69frame/s]

                                                                
📁 Rigs:  50%|█████     | 1/2 [00:00<00:00,  1.07rig/s]

   🔄 500:   0%|          | 0/100 [00:00<?, ?frame/s]

   🔄 500:  18%|█▊        | 18/100 [00:00<00:00, 173.93frame/s]

   🔄 500:  36%|███▌      | 36/100 [00:00<00:00, 172.70frame/s]

   🔄 500:  54%|█████▍    | 54/100 [00:00<00:00, 172.67frame/s]

   🔄 500:  72%|███████▏  | 72/100 [00:00<00:00, 172.61frame/s]

   🔄 500:  90%|█████████ | 90/100 [00:00<00:00, 173.29frame/s]

                                                               
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.07rig/s]
📁 Rigs: 100%|██████████| 2/2 [00:01<00:00,  1.07rig/s]
[✅] Saved PTDs: /working/MDC026_W024_2026-08-25/calibration/debug/4_undistorted/temp_ptd
[WARN] No .tfd files found for serial extraction
[WARN] Failed to extract serial number from .tfd file. Check that .tfd files exist and contain 'LASERbidh' or 'LASERbidg' pattern. Using 'unknown' as fallback.

📤 Writing LUT:   0%|          | 0/3080448 [00:00<?, ?px/s]
📤 Writing LUT:   3%|▎         | 81031/3080448 [00:00<00:03, 810304.98px/s]
📤 Writing LUT:   5%|▌         | 168619/3080448 [00:00<00:03, 848877.71px/s]
📤 Writing LUT:   8%|▊         | 258256/3080448 [00:00<00:03, 870558.34px/s]
📤 Writing LUT:  11%|█         | 345568/3080448 [00:00<00:03, 871567.52px/s]
📤 Writing LUT:  14%|█▍        | 433991/3080448 [00:00<00:03, 876131.72px/s]
📤 Writing LUT:  17%|█▋        | 522849/3080448 [00:00<00:02, 880363.70px/s]
📤 Writing LUT:  20%|█▉        | 610886/3080448 [00:00<00:02, 875031.13px/s]
📤 Writing LUT:  23%|██▎       | 698396/3080448 [00:00<00:02, 871282.89px/s]
📤 Writing LUT:  26%|██▌       | 785531/3080448 [00:00<00:02, 867482.52px/s]
📤 Writing LUT:  28%|██▊       | 872286/3080448 [00:01<00:02, 864558.95px/s]
📤 Writing LUT:  31%|███       | 958747/3080448 [00:01<00:02, 860291.56px/s]
📤 Writing LUT:  34%|███▍      | 1045132/3080448 [00:01<00:02, 861364.50px/s]
📤 Writing LUT:  37%|███▋      | 1132502/3080448 [00:01<00:02, 865077.53px/s]
📤 Writing LUT:  40%|███▉      | 1219040/3080448 [00:01<00:02, 865168.14px/s]
📤 Writing LUT:  42%|████▏     | 1305812/3080448 [00:01<00:02, 865933.50px/s]
📤 Writing LUT:  45%|████▌     | 1394727/3080448 [00:01<00:01, 872905.20px/s]
📤 Writing LUT:  48%|████▊     | 1482021/3080448 [00:01<00:01, 871761.95px/s]
📤 Writing LUT:  51%|█████     | 1570824/3080448 [00:01<00:01, 876638.67px/s]
📤 Writing LUT:  54%|█████▍    | 1659006/3080448 [00:01<00:01, 878192.37px/s]
📤 Writing LUT:  57%|█████▋    | 1746828/3080448 [00:02<00:01, 875371.91px/s]
📤 Writing LUT:  60%|█████▉    | 1835162/3080448 [00:02<00:01, 877753.82px/s]
📤 Writing LUT:  62%|██████▏   | 1923135/3080448 [00:02<00:01, 878344.69px/s]
📤 Writing LUT:  65%|██████▌   | 2010972/3080448 [00:02<00:01, 876598.98px/s]
📤 Writing LUT:  68%|██████▊   | 2098635/3080448 [00:02<00:01, 870307.28px/s]
📤 Writing LUT:  71%|███████   | 2185677/3080448 [00:02<00:01, 868846.51px/s]
📤 Writing LUT:  74%|███████▍  | 2274130/3080448 [00:02<00:00, 873521.94px/s]
📤 Writing LUT:  77%|███████▋  | 2362192/3080448 [00:02<00:00, 875642.29px/s]
📤 Writing LUT:  80%|███████▉  | 2449901/3080448 [00:02<00:00, 876073.11px/s]
📤 Writing LUT:  82%|████████▏ | 2538858/3080448 [00:02<00:00, 880113.05px/s]
📤 Writing LUT:  85%|████████▌ | 2627739/3080448 [00:03<00:00, 882718.09px/s]
📤 Writing LUT:  88%|████████▊ | 2717693/3080448 [00:03<00:00, 887759.31px/s]
📤 Writing LUT:  91%|█████████ | 2807067/3080448 [00:03<00:00, 889549.36px/s]
📤 Writing LUT:  94%|█████████▍| 2896024/3080448 [00:03<00:00, 878530.67px/s]
📤 Writing LUT:  97%|█████████▋| 2984831/3080448 [00:03<00:00, 881366.21px/s]
📤 Writing LUT: 100%|█████████▉| 3072993/3080448 [00:03<00:00, 878473.36px/s]
📤 Writing LUT: 100%|██████████| 3080448/3080448 [00:03<00:00, 873551.75px/s]
[✅] LUT written to: /working/MDC026_W024_2026-08-25/calibration/debug/4_undistorted/output/LUT.lut
[WARN] Folder name 'MDC026_W024_2026-08-25' does not match expected pattern MDC042_W007_5_28_2025. Using simple naming convention.
[✅] LUT copied to: /working/MDC026_W024_2026-08-25/calibration/MDC026_W024_2026-08-25_unknown.lut

-------------------------------------------------------------------
                          Step 5: Heatmap                          
-------------------------------------------------------------------

[ℹ️] Detected device type: MD (rigs: [500, 1000]mm)

📁 Rigs (heatmap):   0%|          | 0/2 [00:00<?, ?rig/s]

   🔥 1000:   0%|          | 0/671 [00:00<?, ?frame/s]

   🔥 1000:   1%|          | 8/671 [00:00<00:08, 75.34frame/s]

   🔥 1000:   3%|▎         | 17/671 [00:00<00:08, 80.50frame/s]

   🔥 1000:   4%|▍         | 26/671 [00:00<00:07, 82.04frame/s]

   🔥 1000:   5%|▌         | 35/671 [00:00<00:07, 83.43frame/s]

   🔥 1000:   7%|▋         | 44/671 [00:00<00:07, 84.24frame/s]

   🔥 1000:   8%|▊         | 53/671 [00:00<00:07, 84.44frame/s]

   🔥 1000:   9%|▉         | 62/671 [00:00<00:07, 85.29frame/s]

   🔥 1000:  11%|█         | 71/671 [00:00<00:07, 84.51frame/s]

   🔥 1000:  12%|█▏        | 80/671 [00:00<00:06, 84.50frame/s]

   🔥 1000:  13%|█▎        | 89/671 [00:01<00:06, 83.94frame/s]

   🔥 1000:  15%|█▍        | 98/671 [00:01<00:06, 83.71frame/s]

   🔥 1000:  16%|█▌        | 107/671 [00:01<00:06, 83.01frame/s]

   🔥 1000:  17%|█▋        | 116/671 [00:01<00:06, 82.98frame/s]

   🔥 1000:  19%|█▊        | 125/671 [00:01<00:06, 82.66frame/s]

   🔥 1000:  20%|█▉        | 134/671 [00:01<00:06, 82.29frame/s]

   🔥 1000:  21%|██▏       | 143/671 [00:01<00:06, 81.46frame/s]

   🔥 1000:  23%|██▎       | 152/671 [00:01<00:06, 80.18frame/s]

   🔥 1000:  24%|██▍       | 161/671 [00:01<00:06, 79.59frame/s]

   🔥 1000:  25%|██▌       | 169/671 [00:02<00:06, 79.05frame/s]

   🔥 1000:  26%|██▋       | 177/671 [00:02<00:06, 77.89frame/s]

   🔥 1000:  28%|██▊       | 185/671 [00:02<00:06, 77.58frame/s]

   🔥 1000:  29%|██▉       | 193/671 [00:02<00:06, 77.40frame/s]

   🔥 1000:  30%|██▉       | 201/671 [00:02<00:06, 77.94frame/s]

   🔥 1000:  31%|███       | 209/671 [00:02<00:05, 77.83frame/s]

   🔥 1000:  32%|███▏      | 217/671 [00:02<00:05, 77.48frame/s]

   🔥 1000:  34%|███▎      | 225/671 [00:02<00:05, 77.93frame/s]

   🔥 1000:  35%|███▍      | 233/671 [00:02<00:05, 77.55frame/s]

   🔥 1000:  36%|███▌      | 241/671 [00:02<00:05, 77.30frame/s]

   🔥 1000:  37%|███▋      | 249/671 [00:03<00:05, 77.97frame/s]

   🔥 1000:  38%|███▊      | 258/671 [00:03<00:05, 78.82frame/s]

   🔥 1000:  40%|███▉      | 267/671 [00:03<00:05, 80.26frame/s]

   🔥 1000:  41%|████      | 276/671 [00:03<00:04, 80.64frame/s]

   🔥 1000:  42%|████▏     | 285/671 [00:03<00:04, 81.68frame/s]

   🔥 1000:  44%|████▍     | 294/671 [00:03<00:04, 81.32frame/s]

   🔥 1000:  45%|████▌     | 303/671 [00:03<00:04, 80.75frame/s]

   🔥 1000:  46%|████▋     | 312/671 [00:03<00:04, 81.96frame/s]

   🔥 1000:  48%|████▊     | 321/671 [00:03<00:04, 82.99frame/s]

   🔥 1000:  49%|████▉     | 330/671 [00:04<00:04, 83.66frame/s]

   🔥 1000:  51%|█████     | 339/671 [00:04<00:03, 84.54frame/s]

   🔥 1000:  52%|█████▏    | 348/671 [00:04<00:03, 85.07frame/s]

   🔥 1000:  53%|█████▎    | 357/671 [00:04<00:03, 84.90frame/s]

   🔥 1000:  55%|█████▍    | 366/671 [00:04<00:03, 84.23frame/s]

   🔥 1000:  56%|█████▌    | 375/671 [00:04<00:03, 83.71frame/s]

   🔥 1000:  57%|█████▋    | 384/671 [00:04<00:03, 83.08frame/s]

   🔥 1000:  59%|█████▊    | 393/671 [00:04<00:03, 82.07frame/s]

   🔥 1000:  60%|█████▉    | 402/671 [00:04<00:03, 81.41frame/s]

   🔥 1000:  61%|██████▏   | 411/671 [00:05<00:03, 81.12frame/s]

   🔥 1000:  63%|██████▎   | 420/671 [00:05<00:03, 80.75frame/s]

   🔥 1000:  64%|██████▍   | 429/671 [00:05<00:02, 80.73frame/s]

   🔥 1000:  65%|██████▌   | 438/671 [00:05<00:02, 80.36frame/s]

   🔥 1000:  67%|██████▋   | 447/671 [00:05<00:02, 79.96frame/s]

   🔥 1000:  68%|██████▊   | 456/671 [00:05<00:02, 79.78frame/s]

   🔥 1000:  69%|██████▉   | 465/671 [00:05<00:02, 80.18frame/s]

   🔥 1000:  71%|███████   | 474/671 [00:05<00:02, 80.36frame/s]

   🔥 1000:  72%|███████▏  | 483/671 [00:05<00:02, 80.84frame/s]

   🔥 1000:  73%|███████▎  | 492/671 [00:06<00:02, 82.36frame/s]

   🔥 1000:  75%|███████▍  | 501/671 [00:06<00:02, 83.78frame/s]

   🔥 1000:  76%|███████▌  | 510/671 [00:06<00:01, 84.89frame/s]

   🔥 1000:  77%|███████▋  | 519/671 [00:06<00:01, 85.26frame/s]

   🔥 1000:  79%|███████▊  | 528/671 [00:06<00:01, 85.70frame/s]

   🔥 1000:  80%|████████  | 537/671 [00:06<00:01, 85.65frame/s]

   🔥 1000:  81%|████████▏ | 546/671 [00:06<00:01, 85.47frame/s]

   🔥 1000:  83%|████████▎ | 555/671 [00:06<00:01, 85.72frame/s]

   🔥 1000:  84%|████████▍ | 564/671 [00:06<00:01, 85.94frame/s]

   🔥 1000:  85%|████████▌ | 573/671 [00:06<00:01, 86.35frame/s]

   🔥 1000:  87%|████████▋ | 582/671 [00:07<00:01, 85.56frame/s]

   🔥 1000:  88%|████████▊ | 591/671 [00:07<00:00, 85.68frame/s]

   🔥 1000:  89%|████████▉ | 600/671 [00:07<00:00, 85.60frame/s]

   🔥 1000:  91%|█████████ | 609/671 [00:07<00:00, 86.04frame/s]

   🔥 1000:  92%|█████████▏| 618/671 [00:07<00:00, 86.84frame/s]

   🔥 1000:  93%|█████████▎| 627/671 [00:07<00:00, 87.69frame/s]

   🔥 1000:  95%|█████████▍| 636/671 [00:07<00:00, 87.95frame/s]

   🔥 1000:  96%|█████████▌| 645/671 [00:07<00:00, 88.14frame/s]

   🔥 1000:  97%|█████████▋| 654/671 [00:07<00:00, 87.38frame/s]

   🔥 1000:  99%|█████████▉| 663/671 [00:08<00:00, 86.45frame/s]

                                                                [✅] 1000: /working/MDC026_W024_2026-08-25/calibration/debug/5_heatmap/1000_heatmap.png
[✅] 1000 (fitted): /working/MDC026_W024_2026-08-25/calibration/debug/5_heatmap/1000_heatmap_fitted.png

📁 Rigs (heatmap):  50%|█████     | 1/2 [00:11<00:11, 11.20s/rig]

   🔥 500:   0%|          | 0/609 [00:00<?, ?frame/s]

   🔥 500:   1%|▏         | 8/609 [00:00<00:07, 75.41frame/s]

   🔥 500:   3%|▎         | 16/609 [00:00<00:07, 77.41frame/s]

   🔥 500:   4%|▍         | 24/609 [00:00<00:07, 77.89frame/s]

   🔥 500:   5%|▌         | 32/609 [00:00<00:07, 78.04frame/s]

   🔥 500:   7%|▋         | 40/609 [00:00<00:07, 77.88frame/s]

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   🔥 500:   9%|▉         | 56/609 [00:00<00:07, 78.15frame/s]

   🔥 500:  11%|█         | 64/609 [00:00<00:06, 78.35frame/s]

   🔥 500:  12%|█▏        | 72/609 [00:00<00:06, 78.42frame/s]

   🔥 500:  13%|█▎        | 80/609 [00:01<00:06, 78.42frame/s]

   🔥 500:  14%|█▍        | 88/609 [00:01<00:06, 78.49frame/s]

   🔥 500:  16%|█▌        | 96/609 [00:01<00:06, 78.46frame/s]

   🔥 500:  17%|█▋        | 104/609 [00:01<00:06, 78.68frame/s]

   🔥 500:  18%|█▊        | 112/609 [00:01<00:06, 78.80frame/s]

   🔥 500:  20%|█▉        | 120/609 [00:01<00:06, 78.40frame/s]

   🔥 500:  21%|██        | 128/609 [00:01<00:06, 78.48frame/s]

   🔥 500:  22%|██▏       | 136/609 [00:01<00:05, 78.91frame/s]

   🔥 500:  24%|██▍       | 145/609 [00:01<00:05, 79.45frame/s]

   🔥 500:  25%|██▌       | 153/609 [00:01<00:05, 79.26frame/s]

   🔥 500:  27%|██▋       | 162/609 [00:02<00:05, 79.73frame/s]

   🔥 500:  28%|██▊       | 171/609 [00:02<00:05, 79.82frame/s]

   🔥 500:  29%|██▉       | 179/609 [00:02<00:05, 79.75frame/s]

   🔥 500:  31%|███       | 187/609 [00:02<00:05, 79.77frame/s]

   🔥 500:  32%|███▏      | 195/609 [00:02<00:05, 79.63frame/s]

   🔥 500:  33%|███▎      | 203/609 [00:02<00:05, 79.41frame/s]

   🔥 500:  35%|███▍      | 211/609 [00:02<00:05, 79.32frame/s]

   🔥 500:  36%|███▌      | 219/609 [00:02<00:04, 79.44frame/s]

   🔥 500:  37%|███▋      | 227/609 [00:02<00:04, 79.08frame/s]

   🔥 500:  39%|███▊      | 235/609 [00:02<00:04, 78.79frame/s]

   🔥 500:  40%|███▉      | 243/609 [00:03<00:04, 78.18frame/s]

   🔥 500:  41%|████      | 251/609 [00:03<00:04, 78.67frame/s]

   🔥 500:  43%|████▎     | 260/609 [00:03<00:04, 79.49frame/s]

   🔥 500:  44%|████▍     | 269/609 [00:03<00:04, 79.81frame/s]

   🔥 500:  46%|████▌     | 278/609 [00:03<00:04, 80.02frame/s]

   🔥 500:  47%|████▋     | 286/609 [00:03<00:04, 79.40frame/s]

   🔥 500:  48%|████▊     | 295/609 [00:03<00:03, 79.65frame/s]

   🔥 500:  50%|████▉     | 303/609 [00:03<00:03, 79.69frame/s]

   🔥 500:  51%|█████     | 311/609 [00:03<00:03, 78.97frame/s]

   🔥 500:  52%|█████▏    | 319/609 [00:04<00:03, 78.40frame/s]

   🔥 500:  54%|█████▎    | 327/609 [00:04<00:03, 77.97frame/s]

   🔥 500:  55%|█████▌    | 335/609 [00:04<00:03, 78.48frame/s]

   🔥 500:  56%|█████▋    | 343/609 [00:04<00:03, 78.29frame/s]

   🔥 500:  58%|█████▊    | 351/609 [00:04<00:03, 78.24frame/s]

   🔥 500:  59%|█████▉    | 359/609 [00:04<00:03, 77.67frame/s]

   🔥 500:  60%|██████    | 367/609 [00:04<00:03, 76.98frame/s]

   🔥 500:  62%|██████▏   | 375/609 [00:04<00:03, 77.29frame/s]

   🔥 500:  63%|██████▎   | 383/609 [00:04<00:02, 76.81frame/s]

   🔥 500:  64%|██████▍   | 391/609 [00:04<00:02, 76.57frame/s]

   🔥 500:  66%|██████▌   | 399/609 [00:05<00:02, 76.64frame/s]

   🔥 500:  67%|██████▋   | 407/609 [00:05<00:02, 76.69frame/s]

   🔥 500:  68%|██████▊   | 415/609 [00:05<00:02, 77.03frame/s]

   🔥 500:  69%|██████▉   | 423/609 [00:05<00:02, 77.50frame/s]

   🔥 500:  71%|███████   | 431/609 [00:05<00:02, 77.50frame/s]

   🔥 500:  72%|███████▏  | 439/609 [00:05<00:02, 78.06frame/s]

   🔥 500:  73%|███████▎  | 447/609 [00:05<00:02, 78.38frame/s]

   🔥 500:  75%|███████▍  | 455/609 [00:05<00:01, 78.47frame/s]

   🔥 500:  76%|███████▌  | 463/609 [00:05<00:01, 78.46frame/s]

   🔥 500:  77%|███████▋  | 471/609 [00:06<00:01, 78.17frame/s]

   🔥 500:  79%|███████▊  | 479/609 [00:06<00:01, 78.43frame/s]

   🔥 500:  80%|███████▉  | 487/609 [00:06<00:01, 78.21frame/s]

   🔥 500:  81%|████████▏ | 495/609 [00:06<00:01, 78.15frame/s]

   🔥 500:  83%|████████▎ | 503/609 [00:06<00:01, 77.86frame/s]

   🔥 500:  84%|████████▍ | 511/609 [00:06<00:01, 77.54frame/s]

   🔥 500:  85%|████████▌ | 519/609 [00:06<00:01, 77.34frame/s]

   🔥 500:  87%|████████▋ | 527/609 [00:06<00:01, 77.31frame/s]

   🔥 500:  88%|████████▊ | 535/609 [00:06<00:00, 77.12frame/s]

   🔥 500:  89%|████████▉ | 543/609 [00:06<00:00, 76.92frame/s]

   🔥 500:  90%|█████████ | 551/609 [00:07<00:00, 76.90frame/s]

   🔥 500:  92%|█████████▏| 559/609 [00:07<00:00, 77.21frame/s]

   🔥 500:  93%|█████████▎| 567/609 [00:07<00:00, 77.26frame/s]

   🔥 500:  94%|█████████▍| 575/609 [00:07<00:00, 77.58frame/s]

   🔥 500:  96%|█████████▌| 583/609 [00:07<00:00, 77.25frame/s]

   🔥 500:  97%|█████████▋| 591/609 [00:07<00:00, 77.00frame/s]

   🔥 500:  98%|█████████▊| 599/609 [00:07<00:00, 77.33frame/s]

   🔥 500: 100%|█████████▉| 607/609 [00:07<00:00, 77.23frame/s]

                                                               [✅] 500: /working/MDC026_W024_2026-08-25/calibration/debug/5_heatmap/500_heatmap.png
[✅] 500 (fitted): /working/MDC026_W024_2026-08-25/calibration/debug/5_heatmap/500_heatmap_fitted.png

📁 Rigs (heatmap): 100%|██████████| 2/2 [00:20<00:00,  9.88s/rig]
📁 Rigs (heatmap): 100%|██████████| 2/2 [00:20<00:00, 10.08s/rig]
[✅] all_rigs_average.png
[✅] all_rigs_best.png
[✅] all_rigs_average_fitted.png
[✅] all_rigs_best_fitted.png
[✅] Heatmaps saved to: /working/MDC026_W024_2026-08-25/calibration/debug/5_heatmap
[🏁] Finished processing MDC026_W024_2026-08-25


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